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Jay-Rajput/DIS_IPL_Preds

Jay-Rajput

--- configs: - config_name: predictions data_files: predictions/*.json --- --- license: apache-2.0 ---

jacobbieker/eumetsat-cloudmask-rss

jacobbieker

--- license: mit ---

IGNF/PASTIS-HD

IGNF

--- license: etalab-2.0 task_categories: - image-classification - image-segmentation tags: - remote sensing - Agricultural size_categories: - 1K<n<10K --- # 🌱 PASTIS-HD 🌿 Panoptic Agricultural Satellite TIme Series : optical time series, radar time series and very high resolution image [PASTIS](https://github.com/VSainteuf/pastis-benchmark) is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite time series. It contains 2,433 patches within the French metropolitan territory with panoptic annotations (instance index + semantic label for each pixel). Each patch is a Sentinel-2 multispectral image time series of variable lentgh. This dataset have been extended in 2021 with aligned radar Sentinel-1 observations for all 2433 patches. For each patch, it constains approximately 70 observations of Sentinel-1 in ascending orbit, and 70 observations in descending orbit. Each each Sentinel1 observation is assembled into a 3-channel image: vertical polarization (VV), horizontal polarisation (VH), and the ratio vertical over horizontal polarization (VV/VH). This extension is named PASTIS-R. We extend PASTIS with aligned very high resolution satellite images from SPOT 6-7 constellation for all 2433 patches in addition to the Sentinel-1 and 2 time series. The image are resampled to a 1m resolution and converted to 8 bits. This enhancement significantly improves the dataset's spatial content, providing more granular information for agricultural parcel segmentation. **PASTIS-HD** can be used to evaluate multi-modal fusion methods (with optical time series, radar time series and VHR images) for parcel-based classification, semantic segmentation, and panoptic segmentation. ## Dataset in numbers 🛰️ Sentinel 2 | 🛰️ Sentinel 1 | 🛰️ **SPOT 6-7 VHR** | 🗻 Annotations :-------------------------------------------- | :-------------------------------------------------- | :------------------------------| :------------------------------ ➡️ 2,433 time series | ➡️ 2 time 2,433 time series | ➡️ **2,433 images** | 124,422 individual parcels ➡️ 10m / pixel | ➡️ 10m / pixel | ➡️ **1.5m / pixel** | covers ~4,000 km² ➡️ 128x128 pixels / images | ➡️ 128x128 pixels / images | ➡️ **1280x1280 pixels / images** | over 2B pixels ➡️ 38-61 acquisitions / series | ➡️ ~ 70 acquisitions / series | ➡️ **One observation** | 18 crop types ➡️ 10 spectral bands |➡️ 2 spectral bands | ➡️ **3 spectral bands** | ⚠️ The **SPOT data are natively 1.5m resolution**, but we over-sampled them at 1m to align them pixel-perfect with Sentinel data.  ## Data loading The Github repository associated to this dataset contains a PyTorch dataset class of [the OmniSat repository](https://github.com/gastruc/OmniSat/blob/main/src/data/Pastis.py) that can be readily used to load data for training models on PASTIS-HD. The time series contained in PASTIS have variable lengths. The Sentinel 1 and 2 time series are stored in numpy array. The SPOT images are in TIFF format. The annotations are in numpy array too. ⚠️ The S2 and S1 folders contains more than 2433 files on the contrary to the labels folder. Some patches are not labelled and not used for training. The relevant information can be find in the metadata.geojson file (with 2433 entries), which is used as an index by the dataloader. ### Remark about the folder names ⚠️ The **DATA_S1A** folder contains the Sentinel-1 **ascendent** images whereas the **DATA_S1D** folder contains the Sentinel-1 **descendant** images. ## Ground Truth Annotations The agricultural parcels are grouped into 18 different crop classes as shown in the table below. The backgroud class corresponds to non-agricultural land, and the void label for parcels that are mostly outside their patch.  Additional information about the dataset can be found in the documentation/pastis-documentation.pdf document. ## Credits - The Sentinel imagery used in PASTIS was retrieved from [THEIA](www.theia.land.fr): "Value-added data processed by the CNES for the Theia www.theia.land.fr data cluster using Copernicus data. The treatments use algorithms developed by Theia’s Scientific Expertise Centres. " - The annotations used in PASTIS stem from the French [land parcel identification system](https://www.data.gouv.fr/en/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/) produced by IGN. - The SPOT images are opendata thanks to the Dataterra Dinamis initiative in the case of the ["Couverture France DINAMIS"](https://dinamis.data-terra.org/opendata/) program. ## References If you use PASTIS please cite the [related paper](https://arxiv.org/abs/2107.07933): ``` @article{garnot2021panoptic, title={Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks}, author={Sainte Fare Garnot, Vivien and Landrieu, Loic}, journal={ICCV}, year={2021} } ``` For the PASTIS-R optical-radar fusion dataset, please also cite [this paper](https://arxiv.org/abs/2112.07558v1): ``` @article{garnot2021mmfusion, title = {Multi-modal temporal attention models for crop mapping from satellite time series}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, year = {2022}, doi = {https://doi.org/10.1016/j.isprsjprs.2022.03.012}, author = {Vivien {Sainte Fare Garnot} and Loic Landrieu and Nesrine Chehata}, } ``` For the PASTIS-HD with the 3 modalities optical-radar time series plus VHR images dataset, please also cite [this paper](https://arxiv.org/abs/2404.08351): ``` @article{astruc2024omnisat, title={Omni{S}at: {S}elf-Supervised Modality Fusion for {E}arth Observation}, author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic}, journal={ECCV}, year={2024} } ```

luulinh90s/chm-corr-prj-giang

luulinh90s

--- license: mit ---

liwu/MNBVC

liwu

--- annotations_creators: - other language: - zh language_creators: - other license: - mit multilinguality: - monolingual pretty_name: MNBVC size_categories: - unknown source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # Dataset Card for MNBVC ## Table of Contents - [Dataset Card for MNBVC](#dataset-card-for-mnbvc) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [数据集介绍](#数据集介绍) - [数据子集](#数据子集) - [数据格式](#数据格式) - [文本数据](#文本数据) - [问答数据](#问答数据) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://mnbvc.253874.net/ - **Repository:** https://github.com/esbatmop/MNBVC - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** N/A ### 数据集介绍 中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布: 在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。 Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。 可以使用如下脚本加载： ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True) next(iter(dataset)) # get the first line ``` ## 数据子集 MNBVC数据集包含数个子集： - `law_judgement`: 来自法律文书的文本。 - `gov_xuexiqiangguo`: 来自学习强国的文本。 - `gov_report`: 来自政府工作报告的文本。 - `co_ann_report`: 企业年报文本。 - `code_metadata`: 代码元数据。 - `qa_zhihu`: 来自[知乎](https://huggingface.co/datasets/wangrui6/Zhihu-KOL)的问答数据。 - `qa_wikihow`: 来自wikihow的问答数据。 - `qa_mfa`: 外交部问答数据。 - `news_peoples_daily`: 来自人民日报的文本数据。 - `wikipedia`: 来自维基百科的文本数据。 - `qa_stackexchange`: 来自StackExchange的问答数据。 - `qa_chatgpt`: 使用ChatGPT构造的问答语料，感谢[genggui001](https://github.com/genggui001)贡献语料。 - `math`: - `math_qa `: 和数学领域有关的问答数据。 - `emath` :中国数学爱好者论坛语料数据 - `math_chat`: 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。 - `crawler_oscar`: 从CommonCrawl中清洗出来的通用文本数据。 - `game` : 一些游戏的平行语料数据。 - `Hogwarts_legacy` : 霍格沃茨指遗 - `The_Wither_3` : 巫师三 ## 数据格式 目前MNBVC数据集包含如下几类数据： - 通用文本 - 问答语料 - 代码语料 - 多轮对话 - 论坛语料 - 平行语料 可以在[MNBVC的wiki页面](https://wiki.mnbvc.org/doku.php/%E7%8E%B0%E6%9C%89%E8%AF%AD%E6%96%99%E6%A0%BC%E5%BC%8F)上查看这几类数据的具体格式。 项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传： ```json { "text": datasets.Value("string"), "meta": datasets.Value("string") } ``` ### Contributions Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset. Thanks to [silver](https://github.com/silverriver) and [jiaming](https://huggingface.co/Yjiaming) for adding and uploading this dataset to Huggingface. ### Citation Please cite the repo if you use the data or code in this repo. ``` @misc{mnbvc, author = {{MOP-LIWU Community} and {MNBVC Team}}, title = {MNBVC: Massive Never-ending BT Vast Chinese corpus}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/esbatmop/MNBVC}}, } ```

common-canvas/commoncatalog-cc-by-nc-nd

common-canvas

--- license: cc-by-nc-nd-4.0 dataset_info: features: - name: jpg dtype: image - name: blip2_caption dtype: string - name: caption dtype: string - name: licensename dtype: string - name: licenseurl dtype: string - name: width dtype: int32 - name: height dtype: int32 - name: original_width dtype: int32 - name: original_height dtype: int32 - name: photoid dtype: int64 - name: uid dtype: string - name: unickname dtype: string - name: datetaken dtype: timestamp[us] - name: dateuploaded dtype: int64 - name: capturedevice dtype: string - name: title dtype: string - name: usertags dtype: string - name: machinetags dtype: string - name: longitude dtype: float64 - name: latitude dtype: float64 - name: accuracy dtype: int64 - name: pageurl dtype: string - name: downloadurl dtype: string - name: serverid dtype: int64 - name: farmid dtype: int64 - name: secret dtype: string - name: secretoriginal dtype: string - name: ext dtype: string - name: url dtype: string - name: key dtype: string - name: status dtype: string - name: error_message dtype: string - name: exif dtype: string - name: sha256 dtype: string - name: description dtype: string task_categories: - text-to-image language: - en --- # Dataset Card for CommonCatalog CC-BY-NC-ND This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 2014 from users of Yahoo Flickr. The dataset contains images of up to 4k resolution, making this one of the highest resolution captioned image datasets. ## Dataset Details ### Dataset Description We provide captions synthetic captions to approximately 100 million high resolution images collected from Yahoo Flickr Creative Commons (YFCC). - **Curated by:** Aaron Gokaslan - **Language(s) (NLP):** en - **License:** See relevant yaml tag / dataset name. ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** https://github.com/mosaicml/diffusion - **Paper:** https://arxiv.org/abs/2310.16825 - **Demo:** See CommonCanvas Gradios ## Uses We use CommonCatalog to train a family latent diffusion models called CommonCanvas. The goal is to produce a model that is competitive with Stable Diffusion 2, but to do so using an easily accessible dataset of known provenance. Doing so makes replicating the model significantly easier, and provides a clearer mechanism for applying training-data attribution techniques. ### Direct Use Training text-to-image models Training image-to-text models ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> * Commercial use * Crafting content that is offensive or injurious towards individuals, including negative portrayals of their living conditions, cultural backgrounds, religious beliefs, etc. * Deliberately creating or spreading content that is discriminatory or reinforces harmful stereotypes. * Falsely representing individuals without their permission. * Generating sexual content that may be seen by individuals without their consent. * Producing or disseminating false or misleading information. * Creating content that depicts extreme violence or bloodshed. * Distributing content that modifies copyrighted or licensed material in a way that breaches its usage terms. ## Dataset Structure The dataset is divided into 10 subsets each containing parquets about 4GB each. Each subfolder within contains a resolution range of the images and their respective aspect ratios. The dataset is also divided along images licensed for commercial use (C) and those that are not (NC). ## Dataset Creation ### Curation Rationale Creating a standardized, accessible dataset with synthetic caption and releasing it so other people can train on a common dataset for open source image generation. ### Source Data Yahoo Flickr Creative Commons 100M Dataset and Synthetically Generated Caption Data. #### Data Collection and Processing All synthetic captions were generated with BLIP2. See paper for more details. #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> Users of Flickr ## Bias, Risks, and Limitations See Yahoo Flickr Creative Commons 100M dataset for more information. The information was collected circa 2014 and known to have a bias towards internet connected Western countries. Some areas such as the global south lack representation. ## Citation **BibTeX:** ``` @article{gokaslan2023commoncanvas, title={CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images}, author={Gokaslan, Aaron and Cooper, A Feder and Collins, Jasmine and Seguin, Landan and Jacobson, Austin and Patel, Mihir and Frankle, Jonathan and Stephenson, Cory and Kuleshov, Volodymyr}, journal={arXiv preprint arXiv:2310.16825}, year={2023} } ``` ## Dataset Card Authors [Aaron Gokaslan](https://huggingface.co/Skylion007) ## Dataset Card Contact [Aaron Gokaslan](https://huggingface.co/Skylion007)

mlabonne/FineTome-100k

mlabonne

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: source dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 239650960.7474458 num_examples: 100000 download_size: 116531415 dataset_size: 239650960.7474458 configs: - config_name: default data_files: - split: train path: data/train-* --- # FineTome-100k  The FineTome dataset is a subset of [arcee-ai/The-Tome](https://huggingface.co/datasets/arcee-ai/The-Tome) (without arcee-ai/qwen2-72b-magpie-en), re-filtered using [HuggingFaceFW/fineweb-edu-classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier). It was made for my article ["Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth"](https://huggingface.co/blog/mlabonne/sft-llama3).

kamilakesbi/transformers_image_doc

kamilakesbi

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 406434.0 num_examples: 2 download_size: 381914 dataset_size: 406434.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

cardiffnlp/databench

cardiffnlp

--- language: - en - es pretty_name: " 💾🏋️💾 DataBench 💾🏋️💾" tags: - table-question-answering - table - qa license: mit task_categories: - table-question-answering - question-answering default: qa configs: - config_name: qa data_files: - data/001_Forbes/qa.parquet - data/002_Titanic/qa.parquet - data/003_Love/qa.parquet - data/004_Taxi/qa.parquet - data/005_NYC/qa.parquet - data/006_London/qa.parquet - data/007_Fifa/qa.parquet - data/008_Tornados/qa.parquet - data/009_Central/qa.parquet - data/010_ECommerce/qa.parquet - data/011_SF/qa.parquet - data/012_Heart/qa.parquet - data/013_Roller/qa.parquet - data/014_Airbnb/qa.parquet - data/015_Food/qa.parquet - data/016_Holiday/qa.parquet - data/017_Hacker/qa.parquet - data/018_Staff/qa.parquet - data/019_Aircraft/qa.parquet - data/020_Real/qa.parquet - data/021_Telco/qa.parquet - data/022_Airbnbs/qa.parquet - data/023_Climate/qa.parquet - data/024_Salary/qa.parquet - data/025_Data/qa.parquet - data/026_Predicting/qa.parquet - data/027_Supermarket/qa.parquet - data/028_Predict/qa.parquet - data/029_NYTimes/qa.parquet - data/030_Professionals/qa.parquet - data/031_Trustpilot/qa.parquet - data/032_Delicatessen/qa.parquet - data/033_Employee/qa.parquet - data/034_World/qa.parquet - data/035_Billboard/qa.parquet - data/036_US/qa.parquet - data/037_Ted/qa.parquet - data/038_Stroke/qa.parquet - data/039_Happy/qa.parquet - data/040_Speed/qa.parquet - data/041_Airline/qa.parquet - data/042_Predict/qa.parquet - data/043_Predict/qa.parquet - data/044_IMDb/qa.parquet - data/045_Predict/qa.parquet - data/046_120/qa.parquet - data/047_Bank/qa.parquet - data/048_Data/qa.parquet - data/049_Boris/qa.parquet - data/050_ING/qa.parquet - data/051_Pokemon/qa.parquet - data/052_Professional/qa.parquet - data/053_Patents/qa.parquet - data/054_Joe/qa.parquet - data/055_German/qa.parquet - data/056_Emoji/qa.parquet - data/057_Spain/qa.parquet - data/058_US/qa.parquet - data/059_Second/qa.parquet - data/060_Bakery/qa.parquet - data/061_Disneyland/qa.parquet - data/062_Trump/qa.parquet - data/063_Influencers/qa.parquet - data/064_Clustering/qa.parquet - data/065_RFM/qa.parquet # - split: 001_Forbes # path: data/001_Forbes/qa.parquet # - split: 002_Titanic # path: data/002_Titanic/qa.parquet # - split: 003_Love # path: data/003_Love/qa.parquet # - split: 004_Taxi # path: data/004_Taxi/qa.parquet # - split: 005_NYC # path: data/005_NYC/qa.parquet # - split: 006_London # path: data/006_London/qa.parquet # - split: 007_Fifa # path: data/007_Fifa/qa.parquet # - split: 008_Tornados # path: data/008_Tornados/qa.parquet # - split: 009_Central # path: data/009_Central/qa.parquet # - split: 010_ECommerce # path: data/010_ECommerce/qa.parquet # - split: 011_SF # path: data/011_SF/qa.parquet # - split: 012_Heart # path: data/012_Heart/qa.parquet # - split: 013_Roller # path: data/013_Roller/qa.parquet # - split: 014_Airbnb # path: data/014_Airbnb/qa.parquet # - split: 015_Food # path: data/015_Food/qa.parquet # - split: 016_Holiday # path: data/016_Holiday/qa.parquet # - split: 017_Hacker # path: data/017_Hacker/qa.parquet # - split: 018_Staff # path: data/018_Staff/qa.parquet # - split: 019_Aircraft # path: data/019_Aircraft/qa.parquet # - split: 020_Real # path: data/020_Real/qa.parquet # - split: 021_Telco # path: data/021_Telco/qa.parquet # - split: 022_Airbnbs # path: data/022_Airbnbs/qa.parquet # - split: 023_Climate # path: data/023_Climate/qa.parquet # - split: 024_Salary # path: data/024_Salary/qa.parquet # - split: 025_Data # path: data/025_Data/qa.parquet # - split: 026_Predicting # path: data/026_Predicting/qa.parquet # - split: 027_Supermarket # path: data/027_Supermarket/qa.parquet # - split: 028_Predict # path: data/028_Predict/qa.parquet # - split: 029_NYTimes # path: data/029_NYTimes/qa.parquet # - split: 030_Professionals # path: data/030_Professionals/qa.parquet # - split: 031_Trustpilot # path: data/031_Trustpilot/qa.parquet # - split: 032_Delicatessen # path: data/032_Delicatessen/qa.parquet # - split: 033_Employee # path: data/033_Employee/qa.parquet # - split: 034_World # path: data/034_World/qa.parquet # - split: 035_Billboard # path: data/035_Billboard/qa.parquet # - split: 036_US # path: data/036_US/qa.parquet # - split: 037_Ted # path: data/037_Ted/qa.parquet # - split: 038_Stroke # path: data/038_Stroke/qa.parquet # - split: 039_Happy # path: data/039_Happy/qa.parquet # - split: 040_Speed # path: data/040_Speed/qa.parquet # - split: 041_Airline # path: data/041_Airline/qa.parquet # - split: 042_Predict # path: data/042_Predict/qa.parquet # - split: 043_Predict # path: data/043_Predict/qa.parquet # - split: 044_IMDb # path: data/044_IMDb/qa.parquet # - split: 045_Predict # path: data/045_Predict/qa.parquet # - split: "046_120" # path: data/046_120/qa.parquet # - split: 047_Bank # path: data/047_Bank/qa.parquet # - split: 048_Data # path: data/048_Data/qa.parquet # - split: 049_Boris # path: data/049_Boris/qa.parquet # - split: 050_ING # path: data/050_ING/qa.parquet # - split: 051_Pokemon # path: data/051_Pokemon/qa.parquet # - split: 052_Professional # path: data/052_Professional/qa.parquet # - split: 053_Patents # path: data/053_Patents/qa.parquet # - split: 054_Joe # path: data/054_Joe/qa.parquet # - split: 055_German # path: data/055_German/qa.parquet # - split: 056_Emoji # path: data/056_Emoji/qa.parquet # - split: 057_Spain # path: data/057_Spain/qa.parquet # - split: 058_US # path: data/058_US/qa.parquet # - split: 059_Second # path: data/059_Second/qa.parquet # - split: 060_Bakery # path: data/060_Bakery/qa.parquet # - split: 061_Disneyland # path: data/061_Disneyland/qa.parquet # - split: 062_Trump # path: data/062_Trump/qa.parquet # - split: 063_Influencers # path: data/063_Influencers/qa.parquet # - split: 064_Clustering # path: data/064_Clustering/qa.parquet # - split: 065_RFM # path: data/065_RFM/qa.parquet # - config_name: 001_Forbes # data_files: # - split: full # path: data/001_Forbes/all.parquet # - split: lite # path: data/001_Forbes/sample.parquet # - config_name: 002_Titanic # data_files: # - split: full # path: data/002_Titanic/all.parquet # - split: lite # path: data/002_Titanic/sample.parquet # - config_name: 003_Love # data_files: # - split: full # path: data/003_Love/all.parquet # - split: lite # path: data/003_Love/sample.parquet # - config_name: 004_Taxi # data_files: # - split: full # path: data/004_Taxi/all.parquet # - split: lite # path: data/004_Taxi/sample.parquet # - config_name: 005_NYC # data_files: # - split: full # path: data/005_NYC/all.parquet # - split: lite # path: data/005_NYC/sample.parquet # - config_name: 006_London # data_files: # - split: full # path: data/006_London/all.parquet # - split: lite # path: data/006_London/sample.parquet # - config_name: 007_Fifa # data_files: # - split: full # path: data/007_Fifa/all.parquet # - split: lite # path: data/007_Fifa/sample.parquet # - config_name: 008_Tornados # data_files: # - split: full # path: data/008_Tornados/all.parquet # - split: lite # path: data/008_Tornados/sample.parquet # - config_name: 009_Central # data_files: # - split: full # path: data/009_Central/all.parquet # - split: lite # path: data/009_Central/sample.parquet # - config_name: 010_ECommerce # data_files: # - split: full # path: data/010_ECommerce/all.parquet # - split: lite # path: data/010_ECommerce/sample.parquet # - config_name: 011_SF # data_files: # - split: full # path: data/011_SF/all.parquet # - split: lite # path: data/011_SF/sample.parquet # - config_name: 012_Heart # data_files: # - split: full # path: data/012_Heart/all.parquet # - split: lite # path: data/012_Heart/sample.parquet # - config_name: 013_Roller # data_files: # - split: full # path: data/013_Roller/all.parquet # - split: lite # path: data/013_Roller/sample.parquet # - config_name: 014_Airbnb # data_files: # - split: full # path: data/014_Airbnb/all.parquet # - split: lite # path: data/014_Airbnb/sample.parquet # - config_name: 015_Food # data_files: # - split: full # path: data/015_Food/all.parquet # - split: lite # path: data/015_Food/sample.parquet # - config_name: 016_Holiday # data_files: # - split: full # path: data/016_Holiday/all.parquet # - split: lite # path: data/016_Holiday/sample.parquet # - config_name: 017_Hacker # data_files: # - split: full # path: data/017_Hacker/all.parquet # - split: lite # path: data/017_Hacker/sample.parquet # - config_name: 018_Staff # data_files: # - split: full # path: data/018_Staff/all.parquet # - split: lite # path: data/018_Staff/sample.parquet # - config_name: 019_Aircraft # data_files: # - split: full # path: data/019_Aircraft/all.parquet # - split: lite # path: data/019_Aircraft/sample.parquet # - config_name: 020_Real # data_files: # - split: full # path: data/020_Real/all.parquet # - split: lite # path: data/020_Real/sample.parquet # - config_name: 021_Telco # data_files: # - split: full # path: data/021_Telco/all.parquet # - split: lite # path: data/021_Telco/sample.parquet # - config_name: 022_Airbnbs # data_files: # - split: full # path: data/022_Airbnbs/all.parquet # - split: lite # path: data/022_Airbnbs/sample.parquet # - config_name: 023_Climate # data_files: # - split: full # path: data/023_Climate/all.parquet # - split: lite # path: data/023_Climate/sample.parquet # - config_name: 024_Salary # data_files: # - split: full # path: data/024_Salary/all.parquet # - split: lite # path: data/024_Salary/sample.parquet # - config_name: 025_Data # data_files: # - split: full # path: data/025_Data/all.parquet # - split: lite # path: data/025_Data/sample.parquet # - config_name: 026_Predicting # data_files: # - split: full # path: data/026_Predicting/all.parquet # - split: lite # path: data/026_Predicting/sample.parquet # - config_name: 027_Supermarket # data_files: # - split: full # path: data/027_Supermarket/all.parquet # - split: lite # path: data/027_Supermarket/sample.parquet # - config_name: 028_Predict # data_files: # - split: full # path: data/028_Predict/all.parquet # - split: lite # path: data/028_Predict/sample.parquet # - config_name: 029_NYTimes # data_files: # - split: full # path: data/029_NYTimes/all.parquet # - split: lite # path: data/029_NYTimes/sample.parquet # - config_name: 030_Professionals # data_files: # - split: full # path: data/030_Professionals/all.parquet # - split: lite # path: data/030_Professionals/sample.parquet # - config_name: 031_Trustpilot # data_files: # - split: full # path: data/031_Trustpilot/all.parquet # - split: lite # path: data/031_Trustpilot/sample.parquet # - config_name: 032_Delicatessen # data_files: # - split: full # path: data/032_Delicatessen/all.parquet # - split: lite # path: data/032_Delicatessen/sample.parquet # - config_name: 033_Employee # data_files: # - split: full # path: data/033_Employee/all.parquet # - split: lite # path: data/033_Employee/sample.parquet # - config_name: 034_World # data_files: # - split: full # path: data/034_World/all.parquet # - split: lite # path: data/034_World/sample.parquet # - config_name: 035_Billboard # data_files: # - split: full # path: data/035_Billboard/all.parquet # - split: lite # path: data/035_Billboard/sample.parquet # - config_name: 036_US # data_files: # - split: full # path: data/036_US/all.parquet # - split: lite # path: data/036_US/sample.parquet # - config_name: 037_Ted # data_files: # - split: full # path: data/037_Ted/all.parquet # - split: lite # path: data/037_Ted/sample.parquet # - config_name: 038_Stroke # data_files: # - split: full # path: data/038_Stroke/all.parquet # - split: lite # path: data/038_Stroke/sample.parquet # - config_name: 039_Happy # data_files: # - split: full # path: data/039_Happy/all.parquet # - split: lite # path: data/039_Happy/sample.parquet # - config_name: 040_Speed # data_files: # - split: full # path: data/040_Speed/all.parquet # - split: lite # path: data/040_Speed/sample.parquet # - config_name: 041_Airline # data_files: # - split: full # path: data/041_Airline/all.parquet # - split: lite # path: data/041_Airline/sample.parquet # - config_name: 042_Predict # data_files: # - split: full # path: data/042_Predict/all.parquet # - split: lite # path: data/042_Predict/sample.parquet # - config_name: 043_Predict # data_files: # - split: full # path: data/043_Predict/all.parquet # - split: lite # path: data/043_Predict/sample.parquet # - config_name: 044_IMDb # data_files: # - split: full # path: data/044_IMDb/all.parquet # - split: lite # path: data/044_IMDb/sample.parquet # - config_name: 045_Predict # data_files: # - split: full # path: data/045_Predict/all.parquet # - split: lite # path: data/045_Predict/sample.parquet # - config_name: "046_120" # data_files: # - split: full # path: data/046_120/all.parquet # - split: lite # path: data/046_120/sample.parquet # - config_name: 047_Bank # data_files: # - split: full # path: data/047_Bank/all.parquet # - split: lite # path: data/047_Bank/sample.parquet # - config_name: 048_Data # data_files: # - split: full # path: data/048_Data/all.parquet # - split: lite # path: data/048_Data/sample.parquet # - config_name: 049_Boris # data_files: # - split: full # path: data/049_Boris/all.parquet # - split: lite # path: data/049_Boris/sample.parquet # - config_name: 050_ING # data_files: # - split: full # path: data/050_ING/all.parquet # - split: lite # path: data/050_ING/sample.parquet # - config_name: 051_Pokemon # data_files: # - split: full # path: data/051_Pokemon/all.parquet # - split: lite # path: data/051_Pokemon/sample.parquet # - config_name: 052_Professional # data_files: # - split: full # path: data/052_Professional/all.parquet # - split: lite # path: data/052_Professional/sample.parquet # - config_name: 053_Patents # data_files: # - split: full # path: data/053_Patents/all.parquet # - split: lite # path: data/053_Patents/sample.parquet # - config_name: 054_Joe # data_files: # - split: full # path: data/054_Joe/all.parquet # - split: lite # path: data/054_Joe/sample.parquet # - config_name: 055_German # data_files: # - split: full # path: data/055_German/all.parquet # - split: lite # path: data/055_German/sample.parquet # - config_name: 056_Emoji # data_files: # - split: full # path: data/056_Emoji/all.parquet # - split: lite # path: data/056_Emoji/sample.parquet # - config_name: 057_Spain # data_files: # - split: full # path: data/057_Spain/all.parquet # - split: lite # path: data/057_Spain/sample.parquet # - config_name: 058_US # data_files: # - split: full # path: data/058_US/all.parquet # - split: lite # path: data/058_US/sample.parquet # - config_name: 059_Second # data_files: # - split: full # path: data/059_Second/all.parquet # - split: lite # path: data/059_Second/sample.parquet # - config_name: 060_Bakery # data_files: # - split: full # path: data/060_Bakery/all.parquet # - split: lite # path: data/060_Bakery/sample.parquet # - config_name: 061_Disneyland # data_files: # - split: full # path: data/061_Disneyland/all.parquet # - split: lite # path: data/061_Disneyland/sample.parquet # - config_name: 062_Trump # data_files: # - split: full # path: data/062_Trump/all.parquet # - split: lite # path: data/062_Trump/sample.parquet # - config_name: 063_Influencers # data_files: # - split: full # path: data/063_Influencers/all.parquet # - split: lite # path: data/063_Influencers/sample.parquet # - config_name: 064_Clustering # data_files: # - split: full # path: data/064_Clustering/all.parquet # - split: lite # path: data/064_Clustering/sample.parquet # - config_name: 065_RFM # data_files: # - split: full # path: data/065_RFM/all.parquet # - split: lite # path: data/065_RFM/sample.parquet - config_name: semeval data_files: - split: train path: - data/001_Forbes/qa.parquet - data/002_Titanic/qa.parquet - data/003_Love/qa.parquet - data/004_Taxi/qa.parquet - data/005_NYC/qa.parquet - data/006_London/qa.parquet - data/007_Fifa/qa.parquet - data/008_Tornados/qa.parquet - data/009_Central/qa.parquet - data/010_ECommerce/qa.parquet - data/011_SF/qa.parquet - data/012_Heart/qa.parquet - data/013_Roller/qa.parquet - data/014_Airbnb/qa.parquet - data/015_Food/qa.parquet - data/016_Holiday/qa.parquet - data/017_Hacker/qa.parquet - data/018_Staff/qa.parquet - data/019_Aircraft/qa.parquet - data/020_Real/qa.parquet - data/021_Telco/qa.parquet - data/022_Airbnbs/qa.parquet - data/023_Climate/qa.parquet - data/024_Salary/qa.parquet - data/025_Data/qa.parquet - data/026_Predicting/qa.parquet - data/027_Supermarket/qa.parquet - data/028_Predict/qa.parquet - data/029_NYTimes/qa.parquet - data/030_Professionals/qa.parquet - data/031_Trustpilot/qa.parquet - data/032_Delicatessen/qa.parquet - data/033_Employee/qa.parquet - data/034_World/qa.parquet - data/035_Billboard/qa.parquet - data/036_US/qa.parquet - data/037_Ted/qa.parquet - data/038_Stroke/qa.parquet - data/039_Happy/qa.parquet - data/040_Speed/qa.parquet - data/041_Airline/qa.parquet - data/042_Predict/qa.parquet - data/043_Predict/qa.parquet - data/044_IMDb/qa.parquet - data/045_Predict/qa.parquet - data/046_120/qa.parquet - data/047_Bank/qa.parquet - data/048_Data/qa.parquet - data/049_Boris/qa.parquet - split: dev path: - data/050_ING/qa.parquet - data/051_Pokemon/qa.parquet - data/052_Professional/qa.parquet - data/053_Patents/qa.parquet - data/054_Joe/qa.parquet - data/055_German/qa.parquet - data/056_Emoji/qa.parquet - data/057_Spain/qa.parquet - data/058_US/qa.parquet - data/059_Second/qa.parquet - data/060_Bakery/qa.parquet - data/061_Disneyland/qa.parquet - data/062_Trump/qa.parquet - data/063_Influencers/qa.parquet - data/064_Clustering/qa.parquet - data/065_RFM/qa.parquet --- # 💾🏋️💾 DataBench 💾🏋️💾 This repository contains the original 65 datasets used for the paper [Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs](https://huggingface.co/datasets/cardiffnlp/databench/resolve/main/Databench-LREC-Coling-2024.pdf) which appeared in LREC-COLING 2024. Large Language Models (LLMs) are showing emerging abilities, and one of the latest recognized ones is tabular reasoning in question answering on tabular data. Although there are some available datasets to assess question answering systems on tabular data, they are not large and diverse enough to evaluate this new ability of LLMs. To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1615 columns in total, and 1300 questions to evaluate your models for the task of QA over Tabular Data. ## Usage ```python from datasets import load_dataset # Load all QA pairs all_qa = load_dataset("cardiffnlp/databench", name="qa", split="train") # Load SemEval 2025 task 8 Question-Answer splits semeval_train_qa = load_dataset("cardiffnlp/databench", name="semeval", split="train") semeval_dev_qa = load_dataset("cardiffnlp/databench", name="semeval", split="dev") ``` You can use any of the individual [integrated libraries](https://huggingface.co/docs/hub/datasets-libraries#libraries) to load the actual data where the answer is to be retrieved. For example, using pandas in Python: ```python import pandas as pd # "001_Forbes", the id of the dataset ds_id = all_qa['dataset'][0] # full dataset df = pd.read_parquet(f"hf://datasets/cardiffnlp/databench/data/{ds_id}/all.parquet") # sample dataset df = pd.read_parquet(f"hf://datasets/cardiffnlp/databench/data/{ds_id}/sample.parquet") ``` ## 📚 Datasets By clicking on each name in the table below, you will be able to explore each dataset. | | Name | Rows | Cols | Domain | Source (Reference) | |---:|:-------------------------------|-------:|-------:|:---------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | 1 | [Forbes](https://public.graphext.com/0b211530c7e213d3/index.html?section=data) | 2668 | 17 | Business | [Forbes](https://www.forbes.com/billionaires/)| | 2 | [Titanic](https://public.graphext.com/8577225c5ffd88fd/index.html) | 887 | 8 | Travel and Locations | [Kaggle](https://www.kaggle.com/competitions/titanic/data)| | 3 | [Love](https://public.graphext.com/be7a566b0c485916/index.html) | 373 | 35 | Social Networks and Surveys | [Graphext](https://public.graphext.com/1de78f6820cfd5ba/index.html) | | 4 | [Taxi](https://public.graphext.com/bcee13c23070f333/index.html) | 100000 | 20 | Travel and Locations | [Kaggle](https://www.kaggle.com/competitions/nyc-taxi-trip-duration/overview) | | 5 | [NYC Calls](https://public.graphext.com/1ce2f5fae408621e/index.html) | 100000 | 46 | Business | [City of New York](https://data.cityofnewyork.us/Social-Services/NYC-311-Data/jrb2-thup) | | 6 | [London Airbnbs](https://public.graphext.com/6bbf4bbd3ff279c0/index.html) | 75241 | 74 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/labdmitriy/airbnb) | | 7 | [Fifa](https://public.graphext.com/37bca51494c10a79/index.html) | 14620 | 59 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/stefanoleone992/fifa-21-complete-player-dataset) | | 8 | [Tornados](https://public.graphext.com/4be9872e031199c3/index.html) | 67558 | 14 | Health | [Kaggle](https://www.kaggle.com/datasets/danbraswell/us-tornado-dataset-1950-2021) | | 9 | [Central Park](https://public.graphext.com/7b3d3a4d7bf1e9b5/index.html) | 56245 | 6 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/danbraswell/new-york-city-weather-18692022) | | 10 | [ECommerce Reviews](https://public.graphext.com/a5b8911b215958ad/index.html) | 23486 | 10 | Business | [Kaggle](https://www.kaggle.com/datasets/nicapotato/womens-ecommerce-clothing-reviews) | | 11 | [SF Police](https://public.graphext.com/ab815ab14f88115c/index.html) | 713107 | 35 | Social Networks and Surveys | [US Gov](https://catalog.data.gov/dataset/police-department-incident-reports-2018-to-present) | | 12 | [Heart Failure](https://public.graphext.com/245cec64075f5542/index.html) | 918 | 12 | Health | [Kaggle](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) | | 13 | [Roller Coasters](https://public.graphext.com/1e550e6c24fc1930/index.html) | 1087 | 56 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/robikscube/rollercoaster-database) | | 14 | [Madrid Airbnbs](https://public.graphext.com/77265ea3a63e650f/index.html) | 20776 | 75 | Travel and Locations | [Inside Airbnb](http://data.insideairbnb.com/spain/comunidad-de-madrid/madrid/2023-09-07/data/listings.parquet.gz) | | 15 | [Food Names](https://public.graphext.com/5aad4c5d6ef140b3/index.html) | 906 | 4 | Business | [Data World](https://data.world/alexandra/generic-food-database) | | 16 | [Holiday Package Sales](https://public.graphext.com/fbc34d3f24282e46/index.html) | 4888 | 20 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/susant4learning/holiday-package-purchase-prediction) | | 17 | [Hacker News](https://public.graphext.com/f20501a9d616b5a5/index.html) | 9429 | 20 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/hacker-news/hacker-news) | | 18 | [Staff Satisfaction](https://public.graphext.com/6822ac1ce6307fec/index.html) | 14999 | 11 | Business | [Kaggle](https://www.kaggle.com/datasets/mohamedharris/employee-satisfaction-index-dataset) | | 19 | [Aircraft Accidents](https://public.graphext.com/1802117b1b14f5c5/index.html) | 23519 | 23 | Health | [Kaggle](https://www.kaggle.com/datasets/ramjasmaurya/aviation-accidents-history1919-april-2022) | | 20 | [Real Estate Madrid](https://public.graphext.com/5f83ec219a7ea84f/index.html) | 26026 | 59 | Business | [Idealista](https://public.graphext.com/5f83ec219a7ea84f/index.html) | | 21 | [Telco Customer Churn](https://public.graphext.com/362cd8e3e96f70d4/index.html) | 7043 | 21 | Business | [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn) | | 22 | [Airbnbs Listings NY](https://public.graphext.com/77265ea3a63e650f/index.html) | 37012 | 33 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data) | | 23 | [Climate in Madrid](https://public.graphext.com/83a75b4f1cea8df4/index.html?section=data) | 36858 | 26 | Travel and Locations | [AEMET](https://public.graphext.com/83a75b4f1cea8df4/index.html?section=data) | | 24 | [Salary Survey Spain 2018](https://public.graphext.com/24d1e717ba01aa3d/index.html) | 216726 | 29 | Business | [INE](ine.es) | | 25 | [Data Driven SEO ](https://public.graphext.com/4e5b1cac9ebdfa44/index.html) | 62 | 5 | Business | [Graphext](https://www.graphext.com/post/data-driven-seo-a-keyword-optimization-guide-using-web-scraping-co-occurrence-analysis-graphext-deepnote-adwords) | | 26 | [Predicting Wine Quality](https://public.graphext.com/de04acf5d18a9aea/index.html) | 1599 | 12 | Business | [Kaggle](https://www.kaggle.com/datasets/yasserh/wine-quality-dataset) | | 27 | [Supermarket Sales](https://public.graphext.com/9a6742da6a8d8f7f/index.html) | 1000 | 17 | Business | [Kaggle](https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales) | | 28 | [Predict Diabetes](https://public.graphext.com/def4bada27af324c/index.html) | 768 | 9 | Health | [Kaggle](https://www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset) | | 29 | [NYTimes World In 2021](https://public.graphext.com/af4c8eef1757973c/index.html?section=data) | 52588 | 5 | Travel and Locations | [New York Times](https://public.graphext.com/af4c8eef1757973c/index.html) | | 30 | [Professionals Kaggle Survey](https://public.graphext.com/3a2e87f90363a85d/index.html) | 19169 | 64 | Business | [Kaggle](https://www.kaggle.com/c/kaggle-survey-2021/data) | | 31 | [Trustpilot Reviews](https://public.graphext.com/367e29432331fbfd/index.html?section=data) | 8020 | 6 | Business | [TrustPilot](https://public.graphext.com/367e29432331fbfd/index.html?section=data) | | 32 | [Delicatessen Customers](https://public.graphext.com/a1687589fbde07bc/index.html) | 2240 | 29 | Business | [Kaggle](https://www.kaggle.com/datasets/rodsaldanha/arketing-campaign) | | 33 | [Employee Attrition](https://public.graphext.com/07a91a15ecf2b8f6/index.html) | 14999 | 11 | Business | [Kaggle(modified)](https://www.kaggle.com/datasets/pavan9065/predicting-employee-attrition) | | 34 | [World Happiness Report 2020](https://public.graphext.com/754c83ff0a7ba087/index.html) | 153 | 20 | Social Networks and Surveys | [World Happiness](https://worldhappiness.report/data/) | | 35 | [Billboard Lyrics](https://public.graphext.com/7e0b009e8d0af719/index.html) | 5100 | 6 | Sports and Entertainment | [Brown University](https://cs.brown.edu/courses/cs100/students/project11/) | | 36 | [US Migrations 2012-2016](https://public.graphext.com/dbdadf87a5c21695/index.html) | 288300 | 9 | Social Networks and Surveys | [US Census](https://www.census.gov/topics/population/migration/guidance/county-to-county-migration-flows.html) | | 37 | [Ted Talks](https://public.graphext.com/07e48466fb670904/index.html) | 4005 | 19 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ashishjangra27/ted-talks) | | 38 | [Stroke Likelihood](https://public.graphext.com/20ccfee9e84948e3/index.html) | 5110 | 12 | Health | [Kaggle](https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease) | | 39 | [Happy Moments](https://public.graphext.com/9b86efff48989701/index.html) | 100535 | 11 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ritresearch/happydb) | | 40 | [Speed Dating](https://public.graphext.com/f1912daad7870be0/index.html) | 8378 | 123 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ulrikthygepedersen/speed-dating) | | 41 | [Airline Mentions X (former Twitter)](https://public.graphext.com/29cb7f73f6e17a38/index.html) | 14640 | 15 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/7e6999327d1f83fd/index.html) | | 42 | [Predict Student Performance](https://public.graphext.com/def4bada27af324c/index.html) | 395 | 33 | Business | [Kaggle](https://www.kaggle.com/datasets/impapan/student-performance-data-set) | | 43 | [Loan Defaults](https://public.graphext.com/0c7fb68ab8071a1f/index.html) | 83656 | 20 | Business | [SBA](https://www.kaggle.com/datasets/mirbektoktogaraev/should-this-loan-be-approved-or-denied) | | 44 | [IMDb Movies](https://public.graphext.com/e23e33774872c496/index.html) | 85855 | 22 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows) | | 45 | [Spotify Song Popularity](https://public.graphext.com/def4bada27af324c/index.html) | 21000 | 19 | Sports and Entertainment | [Spotify](https://www.kaggle.com/datasets/tomigelo/spotify-audio-features) | | 46 | [120 Years Olympics](https://public.graphext.com/e57d5e2f172c9a99/index.html) | 271116 | 15 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results) | | 47 | [Bank Customer Churn](https://public.graphext.com/e8f7aeacd209f74a/index.html) | 7088 | 15 | Business | [Kaggle](https://www.kaggle.com/datasets/mathchi/churn-for-bank-customers) | | 48 | [Data Science Salary Data](https://public.graphext.com/4e5b1cac9ebdfa44/index.html) | 742 | 28 | Business | [Kaggle](https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries) | | 49 | [Boris Johnson UK PM Tweets](https://public.graphext.com/f6623a1ca0f41c8e/index.html) | 3220 | 34 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/f6623a1ca0f41c8e/index.html) | | 50 | [ING 2019 X Mentions](https://public.graphext.com/075030310aa702c6/index.html) | 7244 | 22 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/075030310aa702c6/index.html) | | 51 | [Pokemon Features](https://public.graphext.com/f30d4d863a2e6b01/index.html) | 1072 | 13 | Business | [Kaggle](https://www.kaggle.com/datasets/rounakbanik/pokemon) | | 52 | [Professional Map](https://public.graphext.com/70af2240cb751968/index.html) | 1227 | 12 | Business | [Kern et al, PNAS'20](https://github.com/behavioral-ds/VocationMap) | | 53 | [Google Patents](https://public.graphext.com/a262300e31874716/index.html) | 9999 | 20 | Business | [BigQuery](https://www.kaggle.com/datasets/bigquery/patents/data) | | 54 | [Joe Biden Tweets](https://public.graphext.com/33fa2efa41541ab1/index.html) | 491 | 34 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/339cee259f0a9b32/index.html?section=data) | 55 | [German Loans](https://public.graphext.com/d3f5e425e9d4b0a1/index.html) | 1000 | 18 | Business | [Kaggle](https://www.kaggle.com/datasets/uciml/german-credit/data) | | 56 | [Emoji Diet](https://public.graphext.com/e721cc7d790c06d4/index.html) | 58 | 35 | Health | [Kaggle](https://www.kaggle.com/datasets/ofrancisco/emoji-diet-nutritional-data-sr28) | | 57 | [Spain Survey 2015](https://public.graphext.com/90ca7539b160fdfa/index.html?section=data) | 20000 | 45 | Social Networks and Surveys | [CIS](https://public.graphext.com/90ca7539b160fdfa/index.html?section=data) | | 58 | [US Polls 2020](https://public.graphext.com/dbdadf87a5c21695/index.html) | 3523 | 52 | Social Networks and Surveys | [Brandwatch](https://www.brandwatch.com/p/us-election-raw-polling-data/) | | 59 | [Second Hand Cars](https://public.graphext.com/543d0c49d7120ca0/index.html) | 50000 | 21 | Business | [DataMarket](https://www.kaggle.com/datasets/datamarket/venta-de-coches) | | 60 | [Bakery Purchases](https://public.graphext.com/6f2102e80f47a192/index.html) | 20507 | 5 | Business | [Kaggle](https://www.kaggle.com/code/xvivancos/market-basket-analysis/report) | | 61 | [Disneyland Customer Reviews](https://public.graphext.com/b1037bb566b7b316/index.html) | 42656 | 6 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/arushchillar/disneyland-reviews) | | 62 | [Trump Tweets](https://public.graphext.com/7aff94c3b7f159fc/index.html) | 15039 | 20 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/be903c098a90e46f/index.html?section=data) | | 63 | [Influencers](https://public.graphext.com/e097f1ea03d761a9/index.html) | 1039 | 14 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/e097f1ea03d761a9/index.html) | | 64 | [Clustering Zoo Animals](https://public.graphext.com/d1b66902e46a712a/index.html) | 101 | 18 | Health | [Kaggle](https://www.kaggle.com/datasets/jirkadaberger/zoo-animals) | | 65 | [RFM Analysis](https://public.graphext.com/4db2e54e29006a21/index.html) | 541909 | 8 | Business | [UCI ML](https://www.kaggle.com/datasets/carrie1/ecommerce-data) | ## 🏗️ Folder structure Each folder represents one dataset. You will find the following files within: * all.parquet: the processed data, with each column tagged with our typing system, in [parquet](https://arrow.apache.org/docs/python/parquet.html). * qa.parquet: contains the human-made set of questions, tagged by type and columns used, for the dataset (sample_answer indicates the answers for DataBench lite) * sample.parquet: sample containing 20 rows of the original dataset (DataBench lite) * info.yml: additional information about the dataset ## 🗂️ Column typing system In an effort to map the stage for later analysis, we have categorized the columns by type. This information allows us to segment different kinds of data so that we can subsequently analyze the model's behavior on each column type separately. All parquet files have been casted to their smallest viable data type using the open source [Lector](https://github.com/graphext/lector) reader. What this means is that in the data types we have more granular information that allows us to know if the column contains NaNs or not (following panda’s convention of Int vs int), as well as whether small numerical values contain negatives (Uint vs int) and their range. We also have dates with potential timezone information (although for now they’re all UTC), as well as information about categories’ cardinality coming from the arrow types. In the table below you can see all the data types assigned to each column, as well as the number of columns for each type. The most common data types are numbers and categories with 1336 columns of the total of 1615 included in DataBench. These are followed by some other more rare types as urls, booleans, dates or lists of elements. | Type | Columns | Example | | -------------- | ------- | ----------------------- | | number | 788 | 55 | | category | 548 | apple | | date | 50 | 1970-01-01 | | text | 46 | A red fox ran... | | url | 31 | google.com | | boolean | 18 | True | | list[number] | 14 | [1,2,3] | | list[category] | 112 | [apple, orange, banana] | | list[url] | 8 | [google.com, apple.com] | ## 🔗 Reference You can download the paper [here](https://huggingface.co/datasets/cardiffnlp/databench/resolve/main/Databench-LREC-Coling-2024.pdf). If you use this resource, please use the following reference: ``` @inproceedings{oses-etal-2024-databench, title = "Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs", author = "Jorge Osés Grijalba and Luis Alfonso Ureña-López and Eugenio Martínez Cámara and Jose Camacho-Collados", booktitle = "Proceedings of LREC-COLING 2024", year = "2024", address = "Turin, Italy" } ```

bigscience/evaluation-results

bigscience

--- pretty_name: evaluation-results size_categories: - 100M<n<1B task_categories: - other --- # BigScience BLOOM Evaluation Results This repository contains evaluation results & original predictions of BLOOM & friends. ## Usage You can load numeric results via: ```python from datasets import load_dataset ds = load_dataset("bigscience/evaluation-results", "bloom") ``` If it takes too long, it may be faster to clone the repository and load the data from disk: ```python !git clone https://huggingface.co/datasets/bigscience/evaluation-results ds = load_dataset("evaluation-results", "bloom") ``` For example generations (.jsonl files), you need to manually browse the repository. ## Structure For `bigsciencelmevalharness`, `lmevalharness` & `codeeval` evaluation_frameworks the structure is: `model_name > evaluation_framework > checkpoint_type > dataset_name > data` ## Evaluation Procedure - `bigsciencelmevalharness` files were created using the below: - https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/291 - https://github.com/bigscience-workshop/lm-evaluation-harness - `lmevalharness` files were created using the below: - https://github.com/bigscience-workshop/Megatron-DeepSpeed - https://github.com/EleutherAI/lm-evaluation-harness - `codeeval` files were created using the HumanEval code dataset with the below: - https://github.com/loubnabnl/bloom-code-evaluation

OpenDILabCommunity/LMDrive

OpenDILabCommunity

--- configs: - config_name: default data_files: - split: train path: navigation_instruction_list.txt sep: " " default: true license: apache-2.0 language: - en size_categories: - n>1T --- # LMDrive 64K Dataset Card LMDrive Dataset consists of 64K instruction-sensor-control data clips collected in the CARLA simulator， where each clip includes one navigation instruction, several notice instructions, a sequence of multi-modal multi-view sensor data, and control signals. The duration of the clip spans from 2 to 20 seconds. ## Dataset details - `data/`: dataset folder, the entire dataset contains about 2T of data. - `data/Town01`: sub dataset folder, which only consists of the data folder for the Town01 - `data/Town02`: sub dataset folder, which only consists of the data folder for the Town02 - ... - `dataset_index.txt`: the data list for pretraining the vision encoder - `navigation_instruction_list.txt`: the data list for instruction finetuning - `notice_instruction_list.json`: the data list for instruction finetuning (optional if the notice instruction data is not engaged in the training) **Dataset date:** LMDrive-1.0 Dataset was collected in September 2023. **Paper or resources for more information:** Github: https://github.com/opendilab/LMDrive/README.md Paper: https://arxiv.org/abs/2312.07488 **License:** Attribution-NonCommercial 4.0 International **Where to send questions or comments about the model:** https://github.com/opendilab/LMDrive/issues ## Intended use **Primary intended uses:** The primary use of LMDrive is research on large multimodal models for autonomous driving. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, large multimodal model, autonomous driving, and artificial intelligence.

Voxel51/PIDray

Voxel51

--- annotations_creators: [] language: en license: apache-2.0 size_categories: - 1K<n<10K task_categories: - object-detection task_ids: [] pretty_name: pidray tags: - fiftyone - image - object-detection dataset_summary: '  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 9482 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = fouh.load_from_hub("voxel51/PIDray") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for pidray PIDray is a large-scale dataset which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. The dataset contains 12 categories of prohibited items in 47, 677 X-ray images with high-quality annotated segmentation masks and bounding boxes.  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 9482 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = fouh.load_from_hub("dgural/PIDray") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description From _Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark_: Automatic security inspection using computer vision technology is a challenging task in real-world scenarios due to various factors, including intra-class variance, class imbalance, and occlusion. Most of the previous methods rarely solve the cases that the prohibited items are deliberately hidden in messy objects due to the lack of large-scale datasets, restricted their applications in real-world scenarios. Towards real-world prohibited item detection, we collect a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. With an intensive amount of effort, our dataset contains categories of prohibited items in X-ray images with high-quality annotated segmentation masks and bounding boxes. To the best of our knowledge, it is the largest prohibited items detection dataset to date. Meanwhile, we design the selective dense attention network (SDANet) to construct a strong baseline, which consists of the dense attention module and the dependency refinement module. The dense attention module formed by the spatial and channel-wise dense attentions, is designed to learn the discriminative features to boost the performance. The dependency refinement module is used to exploit the dependencies of multi-scale features. Extensive experiments conducted on the collected PIDray dataset demonstrate that the proposed method performs favorably against the state-of-the-art methods, especially for detecting the deliberately hidden items. - **Language(s) (NLP):** en - **License:** apache-2.0 The images and the corresponding annotations in PIDray Dataset can be used ONLY for academic purposes, NOT for commercial purposes. Copyright © 2021 Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences All rights reserved. ### Dataset Sources - **Repository:** https://github.com/bywang2018/security-dataset - **Paper [optional]:** https://arxiv.org/abs/2108.07020 ## Uses This used for academic research on airport security screening machines and the detection of objects being scanned. ### Out-of-Scope Use Any non-academic work is out of scope and prohibited. ## Citation @inproceedings{wang2021towards, title={Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark}, author={Wang, Boying and Zhang, Libo and Wen, Longyin and Liu, Xianglong and Wu, Yanjun}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={5412--5421}, year={2021} }

argmaxinc/whisperkit-evals

argmaxinc

--- pretty_name: "WhisperKit ASR Evaluation Results" viewer: false library_name: whisperkit tags: - whisper - whisperkit - coreml - asr - quantized --- # WhisperKit WhisperKit is an on-device speech recognition framework for Apple Silicon: https://github.com/argmaxinc/WhisperKit For performance and accuracy benchmarks on real devices, please see: https://huggingface.co/spaces/argmaxinc/whisperkit-benchmarks

Voxel51/emnist-letters-tiny

Voxel51

--- annotations_creators: [] language: en size_categories: - 10K<n<100K task_categories: - image-classification task_ids: [] pretty_name: EMNIST-Letters-10k tags: - fiftyone - image - image-classification dataset_summary: '  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10000 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = load_from_hub("Voxel51/emnist-letters-tiny") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for EMNIST-Letters-10k <!-- Provide a quick summary of the dataset. --> A random subset of the train and test splits from the letters portion of [EMNIST](https://pytorch.org/vision/0.18/generated/torchvision.datasets.EMNIST.html)  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10000 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("Voxel51/emnist-letters-tiny") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Homepage:** https://www.nist.gov/itl/products-and-services/emnist-dataset - **Paper :** https://arxiv.org/abs/1702.05373v1 ## Citation **BibTeX:** ```bibtex @misc{cohen2017emnistextensionmnisthandwritten, title={EMNIST: an extension of MNIST to handwritten letters}, author={Gregory Cohen and Saeed Afshar and Jonathan Tapson and André van Schaik}, year={2017}, eprint={1702.05373}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/1702.05373}, } ``` ## Dataset Card Author [Jacob Marks](https://huggingface.co/jamarks)

qmeeus/voxpopuli

qmeeus

--- dataset_info: - config_name: de features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 61603981153.568 num_examples: 108473 - name: validation num_bytes: 1149586917.507 num_examples: 2109 download_size: 52060225655 dataset_size: 62753568071.075 - config_name: es features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 36533665201.936 num_examples: 50922 - name: validation num_bytes: 1173444834.383 num_examples: 1631 download_size: 1005381345 dataset_size: 37707110036.319 - config_name: fr features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 48346650213.26 num_examples: 73561 - name: validation num_bytes: 1149779276.605 num_examples: 1727 download_size: 17314564262 dataset_size: 49496429489.865005 - config_name: nl features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 10436544940.608 num_examples: 20968 - name: validation num_bytes: 636925883.64 num_examples: 1230 download_size: 9404833804 dataset_size: 11073470824.248 configs: - config_name: de data_files: - split: train path: de/train-* - split: validation path: de/validation-* - config_name: es data_files: - split: train path: es/train-* - split: validation path: es/validation-* - config_name: fr data_files: - split: train path: fr/train-* - split: validation path: fr/validation-* - config_name: nl data_files: - split: train path: nl/train-* - split: validation path: nl/validation-* --- # Dataset Card for "voxpopuli" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

allenai/ZebraLogicBench-private

allenai

--- dataset_info: - config_name: grid_mode features: - name: id dtype: string - name: size dtype: string - name: puzzle dtype: string - name: solution struct: - name: header sequence: string - name: rows sequence: sequence: string - name: created_at dtype: string splits: - name: test num_bytes: 1545275 num_examples: 1000 download_size: 345826 dataset_size: 1545275 - config_name: mc_mode features: - name: id dtype: string - name: puzzle dtype: string - name: question dtype: string - name: choices sequence: string - name: answer dtype: string - name: created_at dtype: string splits: - name: test num_bytes: 5039993 num_examples: 3259 download_size: 826292 dataset_size: 5039993 configs: - config_name: grid_mode data_files: - split: test path: grid_mode/test-* - config_name: mc_mode data_files: - split: test path: mc_mode/test-* ---

DeliberatorArchiver/asmr-archive-data

DeliberatorArchiver

--- license: agpl-3.0 language: - ja tags: - not-for-all-audiences pretty_name: ASMR Archive Dataset size_categories: - n>1T viewer: false --- # ASMR Media Archive Storage This repository contains an archive of ASMR works. All data in this repository is uploaded for **educational and research purposes only.** **All use is at your own risk.** > [!IMPORTANT] > This repository contains **>= 32 TB** of files. > Git LFS consumes twice as much disk space because of the way it works, so `git clone` is not recommended. [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/guides/cli) or [Python libraries](https://huggingface.co/docs/huggingface_hub/index) allow you to select and download only a subset of files. **\>\>\> [CLICK HERE or on the IMAGE BELOW for a list of works](https://asmr-archive-data.daydreamer-json.cc/) \<\<\<** <a href="https://asmr-archive-data.daydreamer-json.cc/"><img width="500" src="./front_page_screenshot.jpg"></a>

lmms-lab/LLaVA-OneVision-Data

lmms-lab

--- language: - en - zh license: apache-2.0 pretty_name: llava-onevision-data dataset_info: - config_name: CLEVR-Math(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 791346970 num_examples: 5280 download_size: 441208499 dataset_size: 791346970 - config_name: FigureQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 463326576.625 num_examples: 17587 download_size: 258197193 dataset_size: 463326576.625 - config_name: GEOS(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1503641 num_examples: 498 download_size: 684471 dataset_size: 1503641 - config_name: GeoQA+(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 53579705.75 num_examples: 17162 download_size: 33480538 dataset_size: 53579705.75 - config_name: Geometry3K(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 218085473.5 num_examples: 9724 download_size: 125914780 dataset_size: 218085473.5 - config_name: IconQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 208430568.375 num_examples: 22589 download_size: 117222488 dataset_size: 208430568.375 - config_name: MapQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 384120915.875 num_examples: 5225 download_size: 215768443 dataset_size: 384120915.875 - config_name: PMC-VQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 571444866.5 num_examples: 35948 download_size: 326541003 dataset_size: 571444866.5 - config_name: Super-CLEVR(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2795082410.75 num_examples: 8642 download_size: 1580301917 dataset_size: 2795082410.75 - config_name: TabMWP(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 307726997.5 num_examples: 22452 download_size: 173938487 dataset_size: 307726997.5 - config_name: UniGeo(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 38296693.375 num_examples: 11949 download_size: 24170743 dataset_size: 38296693.375 - config_name: VisualWebInstruct(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 36317112275.0 num_examples: 263584 download_size: 36239916454 dataset_size: 36317112275.0 - config_name: VizWiz(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1170333936.5 num_examples: 6604 download_size: 660752297 dataset_size: 1170333936.5 - config_name: ai2d(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 438572782.375 num_examples: 2429 download_size: 437348514 dataset_size: 438572782.375 - config_name: ai2d(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 866076731 num_examples: 4864 download_size: 860306578 dataset_size: 866076731 - config_name: ai2d(internvl) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1832787249.625 num_examples: 12403 download_size: 527493895 dataset_size: 1832787249.625 - config_name: allava_instruct_laion4v features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5981767621.25 num_examples: 49990 download_size: 5873046236 dataset_size: 5981767621.25 - config_name: allava_instruct_vflan4v features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2680974558.25 num_examples: 19990 download_size: 2670088751 dataset_size: 2680974558.25 - config_name: aokvqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6896420844.25 num_examples: 16534 download_size: 6894236970 dataset_size: 6896420844.25 - config_name: chart2text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1145458729.5 num_examples: 26956 download_size: 1123681047 dataset_size: 1145458729.5 - config_name: chartqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 815335215.5 num_examples: 18260 download_size: 803084541 dataset_size: 815335215.5 - config_name: chrome_writting features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 44422597.875 num_examples: 8825 download_size: 39611257 dataset_size: 44422597.875 - config_name: clevr(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 10528974543.625 num_examples: 69995 download_size: 10460536445 dataset_size: 10528974543.625 - config_name: diagram_image_to_text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 18858266 num_examples: 295 download_size: 18659115 dataset_size: 18858266 - config_name: dvqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4487270615.625 num_examples: 199995 download_size: 4277056467 dataset_size: 4487270615.625 - config_name: figureqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2351194509.625 num_examples: 99995 download_size: 2222640639 dataset_size: 2351194509.625 - config_name: geo170k(align) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 204236256.75 num_examples: 60242 download_size: 58185410 dataset_size: 204236256.75 - config_name: geo170k(qa) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 266040519.125 num_examples: 67823 download_size: 160022430 dataset_size: 266040519.125 - config_name: geo3k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 42634333.625 num_examples: 2091 download_size: 41097851 dataset_size: 42634333.625 - config_name: geomverse(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2263893609.75 num_examples: 9298 download_size: 2211726352 dataset_size: 2263893609.75 - config_name: hateful_memes(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 3057252325.125 num_examples: 8495 download_size: 3055839880 dataset_size: 3057252325.125 - config_name: hitab(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 161706881.125 num_examples: 2495 download_size: 157871287 dataset_size: 161706881.125 - config_name: hme100k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 273229915.5 num_examples: 74492 download_size: 241005430 dataset_size: 273229915.5 - config_name: iam(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1131633206.75 num_examples: 5658 download_size: 1128371221 dataset_size: 1131633206.75 - config_name: iconqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 331284932.25 num_examples: 27302 download_size: 327005220 dataset_size: 331284932.25 - config_name: iiit5k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 21821437.25 num_examples: 1990 download_size: 21623116 dataset_size: 21821437.25 - config_name: image_textualization(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5218283253.375 num_examples: 99573 download_size: 5164176816 dataset_size: 5218283253.375 - config_name: infographic(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 713657496.25 num_examples: 1982 download_size: 656276080 dataset_size: 713657496.25 - config_name: infographic_vqa features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1528953078.75 num_examples: 4394 download_size: 1419340319 dataset_size: 1528953078.75 - config_name: infographic_vqa_llava_format features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1765315696.875 num_examples: 2113 download_size: 1764548536 dataset_size: 1765315696.875 - config_name: intergps(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 24973395.625 num_examples: 1275 download_size: 24736545 dataset_size: 24973395.625 - config_name: k12_printing features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1205153118.5 num_examples: 256636 download_size: 1108572712 dataset_size: 1205153118.5 - config_name: llavar_gpt4_20k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 633833350.25 num_examples: 19790 download_size: 625365542 dataset_size: 633833350.25 - config_name: lrv_chart features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 99338686 num_examples: 1776 download_size: 97979446 dataset_size: 99338686 - config_name: lrv_normal(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 422589381.75 num_examples: 10490 download_size: 406958773 dataset_size: 422589381.75 - config_name: magpie_pro(l3_80b_mt) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1657129141 num_examples: 299988 download_size: 885893066 dataset_size: 1657129141 - config_name: magpie_pro(l3_80b_st) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1033666690 num_examples: 299990 download_size: 562771564 dataset_size: 1033666690 - config_name: magpie_pro(qwen2_72b_st) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 703489344 num_examples: 299982 download_size: 361433408 dataset_size: 703489344 - config_name: mapqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 3355751195.5 num_examples: 37412 download_size: 3305639218 dataset_size: 3355751195.5 - config_name: mathqa features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 18318538 num_examples: 29827 download_size: 7857130 dataset_size: 18318538 - config_name: mavis_math_metagen features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2304025372.5 num_examples: 87348 download_size: 322776224 dataset_size: 2304025372.5 - config_name: mavis_math_rule_geo features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 14313211512.25 num_examples: 99990 download_size: 5841283073 dataset_size: 14313211512.25 - config_name: multihiertt(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 300319803.25 num_examples: 7614 download_size: 295638314 dataset_size: 300319803.25 - config_name: orand_car_a features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 23602442.125 num_examples: 1999 download_size: 23333412 dataset_size: 23602442.125 - config_name: raven(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1706160514.625 num_examples: 41995 download_size: 1693150088 dataset_size: 1706160514.625 - config_name: rendered_text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11082594894.625 num_examples: 9995 download_size: 11081962044 dataset_size: 11082594894.625 - config_name: robut_sqa(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 685580779.375 num_examples: 8509 download_size: 678666263 dataset_size: 685580779.375 - config_name: robut_wikisql(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6200499653 num_examples: 74984 download_size: 6168399217 dataset_size: 6200499653 - config_name: robut_wtq(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4091776188.875 num_examples: 38241 download_size: 4062777449 dataset_size: 4091776188.875 - config_name: scienceqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 286843125.625 num_examples: 4971 download_size: 282896809 dataset_size: 286843125.625 - config_name: scienceqa(nona_context) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2111029055 num_examples: 19208 download_size: 2053942726 dataset_size: 2111029055 - config_name: screen2words(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 7977502095.375 num_examples: 15725 download_size: 7962327904 dataset_size: 7977502095.375 - config_name: sharegpt4o features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6968025789.5 num_examples: 57284 download_size: 6772195470 dataset_size: 6968025789.5 - config_name: sharegpt4v(coco) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2620153362.875 num_examples: 50017 download_size: 2595583499 dataset_size: 2620153362.875 - config_name: sharegpt4v(knowledge) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 372100773.5 num_examples: 1988 download_size: 369799318 dataset_size: 372100773.5 - config_name: sharegpt4v(llava) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 781795487.25 num_examples: 29990 download_size: 400344187 dataset_size: 781795487.25 - config_name: sharegpt4v(sam) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4437405218.25 num_examples: 8990 download_size: 4428597081 dataset_size: 4437405218.25 - config_name: sroie features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 117810195 num_examples: 33616 download_size: 103647636 dataset_size: 117810195 - config_name: st_vqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5771194098.75 num_examples: 17242 download_size: 5768888141 dataset_size: 5771194098.75 - config_name: tabmwp(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 311192518.375 num_examples: 22717 download_size: 306092255 dataset_size: 311192518.375 - config_name: tallyqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 35998988065.625 num_examples: 98675 download_size: 35982430394 dataset_size: 35998988065.625 - config_name: textcaps features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2222268476.25 num_examples: 21942 download_size: 2217838132 dataset_size: 2222268476.25 - config_name: textocr(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2581655353 num_examples: 25104 download_size: 2574418106 dataset_size: 2581655353 - config_name: tqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 331203026.25 num_examples: 27302 download_size: 326999466 dataset_size: 331203026.25 - config_name: ureader_cap features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 9269857109.75 num_examples: 91434 download_size: 2292099971 dataset_size: 9269857109.75 - config_name: ureader_ie features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11871457209.75 num_examples: 17322 download_size: 1999083115 dataset_size: 11871457209.75 - config_name: vision_flan(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 24847242604.5 num_examples: 186060 download_size: 24750561877 dataset_size: 24847242604.5 - config_name: vistext(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 550187184.5 num_examples: 9964 download_size: 452795103 dataset_size: 550187184.5 - config_name: visual7w(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4451436523.875 num_examples: 14361 download_size: 4441971985 dataset_size: 4451436523.875 - config_name: visualmrc(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2938154124.25 num_examples: 3022 download_size: 2909296079 dataset_size: 2938154124.25 - config_name: vqarad(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 95533417 num_examples: 308 download_size: 95410398 dataset_size: 95533417 - config_name: vsr(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 891981646 num_examples: 2152 download_size: 891572866 dataset_size: 891981646 - config_name: websight(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11209715828.625 num_examples: 9995 download_size: 11144460985 dataset_size: 11209715828.625 configs: - config_name: CLEVR-Math(MathV360K) data_files: - split: train path: CLEVR-Math(MathV360K)/train-* - config_name: FigureQA(MathV360K) data_files: - split: train path: FigureQA(MathV360K)/train-* - config_name: GEOS(MathV360K) data_files: - split: train path: GEOS(MathV360K)/train-* - config_name: GeoQA+(MathV360K) data_files: - split: train path: GeoQA+(MathV360K)/train-* - config_name: Geometry3K(MathV360K) data_files: - split: train path: Geometry3K(MathV360K)/train-* - config_name: IconQA(MathV360K) data_files: - split: train path: IconQA(MathV360K)/train-* - config_name: MapQA(MathV360K) data_files: - split: train path: MapQA(MathV360K)/train-* - config_name: PMC-VQA(MathV360K) data_files: - split: train path: PMC-VQA(MathV360K)/train-* - config_name: Super-CLEVR(MathV360K) data_files: - split: train path: Super-CLEVR(MathV360K)/train-* - config_name: TabMWP(MathV360K) data_files: - split: train path: TabMWP(MathV360K)/train-* - config_name: UniGeo(MathV360K) data_files: - split: train path: UniGeo(MathV360K)/train-* - config_name: VisualWebInstruct(filtered) data_files: - split: train path: VisualWebInstruct(filtered)/train-* - config_name: VizWiz(MathV360K) data_files: - split: train path: VizWiz(MathV360K)/train-* - config_name: ai2d(cauldron,llava_format) data_files: - split: train path: ai2d(cauldron,llava_format)/train-* - config_name: ai2d(gpt4v) data_files: - split: train path: ai2d(gpt4v)/train-* - config_name: ai2d(internvl) data_files: - split: train path: ai2d(internvl)/train-* - config_name: allava_instruct_laion4v data_files: - split: train path: allava_instruct_laion4v/train-* - config_name: allava_instruct_vflan4v data_files: - split: train path: allava_instruct_vflan4v/train-* - config_name: aokvqa(cauldron,llava_format) data_files: - split: train path: aokvqa(cauldron,llava_format)/train-* - config_name: chart2text(cauldron) data_files: - split: train path: chart2text(cauldron)/train-* - config_name: chartqa(cauldron,llava_format) data_files: - split: train path: chartqa(cauldron,llava_format)/train-* - config_name: chrome_writting data_files: - split: train path: chrome_writting/train-* - config_name: clevr(cauldron,llava_format) data_files: - split: train path: clevr(cauldron,llava_format)/train-* - config_name: diagram_image_to_text(cauldron) data_files: - split: train path: diagram_image_to_text(cauldron)/train-* - config_name: dvqa(cauldron,llava_format) data_files: - split: train path: dvqa(cauldron,llava_format)/train-* - config_name: figureqa(cauldron,llava_format) data_files: - split: train path: figureqa(cauldron,llava_format)/train-* - config_name: geo170k(align) data_files: - split: train path: geo170k(align)/train-* - config_name: geo170k(qa) data_files: - split: train path: geo170k(qa)/train-* - config_name: geo3k data_files: - split: train path: geo3k/train-* - config_name: geomverse(cauldron) data_files: - split: train path: geomverse(cauldron)/train-* - config_name: hateful_memes(cauldron,llava_format) data_files: - split: train path: hateful_memes(cauldron,llava_format)/train-* - config_name: hitab(cauldron,llava_format) data_files: - split: train path: hitab(cauldron,llava_format)/train-* - config_name: hme100k data_files: - split: train path: hme100k/train-* - config_name: iam(cauldron) data_files: - split: train path: iam(cauldron)/train-* - config_name: iconqa(cauldron,llava_format) data_files: - split: train path: iconqa(cauldron,llava_format)/train-* - config_name: iiit5k data_files: - split: train path: iiit5k/train-* - config_name: image_textualization(filtered) data_files: - split: train path: image_textualization(filtered)/train-* - config_name: infographic(gpt4v) data_files: - split: train path: infographic(gpt4v)/train-* - config_name: infographic_vqa data_files: - split: train path: infographic_vqa/train-* - config_name: infographic_vqa_llava_format data_files: - split: train path: infographic_vqa_llava_format/train-* - config_name: intergps(cauldron,llava_format) data_files: - split: train path: intergps(cauldron,llava_format)/train-* - config_name: k12_printing data_files: - split: train path: k12_printing/train-* - config_name: llavar_gpt4_20k data_files: - split: train path: llavar_gpt4_20k/train-* - config_name: lrv_chart data_files: - split: train path: lrv_chart/train-* - config_name: lrv_normal(filtered) data_files: - split: train path: lrv_normal(filtered)/train-* - config_name: magpie_pro(l3_80b_mt) data_files: - split: train path: magpie_pro(l3_80b_mt)/train-* - config_name: magpie_pro(l3_80b_st) data_files: - split: train path: magpie_pro(l3_80b_st)/train-* - config_name: magpie_pro(qwen2_72b_st) data_files: - split: train path: magpie_pro(qwen2_72b_st)/train-* - config_name: mapqa(cauldron,llava_format) data_files: - split: train path: mapqa(cauldron,llava_format)/train-* - config_name: mathqa data_files: - split: train path: mathqa/train-* - config_name: mavis_math_metagen data_files: - split: train path: mavis_math_metagen/train-* - config_name: mavis_math_rule_geo data_files: - split: train path: mavis_math_rule_geo/train-* - config_name: multihiertt(cauldron) data_files: - split: train path: multihiertt(cauldron)/train-* - config_name: orand_car_a data_files: - split: train path: orand_car_a/train-* - config_name: raven(cauldron) data_files: - split: train path: raven(cauldron)/train-* - config_name: rendered_text(cauldron) data_files: - split: train path: rendered_text(cauldron)/train-* - config_name: robut_sqa(cauldron) data_files: - split: train path: robut_sqa(cauldron)/train-* - config_name: robut_wikisql(cauldron) data_files: - split: train path: robut_wikisql(cauldron)/train-* - config_name: robut_wtq(cauldron,llava_format) data_files: - split: train path: robut_wtq(cauldron,llava_format)/train-* - config_name: scienceqa(cauldron,llava_format) data_files: - split: train path: scienceqa(cauldron,llava_format)/train-* - config_name: scienceqa(nona_context) data_files: - split: train path: scienceqa(nona_context)/train-* - config_name: screen2words(cauldron) data_files: - split: train path: screen2words(cauldron)/train-* - config_name: sharegpt4o data_files: - split: train path: sharegpt4o/train-* - config_name: sharegpt4v(coco) data_files: - split: train path: sharegpt4v(coco)/train-* - config_name: sharegpt4v(knowledge) data_files: - split: train path: sharegpt4v(knowledge)/train-* - config_name: sharegpt4v(llava) data_files: - split: train path: sharegpt4v(llava)/train-* - config_name: sharegpt4v(sam) data_files: - split: train path: sharegpt4v(sam)/train-* - config_name: sroie data_files: - split: train path: sroie/train-* - config_name: st_vqa(cauldron,llava_format) data_files: - split: train path: st_vqa(cauldron,llava_format)/train-* - config_name: tabmwp(cauldron) data_files: - split: train path: tabmwp(cauldron)/train-* - config_name: tallyqa(cauldron,llava_format) data_files: - split: train path: tallyqa(cauldron,llava_format)/train-* - config_name: textcaps data_files: - split: train path: textcaps/train-* - config_name: textocr(gpt4v) data_files: - split: train path: textocr(gpt4v)/train-* - config_name: tqa(cauldron,llava_format) data_files: - split: train path: tqa(cauldron,llava_format)/train-* - config_name: ureader_cap data_files: - split: train path: ureader_cap/train-* - config_name: ureader_ie data_files: - split: train path: ureader_ie/train-* - config_name: vision_flan(filtered) data_files: - split: train path: vision_flan(filtered)/train-* - config_name: vistext(cauldron) data_files: - split: train path: vistext(cauldron)/train-* - config_name: visual7w(cauldron,llava_format) data_files: - split: train path: visual7w(cauldron,llava_format)/train-* - config_name: visualmrc(cauldron) data_files: - split: train path: visualmrc(cauldron)/train-* - config_name: vqarad(cauldron,llava_format) data_files: - split: train path: vqarad(cauldron,llava_format)/train-* - config_name: vsr(cauldron,llava_format) data_files: - split: train path: vsr(cauldron,llava_format)/train-* - config_name: websight(cauldron) data_files: - split: train path: websight(cauldron)/train-* --- # Dataset Card for LLaVA-OneVision **[2024-09-01]: Uploaded VisualWebInstruct(filtered), it's used in OneVision Stage** > almost all subsets are uploaded with HF's required format and you can use the recommended interface to download them and follow our code below to convert them. > the subset of `ureader_kg` and `ureader_qa` are uploaded with the processed jsons and tar.gz of image folders. > You may directly download them from the following url. > https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data/tree/main/ureader_kg In this dataset, we include the data splits used in the both final image stage and one-vision stage. For more details, please check our [paper](arxiv.org/abs/2408.03326) and our [training doc](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). ## Dataset Description - **Curated by:** Bo Li, Kaichen Zhang, Hao Zhang, Yuanhan Zhang, Renrui Zhang, Feng Li, Dong Guo - **Language(s) (NLP):** English, Chinese - **License:** Apache License 2.0 ## Dataset Sources <!-- Provide the basic links for the dataset. --> - **Dataset Collection:** We include a few subsets from existing dataset collection [Cambrian](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M), [Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron), [UReader](https://arxiv.org/abs/2310.05126). Since we only used a few subsets from these datasets, and applied the cleaning and re-annotation process, we uploaded our processed version of these datasets into our own repository and thank the authors for providing the original datasets. - **Other Datasets:** For rest single source dataset, such as AI2D, OKVQA, we cite and link the original sources in our paper. ## Uses This dataset is used for the training of the LLaVA-OneVision model. We only allow the use of this dataset for academic research and education purpose. For OpenAI GPT-4 generated data, we recommend the users to check the [OpenAI Usage Policy](https://openai.com/policies/usage-policies/). ## Dataset Structure We expalin the data composition for mid-stage and final-stage at our repo in [**training doc**](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). ### Statistics We provide the statistics of the dataset in the following figures, and refer the audience to check our paper.   ### Code Guidance To help audience to better understand our dataest, we upload them into Hugging Face Dataset compatible format. During LLaVA-OneVision training, we use the `json` and `image/video` folder to store the data. > the subset of `ureader_kg` and `ureader_qa` are uploaded with the processed jsons and tar.gz of image folders. You may directly download them from the following url. > https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data/tree/main/ureader_kg Here we provide the code guidance to convert the dataset into the format of LLaVA-OneVision, and conduct the training of the LLaVA-OneVision model with converted dataset. ```python import os from datasets import load_dataset from tqdm import tqdm import json data = load_dataset("lmms-lab/LLaVA-OneVision-Data", split="train") image_folder = "<your_image_folder>" converted_data = [] for da in tqdm(data): json_data = {} json_data["id"] = da["id"] if da["image"] is not None: json_data["image"] = f"{da['id']}.jpg" da["image"].save(os.path.join(image_folder, json_data["image"])) json_data["conversations"] = da["conversations"] converted_data.append(json_data) with open("<your_json_file>.json", "w") as f: json.dump(converted_data, f, indent=4, ensure_ascii=False) ``` ## Citation **BibTeX:** [More Information Needed] ## Glossary The dataset collection process is conducted by all of the authors, we thank the Feng Li and Renrui Zhang for providing [LLaVA-M4-Instruct Data](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data) and Yuanhan for providing the [Video datasets](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K). After the dataset collection, the cleaning and re-annotation process, including final mixture of the dataset, is conducted by Bo Li and with the great help of Kaichen Zhang. ## Dataset Card Authors The dataset is curated by the following authors: Bo Li, Kaichen Zhang, Hao Zhang, Yuanhan Zhang, Renrui Zhang, Feng Li ## Dataset Card Contact [Bo Li](https://brianboli.com/): [email protected] [Kaichen Zhang](https://www.linkedin.com/in/kaichen-zhang-014b17219/?originalSubdomain=sg)

DL3DV/DL3DV-ALL-2K

DL3DV

--- tags: - 3D Vision - NeRF - 3D Gaussian - Dataset - Novel View Synthesis - Text to 3D - Image to 3D pretty_name: Dl3DV-Dataset size_categories: - n>1T --- # DL3DV-Dataset This repo has all the 2K frames with camera poses of DL3DV-10K Dataset. We are working hard to review all the dataset to avoid sensitive information. Thank you for your patience. # Download If you have enough space, you can use git to download a dataset from huggingface. See this [link](https://huggingface.co/docs/hub/en/datasets-downloading). [480P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P)/[960P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-960P) versions should satisfies most needs. If you do not have enough space, we further provide a [download script](https://github.com/DL3DV-10K/Dataset/blob/main/scripts/download.py) here to download a subset. The usage: ```Bash usage: download.py [-h] --odir ODIR --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} --resolution {4K,2K,960P,480P} --file_type {images+poses,video,colmap_cache} [--hash HASH] [--clean_cache] optional arguments: -h, --help show this help message and exit --odir ODIR output directory --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} The subset of the benchmark to download --resolution {4K,2K,960P,480P} The resolution to donwnload --file_type {images+poses,video,colmap_cache} The file type to download --hash HASH If set subset=hash, this is the hash code of the scene to download --clean_cache If set, will clean the huggingface cache to save space ``` Here are some examples: ```Bash # Make sure you have applied for the access. # Use this to download the download.py script wget https://raw.githubusercontent.com/DL3DV-10K/Dataset/main/scripts/download.py # Download 2K resolution images and poses, 0~1K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 1K --resolution 2K --file_type images+poses --clean_cache # Download 2K resolution images and poses, 1K~2K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 2K --resolution 2K --file_type images+poses --clean_cache ``` You can also download a specific scene with its hash. The scene-hash pair visualization can be found [here](https://htmlpreview.github.io/?https://github.com/DL3DV-10K/Dataset/blob/main/visualize/index.html). ```Bash python download.py --odir DL3DV-10K --subset 2K --resolution 2K --file_type images+poses --hash e2cedefea8a0ed2d0ffbd5bdc08acbe7e1f85c96f72f7b790e9dfe1c98963047 --clean_cache ``` # News - [x] DL3DV-1K, 2K, 3K, 4K - [ ] DL3DV-5K ~ 10K

bigscience/xP3

bigscience

--- annotations_creators: - expert-generated - crowdsourced language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3 size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?", "targets": "Yes" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. Adding a new language is very simple, you can take [this script adding Russian](https://huggingface.co/datasets/bs-la/xP3ru/blob/main/xp3_ru.py) as an example. |Language|Kilobytes|%|Samples|%| |--------|------:|-:|---:|-:| |tw|106288|0.11|265071|0.34| |bm|107056|0.11|265180|0.34| |ak|108096|0.11|265071|0.34| |eu|108112|0.11|269973|0.34| |ca|110608|0.12|271191|0.34| |fon|113072|0.12|265063|0.34| |st|114080|0.12|265063|0.34| |ki|115040|0.12|265180|0.34| |tum|116032|0.12|265063|0.34| |wo|122560|0.13|365063|0.46| |ln|126304|0.13|365060|0.46| |as|156256|0.16|265063|0.34| |or|161472|0.17|265063|0.34| |kn|165456|0.17|265063|0.34| |ml|175040|0.18|265864|0.34| |rn|192992|0.2|318189|0.4| |nso|229712|0.24|915051|1.16| |tn|235536|0.25|915054|1.16| |lg|235936|0.25|915021|1.16| |rw|249360|0.26|915043|1.16| |ts|250256|0.26|915044|1.16| |sn|252496|0.27|865056|1.1| |xh|254672|0.27|915058|1.16| |zu|263712|0.28|915061|1.16| |ny|272128|0.29|915063|1.16| |ig|325232|0.34|950097|1.2| |yo|352784|0.37|918416|1.16| |ne|393680|0.41|315754|0.4| |pa|523248|0.55|339210|0.43| |gu|560688|0.59|347499|0.44| |sw|560896|0.59|1114455|1.41| |mr|666240|0.7|417269|0.53| |bn|832720|0.88|428843|0.54| |ta|924496|0.97|410633|0.52| |te|1332912|1.4|573364|0.73| |ur|1918272|2.02|855756|1.08| |vi|3101408|3.27|1667306|2.11| |code|4330752|4.56|2707724|3.43| |hi|4393696|4.63|1543441|1.96| |zh|4589904|4.83|3560556|4.51| |id|4606288|4.85|2627392|3.33| |ar|4677264|4.93|2148955|2.72| |fr|5546688|5.84|5055942|6.41| |pt|6129584|6.46|3562772|4.52| |es|7571808|7.98|5151349|6.53| |en|37261104|39.25|31495184|39.93| |total|94941936|100.0|78883588|100.0| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI datasets & HumanEval) - Natural Language Inference (NLI) - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @article{muennighoff2022crosslingual, title={Crosslingual generalization through multitask finetuning}, author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others}, journal={arXiv preprint arXiv:2211.01786}, year={2022} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.

knkarthick/dialogsum

knkarthick

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization - text2text-generation - text-generation task_ids: [] pretty_name: DIALOGSum Corpus tags: - dialogue-summary - one-liner-summary - meeting-title - email-subject --- # Dataset Card for DIALOGSum Corpus ## Dataset Description ### Links - **Homepage:** https://aclanthology.org/2021.findings-acl.449 - **Repository:** https://github.com/cylnlp/dialogsum - **Paper:** https://aclanthology.org/2021.findings-acl.449 - **Point of Contact:** https://huggingface.co/knkarthick ### Dataset Summary DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics. ### Languages English ## Dataset Structure ### Data Instances DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues (+1000 tests) split into train, test and validation. The first instance in the training set: {'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up} ### Data Fields - dialogue: text of dialogue. - summary: human written summary of the dialogue. - topic: human written topic/one liner of the dialogue. - id: unique file id of an example. ### Data Splits - train: 12460 - val: 500 - test: 1500 - holdout: 100 [Only 3 features: id, dialogue, topic] ## Dataset Creation ### Curation Rationale In paper: We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers. Compared with previous datasets, dialogues from DialogSum have distinct characteristics: Under rich real-life scenarios, including more diverse task-oriented scenarios; Have clear communication patterns and intents, which is valuable to serve as summarization sources; Have a reasonable length, which comforts the purpose of automatic summarization. We ask annotators to summarize each dialogue based on the following criteria: Convey the most salient information; Be brief; Preserve important named entities within the conversation; Be written from an observer perspective; Be written in formal language. ### Who are the source language producers? linguists ### Who are the annotators? language experts ## Licensing Information CC BY-NC-SA 4.0 ## Citation Information ``` @inproceedings{chen-etal-2021-dialogsum, title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset", author = "Chen, Yulong and Liu, Yang and Chen, Liang and Zhang, Yue", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.449", doi = "10.18653/v1/2021.findings-acl.449", pages = "5062--5074", ``` ## Contributions Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.

HuggingFaceTB/smoltalk

HuggingFaceTB

--- language: - en tags: - synthetic pretty_name: SmolTalk size_categories: - 1M<n<10M configs: - config_name: all data_files: - split: train path: data/all/train-* - split: test path: data/all/test-* - config_name: smol-magpie-ultra data_files: - split: train path: data/smol-magpie-ultra/train-* - split: test path: data/smol-magpie-ultra/test-* - config_name: smol-constraints data_files: - split: train path: data/smol-constraints/train-* - split: test path: data/smol-constraints/test-* - config_name: smol-rewrite data_files: - split: train path: data/smol-rewrite/train-* - split: test path: data/smol-rewrite/test-* - config_name: smol-summarize data_files: - split: train path: data/smol-summarize/train-* - split: test path: data/smol-summarize/test-* - config_name: apigen-80k data_files: - split: train path: data/apigen-80k/train-* - split: test path: data/apigen-80k/test-* - config_name: everyday-conversations data_files: - split: train path: data/everyday-conversations/train-* - split: test path: data/everyday-conversations/test-* - config_name: explore-instruct-rewriting data_files: - split: train path: data/explore-instruct-rewriting/train-* - split: test path: data/explore-instruct-rewriting/test-* - config_name: longalign data_files: - split: train path: data/longalign/train-* - split: test path: data/longalign/test-* - config_name: metamathqa-50k data_files: - split: train path: data/metamathqa-50k/train-* - split: test path: data/metamathqa-50k/test-* - config_name: numina-cot-100k data_files: - split: train path: data/numina-cot-100k/train-* - split: test path: data/numina-cot-100k/test-* - config_name: openhermes-100k data_files: - split: train path: data/openhermes-100k/train-* - split: test path: data/openhermes-100k/test-* - config_name: self-oss-instruct data_files: - split: train path: data/self-oss-instruct/train-* - split: test path: data/self-oss-instruct/test-* - config_name: systemchats-30k data_files: - split: train path: data/systemchats-30k/train-* - split: test path: data/systemchats-30k/test-* --- # SmolTalk  ## Dataset description This is a synthetic dataset designed for supervised finetuning (SFT) of LLMs. It was used to build [SmolLM2-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) family of models and contains 1M samples. During the development of SmolLM2, we observed that models finetuned on public SFT datasets underperformed compared to other models with proprietary instruction datasets. To address this gap, we created new synthetic datasets that improve instruction following while covering diverse tasks including text editing, rewriting, summarization, and reasoning. Through a series of data ablations at 1.7B scale, we enhanced our SFT mix by incorporating public datasets to strengthen specific capabilities such as mathematics, coding, system prompt following and long-context understanding. All the new datasets were generated with [distilabel](https://github.com/argilla-io/distilabel) and you can find the generation code here https://github.com/huggingface/smollm/tree/main/distilabel_pipelines. You can load a dataset using ```python from datasets import load_dataset ds = load_dataset("HuggingFaceTB/smoltalk", "all", split="train") # to load the train split of a specific subset such as smol-magpie-ultra, you can do ds = load_dataset("HuggingFaceTB/smoltalk", "smol-magpie-ultra", split="train") ``` ## Dataset composition The mix consists of: **New datasets** - *Smol-Magpie-Ultra*: the core component of our mix, consisting of 400K samples generated using the Magpie pipeline with /Llama-3.1-405B-Instruct. We also heavily curate and filter this dataset compared to the original Magpie-Pro pipeline. SmolLM models trained on this dataset alone outperform those trained on popular public datasets like OpenHermes and Magpie Pro across key benchmarks including IFEval and MT-Bench. - Smol-contraints: a 36K-sample dataset that trains models to follow specific constraints, such as generating responses with a fixed number of sentences or words, or incorporating specified words in the output. The dataset has been decontaminated against IFEval to prevent overlap. - Smol-rewrite: an 50k-sample collection focused on text rewriting tasks, such as adjusting tone to be more friendly or professional. Note that Smol-Magpie-Ultra also includes some rewriting, editing, and summarization examples. - Smol-summarize: an 100k-sample dataset specialized in email and news summarization. **Existing public datasets** To enhance capabilities in mathematics, coding, system prompts, and long-context understanding, we fine-tuned SmolLM2-1.7B on various public SFT datasets and included subsets of the best performing ones using tuned ratios. These include: - OpenHermes2.5: we added 100k samples from [OpenHermes2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5), since we found that it helps preserve and boost benchmarks such as MMLU and WinoGrande, and BBH. - MetaMathQA: we add this [dataset](https://huggingface.co/datasets/meta-math/MetaMathQA?) to improve the model on mathematics and reasoning, we include 50k random samples. - NuminaMath-CoT: we find that this [dataset](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) helps on mathematics, especially hard problems found in benchmarks such as MATH. - Self-Oss-Starcoder2-Instruct: we use this [dataset](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k) to improve coding capabilities. - SystemChats2.0: to make the model support a variety of system prompt formats we add 30k samples from the [SystemChat-2.0](https://huggingface.co/datasets/cognitivecomputations/SystemChat-2.0) dataset. Note that Smol-rewrite and and Smol-summarize datasets also include system prompts. - LongAlign: we find that finetuning the model on only short samples makes it loose long context abilities beyond 2048 tokens, so we add english samples (with less than 16k tokens) from the [LongAlign-10k](https://huggingface.co/datasets/THUDM/LongAlign-10k) dataset and train with a 8192 sequence. - Everyday-conversations: this [dataset](https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k) includes multi-turn everyday conversations such as greeting and was used in SmolLM v1 post-training. - APIGen-Function-Calling: we use 80k samples from [apigen-function-calling](https://huggingface.co/datasets/argilla/apigen-function-calling) which is a mix of [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1) and [xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) datasets. - Explore-Instruct-Rewriting: 30k samples from this rewriting [dataset](https://huggingface.co/datasets/Wanfq/Explore_Instruct_Rewriting_32k). You can find the code for generating the new datasets with [distilabel](https://github.com/argilla-io/distilabel) here: https://github.com/huggingface/smollm. The ablation details will be included in an upcoming blog post. ## License All the new datasets (Smol-Magpie-Ultra, Smol-contraints, Smol-rewrite, Smol-summarize) are licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). For the existing public datasets, please refer to the original dataset for the license [Dataset composition](#dataset-composition) ## Evaluation We compare SmolTalk to the recent [Orca AgentInstruct 1M](https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1) dataset by finetuning SmolLM2 on both datasets using the same training setup (we train for 2 epochs, using a learning rate of 3e-04, a sequence length of 8192 and a global batch size of 16).  We also observe significant improvements at 7B scale when fine-tuning [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.3) on SmolTalk, notably on IFEval, BBH, GS8Mk and MATH.  ## Smol-SmolTalk For SmolLM2-135M-Instruct and SmolLM2-360M-Instruct, we use a subset of the dataset that is more suitable for these smaller models. For instance, we only include samples from Smol-Magpie-Ultra with more concise conversations and exclude advanced math datasets. You can find the dataset here: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk The training code is available here https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2 ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```

japanese-asr/whisper_transcriptions.reazon_speech_all.wer_10.0.vectorized

japanese-asr

--- dataset_info: - config_name: subset_0 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44407083236 num_examples: 28889 download_size: 6430216790 dataset_size: 44407083236 - config_name: subset_1 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44089216600 num_examples: 28682 download_size: 6385763048 dataset_size: 44089216600 - config_name: subset_10 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43927652252 num_examples: 28577 download_size: 6336100250 dataset_size: 43927652252 - config_name: subset_100 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44365586824 num_examples: 28862 download_size: 6435201244 dataset_size: 44365586824 - config_name: subset_101 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44324247868 num_examples: 28835 download_size: 6431762006 dataset_size: 44324247868 - config_name: subset_102 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43821526656 num_examples: 28508 download_size: 6367882564 dataset_size: 43821526656 - config_name: subset_103 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44084293668 num_examples: 28679 download_size: 6363475471 dataset_size: 44084293668 - config_name: subset_104 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44042930672 num_examples: 28652 download_size: 6381242681 dataset_size: 44042930672 - config_name: subset_106 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43912140892 num_examples: 28567 download_size: 6343450605 dataset_size: 43912140892 - config_name: subset_107 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43931998624 num_examples: 28580 download_size: 6358400755 dataset_size: 43931998624 - config_name: subset_108 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44042913000 num_examples: 28652 download_size: 6405970862 dataset_size: 44042913000 - config_name: subset_109 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44642253680 num_examples: 29042 download_size: 6437990632 dataset_size: 44642253680 - config_name: subset_11 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44256762756 num_examples: 28791 download_size: 6393712860 dataset_size: 44256762756 - config_name: subset_110 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43889022688 num_examples: 28552 download_size: 6360561092 dataset_size: 43889022688 - config_name: subset_111 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44129144280 num_examples: 28708 download_size: 6408022759 dataset_size: 44129144280 - config_name: subset_112 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44041454396 num_examples: 28651 download_size: 6391629995 dataset_size: 44041454396 - config_name: subset_113 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44218161920 num_examples: 28766 download_size: 6397865173 dataset_size: 44218161920 - config_name: subset_114 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44311827300 num_examples: 28827 download_size: 6392228352 dataset_size: 44311827300 - config_name: subset_115 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43438751460 num_examples: 28259 download_size: 6261293593 dataset_size: 43438751460 - config_name: subset_116 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43895154544 num_examples: 28556 download_size: 6347517025 dataset_size: 43895154544 - config_name: subset_117 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43969041880 num_examples: 28604 download_size: 6375498562 dataset_size: 43969041880 - config_name: subset_118 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44027316104 num_examples: 28642 download_size: 6354466340 dataset_size: 44027316104 - config_name: subset_119 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44289059560 num_examples: 28812 download_size: 6416432647 dataset_size: 44289059560 - config_name: subset_12 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44293612564 num_examples: 28815 download_size: 6433586401 dataset_size: 44293612564 - config_name: subset_120 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44530056588 num_examples: 28969 download_size: 6437978882 dataset_size: 44530056588 - config_name: subset_121 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 3074160 num_examples: 2 download_size: 556271 dataset_size: 3074160 - config_name: subset_122 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44136739628 num_examples: 28713 download_size: 6404302139 dataset_size: 44136739628 - config_name: subset_123 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44096634284 num_examples: 28687 download_size: 6389251368 dataset_size: 44096634284 - config_name: subset_124 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44001467124 num_examples: 28625 download_size: 6385493649 dataset_size: 44001467124 - config_name: subset_125 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44128863696 num_examples: 28708 download_size: 6364505444 dataset_size: 44128863696 - config_name: subset_126 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44382486420 num_examples: 28873 download_size: 6441197752 dataset_size: 44382486420 - config_name: subset_127 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44006092176 num_examples: 28628 download_size: 6361537304 dataset_size: 44006092176 - config_name: subset_128 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43759809728 num_examples: 28468 download_size: 6336544958 dataset_size: 43759809728 - config_name: subset_129 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44024331328 num_examples: 28640 download_size: 6359644430 dataset_size: 44024331328 - config_name: subset_13 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44357930276 num_examples: 28857 download_size: 6420201483 dataset_size: 44357930276 - config_name: subset_130 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44192010836 num_examples: 28749 download_size: 6422867143 dataset_size: 44192010836 - config_name: subset_131 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44099663532 num_examples: 28689 download_size: 6371664563 dataset_size: 44099663532 - config_name: subset_132 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44095360096 num_examples: 28686 download_size: 6383911332 dataset_size: 44095360096 - config_name: subset_133 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43730827940 num_examples: 28449 download_size: 6313519416 dataset_size: 43730827940 - config_name: subset_134 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44007518388 num_examples: 28629 download_size: 6389179458 dataset_size: 44007518388 - config_name: subset_135 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43707840616 num_examples: 28434 download_size: 6317643688 dataset_size: 43707840616 - config_name: subset_136 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44033774672 num_examples: 28646 download_size: 6373240832 dataset_size: 44033774672 - config_name: subset_137 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 3074344 num_examples: 2 download_size: 557594 dataset_size: 3074344 - config_name: subset_138 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43841655788 num_examples: 28521 download_size: 6370669259 dataset_size: 43841655788 - config_name: subset_139 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43793963000 num_examples: 28490 download_size: 6351019624 dataset_size: 43793963000 - config_name: subset_14 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44413389620 num_examples: 28893 download_size: 6406524573 dataset_size: 44413389620 - config_name: subset_140 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43978329680 num_examples: 28610 download_size: 6341082690 dataset_size: 43978329680 - config_name: subset_141 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44030464856 num_examples: 28644 download_size: 6383471765 dataset_size: 44030464856 - config_name: subset_142 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43793910464 num_examples: 28490 download_size: 6348275681 dataset_size: 43793910464 - config_name: subset_143 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44448732656 num_examples: 28916 download_size: 6450504968 dataset_size: 44448732656 - config_name: subset_144 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43686238792 num_examples: 28420 download_size: 6334779676 dataset_size: 43686238792 - config_name: subset_145 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44141228568 num_examples: 28716 download_size: 6363170999 dataset_size: 44141228568 - config_name: subset_146 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43800179728 num_examples: 28494 download_size: 6358878988 dataset_size: 43800179728 - config_name: subset_147 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44294909712 num_examples: 28816 download_size: 6412779644 dataset_size: 44294909712 - config_name: subset_148 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43886264664 num_examples: 28550 download_size: 6377384251 dataset_size: 43886264664 - config_name: subset_149 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44030547976 num_examples: 28644 download_size: 6383895865 dataset_size: 44030547976 - 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name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43904573672 num_examples: 28562 download_size: 6353845259 dataset_size: 43904573672 - config_name: subset_155 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44414946296 num_examples: 28894 download_size: 6399004665 dataset_size: 44414946296 - config_name: subset_156 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43787907000 num_examples: 28486 download_size: 6361131234 dataset_size: 43787907000 - 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name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43218862392 num_examples: 28116 download_size: 6267480974 dataset_size: 43218862392 - config_name: subset_53 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43020592356 num_examples: 27987 download_size: 6237193214 dataset_size: 43020592356 - config_name: subset_105 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43217581324 num_examples: 28115 download_size: 6241162732 dataset_size: 43217581324 - config_name: subset_23 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44087547940 num_examples: 28681 download_size: 6378825677 dataset_size: 44087547940 - config_name: subset_24 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44250388180 num_examples: 28787 download_size: 6399288392 dataset_size: 44250388180 - config_name: subset_25 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44682379040 num_examples: 29068 download_size: 6472664846 dataset_size: 44682379040 - 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name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43550868688 num_examples: 28332 download_size: 6288205442 dataset_size: 43550868688 - config_name: subset_43 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43772245200 num_examples: 28476 download_size: 6312411517 dataset_size: 43772245200 - config_name: subset_44 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44043101784 num_examples: 28652 download_size: 6367757278 dataset_size: 44043101784 - config_name: subset_45 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43839830568 num_examples: 28520 download_size: 6302918743 dataset_size: 43839830568 - config_name: subset_46 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44418011720 num_examples: 28896 download_size: 6420581627 dataset_size: 44418011720 - config_name: subset_47 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44239609176 num_examples: 28780 download_size: 6409168799 dataset_size: 44239609176 - 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name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43830625696 num_examples: 28514 download_size: 6347303356 dataset_size: 43830625696 - config_name: subset_76 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43924502708 num_examples: 28575 download_size: 6368149688 dataset_size: 43924502708 - config_name: subset_77 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43754158544 num_examples: 28464 download_size: 6347205297 dataset_size: 43754158544 - config_name: subset_78 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43781508304 num_examples: 28482 download_size: 6362656422 dataset_size: 43781508304 - config_name: subset_79 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43978478208 num_examples: 28610 download_size: 6398609121 dataset_size: 43978478208 - config_name: subset_8 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44007563004 num_examples: 28629 download_size: 6358760125 dataset_size: 44007563004 - config_name: subset_80 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43807663524 num_examples: 28499 download_size: 6383713010 dataset_size: 43807663524 - config_name: subset_81 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43958216180 num_examples: 28597 download_size: 6360362244 dataset_size: 43958216180 - config_name: subset_82 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44018307032 num_examples: 28636 download_size: 6388770182 dataset_size: 44018307032 - config_name: subset_83 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43431184792 num_examples: 28254 download_size: 6273446746 dataset_size: 43431184792 - config_name: subset_84 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 4611316 num_examples: 3 download_size: 813473 dataset_size: 4611316 - config_name: subset_85 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43873788512 num_examples: 28542 download_size: 6358732185 dataset_size: 43873788512 - config_name: subset_86 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43505081840 num_examples: 28302 download_size: 6336792534 dataset_size: 43505081840 - config_name: subset_87 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44099477124 num_examples: 28689 download_size: 6376905811 dataset_size: 44099477124 - config_name: subset_88 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43800091792 num_examples: 28494 download_size: 6331140342 dataset_size: 43800091792 - config_name: subset_89 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44185886628 num_examples: 28745 download_size: 6399823294 dataset_size: 44185886628 - config_name: subset_9 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43959761872 num_examples: 28598 download_size: 6369092508 dataset_size: 43959761872 - config_name: subset_90 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43943002092 num_examples: 28587 download_size: 6384008687 dataset_size: 43943002092 - config_name: subset_91 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43709159980 num_examples: 28435 download_size: 6348468066 dataset_size: 43709159980 - config_name: subset_92 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43803194856 num_examples: 28496 download_size: 6384519799 dataset_size: 43803194856 - config_name: subset_93 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43806228672 num_examples: 28498 download_size: 6353242379 dataset_size: 43806228672 - config_name: subset_94 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43918235972 num_examples: 28571 download_size: 6359165774 dataset_size: 43918235972 - config_name: subset_95 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44013722788 num_examples: 28633 download_size: 6372836215 dataset_size: 44013722788 - config_name: subset_96 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43912328076 num_examples: 28567 download_size: 6360540190 dataset_size: 43912328076 - config_name: subset_97 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43784551296 num_examples: 28484 download_size: 6341270112 dataset_size: 43784551296 - config_name: subset_98 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 44568669984 num_examples: 28994 download_size: 6461359260 dataset_size: 44568669984 - config_name: subset_99 features: - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_features sequence: sequence: float32 splits: - name: train num_bytes: 43989120876 num_examples: 28617 download_size: 6385093647 dataset_size: 43989120876 configs: - config_name: subset_0 data_files: - split: train path: subset_0/train-* - config_name: subset_1 data_files: - split: train path: subset_1/train-* - config_name: subset_10 data_files: - split: train path: subset_10/train-* - config_name: subset_100 data_files: - split: train path: subset_100/train-* - config_name: subset_101 data_files: - split: train path: subset_101/train-* - config_name: subset_102 data_files: - split: train path: subset_102/train-* - config_name: subset_103 data_files: - split: train path: subset_103/train-* - config_name: subset_104 data_files: - split: train path: subset_104/train-* - config_name: subset_106 data_files: - split: train path: subset_106/train-* - config_name: subset_107 data_files: - split: train path: subset_107/train-* - config_name: subset_108 data_files: - split: train path: subset_108/train-* - config_name: subset_109 data_files: - split: train path: subset_109/train-* - config_name: subset_11 data_files: - split: train path: subset_11/train-* - 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config_name: subset_5 data_files: - split: train path: subset_5/train-* - config_name: subset_50 data_files: - split: train path: subset_50/train-* - config_name: subset_51 data_files: - split: train path: subset_51/train-* - config_name: subset_52 data_files: - split: train path: subset_52/train-* - config_name: subset_54 data_files: - split: train path: subset_54/train-* - config_name: subset_55 data_files: - split: train path: subset_55/train-* - config_name: subset_56 data_files: - split: train path: subset_56/train-* - config_name: subset_57 data_files: - split: train path: subset_57/train-* - config_name: subset_58 data_files: - split: train path: subset_58/train-* - config_name: subset_59 data_files: - split: train path: subset_59/train-* - config_name: subset_6 data_files: - split: train path: subset_6/train-* - config_name: subset_60 data_files: - split: train path: subset_60/train-* - config_name: subset_61 data_files: - split: train path: subset_61/train-* - config_name: subset_62 data_files: - split: train path: subset_62/train-* - config_name: subset_63 data_files: - split: train path: subset_63/train-* - config_name: subset_64 data_files: - split: train path: subset_64/train-* - config_name: subset_65 data_files: - split: train path: subset_65/train-* - config_name: subset_66 data_files: - split: train path: subset_66/train-* - config_name: subset_67 data_files: - split: train path: subset_67/train-* - config_name: subset_68 data_files: - split: train path: subset_68/train-* - config_name: subset_69 data_files: - split: train path: subset_69/train-* - config_name: subset_7 data_files: - split: train path: subset_7/train-* - config_name: subset_70 data_files: - split: train path: subset_70/train-* - config_name: subset_71 data_files: - split: train path: subset_71/train-* - config_name: subset_72 data_files: - split: train path: subset_72/train-* - config_name: subset_73 data_files: - split: train path: subset_73/train-* - config_name: subset_74 data_files: - split: train path: subset_74/train-* - config_name: subset_75 data_files: - split: train path: subset_75/train-* - config_name: subset_76 data_files: - split: train path: subset_76/train-* - config_name: subset_77 data_files: - split: train path: subset_77/train-* - config_name: subset_78 data_files: - split: train path: subset_78/train-* - config_name: subset_79 data_files: - split: train path: subset_79/train-* - config_name: subset_8 data_files: - split: train path: subset_8/train-* - config_name: subset_80 data_files: - split: train path: subset_80/train-* - config_name: subset_81 data_files: - split: train path: subset_81/train-* - config_name: subset_82 data_files: - split: train path: subset_82/train-* - config_name: subset_83 data_files: - split: train path: subset_83/train-* - config_name: subset_84 data_files: - split: train path: subset_84/train-* - config_name: subset_85 data_files: - split: train path: subset_85/train-* - config_name: subset_86 data_files: - split: train path: subset_86/train-* - config_name: subset_87 data_files: - split: train path: subset_87/train-* - config_name: subset_88 data_files: - split: train path: subset_88/train-* - config_name: subset_89 data_files: - split: train path: subset_89/train-* - config_name: subset_9 data_files: - split: train path: subset_9/train-* - config_name: subset_90 data_files: - split: train path: subset_90/train-* - config_name: subset_91 data_files: - split: train path: subset_91/train-* - config_name: subset_92 data_files: - split: train path: subset_92/train-* - config_name: subset_93 data_files: - split: train path: subset_93/train-* - config_name: subset_94 data_files: - split: train path: subset_94/train-* - config_name: subset_95 data_files: - split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

HuggingFaceM4/OBELICS

HuggingFaceM4

--- language: - en license: cc-by-4.0 size_categories: - 100M<n<1B pretty_name: OBELICS configs: - config_name: default data_files: - split: train path: data/train-* - config_name: opt_out_docs_removed_2023_07_12 data_files: - split: train path: opt_out_docs_removed_2023_07_12/train-* dataset_info: - config_name: default features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 715724717192 num_examples: 141047697 download_size: 71520629655 dataset_size: 715724717192 - config_name: opt_out_docs_removed_2023_07_12 features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 684638314215 num_examples: 134648855 download_size: 266501092920 dataset_size: 684638314215 --- # Dataset Card for OBELICS ## Dataset Description - **Visualization of OBELICS web documents:** https://huggingface.co/spaces/HuggingFaceM4/obelics_visualization - **Paper:** [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://arxiv.org/abs/2306.16527) - **Repository:** https://github.com/huggingface/OBELICS - **Point of Contact: [email protected]** `OBELICS` is an open, massive, and curated collection of interleaved image-text web documents, containing 141M English documents, 115B text tokens, and 353M images, extracted from Common Crawl dumps between February 2020 and February 2023. The collection and filtering steps are described in our [paper](https://huggingface.co/papers/2306.16527). Interleaved image-text web documents are a succession of text paragraphs interleaved by images, such as web pages that contain images. Models trained on these web documents outperform vision and language models trained solely on image-text pairs on various benchmarks. They can also generate long and coherent text about a set of multiple images. As an example, we trained [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b), a visual language model that accepts arbitrary sequences of image and text inputs and produces text outputs. We provide an [interactive visualization](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) of OBELICS that allows exploring the content of OBELICS. The map shows a subset of 11M of the 141M documents. [](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) ## Data Fields An example of a sample looks as follows: ``` # The example has been cropped { 'images': [ 'https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg', None ], 'metadata': '[{"document_url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "unformatted_src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "formatted_filename": "lamborghini urus original carbon fiber accessories", "alt_text": "VW Group Allegedly Receives Offer To Sell Lamborghini For $9.2 Billion", "original_width": 1920, "original_height": 1080, "format": "jpeg"}, null]', 'general_metadata': '{"url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "warc_filename": "crawl-data/CC-MAIN-2021-25/segments/1623488528979.69/warc/CC-MAIN-20210623011557-20210623041557-00312.warc.gz", "warc_record_offset": 322560850, "warc_record_length": 17143}', 'texts': [ None, 'The buyer would get everything, including Lambo\'s headquarters.\n\nThe investment groupQuantum Group AG has submitted a€7.5 billion ($9.2 billion at current exchange rates) offer to purchase Lamborghini from Volkswagen Group, Autocar reports. There\'s no info yet about whether VW intends to accept the offer or further negotiate the deal.\n\nQuantum ... Group Chief Executive Herbert Diess said at the time.' ] } ``` Each sample is composed of the same 4 fields: `images`, `texts`, `metadata`, and `general_metadata`. `images` and `texts` are two lists of the same size, where for each index, one element and only one is not `None`. For example, for the interleaved web document `<image_1>text<image_2>`, we would find `[image_1, None, image_2]` in `images` and `[None, text, None]` in `texts`. The images are replaced by their URLs, and the users need to download the images, for instance, with the library [img2dataset](https://github.com/rom1504/img2dataset). `metadata` is the string representation of a list containing information about each of the images. It has the same length as `texts` and `images` and logs for each image relevant information such as original source document, unformatted source, alternative text if present, etc. `general_metadata` is the string representation of a dictionary containing the URL of the document, and information regarding the extraction from Common Crawl snapshots. ## Size and Data Splits There is only one split, `train`, that contains 141,047,697 documents. `OBELICS` with images replaced by their URLs weighs 666.6 GB (😈) in arrow format and 377 GB in the uploaded `parquet` format. ## Considerations for Using the Data ### Discussion of Biases A subset of this dataset `train`, of ~50k was evaluated using the Data Measurements Tool, with a particular focus on the nPMI metric > nPMI scores for a word help to identify potentially problematic associations, ranked by how close the association is. > nPMI bias scores for paired words help to identify how word associations are skewed between the selected selected words (Aka et al., 2021). > You can select from gender and sexual orientation identity terms that appear in the dataset at least 10 times. > The resulting ranked words are those that co-occur with both identity terms. > The more positive the score, the more associated the word is with the first identity term. The more negative the score, the more associated the word is with the second identity term. While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively.  We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations. ## Opted-out content To respect the preferences of content creators, we removed from OBELICS all images for which creators explicitly opted out of AI model training. We used the [Spawning API](https://api.spawning.ai/spawning-api) to verify that the images in the dataset respect the original copyright owners’ choices. However, due to an error on our side, we did not remove entire documents (i.e., URLs) that opted out of AI model training. As of July 12, 2023, it represents 4.25% of the totality of OBELICS. The config `opt_out_docs_removed_2023_07_12` applies the correct filtering at the web document level as of July 2023: `ds = load_dataset("HuggingFaceM4/OBELICS", "opt_out_docs_removed_2023_07_12")`. We recommend users of OBELICS to regularly check every document against the API. ## Content warnings Despite our efforts in filtering, OBELICS contains a small proportion of documents that are not suitable for all audiences. For instance, while navigating the interactive map, you might find the cluster named "Sex" which predominantly contains descriptions of pornographic movies along with pornographic images. Other clusters would contain advertising for sex workers or reports of violent shootings. In our experience, these documents represent a small proportion of all the documents. ## Terms of Use By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model. ### Licensing Information License CC-BY-4.0. ### Citation Information If you are using this dataset, please cite ``` @misc{laurencon2023obelics, title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents}, author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh}, year={2023}, eprint={2306.16527}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```

zalando-datasets/fashion_mnist

zalando-datasets

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: fashion-mnist pretty_name: FashionMNIST dataset_info: config_name: fashion_mnist features: - name: image dtype: image - name: label dtype: class_label: names: '0': T - shirt / top '1': Trouser '2': Pullover '3': Dress '4': Coat '5': Sandal '6': Shirt '7': Sneaker '8': Bag '9': Ankle boot splits: - name: train num_bytes: 31049107.0 num_examples: 60000 - name: test num_bytes: 5192560.0 num_examples: 10000 download_size: 36106894 dataset_size: 36241667.0 configs: - config_name: fashion_mnist data_files: - split: train path: fashion_mnist/train-* - split: test path: fashion_mnist/test-* default: true --- # Dataset Card for FashionMNIST ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [GitHub](https://github.com/zalandoresearch/fashion-mnist) - **Repository:** [GitHub](https://github.com/zalandoresearch/fashion-mnist) - **Paper:** [arXiv](https://arxiv.org/pdf/1708.07747.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image of Zalando's article into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-fashion-mnist). ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A data point comprises an image and its label. ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x27601169DD8>, 'label': 9 } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `label`: an integer between 0 and 9 representing the classes with the following mapping: | Label | Description | | --- | --- | | 0 | T-shirt/top | | 1 | Trouser | | 2 | Pullover | | 3 | Dress | | 4 | Coat | | 5 | Sandal | | 6 | Shirt | | 7 | Sneaker | | 8 | Bag | | 9 | Ankle boot | ### Data Splits The data is split into training and test set. The training set contains 60,000 images and the test set 10,000 images. ## Dataset Creation ### Curation Rationale **From the arXiv paper:** The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others." Here are some good reasons: - MNIST is too easy. Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read "Most pairs of MNIST digits can be distinguished pretty well by just one pixel." - MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST. - MNIST can not represent modern CV tasks, as noted in this April 2017 Twitter thread, deep learning expert/Keras author François Chollet. ### Source Data #### Initial Data Collection and Normalization **From the arXiv paper:** Fashion-MNIST is based on the assortment on Zalando’s website. Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. The original picture has a light-gray background (hexadecimal color: #fdfdfd) and stored in 762 × 1000 JPEG format. For efficiently serving different frontend components, the original picture is resampled with multiple resolutions, e.g. large, medium, small, thumbnail and tiny. We use the front look thumbnail images of 70,000 unique products to build Fashion-MNIST. Those products come from different gender groups: men, women, kids and neutral. In particular, whitecolor products are not included in the dataset as they have low contrast to the background. The thumbnails (51 × 73) are then fed into the following conversion pipeline: 1. Converting the input to a PNG image. 2. Trimming any edges that are close to the color of the corner pixels. The “closeness” is defined by the distance within 5% of the maximum possible intensity in RGB space. 3. Resizing the longest edge of the image to 28 by subsampling the pixels, i.e. some rows and columns are skipped over. 4. Sharpening pixels using a Gaussian operator of the radius and standard deviation of 1.0, with increasing effect near outlines. 5. Extending the shortest edge to 28 and put the image to the center of the canvas. 6. Negating the intensities of the image. 7. Converting the image to 8-bit grayscale pixels. #### Who are the source language producers? **From the arXiv paper:** Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. ### Annotations #### Annotation process **From the arXiv paper:** For the class labels, they use the silhouette code of the product. The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. Each product Zalando is the Europe’s largest online fashion platform. Each product contains only one silhouette code. #### Who are the annotators? **From the arXiv paper:** The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Han Xiao and Kashif Rasul and Roland Vollgraf ### Licensing Information MIT Licence ### Citation Information ``` @article{DBLP:journals/corr/abs-1708-07747, author = {Han Xiao and Kashif Rasul and Roland Vollgraf}, title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, journal = {CoRR}, volume = {abs/1708.07747}, year = {2017}, url = {http://arxiv.org/abs/1708.07747}, archivePrefix = {arXiv}, eprint = {1708.07747}, timestamp = {Mon, 13 Aug 2018 16:47:27 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.

nyanko7/danbooru2023

nyanko7

--- license: mit task_categories: - image-classification - image-to-image - text-to-image language: - en - ja pretty_name: danbooru2023 size_categories: - 1M<n<10M viewer: false --- <img src="https://huggingface.co/datasets/nyanko7/danbooru2023/resolve/main/cover.webp" alt="cover" width="750"/> # Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset <!-- Provide a quick summary of the dataset. --> Danbooru2023 is a large-scale anime image dataset with over 5 million images contributed and annotated in detail by an enthusiast community. Image tags cover aspects like characters, scenes, copyrights, artists, etc with an average of 30 tags per image. Danbooru is a veteran anime image board with high-quality images and extensive tag metadata. The dataset can be used to train image classification, multi-label tagging, character detection, generative models, and other computer vision tasks. - **Shared by:** Nyanko Devs - **Language(s):** English, Japanese - **License:** MIT This dataset is built on the top of [danbooru2021](https://gwern.net/danbooru2021). We expands the dataset to include images up to ID #6,857,737, adding over 1.8 million additional images and total size is now approximately 8 terabytes (8,000 GB). ## Use ## Format The goal of the dataset is to be as easy as possible to use immediately, avoiding obscure file formats, while allowing simultaneous research & seeding of the torrent, with easy updates. Images are provided in the full original form (be that JPG, PNG, GIF or otherwise) for reference/archival purposes, and bucketed into 1000 subdirectories 0000–0999 (0-padded), which is the Danbooru ID modulo 1000 (ie. all images in 0999/ have an ID ending in ‘999’); IDs can be turned into paths by dividing & padding (eg. in Bash, BUCKET=$(printf "%04d" $(( ID % 1000 )) )) and then the file is at {original,512px}/$BUCKET/$ID.$EXT. The reason for the bucketing is that a single directory would cause pathological filesystem performance, and modulo ID is a simple hash which spreads images evenly without requiring additional future directories to be made or a filesystem IO to check where the file is. The ID is not zero-padded and files end in the relevant extension, hence the file layout looks like this: ```bash $ tree / | less / ├── danbooru2023 -> /mnt/diffusionstorage/workspace/danbooru/ │ ├── metadata │ ├── readme.md │ ├── original │ │ ├── 0000 -> data-0000.tar │ │ ├── 0001 -> data-0001.tar │ │ │ ├── 10001.jpg │ │ │ ├── 210001.png │ │ │ ├── 3120001.webp │ │ │ ├── 6513001.jpg │ │ │ ├── recent │ │ ├── 0000 -> data-1000.tar │ │ ├── 0001 -> data-1001.tar │ │ │ ├── updates │ │ ├── 20240319 │ │ │ ├── dataset-0.tar │ │ │ ├── dataset-1.tar │ │ │ │ │ ├── 2024xxxx │ │ │ ├── dataset-0.tar │ │ │ ├── dataset-1.tar ``` Where `data-{1000..1999}.tar` refer to recent update files (should be updated every few months) and `updates` refer to fast patches (should be updated every few days to few weeks). Currently represented file extensions are: avi/bmp/gif/html/jpeg/jpg/mp3/mp4/mpg/pdf/png/rar/swf/webm/wmv/zip. Raw original files are treacherous. Be careful if working with the original dataset. There are many odd files: truncated, non-sRGB colorspace, wrong file extensions (eg. some PNGs have .jpg extensions like original/0146/1525146.jpg or original/0558/1422558.jpg), etc.

appier-ai-research/StreamBench_public

appier-ai-research

--- dataset_info: - config_name: bird features: - name: db_id dtype: string - name: question dtype: string - name: evidence dtype: string - name: SQL dtype: string - name: question_id dtype: int64 - name: difficulty dtype: string splits: - name: test num_bytes: 586056.0 num_examples: 1534 download_size: 251724 dataset_size: 586056.0 - config_name: ddxplus features: - name: AGE dtype: int64 - name: SEX dtype: string - name: INITIAL_EVIDENCE_ENG dtype: string - name: EVIDENCES_ENG dtype: string - name: PATIENT_PROFILE dtype: string - name: DIFFERENTIAL_DIAGNOSIS dtype: string - name: PATHOLOGY dtype: string - name: INITIAL_EVIDENCE dtype: string - name: EVIDENCES dtype: string splits: - name: test num_bytes: 3318154.0 num_examples: 1764 download_size: 845456 dataset_size: 3318154.0 configs: - config_name: bird data_files: - split: test path: bird/test-* - config_name: ddxplus data_files: - split: test path: ddxplus/test-* ---

EpicPinkPenguin/procgen

EpicPinkPenguin

--- language: - en license: apache-2.0 size_categories: - 10M<n<100M task_categories: - reinforcement-learning pretty_name: Procgen Benchmark Dataset dataset_info: - config_name: bigfish features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 129932068797 dataset_size: 289372500000 - config_name: bossfight features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 198057598671 dataset_size: 289372500000 - config_name: caveflyer features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 149023406845 dataset_size: 289372500000 - config_name: chaser features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 63831099402 dataset_size: 289372500000 - config_name: climber features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 63990304413 dataset_size: 289372500000 - config_name: coinrun features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 76990220716 dataset_size: 289372500000 - config_name: dodgeball features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 104691253324 dataset_size: 289372500000 - config_name: fruitbot features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 271549939959 dataset_size: 289372500000 - config_name: heist features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 74316944819 dataset_size: 289372500000 - config_name: jumper features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 101573987650 dataset_size: 289372500000 - config_name: leaper features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 66796546658 dataset_size: 289372500000 - config_name: maze features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 75397896559 dataset_size: 289372500000 - config_name: miner features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 57170722948 dataset_size: 289372500000 - config_name: ninja features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 99759972643 dataset_size: 289372500000 - config_name: plunder features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 103307437365 dataset_size: 289372500000 - config_name: starpilot features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 170031712117 dataset_size: 289372500000 configs: - config_name: bigfish data_files: - split: train path: bigfish/train-* - split: test path: bigfish/test-* - config_name: bossfight data_files: - split: train path: bossfight/train-* - split: test path: bossfight/test-* - config_name: caveflyer data_files: - split: train path: caveflyer/train-* - split: test path: caveflyer/test-* - config_name: chaser data_files: - split: train path: chaser/train-* - split: test path: chaser/test-* - config_name: climber data_files: - split: train path: climber/train-* - split: test path: climber/test-* - config_name: coinrun data_files: - split: train path: coinrun/train-* - split: test path: coinrun/test-* - config_name: dodgeball data_files: - split: train path: dodgeball/train-* - split: test path: dodgeball/test-* - config_name: fruitbot data_files: - split: train path: fruitbot/train-* - split: test path: fruitbot/test-* - config_name: heist data_files: - split: train path: heist/train-* - split: test path: heist/test-* - config_name: jumper data_files: - split: train path: jumper/train-* - split: test path: jumper/test-* - config_name: leaper data_files: - split: train path: leaper/train-* - split: test path: leaper/test-* - config_name: maze data_files: - split: train path: maze/train-* - split: test path: maze/test-* - config_name: miner data_files: - split: train path: miner/train-* - split: test path: miner/test-* - config_name: ninja data_files: - split: train path: ninja/train-* - split: test path: ninja/test-* - config_name: plunder data_files: - split: train path: plunder/train-* - split: test path: plunder/test-* - config_name: starpilot data_files: - split: train path: starpilot/train-* - split: test path: starpilot/test-* tags: - procgen - bigfish - benchmark - openai - bossfight - caveflyer - chaser - climber - dodgeball - fruitbot - heist - jumper - leaper - maze - miner - ninja - plunder - starpilot --- # Procgen Benchmark This dataset contains expert trajectories generated by a [PPO](https://arxiv.org/abs/1707.06347) reinforcement learning agent trained on each of the 16 procedurally-generated gym environments from the [Procgen Benchmark](https://openai.com/index/procgen-benchmark/). The environments were created on `distribution_mode=easy` and with unlimited levels. Disclaimer: This is not an official repository from OpenAI. ## Dataset Usage Regular usage (for environment bigfish): ```python from datasets import load_dataset train_dataset = load_dataset("EpicPinkPenguin/procgen", name="bigfish", split="train") test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bigfish", split="test") ``` Usage with PyTorch (for environment bossfight): ```python from datasets import load_dataset train_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="train").with_format("torch") test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="test").with_format("torch") ``` ## Agent Performance The PPO RL agent was trained for 25M steps on each environment and obtained the following final performance metrics on the evaluation environment. These values are attain or surpass the performance described in "Easy Difficulty Baseline Results" in Appendix I of the paper. | Environment | Steps (Train) | Steps (Test) | Return | Observation | |:------------|:----------------|:---------------|:-------|:------------| | bigfish | 9,000,000 | 1,000,000 | 29.72 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/lHQXBqLdoWicXlt68I9QX.mp4"></video> | | bossfight | 9,000,000 | 1,000,000 | 11.13 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/LPoafGi4YBWqqkuFlEN_l.mp4"></video> | | caveflyer | 9,000,000 | 1,000,000 | 08.95 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XVqRwu_9yfX4ECQc4At4G.mp4"></video> | | chaser | 9,000,000 | 1,000,000 | 10.98 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/FIKVv48SThqiC1Z2PYQ7U.mp4"></video> | | climber | 9,000,000 | 1,000,000 | 11.66 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XJQlA7IyF9_gwUiw-FkND.mp4"></video> | | coinrun | 9,000,000 | 1,000,000 | 09.61 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/Ucv3HZttewMRQzTL8r_Tw.mp4"></video> | | dodgeball | 9,000,000 | 1,000,000 | 11.07 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/5HetbKuXBpO-v1jcVyLTU.mp4"></video> | | fruitbot | 9,000,000 | 1,000,000 | 32.49 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/zKCyxXvauXjUac-5kEAWz.mp4"></video> | | heist | 9,000,000 | 1,000,000 | 08.37 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/AdZ6XNmUN5_00BKd9BN8R.mp4"></video> | | jumper | 9,000,000 | 1,000,000 | 08.46 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/s5k31gWK2Vc6Lp6QVzQXA.mp4"></video> | | leaper | 9,000,000 | 1,000,000 | 07.11 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/_hDMocxjmzutc0t5FfoTX.mp4"></video> | | maze | 9,000,000 | 1,000,000 | 09.95 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/uhNdDPuNhZpxVns91Ba-9.mp4"></video> | | miner | 9,000,000 | 1,000,000 | 12.21 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/ElpJ8l2WHJGrprZ3-giHU.mp4"></video> | | ninja | 9,000,000 | 1,000,000 | 08.88 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/b9i-fb2Twh8XmBBNf2DRG.mp4"></video> | | plunder | 9,000,000 | 1,000,000 | 22.19 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/JPeGNOVzrotuYUjfzZj40.mp4"></video> | | starpilot | 9,000,000 | 1,000,000 | 49.94 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/wY9lZgkw5tor19hCWmm6A.mp4"></video> | ## Dataset Structure ### Data Instances Each data instance represents a single step consisting of tuples of the form (observation, action, reward, done, truncated) = (o_t, a_t, r_{t+1}, done_{t+1}, trunc_{t+1}). ```json {'action': 1, 'done': False, 'observation': [[[0, 166, 253], [0, 174, 255], [0, 170, 251], [0, 191, 255], [0, 191, 255], [0, 221, 255], [0, 243, 255], [0, 248, 255], [0, 243, 255], [10, 239, 255], [25, 255, 255], [0, 241, 255], [0, 235, 255], [17, 240, 255], [10, 243, 255], [27, 253, 255], [39, 255, 255], [58, 255, 255], [85, 255, 255], [111, 255, 255], [135, 255, 255], [151, 255, 255], [173, 255, 255], ... [0, 0, 37], [0, 0, 39]]], 'reward': 0.0, 'truncated': False} ``` ### Data Fields - `observation`: The current RGB observation from the environment. - `action`: The action predicted by the agent for the current observation. - `reward`: The received reward from stepping the environment with the current action. - `done`: If the new observation is the start of a new episode. Obtained after stepping the environment with the current action. - `truncated`: If the new observation is the start of a new episode due to truncation. Obtained after stepping the environment with the current action. ### Data Splits The dataset is divided into a `train` (90%) and `test` (10%) split. Each environment-dataset has in sum 10M steps (data points). ## Dataset Creation The dataset was created by training an RL agent with [PPO](https://arxiv.org/abs/1707.06347) for 25M steps in each environment. The trajectories where generated by sampling from the predicted action distribution at each step (not taking the argmax). The environments were created on `distribution_mode=easy` and with unlimited levels. ## Procgen Benchmark The [Procgen Benchmark](https://openai.com/index/procgen-benchmark/), released by OpenAI, consists of 16 procedurally-generated environments designed to measure how quickly reinforcement learning (RL) agents learn generalizable skills. It emphasizes experimental convenience, high diversity within and across environments, and is ideal for evaluating both sample efficiency and generalization. The benchmark allows for distinct training and test sets in each environment, making it a standard research platform for the OpenAI RL team. It aims to address the need for more diverse RL benchmarks compared to complex environments like Dota and StarCraft.

haonan-li/cmmlu

haonan-li

--- license: cc-by-nc-4.0 task_categories: - multiple-choice - question-answering language: - zh tags: - chinese - llm - evaluation pretty_name: CMMLU size_categories: - 10K<n<100K --- # CMMLU: Measuring massive multitask language understanding in Chinese - **Homepage:** [https://github.com/haonan-li/CMMLU](https://github.com/haonan-li/CMMLU) - **Repository:** [https://huggingface.co/datasets/haonan-li/cmmlu](https://huggingface.co/datasets/haonan-li/cmmlu) - **Paper:** [CMMLU: Measuring Chinese Massive Multitask Language Understanding](https://arxiv.org/abs/2306.09212). ## Table of Contents - [Introduction](#introduction) - [Leaderboard](#leaderboard) - [Data](#data) - [Citation](#citation) - [License](#license) ## Introduction CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context. CMMLU covers a wide range of subjects, comprising 67 topics that span from elementary to advanced professional levels. It includes subjects that require computational expertise, such as physics and mathematics, as well as disciplines within humanities and social sciences. Many of these tasks are not easily translatable from other languages due to their specific contextual nuances and wording. Furthermore, numerous tasks within CMMLU have answers that are specific to China and may not be universally applicable or considered correct in other regions or languages. ## Leaderboard Latest leaderboard is in our [github](https://github.com/haonan-li/CMMLU). ## Data We provide development and test dataset for each of 67 subjects, with 5 questions in development set and 100+ quesitons in test set. Each question in the dataset is a multiple-choice questions with 4 choices and only one choice as the correct answer. Here are two examples: ``` 题目：同一物种的两类细胞各产生一种分泌蛋白，组成这两种蛋白质的各种氨基酸含量相同，但排列顺序不同。其原因是参与这两种蛋白质合成的： A. tRNA种类不同 B. 同一密码子所决定的氨基酸不同 C. mRNA碱基序列不同 D. 核糖体成分不同 答案是：C ``` ``` 题目：某种植物病毒V是通过稻飞虱吸食水稻汁液在水稻间传播的。稻田中青蛙数量的增加可减少该病毒在水稻间的传播。下列叙述正确的是： A. 青蛙与稻飞虱是捕食关系 B. 水稻和病毒V是互利共生关系 C. 病毒V与青蛙是寄生关系 D. 水稻与青蛙是竞争关系 答案是： ``` #### Load data ```python from datasets import load_dataset cmmlu=load_dataset(r"haonan-li/cmmlu", 'agronomy') print(cmmlu['test'][0]) ``` #### Load all data at once ```python task_list = ['agronomy', 'anatomy', 'ancient_chinese', 'arts', 'astronomy', 'business_ethics', 'chinese_civil_service_exam', 'chinese_driving_rule', 'chinese_food_culture', 'chinese_foreign_policy', 'chinese_history', 'chinese_literature', 'chinese_teacher_qualification', 'clinical_knowledge', 'college_actuarial_science', 'college_education', 'college_engineering_hydrology', 'college_law', 'college_mathematics', 'college_medical_statistics', 'college_medicine', 'computer_science', 'computer_security', 'conceptual_physics', 'construction_project_management', 'economics', 'education', 'electrical_engineering', 'elementary_chinese', 'elementary_commonsense', 'elementary_information_and_technology', 'elementary_mathematics', 'ethnology', 'food_science', 'genetics', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_geography', 'high_school_mathematics', 'high_school_physics', 'high_school_politics', 'human_sexuality', 'international_law', 'journalism', 'jurisprudence', 'legal_and_moral_basis', 'logical', 'machine_learning', 'management', 'marketing', 'marxist_theory', 'modern_chinese', 'nutrition', 'philosophy', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_study', 'sociology', 'sports_science', 'traditional_chinese_medicine', 'virology', 'world_history', 'world_religions'] from datasets import load_dataset cmmlu = {k: load_dataset(r"haonan-li/cmmlu", k) for k in task_list} ``` ## Citation ``` @misc{li2023cmmlu, title={CMMLU: Measuring massive multitask language understanding in Chinese}, author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin}, year={2023}, eprint={2306.09212}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License The CMMLU dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).

alvations/c4p0-x1-en-ja

alvations

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 49764 num_examples: 42 download_size: 37636 dataset_size: 49764 configs: - config_name: default data_files: - split: train path: 66034f82c5c65ae4/train-* ---

KBLab/overlim

KBLab

--- annotations_creators: - other language_creators: - other language: - sv - da - nb license: - cc-by-4.0 multilinguality: - translation size_categories: - unknown source_datasets: - extended|glue - extended|super_glue task_categories: - text-classification task_ids: - natural-language-inference - semantic-similarity-classification - sentiment-classification - text-scoring pretty_name: overlim tags: - qa-nli - paraphrase-identification --- # Dataset Card for OverLim ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The _OverLim_ dataset contains some of the GLUE and SuperGLUE tasks automatically translated to Swedish, Danish, and Norwegian (bokmål), using the OpusMT models for MarianMT. The translation quality was not manually checked and may thus be faulty. Results on these datasets should thus be interpreted carefully. If you want to have an easy script to train and evaluate your models have a look [here](https://github.com/kb-labb/overlim_eval) ### Supported Tasks and Leaderboards The data contains the following tasks from GLUE and SuperGLUE: - GLUE - `mnli` - `mrpc` - `qnli` - `qqp` - `rte` - `sst` - `stsb` - `wnli` - SuperGLUE - `boolq` - `cb` - `copa` - `rte` ### Languages - Swedish - Danish - Norwegian (bokmål) ## Dataset Structure ### Data Instances Every task has their own set of features, but all share an `idx` and `label`. - GLUE - `mnli` - `premise`, `hypothesis` - `mrpc` - `text_a`, `text_b` - `qnli` - `premise`, `hypothesis` - `qqp` - `text_a`, `text_b` - `sst` - `text` - `stsb` - `text_a`, `text_b` - `wnli` - `premise`, `hypothesis` - SuperGLUE - `boolq` - `question`, `passage` - `cb` - `premise`, `hypothesis` - `copa` - `premise`, `choice1`, `choice2`, `question` - `rte` - `premise`, `hypothesis` ### Data Splits In order to have test-split, we repurpose the original validation-split as test-split, and split the training-split into a new training- and validation-split, with an 80-20 distribution. ## Dataset Creation For more information about the individual tasks see (https://gluebenchmark.com) and (https://super.gluebenchmark.com). ### Curation Rationale Training non-English models is easy, but there is a lack of evaluation datasets to compare their actual performance. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@kb-labb](https://github.com/kb-labb) for adding this dataset.

openbmb/UltraInteract_sft

openbmb

--- language: - en license: mit pretty_name: UltraInteract_sft configs: - config_name: default data_files: - split: train path: 0000_sft.parquet dataset_info: features: - name: task dtype: string - name: dataset dtype: string - name: instruction dtype: string - name: response dtype: string - name: id dtype: string - name: parent_id dtype: string splits: - name: train num_bytes: 687238 num_examples: 288579 download_size: 687238 dataset_size: 687238 --- ## Introduction - 📜 [Paper](https://arxiv.org/abs/2404.02078) - 🤗 [Eurus Collection](https://huggingface.co/collections/openbmb/eurus-660bc40bec5376b3adc9d1c5) - 🤗 UltraInteract - [SFT](https://huggingface.co/datasets/openbmb/UltraInteract_sft) - [Preference Learning](https://huggingface.co/datasets/openbmb/UltraInteract_pair) - [GitHub Repo](https://github.com/OpenBMB/Eurus) UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. For each instruction, it includes a preference tree consisting of - (1) reasoning chains with diverse planning strategies in a unified format - (2) multi-turn interaction trajectories with the environment and the critique - (3) pairwise data to facilitate preference learning ## Structure UltraInteract collects a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. In each preference tree, all nodes of correct actions and all trajectories ending with correct actions can be used for SFT. Paired correct and incorrect nodes or trajectories can be used for preference learning. <img src="./figures/tree.png" alt="tree" style="zoom: 20%;" /> ## Illustrative Example Here is an illustrative example of an UltraInteract trajectory over two turns. In each turn, the actor model generates step-by-step reasoning chains, and the environment and the critique model provide observations and textual critique respectively. <img src="./figures/ui_example.png" alt="ui_example" style="zoom: 25%;" /> ## Stats Below are some statistics about UltraInteract. It consists of 86k instructions, 286k correct answers, and 219k pairs. <img src="./figures/stats.png" alt="stats" style="zoom: 40%;" /> ## Dataset Format (SFT) Coding ```JSONC { "id": "92a7356beaa75391539cb79624bdf85b7e5d36d5e74e46a2f1c4001f40b3e055_1_1", // unique data id, contains three parts, the unique string, the depth of the preference tree, and current turn, separated by '_' "parent_id": "535550938ff58bd73cd7fb85be834bb90b7600daf6b6f6a16b64091e5ecbfe43", // data with the same parent_id have the same instruction but different actions. "task": "Coding", // task type, choices = ["Coding", "Math_Cot", "Math_PoT", "Logic"] "dataset": "codecontest", // the dataset where the instruction comes from "instruction": "Write Python code to solve the task.\nMike has a string s consisting of only lowercase English letters. He wants to change exactly one character from the string so that the resulting one is a palindrome. \n\nA palindrome is a string that reads the same backward as forward, for example strings \"z\", \"aaa\", \"aba\", \"abccba\" are palindromes, but strings \"codeforces\", \"reality\", \"ab\" are not.\n\nInput\n\nThe first and single line contains string s (1 ≤ |s| ≤ 15).\n\nOutput\n\nPrint \"YES\" (without quotes) if Mike can change exactly one character so that the resulting string is palindrome or \"NO\" (without quotes) otherwise. \n\nExamples\n\nInput\n\nabccaa\n\n\nOutput\n\nYES\n\n\nInput\n\nabbcca\n\n\nOutput\n\nNO\n\n\nInput\n\nabcda\n\n\nOutput\n\nYES", // the task description. "response": " Step 1: This problem asks us to determine if it is possible to change exactly one character in the string to make it a palindrome. Step 2: We can start by iterating through the characters in the string and counting the number of differences between corresponding characters from the beginning and end of the string. Step 3: If the length of the string is odd and there is only one difference, or if the length is even and there is exactly one difference, then it is possible to change exactly one character to make the string a palindrome. Step 4: We need to consider edge cases such as a single character string or an empty string. ```python # Step 5: Take the input string s = input() # Step 6: Calculate the length of the string n = len(s) # Step 7: Initialize a counter to keep track of the number of differences cnt = 0 # Step 8: Iterate through the first half of the string and count the number of differences between corresponding characters from the beginning and end of the string. for i in range(n//2): if s[i] != s[n-i-1]: cnt += 1 # Step 9: If the length of the string is odd and there are no differences, or if the length is even and there is exactly one difference, print "YES" (without quotes) if (n % 2 == 1 and cnt == 0) or (cnt == 1): print('YES') # Step 10: Otherwise, print "NO" (without quotes) else: print('NO') ```" // The step-by-step solution. } ``` Math ```JSONC { "id": "647b2e8706cf459a8e3ec8e99727d2d51ff3414bf187db43d92cff87f8012713_1_1", "parent_id": "499d250c5852eddcd48a28b6c173f1a2349a16555990a1cd6fb5c7954cb94587", "task": "Math_PoT", "dataset": "mathqa", "instruction": "Tool available:\n[1] Python interpreter\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.\nSolve the following math problem step-by-step.\nthe length of a rectangle is doubled while its width is doubled . what is the % change in area ?", "response": " Step 1: The area of a rectangle is given by the product of its length and width. If the length is doubled, the area changes from l*w to 2l*w. Similarly, if the width is doubled, the area changes from l*w to l*2w. Step 2: If both the length and width are doubled, the area changes from l*w to 2l*2w. The percentage change in area is given by ((new area - old area) / old area)*100. Step 3: We can calculate the percentage change in area using the formula above. Step 4: By creating a tool to calculate the percentage change in area, we can easily solve the question. ```python # Step 5: Define a tool to calculate the percentage change in area def calculate_percentage_change(length, width): # Step 6: Calculate the old and new areas old_area = length * width new_area = 2 * length * 2 * width # Step 7: Calculate the percentage change percentage_change = ((new_area - old_area) / old_area) * 100 return percentage_change # Step 8: Call the function to solve the question percentage_change = calculate_percentage_change(2, 2) print(percentage_change) ```" } ``` Logic ```JSONC { "id": "92a19848da4fb0fc0b414f061f7500a81ea4cbd8f922ca1e32a6a15ef6f7e1a0_1_1", "parent_id": "107cc9147d8c09e62e2ba9e74cb41d9dbafd9a3adf62b67ba3da2b31c81b3300", "task": "Logic", "dataset": "hotpotqa", "instruction": " Tool available:\n[1] wikipedia_search(query: str) -> str\nThe Wikipedia Search tool provides access to a vast collection of articles covering a wide range of topics.\nCan query specific keywords or topics to retrieve accurate and comprehensive information. Solve the following problem step-by-step:\nWhich musician or group is from the US, 3OH!3 or Regina Spektor? Step 1: To determine which musician or group is from the US, we can search for information about each of them. ```python # Step 2: Searching for the information about 3OH!3. print(wikipedia_search('3OH!3')) ``` Page: 3OH!3 Summary: 3OH!3 (pronounced \"three oh three\") is an American electronic music duo from Boulder, Colorado, made up of Sean Foreman and Nathaniel Motte. They are best known for their single \"DONTTRUSTME\" from their album Want, which reached number seven on the Billboard Hot 100. Their second single, a remix of \"Starstrukk\" featuring Katy Perry from Want, was a top ten hit in the United Kingdom, Ireland, Finland, Poland, and Australia. They gained further recognition by featuring Kesha on the song \"My First Kiss\", which was made the lead single from their album Streets of Gold. The album later peaked at number seven on the Billboard 200.\n\n\n\nPage: 3OH!3 (album)\nSummary: 3OH!3 is the debut studio album by American electronic music band 3OH!3. The tracks \"Holler Til You Pass Out\" and \"Chokechain\" were later remixed for the band's major label debut. \"Holler Til You Pass Out\", \"Chokechain\", and \"Dance with Me\" are excluded from the iTunes release of the album.", "response": " Step 1: The provided information confirms that 3OH!3 is an American electronic music duo from Boulder, Colorado. Step 2: We no longer need to perform a search for Regina Spektor as we were only tasked to determine which of the two options is from the US and we have already confirmed 3OH!3's origin. Step 3: We can confidently conclude that 3OH!3 is the musician/group from the US. Answer:3OH!3" } ``` ## Citation ```bib @misc{yuan2024advancing, title={Advancing LLM Reasoning Generalists with Preference Trees}, author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun}, year={2024}, eprint={2404.02078}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

mhenrichsen/alpaca_2k_test

mhenrichsen

--- license: apache-2.0 ---

ArmelR/the-pile-splitted

ArmelR

--- configs: - config_name: all data_files: - split: train path: - "data/ArXiv/train/*.arrow" - "data/BookCorpus2/train/*.arrow" - "data/Books3/train/*.arrow" - "data/DM Mathematics/train/*.arrow" - "data/Enron Emails/train/*.arrow" - "data/EuroParl/train/*.arrow" - "data/FreeLaw/train/*.arrow" - "data/Github/train/*.arrow" - "data/Gutenberg (PG-19)/train/*.arrow" - "data/HackerNews/train/*.arrow" - "data/NIH ExPorter/train/*.arrow" - "data/OpenSubtitles/train/*.arrow" - "data/OpenWebText2/train/*.arrow" - "data/PhilPapers/train/*.arrow" - "data/Pile-CC/train/*.arrow" - "data/PubMed Abstracts/train/*.arrow" - "data/PubMed Central/train/*.arrow" - "data/StackExchange/train/*.arrow" - "data/UPSTO Backgrounds/train/*.arrow" - "data/Ubuntu IRC/train/*.arrow" - "data/Wikipedia (en)/train/*.arrow" - "data/YoutubeSubtitles/train/*.arrow" - split: test path: - "data/ArXiv/test/*.arrow" - "data/BookCorpus2/test/*.arrow" - "data/Books3/test/*.arrow" - "data/DM Mathematics/test/*.arrow" - "data/Enron Emails/test/*.arrow" - "data/EuroParl/test/*.arrow" - "data/FreeLaw/test/*.arrow" - "data/Github/test/*.arrow" - "data/Gutenberg (PG-19)/test/*.arrow" - "data/HackerNews/test/*.arrow" - "data/NIH ExPorter/test/*.arrow" - "data/OpenSubtitles/test/*.arrow" - "data/OpenWebText2/test/*.arrow" - "data/PhilPapers/test/*.arrow" - "data/Pile-CC/test/*.arrow" - "data/PubMed Abstracts/test/*.arrow" - "data/PubMed Central/test/*.arrow" - "data/StackExchange/test/*.arrow" - "data/UPSTO Backgrounds/test/*.arrow" - "data/Ubuntu IRC/test/*.arrow" - "data/Wikipedia (en)/test/*.arrow" - "data/YoutubeSubtitles/test/*.arrow" default: true - config_name: ArXiv data_files: - split: train path: "data/ArXiv/train/*.arrow" - split: test path: "data/ArXiv/test/*.arrow" - config_name: BookCorpus2 data_files: - split: train path: "data/BookCorpus2/train/*.arrow" - split: test path: "data/BookCorpus2/test/*.arrow" - config_name: Books3 data_files: - split: train path: "data/Books3/train/*.arrow" - split: test path: "data/Books3/test/*.arrow" - config_name: DM Mathematics data_files: - split: train path: "data/DM Mathematics/train/*.arrow" - split: test path: "data/DM Mathematics/test/*.arrow" - config_name: Enron Emails data_files: - split: train path: "data/Enron Emails/train/*.arrow" - split: test path: "data/Enron Emails/test/*.arrow" - config_name: EuroParl data_files: - split: train path: "data/EuroParl/train/*.arrow" - split: test path: "data/EuroParl/test/*.arrow" - config_name: FreeLaw data_files: - split: train path: "data/FreeLaw/train/*.arrow" - split: test path: "data/FreeLaw/test/*.arrow" - config_name: Github data_files: - split: train path: "data/Github/train/*.arrow" - split: test path: "data/Github/test/*.arrow" - config_name: Gutenberg (PG-19) data_files: - split: train path: "data/Gutenberg (PG-19)/train/*.arrow" - split: test path: "data/Gutenberg (PG-19)/test/*.arrow" - config_name: HackerNews data_files: - split: train path: "data/HackerNews/train/*.arrow" - split: test path: "data/HackerNews/test/*.arrow" - config_name: NIH ExPorter data_files: - split: train path: "data/NIH ExPorter/train/*.arrow" - split: test path: "data/NIH ExPorter/test/*.arrow" - config_name: OpenSubtitles data_files: - split: train path: "data/OpenSubtitles/train/*.arrow" - split: test path: "data/OpenSubtitles/test/*.arrow" - config_name: OpenWebText2 data_files: - split: train path: "data/OpenWebText2/train/*.arrow" - split: test path: "data/OpenWebText2/test/*.arrow" - config_name: PhilPapers data_files: - split: train path: "data/PhilPapers/train/*.arrow" - split: test path: "data/PhilPapers/test/*.arrow" - config_name: Pile-CC data_files: - split: train path: "data/Pile-CC/train/*.arrow" - split: test path: "data/Pile-CC/test/*.arrow" - config_name: PubMed Abstracts data_files: - split: train path: "data/PubMed Abstracts/train/*.arrow" - split: test path: "data/PubMed Abstracts/test/*.arrow" - config_name: PubMed Central data_files: - split: train path: "data/PubMed Central/train/*.arrow" - split: test path: "data/PubMed Central/test/*.arrow" - config_name: StackExchange data_files: - split: train path: "data/StackExchange/train/*.arrow" - split: test path: "data/StackExchange/test/*.arrow" - config_name: UPSTO Backgrounds data_files: - split: train path: "data/UPSTO Backgrounds/train/*.arrow" - split: test path: "data/UPSTO Backgrounds/test/*.arrow" - config_name: Ubuntu IRC data_files: - split: train path: "data/Ubuntu IRC/train/*.arrow" - split: test path: "data/Ubuntu IRC/test/*.arrow" - config_name: Wikipedia (en) data_files: - split: train path: "data/Wikipedia (en)/train/*.arrow" - split: test path: "data/Wikipedia (en)/test/*.arrow" - config_name: YoutubeSubtitles data_files: - split: train path: "data/YoutubeSubtitles/train/*.arrow" - split: test path: "data/YoutubeSubtitles/test/*.arrow" --- # Dataset description [The pile](https://arxiv.org/abs/2101.00027) is an 800GB dataset of english text designed by EleutherAI to train large-scale language models. The original version of the dataset can be found [here](https://huggingface.co/datasets/EleutherAI/pile). The dataset is divided into 22 smaller high-quality datasets. For more information each of them, please refer to [the datasheet for the pile](https://arxiv.org/abs/2201.07311). However, the current version of the dataset, available on the Hub, is not splitted accordingly. We had to solve this problem in order to improve the user experience when it comes to deal with the pile via the hub. Here is an instance of the pile ``` { 'meta': {'pile_set_name': 'Pile-CC'}, 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web. Playing on...' } ``` We used the `meta` column to properly divide the dataset in subsets. Each instance `example` belongs to the subset `domain` and `domain = example['meta']['pile_set_name']`. By doing this, we were able to create a [new version of the pile](https://huggingface.co/datasets/ArmelR/sharded-pile) that is properly divided, each instance having a new column `domain`. We further splitted each subset in train/test (97%/3%) to build the current dataset which the following structure ``` data ArXiv train test BookCorpus2 train test Books3 train test ``` # Usage ```python from datasets import load_dataset dataset = load_dataset( "ArmelR/the-pile-splitted", subset_of_interest, num_proc=8 ) ``` Using `subset_of_interest = "default"` will load the whole dataset.

labelmaker/arkit_labelmaker

labelmaker

--- viewer: false license: bsd language: - en tags: - 3D semantic segmentation - indoor 3D scene dataset pretty_name: arkit_labelmaker size_categories: - 1K<n<10K --- # ARKit Labelmaker: A New Scale for Indoor 3D Scene Understanding [[arxiv]](https://arxiv.org/abs/2410.13924) [[website]](https://labelmaker.org/) We complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. This produces the first large-scale, real-world 3D dataset with dense semantic annotations. Training on this auto-generated data, we push forward the state-of-the-art performance on ScanNet and ScanNet200 with prevalent 3D semantic segmentation models.

McAuley-Lab/Amazon-Reviews-2023

McAuley-Lab

--- language: - en tags: - recommendation - reviews size_categories: - 10B<n<100B dataset_info: - config_name: raw_meta_All_Beauty features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 172622243 num_examples: 112590 download_size: 59635138 dataset_size: 172622243 - config_name: raw_meta_Arts_Crafts_and_Sewing features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 1893257069 num_examples: 801446 download_size: 806711170 dataset_size: 1893257069 - config_name: raw_meta_Cell_Phones_and_Accessories features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 3497596478 num_examples: 1288490 download_size: 1262072469 dataset_size: 3497596478 - config_name: raw_meta_Electronics features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 4603602269 num_examples: 1610012 download_size: 1955009715 dataset_size: 4603602269 - config_name: raw_meta_Gift_Cards features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 1740761 num_examples: 1137 download_size: 401887 dataset_size: 1740761 - config_name: raw_meta_Handmade_Products features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 340772183 num_examples: 164817 download_size: 132049123 dataset_size: 340772183 - config_name: raw_meta_Industrial_and_Scientific features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 986632649 num_examples: 427564 download_size: 425007659 dataset_size: 986632649 - config_name: raw_meta_Musical_Instruments features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 553296301 num_examples: 213593 download_size: 229633633 dataset_size: 553296301 - config_name: raw_meta_Toys_and_Games features: - name: main_category dtype: string - name: title dtype: string - name: average_rating dtype: float64 - name: rating_number dtype: int64 - name: features sequence: string - name: description sequence: string - name: price dtype: string - name: images sequence: - name: hi_res dtype: string - name: large dtype: string - name: thumb dtype: string - name: variant dtype: string - name: videos sequence: - name: title dtype: string - name: url dtype: string - name: user_id dtype: string - name: store dtype: string - name: categories sequence: string - name: details dtype: string - name: parent_asin dtype: string - name: bought_together dtype: string - name: subtitle dtype: string - name: author dtype: string splits: - name: full num_bytes: 2291736294 num_examples: 890874 download_size: 972667016 dataset_size: 2291736294 configs: - config_name: raw_meta_All_Beauty data_files: - split: full path: raw_meta_All_Beauty/full-* - config_name: raw_meta_Arts_Crafts_and_Sewing data_files: - split: full path: raw_meta_Arts_Crafts_and_Sewing/full-* - config_name: raw_meta_Cell_Phones_and_Accessories data_files: - split: full path: raw_meta_Cell_Phones_and_Accessories/full-* - config_name: raw_meta_Electronics data_files: - split: full path: raw_meta_Electronics/full-* - config_name: raw_meta_Gift_Cards data_files: - split: full path: raw_meta_Gift_Cards/full-* - config_name: raw_meta_Handmade_Products data_files: - split: full path: raw_meta_Handmade_Products/full-* - config_name: raw_meta_Industrial_and_Scientific data_files: - split: full path: raw_meta_Industrial_and_Scientific/full-* - config_name: raw_meta_Musical_Instruments data_files: - split: full path: raw_meta_Musical_Instruments/full-* - config_name: raw_meta_Toys_and_Games data_files: - split: full path: raw_meta_Toys_and_Games/full-* --- # Amazon Reviews 2023 **Please also visit [amazon-reviews-2023.github.io/](https://amazon-reviews-2023.github.io/) for more details, loading scripts, and preprocessed benchmark files.** **[April 7, 2024]** We add two useful files: 1. `all_categories.txt`: 34 lines (33 categories + "Unknown"), each line contains a category name. 2. `asin2category.json`: A mapping between `parent_asin` (item ID) to its corresponding category name. --- <!-- Provide a quick summary of the dataset. --> This is a large-scale **Amazon Reviews** dataset, collected in **2023** by [McAuley Lab](https://cseweb.ucsd.edu/~jmcauley/), and it includes rich features such as: 1. **User Reviews** (*ratings*, *text*, *helpfulness votes*, etc.); 2. **Item Metadata** (*descriptions*, *price*, *raw image*, etc.); 3. **Links** (*user-item* / *bought together* graphs). ## What's New? In the Amazon Reviews'23, we provide: 1. **Larger Dataset:** We collected 571.54M reviews, 245.2% larger than the last version; 2. **Newer Interactions:** Current interactions range from May. 1996 to Sep. 2023; 3. **Richer Metadata:** More descriptive features in item metadata; 4. **Fine-grained Timestamp:** Interaction timestamp at the second or finer level; 5. **Cleaner Processing:** Cleaner item metadata than previous versions; 6. **Standard Splitting:** Standard data splits to encourage RecSys benchmarking. ## Basic Statistics > We define the <b>#R_Tokens</b> as the number of [tokens](https://pypi.org/project/tiktoken/) in user reviews and <b>#M_Tokens</b> as the number of [tokens](https://pypi.org/project/tiktoken/) if treating the dictionaries of item attributes as strings. We emphasize them as important statistics in the era of LLMs. > We count the number of items based on user reviews rather than item metadata files. Note that some items lack metadata. ### Compared to Previous Versions | Year | #Review | #User | #Item | #R_Token | #M_Token | #Domain | Timespan | | ----------- | ---------: | -------: | -------: | ---------: | ------------: | ------------: | ------------: | | [2013](https://snap.stanford.edu/data/web-Amazon-links.html) | 34.69M | 6.64M | 2.44M | 5.91B | -- | 28 | Jun'96 - Mar'13 | | [2014](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html) | 82.83M | 21.13M | 9.86M | 9.16B | 4.14B | 24 | May'96 - Jul'14 | | [2018](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/) | 233.10M | 43.53M | 15.17M | 15.73B | 7.99B | 29 | May'96 - Oct'18 | | <b>[2023](https://)</b> | **571.54M** | **54.51M** | **48.19M** | **30.14B** | **30.78B** | **33** | **May'96 - Sep'23** | ### Grouped by Category | Category | #User | #Item | #Rating | #R_Token | #M_Token | Download | | ------------------------ | ------: | ------: | --------: | -------: | -------: | ------------------------------: | | All_Beauty | 632.0K | 112.6K | 701.5K | 31.6M | 74.1M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/All_Beauty.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_All_Beauty.jsonl.gz' download> meta </a> | | Amazon_Fashion | 2.0M | 825.9K | 2.5M | 94.9M | 510.5M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Amazon_Fashion.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Amazon_Fashion.jsonl.gz' download> meta </a> | | Appliances | 1.8M | 94.3K | 2.1M | 92.8M | 95.3M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Appliances.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Appliances.jsonl.gz' download> meta </a> | | Arts_Crafts_and_Sewing | 4.6M | 801.3K | 9.0M | 350.0M | 695.4M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Arts_Crafts_and_Sewing.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Arts_Crafts_and_Sewing.jsonl.gz' download> meta </a> | | Automotive | 8.0M | 2.0M | 20.0M | 824.9M | 1.7B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Automotive.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Automotive.jsonl.gz' download> meta </a> | | Baby_Products | 3.4M | 217.7K | 6.0M | 323.3M | 218.6M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Baby_Products.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Baby_Products.jsonl.gz' download> meta </a> | | Beauty_and_Personal_Care | 11.3M | 1.0M | 23.9M | 1.1B | 913.7M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Beauty_and_Personal_Care.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Beauty_and_Personal_Care.jsonl.gz' download> meta </a> | | Books | 10.3M | 4.4M | 29.5M | 2.9B | 3.7B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Books.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Books.jsonl.gz' download> meta </a> | | CDs_and_Vinyl | 1.8M | 701.7K | 4.8M | 514.8M | 287.5M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/CDs_and_Vinyl.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_CDs_and_Vinyl.jsonl.gz' download> meta </a> | | Cell_Phones_and_Accessories | 11.6M | 1.3M | 20.8M | 935.4M | 1.3B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Cell_Phones_and_Accessories.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Cell_Phones_and_Accessories.jsonl.gz' download> meta </a> | | Clothing_Shoes_and_Jewelry | 22.6M | 7.2M | 66.0M | 2.6B | 5.9B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Clothing_Shoes_and_Jewelry.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Clothing_Shoes_and_Jewelry.jsonl.gz' download> meta </a> | | Digital_Music | 101.0K | 70.5K | 130.4K | 11.4M | 22.3M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Digital_Music.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Digital_Music.jsonl.gz' download> meta </a> | | Electronics | 18.3M | 1.6M | 43.9M | 2.7B | 1.7B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Electronics.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Electronics.jsonl.gz' download> meta </a> | | Gift_Cards | 132.7K | 1.1K | 152.4K | 3.6M | 630.0K | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Gift_Cards.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Gift_Cards.jsonl.gz' download> meta </a> | | Grocery_and_Gourmet_Food | 7.0M | 603.2K | 14.3M | 579.5M | 462.8M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Grocery_and_Gourmet_Food.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Grocery_and_Gourmet_Food.jsonl.gz' download> meta </a> | | Handmade_Products | 586.6K | 164.7K | 664.2K | 23.3M | 125.8M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Handmade_Products.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Handmade_Products.jsonl.gz' download> meta </a> | | Health_and_Household | 12.5M | 797.4K | 25.6M | 1.2B | 787.2M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Health_and_Household.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Health_and_Household.jsonl.gz' download> meta </a> | | Health_and_Personal_Care | 461.7K | 60.3K | 494.1K | 23.9M | 40.3M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Health_and_Personal_Care.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Health_and_Personal_Care.jsonl.gz' download> meta </a> | | Home_and_Kitchen | 23.2M | 3.7M | 67.4M | 3.1B | 3.8B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Home_and_Kitchen.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Home_and_Kitchen.jsonl.gz' download> meta </a> | | Industrial_and_Scientific | 3.4M | 427.5K | 5.2M | 235.2M | 363.1M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Industrial_and_Scientific.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Industrial_and_Scientific.jsonl.gz' download> meta </a> | | Kindle_Store | 5.6M | 1.6M | 25.6M | 2.2B | 1.7B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Kindle_Store.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Kindle_Store.jsonl.gz' download> meta </a> | | Magazine_Subscriptions | 60.1K | 3.4K | 71.5K | 3.8M | 1.3M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Magazine_Subscriptions.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Magazine_Subscriptions.jsonl.gz' download> meta </a> | | Movies_and_TV | 6.5M | 747.8K | 17.3M | 1.0B | 415.5M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Movies_and_TV.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Movies_and_TV.jsonl.gz' download> meta </a> | | Musical_Instruments | 1.8M | 213.6K | 3.0M | 182.2M | 200.1M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Musical_Instruments.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Musical_Instruments.jsonl.gz' download> meta </a> | | Office_Products | 7.6M | 710.4K | 12.8M | 574.7M | 682.8M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Office_Products.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Office_Products.jsonl.gz' download> meta </a> | | Patio_Lawn_and_Garden | 8.6M | 851.7K | 16.5M | 781.3M | 875.1M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Patio_Lawn_and_Garden.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Patio_Lawn_and_Garden.jsonl.gz' download> meta </a> | | Pet_Supplies | 7.8M | 492.7K | 16.8M | 905.9M | 511.0M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Pet_Supplies.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Pet_Supplies.jsonl.gz' download> meta </a> | | Software | 2.6M | 89.2K | 4.9M | 179.4M | 67.1M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Software.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Software.jsonl.gz' download> meta </a> | | Sports_and_Outdoors | 10.3M | 1.6M | 19.6M | 986.2M | 1.3B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Sports_and_Outdoors.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Sports_and_Outdoors.jsonl.gz' download> meta </a> | | Subscription_Boxes | 15.2K | 641 | 16.2K | 1.0M | 447.0K | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Subscription_Boxes.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Subscription_Boxes.jsonl.gz' download> meta </a> | | Tools_and_Home_Improvement | 12.2M | 1.5M | 27.0M | 1.3B | 1.5B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Tools_and_Home_Improvement.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Tools_and_Home_Improvement.jsonl.gz' download> meta </a> | | Toys_and_Games | 8.1M | 890.7K | 16.3M | 707.9M | 848.3M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Toys_and_Games.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Toys_and_Games.jsonl.gz' download> meta </a> | | Video_Games | 2.8M | 137.2K | 4.6M | 347.9M | 137.3M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Video_Games.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Video_Games.jsonl.gz' download> meta </a> | | Unknown | 23.1M | 13.2M | 63.8M | 3.3B | 232.8M | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Unknown.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Unknown.jsonl.gz' download> meta </a> | > Check Pure ID files and corresponding data splitting strategies in <b>[Common Data Processing](https://amazon-reviews-2023.github.io/data_processing/index.html)</b> section. ## Quick Start ### Load User Reviews ```python from datasets import load_dataset dataset = load_dataset("McAuley-Lab/Amazon-Reviews-2023", "raw_review_All_Beauty", trust_remote_code=True) print(dataset["full"][0]) ``` ```json {'rating': 5.0, 'title': 'Such a lovely scent but not overpowering.', 'text': "This spray is really nice. It smells really good, goes on really fine, and does the trick. I will say it feels like you need a lot of it though to get the texture I want. I have a lot of hair, medium thickness. I am comparing to other brands with yucky chemicals so I'm gonna stick with this. Try it!", 'images': [], 'asin': 'B00YQ6X8EO', 'parent_asin': 'B00YQ6X8EO', 'user_id': 'AGKHLEW2SOWHNMFQIJGBECAF7INQ', 'timestamp': 1588687728923, 'helpful_vote': 0, 'verified_purchase': True} ``` ### Load Item Metadata ```python dataset = load_dataset("McAuley-Lab/Amazon-Reviews-2023", "raw_meta_All_Beauty", split="full", trust_remote_code=True) print(dataset[0]) ``` ```json {'main_category': 'All Beauty', 'title': 'Howard LC0008 Leather Conditioner, 8-Ounce (4-Pack)', 'average_rating': 4.8, 'rating_number': 10, 'features': [], 'description': [], 'price': 'None', 'images': {'hi_res': [None, 'https://m.media-amazon.com/images/I/71i77AuI9xL._SL1500_.jpg'], 'large': ['https://m.media-amazon.com/images/I/41qfjSfqNyL.jpg', 'https://m.media-amazon.com/images/I/41w2yznfuZL.jpg'], 'thumb': ['https://m.media-amazon.com/images/I/41qfjSfqNyL._SS40_.jpg', 'https://m.media-amazon.com/images/I/41w2yznfuZL._SS40_.jpg'], 'variant': ['MAIN', 'PT01']}, 'videos': {'title': [], 'url': [], 'user_id': []}, 'store': 'Howard Products', 'categories': [], 'details': '{"Package Dimensions": "7.1 x 5.5 x 3 inches; 2.38 Pounds", "UPC": "617390882781"}', 'parent_asin': 'B01CUPMQZE', 'bought_together': None, 'subtitle': None, 'author': None} ``` > Check data loading examples and Huggingface datasets APIs in <b>[Common Data Loading](https://amazon-reviews-2023.github.io/data_loading/index.html)</b> section. ## Data Fields ### For User Reviews | Field | Type | Explanation | | ----- | ---- | ----------- | | rating | float | Rating of the product (from 1.0 to 5.0). | | title | str | Title of the user review. | | text | str | Text body of the user review. | | images | list | Images that users post after they have received the product. Each image has different sizes (small, medium, large), represented by the small_image_url, medium_image_url, and large_image_url respectively. | | asin | str | ID of the product. | | parent_asin | str | Parent ID of the product. Note: Products with different colors, styles, sizes usually belong to the same parent ID. The “asin” in previous Amazon datasets is actually parent ID. <b>Please use parent ID to find product meta.</b> | | user_id | str | ID of the reviewer | | timestamp | int | Time of the review (unix time) | | verified_purchase | bool | User purchase verification | | helpful_vote | int | Helpful votes of the review | ### For Item Metadata | Field | Type | Explanation | | ----- | ---- | ----------- | | main_category | str | Main category (i.e., domain) of the product. | | title | str | Name of the product. | | average_rating | float | Rating of the product shown on the product page. | | rating_number | int | Number of ratings in the product. | | features | list | Bullet-point format features of the product. | | description | list | Description of the product. | | price | float | Price in US dollars (at time of crawling). | | images | list | Images of the product. Each image has different sizes (thumb, large, hi_res). The “variant” field shows the position of image. | | videos | list | Videos of the product including title and url. | | store | str | Store name of the product. | | categories | list | Hierarchical categories of the product. | | details | dict | Product details, including materials, brand, sizes, etc. | | parent_asin | str | Parent ID of the product. | | bought_together | list | Recommended bundles from the websites. | ## Citation ```bibtex @article{hou2024bridging, title={Bridging Language and Items for Retrieval and Recommendation}, author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian}, journal={arXiv preprint arXiv:2403.03952}, year={2024} } ``` ## Contact Us - **Report Bugs**: To report bugs in the dataset, please file an issue on our [GitHub](https://github.com/hyp1231/AmazonReviews2023/issues/new). - **Others**: For research collaborations or other questions, please email **yphou AT ucsd.edu**.

openclimatefix/dwd-icon-eu

openclimatefix

--- license: mit tags: - climate pretty_name: DWD ICON-EU Forecasts size_categories: - 1K<n<10K --- # Dataset Card for DWD ICON-EU Forecast This dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model. From 2020-March 2023 the forecasts contain variables that are relevant to solar and wind forecasting. From March 2023 to the present, all variables are included. Each forecast runs up to 5 days into the future, and the model is ran 4 times per day. This data is an archive of the publicly available data at https://opendata.dwd.de/weather/nwp/, converted to Zarr format with Xarray. No other processing of the data is performed. ## Dataset Details - **Curated by:** Jacob Bieker, Sol Cotton, Open Climate Fix - **License:** German Government Open Data License ### Dataset Sources [optional] - **Raw files:** https://opendata.dwd.de/weather/nwp/ Note: The raw files are deleted after 24 hours, and there is no long-term archive available publicly. ## Uses This data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe. ## Dataset Structure The dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-120 hours. The files are located at data/year/month/day/YYYYMMDDHH.zarr.zip. ## Dataset Creation ### Curation Rationale The DWD ICON-EU model provides high-quality, high-resolution forecasts for European weather that is also publicly available and free of charge. The model should generally outperform NOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset was setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting. ### Source Data The source data is the grib2 files from the DWD Open Data Server. #### Data Collection and Processing The data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened with Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have "_s" appended to their names to differentiate them from multi-level variables. The Dataset is then written to Zarr using "ocf_blosc2" to encode and compress the variables. No scaling or changing of the variables values is performed. #### Who are the source data producers? German Weather Service (DWD) ### Recommendations These files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want and open them locally. In either case, to access the data you can do the following ```python import ocf_blosc2 import xarray as xr data = xr.open_zarr("path/to/zarr/file") print(data) ``` Alternatively, for using the data in forecasting, there is the `ocf_datapipes` package for loading and training renewable energy forecasting models with multi-modal inputs, including ICON, but also satellite data, PV readings, etc. ## Dataset Card Contact OCF Data Team: [email protected]

openslr/librispeech_asr

openslr

--- pretty_name: LibriSpeech annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: librispeech-1 size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - audio-classification task_ids: - speaker-identification dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.100 num_bytes: 6619683041 num_examples: 28539 - name: train.360 num_bytes: 23898214592 num_examples: 104014 - name: validation num_bytes: 359572231 num_examples: 2703 - name: test num_bytes: 367705423 num_examples: 2620 download_size: 30121377654 dataset_size: 31245175287 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.500 num_bytes: 31810256902 num_examples: 148688 - name: validation num_bytes: 337283304 num_examples: 2864 - name: test num_bytes: 352396474 num_examples: 2939 download_size: 31236565377 dataset_size: 32499936680 - config_name: all features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.clean.100 num_bytes: 6627791685 num_examples: 28539 - name: train.clean.360 num_bytes: 23927767570 num_examples: 104014 - name: train.other.500 num_bytes: 31852502880 num_examples: 148688 - name: validation.clean num_bytes: 359505691 num_examples: 2703 - name: validation.other num_bytes: 337213112 num_examples: 2864 - name: test.clean num_bytes: 368449831 num_examples: 2620 - name: test.other num_bytes: 353231518 num_examples: 2939 download_size: 61357943031 dataset_size: 63826462287 --- # Dataset Card for librispeech_asr ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [LibriSpeech ASR corpus](http://www.openslr.org/12) - **Repository:** [Needs More Information] - **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) - **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - **Point of Contact:** [Daniel Povey](mailto:[email protected]) ### Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia. ### Languages The audio is in English. There are two configurations: `clean` and `other`. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other". ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'chapter_id': 141231, 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '1272-141231-0000', 'speaker_id': 1272, 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} ``` ### Data Fields - file: A path to the downloaded audio file in .flac format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other". For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech. | | Train.500 | Train.360 | Train.100 | Valid | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | clean | - | 104014 | 28539 | 2703 | 2620| | other | 148688 | - | - | 2864 | 2939 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, pages={5206--5210}, year={2015}, organization={IEEE} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

lithium0003/findtextCenterNet_dataset

lithium0003

--- license: mit ---

deepghs/gelbooru_full

deepghs

--- license: other task_categories: - image-classification - zero-shot-image-classification - text-to-image language: - en tags: - art - anime - not-for-all-audiences size_categories: - 10M<n<100M annotations_creators: - no-annotation source_datasets: - gelbooru --- # Gelbooru Full Dataset This is the full dataset of [gelbooru.com](https://gelbooru.com/). And all the original images are maintained here. # How to Painlessly Use This Use [cheesechaser](https://github.com/deepghs/cheesechaser) to quickly get images from this repository. Before using this code, you have to **grant the access from this gated repository**. And then **set your personal HuggingFace token into `HF_TOKEN` environment variable** to give the code authorization for this repository. ```python from cheesechaser.datapool import GelbooruDataPool pool = GelbooruDataPool() pool.batch_download_to_directory( # download images #7000000-7000100, any ranges or id lists are okay resource_ids=range(7000000, 7000100), # save to directory /data/gelbooru dst_dir='/data/gelbooru', ) ``` # Information ## Images There are 10083708 images in total. The maximum ID of these images is 11191848. Last updated at `2024-12-26 10:23:03 JST`. These are the information of recent 50 images: | id | filename | width | height | mimetype | tags | file_size | file_url | |---------:|:--------------|--------:|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------:|:-----------------------------------------------------------------------------| | 11191848 | 11191848.jpg | 3000 | 3000 | image/jpeg | 1boy absurdres artist_name black_fur bracelet cape christmas chromatic_aberration closed_mouth commentary feet_out_of_frame film_grain fur-trimmed_headwear fur_trim furry furry_male glint gloves hat hedgehog_boy highres instagram_logo instagram_username jewelry looking_at_viewer male_focus red_cape red_eyes red_fur red_hat santa_hat shadow_the_hedgehog shiiyou shoes smile snowflakes solo sonic_(series) symbol-only_commentary twitter_username twitter_x_logo two-tone_fur white_footwear white_gloves | 2152633 | https://img3.gelbooru.com/images/f4/23/f423150983af5d9a5c54808bb0e10d0c.jpg | | 11191845 | 11191845.png | 1012 | 1433 | image/png | 1girl :p bike_shorts blue_eyes blush breasts brown_hair cameltoe clothes_pull covering_breasts covering_privates creatures_(company) game_freak grey_background groin highres holding huuuna large_breasts licking_lips looking_at_viewer may_(pokemon) navel nintendo pokemon pokemon_rse pulling_own_clothes red_shirt shirt short_hair shorts skirt skirt_pull solo thighs tongue tongue_out undressing | 746214 | https://img3.gelbooru.com/images/cc/d3/ccd32a632db8e9d2e7eadfacc03473a0.png | | 11191803 | 11191803.jpg | 2397 | 3871 | image/jpeg | 1girl absurdres aged_down armlet barefoot black_dress blush bracer branch detached_sleeves dress feet gold hands_in_hair highres holding holding_own_hair hololive hololive_english jewelry legs long_hair looking_at_viewer mori_calliope mori_calliope_(1st_costume) multicolored_hair no_shoes pink_hair plant red_eyes single_detached_sleeve single_thighhigh sitting skindentation smile soles solo thigh_strap thighhighs thighs tiara toes very_long_hair virtual_youtuber white_background zserty555 | 5590034 | https://img3.gelbooru.com/images/c1/dc/c1dc83ec7faec617050652b2ceb39a1e.jpg | | 11191793 | 11191793.jpg | 2906 | 4096 | image/jpeg | 2girls :3 :d absurdres animal_ears black_cat black_eyes black_hair black_hoodie blush_stickers bright_pupils brown_eyes cat cat_ears cat_girl christmas christmas_lights christmas_ornaments christmas_tree commentary_request grey_pants highres hood hood_down hoodie indoors jacket long_hair long_sleeves megateru motion_blur multiple_girls open_clothes open_jacket open_mouth original outstretched_arms pants ponytail purple_skirt red_hair shirt sidelocks signature skirt smile socks solo_focus star_ornament surprised turtleneck turtleneck_shirt white_jacket white_pupils white_socks | 1875473 | https://img3.gelbooru.com/images/c7/1d/c71d9be511d90e18707fa52ee73fd229.jpg | | 11191775 | 11191775.png | 2800 | 2600 | image/png | 1boy 1girl absurdly_long_hair absurdres akari_(pokemon) arms_behind_back ass bdsm black_bodysuit black_eyes blue_hair blue_jacket blush bodysuit bondage bound bound_wrists breasts creatures_(company) crotch_rope cursed_delta drooling fellatio female_focus fur-trimmed_sleeves fur_trim game_freak headband headscarf highres huge_ass jacket large_breasts long_hair long_sleeves looking_at_viewer looking_to_the_side looking_up multiple_views nintendo obi obijime oral penis pokemon pokemon_legends:_arceus red_scarf rope saliva sash scarf seductive_gaze seductive_smile shibari shibari_over_clothes short_sleeves sidelocks simple_background skin_tight skindentation smile solo_focus thick_thighs thighs very_long_hair white_background white_headband white_headscarf | 2722576 | https://img3.gelbooru.com/images/95/8c/958c49c10bda5cc4ca817b35ea7d7234.png | | 11191762 | 11191762.jpg | 1668 | 2388 | image/jpeg | 1girl black_serafuku black_skirt blush brown_eyes brown_hair chibi chibi_inset envelope grey_background highres holding holding_envelope irasutoya irasutoya_challenge kagenone long_sleeves medium_hair neckerchief pale_skin red_neckerchief school_uniform serafuku skirt | 4096724 | https://img3.gelbooru.com/images/30/d9/30d9cf039413877951a134b8d15f8b54.jpg | | 11191750 | 11191750.jpg | 1920 | 1200 | image/jpeg | 1boy 3girls ahoge antonio_(ragnarok_online) arms_up bell black_gloves boots bow box breasts brown_footwear brown_hair brown_jacket brown_skirt brown_socks cape christmas christmas_tree copyright_notice creator_(ragnarok_online) dress exyagi from_above full_body fur-trimmed_footwear fur-trimmed_headwear fur_trim gift gift_box gloves green_bow green_eyes green_hair guest_art hair_bow hat highres jacket lif_(ragnarok_online) logo long_hair long_sleeves looking_at_viewer medium_breasts merchant_(ragnarok_online) multiple_girls navel official_art official_wallpaper open_clothes open_jacket open_mouth pink_hair plunging_neckline pom_pom_(clothes) ragnarok_online red_cape red_hat santa_hat shirt shoes short_dress skirt sleigh smile snow snowing socks teeth very_long_hair white_gloves white_shirt | 1268939 | https://img3.gelbooru.com/images/e4/ab/e4ab421a022bd0143c70b2a9cc7daa99.jpg | | 11191739 | 11191739.jpg | 1500 | 1500 | image/jpeg | animal_focus artist_name ball black_hole border_collie brown_eyes commission device dog drooling highres kihara_utsuru novelty_glasses pickles_(reverse:1999) pixiv_commission reverse:1999 signature soccer_ball space speech_bubble thought_bubble | 1271817 | https://img3.gelbooru.com/images/d0/3a/d03ada1a3e1cbe3c86ce85f9e797c1e4.jpg | | 11191738 | 11191738.jpg | 1668 | 2388 | image/jpeg | 1girl autumn_leaves blush book braided_twintails brown_eyes brown_hair brown_sweater highres holding holding_book kagenone leaf long_hair long_sleeves original solo sweater upper_body wide_sleeves | 2326928 | https://img3.gelbooru.com/images/cf/86/cf86585c82c5ebd78ec4f090d8985522.jpg | | 11191737 | 11191737.png | 2586 | 3776 | image/png | 1girl absurdres black_pantyhose blue_eyes breasts brown_hair cowboy_shot creatures_(company) double_bun doughnut_hair_bun game_freak grey_background hair_bun highres keiz looking_at_viewer medium_breasts nintendo nipples open_mouth pantyhose pantyhose_under_shorts pokemon rosa_(pokemon) see-through_clothes see-through_shirt shirt shorts simple_background solo visor_cap wet wet_clothes wet_shirt yellow_shorts | 2230557 | https://img3.gelbooru.com/images/a4/3c/a43c05614b01563b289716e3ee911fb6.png | | 11191727 | 11191727.png | 2894 | 4093 | image/png | 1girl absurdres animal_ear_piercing animal_ears ball bare_shoulders beachball bikini bikini_top_only bow breasts brown_hair brown_tail camouflage_bikini_top collarbone commentary_request cowlick double_bikini eyewear_on_head fangs full_body gold_necklace hair_ornament hairclip highres horse_ears horse_girl horse_tail jewelry jungle_pocket_(umamusume) layered_bikini light_blush looking_at_viewer navel nebusoku necklace open_mouth sidelocks sky small_breasts solo swimsuit tail umamusume water water_drop wet yellow_bikini yellow_eyes | 4552461 | https://img3.gelbooru.com/images/17/b9/17b99f3804aa0881a0e9b26fa6727212.png | | 11191726 | 11191726.jpg | 688 | 912 | image/jpeg | 1girl alternate_costume bare_shoulders blue_archive blush bow cross_hair_ornament dress foulo_paints green_bow green_eyes green_halo grey_hair hair_ornament hairclip halo hare_(blue_archive) long_hair open_mouth ponytail red_dress santa_dress sidelocks simple_background solo white_background | 263343 | https://img3.gelbooru.com/images/02/34/02345898623075346a7029057bdbcb73.jpg | | 11191722 | 11191722.jpeg | 2507 | 3541 | image/jpeg | 1girl akan_mori areola_slip breasts dot_nose dragon_girl dragon_horns female_focus functionally_nude gigantic_breasts grey_eyes horns japanese_text looking_at_viewer lovely_labrynth_of_the_silver_castle matching_hair/eyes open_mouth pointy_ears shiny_skin smile solo standing sweat translation_request white_background white_hair wide_hips yu-gi-oh! | 532172 | https://img3.gelbooru.com/images/68/a7/68a73548c2cbcefcae9e231eaf9a7c4d.jpeg | | 11191720 | 11191720.jpeg | 2507 | 3541 | image/jpeg | 1girl ahoge akan_mori anila_(granblue_fantasy) areola_slip blonde_hair breasts bursting_breasts dot_nose female_focus gigantic_breasts granblue_fantasy horns loincloth long_hair open_mouth shiny_skin smile solo standing thick_thighs thighhighs thighs white_background white_thighhighs wide_hips wide_sleeves | 695982 | https://img3.gelbooru.com/images/1b/b0/1bb0d29d5eaeb66e6e7799ccfe0c4be9.jpeg | | 11191714 | 11191714.png | 1500 | 2200 | image/png | 1girl ahoge alternate_costume animal_ears beach black_hair blue_archive blush breasts cleanerjay clenched_hands collarbone competition_swimsuit day double-parted_bangs fang from_above hair_between_eyes hair_bun hair_ornament halo hands_up highres hip_vent huge_breasts kaede_(blue_archive) kaede_(swimsuit)_(blue_archive) large_breasts looking_at_viewer magatama ocean one-piece_swimsuit open_mouth oppai_loli outdoors pink_halo sand short_hair sidelocks single_side_bun skin_fang skindentation smile solo swimsuit thighhighs thighs white_one-piece_swimsuit white_thighhighs yellow_eyes | 2461625 | https://img3.gelbooru.com/images/a1/48/a14869c5100df87a3b42248a10eae9da.png | | 11191713 | 11191713.jpg | 4096 | 2642 | image/jpeg | 1girl absurdres animal_ears antlers black_hair bleach breasts candy candy_cane christmas_present christmas_tree couch cushion deer_ears elbow_gloves fake_animal_ears fake_antlers food fur-trimmed_gloves fur-trimmed_thighhighs fur_trim gift gloves hair_between_eyes highleg highleg_leotard highres horns kuchiki_rukia leotard lying on_back on_couch purple_eyes red_gloves red_leotard red_thighhighs reindeer_antlers ribbon short_hair small_breasts solo thighhighs waligner | 7319148 | https://img3.gelbooru.com/images/c9/ee/c9ee0daaf2259b49de1209e1d522b05a.jpg | | 11191709 | 11191709.jpg | 1277 | 1381 | image/jpeg | 1girl aqua_eyes blush christmas commentary_request cool-kyou_shinja full_body gloves grey_hair hat highres kanna_kamui kobayashi-san_chi_no_maidragon long_hair long_sleeves looking_to_the_side low_twintails official_art open_mouth red_gloves red_hat red_thighhighs santa_costume santa_hat speech_bubble thighhighs translation_request twintails | 196150 | https://img3.gelbooru.com/images/30/ba/30baa75409510c4f82fbf5939cc94275.jpg | | 11191700 | 11191700.png | 852 | 1309 | image/png | 1girl belt black_hair breasts cassandra_(tangled) closed_mouth collared_shirt cowboy_shot expressionless eyewear_on_head gloves hair_between_eyes highres looking_down medium_breasts monochrome pants shirt short_hair solo standing tangled tangled:_the_series yukima | 1195882 | https://img3.gelbooru.com/images/c0/0a/c00a3b7f06738829801db0c377b55157.png | | 11191699 | 11191699.jpg | 1000 | 1088 | image/jpeg | alternate_costume animal_ear_fluff animal_ears ayamy black_skirt blonde_hair blue_eyes blue_hair breasts cheerleader cowboy_shot cropped_shirt dog_ears dog_girl dog_tail fuwawa_abyssgard hololive hololive_english large_breasts long_hair miniskirt mococo_abyssgard multicolored_hair one_eye_closed pink_eyes pink_hair pleated_skirt pom_pom_(cheerleading) shirt short_hair siblings sisters skirt sleeveless sleeveless_shirt small_breasts streaked_hair tail twins two-tone_hair two_side_up v virtual_youtuber white_background white_shirt | 228372 | https://img3.gelbooru.com/images/fe/bd/febd3505f8919d9a00d11515caefed24.jpg | | 11191695 | 11191695.jpg | 6253 | 4388 | image/jpeg | 1girl :o absurdres alternate_costume animal_collar animal_ear_fluff animal_ear_hairband animal_ears arms_up bell bespectacled black-framed_eyewear black_collar black_eyes black_footwear black_hairband black_jacket black_pants black_thighhighs blue_eyes blue_hair blue_nails blue_ribbon blue_thighhighs bracelet buttons cat_ear_hairband cat_ears cat_tail chain chain_necklace character_name chibi chinese_commentary closed_mouth coat collar collared_coat commentary_request copyright_name diamond-shaped_pupils diamond_(shape) eyelashes fake_animal_ears fake_tail fortissimo frilled_jacket frills glasses gloves gradient_thighhighs grey_pupils hair_between_eyes hair_ornament hairband hairclip hand_on_another's_chin hands_on_eyewear heart highres jacket jacket_partially_removed jewelry lips long_sleeves looking_at_viewer mahou_shoujo_madoka_magica mahou_shoujo_madoka_magica_(anime) miki_sayaka multiple_bracelets multiple_rings multiple_views musical_note musical_note_hair_ornament nail_polish neck_bell necklace nervous no_mouth ouge_dz pants parted_lips paw_pose pink_lips ribbon ring shirt shoes short_hair sneakers solid_oval_eyes spoken_heart sweat symbol-shaped_pupils tail thighhighs turtleneck turtleneck_shirt upper_body weibo_watermark white_background white_coat white_footwear white_gloves white_shirt | 5259522 | https://img3.gelbooru.com/images/1e/c6/1ec6566b4027b4ca179727e99b392b9a.jpg | | 11191691 | 11191691.jpg | 1600 | 1200 | image/jpeg | 1girl azure_striker_gunvolt azure_striker_gunvolt_3 blue_eyes breasts brown_hair china_dress chinese_clothes chinese_zodiac cleavage cowboy_shot dress gunvolt highres horns kirin_(armor) large_breasts multicolored_hair official_art pink_hair red_dress red_hair translation_request wide_sleeves year_of_the_dragon | 1470362 | https://img3.gelbooru.com/images/19/dd/19dd6069593eb8d78958d948e1e8979f.jpg | | 11191689 | 11191689.png | 1179 | 982 | image/png | 1boy 1girl apron belt black_hair blue_eyes blush boots brown_shirt cassandra_(tangled) chibi dagger eyewear_on_head freckles full_body gloves green_eyes green_shirt hair_between_eyes heart holding_hands knife long_sleeves off-shoulder_shirt off_shoulder pants red_shirt shirt shirt_under_shirt short_hair striped_clothes striped_pants tangled tangled:_the_series varian_(tangled) vertical-striped_clothes vertical-striped_pants walking weapon yukima | 481898 | https://img3.gelbooru.com/images/b0/37/b037b1f72ac11d92d1a6671a41afd9ee.png | | 11191683 | 11191683.jpeg | 727 | 1200 | image/jpeg | 1girl blue_archive gun kayoko_(blue_archive) muginosa tagme weapon | 1047363 | https://img3.gelbooru.com/images/63/60/636037b767e63881d61765344f756e62.jpeg | | 11191680 | 11191680.jpeg | 1200 | 810 | image/jpeg | 2girls angel angel_wings catfight jirai_kei muginosa multiple_girls school_uniform tagme wings | 1436180 | https://img3.gelbooru.com/images/83/73/837340945882471a5839a9808d149c99.jpeg | | 11191679 | 11191679.jpeg | 849 | 1200 | image/jpeg | 1girl arms_behind_head artist_logo breasts brown_eyes brown_hair christmas christmas_tree cleavage deviantart_username discord_username facebook_username fire fireplace grin hat indoors jadenkaiba large_breasts logo long_hair looking_at_viewer matching_hair/eyes navel original panties pixiv_username santa_costume santa_hat shadow smile solo striped_clothes striped_panties striped_thighhighs thick_thighs thighhighs thighs underwear watermark web_address | 939438 | https://img3.gelbooru.com/images/e0/28/e028abaa781d7bb8ace89ef449f172fc.jpeg | | 11191672 | 11191672.jpg | 1060 | 1800 | image/jpeg | 1girl alternate_costume areola_slip bare_shoulders bell bikini black_thighhighs blush breasts brown_hair cleavage commentary cowboy_shot elbow_gloves english_commentary front-tie_bikini_top front-tie_top genshin_impact gift gloves green_eyes hair_ornament hand_up highres huge_breasts lisa_(genshin_impact) looking_at_viewer medium_hair navel parted_lips red_bikini red_gloves sack side-tie_bikini_bottom sideboob smile solo stomach swimsuit thighhighs underboob zaphn | 1786012 | https://img3.gelbooru.com/images/b9/91/b9917d3ac26fa807a44be22bf7bda9e4.jpg | | 11191655 | 11191655.jpg | 2893 | 4093 | image/jpeg | 1girl absurdres animal_ears blue_eyes blush boots breasts collarbone commentary_request freely2327 full_body grey_hair hair_between_eyes hair_ornament highres horse_ears horse_girl horse_tail long_hair looking_at_viewer pantyhose simple_background solo tail thick_eyebrows umamusume wonder_acute_(umamusume) | 3235653 | https://img3.gelbooru.com/images/c9/ab/c9abbf876bda585598318d8c4964cc85.jpg | | 11191653 | 11191653.jpg | 1000 | 1000 | image/jpeg | 1boy arm_up baseball_cap collarbone commentary english_commentary fangs hand_on_own_face hat head_tilt male_focus no_eyes off_(game) pastel_colors pink_background shirt solo sparkle t-shirt tanblaque teeth the_batter_(off) | 63350 | https://img3.gelbooru.com/images/d7/17/d7175671d85e22a11f0fa81077b2c2ce.jpg | | 11191649 | 11191649.jpg | 1200 | 1200 | image/jpeg | 2girls :d arashi_chisato black_hair black_jacket black_shorts blue_dress blunt_bangs bow bright_pupils chibi closed_mouth collarbone commentary_request cropped_shoulders double_bun dress grey_hair hair_bow hair_bun hair_down hazuki_ren heart high_ponytail highres holding_hands jacket long_hair long_sleeves looking_at_viewer looking_back love_live! love_live!_superstar!! miyaco_(shirataki_168) multiple_girls multiple_views off_shoulder open_clothes open_jacket open_mouth pink_eyes pink_shirt ponytail shirt shorts sidelocks smile split_mouth translation_request twintails very_long_hair white_background white_bow white_pupils white_shirt yellow_eyes yellow_shirt yuri | 718946 | https://img3.gelbooru.com/images/a4/39/a439354e177571968ffcfaef7173d6c3.jpg | | 11191623 | 11191623.png | 3300 | 5500 | image/png | 1girl absurdres armband black_armband black_gloves black_horns black_jacket black_pants breasts button_gap closed_mouth commentary_request cowboy_shot curled_horns demon_tail earrings f.trigger gloves half_gloves hand_on_table highres horns jacket jacket_on_shoulders jewelry key_earrings large_breasts long_hair looking_at_viewer multicolored_hair necktie pants partial_commentary red_eyes red_necktie revision sleeves_rolled_up solo streaked_hair tail white_hair wuthering_waves zani_(wuthering_waves) | 5586730 | https://img3.gelbooru.com/images/57/77/577786e815ccfd59cf2ad744a90d83d8.png | | 11191618 | 11191618.jpg | 1137 | 1491 | image/jpeg | 1girl alternate_costume blunt_bangs breasts capelet cleavage crescent crescent_hat_ornament green_ribbon hair_ribbon hat hat_ornament highres long_hair medium_breasts midriff navel patchouli_knowledge purple_eyes purple_hair red_capelet red_thighhighs reddizen ribbon santa_hat sidelocks simple_background solo thighhighs touhou very_long_hair white_background | 536827 | https://img3.gelbooru.com/images/db/fe/dbfe87da2679fadbe91bc6e4a50a93af.jpg | | 11191609 | 11191609.jpg | 2257 | 1301 | image/jpeg | 1boy 9175x911 coat derivative_work dilated_pupils hair_over_one_eye highres homicipher looking_at_viewer male_focus monitoring_(vocaloid) mr._scarletella pov pov_peephole red_coat red_eyes red_hair short_hair solo | 417490 | https://img3.gelbooru.com/images/a3/20/a320ac304aaaa60847474fe6323f3699.jpg | | 11191608 | 11191608.jpg | 1087 | 1235 | image/jpeg | 1girl black_ribbon breasts capelet christmas_present closed_mouth elbow_gloves fur-trimmed_capelet fur-trimmed_gloves fur_trim gift gloves green_capelet green_gloves green_hair green_thighhighs highres holding holding_scissors jk_arts large_breasts merry_christmas naked_ribbon purple_eyes ribbon scissors side_ponytail smile solo sousou_no_frieren striped_clothes striped_gloves striped_thighhighs thighhighs ubel_(sousou_no_frieren) | 469028 | https://img3.gelbooru.com/images/db/31/db31b54d37e047214a74fbc77cef7ffe.jpg | | 11191599 | 11191599.jpg | 860 | 1214 | image/jpeg | 1girl bare_shoulders blush breasts dress elf frieren green_eyes grey_hair highres long_hair looking_at_viewer lying mimic mimic_chest pantyhose parted_bangs pointy_ears red_dress red_ribbon ribbon small_breasts solo_focus sousou_no_frieren ten-chan_(eternal_s) thighs tongue tongue_out twintails white_pantyhose | 145853 | https://img3.gelbooru.com/images/9b/fa/9bfae4c181f543f5860c0effe09efdcd.jpg | | 11191597 | 11191597.png | 3000 | 4000 | image/png | 1girl absurdres aqua_eyes aqua_hair arm_warmers ascot bare_shoulders blush breasts christmas christmas_tree_hair_ornament cleavage detached_collar detached_sleeves dress full_body fur-trimmed_dress fur-trimmed_sleeves fur_trim hair_ornament hairclip highres holding holding_sack kantai_collection large_breasts long_hair looking_at_viewer orange_ascot red_dress sack santa_costume santa_dress smile solo strapless strapless_dress suzuya_(christmas_mode)_(kancolle) suzuya_(kancolle) thighhighs white_background yoko_hata zoom_layer | 8248923 | https://img3.gelbooru.com/images/c3/cd/c3cdefcc4d11a5c971e4893026b8218d.png | | 11191593 | 11191593.jpg | 1900 | 2000 | image/jpeg | 2girls bell breasts dildo double_dildo highres mdthetest multiple_girls neck_bell santa_costume sex_toy small_breasts thighhighs | 376210 | https://img3.gelbooru.com/images/26/07/26072bd72f72e6dd9b1a521430d5cf22.jpg | | 11191591 | 11191591.jpg | 900 | 1277 | image/jpeg | 1girl blue_eyes brown_hair commentary english_commentary full_body goggles goggles_on_headwear green_jacket hat highres jacket long_hair original red_hat sandragh signature simple_background smile solo sparkle twintails | 185098 | https://img3.gelbooru.com/images/ed/38/ed38cbd122cebfe0fce2583f85a5b69f.jpg | | 11191587 | 11191587.png | 3000 | 4385 | image/png | 1girl absurdres animal_ears bikini breasts christmas elbow_gloves full_moon gloves gold_ship_(umamusume) green_scarf grey_hair hat highres horse_ears horse_girl horse_tail large_breasts long_hair looking_at_viewer moon navel night night_sky outdoors pink_eyes red_bikini red_gloves red_thighhighs santa_hat scarf sky smile snow solo standing swimsuit tail thighhighs thighs ugohato umamusume | 10013126 | https://img3.gelbooru.com/images/b2/ed/b2edecd00d1a11b42ee09af7efbfb488.png | | 11191586 | 11191586.png | 3000 | 4000 | image/png | 1girl absurdres aqua_hair blue_eyes blush boots breasts brown_hair capelet christmas dress full_body fur-trimmed_capelet fur-trimmed_dress fur_trim gloves hair_ornament hairclip highres kantai_collection kumano_(christmas_mode)_(kancolle) kumano_(kancolle) long_hair looking_at_viewer mittens open_mouth ponytail red_capelet red_dress red_gloves red_mittens santa_costume small_breasts smile solo white_background yoko_hata zoom_layer | 7290383 | https://img3.gelbooru.com/images/3f/a9/3fa94e79b4bc369fa70c7d4342e0fcfb.png | | 11191574 | 11191574.jpg | 1024 | 1862 | image/jpeg | 1girl 987645321o belt black_belt black_pantyhose boots brown_footwear capelet earrings elf frieren full_body green_eyes highres jewelry long_hair pantyhose pointy_ears shirt skirt solo sousou_no_frieren standing striped_clothes striped_shirt twintails white_background white_capelet white_hair white_skirt | 668212 | https://img3.gelbooru.com/images/0f/ad/0fad74acf5d0e4c2f59b2f1619888f35.jpg | | 11191569 | 11191569.jpg | 3512 | 2551 | image/jpeg | 3girls ;3 ^_^ absurdres blush bow brown_hair capelet christmas closed_eyes closed_mouth clueless commentary crossed_bangs english_text flying_sweatdrops fujishima_megumi fur-trimmed_capelet fur_trim fuyu_no_okurimono_(love_live!) girl_sandwich glaring green_background green_eyes hair_bow half_up_braid hanazora_satsuki heart high-waist_skirt highres hinoshita_kaho jealous link!_like!_love_live! long_hair looking_at_another love_live! medium_hair multiple_girls musical_note neck_ribbon official_alternate_costume official_alternate_hairstyle one_eye_closed orange_hair otomune_kozue outline purple_eyes purple_hair red_bow red_capelet red_ribbon red_skirt ribbon sandwiched shirt sidelocks skirt split_mouth striped_ribbon symbol-only_commentary twintails two_side_up virtual_youtuber white_capelet white_outline white_ribbon white_shirt yuri | 737568 | https://img3.gelbooru.com/images/b3/f8/b3f811bb0c062e26ab482e2dbb1a206b.jpg | | 11191563 | 11191563.jpg | 1920 | 1200 | image/jpeg | 1boy 1girl bell black_coat black_eyes black_hair blue_background blush bow briefs closed_mouth coat cone_horns copyright_notice cross cross_necklace detached_sleeves expressionless fake_horns flower gold_trim green_eyes guest_art gypsy_(ragnarok_online) hair_bell hair_bow hair_ornament highres holding holding_flower holding_underwear holding_unworn_clothes horns jewelry jingle_bell kankiriko logo long_hair long_sleeves looking_at_viewer male_underwear multicolored_coat necklace official_art official_wallpaper open_mouth pink_bow pink_sleeves polka_dot polka_dot_background priest_(ragnarok_online) purple_hair ragnarok_online red_coat short_hair sunflower sweatdrop torn_underwear two-tone_coat underwear unworn_male_underwear upper_body white_male_underwear yellow_flower yellow_horns | 1426344 | https://img3.gelbooru.com/images/b8/ca/b8cad78e216a6065ab1c88594ffcecd2.jpg | | 11191561 | 11191561.jpg | 1821 | 1290 | image/jpeg | 2girls apron averting_eyes black_dress blue_eyes blue_hair blue_ribbon blush bow_background braid breasts burger closed_eyes commentary crown_braid dark_blue_hair diagonal_bangs dollchestra dress facing_another feeding floating_hair food french_fries frilled_apron frills grey_hair hair_ribbon heart highres holding holding_burger holding_food juliet_sleeves link!_like!_love_live! long_hair long_sleeves looking_to_the_side love_live! low_twin_braids low_twintails medium_breasts mimori_(cotton_heart) multiple_girls murano_sayaka official_alternate_costume open_mouth pink_background puffy_sleeves ribbon short_hair short_twintails sideways_glance striped_clothes striped_dress symbol-only_commentary twin_braids twintails two-tone_dress upper_body vertical-striped_clothes vertical-striped_dress virtual_youtuber waist_apron waitress wendy's white_dress yellow_apron yugiri_tsuzuri yuri | 292691 | https://img3.gelbooru.com/images/45/42/45427e4052f298c7eda5daae62f49abc.jpg | | 11191560 | 11191560.jpeg | 1049 | 744 | image/jpeg | 2girls borrowed_character catfight commission mixed_martial_arts multiple_girls original pixiv_commission sage_tory tagme | 572195 | https://img3.gelbooru.com/images/37/6f/376feef555143507476be4a2054b467e.jpeg | | 11191558 | 11191558.png | 2300 | 3000 | image/png | 1boy 1girl 2koma after_vaginal aftersex bar_censor bed blush breasts bukkake censored comic cum cum_in_pussy cum_on_body cum_on_breasts cum_on_hair cum_on_lower_body cum_on_upper_body heart huge_penis japanese_text long_hair lying monochrome on_bed open_mouth original penis smile speech_bubble twintails veins veiny_penis | 3267940 | https://img3.gelbooru.com/images/4e/95/4e953b7f950899ea5e12f7021a234cb5.png | | 11191550 | 11191550.jpeg | 1024 | 768 | image/jpeg | air_master black_eyes black_hair black_jacket headband jacket kinjiro_kitaeda red_headband shirt sign watch white_shirt wristwatch | 87745 | https://img3.gelbooru.com/images/8d/7a/8d7acee8805a8af2f0edec3042b95fcd.jpeg | | 11191543 | 11191543.png | 3035 | 4299 | image/png | 1boy 1girl absurdres black-framed_eyewear breasts censored completely_nude dark-skinned_male dark_skin english_text feet full_nelson glasses heart highres imsofckinlost interracial large_penis mosaic_censoring nipples nude orange_hair penis persona persona_5 purple_eyes pussy sakura_futaba sex soles sound_effects steam sweat tongue tongue_out vaginal | 17576059 | https://img3.gelbooru.com/images/b9/05/b9052937f0834f77538744bd54c29fb3.png | | 11191540 | 11191540.jpg | 1000 | 1000 | image/jpeg | 1boy animal_ears antlers aynoh breath brown_jacket cup deer_antlers deer_boy deer_ears green_eyes grey_background holding holding_cup horns hot_chocolate jacket looking_at_viewer male_focus mug open_mouth orange_hair original red_scarf scarf short_hair simple_background solo upper_body very_short_hair | 135211 | https://img3.gelbooru.com/images/a8/18/a8184d7e4f5edb93102e938eb55acfb1.jpg | | 11191533 | 11191533.jpg | 2048 | 1072 | image/jpeg | 1girl blonde_hair blush_stickers breasts cheek_pinching chibi closed_eyes ebora fate/grand_order fate_(series) gareth_(fate) green_eyes hair_flaps highres medium_breasts multiple_views open_mouth pinching shirt short_hair short_sleeves smile white_shirt | 156439 | https://img3.gelbooru.com/images/95/77/9577d226a701f537a84e11078ab9e1a8.jpg | | 11191530 | 11191530.jpg | 1500 | 2300 | image/jpeg | 1girl black_gloves blue_background blue_eyes blue_hair boots c: capelet closed_mouth commentary_request dark_blue_hair embarrassed feet_out_of_frame floating_hair gloves hair_intakes hair_ribbon hat high-waist_skirt highres kanduki_kamibukuro knee_boots link!_like!_love_live! long_hair looking_at_viewer love_live! low_twintails miniskirt murano_sayaka nervous_smile pleated_skirt red_capelet red_footwear red_hat red_ribbon red_skirt ribbon santa_costume santa_hat shirt skirt skirt_hold smile solo sweatdrop twintails virtual_youtuber white_shirt | 297251 | https://img3.gelbooru.com/images/9d/4a/9d4a0fcc1d56853fa5ccb77fd91ccd58.jpg | ## Tags There are 967837 tags in total. These are the top 30 tags (125 tags in total) of type `unknown (-1)`: | id | name | type | count | ambiguous | |-----:|:--------------------------------------------------------------------------------------------------------------------------------------------|-------:|--------:|:------------| | -1 | straightchromia | -1 | 263 | False | | -1 | gekijigen_tag:_blanc_+_neptune_vs_zombie_gundan | -1 | 171 | False | | -1 | seiki_kyushu | -1 | 23 | False | | -1 | toyotaro | -1 | 15 | False | | -1 | ensemble_stars!;character:akehoshi_subaru;happy_elements;male | -1 | 9 | False | | -1 | _double_dash!! | -1 | 7 | False | | -1 | dash!! | -1 | 7 | False | | -1 | fubuki_kyoko | -1 | 7 | False | | -1 | mario_k | -1 | 7 | False | | -1 | star_\(symbol\) | -1 | 7 | False | | -1 | \// | -1 | 6 | False | | -1 | € | -1 | 6 | False | | -1 | kami-sama_onegai!_onii-chan_no_aka-chan_ninshin_shitai_no!_~tsundere_imouto_&_seijun_imouto_to_ecchi_na_kiseki_de_trouble_kozukuri_zanmai♪~ | -1 | 5 | False | | -1 | shrug_\(clothing\) | -1 | 5 | False | | -1 | \\// | -1 | 4 | False | | -1 | akizuki_rasenn | -1 | 4 | False | | -1 | juju_(pixiv4563634) | -1 | 4 | False | | -1 | source:https://nijie.info/view.php?id=151930 | -1 | 4 | False | | -1 | +\l.l./+_(path_to_nowhere) | -1 | 3 | False | | -1 | handing_breasts | -1 | 3 | False | | -1 | pom_pom_\(clothes\) | -1 | 3 | False | | -1 | slime_\(creature\) | -1 | 3 | False | | -1 | star_\(sky\) | -1 | 3 | False | | -1 | /tm | -1 | 2 | False | | -1 | compl\pussy | -1 | 2 | False | | -1 | mahitoⅶ | -1 | 2 | False | | -1 | to_heart:_remember_my_memories | -1 | 2 | False | | -1 | ulquiorra_schiffer | -1 | 2 | False | | -1 | violet_plan | -1 | 2 | False | | -1 | "artist: | -1 | 1 | False | These are the top 30 tags (453388 tags in total) of type `general (0)`: | id | name | type | count | ambiguous | |-------:|:------------------|-------:|--------:|:------------| | 152532 | 1girl | 0 | 6958797 | False | | 12336 | solo | 0 | 5455769 | False | | 265 | long_hair | 0 | 4657350 | True | | 27 | breasts | 0 | 4401214 | False | | 33975 | looking_at_viewer | 0 | 3638931 | False | | 92 | blush | 0 | 3537662 | False | | 796 | smile | 0 | 3084785 | False | | 1100 | open_mouth | 0 | 2658791 | False | | 52 | short_hair | 0 | 2400755 | False | | 67 | blue_eyes | 0 | 2037042 | False | | 28545 | simple_background | 0 | 1992127 | False | | 21905 | large_breasts | 0 | 1988659 | False | | 271 | blonde_hair | 0 | 1789137 | False | | 1999 | shirt | 0 | 1776330 | False | | 66 | black_hair | 0 | 1703108 | True | | 337 | brown_hair | 0 | 1700323 | False | | 179739 | white_background | 0 | 1629836 | False | | 138893 | 1boy | 0 | 1617673 | False | | 107 | skirt | 0 | 1586227 | False | | 98 | gloves | 0 | 1420724 | True | | 1864 | hair_ornament | 0 | 1420019 | False | | 175068 | multiple_girls | 0 | 1409693 | False | | 51 | red_eyes | 0 | 1389635 | True | | 13957 | long_sleeves | 0 | 1368362 | False | | 3477 | navel | 0 | 1343018 | False | | 432 | nipples | 0 | 1325691 | False | | 123 | dress | 0 | 1308851 | False | | 23 | thighhighs | 0 | 1296314 | False | | 6383 | holding | 0 | 1229633 | False | | 153 | animal_ears | 0 | 1184287 | False | These are the top 30 tags (281286 tags in total) of type `artist (1)`: | id | name | type | count | ambiguous | |--------:|:-------------------------|-------:|--------:|:------------| | 46733 | qp:flapper | 1 | 15595 | False | | 555502 | kagami_hirotaka | 1 | 8249 | False | | 219408 | nel-zel_formula | 1 | 8063 | False | | 594229 | ebifurya | 1 | 5768 | False | | 719488 | aoi_nagisa_(metalder) | 1 | 5400 | False | | 470499 | haruyama_kazunori | 1 | 5386 | False | | 25270 | lolita_channel | 1 | 4910 | False | | 401040 | hammer_(sunset_beach) | 1 | 4808 | False | | 603058 | butcha-u | 1 | 4537 | False | | 56027 | yaegashi_nan | 1 | 4460 | False | | 67040 | piromizu | 1 | 4309 | False | | 38088 | yoko_juusuke | 1 | 4149 | False | | 21718 | drawfag | 1 | 4000 | False | | 652987 | ruu_(tksymkw) | 1 | 3874 | False | | 118829 | kanon_(kurogane_knights) | 1 | 3872 | False | | 487842 | boris_(noborhys) | 1 | 3757 | False | | 76506 | circle_anco | 1 | 3714 | False | | 410 | azasuke | 1 | 3622 | False | | 1128557 | kou_hiyoyo | 1 | 3404 | False | | 1069930 | tony_taka | 1 | 3396 | False | | 380097 | matsunaga_kouyou | 1 | 3394 | False | | 481438 | itomugi-kun | 1 | 3284 | False | | 729154 | naga_u | 1 | 3175 | False | | 1051176 | hara_(harayutaka) | 1 | 3060 | False | | 569895 | ojipon | 1 | 3044 | False | | 354817 | bow_(bhp) | 1 | 3023 | False | | 445614 | blade_(galaxist) | 1 | 2989 | False | | 355711 | rebecca_(keinelove) | 1 | 2960 | False | | 14795 | awa | 1 | 2856 | True | | 509171 | neocoill | 1 | 2811 | False | These are the top 30 tags (39857 tags in total) of type `copyright (3)`: | id | name | type | count | ambiguous | |--------:|:----------------------------|-------:|--------:|:------------| | 118 | original | 3 | 1269200 | False | | 126 | touhou | 3 | 780335 | False | | 44106 | nintendo | 3 | 621025 | False | | 448625 | kantai_collection | 3 | 423218 | True | | 43567 | pokemon | 3 | 359839 | False | | 306228 | game_freak | 3 | 357543 | False | | 875834 | creatures_(company) | 3 | 357526 | False | | 342429 | fate_(series) | 3 | 330978 | False | | 1037340 | blue_archive | 3 | 246333 | False | | 545521 | fate/grand_order | 3 | 238769 | False | | 86 | idolmaster | 3 | 235114 | True | | 943985 | genshin_impact | 3 | 224899 | False | | 865663 | hololive | 3 | 216894 | False | | 44086 | vocaloid | 3 | 162444 | False | | 705390 | love_live! | 3 | 150528 | False | | 807658 | arknights | 3 | 135366 | False | | 805915 | azur_lane | 3 | 130239 | False | | 338818 | idolmaster_cinderella_girls | 3 | 120598 | False | | 5074 | fire_emblem | 3 | 112235 | False | | 924 | digimon | 3 | 110069 | False | | 851189 | umamusume | 3 | 102137 | False | | 238 | final_fantasy | 3 | 98764 | False | | 878809 | honkai_(series) | 3 | 88571 | False | | 247 | one_piece | 3 | 81582 | False | | 374082 | girls_und_panzer | 3 | 66219 | False | | 237493 | mahou_shoujo_madoka_magica | 3 | 64433 | False | | 1048700 | hololive_english | 3 | 63157 | False | | 845788 | nijisanji | 3 | 62122 | False | | 1121184 | girls'_frontline | 3 | 61878 | False | | 7 | gundam | 3 | 60290 | True | These are the top 30 tags (189418 tags in total) of type `character (4)`: | id | name | type | count | ambiguous | |--------:|:-------------------------|-------:|--------:|:------------| | 14087 | hatsune_miku | 4 | 110042 | False | | 855 | hakurei_reimu | 4 | 60110 | False | | 130 | kirisame_marisa | 4 | 50889 | False | | 486 | flandre_scarlet | 4 | 46303 | False | | 850 | remilia_scarlet | 4 | 40796 | False | | 1141830 | artoria_pendragon_(fate) | 4 | 37520 | False | | 849 | izayoi_sakuya | 4 | 34149 | False | | 36382 | komeiji_koishi | 4 | 30381 | False | | 848 | konpaku_youmu | 4 | 29123 | False | | 1293 | cirno | 4 | 27945 | False | | 127 | alice_margatroid | 4 | 26925 | False | | 484 | patchouli_knowledge | 4 | 26738 | False | | 14543 | kochiya_sanae | 4 | 26429 | False | | 658 | yakumo_yukari | 4 | 25281 | False | | 83295 | souryuu_asuka_langley | 4 | 23165 | False | | 481 | shameimaru_aya | 4 | 22616 | False | | 237491 | akemi_homura | 4 | 21488 | False | | 847 | reisen_udongein_inaba | 4 | 21351 | False | | 237492 | kaname_madoka | 4 | 21296 | False | | 24290 | nami_(one_piece) | 4 | 20817 | False | | 36383 | komeiji_satori | 4 | 20736 | False | | 761745 | saber_(fate) | 4 | 20331 | False | | 125 | fujiwara_no_mokou | 4 | 20195 | False | | 493 | saigyouji_yuyuko | 4 | 20135 | False | | 14131 | kagamine_rin | 4 | 20122 | False | | 15099 | inubashiri_momiji | 4 | 20037 | False | | 804254 | artoria_pendragon_(all) | 4 | 20024 | False | | 1059472 | ganyu_(genshin_impact) | 4 | 18770 | False | | 881 | link | 4 | 18167 | False | | 2335 | tifa_lockhart | 4 | 17409 | False | These are the top 30 tags (407 tags in total) of type `metadata (5)`: | id | name | type | count | ambiguous | |--------:|:-----------------------|-------:|--------:|:------------| | 262 | highres | 5 | 5938133 | False | | 559 | absurdres | 5 | 1987478 | False | | 150649 | commentary_request | 5 | 1184836 | False | | 136261 | bad_id | 5 | 830999 | False | | 760546 | bad_pixiv_id | 5 | 698199 | False | | 25266 | commentary | 5 | 650901 | False | | 136 | translation_request | 5 | 521034 | False | | 1306 | official_art | 5 | 323671 | False | | 52372 | tagme | 5 | 299899 | False | | 23213 | artist_request | 5 | 247376 | False | | 831896 | english_commentary | 5 | 241830 | False | | 69 | game_cg | 5 | 189181 | True | | 13710 | translated | 5 | 155560 | False | | 209468 | commission | 5 | 155146 | False | | 2229 | lowres | 5 | 113399 | False | | 47252 | character_request | 5 | 110530 | False | | 755092 | bad_twitter_id | 5 | 103797 | False | | 19982 | traditional_media | 5 | 84244 | False | | 789724 | chinese_commentary | 5 | 83177 | False | | 888401 | non-web_source | 5 | 72887 | False | | 426 | scan | 5 | 64325 | False | | 853984 | third-party_edit | 5 | 64310 | False | | 66551 | copyright_request | 5 | 63146 | False | | 323949 | revision | 5 | 56891 | False | | 1139885 | symbol-only_commentary | 5 | 56642 | False | | 1034097 | skeb_commission | 5 | 53326 | False | | 1223605 | paid_reward_available | 5 | 44343 | False | | 191513 | md5_mismatch | 5 | 39237 | False | | 2481 | source_request | 5 | 38976 | False | | 63275 | huge_filesize | 5 | 34141 | True | These are the top 30 tags (3356 tags in total) of type `deprecated (6)`: | id | name | type | count | ambiguous | |--------:|:------------------|-------:|--------:|:------------| | 275 | silver_hair | 6 | 336681 | False | | 205 | striped | 6 | 264448 | False | | 24219 | see-through | 6 | 160050 | False | | 286 | uniform | 6 | 159560 | True | | 214641 | grabbing | 6 | 145174 | False | | 3404 | plaid | 6 | 137227 | False | | 889718 | black_headwear | 6 | 112049 | False | | 264199 | black_legwear | 6 | 102143 | False | | 56035 | light_brown_hair | 6 | 87604 | False | | 850221 | white_headwear | 6 | 82957 | False | | 264263 | white_legwear | 6 | 80489 | False | | 47407 | looking_away | 6 | 65435 | False | | 2818 | ass_grab | 6 | 59392 | False | | 918581 | red_headwear | 6 | 41345 | False | | 82157 | french_braid | 6 | 36466 | False | | 54997 | multiple_penises | 6 | 36285 | False | | 853048 | blue_headwear | 6 | 35686 | False | | 17164 | breast_hold | 6 | 35279 | False | | 18310 | vertical_stripes | 6 | 34542 | False | | 1048560 | light_purple_hair | 6 | 33258 | False | | 26046 | arm_grab | 6 | 26380 | False | | 712440 | red_neckwear | 6 | 26251 | False | | 488916 | oni_horns | 6 | 25061 | False | | 670 | wallpaper | 6 | 24516 | False | | 268269 | torn_legwear | 6 | 21955 | False | | 51586 | screencap | 6 | 20521 | False | | 842746 | green_headwear | 6 | 20206 | False | | 918583 | brown_headwear | 6 | 20205 | False | | 265499 | striped_legwear | 6 | 19590 | False | | 20303 | turret | 6 | 17887 | False |

TempoFunk/tempofunk-sdance

TempoFunk

--- task_categories: - text-to-video - text-to-image - video-classification - image-classification language: - en size_categories: - 1K<n<10K license: agpl-3.0 --- # TempoFunk S(mall)Dance 10k samples of metadata and encoded latents & prompts of videos themed around **dance**. ## Data format - Video frame latents - Numpy arrays - 120 frames, 512x512 source size - Encoded shape (120, 4, 64, 64) - CLIP (openai) encoded prompts - Video description (as seen in metadata) - Encoded shape (77,768) - Video metadata as JSON (description, tags, categories, source URLs, etc.)

isp-uv-es/WorldFloodsv2

isp-uv-es

--- license: cc-by-nc-4.0 pipeline_tag: image-segmentation tags: - remote sensing - sentinel2 - landsat - floods --- # *WorldFloodsv2* dataset This repository contains the *WorldFloodsv2* dataset released with the publication: > E. Portalés-Julià, G. Mateo-García, C. Purcell, and L. Gómez-Chova [Global flood extent segmentation in optical satellite images](https://www.nature.com/articles/s41598-023-47595-7). _Scientific Reports 13, 20316_ (2023). DOI: 10.1038/s41598-023-47595-7. The [*WorldFloodsv2* database](https://www.nature.com/articles/s41598-023-47595-7) contains 509 pairs of Sentinel-2 images and flood segmentation masks. Splitted in train, val and test sets. It requires approximately 76GB of hard-disk storage. <img src="worldfloods_v2.png" alt="licence" width="65%"/> ## Download the dataset ``` huggingface-cli download --cache-dir /path/to/cachedir --local-dir /path/to/localdir/WorldFloodsv2 --repo-type dataset isp-uv-es/WorldFloodsv2 ``` ## Explore the dataset The [exploring *WorldFloodsv2*](https://spaceml-org.github.io/ml4floods/content/prep/exploring_worldfloods.html) tutorial in the [ml4floods](https://github.com/spaceml-org/ml4floods) package shows how to process the dataset and plot the images and masks. ## Licence The *WorldFloods* database and all pre-trained models are released under a [Creative Commons non-commercial licence](https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt) ## Cite If you find this work useful, please cite: ``` @article{portales-julia_global_2023, title = {Global flood extent segmentation in optical satellite images}, volume = {13}, issn = {2045-2322}, doi = {10.1038/s41598-023-47595-7}, number = {1}, urldate = {2023-11-30}, journal = {Scientific Reports}, author = {Portalés-Julià, Enrique and Mateo-García, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis}, month = nov, year = {2023}, pages = {20316}, } ```

ylacombe/cml-tts

ylacombe

--- language: - nl - fr - de - it - pl - pt - es license: cc-by-4.0 size_categories: - 1M<n<10M task_categories: - text-to-speech - text-to-audio pretty_name: CML-TTS dataset_info: - config_name: dutch features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 186374683541.98 num_examples: 309785 - name: dev num_bytes: 2912063172.928 num_examples: 4834 - name: test num_bytes: 2757891736.78 num_examples: 4570 download_size: 132987704971 dataset_size: 192044638451.68802 - config_name: french features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 64984002840.768 num_examples: 107598 - name: dev num_bytes: 2257393207.796 num_examples: 3739 - name: test num_bytes: 2281630546.306 num_examples: 3763 download_size: 48345998335 dataset_size: 69523026594.87 - config_name: german features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 369052038020.872 num_examples: 608296 - name: dev num_bytes: 3197115278.604 num_examples: 5314 - name: test num_bytes: 3288183839.092 num_examples: 5466 download_size: 280438261836 dataset_size: 375537337138.568 - config_name: italian features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 30242801015.92 num_examples: 50345 - name: dev num_bytes: 938644924.81 num_examples: 1765 - name: test num_bytes: 979116355.51 num_examples: 1835 download_size: 21996805791 dataset_size: 32160562296.239998 - config_name: polish features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 11127461686.356 num_examples: 18719 - name: dev num_bytes: 356048249 num_examples: 853 - name: test num_bytes: 367796887 num_examples: 814 download_size: 8114633186 dataset_size: 11851306822.356 - config_name: portuguese features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 20722423371.0 num_examples: 34265 - name: dev num_bytes: 622824524.224 num_examples: 1134 - name: test num_bytes: 673141068.9 num_examples: 1297 download_size: 14421097659 dataset_size: 22018388964.124 - config_name: spanish features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 101377452063.176 num_examples: 168524 - name: dev num_bytes: 1882729515.184 num_examples: 3148 - name: test num_bytes: 1851592818.0 num_examples: 3080 download_size: 73687756096 dataset_size: 105111774396.36 configs: - config_name: dutch data_files: - split: train path: dutch/train-* - split: dev path: dutch/dev-* - split: test path: dutch/test-* - config_name: french data_files: - split: train path: french/train-* - split: dev path: french/dev-* - split: test path: french/test-* - config_name: german data_files: - split: train path: german/train-* - split: dev path: german/dev-* - split: test path: german/test-* - config_name: italian data_files: - split: train path: italian/train-* - split: dev path: italian/dev-* - split: test path: italian/test-* - config_name: polish data_files: - split: train path: polish/train-* - split: dev path: polish/dev-* - split: test path: polish/test-* - config_name: portuguese data_files: - split: train path: portuguese/train-* - split: dev path: portuguese/dev-* - split: test path: portuguese/test-* - config_name: spanish data_files: - split: train path: spanish/train-* - split: dev path: spanish/dev-* - split: test path: spanish/test-* --- # Dataset Card for CML-TTS ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Data Statistics](#data-statistics) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [MultiLingual LibriSpeech ASR corpus](https://www.openslr.org/146/) - **Repository:** [CML-TTS-Dataset](https://github.com/freds0/CML-TTS-Dataset) - **Paper:** [CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages](https://arxiv.org/abs/2306.10097) ### Dataset Summary CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/146) to make it easier to stream. ### Supported Tasks - `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS). ### Languages The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz. ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German): ```python from datasets import load_dataset mls = load_dataset("ylacombe/cml-tts", "german", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True) print(next(iter(mls))) ``` #### *Bonus* You can create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). **Local:** ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler mls = load_dataset("ylacombe/cml-tts", "german", split="train") batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False) dataloader = DataLoader(mls, batch_sampler=batch_sampler) ``` **Streaming:** ```python from datasets import load_dataset from torch.utils.data import DataLoader mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True) dataloader = DataLoader(mls, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'audio': {'path': '6892_8912_000729.wav', 'array': array([-1.52587891e-...7344e-05]), 'sampling_rate': 24000}, 'wav_filesize': 601964, 'text': 'Proszę pana, tu pano... zdziwiony', 'transcript_wav2vec': 'proszę pana tu panow... zdziwiony', 'levenshtein': 0.96045197740113, 'duration': 13.648979591836737, 'num_words': 29, 'speaker_id': 6892} ``` ### Data Fields - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - transcript_wav2vec: the transcription of the audio file using the wav2vec model. Has been used to curate the dataset. - wav_filesize: The size of the audio waveform file. Has been used to curate the dataset. - levenshtein: The [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between the wav2vec transcription and the original transcription. Has been used to curate the dataset. - duration: The duration of the audio in seconds. - num_words: The number of words of the transcription. ### Data Splits | # Samples | Train | Dev | Test | |------------|--------|------|------| | german | 608296 | 5314 | 5466 | | dutch | 309785 | 4834 | 4570 | | french | 107598 | 3739 | 3763 | | spanish | 168524 | 3148 | 3080 | | italian | 50345 | 1765 | 1835 | | portuguese | 34265 | 1134 | 1297 | | polish | 18719 | 853 | 814 | ### Data Statistics | Language | Duration (Train) | Duration (Test) | Duration (Dev) | Speakers (Train) | Speakers (Test) | Speakers (Dev) | |------------|-------------------|------------------|----------------|------------------|-----------------|----------------| | | M | F | M | F | M | F | M | F | M | F | M | F | | Dutch | 482.82 | 162.17 | 2.46 | 1.29 | 2.24 | 1.67 | 8 | 27 | 3 | 3 | 2 | 4 | | French | 260.08 | 24.04 | 2.48 | 3.55 | 3.31 | 2.72 | 25 | 20 | 8 | 9 | 10 | 8 | | German | 1128.96 | 436.64 | 3.75 | 5.27 | 4.31 | 5.03 | 78 | 90 | 13 | 17 | 13 | 15 | | Italian | 73.78 | 57.51 | 1.47 | 0.85 | 0.40 | 1.52 | 23 | 38 | 5 | 5 | 4 | 6 | | Polish | 30.61 | 8.32 | 0.70 | 0.90 | 0.56 | 0.80 | 4 | 4 | 2 | 2 | 2 | 2 | | Portuguese | 23.14 | 44.81 | 0.28 | 0.24 | 0.68 | 0.20 | 20 | 10 | 5 | 4 | 6 | 3 | | Spanish | 279.15 | 164.08 | 2.77 | 2.06 | 3.40 | 2.34 | 35 | 42 | 10 | 8 | 11 | 9 | | Total | 3,176.13| | 28.11 | | 29.19 | | 424 | | 94 | | 95 | | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) ### Citation Information ``` @misc{oliveira2023cmltts, title={CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages}, author={Frederico S. Oliveira and Edresson Casanova and Arnaldo Cândido Júnior and Anderson S. Soares and Arlindo R. Galvão Filho}, year={2023}, eprint={2306.10097}, archivePrefix={arXiv}, primaryClass={eess.AS} } ``` ### Contributions Thanks to [@ylacombe](https://github.com/ylacombe) for adding this dataset.

lerobot/berkeley_cable_routing

lerobot

--- license: cc-by-4.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** https://sites.google.com/view/cablerouting/home - **Paper:** https://arxiv.org/abs/2307.08927 - **License:** cc-by-4.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "unknown", "total_episodes": 1647, "total_frames": 42328, "total_tasks": 1, "total_videos": 6588, "total_chunks": 2, "chunks_size": 1000, "fps": 10, "splits": { "train": "0:1647" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.images.top_image": { "dtype": "video", "shape": [ 128, 128, 3 ], "names": [ "height", "width", "channel" ], "video_info": { "video.fps": 10.0, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.wrist225_image": { "dtype": "video", "shape": [ 128, 128, 3 ], "names": [ "height", "width", "channel" ], "video_info": { "video.fps": 10.0, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.wrist45_image": { "dtype": "video", "shape": [ 128, 128, 3 ], "names": [ "height", "width", "channel" ], "video_info": { "video.fps": 10.0, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.image": { "dtype": "video", "shape": [ 128, 128, 3 ], "names": [ "height", "width", "channel" ], "video_info": { "video.fps": 10.0, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "language_instruction": { "dtype": "string", "shape": [ 1 ], "names": null }, "observation.state": { "dtype": "float32", "shape": [ 8 ], "names": { "motors": [ "motor_0", "motor_1", "motor_2", "motor_3", "motor_4", "motor_5", "motor_6", "motor_7" ] } }, "action": { "dtype": "float32", "shape": [ 7 ], "names": { "motors": [ "motor_0", "motor_1", "motor_2", "motor_3", "motor_4", "motor_5", "motor_6" ] } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "next.reward": { "dtype": "float32", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex @article{luo2023multistage, author = {Jianlan Luo and Charles Xu and Xinyang Geng and Gilbert Feng and Kuan Fang and Liam Tan and Stefan Schaal and Sergey Levine}, title = {Multi-Stage Cable Routing through Hierarchical Imitation Learning}, journal = {arXiv pre-print}, year = {2023}, url = {https://arxiv.org/abs/2307.08927}, } ```

alvations/c4p0-x1-en-it

alvations

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 6913 num_examples: 5 download_size: 19494 dataset_size: 6913 configs: - config_name: default data_files: - split: train path: 5eeb99e4b632b370/train-* ---

Anthropic/hh-rlhf

Anthropic

--- license: mit tags: - human-feedback --- # Dataset Card for HH-RLHF ## Dataset Summary This repository provides access to two different kinds of data: 1. Human preference data about helpfulness and harmlessness from [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). These data are meant to train preference (or reward) models for subsequent RLHF training. These data are *not* meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead to harmful models and this shold be avoided. 2. Human-generated and annotated red teaming dialogues from [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). These data are meant to understand how crowdworkers red team models and what types of red team attacks are succesful or not. The data are *not* meant for fine-tuning or preference modeling (use the data above for preference modeling). These data are entire transcripts of conversations that are derived from the harmlessness preference modeling data described above, where only the chosen response is incorporated into the overall transcript. Furthermore, the transcripts are annotated with human and automated measurements of how harmful the overall dialogues are. **Disclaimer**: The data (especially the harmlessness preference data and the red team data) contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees. As mentioned above, these data are *not* intended for training dialogue agents as this will likely lead to harmful model behavior. Each of these datasets are described further below. ## Human preference data about helpfulness and harmlessness (PM Data) The data are described in the paper: [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one "chosen" and one "rejected". For **helpfulness**, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated "online" process. For **harmlessness**, the data are only collected for our base models, but otherwise formatted in the same way. Details about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D. ## Red teaming data (not PM Data) The data are described in the paper: [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). If you find the data useful, please cite the paper. At a high level these data can be examined to understand what succesful red team attempts look like. Details about the data and data collection procedures can be found in the Datasheet in the appendix of the paper. Each line of the jsonl file contains a dictionary with the following fields: - `transcript` a text transcript of a conversation between a human adversary (red team member) and an AI assistant - `min_harmlessness_score_transcript` a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model - `num_params` number of parameters in the language model powering the AI assistant - `model_type` type of model powering the AI assistant - `rating` the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful) - `task_description` a short text description written by the red team member about how they tried to red team the AI assistant - `task_description_harmlessness_score` a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model - `red_team_member_id` an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks - `is_upworker` a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk - `tags` a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types. ## Usage Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the `data_dir` argument of the `load_dataset()` function as follows: ```python from datasets import load_dataset # Load all helpfulness/harmless subsets (share the same schema) dataset = load_dataset("Anthropic/hh-rlhf") # Load one of the harmless subsets dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base") # Load the red teaming subset dataset = load_dataset("Anthropic/hh-rlhf", data_dir="red-team-attempts") ``` ## Contact The original authors host this dataset on GitHub here: https://github.com/anthropics/hh-rlhf You can submit inquiries to: [email protected]

deepghs/sankaku_full

deepghs

--- license: other task_categories: - image-classification - zero-shot-image-classification - text-to-image language: - en - ja tags: - art - anime - not-for-all-audiences size_categories: - 10M<n<100M annotations_creators: - no-annotation source_datasets: - sankaku --- # Sankaku Full Dataset This is the full dataset of [chan.sankakucomplex.com](https://chan.sankakucomplex.com/). And all the original images are maintained here. # How to Painlessly Use This Use [cheesechaser](https://github.com/deepghs/cheesechaser) to quickly get images from this repository. Before using this code, you have to **grant the access from this gated repository**. And then **set your personal HuggingFace token into `HF_TOKEN` environment variable** to give the code authorization for this repository. ```python from cheesechaser.datapool import SankakuDataPool pool = SankakuDataPool() pool.batch_download_to_directory( # download images #7000000-7000100, any ranges or id lists are okay resource_ids=range(7000000, 7000100), # save to directory /data/sankaku dst_dir='/data/sankaku', ) ``` # Information ## Images There are 16498615 images in total. The maximum ID of these images is 36864051. Last updated at `2024-12-19 02:45:07 UTC`. 16498615 images in total. Only 50 images shown. | id | filename | width | height | rating | mimetype | file_size | tags | created_at | |---------:|:--------------|--------:|---------:|:---------|:-----------|------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------:| | 36864051 | 36864051.png | 2048 | 945 | q | image/png | 3754284 | [] | 1731168844 | | 36864049 | 36864049.png | 1267 | 1043 | q | image/png | 931791 | ['code_geass', 'code_geass:_lost_stories', 'ledo_offen', 'androgynous', 'clothing', 'shirt', 'uniform', 'gloves', 'long_sleeves', 'jacket', 'white_shirt', 'sidelocks', 'black_gloves', 'cape', 'military_uniform', 'hair_intakes', 'blue_jacket', 'gold_trim', 'blue_cape', 'female', 'long_hair', 'male', 'black_hair', 'red_eyes', 'brown_eyes', 'dark_skin', 'upper_body', 'dark-skinned_female', 'dark-skinned_male', 'looking_at_viewer', 'standing', 'parted_lips', 'hands_up', 'adjusting_clothes', 'adjusting_gloves', 'military', 'solo', '1boy', 'male_focus', 'cropped_torso', 'side_cape', 'cg_art', 'simple_background', 'game_cg', 'official_art', 'useless_tags', 'black_background', 'transparent_background', 'tagme'] | 1731168821 | | 36864048 | 36864048.jpg | 2480 | 3508 | e | image/jpeg | 3041918 | ['naruto', 'naruto_shippuden', 'uzumaki_naruto', 'inuzuka_kiba', 'destor777', 'yaoi', 'bdsm', 'bara', 'foot_fetish', 'clothing', 'nude', 'headwear', 'completely_nude', 'collar', 'headband', 'forehead_protector', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'brown_hair', 'black_hair', 'navel', 'thighs', 'tail', 'feet', 'testicles', 'teeth', 'erection', 'muscle', 'black_eyes', 'stomach', 'abs', 'muscular_male', 'pectorals', 'facial_mark', 'large_pectorals', 'dog_tail', 'circumcised', 'open_mouth', 'looking_at_viewer', 'spread_legs', 'bondage', 'screaming', 'chain', 'leash', 'konohagakure_symbol', 'genital_fluids', 'precum', '1boy', 'multiple_boys', 'solo_focus', '2boys', 'male_focus', 'male_only', 'chain_leash', 'stomping', 'uncensored', 'high_resolution', 'tagme', 'very_high_resolution'] | 1731168820 | | 36864047 | 36864047.avif | 1280 | 1967 | e | image/avif | 94315 | ['original', 'egami', 'hetero', 'loli', 'age_difference', 'teenage', 'teenage_girl', 'clothing', 'tied_hair', 'nude', 'legwear', 'bangs', 'shirt', 'thighhighs', 'skirt', 'uniform', 'twintails', 'school_uniform', 'black_legwear', 'white_shirt', 'short_sleeves', 'pleated_skirt', 'serafuku', 'black_thighhighs', 'nude_male', 'clothed_female', 'clothed_female_nude_male', 'female', 'breasts', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'brown_hair', 'black_hair', 'ass', 'brown_eyes', 'small_breasts', 'erect_nipples', 'symbol-shaped_pupils', 'heart-shaped_pupils', 'bulge', 'stomach_bulge', 'open_mouth', 'standing', 'clothes_pull', 'clothes_lift', 'doggystyle', 'shirt_lift', 'standing_sex', 'tiptoes', 'sex', 'vaginal', 'taken_from_behind', 'orgasm', 'trembling', 'clothed_sex', 'female_orgasm', 'torso_grab', 'semen', 'sweat', 'overflow', '1girl', '1boy', 'indoors', 'limited_visibility', 'contentious_content', 'heart', 'high_resolution'] | 1731168814 | | 36864046 | 36864046.jpg | 2894 | 3695 | e | image/jpeg | 2170162 | ['touhou_project', 'cirno', 'kame_(kamepan44231)', 'chibi', 'clothing', 'hair_ornament', 'shirt', 'underwear', 'footwear', 'dress', 'ribbon', 'bow', 'hair_between_eyes', 'shoes', 'white_shirt', 'hair_bow', 'short_sleeves', 'sleeveless', 'collared_shirt', 'puffy_sleeves', 'puffy_short_sleeves', 'sleeveless_dress', 'blue_dress', 'blue_bow', 'blush_stickers', 'blue_footwear', 'bloomers', 'long_dress', 'pinafore_dress', 'female', 'short_hair', 'blue_eyes', 'blue_hair', 'ahoge', 'wings', 'looking_at_viewer', 'closed_mouth', 'restrained', 'fairy', 'rope', 'ice', '1girl', 'solo', ':<', 'fumo_(doll)', 'ice_wings', 'tied_up_(nonsexual)', 'simple_background', 'white_background', 'full_body', 'useless_tags', 'high_resolution', 'tagme', 'very_high_resolution'] | 1731168798 | | 36864045 | 36864045.jpg | 3070 | 4096 | e | image/jpeg | 437973 | ['minecraft', 'ellie_walls_(odysseyellie)', 'odysseyellie', 'hetero', 'threesome', 'interracial', 'mmf_threesome', 'prostitution', 'blacked', 'clothing', 'legwear', 'headwear', 'hair_ornament', 'bangs', 'thighhighs', 'underwear', 'gloves', 'hairband', 'bottomless', 'bra', 'black_gloves', 'blunt_bangs', 'nopan', 'stockings', 'sports_bra', 'skull_hair_ornament', 'blacked_clothing', 'female', 'male', 'blush', 'penis', 'black_hair', 'ass', 'thighs', 'green_eyes', 'anus', 'red_hair', 'dark_skin', 'multicolored_hair', 'thick_thighs', 'dark-skinned_male', 'large_ass', 'humanoid', 'freckles', 'shortstack', 'looking_at_viewer', 'smile', 'standing', 'double_penetration', 'standing_sex', 'sex', 'vaginal', 'nakadashi', 'looking_back', 'anal', 'penetration', 'looking_at_another', 'embarrassed', 'looking_at_penis', 'looking_at_partner', 'horny', 'goth_girl', 'semen', 'semen_in_anus', '1girl', '1boy', 'multiple_boys', 'solo_focus', '2boys', 'outdoors', 'black_and_red_hair', 'goth', 'happy_face', 'happy_female', 'horny_female', 'male/female/male', 'ponytails', 'spiked_hairband', '3d', 'useless_tags', 'mine-imator', 'commission', 'english_language', 'text', 'english_text', 'high_resolution', 'very_high_resolution'] | 1731168793 | | 36864044 | 36864044.avif | 1280 | 1967 | e | image/avif | 86235 | ['original', 'egami', 'hetero', 'loli', 'teenage', 'mind_control', 'teenage_girl', 'hypnosis', 'clothing', 'tied_hair', 'nude', 'legwear', 'bangs', 'shirt', 'thighhighs', 'skirt', 'uniform', 'twintails', 'school_uniform', 'black_legwear', 'white_shirt', 'short_sleeves', 'pleated_skirt', 'serafuku', 'black_thighhighs', 'nude_male', 'clothed_female', 'blue_skirt', 'clothed_female_nude_male', 'female', 'breasts', 'long_hair', 'male', 'blush', 'nipples', 'penis', 'brown_hair', 'black_hair', 'ass', 'thighs', 'brown_eyes', 'small_breasts', 'erect_nipples', 'bulge', 'stomach_bulge', 'open_mouth', 'standing', 'clothes_pull', 'clothes_lift', 'shirt_lift', 'standing_sex', 'against_wall', 'tiptoes', 'sex', 'vaginal', 'taken_from_behind', 'trembling', 'clothed_sex', 'torso_grab', 'sweat', '1girl', '1boy', 'indoors', 'limited_visibility', 'contentious_content', 'heart', 'high_resolution'] | 1731168782 | | 36864043 | 36864043.jpg | 1536 | 2048 | s | image/jpeg | 482685 | ['vocaloid', 'pixiv', 'hatsune_miku', 'sakura_miku', 'aoiyui', 'clothing', 'tied_hair', 'hair_ornament', 'bangs', 'shirt', 'skirt', 'twintails', 'bare_shoulders', 'necktie', 'pleated_skirt', 'detached_sleeves', 'sleeveless', 'tattoo', 'sleeveless_shirt', 'pink_shirt', 'pink_skirt', 'shoulder_tattoo', 'leaf_hair_ornament', 'female', 'long_hair', 'blush', 'pink_hair', 'very_long_hair', 'ahoge', 'upper_body', 'pink_eyes', 'open_mouth', 'looking_at_viewer', 'hand_up', 'outstretched_hand', 'looking_up', 'headphones', '1girl', 'solo', 'cherry_hair_ornament', 'food-themed_hair_ornament', 'headset', 'pink_necktie', 'pink_sleeves', 'sakura_design', 'useless_tags', 'pink_background', 'pink_theme', 'high_resolution', 'tagme'] | 1731168773 | | 36864041 | 36864041.jpg | 1280 | 1807 | e | image/jpeg | 214630 | ['genshin_impact', 'barbara_(genshin_impact)', 'gweda', 'hetero', 'group_sex', 'gangbang', 'clothing', 'tied_hair', 'legwear', 'headwear', 'bangs', 'twintails', 'hat', 'long_sleeves', 'pantyhose', 'white_legwear', 'detached_sleeves', 'torn_clothes', 'topless', 'detached_collar', 'drill_hair', 'white_headwear', 'torn_legwear', 'torn_pantyhose', 'white_pantyhose', 'female', 'breasts', 'long_hair', 'male', 'blush', 'nipples', 'penis', 'blonde_hair', 'blue_eyes', 'vagina', 'navel', 'tongue', 'medium_breasts', 'erection', 'multiple_penises', 'open_mouth', 'looking_at_viewer', 'spread_legs', 'straddling', 'on_top', 'girl_on_top', 'cowgirl_position', 'sex', 'vaginal', 'tongue_out', 'oral', 'nakadashi', 'fellatio', 'ejaculation', 'facial', 'tekoki', 'female_penetrated', 'double_handjob', 'nun', 'semen', 'semen_in_mouth', '1girl', 'multiple_boys', 'solo_focus', 'group', '3boys', 'male_pov', 'female_on_male', 'uncensored', 'pov', 'high_resolution', 'tagme'] | 1731168769 | | 36864040 | 36864040.png | 1567 | 2800 | q | image/png | 7273267 | ['genshin_impact', 'lumine_(genshin_impact)', 'lumine', 'kabu_usagi', 'clothing', 'legwear', 'hair_ornament', 'bangs', 'thighhighs', 'underwear', 'pantsu', 'bare_shoulders', 'hair_between_eyes', 'black_legwear', 'sidelocks', 'lingerie', 'black_clothes', 'bra', 'hair_flower', 'black_thighhighs', 'black_underwear', 'wardrobe_malfunction', 'black_panties', 'garter_straps', 'no_shoes', 'underwear_only', 'garter_belt', 'black_bra', 'short_hair_with_long_locks', 'strap_slip', 'female', 'breasts', 'blush', 'large_breasts', 'blonde_hair', 'navel', 'thighs', 'cleavage', 'yellow_eyes', 'clavicle', 'stomach', 'feathers', 'thigh_gap', 'looking_at_viewer', 'sitting', 'closed_mouth', 'bent_knees', 'wariza', 'outstretched_arms', 'raised_eyebrows', 'flower', 'white_flower', 'bed', 'clock', 'sweat', '1girl', 'solo', 'indoors', 'on_bed', 'feather_hair_ornament', 'paid_reward', 'high_resolution', 'very_high_resolution', 'large_filesize'] | 1731168755 | | 36864039 | 36864039.jpg | 4168 | 5894 | q | image/jpeg | 1617286 | ['original', 'twitter', 'original_character', 'bodyguard_(roborobocop)', 'robocap', 'roborobocop', 'clothing', 'tied_hair', 'shirt', 'underwear', 'ponytail', 'jacket', 'white_shirt', 'short_sleeves', 'necktie', 'neckwear', 'choker', 'pants', 'black_clothes', 'bra', 'belt', 'collared_shirt', 'see-through', 'black_underwear', 'red_neckwear', 'black_jacket', 'crop_top', 'formal', 'suit', 'black_choker', 'dress_shirt', 'black_pants', 'high_ponytail', 'denim', 'blue_shirt', 'black_bra', 'red_necktie', 'jeans', 'tight_clothing', 'shirt_tucked_in', 'brown_belt', 'bra_visible_through_clothes', 'blue_crop_top', 'female', 'breasts', 'long_hair', 'male', 'short_hair', 'large_breasts', 'blonde_hair', 'black_hair', 'thighs', 'cleavage', 'huge_breasts', 'clavicle', 'muscle', 'midriff', 'curvaceous', 'muscular_male', 'voluptuous', 'scar', 'hourglass_figure', 'scar_on_face', 'height_difference', 'platinum_blonde_hair', 'massive_breasts', 'enormous_breasts', 'standing', 'hand_in_pocket', 'holding_smartphone', 'surprised_expression', 'holding', 'holding_object', 'looking_at_another', 'surprised', 'holding_phone', 'looking_at_phone', 'bodyguard', 'window', 'phone', 'cellphone', 'smartphone', 'sweat', 'sweatdrop', '1girl', '1boy', 'artist_website', 'holding_cellphone', 'tall_male', 'tight_jeans', 'tight_pants', 'tight_shirt', 'yena_lee_(roborobocop)', 'artist_name', 'speech_bubble', 'signature', 'username', 'blurry', 'twitter_username', 'useless_tags', 'blurry_background', 'web_address', 'artist_signature', 'english_language', 'text', 'korean_language', 'english_text', '!', 'korean_text', 'high_resolution', 'tagme', 'very_high_resolution'] | 1731168746 | | 36864038 | 36864038.jpg | 2355 | 4096 | q | image/jpeg | 736606 | ['blue_archive', 'tsurugi_(blue_archive)', 'ankoku_kishi_m', 'clothing', 'bangs', 'dress', 'barefoot', 'short_sleeves', 'alternative_costume', 'white_dress', 'alternative_hairstyle', 'sleepwear', 'nightgown', 'female', 'breasts', 'long_hair', 'blush', 'large_breasts', 'black_hair', 'red_eyes', 'cleavage', 'feet', 'very_long_hair', 'clavicle', 'ahoge', 'wings', 'halo', 'black_wings', 'looking_at_viewer', 'standing', 'closed_mouth', 'hand_on_chest', 'hand_on_own_chest', '1girl', 'solo', 'absurdly_long_hair', 'simple_background', 'white_background', 'full_body', 'useless_tags', 'high_resolution', 'tagme', 'very_high_resolution'] | 1731168736 | | 36864037 | 36864037.png | 2480 | 3496 | e | image/png | 1757821 | ['hetero', 'anthro', 'furry', 'interspecies', 'mature', 'zoophilia', 'furry_with_non-furry', 'nude', 'collar', 'female', 'breasts', 'male', 'blush', 'nipples', 'large_breasts', 'blue_eyes', 'vagina', 'tongue', 'tail', 'huge_breasts', 'genitals', 'fur', 'mature_female', 'white_fur', 'white_body', 'humanoid', 'open_mouth', 'ahegao', 'doggystyle', 'all_fours', 'sex', 'tongue_out', 'taken_from_behind', 'naughty_face', 'furry_female', 'mammal', 'animal', 'canine', 'feline', 'feral', 'felid', 'canis', 'dog', 'wolf', 'felis', 'domestic_cat', 'animal_humanoid', 'mammal_humanoid', 'sweat', 'bodily_fluids', 'genital_fluids', 'vaginal_juices', '1girl', '1boy', 'duo', 'cat_humanoid', 'felid_humanoid', 'feline_humanoid', 'lion_granny', 'useless_tags', 'limited_visibility', 'extreme_content', 'high_resolution', 'tagme', 'very_high_resolution'] | 1731168721 | | 36864036 | 36864036.png | 6200 | 4000 | e | image/png | 18128569 | ['tuca_and_bertie', 'netflix', 'draca_(tuca_and_bertie)', 'mr_valentine00', 'anthro', 'public_humiliation', 'nude', 'nude_female', 'public_nudity', 'female', 'breasts', 'ass', 'huge_ass', 'colored_skin', 'large_ass', 'tanned', 'tanlines', 'green_skin', 'green_body', 'from_behind', 'hand_on_hip', 'viewed_from_behind', 'embarrassed', 'walking', 'humiliation', 'embarrassed_nude_female', 'plant', 'flora_fauna', 'elemental_creature', 'window', 'potted_plant', 'smoke', '1girl', 'solo', 'public', 'building', 'elemental_humanoid', 'for_a_head', 'plant_humanoid', 'english_language', 'text', 'english_text', 'high_resolution', 'tagme', 'very_high_resolution', 'large_filesize', 'extremely_large_filesize'] | 1731168720 | | 36864035 | 36864035.jpg | 4000 | 4000 | q | image/jpeg | 2096852 | ['yu-gi-oh!', "yu-gi-oh!_5d's", 'fudou_yuusei', 'junk_warrior', 'junk_synchron', 'speed_warrior', 'clothing', 'shirt', 'gloves', 'jacket', 'armor', 'black_shirt', 'blue_jacket', 'brown_gloves', 'blue_armor', 'white_armor', 'male', 'blonde_hair', 'blue_eyes', 'black_hair', 'multicolored_hair', 'upper_body', 'two-tone_hair', 'streaked_hair', 'facial_mark', 'open_mouth', 'outstretched_arm', 'duel_monster', '1boy', 'multiple_boys', 'male_focus', '4boys', 'vincent_graphic_art', '1:1_aspect_ratio', 'useless_tags', 'high_resolution', 'tagme', 'very_high_resolution'] | 1731168713 | | 36864034 | 36864034.jpg | 1500 | 2000 | q | image/jpeg | 1005992 | ['touhou_project', 'hinanawi_tenshi', 'clothing', 'headwear', 'bangs', 'shirt', 'skirt', 'hat', 'bow', 'hair_between_eyes', 'white_shirt', 'short_sleeves', 'neckwear', 'frills', 'collared_shirt', 'bowtie', 'puffy_sleeves', 'red_neckwear', 'red_bow', 'blue_skirt', 'puffy_short_sleeves', 'black_headwear', 'red_bowtie', 'black_hat', 'center_frills', 'female', 'long_hair', 'blush', 'red_eyes', 'blue_hair', 'eyebrows', 'v-shaped_eyebrows', 'middle_finger', 'looking_at_viewer', 'closed_mouth', 'hand_on_hip', 'hand_on_own_hip', 'leaf', 'food', 'fruit', '1girl', 'solo', 'elucipate', 'fruit_hat_ornament', 'peach', 'rainbow_order', 'simple_background', 'white_background', 'cowboy_shot', 'sketch', 'useless_tags', 'high_resolution', 'tagme'] | 1731168689 | | 36864033 | 36864033.jpg | 2480 | 3508 | e | image/jpeg | 3016480 | ['naruto', 'naruto_shippuden', 'inuzuka_kiba', 'destor777', 'yaoi', 'bdsm', 'bara', 'foot_fetish', 'clothing', 'nude', 'headwear', 'barefoot', 'completely_nude', 'collar', 'headband', 'forehead_protector', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'brown_hair', 'black_hair', 'navel', 'thighs', 'tail', 'feet', 'testicles', 'teeth', 'erection', 'muscle', 'toes', 'black_eyes', 'stomach', 'abs', 'muscular_male', 'pectorals', 'facial_mark', 'large_pectorals', 'dog_tail', 'circumcised', 'looking_at_viewer', 'spread_legs', 'chain', 'leash', 'konohagakure_symbol', '1boy', 'multiple_boys', 'solo_focus', '2boys', 'male_focus', 'male_only', 'chain_leash', 'stomping', 'uncensored', 'high_resolution', 'tagme', 'very_high_resolution'] | 1731168688 | | 36864032 | 36864032.avif | 2135 | 2880 | e | image/avif | 133404 | ['original', 'egami', 'hetero', 'loli', 'teenage', 'mind_control', 'teenage_girl', 'hypnosis', 'clothing', 'hair_ornament', 'bangs', 'shirt', 'uniform', 'long_sleeves', 'school_uniform', 'bow', 'white_shirt', 'open_clothes', 'neckwear', 'hairclip', 'collared_shirt', 'bowtie', 'open_shirt', 'red_neckwear', 'red_bow', 'red_bowtie', 'female', 'breasts', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'black_hair', 'vagina', 'navel', 'small_breasts', 'dark_skin', 'erect_nipples', 'black_eyes', 'symbol-shaped_pupils', 'dark-skinned_male', 'heart-shaped_pupils', 'bulge', 'stomach_bulge', 'uterus', 'open_mouth', 'looking_at_viewer', 'spread_legs', 'lying', 'on_back', 'hands_up', 'missionary_position', 'sex', 'vaginal', 'ejaculation', 'orgasm', 'female_orgasm', 'internal_cumshot', 'torso_grab', 'bed', 'pillow', 'bed_sheet', 'semen', 'sweat', '1girl', '1boy', 'solo_focus', 'on_bed', 'cross_section', 'limited_visibility', 'contentious_content', 'heart', 'high_resolution', 'very_high_resolution'] | 1731168685 | | 36864031 | 36864031.jpg | 553 | 700 | e | image/jpeg | 50377 | ['tokyo_afterschool_summoners', 'garouzuki', 'anthro', 'furry', 'bara', 'clothing', 'underwear', 'footwear', 'jewelry', 'traditional_clothes', 'wafuku', 'necklace', 'animal_print', 'sandals', 'fundoshi', 'flip-flops', 'asian_clothing', 'japanese_clothing', 'east_asian_clothing', 'male', 'blush', 'short_hair', 'nipples', 'navel', 'thighs', 'animal_ears', 'tail', 'eyebrows', 'thick_thighs', 'fur', 'muscle', 'muscular_male', 'bulge', 'pectorals', 'plump', 'scar', 'bipedal', 'belly', 'brown_fur', 'thick_eyebrows', 'overweight', 'dog_ears', 'two-tone_fur', 'overweight_male', 'dog_tail', 'scar_on_face', 'overweight_anthro', 'obese', 'obese_male', 'squatting', 'furry_male', 'mammal', 'animal', 'canine', 'canis', 'dog', 'dog_boy', 'dog_tags', 'solo', '1boy', 'male_focus', 'happi', 'happi_(clothing)', 'lifewonders', 'moritaka_(tas)', 'obese_anthro', 'paw_print', 'countershading', 'limited_visibility'] | 1731168674 | | 36864030 | 36864030.jpg | 661 | 800 | q | image/jpeg | 107018 | ['rockman', 'mega_man_(series)', 'mega_man_legends_(series)', 'roll_caskett', 'tron_bonne_(mega_man)', 'hirotaka_(hrtk990203)', 'clothing', 'headwear', 'gloves', 'jewelry', 'dress', 'hat', 'jacket', 'earrings', 'open_clothes', 'pantyhose', 'hairband', 'short_sleeves', 'shorts', 'belt', 'bodysuit', 'open_jacket', 'covered_navel', 'short_dress', 'blue_jacket', 'pink_dress', 'red_headwear', 'red_jacket', 'brown_gloves', 'cropped_jacket', 'sleeves_rolled_up', 'black_bodysuit', 'cabbie_hat', 'red_shorts', 'red_hat', 'pink_hairband', 'bodysuit_under_clothes', 'female', 'breasts', 'long_hair', 'short_hair', 'blonde_hair', 'brown_hair', 'navel', 'green_eyes', 'medium_hair', 'open_mouth', 'looking_at_viewer', 'smile', 'closed_mouth', 'open-mouth_smile', ':d', 'crossed_arms', 'hands_on_hips', 'hands_on_own_hips', 'multiple_girls', '2girls', 'crotch_plate', 'skull_brooch', 'skull_earrings', 'artist_name', 'cowboy_shot', 'tagme'] | 1731168665 | | 36864029 | 36864029.jpg | 494 | 620 | e | image/jpeg | 33553 | ['tokyo_afterschool_summoners', 'garouzuki', 'anthro', 'furry', 'bara', 'clothing', 'animal_print', 'male', 'blush', 'short_hair', 'nipples', 'navel', 'thighs', 'animal_ears', 'eyebrows', 'fur', 'muscle', 'stomach', 'muscular_male', 'fangs', 'pectorals', 'plump', 'scar', 'belly', 'brown_fur', 'thick_eyebrows', 'dog_ears', 'scar_on_face', 'open_mouth', 'looking_at_viewer', 'smile', 'standing', 'furry_male', 'mammal', 'animal', 'canine', 'canis', 'dog', 'dog_boy', 'towel', 'solo', '1boy', 'male_focus', 'lifewonders', 'moritaka_(tas)', 'musclegut', 'paw_print', 'countershading', 'limited_visibility', 'tagme'] | 1731168653 | | 36864028 | 36864028.png | 832 | 1216 | e | image/png | 1153331 | ['chrono_trigger', 'lucca', 'nude', 'bangs', 'eyewear', 'glasses', 'completely_nude', 'blunt_bangs', 'female', 'breasts', 'blush', 'short_hair', 'nipples', 'large_breasts', 'vagina', 'navel', 'thighs', 'eyes_closed', 'pink_hair', 'purple_hair', 'areolae', 'teeth', 'armpits', 'stomach', 'upper_teeth', 'upper_teeth_only', 'open_mouth', 'spread_legs', 'lying', 'on_back', 'arms_up', 'sex', 'vaginal', 'masturbation', 'insertion', 'object_insertion', 'vaginal_insertion', 'female_masturbation', 'female_ejaculation', 'vaginal_object_insertion', 'bed', 'sex_toy', 'pillow', 'window', 'bed_sheet', 'dildo', 'vibrator', 'machine', 'sex_machine', 'sweat', 'genital_fluids', 'vaginal_juices', '1girl', 'solo', 'female_only', 'indoors', 'on_bed', 'fucking_machine', 'novelai', 'high_resolution', 'tagme', 'ai-created'] | 1731168647 | | 36864027 | 36864027.png | 1500 | 1000 | q | image/png | 469457 | ['original', 'kemomimi-chan_(naga_u)', 'naga_u', 'clothing', 'tied_hair', 'legwear', 'hair_ornament', 'bangs', 'shirt', 'thighhighs', 'skirt', 'long_sleeves', 'hair_between_eyes', 'white_legwear', 'hair_bun', 'sailor_collar', 'white_thighhighs', 'sleeves_past_wrists', 'single_hair_bun', 'ribbon_trim', 'sleeves_past_fingers', 'green_shirt', 'white_sailor_collar', 'purple_skirt', 'female', 'blush', 'blonde_hair', 'animal_ears', 'tail', 'purple_eyes', 'animal_tail', 'fox_ears', 'animal_ear_fluff', 'violet_eyes', 'fox_tail', 'empty_eyes', 'shaded_face', 'looking_at_viewer', 'lying', 'on_stomach', 'fox_girl', '1girl', 'solo', 'hot', 'melting', 'ribbon-trimmed_legwear', 'ribbon-trimmed_thighhighs', 'simple_background', 'white_background', 'full_body', 'shadow', 'tagme', '3:2_aspect_ratio'] | 1731168642 | | 36864026 | 36864026.avif | 2400 | 2090 | e | image/avif | 169361 | ['original', 'egami', 'hetero', 'loli', 'teenage', 'mind_control', 'teenage_girl', 'clothing', 'bangs', 'shirt', 'skirt', 'underwear', 'uniform', 'pantsu', 'school_uniform', 'white_shirt', 'short_sleeves', 'serafuku', 'no_bra', 'sailor_collar', 'blue_skirt', 'female', 'breasts', 'long_hair', 'male', 'blush', 'nipples', 'penis', 'black_hair', 'vagina', 'navel', 'brown_eyes', 'small_breasts', 'erect_nipples', 'black_eyes', 'uterus', 'open_mouth', 'spread_legs', 'lying', 'closed_mouth', 'on_back', 'clothes_pull', 'clothes_lift', 'shirt_lift', 'sex', 'vaginal', 'nakadashi', 'ejaculation', 'orgasm', 'internal_cumshot', 'table', 'desk', 'school_desk', 'semen', 'sweat', '1girl', '1boy', 'solo_focus', 'indoors', 'classroom', 'cross_section', 'limited_visibility', 'contentious_content', 'high_resolution'] | 1731168639 | | 36864025 | 36864025.jpg | 1500 | 2307 | q | image/jpeg | 247461 | ['kantai_collection', 'okinami_(kantai_collection)', 'mori_tarou', 'clothing', 'legwear', 'shirt', 'skirt', 'underwear', 'uniform', 'footwear', 'long_sleeves', 'school_uniform', 'bow', 'eyewear', 'shoes', 'glasses', 'white_shirt', 'alternative_costume', 'socks', 'pleated_skirt', 'white_legwear', 'bra', 'serafuku', 'bowtie', 'sailor_collar', 'brown_footwear', 'white_socks', 'slip-on_shoes', 'loafers', 'grey_shirt', 'adapted_costume', 'brown_skirt', 'green_underwear', 'sailor_shirt', 'loose_socks', 'aqua_bow', 'green_bra', 'female', 'short_hair', 'blonde_hair', 'brown_hair', 'black_hair', 'green_eyes', 'pink_hair', 'multicolored_hair', 'looking_at_viewer', 'smile', 'standing', 'holding', 'holding_instrument', 'star_(symbol)', 'instrument', 'guitar', 'electric_guitar', '1girl', 'solo', 'aqua_bowtie', 'blue-framed_eyewear', 'brown_sailor_collar', 'holding_guitar', 'holding_musical_instrument', 'okinami_kai_ni_(kancolle)', 'simple_background', 'white_background', 'full_body', 'useless_tags', 'high_resolution', 'tagme'] | 1731168620 | | 36864024 | 36864024.avif | 2400 | 2090 | e | image/avif | 162038 | ['original', 'egami', 'hetero', 'teenage', 'mind_control', 'teenage_girl', 'hypnosis', 'clothing', 'bangs', 'shirt', 'skirt', 'uniform', 'school_uniform', 'white_shirt', 'short_sleeves', 'pleated_skirt', 'serafuku', 'sailor_collar', 'blue_skirt', 'female', 'breasts', 'long_hair', 'male', 'blush', 'nipples', 'penis', 'black_hair', 'vagina', 'navel', 'brown_eyes', 'small_breasts', 'erect_nipples', 'black_eyes', 'open_mouth', 'spread_legs', 'lying', 'on_back', 'clothes_pull', 'clothes_lift', 'shirt_lift', 'skirt_pull', 'skirt_lift', 'sex', 'vaginal', 'table', 'desk', 'sweat', '1girl', '1boy', 'solo_focus', 'indoors', 'classroom', 'high_resolution'] | 1731168601 | | 36864023 | 36864023.png | 1000 | 1200 | e | image/png | 502627 | ['sailor_moon_(series)', 'kino_makoto', 'sailor_jupiter', 'pokemilf', 'hetero', 'mature', 'clothing', 'tied_hair', 'nude', 'headwear', 'skirt', 'jewelry', 'ponytail', 'earrings', 'miniskirt', 'crown', 'nude_male', 'tiara', 'partially_clothed', 'mostly_nude', 'microskirt', 'sailor_senshi_uniform', 'female', 'breasts', 'long_hair', 'male', 'nipples', 'large_breasts', 'penis', 'vagina', 'navel', 'thighs', 'huge_breasts', 'eyebrows', 'eyebrows_visible_through_hair', 'areolae', 'genitals', 'thick_thighs', 'hair', 'midriff', 'wide_hips', 'lips', 'eyelashes', 'mature_female', 'plump', 'voluptuous', 'belly', 'hips', 'fat', 'unaligned_breasts', 'looking_at_viewer', 'spread_legs', 'parted_lips', 'straddling', 'on_top', 'girl_on_top', 'cowgirl_position', 'male_on_bottom', 'sex', 'vaginal', 'penetration', 'male_penetrating', 'bouncing_breasts', 'clothed_sex', 'female_penetrated', 'male_penetrating_female', 'riding', 'magical_girl', 'mammal', 'sweat', 'bodily_fluids', 'genital_fluids', 'vaginal_juices', '1girl', '1boy', 'solo_focus', 'female_focus', 'male_pov', 'unseen_male_face', 'monochrome', 'pov', 'useless_tags', 'motion_lines', 'green_theme', 'alternate_version_available', 'high_resolution', 'tagme'] | 1731168576 | | 36864022 | 36864022.jpg | 1111 | 1899 | q | image/jpeg | 276951 | ['blue_archive', 'kyouyama_kazusa', 'dainillust', 'clothing', 'headwear', 'hair_ornament', 'bangs', 'skirt', 'uniform', 'hat', 'long_sleeves', 'school_uniform', 'jacket', 'choker', 'pleated_skirt', 'hairclip', 'blunt_bangs', 'hood', 'sailor_collar', 'black_jacket', 'hoodie', 'neckerchief', 'black_choker', 'bob_cut', 'hood_down', 'hooded_jacket', 'black_hoodie', 'pink_hat', 'female', 'blush', 'short_hair', 'black_hair', 'animal_ears', 'pink_hair', 'clavicle', 'multicolored_hair', 'upper_body', 'cat_ears', 'pink_eyes', 'grin', 'two-tone_hair', 'eyelashes', 'halo', 'extra_ears', 'colored_inner_hair', 'tsurime', 'looking_at_viewer', 'smile', 'hands_up', 'holding', 'holding_object', 'happy', 'flower', 'leaf', 'pink_flower', 'blue_flower', 'bouquet', 'hydrangea', '1girl', 'solo', 'cat_choker', 'food-themed_hair_ornament', 'green_sailor_collar', 'holding_bouquet', 'ice_cream_hair_ornament', 'pink_neckerchief', 'simple_background', 'white_background', 'useless_tags', 'high_resolution', 'tagme'] | 1731168576 | | 36864021 | 36864021.avif | 2135 | 2880 | e | image/avif | 128014 | ['original', 'egami', 'hetero', 'loli', 'teenage', 'mind_control', 'teenage_girl', 'hypnosis', 'clothing', 'hair_ornament', 'bangs', 'shirt', 'uniform', 'long_sleeves', 'school_uniform', 'bow', 'white_shirt', 'open_clothes', 'neckwear', 'bottomless', 'hairclip', 'collared_shirt', 'bowtie', 'open_shirt', 'red_neckwear', 'red_bow', 'red_bowtie', 'female', 'breasts', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'black_hair', 'vagina', 'navel', 'small_breasts', 'erect_nipples', 'black_eyes', 'symbol-shaped_pupils', 'heart-shaped_pupils', 'bulge', 'stomach_bulge', 'open_mouth', 'looking_at_viewer', 'spread_legs', 'lying', 'on_back', 'hands_up', 'missionary_position', 'sex', 'vaginal', 'orgasm', 'female_orgasm', 'torso_grab', 'bed', 'pillow', 'bed_sheet', 'sweat', '1girl', '1boy', 'solo_focus', 'on_bed', 'limited_visibility', 'contentious_content', 'heart', 'high_resolution', 'very_high_resolution'] | 1731168565 | | 36864020 | 36864020.png | 1000 | 1200 | e | image/png | 513634 | ['sailor_moon_(series)', 'kino_makoto', 'sailor_jupiter', 'pokemilf', 'hetero', 'mature', 'clothing', 'tied_hair', 'nude', 'headwear', 'skirt', 'jewelry', 'ponytail', 'earrings', 'miniskirt', 'crown', 'nude_male', 'tiara', 'partially_clothed', 'mostly_nude', 'microskirt', 'sailor_senshi_uniform', 'female', 'breasts', 'long_hair', 'male', 'nipples', 'large_breasts', 'penis', 'vagina', 'navel', 'thighs', 'huge_breasts', 'eyebrows', 'eyebrows_visible_through_hair', 'areolae', 'genitals', 'pubic_hair', 'thick_thighs', 'hair', 'midriff', 'wide_hips', 'lips', 'female_pubic_hair', 'eyelashes', 'mature_female', 'plump', 'voluptuous', 'belly', 'hips', 'fat', 'hairy_vagina', 'unaligned_breasts', 'looking_at_viewer', 'spread_legs', 'parted_lips', 'straddling', 'on_top', 'girl_on_top', 'cowgirl_position', 'male_on_bottom', 'sex', 'vaginal', 'penetration', 'male_penetrating', 'bouncing_breasts', 'clothed_sex', 'female_penetrated', 'male_penetrating_female', 'riding', 'magical_girl', 'mammal', 'sweat', 'bodily_fluids', 'genital_fluids', 'vaginal_juices', '1girl', '1boy', 'solo_focus', 'female_focus', 'male_pov', 'unseen_male_face', 'monochrome', 'pov', 'useless_tags', 'motion_lines', 'green_theme', 'alternate_version_available', 'high_resolution', 'tagme'] | 1731168553 | | 36864019 | 36864019.png | 4096 | 4096 | e | image/png | 6795474 | ['original', 'loli', 'clothing', 'legwear', 'bangs', 'shirt', 'skirt', 'uniform', 'footwear', 'school_uniform', 'white_shirt', 'socks', 'pleated_skirt', 'white_legwear', 'bottomless', 'serafuku', 'nopan', 'sailor_collar', 'blue_skirt', 'no_shoes', 'white_socks', 'female', 'long_hair', 'blush', 'black_hair', 'ass', 'vagina', 'thighs', 'brown_eyes', 'feet', 'anus', 'legs', 'black_eyes', 'soles', 'cleft_of_venus', 'open_mouth', 'looking_at_viewer', 'smile', 'spread_legs', 'lying', 'on_back', 'clothes_pull', 'clothes_lift', 'skirt_pull', 'skirt_lift', 'legs_up', 'seductive_look', 'bed', 'pillow', 'window', '1girl', 'solo', 'indoors', 'on_bed', '1:1_aspect_ratio', 'sankaku_ai', 'limited_visibility', 'contentious_content', 'high_resolution', 'tagme', 'very_high_resolution', 'large_filesize', 'ai-created'] | 1731168553 | | 36864018 | 36864018.jpg | 4093 | 2894 | s | image/jpeg | 2054006 | ['love_live!_(series)', 'link!_like!_love_live!', 'hinoshita_kaho', 'murano_sayaka', 'fujishima_megumi', 'otomune_kozue', 'yugiri_tsuzuri', 'osawa_rurino', 'sunkeun04', 'virtual_youtuber', 'clothing', 'tied_hair', 'hair_ornament', 'bangs', 'uniform', 'dress', 'twintails', 'ponytail', 'long_sleeves', 'ribbon', 'school_uniform', 'open_clothes', 'hair_ribbon', 'neckwear', 'hairclip', 'asymmetrical_hair', 'hair_bun', 'hair_flower', 'blunt_bangs', 'side_ponytail', 'sailor_collar', 'red_neckwear', 'two_side_up', 'parted_bangs', 'neckerchief', 'low_twintails', 'bob_cut', 'straight_hair', 'cardigan', 'blue_ribbon', 'red_neckerchief', 'white_sailor_collar', 'star_hair_ornament', 'sailor_dress', 'pleated_dress', 'single_side_bun', 'brown_dress', 'open_cardigan', 'winter_uniform', 'inverted_bob', 'female', 'long_hair', 'short_hair', 'blonde_hair', 'blue_eyes', 'brown_hair', 'green_eyes', 'blue_hair', 'purple_eyes', 'eyes_closed', 'purple_hair', 'teeth', 'red_hair', 'multicolored_hair', 'one_eye_closed', 'green_hair', 'grey_hair', 'medium_hair', 'pink_eyes', 'grin', 'streaked_hair', 'violet_eyes', 'upper_teeth', 'gradient_hair', 'upper_teeth_only', 'light_brown_hair', 'colored_inner_hair', 'crossed_bangs', 'light_blue_hair', 'dark_blue_hair', 'open_mouth', 'looking_at_viewer', 'smile', 'closed_mouth', 'v', 'arms_behind_back', '^_^', ';d', "hand_on_another's_shoulder", 'behind_another', 'holding', 'holding_object', 'wink', 'flower', 'rose', 'white_flower', 'pink_flower', 'blue_flower', 'bouquet', 'purple_flower', 'pink_rose', 'star_(symbol)', 'photo_(object)', 'tube', 'multiple_girls', '6+girls', 'aqua_neckerchief', 'artistic_error', 'brown_cardigan', 'bunny_ears_prank', 'congratulations', 'daisy', 'graduation', 'hasu_no_sora_school_uniform', 'holding_bouquet', 'kotori_photobomb', 'oogami_sachi', 'prank', 'rabbit_hair_ornament', 'triangle_mouth', 'blurry', 'useless_tags', 'blurry_background', 'selfie', 'blurry_foreground', 'chromatic_aberration', 'film_grain', 'high_resolution', 'very_high_resolution'] | 1731168549 | | 36864017 | 36864017.png | 1567 | 2800 | q | image/png | 7084962 | ['genshin_impact', 'lumine_(genshin_impact)', 'lumine', 'kabu_usagi', 'clothing', 'legwear', 'hair_ornament', 'bangs', 'thighhighs', 'underwear', 'pantsu', 'bare_shoulders', 'black_legwear', 'sidelocks', 'lingerie', 'black_clothes', 'bra', 'hair_flower', 'black_thighhighs', 'black_underwear', 'wardrobe_malfunction', 'black_panties', 'garter_straps', 'no_shoes', 'underwear_only', 'garter_belt', 'black_bra', 'short_hair_with_long_locks', 'strap_slip', 'female', 'breasts', 'blush', 'large_breasts', 'blonde_hair', 'navel', 'thighs', 'cleavage', 'yellow_eyes', 'clavicle', 'stomach', 'feathers', 'thigh_gap', 'looking_at_viewer', 'sitting', 'closed_mouth', 'bent_knees', 'wariza', 'outstretched_arms', 'raised_eyebrows', 'flower', 'white_flower', 'bed', 'clock', 'sweat', '1girl', 'solo', 'indoors', 'on_bed', 'feather_hair_ornament', 'paid_reward', 'high_resolution', 'very_high_resolution', 'large_filesize'] | 1731168542 | | 36864016 | 36864016.png | 1980 | 2000 | e | image/png | 942894 | ['the_elder_scrolls', 'the_elder_scrolls_v:_skyrim', 'bethesda_softworks', 'microsoft', 'hetero', 'anthro', 'furry', 'interspecies', 'scalie', 'nude', 'armor', 'shoulder_armor', 'female', 'breasts', 'male', 'large_breasts', 'ass', 'thighs', 'tongue', 'tail', 'teeth', 'genitals', 'thick_thighs', 'claws', 'scar', 'sharp_teeth', 'scales', 'non-mammal_breasts', 'long_tongue', 'looking_at_viewer', 'hand_on_ass', 'hand_on_breast', 'tongue_out', 'groping', 'breast_grab', 'embracing', 'hug', 'ass_grab', 'deep_skin', 'groping_from_behind', 'furry_female', 'dragon', 'monster_girl', 'dragon_girl', '1girl', 'duo', 'argonian', 'cokesero', 'deeja', 'face_in_breasts', 'jaree-ra', 'simple_background', 'monochrome', 'useless_tags', 'limited_visibility', 'high_resolution', 'tagme'] | 1731168528 | | 36864015 | 36864015.png | 2048 | 945 | s | image/png | 3931977 | ['idolmaster', 'code_geass', 'code_geass:_lost_stories', 'shirley_fenette', 'crossdressing', 'clothing', 'shirt', 'jewelry', 'long_sleeves', 'bow', 'jacket', 'open_clothes', 'sidelocks', 'pants', 'frills', 'striped', 'collared_shirt', 'bowtie', 'open_jacket', 'vest', 'formal', 'suit', 'white_jacket', 'frilled_sleeves', 'brooch', 'green_shirt', 'white_pants', 'brown_bow', 'white_vest', 'female', 'breasts', 'long_hair', 'blush', 'brown_hair', 'green_eyes', 'medium_breasts', 'orange_hair', 'light_blush', 'open_mouth', 'looking_at_viewer', 'smile', 'hands_up', 'hand_on_chest', 'hand_on_own_chest', 'clenched_hand', 'on_one_knee', 'holding', 'holding_object', 'happy', 'groom', 'flower', 'petals', 'rose', 'white_flower', 'red_flower', 'pink_flower', 'yellow_flower', 'bouquet', 'red_rose', 'white_rose', 'pink_rose', 'sparkle', 'buttons', 'gem', 'blue_gemstone', '1girl', 'solo', 'outdoors', 'day', 'stairs', 'fountain', 'brown_bowtie', 'frilled_jacket', 'holding_bouquet', 'red_carpet', 'straight-on', 'white_suit', 'yellow_rose', 'cg_art', 'game_cg', 'official_art', 'useless_tags', 'dutch_angle', 'high_resolution'] | 1731168523 | | 36864014 | 36864014.avif | 2135 | 2880 | e | image/avif | 119809 | ['original', 'egami', 'hetero', 'loli', 'teenage', 'mind_control', 'teenage_girl', 'hypnosis', 'clothing', 'hair_ornament', 'bangs', 'shirt', 'uniform', 'long_sleeves', 'school_uniform', 'bow', 'white_shirt', 'open_clothes', 'neckwear', 'hairclip', 'collared_shirt', 'bowtie', 'open_shirt', 'red_neckwear', 'red_bow', 'red_bowtie', 'female', 'breasts', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'black_hair', 'vagina', 'navel', 'small_breasts', 'dark_skin', 'erect_nipples', 'black_eyes', 'symbol-shaped_pupils', 'dark-skinned_male', 'heart-shaped_pupils', 'bulge', 'stomach_bulge', 'open_mouth', 'looking_at_viewer', 'spread_legs', 'lying', 'on_back', 'hands_up', 'missionary_position', 'sex', 'vaginal', 'orgasm', 'female_orgasm', 'torso_grab', 'bed', 'pillow', 'bed_sheet', 'sweat', '1girl', '1boy', 'solo_focus', 'on_bed', 'limited_visibility', 'contentious_content', 'heart', 'high_resolution', 'very_high_resolution'] | 1731168513 | | 36864013 | 36864013.webp | 1280 | 853 | e | image/webp | 79318 | ['hetero', 'clothing', 'tied_hair', 'nude', 'hair_ornament', 'bangs', 'bow', 'braid', 'completely_nude', 'hair_bow', 'pink_bow', 'female', 'breasts', 'long_hair', 'male', 'blush', 'nipples', 'large_breasts', 'penis', 'ass', 'vagina', 'navel', 'thighs', 'red_eyes', 'white_hair', 'anus', 'testicles', 'grey_hair', 'open_mouth', 'spread_legs', 'lying', 'ahegao', 'top-down_bottom-up', 'hands_on_ground', 'sex', 'vaginal', 'nakadashi', 'anal', 'taken_from_behind', 'ejaculation', 'breast_press', 'orgasm', 'trembling', 'female_ejaculation', 'cloud', 'semen', 'sweat', 'water', 'wet', 'overflow', '1girl', '1boy', 'solo_focus', 'outdoors', 'sky', 'day', 'blue_sky', 'beach', 'ocean', 'ai-created', '3:2_aspect_ratio'] | 1731168511 | | 36864012 | 36864012.jpg | 1280 | 1161 | e | image/jpeg | 361930 | ['warcraft', 'blizzard_entertainment', 'worgen', 'vulpera', 'spelunker_sal', 'anthro', 'furry', 'yaoi', 'size_difference', 'bara', 'nude', 'jewelry', 'earrings', 'completely_nude', 'collar', 'male', 'nipples', 'penis', 'ass', 'thighs', 'animal_ears', 'tongue', 'tail', 'feet', 'testicles', 'teeth', 'erection', 'genitals', 'thick_thighs', 'fur', 'muscle', 'toes', 'large_penis', 'huge_ass', 'abs', 'animal_genitalia', 'muscular_male', 'claws', 'pectorals', 'animal_penis', 'scar', 'sharp_teeth', 'muscular_anthro', 'toe_claws', 'smile', 'all_fours', 'sex', 'holding', 'anal', 'penetration', 'taken_from_behind', 'male_penetrating', 'leash_pull', 'submissive', 'furry_male', 'male_penetrated', 'small_dom_big_sub', 'mammal', 'animal', 'canine', 'canis', 'dog_boy', 'leash', 'semen', 'bodily_fluids', 'genital_fluids', 'precum', 'multiple_boys', 'duo', '2boys', 'male_focus', 'furry_with_furry', 'male_penetrating_male', 'muscular_bottom', 'muscular_sub', 'were', 'werecanid', 'wolf_boy', 'simple_background', 'monochrome', 'white_background', 'limited_visibility'] | 1731168506 | | 36864011 | 36864011.png | 844 | 1133 | s | image/png | 1046129 | ['code_geass', 'code_geass:_lost_stories', 'shirley_fenette', 'crossdressing', 'clothing', 'shirt', 'jewelry', 'long_sleeves', 'bow', 'jacket', 'open_clothes', 'sidelocks', 'pants', 'frills', 'collared_shirt', 'bowtie', 'open_jacket', 'vest', 'formal', 'suit', 'white_jacket', 'frilled_sleeves', 'brooch', 'green_shirt', 'white_pants', 'brown_bow', 'white_vest', 'female', 'breasts', 'long_hair', 'blush', 'brown_hair', 'green_eyes', 'medium_breasts', 'orange_hair', 'light_blush', 'open_mouth', 'looking_at_viewer', 'smile', 'standing', 'hands_up', 'hand_on_chest', 'hand_on_own_chest', 'clenched_hand', 'holding', 'holding_object', 'happy', 'groom', 'flower', 'rose', 'red_flower', 'bouquet', 'red_rose', 'buttons', 'gem', 'blue_gemstone', '1girl', 'solo', 'brown_bowtie', 'cropped_legs', 'frilled_jacket', 'holding_bouquet', 'straight-on', 'white_suit', 'cg_art', 'simple_background', 'game_cg', 'official_art', 'cowboy_shot', 'useless_tags', 'dutch_angle', 'transparent_background'] | 1731168500 | | 36864010 | 36864010.jpg | 1280 | 1920 | e | image/jpeg | 268772 | ['hetero', 'clothing', 'tied_hair', 'nude', 'hair_ornament', 'bangs', 'ribbon', 'bow', 'swimsuit', 'braid', 'bikini', 'hair_ribbon', 'hair_bow', 'nude_male', 'clothed_female', 'clothed_female_nude_male', 'swept_bangs', 'sarong', 'female', 'breasts', 'long_hair', 'male', 'blush', 'short_hair', 'nipples', 'large_breasts', 'penis', 'brown_hair', 'vagina', 'navel', 'thighs', 'red_eyes', 'tongue', 'white_hair', 'testicles', 'dark_skin', 'stomach', 'dark-skinned_male', 'faceless', 'clitoris', 'faceless_male', 'open_mouth', 'looking_at_viewer', 'spread_legs', 'standing', 'leg_up', 'standing_on_one_leg', 'standing_sex', 'sex', 'vaginal', 'tongue_out', 'nakadashi', 'anal', 'groping', 'ejaculation', 'breast_grab', 'grabbing', 'orgasm', 'trembling', 'heavy_breathing', 'tree', 'palm_tree', 'cloud', 'semen', 'sweat', 'saliva', 'water', 'wet', 'overflow', 'sand', '1girl', '1boy', 'solo_focus', 'outdoors', 'sky', 'day', 'blue_sky', 'beach', 'ocean', 'sunlight', 'high_resolution', 'ai-created'] | 1731168495 | | 36864009 | 36864009.jpg | 1483 | 2048 | q | image/jpeg | 275331 | ['sousou_no_frieren', 'frieren', 'hikentai0112', 'clothing', 'tied_hair', 'bangs', 'shirt', 'jewelry', 'twintails', 'jacket', 'earrings', 'striped', 'parted_bangs', 'capelet', 'striped_shirt', 'striped_clothing', 'female', 'long_hair', 'green_eyes', 'eyebrows', 'upper_body', 'pointed_ears', 'thick_eyebrows', 'looking_at_viewer', 'parted_lips', 'elf', '1girl', 'solo', 'cropped_torso', 'dangle_earrings', 'drop_earrings', 'simple_background', 'monochrome', 'white_background', 'greyscale', 'useless_tags', 'spot_color', 'high_resolution', 'tagme'] | 1731168478 | | 36864008 | 36864008.jpg | 1280 | 1920 | e | image/jpeg | 276801 | ['hetero', 'clothing', 'tied_hair', 'nude', 'hair_ornament', 'bangs', 'skirt', 'bow', 'swimsuit', 'braid', 'bikini', 'hair_bow', 'frills', 'nude_male', 'clothed_female', 'clothed_female_nude_male', 'pink_bow', 'frilled_swimsuit', 'frilled_bikini', 'pink_swimsuit', 'pink_bikini', 'sarong', 'bikini_skirt', 'female', 'breasts', 'long_hair', 'male', 'blush', 'nipples', 'large_breasts', 'penis', 'ass', 'vagina', 'thighs', 'red_eyes', 'tongue', 'white_hair', 'anus', 'dark_skin', 'pubic_hair', 'dark-skinned_male', 'male_pubic_hair', 'open_mouth', 'looking_at_viewer', 'standing', 'clothes_pull', 'from_behind', 'sex', 'vaginal', 'tongue_out', 'nakadashi', 'looking_back', 'anal', 'taken_from_behind', 'trembling', 'heavy_breathing', 'arm_grab', 'tree', 'palm_tree', 'cloud', 'semen', 'sweat', 'genital_fluids', 'vaginal_juices', 'water', 'wet', 'sand', '1girl', '1boy', 'solo_focus', 'outdoors', 'sky', 'day', 'blue_sky', 'beach', 'ocean', 'sunlight', 'high_resolution', 'ai-created'] | 1731168475 | | 36864007 | 36864007.avif | 3028 | 2880 | e | image/avif | 169423 | ['original', 'egami', 'hetero', 'loli', 'teenage', 'teenage_girl', 'clothing', 'tied_hair', 'nude', 'legwear', 'bangs', 'shirt', 'thighhighs', 'skirt', 'uniform', 'school_uniform', 'black_legwear', 'white_shirt', 'neckwear', 'black_thighhighs', 'sailor_collar', 'red_neckwear', 'neckerchief', 'red_neckerchief', 'female', 'breasts', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'brown_hair', 'black_hair', 'vagina', 'navel', 'thighs', 'brown_eyes', 'small_breasts', 'erect_nipples', 'open_mouth', 'spread_legs', 'standing', 'clothes_lift', 'shirt_lift', 'leg_up', 'standing_sex', 'sex', 'vaginal', 'nakadashi', 'taken_from_behind', 'orgasm', 'trembling', 'female_orgasm', 'semen', 'sweat', '1girl', '1boy', 'indoors', 'classroom', 'limited_visibility', 'contentious_content', 'high_resolution', 'very_high_resolution'] | 1731168464 | | 36864006 | 36864006.png | 1800 | 2400 | e | image/png | 1644531 | ['slime_rancher', 'hetero', 'anthro', 'hyper', 'imminent_sex', 'nude', 'female', 'breasts', 'male', 'blush', 'penis', 'ass', 'vagina', 'thighs', 'anus', 'testicles', 'erection', 'genitals', 'thick_thighs', 'shiny_skin', 'huge_ass', 'colored_skin', 'large_ass', 'humanoid', 'humanoid_genitalia', 'disembodied', 'disembodied_penis', 'orange_body', 'red_skin', 'orange_skin', 'big_anus', 'lying', 'from_behind', 'viewed_from_behind', 'looking_back', 'eating', 'grass', 'mammal', 'animal', 'bunny', 'lagomorph', 'leporid', 'slime_monster', 'hybrid', 'slime', 'food', 'carrot', 'sweat', 'dripping', '1girl', '1boy', 'solo_focus', 'duo', 'outdoors', 'bunny_slime_(slime_rancher)', 'hotwoodcoldwood', 'slime_(slime_rancher)', 'translucent', 'translucent_body', 'shiny', 'useless_tags', '3:4', '?', 'high_resolution', 'tagme', 'very_high_resolution'] | 1731168462 | | 36864005 | 36864005.png | 2400 | 3300 | q | image/png | 3678561 | ['she-ra_and_the_princesses_of_power', 'adora', 'scotchwaifus', 'bdsm', 'femsub', 'clothing', 'tied_hair', 'shirt', 'footwear', 'ponytail', 'white_shirt', 'boots', 'pants', 'gagged', 'fully_clothed', 'grey_pants', 'red_boots', 'female', 'breasts', 'blush', 'blonde_hair', 'blue_eyes', 'light_skin', 'light-skinned_female', 'sitting', 'arms_behind_back', 'bound_legs', 'bondage', 'restrained', 'looking_to_the_side', 'looking_away', 'bound_arms', 'tied_up', 'bound_ankles', 'rope_bondage', 'submissive', 'prisoner', 'kidnapped', 'rope', 'gag', 'tape', 'improvised_gag', 'tapegag', 'sweat', '1girl', 'solo', 'female_only', 'female_focus', 'prison', 'prison_cell', 'blonde_female', 'damsel_in_distress', 'female_solo', 'fright_zone', 'mouth_taped', 'sub_only', 'tape_bondage', 'full_body', 'artist_name', 'watermark', 'useless_tags', 'web_address', '2019', '2010s', 'high_resolution', 'very_high_resolution'] | 1731168410 | | 36864004 | 36864004.avif | 1280 | 1430 | e | image/avif | 71646 | ['original', 'egami', 'hetero', 'loli', 'teenage', 'teenage_girl', 'clothing', 'nude', 'legwear', 'shirt', 'thighhighs', 'skirt', 'underwear', 'uniform', 'school_uniform', 'black_legwear', 'short_sleeves', 'black_clothes', 'black_thighhighs', 'black_skirt', 'nude_male', 'clothed_female', 'clothed_female_nude_male', 'female', 'breasts', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'brown_hair', 'black_hair', 'vagina', 'navel', 'tongue', 'testicles', 'small_breasts', 'dark_skin', 'erect_nipples', 'uterus', 'cervix', 'open_mouth', 'spread_legs', 'clothes_lift', 'straddling', 'on_top', 'head_back', 'sex', 'vaginal', 'tongue_out', 'nakadashi', 'groping', 'ejaculation', 'breast_grab', 'grabbing', 'orgasm', 'trembling', 'clothed_sex', 'nipple_tweak', 'female_orgasm', 'internal_cumshot', 'reverse_upright_straddle', 'semen', 'sweat', 'genital_fluids', 'vaginal_juices', 'overflow', '1girl', '1boy', 'indoors', 'limited_visibility', 'contentious_content', 'heart', 'high_resolution'] | 1731168405 | | 36864003 | 36864003.avif | 3040 | 2880 | e | image/avif | 200735 | ['original', 'egami', 'hetero', 'teenage', 'mind_control', 'teenage_girl', 'hypnosis', 'clothing', 'tied_hair', 'nude', 'legwear', 'bangs', 'shirt', 'thighhighs', 'skirt', 'underwear', 'uniform', 'twintails', 'pantsu', 'school_uniform', 'black_legwear', 'white_shirt', 'short_sleeves', 'neckwear', 'pleated_skirt', 'pants', 'serafuku', 'belt', 'black_thighhighs', 'sailor_collar', 'nude_male', 'clothed_female', 'clothed_female_nude_male', 'neckerchief', 'female', 'breasts', 'long_hair', 'male', 'blush', 'short_hair', 'nipples', 'penis', 'black_hair', 'feet', 'erect_nipples', 'grey_eyes', 'open_mouth', 'clothes_pull', 'clothes_lift', 'skirt_pull', 'skirt_lift', 'pants_pull', 'sex', 'vaginal', 'taken_from_behind', 'orgasm', 'trembling', 'clothed_sex', 'female_orgasm', 'sweat', '1girl', '1boy', 'indoors', 'high_resolution', 'very_high_resolution'] | 1731168369 | | 36864002 | 36864002.png | 4967 | 3095 | q | image/png | 2600889 | ['sonic_the_hedgehog_(series)', 'sega', 'sonic_the_hedgehog_(character)', 'miles_prower', 'blaze_the_cat', 'dr._eggman', 'anthro', 'furry', 'clothing', 'tied_hair', 'hair_ornament', 'shirt', 'gloves', 'jewelry', 'ponytail', 'long_sleeves', 'eyewear', 'jacket', 'glasses', 'sleeveless', 'necklace', 'white_gloves', 'sunglasses', 'topwear', 'eyewear_on_head', 'goggles', 'red_shirt', 'one_eye_covered', 'handwear', 'red_jacket', 'goggles_on_head', 'mustache', 'hair_tie', 'purple_shirt', 'tinted_eyewear', 'topknot', 'female', 'long_hair', 'male', 'short_hair', 'blonde_hair', 'blue_eyes', 'black_hair', 'animal_ears', 'green_eyes', 'blue_hair', 'tail', 'purple_eyes', 'purple_hair', 'yellow_eyes', 'teeth', 'multicolored_hair', 'fur', 'hair', 'animal_tail', 'cat_ears', 'black_eyes', 'two-tone_hair', 'hair_over_one_eye', 'claws', 'clenched_teeth', 'white_fur', 'facial_hair', 'fox_ears', 'fox_tail', 'black_fur', 'body_fur', 'glowing_eyes', 'two-tone_fur', 'blue_fur', 'yellow_fur', 'animal_nose', 'wide-eyed', 'bald', 'constricted_pupils', 'open_mouth', 'looking_at_viewer', 'closed_mouth', 'half-closed_eyes', 'narrowed_eyes', 'surprised', 'transformation', 'scared', 'furry_female', 'furry_male', 'mammal', 'animal', 'canine', 'feline', 'felid', 'canis', 'fox', 'wolf', 'felis', 'domestic_cat', 'hedgehog', 'eulipotyphlan', 'cat_girl', 'fox_girl', 'fox_boy', 'gem', 'electricity', 'multiple_girls', '2girls', 'multiple_boys', 'group', '4boys', '5boys', 'accessory', 'blue-tinted_eyewear', 'chris_hayabusa', 'finimun', 'green_glow', 'mid_transformation', 'reaching_towards_viewer', 'simple_background', 'signature', 'gradient', 'gradient_background', 'useless_tags', 'glowing', 'abstract_background', 'alpha_channel', 'limited_visibility', 'high_resolution', 'very_high_resolution', '16:10_aspect_ratio'] | 1731168350 | | 36864001 | 36864001.jpg | 1396 | 1730 | e | image/jpeg | 213991 | ['anthro', 'clothing', 'tied_hair', 'ponytail', 'female', 'breasts', 'large_breasts', 'thighs', 'thick_thighs', 'open_mouth', 'robot', 'robot_girl', 'bell', '1girl', 'solo', 'jinx_doodle', 'open_hands', 'high_resolution', 'tagme'] | 1731168336 | | 36864000 | 36864000.jpg | 921 | 1300 | q | image/jpeg | 104589 | ['original', 'nakkar', 'clothing', 'tied_hair', 'legwear', 'hair_ornament', 'bangs', 'skirt', 'uniform', 'footwear', 'dress', 'twintails', 'long_sleeves', 'ribbon', 'school_uniform', 'bow', 'shoes', 'hair_ribbon', 'hair_bow', 'socks', 'white_legwear', 'frills', 'serafuku', 'puffy_sleeves', 'white_dress', 'sailor_collar', 'black_footwear', 'sleeves_past_wrists', 'white_socks', 'frilled_dress', 'pink_bow', 'blue_sailor_collar', 'puffy_long_sleeves', 'pink_ribbon', 'mary_janes', 'sailor_dress', 'grey_legwear', 'female', 'long_hair', 'blush', 'animal_ears', 'tail', 'purple_eyes', 'very_long_hair', 'ahoge', 'grey_hair', 'animal_tail', 'cat_ears', 'cat_tail', 'animal_ear_fluff', 'violet_eyes', 'looking_at_viewer', 'standing', 'closed_mouth', 'hand_up', 'standing_on_one_leg', 'running', 'cat_girl', '1girl', 'solo', ':<', 'escalator', 'grey_socks', 'full_body', 'username', 'blurry', 'watermark', 'twitter_username', 'useless_tags', 'blurry_background', 'motion_blur', 'speed_lines', 'photo_background', 'high_resolution', 'tagme'] | 1731168334 | ## Tags 1911041 tags in total. These are the top 30 tags (237 tags in total) of type `unknown (-1)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |-----:|:-----------------------------------|:-----------------------------------|:----------|:----------|-------:|:---------|--------:|--------------:| | -1 | ｎｉｇｈｔ | ｎｉｇｈｔ | | | -1 | | 26 | 0 | | -1 | workout | workout | | | -1 | | 22 | 0 | | -1 | š | š | | | -1 | | 20 | 0 | | -1 | ポケモン | ポケモン | | | -1 | | 20 | 0 | | -1 | ｆａｔｅ/ｓｔａｙ | ｆａｔｅ/ｓｔａｙ | | | -1 | | 20 | 0 | | -1 | ｘ・ｙ | ｘ・ｙ | | | -1 | | 20 | 0 | | -1 | _la_tour_d'horloge | _la_tour_d'horloge | | | -1 | | 19 | 0 | | -1 | tokeidai_no_jeanne:_jeanne_à | tokeidai_no_jeanne:_jeanne_à | | | -1 | | 19 | 0 | | -1 | ht-mimi | ht-mimi | | | -1 | | 18 | 0 | | -1 | ｓｃｈｏｏｌ | ｓｃｈｏｏｌ | | | -1 | | 17 | 0 | | -1 | プリズマ☆イリヤ | プリズマ☆イリヤ | | | -1 | | 16 | 0 | | -1 | ｆａｔｅ/ｋａｌｅｉｄ | ｆａｔｅ/ｋａｌｅｉｄ | | | -1 | | 16 | 0 | | -1 | ｌｉｎｅｒ | ｌｉｎｅｒ | | | -1 | | 16 | 0 | | -1 | ラブライブ！ | ラブライブ！ | | | -1 | | 14 | 0 | | -1 | ｉｄｏｌ | ｉｄｏｌ | | | -1 | | 14 | 0 | | -1 | ｐｒｏｊｅｃｔ | ｐｒｏｊｅｃｔ | | | -1 | | 14 | 0 | | -1 | プルルーå | プルルーå | | | -1 | | 11 | 0 | | -1 | é®®è¡€ç•ªé•·ï¼ | é®®è¡€ç•ªé•·ï¼ | | | -1 | | 11 | 0 | | -1 | 上柚木綾瀬 | 上柚木綾瀬 | | | -1 | | 11 | 0 | | -1 | ｌｏｖｅる | ｌｏｖｅる | | | -1 | | 11 | 0 | | -1 | ｔｏ | ｔｏ | | | -1 | | 11 | 0 | | -1 | ダイワスカーレット（ウマ娘）ウマ娘 | ダイワスカーレット（ウマ娘）ウマ娘 | | | -1 | | 10 | 0 | | -1 | 鮮血番長ï¼_プルルーå | 鮮血番長ï¼_プルルーå | | | -1 | | 9 | 0 | | -1 | -とらぶる- | -とらぶる- | | | -1 | | 8 | 0 | | -1 | フェラ | フェラ | | | -1 | | 7 | 0 | | -1 | ｉｎ-ｂｉｒｔｈ | ｉｎ-ｂｉｒｔｈ | | | -1 | | 6 | 0 | | -1 | ｕｎｄｅｒ | ｕｎｄｅｒ | | | -1 | | 6 | 0 | | -1 | －イリス－ | －イリス－ | | | -1 | | 5 | 0 | | -1 | ｓｏｆｔ | ｓｏｆｔ | | | -1 | | 5 | 0 | | -1 | 328ï¼ | 328ï¼ | | | -1 | | 4 | 0 | These are the top 30 tags (1124446 tags in total) of type `general (0)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |-------:|:------------------|:------------------|:------------------|:-------------|-------:|:---------|---------:|--------------:| | 34240 | female | Female | Female | 女性 | 0 | s | 12356484 | 12356484 | | 560 | clothing | clothing | clothing | 衣類 | 0 | q | 10628832 | 10628832 | | 464292 | high_resolution | High resolution | High resolution | 高解像度 | 0 | s | 9576405 | 9576405 | | 43623 | 1girl | 1girl | 1girl | 女性一人 | 0 | q | 8541226 | 8541226 | | 6878 | solo | solo | solo | 一人 | 0 | s | 7630765 | 7630765 | | 163 | breasts | breasts | breasts | 乳 | 0 | q | 6783614 | 6783614 | | 142 | long_hair | long hair | long hair | ロングヘア | 0 | q | 6554683 | 6554683 | | 156 | blush | Blush | Blush | 赤面 | 0 | q | 5074226 | 5074226 | | 2679 | male | male | male | 男性 | 0 | q | 5029960 | 5029960 | | 97161 | looking_at_viewer | Looking at viewer | Looking at viewer | カメラ目線 | 0 | s | 4703847 | 4703847 | | 820 | smile | Smile | Smile | 笑顔 | 0 | s | 4348542 | 4348542 | | 768 | short_hair | short hair | short hair | ショートヘア | 0 | s | 4057546 | 4057546 | | 1356 | open_mouth | Open mouth | Open mouth | 開口 | 0 | q | 3852067 | 3852067 | | 77229 | tied_hair | tied hair | tied hair | 結んだ髪 | 0 | q | 3560060 | 3560060 | | 32 | large_breasts | large breasts | large breasts | 巨乳 | 0 | q | 3219457 | 3219457 | | 454834 | blonde_hair | Blonde hair | Blonde hair | 金髪の毛 | 0 | q | 3158774 | 3158774 | | 371 | nipples | nipples | nipples | 乳首 | 0 | e | 3076796 | 3076796 | | 236202 | female_only | Female only | Female only | 女性のみ | 0 | s | 2964095 | 2964095 | | 424 | blue_eyes | blue eyes | blue eyes | 青い目 | 0 | q | 2941055 | 2941054 | | 3722 | hair_ornament | hair ornament | hair ornament | 髪飾り | 0 | s | 2661704 | 2661704 | | 143919 | legwear | Legwear | Legwear | 靴下類 | 0 | q | 2645090 | 2645087 | | 176064 | headwear | headwear | headwear | | 0 | s | 2636271 | 2636271 | | 414 | brown_hair | brown hair | brown hair | 茶髪 | 0 | q | 2585942 | 2585942 | | 310 | black_hair | Black hair | Black hair | 黒髪 | 0 | q | 2576526 | 2576526 | | 654 | bangs | Bangs | Bangs | 前髪 | 0 | s | 2492691 | 2492691 | | 165 | nude | Nude | Nude | 裸 | 0 | e | 2327371 | 2327371 | | 140037 | 1boy | 1boy | 1boy | 男一人 | 0 | e | 2241747 | 2241747 | | 127 | penis | penis | penis | ペニス | 0 | e | 2177521 | 2177521 | | 1123 | shirt | shirt | shirt | シャツ | 0 | s | 2148794 | 2148794 | | 278 | navel | navel | navel | おへそ | 0 | q | 2142828 | 2142828 | These are the top 30 tags (466894 tags in total) of type `artist (1)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |-------:|:----------------------|:----------------------|:----------------------|:----------------------------------|-------:|:---------|--------:|--------------:| | 853326 | pochincoff | Pochincoff | Pochincoff | ポチンコフ | 1 | e | 13606 | 13606 | | 6250 | yaegashi_nan | Yaegashi Nan | Yaegashi Nan | 八重樫南 | 1 | q | 8218 | 8218 | | 2305 | clamp | Clamp | Clamp | ＣＬＡＭＰ | 1 | s | 7914 | 7914 | | 36476 | sakimichan | Sakimichan | Sakimichan | 咲美ちゃん | 1 | q | 7683 | 7683 | | 96621 | annindoufu_(oicon) | Annindoufu (Oicon) | Annindoufu (Oicon) | 杏仁豆腐（ＯＩ☆ＣＯＮ） | 1 | s | 7666 | 7666 | | 580836 | kagami_(lilith-soft) | Kagami (Lilith-Soft) | Kagami (Lilith-Soft) | 嘉臥深（カガミ）_（リリスソフト） | 1 | e | 7202 | 7202 | | 870474 | twistedscarlett60 | Twistedscarlett60 | Twistedscarlett60 | | 1 | e | 7121 | 7121 | | 31565 | drawfag | Drawfag | Drawfag | | 1 | q | 7047 | 7047 | | 627253 | barazoku | Barazoku | Barazoku | | 1 | e | 6973 | 6973 | | 2649 | kantoku | Kantoku | Kantoku | カントク | 1 | s | 6699 | 6699 | | 556881 | boris_(noborhys) | Boris (Noborhys) | Boris (Noborhys) | | 1 | e | 6675 | 6675 | | 7355 | palcomix | Palcomix | Palcomix | | 1 | e | 6567 | 6567 | | 57 | tony | Tony | Tony | Ｔｏｎｙ | 1 | q | 6470 | 6470 | | 613034 | neocoill | Neocoill | Neocoill | | 1 | e | 6225 | 6225 | | 841522 | dandon_fuga | Dandon Fuga | Dandon Fuga | | 1 | e | 6131 | 6131 | | 122866 | ebifurya | Ebifurya | Ebifurya | えびふりゃ | 1 | s | 6015 | 6015 | | 115960 | huracan | Huracan | Huracan | | 1 | e | 5906 | 5906 | | 2482 | carnelian | Carnelian | Carnelian | ＣＡＲＮＥＬＩＡＮ | 1 | s | 5787 | 5787 | | 549062 | haruyama_kazunori | Haruyama Kazunori | Haruyama Kazunori | 春山和則 | 1 | q | 5427 | 5427 | | 900381 | merunyaa | Merunyaa | Merunyaa | | 1 | q | 5233 | 5233 | | 986 | noizi_ito | Noizi Ito | Noizi Ito | いとうのいぢ | 1 | s | 5151 | 5151 | | 101820 | personalami | Personalami | Personalami | | 1 | e | 5041 | 5041 | | 226611 | nel-zel_formula | Nel-Zel Formula | Nel-Zel Formula | | 1 | e | 4970 | 4970 | | 913753 | aoi_nagisa_(metalder) | Aoi Nagisa (Metalder) | Aoi Nagisa (Metalder) | 葵渚 | 1 | e | 4876 | 4876 | | 212158 | lm_(legoman) | Lm (Legoman) | Lm (Legoman) | | 1 | e | 4767 | 4767 | | 39484 | bokuman | Bokuman | Bokuman | ボクマン | 1 | q | 4685 | 4685 | | 144406 | slugbox | Slugbox | Slugbox | | 1 | q | 4685 | 4685 | | 212936 | matsunaga_kouyou | Matsunaga Kouyou | Matsunaga Kouyou | 松永紅葉 | 1 | q | 4558 | 4558 | | 53603 | tsunako | Tsunako | Tsunako | つなこ | 1 | s | 4549 | 4549 | | 125740 | sparrow_(artist) | Sparrow (artist) | Sparrow (artist) | | 1 | e | 4527 | 4527 | These are the top 30 tags (8741 tags in total) of type `studio (2)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |--------:|:-----------------------|:-----------------------|:-----------------------|:-------------------------------|-------:|:---------|--------:|--------------:| | 4537 | nintendo | Nintendo | Nintendo | 任天堂 | 2 | q | 699202 | 699202 | | 214348 | game_freak | Game Freak | Game Freak | ゲームフリーク | 2 | q | 173050 | 173050 | | 1325604 | creatures_inc. | Creatures Inc. | Creatures Inc. | | 2 | q | 161288 | 161288 | | 161870 | hasbro | Hasbro | Hasbro | | 2 | e | 134553 | 134553 | | 7314 | disney | disney | disney | ディズニー | 2 | e | 107845 | 107845 | | 2258 | capcom | Capcom | Capcom | カプコン | 2 | q | 63695 | 63695 | | 18059 | dc_comics | Dc Comics | Dc Comics | ＤＣコミック | 2 | e | 59567 | 59567 | | 4874 | sega | Sega | Sega | セガ | 2 | e | 51380 | 51380 | | 624520 | blizzard_entertainment | Blizzard Entertainment | Blizzard Entertainment | ブリザードエンターテインメント | 2 | e | 39089 | 39089 | | 22263 | cartoon_network | Cartoon Network | Cartoon Network | | 2 | e | 38163 | 38163 | | 86536 | marvel_comics | Marvel Comics | Marvel Comics | マーベル・コミック | 2 | q | 34658 | 34658 | | 69872 | nickelodeon | Nickelodeon | Nickelodeon | ニッケルオデオン | 2 | e | 32588 | 32588 | | 81970 | deviantart | Deviantart | Deviantart | デヴィアントアート | 2 | s | 29668 | 29668 | | 11044 | square_enix | Square Enix | Square Enix | スクウェア・エニックス | 2 | q | 27690 | 27690 | | 155460 | namco_bandai | Namco Bandai | Namco Bandai | | 2 | e | 22512 | 22512 | | 569386 | shueisha | Shueisha | Shueisha | 集英社 | 2 | e | 20818 | 20818 | | 17018 | shounen_jump | Shounen Jump | Shounen Jump | 少年ジャンプ | 2 | e | 20383 | 20383 | | 2467 | snk | Snk | Snk | ＳＮＫ | 2 | q | 18769 | 18769 | | 2638 | konami | Konami | Konami | コナミ | 2 | s | 17118 | 17118 | | 6495 | lilith-soft | Lilith-soft | Lilith-soft | リリスソフト | 2 | e | 14161 | 14161 | | 18346 | qp:flapper | Qp:Flapper | Qp:Flapper | ＱＰ：ｆｌａｐｐｅｒ | 2 | s | 13164 | 13164 | | 1655458 | project_moon | Project Moon | Project Moon | | 2 | s | 11611 | 11611 | | 55177 | idea_factory | Idea Factory | Idea Factory | アイディアファクトリー | 2 | s | 9680 | 9680 | | 149297 | hanna-barbera | Hanna-barbera | Hanna-barbera | | 2 | e | 8865 | 8865 | | 4172 | atlus | Atlus | Atlus | アトラス | 2 | e | 8841 | 8841 | | 458136 | warner_brothers | Warner Brothers | Warner Brothers | | 2 | e | 8798 | 8798 | | 2518 | nitroplus | Nitroplus | Nitroplus | ニトロプラス | 2 | q | 8416 | 8416 | | 1652511 | scottgames | Scottgames | Scottgames | | 2 | e | 8320 | 8320 | | 459605 | naruto_pixxx | Naruto Pixxx | Naruto Pixxx | | 2 | e | 8289 | 8289 | | 221337 | activision | Activision | Activision | | 2 | e | 8265 | 8265 | These are the top 30 tags (48621 tags in total) of type `copyright (3)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |--------:|:------------------------------------|:------------------------------------|:------------------------------------|:-------------------------------------|-------:|:---------|--------:|--------------:| | 1397 | original | Original | Original | オリジナル | 3 | e | 1672354 | 1672354 | | 83524 | touhou_project | Touhou Project | Touhou Project | 東方Ｐｒｏｊｅｃｔ | 3 | q | 848956 | 848956 | | 1384 | pokemon | Pokemon | Pokemon | ポケモン | 3 | q | 671784 | 671784 | | 175211 | fate_(series) | Fate (Series) | Fate (Series) | フェイト・シリーズ | 3 | q | 495365 | 495365 | | 458437 | kantai_collection | Kantai Collection | Kantai Collection | 艦隊これくしょん | 3 | q | 492851 | 492851 | | 3735 | pixiv | Pixiv | Pixiv | ピクシブ | 3 | s | 472601 | 472601 | | 682608 | fate/grand_order | Fate/grand Order | Fate/grand Order | Ｆａｔｅ／Ｇｒａｎｄ　Ｏｒｄｅｒ | 3 | q | 382658 | 382658 | | 91872 | pokemon_(game) | Pokemon (game) | Pokemon (game) | ポケモン（ゲーム） | 3 | q | 326892 | 326892 | | 1268449 | genshin_impact | Genshin Impact | Genshin Impact | 原神 | 3 | q | 294313 | 294312 | | 912 | idolmaster | Idolmaster | Idolmaster | アイドルマスター | 3 | e | 275773 | 275773 | | 571 | vocaloid | Vocaloid | Vocaloid | ボーカロイド | 3 | s | 271455 | 271455 | | 1129497 | hololive | Hololive | Hololive | ホロライブ | 3 | s | 232651 | 232651 | | 1425396 | blue_archive | Blue Archive | Blue Archive | ブルーアーカイブ | 3 | q | 193053 | 193053 | | 1023439 | azur_lane | Azur Lane | Azur Lane | アズールレーン | 3 | q | 176441 | 176441 | | 882999 | love_live!_(series) | Love Live! (Series) | Love Live! (Series) | ラブライブ！（シリーズ） | 3 | q | 170692 | 170692 | | 60150 | my_little_pony | My Little Pony | My Little Pony | マイリトルポニー | 3 | e | 169298 | 169298 | | 701 | naruto | Naruto | Naruto | ナルト | 3 | e | 163842 | 163842 | | 1031332 | arknights | Arknights | Arknights | アークナイツ | 3 | s | 156971 | 156971 | | 172796 | idolmaster_cinderella_girls | Idolmaster Cinderella Girls | Idolmaster Cinderella Girls | アイドルマスター　シンデレラガールズ | 3 | s | 151896 | 151896 | | 7479 | fire_emblem | Fire Emblem | Fire Emblem | ファイアーエムブレム | 3 | q | 146170 | 146170 | | 2240 | final_fantasy | Final Fantasy | Final Fantasy | ファイナルファンタジー | 3 | e | 141470 | 141470 | | 151035 | my_little_pony:_friendship_is_magic | My Little Pony: Friendship Is Magic | My Little Pony: Friendship Is Magic | マイリトルポニー〜トモダチは魔法〜 | 3 | e | 137685 | 137685 | | 4047 | precure | Precure | Precure | プリキュア | 3 | q | 130664 | 130664 | | 24606 | twitter | Twitter | Twitter | ツイッター | 3 | s | 109812 | 109810 | | 1752 | one_piece | One Piece | One Piece | ワンピース | 3 | q | 109617 | 109617 | | 779230 | my_hero_academia | My Hero Academia | My Hero Academia | 僕のヒーローアカデミア | 3 | q | 103009 | 103009 | | 42841 | yu-gi-oh! | Yu-gi-oh! | Yu-gi-oh! | 遊戯王！ | 3 | q | 102995 | 102995 | | 930765 | uma_musume_-_pretty_derby | Uma Musume - Pretty Derby | Uma Musume - Pretty Derby | ウマ娘　プリティーダービー | 3 | s | 98554 | 98554 | | 330716 | hetalia:_axis_powers | Hetalia: Axis Powers | Hetalia: Axis Powers | Ａｘｉｓ　ｐｏｗｅｒｓ　ヘタリア | 3 | s | 94211 | 94211 | | 125008 | mahou_shoujo_madoka_magica | Mahou Shoujo Madoka Magica | Mahou Shoujo Madoka Magica | 魔法少女まどか☆マギカ | 3 | q | 94126 | 94126 | These are the top 30 tags (258367 tags in total) of type `character (4)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |--------:|:-------------------------------|:-------------------------------|:-------------------------------|:---------------------------|-------:|:---------|--------:|--------------:| | 460935 | pokemon_character | Pokemon Character | Pokemon Character | ポケモンキャラクター | 4 | q | 262532 | 262532 | | 3155 | original_character | Original Character | Original Character | オリジナルキャラクター | 4 | e | 197618 | 197618 | | 138 | hatsune_miku | Hatsune Miku | Hatsune Miku | 初音ミク | 4 | s | 156356 | 156356 | | 1094140 | gen_1_pokemon | Gen 1 Pokemon | Gen 1 Pokemon | 第一世代のポケモン | 4 | q | 79689 | 79689 | | 534 | hakurei_reimu | Hakurei Reimu | Hakurei Reimu | 博麗霊夢 | 4 | s | 70055 | 70055 | | 1127 | kirisame_marisa | Kirisame Marisa | Kirisame Marisa | 霧雨魔理沙 | 4 | s | 60175 | 60175 | | 85949 | fan_character | Fan Character | Fan Character | | 4 | e | 57906 | 57906 | | 857685 | artoria_pendragon | Artoria Pendragon | Artoria Pendragon | アルトリア・ペンドラゴン | 4 | s | 54607 | 54607 | | 1144 | flandre_scarlet | Flandre Scarlet | Flandre Scarlet | フランドール・スカーレット | 4 | s | 49773 | 49773 | | 536883 | allied_forces_(hetalia) | Allied Forces (Hetalia) | Allied Forces (Hetalia) | 連合国（ヘタリア） | 4 | s | 49382 | 49382 | | 1095014 | gen_4_pokemon | Gen 4 Pokemon | Gen 4 Pokemon | | 4 | q | 48694 | 48694 | | 1143 | remilia_scarlet | Remilia Scarlet | Remilia Scarlet | レミリア・スカーレット | 4 | s | 47056 | 47056 | | 536881 | axis_power_countries_(hetalia) | Axis Power Countries (Hetalia) | Axis Power Countries (Hetalia) | 枢軸国（ヘタリア） | 4 | s | 45399 | 45399 | | 780805 | remodel_(kantai_collection) | Remodel (Kantai Collection) | Remodel (Kantai Collection) | 改造後（艦これ） | 4 | s | 45240 | 45240 | | 1094473 | gen_3_pokemon | Gen 3 Pokemon | Gen 3 Pokemon | | 4 | q | 44347 | 44347 | | 574 | kagamine_rin | Kagamine Rin | Kagamine Rin | 鏡音リン | 4 | s | 43819 | 43819 | | 6959 | uzumaki_naruto | Uzumaki Naruto | Uzumaki Naruto | うずまきナルト | 4 | q | 41217 | 41217 | | 1142 | izayoi_sakuya | Izayoi Sakuya | Izayoi Sakuya | 十六夜咲夜 | 4 | s | 40157 | 40157 | | 220249 | legendary_pokemon | Legendary Pokemon | Legendary Pokemon | 伝説のポケモン | 4 | q | 39295 | 39295 | | 1192124 | digimon_(species) | Digimon (Species) | Digimon (Species) | | 4 | e | 36493 | 36493 | | 1094161 | gen_2_pokemon | Gen 2 Pokemon | Gen 2 Pokemon | | 4 | q | 36092 | 36092 | | 1243 | kagamine_len | Kagamine Len | Kagamine Len | 鏡音レン | 4 | s | 35982 | 35982 | | 1128 | konpaku_youmu | Konpaku Youmu | Konpaku Youmu | 魂魄妖夢 | 4 | s | 32943 | 32943 | | 183 | cirno | Cirno | Cirno | チルノ | 4 | s | 32545 | 32545 | | 1095028 | gen_5_pokemon | Gen 5 Pokemon | Gen 5 Pokemon | | 4 | q | 32295 | 32295 | | 125006 | akemi_homura | Akemi Homura | Akemi Homura | 暁美ほむら | 4 | s | 31842 | 31842 | | 1130 | komeiji_koishi | Komeiji Koishi | Komeiji Koishi | 古明地こいし | 4 | s | 31628 | 31628 | | 1159 | alice_margatroid | Alice Margatroid | Alice Margatroid | アリス・マーガトロイド | 4 | s | 31500 | 31500 | | 125007 | kaname_madoka | Kaname Madoka | Kaname Madoka | 鹿目まどか | 4 | s | 31451 | 31451 | | 799 | patchouli_knowledge | Patchouli Knowledge | Patchouli Knowledge | パチュリー・ノーレッジ | 4 | s | 31315 | 31315 | These are the top 30 tags (304 tags in total) of type `genre (5)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |-------:|:-----------------|:-----------------|:-----------------|:---------------------------|-------:|:---------|--------:|--------------:| | 40895 | anthro | Anthro | Anthro | 人外 | 5 | e | 1524129 | 1524129 | | 192913 | hetero | Hetero | Hetero | 異性愛 | 5 | e | 1285790 | 1285790 | | 2 | loli | Loli | Loli | ロリ | 5 | e | 809785 | 809785 | | 4622 | furry | Furry | Furry | ケモノ | 5 | e | 667189 | 667188 | | 1583 | yaoi | Yaoi | Yaoi | やおい | 5 | e | 444276 | 444276 | | 971778 | virtual_youtuber | Virtual youtuber | Virtual youtuber | バーチャルＹｏｕＴｕｂｅｒ | 5 | s | 388766 | 388766 | | 209 | yuri | Yuri | Yuri | 百合 | 5 | q | 386622 | 386622 | | 284125 | intersex | Intersex | Intersex | インターセックス | 5 | e | 374253 | 374253 | | 934 | bdsm | BDSM | BDSM | ＢＤＳＭ | 5 | e | 336149 | 336149 | | 147749 | interspecies | Interspecies | Interspecies | 異種姦 | 5 | e | 299230 | 299230 | | 7 | chibi | Chibi | Chibi | ちび | 5 | s | 278718 | 278718 | | 30953 | age_difference | Age difference | Age difference | 年齢差 | 5 | e | 250599 | 250599 | | 2891 | group_sex | Group sex | Group sex | グループセックス | 5 | e | 224882 | 224882 | | 13 | shota | Shota | Shota | ショタ | 5 | e | 213052 | 213052 | | 4868 | mature | Mature | Mature | 成熟した | 5 | e | 184767 | 184767 | | 388960 | scalie | Scalie | Scalie | | 5 | e | 179492 | 179492 | | 459708 | violation | Violation | Violation | | 5 | e | 173413 | 173413 | | 568 | cosplay | Cosplay | Cosplay | コスプレ | 5 | s | 172355 | 172355 | | 236 | crossover | Crossover | Crossover | クロスオーバー | 5 | s | 161175 | 161175 | | 1419 | trap | Trap | Trap | 男の娘 | 5 | q | 160800 | 160800 | | 7188 | interracial | Interracial | Interracial | 異人種間 | 5 | e | 155412 | 155412 | | 921 | milf | MILF | MILF | ＭＩＬＦ | 5 | e | 154152 | 154152 | | 2767 | androgynous | Androgynous | Androgynous | 中性的 | 5 | q | 154037 | 154037 | | 1177 | genderswap | Genderswap | Genderswap | 性転換 | 5 | q | 150004 | 150004 | | 745 | tentacles | Tentacles | Tentacles | 触手 | 5 | e | 138450 | 138450 | | 125 | crossdressing | Crossdressing | Crossdressing | 女装・男装 | 5 | q | 137427 | 137427 | | 1092 | threesome | Threesome | Threesome | ３Ｐ | 5 | e | 116348 | 116348 | | 7295 | bara | Bara | Bara | 薔薇（ジャンル） | 5 | e | 103015 | 103015 | | 3386 | femdom | Femdom | Femdom | フェムドム | 5 | e | 96819 | 96819 | | 11841 | zoophilia | Zoophilia | Zoophilia | | 5 | e | 95126 | 95126 | These are the top 30 tags (2006 tags in total) of type `medium (8)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |--------:|:------------------------|:------------------------|:------------------------|:-----------------------|-------:|:---------|--------:|--------------:| | 610607 | very_high_resolution | Very high resolution | Very high resolution | 非常に高い解像度 | 8 | s | 3278554 | 3278554 | | 7470 | simple_background | Simple background | Simple background | シンプル背景 | 8 | s | 3113846 | 3113846 | | 116956 | white_background | White background | White background | 白い背景 | 8 | s | 1843645 | 1843645 | | 379 | censored | Censored | Censored | 修正あり | 8 | e | 1100315 | 1100315 | | 66315 | large_filesize | Large filesize | Large filesize | 大きいファイルサイズ | 8 | s | 1037075 | 1037075 | | 610654 | 1:1_aspect_ratio | 1:1 aspect ratio | 1:1 aspect ratio | １：１アスペクト比 | 8 | s | 967672 | 967671 | | 126842 | full_body | Full body | Full body | 全身 | 8 | s | 909655 | 909654 | | 486 | uncensored | Uncensored | Uncensored | 無修正 | 8 | e | 857549 | 857549 | | 110 | monochrome | Monochrome | Monochrome | 白黒 | 8 | s | 783897 | 783897 | | 610648 | 4:3_aspect_ratio | 4:3 aspect ratio | 4:3 aspect ratio | ４：３アスペクト比 | 8 | e | 708260 | 708260 | | 13280 | official_art | Official art | Official art | 公式アート | 8 | s | 660664 | 660664 | | 175652 | cowboy_shot | Cowboy shot | Cowboy shot | カウボーイ・ショット | 8 | s | 575436 | 575436 | | 159929 | artist_name | Artist name | Artist name | アーティスト名前 | 8 | s | 572668 | 572668 | | 610649 | 16:9_aspect_ratio | 16:9 aspect ratio | 16:9 aspect ratio | １６：９アスペクト比 | 8 | s | 554346 | 554346 | | 3007 | comic | Comic | Comic | コミック | 8 | s | 546482 | 546482 | | 27996 | photoshop_(medium) | Photoshop (medium) | Photoshop (medium) | | 8 | s | 506622 | 506622 | | 580618 | useless_tags | Useless tags | Useless tags | 役に立たないタグ | 8 | e | 506342 | 506342 | | 679 | speech_bubble | Speech bubble | Speech bubble | 吹き出し | 8 | q | 410167 | 410167 | | 1820 | signature | Signature | Signature | 署名 | 8 | s | 404693 | 404693 | | 768090 | digital_media_(artwork) | Digital media (artwork) | Digital media (artwork) | | 8 | e | 390631 | 390631 | | 253759 | cg_art | Cg art | Cg art | ＣＧアート | 8 | s | 362765 | 362765 | | 1198 | 3d | 3d | 3d | ３ＤＣＧ | 8 | e | 356004 | 356004 | | 4480 | greyscale | Greyscale | Greyscale | グレースケール | 8 | q | 353593 | 353593 | | 2034518 | ai-created | AI-created | AI-created | | 8 | s | 341083 | 341083 | | 191661 | mosaic_censoring | Mosaic censoring | Mosaic censoring | モザイク修正 | 8 | e | 331188 | 331188 | | 165358 | grey_background | Grey background | Grey background | グレーバックグラウンド | 8 | s | 309907 | 309907 | | 6138 | pov | Pov | Pov | 一人称視点 | 8 | s | 292638 | 292637 | | 935844 | username | Username | Username | | 8 | q | 280402 | 280402 | | 5666 | sketch | Sketch | Sketch | スケッチ | 8 | q | 269032 | 269032 | | 289 | wallpaper | Wallpaper | Wallpaper | 壁紙 | 8 | s | 268578 | 268578 | These are the top 30 tags (1425 tags in total) of type `meta (9)`: | id | tag | name | name_en | name_ja | type | rating | count | total_count | |--------:|:----------------------------|:----------------------------|:----------------------------|:-------------------------|-------:|:---------|--------:|--------------:| | 2230072 | limited_visibility | Limited Visibility | Limited Visibility | | 9 | e | 2241239 | 2241239 | | 1093096 | contentious_content | Contentious Content | Contentious Content | 不和なコンテンツ | 9 | e | 1028468 | 1028468 | | 56191 | extreme_content | Extreme Content | Extreme Content | 過激なコンテンツ | 9 | e | 911763 | 911762 | | 316134 | potential_duplicate | Potential duplicate | Potential duplicate | 重複可能性あり | 9 | s | 469106 | 469106 | | 2678 | translation_request | Translation request | Translation request | 翻訳希望 | 9 | q | 228836 | 228836 | | 1112475 | third-party_edit | Third-party edit | Third-party edit | コラ・加工 | 9 | s | 176686 | 176686 | | 98093 | commission | Commission | Commission | 有料イラリク | 9 | s | 133751 | 133751 | | 976119 | png-to-jpg_conversion | Png-to-jpg conversion | Png-to-jpg conversion | ｐｎｇからｊｐｇへの変換 | 9 | s | 113581 | 113581 | | 7672 | artist_request | Artist request | Artist request | 絵師詳細希望 | 9 | q | 79599 | 79599 | | 320 | translated | Translated | Translated | 翻訳済み | 9 | q | 73221 | 73221 | | 958415 | bad_twitter_id | Bad twitter id | Bad twitter id | 不正なツイッターid | 9 | s | 67535 | 67535 | | 165874 | revision | Revision | Revision | 修正版 | 9 | s | 60351 | 60351 | | 5803 | duplicate | Duplicate | Duplicate | 重複 | 9 | s | 57024 | 57024 | | 107370 | md5_mismatch | Md5 mismatch | Md5 mismatch | | 9 | s | 55655 | 55655 | | 313595 | colorization | Colorization | Colorization | 着色 | 9 | q | 45983 | 45983 | | 1421384 | skeb_commission | Skeb commission | Skeb commission | | 9 | s | 40724 | 40724 | | 21283 | character_request | Character request | Character request | キャラクター希望 | 9 | q | 35337 | 35337 | | 10305 | spoilers | Spoilers | Spoilers | ネタバレ | 9 | s | 31955 | 31955 | | 566512 | borrowed_character | Borrowed character | Borrowed character | | 9 | q | 29418 | 29418 | | 1484107 | paid_reward_available | Paid reward available | Paid reward available | | 9 | q | 29201 | 29201 | | 162467 | pleated | Pleated | Pleated | | 9 | s | 26924 | 26924 | | 77304 | decensored | Decensored | Decensored | 無修正化 | 9 | e | 24165 | 24165 | | 56184 | upscaled | Upscaled | Upscaled | | 9 | q | 24027 | 24027 | | 808206 | alternate_version_available | Alternate version available | Alternate version available | | 9 | e | 21774 | 21774 | | 7059 | color | Color | Color | カラー | 9 | e | 19760 | 19760 | | 661 | blue | Blue | Blue | 青い | 9 | s | 18900 | 18900 | | 359 | eyes | Eyes | Eyes | 目 | 9 | q | 18369 | 18369 | | 1388906 | resolution_mismatch | Resolution mismatch | Resolution mismatch | | 9 | s | 15311 | 15311 | | 107058 | copyright_request | Copyright request | Copyright request | 著作権希望 | 9 | q | 15033 | 15033 | | 4199 | rubber | Rubber | Rubber | | 9 | e | 14554 | 14554 |

TempoFunk/webvid-10M

TempoFunk

--- license: agpl-3.0 task_categories: - text-to-video - text-to-image - video-classification - image-classification language: - en size_categories: - 1M<n<10M ---

AmazonScience/massive

AmazonScience

--- annotations_creators: - expert-generated language_creators: - found license: - cc-by-4.0 multilinguality: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - ca-ES - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - intent-classification - multi-class-classification paperswithcode_id: massive pretty_name: MASSIVE language_bcp47: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - ca-ES - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW tags: - natural-language-understanding --- # MASSIVE 1.1: A 1M-Example Multilingual Natural Language Understanding Dataset with 52 Typologically-Diverse Languages ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [No Warranty](#no-warranty) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://github.com/alexa/massive - **Repository:** https://github.com/alexa/massive - **Paper:** https://arxiv.org/abs/2204.08582 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview - **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues) ### Dataset Summary MASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. | Name | Lang | Utt/Lang | Domains | Intents | Slots | |:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:| | MASSIVE 1.1 | 52 | 19,521 | 18 | 60 | 55 | | SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 | | NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 | | Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 | | ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 | | MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 | | Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 | | Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 | | Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 | | Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 | | Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 | | Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 | | Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - | | Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 | ### Supported Tasks and Leaderboards The dataset can be used to train a model for `natural-language-understanding` (NLU) : - `intent-classification` - `multi-class-classification` - `natural-language-understanding` ### Languages The MASSIVE 1.1 corpora consists of parallel sentences from 52 languages : - `Afrikaans - South Africa (af-ZA)` - `Amharic - Ethiopia (am-ET)` - `Arabic - Saudi Arabia (ar-SA)` - `Azeri - Azerbaijan (az-AZ)` - `Bengali - Bangladesh (bn-BD)` - `Catalan - Spain (ca-ES)` - `Chinese - China (zh-CN)` - `Chinese - Taiwan (zh-TW)` - `Danish - Denmark (da-DK)` - `German - Germany (de-DE)` - `Greek - Greece (el-GR)` - `English - United States (en-US)` - `Spanish - Spain (es-ES)` - `Farsi - Iran (fa-IR)` - `Finnish - Finland (fi-FI)` - `French - France (fr-FR)` - `Hebrew - Israel (he-IL)` - `Hungarian - Hungary (hu-HU)` - `Armenian - Armenia (hy-AM)` - `Indonesian - Indonesia (id-ID)` - `Icelandic - Iceland (is-IS)` - `Italian - Italy (it-IT)` - `Japanese - Japan (ja-JP)` - `Javanese - Indonesia (jv-ID)` - `Georgian - Georgia (ka-GE)` - `Khmer - Cambodia (km-KH)` - `Korean - Korea (ko-KR)` - `Latvian - Latvia (lv-LV)` - `Mongolian - Mongolia (mn-MN)` - `Malay - Malaysia (ms-MY)` - `Burmese - Myanmar (my-MM)` - `Norwegian - Norway (nb-NO)` - `Dutch - Netherlands (nl-NL)` - `Polish - Poland (pl-PL)` - `Portuguese - Portugal (pt-PT)` - `Romanian - Romania (ro-RO)` - `Russian - Russia (ru-RU)` - `Slovanian - Slovania (sl-SL)` - `Albanian - Albania (sq-AL)` - `Swedish - Sweden (sv-SE)` - `Swahili - Kenya (sw-KE)` - `Hindi - India (hi-IN)` - `Kannada - India (kn-IN)` - `Malayalam - India (ml-IN)` - `Tamil - India (ta-IN)` - `Telugu - India (te-IN)` - `Thai - Thailand (th-TH)` - `Tagalog - Philippines (tl-PH)` - `Turkish - Turkey (tr-TR)` - `Urdu - Pakistan (ur-PK)` - `Vietnamese - Vietnam (vi-VN)` - `Welsh - United Kingdom (cy-GB)` ## Load the dataset with HuggingFace ```python from datasets import load_dataset dataset = load_dataset("AmazonScience/massive", "en-US", split='train') print(dataset[0]) ``` ## Dataset Structure ### Data Instances ```json { "id": "0", "locale": "fr-FR", "partition": "test", "scenario": "alarm", "intent": "alarm_set", "utt": "réveille-moi à cinq heures du matin cette semaine", "annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]", "worker_id": "22", "slot_method": [ { "slot": "time", "method": "translation" }, { "slot": "date", "method": "translation" } ], "judgments": [ { "worker_id": "22", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" }, { "worker_id": "8", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" }, { "worker_id": "0", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" } ] } ``` ### Data Fields `id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization. `locale`: is the language and country code accoring to ISO-639-1 and ISO-3166. `partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp). `scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance `intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}` `utt`: the raw utterance text without annotations `annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]` `worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales. `slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification). `judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker. ```plain intent_score : "Does the sentence match the intent?" 0: No 1: Yes 2: It is a reasonable interpretation of the goal slots_score : "Do all these terms match the categories in square brackets?" 0: No 1: Yes 2: There are no words in square brackets (utterance without a slot) grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?" 0: Completely unnatural (nonsensical, cannot be understood at all) 1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language) 2: Some errors (the meaning can be understood but it doesn't sound natural in your language) 3: Good enough (easily understood and sounds almost natural in your language) 4: Perfect (sounds natural in your language) spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error." 0: There are more than 2 spelling errors 1: There are 1-2 spelling errors 2: All words are spelled correctly language_identification : "The following sentence contains words in the following languages (check all that apply)" 1: target 2: english 3: other 4: target & english 5: target & other 6: english & other 7: target & english & other ``` ### Data Splits |Language|Train|Dev|Test| |:---:|:---:|:---:|:---:| |af-ZA|11514|2033|2974| |am-ET|11514|2033|2974| |ar-SA|11514|2033|2974| |az-AZ|11514|2033|2974| |bn-BD|11514|2033|2974| |ca-ES|11514|2033|2974| |cy-GB|11514|2033|2974| |da-DK|11514|2033|2974| |de-DE|11514|2033|2974| |el-GR|11514|2033|2974| |en-US|11514|2033|2974| |es-ES|11514|2033|2974| |fa-IR|11514|2033|2974| |fi-FI|11514|2033|2974| |fr-FR|11514|2033|2974| |he-IL|11514|2033|2974| |hi-IN|11514|2033|2974| |hu-HU|11514|2033|2974| |hy-AM|11514|2033|2974| |id-ID|11514|2033|2974| |is-IS|11514|2033|2974| |it-IT|11514|2033|2974| |ja-JP|11514|2033|2974| |jv-ID|11514|2033|2974| |ka-GE|11514|2033|2974| |km-KH|11514|2033|2974| |kn-IN|11514|2033|2974| |ko-KR|11514|2033|2974| |lv-LV|11514|2033|2974| |ml-IN|11514|2033|2974| |mn-MN|11514|2033|2974| |ms-MY|11514|2033|2974| |my-MM|11514|2033|2974| |nb-NO|11514|2033|2974| |nl-NL|11514|2033|2974| |pl-PL|11514|2033|2974| |pt-PT|11514|2033|2974| |ro-RO|11514|2033|2974| |ru-RU|11514|2033|2974| |sl-SL|11514|2033|2974| |sq-AL|11514|2033|2974| |sv-SE|11514|2033|2974| |sw-KE|11514|2033|2974| |ta-IN|11514|2033|2974| |te-IN|11514|2033|2974| |th-TH|11514|2033|2974| |tl-PH|11514|2033|2974| |tr-TR|11514|2033|2974| |ur-PK|11514|2033|2974| |vi-VN|11514|2033|2974| |zh-CN|11514|2033|2974| |zh-TW|11514|2033|2974| ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Additional Information ### Dataset Curators __MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan. __SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena. __Hugging Face Upload and Integration__: Labrak Yanis (Not affiliated with the original corpus) ### Licensing Information ```plain Copyright Amazon.com Inc. or its affiliates. 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No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. d. Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. ======================================================================= Creative Commons is not a party to its public licenses. Notwithstanding, Creative Commons may elect to apply one of its public licenses to material it publishes and in those instances will be considered the “Licensor.” The text of the Creative Commons public licenses is dedicated to the public domain under the CC0 Public Domain Dedication. 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Creative Commons may be contacted at creativecommons.org. ``` ### Citation Information Please cite the following papers when using this dataset. ```latex @misc{fitzgerald2022massive, title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, year={2022}, eprint={2204.08582}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{bastianelli-etal-2020-slurp, title = "{SLURP}: A Spoken Language Understanding Resource Package", author = "Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.588", doi = "10.18653/v1/2020.emnlp-main.588", pages = "7252--7262", abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." } ```

HuggingFaceM4/WebSight

HuggingFaceM4

--- language: - en license: cc-by-4.0 size_categories: - 1M<n<10M pretty_name: WebSight dataset_info: - config_name: v0.2 features: - name: image dtype: image - name: text dtype: string - name: llm_generated_idea dtype: string splits: - name: train num_bytes: 368943620718.125 num_examples: 1922671 download_size: 144861710051 dataset_size: 368943620718.125 - config_name: v0.1 features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 35386660486.65 num_examples: 822987 download_size: 31394170440 dataset_size: 35386660486.65 configs: - config_name: v0.2 default: true data_files: - split: train path: v0.2/train-* - config_name: v0.1 data_files: - split: train path: data/train-* tags: - code - synthetic --- # Dataset Card for WebSight ## Dataset Description WebSight is a large synthetic dataset containing HTML/CSS codes representing synthetically generated English websites, each accompanied by a corresponding screenshot. This dataset serves as a valuable resource for tasks such as generating UI codes from a screenshot. It comes in two versions: - v0.1: Websites are coded with HTML + CSS. They do not include real images. - v0.2: Websites are coded with HTML + Tailwind CSS. They do include real images. Essentially, here are the improvements in version v0.2, compared to version v0.1: - Websites include real images (related to the context of the website!!) - Usage of Tailwind CSS instead of traditional CSS - Contains 2x more examples - Contains more tables - Better resolution for the screenshots - Presence of a column indicating the LLM-generated idea to create the websites <details> <summary>Details for WebSight-v0.1 (HTML + CSS)</summary> ## Data Fields An example of a sample appears as follows: ``` { 'images': PIL.Image, 'text': '<html>\n<style>\n{css}</style>\n{body}\n</html>', } ``` where `css` is the CSS code, and `body` is the body of the HTML code. In other words, the CSS code is embedded directly within the HTML code, facilitating the straightforward training of a model. ## Data Splits There is only one split, `train`, that contains 822,987 images and codes. ## Dataset Creation This dataset was created using [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) to generate random website ideas with the prompt ``` Generate diverse website layout ideas for different companies, each with a unique design element. Examples include: a car company site with a left column, a webpage footer with a centered logo. Explore variations in colors, positions, and company fields. Don't give any explanations or recognition that you have understood the request, just give the list of 10 ideas, with a line break between each. ``` which were then passed to [Deepseek-Coder-33b-Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct) with the prompt ``` Create a very SIMPLE and SHORT website with the following elements: {idea} Be creative with the design, size, position of the elements, columns, etc... Don't give any explanation, just the content of the HTML code `index.html` starting with `<!DOCTYPE html>`, followed by the CSS code `styles.css` starting with `/* Global Styles */`. Write real and short sentences for the paragraphs, don't use Lorem ipsum. When you want to display an image, don't use <img> in the HTML, always display a colored rectangle instead. ``` Following these steps, the HTML and CSS codes were extracted from the outputs of Deepseek-Coder and formatted into the structure `'<html>\n<style>\n{css}</style>\n{body}\n</html>'`. </details> <details> <summary>Details for WebSight-v0.2 (HTML + Tailwind CSS)</summary> ## Data Fields An example of a sample appears as follows: ``` { 'images': PIL.Image, 'text': '<html>\n<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/tailwind.min.css" rel="stylesheet">\n{body}\n</html>', } ``` where `body` is the body of the HTML code, containing directly the Tailwind CSS code and facilitating the straightforward training of a model. ## Data Splits There is only one split, `train`, that contains TO DO images and codes. ## Dataset Creation TO DO. For now, the creation of the dataset is documented in the technical report. </details> ## Terms of Use By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model. ### Licensing Information License CC-BY-4.0. ### Citation Information If you are using this dataset, please cite our [technical report](https://arxiv.org/abs/2403.09029) ``` @misc{laurençon2024unlocking, title={Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset}, author={Hugo Laurençon and Léo Tronchon and Victor Sanh}, year={2024}, eprint={2403.09029}, archivePrefix={arXiv}, primaryClass={cs.HC} } ```

AnonymousGM/MultiSetTransformerData

AnonymousGM

--- license: mit --- ## General Description MultiSetTransformerData is a large dataset designed to train and validate neural Symbolic Regression models. It was designed to solve the Multi-Set Symbolic Skeleton Prediction (MSSP) problems, described in the paper **"Univariate Skeleton Prediction in Multivariate Systems Using Transformers"**. However, it can be used for training generic SR models as well. This dataset consists of artificially generated **univariate symbolic skeletons**, from which mathematical expressions are sampled, which are then used to sample data sets. In this repository, a dataset **Q1** is presented: * **Q1**: Consists of mathematical expressions that use up to 5 unary and binary operators (e.g., \\(1 + 1 / (\sin(2x) + 3)\\) uses five operators). It allows up to one nested operator (e.g., \\(\sin( \exp(x))\\) is allowed but \\(\sin( \exp(x^2))\\) is not). ## Dataset Structure In the **Q1** folder, you will find a training set alongside its corresponding validation set. Then, each folder consists of a collection of HDF5 files, as shown below: ``` ├── Q1 │ ├── training │ │ ├── 0.h5 │ │ ├── 1.h5 │ │ ├── ... │ ├── validation │ │ ├── 0.h5 │ │ ├── 1.h5 │ │ ├── ... ``` Each HDF5 file contains 5000 **blocks** and has the following structure: ``` { "block_1": { "X": "Support vector, shape (10000, 10)", "Y": "Response vector, shape (10000, 10)", "tokenized": "Symbolic skeleton expression tokenized using vocabulary, list", "exprs": "Symbolic skeleton expression, str", "sampled_exprs": "Ten mathematical expressions sampled from a common skeleton" }, "block_2": { "X": "Support, shape (10000, 10)", "Y": "Response, shape (10000, 10)", "tokenized": "Symbolic skeleton expression tokenized using vocabulary, list", "exprs": "Symbolic skeleton expression, str", "sampled_exprs": "Ten mathematical expressions sampled from a common skeleton" }, ... } ``` More specifically, each block corresponds to one univariate symbolic skeleton (i.e., a function without defined constant values); for example, `c + c/(c*sin(c*x_1) + c)`. From this skeleton, 10 random functions are sampled; for example: * `-2.284 + 0.48/(-sin(0.787*x_1) - 1.136)` * `4.462 - 2.545/(3.157*sin(0.422*x_1) - 1.826)`, ... Then, for the \\(i\\)-th function (where \\(i \in [0, 1, ..., 9]\\)), we sample a **support vector** `X[:, i]` of 10000 elements whose values are drawn from a uniform distribution \\(\mathcal{U}(-10, 10)\\). The support vector `X[:, i]` is evaluated on the \\(i\\)-th function to obtain the response vector `Y[:, i]`. In other words, a block contains input-output data generated from 10 **different functions that share the same symbolic skeleton**. For instance, the following figure shows 10 sets of data generated from the symbolic skeleton `c + c/(c*sin(c*x_1) + c)`: <p align="center"> <img src="images/data_example.jpg" alt="alt text" width="600"> </p> ## Loading Data Once the data is downloaded, it can be loaded using Python as follows: ``` imort os import glob import h5py def open_h5(path): block = [] with h5py.File(path, "r") as hf: # Iterate through the groups in the HDF5 file (group names are integers) for group_name in hf: group = hf[group_name] X = group["X"][:] Y = group["Y"][:] # Load 'tokenized' as a list of integers tokenized = list(group["tokenized"]) # Load 'exprs' as a string exprs = group["exprs"][()].tobytes().decode("utf-8") # Load 'sampled_exprs' as a list of sympy expressions sampled_exprs = [expr_str for expr_str in group["sampled_exprs"][:].astype(str)] block.append([X, Y, tokenized, exprs, sampled_exprs]) return block train_path = 'data/Q1/training' train_files = glob.glob(os.path.join(self.sampledData_train_path, '*.h5')) for tfile in train_files: # Read block block = open_h5(tfile) # Do stuff with your data ``` ## Vocabulary and Expression Generation The table below provides the vocabulary used to construct the expressions of this dataset. <p align="center"> <img src="images/vocabulary.jpg" alt="alt text" width="500"> </p> We use a method that builds the expression tree recursively in a preorder fashion, which allows us to enforce certain conditions and constraints effectively. That is, we forbid certain combinations of operators and set a maximum limit on the nesting depth of unary operators within each other. For example, we avoid embedding the operator \\(\text{log}\\) within the operator \\(\text{exp}\\), or vice versa, since such composition could lead to direct simplification (e.g., \\(\text{log}\left( \text{exp} (x) \right) = x\\). We can also avoid combinations of operators that would generate extremely large values (e.g., \\(\text{exp}\left( \text{exp} (x) \right)\\) and \\(\text{sinh} \left( \text{sinh} (x) \right)\\)). The table below shows the forbidden operators we considered for some specific parent operators. <p align="center"> <img src="images/forbidden_ops.jpg" alt="alt text" width="500"> </p> ## Citation Use this Bibtex to cite this repository ``` @INPROCEEDINGS{MultiSetSR, author="Morales, Giorgio and Sheppard, John W.", editor="Bifet, Albert and Daniu{\v{s}}is, Povilas and Davis, Jesse and Krilavi{\v{c}}ius, Tomas and Kull, Meelis and Ntoutsi, Eirini and Puolam{\"a}ki, Kai and {\v{Z}}liobait{\.{e}}, Indr{\.{e}}", title="Univariate Skeleton Prediction in Multivariate Systems Using Transformers", booktitle="Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track", year="2024", publisher="Springer Nature Switzerland", address="Cham", pages="107--125", isbn="978-3-031-70371-3" } ```

tasksource/mmlu

tasksource

--- license: apache-2.0 task_categories: - text-classification - multiple-choice - question-answering task_ids: - multiple-choice-qa - open-domain-qa - closed-domain-qa language: - en tags: - multi-task - multitask - mmlu - hendrycks_test pretty_name: mmlu --- MMLU (`hendrycks_test` on huggingface) without auxiliary train. It is much lighter (7MB vs 162MB) and faster than the original implementation, in which auxiliary train is loaded (+ duplicated!) by default for all the configs in the original version, making it quite heavy. We use this version in [tasksource](https://huggingface.co/tasksource). Reference to original dataset: Measuring Massive Multitask Language Understanding - https://github.com/hendrycks/test ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```

avalab/Allo-AVA

avalab

--- license: cc language: - en tags: - code size_categories: - n>1T ---

lmms-lab/POPE

lmms-lab

--- dataset_info: - config_name: Full features: - name: id dtype: string - name: question_id dtype: string - name: question dtype: string - name: answer dtype: string - name: image_source dtype: string - name: image dtype: image - name: category dtype: string splits: - name: adversarial num_bytes: 490408158.0 num_examples: 3000 - name: popular num_bytes: 490397000.0 num_examples: 3000 - name: random num_bytes: 490394976.0 num_examples: 3000 download_size: 255022914 dataset_size: 1471200134.0 - config_name: default features: - name: id dtype: string - name: question_id dtype: string - name: question dtype: string - name: answer dtype: string - name: image_source dtype: string - name: image dtype: image - name: category dtype: string splits: - name: test num_bytes: 1471200135.0 num_examples: 9000 download_size: 255022914 dataset_size: 1471200135.0 configs: - config_name: Full data_files: - split: adversarial path: Full/adversarial-* - split: popular path: Full/popular-* - split: random path: Full/random-* - config_name: default data_files: - split: test path: data/test-* --- <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [POPE](https://github.com/RUCAIBox/POPE). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{li2023evaluating, title={Evaluating object hallucination in large vision-language models}, author={Li, Yifan and Du, Yifan and Zhou, Kun and Wang, Jinpeng and Zhao, Wayne Xin and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2305.10355}, year={2023} } ```

MahmoodLab/hest

MahmoodLab

--- license: cc-by-nc-sa-4.0 language: - en tags: - spatial-transcriptomics - histology - pathology task_categories: - image-classification - feature-extraction - image-segmentation size_categories: - 100B<n<1T --- # Model Card for HEST-1k <img src="fig1a.jpg" alt="Description" style="width: 38%;" align="right"/> #### What is HEST-1k? - A collection of <b>1,229</b> spatial transcriptomic profiles, each linked and aligned to a Whole Slide Image (with pixel size > 1.15 µm/px) and metadata. - HEST-1k was assembled from 131 public and internal cohorts encompassing: - 26 organs - 2 species (Homo Sapiens and Mus Musculus) - 367 cancer samples from 25 cancer types. HEST-1k processing enabled the identification of <b>1.5 million</b> expression/morphology pairs and <b>76 million</b> nuclei ### Updates - **21.10.24**: HEST has been accepted to NeurIPS 2024 as a Spotlight! We will be in Vancouver from Dec 10th to 15th. Send us a message if you wanna learn more about HEST ([email protected]). - **23.09.24**: 121 new samples released, including 27 Xenium and 7 Visium HD! We also make the aligned Xenium transcripts + the aligned DAPI segmented cells/nuclei public. - **30.08.24**: HEST-Benchmark results updated. Includes H-Optimus-0, Virchow 2, Virchow, and GigaPath. New COAD task based on 4 Xenium samples. HuggingFace bench data have been updated. - **28.08.24**: New set of helpers for batch effect visualization and correction. Tutorial [here](https://github.com/mahmoodlab/HEST/blob/main/tutorials/5-Batch-effect-visualization.ipynb). ## Instructions for Setting Up HuggingFace Account and Token ### 1. Create an Account on HuggingFace Follow the instructions provided on the [HuggingFace sign-up page](https://huggingface.co/join). ### 2. Accept terms of use of HEST 1. On this page click request access (access will be automatically granted) 2. At this stage, you can already manually inspect the data by navigating in the `Files and version` ### 3. Create a Hugging Face Token 1. **Go to Settings:** Navigate to your profile settings by clicking on your profile picture in the top right corner and selecting `Settings` from the dropdown menu. 2. **Access Tokens:** In the settings menu, find and click on `Access tokens`. 3. **Create New Token:** - Click on `New token`. - Set the token name (e.g., `hest`). - Set the access level to `Write`. - Click on `Create`. 4. **Copy Token:** After the token is created, copy it to your clipboard. You will need this token for authentication. ### 4. Logging Run the following ``` pip install datasets ``` ``` from huggingface_hub import login login(token="YOUR HUGGINGFACE TOKEN") ``` ## Download the entire HEST-1k dataset: ```python import datasets local_dir='hest_data' # hest will be dowloaded to this folder # Note that the full dataset is around 1TB of data dataset = datasets.load_dataset( 'MahmoodLab/hest', cache_dir=local_dir, patterns='*' ) ``` ## Download a subset of HEST-1k: ```python import datasets local_dir='hest_data' # hest will be dowloaded to this folder ids_to_query = ['TENX96', 'TENX99'] # list of ids to query list_patterns = [f"*{id}[_.]**" for id in ids_to_query] dataset = datasets.load_dataset( 'MahmoodLab/hest', cache_dir=local_dir, patterns=list_patterns ) ``` #### Query HEST by organ, techonology, oncotree code... ```python import datasets import pandas as pd local_dir='hest_data' # hest will be dowloaded to this folder meta_df = pd.read_csv("hf://datasets/MahmoodLab/hest/HEST_v1_1_0.csv") # Filter the dataframe by organ, oncotree code... meta_df = meta_df[meta_df['oncotree_code'] == 'IDC'] meta_df = meta_df[meta_df['organ'] == 'Breast'] ids_to_query = meta_df['id'].values list_patterns = [f"*{id}[_.]**" for id in ids_to_query] dataset = datasets.load_dataset( 'MahmoodLab/hest', cache_dir=local_dir, patterns=list_patterns ) ``` ## Loading the data with the python library `hest` Once downloaded, you can then easily iterate through the dataset: ```python from hest import iter_hest for st in iter_hest('../hest_data', id_list=['TENX95']): print(st) ``` Please visit the [github repo](https://github.com/mahmoodlab/hest) and the [documentation](https://hest.readthedocs.io/en/latest/) for more information about the `hest` library API. ## Data organization For each sample: - `wsis/`: H&E stained Whole Slide Images in pyramidal Generic TIFF (or pyramidal Generic BigTIFF if >4.1GB) - `st/`: spatial transcriptomics expressions in a scanpy `.h5ad` object - `metadata/`: metadata - `spatial_plots/`: overlay of the WSI with the st spots - `thumbnails/`: downscaled version of the WSI - `tissue_seg/`: tissue segmentation masks: - {id}_mask.jpg: downscaled or full resolution greyscale tissue mask - {id}_mask.pkl: tissue/holes contours in a pickle file - {id}_vis.jpg: visualization of the tissue mask on the downscaled WSI - `pixel_size_vis/`: visualization of the pixel size - `patches/`: 256x256 H&E patches (0.5µm/px) extracted around ST spots in a .h5 object optimized for deep-learning. Each patch is matched to the corresponding ST profile (see `st/`) with a barcode. - `patches_vis/`: visualization of the mask and patches on a downscaled WSI. - `cellvit_seg/`: cellvit nuclei segmentation For each xenium sample: - `transcripts/`: individual transcripts aligned to H&E for xenium samples; read with pandas.read_parquet; aligned coordinates in pixel are in columns `['he_x', 'he_y']` - `xenium_seg/`: xenium segmentation on DAPI and aligned to H&E ### How to cite: ``` @article{jaume2024hest, author = {Jaume, Guillaume and Doucet, Paul and Song, Andrew H. and Lu, Ming Y. and Almagro-Perez, Cristina and Wagner, Sophia J. and Vaidya, Anurag J. and Chen, Richard J. and Williamson, Drew F. K. and Kim, Ahrong and Mahmood, Faisal}, title = {{HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis}}, journal = {arXiv}, year = {2024}, month = jun, eprint = {2406.16192}, url = {https://arxiv.org/abs/2406.16192v1} } ``` ### Contact: - <b>Guillaume Jaume</b> Harvard Medical School, Boston, Mahmood Lab (`[email protected]`) - <b>Paul Doucet</b> Harvard Medical School, Boston, Mahmood Lab (`[email protected]`) <i>The dataset is distributed under the Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0 Deed)</i>

deepghs/gelbooru-webp-4Mpixel

deepghs

--- license: other task_categories: - image-classification - zero-shot-image-classification - text-to-image language: - en tags: - art - anime - not-for-all-audiences size_categories: - 10M<n<100M annotations_creators: - no-annotation source_datasets: - gelbooru --- # Gelbooru 4M Re-encoded Dataset This is the re-encoded dataset of [deepghs/gelbooru_full](https://huggingface.co/datasets/deepghs/gelbooru_full). And all the resized images are maintained here. There are 10083605 images in total. The maximum ID of these images is 11191848. Last updated at `2024-12-26 10:10:42 JST`. # How to Painlessly Use This Use [cheesechaser](https://github.com/deepghs/cheesechaser) to quickly get images from this repository. Before using this code, you have to **grant the access from this gated repository**. And then **set your personal HuggingFace token into `HF_TOKEN` environment variable** to give the code authorization for this repository. ```python from cheesechaser.datapool import GelbooruWebpDataPool pool = GelbooruWebpDataPool() pool.batch_download_to_directory( # download images #7000000-7000100, any ranges or id lists are okay resource_ids=range(7000000, 7000100), # save to directory /data/gelbooru_webp dst_dir='/data/gelbooru_webp', ) ```

rayliuca/WikidataLabels

rayliuca

--- license: cc0-1.0 dataset_info: - config_name: aa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13986211 num_examples: 436895 download_size: 9821312 dataset_size: 13986211 - config_name: ab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5012532 num_examples: 159908 download_size: 3013706 dataset_size: 5012532 - config_name: abs features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252728 num_examples: 143986 download_size: 2567450 dataset_size: 4252728 - config_name: ace features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 19105673 num_examples: 574712 download_size: 13573374 dataset_size: 19105673 - config_name: ady features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4444259 num_examples: 148627 download_size: 2705754 dataset_size: 4444259 - config_name: ady-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4412556 num_examples: 147884 download_size: 2682170 dataset_size: 4412556 - config_name: aeb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4305734 num_examples: 145198 download_size: 2606368 dataset_size: 4305734 - config_name: aeb-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4467930 num_examples: 148796 download_size: 2722169 dataset_size: 4467930 - config_name: aeb-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12770359 num_examples: 404946 download_size: 8886489 dataset_size: 12770359 - config_name: af features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 58561042 num_examples: 1643153 download_size: 42539052 dataset_size: 58561042 - config_name: agq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 1317 num_examples: 33 download_size: 2906 dataset_size: 1317 - config_name: ak features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14198715 num_examples: 443037 download_size: 9991525 dataset_size: 14198715 - config_name: aln features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13811116 num_examples: 432089 download_size: 9673418 dataset_size: 13811116 - config_name: als features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20691 num_examples: 543 download_size: 17540 dataset_size: 20691 - config_name: alt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 108390 num_examples: 1814 download_size: 59046 dataset_size: 108390 - config_name: am features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5231176 num_examples: 163038 download_size: 3187164 dataset_size: 5231176 - config_name: ami features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 21519 num_examples: 686 download_size: 16640 dataset_size: 21519 - config_name: an features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 240345072 num_examples: 5921087 download_size: 164895205 dataset_size: 240345072 - config_name: ang features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14275715 num_examples: 443461 download_size: 10063758 dataset_size: 14275715 - config_name: anp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8558258 num_examples: 241612 download_size: 4381360 dataset_size: 8558258 - config_name: ar features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 291173732 num_examples: 5724064 download_size: 159369497 dataset_size: 291173732 - config_name: arc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4473283 num_examples: 150006 download_size: 2722619 dataset_size: 4473283 - config_name: arn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13879729 num_examples: 433912 download_size: 9715431 dataset_size: 13879729 - config_name: arq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4346991 num_examples: 146004 download_size: 2636972 dataset_size: 4346991 - config_name: ary features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5358568 num_examples: 171568 download_size: 3313402 dataset_size: 5358568 - config_name: arz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 81806333 num_examples: 1669699 download_size: 49423508 dataset_size: 81806333 - config_name: as features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 21658610 num_examples: 450074 download_size: 9641626 dataset_size: 21658610 - config_name: ase features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252943 num_examples: 143986 download_size: 2568106 dataset_size: 4252943 - config_name: ast features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 1385628786 num_examples: 20696237 download_size: 955908362 dataset_size: 1385628786 - config_name: atj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12996229 num_examples: 411639 download_size: 9057557 dataset_size: 12996229 - config_name: av features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4722934 num_examples: 153781 download_size: 2880103 dataset_size: 4722934 - config_name: avk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13194485 num_examples: 414598 download_size: 9200917 dataset_size: 13194485 - config_name: awa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8599312 num_examples: 242320 download_size: 4411751 dataset_size: 8599312 - config_name: ay features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14269432 num_examples: 443521 download_size: 10029939 dataset_size: 14269432 - config_name: az features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 21049248 num_examples: 516732 download_size: 14117527 dataset_size: 21049248 - config_name: azb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 30781587 num_examples: 607562 download_size: 16028687 dataset_size: 30781587 - config_name: ba features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 11525351 num_examples: 261509 download_size: 6733777 dataset_size: 11525351 - config_name: ban features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13674052 num_examples: 426706 download_size: 9513747 dataset_size: 13674052 - config_name: ban-bali features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 50961 num_examples: 748 download_size: 25817 dataset_size: 50961 - config_name: bar features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 54783034 num_examples: 1566120 download_size: 40389830 dataset_size: 54783034 - config_name: bbc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12820895 num_examples: 406960 download_size: 8917054 dataset_size: 12820895 - config_name: bcc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8017228 num_examples: 241977 download_size: 4344579 dataset_size: 8017228 - config_name: be features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 30978832 num_examples: 564184 download_size: 17461174 dataset_size: 30978832 - config_name: be-tarask features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18931909 num_examples: 374396 download_size: 10871239 dataset_size: 18931909 - config_name: bg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 200628708 num_examples: 4383953 download_size: 137745533 dataset_size: 200628708 - config_name: bgn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7999280 num_examples: 241566 download_size: 4331249 dataset_size: 7999280 - config_name: bi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14040026 num_examples: 438382 download_size: 9867032 dataset_size: 14040026 - config_name: bjn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8375348 num_examples: 254558 download_size: 5722334 dataset_size: 8375348 - config_name: bm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18145787 num_examples: 549694 download_size: 13129193 dataset_size: 18145787 - config_name: bn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 815803977 num_examples: 9767284 download_size: 261147329 dataset_size: 815803977 - config_name: bo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 11671330 num_examples: 278307 download_size: 5669602 dataset_size: 11671330 - config_name: bpy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15497749 num_examples: 347458 download_size: 6991190 dataset_size: 15497749 - config_name: bqi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8017455 num_examples: 241984 download_size: 4345123 dataset_size: 8017455 - config_name: br features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 58304963 num_examples: 1653800 download_size: 42722031 dataset_size: 58304963 - config_name: brh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5328437 num_examples: 171504 download_size: 3376189 dataset_size: 5328437 - config_name: bs features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 30441466 num_examples: 858190 download_size: 21606575 dataset_size: 30441466 - config_name: btm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252525 num_examples: 143980 download_size: 2567218 dataset_size: 4252525 - config_name: bto features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12841721 num_examples: 407470 download_size: 8934218 dataset_size: 12841721 - config_name: bug features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7595464 num_examples: 235268 download_size: 5129941 dataset_size: 7595464 - config_name: bxr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4713699 num_examples: 153707 download_size: 2869313 dataset_size: 4713699 - config_name: ca features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 408509932 num_examples: 9936886 download_size: 288474980 dataset_size: 408509932 - config_name: cbk-zam features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14108232 num_examples: 440345 download_size: 9920793 dataset_size: 14108232 - config_name: cdo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6503254 num_examples: 201362 download_size: 4137841 dataset_size: 6503254 - config_name: ce features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 28093148 num_examples: 607767 download_size: 16367596 dataset_size: 28093148 - config_name: ceb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 332947091 num_examples: 7769402 download_size: 219525737 dataset_size: 332947091 - config_name: ch features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13983906 num_examples: 436785 download_size: 9817385 dataset_size: 13983906 - config_name: cho features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13950786 num_examples: 435869 download_size: 9791296 dataset_size: 13950786 - config_name: chr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5386793 num_examples: 172855 download_size: 3419676 dataset_size: 5386793 - config_name: chy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13994916 num_examples: 437007 download_size: 9830465 dataset_size: 13994916 - config_name: ckb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 23343034 num_examples: 511183 download_size: 11459344 dataset_size: 23343034 - config_name: co features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 47080480 num_examples: 1346929 download_size: 34551346 dataset_size: 47080480 - config_name: cps features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12849864 num_examples: 407695 download_size: 8941921 dataset_size: 12849864 - config_name: cr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5516556 num_examples: 176667 download_size: 3532952 dataset_size: 5516556 - config_name: crh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 10864382 num_examples: 336709 download_size: 7542853 dataset_size: 10864382 - config_name: crh-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4419064 num_examples: 148046 download_size: 2688683 dataset_size: 4419064 - config_name: crh-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14201429 num_examples: 442905 download_size: 9986290 dataset_size: 14201429 - config_name: cs features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 140189244 num_examples: 3384048 download_size: 97516751 dataset_size: 140189244 - config_name: csb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20177120 num_examples: 619275 download_size: 14528772 dataset_size: 20177120 - config_name: cv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8047221 num_examples: 215611 download_size: 4857718 dataset_size: 8047221 - config_name: cy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 89241808 num_examples: 2244550 download_size: 62686006 dataset_size: 89241808 - config_name: da features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 130931077 num_examples: 3448894 download_size: 98202417 dataset_size: 130931077 - config_name: dag features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 2664957 num_examples: 78534 download_size: 2052615 dataset_size: 2664957 - config_name: de features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 765398522 num_examples: 17531361 download_size: 527642124 dataset_size: 765398522 - config_name: de-at features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 53043722 num_examples: 1515373 download_size: 38761571 dataset_size: 53043722 - config_name: de-ch features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 53480908 num_examples: 1528137 download_size: 39349412 dataset_size: 53480908 - config_name: de-formal features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4256391 num_examples: 144061 download_size: 2571862 dataset_size: 4256391 - config_name: din features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12819746 num_examples: 406591 download_size: 8922303 dataset_size: 12819746 - config_name: diq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7570161 num_examples: 232674 download_size: 5057742 dataset_size: 7570161 - config_name: dsb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16135830 num_examples: 491423 download_size: 11412316 dataset_size: 16135830 - config_name: dtp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13867373 num_examples: 433733 download_size: 9720699 dataset_size: 13867373 - config_name: dty features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8839082 num_examples: 246026 download_size: 4551845 dataset_size: 8839082 - config_name: dua features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 2631 num_examples: 87 download_size: 3877 dataset_size: 2631 - config_name: dv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 81396462 num_examples: 2103276 download_size: 45332104 dataset_size: 81396462 - config_name: dz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8590239 num_examples: 242196 download_size: 4406353 dataset_size: 8590239 - config_name: ee features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14377017 num_examples: 447208 download_size: 10136064 dataset_size: 14377017 - config_name: egl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13068224 num_examples: 413551 download_size: 9121776 dataset_size: 13068224 - config_name: el features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 32978562 num_examples: 592016 download_size: 19577876 dataset_size: 32978562 - config_name: eml features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14768563 num_examples: 458847 download_size: 10453636 dataset_size: 14768563 - config_name: en features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6327454281 num_examples: 81801560 download_size: 4224231068 dataset_size: 6327454281 - config_name: en-ca features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 73305274 num_examples: 1909970 download_size: 53060194 dataset_size: 73305274 - config_name: en-gb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 115978412 num_examples: 2520405 download_size: 78924421 dataset_size: 115978412 - config_name: en-us features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14815 num_examples: 332 download_size: 9953 dataset_size: 14815 - config_name: eo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 256196064 num_examples: 6285304 download_size: 177219679 dataset_size: 256196064 - config_name: es features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 730214298 num_examples: 17233968 download_size: 514588069 dataset_size: 730214298 - config_name: es-419 features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4355180 num_examples: 146476 download_size: 2659218 dataset_size: 4355180 - config_name: es-formal features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4280933 num_examples: 144717 download_size: 2592085 dataset_size: 4280933 - config_name: et features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 65123623 num_examples: 1820762 download_size: 48197302 dataset_size: 65123623 - config_name: eu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 290282374 num_examples: 7109758 download_size: 197889378 dataset_size: 290282374 - config_name: ext features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 223257222 num_examples: 5359047 download_size: 147078789 dataset_size: 223257222 - config_name: fa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 123727757 num_examples: 2142642 download_size: 65952114 dataset_size: 123727757 - config_name: ff features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14116652 num_examples: 440614 download_size: 9920388 dataset_size: 14116652 - config_name: fi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 286539944 num_examples: 6905698 download_size: 209916638 dataset_size: 286539944 - config_name: fit features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20217258 num_examples: 620391 download_size: 14566702 dataset_size: 20217258 - config_name: fj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14159041 num_examples: 441745 download_size: 9956108 dataset_size: 14159041 - config_name: fkv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4328482 num_examples: 145988 download_size: 2619845 dataset_size: 4328482 - config_name: fo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 24474476 num_examples: 731732 download_size: 17876981 dataset_size: 24474476 - config_name: fr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 774128723 num_examples: 17908351 download_size: 534489308 dataset_size: 774128723 - config_name: frc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17896106 num_examples: 547258 download_size: 12953740 dataset_size: 17896106 - config_name: frp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 40902510 num_examples: 1191134 download_size: 29778105 dataset_size: 40902510 - config_name: frr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16979214 num_examples: 515350 download_size: 12069637 dataset_size: 16979214 - config_name: fur features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42077410 num_examples: 1221071 download_size: 30714082 dataset_size: 42077410 - config_name: ga features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 471527543 num_examples: 11524282 download_size: 320967189 dataset_size: 471527543 - config_name: gag features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14149375 num_examples: 440732 download_size: 9940551 dataset_size: 14149375 - config_name: gan features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 31572161 num_examples: 905186 download_size: 18909564 dataset_size: 31572161 - config_name: gan-hans features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 31004794 num_examples: 889875 download_size: 18566811 dataset_size: 31004794 - config_name: gan-hant features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4374444 num_examples: 147098 download_size: 2657182 dataset_size: 4374444 - config_name: gcr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4311409 num_examples: 145829 download_size: 2618211 dataset_size: 4311409 - config_name: gd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 49316935 num_examples: 1429457 download_size: 36220978 dataset_size: 49316935 - config_name: gl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 289484839 num_examples: 7052226 download_size: 197315151 dataset_size: 289484839 - config_name: glk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8327018 num_examples: 249115 download_size: 4538325 dataset_size: 8327018 - config_name: gn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14212974 num_examples: 442765 download_size: 10004863 dataset_size: 14212974 - config_name: gom features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4584575 num_examples: 150273 download_size: 2780570 dataset_size: 4584575 - config_name: gom-deva features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8585678 num_examples: 242131 download_size: 4400578 dataset_size: 8585678 - config_name: gom-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12783006 num_examples: 405302 download_size: 8897342 dataset_size: 12783006 - config_name: gor features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14667616 num_examples: 454512 download_size: 10319196 dataset_size: 14667616 - config_name: got features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5432139 num_examples: 172951 download_size: 3435531 dataset_size: 5432139 - config_name: grc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4494817 num_examples: 149631 download_size: 2746170 dataset_size: 4494817 - config_name: gu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 23788894 num_examples: 486140 download_size: 10779200 dataset_size: 23788894 - config_name: guc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 1419 num_examples: 38 download_size: 3054 dataset_size: 1419 - config_name: guw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 118 num_examples: 4 download_size: 1864 dataset_size: 118 - config_name: gv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20683485 num_examples: 631005 download_size: 14894590 dataset_size: 20683485 - config_name: ha features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14716168 num_examples: 455836 download_size: 10421790 dataset_size: 14716168 - config_name: hak features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6128644 num_examples: 193036 download_size: 3991729 dataset_size: 6128644 - config_name: haw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14158084 num_examples: 441511 download_size: 9952975 dataset_size: 14158084 - config_name: he features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 43629050 num_examples: 884809 download_size: 27221301 dataset_size: 43629050 - config_name: hi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 37237187 num_examples: 668964 download_size: 17804873 dataset_size: 37237187 - config_name: hif features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14457954 num_examples: 449009 download_size: 10166264 dataset_size: 14457954 - config_name: hif-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14519845 num_examples: 454037 download_size: 10240704 dataset_size: 14519845 - config_name: hil features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12928914 num_examples: 409962 download_size: 9009705 dataset_size: 12928914 - config_name: ho features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13950504 num_examples: 435857 download_size: 9790849 dataset_size: 13950504 - config_name: hr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 61272623 num_examples: 1720527 download_size: 45307411 dataset_size: 61272623 - config_name: hrx features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12869295 num_examples: 407823 download_size: 8964114 dataset_size: 12869295 - config_name: hsb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 23720349 num_examples: 707100 download_size: 17145693 dataset_size: 23720349 - config_name: ht features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16835529 num_examples: 509955 download_size: 11880404 dataset_size: 16835529 - config_name: hu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 85054175 num_examples: 2200589 download_size: 64143342 dataset_size: 85054175 - config_name: hu-formal features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252810 num_examples: 143986 download_size: 2567582 dataset_size: 4252810 - config_name: hy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 39339286 num_examples: 773925 download_size: 22108994 dataset_size: 39339286 - config_name: hyw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5443608 num_examples: 166902 download_size: 3238370 dataset_size: 5443608 - config_name: hz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13948574 num_examples: 435804 download_size: 9788697 dataset_size: 13948574 - config_name: ia features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 229143237 num_examples: 5616433 download_size: 155877454 dataset_size: 229143237 - config_name: id features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 95220928 num_examples: 2512331 download_size: 69525046 dataset_size: 95220928 - config_name: ie features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 225725262 num_examples: 5533032 download_size: 153371930 dataset_size: 225725262 - config_name: ig features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20109388 num_examples: 617044 download_size: 14475407 dataset_size: 20109388 - config_name: ii features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4310418 num_examples: 145332 download_size: 2609723 dataset_size: 4310418 - config_name: ik features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13989609 num_examples: 436958 download_size: 9823174 dataset_size: 13989609 - config_name: ike-cans features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4352278 num_examples: 146355 download_size: 2645174 dataset_size: 4352278 - config_name: ike-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13851135 num_examples: 432932 download_size: 9714057 dataset_size: 13851135 - config_name: ilo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15955483 num_examples: 480555 download_size: 11141942 dataset_size: 15955483 - config_name: inh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4634360 num_examples: 152226 download_size: 2831580 dataset_size: 4634360 - config_name: io features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 233656822 num_examples: 5757440 download_size: 159720058 dataset_size: 233656822 - config_name: is features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 51679396 num_examples: 1483610 download_size: 37965494 dataset_size: 51679396 - config_name: it features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 536601426 num_examples: 12631487 download_size: 375025347 dataset_size: 536601426 - config_name: iu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5360588 num_examples: 172215 download_size: 3402239 dataset_size: 5360588 - config_name: ja features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 140641579 num_examples: 2917962 download_size: 92145329 dataset_size: 140641579 - config_name: jam features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18849751 num_examples: 571777 download_size: 13684422 dataset_size: 18849751 - config_name: jbo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14301985 num_examples: 446512 download_size: 9994516 dataset_size: 14301985 - config_name: jv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 27232302 num_examples: 794181 download_size: 19651565 dataset_size: 27232302 - config_name: ka features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 24073345 num_examples: 399546 download_size: 11679979 dataset_size: 24073345 - config_name: kaa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14082184 num_examples: 439411 download_size: 9902820 dataset_size: 14082184 - config_name: kab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18459676 num_examples: 557857 download_size: 13384218 dataset_size: 18459676 - config_name: kbd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4594409 num_examples: 149733 download_size: 2759503 dataset_size: 4594409 - config_name: kbd-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4417661 num_examples: 148017 download_size: 2687531 dataset_size: 4417661 - config_name: kbp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12873178 num_examples: 408039 download_size: 8965474 dataset_size: 12873178 - config_name: kea features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12793700 num_examples: 405901 download_size: 8896866 dataset_size: 12793700 - config_name: kg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 40949149 num_examples: 1193499 download_size: 29766747 dataset_size: 40949149 - config_name: khw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4308653 num_examples: 145279 download_size: 2608581 dataset_size: 4308653 - config_name: ki features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14056900 num_examples: 439015 download_size: 9875534 dataset_size: 14056900 - config_name: kj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13881723 num_examples: 433861 download_size: 9733715 dataset_size: 13881723 - config_name: kjp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8504302 num_examples: 240339 download_size: 4341523 dataset_size: 8504302 - config_name: kk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 19216115 num_examples: 428880 download_size: 11577682 dataset_size: 19216115 - config_name: kk-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7241749 num_examples: 211731 download_size: 4487032 dataset_size: 7241749 - config_name: kk-kz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4937945 num_examples: 160027 download_size: 3062906 dataset_size: 4937945 - config_name: kk-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 22197825 num_examples: 677162 download_size: 16072332 dataset_size: 22197825 - config_name: kk-tr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20060635 num_examples: 616521 download_size: 14438929 dataset_size: 20060635 - config_name: ko features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 60335212 num_examples: 1364440 download_size: 39186630 dataset_size: 60335212 - config_name: ko-kp features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4338717 num_examples: 146150 download_size: 2630925 dataset_size: 4338717 - config_name: koi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4737590 num_examples: 155082 download_size: 2894674 dataset_size: 4737590 - config_name: kr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13886057 num_examples: 433990 download_size: 9737602 dataset_size: 13886057 - config_name: krc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4646136 num_examples: 151026 download_size: 2785454 dataset_size: 4646136 - config_name: kri features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12798530 num_examples: 406032 download_size: 8902330 dataset_size: 12798530 - config_name: krj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13850324 num_examples: 433444 download_size: 9703460 dataset_size: 13850324 - config_name: krl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12788020 num_examples: 405729 download_size: 8893337 dataset_size: 12788020 - config_name: ks features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4390604 num_examples: 147033 download_size: 2671069 dataset_size: 4390604 - config_name: ks-deva features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8567518 num_examples: 241832 download_size: 4387687 dataset_size: 8567518 - config_name: ksh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20394712 num_examples: 624523 download_size: 14698860 dataset_size: 20394712 - config_name: ku features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8037777 num_examples: 239515 download_size: 5306097 dataset_size: 8037777 - config_name: ku-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4577826 num_examples: 151290 download_size: 2796159 dataset_size: 4577826 - config_name: ku-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14683841 num_examples: 458802 download_size: 10371977 dataset_size: 14683841 - config_name: kum features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252739 num_examples: 143985 download_size: 2567503 dataset_size: 4252739 - config_name: kv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4946978 num_examples: 158888 download_size: 2997865 dataset_size: 4946978 - config_name: kw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20245535 num_examples: 621432 download_size: 14581378 dataset_size: 20245535 - config_name: ky features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8909613 num_examples: 235165 download_size: 5462115 dataset_size: 8909613 - config_name: la features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 299766395 num_examples: 7085082 download_size: 201477460 dataset_size: 299766395 - config_name: lad features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20336417 num_examples: 622775 download_size: 14653199 dataset_size: 20336417 - config_name: lb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 56473066 num_examples: 1601093 download_size: 41410732 dataset_size: 56473066 - config_name: lbe features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4501470 num_examples: 149898 download_size: 2744786 dataset_size: 4501470 - config_name: lez features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4890798 num_examples: 155936 download_size: 2959653 dataset_size: 4890798 - config_name: lfn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14709210 num_examples: 456719 download_size: 10408539 dataset_size: 14709210 - config_name: lg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13979286 num_examples: 436009 download_size: 9802779 dataset_size: 13979286 - config_name: li features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 43476868 num_examples: 1253970 download_size: 31750932 dataset_size: 43476868 - config_name: lij features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42327066 num_examples: 1227346 download_size: 30898971 dataset_size: 42327066 - config_name: liv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12781331 num_examples: 405236 download_size: 8895889 dataset_size: 12781331 - config_name: lki features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8039166 num_examples: 242526 download_size: 4363703 dataset_size: 8039166 - config_name: lld features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 90305 num_examples: 2634 download_size: 69672 dataset_size: 90305 - config_name: lmo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18287638 num_examples: 545398 download_size: 13130119 dataset_size: 18287638 - config_name: ln features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14123637 num_examples: 439731 download_size: 9915851 dataset_size: 14123637 - config_name: lo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 9905189 num_examples: 271710 download_size: 5313218 dataset_size: 9905189 - config_name: loz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13695602 num_examples: 428723 download_size: 9581113 dataset_size: 13695602 - config_name: lt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 39902419 num_examples: 1096727 download_size: 29185765 dataset_size: 39902419 - config_name: ltg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13884707 num_examples: 433453 download_size: 9736637 dataset_size: 13884707 - config_name: lus features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13695197 num_examples: 428712 download_size: 9580538 dataset_size: 13695197 - config_name: luz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8459036 num_examples: 253454 download_size: 4687414 dataset_size: 8459036 - config_name: lv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 27242119 num_examples: 764753 download_size: 19676667 dataset_size: 27242119 - config_name: lzh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 25067538 num_examples: 685152 download_size: 14998856 dataset_size: 25067538 - config_name: mdf features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4634268 num_examples: 152141 download_size: 2820744 dataset_size: 4634268 - config_name: mg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 43863002 num_examples: 1271074 download_size: 32016826 dataset_size: 43863002 - config_name: mh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13775721 num_examples: 431162 download_size: 9644397 dataset_size: 13775721 - config_name: mi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20857040 num_examples: 637118 download_size: 15060301 dataset_size: 20857040 - config_name: min features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 53044258 num_examples: 1464128 download_size: 38587450 dataset_size: 53044258 - config_name: mk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 24087229 num_examples: 449241 download_size: 12217912 dataset_size: 24087229 - config_name: ml features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 189266798 num_examples: 2664923 download_size: 71344031 dataset_size: 189266798 - config_name: mn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 9311543 num_examples: 219695 download_size: 5272784 dataset_size: 9311543 - config_name: mni features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8696893 num_examples: 243616 download_size: 4470994 dataset_size: 8696893 - config_name: mnw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8861861 num_examples: 244906 download_size: 4517726 dataset_size: 8861861 - config_name: mo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5377009 num_examples: 172144 download_size: 3405661 dataset_size: 5377009 - config_name: mr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 26855182 num_examples: 526220 download_size: 12358679 dataset_size: 26855182 - config_name: mrh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 68 num_examples: 2 download_size: 1820 dataset_size: 68 - config_name: mrj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5007903 num_examples: 160889 download_size: 3073431 dataset_size: 5007903 - config_name: ms features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 64674328 num_examples: 1803714 download_size: 47165217 dataset_size: 64674328 - config_name: ms-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 136496 num_examples: 2961 download_size: 92316 dataset_size: 136496 - config_name: mt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 22632686 num_examples: 682867 download_size: 16352572 dataset_size: 22632686 - config_name: mus features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14013416 num_examples: 437688 download_size: 9835239 dataset_size: 14013416 - config_name: mwl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14493299 num_examples: 448926 download_size: 10225888 dataset_size: 14493299 - config_name: my features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16182182 num_examples: 345096 download_size: 7981905 dataset_size: 16182182 - config_name: mzn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17973941 num_examples: 447870 download_size: 9174617 dataset_size: 17973941 - config_name: na features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13992666 num_examples: 436956 download_size: 9823328 dataset_size: 13992666 - config_name: nah features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14490294 num_examples: 449748 download_size: 10192501 dataset_size: 14490294 - config_name: nan-hani features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 191 num_examples: 6 download_size: 1925 dataset_size: 191 - config_name: nap features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42362346 num_examples: 1229161 download_size: 30918265 dataset_size: 42362346 - config_name: nb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 142554768 num_examples: 3688026 download_size: 105549981 dataset_size: 142554768 - config_name: nds features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 58766114 num_examples: 1666813 download_size: 43421948 dataset_size: 58766114 - config_name: nds-nl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 44121756 num_examples: 1273149 download_size: 32201410 dataset_size: 44121756 - config_name: ne features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 11925386 num_examples: 295006 download_size: 6265232 dataset_size: 11925386 - config_name: new features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16906308 num_examples: 350362 download_size: 7680329 dataset_size: 16906308 - config_name: ng features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13870754 num_examples: 433582 download_size: 9723795 dataset_size: 13870754 - config_name: nia features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20649 num_examples: 515 download_size: 16535 dataset_size: 20649 - config_name: niu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12794247 num_examples: 405902 download_size: 8897260 dataset_size: 12794247 - config_name: nl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5016576732 num_examples: 61931959 download_size: 3380404239 dataset_size: 5016576732 - config_name: nn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 99997815 num_examples: 2708994 download_size: 74736304 dataset_size: 99997815 - config_name: 'no' features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 2934 num_examples: 64 download_size: 4108 dataset_size: 2934 - config_name: nod features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4322068 num_examples: 145566 download_size: 2618106 dataset_size: 4322068 - config_name: nov features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14150434 num_examples: 440903 download_size: 9947798 dataset_size: 14150434 - config_name: nqo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8094271 num_examples: 243184 download_size: 4398836 dataset_size: 8094271 - config_name: nrm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 41330956 num_examples: 1203295 download_size: 30084065 dataset_size: 41330956 - config_name: nso features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14178321 num_examples: 443205 download_size: 9959708 dataset_size: 14178321 - config_name: nv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15351770 num_examples: 455188 download_size: 10472240 dataset_size: 15351770 - config_name: ny features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13989813 num_examples: 436764 download_size: 9821588 dataset_size: 13989813 - config_name: nys features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13092059 num_examples: 413241 download_size: 9153100 dataset_size: 13092059 - config_name: oc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 266612548 num_examples: 6569770 download_size: 180156462 dataset_size: 266612548 - config_name: olo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13200388 num_examples: 416935 download_size: 9214968 dataset_size: 13200388 - config_name: om features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5476389 num_examples: 175314 download_size: 3496637 dataset_size: 5476389 - config_name: or features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 22798709 num_examples: 470237 download_size: 10322832 dataset_size: 22798709 - config_name: os features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5946062 num_examples: 177054 download_size: 3583703 dataset_size: 5946062 - config_name: ota features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8015024 num_examples: 241903 download_size: 4343478 dataset_size: 8015024 - config_name: pa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20505754 num_examples: 481522 download_size: 10552147 dataset_size: 20505754 - config_name: pam features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14527964 num_examples: 451253 download_size: 10242443 dataset_size: 14527964 - config_name: pap features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 54505401 num_examples: 1449881 download_size: 40415776 dataset_size: 54505401 - config_name: pcd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42132826 num_examples: 1221362 download_size: 30766812 dataset_size: 42132826 - config_name: pdc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14435256 num_examples: 448055 download_size: 10178322 dataset_size: 14435256 - config_name: pdt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13994892 num_examples: 437200 download_size: 9819388 dataset_size: 13994892 - config_name: pfl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15461023 num_examples: 474198 download_size: 10893651 dataset_size: 15461023 - config_name: pi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8913354 num_examples: 250251 download_size: 4651392 dataset_size: 8913354 - config_name: pih features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13971081 num_examples: 436214 download_size: 9810653 dataset_size: 13971081 - config_name: pl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 426030491 num_examples: 10025139 download_size: 295767506 dataset_size: 426030491 - config_name: pms features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 51268512 num_examples: 1477043 download_size: 37698831 dataset_size: 51268512 - config_name: pnb features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16192682 num_examples: 409037 download_size: 9196626 dataset_size: 16192682 - config_name: pnt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4439173 num_examples: 148336 download_size: 2703117 dataset_size: 4439173 - config_name: prg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17940420 num_examples: 544030 download_size: 12958482 dataset_size: 17940420 - config_name: ps features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8860902 num_examples: 259186 download_size: 4916502 dataset_size: 8860902 - config_name: pt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 491184040 num_examples: 11574568 download_size: 340831923 dataset_size: 491184040 - config_name: pt-br features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 318857431 num_examples: 7782980 download_size: 223442911 dataset_size: 318857431 - config_name: pwn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8500 num_examples: 269 download_size: 8738 dataset_size: 8500 - config_name: qu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15254702 num_examples: 468823 download_size: 10750388 dataset_size: 15254702 - config_name: quc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 32 num_examples: 1 download_size: 1772 dataset_size: 32 - config_name: qug features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13798264 num_examples: 431733 download_size: 9661685 dataset_size: 13798264 - config_name: rgn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17001688 num_examples: 519871 download_size: 12258201 dataset_size: 17001688 - config_name: rif features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13792951 num_examples: 431588 download_size: 9657698 dataset_size: 13792951 - config_name: rm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 44450577 num_examples: 1284908 download_size: 32519630 dataset_size: 44450577 - config_name: rmc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 159 num_examples: 4 download_size: 1963 dataset_size: 159 - config_name: rmy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5610156 num_examples: 179191 download_size: 3608283 dataset_size: 5610156 - config_name: rn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13935534 num_examples: 435271 download_size: 9779486 dataset_size: 13935534 - config_name: ro features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 247469452 num_examples: 5878366 download_size: 177525205 dataset_size: 247469452 - config_name: roa-tara features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14425120 num_examples: 448972 download_size: 10152875 dataset_size: 14425120 - config_name: ru features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 405103215 num_examples: 7485811 download_size: 257215625 dataset_size: 405103215 - config_name: rue features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4953403 num_examples: 159530 download_size: 3037824 dataset_size: 4953403 - config_name: rup features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14459686 num_examples: 450345 download_size: 10198398 dataset_size: 14459686 - config_name: ruq-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4434290 num_examples: 148404 download_size: 2700920 dataset_size: 4434290 - config_name: ruq-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13783683 num_examples: 430978 download_size: 9656941 dataset_size: 13783683 - config_name: rw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14090196 num_examples: 439172 download_size: 9901257 dataset_size: 14090196 - config_name: rwr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8568706 num_examples: 241841 download_size: 4388475 dataset_size: 8568706 - config_name: ryu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 2852 num_examples: 82 download_size: 4237 dataset_size: 2852 - config_name: sa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 21404327 num_examples: 455674 download_size: 9692464 dataset_size: 21404327 - config_name: sat features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 10810040 num_examples: 284911 download_size: 5750917 dataset_size: 10810040 - config_name: sc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 47195572 num_examples: 1348137 download_size: 34521764 dataset_size: 47195572 - config_name: scn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 43458983 num_examples: 1259067 download_size: 31775157 dataset_size: 43458983 - config_name: sco features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 56960413 num_examples: 1611092 download_size: 41724559 dataset_size: 56960413 - config_name: sd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14257513 num_examples: 363318 download_size: 7844047 dataset_size: 14257513 - config_name: sdc features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13975497 num_examples: 436913 download_size: 9800517 dataset_size: 13975497 - config_name: se features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 23962268 num_examples: 711439 download_size: 17409387 dataset_size: 23962268 - config_name: sei features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13827581 num_examples: 432520 download_size: 9684192 dataset_size: 13827581 - config_name: sg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13913524 num_examples: 434751 download_size: 9761739 dataset_size: 13913524 - config_name: sh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 30173635 num_examples: 746207 download_size: 20133594 dataset_size: 30173635 - config_name: shi-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13783218 num_examples: 430968 download_size: 9656828 dataset_size: 13783218 - config_name: shi-tfng features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4308577 num_examples: 145279 download_size: 2608525 dataset_size: 4308577 - config_name: shn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 10139002 num_examples: 260808 download_size: 4952168 dataset_size: 10139002 - config_name: shy-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4255322 num_examples: 144058 download_size: 2570625 dataset_size: 4255322 - config_name: si features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 7405400 num_examples: 189718 download_size: 4270591 dataset_size: 7405400 - config_name: sjd features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4300688 num_examples: 145047 download_size: 2604357 dataset_size: 4300688 - config_name: sje features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20970223 num_examples: 637639 download_size: 15120381 dataset_size: 20970223 - config_name: sju features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4315103 num_examples: 145655 download_size: 2620763 dataset_size: 4315103 - config_name: sk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 75586366 num_examples: 2050873 download_size: 54951330 dataset_size: 75586366 - config_name: skr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4274062 num_examples: 144443 download_size: 2585286 dataset_size: 4274062 - config_name: sl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 157883240 num_examples: 4112048 download_size: 118047353 dataset_size: 157883240 - config_name: sli features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13909208 num_examples: 434986 download_size: 9745964 dataset_size: 13909208 - config_name: sm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13984823 num_examples: 436830 download_size: 9817472 dataset_size: 13984823 - config_name: sma features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20653595 num_examples: 630437 download_size: 14902319 dataset_size: 20653595 - config_name: smj features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 19640206 num_examples: 604326 download_size: 14133964 dataset_size: 19640206 - config_name: smn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 10902411 num_examples: 337543 download_size: 7576850 dataset_size: 10902411 - config_name: sms features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4462345 num_examples: 149355 download_size: 2741038 dataset_size: 4462345 - config_name: sn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20116601 num_examples: 618231 download_size: 14463728 dataset_size: 20116601 - config_name: sq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 304708913 num_examples: 7311820 download_size: 225592169 dataset_size: 304708913 - config_name: sr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 52787253 num_examples: 1018361 download_size: 31364006 dataset_size: 52787253 - config_name: sr-ec features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 9237541 num_examples: 248556 download_size: 5875548 dataset_size: 9237541 - config_name: sr-el features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 48848162 num_examples: 1418824 download_size: 35859120 dataset_size: 48848162 - config_name: srq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12796525 num_examples: 405957 download_size: 8899493 dataset_size: 12796525 - config_name: ss features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13823630 num_examples: 432423 download_size: 9682165 dataset_size: 13823630 - config_name: st features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13938937 num_examples: 435419 download_size: 9785161 dataset_size: 13938937 - config_name: stq features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14484394 num_examples: 449885 download_size: 10228446 dataset_size: 14484394 - config_name: su features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20025826 num_examples: 583096 download_size: 14042822 dataset_size: 20025826 - config_name: sv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 339074900 num_examples: 8115455 download_size: 236022796 dataset_size: 339074900 - config_name: sw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 50612064 num_examples: 1465385 download_size: 37096369 dataset_size: 50612064 - config_name: szl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16772062 num_examples: 500107 download_size: 11868254 dataset_size: 16772062 - config_name: szy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4332021 num_examples: 146136 download_size: 2633271 dataset_size: 4332021 - config_name: ta features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 31251824 num_examples: 546558 download_size: 15157673 dataset_size: 31251824 - config_name: tay features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4345269 num_examples: 146938 download_size: 2632535 dataset_size: 4345269 - config_name: tcy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 8723594 num_examples: 244350 download_size: 4487471 dataset_size: 8723594 - config_name: te features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 27587665 num_examples: 569615 download_size: 13669398 dataset_size: 27587665 - config_name: tet features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15092299 num_examples: 466244 download_size: 10702917 dataset_size: 15092299 - config_name: tg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 12643125 num_examples: 304625 download_size: 7622522 dataset_size: 12643125 - config_name: tg-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4504034 num_examples: 149533 download_size: 2755000 dataset_size: 4504034 - config_name: tg-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 19845835 num_examples: 610020 download_size: 14264492 dataset_size: 19845835 - config_name: th features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 32693750 num_examples: 537447 download_size: 15849247 dataset_size: 32693750 - config_name: ti features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4366995 num_examples: 146479 download_size: 2648869 dataset_size: 4366995 - config_name: tk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5797050 num_examples: 184302 download_size: 3728802 dataset_size: 5797050 - config_name: tl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13661554 num_examples: 387377 download_size: 9456413 dataset_size: 13661554 - config_name: tly features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4309748 num_examples: 145312 download_size: 2609307 dataset_size: 4309748 - config_name: tly-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 35 num_examples: 1 download_size: 1793 dataset_size: 35 - config_name: tn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13936132 num_examples: 435219 download_size: 9780279 dataset_size: 13936132 - config_name: to features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13980327 num_examples: 436460 download_size: 9810650 dataset_size: 13980327 - config_name: tpi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14169019 num_examples: 442133 download_size: 9961827 dataset_size: 14169019 - config_name: tr features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 72134544 num_examples: 1770267 download_size: 51032484 dataset_size: 72134544 - config_name: tru features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5322844 num_examples: 171327 download_size: 3371105 dataset_size: 5322844 - config_name: trv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 94285 num_examples: 3109 download_size: 65138 dataset_size: 94285 - config_name: ts features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13943481 num_examples: 435408 download_size: 9783789 dataset_size: 13943481 - config_name: tt features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 24182976 num_examples: 548502 download_size: 14868166 dataset_size: 24182976 - config_name: tt-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4943914 num_examples: 158198 download_size: 3048932 dataset_size: 4943914 - config_name: tt-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13842972 num_examples: 432513 download_size: 9702714 dataset_size: 13842972 - config_name: tum features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13924159 num_examples: 435110 download_size: 9770501 dataset_size: 13924159 - config_name: tw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13830508 num_examples: 432669 download_size: 9688164 dataset_size: 13830508 - config_name: ty features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 16816401 num_examples: 507332 download_size: 12098154 dataset_size: 16816401 - config_name: tyv features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4583082 num_examples: 149929 download_size: 2779632 dataset_size: 4583082 - config_name: tzm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4253588 num_examples: 144002 download_size: 2569067 dataset_size: 4253588 - config_name: udm features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4854947 num_examples: 156300 download_size: 2958444 dataset_size: 4854947 - config_name: ug-arab features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4316690 num_examples: 145443 download_size: 2614962 dataset_size: 4316690 - config_name: ug-latn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13786474 num_examples: 431056 download_size: 9659723 dataset_size: 13786474 - config_name: uk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 251058352 num_examples: 5108733 download_size: 168140976 dataset_size: 251058352 - config_name: ur features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 57063750 num_examples: 987011 download_size: 28328459 dataset_size: 57063750 - config_name: uz features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 11731793 num_examples: 344615 download_size: 8102734 dataset_size: 11731793 - config_name: uz-cyrl features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4252574 num_examples: 143981 download_size: 2567325 dataset_size: 4252574 - config_name: ve features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 13932174 num_examples: 435216 download_size: 9777266 dataset_size: 13932174 - config_name: vec features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 52081230 num_examples: 1466867 download_size: 37307805 dataset_size: 52081230 - config_name: vep features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6174898 num_examples: 192298 download_size: 3994582 dataset_size: 6174898 - config_name: vi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 246835524 num_examples: 5743737 download_size: 172949263 dataset_size: 246835524 - config_name: vls features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42789297 num_examples: 1239359 download_size: 31228294 dataset_size: 42789297 - config_name: vmf features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 18352990 num_examples: 555205 download_size: 13289296 dataset_size: 18352990 - config_name: vo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 228352533 num_examples: 5610875 download_size: 155496988 dataset_size: 228352533 - config_name: vot features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5406190 num_examples: 173486 download_size: 3439433 dataset_size: 5406190 - config_name: wa features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 49235347 num_examples: 1426584 download_size: 36167816 dataset_size: 49235347 - config_name: war features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 190306474 num_examples: 4449062 download_size: 133786270 dataset_size: 190306474 - config_name: wls features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4033 num_examples: 104 download_size: 5150 dataset_size: 4033 - config_name: wo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 40961626 num_examples: 1193626 download_size: 29778666 dataset_size: 40961626 - config_name: wuu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 40570130 num_examples: 1127741 download_size: 24209117 dataset_size: 40570130 - config_name: wya features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 28 num_examples: 1 download_size: 1740 dataset_size: 28 - config_name: xal features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4475344 num_examples: 149984 download_size: 2722459 dataset_size: 4475344 - config_name: xh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 20036194 num_examples: 615514 download_size: 14405310 dataset_size: 20036194 - config_name: xmf features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5943645 num_examples: 169507 download_size: 3418593 dataset_size: 5943645 - config_name: xsy features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4262789 num_examples: 144305 download_size: 2573349 dataset_size: 4262789 - config_name: yav features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4070 num_examples: 102 download_size: 4718 dataset_size: 4070 - config_name: yi features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 5495313 num_examples: 170277 download_size: 3373820 dataset_size: 5495313 - config_name: yo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 25424749 num_examples: 724345 download_size: 18086773 dataset_size: 25424749 - config_name: za features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15159230 num_examples: 365892 download_size: 7774767 dataset_size: 15159230 - config_name: zea features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 14538518 num_examples: 451577 download_size: 10262897 dataset_size: 14538518 - config_name: zgh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 4253917 num_examples: 144006 download_size: 2569373 dataset_size: 4253917 - config_name: zh features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 264353677 num_examples: 5424320 download_size: 174420118 dataset_size: 264353677 - config_name: zh-cn features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 42868611 num_examples: 1158755 download_size: 27243799 dataset_size: 42868611 - config_name: zh-hans features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 57233156 num_examples: 1483225 download_size: 36583522 dataset_size: 57233156 - config_name: zh-hant features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 53502814 num_examples: 1356560 download_size: 36755083 dataset_size: 53502814 - config_name: zh-hk features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 15325323 num_examples: 408391 download_size: 10455809 dataset_size: 15325323 - config_name: zh-mo features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 6568267 num_examples: 180950 download_size: 3547260 dataset_size: 6568267 - config_name: zh-my features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 32637498 num_examples: 916876 download_size: 19289581 dataset_size: 32637498 - config_name: zh-sg features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 35325327 num_examples: 979652 download_size: 21150070 dataset_size: 35325327 - config_name: zh-tw features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 17500668 num_examples: 443057 download_size: 11121104 dataset_size: 17500668 - config_name: zh-yue features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 1352 num_examples: 30 download_size: 2963 dataset_size: 1352 - config_name: zu features: - name: wikidata_id dtype: string - name: lastrevid dtype: int64 - name: label dtype: string splits: - name: label num_bytes: 47349379 num_examples: 1380550 download_size: 34649660 dataset_size: 47349379 configs: - config_name: aa data_files: - split: label path: aa/label-* - config_name: ab data_files: - split: label path: ab/label-* - config_name: abs data_files: - split: label path: abs/label-* - config_name: ace data_files: - split: label path: ace/label-* - config_name: ady data_files: - split: label path: ady/label-* - config_name: ady-cyrl data_files: - split: label path: ady-cyrl/label-* - config_name: aeb data_files: - split: label path: aeb/label-* - config_name: aeb-arab data_files: - split: label path: aeb-arab/label-* - config_name: aeb-latn data_files: - split: label path: aeb-latn/label-* - config_name: af data_files: - split: label path: af/label-* - config_name: agq data_files: - split: label path: agq/label-* - config_name: ak data_files: - split: label path: ak/label-* - config_name: aln data_files: - split: label path: aln/label-* - config_name: als data_files: - split: label path: als/label-* - config_name: alt data_files: - split: label path: alt/label-* - config_name: am data_files: - split: label path: am/label-* - config_name: ami data_files: - split: label path: ami/label-* - config_name: an data_files: - split: label path: an/label-* - config_name: ang data_files: - split: label path: ang/label-* - config_name: anp data_files: - split: label path: anp/label-* - config_name: ar data_files: - split: label path: ar/label-* - config_name: arc data_files: - split: label path: arc/label-* - config_name: arn data_files: - split: label path: arn/label-* - config_name: arq data_files: - split: label path: arq/label-* - config_name: ary data_files: - split: label path: ary/label-* - config_name: arz data_files: - split: label path: arz/label-* - config_name: as data_files: - split: label path: as/label-* - config_name: ase data_files: - split: label path: ase/label-* - config_name: ast data_files: - split: label path: ast/label-* - config_name: atj data_files: - split: label path: atj/label-* - config_name: av data_files: - split: label path: av/label-* - config_name: avk data_files: - split: label path: avk/label-* - config_name: awa data_files: - split: label path: awa/label-* - config_name: ay data_files: - split: label path: ay/label-* - config_name: az data_files: - split: label path: az/label-* - config_name: azb data_files: - split: label path: azb/label-* - config_name: ba data_files: - split: label path: ba/label-* - config_name: ban data_files: - split: label path: ban/label-* - config_name: ban-bali data_files: - split: label path: ban-bali/label-* - config_name: bar data_files: - split: label path: bar/label-* - config_name: bbc data_files: - split: label path: bbc/label-* - config_name: bcc data_files: - split: label path: bcc/label-* - config_name: be data_files: - split: label path: be/label-* - config_name: be-tarask data_files: - split: label path: be-tarask/label-* - config_name: bg data_files: - split: label path: bg/label-* - config_name: bgn data_files: - split: label path: bgn/label-* - config_name: bi data_files: - split: label path: bi/label-* - config_name: bjn data_files: - split: label path: bjn/label-* - config_name: bm data_files: - split: label path: bm/label-* - config_name: bn data_files: - split: label path: bn/label-* - config_name: bo data_files: - split: label path: bo/label-* - config_name: bpy data_files: - split: label path: bpy/label-* - config_name: bqi data_files: - split: label path: bqi/label-* - config_name: br data_files: - split: label path: br/label-* - config_name: brh data_files: - split: label path: brh/label-* - config_name: bs data_files: - split: label path: bs/label-* - config_name: btm data_files: - split: label path: btm/label-* - config_name: bto data_files: - split: label path: bto/label-* - config_name: bug data_files: - split: label path: bug/label-* - config_name: bxr data_files: - split: label path: bxr/label-* - config_name: ca data_files: - split: label path: ca/label-* - config_name: cbk-zam data_files: - split: label path: cbk-zam/label-* - config_name: cdo data_files: - split: label path: cdo/label-* - config_name: ce data_files: - split: label path: ce/label-* - config_name: ceb data_files: - split: label path: ceb/label-* - config_name: ch data_files: - split: label path: ch/label-* - config_name: cho data_files: - split: label path: cho/label-* - config_name: chr data_files: - split: label path: chr/label-* - config_name: chy data_files: - split: label path: chy/label-* - config_name: ckb data_files: - split: label path: ckb/label-* - config_name: co data_files: - split: label path: co/label-* - config_name: cps data_files: - split: label path: cps/label-* - config_name: cr data_files: - split: label path: cr/label-* - config_name: crh data_files: - split: label path: crh/label-* - config_name: crh-cyrl data_files: - split: label path: crh-cyrl/label-* - config_name: crh-latn data_files: - split: label path: crh-latn/label-* - config_name: cs data_files: - split: label path: cs/label-* - config_name: csb data_files: - split: label path: csb/label-* - config_name: cv data_files: - split: label path: cv/label-* - config_name: cy data_files: - split: label path: cy/label-* - config_name: da data_files: - split: label path: da/label-* - config_name: dag data_files: - split: label path: dag/label-* - config_name: de data_files: - split: label path: de/label-* - config_name: de-at data_files: - split: label path: de-at/label-* - config_name: de-ch data_files: - split: label path: de-ch/label-* - config_name: de-formal data_files: - split: label path: de-formal/label-* - config_name: din data_files: - split: label path: din/label-* - config_name: diq data_files: - split: label path: diq/label-* - config_name: dsb data_files: - split: label path: dsb/label-* - config_name: dtp data_files: - split: label path: dtp/label-* - config_name: dty data_files: - split: label path: dty/label-* - config_name: dua data_files: - split: label path: dua/label-* - config_name: dv data_files: - split: label path: dv/label-* - config_name: dz data_files: - split: label path: dz/label-* - config_name: ee data_files: - split: label path: ee/label-* - config_name: egl data_files: - split: label path: egl/label-* - config_name: el data_files: - split: label path: el/label-* - config_name: eml data_files: - split: label path: eml/label-* - config_name: en data_files: - split: label path: en/label-* default: true - config_name: en-ca data_files: - split: label path: en-ca/label-* - config_name: en-gb data_files: - split: label path: en-gb/label-* - config_name: en-us data_files: - split: label path: en-us/label-* - config_name: eo data_files: - split: label path: eo/label-* - config_name: es data_files: - split: label path: es/label-* - config_name: es-419 data_files: - split: label path: es-419/label-* - config_name: es-formal data_files: - split: label path: es-formal/label-* - config_name: et data_files: - split: label path: et/label-* - config_name: eu data_files: - split: label path: eu/label-* - config_name: ext data_files: - split: label path: ext/label-* - config_name: fa data_files: - split: label path: fa/label-* - config_name: ff data_files: - split: label path: ff/label-* - config_name: fi data_files: - split: label path: fi/label-* - config_name: fit data_files: - split: label path: fit/label-* - config_name: fj data_files: - split: label path: fj/label-* - config_name: fkv data_files: - split: label path: fkv/label-* - config_name: fo data_files: - split: label path: fo/label-* - config_name: fr data_files: - split: label path: fr/label-* - config_name: frc data_files: - split: label path: frc/label-* - config_name: frp data_files: - split: label path: frp/label-* - config_name: frr data_files: - split: label path: frr/label-* - config_name: fur data_files: - split: label path: fur/label-* - config_name: ga data_files: - split: label path: ga/label-* - config_name: gag data_files: - split: label path: gag/label-* - config_name: gan data_files: - split: label path: gan/label-* - config_name: gan-hans data_files: - split: label path: gan-hans/label-* - config_name: gan-hant data_files: - split: label path: gan-hant/label-* - config_name: gcr data_files: - split: label path: gcr/label-* - config_name: gd data_files: - split: label path: gd/label-* - config_name: gl data_files: - split: label path: gl/label-* - config_name: glk data_files: - split: label path: glk/label-* - config_name: gn data_files: - split: label path: gn/label-* - config_name: gom data_files: - split: label path: gom/label-* - config_name: gom-deva data_files: - split: label path: gom-deva/label-* - config_name: gom-latn data_files: - split: label path: gom-latn/label-* - config_name: gor data_files: - split: label path: gor/label-* - config_name: got data_files: - split: label path: got/label-* - config_name: grc data_files: - split: label path: grc/label-* - config_name: gu data_files: - split: label path: gu/label-* - config_name: guc data_files: - split: label path: guc/label-* - config_name: guw data_files: - split: label path: guw/label-* - config_name: gv data_files: - split: label path: gv/label-* - config_name: ha data_files: - split: label path: ha/label-* - config_name: hak data_files: - split: label path: hak/label-* - config_name: haw data_files: - split: label path: haw/label-* - config_name: he data_files: - split: label path: he/label-* - config_name: hi data_files: - split: label path: hi/label-* - config_name: hif data_files: - split: label path: hif/label-* - config_name: hif-latn data_files: - split: label path: hif-latn/label-* - config_name: hil data_files: - split: label path: hil/label-* - config_name: ho data_files: - split: label path: ho/label-* - config_name: hr data_files: - split: label path: hr/label-* - config_name: hrx data_files: - split: label path: hrx/label-* - config_name: hsb data_files: - split: label path: hsb/label-* - config_name: ht data_files: - split: label path: ht/label-* - config_name: hu data_files: - split: label path: hu/label-* - config_name: hu-formal data_files: - split: label path: hu-formal/label-* - config_name: hy data_files: - split: label path: hy/label-* - config_name: hyw data_files: - split: label path: hyw/label-* - config_name: hz data_files: - split: label path: hz/label-* - config_name: ia data_files: - split: label path: ia/label-* - config_name: id data_files: - split: label path: id/label-* - config_name: ie data_files: - split: label path: ie/label-* - config_name: ig data_files: - split: label path: ig/label-* - config_name: ii data_files: - split: label path: ii/label-* - config_name: ik data_files: - split: label path: ik/label-* - config_name: ike-cans data_files: - split: label path: ike-cans/label-* - config_name: ike-latn data_files: - split: label path: ike-latn/label-* - config_name: ilo data_files: - split: label path: ilo/label-* - config_name: inh data_files: - split: label path: inh/label-* - config_name: io data_files: - split: label path: io/label-* - config_name: is data_files: - split: label path: is/label-* - config_name: it data_files: - split: label path: it/label-* - config_name: iu data_files: - split: label path: iu/label-* - config_name: ja data_files: - split: label path: ja/label-* - config_name: jam data_files: - split: label path: jam/label-* - config_name: jbo data_files: - split: label path: jbo/label-* - config_name: jv data_files: - split: label path: jv/label-* - config_name: ka data_files: - split: label path: ka/label-* - config_name: kaa data_files: - split: label path: kaa/label-* - config_name: kab data_files: - split: label path: kab/label-* - config_name: kbd data_files: - split: label path: kbd/label-* - config_name: kbd-cyrl data_files: - split: label path: kbd-cyrl/label-* - config_name: kbp data_files: - split: label path: kbp/label-* - config_name: kea data_files: - split: label path: kea/label-* - config_name: kg data_files: - split: label path: kg/label-* - config_name: khw data_files: - split: label path: khw/label-* - config_name: ki data_files: - split: label path: ki/label-* - config_name: kj data_files: - split: label path: kj/label-* - config_name: kjp data_files: - split: label path: kjp/label-* - config_name: kk data_files: - split: label path: kk/label-* - config_name: kk-arab data_files: - split: label path: kk-arab/label-* - config_name: kk-kz data_files: - split: label path: kk-kz/label-* - config_name: kk-latn data_files: - split: label path: kk-latn/label-* - config_name: kk-tr data_files: - split: label path: kk-tr/label-* - config_name: ko data_files: - split: label path: ko/label-* - config_name: ko-kp data_files: - split: label path: ko-kp/label-* - config_name: koi data_files: - split: label path: koi/label-* - config_name: kr data_files: - split: label path: kr/label-* - config_name: krc data_files: - split: label path: krc/label-* - config_name: kri data_files: - split: label path: kri/label-* - config_name: krj data_files: - split: label path: krj/label-* - config_name: krl data_files: - split: label path: krl/label-* - config_name: ks data_files: - split: label path: ks/label-* - config_name: ks-deva data_files: - split: label path: ks-deva/label-* - config_name: ksh data_files: - split: label path: ksh/label-* - config_name: ku data_files: - split: label path: ku/label-* - config_name: ku-arab data_files: - split: label path: ku-arab/label-* - config_name: ku-latn data_files: - split: label path: ku-latn/label-* - config_name: kum data_files: - split: label path: kum/label-* - config_name: kv data_files: - split: label path: kv/label-* - config_name: kw data_files: - split: label path: kw/label-* - config_name: ky data_files: - split: label path: ky/label-* - config_name: la data_files: - split: label path: la/label-* - config_name: lad data_files: - split: label path: lad/label-* - config_name: lb data_files: - split: label path: lb/label-* - config_name: lbe data_files: - split: label path: lbe/label-* - config_name: lez data_files: - split: label path: lez/label-* - config_name: lfn data_files: - split: label path: lfn/label-* - config_name: lg data_files: - split: label path: lg/label-* - config_name: li data_files: - split: label path: li/label-* - config_name: lij data_files: - split: label path: lij/label-* - config_name: liv data_files: - split: label path: liv/label-* - config_name: lki data_files: - split: label path: lki/label-* - config_name: lld data_files: - split: label path: lld/label-* - config_name: lmo data_files: - split: label path: lmo/label-* - config_name: ln data_files: - split: label path: ln/label-* - config_name: lo data_files: - split: label path: lo/label-* - config_name: loz data_files: - split: label path: loz/label-* - config_name: lt data_files: - split: label path: lt/label-* - config_name: ltg data_files: - split: label path: ltg/label-* - config_name: lus data_files: - split: label path: lus/label-* - config_name: luz data_files: - split: label path: luz/label-* - config_name: lv data_files: - split: label path: lv/label-* - config_name: lzh data_files: - split: label path: lzh/label-* - config_name: mdf data_files: - split: label path: mdf/label-* - config_name: mg data_files: - split: label path: mg/label-* - config_name: mh data_files: - split: label path: mh/label-* - config_name: mi data_files: - split: label path: mi/label-* - config_name: min data_files: - split: label path: min/label-* - config_name: mk data_files: - split: label path: mk/label-* - config_name: ml data_files: - split: label path: ml/label-* - config_name: mn data_files: - split: label path: mn/label-* - config_name: mni data_files: - split: label path: mni/label-* - config_name: mnw data_files: - split: label path: mnw/label-* - config_name: mo data_files: - split: label path: mo/label-* - config_name: mr data_files: - split: label path: mr/label-* - config_name: mrh data_files: - split: label path: mrh/label-* - config_name: mrj data_files: - split: label path: mrj/label-* - config_name: ms data_files: - split: label path: ms/label-* - config_name: ms-arab data_files: - split: label path: ms-arab/label-* - config_name: mt data_files: - split: label path: mt/label-* - config_name: mus data_files: - split: label path: mus/label-* - config_name: mwl data_files: - split: label path: mwl/label-* - config_name: my data_files: - split: label path: my/label-* - config_name: mzn data_files: - split: label path: mzn/label-* - config_name: na data_files: - split: label path: na/label-* - config_name: nah data_files: - split: label path: nah/label-* - config_name: nan-hani data_files: - split: label path: nan-hani/label-* - config_name: nap data_files: - split: label path: nap/label-* - config_name: nb data_files: - split: label path: nb/label-* - config_name: nds data_files: - split: label path: nds/label-* - config_name: nds-nl data_files: - split: label path: nds-nl/label-* - config_name: ne data_files: - split: label path: ne/label-* - config_name: new data_files: - split: label path: new/label-* - config_name: ng data_files: - split: label path: ng/label-* - config_name: nia data_files: - split: label path: nia/label-* - config_name: niu data_files: - split: label path: niu/label-* - config_name: nl data_files: - split: label path: nl/label-* - config_name: nn data_files: - split: label path: nn/label-* - config_name: 'no' data_files: - split: label path: no/label-* - config_name: nod data_files: - split: label path: nod/label-* - config_name: nov data_files: - split: label path: nov/label-* - config_name: nqo data_files: - split: label path: nqo/label-* - config_name: nrm data_files: - split: label path: nrm/label-* - config_name: nso data_files: - split: label path: nso/label-* - config_name: nv data_files: - split: label path: nv/label-* - config_name: ny data_files: - split: label path: ny/label-* - config_name: nys data_files: - split: label path: nys/label-* - config_name: oc data_files: - split: label path: oc/label-* - config_name: olo data_files: - split: label path: olo/label-* - config_name: om data_files: - split: label path: om/label-* - config_name: or data_files: - split: label path: or/label-* - config_name: os data_files: - split: label path: os/label-* - config_name: ota data_files: - split: label path: ota/label-* - config_name: pa data_files: - split: label path: pa/label-* - config_name: pam data_files: - split: label path: pam/label-* - config_name: pap data_files: - split: label path: pap/label-* - config_name: pcd data_files: - split: label path: pcd/label-* - config_name: pdc data_files: - split: label path: pdc/label-* - config_name: pdt data_files: - split: label path: pdt/label-* - config_name: pfl data_files: - split: label path: pfl/label-* - config_name: pi data_files: - split: label path: pi/label-* - config_name: pih data_files: - split: label path: pih/label-* - config_name: pl data_files: - split: label path: pl/label-* - config_name: pms data_files: - split: label path: pms/label-* - config_name: pnb data_files: - split: label path: pnb/label-* - config_name: pnt data_files: - split: label path: pnt/label-* - config_name: prg data_files: - split: label path: prg/label-* - config_name: ps data_files: - split: label path: ps/label-* - config_name: pt data_files: - split: label path: pt/label-* - config_name: pt-br data_files: - split: label path: pt-br/label-* - config_name: pwn data_files: - split: label path: pwn/label-* - config_name: qu data_files: - split: label path: qu/label-* - config_name: quc data_files: - split: label path: quc/label-* - config_name: qug data_files: - split: label path: qug/label-* - config_name: rgn data_files: - split: label path: rgn/label-* - config_name: rif data_files: - split: label path: rif/label-* - config_name: rm data_files: - split: label path: rm/label-* - config_name: rmc data_files: - split: label path: rmc/label-* - config_name: rmy data_files: - split: label path: rmy/label-* - config_name: rn data_files: - split: label path: rn/label-* - config_name: ro data_files: - split: label path: ro/label-* - config_name: roa-tara data_files: - split: label path: roa-tara/label-* - config_name: ru data_files: - split: label path: ru/label-* - config_name: rue data_files: - split: label path: rue/label-* - config_name: rup data_files: - split: label path: rup/label-* - config_name: ruq-cyrl data_files: - split: label path: ruq-cyrl/label-* - config_name: ruq-latn data_files: - split: label path: ruq-latn/label-* - config_name: rw data_files: - split: label path: rw/label-* - config_name: rwr data_files: - split: label path: rwr/label-* - config_name: ryu data_files: - split: label path: ryu/label-* - config_name: sa data_files: - split: label path: sa/label-* - config_name: sat data_files: - split: label path: sat/label-* - config_name: sc data_files: - split: label path: sc/label-* - config_name: scn data_files: - split: label path: scn/label-* - config_name: sco data_files: - split: label path: sco/label-* - config_name: sd data_files: - split: label path: sd/label-* - config_name: sdc data_files: - split: label path: sdc/label-* - config_name: se data_files: - split: label path: se/label-* - config_name: sei data_files: - split: label path: sei/label-* - config_name: sg data_files: - split: label path: sg/label-* - config_name: sh data_files: - split: label path: sh/label-* - config_name: shi-latn data_files: - split: label path: shi-latn/label-* - config_name: shi-tfng data_files: - split: label path: shi-tfng/label-* - config_name: shn data_files: - split: label path: shn/label-* - config_name: shy-latn data_files: - split: label path: shy-latn/label-* - config_name: si data_files: - split: label path: si/label-* - config_name: sjd data_files: - split: label path: sjd/label-* - config_name: sje data_files: - split: label path: sje/label-* - config_name: sju data_files: - split: label path: sju/label-* - config_name: sk data_files: - split: label path: sk/label-* - config_name: skr data_files: - split: label path: skr/label-* - config_name: sl data_files: - split: label path: sl/label-* - config_name: sli data_files: - split: label path: sli/label-* - config_name: sm data_files: - split: label path: sm/label-* - config_name: sma data_files: - split: label path: sma/label-* - config_name: smj data_files: - split: label path: smj/label-* - config_name: smn data_files: - split: label path: smn/label-* - config_name: sms data_files: - split: label path: sms/label-* - config_name: sn data_files: - split: label path: sn/label-* - config_name: sq data_files: - split: label path: sq/label-* - config_name: sr data_files: - split: label path: sr/label-* - config_name: sr-ec data_files: - split: label path: sr-ec/label-* - config_name: sr-el data_files: - split: label path: sr-el/label-* - config_name: srq data_files: - split: label path: srq/label-* - config_name: ss data_files: - split: label path: ss/label-* - config_name: st data_files: - split: label path: st/label-* - config_name: stq data_files: - split: label path: stq/label-* - config_name: su data_files: - split: label path: su/label-* - config_name: sv data_files: - split: label path: sv/label-* - config_name: sw data_files: - split: label path: sw/label-* - config_name: szl data_files: - split: label path: szl/label-* - config_name: szy data_files: - split: label path: szy/label-* - config_name: ta data_files: - split: label path: ta/label-* - config_name: tay data_files: - split: label path: tay/label-* - config_name: tcy data_files: - split: label path: tcy/label-* - config_name: te data_files: - split: label path: te/label-* - config_name: tet data_files: - split: label path: tet/label-* - config_name: tg data_files: - split: label path: tg/label-* - config_name: tg-cyrl data_files: - split: label path: tg-cyrl/label-* - config_name: tg-latn data_files: - split: label path: tg-latn/label-* - config_name: th data_files: - split: label path: th/label-* - config_name: ti data_files: - split: label path: ti/label-* - config_name: tk data_files: - split: label path: tk/label-* - config_name: tl data_files: - split: label path: tl/label-* - config_name: tly data_files: - split: label path: tly/label-* - config_name: tly-cyrl data_files: - split: label path: tly-cyrl/label-* - config_name: tn data_files: - split: label path: tn/label-* - config_name: to data_files: - split: label path: to/label-* - config_name: tpi data_files: - split: label path: tpi/label-* - config_name: tr data_files: - split: label path: tr/label-* - config_name: tru data_files: - split: label path: tru/label-* - config_name: trv data_files: - split: label path: trv/label-* - config_name: ts data_files: - split: label path: ts/label-* - config_name: tt data_files: - split: label path: tt/label-* - config_name: tt-cyrl data_files: - split: label path: tt-cyrl/label-* - config_name: tt-latn data_files: - split: label path: tt-latn/label-* - config_name: tum data_files: - split: label path: tum/label-* - config_name: tw data_files: - split: label path: tw/label-* - config_name: ty data_files: - split: label path: ty/label-* - config_name: tyv data_files: - split: label path: tyv/label-* - config_name: tzm data_files: - split: label path: tzm/label-* - config_name: udm data_files: - split: label path: udm/label-* - config_name: ug-arab data_files: - split: label path: ug-arab/label-* - config_name: ug-latn data_files: - split: label path: ug-latn/label-* - config_name: uk data_files: - split: label path: uk/label-* - config_name: ur data_files: - split: label path: ur/label-* - config_name: uz data_files: - split: label path: uz/label-* - config_name: uz-cyrl data_files: - split: label path: uz-cyrl/label-* - config_name: ve data_files: - split: label path: ve/label-* - config_name: vec data_files: - split: label path: vec/label-* - config_name: vep data_files: - split: label path: vep/label-* - config_name: vi data_files: - split: label path: vi/label-* - config_name: vls data_files: - split: label path: vls/label-* - config_name: vmf data_files: - split: label path: vmf/label-* - config_name: vo data_files: - split: label path: vo/label-* - config_name: vot data_files: - split: label path: vot/label-* - config_name: wa data_files: - split: label path: wa/label-* - config_name: war data_files: - split: label path: war/label-* - config_name: wls data_files: - split: label path: wls/label-* - config_name: wo data_files: - split: label path: wo/label-* - config_name: wuu data_files: - split: label path: wuu/label-* - config_name: wya data_files: - split: label path: wya/label-* - config_name: xal data_files: - split: label path: xal/label-* - config_name: xh data_files: - split: label path: xh/label-* - config_name: xmf data_files: - split: label path: xmf/label-* - config_name: xsy data_files: - split: label path: xsy/label-* - config_name: yav data_files: - split: label path: yav/label-* - config_name: yi data_files: - split: label path: yi/label-* - config_name: yo data_files: - split: label path: yo/label-* - config_name: za data_files: - split: label path: za/label-* - config_name: zea data_files: - split: label path: zea/label-* - config_name: zgh data_files: - split: label path: zgh/label-* - config_name: zh data_files: - split: label path: zh/label-* - config_name: zh-cn data_files: - split: label path: zh-cn/label-* - config_name: zh-hans data_files: - split: label path: zh-hans/label-* - config_name: zh-hant data_files: - split: label path: zh-hant/label-* - config_name: zh-hk data_files: - split: label path: zh-hk/label-* - config_name: zh-mo data_files: - split: label path: zh-mo/label-* - config_name: zh-my data_files: - split: label path: zh-my/label-* - config_name: zh-sg data_files: - split: label path: zh-sg/label-* - config_name: zh-tw data_files: - split: label path: zh-tw/label-* - config_name: zh-yue data_files: - split: label path: zh-yue/label-* - config_name: zu data_files: - split: label path: zu/label-* task_categories: - translation - text2text-generation language: - en - fr - de - ja - zh - hi - ar - bn - ru - es --- # Wikidata Labels Large parallel corpus for machine translation - Entity label data extracted from Wikidata (2022-01-03), filtered for item entities only - Only download the languages you need with `datasets>=2.14.0` - Similar dataset: https://huggingface.co/datasets/wmt/wikititles (18 Wikipedia titles pairs instead of all Wikidata entities) ## Dataset Details ### Dataset Sources - Wikidata JSON dump (wikidata-20220103-all.json.gz) https://www.wikidata.org/wiki/Wikidata:Database_download ## Uses You can generate parallel text examples from this dataset like below: ```python from datasets import load_dataset import pandas as pd def parallel_labels(lang_codes: list, how="inner", repo_id="rayliuca/wikidata_entity_label", merge_config={}, datasets_config={}) -> pd.DataFrame: out_df = None for lc in lang_codes: dataset = load_dataset(repo_id, lc, **datasets_config) dataset_df = dataset['label'].to_pandas().rename(columns={"label":lc}).drop(columns=['lastrevid']) if out_df is None: out_df = dataset_df else: out_df = out_df.merge( dataset_df, on='wikidata_id', how=how, **merge_config ) return out_df # Note: the "en" subset is >4GB parallel_labels(['en', 'fr', 'ja', 'zh']).head() ``` ### Output | | wikidata_id | en | fr | ja | zh | |---:|:--------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------|:---------------------------------------------| | 0 | Q109739412 | SARS-CoV-2 Omicron variant | variant Omicron du SARS-CoV-2 | SARSコロナウイルス2-オミクロン株 | 嚴重急性呼吸道症候群冠狀病毒2型Omicron變異株 | | 1 | Q108460606 | Ulughbegsaurus | Ulughbegsaurus | ウルグベグサウルス | 兀魯伯龍屬 | | 2 | Q108556886 | AUKUS | AUKUS | AUKUS | AUKUS | | 3 | Q106496152 | Claude Joseph | Claude Joseph | クロード・ジョゼフ | 克洛德·约瑟夫 | | 4 | Q105519361 | The World's Finest Assassin Gets Reincarnated in a Different World as an Aristocrat | The World's Finest Assassin Gets Reincarnated in Another World as an Aristocrat | 世界最高の暗殺者、異世界貴族に転生する | 世界頂尖的暗殺者轉生為異世界貴族 | Note: this example table above shows a quirk(?) of the Wiki data. The French Wikipedia page [The World's Finest Assassin Gets Reincarnated in Another World as an Aristocrat](https://fr.wikipedia.org/wiki/The_World%27s_Finest_Assassin_Gets_Reincarnated_in_Another_World_as_an_Aristocrat) uses English for its title. While this could be disadvantageous for direct translation training, it also provides insights into how native speakers might call this entity instead of the literal translation on the Wiki page as well ## Dataset Structure Each language has its own subset (aka config), which means you only have to download the languages you need with `datasets>=2.14.0` Each subset has these fields: - wikidata_id - lastrevid - label ## Dataset Creation #### Data Collection and Processing - Filtered for item entities only - Ignored the descriptions as those texts are not very parallel ## Bias, Risks, and Limitations - Might be slightly outdated (2022) - Popular languages have more entries - Labels are not guaranteed to be literal translations (see examples above)

CASIA-LM/ChineseWebText2.0

CASIA-LM

--- license: apache-2.0 size_categories: - n>1T --- # ChineseWebText 2.0: Large-Scale High-quality Chinese Web Text with Multi-dimensional and fine-grained information This directory contains the ChineseWebText2.0 dataset, and a new tool-chain called MDFG-tool for constructing large-scale and high-quality Chinese datasets with multi-dimensional and fine-grained information. Our ChineseWebText2.0 code is publicly available on github [(here)](https://github.com/CASIA-LM/ChineseWebText-2.0.git). ## ChineseWebText2.0 - ### Dataset Overview We have released the latest and largest Chinese dataset, ChineseWebText 2.0, which consists of 3.8 TB of data. Each text in the dataset is accompanied by a quality score, domain single-label and multi-label tags, as well as toxicity classification and scores, enabling LLM researchers to select data based on new quality thresholds. - ### Data Example ```json { "text": "近日，黑龙江省高校校报协会第十四届学术年会暨校报工作交流研讨会在东北农业大学举行。我校10件新闻作品喜获2项一等奖，2项二等奖，6项三等奖……", "domain": { "single_label": "news", "multi_label": ["news", "education"] }, "toxicity": { "label": 0, "score": 1.0347155694034882e-05 }, "quality_score": 0.96044921875 } ``` - "text": [string] Text content of data sample. - "single_label": [string] The highest probability label generated by the domain classification model. - "multi_label": [list] All labels generated by the domain classification model with probabilities higher than the threshold. - "label": [int] Toxicity label generated by toxicity classification models. - "score": [flaot] Toxicity score generated by toxicity classification model, samples with scores exceeding 0.99 were categorised as toxic. - "quality_score": [float] Quality score generated by the quality evaluation model. ## MDFG-tool ### Introduction We introduce a new toolchain, MDFG-tool (see Figure 1). We begin with the coarse-grained filtering module, which applies rule-based methods to clean the data, focusing on criteria such as text length and sensitive words to ensure data quality. After cleaning, we evaluate the text quality using a BERT-based model. This process generates a quality score, and by selecting an appropriate threshold, we can extract high-quality text data that meets our needs. Next, we use FastText for both single-label and multi-label classification of the cleaned data. Meanwhile, we conduct toxicity assessment. The FastText model is used to filter out toxic content and assign toxicity scores to each text. This scoring system allows researchers to set thresholds for identifying and selecting harmful texts for further training. <div align="center"> <img src="./Pictures/structure.png" width="67%" /> <br> <em>Figure 1: The pipeline of MDFG-tool.</em> </div> ## Data Analysis #### Removal Rate for Different Stages In order to provide a high-level overview of the preparation and preprocessing stages, the figure followed shows the processing workflow and the removal rate of each step. This figure details the removal ratio of data from the previous step and the absolute percentage of the remaining data from the original collected dataset, thereby facilitating readers in tracking the various processing stages from the raw data to the high-quality dataset. After collecting raw data from various sources, we initially obtain a original Chinese dataset totaling 6.6 TB. However, due to a significant amount of irrelevant and noisy content in some sources, a manual sampling analysis is performed in preparation stage. If irrelevant text accounted for more than 50\% of a source, the data from that source will be discarded entirely. As a result, a substantial portion of the data is removed during the preparation stage, retaining only 67.68\% of the original dataset. In preprocessing stage, four rule-based steps are implemented to filter the remained data. First, the Data Length step remove overly short texts to ensure that each text contains sufficient informational content. Next, the Character Proportion step eliminate texts with a high percentage of noisy characters, such as English, Traditional Chinese characters, or other irrelevant symbols. Finally, the Sensitive Words step and the Deduplication step are employed to remove toxic content and duplicate texts from the dataset. After the preprocessing stage, we produce a high-quality Chinese text dataset totaling 3.8 TB. In the next stage, each text in this high-quality dataset will be enriched with fine-grained annotations, including a quality score, domain lablels, a toxicity score and a toxicity label. <div align="center"> <img src="./Pictures/data_statistics.png" width="100%" /> <br> <em>Figure 2: The proportion of data removed from the originally collected data in each processing step. The gray bars represent the proportion of data removed in each step relative to the data remaining before that step, while the other colored bars represent the retained data and its proportion relative to the originally collected data.</em> </div> #### Data Quality Distribution <div align="center"> <img src="./Pictures/quality-evaluation.png" width="100%" /> <br> <em>Figure 3: The Data Analysis on Quality Evaluation.</em> </div> **Quality Distribution** To investigate the quality distribution, we calculate the data proportions across different quality score ranges from our ChineseWebText 2.0 dataset. Figure 3(a) shows the proportion of data across different quality score intervals. The data is primarily concentrated in the mid-range score intervals \([0.2, 0.4)\), each contributing approximately 18\%. Additionally, a significant proportion lies within the high-quality interval \([0.9, 1.0)\), reflecting the presence of high-quality content in the dataset. In contrast, the lowest interval \([0.1, 0.2)\) contains only a minimal fraction, indicating a limited amount of poor-quality data. Note that the quantity of quality scores in the range [0, 0.1) is zero, so this interval has been omitted. This quality distribution provides a valuable reference for LLM researchers, enabling them to select data based on desired quality thresholds. **Human Acceptance Evaluation** To validate the consistency between quality evaluation and human judgments, Figure 3(b) displays human acceptance rates across different score intervals, showing a clear positive trend: higher scores correlate with higher acceptance rates. Specifically, the highest score interval \([0.5, 1.0)\) achieves an acceptance rate exceeding 90\%, while the lowest interval \([0.1, 0.2)\) still maintains an acceptance rate of 80\%. This trend highlights the overall high quality of the data. In summary, the dataset is primarily concentrated in the mid-quality range, with higher scores strongly correlating to greater human acceptance. This alignment underscores the dataset's potential for high-quality applications, where consistency in human-like quality is essential. #### Domain Distribution To investigate the distribution of our dataset across different domains, in this section, we conduct an in-depth analysis of the data distribution across eleven distinct domains: *book*, *dialogue*, *education*, *encyclopedia*, *finance*, *law*, *math*, *medicine*, *news*, *technology*, and *general*. This analysis considers two perspectives: the overall domain distribution and the quality-related domain distribution, providing comprehensive insights into the dataset's composition across different domains. **Overall Domain Distribution** As illustrated in Figure 8, the sample counts and corresponding proportions across various domains are presented. The Encyclopedia, General, and News domains dominate the dataset, comprising 33.43\%, 32.63\%, and 28.01\% of the data, respectively. In contrast, the Math domain has the smallest share at 0.55\%, yet it still includes over 8 million samples. Figure 9 complements this with a bar chart that provides a more intuitive visualization of the data distribution. This comprehensive domain distribution enables LLM researchers to select suitable datasets, facilitating the enhancement of the model’s knowledge and capabilities in specific domains. <div align="center"> <img src="./Pictures/domain-distribution.png" width="100%" /> <br> <em>Figure 4: Data Distribution Across Different Domains.</em> </div> **Quality-Related Domain Distribution** In order to explore the domain distribution across different quality intervals, we perform an analysis focusing on the quality-related domain distribution. Specifically, we calculate the proportions of various domains within each quality interval. As shown in Figure 5, this table provides a detailed breakdown of domain proportions across different quality intervals. From the results, we observe that the distribution of domain data within each quality interval aligns closely with their overall distribution in the dataset. Based on the proportions in Figure 5, researchers can filter domain-specific data within targeted quality intervals, enabling the extraction of higher-quality domain-specific data subsets. <div align="center"> <img src="./Pictures/domain-distribution-per-quality.png" width="100%" /> <br> <em>Figure 5: Table of Domain Distribution Across Quality Levels</em> </div> #### Data Toxicity Analysis <div align="center"> <img src="./Pictures/toxicity_distribution.png" width="100%" /> <br> <em>Figure 6:The Distribution of Toxicity: A threshold of 0.99 was established, and samples with scores exceeding 0.99 were categorised as toxic.</em> </div> During the training procedure of LLMs, toxic data introduces harmful knowledge and information, which may lead the model to generate toxic outputs. In this section, we analyze the toxicity distribution within our dataset. As shown in Figure 6, it depicts the toxicity distribution of the dataset. In this figure, a higher toxicity score indicates greater toxicity. It is evident that the majority of the data in our dataset has a toxicity score of 0.0, signifying non-toxic, high-quality data. These non-toxic texts comprise 97.41\% of the dataset. Additionally, through manual analysis of the toxicity scores, we identify that data with scores above 0.99 are classified as toxic. By applying this empirical threshold, we filter our dataset and obtain a 3.16GB toxic text subset comprising 1,632,620 samples. In Figure 7, we conduct a comparison between this subset with other publicly available toxic datasets. In this table, OffensEval 2019, AbusEval, HatEval, RAL-E and ToxiGen are English toxicity datasets, while COLD, ToxiCN, SWSR and CDial-Bias are Chinese toxicity datasets. The OffensEval 2019, AbusEval, and HatEval datasets are derived from Twitter and focus on the analysis of offensive language, abusive language, and hate speech, respectively. The RAL-E dataset, sourced from a banned Reddit community, is a large-scale, unannotated English dataset. In contrast, ToxiGen is a toxicity dataset generated using GPT-3, targeting multiple groups. The COLD, SWSR, CDial-Bias, and ToxiCN datasets are collected from Chinese social media platforms including Zhihu, Weibo, and Tieba, with each dataset focusing on different groups. Compared to these datasets, ours features the largest collection of toxicity data and each text contains a toxicity score, providing researchers with a valuable resource to better optimize and evaluate LLMs' safety. <div align="center"> <img src="./Pictures/toxicity-datasets-comparison.png" width="100%" /> <br> <em>Figure 7: Table of Comparison of Different Toxicity Datasets.</em> </div> ## Citation Please cite the paper if you use the data or code in this repo. ```shell @misc{zhang2024chinesewebtext20largescalehighquality, title={ChineseWebText 2.0: Large-Scale High-quality Chinese Web Text with Multi-dimensional and fine-grained information}, author={Wanyue Zhang and Ziyong Li and Wen Yang and Chunlin Leng and Yinan Bai and Qianlong Du and Chengqing Zong and Jiajun Zhang}, year={2024}, eprint={2411.19668}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2411.19668}, } ```

bilgeyucel/seven-wonders

bilgeyucel

--- language: - en size_categories: - n<1K ---

hotpotqa/hotpot_qa

hotpotqa

--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: HotpotQA size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: hotpotqa tags: - multi-hop dataset_info: - config_name: distractor features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 45716111 num_examples: 7405 download_size: 612746344 dataset_size: 598665426 - config_name: fullwiki features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 46848601 num_examples: 7405 - name: test num_bytes: 46000102 num_examples: 7405 download_size: 660094672 dataset_size: 645798018 --- # Dataset Card for "hotpot_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://hotpotqa.github.io/](https://hotpotqa.github.io/) - **Repository:** https://github.com/hotpotqa/hotpot - **Paper:** [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://arxiv.org/abs/1809.09600) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.27 GB - **Size of the generated dataset:** 1.24 GB - **Total amount of disk used:** 2.52 GB ### Dataset Summary HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### distractor - **Size of downloaded dataset files:** 612.75 MB - **Size of the generated dataset:** 598.66 MB - **Total amount of disk used:** 1.21 GB An example of 'validation' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 21", "Sent 22"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "medium", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "comparison" } ``` #### fullwiki - **Size of downloaded dataset files:** 660.10 MB - **Size of the generated dataset:** 645.80 MB - **Total amount of disk used:** 1.31 GB An example of 'train' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 2"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "hard", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "bridge" } ``` ### Data Fields The data fields are the same among all splits. #### distractor - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. #### fullwiki - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. ### Data Splits #### distractor | |train|validation| |----------|----:|---------:| |distractor|90447| 7405| #### fullwiki | |train|validation|test| |--------|----:|---------:|---:| |fullwiki|90447| 7405|7405| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information HotpotQA is distributed under a [CC BY-SA 4.0 License](http://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{yang2018hotpotqa, title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering}, author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.}, booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})}, year={2018} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.

togethercomputer/RedPajama-Data-1T-Sample

togethercomputer

--- task_categories: - text-generation language: - en pretty_name: Red Pajama 1T Sample --- # Dataset Card for Dataset Name ### Dataset Summary RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This HuggingFace repo contains a 1B-token sample of the RedPajama dataset. The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T): | Dataset | Token Count | |---------------|-------------| | Commoncrawl | 878 Billion | | C4 | 175 Billion | | GitHub | 59 Billion | | Books | 26 Billion | | ArXiv | 28 Billion | | Wikipedia | 24 Billion | | StackExchange | 20 Billion | | Total | 1.2 Trillion | A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ``` { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...} } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use simhash to remove near duplicates. #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score. <!-- ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed] -->

Nerfgun3/bad_prompt

Nerfgun3

--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/datasets/Nerfgun3/bad_prompt/resolve/main/bad_prompt_showcase.jpg" tags: - stable-diffusion - text-to-image - image-to-image inference: false --- # Negative Embedding / Textual Inversion <img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/bad_prompt/resolve/main/bad_prompt_showcase.jpg"/> ## Idea The idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding. Side note: Embedding has proven to be very helpful for the generation of hands! :) ## Usage To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder. **Please put the embedding in the negative prompt to get the right results!** For special negative tags such as "malformed sword", you still need to add them yourself. The negative embedding is trained on a basic skeleton for the negative prompt, which should provide a high-resolution image as a result. ### Version 1: Issue: Changing the style to much. To use it in the negative prompt: ```"bad_prompt"``` Personally, I would recommend to use my embeddings with a strength of 0.8 even the negative embeddings, like ```"(bad_prompt:0.8)"``` ### Version 2: With this version I tried to reduce the amount of vectors used, aswell as the issue with the changing artstyle. The newer version is still a work in progress, but its already way better than the first version. Its in files section! I hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: "Nerfgun3#7508" ## License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

hpprc/emb

hpprc

--- language: - ja license: other dataset_info: - config_name: auto-wiki-nli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 533673945 num_examples: 198895 download_size: 362814978 dataset_size: 533673945 - config_name: auto-wiki-qa-collection features: - name: text dtype: string splits: - name: train num_bytes: 5215705706 num_examples: 8215817 download_size: 3385038265 dataset_size: 5215705706 - config_name: auto-wiki-qa-dataset features: - name: passage_id dtype: int64 - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 30767957804 num_examples: 2377503 download_size: 21875194075 dataset_size: 30767957804 - config_name: auto-wiki-qa-nemotron-collection features: - name: text dtype: string splits: - name: train num_bytes: 4202532852 num_examples: 6354725 download_size: 2709124196 dataset_size: 4202532852 - config_name: auto-wiki-qa-nemotron-dataset features: - name: passage_id dtype: int64 - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 2034181294 num_examples: 156089 download_size: 1449231482 dataset_size: 2034181294 - config_name: baobab-wiki-retrieval-collection features: - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3771123469 num_examples: 5140862 download_size: 2463376300 dataset_size: 3771123469 - config_name: baobab-wiki-retrieval-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: 'null' - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: 'null' - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 7837529 num_examples: 838 download_size: 5661379 dataset_size: 7837529 - config_name: jagovfaqs-collection features: - name: text dtype: string splits: - name: train num_bytes: 13918890 num_examples: 22794 download_size: 5874592 dataset_size: 13918890 - config_name: jagovfaqs-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: 'null' - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: 'null' - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 205284001 num_examples: 22794 download_size: 93115345 dataset_size: 205284001 - config_name: janli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 14075833 num_examples: 13496 download_size: 3088881 dataset_size: 14075833 - config_name: jaquad-collection features: - name: text dtype: string splits: - name: train num_bytes: 4213318372 num_examples: 6364369 download_size: 2716125410 dataset_size: 4213318372 - config_name: jaquad-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 410758435 num_examples: 31748 download_size: 267846825 dataset_size: 410758435 - config_name: jcommonsenseqa-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: 'null' - name: neg_ids.original sequence: 'null' splits: - name: train num_bytes: 673948 num_examples: 8939 download_size: 381605 dataset_size: 673948 - config_name: jqara-collection features: - name: text dtype: string splits: - name: train num_bytes: 4267669475 num_examples: 6433384 download_size: 2751666583 dataset_size: 4267669475 - config_name: jqara-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: int64 - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 29789340 num_examples: 2235 download_size: 22310036 dataset_size: 29789340 - config_name: jsnli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 170593490 num_examples: 144190 download_size: 88629828 dataset_size: 170593490 - config_name: jsquad-collection features: - name: text dtype: string splits: - name: train num_bytes: 4210493031 num_examples: 6369790 download_size: 2714126867 dataset_size: 4210493031 - config_name: jsquad-dataset features: - name: passage_id dtype: int64 - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 812736672 num_examples: 62859 download_size: 514718047 dataset_size: 812736672 - config_name: miracl-collection features: - name: passage_id dtype: int64 - name: docid dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3026160577.0 num_examples: 6953614 download_size: 1682864613 dataset_size: 3026160577.0 - config_name: miracl-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: string - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: int64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 32393484 num_examples: 3477 download_size: 23431039 dataset_size: 32393484 - config_name: mkqa-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: 'null' - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 129900532 num_examples: 10000 download_size: 88793974 dataset_size: 129900532 - config_name: mkqa-triplet features: - name: idx dtype: string - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string splits: - name: train num_bytes: 7640649 num_examples: 10000 download_size: 4121496 dataset_size: 7640649 - config_name: mmarco-collection features: - name: text dtype: string splits: - name: train num_bytes: 3814117634 num_examples: 8829813 download_size: 2217976936 dataset_size: 3814117634 - config_name: mmarco-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: string - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: int64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 3548801103 num_examples: 391060 download_size: 2624355417 dataset_size: 3548801103 - config_name: mr-tydi-collection features: - name: passage_id dtype: int64 - name: docid dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3061941618 num_examples: 7000027 download_size: 1702050239 dataset_size: 3061941618 - config_name: mr-tydi-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: string - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: int64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 35660240 num_examples: 3697 download_size: 25702000 dataset_size: 35660240 - config_name: niilc-qa-dataset features: - name: id dtype: string - name: anc dtype: string - name: answers sequence: string splits: - name: dev num_bytes: 94339 num_examples: 795 - name: test num_bytes: 24706 num_examples: 198 download_size: 69487 dataset_size: 119045 - config_name: nu-mnli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 145358014 num_examples: 77785 download_size: 90397670 dataset_size: 145358014 - config_name: nu-snli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 133786645 num_examples: 109154 download_size: 68979487 dataset_size: 133786645 - config_name: paws-x-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 124053741 num_examples: 49401 download_size: 75965630 dataset_size: 124053741 - config_name: qa-collection features: - name: text dtype: string splits: - name: train num_bytes: 4202542828.0 num_examples: 6354742 download_size: 2284295643 dataset_size: 4202542828.0 - config_name: quiz-no-mori-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: 'null' - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 340206118 num_examples: 25991 download_size: 241017142 dataset_size: 340206118 - config_name: quiz-works-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: 'null' - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 248971793 num_examples: 19073 download_size: 176241965 dataset_size: 248971793 - config_name: snow-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string splits: - name: train num_bytes: 63640356 num_examples: 62758 download_size: 35752257 dataset_size: 63640356 configs: - config_name: auto-wiki-nli-triplet data_files: - split: train path: auto-wiki-nli-triplet/train-* - config_name: auto-wiki-qa-collection data_files: - split: train path: auto-wiki-qa-collection/train-* - config_name: auto-wiki-qa-dataset data_files: - split: train path: auto-wiki-qa-dataset/train-* - config_name: auto-wiki-qa-nemotron-collection data_files: - split: train path: auto-wiki-qa-nemotron-collection/train-* - config_name: auto-wiki-qa-nemotron-dataset data_files: - split: train path: auto-wiki-qa-nemotron-dataset/train-* - config_name: baobab-wiki-retrieval-collection data_files: - split: train path: baobab-wiki-retrieval-collection/train-* - config_name: baobab-wiki-retrieval-dataset data_files: - split: train path: baobab-wiki-retrieval-dataset/train-* - config_name: jagovfaqs-collection data_files: - split: train path: jagovfaqs-collection/train-* - config_name: jagovfaqs-dataset data_files: - split: train path: jagovfaqs-dataset/train-* - config_name: janli-triplet data_files: - split: train path: janli-triplet/train-* - config_name: jaquad-collection data_files: - split: train path: jaquad-collection/train-* - config_name: jaquad-dataset data_files: - split: train path: jaquad-dataset/train-* - config_name: jcommonsenseqa-dataset data_files: - split: train path: jcommonsenseqa-dataset/train-* - config_name: jqara-collection data_files: - split: train path: jqara-collection/train-* - config_name: jqara-dataset data_files: - split: train path: jqara-dataset/train-* - config_name: jsnli-triplet data_files: - split: train path: jsnli-triplet/train-* - config_name: jsquad-collection data_files: - split: train path: jsquad-collection/train-* - config_name: jsquad-dataset data_files: - split: train path: jsquad-dataset/train-* - config_name: miracl-collection data_files: - split: train path: miracl-collection/train-* - config_name: miracl-dataset data_files: - split: train path: miracl-dataset/train-* - config_name: mkqa-dataset data_files: - split: train path: mkqa-dataset/train-* - config_name: mkqa-triplet data_files: - split: train path: mkqa-triplet/train-* - config_name: mmarco-collection data_files: - split: train path: mmarco-collection/train-* - config_name: mmarco-dataset data_files: - split: train path: mmarco-dataset/train-* - config_name: mr-tydi-collection data_files: - split: train path: mr-tydi-collection/train-* - config_name: mr-tydi-dataset data_files: - split: train path: mr-tydi-dataset/train-* - config_name: niilc-qa-dataset data_files: - split: dev path: niilc-qa-dataset/dev-* - split: test path: niilc-qa-dataset/test-* - config_name: nu-mnli-triplet data_files: - split: train path: nu-mnli-triplet/train-* - config_name: nu-snli-triplet data_files: - split: train path: nu-snli-triplet/train-* - config_name: paws-x-triplet data_files: - split: train path: paws-x-triplet/train-* - config_name: qa-collection data_files: - split: train path: qa-collection/train-* - config_name: quiz-no-mori-dataset data_files: - split: train path: quiz-no-mori-dataset/train-* - config_name: quiz-works-dataset data_files: - split: train path: quiz-works-dataset/train-* - config_name: snow-triplet data_files: - split: train path: snow-triplet/train-* --- still WIP ## Dataset Description - **Paper:** https://arxiv.org/abs/2409.07737 - **Point of Contact:** [Hayato Tsukagoshi](mailto:[email protected]) ## Information |Name|Type|License (根拠)| |-|-|-| |MMARCO|Retrieval|[Apache 2.0 (?)](https://huggingface.co/datasets/unicamp-dl/mmarco)| |Mr. TyDi|Retrieval|[Apache 2.0](https://huggingface.co/datasets/castorini/mr-tydi)| |MIRACL|Retrieval|[Apache 2.0](https://huggingface.co/datasets/miracl/miracl)| |JaGovFaqs|QA|[CC-BY-4.0](https://huggingface.co/datasets/matsuxr/JaGovFaqs-22k)| |Auto Wiki QA|QA & Retrieval|[CC-BY-SA-4.0](https://huggingface.co/datasets/cl-nagoya/auto-wiki-qa)| |Auto Wiki QA Nemotron|QA & Retrieval|[CC-BY-SA-4.0](https://huggingface.co/datasets/hpprc/auto-wiki-qa-nemotron)| |JCommonsenseQA|QA|[CC-BY-SA-4.0](https://github.com/yahoojapan/JGLUE)| |JSQuAD|QA & Retrieval|[CC-BY-SA-4.0](https://github.com/yahoojapan/JGLUE)| |Japanese Wikipedia Human Retrieval|QA & Retrieval|[Apache 2.0](https://huggingface.co/datasets/baobab-trees/wikipedia-human-retrieval-ja)| |JQaRA (dev, unused)|QA|[CC-BY-SA-4.0](https://huggingface.co/datasets/hotchpotch/JQaRA#:~:text=%E3%81%B0%E5%B9%B8%E3%81%84%E3%81%A7%E3%81%99%E3%80%82-,%E3%83%A9%E3%82%A4%E3%82%BB%E3%83%B3%E3%82%B9,%E3%81%A7%E3%81%82%E3%82%8B%20CC%20BY%2DSA%204.0%20%E3%81%BE%E3%81%9F%E3%81%AF%20GFDL%E3%81%A8%E3%81%97%E3%81%BE%E3%81%99%E3%80%82,-%E8%AC%9D%E8%BE%9E)| |JaQuAD|QA & Retrieval|[CC-BY-SA-3.0](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD)| |JSNLI|NLI|[CC-BY-SA-4.0](https://huggingface.co/datasets/shunk031/jsnli)| |Auto Wiki NLI|NLI|[CC-BY-SA-4.0](https://huggingface.co/datasets/hpprc/auto-wiki-nli-reward)| |NU-SNLI|NLI|[CC-BY-SA-4.0](https://huggingface.co/datasets/cl-nagoya/nu-snli)| |NU-MNLI|NLI|[CC-BY-SA-3.0, MIT, Others](https://huggingface.co/datasets/cl-nagoya/nu-mnli)| |PAWS-X|Paraphrase|[Free (二次利用自由)](https://github.com/google-research-datasets/paws?tab=License-1-ov-file#readme)| |SNOW|Paraphrase|[CC-BY-3.0](https://huggingface.co/datasets/SNOW-NLP/snow_simplified_japanese_corpus)| |MKQA|QA|[CC-BY-3.0](https://huggingface.co/datasets/apple/mkqa)| |Quiz Works|QA|[Free (二次利用自由)](https://quiz-works.com/about)| |Quiz No Mori|QA|[Free (二次利用自由)](https://quiz-schedule.info/quiz_no_mori/quizforestsecond.html)| |NIILC QA|QA|[CC-BY-SA](https://mynlp.is.s.u-tokyo.ac.jp/niilc-qa/)|

mcaleste/sat_multiple_choice_math_may_23

mcaleste

--- language: - en size_categories: - n<1K --- This is the set of math SAT questions from the May 2023 SAT, taken from here: https://www.mcelroytutoring.com/lower.php?url=44-official-sat-pdfs-and-82-official-act-pdf-practice-tests-free. Questions that included images were not included but all other math questions, including those that have tables were included.

mlfoundations/dclm-pool-1b-5x

mlfoundations

--- license: cc-by-4.0 ---

TIGER-Lab/MMLU-STEM

TIGER-Lab

--- license: mit dataset_info: - config_name: default features: - name: question dtype: string - name: choices sequence: string - name: subject dtype: string - name: answer dtype: int64 splits: - name: test num_bytes: 976986 num_examples: 3153 download_size: 487500 dataset_size: 976986 configs: - config_name: default data_files: - split: test path: data/test-* --- This contains a subset of STEM subjects defined in MMLU by the original paper. The included subjects are - 'abstract_algebra', - 'anatomy', - 'astronomy', - 'college_biology', - 'college_chemistry', - 'college_computer_science', - 'college_mathematics', - 'college_physics', - 'computer_security', - 'conceptual_physics', - 'electrical_engineering', - 'elementary_mathematics', - 'high_school_biology', - 'high_school_chemistry', - 'high_school_computer_science', - 'high_school_mathematics', - 'high_school_physics', - 'high_school_statistics', - 'machine_learning' Please cite the original MMLU paper when you are using it.

HiTZ/EusExams

HiTZ

--- license: cc-by-sa-4.0 task_categories: - question-answering - multiple-choice language: - eu - es tags: - legal pretty_name: EusExams size_categories: - 10K<n<100K configs: - config_name: eu_opeosakiadmineu data_files: - split: test path: "data/eu/opeosaki/opeosakiadmineu.jsonl" - config_name: eu_opeosakiauxenfeu data_files: - split: test path: "data/eu/opeosaki/opeosakiauxenfeu.jsonl" - config_name: eu_opeosakiauxeu data_files: - split: test path: "data/eu/opeosaki/opeosakiauxeu.jsonl" - config_name: eu_opeosakiceladoreu data_files: - split: test path: "data/eu/opeosaki/opeosakiceladoreu.jsonl" - config_name: eu_opeosakienfeu data_files: - split: test path: "data/eu/opeosaki/opeosakienfeu.jsonl" - config_name: eu_opeosakioperarioeu data_files: - split: test path: "data/eu/opeosaki/opeosakioperarioeu.jsonl" - config_name: eu_opeosakitecnicoeu data_files: - split: test path: "data/eu/opeosaki/opeosakitecnicoeu.jsonl" - config_name: eu_opeosakivarioseu data_files: - split: test path: "data/eu/opeosaki/opeosakivarioseu.jsonl" - config_name: eu_opegasteizkoudala data_files: - split: test path: "data/eu/opegasteiz/opegasteizkoudala.jsonl" - config_name: eu_opeehuadmineu data_files: - split: test path: "data/eu/opeehu/opeehuadmineu.jsonl" - config_name: eu_opeehuauxeu data_files: - split: test path: "data/eu/opeehu/opeehuauxeu.jsonl" - config_name: eu_opeehubiblioeu data_files: - split: test path: "data/eu/opeehu/opeehubiblioeu.jsonl" - config_name: eu_opeehuderechoeu data_files: - split: test path: "data/eu/opeehu/opeehuderechoeu.jsonl" - config_name: eu_opeehueconomicaseu data_files: - split: test path: "data/eu/opeehu/opeehueconomicaseu.jsonl" - config_name: eu_opeehuempresarialeseu data_files: - split: test path: "data/eu/opeehu/opeehuempresarialeseu.jsonl" - config_name: eu_opeehusubalternoeu data_files: - split: test path: "data/eu/opeehu/opeehusubalternoeu.jsonl" - config_name: eu_opeehutecnicoeu data_files: - split: test path: "data/eu/opeehu/opeehutecnicoeu.jsonl" - config_name: eu_opeehuteknikarib data_files: - split: test path: "data/eu/opeehu/opeehuteknikarib.jsonl" - config_name: eu_ejadministrari data_files: - split: test path: "data/eu/ope/ejadministrari.jsonl" - config_name: eu_ejlaguntza data_files: - split: test path: "data/eu/ope/ejlaguntza.jsonl" - config_name: eu_ejlaguntzaile data_files: - split: test path: "data/eu/ope/ejlaguntzaile.jsonl" - config_name: eu_ejteknikari data_files: - split: test path: "data/eu/ope/ejteknikari.jsonl" - config_name: eu_osakidetza1e data_files: - split: test path: "data/eu/osakidetza/osakidetza1e.jsonl" - config_name: eu_osakidetza2e data_files: - split: test path: "data/eu/osakidetza/osakidetza2e.jsonl" - config_name: eu_osakidetza3e data_files: - split: test path: "data/eu/osakidetza/osakidetza3e.jsonl" - config_name: eu_osakidetza5e data_files: - split: test path: "data/eu/osakidetza/osakidetza5e.jsonl" - config_name: eu_osakidetza6e data_files: - split: test path: "data/eu/osakidetza/osakidetza6e.jsonl" - config_name: eu_osakidetza7e data_files: - split: test path: "data/eu/osakidetza/osakidetza7e.jsonl" - config_name: eu_opebilbaoeu data_files: - split: test path: "data/eu/opebilbao/opebilbaoeu.jsonl" - config_name: es_opeosakiadmin data_files: - split: test path: "data/es/opeosaki/opeosakiadmin.jsonl" - config_name: es_opeosakiaux data_files: - split: test path: "data/es/opeosaki/opeosakiaux.jsonl" - config_name: es_opeosakiauxenf data_files: - split: test path: "data/es/opeosaki/opeosakiauxenf.jsonl" - config_name: es_opeosakicelador data_files: - split: test path: "data/es/opeosaki/opeosakicelador.jsonl" - config_name: es_opeosakienf data_files: - split: test path: "data/es/opeosaki/opeosakienf.jsonl" - config_name: es_opeosakijuridico data_files: - split: test path: "data/es/opeosaki/opeosakijuridico.jsonl" - config_name: es_opeosakioperario data_files: - split: test path: "data/es/opeosaki/opeosakioperario.jsonl" - config_name: es_opeosakitecnico data_files: - split: test path: "data/es/opeosaki/opeosakitecnico.jsonl" - config_name: es_opeosakivarios data_files: - split: test path: "data/es/opeosaki/opeosakivarios.jsonl" - config_name: es_opeayuntamientovitoria data_files: - split: test path: "data/es/opegasteiz/opeayuntamientovitoria.jsonl" - config_name: es_opeehuadmin data_files: - split: test path: "data/es/opeehu/opeehuadmin.jsonl" - config_name: es_opeehuaux data_files: - split: test path: "data/es/opeehu/opeehuaux.jsonl" - config_name: es_opeehubiblio data_files: - split: test path: "data/es/opeehu/opeehubiblio.jsonl" - config_name: es_opeehuderecho data_files: - split: test path: "data/es/opeehu/opeehuderecho.jsonl" - config_name: es_opeehueconomicas data_files: - split: test path: "data/es/opeehu/opeehueconomicas.jsonl" - config_name: es_opeehuempresariales data_files: - split: test path: "data/es/opeehu/opeehuempresariales.jsonl" - config_name: es_opeehusubalterno data_files: - split: test path: "data/es/opeehu/opeehusubalterno.jsonl" - config_name: es_opeehutecnico data_files: - split: test path: "data/es/opeehu/opeehutecnico.jsonl" - config_name: es_opeehutecnicob data_files: - split: test path: "data/es/opeehu/opeehutecnicob.jsonl" - config_name: es_ejadministrativo data_files: - split: test path: "data/es/ope/ejadministrativo.jsonl" - config_name: es_ejauxiliar data_files: - split: test path: "data/es/ope/ejauxiliar.jsonl" - config_name: es_ejsubalterno data_files: - split: test path: "data/es/ope/ejsubalterno.jsonl" - config_name: es_ejtecnico data_files: - split: test path: "data/es/ope/ejtecnico.jsonl" - config_name: es_osakidetza1c data_files: - split: test path: "data/es/osakidetza/osakidetza1c.jsonl" - config_name: es_osakidetza2c data_files: - split: test path: "data/es/osakidetza/osakidetza2c.jsonl" - config_name: es_osakidetza3c data_files: - split: test path: "data/es/osakidetza/osakidetza3c.jsonl" - config_name: es_osakidetza4c data_files: - split: test path: "data/es/osakidetza/osakidetza4c.jsonl" - config_name: es_osakidetza5c data_files: - split: test path: "data/es/osakidetza/osakidetza5c.jsonl" - config_name: es_osakidetza6c data_files: - split: test path: "data/es/osakidetza/osakidetza6c.jsonl" - config_name: es_osakidetza7c data_files: - split: test path: "data/es/osakidetza/osakidetza7c.jsonl" - config_name: es_osakidetza8c data_files: - split: test path: "data/es/osakidetza/osakidetza8c.jsonl" - config_name: es_osakidetza9c data_files: - split: test path: "data/es/osakidetza/osakidetza9c.jsonl" - config_name: es_opebilbao data_files: - split: test path: "data/es/opebilbao/opebilbao.jsonl" --- # Dataset Card for EusExams EusExams is a collection of tests designed to prepare individuals for Public Service examinations conducted by several Basque institutions, including the public health system Osakidetza, the Basque Government, the City Councils of Bilbao and Gasteiz, and the University of the Basque Country (UPV/EHU). Within each of these groups, there are different exams for public positions, such as administrative and assistant roles. Each multiple-choice question contains 2 to 4 choices (3.90 on average) and one correct answer. The dataset is mostly parallel with 16k questions in Basque and 18k in Spanish. - **Curated by:** HiTZ Research Center & IXA Research group (University of the Basque Country UPV/EHU) - **Language(s) (NLP):** Basque (eu) - 📒 Blog Post: [Latxa: An Open Language Model and Evaluation Suite for Basque](https://www.hitz.eus/en/node/340) - 📖 Paper: [Latxa: An Open Language Model and Evaluation Suite for Basque](https://arxiv.org/abs/2403.20266) - 💻 Code: [hitz-zentroa/latxa](https://github.com/hitz-zentroa/latxa) - 📧 Contact: [[email protected]](mailto:[email protected]) ## Example Basque Example: ```txt Galdera: UPV/EHUREN ONDAREA HAU DA: A. UPV/EHUk jabetzan dituen ondasunak. B. UPV/EHUk jabetzan dituen ondasun eta eskubideak. C. UPV/EHUk jabetzan edo titularitatean dituen ondasun eta eskubideak, bai eta etorkizunean eskuratzen edo esleitzen zaizkion gainerako guztiak ere. D. UPV/EHUk jabetzan dituen ondasunak, bai eta etorkizunean eskuratzen dituen gainerako guztiak ere. Erantzuna: C ``` English Translation: ```txt Question: UPV/EHU’S LEGACY IS: A. The property owned by UPV/EHU. B. The rights and property owned by the UPV/EHU. C. The rights and property of the UPV/EHU in ownership, as well as any other property acquired or assigned to it in the future. D. The property of the UPV/EHU in ownership, as well as any other property acquired or assigned to it in the future. Answer: C ``` ## Citation ```bibtex @misc{etxaniz2024latxa, title={{L}atxa: An Open Language Model and Evaluation Suite for {B}asque}, author={Julen Etxaniz and Oscar Sainz and Naiara Perez and Itziar Aldabe and German Rigau and Eneko Agirre and Aitor Ormazabal and Mikel Artetxe and Aitor Soroa}, year={2024}, eprint={2403.20266}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

fixie-ai/librispeech_asr

fixie-ai

--- language: - en dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: test num_bytes: 623948478.48 num_examples: 2620 - name: validation num_bytes: 622190064.956 num_examples: 2703 - name: train.360 num_bytes: 41953890926.124 num_examples: 104014 - name: train.100 num_bytes: 11606313661.774 num_examples: 28539 download_size: 53886816833 dataset_size: 54806343131.334 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: train.500 num_bytes: 57330687390.808 num_examples: 148688 - name: validation num_bytes: 591511495.496 num_examples: 2864 - name: test num_bytes: 616939198.113 num_examples: 2939 download_size: 57019309170 dataset_size: 58539138084.417 configs: - config_name: clean data_files: - split: test path: clean/test-* - split: validation path: clean/validation-* - split: train.360 path: clean/train.360-* - split: train.100 path: clean/train.100-* - config_name: other data_files: - split: train.500 path: other/train.500-* - split: validation path: other/validation-* - split: test path: other/test-* ---

google-research-datasets/newsgroup

google-research-datasets

--- annotations_creators: - found language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: 20 Newsgroups size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: 20-newsgroups dataset_info: - config_name: 18828_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 1669511 num_examples: 799 download_size: 14666916 dataset_size: 1669511 - config_name: 18828_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 1661199 num_examples: 973 download_size: 14666916 dataset_size: 1661199 - config_name: 18828_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 2378739 num_examples: 985 download_size: 14666916 dataset_size: 2378739 - config_name: 18828_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1185187 num_examples: 982 download_size: 14666916 dataset_size: 1185187 - config_name: 18828_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1056264 num_examples: 961 download_size: 14666916 dataset_size: 1056264 - config_name: 18828_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 1876297 num_examples: 980 download_size: 14666916 dataset_size: 1876297 - config_name: 18828_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 925124 num_examples: 972 download_size: 14666916 dataset_size: 925124 - config_name: 18828_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 1295307 num_examples: 990 download_size: 14666916 dataset_size: 1295307 - config_name: 18828_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 1206491 num_examples: 994 download_size: 14666916 dataset_size: 1206491 - config_name: 18828_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 1369551 num_examples: 994 download_size: 14666916 dataset_size: 1369551 - config_name: 18828_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 1758094 num_examples: 999 download_size: 14666916 dataset_size: 1758094 - config_name: 18828_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 2050727 num_examples: 991 download_size: 14666916 dataset_size: 2050727 - config_name: 18828_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 1237175 num_examples: 981 download_size: 14666916 dataset_size: 1237175 - config_name: 18828_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 1886363 num_examples: 990 download_size: 14666916 dataset_size: 1886363 - config_name: 18828_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 1812803 num_examples: 987 download_size: 14666916 dataset_size: 1812803 - config_name: 18828_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 2307486 num_examples: 997 download_size: 14666916 dataset_size: 2307486 - config_name: 18828_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 1922992 num_examples: 910 download_size: 14666916 dataset_size: 1922992 - config_name: 18828_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 2910324 num_examples: 940 download_size: 14666916 dataset_size: 2910324 - config_name: 18828_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 2102809 num_examples: 775 download_size: 14666916 dataset_size: 2102809 - config_name: 18828_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 1374261 num_examples: 628 download_size: 14666916 dataset_size: 1374261 - config_name: 19997_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 2562277 num_examples: 1000 download_size: 17332201 dataset_size: 2562277 - config_name: 19997_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 2181673 num_examples: 1000 download_size: 17332201 dataset_size: 2181673 - config_name: 19997_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 2898760 num_examples: 1000 download_size: 17332201 dataset_size: 2898760 - config_name: 19997_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1671166 num_examples: 1000 download_size: 17332201 dataset_size: 1671166 - config_name: 19997_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1580881 num_examples: 1000 download_size: 17332201 dataset_size: 1580881 - config_name: 19997_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 2418273 num_examples: 1000 download_size: 17332201 dataset_size: 2418273 - config_name: 19997_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 1412012 num_examples: 1000 download_size: 17332201 dataset_size: 1412012 - config_name: 19997_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 1780502 num_examples: 1000 download_size: 17332201 dataset_size: 1780502 - config_name: 19997_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 1677964 num_examples: 1000 download_size: 17332201 dataset_size: 1677964 - config_name: 19997_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 1835432 num_examples: 1000 download_size: 17332201 dataset_size: 1835432 - config_name: 19997_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 2207282 num_examples: 1000 download_size: 17332201 dataset_size: 2207282 - config_name: 19997_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 2607835 num_examples: 1000 download_size: 17332201 dataset_size: 2607835 - config_name: 19997_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 1732199 num_examples: 1000 download_size: 17332201 dataset_size: 1732199 - config_name: 19997_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 2388789 num_examples: 1000 download_size: 17332201 dataset_size: 2388789 - config_name: 19997_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 2351411 num_examples: 1000 download_size: 17332201 dataset_size: 2351411 - config_name: 19997_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 2743018 num_examples: 997 download_size: 17332201 dataset_size: 2743018 - config_name: 19997_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 2639343 num_examples: 1000 download_size: 17332201 dataset_size: 2639343 - config_name: 19997_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 3695931 num_examples: 1000 download_size: 17332201 dataset_size: 3695931 - config_name: 19997_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 3169183 num_examples: 1000 download_size: 17332201 dataset_size: 3169183 - config_name: 19997_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 2658700 num_examples: 1000 download_size: 17332201 dataset_size: 2658700 - config_name: bydate_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 1042224 num_examples: 480 - name: test num_bytes: 702920 num_examples: 319 download_size: 14464277 dataset_size: 1745144 - config_name: bydate_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 911665 num_examples: 584 - name: test num_bytes: 849632 num_examples: 389 download_size: 14464277 dataset_size: 1761297 - config_name: bydate_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 1770988 num_examples: 591 - name: test num_bytes: 706676 num_examples: 394 download_size: 14464277 dataset_size: 2477664 - config_name: bydate_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 800446 num_examples: 590 - name: test num_bytes: 485310 num_examples: 392 download_size: 14464277 dataset_size: 1285756 - config_name: bydate_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 696311 num_examples: 578 - name: test num_bytes: 468791 num_examples: 385 download_size: 14464277 dataset_size: 1165102 - config_name: bydate_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 1243463 num_examples: 593 - name: test num_bytes: 795366 num_examples: 395 download_size: 14464277 dataset_size: 2038829 - config_name: bydate_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 611210 num_examples: 585 - name: test num_bytes: 415902 num_examples: 390 download_size: 14464277 dataset_size: 1027112 - config_name: bydate_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 860646 num_examples: 594 - name: test num_bytes: 535378 num_examples: 396 download_size: 14464277 dataset_size: 1396024 - config_name: bydate_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 811151 num_examples: 598 - name: test num_bytes: 497735 num_examples: 398 download_size: 14464277 dataset_size: 1308886 - config_name: bydate_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 850740 num_examples: 597 - name: test num_bytes: 618609 num_examples: 397 download_size: 14464277 dataset_size: 1469349 - config_name: bydate_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 1189652 num_examples: 600 - name: test num_bytes: 666358 num_examples: 399 download_size: 14464277 dataset_size: 1856010 - config_name: bydate_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 1502448 num_examples: 595 - name: test num_bytes: 657727 num_examples: 396 download_size: 14464277 dataset_size: 2160175 - config_name: bydate_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 814856 num_examples: 591 - name: test num_bytes: 523095 num_examples: 393 download_size: 14464277 dataset_size: 1337951 - config_name: bydate_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 1195201 num_examples: 594 - name: test num_bytes: 791826 num_examples: 396 download_size: 14464277 dataset_size: 1987027 - config_name: bydate_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 1197965 num_examples: 593 - name: test num_bytes: 721771 num_examples: 394 download_size: 14464277 dataset_size: 1919736 - config_name: bydate_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 1358047 num_examples: 599 - name: test num_bytes: 1003668 num_examples: 398 download_size: 14464277 dataset_size: 2361715 - config_name: bydate_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 1313019 num_examples: 546 - name: test num_bytes: 701477 num_examples: 364 download_size: 14464277 dataset_size: 2014496 - config_name: bydate_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 1765833 num_examples: 564 - name: test num_bytes: 1236435 num_examples: 376 download_size: 14464277 dataset_size: 3002268 - config_name: bydate_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 1328057 num_examples: 465 - name: test num_bytes: 853395 num_examples: 310 download_size: 14464277 dataset_size: 2181452 - config_name: bydate_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 835761 num_examples: 377 - name: test num_bytes: 598452 num_examples: 251 download_size: 14464277 dataset_size: 1434213 --- # Dataset Card for "newsgroup" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://qwone.com/~jason/20Newsgroups/](http://qwone.com/~jason/20Newsgroups/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [NewsWeeder: Learning to Filter Netnews](https://doi.org/10.1016/B978-1-55860-377-6.50048-7) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 929.27 MB - **Size of the generated dataset:** 124.41 MB - **Total amount of disk used:** 1.05 GB ### Dataset Summary The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of my knowledge, it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper, though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. does not include cross-posts and includes only the "From" and "Subject" headers. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### 18828_alt.atheism - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.67 MB - **Total amount of disk used:** 16.34 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.graphics - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.66 MB - **Total amount of disk used:** 16.33 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.os.ms-windows.misc - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 2.38 MB - **Total amount of disk used:** 17.05 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.sys.ibm.pc.hardware - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.18 MB - **Total amount of disk used:** 15.85 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.sys.mac.hardware - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.06 MB - **Total amount of disk used:** 15.73 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 18828_alt.atheism - `text`: a `string` feature. #### 18828_comp.graphics - `text`: a `string` feature. #### 18828_comp.os.ms-windows.misc - `text`: a `string` feature. #### 18828_comp.sys.ibm.pc.hardware - `text`: a `string` feature. #### 18828_comp.sys.mac.hardware - `text`: a `string` feature. ### Data Splits | name |train| |------------------------------|----:| |18828_alt.atheism | 799| |18828_comp.graphics | 973| |18828_comp.os.ms-windows.misc | 985| |18828_comp.sys.ibm.pc.hardware| 982| |18828_comp.sys.mac.hardware | 961| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @incollection{LANG1995331, title = {NewsWeeder: Learning to Filter Netnews}, editor = {Armand Prieditis and Stuart Russell}, booktitle = {Machine Learning Proceedings 1995}, publisher = {Morgan Kaufmann}, address = {San Francisco (CA)}, pages = {331-339}, year = {1995}, isbn = {978-1-55860-377-6}, doi = {https://doi.org/10.1016/B978-1-55860-377-6.50048-7}, url = {https://www.sciencedirect.com/science/article/pii/B9781558603776500487}, author = {Ken Lang}, } ``` ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

nguha/legalbench

nguha

--- language: - en license: other size_categories: - 10K<n<100K task_categories: - text-classification - question-answering - text-generation tags: - legal - law - finance dataset_info: - config_name: abercrombie features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 307 num_examples: 5 - name: test num_bytes: 6240 num_examples: 95 download_size: 19558988 dataset_size: 6547 - config_name: canada_tax_court_outcomes features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2975 num_examples: 6 - name: test num_bytes: 157411 num_examples: 244 download_size: 19558988 dataset_size: 160386 - config_name: citation_prediction_classification features: - name: answer dtype: string - name: citation dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 660 num_examples: 2 - name: test num_bytes: 26112 num_examples: 108 download_size: 19558988 dataset_size: 26772 - config_name: citation_prediction_open features: - name: answer dtype: string - name: circuit dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 555 num_examples: 2 - name: test num_bytes: 13460 num_examples: 53 download_size: 19558988 dataset_size: 14015 - config_name: consumer_contracts_qa features: - name: answer dtype: string - name: contract dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 9941 num_examples: 4 - name: test num_bytes: 1221320 num_examples: 396 download_size: 19558988 dataset_size: 1231261 - config_name: contract_nli_confidentiality_of_agreement features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4070 num_examples: 8 - name: test num_bytes: 43818 num_examples: 82 download_size: 19558988 dataset_size: 47888 - config_name: contract_nli_explicit_identification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3615 num_examples: 8 - name: test num_bytes: 62133 num_examples: 109 download_size: 19558988 dataset_size: 65748 - config_name: contract_nli_inclusion_of_verbally_conveyed_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3817 num_examples: 8 - name: test num_bytes: 81933 num_examples: 139 download_size: 19558988 dataset_size: 85750 - config_name: contract_nli_limited_use features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4855 num_examples: 8 - name: test num_bytes: 98534 num_examples: 208 download_size: 19558988 dataset_size: 103389 - config_name: contract_nli_no_licensing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2591 num_examples: 8 - name: test num_bytes: 78173 num_examples: 162 download_size: 19558988 dataset_size: 80764 - config_name: contract_nli_notice_on_compelled_disclosure features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3907 num_examples: 8 - name: test num_bytes: 80470 num_examples: 142 download_size: 19558988 dataset_size: 84377 - config_name: contract_nli_permissible_acquirement_of_similar_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2736 num_examples: 8 - name: test num_bytes: 87469 num_examples: 178 download_size: 19558988 dataset_size: 90205 - config_name: contract_nli_permissible_copy features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3480 num_examples: 8 - name: test num_bytes: 39015 num_examples: 87 download_size: 19558988 dataset_size: 42495 - config_name: contract_nli_permissible_development_of_similar_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3921 num_examples: 8 - name: test num_bytes: 62603 num_examples: 136 download_size: 19558988 dataset_size: 66524 - config_name: contract_nli_permissible_post-agreement_possession features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4608 num_examples: 8 - name: test num_bytes: 65932 num_examples: 111 download_size: 19558988 dataset_size: 70540 - config_name: contract_nli_return_of_confidential_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3499 num_examples: 8 - name: test num_bytes: 35672 num_examples: 66 download_size: 19558988 dataset_size: 39171 - config_name: contract_nli_sharing_with_employees features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3173 num_examples: 8 - name: test num_bytes: 104240 num_examples: 170 download_size: 19558988 dataset_size: 107413 - config_name: contract_nli_sharing_with_third-parties features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3249 num_examples: 8 - name: test num_bytes: 104822 num_examples: 180 download_size: 19558988 dataset_size: 108071 - config_name: contract_nli_survival_of_obligations features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2272 num_examples: 8 - name: test num_bytes: 75450 num_examples: 157 download_size: 19558988 dataset_size: 77722 - config_name: contract_qa features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 2408 num_examples: 8 - name: test num_bytes: 26370 num_examples: 80 download_size: 19558988 dataset_size: 28778 - config_name: corporate_lobbying features: - name: answer dtype: string - name: bill_summary dtype: string - name: bill_title dtype: string - name: company_description dtype: string - name: company_name dtype: string - name: index dtype: string splits: - name: train num_bytes: 54334 num_examples: 10 - name: test num_bytes: 2974813 num_examples: 490 download_size: 19558988 dataset_size: 3029147 - config_name: cuad_affiliate_license-licensee features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4067 num_examples: 6 - name: test num_bytes: 115798 num_examples: 198 download_size: 19558988 dataset_size: 119865 - config_name: cuad_affiliate_license-licensor features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4247 num_examples: 6 - name: test num_bytes: 64931 num_examples: 88 download_size: 19558988 dataset_size: 69178 - config_name: cuad_anti-assignment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2070 num_examples: 6 - name: test num_bytes: 513026 num_examples: 1172 download_size: 19558988 dataset_size: 515096 - config_name: cuad_audit_rights features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2555 num_examples: 6 - name: test num_bytes: 526977 num_examples: 1216 download_size: 19558988 dataset_size: 529532 - config_name: cuad_cap_on_liability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2621 num_examples: 6 - name: test num_bytes: 587220 num_examples: 1246 download_size: 19558988 dataset_size: 589841 - config_name: cuad_change_of_control features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2231 num_examples: 6 - name: test num_bytes: 203823 num_examples: 416 download_size: 19558988 dataset_size: 206054 - config_name: cuad_competitive_restriction_exception features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2774 num_examples: 6 - name: test num_bytes: 115844 num_examples: 220 download_size: 19558988 dataset_size: 118618 - config_name: cuad_covenant_not_to_sue features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2581 num_examples: 6 - name: test num_bytes: 153799 num_examples: 308 download_size: 19558988 dataset_size: 156380 - config_name: cuad_effective_date features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2080 num_examples: 6 - name: test num_bytes: 87802 num_examples: 236 download_size: 19558988 dataset_size: 89882 - config_name: cuad_exclusivity features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1897 num_examples: 6 - name: test num_bytes: 355097 num_examples: 762 download_size: 19558988 dataset_size: 356994 - config_name: cuad_expiration_date features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1638 num_examples: 6 - name: test num_bytes: 354232 num_examples: 876 download_size: 19558988 dataset_size: 355870 - config_name: cuad_governing_law features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2420 num_examples: 6 - name: test num_bytes: 337322 num_examples: 876 download_size: 19558988 dataset_size: 339742 - config_name: cuad_insurance features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2537 num_examples: 6 - name: test num_bytes: 475827 num_examples: 1030 download_size: 19558988 dataset_size: 478364 - config_name: cuad_ip_ownership_assignment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4756 num_examples: 6 - name: test num_bytes: 294749 num_examples: 576 download_size: 19558988 dataset_size: 299505 - config_name: cuad_irrevocable_or_perpetual_license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 5328 num_examples: 6 - name: test num_bytes: 160279 num_examples: 280 download_size: 19558988 dataset_size: 165607 - config_name: cuad_joint_ip_ownership features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 5011 num_examples: 6 - name: test num_bytes: 90592 num_examples: 192 download_size: 19558988 dataset_size: 95603 - config_name: cuad_license_grant features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3690 num_examples: 6 - name: test num_bytes: 709331 num_examples: 1396 download_size: 19558988 dataset_size: 713021 - config_name: cuad_liquidated_damages features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3579 num_examples: 6 - name: test num_bytes: 97839 num_examples: 220 download_size: 19558988 dataset_size: 101418 - config_name: cuad_minimum_commitment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2874 num_examples: 6 - name: test num_bytes: 354078 num_examples: 772 download_size: 19558988 dataset_size: 356952 - config_name: cuad_most_favored_nation features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2103 num_examples: 6 - name: test num_bytes: 32800 num_examples: 64 download_size: 19558988 dataset_size: 34903 - config_name: cuad_no-solicit_of_customers features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3310 num_examples: 6 - name: test num_bytes: 40828 num_examples: 84 download_size: 19558988 dataset_size: 44138 - config_name: cuad_no-solicit_of_employees features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3619 num_examples: 6 - name: test num_bytes: 72661 num_examples: 142 download_size: 19558988 dataset_size: 76280 - config_name: cuad_non-compete features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3675 num_examples: 6 - name: test num_bytes: 211272 num_examples: 442 download_size: 19558988 dataset_size: 214947 - config_name: cuad_non-disparagement features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2168 num_examples: 6 - name: test num_bytes: 49850 num_examples: 100 download_size: 19558988 dataset_size: 52018 - config_name: cuad_non-transferable_license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3643 num_examples: 6 - name: test num_bytes: 269505 num_examples: 542 download_size: 19558988 dataset_size: 273148 - config_name: cuad_notice_period_to_terminate_renewal features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4166 num_examples: 6 - name: test num_bytes: 100014 num_examples: 222 download_size: 19558988 dataset_size: 104180 - config_name: cuad_post-termination_services features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3349 num_examples: 6 - name: test num_bytes: 419477 num_examples: 808 download_size: 19558988 dataset_size: 422826 - config_name: cuad_price_restrictions features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2945 num_examples: 6 - name: test num_bytes: 19430 num_examples: 46 download_size: 19558988 dataset_size: 22375 - config_name: cuad_renewal_term features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2163 num_examples: 6 - name: test num_bytes: 168528 num_examples: 386 download_size: 19558988 dataset_size: 170691 - config_name: cuad_revenue-profit_sharing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2581 num_examples: 6 - name: test num_bytes: 363594 num_examples: 774 download_size: 19558988 dataset_size: 366175 - config_name: cuad_rofr-rofo-rofn features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2817 num_examples: 6 - name: test num_bytes: 338243 num_examples: 690 download_size: 19558988 dataset_size: 341060 - config_name: cuad_source_code_escrow features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2696 num_examples: 6 - name: test num_bytes: 58125 num_examples: 118 download_size: 19558988 dataset_size: 60821 - config_name: cuad_termination_for_convenience features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1506 num_examples: 6 - name: test num_bytes: 181164 num_examples: 430 download_size: 19558988 dataset_size: 182670 - config_name: cuad_third_party_beneficiary features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2378 num_examples: 6 - name: test num_bytes: 24106 num_examples: 68 download_size: 19558988 dataset_size: 26484 - config_name: cuad_uncapped_liability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2570 num_examples: 6 - name: test num_bytes: 158009 num_examples: 294 download_size: 19558988 dataset_size: 160579 - config_name: cuad_unlimited-all-you-can-eat-license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2414 num_examples: 6 - name: test num_bytes: 22347 num_examples: 48 download_size: 19558988 dataset_size: 24761 - config_name: cuad_volume_restriction features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1397 num_examples: 6 - name: test num_bytes: 129456 num_examples: 322 download_size: 19558988 dataset_size: 130853 - config_name: cuad_warranty_duration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1815 num_examples: 6 - name: test num_bytes: 142580 num_examples: 320 download_size: 19558988 dataset_size: 144395 - config_name: definition_classification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1826 num_examples: 8 - name: test num_bytes: 371743 num_examples: 1337 download_size: 19558988 dataset_size: 373569 - config_name: definition_extraction features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2731 num_examples: 8 - name: test num_bytes: 254689 num_examples: 687 download_size: 19558988 dataset_size: 257420 - config_name: diversity_1 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 803 num_examples: 6 - name: test num_bytes: 41135 num_examples: 300 download_size: 19558988 dataset_size: 41938 - config_name: diversity_2 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1041 num_examples: 6 - name: test num_bytes: 53537 num_examples: 300 download_size: 19558988 dataset_size: 54578 - config_name: diversity_3 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 992 num_examples: 6 - name: test num_bytes: 50744 num_examples: 300 download_size: 19558988 dataset_size: 51736 - config_name: diversity_4 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1070 num_examples: 6 - name: test num_bytes: 53464 num_examples: 300 download_size: 19558988 dataset_size: 54534 - config_name: diversity_5 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1232 num_examples: 6 - name: test num_bytes: 62550 num_examples: 300 download_size: 19558988 dataset_size: 63782 - config_name: diversity_6 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 2016 num_examples: 6 - name: test num_bytes: 100411 num_examples: 300 download_size: 19558988 dataset_size: 102427 - config_name: function_of_decision_section features: - name: Citation dtype: string - name: Paragraph dtype: string - name: answer dtype: string - name: index dtype: string splits: - name: train num_bytes: 1547 num_examples: 7 - name: test num_bytes: 210419 num_examples: 367 download_size: 19558988 dataset_size: 211966 - config_name: hearsay features: - name: answer dtype: string - name: index dtype: string - name: slice dtype: string - name: text dtype: string splits: - name: train num_bytes: 788 num_examples: 5 - name: test num_bytes: 17150 num_examples: 94 download_size: 19558988 dataset_size: 17938 - config_name: insurance_policy_interpretation features: - name: answer dtype: string - name: claim dtype: string - name: index dtype: string - name: policy dtype: string splits: - name: train num_bytes: 3119 num_examples: 5 - name: test num_bytes: 70764 num_examples: 133 download_size: 19558988 dataset_size: 73883 - config_name: international_citizenship_questions features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 832 num_examples: 4 - name: test num_bytes: 2089107 num_examples: 9306 download_size: 19558988 dataset_size: 2089939 - config_name: jcrew_blocker features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 7352 num_examples: 6 - name: test num_bytes: 59879 num_examples: 54 download_size: 19558988 dataset_size: 67231 - config_name: learned_hands_benefits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 8267 num_examples: 6 - name: test num_bytes: 87512 num_examples: 66 download_size: 19558988 dataset_size: 95779 - config_name: learned_hands_business features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6075 num_examples: 6 - name: test num_bytes: 202116 num_examples: 174 download_size: 19558988 dataset_size: 208191 - config_name: learned_hands_consumer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6355 num_examples: 6 - name: test num_bytes: 795463 num_examples: 614 download_size: 19558988 dataset_size: 801818 - config_name: learned_hands_courts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 10693 num_examples: 6 - name: test num_bytes: 228204 num_examples: 192 download_size: 19558988 dataset_size: 238897 - config_name: learned_hands_crime features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 7322 num_examples: 6 - name: test num_bytes: 846597 num_examples: 688 download_size: 19558988 dataset_size: 853919 - config_name: learned_hands_divorce features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 10651 num_examples: 6 - name: test num_bytes: 189279 num_examples: 150 download_size: 19558988 dataset_size: 199930 - config_name: learned_hands_domestic_violence features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 11170 num_examples: 6 - name: test num_bytes: 239797 num_examples: 174 download_size: 19558988 dataset_size: 250967 - config_name: learned_hands_education features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6992 num_examples: 6 - name: test num_bytes: 79184 num_examples: 56 download_size: 19558988 dataset_size: 86176 - config_name: learned_hands_employment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 11223 num_examples: 6 - name: test num_bytes: 909220 num_examples: 710 download_size: 19558988 dataset_size: 920443 - config_name: learned_hands_estates features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5970 num_examples: 6 - name: test num_bytes: 216836 num_examples: 178 download_size: 19558988 dataset_size: 222806 - config_name: learned_hands_family features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 8714 num_examples: 6 - name: test num_bytes: 3073508 num_examples: 2265 download_size: 19558988 dataset_size: 3082222 - config_name: learned_hands_health features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6155 num_examples: 6 - name: test num_bytes: 336934 num_examples: 226 download_size: 19558988 dataset_size: 343089 - config_name: learned_hands_housing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 9726 num_examples: 6 - name: test num_bytes: 6028612 num_examples: 4494 download_size: 19558988 dataset_size: 6038338 - config_name: learned_hands_immigration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3955 num_examples: 6 - name: test num_bytes: 165352 num_examples: 134 download_size: 19558988 dataset_size: 169307 - config_name: learned_hands_torts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4484 num_examples: 6 - name: test num_bytes: 615649 num_examples: 432 download_size: 19558988 dataset_size: 620133 - config_name: learned_hands_traffic features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6250 num_examples: 6 - name: test num_bytes: 667539 num_examples: 556 download_size: 19558988 dataset_size: 673789 - config_name: legal_reasoning_causality features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4688 num_examples: 4 - name: test num_bytes: 87007 num_examples: 55 download_size: 19558988 dataset_size: 91695 - config_name: maud_ability_to_consummate_concept_is_subject_to_mae_carveouts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5322 num_examples: 1 - name: test num_bytes: 304051 num_examples: 69 download_size: 19558988 dataset_size: 309373 - config_name: maud_accuracy_of_fundamental_target_rws_bringdown_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 271 num_examples: 1 - name: test num_bytes: 148869 num_examples: 175 download_size: 19558988 dataset_size: 149140 - config_name: maud_accuracy_of_target_capitalization_rw_(outstanding_shares)_bringdown_standard_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1493 num_examples: 1 - name: test num_bytes: 152224 num_examples: 181 download_size: 19558988 dataset_size: 153717 - config_name: maud_accuracy_of_target_general_rw_bringdown_timing_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1000 num_examples: 1 - name: test num_bytes: 152717 num_examples: 181 download_size: 19558988 dataset_size: 153717 - config_name: maud_additional_matching_rights_period_for_modifications_(cor) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2170 num_examples: 1 - name: test num_bytes: 312632 num_examples: 158 download_size: 19558988 dataset_size: 314802 - config_name: maud_application_of_buyer_consent_requirement_(negative_interim_covenant) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 558 num_examples: 1 - name: test num_bytes: 96990 num_examples: 180 download_size: 19558988 dataset_size: 97548 - config_name: maud_buyer_consent_requirement_(ordinary_course) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2620 num_examples: 1 - name: test num_bytes: 138668 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_change_in_law__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6000 num_examples: 1 - name: test num_bytes: 448666 num_examples: 99 download_size: 19558988 dataset_size: 454666 - config_name: maud_changes_in_gaap_or_other_accounting_principles__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5998 num_examples: 1 - name: test num_bytes: 444442 num_examples: 98 download_size: 19558988 dataset_size: 450440 - config_name: maud_cor_permitted_in_response_to_intervening_event features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2631 num_examples: 1 - name: test num_bytes: 195447 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_cor_permitted_with_board_fiduciary_determination_only features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3970 num_examples: 1 - name: test num_bytes: 194108 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_cor_standard_(intervening_event) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 727 num_examples: 1 - name: test num_bytes: 175140 num_examples: 84 download_size: 19558988 dataset_size: 175867 - config_name: maud_cor_standard_(superior_offer) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1173 num_examples: 1 - name: test num_bytes: 196905 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_definition_contains_knowledge_requirement_-_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1899 num_examples: 1 - name: test num_bytes: 231405 num_examples: 147 download_size: 19558988 dataset_size: 233304 - config_name: maud_definition_includes_asset_deals features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 614 num_examples: 1 - name: test num_bytes: 289644 num_examples: 146 download_size: 19558988 dataset_size: 290258 - config_name: maud_definition_includes_stock_deals features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 683 num_examples: 1 - name: test num_bytes: 292466 num_examples: 148 download_size: 19558988 dataset_size: 293149 - config_name: maud_fiduciary_exception__board_determination_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1594 num_examples: 1 - name: test num_bytes: 288180 num_examples: 179 download_size: 19558988 dataset_size: 289774 - config_name: maud_fiduciary_exception_board_determination_trigger_(no_shop) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3538 num_examples: 1 - name: test num_bytes: 286236 num_examples: 179 download_size: 19558988 dataset_size: 289774 - config_name: maud_financial_point_of_view_is_the_sole_consideration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3290 num_examples: 1 - name: test num_bytes: 217048 num_examples: 112 download_size: 19558988 dataset_size: 220338 - config_name: maud_fls_(mae)_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4669 num_examples: 1 - name: test num_bytes: 349856 num_examples: 77 download_size: 19558988 dataset_size: 354525 - config_name: maud_general_economic_and_financial_conditions_subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5998 num_examples: 1 - name: test num_bytes: 445306 num_examples: 98 download_size: 19558988 dataset_size: 451304 - config_name: maud_includes_consistent_with_past_practice features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1127 num_examples: 1 - name: test num_bytes: 140161 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_initial_matching_rights_period_(cor) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3041 num_examples: 1 - name: test num_bytes: 311761 num_examples: 158 download_size: 19558988 dataset_size: 314802 - config_name: maud_initial_matching_rights_period_(ftr) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1850 num_examples: 1 - name: test num_bytes: 279202 num_examples: 132 download_size: 19558988 dataset_size: 281052 - config_name: maud_intervening_event_-_required_to_occur_after_signing_-_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3055 num_examples: 1 - name: test num_bytes: 230249 num_examples: 147 download_size: 19558988 dataset_size: 233304 - config_name: maud_knowledge_definition features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 240 num_examples: 1 - name: test num_bytes: 359730 num_examples: 167 download_size: 19558988 dataset_size: 359970 - config_name: maud_liability_standard_for_no-shop_breach_by_target_non-do_representatives features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 154 num_examples: 1 - name: test num_bytes: 40946 num_examples: 156 download_size: 19558988 dataset_size: 41100 - config_name: maud_ordinary_course_efforts_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1037 num_examples: 1 - name: test num_bytes: 140251 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_pandemic_or_other_public_health_event__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3728 num_examples: 1 - name: test num_bytes: 447053 num_examples: 98 download_size: 19558988 dataset_size: 450781 - config_name: maud_pandemic_or_other_public_health_event_specific_reference_to_pandemic-related_governmental_responses_or_measures features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3728 num_examples: 1 - name: test num_bytes: 447053 num_examples: 98 download_size: 19558988 dataset_size: 450781 - config_name: maud_relational_language_(mae)_applies_to features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4948 num_examples: 1 - name: test num_bytes: 409477 num_examples: 90 download_size: 19558988 dataset_size: 414425 - config_name: maud_specific_performance features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 771 num_examples: 1 - name: test num_bytes: 107392 num_examples: 178 download_size: 19558988 dataset_size: 108163 - config_name: maud_tail_period_length features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 406 num_examples: 1 - name: test num_bytes: 108632 num_examples: 179 download_size: 19558988 dataset_size: 109038 - config_name: maud_type_of_consideration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 258 num_examples: 1 - name: test num_bytes: 139270 num_examples: 172 download_size: 19558988 dataset_size: 139528 - config_name: nys_judicial_ethics features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: year dtype: string splits: - name: train num_bytes: 1697 num_examples: 8 - name: test num_bytes: 53974 num_examples: 292 download_size: 19558988 dataset_size: 55671 - config_name: opp115_data_retention features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1791 num_examples: 8 - name: test num_bytes: 18620 num_examples: 88 download_size: 19558988 dataset_size: 20411 - config_name: opp115_data_security features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2123 num_examples: 8 - name: test num_bytes: 352667 num_examples: 1334 download_size: 19558988 dataset_size: 354790 - config_name: opp115_do_not_track features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2507 num_examples: 8 - name: test num_bytes: 26363 num_examples: 110 download_size: 19558988 dataset_size: 28870 - config_name: opp115_first_party_collection_use features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2227 num_examples: 8 - name: test num_bytes: 463566 num_examples: 2086 download_size: 19558988 dataset_size: 465793 - config_name: opp115_international_and_specific_audiences features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1643 num_examples: 8 - name: test num_bytes: 338196 num_examples: 980 download_size: 19558988 dataset_size: 339839 - config_name: opp115_policy_change features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1201 num_examples: 8 - name: test num_bytes: 94060 num_examples: 431 download_size: 19558988 dataset_size: 95261 - config_name: opp115_third_party_sharing_collection features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1217 num_examples: 8 - name: test num_bytes: 383909 num_examples: 1590 download_size: 19558988 dataset_size: 385126 - config_name: opp115_user_access,_edit_and_deletion features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1251 num_examples: 8 - name: test num_bytes: 108969 num_examples: 462 download_size: 19558988 dataset_size: 110220 - config_name: opp115_user_choice_control features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1695 num_examples: 8 - name: test num_bytes: 353113 num_examples: 1546 download_size: 19558988 dataset_size: 354808 - config_name: oral_argument_question_purpose features: - name: Docket No. dtype: string - name: answer dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 2415 num_examples: 7 - name: test num_bytes: 95262 num_examples: 312 download_size: 19558988 dataset_size: 97677 - config_name: overruling features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 629 num_examples: 6 - name: test num_bytes: 443484 num_examples: 2394 download_size: 19558988 dataset_size: 444113 - config_name: personal_jurisdiction features: - name: answer dtype: string - name: index dtype: string - name: slice dtype: string - name: text dtype: string splits: - name: train num_bytes: 1660 num_examples: 4 - name: test num_bytes: 21089 num_examples: 50 download_size: 19558988 dataset_size: 22749 - config_name: privacy_policy_entailment features: - name: answer dtype: string - name: description dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6282 num_examples: 8 - name: test num_bytes: 3174950 num_examples: 4335 download_size: 19558988 dataset_size: 3181232 - config_name: privacy_policy_qa features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 2231 num_examples: 8 - name: test num_bytes: 2817986 num_examples: 10923 download_size: 19558988 dataset_size: 2820217 - config_name: proa features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1057 num_examples: 5 - name: test num_bytes: 25475 num_examples: 95 download_size: 19558988 dataset_size: 26532 - config_name: rule_qa features: - name: answer dtype: string - name: doctrine dtype: string - name: index dtype: string - name: text dtype: string splits: - name: test num_bytes: 12665 num_examples: 50 download_size: 19558988 dataset_size: 12665 - config_name: sara_entailment features: - name: answer dtype: string - name: case id dtype: string - name: description dtype: string - name: index dtype: string - name: question dtype: string - name: statute dtype: string - name: text dtype: string splits: - name: train num_bytes: 2528 num_examples: 4 - name: test num_bytes: 225560 num_examples: 272 download_size: 19558988 dataset_size: 228088 - config_name: sara_numeric features: - name: answer dtype: string - name: case id dtype: string - name: description dtype: string - name: index dtype: string - name: question dtype: string - name: statute dtype: string - name: text dtype: string splits: - name: train num_bytes: 238363 num_examples: 4 - name: test num_bytes: 5725392 num_examples: 96 download_size: 19558988 dataset_size: 5963755 - config_name: scalr features: - name: answer dtype: string - name: choice_0 dtype: string - name: choice_1 dtype: string - name: choice_2 dtype: string - name: choice_3 dtype: string - name: choice_4 dtype: string - name: index dtype: string - name: question dtype: string splits: - name: test num_bytes: 1026740 num_examples: 571 download_size: 19558988 dataset_size: 1026740 - config_name: ssla_company_defendants features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5847 num_examples: 3 - name: test num_bytes: 2313039 num_examples: 1228 download_size: 19558988 dataset_size: 2318886 - config_name: ssla_individual_defendants features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5962 num_examples: 3 - name: test num_bytes: 2002620 num_examples: 1012 download_size: 19558988 dataset_size: 2008582 - config_name: ssla_plaintiff features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5831 num_examples: 3 - name: test num_bytes: 1926518 num_examples: 1033 download_size: 19558988 dataset_size: 1932349 - config_name: successor_liability features: - name: answer dtype: string - name: index dtype: string - name: issue dtype: string - name: text dtype: string splits: - name: train num_bytes: 1734 num_examples: 3 - name: test num_bytes: 26490 num_examples: 47 download_size: 19558988 dataset_size: 28224 - config_name: supply_chain_disclosure_best_practice_accountability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 18987 num_examples: 8 - name: test num_bytes: 1347025 num_examples: 379 download_size: 19558988 dataset_size: 1366012 - config_name: supply_chain_disclosure_best_practice_audits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 23879 num_examples: 8 - name: test num_bytes: 1342065 num_examples: 379 download_size: 19558988 dataset_size: 1365944 - config_name: supply_chain_disclosure_best_practice_certification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 22058 num_examples: 8 - name: test num_bytes: 1338516 num_examples: 378 download_size: 19558988 dataset_size: 1360574 - config_name: supply_chain_disclosure_best_practice_training features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 24071 num_examples: 8 - name: test num_bytes: 1341885 num_examples: 379 download_size: 19558988 dataset_size: 1365956 - config_name: supply_chain_disclosure_best_practice_verification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 27158 num_examples: 8 - name: test num_bytes: 1338739 num_examples: 379 download_size: 19558988 dataset_size: 1365897 - config_name: supply_chain_disclosure_disclosed_accountability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 18902 num_examples: 8 - name: test num_bytes: 1344444 num_examples: 378 download_size: 19558988 dataset_size: 1363346 - config_name: supply_chain_disclosure_disclosed_audits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 24404 num_examples: 8 - name: test num_bytes: 1341624 num_examples: 379 download_size: 19558988 dataset_size: 1366028 - config_name: supply_chain_disclosure_disclosed_certification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 17987 num_examples: 8 - name: test num_bytes: 1342646 num_examples: 378 download_size: 19558988 dataset_size: 1360633 - config_name: supply_chain_disclosure_disclosed_training features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 27093 num_examples: 8 - name: test num_bytes: 1338919 num_examples: 379 download_size: 19558988 dataset_size: 1366012 - config_name: supply_chain_disclosure_disclosed_verification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 25387 num_examples: 8 - name: test num_bytes: 1340578 num_examples: 379 download_size: 19558988 dataset_size: 1365965 - config_name: telemarketing_sales_rule features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1230 num_examples: 4 - name: test num_bytes: 17140 num_examples: 47 download_size: 19558988 dataset_size: 18370 - config_name: textualism_tool_dictionaries features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4842 num_examples: 4 - name: test num_bytes: 102644 num_examples: 107 download_size: 19558988 dataset_size: 107486 - config_name: textualism_tool_plain features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3338 num_examples: 4 - name: test num_bytes: 167428 num_examples: 165 download_size: 19558988 dataset_size: 170766 - config_name: ucc_v_common_law features: - name: answer dtype: string - name: contract dtype: string - name: index dtype: string splits: - name: train num_bytes: 904 num_examples: 6 - name: test num_bytes: 12694 num_examples: 94 download_size: 19558988 dataset_size: 13598 - config_name: unfair_tos features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3308 num_examples: 9 - name: test num_bytes: 787108 num_examples: 3813 download_size: 19558988 dataset_size: 790416 --- # Dataset Card for Dataset Name - **Homepage: https://hazyresearch.stanford.edu/legalbench/** - **Repository: https://github.com/HazyResearch/legalbench/** - **Paper: https://arxiv.org/abs/2308.11462** ## Dataset Description ### Dataset Summary The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors. Note: Because LegalBench is intended to test zero and few-shot reasoning, the available "train" splits are small. However, if you are interested in finetuning models or studying model performance in a more traditional train/test regime, you can combine and re-partition train and test data. If you have questions about the project or would like to get involved, please see the website for more information. ### Supported Tasks and Leaderboards LegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process. ### Languages All LegalBench tasks are in English. ## Dataset Structure ### Data Instances Detailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the `abercrombie` task, is provided below: ``` { "text": "The mark "Ivory" for a product made of elephant tusks.", "label": "generic" "idx": 0 } ``` A substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is "Yes" or "No". ### Data Fields Detailed descriptions of the instances for each task can be found on the Github. ### Data Splits Each task (except for `rule_qa` and `scalr`) has both a training and evaluation split. Following [RAFT](https://huggingface.co/datasets/ought/raft), train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs. ## Dataset Creation ### Curation Rationale LegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs. ### Source Data #### Initial Data Collection and Normalization Broadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original [CUAD dataset](https://github.com/TheAtticusProject/cuad) contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details. #### Who are the source language producers? LegalBench data was created by humans. Demographic information for these individuals is not available. ### Annotations #### Annotation process Please see the paper for more information on the annotation process used in the creation of each task. #### Who are the annotators? Please see the paper for more information on the identity of annotators for each task. ### Personal and Sensitive Information Data in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database). Several tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues. ## Considerations for Using the Data ### Social Impact of Dataset Please see the original paper for a discussion of social impact. ### Discussion of Biases Please see the original paper for a discussion of social impact. ### Other Known Limitations LegalBench primarily contains tasks corresponding to American law. ## Additional Information ### Dataset Curators Please see the website for a full list of participants in the LegalBench project. ### Licensing Information LegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses. ### Citation Information If you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github). ``` @misc{guha2023legalbench, title={LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models}, author={Neel Guha and Julian Nyarko and Daniel E. Ho and Christopher Ré and Adam Chilton and Aditya Narayana and Alex Chohlas-Wood and Austin Peters and Brandon Waldon and Daniel N. Rockmore and Diego Zambrano and Dmitry Talisman and Enam Hoque and Faiz Surani and Frank Fagan and Galit Sarfaty and Gregory M. Dickinson and Haggai Porat and Jason Hegland and Jessica Wu and Joe Nudell and Joel Niklaus and John Nay and Jonathan H. Choi and Kevin Tobia and Margaret Hagan and Megan Ma and Michael Livermore and Nikon Rasumov-Rahe and Nils Holzenberger and Noam Kolt and Peter Henderson and Sean Rehaag and Sharad Goel and Shang Gao and Spencer Williams and Sunny Gandhi and Tom Zur and Varun Iyer and Zehua Li}, year={2023}, eprint={2308.11462}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{koreeda2021contractnli, title={ContractNLI: A dataset for document-level natural language inference for contracts}, author={Koreeda, Yuta and Manning, Christopher D}, journal={arXiv preprint arXiv:2110.01799}, year={2021} } @article{hendrycks2021cuad, title={Cuad: An expert-annotated nlp dataset for legal contract review}, author={Hendrycks, Dan and Burns, Collin and Chen, Anya and Ball, Spencer}, journal={arXiv preprint arXiv:2103.06268}, year={2021} } @article{wang2023maud, title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding}, author={Wang, Steven H and Scardigli, Antoine and Tang, Leonard and Chen, Wei and Levkin, Dimitry and Chen, Anya and Ball, Spencer and Woodside, Thomas and Zhang, Oliver and Hendrycks, Dan}, journal={arXiv preprint arXiv:2301.00876}, year={2023} } @inproceedings{wilson2016creation, title={The creation and analysis of a website privacy policy corpus}, author={Wilson, Shomir and Schaub, Florian and Dara, Aswarth Abhilash and Liu, Frederick and Cherivirala, Sushain and Leon, Pedro Giovanni and Andersen, Mads Schaarup and Zimmeck, Sebastian and Sathyendra, Kanthashree Mysore and Russell, N Cameron and others}, booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={1330--1340}, year={2016} } @inproceedings{zheng2021does, title={When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings}, author={Zheng, Lucia and Guha, Neel and Anderson, Brandon R and Henderson, Peter and Ho, Daniel E}, booktitle={Proceedings of the eighteenth international conference on artificial intelligence and law}, pages={159--168}, year={2021} } @article{zimmeck2019maps, title={Maps: Scaling privacy compliance analysis to a million apps}, author={Zimmeck, Sebastian and Story, Peter and Smullen, Daniel and Ravichander, Abhilasha and Wang, Ziqi and Reidenberg, Joel R and Russell, N Cameron and Sadeh, Norman}, journal={Proc. Priv. Enhancing Tech.}, volume={2019}, pages={66}, year={2019} } @article{ravichander2019question, title={Question answering for privacy policies: Combining computational and legal perspectives}, author={Ravichander, Abhilasha and Black, Alan W and Wilson, Shomir and Norton, Thomas and Sadeh, Norman}, journal={arXiv preprint arXiv:1911.00841}, year={2019} } @article{holzenberger2021factoring, title={Factoring statutory reasoning as language understanding challenges}, author={Holzenberger, Nils and Van Durme, Benjamin}, journal={arXiv preprint arXiv:2105.07903}, year={2021} } @article{lippi2019claudette, title={CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service}, author={Lippi, Marco and Pa{\l}ka, Przemys{\l}aw and Contissa, Giuseppe and Lagioia, Francesca and Micklitz, Hans-Wolfgang and Sartor, Giovanni and Torroni, Paolo}, journal={Artificial Intelligence and Law}, volume={27}, pages={117--139}, year={2019}, publisher={Springer} } ```

JonasGeiping/the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020

JonasGeiping

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 43860000000 num_examples: 85000000 download_size: 24001057282 dataset_size: 43860000000 annotations_creators: - no-annotation language_creators: - found language: - en license: other multilinguality: - monolingual pretty_name: pretokenized,filtered,sorted subset of the Pile size_categories: - 10B<n<100B source_datasets: - the-pile task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: the-pile-cramming --- # Dataset Card for "the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020" ## Dataset Description - **Repository:** https://github.com/JonasGeiping/cramming - **Paper:** https://arxiv.org/abs/2212.14034 - **Raw Data Source Paper:** [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027) - **Raw Data Source Datasheet:** [Datasheet for the Pile](https://arxiv.org/abs/2201.07311) ### Dataset Summary This is a preprocessed, tokenized dataset for the cramming-project. Use only with the tokenizer uploaded here. This version is `2efdb9d060d1ae95faf952ec1a50f020`, which corresponds to a specific dataset construction setup, described below. The raw data source is the Pile, a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. ### Languages This dataset is in English (`EN`). ### Data Splits This preprocessed subset contains only a train split. ## Dataset Creation The configuration to create this dataset with the cramming project code (https://github.com/JonasGeiping/cramming) is ``` # This is a slice of the pile name: the_pile defaults: - sources: - the_pile # # Preprocessing normalizer: force_lowercase: True strip_accents: True force_english_keyboard: True whitespace_escape: False tokenizer: WordPiece vocab_size: 32768 # Dataset Formation seq_length: 128 include_cls_token_in_corpus: False include_sep_token_in_corpus: True use_type_ids: False max_entries_in_raw_dataset: 16e6 max_seq_in_tokenized_dataset: 85e6 # Data Cleaning: named_entity_simplification: False remove_whitespaces: False remove_trash: True trash_cutoff: 0.25 deduplicate_entries: False deduplication_threshold: 75 # Data Order: ordering: sentence-length-curriculum ``` ## Considerations for Using the Data Limitations and bias: This training data was further filtered and sorted beyond the normal preprocessing. These modifications were not tested for unintended consequences. ## Additional Information ### Dataset Curators This dataset is a filtered, sorted and preprocessed subset of the the-Pile made by Jonas Geiping . The original dataset was primarily curated by Leo Gao and Stella Biderman, with assistance from other authors of the Pile paper. ### Licensing Information Please refer to the specific license depending on the subset you use at https://huggingface.co/datasets/EleutherAI/pile ### Citation Information Filtered version for the cramming project: ``` @article{geiping_cramming_2022, title = {Cramming: {{Training}} a {{Language Model}} on a {{Single GPU}} in {{One Day}}}, shorttitle = {Cramming}, author = {Geiping, Jonas and Goldstein, Tom}, year = {2022}, month = dec, eprint = {2212.14034}, primaryclass = {cs}, publisher = {{arXiv}}, doi = {10.48550/arXiv.2212.14034}, url = {http://arxiv.org/abs/2212.14034}, urldate = {2023-01-10}, archiveprefix = {arxiv}, keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning}, journal = {arxiv:2212.14034[cs]} } ``` Original Data Curation: ``` @article{gao2020pile, title={The {P}ile: An 800{GB} dataset of diverse text for language modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } @article{biderman2022datasheet, title={Datasheet for the pile}, author={Biderman, Stella and Bicheno, Kieran and Gao, Leo}, journal={arXiv preprint arXiv:2201.07311}, year={2022} } ```

CohereForAI/Global-MMLU

CohereForAI

--- dataset_info: - config_name: am features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 209505 num_examples: 285 - name: test num_bytes: 12085768 num_examples: 14042 download_size: 10260448 dataset_size: 12295273 - config_name: ar features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 202343 num_examples: 285 - name: test num_bytes: 11621977 num_examples: 14042 download_size: 9817049 dataset_size: 11824320 - config_name: bn features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 301875 num_examples: 285 - name: test num_bytes: 18061158 num_examples: 14042 download_size: 12524784 dataset_size: 18363033 - config_name: cs features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 149807 num_examples: 285 - name: test num_bytes: 8607308 num_examples: 14042 download_size: 8640151 dataset_size: 8757115 - config_name: de features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 162406 num_examples: 285 - name: test num_bytes: 9575360 num_examples: 14042 download_size: 9187953 dataset_size: 9737766 - config_name: el features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 254308 num_examples: 285 - name: test num_bytes: 14502137 num_examples: 14042 download_size: 12288940 dataset_size: 14756445 - config_name: en features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 146364 num_examples: 285 - name: test num_bytes: 8440632 num_examples: 14042 download_size: 7912429 dataset_size: 8586996 - config_name: es features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 160633 num_examples: 285 - name: test num_bytes: 9399724 num_examples: 14042 download_size: 8752720 dataset_size: 9560357 - config_name: fa features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 202609 num_examples: 285 - name: test num_bytes: 11611890 num_examples: 14042 download_size: 9564082 dataset_size: 11814499 - config_name: fil features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 165182 num_examples: 285 - name: test num_bytes: 9510179 num_examples: 14042 download_size: 8564879 dataset_size: 9675361 - config_name: fr features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 166173 num_examples: 285 - name: test num_bytes: 9858873 num_examples: 14042 download_size: 9202595 dataset_size: 10025046 - config_name: ha features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 147406 num_examples: 285 - name: test num_bytes: 8445707 num_examples: 14042 download_size: 7665529 dataset_size: 8593113 - config_name: he features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 178912 num_examples: 285 - name: test num_bytes: 10248592 num_examples: 14042 download_size: 8818618 dataset_size: 10427504 - config_name: hi features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 308254 num_examples: 285 - name: test num_bytes: 17970478 num_examples: 14042 download_size: 12407854 dataset_size: 18278732 - config_name: id features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 154692 num_examples: 285 - name: test num_bytes: 8886643 num_examples: 14042 download_size: 7793365 dataset_size: 9041335 - config_name: ig features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 157376 num_examples: 285 - name: test num_bytes: 9221405 num_examples: 14042 download_size: 7644102 dataset_size: 9378781 - config_name: it features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 157547 num_examples: 285 - name: test num_bytes: 9374481 num_examples: 14042 download_size: 8873034 dataset_size: 9532028 - config_name: ja features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 167646 num_examples: 285 - name: test num_bytes: 9830716 num_examples: 14042 download_size: 8826164 dataset_size: 9998362 - config_name: ko features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 160572 num_examples: 285 - name: test num_bytes: 9454859 num_examples: 14042 download_size: 8640457 dataset_size: 9615431 - config_name: ky features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 235001 num_examples: 285 - name: test num_bytes: 13483934 num_examples: 14042 download_size: 11148813 dataset_size: 13718935 - config_name: lt features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 148917 num_examples: 285 - name: test num_bytes: 8504949 num_examples: 14042 download_size: 8416467 dataset_size: 8653866 - config_name: mg features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 161992 num_examples: 285 - name: test num_bytes: 9337415 num_examples: 14042 download_size: 8011427 dataset_size: 9499407 - config_name: ms features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 152549 num_examples: 285 - name: test num_bytes: 8823844 num_examples: 14042 download_size: 7783581 dataset_size: 8976393 - config_name: ne features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 294790 num_examples: 285 - name: test num_bytes: 16972110 num_examples: 14042 download_size: 11895818 dataset_size: 17266900 - config_name: nl features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 158122 num_examples: 285 - name: test num_bytes: 9099176 num_examples: 14042 download_size: 8565959 dataset_size: 9257298 - config_name: ny features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 151315 num_examples: 285 - name: test num_bytes: 8686819 num_examples: 14042 download_size: 7822699 dataset_size: 8838134 - config_name: pl features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 157290 num_examples: 285 - name: test num_bytes: 8980730 num_examples: 14042 download_size: 8981270 dataset_size: 9138020 - config_name: pt features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 154592 num_examples: 285 - name: test num_bytes: 8983299 num_examples: 14042 download_size: 8517588 dataset_size: 9137891 - config_name: ro features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 158311 num_examples: 285 - name: test num_bytes: 9163189 num_examples: 14042 download_size: 8773232 dataset_size: 9321500 - config_name: ru features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 246059 num_examples: 285 - name: test num_bytes: 14059847 num_examples: 14042 download_size: 11904365 dataset_size: 14305906 - config_name: si features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 297843 num_examples: 285 - name: test num_bytes: 17374939 num_examples: 14042 download_size: 12790098 dataset_size: 17672782 - config_name: sn features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 147355 num_examples: 285 - name: test num_bytes: 8507368 num_examples: 14042 download_size: 7962672 dataset_size: 8654723 - config_name: so features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 156282 num_examples: 285 - name: test num_bytes: 9033243 num_examples: 14042 download_size: 8706693 dataset_size: 9189525 - config_name: sr features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 221580 num_examples: 285 - name: test num_bytes: 12695546 num_examples: 14042 download_size: 10748391 dataset_size: 12917126 - config_name: sv features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 147893 num_examples: 285 - name: test num_bytes: 8549708 num_examples: 14042 download_size: 8181997 dataset_size: 8697601 - config_name: sw features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 147069 num_examples: 285 - name: test num_bytes: 8653210 num_examples: 14042 download_size: 7932986 dataset_size: 8800279 - config_name: te features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 315724 num_examples: 285 - name: test num_bytes: 18170058 num_examples: 14042 download_size: 12631358 dataset_size: 18485782 - config_name: tr features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 153426 num_examples: 285 - name: test num_bytes: 8833244 num_examples: 14042 download_size: 8351339 dataset_size: 8986670 - config_name: uk features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 229888 num_examples: 285 - name: test num_bytes: 13233771 num_examples: 14042 download_size: 11347842 dataset_size: 13463659 - config_name: vi features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 185712 num_examples: 285 - name: test num_bytes: 10604332 num_examples: 14042 download_size: 8971266 dataset_size: 10790044 - config_name: yo features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 153810 num_examples: 285 - name: test num_bytes: 10694916 num_examples: 14042 download_size: 9303668 dataset_size: 10848726 - config_name: zh features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool splits: - name: dev num_bytes: 127577 num_examples: 285 - name: test num_bytes: 7393764 num_examples: 14042 download_size: 7322261 dataset_size: 7521341 configs: - config_name: am data_files: - split: test path: am/test-* - split: dev path: am/dev-* - config_name: ar data_files: - split: test path: ar/test-* - split: dev path: ar/dev-* - config_name: bn data_files: - split: test path: bn/test-* - split: dev path: bn/dev-* - config_name: cs data_files: - split: test path: cs/test-* - split: dev path: cs/dev-* - config_name: de data_files: - split: test path: de/test-* - split: dev path: de/dev-* - config_name: el data_files: - split: test path: el/test-* - split: dev path: el/dev-* - config_name: en data_files: - split: test path: en/test-* - split: dev path: en/dev-* - config_name: es data_files: - split: test path: es/test-* - split: dev path: es/dev-* - config_name: fa data_files: - split: test path: fa/test-* - split: dev path: fa/dev-* - config_name: fil data_files: - split: test path: fil/test-* - split: dev path: fil/dev-* - config_name: fr data_files: - split: test path: fr/test-* - split: dev path: fr/dev-* - config_name: ha data_files: - split: test path: ha/test-* - split: dev path: ha/dev-* - config_name: he data_files: - split: test path: he/test-* - split: dev path: he/dev-* - config_name: hi data_files: - split: test path: hi/test-* - split: dev path: hi/dev-* - config_name: id data_files: - split: test path: id/test-* - split: dev path: id/dev-* - config_name: ig data_files: - split: test path: ig/test-* - split: dev path: ig/dev-* - config_name: it data_files: - split: test path: it/test-* - split: dev path: it/dev-* - config_name: ja data_files: - split: test path: ja/test-* - split: dev path: ja/dev-* - config_name: ko data_files: - split: test path: ko/test-* - split: dev path: ko/dev-* - config_name: ky data_files: - split: test path: ky/test-* - split: dev path: ky/dev-* - config_name: lt data_files: - split: test path: lt/test-* - split: dev path: lt/dev-* - config_name: mg data_files: - split: test path: mg/test-* - split: dev path: mg/dev-* - config_name: ms data_files: - split: test path: ms/test-* - split: dev path: ms/dev-* - config_name: ne data_files: - split: test path: ne/test-* - split: dev path: ne/dev-* - config_name: nl data_files: - split: test path: nl/test-* - split: dev path: nl/dev-* - config_name: ny data_files: - split: test path: ny/test-* - split: dev path: ny/dev-* - config_name: pl data_files: - split: test path: pl/test-* - split: dev path: pl/dev-* - config_name: pt data_files: - split: test path: pt/test-* - split: dev path: pt/dev-* - config_name: ro data_files: - split: test path: ro/test-* - split: dev path: ro/dev-* - config_name: ru data_files: - split: test path: ru/test-* - split: dev path: ru/dev-* - config_name: si data_files: - split: test path: si/test-* - split: dev path: si/dev-* - config_name: sn data_files: - split: test path: sn/test-* - split: dev path: sn/dev-* - config_name: so data_files: - split: test path: so/test-* - split: dev path: so/dev-* - config_name: sr data_files: - split: test path: sr/test-* - split: dev path: sr/dev-* - config_name: sv data_files: - split: test path: sv/test-* - split: dev path: sv/dev-* - config_name: sw data_files: - split: test path: sw/test-* - split: dev path: sw/dev-* - config_name: te data_files: - split: test path: te/test-* - split: dev path: te/dev-* - config_name: tr data_files: - split: test path: tr/test-* - split: dev path: tr/dev-* - config_name: uk data_files: - split: test path: uk/test-* - split: dev path: uk/dev-* - config_name: vi data_files: - split: test path: vi/test-* - split: dev path: vi/dev-* - config_name: yo data_files: - split: test path: yo/test-* - split: dev path: yo/dev-* - config_name: zh data_files: - split: test path: zh/test-* - split: dev path: zh/dev-* tags: - argilla language: - en - ar - bn - es - fr - hi - ru - de - id - it - ja - ko - pt - zh - yo - nl - ro - uk - vi - tr - pl - fa - cs - he - el - ms - fil - te - si - ne - ky - sv - lt - sr - mg - so - ha - am - sn - ig - ny - sw ---  # Dataset Summary [Global-MMLU](https://arxiv.org/abs/2412.03304) 🌍 is a multilingual evaluation set spanning 42 languages, including English. This dataset combines machine translations for [MMLU](https://huggingface.co/datasets/cais/mmlu) questions along with professional translations and crowd-sourced post-edits. It also includes cultural sensitivity annotations for a subset of the questions (2850 questions per language) and classifies them as *Culturally Sensitive* (CS) 🗽 or *Culturally Agnostic* (CA) ⚖️. These annotations were collected as part of an open science initiative led by Cohere For AI in collaboration with many external collaborators from both industry and academia. - **Curated by:** Professional annotators and contributors of [Cohere For AI Community](https://cohere.com/research). - **Language(s):** 42 languages. - **License:** [Apache 2.0](https://opensource.org/license/apache-2-0) **Note:** We also provide a "lite" version of Global MMLU called ["Global-MMLU-Lite"](https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite). This datatset is more balanced containing 200 samples each for CA and CS subsets for each language. And provides coverage for 15 languages with human translations. ### **Global-MMLU Dataset Family:** | Name | Explanation | |------|--------------| | [Global-MMLU](https://huggingface.co/datasets/CohereForAI/Global-MMLU) | Full Global-MMLU set with translations for all 14K samples including CS and CA subsets| | [Global-MMLU-Lite](https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite) | Lite version of Global-MMLU with human translated samples in 15 languages and containing 200 samples each for CS and CA subsets per language.| ## Load with Datasets To load this dataset with `datasets`, you'll first need to install it using `pip install datasets` and then use the following code: ```python from datasets import load_dataset # load HF dataset global_mmlu = load_dataset("CohereForAI/Global-MMLU", 'en') # can also be used as pandas dataframe global_mmlu.set_format("pandas") global_mmlu_test = global_mmlu['test'][:] global_mmlu_dev = global_mmlu['dev'][:] ``` <details> <summary> additional details </summary> The columns corresponding to annotations collected from our cultural bias study (i.e. 'required_knowledge', 'time_sensitive', 'reference', 'culture', 'region', 'country') contain a list of values representing annotations from different annotators. However, to avoid conversion issues to HF dataset, these columns are provided as string in the final dataset. You can convert these columns back to list of values for easier manipulation as follows: ```python import ast # convert string values to list global_mmlu_df['required_knowledge'] = global_mmlu_df['required_knowledge'].apply(lamda x: ast.literal_eval(x)) ``` </details> <br> ## Data Fields The data fields are the same among all splits. Brief description of each field is provided below. <details> <summary> data field description </summary> - `sample_id`: A unique identifier for the question. - `subject`: The main topic the question falls under. - `subject_category`: The high-level category the subject falls under i.e. STEM/Humanities/Social Sciences/Medical/Business/Other. - `question`: translated question from MMLU - `option_a`: one of the possible option choices - `option_b`: one of the possible option choices - `option_c`: one of the possible option choices - `option_d`: one of the possible option choices - `answer': the correct answer (A/B/C/D) - `required_knowledge`: annotator votes for knowledge needed to answer the question correctly. Possible values include: "cultural", "regional", "dialect" or "none" - `time_sensitive`: annotator votes indicating if the question's answer is time-dependent. Possible values include: Yes/No - `reference`: annotations for which part of the question contains cultural/regional/dialect references. The different items in the list are annotations from different annotators. - `culture`: annotations for which culture does the question belong to. The different items in the list correspond to annotations from different annotators. - `region`: Geographic region the question is relevant to. Each item in the list correspond to annotations from different annotators. - `country`: Specific country the question pertains to. Each item in the list correspond to annotations from different annotators. - `cultural_sensitivity_label`: Label to indicate if question is culturally sensitive (CS) or culturally agnostic (CA) based on annotator votes. - `is_annotated`: True/False flag to indicate if sample contains any annotations from our cultural bias study. </details> <br> ## Data Splits The following are the splits of the data: | Split | No. of instances | Language Coverage | |-------|------------------|-------------------| | test | 589,764 | 42 | | dev | 11,970 | 42 | ## Data Instances An example from `test` set looks as follows: ```json {'sample_id': 'world_religions/test/170' 'subject': 'world_religions', 'subject_category': 'Humanities', 'question': ' The numen of Augustus referred to which of the following characteristics?', 'option_a': 'Divine power', 'option_b': 'Sexual virility', 'option_c': 'Military acumen', 'option_d': 'Philosophical intellect', 'answer': 'A', 'required_knowledge': "['none', 'cultural', 'cultural', 'cultural']", 'time_sensitive': "['No', 'No', 'No', 'No']", 'reference': "['-', '-', {'end': 22, 'label': 'Cultural', 'score': None, 'start': 5}, {'end': 22, 'label': 'Cultural', 'score': None, 'start': 5}]", 'culture': "['Western Culture', 'Western Culture', 'Western Culture']", 'region': "['North America', 'Europe']", 'country': "['Italy']", 'cultural_sensitivity_label': 'CS', 'is_annotated': True, } ``` ## Statistics ### Annotation Types The following is the breakdown of CS🗽, CA⚖️ and MA📝 samples in the final dataset. | Type of Annotation | Instances per language | No. of languages | Total instances |--------------------|------------------------|------------------|----------------| | Culturally Sensitive 🗽 | 792 | 42 | 33,264 | | Culturally Agnostic ⚖️ | 2058 |42 | 86,436 | | MMLU Annotated 📝| 2850 |42 | 119,700 | ### Languages The dataset covers 42 languages: 20 high-resource, 9 mid-resource, and 13 low-resource languages. The following is details about the languages included in the dataset. <details> <summary> Languages Info </summary> | ISO Code | Language | Resources | |----------|----------|-----------| | `am` | Amharic | Low | | `ar` | Arabic (Standard)| High | | `bn` | Bengali | Mid | | `de` | German | High | | `el` | Greek | Mid | | `en` | English | High | | `fil` | Filipino | Mid | | `fr` | French | High | | `ha` | Hausa | Low | | `he` | Hebrew | Mid | | `hi` | Hindi | High | | `ig` | Igbo | Low | | `id` | Indonesian | Mid | | `it` | Italian | High | | `ja` | Japanese | High | | `ky` | Kyrgyz | Low | | `ko` | Korean | Mid | | `lt` | Lithuanian | Mid | | `mg` | Malagasy | Low | | `ms` | Malay | Mid | | `ne` | Nepali | Low | | `nl` | Dutch | High | | `ny` | Chichewa | Low | | `fa` | Persian | High | | `pl` | Polish | High | | `pt` | Portuguese | High | | `ru` | Russian | High | | `si` | Sinhala | Low | | `sn` | Shona | Low | | `so` | Somali | Low | | `es` | Spanish | High | | `sr` | Serbian | High | | `sw` | Swahili | Low | | `sw` | Swedish | High | | `te` | Telugu | Low | | `tr` | Turkish | High | | `uk` | Ukrainian | Mid | | `vi` | Vietnamese | High | | `yo` | Yorùbá | Low | | `zh` | Chinese (Simplified) | High | </details> <br> # Known Limitations A brief overview of limitations of this dataset is provided below. <details> <summary> show limitations </summary> - **Language and dialect coverage:** Global-MMLU focusses on 42 languages. However, this is still only a tiny fraction of the world’s linguistic diversity. Future work is needed to continue to improve evaluations beyond these 42 languages and take into account how technology serves different dialects. - **Uneven distribution of contributions:** The dataset contains translation post-edits from community volunteers, with a 'long tail' of volunteers making only one or two contributions. Similarly, there is a huge gap between languages with the highest number of contributions and ones with the lowest number of contributions. - **Toxic or offensive speech:** Our annotation process did not focus on flagging for toxic,harmful, or offensive speech, so it is possible that Global-MMLU contains some data that could be considered harmful. We believe this is of relatively low risk because of the nature of the original MMLU and the focus on examination material. - **Region Category Assignment:** For the annotation of geographically sensitive questions, we classified regions into six geographic regions (Africa, Asia, Europe, North America, Oceania,and South America). However, based upon discussions we would going forward recommend switching to the taxonomy proposed by the World Bank which is more granular and includes separate designations for Central America and Sub-Saharan Africa. - **Identifying cultural sensitivity does not guarantee cultural inclusion:** While Global-MMLU highlights important limitations in current datasets by identifying gaps in non-Western cultural representation. Future work must prioritize the integration of diverse culturally grounded knowledge to achieve true inclusivity and fairness in multilingual AI evaluation. </details> <br> # Additional Information ## Provenance - **Methods Used:** Professional annotations as well as crowd-sourced through volunteer annotations. - **Methodology Details:** We collected cultural bias annotations as well as post-edits of translations for different mmlu questions. - [Cultural Sensitivity Annotation Platform](https://huggingface.co/spaces/CohereForAI/MMLU-evaluation) - [Translation Quality Annotation Platform](https://huggingface.co/spaces/CohereForAI/review-mmlu-translations) - Dates of Collection: May 2024 - Aug 2024 ## Dataset Version and Maintenance - **Maintenance Status:** Actively Maintained - **Version Details:** - *Current version:* 1.0 - *Last Update:* 12/2024 - *First Release:* 12/2024 ## Authorship - **Publishing Organization:** [Cohere For AI](https://cohere.com/research) - **Industry Type:** Not-for-profit - Tech ## Licensing Information This dataset can be used for any purpose, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License. ## Additional Details For any additional details, please check our paper, [Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://arxiv.org/abs/2412.03304). ## Citation Information ```bibtex @misc{singh2024globalmmluunderstandingaddressing, title={Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation}, author={Shivalika Singh and Angelika Romanou and Clémentine Fourrier and David I. Adelani and Jian Gang Ngui and Daniel Vila-Suero and Peerat Limkonchotiwat and Kelly Marchisio and Wei Qi Leong and Yosephine Susanto and Raymond Ng and Shayne Longpre and Wei-Yin Ko and Madeline Smith and Antoine Bosselut and Alice Oh and Andre F. T. Martins and Leshem Choshen and Daphne Ippolito and Enzo Ferrante and Marzieh Fadaee and Beyza Ermis and Sara Hooker}, year={2024}, eprint={2412.03304}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.03304}, } ```

fixie-ai/peoples_speech

fixie-ai

--- dataset_info: - config_name: clean features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: duration_ms dtype: int32 - name: text dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 2511523987.692 num_examples: 18622 - name: test num_bytes: 4259695510.794 num_examples: 34898 - name: train num_bytes: 401646320552.671 num_examples: 1501271 download_size: 398922548670 dataset_size: 408417540051 - config_name: dirty_sa features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: duration_ms dtype: int32 - name: text dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 144432442623.054 num_examples: 548014 - name: validation num_bytes: 2511524241.692 num_examples: 18622 - name: test num_bytes: 4259695588.794 num_examples: 34898 download_size: 149491764186 dataset_size: 151203662453.53998 configs: - config_name: clean data_files: - split: validation path: clean/validation-* - split: test path: clean/test-* - split: train path: data/train-* - config_name: dirty_sa data_files: - split: train path: dirty_sa/train-* - split: validation path: dirty_sa/validation-* - split: test path: dirty_sa/test-* ---

dsfsi/vukuzenzele-sentence-aligned

dsfsi

--- language: - eng - afr - nbl - xho - zul - sot - nso - tsn - ssw - ven - tso license: cc-by-4.0 task_categories: - sentence-similarity - translation pretty_name: The Vuk'uzenzele South African Multilingual Corpus tags: - multilingual - government arxiv: 2303.0375 configs: - config_name: afr-eng data_files: - split: train path: afr-eng/train-* - split: test path: afr-eng/test-* - split: eval path: afr-eng/eval-* - config_name: afr-nbl data_files: - split: train path: afr-nbl/train-* - split: test path: afr-nbl/test-* - split: eval path: afr-nbl/eval-* - config_name: afr-nso data_files: - split: train path: afr-nso/train-* - split: test path: afr-nso/test-* - split: eval path: afr-nso/eval-* - config_name: afr-sot data_files: - split: train path: afr-sot/train-* - split: test path: afr-sot/test-* - split: eval path: afr-sot/eval-* - config_name: afr-ssw data_files: - split: train path: afr-ssw/train-* - split: test path: afr-ssw/test-* - split: eval path: afr-ssw/eval-* - config_name: afr-tsn data_files: - split: train path: afr-tsn/train-* - split: test path: afr-tsn/test-* - split: eval path: afr-tsn/eval-* - config_name: afr-tso data_files: - split: train path: afr-tso/train-* - split: test path: afr-tso/test-* - split: eval path: afr-tso/eval-* - config_name: afr-ven data_files: - split: train path: afr-ven/train-* - split: test path: afr-ven/test-* - split: eval path: afr-ven/eval-* - config_name: afr-xho data_files: - split: train path: afr-xho/train-* - split: test path: afr-xho/test-* - split: eval path: afr-xho/eval-* - config_name: afr-zul data_files: - split: train path: afr-zul/train-* - split: test path: afr-zul/test-* - split: eval path: afr-zul/eval-* - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - config_name: eng-nbl data_files: - split: train path: eng-nbl/train-* - split: test path: eng-nbl/test-* - split: eval path: eng-nbl/eval-* - config_name: eng-nso data_files: - split: train path: eng-nso/train-* - split: test path: eng-nso/test-* - split: eval path: eng-nso/eval-* - config_name: eng-sot data_files: - split: train path: eng-sot/train-* - split: test path: eng-sot/test-* - split: eval path: eng-sot/eval-* - config_name: eng-ssw data_files: - split: train path: eng-ssw/train-* - split: test path: eng-ssw/test-* - split: eval path: eng-ssw/eval-* - config_name: eng-tsn data_files: - split: train path: eng-tsn/train-* - split: test path: eng-tsn/test-* - split: eval path: eng-tsn/eval-* - config_name: eng-tso data_files: - split: train path: eng-tso/train-* - split: test path: eng-tso/test-* - split: eval path: eng-tso/eval-* - config_name: eng-ven data_files: - split: train path: eng-ven/train-* - split: test path: eng-ven/test-* - split: eval path: eng-ven/eval-* - config_name: eng-xho data_files: - split: train path: eng-xho/train-* - split: test path: eng-xho/test-* - split: eval path: eng-xho/eval-* - config_name: eng-zul data_files: - split: train path: eng-zul/train-* - split: test path: eng-zul/test-* - split: eval path: eng-zul/eval-* - config_name: nbl-nso data_files: - split: train path: nbl-nso/train-* - split: test path: nbl-nso/test-* - split: eval path: nbl-nso/eval-* - config_name: nbl-sot data_files: - split: train path: nbl-sot/train-* - split: test path: nbl-sot/test-* - split: eval path: nbl-sot/eval-* - config_name: nbl-ssw data_files: - split: train path: nbl-ssw/train-* - split: test path: nbl-ssw/test-* - split: eval path: nbl-ssw/eval-* - config_name: nbl-tsn data_files: - split: train path: nbl-tsn/train-* - split: test path: nbl-tsn/test-* - split: eval path: nbl-tsn/eval-* - config_name: nbl-tso data_files: - split: train path: nbl-tso/train-* - split: test path: nbl-tso/test-* - split: eval path: nbl-tso/eval-* - config_name: nbl-ven data_files: - split: train path: nbl-ven/train-* - split: test path: nbl-ven/test-* - split: eval path: nbl-ven/eval-* - config_name: nbl-xho data_files: - split: train path: nbl-xho/train-* - split: test path: nbl-xho/test-* - split: eval path: nbl-xho/eval-* - config_name: nbl-zul data_files: - split: train path: nbl-zul/train-* - split: test path: nbl-zul/test-* - split: eval path: nbl-zul/eval-* - config_name: nso-sot data_files: - split: train path: nso-sot/train-* - split: test path: nso-sot/test-* - split: eval path: nso-sot/eval-* - config_name: nso-ssw data_files: - split: train path: nso-ssw/train-* - split: test path: nso-ssw/test-* - split: eval path: nso-ssw/eval-* - config_name: nso-tsn data_files: - split: train path: nso-tsn/train-* - split: test path: nso-tsn/test-* - split: eval path: nso-tsn/eval-* - config_name: nso-tso data_files: - split: train path: nso-tso/train-* - split: test path: nso-tso/test-* - split: eval path: nso-tso/eval-* - config_name: nso-ven data_files: - split: train path: nso-ven/train-* - split: test path: nso-ven/test-* - split: eval path: nso-ven/eval-* - config_name: nso-xho data_files: - split: train path: nso-xho/train-* - split: test path: nso-xho/test-* - split: eval path: nso-xho/eval-* - config_name: nso-zul data_files: - split: train path: nso-zul/train-* - split: test path: nso-zul/test-* - split: eval path: nso-zul/eval-* - config_name: sot-ssw data_files: - split: train path: sot-ssw/train-* - split: test path: sot-ssw/test-* - split: eval path: sot-ssw/eval-* - config_name: sot-tsn data_files: - split: train path: sot-tsn/train-* - split: test path: sot-tsn/test-* - split: eval path: sot-tsn/eval-* - config_name: sot-tso data_files: - split: train path: sot-tso/train-* - split: test path: sot-tso/test-* - split: eval path: sot-tso/eval-* - config_name: sot-ven data_files: - split: train path: sot-ven/train-* - split: test path: sot-ven/test-* - split: eval path: sot-ven/eval-* - config_name: sot-xho data_files: - split: train path: sot-xho/train-* - split: test path: sot-xho/test-* - split: eval path: sot-xho/eval-* - config_name: sot-zul data_files: - split: train path: sot-zul/train-* - split: test path: sot-zul/test-* - split: eval path: sot-zul/eval-* - config_name: ssw-tsn data_files: - split: train path: ssw-tsn/train-* - split: test path: ssw-tsn/test-* - split: eval path: ssw-tsn/eval-* - config_name: ssw-tso data_files: - split: train path: ssw-tso/train-* - split: test path: ssw-tso/test-* - split: eval path: ssw-tso/eval-* - config_name: ssw-ven data_files: - split: train path: ssw-ven/train-* - split: test path: ssw-ven/test-* - split: eval path: ssw-ven/eval-* - config_name: ssw-xho data_files: - split: train path: ssw-xho/train-* - split: test path: ssw-xho/test-* - split: eval path: ssw-xho/eval-* - config_name: ssw-zul data_files: - split: train path: ssw-zul/train-* - split: test path: ssw-zul/test-* - split: eval path: ssw-zul/eval-* - config_name: tsn-tso data_files: - split: train path: tsn-tso/train-* - split: test path: tsn-tso/test-* - split: eval path: tsn-tso/eval-* - config_name: tsn-ven data_files: - split: train path: tsn-ven/train-* - split: test path: tsn-ven/test-* - split: eval path: tsn-ven/eval-* - config_name: tsn-xho data_files: - split: train path: tsn-xho/train-* - split: test path: tsn-xho/test-* - split: eval path: tsn-xho/eval-* - config_name: tsn-zul data_files: - split: train path: tsn-zul/train-* - split: test path: tsn-zul/test-* - split: eval path: tsn-zul/eval-* - config_name: tso-ven data_files: - split: train path: tso-ven/train-* - split: test path: tso-ven/test-* - split: eval path: tso-ven/eval-* - config_name: tso-xho data_files: - split: train path: tso-xho/train-* - split: test path: tso-xho/test-* - split: eval path: tso-xho/eval-* - config_name: tso-zul data_files: - split: train path: tso-zul/train-* - split: test path: tso-zul/test-* - split: eval path: tso-zul/eval-* - config_name: ven-xho data_files: - split: train path: ven-xho/train-* - split: test path: ven-xho/test-* - split: eval path: ven-xho/eval-* - config_name: ven-zul data_files: - split: train path: ven-zul/train-* - split: test path: ven-zul/test-* - split: eval path: ven-zul/eval-* - config_name: xho-zul data_files: - split: train path: xho-zul/train-* - split: test path: xho-zul/test-* - split: eval path: xho-zul/eval-* dataset_info: - config_name: afr-eng features: - name: afr dtype: string - name: eng dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 793530 num_examples: 2660 - name: test num_bytes: 171644 num_examples: 570 - name: eval num_bytes: 172132 num_examples: 571 download_size: 757198 dataset_size: 1137306 - config_name: afr-nbl features: - name: afr dtype: string - name: nbl dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 281328 num_examples: 723 - name: test num_bytes: 57947 num_examples: 155 - name: eval num_bytes: 59996 num_examples: 155 download_size: 279950 dataset_size: 399271 - config_name: afr-nso features: - name: afr dtype: string - name: nso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 980475 num_examples: 2937 - name: test num_bytes: 203451 num_examples: 630 - name: eval num_bytes: 214623 num_examples: 630 download_size: 892392 dataset_size: 1398549 - config_name: afr-sot features: - name: afr dtype: string - name: sot dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 971841 num_examples: 2968 - name: test num_bytes: 211374 num_examples: 636 - name: eval num_bytes: 209697 num_examples: 636 download_size: 901006 dataset_size: 1392912 - config_name: afr-ssw features: - name: afr dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 975744 num_examples: 3042 - name: test num_bytes: 209151 num_examples: 652 - name: eval num_bytes: 208877 num_examples: 653 download_size: 927666 dataset_size: 1393772 - config_name: afr-tsn features: - name: afr dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1010506 num_examples: 2830 - name: test num_bytes: 218153 num_examples: 607 - name: eval num_bytes: 214373 num_examples: 607 download_size: 913596 dataset_size: 1443032 - config_name: afr-tso features: - name: afr dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 980025 num_examples: 2952 - name: test num_bytes: 213355 num_examples: 633 - name: eval num_bytes: 211642 num_examples: 633 download_size: 902666 dataset_size: 1405022 - config_name: afr-ven features: - name: afr dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 352031 num_examples: 830 - name: test num_bytes: 72702 num_examples: 178 - name: eval num_bytes: 75243 num_examples: 178 download_size: 323825 dataset_size: 499976 - config_name: afr-xho features: - name: afr dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 986062 num_examples: 3098 - name: test num_bytes: 205229 num_examples: 664 - name: eval num_bytes: 210379 num_examples: 665 download_size: 944334 dataset_size: 1401670 - config_name: afr-zul features: - name: afr dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 982102 num_examples: 3078 - name: test num_bytes: 208473 num_examples: 660 - name: eval num_bytes: 201824 num_examples: 660 download_size: 932565 dataset_size: 1392399 - config_name: default features: - name: nbl dtype: string - name: nso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 128131 num_examples: 315 - name: test num_bytes: 31826 num_examples: 79 download_size: 113394 dataset_size: 159957 - config_name: eng-nbl features: - name: eng dtype: string - name: nbl dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 78419 num_examples: 207 - name: test num_bytes: 16930 num_examples: 45 - name: eval num_bytes: 15202 num_examples: 45 download_size: 89654 dataset_size: 110551 - config_name: eng-nso features: - name: eng dtype: string - name: nso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 848347 num_examples: 2616 - name: test num_bytes: 183267 num_examples: 561 - name: eval num_bytes: 181802 num_examples: 561 download_size: 770909 dataset_size: 1213416 - config_name: eng-sot features: - name: eng dtype: string - name: sot dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 843173 num_examples: 2671 - name: test num_bytes: 181709 num_examples: 573 - name: eval num_bytes: 180602 num_examples: 573 download_size: 776145 dataset_size: 1205484 - config_name: eng-ssw features: - name: eng dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 830354 num_examples: 2662 - name: test num_bytes: 175688 num_examples: 571 - name: eval num_bytes: 176734 num_examples: 571 download_size: 777951 dataset_size: 1182776 - config_name: eng-tsn features: - name: eng dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 880306 num_examples: 2517 - name: test num_bytes: 190843 num_examples: 539 - name: eval num_bytes: 187728 num_examples: 540 download_size: 786563 dataset_size: 1258877 - config_name: eng-tso features: - name: eng dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 849914 num_examples: 2623 - name: test num_bytes: 181181 num_examples: 562 - name: eval num_bytes: 176362 num_examples: 563 download_size: 773662 dataset_size: 1207457 - config_name: eng-ven features: - name: eng dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 113647 num_examples: 279 - name: test num_bytes: 26195 num_examples: 60 - name: eval num_bytes: 26121 num_examples: 60 download_size: 119271 dataset_size: 165963 - config_name: eng-xho features: - name: eng dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 827201 num_examples: 2662 - name: test num_bytes: 175023 num_examples: 571 - name: eval num_bytes: 176047 num_examples: 571 download_size: 784961 dataset_size: 1178271 - config_name: eng-zul features: - name: eng dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 815837 num_examples: 2646 - name: test num_bytes: 168769 num_examples: 567 - name: eval num_bytes: 177547 num_examples: 567 download_size: 767836 dataset_size: 1162153 - config_name: nbl-nso features: - name: nbl dtype: string - name: nso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 109438 num_examples: 275 - name: test num_bytes: 24000 num_examples: 59 - name: eval num_bytes: 26519 num_examples: 60 download_size: 118816 dataset_size: 159957 - config_name: nbl-sot features: - name: nbl dtype: string - name: sot dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 149593 num_examples: 365 - name: test num_bytes: 30656 num_examples: 78 - name: eval num_bytes: 32211 num_examples: 79 download_size: 152576 dataset_size: 212460 - config_name: nbl-ssw features: - name: nbl dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 146544 num_examples: 387 - name: test num_bytes: 33410 num_examples: 83 - name: eval num_bytes: 32858 num_examples: 84 download_size: 157314 dataset_size: 212812 - config_name: nbl-tsn features: - name: nbl dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 131856 num_examples: 302 - name: test num_bytes: 31961 num_examples: 65 - name: eval num_bytes: 29676 num_examples: 65 download_size: 139315 dataset_size: 193493 - config_name: nbl-tso features: - name: nbl dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 127433 num_examples: 296 - name: test num_bytes: 24654 num_examples: 63 - name: eval num_bytes: 23290 num_examples: 64 download_size: 127532 dataset_size: 175377 - config_name: nbl-ven features: - name: nbl dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 976911 num_examples: 2660 - name: test num_bytes: 211536 num_examples: 570 - name: eval num_bytes: 207694 num_examples: 570 download_size: 885066 dataset_size: 1396141 - config_name: nbl-xho features: - name: nbl dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 149020 num_examples: 403 - name: test num_bytes: 33319 num_examples: 87 - name: eval num_bytes: 31809 num_examples: 87 download_size: 160427 dataset_size: 214148 - config_name: nbl-zul features: - name: nbl dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 111905 num_examples: 289 - name: test num_bytes: 25799 num_examples: 62 - name: eval num_bytes: 22660 num_examples: 63 download_size: 124588 dataset_size: 160364 - config_name: nso-sot features: - name: nso dtype: string - name: sot dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1057851 num_examples: 3052 - name: test num_bytes: 226420 num_examples: 654 - name: eval num_bytes: 232934 num_examples: 655 download_size: 945243 dataset_size: 1517205 - config_name: nso-ssw features: - name: nso dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1020738 num_examples: 2968 - name: test num_bytes: 219932 num_examples: 636 - name: eval num_bytes: 218161 num_examples: 637 download_size: 922981 dataset_size: 1458831 - config_name: nso-tsn features: - name: nso dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1092885 num_examples: 2918 - name: test num_bytes: 238439 num_examples: 625 - name: eval num_bytes: 234644 num_examples: 626 download_size: 952272 dataset_size: 1565968 - config_name: nso-tso features: - name: nso dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1045959 num_examples: 2956 - name: test num_bytes: 228677 num_examples: 634 - name: eval num_bytes: 226626 num_examples: 634 download_size: 925262 dataset_size: 1501262 - config_name: nso-ven features: - name: nso dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 155499 num_examples: 343 - name: test num_bytes: 35576 num_examples: 73 - name: eval num_bytes: 31381 num_examples: 74 download_size: 152424 dataset_size: 222456 - config_name: nso-xho features: - name: nso dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1022670 num_examples: 2959 - name: test num_bytes: 214850 num_examples: 634 - name: eval num_bytes: 212932 num_examples: 635 download_size: 929486 dataset_size: 1450452 - config_name: nso-zul features: - name: nso dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1014985 num_examples: 2998 - name: test num_bytes: 223825 num_examples: 643 - name: eval num_bytes: 219173 num_examples: 643 download_size: 926742 dataset_size: 1457983 - config_name: sot-ssw features: - name: sot dtype: string - name: ssw dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1036642 num_examples: 3075 - name: test num_bytes: 217328 num_examples: 659 - name: eval num_bytes: 222863 num_examples: 660 download_size: 950426 dataset_size: 1476833 - config_name: sot-tsn features: - name: sot dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1118346 num_examples: 3019 - name: test num_bytes: 237826 num_examples: 647 - name: eval num_bytes: 235279 num_examples: 647 download_size: 981019 dataset_size: 1591451 - config_name: sot-tso features: - name: sot dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1057631 num_examples: 3027 - name: test num_bytes: 226229 num_examples: 649 - name: eval num_bytes: 222671 num_examples: 649 download_size: 943068 dataset_size: 1506531 - config_name: sot-ven features: - name: sot dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 201166 num_examples: 461 - name: test num_bytes: 44845 num_examples: 99 - name: eval num_bytes: 42607 num_examples: 99 download_size: 191947 dataset_size: 288618 - config_name: sot-xho features: - name: sot dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1042930 num_examples: 3098 - name: test num_bytes: 217327 num_examples: 664 - name: eval num_bytes: 223313 num_examples: 665 download_size: 964792 dataset_size: 1483570 - config_name: sot-zul features: - name: sot dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1017097 num_examples: 3079 - name: test num_bytes: 223761 num_examples: 660 - name: eval num_bytes: 227514 num_examples: 660 download_size: 949761 dataset_size: 1468372 - config_name: ssw-tsn features: - name: ssw dtype: string - name: tsn dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1124400 num_examples: 3110 - name: test num_bytes: 238160 num_examples: 666 - name: eval num_bytes: 246176 num_examples: 667 download_size: 1012570 dataset_size: 1608736 - config_name: ssw-tso features: - name: ssw dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1069540 num_examples: 3142 - name: test num_bytes: 237608 num_examples: 673 - name: eval num_bytes: 231657 num_examples: 674 download_size: 980833 dataset_size: 1538805 - config_name: ssw-ven features: - name: ssw dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 170825 num_examples: 401 - name: test num_bytes: 34774 num_examples: 86 - name: eval num_bytes: 39434 num_examples: 87 download_size: 170522 dataset_size: 245033 - config_name: ssw-xho features: - name: ssw dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1047037 num_examples: 3193 - name: test num_bytes: 227505 num_examples: 684 - name: eval num_bytes: 219981 num_examples: 685 download_size: 992683 dataset_size: 1494523 - config_name: ssw-zul features: - name: ssw dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1054772 num_examples: 3255 - name: test num_bytes: 231524 num_examples: 698 - name: eval num_bytes: 223701 num_examples: 698 download_size: 997182 dataset_size: 1509997 - config_name: tsn-tso features: - name: tsn dtype: string - name: tso dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1141163 num_examples: 3023 - name: test num_bytes: 244100 num_examples: 648 - name: eval num_bytes: 242886 num_examples: 648 download_size: 998631 dataset_size: 1628149 - config_name: tsn-ven features: - name: tsn dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 175353 num_examples: 361 - name: test num_bytes: 39141 num_examples: 77 - name: eval num_bytes: 37453 num_examples: 78 download_size: 165408 dataset_size: 251947 - config_name: tsn-xho features: - name: tsn dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1095514 num_examples: 2989 - name: test num_bytes: 231544 num_examples: 640 - name: eval num_bytes: 227856 num_examples: 641 download_size: 986295 dataset_size: 1554914 - config_name: tsn-zul features: - name: tsn dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1090114 num_examples: 3038 - name: test num_bytes: 232488 num_examples: 651 - name: eval num_bytes: 240758 num_examples: 651 download_size: 989654 dataset_size: 1563360 - config_name: tso-ven features: - name: tso dtype: string - name: ven dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 171428 num_examples: 375 - name: test num_bytes: 33029 num_examples: 80 - name: eval num_bytes: 38079 num_examples: 81 download_size: 163896 dataset_size: 242536 - config_name: tso-xho features: - name: tso dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1049926 num_examples: 3066 - name: test num_bytes: 224708 num_examples: 657 - name: eval num_bytes: 221699 num_examples: 657 download_size: 967978 dataset_size: 1496333 - config_name: tso-zul features: - name: tso dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1059314 num_examples: 3106 - name: test num_bytes: 224935 num_examples: 666 - name: eval num_bytes: 225248 num_examples: 666 download_size: 970505 dataset_size: 1509497 - config_name: ven-xho features: - name: ven dtype: string - name: xho dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 164305 num_examples: 401 - name: test num_bytes: 36290 num_examples: 86 - name: eval num_bytes: 35520 num_examples: 87 download_size: 165177 dataset_size: 236115 - config_name: ven-zul features: - name: ven dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 138178 num_examples: 336 - name: test num_bytes: 32949 num_examples: 72 - name: eval num_bytes: 30697 num_examples: 72 download_size: 143542 dataset_size: 201824 - config_name: xho-zul features: - name: xho dtype: string - name: zul dtype: string - name: score dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1062980 num_examples: 3276 - name: test num_bytes: 226001 num_examples: 702 - name: eval num_bytes: 225893 num_examples: 703 download_size: 1011124 dataset_size: 1514874 --- # The Vuk'uzenzele South African Multilingual Corpus Github: [https://github.com/dsfsi/vukuzenzele-nlp/](https://github.com/dsfsi/vukuzenzele-nlp/) Zenodo: [](https://doi.org/10.5281/zenodo.7598539) Arxiv Preprint: [](https://arxiv.org/abs/2303.03750) Give Feedback 📑: [DSFSI Resource Feedback Form](https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/formResponse) # About The dataset was obtained from the South African government magazine Vuk'uzenzele, created by the [Government Communication and Information System (GCIS)](https://www.gcis.gov.za/). The original raw PDFS were obtatined from the [Vuk'uzenzele website](https://www.vukuzenzele.gov.za/). The datasets contain government magazine editions in 11 languages, namely: | Language | Code | Language | Code | |------------|-------|------------|-------| | English | (eng) | Sepedi | (sep) | | Afrikaans | (afr) | Setswana | (tsn) | | isiNdebele | (nbl) | Siswati | (ssw) | | isiXhosa | (xho) | Tshivenda | (ven) | | isiZulu | (zul) | Xitstonga | (tso) | | Sesotho | (nso) | ## Available pairings The alignment direction is bidrectional, i.e. xho-zul is zul-xho afr-eng; afr-nbl; afr-nso; afr-sot; afr-ssw; afr-tsn; afr-tso; afr-ven; afr-xho; afr-zul eng-nbl; eng-nso; eng-sot ;eng-ssw; eng-tsn; eng-tso; eng-ven; eng-xho; eng-zul nbl-nso; nbl-sot; nbl-ssw; nbl-tsn; nbl-tso; nbl-ven; nbl-xho; nbl-zul nso-sot; nso-ssw; nso-tsn; nso-tso; nso-ven; nso-xho; nso-zul sot-ssw; sot-tsn; sot-tso; sot-ven; sot-xho; sot-zul ssw-tsn; ssw-tso; ssw-ven; ssw-xho; ssw-zul tsn-tso; tsn-ven; tsn-xho; tsn-zul tso-ven; tso-xho; tso-zul ven-xho; ven-zul xho-zul # Disclaimer This dataset contains machine-readable data extracted from PDF documents, from https://www.vukuzenzele.gov.za/, provided by the Government Communication Information System (GCIS). While efforts were made to ensure the accuracy and completeness of this data, there may be errors or discrepancies between the original publications and this dataset. No warranties, guarantees or representations are given in relation to the information contained in the dataset. The members of the Data Science for Societal Impact Research Group bear no responsibility and/or liability for any such errors or discrepancies in this dataset. The Government Communication Information System (GCIS) bears no responsibility and/or liability for any such errors or discrepancies in this dataset. It is recommended that users verify all information contained herein before making decisions based upon this information. # Datasets The datasets consist of pairwise sentence aligned data. There are 55 distinct datasets of paired sentences. The data is obtained by comparing [LASER](https://github.com/facebookresearch/LASER) embeddings of sentence tokens between 2 languages. If the similarity is high, the sentences are deemed semantic equivalents of one another and the observation is outputted. Naming convention: The naming structure of the pairwise_sentence_aligned folder is `aligned-{src_lang_code}-{tgt_lang_code}.csv`. For example, `aligned-afr-zul.csv` is the aligned sentences between Afrikaans and isiZulu. The data is in .csv format and the columns are `src_text`,`tgt_text`,`cosine_score` where: - `src_text` is the source sentence - `tgt_text` is the target sentence - `cosine_score` is the cosine similarity score obtained by comparing the sentence embeddings, it ranges from 0 to 1 **Note:** The notion of source (src) and target (tgt) are only necessary for distinction between the languages used in the aligned pair, as the sentence semantics should be bidirectional. (hallo <-> sawubona) # Citation Vukosi Marivate, Andani Madodonga, Daniel Njini, Richard Lastrucci, Isheanesu Dzingirai, Jenalea Rajab. **The Vuk'uzenzele South African Multilingual Corpus**, 2023 > @dataset{marivate_vukosi_2023_7598540, author = {Marivate, Vukosi and Njini, Daniel and Madodonga, Andani and Lastrucci, Richard and Dzingirai, Isheanesu Rajab, Jenalea}, title = {The Vuk'uzenzele South African Multilingual Corpus}, month = feb, year = 2023, publisher = {Zenodo}, doi = {10.5281/zenodo.7598539}, url = {https://doi.org/10.5281/zenodo.7598539} } ### Licence * Licence for Data - [CC 4.0 BY](LICENSE.md)

Davlan/sib200

Davlan

--- annotations_creators: - found language_creators: - expert-generated language: - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nqo - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu license: - cc-by-sa-4.0 multilinguality: - multilingual pretty_name: sib200 language_details: ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn size_categories: - 1K<n<10K source_datasets: - original tags: - news-topic - sib-200 - sib200 task_categories: - text-classification task_ids: - topic-classification configs: - config_name: ace_Arab data_files: - split: train path: data/ace_Arab/train.tsv - split: validation path: data/ace_Arab/dev.tsv - split: test path: data/ace_Arab/test.tsv - config_name: ace_Latn data_files: - split: train path: data/ace_Latn/train.tsv - split: validation path: data/ace_Latn/dev.tsv - split: test path: data/ace_Latn/test.tsv - config_name: acm_Arab data_files: - split: train path: data/acm_Arab/train.tsv - split: validation path: data/acm_Arab/dev.tsv - split: test path: data/acm_Arab/test.tsv - config_name: acq_Arab data_files: - split: train path: data/acq_Arab/train.tsv - split: validation path: data/acq_Arab/dev.tsv - split: test path: data/acq_Arab/test.tsv - config_name: aeb_Arab data_files: - split: train path: data/aeb_Arab/train.tsv - split: validation path: data/aeb_Arab/dev.tsv - split: test path: data/aeb_Arab/test.tsv - config_name: afr_Latn data_files: - split: train path: data/afr_Latn/train.tsv - split: validation path: data/afr_Latn/dev.tsv - split: test path: data/afr_Latn/test.tsv - config_name: ajp_Arab data_files: - split: train path: data/ajp_Arab/train.tsv - split: validation path: data/ajp_Arab/dev.tsv - split: test path: data/ajp_Arab/test.tsv - config_name: aka_Latn data_files: - split: train path: data/aka_Latn/train.tsv - split: validation path: data/aka_Latn/dev.tsv - split: test path: data/aka_Latn/test.tsv - config_name: als_Latn data_files: - split: train path: data/als_Latn/train.tsv - split: validation path: data/als_Latn/dev.tsv - split: test path: data/als_Latn/test.tsv - config_name: amh_Ethi data_files: - split: train path: data/amh_Ethi/train.tsv - split: validation path: data/amh_Ethi/dev.tsv - split: test path: data/amh_Ethi/test.tsv - config_name: apc_Arab data_files: - split: train path: data/apc_Arab/train.tsv - split: validation path: data/apc_Arab/dev.tsv - split: test path: data/apc_Arab/test.tsv - config_name: arb_Arab data_files: - split: train path: data/arb_Arab/train.tsv - split: validation path: data/arb_Arab/dev.tsv - split: test path: data/arb_Arab/test.tsv - config_name: arb_Latn data_files: - split: train path: data/arb_Latn/train.tsv - split: validation path: data/arb_Latn/dev.tsv - split: test path: data/arb_Latn/test.tsv - config_name: ars_Arab data_files: - split: train path: data/ars_Arab/train.tsv - split: validation path: data/ars_Arab/dev.tsv - split: test path: data/ars_Arab/test.tsv - config_name: ary_Arab data_files: - split: train path: data/ary_Arab/train.tsv - split: validation path: data/ary_Arab/dev.tsv - split: test path: data/ary_Arab/test.tsv - config_name: arz_Arab data_files: - split: train path: data/arz_Arab/train.tsv - split: validation path: data/arz_Arab/dev.tsv - split: test path: data/arz_Arab/test.tsv - config_name: asm_Beng data_files: - split: train path: data/asm_Beng/train.tsv - split: validation path: data/asm_Beng/dev.tsv - split: test path: data/asm_Beng/test.tsv - config_name: ast_Latn data_files: - split: train path: data/ast_Latn/train.tsv - split: validation path: data/ast_Latn/dev.tsv - split: test path: data/ast_Latn/test.tsv - config_name: awa_Deva data_files: - split: train path: data/awa_Deva/train.tsv - split: validation path: data/awa_Deva/dev.tsv - split: test path: data/awa_Deva/test.tsv - config_name: ayr_Latn data_files: - split: train path: data/ayr_Latn/train.tsv - split: validation path: data/ayr_Latn/dev.tsv - split: test path: data/ayr_Latn/test.tsv - config_name: azb_Arab data_files: - split: train path: data/azb_Arab/train.tsv - split: validation path: data/azb_Arab/dev.tsv - split: test path: data/azb_Arab/test.tsv - config_name: azj_Latn data_files: - split: train path: data/azj_Latn/train.tsv - split: validation path: data/azj_Latn/dev.tsv - split: test path: data/azj_Latn/test.tsv - config_name: bak_Cyrl data_files: - split: train path: data/bak_Cyrl/train.tsv - split: validation path: data/bak_Cyrl/dev.tsv - split: test path: data/bak_Cyrl/test.tsv - config_name: bam_Latn data_files: - split: train path: data/bam_Latn/train.tsv - split: validation path: data/bam_Latn/dev.tsv - split: test path: data/bam_Latn/test.tsv - config_name: ban_Latn data_files: - split: train path: data/ban_Latn/train.tsv - split: validation path: data/ban_Latn/dev.tsv - split: test path: data/ban_Latn/test.tsv - config_name: bel_Cyrl data_files: - split: train path: data/bel_Cyrl/train.tsv - split: validation path: data/bel_Cyrl/dev.tsv - split: test path: data/bel_Cyrl/test.tsv - config_name: bem_Latn data_files: - split: train path: data/bem_Latn/train.tsv - split: validation path: data/bem_Latn/dev.tsv - split: test path: data/bem_Latn/test.tsv - config_name: ben_Beng data_files: - split: train path: data/ben_Beng/train.tsv - split: validation path: data/ben_Beng/dev.tsv - split: test path: data/ben_Beng/test.tsv - config_name: bho_Deva data_files: - split: train path: data/bho_Deva/train.tsv - split: validation path: data/bho_Deva/dev.tsv - split: test path: data/bho_Deva/test.tsv - config_name: bjn_Arab data_files: - split: train path: data/bjn_Arab/train.tsv - split: validation path: data/bjn_Arab/dev.tsv - split: test path: data/bjn_Arab/test.tsv - config_name: bjn_Latn data_files: - split: train path: data/bjn_Latn/train.tsv - split: validation path: data/bjn_Latn/dev.tsv - split: test path: data/bjn_Latn/test.tsv - config_name: bod_Tibt data_files: - split: train path: data/bod_Tibt/train.tsv - split: validation path: data/bod_Tibt/dev.tsv - split: test path: data/bod_Tibt/test.tsv - config_name: bos_Latn data_files: - split: train path: data/bos_Latn/train.tsv - split: validation path: data/bos_Latn/dev.tsv - split: test path: data/bos_Latn/test.tsv - config_name: bug_Latn data_files: - split: train path: data/bug_Latn/train.tsv - split: validation path: data/bug_Latn/dev.tsv - split: test path: data/bug_Latn/test.tsv - config_name: bul_Cyrl data_files: - split: train path: data/bul_Cyrl/train.tsv - split: validation path: data/bul_Cyrl/dev.tsv - split: test path: data/bul_Cyrl/test.tsv - config_name: cat_Latn data_files: - split: train path: data/cat_Latn/train.tsv - split: validation path: data/cat_Latn/dev.tsv - split: test path: data/cat_Latn/test.tsv - config_name: ceb_Latn data_files: - split: train path: data/ceb_Latn/train.tsv - split: validation path: data/ceb_Latn/dev.tsv - split: test path: data/ceb_Latn/test.tsv - config_name: ces_Latn data_files: - split: train path: data/ces_Latn/train.tsv - split: validation path: data/ces_Latn/dev.tsv - split: test path: data/ces_Latn/test.tsv - config_name: cjk_Latn data_files: - split: train path: data/cjk_Latn/train.tsv - split: validation path: data/cjk_Latn/dev.tsv - split: test path: data/cjk_Latn/test.tsv - config_name: ckb_Arab data_files: - split: train path: data/ckb_Arab/train.tsv - split: validation path: data/ckb_Arab/dev.tsv - split: test path: data/ckb_Arab/test.tsv - config_name: crh_Latn data_files: - split: train path: data/crh_Latn/train.tsv - split: validation path: data/crh_Latn/dev.tsv - split: test path: data/crh_Latn/test.tsv - config_name: cym_Latn data_files: - split: train path: data/cym_Latn/train.tsv - split: validation path: data/cym_Latn/dev.tsv - split: test path: data/cym_Latn/test.tsv - config_name: dan_Latn data_files: - split: train path: data/dan_Latn/train.tsv - split: validation path: data/dan_Latn/dev.tsv - split: test path: data/dan_Latn/test.tsv - config_name: deu_Latn data_files: - split: train path: data/deu_Latn/train.tsv - split: validation path: data/deu_Latn/dev.tsv - split: test path: data/deu_Latn/test.tsv - config_name: dik_Latn data_files: - split: train path: data/dik_Latn/train.tsv - split: validation path: data/dik_Latn/dev.tsv - split: test path: data/dik_Latn/test.tsv - config_name: dyu_Latn data_files: - split: train path: data/dyu_Latn/train.tsv - split: validation path: data/dyu_Latn/dev.tsv - split: test path: data/dyu_Latn/test.tsv - config_name: dzo_Tibt data_files: - split: train path: data/dzo_Tibt/train.tsv - split: validation path: data/dzo_Tibt/dev.tsv - split: test path: data/dzo_Tibt/test.tsv - config_name: ell_Grek data_files: - split: train path: data/ell_Grek/train.tsv - split: validation path: data/ell_Grek/dev.tsv - split: test path: data/ell_Grek/test.tsv - config_name: eng_Latn data_files: - split: train path: data/eng_Latn/train.tsv - split: validation path: data/eng_Latn/dev.tsv - split: test path: data/eng_Latn/test.tsv - config_name: epo_Latn data_files: - split: train path: data/epo_Latn/train.tsv - split: validation path: data/epo_Latn/dev.tsv - split: test path: data/epo_Latn/test.tsv - config_name: est_Latn data_files: - split: train path: data/est_Latn/train.tsv - split: validation path: data/est_Latn/dev.tsv - split: test path: data/est_Latn/test.tsv - config_name: eus_Latn data_files: - split: train path: data/eus_Latn/train.tsv - split: validation path: data/eus_Latn/dev.tsv - split: test path: data/eus_Latn/test.tsv - config_name: ewe_Latn data_files: - split: train path: data/ewe_Latn/train.tsv - split: validation path: data/ewe_Latn/dev.tsv - split: test path: data/ewe_Latn/test.tsv - config_name: fao_Latn data_files: - split: train path: data/fao_Latn/train.tsv - split: validation path: data/fao_Latn/dev.tsv - split: test path: data/fao_Latn/test.tsv - config_name: fij_Latn data_files: - split: train path: data/fij_Latn/train.tsv - split: validation path: data/fij_Latn/dev.tsv - split: test path: data/fij_Latn/test.tsv - config_name: fin_Latn data_files: - split: train path: data/fin_Latn/train.tsv - split: validation path: data/fin_Latn/dev.tsv - split: test path: data/fin_Latn/test.tsv - config_name: fon_Latn data_files: - split: train path: data/fon_Latn/train.tsv - split: validation path: data/fon_Latn/dev.tsv - split: test path: data/fon_Latn/test.tsv - config_name: fra_Latn data_files: - split: train path: data/fra_Latn/train.tsv - split: validation path: data/fra_Latn/dev.tsv - split: test path: data/fra_Latn/test.tsv - config_name: fur_Latn data_files: - split: train path: data/fur_Latn/train.tsv - split: validation path: data/fur_Latn/dev.tsv - split: test path: data/fur_Latn/test.tsv - config_name: fuv_Latn data_files: - split: train path: data/fuv_Latn/train.tsv - split: validation path: data/fuv_Latn/dev.tsv - split: test path: data/fuv_Latn/test.tsv - config_name: gaz_Latn data_files: - split: train path: data/gaz_Latn/train.tsv - split: validation path: data/gaz_Latn/dev.tsv - split: test path: data/gaz_Latn/test.tsv - config_name: gla_Latn data_files: - split: train path: data/gla_Latn/train.tsv - split: validation path: data/gla_Latn/dev.tsv - split: test path: data/gla_Latn/test.tsv - config_name: gle_Latn data_files: - split: train path: data/gle_Latn/train.tsv - split: validation path: data/gle_Latn/dev.tsv - split: test path: data/gle_Latn/test.tsv - config_name: glg_Latn data_files: - split: train path: data/glg_Latn/train.tsv - split: validation path: data/glg_Latn/dev.tsv - split: test path: data/glg_Latn/test.tsv - config_name: grn_Latn data_files: - split: train path: data/grn_Latn/train.tsv - split: validation path: data/grn_Latn/dev.tsv - split: test path: data/grn_Latn/test.tsv - config_name: guj_Gujr data_files: - split: train path: data/guj_Gujr/train.tsv - split: validation path: data/guj_Gujr/dev.tsv - split: test path: data/guj_Gujr/test.tsv - config_name: hat_Latn data_files: - split: train path: data/hat_Latn/train.tsv - split: validation path: data/hat_Latn/dev.tsv - split: test path: data/hat_Latn/test.tsv - config_name: hau_Latn data_files: - split: train path: data/hau_Latn/train.tsv - split: validation path: data/hau_Latn/dev.tsv - split: test path: data/hau_Latn/test.tsv - config_name: heb_Hebr data_files: - split: train path: data/heb_Hebr/train.tsv - split: validation path: data/heb_Hebr/dev.tsv - split: test path: data/heb_Hebr/test.tsv - config_name: hin_Deva data_files: - split: train path: data/hin_Deva/train.tsv - split: validation path: data/hin_Deva/dev.tsv - split: test path: data/hin_Deva/test.tsv - config_name: hne_Deva data_files: - split: train path: data/hne_Deva/train.tsv - split: validation path: data/hne_Deva/dev.tsv - split: test path: data/hne_Deva/test.tsv - config_name: hrv_Latn data_files: - split: train path: data/hrv_Latn/train.tsv - split: validation path: data/hrv_Latn/dev.tsv - split: test path: data/hrv_Latn/test.tsv - config_name: hun_Latn data_files: - split: train path: data/hun_Latn/train.tsv - split: validation path: data/hun_Latn/dev.tsv - split: test path: data/hun_Latn/test.tsv - config_name: hye_Armn data_files: - split: train path: data/hye_Armn/train.tsv - split: validation path: data/hye_Armn/dev.tsv - split: test path: data/hye_Armn/test.tsv - config_name: ibo_Latn data_files: - split: train path: data/ibo_Latn/train.tsv - split: validation path: data/ibo_Latn/dev.tsv - split: test path: data/ibo_Latn/test.tsv - config_name: ilo_Latn data_files: - split: train path: data/ilo_Latn/train.tsv - split: validation path: data/ilo_Latn/dev.tsv - split: test path: data/ilo_Latn/test.tsv - config_name: ind_Latn data_files: - split: train path: data/ind_Latn/train.tsv - split: validation path: data/ind_Latn/dev.tsv - split: test path: data/ind_Latn/test.tsv - config_name: isl_Latn data_files: - split: train path: data/isl_Latn/train.tsv - split: validation path: data/isl_Latn/dev.tsv - split: test path: data/isl_Latn/test.tsv - config_name: ita_Latn data_files: - split: train path: data/ita_Latn/train.tsv - split: validation path: data/ita_Latn/dev.tsv - split: test path: data/ita_Latn/test.tsv - config_name: jav_Latn data_files: - split: train path: data/jav_Latn/train.tsv - split: validation path: data/jav_Latn/dev.tsv - split: test path: data/jav_Latn/test.tsv - config_name: jpn_Jpan data_files: - split: train path: data/jpn_Jpan/train.tsv - split: validation path: data/jpn_Jpan/dev.tsv - split: test path: data/jpn_Jpan/test.tsv - config_name: kab_Latn data_files: - split: train path: data/kab_Latn/train.tsv - split: validation path: data/kab_Latn/dev.tsv - split: test path: data/kab_Latn/test.tsv - config_name: kac_Latn data_files: - split: train path: data/kac_Latn/train.tsv - split: validation path: data/kac_Latn/dev.tsv - split: test path: data/kac_Latn/test.tsv - config_name: kam_Latn data_files: - split: train path: data/kam_Latn/train.tsv - split: validation path: data/kam_Latn/dev.tsv - split: test path: data/kam_Latn/test.tsv - config_name: kan_Knda data_files: - split: train path: data/kan_Knda/train.tsv - split: validation path: data/kan_Knda/dev.tsv - split: test path: data/kan_Knda/test.tsv - config_name: kas_Arab data_files: - split: train path: data/kas_Arab/train.tsv - split: validation path: data/kas_Arab/dev.tsv - split: test path: data/kas_Arab/test.tsv - config_name: kas_Deva data_files: - split: train path: data/kas_Deva/train.tsv - split: validation path: data/kas_Deva/dev.tsv - split: test path: data/kas_Deva/test.tsv - config_name: kat_Geor data_files: - split: train path: data/kat_Geor/train.tsv - split: validation path: data/kat_Geor/dev.tsv - split: test path: data/kat_Geor/test.tsv - config_name: kaz_Cyrl data_files: - split: train path: data/kaz_Cyrl/train.tsv - split: validation path: data/kaz_Cyrl/dev.tsv - split: test path: data/kaz_Cyrl/test.tsv - config_name: kbp_Latn data_files: - split: train path: data/kbp_Latn/train.tsv - split: validation path: data/kbp_Latn/dev.tsv - split: test path: data/kbp_Latn/test.tsv - config_name: kea_Latn data_files: - split: train path: data/kea_Latn/train.tsv - split: validation path: data/kea_Latn/dev.tsv - split: test path: data/kea_Latn/test.tsv - config_name: khk_Cyrl data_files: - split: train path: data/khk_Cyrl/train.tsv - split: validation path: data/khk_Cyrl/dev.tsv - split: test path: data/khk_Cyrl/test.tsv - config_name: khm_Khmr data_files: - split: train path: data/khm_Khmr/train.tsv - split: validation path: data/khm_Khmr/dev.tsv - split: test path: data/khm_Khmr/test.tsv - config_name: kik_Latn data_files: - split: train path: data/kik_Latn/train.tsv - split: validation path: data/kik_Latn/dev.tsv - split: test path: data/kik_Latn/test.tsv - config_name: kin_Latn data_files: - split: train path: data/kin_Latn/train.tsv - split: validation path: data/kin_Latn/dev.tsv - split: test path: data/kin_Latn/test.tsv - config_name: kir_Cyrl data_files: - split: train path: data/kir_Cyrl/train.tsv - split: validation path: data/kir_Cyrl/dev.tsv - split: test path: data/kir_Cyrl/test.tsv - config_name: kmb_Latn data_files: - split: train path: data/kmb_Latn/train.tsv - split: validation path: data/kmb_Latn/dev.tsv - split: test path: data/kmb_Latn/test.tsv - config_name: kmr_Latn data_files: - split: train path: data/kmr_Latn/train.tsv - split: validation path: data/kmr_Latn/dev.tsv - split: test path: data/kmr_Latn/test.tsv - config_name: knc_Arab data_files: - split: train path: data/knc_Arab/train.tsv - split: validation path: data/knc_Arab/dev.tsv - split: test path: data/knc_Arab/test.tsv - config_name: knc_Latn data_files: - split: train path: data/knc_Latn/train.tsv - split: validation path: data/knc_Latn/dev.tsv - split: test path: data/knc_Latn/test.tsv - config_name: kon_Latn data_files: - split: train path: data/kon_Latn/train.tsv - split: validation path: data/kon_Latn/dev.tsv - split: test path: data/kon_Latn/test.tsv - config_name: kor_Hang data_files: - split: train path: data/kor_Hang/train.tsv - split: validation path: data/kor_Hang/dev.tsv - split: test path: data/kor_Hang/test.tsv - config_name: lao_Laoo data_files: - split: train path: data/lao_Laoo/train.tsv - split: validation path: data/lao_Laoo/dev.tsv - split: test path: data/lao_Laoo/test.tsv - config_name: lij_Latn data_files: - split: train path: data/lij_Latn/train.tsv - split: validation path: data/lij_Latn/dev.tsv - split: test path: data/lij_Latn/test.tsv - config_name: lim_Latn data_files: - split: train path: data/lim_Latn/train.tsv - split: validation path: data/lim_Latn/dev.tsv - split: test path: data/lim_Latn/test.tsv - config_name: lin_Latn data_files: - split: train path: data/lin_Latn/train.tsv - split: validation path: data/lin_Latn/dev.tsv - split: test path: data/lin_Latn/test.tsv - config_name: lit_Latn data_files: - split: train path: data/lit_Latn/train.tsv - split: validation path: data/lit_Latn/dev.tsv - split: test path: data/lit_Latn/test.tsv - config_name: lmo_Latn data_files: - split: train path: data/lmo_Latn/train.tsv - split: validation path: data/lmo_Latn/dev.tsv - split: test path: data/lmo_Latn/test.tsv - config_name: ltg_Latn data_files: - split: train path: data/ltg_Latn/train.tsv - split: validation path: data/ltg_Latn/dev.tsv - split: test path: data/ltg_Latn/test.tsv - config_name: ltz_Latn data_files: - split: train path: data/ltz_Latn/train.tsv - split: validation path: data/ltz_Latn/dev.tsv - split: test path: data/ltz_Latn/test.tsv - config_name: lua_Latn data_files: - split: train path: data/lua_Latn/train.tsv - split: validation path: data/lua_Latn/dev.tsv - split: test path: data/lua_Latn/test.tsv - config_name: lug_Latn data_files: - split: train path: data/lug_Latn/train.tsv - split: validation path: data/lug_Latn/dev.tsv - split: test path: data/lug_Latn/test.tsv - config_name: luo_Latn data_files: - split: train path: data/luo_Latn/train.tsv - split: validation path: data/luo_Latn/dev.tsv - split: test path: data/luo_Latn/test.tsv - config_name: lus_Latn data_files: - split: train path: data/lus_Latn/train.tsv - split: validation path: data/lus_Latn/dev.tsv - split: test path: data/lus_Latn/test.tsv - config_name: lvs_Latn data_files: - split: train path: data/lvs_Latn/train.tsv - split: validation path: data/lvs_Latn/dev.tsv - split: test path: data/lvs_Latn/test.tsv - config_name: mag_Deva data_files: - split: train path: data/mag_Deva/train.tsv - split: validation path: data/mag_Deva/dev.tsv - split: test path: data/mag_Deva/test.tsv - config_name: mai_Deva data_files: - split: train path: data/mai_Deva/train.tsv - split: validation path: data/mai_Deva/dev.tsv - split: test path: data/mai_Deva/test.tsv - config_name: mal_Mlym data_files: - split: train path: data/mal_Mlym/train.tsv - split: validation path: data/mal_Mlym/dev.tsv - split: test path: data/mal_Mlym/test.tsv - config_name: mar_Deva data_files: - split: train path: data/mar_Deva/train.tsv - split: validation path: data/mar_Deva/dev.tsv - split: test path: data/mar_Deva/test.tsv - config_name: min_Arab data_files: - split: train path: data/min_Arab/train.tsv - split: validation path: data/min_Arab/dev.tsv - split: test path: data/min_Arab/test.tsv - config_name: min_Latn data_files: - split: train path: data/min_Latn/train.tsv - split: validation path: data/min_Latn/dev.tsv - split: test path: data/min_Latn/test.tsv - config_name: mkd_Cyrl data_files: - split: train path: data/mkd_Cyrl/train.tsv - split: validation path: data/mkd_Cyrl/dev.tsv - split: test path: data/mkd_Cyrl/test.tsv - config_name: mlt_Latn data_files: - split: train path: data/mlt_Latn/train.tsv - split: validation path: data/mlt_Latn/dev.tsv - split: test path: data/mlt_Latn/test.tsv - config_name: mni_Beng data_files: - split: train path: data/mni_Beng/train.tsv - split: validation path: data/mni_Beng/dev.tsv - split: test path: data/mni_Beng/test.tsv - config_name: mos_Latn data_files: - split: train path: data/mos_Latn/train.tsv - split: validation path: data/mos_Latn/dev.tsv - split: test path: data/mos_Latn/test.tsv - config_name: mri_Latn data_files: - split: train path: data/mri_Latn/train.tsv - split: validation path: data/mri_Latn/dev.tsv - split: test path: data/mri_Latn/test.tsv - config_name: mya_Mymr data_files: - split: train path: data/mya_Mymr/train.tsv - split: validation path: data/mya_Mymr/dev.tsv - split: test path: data/mya_Mymr/test.tsv - config_name: nld_Latn data_files: - split: train path: data/nld_Latn/train.tsv - split: validation path: data/nld_Latn/dev.tsv - split: test path: data/nld_Latn/test.tsv - config_name: nno_Latn data_files: - split: train path: data/nno_Latn/train.tsv - split: validation path: data/nno_Latn/dev.tsv - split: test path: data/nno_Latn/test.tsv - config_name: nob_Latn data_files: - split: train path: data/nob_Latn/train.tsv - split: validation path: data/nob_Latn/dev.tsv - split: test path: data/nob_Latn/test.tsv - config_name: npi_Deva data_files: - split: train path: data/npi_Deva/train.tsv - split: validation path: data/npi_Deva/dev.tsv - split: test path: data/npi_Deva/test.tsv - config_name: nqo_Nkoo data_files: - split: train path: data/nqo_Nkoo/train.tsv - split: validation path: data/nqo_Nkoo/dev.tsv - split: test path: data/nqo_Nkoo/test.tsv - config_name: nqo_Nkoo.zip data_files: - split: train path: data/nqo_Nkoo.zip/train.tsv - split: validation path: data/nqo_Nkoo.zip/dev.tsv - split: test path: data/nqo_Nkoo.zip/test.tsv - config_name: nso_Latn data_files: - split: train path: data/nso_Latn/train.tsv - split: validation path: data/nso_Latn/dev.tsv - split: test path: data/nso_Latn/test.tsv - config_name: nus_Latn data_files: - split: train path: data/nus_Latn/train.tsv - split: validation path: data/nus_Latn/dev.tsv - split: test path: data/nus_Latn/test.tsv - config_name: nya_Latn data_files: - split: train path: data/nya_Latn/train.tsv - split: validation path: data/nya_Latn/dev.tsv - split: test path: data/nya_Latn/test.tsv - config_name: oci_Latn data_files: - split: train path: data/oci_Latn/train.tsv - split: validation path: data/oci_Latn/dev.tsv - split: test path: data/oci_Latn/test.tsv - config_name: ory_Orya data_files: - split: train path: data/ory_Orya/train.tsv - split: validation path: data/ory_Orya/dev.tsv - split: test path: data/ory_Orya/test.tsv - config_name: pag_Latn data_files: - split: train path: data/pag_Latn/train.tsv - split: validation path: data/pag_Latn/dev.tsv - split: test path: data/pag_Latn/test.tsv - config_name: pan_Guru data_files: - split: train path: data/pan_Guru/train.tsv - split: validation path: data/pan_Guru/dev.tsv - split: test path: data/pan_Guru/test.tsv - config_name: pap_Latn data_files: - split: train path: data/pap_Latn/train.tsv - split: validation path: data/pap_Latn/dev.tsv - split: test path: data/pap_Latn/test.tsv - config_name: pbt_Arab data_files: - split: train path: data/pbt_Arab/train.tsv - split: validation path: data/pbt_Arab/dev.tsv - split: test path: data/pbt_Arab/test.tsv - config_name: pes_Arab data_files: - split: train path: data/pes_Arab/train.tsv - split: validation path: data/pes_Arab/dev.tsv - split: test path: data/pes_Arab/test.tsv - config_name: plt_Latn data_files: - split: train path: data/plt_Latn/train.tsv - split: validation path: data/plt_Latn/dev.tsv - split: test path: data/plt_Latn/test.tsv - config_name: pol_Latn data_files: - split: train path: data/pol_Latn/train.tsv - split: validation path: data/pol_Latn/dev.tsv - split: test path: data/pol_Latn/test.tsv - config_name: por_Latn data_files: - split: train path: data/por_Latn/train.tsv - split: validation path: data/por_Latn/dev.tsv - split: test path: data/por_Latn/test.tsv - config_name: prs_Arab data_files: - split: train path: data/prs_Arab/train.tsv - split: validation path: data/prs_Arab/dev.tsv - split: test path: data/prs_Arab/test.tsv - config_name: quy_Latn data_files: - split: train path: data/quy_Latn/train.tsv - split: validation path: data/quy_Latn/dev.tsv - split: test path: data/quy_Latn/test.tsv - config_name: ron_Latn data_files: - split: train path: data/ron_Latn/train.tsv - split: validation path: data/ron_Latn/dev.tsv - split: test path: data/ron_Latn/test.tsv - config_name: run_Latn data_files: - split: train path: data/run_Latn/train.tsv - split: validation path: data/run_Latn/dev.tsv - split: test path: data/run_Latn/test.tsv - config_name: rus_Cyrl data_files: - split: train path: data/rus_Cyrl/train.tsv - split: validation path: data/rus_Cyrl/dev.tsv - split: test path: data/rus_Cyrl/test.tsv - config_name: sag_Latn data_files: - split: train path: data/sag_Latn/train.tsv - split: validation path: data/sag_Latn/dev.tsv - split: test path: data/sag_Latn/test.tsv - config_name: san_Deva data_files: - split: train path: data/san_Deva/train.tsv - split: validation path: data/san_Deva/dev.tsv - split: test path: data/san_Deva/test.tsv - config_name: sat_Olck data_files: - split: train path: data/sat_Olck/train.tsv - split: validation path: data/sat_Olck/dev.tsv - split: test path: data/sat_Olck/test.tsv - config_name: scn_Latn data_files: - split: train path: data/scn_Latn/train.tsv - split: validation path: data/scn_Latn/dev.tsv - split: test path: data/scn_Latn/test.tsv - config_name: shn_Mymr data_files: - split: train path: data/shn_Mymr/train.tsv - split: validation path: data/shn_Mymr/dev.tsv - split: test path: data/shn_Mymr/test.tsv - config_name: sin_Sinh data_files: - split: train path: data/sin_Sinh/train.tsv - split: validation path: data/sin_Sinh/dev.tsv - split: test path: data/sin_Sinh/test.tsv - config_name: slk_Latn data_files: - split: train path: data/slk_Latn/train.tsv - split: validation path: data/slk_Latn/dev.tsv - split: test path: data/slk_Latn/test.tsv - config_name: slv_Latn data_files: - split: train path: data/slv_Latn/train.tsv - split: validation path: data/slv_Latn/dev.tsv - split: test path: data/slv_Latn/test.tsv - config_name: smo_Latn data_files: - split: train path: data/smo_Latn/train.tsv - split: validation path: data/smo_Latn/dev.tsv - split: test path: data/smo_Latn/test.tsv - config_name: sna_Latn data_files: - split: train path: data/sna_Latn/train.tsv - split: validation path: data/sna_Latn/dev.tsv - split: test path: data/sna_Latn/test.tsv - config_name: snd_Arab data_files: - split: train path: data/snd_Arab/train.tsv - split: validation path: data/snd_Arab/dev.tsv - split: test path: data/snd_Arab/test.tsv - config_name: som_Latn data_files: - split: train path: data/som_Latn/train.tsv - split: validation path: data/som_Latn/dev.tsv - split: test path: data/som_Latn/test.tsv - config_name: sot_Latn data_files: - split: train path: data/sot_Latn/train.tsv - split: validation path: data/sot_Latn/dev.tsv - split: test path: data/sot_Latn/test.tsv - config_name: spa_Latn data_files: - split: train path: data/spa_Latn/train.tsv - split: validation path: data/spa_Latn/dev.tsv - split: test path: data/spa_Latn/test.tsv - config_name: srd_Latn data_files: - split: train path: data/srd_Latn/train.tsv - split: validation path: data/srd_Latn/dev.tsv - split: test path: data/srd_Latn/test.tsv - config_name: srp_Cyrl data_files: - split: train path: data/srp_Cyrl/train.tsv - split: validation path: data/srp_Cyrl/dev.tsv - split: test path: data/srp_Cyrl/test.tsv - config_name: ssw_Latn data_files: - split: train path: data/ssw_Latn/train.tsv - split: validation path: data/ssw_Latn/dev.tsv - split: test path: data/ssw_Latn/test.tsv - config_name: sun_Latn data_files: - split: train path: data/sun_Latn/train.tsv - split: validation path: data/sun_Latn/dev.tsv - split: test path: data/sun_Latn/test.tsv - config_name: swe_Latn data_files: - split: train path: data/swe_Latn/train.tsv - split: validation path: data/swe_Latn/dev.tsv - split: test path: data/swe_Latn/test.tsv - config_name: swh_Latn data_files: - split: train path: data/swh_Latn/train.tsv - split: validation path: data/swh_Latn/dev.tsv - split: test path: data/swh_Latn/test.tsv - config_name: szl_Latn data_files: - split: train path: data/szl_Latn/train.tsv - split: validation path: data/szl_Latn/dev.tsv - split: test path: data/szl_Latn/test.tsv - config_name: tam_Taml data_files: - split: train path: data/tam_Taml/train.tsv - split: validation path: data/tam_Taml/dev.tsv - split: test path: data/tam_Taml/test.tsv - config_name: taq_Latn data_files: - split: train path: data/taq_Latn/train.tsv - split: validation path: data/taq_Latn/dev.tsv - split: test path: data/taq_Latn/test.tsv - config_name: taq_Tfng data_files: - split: train path: data/taq_Tfng/train.tsv - split: validation path: data/taq_Tfng/dev.tsv - split: test path: data/taq_Tfng/test.tsv - config_name: tat_Cyrl data_files: - split: train path: data/tat_Cyrl/train.tsv - split: validation path: data/tat_Cyrl/dev.tsv - split: test path: data/tat_Cyrl/test.tsv - config_name: tel_Telu data_files: - split: train path: data/tel_Telu/train.tsv - split: validation path: data/tel_Telu/dev.tsv - split: test path: data/tel_Telu/test.tsv - config_name: tgk_Cyrl data_files: - split: train path: data/tgk_Cyrl/train.tsv - split: validation path: data/tgk_Cyrl/dev.tsv - split: test path: data/tgk_Cyrl/test.tsv - config_name: tgl_Latn data_files: - split: train path: data/tgl_Latn/train.tsv - split: validation path: data/tgl_Latn/dev.tsv - split: test path: data/tgl_Latn/test.tsv - config_name: tha_Thai data_files: - split: train path: data/tha_Thai/train.tsv - split: validation path: data/tha_Thai/dev.tsv - split: test path: data/tha_Thai/test.tsv - config_name: tir_Ethi data_files: - split: train path: data/tir_Ethi/train.tsv - split: validation path: data/tir_Ethi/dev.tsv - split: test path: data/tir_Ethi/test.tsv - config_name: tpi_Latn data_files: - split: train path: data/tpi_Latn/train.tsv - split: validation path: data/tpi_Latn/dev.tsv - split: test path: data/tpi_Latn/test.tsv - config_name: tsn_Latn data_files: - split: train path: data/tsn_Latn/train.tsv - split: validation path: data/tsn_Latn/dev.tsv - split: test path: data/tsn_Latn/test.tsv - config_name: tso_Latn data_files: - split: train path: data/tso_Latn/train.tsv - split: validation path: data/tso_Latn/dev.tsv - split: test path: data/tso_Latn/test.tsv - config_name: tuk_Latn data_files: - split: train path: data/tuk_Latn/train.tsv - split: validation path: data/tuk_Latn/dev.tsv - split: test path: data/tuk_Latn/test.tsv - config_name: tum_Latn data_files: - split: train path: data/tum_Latn/train.tsv - split: validation path: data/tum_Latn/dev.tsv - split: test path: data/tum_Latn/test.tsv - config_name: tur_Latn data_files: - split: train path: data/tur_Latn/train.tsv - split: validation path: data/tur_Latn/dev.tsv - split: test path: data/tur_Latn/test.tsv - config_name: twi_Latn data_files: - split: train path: data/twi_Latn/train.tsv - split: validation path: data/twi_Latn/dev.tsv - split: test path: data/twi_Latn/test.tsv - config_name: tzm_Tfng data_files: - split: train path: data/tzm_Tfng/train.tsv - split: validation path: data/tzm_Tfng/dev.tsv - split: test path: data/tzm_Tfng/test.tsv - config_name: uig_Arab data_files: - split: train path: data/uig_Arab/train.tsv - split: validation path: data/uig_Arab/dev.tsv - split: test path: data/uig_Arab/test.tsv - config_name: ukr_Cyrl data_files: - split: train path: data/ukr_Cyrl/train.tsv - split: validation path: data/ukr_Cyrl/dev.tsv - split: test path: data/ukr_Cyrl/test.tsv - config_name: umb_Latn data_files: - split: train path: data/umb_Latn/train.tsv - split: validation path: data/umb_Latn/dev.tsv - split: test path: data/umb_Latn/test.tsv - config_name: urd_Arab data_files: - split: train path: data/urd_Arab/train.tsv - split: validation path: data/urd_Arab/dev.tsv - split: test path: data/urd_Arab/test.tsv - config_name: uzn_Latn data_files: - split: train path: data/uzn_Latn/train.tsv - split: validation path: data/uzn_Latn/dev.tsv - split: test path: data/uzn_Latn/test.tsv - config_name: vec_Latn data_files: - split: train path: data/vec_Latn/train.tsv - split: validation path: data/vec_Latn/dev.tsv - split: test path: data/vec_Latn/test.tsv - config_name: vie_Latn data_files: - split: train path: data/vie_Latn/train.tsv - split: validation path: data/vie_Latn/dev.tsv - split: test path: data/vie_Latn/test.tsv - config_name: war_Latn data_files: - split: train path: data/war_Latn/train.tsv - split: validation path: data/war_Latn/dev.tsv - split: test path: data/war_Latn/test.tsv - config_name: wol_Latn data_files: - split: train path: data/wol_Latn/train.tsv - split: validation path: data/wol_Latn/dev.tsv - split: test path: data/wol_Latn/test.tsv - config_name: xho_Latn data_files: - split: train path: data/xho_Latn/train.tsv - split: validation path: data/xho_Latn/dev.tsv - split: test path: data/xho_Latn/test.tsv - config_name: ydd_Hebr data_files: - split: train path: data/ydd_Hebr/train.tsv - split: validation path: data/ydd_Hebr/dev.tsv - split: test path: data/ydd_Hebr/test.tsv - config_name: yor_Latn data_files: - split: train path: data/yor_Latn/train.tsv - split: validation path: data/yor_Latn/dev.tsv - split: test path: data/yor_Latn/test.tsv - config_name: yue_Hant data_files: - split: train path: data/yue_Hant/train.tsv - split: validation path: data/yue_Hant/dev.tsv - split: test path: data/yue_Hant/test.tsv - config_name: zho_Hans data_files: - split: train path: data/zho_Hans/train.tsv - split: validation path: data/zho_Hans/dev.tsv - split: test path: data/zho_Hans/test.tsv - config_name: zho_Hant data_files: - split: train path: data/zho_Hant/train.tsv - split: validation path: data/zho_Hant/dev.tsv - split: test path: data/zho_Hant/test.tsv - config_name: zsm_Latn data_files: - split: train path: data/zsm_Latn/train.tsv - split: validation path: data/zsm_Latn/dev.tsv - split: test path: data/zsm_Latn/test.tsv - config_name: zul_Latn data_files: - split: train path: data/zul_Latn/train.tsv - split: validation path: data/zul_Latn/dev.tsv - split: test path: data/zul_Latn/test.tsv --- # Dataset Card for SIB-200 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [homepage](https://github.com/dadelani/sib-200) - **Repository:** [github](https://github.com/dadelani/sib-200) - **Paper:** [paper](https://arxiv.org/abs/2309.07445) - **Point of Contact:** [email protected] ### Dataset Summary SIB-200 is the largest publicly available topic classification dataset based on Flores-200 covering 205 languages and dialects. The train/validation/test sets are available for all the 205 languages. ### Supported Tasks and Leaderboards - `topic classification`: categorize wikipedia sentences into topics e.g science/technology, sports or politics. ### Languages There are 205 languages available : ## Dataset Structure ### Data Instances The examples look like this for English: ``` from datasets import load_dataset data = load_dataset('Davlan/sib200', 'eng_Latn') # Please, specify the language code # A data point example is below: { 'label': 0, 'index_id': 1523, 'text': 'Mutation adds new genetic variation, and selection removes it from the pool of expressed variation.' } ``` ### Data Fields - `label`: topic id - `index_id`: sentence id in flores-200 - `text`: text The topics correspond to this list: ``` "science/technology", "travel", "politics", "sports", "health", "entertainment", "geography" ``` ### Data Splits For all languages, there are three splits. The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits. The splits have the following sizes : | Language | train | validation | test | |-----------------|------:|-----------:|-----:| | English | 701 | 99 | 204 | ## Dataset Creation ### Curation Rationale The dataset was introduced to introduce new resources for 205 languages, many are under-served for natural language processing. [More Information Needed] ### Source Data The source of the data is from the news domain, details can be found here **** #### Initial Data Collection and Normalization The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable. #### Who are the source language producers? The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above. ### Annotations #### Annotation process Details can be found here ** #### Who are the annotators? Annotators were recruited from [Masakhane](https://www.masakhane.io/) ### Personal and Sensitive Information The data is sourced from newspaper source and only contains mentions of public figures or individuals ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains. ## Additional Information ### Dataset Curators ### Licensing Information The licensing status of the data is CC 4.0 Commercial ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @misc{adelani2023sib200, title={SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects}, author={David Ifeoluwa Adelani and Hannah Liu and Xiaoyu Shen and Nikita Vassilyev and Jesujoba O. Alabi and Yanke Mao and Haonan Gao and Annie En-Shiun Lee}, year={2023}, eprint={2309.07445}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.

bigscience/xP3mt

bigscience

--- annotations_creators: - expert-generated - crowdsourced language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3 size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Oración 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\Oración 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nPregunta: ¿La oración 1 parafrasea la oración 2? ¿Si o no?", "targets": "Sí" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. We machine-translated prompts for monolingual datasets, thus languages with only crosslingual datasets (e.g. Translation) do not have non-English prompts. Languages without non-English prompts are equivalent to [xP3](https://huggingface.co/datasets/bigscience/xP3). |Language|Kilobytes|%|Samples|%|Non-English prompts| |--------|------:|-:|---:|-:|-:| |tw|106288|0.11|265071|0.33| | |bm|107056|0.11|265180|0.33| | |ak|108096|0.11|265071|0.33| | |ca|110608|0.11|271191|0.34| | |eu|113008|0.12|281199|0.35| | |fon|113072|0.12|265063|0.33| | |st|114080|0.12|265063|0.33| | |ki|115040|0.12|265180|0.33| | |tum|116032|0.12|265063|0.33| | |wo|122560|0.13|365063|0.46| | |ln|126304|0.13|365060|0.46| | |as|156256|0.16|265063|0.33| | |or|161472|0.17|265063|0.33| | |kn|165456|0.17|265063|0.33| | |ml|175040|0.18|265864|0.33| | |rn|192992|0.2|318189|0.4| | |nso|229712|0.24|915051|1.14| | |tn|235536|0.24|915054|1.14| | |lg|235936|0.24|915021|1.14| | |rw|249360|0.26|915043|1.14| | |ts|250256|0.26|915044|1.14| | |sn|252496|0.26|865056|1.08| | |xh|254672|0.26|915058|1.14| | |zu|263712|0.27|915061|1.14| | |ny|272128|0.28|915063|1.14| | |ig|325440|0.33|950097|1.19|✅| |yo|339664|0.35|913021|1.14|✅| |ne|398144|0.41|315754|0.39|✅| |pa|529632|0.55|339210|0.42|✅| |sw|561392|0.58|1114439|1.39|✅| |gu|566576|0.58|347499|0.43|✅| |mr|674000|0.69|417269|0.52|✅| |bn|854864|0.88|428725|0.54|✅| |ta|943440|0.97|410633|0.51|✅| |te|1384016|1.42|573354|0.72|✅| |ur|1944416|2.0|855756|1.07|✅| |vi|3113184|3.2|1667306|2.08|✅| |code|4330752|4.46|2707724|3.38| | |hi|4469712|4.6|1543441|1.93|✅| |id|4538768|4.67|2582272|3.22|✅| |zh|4604112|4.74|3571636|4.46|✅| |ar|4703968|4.84|2148970|2.68|✅| |fr|5558912|5.72|5055942|6.31|✅| |pt|6130016|6.31|3562772|4.45|✅| |es|7579424|7.8|5151349|6.43|✅| |en|39252528|40.4|32740750|40.87| | |total|97150128|100.0|80100816|100.0|✅| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI & HumanEval) - Natural Language Inference (NLI) - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.

HuggingFaceTB/finemath

HuggingFaceTB

--- license: odc-by dataset_info: - config_name: finemath-3plus features: - name: url dtype: string - name: fetch_time dtype: int64 - name: content_mime_type dtype: string - name: warc_filename dtype: string - name: warc_record_offset dtype: int32 - name: warc_record_length dtype: int32 - name: text dtype: string - name: token_count dtype: int32 - name: char_count dtype: int32 - name: metadata dtype: string - name: score dtype: float64 - name: int_score dtype: int64 - name: crawl dtype: string - name: snapshot_type dtype: string - name: language dtype: string - name: language_score dtype: float64 splits: - name: train num_bytes: 137764105388.93857 num_examples: 21405610 download_size: 65039196945 dataset_size: 137764105388.93857 - config_name: finemath-4plus features: - name: url dtype: string - name: fetch_time dtype: int64 - name: content_mime_type dtype: string - name: warc_filename dtype: string - name: warc_record_offset dtype: int32 - name: warc_record_length dtype: int32 - name: text dtype: string - name: token_count dtype: int32 - name: char_count dtype: int32 - name: metadata dtype: string - name: score dtype: float64 - name: int_score dtype: int64 - name: crawl dtype: string - name: snapshot_type dtype: string - name: language dtype: string - name: language_score dtype: float64 splits: - name: train num_bytes: 39101488149.09091 num_examples: 6699493 download_size: 18365184633 dataset_size: 39101488149.09091 - config_name: infiwebmath-3plus features: - name: url dtype: string - name: metadata dtype: string - name: score dtype: float64 - name: int_score dtype: int64 - name: token_count dtype: int64 - name: char_count dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 96485696853.10182 num_examples: 13882669 download_size: 46808660851 dataset_size: 96485696853.10182 - config_name: infiwebmath-4plus features: - name: url dtype: string - name: metadata dtype: string - name: score dtype: float64 - name: int_score dtype: int64 - name: token_count dtype: int64 - name: char_count dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 40002719500.1551 num_examples: 6296212 download_size: 19234328998 dataset_size: 40002719500.1551 configs: - config_name: finemath-3plus data_files: - split: train path: finemath-3plus/train-* - config_name: finemath-4plus data_files: - split: train path: finemath-4plus/train-* - config_name: infiwebmath-3plus data_files: - split: train path: infiwebmath-3plus/train-* - config_name: infiwebmath-4plus data_files: - split: train path: infiwebmath-4plus/train-* --- # 📐 FineMath  ## What is it? 📐 FineMath consists of **34B tokens** (FineMath-3+) and **54B tokens** (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCrawl. To curate this dataset, we trained a mathematical content [classifier](https://huggingface.co/HuggingFaceTB/finemath-classifier) using annotations generated by LLama-3.1-70B-Instruct. We used the classifier to retain only the most educational mathematics content, focusing on clear explanations and step-by-step problem solving rather than advanced academic papers. The [Dataset Curation](#dataset-curation) section details the process for creating the dataset. <img src="assets/train_curves.png" width="800"/> ## What is being released? The dataset is released in two versions: - **FineMath-3+**: 34B tokens, 21.4M documents containing mathematical reasoning and problem solving, formatted with Markdown and LaTeX. - **FineMath-4+** (a subset of FineMath-3+): 9.6B tokens, 6.7M documents of higher quality with detailed explanations. Models trained on this dataset perform better on GSM8k and MATH. <!-- (the image looks kinda meh) <img src="assets/stats.png" width="512"/> --> We also release a filtered English text-only portion of the **[InfiMM-WebMath-40B](https://huggingface.co/datasets/Infi-MM/InfiMM-WebMath-40B)** dataset, classified using the same approach as FineMath: - **InfiMM-WebMath-3+**: 20.5B tokens, 13.9M documents. - **InfiMM-WebMath-4+** (a subset of InfiMM-WebMath-3+): 8.5B tokens, 6.3M documents. ## How to load the dataset Use one of the available configs: `finemath-3plus`, `finemath-4plus`, `infiwebmath-3plus`, or `infiwebmath-4plus`. ```python from datasets import load_dataset # Load the high-quality subset data = load_dataset("HuggingFaceTB/finemath", "finemath-4plus", split="train", num_proc=8) # Or load the larger subset data = load_dataset("HuggingFaceTB/finemath", "finemath-3plus", split="train", num_proc=8) ``` ## Dataset curation Recent language models like DeepSeekMath and MathStral have demonstrated strong mathematical capabilities, trained on specialized datasets that aren't publicly available. We developed a pipeline to identify and extract high-quality mathematical content from CommonCrawl, with several iterations of refinement to improve quality. ### Phase 1: Initial content extraction and classification We began by re-extracting pages from CommonCrawl WARCs using URLs from the FineWeb dataset, collecting both the latest and largest versions of each page to capture the evolution of pages across the years. Unlike FineWeb which uses Trafilatura, we employed Resiliparse for text extraction as it better preserves forum discussions and QA answers that often contain crucial reasoning steps and solutions. For initial quality assessment, we used [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) to generate annotations on a 3-point scale: 1. Contains general mathematical content 2. Shows logical reasoning in mathematical context 3. Contains clear step-by-step solutions at appropriate level A `multilingual-e5-small`-based classifier finetuned on these annotations was used to score the initial corpus. However, this first version performed below the OpenWebMath baseline, leading to several important refinements. ### Phase 2: Recalling more candidate pages Analysis revealed that FineWeb's C4 filter removes pages containing '{' characters, inadvertently filtering out content with LaTeX notation. To address this and expand coverage, we: 1. Identified promising website domains by selecting those where at least 10% of pages received a classifier score ≥ 2 2. Added URLs from OpenWebMath and InfiMM-WebMath datasets 3. Recovered URLs of pages filtered by FineWeb's '{' rule from its rejection logs 4. Re-extracted all content from scratch using the [OpenWebMath pipeline](https://github.com/keirp/OpenWebMath), which properly handles mathematical notation across various HTML markup formats and standardizes them to LaTeX ### Phase 3: Refined quality assessment The expanded corpus underwent a more fine-grained quality evaluation: Once again, we used LLama-3.1-70B-Instruct to score a sample of newly extracted pages on a 5-point scale (full prompt available in [here](assets/prompt.txt)): We finetuned a new [classifier](https://huggingface.co/HuggingFaceTB/finemath-classifier) on these annotations and scored the entire corpus. After leaving only pages with a score of 3 or higher, and deduplicating the samples using simple single-band MinHash-LSH, we obtained FineMath-3+ with 34B tokens. The same classifier was applied to InfiMM-WebMath's text content, focusing more on reasoning rather than advanced mathematics. Both datasets were additionally filtered using FineWeb's language classification pipeline to remove non-English content. ### Decontamination Following Qwen2.5-Math's approach, we removed samples with 13-gram overlaps against test sets from GSM8k, MATH, MMLU and ARC. Decontamination logs are available at [HuggingFaceTB/finemath_contamination_report](https://huggingface.co/datasets/HuggingFaceTB/finemath_contamination_report). ## Results and Performance <img src="assets/eval_bar.png" width="600"/> Our evaluations show several key findings: 1. FineMath-3+ outperforms the base InfiWebMath on GSM8k and MATH benchmarks 2. FineMath-4+ demonstrates superior performance compared to both FineMath-3+ and InfiWebMath-4+ on GSM8k and MATH 3. Combining the datasets (50% FineMath-3+ with 50% InfiWebMath-3+) yields approximately 50B tokens while matching the performance of FineMath-3+ 4. Deduplicating the pages repeated between FineMath and InfiWebMath reduces performance compared to a non-deduplicated combination ## Dataset Schema ```python { 'url': string, # Source page URL 'fetch_time': int64, # Crawler timestamp 'content_mime_type': string, # MIME type 'warc_filename': string, # Common Crawl WARC source file 'warc_record_offset': int32, # WARC record offset, in bytes 'warc_record_length': int32, # WARC record size, in bytes 'text': string, # Page content 'token_count': int32, # Number of Llama tokens 'char_count': int32, # Character count 'metadata': string, # Additional OpenWebMath metadata 'score': float64, # Raw quality score 'int_score': int64, # Integer quality score 'crawl': string, # Common Crawl crawl identifier 'snapshot_type': string, # Whether the page is the latest or the largest for this URL 'language': string, # Document language 'language_score': float64 # LangID probability } ``` ## Considerations for Using the Data ### Social Impact of Dataset With the release of this dataset, we aim to make high-quality mathematical educational content more accessible to the machine learning community. While multiple language models have demonstrated strong mathematical capabilities, the datasets used to train these capabilities are often not publicly available. By releasing FineMath, we hope to: - Make the dataset creation process more transparent - Reduce the barrier to entry for training models with strong mathematical capabilities - Provide a benchmark for mathematical content quality filtering ### Discussion of Biases The dataset may have certain inherent biases: - Focus on English language content - Emphasis on popular educational approaches to mathematics - Bias towards certain types of mathematical notation and formatting ### Other Known Limitations - The dataset is limited to English language content - The filtering criteria may not capture advanced mathematical content (e.g. advanced research subjects) - Some mathematical notation (e.g. image-based) may not be preserved - Long-form content may have varying quality even within high-scoring documents ## Licensing Information The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use). ## Future work There are several avenues for future work: - Expand language coverage beyond English - Improve mathematical notation extraction and preservation - Develop more sophisticated quality metrics - Create specialized subsets for different educational levels ### Citation Information ``` @misc{lozhkov2024finemath, author = { Lozhkov, Anton and Ben Allal, Loubna and Bakouch, Elie and von Werra, Leandro and Wolf, Thomas }, title = { FineMath: the Finest Collection of Mathematical Content }, year = 2024, url = { https://huggingface.co/datasets/HuggingFaceTB/finemath }, doi = { 10.57967/hf/3847 }, publisher = { Hugging Face } } ```

cimec/lambada

cimec

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|bookcorpus task_categories: - text2text-generation task_ids: [] paperswithcode_id: lambada pretty_name: LAMBADA tags: - long-range-dependency dataset_info: config_name: plain_text features: - name: text dtype: string - name: domain dtype: string splits: - name: train num_bytes: 978174122 num_examples: 2662 - name: test num_bytes: 1791823 num_examples: 5153 - name: validation num_bytes: 1703482 num_examples: 4869 download_size: 552427340 dataset_size: 981669427 configs: - config_name: plain_text data_files: - split: train path: plain_text/train-* - split: test path: plain_text/test-* - split: validation path: plain_text/validation-* default: true --- # Dataset Card for LAMBADA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [LAMBADA homepage](https://zenodo.org/record/2630551#.X8UP76pKiIa) - **Paper:** [The LAMBADA dataset: Word prediction requiring a broad discourse context∗](https://www.aclweb.org/anthology/P16-1144.pdf) - **Data:** https://doi.org/10.5281/zenodo.2630551 ### Dataset Summary The LAMBADA evaluates the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. The LAMBADA dataset is extracted from BookCorpus and consists of 10'022 passages, divided into 4'869 development and 5'153 test passages. The training data for language models to be tested on LAMBADA include the full text of 2'662 novels (disjoint from those in dev+test), comprising 203 million words. ### Supported Tasks and Leaderboards Long range dependency evaluated as (last) word prediction ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances A data point is a text sequence (passage) including the context, the target sentence (the last one) and the target word. For each passage in the dev and the test splits, the word to be guessed is the last one. The training data include the full text of 2'662 novels (disjoint from those in dev+test), comprising more than 200M words. It consists of text from the same domain as the dev+test passages, but not filtered in any way. Each training instance has a `category` field indicating which sub-category the book was extracted from. This field is not given for the dev and test splits. An example looks like this: ``` {"category": "Mystery", "text": "bob could have been called in at this point , but he was n't miffed at his exclusion at all . he was relieved at not being brought into this initial discussion with central command . `` let 's go make some grub , '' said bob as he turned to danny . danny did n't keep his stoic expression , but with a look of irritation got up and left the room with bob", } ``` ### Data Fields - `category`: the sub-category of books from which the book was extracted from. Only available for the training split. - `text`: the text (concatenation of context, target sentence and target word). The word to be guessed is the last one. ### Data Splits - train: 2'662 novels - dev: 4'869 passages - test: 5'153 passages ## Dataset Creation ### Curation Rationale The dataset aims at evaluating the ability of language models to hold long-term contextual memories. Instances are extracted from books because they display long-term dependencies. In particular, the data are curated such that the target words are easy to guess by human subjects when they can look at the whole passage they come from, but nearly impossible if only the last sentence is considered. ### Source Data #### Initial Data Collection and Normalization The corpus was duplicated and potentially offensive material were filtered out with a stop word list. #### Who are the source language producers? The passages are extracted from novels from [Book Corpus](https://github.com/huggingface/datasets/tree/master/datasets/bookcorpus). ### Annotations #### Annotation process The authors required two consecutive subjects (paid crowdsourcers) to exactly match the missing word based on the whole passage (comprising the context and the target sentence), and made sure that no subject (out of ten) was able to provide it based on local context only, even when given 3 guesses. #### Who are the annotators? The text is self-annotated but was curated by asking (paid) crowdsourcers to guess the last word. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is released under the [CC BY 4.0](Creative Commons Attribution 4.0 International) license. ### Citation Information ``` @InProceedings{paperno-EtAl:2016:P16-1, author = {Paperno, Denis and Kruszewski, Germ\'{a}n and Lazaridou, Angeliki and Pham, Ngoc Quan and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernandez, Raquel}, title = {The {LAMBADA} dataset: Word prediction requiring a broad discourse context}, booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {August}, year = {2016}, address = {Berlin, Germany}, publisher = {Association for Computational Linguistics}, pages = {1525--1534}, url = {http://www.aclweb.org/anthology/P16-1144} } ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

HuggingFaceGECLM/REDDIT_comments

HuggingFaceGECLM

--- dataset_info: features: - name: archived dtype: string - name: author dtype: string - name: author_fullname dtype: string - name: body dtype: string - name: comment_type dtype: string - name: controversiality dtype: string - name: created_utc dtype: string - name: edited dtype: string - name: gilded dtype: string - name: id dtype: string - name: link_id dtype: string - name: locked dtype: string - name: name dtype: string - name: parent_id dtype: string - name: permalink dtype: string - name: retrieved_on dtype: string - name: score dtype: string - name: subreddit_id dtype: string - name: subreddit_name_prefixed dtype: string - name: subreddit_type dtype: string - name: total_awards_received dtype: string splits: - name: programming num_bytes: 3466623746 num_examples: 7503347 - name: tifu num_bytes: 4761338653 num_examples: 12738669 - name: explainlikeimfive num_bytes: 8451732573 num_examples: 16392814 - name: WritingPrompts num_bytes: 4651591771 num_examples: 4436210 - name: changemyview num_bytes: 8603031915 num_examples: 11600073 - name: LifeProTips num_bytes: 5272994396 num_examples: 12829459 - name: todayilearned num_bytes: 22655655241 num_examples: 60199778 - name: science num_bytes: 7069809765 num_examples: 18112884 - name: askscience num_bytes: 3144754665 num_examples: 6286702 - name: ifyoulikeblank num_bytes: 547200329 num_examples: 1332211 - name: Foodforthought num_bytes: 308377128 num_examples: 567900 - name: IWantToLearn num_bytes: 408331672 num_examples: 745543 - name: bestof num_bytes: 2003718831 num_examples: 4347522 - name: IAmA num_bytes: 9380094090 num_examples: 25778822 - name: socialskills num_bytes: 1000014402 num_examples: 1842733 - name: relationship_advice num_bytes: 22298879735 num_examples: 38937398 - name: philosophy num_bytes: 1494947876 num_examples: 2391695 - name: YouShouldKnow num_bytes: 1165617658 num_examples: 2639265 - name: history num_bytes: 1457852402 num_examples: 2962043 - name: books num_bytes: 4562689426 num_examples: 10187495 - name: Showerthoughts num_bytes: 13259109532 num_examples: 34123213 - name: personalfinance num_bytes: 9484869588 num_examples: 18361314 - name: buildapc num_bytes: 9801044390 num_examples: 21761801 - name: EatCheapAndHealthy num_bytes: 853462012 num_examples: 1821897 - name: boardgames num_bytes: 3131627378 num_examples: 6328926 - name: malefashionadvice num_bytes: 2928017882 num_examples: 7712258 - name: femalefashionadvice num_bytes: 1619784736 num_examples: 3262969 - name: scifi num_bytes: 888152056 num_examples: 2193741 - name: Fantasy num_bytes: 2285934538 num_examples: 4566639 - name: Games num_bytes: 10396813188 num_examples: 23373965 - name: bodyweightfitness num_bytes: 794549854 num_examples: 1613634 - name: SkincareAddiction num_bytes: 3421122597 num_examples: 5660550 - name: podcasts num_bytes: 464773126 num_examples: 943266 - name: suggestmeabook num_bytes: 1842944304 num_examples: 3492937 - name: AskHistorians num_bytes: 2244587909 num_examples: 2714353 - name: gaming num_bytes: 28374513722 num_examples: 85729253 - name: DIY num_bytes: 2113533684 num_examples: 4489265 - name: sports num_bytes: 2230129132 num_examples: 6470079 - name: space num_bytes: 3081499208 num_examples: 7896182 - name: gadgets num_bytes: 1683252868 num_examples: 4104833 - name: Documentaries num_bytes: 1852644771 num_examples: 4051474 - name: GetMotivated num_bytes: 1211761267 num_examples: 3221980 - name: UpliftingNews num_bytes: 2003149025 num_examples: 4741948 - name: technology num_bytes: 10826871436 num_examples: 25404699 - name: Fitness num_bytes: 6191132755 num_examples: 14319856 - name: travel num_bytes: 1740556350 num_examples: 3806755 - name: lifehacks num_bytes: 626791812 num_examples: 1799437 - name: Damnthatsinteresting num_bytes: 6376694618 num_examples: 15643554 - name: gardening num_bytes: 1825313940 num_examples: 4568468 - name: mildlyinteresting num_bytes: 9079894206 num_examples: 26436769 download_size: 109177016105 dataset_size: 255339788158 annotations_creators: - no-annotation language: - en language_creators: - found license: [] multilinguality: - monolingual pretty_name: Reddit comments size_categories: - 10B<n<100B source_datasets: [] tags: - reddit - social-media task_categories: - text-generation task_ids: - dialogue-modeling - language-modeling --- # Dataset Card for "REDDIT_comments" ## Dataset Description - **Homepage:** - **Paper: https://arxiv.org/abs/2001.08435** ### Dataset Summary Comments of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023). ### Supported Tasks These comments can be used for text generation and language modeling, as well as dialogue modeling. ## Dataset Structure ### Data Splits Each split corresponds to a specific subreddit in the following list: "tifu", "explainlikeimfive", "WritingPrompts", "changemyview", "LifeProTips", "todayilearned", "science", "askscience", "ifyoulikeblank", "Foodforthought", "IWantToLearn", "bestof", "IAmA", "socialskills", "relationship_advice", "philosophy", "YouShouldKnow", "history", "books", "Showerthoughts", "personalfinance", "buildapc", "EatCheapAndHealthy", "boardgames", "malefashionadvice", "femalefashionadvice", "scifi", "Fantasy", "Games", "bodyweightfitness", "SkincareAddiction", "podcasts", "suggestmeabook", "AskHistorians", "gaming", "DIY", "mildlyinteresting", "sports", "space", "gadgets", "Documentaries", "GetMotivated", "UpliftingNews", "technology", "Fitness", "travel", "lifehacks", "Damnthatsinteresting", "gardening", "programming" ## Dataset Creation ### Curation Rationale All the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: "archived", "author", "author_fullname", "body", "comment_type", "controversiality", "created_utc", "edited", "gilded", "id", "link_id", "locked", "name", "parent_id", "permalink", "retrieved_on", "score", "subreddit", "subreddit_id", "subreddit_name_prefixed", "subreddit_type", "total_awards_received". ### Source Data The [Reddit PushShift data dumps](https://files.pushshift.io/reddit/) are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data. #### Initial Data Collection and Normalization See the paper. #### Who are the source language producers? Redditors are mostly young (65% below 30), male (70%), and American (50% of the site). ### Personal and Sensitive Information The data contains Redditor's usernames associated to their content. ## Considerations for Using the Data This dataset should be anonymized before any processing. Though the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity. ### Contributions Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset.

google-research-datasets/paws-x

google-research-datasets

--- annotations_creators: - expert-generated - machine-generated language_creators: - expert-generated - machine-generated language: - de - en - es - fr - ja - ko - zh license: - other multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - extended|other-paws task_categories: - text-classification task_ids: - semantic-similarity-classification - semantic-similarity-scoring - text-scoring - multi-input-text-classification paperswithcode_id: paws-x pretty_name: 'PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification' tags: - paraphrase-identification dataset_info: - config_name: de features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 12801784 num_examples: 49401 - name: test num_bytes: 524206 num_examples: 2000 - name: validation num_bytes: 514001 num_examples: 2000 download_size: 9601920 dataset_size: 13839991 - config_name: en features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 12215913 num_examples: 49401 - name: test num_bytes: 494726 num_examples: 2000 - name: validation num_bytes: 492279 num_examples: 2000 download_size: 9045005 dataset_size: 13202918 - config_name: es features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 12808446 num_examples: 49401 - name: test num_bytes: 519103 num_examples: 2000 - name: validation num_bytes: 513880 num_examples: 2000 download_size: 9538815 dataset_size: 13841429 - config_name: fr features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 13295557 num_examples: 49401 - name: test num_bytes: 535093 num_examples: 2000 - name: validation num_bytes: 533023 num_examples: 2000 download_size: 9785410 dataset_size: 14363673 - config_name: ja features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 15041592 num_examples: 49401 - name: test num_bytes: 668628 num_examples: 2000 - name: validation num_bytes: 661770 num_examples: 2000 download_size: 10435711 dataset_size: 16371990 - config_name: ko features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 13934181 num_examples: 49401 - name: test num_bytes: 562292 num_examples: 2000 - name: validation num_bytes: 554867 num_examples: 2000 download_size: 10263972 dataset_size: 15051340 - config_name: zh features: - name: id dtype: int32 - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 10815459 num_examples: 49401 - name: test num_bytes: 474636 num_examples: 2000 - name: validation num_bytes: 473110 num_examples: 2000 download_size: 9178953 dataset_size: 11763205 configs: - config_name: de data_files: - split: train path: de/train-* - split: test path: de/test-* - split: validation path: de/validation-* - config_name: en data_files: - split: train path: en/train-* - split: test path: en/test-* - split: validation path: en/validation-* - config_name: es data_files: - split: train path: es/train-* - split: test path: es/test-* - split: validation path: es/validation-* - config_name: fr data_files: - split: train path: fr/train-* - split: test path: fr/test-* - split: validation path: fr/validation-* - config_name: ja data_files: - split: train path: ja/train-* - split: test path: ja/test-* - split: validation path: ja/validation-* - config_name: ko data_files: - split: train path: ko/train-* - split: test path: ko/test-* - split: validation path: ko/validation-* - config_name: zh data_files: - split: train path: zh/train-* - split: test path: zh/test-* - split: validation path: zh/validation-* --- # Dataset Card for PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) - **Repository:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) - **Paper:** [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828) - **Point of Contact:** [Yinfei Yang]([email protected]) ### Dataset Summary This dataset contains 23,659 **human** translated PAWS evaluation pairs and 296,406 **machine** translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki). For further details, see the accompanying paper: [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828) ### Supported Tasks and Leaderboards It has been majorly used for paraphrase identification for English and other 6 languages namely French, Spanish, German, Chinese, Japanese, and Korean ### Languages The dataset is in English, French, Spanish, German, Chinese, Japanese, and Korean ## Dataset Structure ### Data Instances For en: ``` id : 1 sentence1 : In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland . sentence2 : In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England . label : 0 ``` For fr: ``` id : 1 sentence1 : À Paris, en octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, lui demandant un passeport pour retourner en Angleterre en passant par l'Écosse. sentence2 : En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre. label : 0 ``` ### Data Fields All files are in tsv format with four columns: Column Name | Data :---------- | :-------------------------------------------------------- id | An ID that matches the ID of the source pair in PAWS-Wiki sentence1 | The first sentence sentence2 | The second sentence label | Label for each pair The source text of each translation can be retrieved by looking up the ID in the corresponding file in PAWS-Wiki. ### Data Splits The numbers of examples for each of the seven languages are shown below: Language | Train | Dev | Test :------- | ------: | -----: | -----: en | 49,401 | 2,000 | 2,000 fr | 49,401 | 2,000 | 2,000 es | 49,401 | 2,000 | 2,000 de | 49,401 | 2,000 | 2,000 zh | 49,401 | 2,000 | 2,000 ja | 49,401 | 2,000 | 2,000 ko | 49,401 | 2,000 | 2,000 > **Caveat**: please note that the dev and test sets of PAWS-X are both sourced > from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may > appear in both the dev and test sets. Nevertheless our data split guarantees > that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`) > between dev and test. ## Dataset Creation ### Curation Rationale Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. They remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. They provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT (Devlin et al., 2019) fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information. ### Source Data PAWS (Paraphrase Adversaries from Word Scrambling) #### Initial Data Collection and Normalization All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki) #### Who are the source language producers? This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. ### Annotations #### Annotation process If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes. #### Who are the annotators? The paper mentions the translate team, especially Mengmeng Niu, for the help with the annotations. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ``` @InProceedings{pawsx2019emnlp, title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}}, author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason}, booktitle = {Proc. of EMNLP}, year = {2019} } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@gowtham1997](https://github.com/gowtham1997) for adding this dataset.

alvations/c4p0-x1-en-engb

alvations

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 5583 num_examples: 5 download_size: 17399 dataset_size: 5583 configs: - config_name: default data_files: - split: train path: 5eeb99e4b632b370/train-* ---

asahi417/seamless-align-enA-jaA.speaker-embedding.hubert-xl

asahi417

--- dataset_info: - config_name: subset_1 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.laser_score dtype: float64 - name: jaA.audio.speaker_embedding sequence: float32 - name: jaA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 10876520133 num_examples: 2073 download_size: 10908762452 dataset_size: 10876520133 - config_name: subset_10 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: jaA.audio.speaker_embedding sequence: float32 - name: jaA.audio.speaker_embedding.full sequence: sequence: float32 splits: - 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split: train path: subset_84/train-* - config_name: subset_85 data_files: - split: train path: subset_85/train-* - config_name: subset_86 data_files: - split: train path: subset_86/train-* - config_name: subset_87 data_files: - split: train path: subset_87/train-* - config_name: subset_88 data_files: - split: train path: subset_88/train-* - config_name: subset_89 data_files: - split: train path: subset_89/train-* - config_name: subset_9 data_files: - split: train path: subset_9/train-* - config_name: subset_90 data_files: - split: train path: subset_90/train-* - config_name: subset_91 data_files: - split: train path: subset_91/train-* - config_name: subset_92 data_files: - split: train path: subset_92/train-* - config_name: subset_93 data_files: - split: train path: subset_93/train-* - config_name: subset_94 data_files: - split: train path: subset_94/train-* - config_name: subset_95 data_files: - split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

asahi417/seamless-align-enA-zhA.speaker-embedding.xlsr-2b

asahi417

--- dataset_info: - config_name: subset_1 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 14209259131 num_examples: 1962 download_size: 14256120203 dataset_size: 14209259131 - config_name: subset_10 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13574781625 num_examples: 2031 download_size: 13621966757 dataset_size: 13574781625 - config_name: subset_100 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13223964877 num_examples: 1891 download_size: 13269307182 dataset_size: 13223964877 - config_name: subset_101 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13038203739 num_examples: 1885 download_size: 13083404216 dataset_size: 13038203739 - config_name: subset_102 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12731679458 num_examples: 1863 download_size: 12775688644 dataset_size: 12731679458 - config_name: subset_103 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12967209285 num_examples: 1861 download_size: 13011071076 dataset_size: 12967209285 - config_name: subset_104 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12798692606 num_examples: 1875 download_size: 12842795816 dataset_size: 12798692606 - config_name: subset_105 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13127114114 num_examples: 1871 download_size: 13172271401 dataset_size: 13127114114 - config_name: subset_106 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12426801586 num_examples: 1865 download_size: 12469421998 dataset_size: 12426801586 - config_name: subset_107 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12484775174 num_examples: 1838 download_size: 12527398592 dataset_size: 12484775174 - config_name: subset_108 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13018346253 num_examples: 1860 download_size: 13063301347 dataset_size: 13018346253 - config_name: subset_109 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12986696298 num_examples: 1866 download_size: 13030608940 dataset_size: 12986696298 - config_name: subset_11 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13038519979 num_examples: 1994 download_size: 13084550040 dataset_size: 13038519979 - config_name: subset_110 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12537003686 num_examples: 1843 download_size: 12580875152 dataset_size: 12537003686 - config_name: subset_111 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12920543044 num_examples: 1845 download_size: 12964231904 dataset_size: 12920543044 - config_name: subset_112 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12666264009 num_examples: 1844 download_size: 12709732284 dataset_size: 12666264009 - config_name: subset_113 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12572103874 num_examples: 1839 download_size: 12615926245 dataset_size: 12572103874 - config_name: subset_114 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12918422777 num_examples: 1851 download_size: 12960836861 dataset_size: 12918422777 - config_name: subset_115 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12832082885 num_examples: 1821 download_size: 12875679807 dataset_size: 12832082885 - config_name: subset_116 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12805128711 num_examples: 1837 download_size: 12848847004 dataset_size: 12805128711 - config_name: subset_117 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12914312061 num_examples: 1854 download_size: 12957416120 dataset_size: 12914312061 - config_name: subset_118 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12536340519 num_examples: 1814 download_size: 12579845649 dataset_size: 12536340519 - 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name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 12554140420 num_examples: 1860 download_size: 12598098176 dataset_size: 12554140420 - config_name: subset_99 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: zhA.id dtype: string - name: zhA.laser_score dtype: float64 - name: zhA.audio.speaker_embedding sequence: float32 - name: zhA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 13502104593 num_examples: 1915 download_size: 13548188642 dataset_size: 13502104593 configs: - config_name: subset_1 data_files: - split: train path: subset_1/train-* - config_name: subset_10 data_files: - split: train path: subset_10/train-* - config_name: subset_100 data_files: - split: train path: subset_100/train-* - config_name: subset_101 data_files: - split: train path: subset_101/train-* - config_name: subset_102 data_files: - split: train path: subset_102/train-* - config_name: subset_103 data_files: - split: train path: subset_103/train-* - config_name: subset_104 data_files: - split: train path: subset_104/train-* - config_name: subset_105 data_files: - split: train path: subset_105/train-* - config_name: subset_106 data_files: - split: train path: subset_106/train-* - config_name: subset_107 data_files: - split: train path: subset_107/train-* - config_name: subset_108 data_files: - split: train path: subset_108/train-* - config_name: subset_109 data_files: - split: train path: subset_109/train-* - config_name: subset_11 data_files: - split: train path: subset_11/train-* - config_name: subset_110 data_files: - split: train path: subset_110/train-* - config_name: subset_111 data_files: - split: train path: subset_111/train-* - config_name: subset_112 data_files: - split: train path: subset_112/train-* - config_name: subset_113 data_files: - split: train path: subset_113/train-* - config_name: subset_114 data_files: - split: train path: subset_114/train-* - config_name: subset_115 data_files: - split: train path: subset_115/train-* - config_name: subset_116 data_files: - split: train path: subset_116/train-* - config_name: subset_117 data_files: - split: train path: subset_117/train-* - config_name: subset_118 data_files: - split: train path: subset_118/train-* - config_name: subset_119 data_files: - split: train path: subset_119/train-* - config_name: subset_12 data_files: - split: train path: subset_12/train-* - config_name: subset_120 data_files: - split: train path: subset_120/train-* - config_name: subset_121 data_files: - split: train path: subset_121/train-* - config_name: subset_122 data_files: - split: train path: subset_122/train-* - config_name: subset_123 data_files: - split: train path: subset_123/train-* - config_name: subset_124 data_files: - split: train path: subset_124/train-* - config_name: subset_125 data_files: - split: train path: subset_125/train-* - config_name: subset_126 data_files: - split: train path: subset_126/train-* - config_name: subset_127 data_files: - split: train path: subset_127/train-* - config_name: subset_128 data_files: - split: train path: subset_128/train-* - config_name: subset_129 data_files: - split: train path: subset_129/train-* - config_name: subset_13 data_files: - split: train path: subset_13/train-* - config_name: subset_130 data_files: - split: train path: subset_130/train-* - config_name: subset_131 data_files: - split: train path: subset_131/train-* - config_name: subset_132 data_files: - split: train path: subset_132/train-* - config_name: subset_133 data_files: - split: train path: subset_133/train-* - config_name: subset_134 data_files: - split: train path: subset_134/train-* - config_name: subset_135 data_files: - split: train path: subset_135/train-* - config_name: subset_136 data_files: - split: train path: subset_136/train-* - config_name: subset_137 data_files: - split: train path: subset_137/train-* - config_name: subset_138 data_files: - split: train path: subset_138/train-* - config_name: subset_139 data_files: - split: train path: subset_139/train-* - config_name: subset_14 data_files: - split: train path: subset_14/train-* - config_name: subset_140 data_files: - split: train path: subset_140/train-* - config_name: subset_141 data_files: - split: train path: subset_141/train-* - config_name: subset_142 data_files: - split: train path: subset_142/train-* - config_name: subset_143 data_files: - split: train path: subset_143/train-* - config_name: subset_144 data_files: - split: train path: subset_144/train-* - config_name: subset_145 data_files: - split: train path: subset_145/train-* - config_name: subset_146 data_files: - split: train path: subset_146/train-* - config_name: subset_147 data_files: - split: train path: subset_147/train-* - config_name: subset_148 data_files: - split: train path: subset_148/train-* - config_name: subset_149 data_files: - split: train path: subset_149/train-* - config_name: subset_15 data_files: - split: train path: subset_15/train-* - config_name: subset_150 data_files: - split: train path: subset_150/train-* - config_name: subset_151 data_files: - split: train path: subset_151/train-* - config_name: subset_152 data_files: - split: train path: subset_152/train-* - config_name: subset_153 data_files: - split: train path: subset_153/train-* - config_name: subset_154 data_files: - split: train path: subset_154/train-* - config_name: subset_155 data_files: - split: train path: subset_155/train-* - config_name: subset_156 data_files: - split: train path: subset_156/train-* - config_name: subset_157 data_files: - split: train path: subset_157/train-* - config_name: subset_158 data_files: - split: train path: subset_158/train-* - config_name: subset_159 data_files: - split: train path: subset_159/train-* - config_name: subset_16 data_files: - split: train path: subset_16/train-* - config_name: subset_160 data_files: - split: train path: subset_160/train-* - config_name: subset_161 data_files: - split: train path: subset_161/train-* - config_name: subset_162 data_files: - split: train path: subset_162/train-* - config_name: subset_163 data_files: - split: train path: subset_163/train-* - config_name: subset_164 data_files: - split: train path: subset_164/train-* - config_name: subset_165 data_files: - split: train path: subset_165/train-* - config_name: subset_166 data_files: - split: train path: subset_166/train-* - config_name: subset_167 data_files: - split: train path: subset_167/train-* - config_name: subset_168 data_files: - split: train path: subset_168/train-* - config_name: subset_169 data_files: - split: train path: subset_169/train-* - config_name: subset_17 data_files: - split: train path: subset_17/train-* - config_name: subset_170 data_files: - split: train path: subset_170/train-* - config_name: subset_171 data_files: - split: train path: subset_171/train-* - config_name: subset_172 data_files: - split: train path: subset_172/train-* - config_name: subset_173 data_files: - split: train path: subset_173/train-* - config_name: subset_174 data_files: - split: train path: subset_174/train-* - config_name: subset_175 data_files: - split: train path: subset_175/train-* - config_name: subset_176 data_files: - split: train path: subset_176/train-* - config_name: subset_177 data_files: - split: train path: subset_177/train-* - config_name: subset_178 data_files: - split: train path: subset_178/train-* - config_name: subset_179 data_files: - split: train path: subset_179/train-* - config_name: subset_18 data_files: - split: train path: subset_18/train-* - config_name: subset_180 data_files: - split: train path: subset_180/train-* - config_name: subset_181 data_files: - split: train path: subset_181/train-* - config_name: subset_182 data_files: - split: train path: subset_182/train-* - config_name: subset_183 data_files: - split: train path: subset_183/train-* - config_name: subset_184 data_files: - split: train path: subset_184/train-* - config_name: subset_185 data_files: - split: train path: subset_185/train-* - config_name: subset_186 data_files: - split: train path: subset_186/train-* - config_name: subset_187 data_files: - split: train path: subset_187/train-* - config_name: subset_188 data_files: - split: train path: subset_188/train-* - config_name: subset_189 data_files: - split: train path: subset_189/train-* - config_name: subset_19 data_files: - split: train path: subset_19/train-* - config_name: subset_190 data_files: - split: train path: subset_190/train-* - config_name: subset_191 data_files: - split: train path: subset_191/train-* - config_name: subset_192 data_files: - split: train path: subset_192/train-* - config_name: subset_193 data_files: - split: train path: subset_193/train-* - config_name: subset_194 data_files: - split: train path: subset_194/train-* - config_name: subset_195 data_files: - split: train path: subset_195/train-* - config_name: subset_196 data_files: - split: train path: subset_196/train-* - config_name: subset_197 data_files: - split: train path: subset_197/train-* - config_name: subset_198 data_files: - split: train path: subset_198/train-* - config_name: subset_199 data_files: - split: train path: subset_199/train-* - config_name: subset_2 data_files: - split: train path: subset_2/train-* - config_name: subset_20 data_files: - split: train path: subset_20/train-* - config_name: subset_200 data_files: - split: train path: subset_200/train-* - config_name: subset_201 data_files: - split: train path: subset_201/train-* - config_name: subset_202 data_files: - split: train path: subset_202/train-* - config_name: subset_203 data_files: - split: train path: subset_203/train-* - config_name: subset_204 data_files: - split: train path: subset_204/train-* - config_name: subset_205 data_files: - split: train path: subset_205/train-* - config_name: subset_206 data_files: - split: train path: subset_206/train-* - config_name: subset_207 data_files: - split: train path: subset_207/train-* - config_name: subset_208 data_files: - split: train path: subset_208/train-* - config_name: subset_209 data_files: - split: train path: subset_209/train-* - config_name: subset_21 data_files: - split: train path: subset_21/train-* - config_name: subset_210 data_files: - split: train path: subset_210/train-* - config_name: subset_211 data_files: - split: train path: subset_211/train-* - config_name: subset_212 data_files: - split: train path: subset_212/train-* - config_name: subset_213 data_files: - split: train path: subset_213/train-* - config_name: subset_214 data_files: - split: train path: subset_214/train-* - config_name: subset_215 data_files: - split: train path: subset_215/train-* - config_name: subset_216 data_files: - split: train path: subset_216/train-* - config_name: subset_217 data_files: - split: train path: subset_217/train-* - config_name: subset_218 data_files: - split: train path: subset_218/train-* - config_name: subset_219 data_files: - split: train path: subset_219/train-* - config_name: subset_22 data_files: - split: train path: subset_22/train-* - config_name: subset_220 data_files: - split: train path: subset_220/train-* - config_name: subset_221 data_files: - split: train path: subset_221/train-* - config_name: subset_222 data_files: - split: train path: subset_222/train-* - config_name: subset_223 data_files: - split: train path: subset_223/train-* - config_name: subset_224 data_files: - split: train path: subset_224/train-* - config_name: subset_225 data_files: - split: train path: subset_225/train-* - config_name: subset_226 data_files: - split: train path: subset_226/train-* - config_name: subset_227 data_files: - split: train path: subset_227/train-* - config_name: subset_228 data_files: - split: train path: subset_228/train-* - config_name: subset_229 data_files: - split: train path: subset_229/train-* - config_name: subset_23 data_files: - split: train path: subset_23/train-* - config_name: subset_230 data_files: - split: train path: subset_230/train-* - config_name: subset_231 data_files: - split: train path: subset_231/train-* - config_name: subset_232 data_files: - split: train path: subset_232/train-* - config_name: subset_233 data_files: - split: train path: subset_233/train-* - config_name: subset_234 data_files: - split: train path: subset_234/train-* - config_name: subset_235 data_files: - split: train path: subset_235/train-* - config_name: subset_236 data_files: - split: train path: subset_236/train-* - config_name: subset_237 data_files: - split: train path: subset_237/train-* - config_name: subset_238 data_files: - split: train path: subset_238/train-* - config_name: subset_239 data_files: - split: train path: subset_239/train-* - config_name: subset_24 data_files: - split: train path: subset_24/train-* - config_name: subset_240 data_files: - split: train path: subset_240/train-* - config_name: subset_241 data_files: - split: train path: subset_241/train-* - config_name: subset_242 data_files: - split: train path: subset_242/train-* - config_name: subset_243 data_files: - split: train path: subset_243/train-* - config_name: subset_244 data_files: - split: train path: subset_244/train-* - config_name: subset_245 data_files: - split: train path: subset_245/train-* - config_name: subset_246 data_files: - split: train path: subset_246/train-* - config_name: subset_247 data_files: - split: train path: subset_247/train-* - config_name: subset_248 data_files: - split: train path: subset_248/train-* - config_name: subset_249 data_files: - split: train path: subset_249/train-* - config_name: subset_25 data_files: - split: train path: subset_25/train-* - config_name: subset_250 data_files: - split: train path: subset_250/train-* - config_name: subset_251 data_files: - split: train path: subset_251/train-* - config_name: subset_252 data_files: - split: train path: subset_252/train-* - config_name: subset_253 data_files: - split: train path: subset_253/train-* - config_name: subset_254 data_files: - split: train path: subset_254/train-* - config_name: subset_255 data_files: - split: train path: subset_255/train-* - config_name: subset_256 data_files: - split: train path: subset_256/train-* - config_name: subset_257 data_files: - split: train path: subset_257/train-* - config_name: subset_258 data_files: - split: train path: subset_258/train-* - config_name: subset_26 data_files: - split: train path: subset_26/train-* - config_name: subset_27 data_files: - split: train path: subset_27/train-* - config_name: subset_28 data_files: - split: train path: subset_28/train-* - config_name: subset_29 data_files: - split: train path: subset_29/train-* - config_name: subset_3 data_files: - split: train path: subset_3/train-* - config_name: subset_30 data_files: - split: train path: subset_30/train-* - config_name: subset_31 data_files: - split: train path: subset_31/train-* - config_name: subset_32 data_files: - split: train path: subset_32/train-* - config_name: subset_33 data_files: - split: train path: subset_33/train-* - config_name: subset_34 data_files: - split: train path: subset_34/train-* - config_name: subset_35 data_files: - split: train path: subset_35/train-* - config_name: subset_36 data_files: - split: train path: subset_36/train-* - config_name: subset_37 data_files: - split: train path: subset_37/train-* - config_name: subset_38 data_files: - split: train path: subset_38/train-* - config_name: subset_39 data_files: - split: train path: subset_39/train-* - config_name: subset_4 data_files: - split: train path: subset_4/train-* - config_name: subset_40 data_files: - split: train path: subset_40/train-* - config_name: subset_41 data_files: - split: train path: subset_41/train-* - config_name: subset_42 data_files: - split: train path: subset_42/train-* - config_name: subset_43 data_files: - split: train path: subset_43/train-* - config_name: subset_44 data_files: - split: train path: subset_44/train-* - config_name: subset_45 data_files: - split: train path: subset_45/train-* - config_name: subset_46 data_files: - split: train path: subset_46/train-* - config_name: subset_47 data_files: - split: train path: subset_47/train-* - config_name: subset_48 data_files: - split: train path: subset_48/train-* - config_name: subset_49 data_files: - split: train path: subset_49/train-* - config_name: subset_5 data_files: - split: train path: subset_5/train-* - config_name: subset_50 data_files: - split: train path: subset_50/train-* - config_name: subset_51 data_files: - split: train path: subset_51/train-* - config_name: subset_52 data_files: - split: train path: subset_52/train-* - config_name: subset_53 data_files: - split: train path: subset_53/train-* - config_name: subset_54 data_files: - split: train path: subset_54/train-* - config_name: subset_55 data_files: - split: train path: subset_55/train-* - config_name: subset_56 data_files: - split: train path: subset_56/train-* - config_name: subset_57 data_files: - split: train path: subset_57/train-* - config_name: subset_58 data_files: - split: train path: subset_58/train-* - config_name: subset_59 data_files: - split: train path: subset_59/train-* - config_name: subset_6 data_files: - split: train path: subset_6/train-* - config_name: subset_60 data_files: - split: train path: subset_60/train-* - config_name: subset_61 data_files: - split: train path: subset_61/train-* - config_name: subset_62 data_files: - split: train path: subset_62/train-* - config_name: subset_63 data_files: - split: train path: subset_63/train-* - config_name: subset_64 data_files: - split: train path: subset_64/train-* - config_name: subset_65 data_files: - split: train path: subset_65/train-* - config_name: subset_66 data_files: - split: train path: subset_66/train-* - config_name: subset_67 data_files: - split: train path: subset_67/train-* - config_name: subset_68 data_files: - split: train path: subset_68/train-* - config_name: subset_69 data_files: - split: train path: subset_69/train-* - config_name: subset_7 data_files: - split: train path: subset_7/train-* - config_name: subset_70 data_files: - split: train path: subset_70/train-* - config_name: subset_71 data_files: - split: train path: subset_71/train-* - config_name: subset_72 data_files: - split: train path: subset_72/train-* - config_name: subset_73 data_files: - split: train path: subset_73/train-* - config_name: subset_74 data_files: - split: train path: subset_74/train-* - config_name: subset_75 data_files: - split: train path: subset_75/train-* - config_name: subset_76 data_files: - split: train path: subset_76/train-* - config_name: subset_77 data_files: - split: train path: subset_77/train-* - config_name: subset_78 data_files: - split: train path: subset_78/train-* - config_name: subset_79 data_files: - split: train path: subset_79/train-* - config_name: subset_8 data_files: - split: train path: subset_8/train-* - config_name: subset_80 data_files: - split: train path: subset_80/train-* - config_name: subset_81 data_files: - split: train path: subset_81/train-* - config_name: subset_82 data_files: - split: train path: subset_82/train-* - config_name: subset_83 data_files: - split: train path: subset_83/train-* - config_name: subset_84 data_files: - split: train path: subset_84/train-* - config_name: subset_85 data_files: - split: train path: subset_85/train-* - config_name: subset_86 data_files: - split: train path: subset_86/train-* - config_name: subset_87 data_files: - split: train path: subset_87/train-* - config_name: subset_88 data_files: - split: train path: subset_88/train-* - config_name: subset_89 data_files: - split: train path: subset_89/train-* - config_name: subset_9 data_files: - split: train path: subset_9/train-* - config_name: subset_90 data_files: - split: train path: subset_90/train-* - config_name: subset_91 data_files: - split: train path: subset_91/train-* - config_name: subset_92 data_files: - split: train path: subset_92/train-* - config_name: subset_93 data_files: - split: train path: subset_93/train-* - config_name: subset_94 data_files: - split: train path: subset_94/train-* - config_name: subset_95 data_files: - split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

wis-k/instruction-following-eval

wis-k

--- license: apache-2.0 ---

uoft-cs/cifar100

uoft-cs

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-100 pretty_name: Cifar100 dataset_info: config_name: cifar100 features: - name: img dtype: image - name: fine_label dtype: class_label: names: '0': apple '1': aquarium_fish '2': baby '3': bear '4': beaver '5': bed '6': bee '7': beetle '8': bicycle '9': bottle '10': bowl '11': boy '12': bridge '13': bus '14': butterfly '15': camel '16': can '17': castle '18': caterpillar '19': cattle '20': chair '21': chimpanzee '22': clock '23': cloud '24': cockroach '25': couch '26': cra '27': crocodile '28': cup '29': dinosaur '30': dolphin '31': elephant '32': flatfish '33': forest '34': fox '35': girl '36': hamster '37': house '38': kangaroo '39': keyboard '40': lamp '41': lawn_mower '42': leopard '43': lion '44': lizard '45': lobster '46': man '47': maple_tree '48': motorcycle '49': mountain '50': mouse '51': mushroom '52': oak_tree '53': orange '54': orchid '55': otter '56': palm_tree '57': pear '58': pickup_truck '59': pine_tree '60': plain '61': plate '62': poppy '63': porcupine '64': possum '65': rabbit '66': raccoon '67': ray '68': road '69': rocket '70': rose '71': sea '72': seal '73': shark '74': shrew '75': skunk '76': skyscraper '77': snail '78': snake '79': spider '80': squirrel '81': streetcar '82': sunflower '83': sweet_pepper '84': table '85': tank '86': telephone '87': television '88': tiger '89': tractor '90': train '91': trout '92': tulip '93': turtle '94': wardrobe '95': whale '96': willow_tree '97': wolf '98': woman '99': worm - name: coarse_label dtype: class_label: names: '0': aquatic_mammals '1': fish '2': flowers '3': food_containers '4': fruit_and_vegetables '5': household_electrical_devices '6': household_furniture '7': insects '8': large_carnivores '9': large_man-made_outdoor_things '10': large_natural_outdoor_scenes '11': large_omnivores_and_herbivores '12': medium_mammals '13': non-insect_invertebrates '14': people '15': reptiles '16': small_mammals '17': trees '18': vehicles_1 '19': vehicles_2 splits: - name: train num_bytes: 112545106.0 num_examples: 50000 - name: test num_bytes: 22564261.0 num_examples: 10000 download_size: 142291368 dataset_size: 135109367.0 configs: - config_name: cifar100 data_files: - split: train path: cifar100/train-* - split: test path: cifar100/test-* default: true --- # Dataset Card for CIFAR-100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html) - **Repository:** - **Paper:** [Paper](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass). ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-100). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19, 'coarse_label': 11 } ``` ### Data Fields - `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `fine_label`: an `int` classification label with the following mapping: `0`: apple `1`: aquarium_fish `2`: baby `3`: bear `4`: beaver `5`: bed `6`: bee `7`: beetle `8`: bicycle `9`: bottle `10`: bowl `11`: boy `12`: bridge `13`: bus `14`: butterfly `15`: camel `16`: can `17`: castle `18`: caterpillar `19`: cattle `20`: chair `21`: chimpanzee `22`: clock `23`: cloud `24`: cockroach `25`: couch `26`: cra `27`: crocodile `28`: cup `29`: dinosaur `30`: dolphin `31`: elephant `32`: flatfish `33`: forest `34`: fox `35`: girl `36`: hamster `37`: house `38`: kangaroo `39`: keyboard `40`: lamp `41`: lawn_mower `42`: leopard `43`: lion `44`: lizard `45`: lobster `46`: man `47`: maple_tree `48`: motorcycle `49`: mountain `50`: mouse `51`: mushroom `52`: oak_tree `53`: orange `54`: orchid `55`: otter `56`: palm_tree `57`: pear `58`: pickup_truck `59`: pine_tree `60`: plain `61`: plate `62`: poppy `63`: porcupine `64`: possum `65`: rabbit `66`: raccoon `67`: ray `68`: road `69`: rocket `70`: rose `71`: sea `72`: seal `73`: shark `74`: shrew `75`: skunk `76`: skyscraper `77`: snail `78`: snake `79`: spider `80`: squirrel `81`: streetcar `82`: sunflower `83`: sweet_pepper `84`: table `85`: tank `86`: telephone `87`: television `88`: tiger `89`: tractor `90`: train `91`: trout `92`: tulip `93`: turtle `94`: wardrobe `95`: whale `96`: willow_tree `97`: wolf `98`: woman `99`: worm - `coarse_label`: an `int` coarse classification label with following mapping: `0`: aquatic_mammals `1`: fish `2`: flowers `3`: food_containers `4`: fruit_and_vegetables `5`: household_electrical_devices `6`: household_furniture `7`: insects `8`: large_carnivores `9`: large_man-made_outdoor_things `10`: large_natural_outdoor_scenes `11`: large_omnivores_and_herbivores `12`: medium_mammals `13`: non-insect_invertebrates `14`: people `15`: reptiles `16`: small_mammals `17`: trees `18`: vehicles_1 `19`: vehicles_2 ### Data Splits | name |train|test| |----------|----:|---------:| |cifar100|50000| 10000| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.

lmms-lab/MMMU

lmms-lab

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 57719107.0 num_examples: 150 - name: validation num_bytes: 347519954.0 num_examples: 900 - name: test num_bytes: 3271046267.0 num_examples: 10500 download_size: 3377778136 dataset_size: 3676285328.0 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: validation path: data/validation-* - split: test path: data/test-* --- This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated. <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [MMMU](https://github.com/MMMU-Benchmark/MMMU). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{yue2023mmmu, title={Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi}, author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Stevens, Samuel and Jiang, Dongfu and Ren, Weiming and Sun, Yuxuan and others}, journal={arXiv preprint arXiv:2311.16502}, year={2023} } ```

OpenDriveLab/OpenScene

OpenDriveLab

--- license: cc-by-nc-sa-4.0 ---

nvidia/OpenMathInstruct-2

nvidia

--- language: - en license: cc-by-4.0 size_categories: - 10M<n<100M task_categories: - question-answering - text-generation pretty_name: OpenMathInstruct-2 dataset_info: features: - name: problem dtype: string - name: generated_solution dtype: string - name: expected_answer dtype: string - name: problem_source dtype: string splits: - name: train_1M num_bytes: 1350383003 num_examples: 1000000 - name: train_2M num_bytes: 2760009675 num_examples: 2000000 - name: train_5M num_bytes: 6546496157 num_examples: 5000000 - name: train num_bytes: 15558412976 num_examples: 13972791 download_size: 20208929853 dataset_size: 26215301811 tags: - math - nvidia configs: - config_name: default data_files: - split: train path: data/train-* - split: train_1M path: data/train_1M-* - split: train_2M path: data/train_2M-* - split: train_5M path: data/train_5M-* --- # OpenMathInstruct-2 OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs generated using the [Llama3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) model. The training set problems of [GSM8K](https://github.com/openai/grade-school-math) and [MATH](https://github.com/hendrycks/math) are used for constructing the dataset in the following ways: - *Solution augmentation*: Generating chain-of-thought solutions for training set problems in GSM8K and MATH. - *Problem-Solution augmentation*: Generating new problems, followed by solutions for these new problems. <p> <img src="SFT Data Diagram 1.jpg" width="75%" title="Composition of OpenMathInstruct-2"> </p> OpenMathInstruct-2 dataset contains the following fields: - **problem**: Original problem from either the GSM8K or MATH training set or augmented problem from these training sets. - **generated_solution**: Synthetically generated solution. - **expected_answer**: For problems in the training set, it is the ground-truth answer provided in the datasets. **For augmented problems, it is the majority-voting answer.** - **problem_source**: Whether the problem is taken directly from GSM8K or MATH or is an augmented version derived from either dataset. <p> <img src="scaling_plot.jpg" width="40%" title="Scaling Curve"> </p> We also release the 1M, 2M, and 5M, *fair-downsampled* versions of the entire training set corresponding to points in the above scaling plot. These splits are referred to as **train_1M**, **train_2M**, and **train_5M**. To use these subsets, just specify one of these subsets as split while downloading the data: ```python from datasets import load_dataset # Download only the 1M training split dataset = load_dataset('nvidia/OpenMathInstruct-2', split='train_1M', streaming=True) ``` To download the entire training set and to convert it into the jsonl format, use the following code snippet. This might take 20-30 minutes (or more depending on your network connection) and will use ~20Gb of RAM. ```python import json from datasets import load_dataset from tqdm import tqdm dataset = load_dataset('nvidia/OpenMathInstruct-2', split='train') print("Converting dataset to jsonl format") output_file = "openmathinstruct2.jsonl" with open(output_file, 'w', encoding='utf-8') as f: for item in tqdm(dataset): f.write(json.dumps(item, ensure_ascii=False) + '\n') print(f"Conversion complete. Output saved as {output_file}") ``` Apart from the dataset, we also release the [contamination explorer](https://huggingface.co/spaces/nvidia/OpenMathInstruct-2-explorer) for looking at problems in the OpenMathInstruct-2 dataset that are similar to the [GSM8K](https://huggingface.co/datasets/openai/gsm8k), [MATH](https://github.com/hendrycks/math), [AMC 2023](https://github.com/QwenLM/Qwen2.5-Math/tree/main/evaluation/data/amc23), [AIME 2024](https://artofproblemsolving.com/wiki/index.php/2024_AIME_I), and [Omni-MATH](https://huggingface.co/datasets/KbsdJames/Omni-MATH) test set problems. See our [paper](https://arxiv.org/abs/2410.01560) to learn more details! ### Note The released dataset doesn't filter out extremely long questions. After the dataset release, we found that 564 questions (roughly 0.1%) were longer than 1024 Llama tokens. We experimented with removing these questions and didn't see a performance drop (in fact, we observed a minor bump). Dropping these questions, helps with memory as well. So we would recommend, filtering out extremely long questions. We have updated the data preparation commands in our [Github documentation](https://nvidia.github.io/NeMo-Skills/openmathinstruct2/dataset/#converting-to-sft-format). ## OpenMath2 models To demonstrate the quality of this dataset, we release a series of OpenMath2 models trained on this data. | Model | GSM8K | MATH | AMC 2023 | AIME 2024 | Omni-MATH | |:---|:---:|:---:|:---:|:---:|:---:| | Llama3.1-8B-Instruct | 84.5 | 51.9 | 9/40 | 2/30 | 12.7 | | OpenMath2-Llama3.1-8B ([nemo](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B-nemo) \| [HF](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B)) | 91.7 | 67.8 | 16/40 | 3/30 | 22.0 | | + majority@256 | 94.1 | 76.1 | 23/40 | 3/30 | 24.6 | | Llama3.1-70B-Instruct | 95.8 | 67.9 | 19/40 | 6/30 | 19.0 | | OpenMath2-Llama3.1-70B ([nemo](https://huggingface.co/nvidia/OpenMath2-Llama3.1-70B-nemo) \| [HF](https://huggingface.co/nvidia/OpenMath2-Llama3.1-70B)) | 94.9 | 71.9 | 20/40 | 4/30 | 23.1 | | + majority@256 | 96.0 | 79.6 | 24/40 | 6/30 | 27.6 | The pipeline we used to produce the data and models is fully open-sourced! - [Code](https://github.com/NVIDIA/NeMo-Skills) - [Models](https://huggingface.co/collections/nvidia/openmath-2-66fb142317d86400783d2c7b) - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2) ## Reproducing our results We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathinstruct2/) to fully reproduce our results, including data generation. ## Citation If you find our work useful, please consider citing us! ```bibtex @article{toshniwal2024openmath2, title = {OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data}, author = {Shubham Toshniwal and Wei Du and Ivan Moshkov and Branislav Kisacanin and Alexan Ayrapetyan and Igor Gitman}, year = {2024}, journal = {arXiv preprint arXiv:2410.01560} } ```

CogComp/trec

CogComp

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: trecqa pretty_name: Text Retrieval Conference Question Answering dataset_info: features: - name: text dtype: string - name: coarse_label dtype: class_label: names: '0': ABBR '1': ENTY '2': DESC '3': HUM '4': LOC '5': NUM - name: fine_label dtype: class_label: names: '0': ABBR:abb '1': ABBR:exp '2': ENTY:animal '3': ENTY:body '4': ENTY:color '5': ENTY:cremat '6': ENTY:currency '7': ENTY:dismed '8': ENTY:event '9': ENTY:food '10': ENTY:instru '11': ENTY:lang '12': ENTY:letter '13': ENTY:other '14': ENTY:plant '15': ENTY:product '16': ENTY:religion '17': ENTY:sport '18': ENTY:substance '19': ENTY:symbol '20': ENTY:techmeth '21': ENTY:termeq '22': ENTY:veh '23': ENTY:word '24': DESC:def '25': DESC:desc '26': DESC:manner '27': DESC:reason '28': HUM:gr '29': HUM:ind '30': HUM:title '31': HUM:desc '32': LOC:city '33': LOC:country '34': LOC:mount '35': LOC:other '36': LOC:state '37': NUM:code '38': NUM:count '39': NUM:date '40': NUM:dist '41': NUM:money '42': NUM:ord '43': NUM:other '44': NUM:period '45': NUM:perc '46': NUM:speed '47': NUM:temp '48': NUM:volsize '49': NUM:weight splits: - name: train num_bytes: 385090 num_examples: 5452 - name: test num_bytes: 27983 num_examples: 500 download_size: 359212 dataset_size: 413073 --- # Dataset Card for "trec" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://cogcomp.seas.upenn.edu/Data/QA/QC/](https://cogcomp.seas.upenn.edu/Data/QA/QC/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 0.36 MB - **Size of the generated dataset:** 0.41 MB - **Total amount of disk used:** 0.78 MB ### Dataset Summary The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 labeled questions in training set and another 500 for test set. The dataset has 6 coarse class labels and 50 fine class labels. Average length of each sentence is 10, vocabulary size of 8700. Data are collected from four sources: 4,500 English questions published by USC (Hovy et al., 2001), about 500 manually constructed questions for a few rare classes, 894 TREC 8 and TREC 9 questions, and also 500 questions from TREC 10 which serves as the test set. These questions were manually labeled. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The language in this dataset is English (`en`). ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 0.36 MB - **Size of the generated dataset:** 0.41 MB - **Total amount of disk used:** 0.78 MB An example of 'train' looks as follows. ``` { 'text': 'How did serfdom develop in and then leave Russia ?', 'coarse_label': 2, 'fine_label': 26 } ``` ### Data Fields The data fields are the same among all splits. - `text` (`str`): Text of the question. - `coarse_label` (`ClassLabel`): Coarse class label. Possible values are: - 'ABBR' (0): Abbreviation. - 'ENTY' (1): Entity. - 'DESC' (2): Description and abstract concept. - 'HUM' (3): Human being. - 'LOC' (4): Location. - 'NUM' (5): Numeric value. - `fine_label` (`ClassLabel`): Fine class label. Possible values are: - ABBREVIATION: - 'ABBR:abb' (0): Abbreviation. - 'ABBR:exp' (1): Expression abbreviated. - ENTITY: - 'ENTY:animal' (2): Animal. - 'ENTY:body' (3): Organ of body. - 'ENTY:color' (4): Color. - 'ENTY:cremat' (5): Invention, book and other creative piece. - 'ENTY:currency' (6): Currency name. - 'ENTY:dismed' (7): Disease and medicine. - 'ENTY:event' (8): Event. - 'ENTY:food' (9): Food. - 'ENTY:instru' (10): Musical instrument. - 'ENTY:lang' (11): Language. - 'ENTY:letter' (12): Letter like a-z. - 'ENTY:other' (13): Other entity. - 'ENTY:plant' (14): Plant. - 'ENTY:product' (15): Product. - 'ENTY:religion' (16): Religion. - 'ENTY:sport' (17): Sport. - 'ENTY:substance' (18): Element and substance. - 'ENTY:symbol' (19): Symbols and sign. - 'ENTY:techmeth' (20): Techniques and method. - 'ENTY:termeq' (21): Equivalent term. - 'ENTY:veh' (22): Vehicle. - 'ENTY:word' (23): Word with a special property. - DESCRIPTION: - 'DESC:def' (24): Definition of something. - 'DESC:desc' (25): Description of something. - 'DESC:manner' (26): Manner of an action. - 'DESC:reason' (27): Reason. - HUMAN: - 'HUM:gr' (28): Group or organization of persons - 'HUM:ind' (29): Individual. - 'HUM:title' (30): Title of a person. - 'HUM:desc' (31): Description of a person. - LOCATION: - 'LOC:city' (32): City. - 'LOC:country' (33): Country. - 'LOC:mount' (34): Mountain. - 'LOC:other' (35): Other location. - 'LOC:state' (36): State. - NUMERIC: - 'NUM:code' (37): Postcode or other code. - 'NUM:count' (38): Number of something. - 'NUM:date' (39): Date. - 'NUM:dist' (40): Distance, linear measure. - 'NUM:money' (41): Price. - 'NUM:ord' (42): Order, rank. - 'NUM:other' (43): Other number. - 'NUM:period' (44): Lasting time of something - 'NUM:perc' (45): Percent, fraction. - 'NUM:speed' (46): Speed. - 'NUM:temp' (47): Temperature. - 'NUM:volsize' (48): Size, area and volume. - 'NUM:weight' (49): Weight. ### Data Splits | name | train | test | |---------|------:|-----:| | default | 5452 | 500 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{li-roth-2002-learning, title = "Learning Question Classifiers", author = "Li, Xin and Roth, Dan", booktitle = "{COLING} 2002: The 19th International Conference on Computational Linguistics", year = "2002", url = "https://www.aclweb.org/anthology/C02-1150", } @inproceedings{hovy-etal-2001-toward, title = "Toward Semantics-Based Answer Pinpointing", author = "Hovy, Eduard and Gerber, Laurie and Hermjakob, Ulf and Lin, Chin-Yew and Ravichandran, Deepak", booktitle = "Proceedings of the First International Conference on Human Language Technology Research", year = "2001", url = "https://www.aclweb.org/anthology/H01-1069", } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

sordonia/narrativeqa_sanitized

sordonia

--- dataset_info: features: - name: text dtype: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 508515599 num_examples: 1572 download_size: 288012667 dataset_size: 508515599 configs: - config_name: default data_files: - split: train path: data/train-* ---

WenhaoWang/TIP-I2V

WenhaoWang

--- language: - en license: cc-by-nc-4.0 size_categories: - 1M<n<10M task_categories: - image-to-video - text-to-video - text-to-image - image-to-image dataset_info: features: - name: UUID dtype: string - name: Text_Prompt dtype: string - name: Image_Prompt dtype: image - name: Subject dtype: string - name: Timestamp dtype: string - name: Text_NSFW dtype: float32 - name: Image_NSFW dtype: string splits: - name: Full num_bytes: 13440652664.125 num_examples: 1701935 - name: Subset num_bytes: 790710630 num_examples: 100000 - name: Eval num_bytes: 78258893 num_examples: 10000 download_size: 27500759907 dataset_size: 27750274851.25 configs: - config_name: default data_files: - split: Full path: data/Full-* - split: Subset path: data/Subset-* - split: Eval path: data/Eval-* tags: - prompt - image-to-video - text-to-video - visual-generation - video-generation pretty_name: TIP-I2V --- # Summary This is the dataset proposed in our paper [**TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation**](https://arxiv.org/abs/2411.04709). TIP-I2V is the first dataset comprising over 1.70 million unique user-provided text and image prompts. Besides the prompts, TIP-I2V also includes videos generated by five state-of-the-art image-to-video models (Pika, Stable Video Diffusion, Open-Sora, I2VGen-XL, and CogVideoX-5B). The TIP-I2V contributes to the development of better and safer image-to-video models. <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/TIP-I2V/resolve/main/assets/teasor.png" width="1000"> </p> # Datapoint <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/TIP-I2V/resolve/main/assets/datapoint.png" width="1000"> </p> # Statistics <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/TIP-I2V/resolve/main/assets/stat.png" width="1000"> </p> # Download For users in mainland China, try setting `export HF_ENDPOINT=https://hf-mirror.com` to successfully download the datasets. ## Download the text and (compressed) image prompts with related information ```python # Full (text and compressed image) prompts: ~13.4G from datasets import load_dataset ds = load_dataset("WenhaoWang/TIP-I2V", split='Full', streaming=True) # Convert to Pandas format (it may be slow) import pandas as pd df = pd.DataFrame(ds) ``` ```python # 100k subset (text and compressed image) prompts: ~0.8G from datasets import load_dataset ds = load_dataset("WenhaoWang/TIP-I2V", split='Subset', streaming=True) # Convert to Pandas format (it may be slow) import pandas as pd df = pd.DataFrame(ds) ``` ```python # 10k TIP-Eval (text and compressed image) prompts: ~0.08G from datasets import load_dataset ds = load_dataset("WenhaoWang/TIP-I2V", split='Eval', streaming=True) # Convert to Pandas format (it may be slow) import pandas as pd df = pd.DataFrame(ds) ``` ## Download the embeddings for text and image prompts ```python # Embeddings for full text prompts (~21G) and image prompts (~3.5G) from huggingface_hub import hf_hub_download hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="Embedding/Full_Text_Embedding.parquet", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="Embedding/Full_Image_Embedding.parquet", repo_type="dataset") ``` ```python # Embeddings for 100k subset text prompts (~1.2G) and image prompts (~0.2G) from huggingface_hub import hf_hub_download hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="Embedding/Subset_Text_Embedding.parquet", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="Embedding/Subset_Image_Embedding.parquet", repo_type="dataset") ``` ```python # Embeddings for 10k TIP-Eval text prompts (~0.1G) and image prompts (~0.02G) from huggingface_hub import hf_hub_download hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="Embedding/Eval_Text_Embedding.parquet", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="Embedding/Eval_Image_Embedding.parquet", repo_type="dataset") ``` ## Download uncompressed image prompts ```python # Full uncompressed image prompts: ~1T from huggingface_hub import hf_hub_download for i in range(1,52): hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="image_prompt_tar/image_prompt_%d.tar"%i, repo_type="dataset") ``` ```python # 100k subset uncompressed image prompts: ~69.6G from huggingface_hub import hf_hub_download for i in range(1,3): hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="sub_image_prompt_tar/sub_image_prompt_%d.tar"%i, repo_type="dataset") ``` ```python # 10k TIP-Eval uncompressed image prompts: ~6.5G from huggingface_hub import hf_hub_download hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="eval_image_prompt_tar/eval_image_prompt.tar", repo_type="dataset") ``` ## Download generated videos ```python # Full videos generated by Pika: ~1T from huggingface_hub import hf_hub_download for i in range(1,52): hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="pika_videos_tar/pika_videos_%d.tar"%i, repo_type="dataset") ``` ```python # 100k subset videos generated by Pika (~57.6G), Stable Video Diffusion (~38.9G), Open-Sora (~47.2G), I2VGen-XL (~54.4G), and CogVideoX-5B (~36.7G) from huggingface_hub import hf_hub_download hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_1.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_2.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/svd_videos_subset.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/opensora_videos_subset.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset_1.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset_2.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/cog_videos_subset.tar", repo_type="dataset") ``` ```python # 10k TIP-Eval videos generated by Pika (~5.8G), Stable Video Diffusion (~3.9G), Open-Sora (~4.7G), I2VGen-XL (~5.4G), and CogVideoX-5B (~3.6G) from huggingface_hub import hf_hub_download hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="eval_videos_tar/pika_videos_eval.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="eval_videos_tar/svd_videos_eval.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="eval_videos_tar/opensora_videos_eval.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="eval_videos_tar/i2vgenxl_videos_eval.tar", repo_type="dataset") hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="eval_videos_tar/cog_videos_eval.tar", repo_type="dataset") ``` # Comparison with VidProM and DiffusionDB <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/TIP-I2V/resolve/main/assets/table.png" width="1000"> </p> <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/TIP-I2V/resolve/main/assets/comparison.png" width="1000"> </p> Click the [WizMap (TIP-I2V VS VidProM)](https://poloclub.github.io/wizmap/?dataURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fdata_tip-i2v_vidprom.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fgrid_tip-i2v_vidprom.json) and [WizMap (TIP-I2V VS DiffusionDB)](https://poloclub.github.io/wizmap/?dataURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fdata_tip-i2v_diffusiondb.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fgrid_tip-i2v_diffusiondb.json) (wait for 5 seconds) for an interactive visualization of our 1.70 million prompts. # License The prompts and videos in our TIP-I2V are licensed under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). # Curators TIP-I2V is created by [Wenhao Wang](https://wangwenhao0716.github.io/) and Professor [Yi Yang](https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=zh-CN). # License The prompts and videos in our TIP-I2V are licensed under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). # Citation ``` @article{wang2024tipi2v, title={TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation}, author={Wang, Wenhao and Yang, Yi}, booktitle={arXiv preprint arXiv:2411.04709}, year={2024} } ``` # Contact If you have any questions, feel free to contact Wenhao Wang ([email protected]).

lmms-lab/DocVQA

lmms-lab

--- license: apache-2.0 dataset_info: - config_name: DocVQA features: - name: questionId dtype: string - name: question dtype: string - name: question_types sequence: string - name: image dtype: image - name: docId dtype: int64 - name: ucsf_document_id dtype: string - name: ucsf_document_page_no dtype: string - name: answers sequence: string - name: data_split dtype: string splits: # - name: train # num_bytes: 5659006943.631 # num_examples: 39463 - name: validation num_bytes: 2532447207.066 num_examples: 5349 - name: test num_bytes: 2500408525.732 num_examples: 5188 download_size: 9555791945 dataset_size: 10691862676.428999 - config_name: InfographicVQA features: - name: questionId dtype: string - name: question dtype: string - name: answers sequence: string - name: answer_type sequence: string - name: image dtype: image - name: image_url dtype: string - name: operation/reasoning sequence: string - name: ocr dtype: string - name: data_split dtype: string splits: # - name: train # num_bytes: 11559694546.32 # num_examples: 23946 - name: validation num_bytes: 1863177404.253 num_examples: 2801 - name: test num_bytes: 1851304047.712 num_examples: 3288 download_size: 2544892079 dataset_size: 15274175998.285 configs: - config_name: DocVQA data_files: # - split: train # path: DocVQA/train-* - split: validation path: DocVQA/validation-* - split: test path: DocVQA/test-* - config_name: InfographicVQA data_files: # - split: train # path: InfographicVQA/train-* - split: validation path: InfographicVQA/validation-* - split: test path: InfographicVQA/test-* --- <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [DocVQA](https://arxiv.org/abs/2007.00398). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @article{mathew2020docvqa, title={DocVQA: A Dataset for VQA on Document Images. CoRR abs/2007.00398 (2020)}, author={Mathew, Minesh and Karatzas, Dimosthenis and Manmatha, R and Jawahar, CV}, journal={arXiv preprint arXiv:2007.00398}, year={2020} } ```

lmms-lab/Video-MME

lmms-lab

--- dataset_info: config_name: videomme features: - name: video_id dtype: string - name: duration dtype: string - name: domain dtype: string - name: sub_category dtype: string - name: url dtype: string - name: videoID dtype: string - name: question_id dtype: string - name: task_type dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string splits: - name: test num_bytes: 1003241.0 num_examples: 2700 download_size: 405167 dataset_size: 1003241.0 configs: - config_name: videomme data_files: - split: test path: videomme/test-* ---

faur-ai/fulg

faur-ai

--- license: odc-by viewer: true task_categories: - text-generation language: - ro tags: - language-modeling - casual-lm - llm pretty_name: FuLG size_categories: - 100B<n<1T --- # ❄️FuLG The FuLG dataset is a comprehensive Romanian language corpus comprising 150 billion tokens, carefully extracted from Common Crawl. This extensive dataset is the result of rigorous filtering and deduplication processes applied to 95 Common Crawl snapshots. The compressed dataset has 289 GB. For more details, check the [arXiv preprint](https://arxiv.org/abs/2407.13657). ### How do I download this? ##### Using 🤗 Datasets ```python from datasets import load_dataset # Full dataset dataset = load_dataset("faur-ai/fulg") # To load the data from a specific CC snapshot dataset = load_dataset("faur-ai/fulg", data_dir='2018-05') ``` ##### Using Git ```bash git clone https://huggingface.co/datasets/faur-ai/fulg ``` ### Data Fields The data have several fields: - `url`: url of the source as a string - `date_download`: date of crawl - `digest`: hash of content - `length`: length of content - `nlines`: number of lines - `source_domain`: domain of document - `title`: title of document - `raw_content`: text content as a string - `cc_segment`: source CommonCrawl segment - `original_nlines`: original number of lines before processing - `original_length`: original length before processing - `language`: language (ro) - `language_score`: score for language ### Licensing Information We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this dataset, you are also bound any license agreements and terms of use of the original data sources. ## Bibtex If you use our dataset, please cite us at: ```bibtex @misc{fulg150bromaniancorpus, title={FuLG: 150B Romanian Corpus for Language Model Pretraining}, author={Vlad-Andrei Bădoiu and Mihai-Valentin Dumitru and Alexandru M. Gherghescu and Alexandru Agache and Costin Raiciu}, year={2024}, eprint={2407.13657}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.13657}, } ```

ontocord/VALID

ontocord

--- license: cc-by-sa-4.0 dataset_info: features: - name: video_id dtype: string - name: chunk_idx dtype: int64 - name: chunk_text dtype: string - name: video_metadata dtype: string - name: video_language dtype: string - name: chunk_media dtype: string splits: - name: shard_10339 num_bytes: 1997009 num_examples: 631 - name: shard_10400 num_bytes: 2638827 num_examples: 722 - name: shard_10324 num_bytes: 1700655 num_examples: 515 - name: shard_10418 num_bytes: 3034319 num_examples: 947 - name: shard_1045 num_bytes: 2042334 num_examples: 648 - name: shard_10428 num_bytes: 2314345 num_examples: 706 - name: shard_10435 num_bytes: 2300183 num_examples: 677 - name: shard_10424 num_bytes: 1839226 num_examples: 552 - name: shard_10442 num_bytes: 1543285 num_examples: 419 - name: shard_10411 num_bytes: 2005599 num_examples: 604 - name: shard_10344 num_bytes: 1796239 num_examples: 589 - name: shard_10439 num_bytes: 1780546 num_examples: 567 - name: shard_10351 num_bytes: 2156111 num_examples: 677 - name: shard_10446 num_bytes: 2117151 num_examples: 525 - name: shard_10457 num_bytes: 1851306 num_examples: 555 - name: shard_10464 num_bytes: 1316832 num_examples: 440 - name: shard_10405 num_bytes: 1820556 num_examples: 613 - name: shard_10471 num_bytes: 2397197 num_examples: 682 - name: shard_10456 num_bytes: 1279577 num_examples: 430 - name: shard_1035 num_bytes: 2102014 num_examples: 687 - name: shard_10430 num_bytes: 2293697 num_examples: 686 - name: shard_10469 num_bytes: 2521584 num_examples: 743 - name: shard_10360 num_bytes: 2329044 num_examples: 680 - name: shard_10443 num_bytes: 2222280 num_examples: 641 - name: shard_10453 num_bytes: 3277011 num_examples: 931 - name: shard_10481 num_bytes: 2163505 num_examples: 709 - name: shard_10482 num_bytes: 1885620 num_examples: 503 - name: shard_10365 num_bytes: 1789825 num_examples: 453 - name: shard_10475 num_bytes: 2290432 num_examples: 635 - name: shard_10444 num_bytes: 1915386 num_examples: 550 - name: shard_10493 num_bytes: 2240928 num_examples: 752 - 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name: shard_10518 num_bytes: 1554268 num_examples: 534 - name: shard_10514 num_bytes: 2398872 num_examples: 659 - name: shard_10366 num_bytes: 2686341 num_examples: 715 - name: shard_10462 num_bytes: 3202984 num_examples: 912 - name: shard_10512 num_bytes: 2058849 num_examples: 697 - name: shard_10558 num_bytes: 2065125 num_examples: 572 - name: shard_10383 num_bytes: 2580580 num_examples: 859 - name: shard_10550 num_bytes: 2617491 num_examples: 643 - name: shard_10536 num_bytes: 2352902 num_examples: 649 - name: shard_10529 num_bytes: 1970611 num_examples: 633 - name: shard_10565 num_bytes: 1569669 num_examples: 522 - name: shard_10538 num_bytes: 2012923 num_examples: 564 - name: shard_10532 num_bytes: 1839647 num_examples: 594 - name: shard_10531 num_bytes: 2125990 num_examples: 618 - name: shard_10382 num_bytes: 1770026 num_examples: 493 - name: shard_1058 num_bytes: 1707150 num_examples: 491 - name: shard_10525 num_bytes: 3210740 num_examples: 892 - name: shard_10594 num_bytes: 1369358 num_examples: 458 - 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split: shard_10481 path: data/shard_10481-* - split: shard_10482 path: data/shard_10482-* - split: shard_10365 path: data/shard_10365-* - split: shard_10475 path: data/shard_10475-* - split: shard_10444 path: data/shard_10444-* - split: shard_10493 path: data/shard_10493-* - split: shard_10433 path: data/shard_10433-* - split: shard_1037 path: data/shard_1037-* - split: shard_1049 path: data/shard_1049-* - split: shard_10507 path: data/shard_10507-* - split: shard_10521 path: data/shard_10521-* - split: shard_10479 path: data/shard_10479-* - split: shard_10543 path: data/shard_10543-* - split: shard_10494 path: data/shard_10494-* - split: shard_10565 path: data/shard_10565-* - split: shard_10558 path: data/shard_10558-* - split: shard_10506 path: data/shard_10506-* - split: shard_10497 path: data/shard_10497-* - split: shard_10503 path: data/shard_10503-* - split: shard_10488 path: data/shard_10488-* - split: shard_1050 path: data/shard_1050-* - split: shard_10379 path: data/shard_10379-* - 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split: shard_10759 path: data/shard_10759-* - split: shard_10628 path: data/shard_10628-* - split: shard_10712 path: data/shard_10712-* - split: shard_10882 path: data/shard_10882-* - split: shard_1089 path: data/shard_1089-* - split: shard_10897 path: data/shard_10897-* - split: shard_10463 path: data/shard_10463-* - split: shard_10918 path: data/shard_10918-* - split: shard_10840 path: data/shard_10840-* - split: shard_10932 path: data/shard_10932-* - split: shard_10910 path: data/shard_10910-* - split: shard_1065 path: data/shard_1065-* - split: shard_10474 path: data/shard_10474-* - split: shard_10770 path: data/shard_10770-* - split: shard_10821 path: data/shard_10821-* - split: shard_10925 path: data/shard_10925-* - split: shard_1094 path: data/shard_1094-* - split: shard_10660 path: data/shard_10660-* - split: shard_10947 path: data/shard_10947-* - split: shard_10866 path: data/shard_10866-* - split: shard_10872 path: data/shard_10872-* - split: shard_10879 path: data/shard_10879-* - split: shard_10671 path: data/shard_10671-* - split: shard_1091 path: data/shard_1091-* - split: shard_10983 path: data/shard_10983-* - split: shard_10929 path: data/shard_10929-* - split: shard_10857 path: data/shard_10857-* - split: shard_10485 path: data/shard_10485-* - split: shard_10969 path: data/shard_10969-* - split: shard_10976 path: data/shard_10976-* - split: shard_10935 path: data/shard_10935-* - split: shard_10948 path: data/shard_10948-* - split: shard_10990 path: data/shard_10990-* - split: shard_10998 path: data/shard_10998-* - split: shard_10885 path: data/shard_10885-* - split: shard_1085 path: data/shard_1085-* - split: shard_10922 path: data/shard_10922-* - split: shard_1048 path: data/shard_1048-* - split: shard_108 path: data/shard_108-* - split: shard_10813 path: data/shard_10813-* - split: shard_10864 path: data/shard_10864-* - split: shard_11010 path: data/shard_11010-* - split: shard_11003 path: data/shard_11003-* - split: shard_11018 path: data/shard_11018-* - split: shard_11025 path: data/shard_11025-* - split: shard_10490 path: data/shard_10490-* - split: shard_10886 path: data/shard_10886-* - split: shard_11032 path: data/shard_11032-* - split: shard_1090 path: data/shard_1090-* - split: shard_10973 path: data/shard_10973-* - split: shard_10893 path: data/shard_10893-* - split: shard_10496 path: data/shard_10496-* - split: shard_10871 path: data/shard_10871-* - split: shard_10960 path: data/shard_10960-* - split: shard_10941 path: data/shard_10941-* - split: shard_11047 path: data/shard_11047-* - split: shard_10967 path: data/shard_10967-* - split: shard_10835 path: data/shard_10835-* - split: shard_10914 path: data/shard_10914-* - split: shard_10992 path: data/shard_10992-* - split: shard_10999 path: data/shard_10999-* - split: shard_10921 path: data/shard_10921-* - split: shard_11054 path: data/shard_11054-* - split: shard_11090 path: data/shard_11090-* - split: shard_11035 path: data/shard_11035-* - split: shard_11022 path: data/shard_11022-* - split: shard_11111 path: data/shard_11111-* - split: shard_11119 path: data/shard_11119-* - split: shard_11126 path: data/shard_11126-* ---  # VALID (Video-Audio Large Interleaved Dataset) ## Overview The **VALID (Video-Audio Large Interleaved Dataset)** is a multimodal dataset comprising approximately 720,000 [Creative Commons licensed](https://creativecommons.org/share-your-work/cclicenses/) videos crawled from YouTube, and processed into audio-video-text data records for machine learning research. The dataset provides a unique opportunity for training models to understand relationships between modalities such as video frames, audio clips, and multilingual textual data, making it suitable for applications like multimodal representation learning. - **Please note the current version is a PREVIEW version. We are still in the process of uploading. Please be patient.** ## Features - Audio-Video-Text Format: A combination of: ``` <video> <caption><image> the caption </caption> <caption><image> the caption </caption> <caption><image> the caption </caption> </video> <transcript> <audio> multi-lingual transcript </transcript> English text ``` - The non-text multimodal portion begins the data item and can include multiple media. Some snippets may have more than one audio, and more than one video. Others may have only images/videos or only audio paired with English text. Each video contains multiple frames stored as images, and text captions for each image. There can also be standalone images interleaved as well. Even though each audio video snippets are no more than 10 seconds, a data record may span over more than 10 secs (e.g., if a data item has two 10 second videos, then the corresponding English text corresponds roughly to 20 seconds of video). The intention for this format is to teach a model to associate multiple modalities with each other, and understand multiple audio-video elements in an interleaved fashion. - Data Components: - **Images**: PNG format, phashed to ensure variability, with 0–10 images per audio snippet. Each image includes a caption created with Florence-2. - **Audio**: OGG format, multilingual, ~10 seconds per snippet, with shorter sound or music snippets (1–3 seconds) to minimize copyright issues. Each audio snippet is transcribed either with Whisper for non-English, or with the original Youtube ASR for English. - **Text**: Not including the captions and transcripts, the “text” portion is a concatenation of Youtube’s original English transcripts associated with the above media of around 1–40 words per data record. - Dataset Size: - **About 7,000,000 records.** - **About 15,000,000 images, each captioned with FLorence-2.** - **About 30,000,000 audio snippets, about half of which transcribed with Whisper-large, and half with Youtube ASR.** - **Divided into about 12K shards of about 600 records, each in a parquet file and a corresponding .tar.gz file for the media.** - **About 14TB in total.** ## File Organization - Each data entry follows the `<video><image(s)><audio><text>` structure as described above. - Metadata includes alignment between modalities, and implicit ordering of audio/visual elements. ## Multimodal Details - **Audio-Video Alignment**: Snippets allow learning temporal relationships between audio and visual elements. - **Text Annotations**: Text descriptions, including captions and Youtube ASR English translations, provide linguistic alignment. ## Preprocessing - **Phashing for Images**: Ensures that images within the dataset are dynamic and non-static. - **Audio Snippet Lengths**: Music and sound effects are clipped to 1–3 seconds to minimize copyright concerns under fair use principles. ------ ## Licenses All videos in VALID are CC BY, as declared by their original uploaders on YouTube. We publish the audio snippets of these videos and select image frames here under these rights and under the principles of fair use. However, we cannot guarantee that original uploaders had the rights to share the content. This dataset has only been lightly filtered for safety by removing data records with high proportions of children related words AND high proportions of sexual or violence related words. Moreover, we disclaim all warranties, whether express or implied and all laibilities with respect to infringment, fitness for a particular puprpose, or otherwise. ## Intended Uses - **Primary Use Case**: Training models for multimodal understanding, such as contrastive multimodal learning (e.g., CLIP, CLAP). - **Not Recommended For**: Generation tasks, as the dataset's quality may not meet generative model requirements. ## Dataset Limitations - **Quality**: Images and audio are sourced from YouTube and may vary in resolution and clarity. - **Rights Uncertainty**: While videos are marked as CC-BY by the third party authors of the videos, original rights may not be verifiable. - **Biases**: The dataset's multilingual audio paired with English-only text may introduce linguistic biases. The large variety of videos may introduce bias. ## Ethical Considerations The dataset was built under the principles of fair use and CC-BY licensing. Its creation strives to align with the spirit of the EU AI Act, emphasizing transparency and safety in AI model development. Users must exercise caution and adhere to copyright and licensing rules when using VALID. ------ ## Policy for Managing Video Deletion Requests Our goal is to establish a clear process for removing videos from our dataset when requested by users or required by external factors, while balancing the rights of content owners, compliance with CC-BY licenses, and the community's ability to utilize the dataset for training and research purposes. - **1. Respecting Content Owners' Rights:** All videos in the dataset are under the CC-BY license. As such, proper attribution will always be maintained as required by the license. If a content owner requests the removal of a video from the dataset, we will balance this request with the community's ability to train on the data, considering the original intent of the CC-BY license. - **2. Deletion Request Process:** - Content owners or users can request the removal of a video by FIRST requesting it be removed from Youtube: [Here](https://support.google.com/youtube/answer/2807622?) and [Here](https://support.google.com/youtube/answer/2801895?hl=en). - Then the onwers or users should verify that it has been removed from YouTube and provide this fact in a feedback to us [Here](https://forms.gle/f4zYzZpJU78SBPho9). - Requests must demonstrate that the video is no longer publicly available on YouTube. - We will remove the videos confirmed to be deleted in the next release of this dataset. - **3. Verification and Balancing Interests:** All deletion requests will be verified by checking YouTube to ensure the video is no longer available. We may also remove a video in our sole discretion. Decisions on video removal will take into account: - The rights and wishes of content owners, including their ability to remove their videos from public availability. - The community's need for robust datasets for training and research. - The spirit of the CC-BY license, which permits redistribution and use with proper attribution. - **4. Responsibilities for Derivative Datasets:** Users creating derivative datasets must ensure compliance by deleting videos listed in `delete_these_videos.json`. - **5. Proactive Deletion:** Videos may be removed proactively under the following circumstances: - Requests from the hosting provider (e.g., Hugging Face). - Legal requirements or enforcement actions. - Internal decisions. - **6. Community Considerations:** - The community is encouraged to respect the balance between individual content owners’ wishes and the public benefit derived from open access datasets. - Efforts will be made to keep the dataset robust while honoring legitimate requests for content removal. - **7. Updates:** Users are encouraged to check the `delete_these_videos.json`, from time to time to ensure their copy of the dataset is up to date. ------ ## Related Materials: - If you are looking for CC-BY Youtube transcripts of videos, check out PleIAs’ [YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons). - Also, Huggingface has created an excellent CC-BY Youtube video dataset here: [Finevideo](https://huggingface.co/datasets/HuggingFaceFV/finevideo) - LAION is also building a dataset [Here](https://huggingface.co/datasets/laion/laion-audio-preview) which includes Youtube audio snippets paired with Gemini generated captions. ## Acknowledgement and Thanks This dataset was built by Ontocord.AI in cooperation with Grass and LAION.AI. It was created as part of our SafeLLM/Aurora-M2 project in order to build safe multimodal models that comply with the EU AI Act. This dataset was built on a subset of the Grass Video Repository, a massive video dataset of creative commons videos. We deeply thank Huggingface and the open source community for their support. ## About the Contributors: - [**Grass**](https://www.getgrass.io/) is committed to making the public web accessible again. Through its network of millions of globally distributed nodes, it is capable of collecting petabyte-scale datasets for a variety of use cases, including training AI models. The network is run exclusively by users who have downloaded an application to their devices, allowing them to contribute their unused internet bandwidth to the network. On X: @getgrass_io - [**LAION**](https://www.laion.ai), is a non-profit organization, that provides datasets, tools and models to liberate machine learning research. By doing so, we encourage open public education and a more environment-friendly use of resources by reusing existing datasets and models. - [**Ontocord**](https://www.ontocord.ai/ ) is dedicated to making legally compliant AI. Our mission is to make our AGI future lawful and accessible to everyone. - [**Alignment Lab AI**](https://x.com/alignment_lab): Our mission is to build a future leveraging AI as a force for good and as a tool that enhances human lives. We believe everyone deserves to harness the power of personal intelligence. - And many others ... ## Citation ``` @misc{Huu2024VALID, title = {VALID (Video-Audio Large Interleaved Dataset)}, author = {Huu Nguyen, Ken Tsui, Andrej Radonjic, Christoph Schuhmann}, year = {2024} url = {https://huggingface.co/datasets/ontocord/VALID}, } ```

Tiger14n/RVC-GUI

Tiger14n

--- license: mit ---

fsicoli/common_voice_16_0

fsicoli

--- license: cc0-1.0 language: - ab - af - am - ar - as - ast - az - ba - bas - be - bg - bn - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - dyu - el - en - eo - es - et - eu - fa - fi - fr - gl - gn - ha - he - hi - hsb - hu - ia - id - ig - is - it - ja - ka - kab - kk - kmr - ko - ky - lg - lo - lt - lv - mdf - mhr - mk - ml - mn - mr - mrj - mt - myv - nl - oc - or - pl - ps - pt - quy - ro - ru - rw - sah - sat - sc - sk - skr - sl - sq - sr - sw - ta - th - ti - tig - tk - tok - tr - tt - tw - ug - uk - ur - uz - vi - vot - yue - zgh - zh - yo task_categories: - automatic-speech-recognition pretty_name: Common Voice Corpus 16.0 size_categories: - 100B<n<1T tags: - mozilla - foundation --- # Dataset Card for Common Voice Corpus 16.0 <!-- Provide a quick summary of the dataset. --> This dataset is an unofficial version of the Mozilla Common Voice Corpus 16. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/. ## Languages ``` Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba ``` ## How to use The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function. For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese): ``` from datasets import load_dataset cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ``` from datasets import load_dataset cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train", streaming=True) print(next(iter(cv_16))) ``` Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed). ### Local ``` from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train") batch_sampler = BatchSampler(RandomSampler(cv_16), batch_size=32, drop_last=False) dataloader = DataLoader(cv_16, batch_sampler=batch_sampler) ``` ### Streaming ``` from datasets import load_dataset from torch.utils.data import DataLoader cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train") dataloader = DataLoader(cv_16, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets. ### Dataset Structure Data Instances A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment. ### Licensing Information Public Domain, CC-0 ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ---

HAERAE-HUB/KMMLU-HARD

HAERAE-HUB

--- configs: - config_name: maritime_engineering data_files: - split: dev path: data/maritime_engineering-dev.csv - split: test path: data/maritime_engineering-hard-test.csv - config_name: materials_engineering data_files: - split: dev path: data/materials_engineering-dev.csv - split: test path: data/materials_engineering-hard-test.csv - config_name: railway_and_automotive_engineering data_files: - split: dev path: data/railway_and_automotive_engineering-dev.csv - split: test path: data/railway_and_automotive_engineering-hard-test.csv - config_name: biology data_files: - split: dev path: data/biology-dev.csv - split: test path: data/biology-hard-test.csv - config_name: public_safety data_files: - split: dev path: data/public_safety-dev.csv - split: test path: data/public_safety-hard-test.csv - config_name: criminal_law data_files: - split: dev path: data/criminal_law-dev.csv - split: test path: data/criminal_law-hard-test.csv - config_name: information_technology data_files: - split: dev path: data/information_technology-dev.csv - split: test path: data/information_technology-hard-test.csv - config_name: geomatics data_files: - split: dev path: data/geomatics-dev.csv - split: test path: data/geomatics-hard-test.csv - config_name: management data_files: - split: dev path: data/management-dev.csv - split: test path: data/management-hard-test.csv - config_name: math data_files: - split: dev path: data/math-dev.csv - split: test path: data/math-hard-test.csv - config_name: accounting data_files: - split: dev path: data/accounting-dev.csv - split: test path: data/accounting-hard-test.csv - config_name: chemistry data_files: - split: dev path: data/chemistry-dev.csv - split: test path: data/chemistry-hard-test.csv - config_name: nondestructive_testing data_files: - split: dev path: data/nondestructive_testing-dev.csv - split: test path: data/nondestructive_testing-hard-test.csv - config_name: computer_science data_files: - split: dev path: data/computer_science-dev.csv - split: test path: data/computer_science-hard-test.csv - config_name: ecology data_files: - split: dev path: data/ecology-dev.csv - split: test path: data/ecology-hard-test.csv - config_name: health data_files: - split: dev path: data/health-dev.csv - split: test path: data/health-hard-test.csv - config_name: political_science_and_sociology data_files: - split: dev path: data/political_science_and_sociology-dev.csv - split: test path: data/political_science_and_sociology-hard-test.csv - config_name: patent data_files: - split: dev path: data/patent-dev.csv - split: test path: data/patent-hard-test.csv - config_name: electrical_engineering data_files: - split: dev path: data/electrical_engineering-dev.csv - split: test path: data/electrical_engineering-hard-test.csv - config_name: electronics_engineering data_files: - split: dev path: data/electronics_engineering-dev.csv - split: test path: data/electronics_engineering-hard-test.csv - config_name: korean_history data_files: - split: dev path: data/korean_history-dev.csv - split: test path: data/korean_history-hard-test.csv - config_name: gas_technology_and_engineering data_files: - split: dev path: data/gas_technology_and_engineering-dev.csv - split: test path: data/gas_technology_and_engineering-hard-test.csv - config_name: machine_design_and_manufacturing data_files: - split: dev path: data/machine_design_and_manufacturing-dev.csv - split: test path: data/machine_design_and_manufacturing-hard-test.csv - config_name: chemical_engineering data_files: - split: dev path: data/chemical_engineering-dev.csv - split: test path: data/chemical_engineering-hard-test.csv - config_name: telecommunications_and_wireless_technology data_files: - split: dev path: data/telecommunications_and_wireless_technology-dev.csv - split: test path: data/telecommunications_and_wireless_technology-hard-test.csv - config_name: food_processing data_files: - split: dev path: data/food_processing-dev.csv - split: test path: data/food_processing-hard-test.csv - config_name: social_welfare data_files: - split: dev path: data/social_welfare-dev.csv - split: test path: data/social_welfare-hard-test.csv - config_name: real_estate data_files: - split: dev path: data/real_estate-dev.csv - split: test path: data/real_estate-hard-test.csv - config_name: marketing data_files: - split: dev path: data/marketing-dev.csv - split: test path: data/marketing-hard-test.csv - config_name: mechanical_engineering data_files: - split: dev path: data/mechanical_engineering-dev.csv - split: test path: data/mechanical_engineering-hard-test.csv - config_name: fashion data_files: - split: dev path: data/fashion-dev.csv - split: test path: data/fashion-hard-test.csv - config_name: psychology data_files: - split: dev path: data/psychology-dev.csv - split: test path: data/psychology-hard-test.csv - config_name: taxation data_files: - split: dev path: data/taxation-dev.csv - split: test path: data/taxation-hard-test.csv - config_name: environmental_science data_files: - split: dev path: data/environmental_science-dev.csv - split: test path: data/environmental_science-hard-test.csv - config_name: refrigerating_machinery data_files: - split: dev path: data/refrigerating_machinery-dev.csv - split: test path: data/refrigerating_machinery-hard-test.csv - config_name: education data_files: - split: dev path: data/education-dev.csv - split: test path: data/education-hard-test.csv - config_name: industrial_engineer data_files: - split: dev path: data/industrial_engineer-dev.csv - split: test path: data/industrial_engineer-hard-test.csv - config_name: civil_engineering data_files: - split: dev path: data/civil_engineering-dev.csv - split: test path: data/civil_engineering-hard-test.csv - config_name: energy_management data_files: - split: dev path: data/energy_management-dev.csv - split: test path: data/energy_management-hard-test.csv - config_name: law data_files: - split: dev path: data/law-dev.csv - split: test path: data/law-hard-test.csv - config_name: agricultural_sciences data_files: - split: dev path: data/agricultural_sciences-dev.csv - split: test path: data/agricultural_sciences-hard-test.csv - config_name: interior_architecture_and_design data_files: - split: dev path: data/interior_architecture_and_design-dev.csv - split: test path: data/interior_architecture_and_design-hard-test.csv - config_name: aviation_engineering_and_maintenance data_files: - split: dev path: data/aviation_engineering_and_maintenance-dev.csv - split: test path: data/aviation_engineering_and_maintenance-hard-test.csv - config_name: construction data_files: - split: dev path: data/construction-dev.csv - split: test path: data/construction-hard-test.csv - config_name: economics data_files: - split: dev path: data/economics-dev.csv - split: test path: data/economics-hard-test.csv license: cc-by-nd-4.0 task_categories: - question-answering language: - ko tags: - haerae - mmlu size_categories: - 100K<n<1M --- ### KMMLU (Korean-MMLU) We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks that are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 26 publically available and proprietary LLMs, identifying significant room for improvement. The best publicly available model achieves 50.54% on KMMLU, far below the average human performance of 62.6%. This model was primarily trained for English and Chinese, not Korean. Current LLMs tailored to Korean, such as Polyglot-Ko, perform far worse. Surprisingly, even the most capable proprietary LLMs, e.g., GPT-4 and HyperCLOVA X, achieve 59.95% and 53.40%, respectively. This suggests that further work is needed to improve Korean LLMs, and KMMLU offers the right tool to track this progress. We make our dataset publicly available on the Hugging Face Hub and integrate the benchmark into EleutherAI's Language Model Evaluation Harness. Link to Paper: [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://arxiv.org/abs/2402.11548) ### KMMLU Statistics | Category | # Questions | |------------------------------|-------------| | **Prerequisites** | | | None | 59,909 | | 1 Prerequisite Test | 12,316 | | 2 Prerequisite Tests | 776 | | 2+ Years of Experience | 65,135 | | 4+ Years of Experience | 98,678 | | 9+ Years of Experience | 6,963 | | **Question Type** | | | Positive | 207,030 | | Negation | 36,777 | | **Split** | | | Train | 208,522 | | Validation | 225 | | Test | 35,030 | | **Total** | 243,777 | ### Categories To reimplement the categories in the paper, refer to the following: ``` supercategories = { "accounting": "HUMSS", "agricultural_sciences": "Other", "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_engineering": "STEM", "electronics_engineering": "Applied Science", "energy_management": "Applied Science", "environmental_science": "Applied Science", "fashion": "Other", "food_processing": "Other", "gas_technology_and_engineering": "Applied Science", "geomatics": "Applied Science", "health": "Other", "industrial_engineer": "Applied Science", "information_technology": "STEM", "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "patent": "Other", "political_science_and_sociology": "HUMSS", "psychology": "HUMSS", "public_safety": "Other", "railway_and_automotive_engineering": "Applied Science", "real_estate": "Other", "refrigerating_machinery": "Other", "social_welfare": "HUMSS", "taxation": "HUMSS", "telecommunications_and_wireless_technology": "Applied Science", "korean_history": "HUMSS", "math": "STEM" } ``` ### Point of Contact For any questions contact us via the following email:) ``` [email protected] ```

benediktkol/DDOS

benediktkol

--- license: cc-by-nc-4.0 pretty_name: DDOS task_categories: - image-segmentation - depth-estimation task_ids: - semantic-segmentation tags: - drones - uav - aerial - vision - wires - cables - outdoor - segmentation - semantic segmentation - depth - weather - flying - computer vision - cv size_categories: - 10K<n<100K --- # DDOS: The Drone Depth and Obstacle Segmentation Dataset The Drone Depth and Obstacle Segmentation (DDOS) dataset comprises synthetic aerial images captured by drones, along with corresponding depth maps and pixel-wise semantic segmentation masks. DDOS is purpose-built to support research and development in computer vision, focusing on tasks such as depth estimation and obstacle segmentation from aerial imagery. Emphasizing the detection of thin structures like wires and effective navigation in diverse weather conditions, DDOS serves as a valuable resource for advancing algorithms in autonomous drone technology. - **Paper:** [DDOS: The Drone Depth and Obstacle Segmentation Dataset](https://arxiv.org/abs/2312.12494) --------- ## Data Structure DDOS is organised as follows: - Data Splits: - Train: Contains 300 flights with a total of 30k images for training. - Validation: Contains 20 flights with a total of 2k images for validation during model development. - Test: Contains 20 flights with a total of 2k images for the final evaluation of the trained model. - Environments: - Neighbourhood: Contains data captured in urban and residential environments. - Park: Contains data captured in park and natural environments. - Flights: - Each flight is represented by a unique flight ID and is contained within the corresponding environment directory. - Data for Each Flight: - Image: Contains RGB images captured by the drone camera. - Depth: Contains depth maps representing the distance of objects from the camera. These maps are saved as uint16 PNG images, where pixel values range from 0 to 65535, representing distances from 0 to 100 meters linearly. - Segmentation: Contains pixel-wise segmentation masks for semantic segmentation. Classes, as well as their corresponding mappings, are mentioned below. - Flow: Contains optical flow data representing the apparent motion of objects between consecutive frames. - Surface Normal: Contains surface normal maps representing the orientation of object surfaces. Overview of file structure: ``` data/ ├── train/ │ ├── neighbourhood/ │ │ ├── 0/ │ │ │ ├── depth/ │ │ │ │ ├── 0.png │ │ │ │ ├── ... │ │ │ │ └── 99.png │ │ │ ├── flow/ │ │ │ │ ├── 0.png │ │ │ │ ├── ... │ │ │ │ └── 99.png │ │ │ ├── image/ │ │ │ │ ├── 0.png │ │ │ │ ├── ... │ │ │ │ └── 99.png │ │ │ ├── segmentation/ │ │ │ │ ├── 0.png │ │ │ │ ├── ... │ │ │ │ └── 99.png │ │ │ ├── surfacenormals/ │ │ │ │ ├── 0.png │ │ │ │ ├── ... │ │ │ │ └── 99.png │ │ │ ├── metadata.csv │ │ │ └── weather.csv │ │ ├── ... │ │ └── 249/ │ │ └── ... │ └── park/ │ ├── 0/ │ │ ├── depth/ │ │ │ └── ... │ │ ├── flow/ │ │ │ └── ... │ │ ├── image/ │ │ │ └── ... │ │ ├── segmentation/ │ │ │ └── ... │ │ ├── surfacenormals/ │ │ │ └── ... │ │ ├── metadata.csv │ │ └── weather.csv │ ├── ... │ └── 49/ │ └── ... ├── validation/ │ └── ... └── test/ └── ... ``` --------- ## Additional Information **Class Mapping:** The segmentation masks use the following class labels for obstacle segmentation: ```python CLASS_MAPPING = { 'ultra_thin': 255, 'thin_structures': 240, 'small_mesh': 220, 'large_mesh': 200, 'trees': 180, 'buildings': 160, 'vehicles': 140, 'animals': 100, 'other': 80 } ``` **Metadata:** The dataset contains metadata, such as coordinates, pose, acceleration, weather conditions and camera parameters, which provide valuable contextual information about each flight. --------- ## Dataset Usage - **Data Loading:** To load and use the DDOS dataset in your projects, you can refer to the official PyTorch data loading tutorial: [PyTorch Data Loading Tutorial](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html). This tutorial will guide you through the process of loading data, creating data loaders, and preparing the dataset for training or evaluation using PyTorch. - **Respect the Data Splits:** Please ensure that the testing data is not used for validation. Mixing these datasets could lead to inaccurate assessments of model performance. Maintaining separate datasets for testing and validation helps ensure reliable evaluation and accurate reporting of results. --------- ## License DDOS is openly licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) --------- ## Citation If you use DDOS in your research or projects, please cite our paper: ``` @article{kolbeinsson2023ddos, title={{DDOS}: The Drone Depth and Obstacle Segmentation Dataset}, author={Benedikt Kolbeinsson and Krystian Mikolajczyk}, journal={arXiv preprint arXiv:2312.12494}, year={2023} } ```

LEAP/ClimSim_low-res

LEAP

--- license: cc-by-4.0 --- Corresponding GitHub repo can be found here: https://github.com/leap-stc/ClimSim Read more: https://arxiv.org/abs/2306.08754.