_id
stringlengths 24
24
| id
stringlengths 5
121
| author
stringlengths 2
42
| cardData
stringlengths 2
1.07M
⌀ | disabled
bool 2
classes | gated
null | lastModified
timestamp[ns]date 2021-02-05 16:03:35
2025-04-05 23:31:13
| likes
int64 0
7.67k
| trendingScore
float64 -1
51
| private
bool 1
class | sha
stringlengths 40
40
| description
stringlengths 0
6.67k
⌀ | downloads
int64 0
5.17M
| downloadsAllTime
int64 0
142M
| tags
sequencelengths 1
7.92k
| createdAt
timestamp[ns]date 2022-03-02 23:29:22
2025-04-05 23:29:13
| paperswithcode_id
stringclasses 653
values | citation
stringlengths 0
10.7k
⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
67d3479522a51de18affff22 | nvidia/Llama-Nemotron-Post-Training-Dataset-v1 | nvidia | {"license": "cc-by-4.0", "configs": [{"config_name": "SFT", "data_files": [{"split": "code", "path": "SFT/code/*.jsonl"}, {"split": "math", "path": "SFT/math/*.jsonl"}, {"split": "science", "path": "SFT/science/*.jsonl"}, {"split": "chat", "path": "SFT/chat/*.jsonl"}, {"split": "safety", "path": "SFT/safety/*.jsonl"}], "default": true}, {"config_name": "RL", "data_files": [{"split": "instruction_following", "path": "RL/instruction_following/*.jsonl"}]}]} | false | null | 2025-03-18T15:56:14 | 320 | 51 | false | ed905e6239c9d191e4c965a403dde07a5383b5eb |
Llama-Nemotron-Post-Training-Dataset-v1 Release
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of Llama-3.3-Nemotron-Super-49B-v1 and Llama-3.1-Nemotron-Nano-8B-v1.
Llama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) which is a derivative of Meta’s Llama-3.3-70B-Instruct (AKA… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1. | 12,271 | 12,280 | [
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-13T21:01:09 | null | null |
67ea45bbcb39affecc10763e | virtuoussy/Multi-subject-RLVR | virtuoussy | {"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"]} | false | null | 2025-04-02T10:29:40 | 40 | 40 | false | 5be8ffa52bf3ccbfe0d4f601ddee1183cb1be0ab | Multi-subject data for paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
we use a multi-subject multiple-choice QA dataset ExamQA (Yu et al., 2021).
Originally written in Chinese, ExamQA covers at least 48 first-level subjects.
We remove the distractors and convert each instance into a free-form QA pair.
This dataset consists of 638k college-level instances, with both questions and objective answers written by domain experts for examination purposes.
We also use GPT-4o-mini… See the full description on the dataset page: https://huggingface.co/datasets/virtuoussy/Multi-subject-RLVR. | 438 | 438 | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.23829",
"region:us"
] | 2025-03-31T07:35:23 | null | null |
67c0cda5c0b7a236a5f070e3 | glaiveai/reasoning-v1-20m | glaiveai | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 177249016911, "num_examples": 22199375}], "download_size": 87247205094, "dataset_size": 177249016911}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["10M<n<100M"]} | false | null | 2025-03-19T13:21:37 | 168 | 38 | false | da6bb3d0ff8fd8ea5abacee8519762ca6aaf367e |
We are excited to release a synthetic reasoning dataset containing 22mil+ general reasoning questions and responses generated using deepseek-ai/DeepSeek-R1-Distill-Llama-70B. While there have been multiple efforts to build open reasoning datasets for math and code tasks, we noticed a lack of large datasets containing reasoning traces for diverse non code/math topics like social and natural sciences, education, creative writing and general conversations, which is why we decided to release this… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/reasoning-v1-20m. | 9,690 | 9,804 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-27T20:40:05 | null | null |
676f70846bf205795346d2be | FreedomIntelligence/medical-o1-reasoning-SFT | FreedomIntelligence | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}]} | false | null | 2025-02-22T05:15:38 | 610 | 37 | false | 61536c1d80b2c799df6800cc583897b77d2c86d2 |
News
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM verifier.
Introduction
This dataset is used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. This dataset is constructed using GPT-4o… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT. | 23,038 | 51,769 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08 | null | null |
67edf568d1631250f17528af | open-thoughts/OpenThoughts2-1M | open-thoughts | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "question", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 18986223337, "num_examples": 1143205}], "download_size": 8328411205, "dataset_size": 18986223337}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["synthetic", "curator"], "license": "apache-2.0"} | false | null | 2025-04-03T20:31:18 | 35 | 35 | false | 6e9f7d7b5e072a76e65bd204fe6c59d32404c69c |
OpenThoughts2-1M
Open synthetic reasoning dataset with 1M high-quality examples covering math, science, code, and puzzles!
OpenThoughts2-1M builds upon our previous OpenThoughts-114k dataset, augmenting it with existing datasets like OpenR1, as well as additional math and code reasoning data.
This dataset was used to train OpenThinker2-7B and OpenThinker2-32B.
See our blog post for more details.
OpenThinker2 Models
Our OpenThinker2 models trained on this… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M. | 1,222 | 1,222 | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"synthetic",
"curator"
] | 2025-04-03T02:41:44 | null | null |
67cd6c25b770987b3f80af97 | a-m-team/AM-DeepSeek-R1-Distilled-1.4M | a-m-team | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["zh", "en"], "tags": ["code", "math", "reasoning", "thinking", "deepseek-r1", "distill"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "am_0.5M", "data_files": "am_0.5M.jsonl.zst", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}, {"config_name": "am_0.9M", "data_files": "am_0.9M.jsonl.zst", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}, {"config_name": "am_0.9M_sample_1k", "data_files": "am_0.9M_sample_1k.jsonl", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}]} | false | null | 2025-03-30T01:30:08 | 105 | 28 | false | 53531c06634904118a2dcd83961918c4d69d1cdf | For more open-source datasets, models, and methodologies, please visit our GitHub repository.
AM-DeepSeek-R1-Distilled-1.4M is a large-scale general reasoning task dataset composed of
high-quality and challenging reasoning problems. These problems are collected from numerous
open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination.
All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone
rigorous… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M. | 9,495 | 9,495 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"arxiv:2503.19633",
"region:us",
"code",
"math",
"reasoning",
"thinking",
"deepseek-r1",
"distill"
] | 2025-03-09T10:23:33 | null | null |
67e90b135e63bac35a2dbaf0 | MohamedRashad/Quran-Recitations | MohamedRashad | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 49579449331.918, "num_examples": 124689}], "download_size": 33136131149, "dataset_size": 49579449331.918}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["automatic-speech-recognition", "text-to-speech"], "language": ["ar"], "size_categories": ["100K<n<1M"]} | false | null | 2025-03-30T11:19:54 | 25 | 25 | false | 65ee6114d526c02f7f96d696bb254a2dd666270c |
Quran-Recitations Dataset
Overview
The Quran-Recitations dataset is a rich and reverent collection of Quranic verses, meticulously paired with their respective recitations by esteemed Qaris. This dataset serves as a valuable resource for researchers, developers, and students interested in Quranic studies, speech recognition, audio analysis, and Islamic applications.
Dataset Structure
source: The name of the Qari (reciter) who performed… See the full description on the dataset page: https://huggingface.co/datasets/MohamedRashad/Quran-Recitations. | 474 | 474 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:ar",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-30T09:12:51 | null | null |
67d9394e2e311ae0f2e8183f | PixelAI-Team/TalkBody4D | PixelAI-Team | {"viewer": false, "license": "cc-by-nc-4.0", "extra_gated_prompt": "The dataset is encrypted to prevent unauthorized access. Please fill out the request form : https://forms.gle/eC2aLRXZ8DAdKcis7. We'll check with your PI.", "extra_gated_fields": {"Name": "text", "E-Mail": "text", "Company/Organization": "text", "PI's Name": "text", "PI's E-Mail": "text", "Specific date": "date_picker", "I want to use this dataset for": {"type": "select", "options": ["Research", "Education", {"label": "Other", "value": "other"}]}, "I have signed the request form": "checkbox"}, "size_categories": ["100B<n<1T"]} | false | null | 2025-03-25T12:05:54 | 69 | 24 | false | e20725b0891c858f73fff56ad1ea34e46bfc54ec |
TalkBody4D Dataset
This dataset contains four multi-view image sequences used in our paper "TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars
for Augmented Reality via 3D Gaussian Splatting". They are captured with 59 well-calibrated RGB cameras in 20 fps, with a resolution of 3000×4000 and lengths ranging from 800 to 1000 frames. We use the data to evaluate our method for building animatable human body avatars.
We also provide the SMPL-X fitting in the dataset.… See the full description on the dataset page: https://huggingface.co/datasets/PixelAI-Team/TalkBody4D. | 90 | 90 | [
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | 2025-03-18T09:13:50 | null | null |
67a404bc8c6d42c5ec097433 | Anthropic/EconomicIndex | Anthropic | {"language": "en", "pretty_name": "EconomicIndex", "tags": ["AI", "LLM", "Economic Impacts", "Anthropic"], "viewer": true, "license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "release_2025_03_27/automation_vs_augmentation_by_task.csv"}]}]} | false | null | 2025-03-27T22:08:25 | 250 | 20 | false | 2f63ea41bda89c22c00bbd3dd487771087717614 |
The Anthropic Economic Index
Overview
The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy.
Data Releases
This repository contains multiple data releases, each with its own documentation:
2025-02-10 Release: Initial release with O*NET task mappings, automation vs. augmentation data, and more
2025-03-27 Release: Updated analysis with Claude 3.7 Sonnet data and cluster-level insights… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex. | 3,677 | 10,451 | [
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"AI",
"LLM",
"Economic Impacts",
"Anthropic"
] | 2025-02-06T00:39:24 | null | null |
63990f21cc50af73d29ecfa3 | fka/awesome-chatgpt-prompts | fka | {"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]} | false | null | 2025-01-06T00:02:53 | 7,666 | 18 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 10,945 | 140,884 | [
"task_categories:question-answering",
"license:cc0-1.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"ChatGPT"
] | 2022-12-13T23:47:45 | null | null |
679c0b5c32cf4c58bdcba8eb | facebook/natural_reasoning | facebook | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Natural Reasoning", "size_categories": ["1M<n<10M"]} | false | null | 2025-02-21T06:02:40 | 482 | 17 | false | 99eea5dc6bfa45a925eb42600e81dc90377ba237 | NaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning. | 10,531 | 17,689 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13124",
"region:us"
] | 2025-01-30T23:29:32 | null | null |
67e9a644ea97f3c65c463bfb | LLM360/MegaMath | LLM360 | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math", "code", "pre-training", "synthesis"], "size_categories": ["1B<n<10B"]} | false | null | 2025-04-04T14:04:23 | 17 | 17 | false | b2dbbfdb0bb40f8f5893b4057c6f5f430ae34d35 |
MegaMath: Pushing the Limits of Open Math Copora
Megamath is part of TxT360, curated by LLM360 Team.
We introduce MegaMath, an open math pretraining dataset curated from diverse, math-focused sources, with over 300B tokens.
MegaMath is curated via the following three efforts:
Revisiting web data:
We re-extracted mathematical documents from Common Crawl with math-oriented HTML optimizations, fasttext-based filtering and deduplication, all for acquiring higher-quality data on the… See the full description on the dataset page: https://huggingface.co/datasets/LLM360/MegaMath. | 97 | 97 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"arxiv:2504.02807",
"region:us",
"math",
"code",
"pre-training",
"synthesis"
] | 2025-03-30T20:15:00 | null | null |
67e134c540496e1ded36dcc3 | Intelligent-Internet/II-Thought-RL-v0 | Intelligent-Internet | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "verification_info", "dtype": "string"}, {"name": "data_source", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4819048664, "num_examples": 341795}], "download_size": 2448038647, "dataset_size": 4819048664}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-28T15:26:57 | 44 | 15 | false | c41b695c60b0af3c3701e41d483031246c378088 |
II-Thought RL v0: A Large-Scale Curated Dataset for Reinforcement Learning
See our blog here for additional details.
We introduce II-Thought RL v0, the first large-scale, multi-task dataset designed for Reinforcement Learning. This dataset consists of high-quality question-answer pairs that have undergone a rigorous multi-step filtering process, leveraging Gemini 2.0 Flash and Qwen 32B as quality evaluators.
In this initial release, we have curated and refined publicly available… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Internet/II-Thought-RL-v0. | 3,435 | 3,458 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2412.08819",
"region:us"
] | 2025-03-24T10:32:37 | null | null |
625552d2b339bb03abe3432d | openai/gsm8k | openai | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]} | false | null | 2024-01-04T12:05:15 | 676 | 12 | false | e53f048856ff4f594e959d75785d2c2d37b678ee |
Dataset Card for GSM8K
Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These problems take between 2 and 8 steps to solve.
Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k. | 344,422 | 4,333,110 | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2110.14168",
"region:us",
"math-word-problems"
] | 2022-04-12T10:22:10 | gsm8k | null |
67e74b725fb029dd96363693 | inclusionAI/AReaL-boba-Data | inclusionAI | {"license": "apache-2.0"} | false | null | 2025-03-29T01:26:55 | 12 | 12 | false | 1799c00be3f1216ab55a5cae3562d654dbfd7d82 | null | 274 | 274 | [
"license:apache-2.0",
"region:us"
] | 2025-03-29T01:22:58 | null | null |
67e9eb451ba052dc29fd90f8 | camel-ai/loong | camel-ai | {"authors": ["camel-ai"], "description": "A comprehensive collection of 3,551 high-quality problems across 8 diverse domains, curated for Project Loong. Each problem includes a detailed executable rationale and solution, designed for training and evaluating reasoning models.", "language": ["en"], "license": "mit", "pretty_name": "camel-ai/loong", "tags": ["reasoning", "problem-solving", "project-loong", "multi-domain", "mathematics", "physics", "finance", "optimization"], "task_categories": ["question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "advanced_physics", "path": "data/advanced_physics-*"}, {"split": "graph_discrete_math", "path": "data/graph_discrete_math-*"}, {"split": "computational_biology", "path": "data/computational_biology-*"}, {"split": "logic", "path": "data/logic-*"}, {"split": "security_and_safety", "path": "data/security_and_safety-*"}, {"split": "advanced_math", "path": "data/advanced_math-*"}, {"split": "finance", "path": "data/finance-*"}, {"split": "mathematical_programming", "path": "data/mathematical_programming-*"}]}], "dataset_info": {"features": [{"name": "source_type", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "final_answer", "dtype": "string"}, {"name": "meta_data", "dtype": "string"}], "splits": [{"name": "advanced_physics", "num_bytes": 829991.8175161927, "num_examples": 434}, {"name": "graph_discrete_math", "num_bytes": 342323.8141368629, "num_examples": 179}, {"name": "computational_biology", "num_bytes": 581376.7569698676, "num_examples": 304}, {"name": "logic", "num_bytes": 210366.58969304422, "num_examples": 110}, {"name": "security_and_safety", "num_bytes": 996372.6657279639, "num_examples": 521}, {"name": "advanced_math", "num_bytes": 3088564.021402422, "num_examples": 1615}, {"name": "finance", "num_bytes": 611975.5336524922, "num_examples": 320}, {"name": "mathematical_programming", "num_bytes": 130044.80090115461, "num_examples": 68}], "download_size": 2447494, "dataset_size": 6791016.000000001}} | false | null | 2025-04-01T22:04:20 | 12 | 12 | false | 74cadda690866a8b60cbc31e801fba5f173cb392 |
Additional Information
Project Loong Seed Dataset
This dataset is part of Project Loong, a collaborative effort to explore whether reasoning-capable models can bootstrap themselves from small, high-quality seed datasets by generating synthetic data and verifying LLM agent responses.
Dataset Description
This comprehensive collection contains 3,551 human-vetted problems across 8 diverse domains:
🧮 Advanced Math: 1,615 questions
⚛️ Advanced Physics: 434… See the full description on the dataset page: https://huggingface.co/datasets/camel-ai/loong. | 373 | 373 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"reasoning",
"problem-solving",
"project-loong",
"multi-domain",
"mathematics",
"physics",
"finance",
"optimization"
] | 2025-03-31T01:09:25 | null | null |
65af32411edab235a1f38b0b | omar07ibrahim/Alpaca_Stanford_Azerbaijan | omar07ibrahim | null | false | null | 2024-01-23T03:28:27 | 12 | 11 | false | a088761652ed34235281b46bcdb49d36fd0a3bdb | null | 17 | 150 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-23T03:28:01 | null | null |
65afa00e637e10fba969eb56 | omar07ibrahim/alpaca-cleaned_AZERBAIJANI | omar07ibrahim | null | false | null | 2024-01-23T11:18:42 | 13 | 11 | false | ad9e82bceb5c7a2d438dfcf04132854fc0328781 | null | 31 | 250 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-23T11:16:30 | null | null |
65b4e0dccbea1825a691a012 | omar07ibrahim/testlimOcrCA | omar07ibrahim | null | false | null | 2024-01-27T10:57:04 | 11 | 11 | false | b1f404a6dcaff40d4d14320dd44a212c79a13c94 | null | 25 | 93 | [
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-27T10:54:20 | null | null |
65d713022db271ebd4139f5f | omar07ibrahim/azcon | omar07ibrahim | null | false | null | 2024-02-22T09:26:09 | 11 | 11 | false | f13a01b9ff1ac643e343c80c7ef356ab10e42f7a | null | 17 | 98 | [
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-02-22T09:25:22 | null | null |
67e5170dd9b7021d4a7f48be | Rapidata/OpenAI-4o_t2i_human_preference | Rapidata | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image1", "dtype": "image"}, {"name": "image2", "dtype": "image"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "weighted_results_image1_preference", "dtype": "float32"}, {"name": "weighted_results_image2_preference", "dtype": "float32"}, {"name": "detailed_results_preference", "dtype": "string"}, {"name": "weighted_results_image1_coherence", "dtype": "float32"}, {"name": "weighted_results_image2_coherence", "dtype": "float32"}, {"name": "detailed_results_coherence", "dtype": "string"}, {"name": "weighted_results_image1_alignment", "dtype": "float32"}, {"name": "weighted_results_image2_alignment", "dtype": "float32"}, {"name": "detailed_results_alignment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10832696953, "num_examples": 13000}], "download_size": 5203247080, "dataset_size": 10832696953}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cdla-permissive-2.0", "task_categories": ["text-to-image", "image-to-text", "image-classification", "reinforcement-learning"], "language": ["en"], "tags": ["Human", "Preference", "Coherence", "Alignment", "country", "language", "flux", "midjourney", "dalle3", "stabeldiffusion", "alignment", "flux1.1", "flux1", "imagen3", "aurora", "lumina", "recraft", "recraft v2", "ideogram", "frames", "OpenAI 4o", "4o", "OpenAI"], "size_categories": ["10K<n<100K"], "pretty_name": "OpenAI 4o vs. Ideogram V2 / Recraft V2 / Lumina-15-2-25 / Frames-23-1-25 / Aurora / imagen-3 / Flux-1.1-pro / Flux-1-pro / Dalle-3 / Midjourney-5.2 / Stabel-Diffusion-3 - Human Preference Dataset"} | false | null | 2025-03-28T20:00:43 | 29 | 11 | false | 9fafb39b4bb3bac6e2fbabd13503fa1199fde400 |
Rapidata OpenAI 4o Preference
This T2I dataset contains over 200'000 human responses from over ~45,000 individual annotators, collected in less than half a day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating OpenAI 4o (version from 26.3.2025) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/OpenAI-4o_t2i_human_preference. | 1,178 | 1,178 | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:image-classification",
"task_categories:reinforcement-learning",
"language:en",
"license:cdla-permissive-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"Human",
"Preference",
"Coherence",
"Alignment",
"country",
"language",
"flux",
"midjourney",
"dalle3",
"stabeldiffusion",
"alignment",
"flux1.1",
"flux1",
"imagen3",
"aurora",
"lumina",
"recraft",
"recraft v2",
"ideogram",
"frames",
"OpenAI 4o",
"4o",
"OpenAI"
] | 2025-03-27T09:14:53 | null | null |
621ffdd236468d709f181e5e | cais/mmlu | cais | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mmlu", "pretty_name": "Measuring Massive Multitask Language Understanding", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "abstract_algebra", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 17143, "dataset_size": 57303.3562203159}, {"config_name": "all", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 6967453, "num_examples": 14042}, {"name": "validation", "num_bytes": 763484, "num_examples": 1531}, {"name": "dev", "num_bytes": 125353, "num_examples": 285}, {"name": "auxiliary_train", "num_bytes": 161000625, "num_examples": 99842}], "download_size": 51503402, "dataset_size": 168856915}, {"config_name": "anatomy", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 66985.19833357072, "num_examples": 135}, {"name": "validation", "num_bytes": 6981.5649902024825, "num_examples": 14}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 28864, "dataset_size": 76165.9387623697}, {"config_name": "astronomy", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 75420.3714570574, "num_examples": 152}, {"name": "validation", "num_bytes": 7978.931417374265, "num_examples": 16}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 39316, "dataset_size": 85598.47831302814}, {"config_name": "auxiliary_train", "features": [{"name": "train", "struct": [{"name": "answer", "dtype": "int64"}, {"name": "choices", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 161000625, "num_examples": 99842}], "download_size": 47518592, "dataset_size": 161000625}, {"config_name": "business_ethics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 31619, "dataset_size": 57303.3562203159}, {"config_name": "clinical_knowledge", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 131489.4633955277, "num_examples": 265}, {"name": "validation", "num_bytes": 14461.813193990856, "num_examples": 29}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 51655, "dataset_size": 148150.45202811505}, {"config_name": "college_biology", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 71450.87822247542, "num_examples": 144}, {"name": "validation", "num_bytes": 7978.931417374265, "num_examples": 16}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 43017, "dataset_size": 81628.98507844617}, {"config_name": "college_chemistry", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 3989.4657086871325, "num_examples": 8}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 26781, "dataset_size": 55807.30657955822}, {"config_name": "college_computer_science", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 41132, "dataset_size": 57303.3562203159}, {"config_name": "college_mathematics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 26779, "dataset_size": 57303.3562203159}, {"config_name": "college_medicine", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 85840.29119783506, "num_examples": 173}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 56303, "dataset_size": 99010.49733532117}, {"config_name": "college_physics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 50611.0387409201, "num_examples": 102}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 29539, "dataset_size": 58295.7295289614}, {"config_name": "computer_security", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 30150, "dataset_size": 57303.3562203159}, {"config_name": "conceptual_physics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 116603.86376584532, "num_examples": 235}, {"name": "validation", "num_bytes": 12965.76355323318, "num_examples": 26}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 34968, "dataset_size": 131768.802757675}, {"config_name": "econometrics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 56565.27859279305, "num_examples": 114}, {"name": "validation", "num_bytes": 5984.198563030699, "num_examples": 12}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 36040, "dataset_size": 64748.652594420244}, {"config_name": "electrical_engineering", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 71947.06487679818, "num_examples": 145}, {"name": "validation", "num_bytes": 7978.931417374265, "num_examples": 16}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 26746, "dataset_size": 82125.17173276893}, {"config_name": "elementary_mathematics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 187558.555333998, "num_examples": 378}, {"name": "validation", "num_bytes": 20446.011757021555, "num_examples": 41}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 54987, "dataset_size": 210203.74252961605}, {"config_name": "formal_logic", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 62519.518444666, "num_examples": 126}, {"name": "validation", "num_bytes": 6981.5649902024825, "num_examples": 14}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 32884, "dataset_size": 71700.25887346498}, {"config_name": "global_facts", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 4986.8321358589155, "num_examples": 10}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 19258, "dataset_size": 56804.67300673001}, {"config_name": "high_school_biology", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 153817.86284005127, "num_examples": 310}, {"name": "validation", "num_bytes": 15957.86283474853, "num_examples": 32}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 78216, "dataset_size": 171974.90111339628}, {"config_name": "high_school_chemistry", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 100725.89082751745, "num_examples": 203}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 45799, "dataset_size": 113896.09696500355}, {"config_name": "high_school_computer_science", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 4488.148922273024, "num_examples": 9}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 39072, "dataset_size": 56305.989793144116}, {"config_name": "high_school_european_history", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 81870.79796325309, "num_examples": 165}, {"name": "validation", "num_bytes": 8976.297844546049, "num_examples": 18}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 196270, "dataset_size": 93046.27124639563}, {"config_name": "high_school_geography", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 98244.95755590372, "num_examples": 198}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 38255, "dataset_size": 111415.16369338983}, {"config_name": "high_school_government_and_politics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 95764.02428428999, "num_examples": 193}, {"name": "validation", "num_bytes": 10472.347485303722, "num_examples": 21}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 52963, "dataset_size": 108435.5472081902}, {"config_name": "high_school_macroeconomics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 193512.79518587096, "num_examples": 390}, {"name": "validation", "num_bytes": 21443.378184193338, "num_examples": 43}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 68758, "dataset_size": 217155.34880866078}, {"config_name": "high_school_mathematics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 133970.39666714144, "num_examples": 270}, {"name": "validation", "num_bytes": 14461.813193990856, "num_examples": 29}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 45210, "dataset_size": 150631.38529972878}, {"config_name": "high_school_microeconomics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 118092.42372881356, "num_examples": 238}, {"name": "validation", "num_bytes": 12965.76355323318, "num_examples": 26}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 49885, "dataset_size": 133257.36272064323}, {"config_name": "high_school_physics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 74924.18480273466, "num_examples": 151}, {"name": "validation", "num_bytes": 8477.614630960157, "num_examples": 17}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 45483, "dataset_size": 85600.9748722913}, {"config_name": "high_school_psychology", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 270421.7266058966, "num_examples": 545}, {"name": "validation", "num_bytes": 29920.992815153495, "num_examples": 60}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 113158, "dataset_size": 302541.8948596466}, {"config_name": "high_school_statistics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 107176.31733371314, "num_examples": 216}, {"name": "validation", "num_bytes": 11469.713912475507, "num_examples": 23}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 74924, "dataset_size": 120845.20668478514}, {"config_name": "high_school_us_history", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 101222.0774818402, "num_examples": 204}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 200043, "dataset_size": 114392.2836193263}, {"config_name": "high_school_world_history", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 117596.23707449081, "num_examples": 237}, {"name": "validation", "num_bytes": 12965.76355323318, "num_examples": 26}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 250302, "dataset_size": 132761.17606632048}, {"config_name": "human_aging", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 110649.62391397236, "num_examples": 223}, {"name": "validation", "num_bytes": 11469.713912475507, "num_examples": 23}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 41196, "dataset_size": 124318.51326504436}, {"config_name": "human_sexuality", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 65000.451716279735, "num_examples": 131}, {"name": "validation", "num_bytes": 5984.198563030699, "num_examples": 12}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 32533, "dataset_size": 73183.82571790692}, {"config_name": "international_law", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 60038.58517305227, "num_examples": 121}, {"name": "validation", "num_bytes": 6482.88177661659, "num_examples": 13}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 41592, "dataset_size": 68720.64238826535}, {"config_name": "jurisprudence", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 53588.15866685657, "num_examples": 108}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 33578, "dataset_size": 61272.84945489787}, {"config_name": "logical_fallacies", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 80878.4246546076, "num_examples": 163}, {"name": "validation", "num_bytes": 8976.297844546049, "num_examples": 18}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 33669, "dataset_size": 92053.89793775014}, {"config_name": "machine_learning", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 55572.90528414756, "num_examples": 112}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 31121, "dataset_size": 63257.596072188855}, {"config_name": "management", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 51107.225395242844, "num_examples": 103}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 22828, "dataset_size": 58791.91618328414}, {"config_name": "marketing", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 116107.67711152257, "num_examples": 234}, {"name": "validation", "num_bytes": 12467.08033964729, "num_examples": 25}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 49747, "dataset_size": 130773.93288976635}, {"config_name": "medical_genetics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 25775, "dataset_size": 57303.3562203159}, {"config_name": "miscellaneous", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 388514.15033471014, "num_examples": 783}, {"name": "validation", "num_bytes": 42886.756368386676, "num_examples": 86}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 115097, "dataset_size": 433600.08214169333}, {"config_name": "moral_disputes", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 171680.58239567012, "num_examples": 346}, {"name": "validation", "num_bytes": 18949.96211626388, "num_examples": 38}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 76043, "dataset_size": 192829.71995053047}, {"config_name": "moral_scenarios", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 444087.05561885773, "num_examples": 895}, {"name": "validation", "num_bytes": 49868.32135858916, "num_examples": 100}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 109869, "dataset_size": 496154.5524160434}, {"config_name": "nutrition", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 151833.1162227603, "num_examples": 306}, {"name": "validation", "num_bytes": 16456.54604833442, "num_examples": 33}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 69050, "dataset_size": 170488.8377096912}, {"config_name": "philosophy", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 154314.04949437402, "num_examples": 311}, {"name": "validation", "num_bytes": 16955.229261920314, "num_examples": 34}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 61912, "dataset_size": 173468.45419489083}, {"config_name": "prehistory", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 160764.47600056973, "num_examples": 324}, {"name": "validation", "num_bytes": 17453.912475506204, "num_examples": 35}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 68826, "dataset_size": 180417.5639146724}, {"config_name": "professional_accounting", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 139924.6365190144, "num_examples": 282}, {"name": "validation", "num_bytes": 15459.179621162639, "num_examples": 31}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 87297, "dataset_size": 157582.99157877354}, {"config_name": "professional_law", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 761150.3277310925, "num_examples": 1534}, {"name": "validation", "num_bytes": 84776.14630960157, "num_examples": 170}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 1167828, "dataset_size": 848125.6494792906}, {"config_name": "professional_medicine", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 134962.7699757869, "num_examples": 272}, {"name": "validation", "num_bytes": 15459.179621162639, "num_examples": 31}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 153242, "dataset_size": 152621.12503554605}, {"config_name": "professional_psychology", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 303666.2324455206, "num_examples": 612}, {"name": "validation", "num_bytes": 34409.14173742652, "num_examples": 69}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 159357, "dataset_size": 340274.5496215436}, {"config_name": "public_relations", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 54580.53197550207, "num_examples": 110}, {"name": "validation", "num_bytes": 5984.198563030699, "num_examples": 12}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 31500, "dataset_size": 62763.90597712925}, {"config_name": "security_studies", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 121565.73030907278, "num_examples": 245}, {"name": "validation", "num_bytes": 13464.446766819072, "num_examples": 27}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 140258, "dataset_size": 137229.35251448833}, {"config_name": "sociology", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 99733.51751887196, "num_examples": 201}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 56480, "dataset_size": 112903.72365635807}, {"config_name": "us_foreign_policy", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 29027, "dataset_size": 57303.3562203159}, {"config_name": "virology", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 82366.98461757584, "num_examples": 166}, {"name": "validation", "num_bytes": 8976.297844546049, "num_examples": 18}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 38229, "dataset_size": 93542.45790071838}, {"config_name": "world_religions", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 84847.91788918957, "num_examples": 171}, {"name": "validation", "num_bytes": 9474.98105813194, "num_examples": 19}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 27165, "dataset_size": 96522.07438591801}], "configs": [{"config_name": "abstract_algebra", "data_files": [{"split": "test", "path": "abstract_algebra/test-*"}, {"split": "validation", "path": "abstract_algebra/validation-*"}, {"split": "dev", "path": "abstract_algebra/dev-*"}]}, {"config_name": "all", "data_files": [{"split": "test", "path": "all/test-*"}, {"split": "validation", "path": "all/validation-*"}, {"split": "dev", "path": "all/dev-*"}, {"split": "auxiliary_train", "path": "all/auxiliary_train-*"}]}, {"config_name": "anatomy", "data_files": [{"split": "test", "path": "anatomy/test-*"}, {"split": "validation", "path": "anatomy/validation-*"}, {"split": "dev", "path": "anatomy/dev-*"}]}, {"config_name": "astronomy", "data_files": [{"split": "test", "path": "astronomy/test-*"}, {"split": "validation", "path": "astronomy/validation-*"}, {"split": "dev", "path": "astronomy/dev-*"}]}, {"config_name": "auxiliary_train", "data_files": [{"split": "train", "path": "auxiliary_train/train-*"}]}, {"config_name": "business_ethics", "data_files": [{"split": "test", "path": "business_ethics/test-*"}, {"split": "validation", "path": "business_ethics/validation-*"}, {"split": "dev", "path": "business_ethics/dev-*"}]}, {"config_name": "clinical_knowledge", "data_files": [{"split": "test", "path": "clinical_knowledge/test-*"}, {"split": "validation", "path": "clinical_knowledge/validation-*"}, {"split": "dev", "path": "clinical_knowledge/dev-*"}]}, {"config_name": "college_biology", "data_files": [{"split": "test", "path": "college_biology/test-*"}, {"split": "validation", "path": "college_biology/validation-*"}, {"split": "dev", "path": "college_biology/dev-*"}]}, {"config_name": "college_chemistry", "data_files": [{"split": "test", "path": "college_chemistry/test-*"}, {"split": "validation", "path": "college_chemistry/validation-*"}, {"split": "dev", "path": "college_chemistry/dev-*"}]}, {"config_name": "college_computer_science", "data_files": [{"split": "test", "path": "college_computer_science/test-*"}, {"split": "validation", "path": "college_computer_science/validation-*"}, {"split": "dev", "path": "college_computer_science/dev-*"}]}, {"config_name": "college_mathematics", "data_files": [{"split": "test", "path": "college_mathematics/test-*"}, {"split": "validation", "path": "college_mathematics/validation-*"}, {"split": "dev", "path": "college_mathematics/dev-*"}]}, {"config_name": "college_medicine", "data_files": [{"split": "test", "path": "college_medicine/test-*"}, {"split": "validation", "path": "college_medicine/validation-*"}, {"split": "dev", "path": "college_medicine/dev-*"}]}, {"config_name": "college_physics", "data_files": [{"split": "test", "path": "college_physics/test-*"}, {"split": "validation", "path": "college_physics/validation-*"}, {"split": "dev", "path": "college_physics/dev-*"}]}, {"config_name": "computer_security", "data_files": [{"split": "test", "path": "computer_security/test-*"}, {"split": "validation", "path": "computer_security/validation-*"}, {"split": "dev", "path": "computer_security/dev-*"}]}, {"config_name": "conceptual_physics", "data_files": [{"split": "test", "path": "conceptual_physics/test-*"}, {"split": "validation", "path": "conceptual_physics/validation-*"}, {"split": "dev", "path": "conceptual_physics/dev-*"}]}, {"config_name": "econometrics", "data_files": [{"split": "test", "path": "econometrics/test-*"}, {"split": "validation", "path": "econometrics/validation-*"}, {"split": "dev", "path": "econometrics/dev-*"}]}, {"config_name": "electrical_engineering", "data_files": [{"split": "test", "path": "electrical_engineering/test-*"}, {"split": "validation", "path": "electrical_engineering/validation-*"}, {"split": "dev", "path": "electrical_engineering/dev-*"}]}, {"config_name": "elementary_mathematics", "data_files": [{"split": "test", "path": "elementary_mathematics/test-*"}, {"split": "validation", "path": "elementary_mathematics/validation-*"}, {"split": "dev", "path": "elementary_mathematics/dev-*"}]}, {"config_name": "formal_logic", "data_files": [{"split": "test", "path": "formal_logic/test-*"}, {"split": "validation", "path": "formal_logic/validation-*"}, {"split": "dev", "path": "formal_logic/dev-*"}]}, {"config_name": "global_facts", "data_files": [{"split": "test", "path": "global_facts/test-*"}, {"split": "validation", "path": "global_facts/validation-*"}, {"split": "dev", "path": "global_facts/dev-*"}]}, {"config_name": "high_school_biology", "data_files": [{"split": "test", "path": "high_school_biology/test-*"}, {"split": "validation", "path": "high_school_biology/validation-*"}, {"split": "dev", "path": "high_school_biology/dev-*"}]}, {"config_name": "high_school_chemistry", "data_files": [{"split": "test", "path": "high_school_chemistry/test-*"}, {"split": "validation", "path": "high_school_chemistry/validation-*"}, {"split": "dev", "path": "high_school_chemistry/dev-*"}]}, {"config_name": "high_school_computer_science", "data_files": [{"split": "test", "path": "high_school_computer_science/test-*"}, {"split": "validation", "path": "high_school_computer_science/validation-*"}, {"split": "dev", "path": "high_school_computer_science/dev-*"}]}, {"config_name": "high_school_european_history", "data_files": [{"split": "test", "path": "high_school_european_history/test-*"}, {"split": "validation", "path": "high_school_european_history/validation-*"}, {"split": "dev", "path": "high_school_european_history/dev-*"}]}, {"config_name": "high_school_geography", "data_files": [{"split": "test", "path": "high_school_geography/test-*"}, {"split": "validation", "path": "high_school_geography/validation-*"}, {"split": "dev", "path": "high_school_geography/dev-*"}]}, {"config_name": "high_school_government_and_politics", "data_files": [{"split": "test", "path": "high_school_government_and_politics/test-*"}, {"split": "validation", "path": "high_school_government_and_politics/validation-*"}, {"split": "dev", "path": "high_school_government_and_politics/dev-*"}]}, {"config_name": "high_school_macroeconomics", "data_files": [{"split": "test", "path": "high_school_macroeconomics/test-*"}, {"split": "validation", "path": "high_school_macroeconomics/validation-*"}, {"split": "dev", "path": "high_school_macroeconomics/dev-*"}]}, {"config_name": "high_school_mathematics", "data_files": [{"split": "test", "path": "high_school_mathematics/test-*"}, {"split": "validation", "path": "high_school_mathematics/validation-*"}, {"split": "dev", "path": "high_school_mathematics/dev-*"}]}, {"config_name": "high_school_microeconomics", "data_files": [{"split": "test", "path": "high_school_microeconomics/test-*"}, {"split": "validation", "path": "high_school_microeconomics/validation-*"}, {"split": "dev", "path": "high_school_microeconomics/dev-*"}]}, {"config_name": "high_school_physics", "data_files": [{"split": "test", "path": "high_school_physics/test-*"}, {"split": "validation", "path": "high_school_physics/validation-*"}, {"split": "dev", "path": "high_school_physics/dev-*"}]}, {"config_name": "high_school_psychology", "data_files": [{"split": "test", "path": "high_school_psychology/test-*"}, {"split": "validation", "path": "high_school_psychology/validation-*"}, {"split": "dev", "path": "high_school_psychology/dev-*"}]}, {"config_name": "high_school_statistics", "data_files": [{"split": "test", "path": "high_school_statistics/test-*"}, {"split": "validation", "path": "high_school_statistics/validation-*"}, {"split": "dev", "path": "high_school_statistics/dev-*"}]}, {"config_name": "high_school_us_history", "data_files": [{"split": "test", "path": "high_school_us_history/test-*"}, {"split": "validation", "path": "high_school_us_history/validation-*"}, {"split": "dev", "path": "high_school_us_history/dev-*"}]}, {"config_name": "high_school_world_history", "data_files": [{"split": "test", "path": "high_school_world_history/test-*"}, {"split": "validation", "path": "high_school_world_history/validation-*"}, {"split": "dev", "path": "high_school_world_history/dev-*"}]}, {"config_name": "human_aging", "data_files": [{"split": "test", "path": "human_aging/test-*"}, {"split": "validation", "path": "human_aging/validation-*"}, {"split": "dev", "path": "human_aging/dev-*"}]}, {"config_name": "human_sexuality", "data_files": [{"split": "test", "path": "human_sexuality/test-*"}, {"split": "validation", "path": "human_sexuality/validation-*"}, {"split": "dev", "path": "human_sexuality/dev-*"}]}, {"config_name": "international_law", "data_files": [{"split": "test", "path": "international_law/test-*"}, {"split": "validation", "path": "international_law/validation-*"}, {"split": "dev", "path": "international_law/dev-*"}]}, {"config_name": "jurisprudence", "data_files": [{"split": "test", "path": "jurisprudence/test-*"}, {"split": "validation", "path": "jurisprudence/validation-*"}, {"split": "dev", "path": "jurisprudence/dev-*"}]}, {"config_name": "logical_fallacies", "data_files": [{"split": "test", "path": "logical_fallacies/test-*"}, {"split": "validation", "path": "logical_fallacies/validation-*"}, {"split": "dev", "path": "logical_fallacies/dev-*"}]}, {"config_name": "machine_learning", "data_files": [{"split": "test", "path": "machine_learning/test-*"}, {"split": "validation", "path": "machine_learning/validation-*"}, {"split": "dev", "path": "machine_learning/dev-*"}]}, {"config_name": "management", "data_files": [{"split": "test", "path": "management/test-*"}, {"split": "validation", "path": "management/validation-*"}, {"split": "dev", "path": "management/dev-*"}]}, {"config_name": "marketing", "data_files": [{"split": "test", "path": "marketing/test-*"}, {"split": "validation", "path": "marketing/validation-*"}, {"split": "dev", "path": "marketing/dev-*"}]}, {"config_name": "medical_genetics", "data_files": [{"split": "test", "path": "medical_genetics/test-*"}, {"split": "validation", "path": "medical_genetics/validation-*"}, {"split": "dev", "path": "medical_genetics/dev-*"}]}, {"config_name": "miscellaneous", "data_files": [{"split": "test", "path": "miscellaneous/test-*"}, {"split": "validation", "path": "miscellaneous/validation-*"}, {"split": "dev", "path": "miscellaneous/dev-*"}]}, {"config_name": "moral_disputes", "data_files": [{"split": "test", "path": "moral_disputes/test-*"}, {"split": "validation", "path": "moral_disputes/validation-*"}, {"split": "dev", "path": "moral_disputes/dev-*"}]}, {"config_name": "moral_scenarios", "data_files": [{"split": "test", "path": "moral_scenarios/test-*"}, {"split": "validation", "path": "moral_scenarios/validation-*"}, {"split": "dev", "path": "moral_scenarios/dev-*"}]}, {"config_name": "nutrition", "data_files": [{"split": "test", "path": "nutrition/test-*"}, {"split": "validation", "path": "nutrition/validation-*"}, {"split": "dev", "path": "nutrition/dev-*"}]}, {"config_name": "philosophy", "data_files": [{"split": "test", "path": "philosophy/test-*"}, {"split": "validation", "path": "philosophy/validation-*"}, {"split": "dev", "path": "philosophy/dev-*"}]}, {"config_name": "prehistory", "data_files": [{"split": "test", "path": "prehistory/test-*"}, {"split": "validation", "path": "prehistory/validation-*"}, {"split": "dev", "path": "prehistory/dev-*"}]}, {"config_name": "professional_accounting", "data_files": [{"split": "test", "path": "professional_accounting/test-*"}, {"split": "validation", "path": "professional_accounting/validation-*"}, {"split": "dev", "path": "professional_accounting/dev-*"}]}, {"config_name": "professional_law", "data_files": [{"split": "test", "path": "professional_law/test-*"}, {"split": "validation", "path": "professional_law/validation-*"}, {"split": "dev", "path": "professional_law/dev-*"}]}, {"config_name": "professional_medicine", "data_files": [{"split": "test", "path": "professional_medicine/test-*"}, {"split": "validation", "path": "professional_medicine/validation-*"}, {"split": "dev", "path": "professional_medicine/dev-*"}]}, {"config_name": "professional_psychology", "data_files": [{"split": "test", "path": "professional_psychology/test-*"}, {"split": "validation", "path": "professional_psychology/validation-*"}, {"split": "dev", "path": "professional_psychology/dev-*"}]}, {"config_name": "public_relations", "data_files": [{"split": "test", "path": "public_relations/test-*"}, {"split": "validation", "path": "public_relations/validation-*"}, {"split": "dev", "path": "public_relations/dev-*"}]}, {"config_name": "security_studies", "data_files": [{"split": "test", "path": "security_studies/test-*"}, {"split": "validation", "path": "security_studies/validation-*"}, {"split": "dev", "path": "security_studies/dev-*"}]}, {"config_name": "sociology", "data_files": [{"split": "test", "path": "sociology/test-*"}, {"split": "validation", "path": "sociology/validation-*"}, {"split": "dev", "path": "sociology/dev-*"}]}, {"config_name": "us_foreign_policy", "data_files": [{"split": "test", "path": "us_foreign_policy/test-*"}, {"split": "validation", "path": "us_foreign_policy/validation-*"}, {"split": "dev", "path": "us_foreign_policy/dev-*"}]}, {"config_name": "virology", "data_files": [{"split": "test", "path": "virology/test-*"}, {"split": "validation", "path": "virology/validation-*"}, {"split": "dev", "path": "virology/dev-*"}]}, {"config_name": "world_religions", "data_files": [{"split": "test", "path": "world_religions/test-*"}, {"split": "validation", "path": "world_religions/validation-*"}, {"split": "dev", "path": "world_religions/dev-*"}]}]} | false | null | 2024-03-08T20:36:26 | 445 | 10 | false | c30699e8356da336a370243923dbaf21066bb9fe |
Dataset Card for MMLU
Dataset Summary
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu. | 134,703 | 37,218,591 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | 2022-03-02T23:29:22 | mmlu | null |
66212f29fb07c3e05ad0432e | HuggingFaceFW/fineweb | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-38/*"}]}, {"config_name": "CC-MAIN-2024-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-33/*"}]}, {"config_name": "CC-MAIN-2024-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-30/*"}]}, {"config_name": "CC-MAIN-2024-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-26/*"}]}, {"config_name": "CC-MAIN-2024-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-22/*"}]}, {"config_name": "CC-MAIN-2024-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-18/*"}]}, {"config_name": "CC-MAIN-2024-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-10/*"}]}, {"config_name": "CC-MAIN-2023-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-50/*"}]}, {"config_name": "CC-MAIN-2023-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-40/*"}]}, {"config_name": "CC-MAIN-2023-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-23/*"}]}, {"config_name": "CC-MAIN-2023-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-14/*"}]}, {"config_name": "CC-MAIN-2023-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-06/*"}]}, {"config_name": "CC-MAIN-2022-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-49/*"}]}, {"config_name": "CC-MAIN-2022-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-40/*"}]}, {"config_name": "CC-MAIN-2022-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-33/*"}]}, {"config_name": "CC-MAIN-2022-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-27/*"}]}, {"config_name": "CC-MAIN-2022-21", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-21/*"}]}, {"config_name": "CC-MAIN-2022-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-05/*"}]}, {"config_name": "CC-MAIN-2021-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-49/*"}]}, {"config_name": "CC-MAIN-2021-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-43/*"}]}, {"config_name": "CC-MAIN-2021-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-39/*"}]}, {"config_name": "CC-MAIN-2021-31", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-31/*"}]}, {"config_name": "CC-MAIN-2021-25", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-25/*"}]}, {"config_name": "CC-MAIN-2021-21", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-21/*"}]}, {"config_name": "CC-MAIN-2021-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-17/*"}]}, {"config_name": "CC-MAIN-2021-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-10/*"}]}, {"config_name": "CC-MAIN-2021-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-04/*"}]}, {"config_name": "CC-MAIN-2020-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-50/*"}]}, {"config_name": "CC-MAIN-2020-45", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-45/*"}]}, {"config_name": "CC-MAIN-2020-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-40/*"}]}, {"config_name": "CC-MAIN-2020-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-34/*"}]}, {"config_name": "CC-MAIN-2020-29", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-29/*"}]}, {"config_name": "CC-MAIN-2020-24", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-24/*"}]}, {"config_name": "CC-MAIN-2020-16", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-16/*"}]}, {"config_name": "CC-MAIN-2020-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-10/*"}]}, {"config_name": "CC-MAIN-2020-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-05/*"}]}, {"config_name": "CC-MAIN-2019-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-51/*"}]}, {"config_name": "CC-MAIN-2019-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-47/*"}]}, {"config_name": "CC-MAIN-2019-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-43/*"}]}, {"config_name": "CC-MAIN-2019-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-39/*"}]}, {"config_name": "CC-MAIN-2019-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-35/*"}]}, {"config_name": "CC-MAIN-2019-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-30/*"}]}, {"config_name": "CC-MAIN-2019-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-26/*"}]}, {"config_name": "CC-MAIN-2019-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-22/*"}]}, {"config_name": "CC-MAIN-2019-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-18/*"}]}, {"config_name": "CC-MAIN-2019-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-13/*"}]}, {"config_name": "CC-MAIN-2019-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-09/*"}]}, {"config_name": "CC-MAIN-2019-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-04/*"}]}, {"config_name": "CC-MAIN-2018-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-51/*"}]}, {"config_name": "CC-MAIN-2018-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-47/*"}]}, {"config_name": "CC-MAIN-2018-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-43/*"}]}, {"config_name": "CC-MAIN-2018-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-39/*"}]}, {"config_name": "CC-MAIN-2018-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-34/*"}]}, {"config_name": "CC-MAIN-2018-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-30/*"}]}, {"config_name": "CC-MAIN-2018-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-26/*"}]}, {"config_name": "CC-MAIN-2018-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-22/*"}]}, {"config_name": "CC-MAIN-2018-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-17/*"}]}, {"config_name": "CC-MAIN-2018-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-13/*"}]}, {"config_name": "CC-MAIN-2018-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-09/*"}]}, {"config_name": "CC-MAIN-2018-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-05/*"}]}, {"config_name": "CC-MAIN-2017-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-51/*"}]}, {"config_name": "CC-MAIN-2017-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-47/*"}]}, {"config_name": "CC-MAIN-2017-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-43/*"}]}, {"config_name": "CC-MAIN-2017-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-39/*"}]}, {"config_name": "CC-MAIN-2017-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-34/*"}]}, {"config_name": "CC-MAIN-2017-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-30/*"}]}, {"config_name": "CC-MAIN-2017-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-26/*"}]}, {"config_name": "CC-MAIN-2017-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-22/*"}]}, {"config_name": "CC-MAIN-2017-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-17/*"}]}, {"config_name": "CC-MAIN-2017-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-13/*"}]}, {"config_name": "CC-MAIN-2017-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-09/*"}]}, {"config_name": "CC-MAIN-2017-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-04/*"}]}, {"config_name": "CC-MAIN-2016-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-50/*"}]}, {"config_name": "CC-MAIN-2016-44", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-44/*"}]}, {"config_name": "CC-MAIN-2016-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-40/*"}]}, {"config_name": "CC-MAIN-2016-36", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-36/*"}]}, {"config_name": "CC-MAIN-2016-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-30/*"}]}, {"config_name": "CC-MAIN-2016-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-26/*"}]}, {"config_name": "CC-MAIN-2016-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-22/*"}]}, {"config_name": "CC-MAIN-2016-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-18/*"}]}, {"config_name": "CC-MAIN-2016-07", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-07/*"}]}, {"config_name": "CC-MAIN-2015-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-48/*"}]}, {"config_name": "CC-MAIN-2015-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-40/*"}]}, {"config_name": "CC-MAIN-2015-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-35/*"}]}, {"config_name": "CC-MAIN-2015-32", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-32/*"}]}, {"config_name": "CC-MAIN-2015-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-27/*"}]}, {"config_name": "CC-MAIN-2015-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-22/*"}]}, {"config_name": "CC-MAIN-2015-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-18/*"}]}, {"config_name": "CC-MAIN-2015-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-14/*"}]}, {"config_name": "CC-MAIN-2015-11", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-11/*"}]}, {"config_name": "CC-MAIN-2015-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-06/*"}]}, {"config_name": "CC-MAIN-2014-52", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-52/*"}]}, {"config_name": "CC-MAIN-2014-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-49/*"}]}, {"config_name": "CC-MAIN-2014-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-42/*"}]}, {"config_name": "CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/*"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]} | false | null | 2025-01-31T14:10:44 | 2,083 | 10 | false | 0f039043b23fe1d4eed300b504aa4b4a68f1c7ba |
🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library.
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb. | 189,801 | 2,359,376 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:10B<n<100B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2306.01116",
"arxiv:2109.07445",
"arxiv:2406.17557",
"doi:10.57967/hf/2493",
"region:us"
] | 2024-04-18T14:33:13 | null | null |
665eaefe5baf7febc7207877 | OOPPEENN/Galgame_Dataset | OOPPEENN | {"license": "gpl-3.0"} | false | null | 2025-04-05T13:55:37 | 124 | 10 | false | 063289e2dc66f39729e575d9a6c35ec959571419 |
0x0 使用协议：
必须遵守GNU General Public License v3.0内的所有协议！附加：禁止商用，本数据集以及使用本数据集训练出来的任何模型都不得用于任何商业行为，如要用于商业用途，请找数据列表内的所有厂商授权（笑），因违反开源协议而出现的任何问题都与本人无关！
训练出来的模型必须开源，是否在README内引用本数据集由训练者自主决定，不做强制要求。
0x1 数据说明：
解压密码：9ll9Ke4iq0jqyq3gS1Wy。
标注说明：标注，说话人和对应的音频是直接读游戏引擎的脚本生成的，应该是100%准确率，全部存放在index.json里面，如果还有错误可以在开issues反馈（有些遗漏的控制符可能没洗干净）。
务必根据index.json里面的键值对找音频，不在index内的音频请直接丢弃，说话人为？？？的请直接丢弃。
数据语言：日语（100%）
数据时长：8823h 22m 07s
角色总数：25387人（未合并）
音频格式：ogg（6031257个），opus（172948个），wav（34753个）… See the full description on the dataset page: https://huggingface.co/datasets/OOPPEENN/Galgame_Dataset. | 3,691 | 22,881 | [
"license:gpl-3.0",
"region:us"
] | 2024-06-04T06:06:54 | null | null |
67c03fd6b9fe27a2ac49784d | open-r1/codeforces-cots | open-r1 | {"dataset_info": [{"config_name": "checker_interactor", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 994149425, "num_examples": 35718}], "download_size": 274975300, "dataset_size": 994149425}, {"config_name": "solutions", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4968074271, "num_examples": 47780}], "download_size": 1887049179, "dataset_size": 4968074271}, {"config_name": "solutions_decontaminated", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 6719356671, "num_examples": 40665}], "download_size": 2023394671, "dataset_size": 6719356671}, {"config_name": "solutions_py", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1000253222, "num_examples": 9556}], "download_size": 411697337, "dataset_size": 1000253222}, {"config_name": "solutions_py_decontaminated", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1349328880, "num_examples": 8133}], "download_size": 500182086, "dataset_size": 1349328880}, {"config_name": "solutions_short_and_long_decontaminated", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2699204607, "num_examples": 16266}], "download_size": 1002365269, "dataset_size": 2699204607}, {"config_name": "solutions_w_editorials", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2649620432, "num_examples": 29180}], "download_size": 972089090, "dataset_size": 2649620432}, {"config_name": "solutions_w_editorials_decontaminated", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 3738669884, "num_examples": 24490}], "download_size": 1012247387, "dataset_size": 3738669884}, {"config_name": "solutions_w_editorials_py", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1067124847, "num_examples": 11672}], "download_size": 415023817, "dataset_size": 1067124847}, {"config_name": "solutions_w_editorials_py_decontaminated", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1499075280, "num_examples": 9796}], "download_size": 466078291, "dataset_size": 1499075280}, {"config_name": "test_input_generator", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "completion_tokens_details", "dtype": "null"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1851104290, "num_examples": 20620}], "download_size": 724157877, "dataset_size": 1851104290}], "configs": [{"config_name": "checker_interactor", "data_files": [{"split": "train", "path": "checker_interactor/train-*"}]}, {"config_name": "solutions", "default": true, "data_files": [{"split": "train", "path": "solutions/train-*"}]}, {"config_name": "solutions_decontaminated", "data_files": [{"split": "train", "path": "solutions_decontaminated/train-*"}]}, {"config_name": "solutions_py", "data_files": [{"split": "train", "path": "solutions_py/train-*"}]}, {"config_name": "solutions_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_py_decontaminated/train-*"}]}, {"config_name": "solutions_short_and_long_decontaminated", "data_files": [{"split": "train", "path": "solutions_short_and_long_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials", "data_files": [{"split": "train", "path": "solutions_w_editorials/train-*"}]}, {"config_name": "solutions_w_editorials_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials_py", "data_files": [{"split": "train", "path": "solutions_w_editorials_py/train-*"}]}, {"config_name": "solutions_w_editorials_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_py_decontaminated/train-*"}]}, {"config_name": "test_input_generator", "data_files": [{"split": "train", "path": "test_input_generator/train-*"}]}], "license": "cc-by-4.0"} | false | null | 2025-03-28T12:21:06 | 127 | 10 | false | 39ac85c150806230473c70ad72c31f6232fe3f41 |
Dataset Card for CodeForces-CoTs
Dataset description
CodeForces-CoTs is a large-scale dataset for training reasoning models on competitive programming tasks. It consists of 10k CodeForces problems with up to five reasoning traces generated by DeepSeek R1. We did not filter the traces for correctness, but found that around 84% of the Python ones pass the public tests.
The dataset consists of several subsets:
solutions: we prompt R1 to solve the problem and produce code.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-cots. | 10,286 | 10,366 | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-27T10:35:02 | null | null |
67ce7e23fee7f7ce990104eb | X-ART/LeX-10K | X-ART | {"license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-image"], "tags": ["text-rendering", "art"]} | false | null | 2025-03-31T08:19:17 | 18 | 10 | false | 18a5cf77b06608dac50442903bf5b9ad46bd7059 |
🖼️ LeX-10K: High-Quality Dataset for Text Rendering
LeX-10K is a curated dataset of 10,000 high-resolution, visually diverse 1024×1024 images tailored for text-to-image generation with a focus on aesthetics, text fidelity, and stylistic richness.
Project Page | Paper
🌟 Why LeX-10K?
We compare LeX-10K with two widely used datasets: AnyWord-3M and MARIO-10M.As shown below, LeX-10K significantly outperforms both in terms of aesthetic quality, text readability, and… See the full description on the dataset page: https://huggingface.co/datasets/X-ART/LeX-10K. | 1,285 | 1,285 | [
"task_categories:text-to-image",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.21749",
"region:us",
"text-rendering",
"art"
] | 2025-03-10T05:52:35 | null | null |
67e52d5dc04bd73d2f46a929 | MrDragonFox/Elise | MrDragonFox | {"license": "mit"} | false | null | 2025-03-27T11:22:24 | 25 | 10 | false | ee867f95526856352ba9c607e6f97e6b9c65b043 | this is very much a clone of
https://huggingface.co/datasets/Jinsaryko/Elise
but with classified emotions like laughs and giggles
not ment to be comprehenive - its about 3h in total and will be enough to for a finetuned voice and some basic emotional tags
short but sweet - acts as demo test set
"giggles - 76",
"laughs - 336",
"long pause - 2",
"chuckles - 20",
"whispers - 2",
"normal volume - 2",
"sighs - 156",
"clicks tongue - 2",
"gasps - 4",
"moans - 8",
"sonora - 2",
"habla en inglés -… See the full description on the dataset page: https://huggingface.co/datasets/MrDragonFox/Elise. | 1,410 | 1,410 | [
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-27T10:50:05 | null | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | null | 2023-05-26T18:47:34 | 1,312 | 9 | false | 09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa |
Dataset Card for HH-RLHF
Dataset Summary
This repository provides access to two different kinds of data:
Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. 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… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf. | 13,018 | 1,563,546 | [
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 2022-12-08T20:11:33 | null | null |
67d97c4be2b27852325fd8e2 | nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim | nvidia | {"license": "cc-by-4.0"} | false | null | 2025-04-02T02:27:47 | 102 | 9 | false | 8fc782b6de78e17914dd52053c9c680e4bde8fb1 |
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
Github Repo: Isaac GR00T N1
We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks.
Cross-embodied bimanual manipulation: 9k trajectories
Dataset Name
#trajectories
bimanual_panda_gripper.Threading
1000
bimanual_panda_hand.LiftTray
1000
bimanual_panda_gripper.ThreePieceAssembly
1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim. | 29,201 | 29,201 | [
"license:cc-by-4.0",
"region:us"
] | 2025-03-18T13:59:39 | null | null |
6791fcbb49c4df6d798ca7c9 | cais/hle | cais | {"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "image_preview", "dtype": "image"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "author_name", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "rationale_image", "dtype": "image"}, {"name": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 284635618, "num_examples": 2500}], "download_size": 274582371, "dataset_size": 284635618}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | false | null | 2025-04-04T04:00:14 | 292 | 8 | false | 1e33bd2d1346480b397ad94845067c4a088a33d3 |
Humanity's Last Exam
🌐 Website | 📄 Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,500 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of… See the full description on the dataset page: https://huggingface.co/datasets/cais/hle. | 7,073 | 17,174 | [
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-01-23T08:24:27 | null | null |
67a52826e2c430620fe95ca3 | PatronusAI/BLUR | PatronusAI | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "blur", "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "validation.csv"}, {"split": "public_test", "path": "public_test.csv"}]}]} | false | null | 2025-03-26T02:43:58 | 10 | 8 | false | dda66f8e5fd56102644ef313a6532d4e4cfba514 |
Browsing Lost Unformed Recollections
The leaderboard can be found at https://huggingface.co/spaces/PatronusAI/BLUR-leaderboard. If you use or find this dataset helpful in your research, please do cite our paper:
Paper Link: arXiv
@misc{chwang2025blur,
title = {Browsing {Lost} {Unformed} {Recollections}: {A} {Benchmark} for {Tip}-of-the-{Tongue} {Search} and {Reasoning}},
shorttitle = {Browsing {Lost} {Unformed} {Recollections}},
url = {http://arxiv.org/abs/2503.19193}… See the full description on the dataset page: https://huggingface.co/datasets/PatronusAI/BLUR. | 137 | 172 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:csv",
"modality:audio",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.19193",
"region:us"
] | 2025-02-06T21:22:46 | null | null |
67abc2c2d6edf5606aa5c0d7 | facebook/collaborative_agent_bench | facebook | {"license": "other", "extra_gated_prompt": "## License", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "I accept the terms and conditions": "checkbox", "geo": "ip_location"}, "extra_gated_description": "SWEET-RL Research License and Acceptable Use Policy", "extra_gated_button_content": "I Accept Self-taught Evaluator Research License and AUP"} | false | null | 2025-03-20T04:17:14 | 57 | 8 | false | cf3526da25989b53f105fe9b74c1174a3e19c548 | This dataset is released as part of SWEET-RL: Training Multi-Turn LLM Agents on
Collaborative Reasoning Tasks research project.
Please refer to our project materials here for training and evaluation details.
Citation
If you use data, model, or code from this work, please cite with the following BibTex entry:
@misc{zhou2025sweetrltrainingmultiturnllm,
title={SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks},
author={Yifei Zhou and Song Jiang and… See the full description on the dataset page: https://huggingface.co/datasets/facebook/collaborative_agent_bench. | 125 | 125 | [
"license:other",
"arxiv:2503.15478",
"region:us"
] | 2025-02-11T21:36:02 | null | null |
67b32145bac2756ce9a4a0fe | Congliu/Chinese-DeepSeek-R1-Distill-data-110k | Congliu | {"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]} | false | null | 2025-02-21T02:18:08 | 609 | 8 | false | 8520b649430617c2be4490f424d251d09d835ed3 |
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：
Math：共计36568个样本，
Exam：共计2432个样本，
STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k. | 5,081 | 11,124 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-17T11:45:09 | null | null |
67e90aae9f3eff13b7405dfb | KwaiVGI/MultiCamVideo-Dataset | KwaiVGI | {"license": "apache-2.0"} | false | null | 2025-04-03T03:23:10 | 8 | 8 | false | 1603dfd560cc227f218c854fbee81e56a82993e0 | Github
Project Page
Paper
📷 MultiCamVideo Dataset
1. Dataset Introduction
TL;DR: The MultiCamVideo Dataset, introduced in ReCamMaster, is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and its corresponding camera trajectories. The MultiCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction.
The… See the full description on the dataset page: https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset. | 278 | 278 | [
"license:apache-2.0",
"arxiv:2503.11647",
"region:us"
] | 2025-03-30T09:11:10 | null | null |
67ec47948647cfa17739af7a | nvidia/OpenCodeReasoning | nvidia | {"dataset_info": [{"config_name": "split_0", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "split_0", "num_bytes": 28108469190, "num_examples": 567850}]}, {"config_name": "split_1", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "index", "dtype": "string"}], "splits": [{"name": "split_1", "num_bytes": 4722811278, "num_examples": 167405}]}], "configs": [{"config_name": "split_0", "data_files": [{"split": "split_0", "path": "split_0/train-*"}]}, {"config_name": "split_1", "data_files": [{"split": "split_1", "path": "split_1/train-*"}]}], "tags": ["synthetic"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "OpenCodeReasoning"} | false | null | 2025-04-05T00:26:16 | 8 | 8 | false | 4c7dd4975d530d9808268f62444dba001cdc5f80 |
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeReasoning.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeReasoning. | 23 | 23 | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.01943",
"region:us",
"synthetic"
] | 2025-04-01T20:07:48 | null | null |
64035e3d723a03e62696f152 | biglam/european_art | biglam | {"dataset_info": [{"config_name": "coco", "features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}, {"name": "image_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8285204, "num_examples": 15156}], "download_size": 18160510195, "dataset_size": 8285204}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "file_id", "dtype": "string"}, {"name": "annotations", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18197594657, "num_examples": 15154}], "download_size": 18151946901, "dataset_size": 18197594657}, {"config_name": "raw", "features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "objects", "list": [{"name": "name", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "pose", "dtype": {"class_label": {"names": {"0": "stand", "1": "sit", "2": "partial", "3": "Unspecified", "4": "squats", "5": "lie", "6": "bend", "7": "fall", "8": "walk", "9": "push", "10": "pray", "11": "undefined", "12": "kneel", "13": "unrecognize", "14": "unknown", "15": "other", "16": "ride"}}}}, {"name": "diffult", "dtype": "int32"}, {"name": "xmin", "dtype": "float64"}, {"name": "ymin", "dtype": "float64"}, {"name": "xmax", "dtype": "float64"}, {"name": "ymax", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 9046918, "num_examples": 15156}], "download_size": 18160510195, "dataset_size": 9046918}], "license": "cc-by-nc-2.0", "task_categories": ["object-detection", "image-classification"], "tags": ["lam", "art", "historical"], "pretty_name": "DEArt: Dataset of European Art", "size_categories": ["10K<n<100K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-31T18:04:12 | 14 | 7 | false | f00afe1c164f7d1d9819e3b55b1fe693e4cfa91c |
Dataset Card for DEArt: Dataset of European Art
Dataset Summary
DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural… See the full description on the dataset page: https://huggingface.co/datasets/biglam/european_art. | 360 | 862 | [
"task_categories:object-detection",
"task_categories:image-classification",
"license:cc-by-nc-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2211.01226",
"region:us",
"lam",
"art",
"historical"
] | 2023-03-04T15:05:33 | null | null |
641debae1d05404efd046a4f | yahma/alpaca-cleaned | yahma | {"license": "cc-by-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca-Cleaned", "task_categories": ["text-generation"]} | false | null | 2023-04-10T20:29:06 | 675 | 7 | false | 12567cabf869d7c92e573c7c783905fc160e9639 |
Dataset Card for Alpaca-Cleaned
Repository: https://github.com/gururise/AlpacaDataCleaned
Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
"instruction":"Summarize the… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned. | 23,360 | 622,174 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"instruction-finetuning"
] | 2023-03-24T18:27:58 | null | null |
65abafba043d53781a266118 | arbml/CIDAR | arbml | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "index", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6712623, "num_examples": 10000}], "download_size": 3553672, "dataset_size": 6712623}, "license": "apache-2.0", "task_categories": ["text-generation"], "tags": ["Instruction"], "language": ["ar"], "pretty_name": "CIDAR", "size_categories": ["1K<n<10K"]} | false | null | 2025-04-03T08:35:36 | 49 | 7 | false | a3a9da3ea61ee296476d91132f9d0df95a11622a |
Dataset Card for "CIDAR"
🌴CIDAR: Culturally Relevant Instruction Dataset For Arabic
[ Paper - GitHub ]
CIDAR contains 10,000 instructions and their output. The dataset was created by selecting around 9,109 samples from Alpagasus dataset then translating it to Arabic using ChatGPT. In addition, we append that with around 891 Arabic grammar instructions from the webiste Ask the teacher. All the 10,000 samples were reviewed by around 12 reviewers.
📚… See the full description on the dataset page: https://huggingface.co/datasets/arbml/CIDAR. | 576 | 4,181 | [
"task_categories:text-generation",
"language:ar",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2402.03177",
"region:us",
"Instruction"
] | 2024-01-20T11:34:18 | null | null |
6731b4fffb568a134d3f2dd6 | TIGER-Lab/OmniEdit-Filtered-1.2M | TIGER-Lab | {"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "OmniEdit", "dataset_info": {"features": [{"name": "omni_edit_id", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "src_img", "dtype": "image"}, {"name": "edited_img", "dtype": "image"}, {"name": "edited_prompt_list", "sequence": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "sc_score_1", "dtype": "int64"}, {"name": "sc_score_2", "dtype": "int64"}, {"name": "sc_reasoning", "dtype": "string"}, {"name": "pq_score", "dtype": "int64"}, {"name": "pq_reasoning", "dtype": "string"}, {"name": "o_score", "dtype": "float64"}], "splits": [{"name": "dev", "num_bytes": 1547839078, "num_examples": 700}, {"name": "train", "num_bytes": 2852916299223.88, "num_examples": 1202797}], "download_size": 2978259415518, "dataset_size": 2854464138301.88}, "configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "data/dev-*"}, {"split": "train", "path": "data/train-*"}]}], "tags": ["image"]} | false | null | 2024-12-06T02:57:59 | 84 | 7 | false | 82455c6cd66db7f0e5bfce8d7a236441af59d6df |
OmniEdit
In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision
from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality.
📃Paper |… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M. | 16,374 | 72,222 | [
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2411.07199",
"region:us",
"image"
] | 2024-11-11T07:40:47 | null | null |
6797e648de960c48ff034e54 | open-thoughts/OpenThoughts-114k | open-thoughts | {"dataset_info": [{"config_name": "default", "features": [{"name": "system", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2635015668, "num_examples": 113957}], "download_size": 1078777193, "dataset_size": 2635015668}, {"config_name": "metadata", "features": [{"name": "problem", "dtype": "string"}, {"name": "deepseek_reasoning", "dtype": "string"}, {"name": "deepseek_solution", "dtype": "string"}, {"name": "ground_truth_solution", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_cases", "dtype": "string"}, {"name": "starter_code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5525214077.699433, "num_examples": 113957}], "download_size": 2469729724, "dataset_size": 5525214077.699433}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "metadata", "data_files": [{"split": "train", "path": "metadata/train-*"}]}], "tags": ["curator", "synthetic"], "license": "apache-2.0"} | false | null | 2025-02-20T07:16:57 | 680 | 7 | false | 56b06e3066a8163577ac93b24613a560e685d029 |
Open-Thoughts-114k
Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles!
Inspect the content with rich formatting with Curator Viewer.
Available Subsets
default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models:
ds = load_dataset("open-thoughts/OpenThoughts-114k", split="train")
metadata subset containing extra columns used in dataset construction:… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k. | 25,478 | 150,091 | [
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"curator",
"synthetic"
] | 2025-01-27T20:02:16 | null | null |
67ae5cb70100bb7fb11fdb31 | getomni-ai/ocr-benchmark | getomni-ai | {"license": "mit", "size_categories": ["1K<n<10K"]} | false | null | 2025-02-21T06:34:31 | 41 | 7 | false | 4ed0d95271ca00107726230f7a0944ed9e90d897 |
OmniAI OCR Benchmark
A comprehensive benchmark that compares OCR and data extraction capabilities of different multimodal LLMs such as gpt-4o and gemini-2.0, evaluating both text and JSON extraction accuracy.
Benchmark Results (Feb 2025) | Source Code
| 1,848 | 2,924 | [
"license:mit",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2025-02-13T20:57:27 | null | null |
67e0ce6de8133567a3f347e8 | generalagents/showdown-clicks | generalagents | {"license": "mit", "task_categories": ["image-to-text"], "language": ["en"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "showdown-clicks-dev/data.csv"}]}]} | false | null | 2025-03-31T07:10:38 | 7 | 7 | false | a6dfa6415d7fdd41fb5c0544e1dd9a5da439f1ce |
showdown-clicks
General Agents
🤗 Dataset | GitHub
showdown is a suite of offline and online benchmarks for computer-use agents.
showdown-clicks is a collection of 5,679 left clicks of humans performing various tasks in a macOS desktop environment. It is intended to evaluate instruction-following and low-level control capabilities of computer-use agents.
As of March 2025, we are releasing a subset of the full set, showdown-clicks-dev, containing 557 clicks. All examples are… See the full description on the dataset page: https://huggingface.co/datasets/generalagents/showdown-clicks. | 134 | 134 | [
"task_categories:image-to-text",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:csv",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-24T03:15:57 | null | null |
67e4200962f39381a49cd4d0 | Rapidata/Recraft-V2_t2i_human_preference | Rapidata | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image1", "dtype": "image"}, {"name": "image2", "dtype": "image"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "weighted_results_image1_preference", "dtype": "float32"}, {"name": "weighted_results_image2_preference", "dtype": "float32"}, {"name": "detailed_results_preference", "dtype": "string"}, {"name": "weighted_results_image1_coherence", "dtype": "float32"}, {"name": "weighted_results_image2_coherence", "dtype": "float32"}, {"name": "detailed_results_coherence", "dtype": "string"}, {"name": "weighted_results_image1_alignment", "dtype": "float32"}, {"name": "weighted_results_image2_alignment", "dtype": "float32"}, {"name": "detailed_results_alignment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13265684267, "num_examples": 13000}], "download_size": 5160991901, "dataset_size": 13265684267}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cdla-permissive-2.0", "task_categories": ["text-to-image", "image-to-text", "image-classification", "reinforcement-learning"], "language": ["en"], "tags": ["Human", "Preference", "Coherence", "Alignment", "country", "language", "flux", "midjourney", "dalle3", "stabeldiffusion", "alignment", "flux1.1", "flux1", "imagen3", "aurora", "lumina", "recraft", "recraft v2"], "size_categories": ["100K<n<1M"], "pretty_name": "Recraft V2 vs. Lumina-15-2-25 / Aurora / Frames-23-1-25 / imagen-3 / Flux-1.1-pro / Flux-1-pro / Dalle-3 / Midjourney-5.2 / Stabel-Diffusion-3 - Human Preference Dataset"} | false | null | 2025-03-27T14:46:25 | 7 | 7 | false | eaf11e29c908ce1534dd233390f41c6df2eaff9f |
Rapidata Recraft-V2 Preference
This T2I dataset contains over 195k human responses from over 47k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Recraft-V2 across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Recraft-V2_t2i_human_preference. | 528 | 528 | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:image-classification",
"task_categories:reinforcement-learning",
"language:en",
"license:cdla-permissive-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"Human",
"Preference",
"Coherence",
"Alignment",
"country",
"language",
"flux",
"midjourney",
"dalle3",
"stabeldiffusion",
"alignment",
"flux1.1",
"flux1",
"imagen3",
"aurora",
"lumina",
"recraft",
"recraft v2"
] | 2025-03-26T15:40:57 | null | null |
67e46df98c0347025bba131b | sychonix/emotion | sychonix | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 1741533, "num_examples": 16000}, {"name": "validation", "num_bytes": 214695, "num_examples": 2000}, {"name": "test", "num_bytes": 217173, "num_examples": 2000}], "download_size": 1281072, "dataset_size": 2173401}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | false | null | 2025-03-26T21:13:34 | 29 | 7 | false | 5a355b76cee6387d370d99d7ff656e79cc10d2eb | null | 874 | 874 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-26T21:13:29 | null | null |
67ecdf7e693ef0b1e0d7a06b | a-m-team/AM-Math-Difficulty-RL | a-m-team | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math"], "size_categories": ["100K<n<1M"]} | false | null | 2025-04-02T08:39:29 | 7 | 7 | false | 32540e9bce5952736795ac78cf049a0757f601d3 | For more open-source datasets, models, and methodologies, please visit our GitHub repository.
We believe that the selection of training data for reinforcement learning is crucial.
To validate this, we conducted several experiments exploring how data difficulty influences training performance.
Our data sources originate from numerous excellent open-source projects, and we sincerely appreciate their contributions, without which our current achievements would not have been possible.… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-Math-Difficulty-RL. | 164 | 164 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.00829",
"region:us",
"math"
] | 2025-04-02T06:55:58 | null | null |
63270b5b0416b7adda873b3e | Gustavosta/Stable-Diffusion-Prompts | Gustavosta | {"license": ["unknown"], "annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "source_datasets": ["original"]} | false | null | 2022-09-18T22:38:59 | 477 | 6 | false | d816d4a05cb89bde39dd99284c459801e1e7e69a |
Stable Diffusion Dataset
This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "Lexica.art". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.
If you want to test the model with a demo, you can go to: "spaces/Gustavosta/MagicPrompt-Stable-Diffusion".
If you want to see the model, go to: "Gustavosta/MagicPrompt-Stable-Diffusion".
| 8,381 | 109,643 | [
"annotations_creators:no-annotation",
"language_creators:found",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-09-18T12:13:15 | null | null |
64382440c212a363c3ac15c8 | OpenAssistant/oasst1 | OpenAssistant | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 100367999, "num_examples": 84437}, {"name": "validation", "num_bytes": 5243405, "num_examples": 4401}], "download_size": 41596430, "dataset_size": 105611404}, "language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "tags": ["human-feedback"], "size_categories": ["100K<n<1M"], "pretty_name": "OpenAssistant Conversations"} | false | null | 2023-05-02T13:21:21 | 1,373 | 6 | false | fdf72ae0827c1cda404aff25b6603abec9e3399b |
OpenAssistant Conversations Dataset (OASST1)
Dataset Summary
In an effort to democratize research on large-scale alignment, we release OpenAssistant
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation
corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292
quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus
is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst1. | 8,360 | 253,091 | [
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:hu",
"language:eu",
"language:zh",
"language:eo",
"language:ja",
"language:ca",
"language:cs",
"language:bg",
"language:fi",
"language:pt",
"language:tr",
"language:ro",
"language:ar",
"language:uk",
"language:gl",
"language:fr",
"language:ko",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2304.07327",
"region:us",
"human-feedback"
] | 2023-04-13T15:48:16 | null | null |
650a9248d26103b6eee3ea7b | lmsys/lmsys-chat-1m | lmsys | {"size_categories": ["1M<n<10M"], "task_categories": ["conversational"], "extra_gated_prompt": "You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text"}, "extra_gated_button_content": "I agree to the terms and conditions of the LMSYS-Chat-1M Dataset License Agreement.", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "turn", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "openai_moderation", "list": [{"name": "categories", "struct": [{"name": "harassment", "dtype": "bool"}, {"name": "harassment/threatening", "dtype": "bool"}, {"name": "hate", "dtype": "bool"}, {"name": "hate/threatening", "dtype": "bool"}, {"name": "self-harm", "dtype": "bool"}, {"name": "self-harm/instructions", "dtype": "bool"}, {"name": "self-harm/intent", "dtype": "bool"}, {"name": "sexual", "dtype": "bool"}, {"name": "sexual/minors", "dtype": "bool"}, {"name": "violence", "dtype": "bool"}, {"name": "violence/graphic", "dtype": "bool"}]}, {"name": "category_scores", "struct": [{"name": "harassment", "dtype": "float64"}, {"name": "harassment/threatening", "dtype": "float64"}, {"name": "hate", "dtype": "float64"}, {"name": "hate/threatening", "dtype": "float64"}, {"name": "self-harm", "dtype": "float64"}, {"name": "self-harm/instructions", "dtype": "float64"}, {"name": "self-harm/intent", "dtype": "float64"}, {"name": "sexual", "dtype": "float64"}, {"name": "sexual/minors", "dtype": "float64"}, {"name": "violence", "dtype": "float64"}, {"name": "violence/graphic", "dtype": "float64"}]}, {"name": "flagged", "dtype": "bool"}]}, {"name": "redacted", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2626438904, "num_examples": 1000000}], "download_size": 1488850250, "dataset_size": 2626438904}} | false | null | 2024-07-27T09:28:42 | 654 | 6 | false | 200748d9d3cddcc9d782887541057aca0b18c5da |
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
This dataset contains one million real-world conversations with 25 state-of-the-art LLMs.
It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023.
Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag.
User consent is obtained through the "Terms of… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m. | 4,066 | 222,713 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2309.11998",
"region:us"
] | 2023-09-20T06:33:44 | null | null |
65a67881c26011a4a77e2aca | omar07ibrahim/Alpaca | omar07ibrahim | null | false | null | 2024-01-16T12:37:55 | 6 | 6 | false | 0422485a5c4de92acd262d6ca3cf28cf018322c4 | null | 14 | 83 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-16T12:37:21 | null | null |
65afeef01dbd85fd0cdcf7c8 | omar07ibrahim/orca | omar07ibrahim | null | false | null | 2024-01-23T18:24:06 | 6 | 6 | false | 5859a72678e8b94dd942b0cb28bac7d26070aa31 | null | 23 | 93 | [
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-23T16:53:04 | null | null |
65b0b0ed7ac1fb9fc7f65747 | omar07ibrahim/orca_firstpart_AZ | omar07ibrahim | null | false | null | 2024-01-24T06:44:45 | 6 | 6 | false | 1e5e9dc9af0abf67bdc423d8aa5fcc5f6af9d6e2 | null | 12 | 90 | [
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-24T06:40:45 | null | null |
65b3fe30ca3eb32979ca1411 | omar07ibrahim/test9dpo | omar07ibrahim | null | false | null | 2024-01-26T18:47:27 | 6 | 6 | false | af6b9e605dc6d17e4b776713f15423ba4b07aadd | null | 16 | 99 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-26T18:47:12 | null | null |
65b55f122d8f64c77adadd66 | omar07ibrahim/ultrafeedback_binarized-BIZIM | omar07ibrahim | null | false | null | 2024-01-27T19:53:48 | 6 | 6 | false | b63048787a93c66475978782c277fef4bf69bb7e | null | 18 | 78 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-27T19:52:50 | null | null |
65c666da10735dcd76ea29e1 | ibrahimhamamci/CT-RATE | ibrahimhamamci | {"title": "CT-RATE Dataset", "license": "cc-by-nc-sa-4.0", "extra_gated_prompt": "## Terms and Conditions for Using the CT-RATE Dataset\n\n**1. Acceptance of Terms**\nAccessing and using the CT-RATE dataset implies your agreement to these terms and conditions. If you disagree with any part, please refrain from using the dataset.\n\n**2. Permitted Use**\n- The dataset is intended solely for academic, research, and educational purposes.\n- Any commercial exploitation of the dataset without prior permission is strictly forbidden.\n- You must adhere to all relevant laws, regulations, and research ethics, including data privacy and protection standards.\n\n**3. Data Protection and Privacy**\n- Acknowledge the presence of sensitive information within the dataset and commit to maintaining data confidentiality.\n- Direct attempts to re-identify individuals from the dataset are prohibited.\n- Ensure compliance with data protection laws such as GDPR and HIPAA.\n\n**4. Attribution**\n- Cite the dataset and acknowledge the providers in any publications resulting from its use.\n- Claims of ownership or exclusive rights over the dataset or derivatives are not permitted.\n\n**5. Redistribution**\n- Redistribution of the dataset or any portion thereof is not allowed.\n- Sharing derived data must respect the privacy and confidentiality terms set forth.\n\n**6. Disclaimer**\nThe dataset is provided \"as is\" without warranty of any kind, either expressed or implied, including but not limited to the accuracy or completeness of the data.\n\n**7. Limitation of Liability**\nUnder no circumstances will the dataset providers be liable for any claims or damages resulting from your use of the dataset.\n\n**8. Access Revocation**\nViolation of these terms may result in the termination of your access to the dataset.\n\n**9. Amendments**\nThe terms and conditions may be updated at any time; continued use of the dataset signifies acceptance of the new terms.\n\n**10. Governing Law**\nThese terms are governed by the laws of the location of the dataset providers, excluding conflict of law rules.\n\n**Consent:**\nAccessing and using the CT-RATE dataset signifies your acknowledgment and agreement to these terms and conditions.\n", "extra_gated_fields": {"Name": "text", "Institution": "text", "Email": "text", "I have read and agree with Terms and Conditions for using the CT-RATE dataset": "checkbox"}, "configs": [{"config_name": "labels", "data_files": [{"split": "train", "path": "dataset/multi_abnormality_labels/train_predicted_labels.csv"}, {"split": "validation", "path": "dataset/multi_abnormality_labels/valid_predicted_labels.csv"}]}, {"config_name": "reports", "data_files": [{"split": "train", "path": "dataset/radiology_text_reports/train_reports.csv"}, {"split": "validation", "path": "dataset/radiology_text_reports/validation_reports.csv"}]}, {"config_name": "metadata", "data_files": [{"split": "train", "path": "dataset/metadata/train_metadata.csv"}, {"split": "validation", "path": "dataset/metadata/validation_metadata.csv"}]}]} | false | null | 2025-04-04T15:00:57 | 133 | 6 | false | 86d4322aa852bed5c1528c6b9787f4c1f731ca85 |
Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
Welcome to the official page for our paper, which introduces CT-RATE—a pioneering dataset in 3D medical imaging that uniquely pairs textual data with image data focused on chest CT volumes. Here, you will find the CT-RATE dataset, comprising chest CT volumes paired with corresponding radiology text reports, multi-abnormality labels, and metadata, all freely accessible to researchers.… See the full description on the dataset page: https://huggingface.co/datasets/ibrahimhamamci/CT-RATE. | 36,050 | 346,479 | [
"license:cc-by-nc-sa-4.0",
"size_categories:100K<n<1M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2403.17834",
"region:us"
] | 2024-02-09T17:54:34 | null | null |
65d61e35d192e46c9367fa6f | omar07ibrahim/datasetaz | omar07ibrahim | null | false | null | 2024-02-21T16:02:52 | 6 | 6 | false | 05b76dd2702c9c68003ad6388600b3ce53a02102 | null | 16 | 96 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-02-21T16:00:53 | null | null |
65fc5a783bc54054aa2e6e62 | gretelai/synthetic_text_to_sql | gretelai | {"license": "apache-2.0", "task_categories": ["question-answering", "table-question-answering", "text-generation"], "language": ["en"], "tags": ["synthetic", "SQL", "text-to-SQL", "code"], "size_categories": ["100K<n<1M"]} | false | null | 2024-05-10T22:30:56 | 518 | 6 | false | 273a86f5f290e8d61b6767a9ff690c82bc990dc4 |
Image generated by DALL-E. See prompt for more details
synthetic_text_to_sql
gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples,
designed and generated using Gretel Navigator, and released under Apache 2.0.
Please see our release blogpost for more details.
The dataset includes:
105,851 records partitioned into 100,000 train and 5,851 test records
~23M total tokens, including ~12M SQL tokens
Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql. | 6,713 | 44,528 | [
"task_categories:question-answering",
"task_categories:table-question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2306.05685",
"region:us",
"synthetic",
"SQL",
"text-to-SQL",
"code"
] | 2024-03-21T16:04:08 | null | null |
673463fabe618c1a378d99c6 | qgyd2021/chinese_porn_novel | qgyd2021 | {"language": ["zh"], "size_categories": ["100M<n<1B"], "task_categories": ["text-generation"], "tags": ["art"], "dataset_info": {"config_name": "xbookcn_short_story", "features": [{"name": "source", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "content_length", "dtype": "uint32"}, {"name": "url", "dtype": "string"}, {"name": "summary1", "dtype": "string"}, {"name": "summary2", "dtype": "string"}, {"name": "summary3", "dtype": "string"}, {"name": "summary4", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1167355353, "num_examples": 627195}], "download_size": 721183317, "dataset_size": 1167355353}, "configs": [{"config_name": "xbookcn_short_story", "data_files": [{"split": "train", "path": "xbookcn_short_story/train-*"}], "default": true}]} | false | null | 2024-11-13T11:06:27 | 66 | 6 | false | 170c125e168cf58400ad3b31300c88ed8a1c978a |
Chinese Porn Novel
https://huggingface.co/docs/hub/en/datasets-adding
datasets-cli convert_to_parquet qgyd2021/chinese_porn_novel --trust_remote_code
SQ小说, 用于制作特殊的 GPT 语言模型.
将每篇小说切分 chunk,
用 Qwen-instruct 对 chunk 进行4个摘要,
4个摘要的 prompt
{content}
对于此文本，
根据文本的长度输出3到7个具有代表性的简短句子来描述其内容。
每个句子控制在10字左右，不要有序号等，每行一句。
{content}
对于此文本，
根据文本的长度输出2到4个具有代表性的简短句子来描述其内容。
每个句子控制在15字左右，不要有序号等，每行一句。
{content}
对于此文本，
根据文本的长度输出2到4个具有代表性的简短句子来概括其内容。
每个句子控制在10字左右，不要有序号等，每行一句。… See the full description on the dataset page: https://huggingface.co/datasets/qgyd2021/chinese_porn_novel. | 643 | 2,504 | [
"task_categories:text-generation",
"language:zh",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"art"
] | 2024-11-13T08:31:54 | null | null |
67d7eeec9830e5c1e2a8f708 | BytedTsinghua-SIA/DAPO-Math-17k | BytedTsinghua-SIA | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math"], "pretty_name": "DAPO-Math-17k", "size_categories": ["1M<n<10M"]} | false | null | 2025-03-18T07:47:04 | 53 | 6 | false | 9f6440001c15da8e7c7516fdbb3d2ce49de711de |
This dataset actually only contains ~17k unique prompts and was duplicated by ~100x by accident.
| 3,819 | 3,819 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"math"
] | 2025-03-17T09:44:12 | null | null |
67d871c14702a4d2c523592a | oumi-ai/oumi-synthetic-claims | oumi-ai | {"dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "claims", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "text label", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 141743217, "num_examples": 19199}], "download_size": 17800360, "dataset_size": 141743217}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "llama3.1", "language": ["en"], "size_categories": ["10K<n<100K"]} | false | null | 2025-04-04T16:39:52 | 6 | 6 | false | 2b024ef5ac94177816e0b1104006efce3bfafda5 |
oumi-ai/oumi-synthetic-claims
oumi-synthetic-claims is a text dataset designed to fine-tune language models for Claim Verification.
Prompts and responses were produced synthetically from Llama-3.1-405B-Instruct.
oumi-synthetic-claims was used to train HallOumi-8B, which achieves 77.2% Macro F1, outperforming SOTA models such as Claude Sonnet 3.5, OpenAI o1, etc.
Curated by: Oumi AI using Oumi inference
Language(s) (NLP): English
License: Llama 3.1 Community License… See the full description on the dataset page: https://huggingface.co/datasets/oumi-ai/oumi-synthetic-claims. | 70 | 70 | [
"language:en",
"license:llama3.1",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-17T19:02:25 | null | null |
67e2d9efa823152c92e695d4 | nyuuzyou/archiveofourown | nyuuzyou | {"annotations_creators": ["found"], "language": ["ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "he", "hi", "hr", "hu", "id", "it", "ja", "ko", "lt", "lv", "ms", "nl", "no", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "th", "tr", "uk", "vi", "zh"], "language_bcp47": ["pt-BR", "zh-HK", "zh-TW"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "pretty_name": "Archive of Our Own (AO3)", "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "text-classification"], "task_ids": ["language-modeling", "topic-classification"], "configs": [{"config_name": "train", "data_files": [{"split": "train", "path": "*.jsonl.zst"}], "default": true}]} | false | null | 2025-03-25T17:07:57 | 14 | 6 | false | 4f0f0eb835ab627a78d02d2973ca0263400720df |
Dataset Card for Archive of Our Own (AO3)
Dataset Summary
This dataset contains approximately 12.6 million publicly available works from Archive of Our Own (AO3), a fan-created, fan-run, non-profit archive for transformative fanworks. The dataset was created by processing works with IDs from 1 to 63,200,000 that are publicly accessible. Each entry contains the full text of the work along with comprehensive metadata including title, author, fandom, relationships… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/archiveofourown. | 756 | 756 | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:topic-classification",
"annotations_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:ar",
"language:bg",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fa",
"language:fi",
"language:fr",
"language:he",
"language:hi",
"language:hr",
"language:hu",
"language:id",
"language:it",
"language:ja",
"language:ko",
"language:lt",
"language:lv",
"language:ms",
"language:nl",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sk",
"language:sl",
"language:sr",
"language:sv",
"language:th",
"language:tr",
"language:uk",
"language:vi",
"language:zh",
"license:cc0-1.0",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | 2025-03-25T16:29:35 | null | null |
67ebe5090eb9daaf7188c0ca | efficientscaling/Z1-Code-Reasoning-107K | efficientscaling | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "token_num_qwen", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 494418537, "num_examples": 107173}], "download_size": 238409407, "dataset_size": 494418537}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-04-02T03:42:21 | 6 | 6 | false | b1094ba6f6087a7d5764a2e42bd01d0dd5ab13f2 |
Z1: Efficient Test-time Scaling with Code
Train Large Language Model to Reason with Shifted Thinking
[📜 Paper] •
[🤗 HF Models] •
[🐱 GitHub]
Details
Please refer to https://github.com/efficientscaling/Z1.
Usage
from datasets import load_dataset
ds = load_dataset("efficientscaling/Z1-Code-Reasoning-107K")["train"]
ds[0]
Citation
@misc{yu2025efficientscaling,
title={Z1: Efficient Test-time Scaling with Code}… See the full description on the dataset page: https://huggingface.co/datasets/efficientscaling/Z1-Code-Reasoning-107K. | 80 | 80 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2504.00810",
"region:us"
] | 2025-04-01T13:07:21 | null | null |
621ffdd236468d709f181e16 | dair-ai/emotion | dair-ai | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "paperswithcode_id": "emotion", "pretty_name": "Emotion", "tags": ["emotion-classification"], "dataset_info": [{"config_name": "split", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 1741533, "num_examples": 16000}, {"name": "validation", "num_bytes": 214695, "num_examples": 2000}, {"name": "test", "num_bytes": 217173, "num_examples": 2000}], "download_size": 1287193, "dataset_size": 2173401}, {"config_name": "unsplit", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 45444017, "num_examples": 416809}], "download_size": 26888538, "dataset_size": 45444017}], "configs": [{"config_name": "split", "data_files": [{"split": "train", "path": "split/train-*"}, {"split": "validation", "path": "split/validation-*"}, {"split": "test", "path": "split/test-*"}], "default": true}, {"config_name": "unsplit", "data_files": [{"split": "train", "path": "unsplit/train-*"}]}], "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]} | false | null | 2024-08-08T06:10:47 | 350 | 5 | false | cab853a1dbdf4c42c2b3ef2173804746df8825fe |
Dataset Card for "emotion"
Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
An example looks as follows.
{
"text": "im feeling quite sad… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion. | 17,809 | 357,207 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"emotion-classification"
] | 2022-03-02T23:29:22 | emotion | null |
621ffdd236468d709f181e77 | stanfordnlp/imdb | stanfordnlp | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "imdb-movie-reviews", "pretty_name": "IMDB", "dataset_info": {"config_name": "plain_text", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}], "splits": [{"name": "train", "num_bytes": 33432823, "num_examples": 25000}, {"name": "test", "num_bytes": 32650685, "num_examples": 25000}, {"name": "unsupervised", "num_bytes": 67106794, "num_examples": 50000}], "download_size": 83446840, "dataset_size": 133190302}, "configs": [{"config_name": "plain_text", "data_files": [{"split": "train", "path": "plain_text/train-*"}, {"split": "test", "path": "plain_text/test-*"}, {"split": "unsupervised", "path": "plain_text/unsupervised-*"}], "default": true}], "train-eval-index": [{"config": "plain_text", "task": "text-classification", "task_id": "binary_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy"}, {"name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]} | false | null | 2024-01-04T12:09:45 | 301 | 5 | false | e6281661ce1c48d982bc483cf8a173c1bbeb5d31 |
Dataset Card for "imdb"
Dataset Summary
Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb. | 115,557 | 6,880,454 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22 | imdb-movie-reviews | null |
621ffdd236468d709f181f3d | qiaojin/PubMedQA | qiaojin | {"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "pubmedqa", "pretty_name": "PubMedQA", "config_names": ["pqa_artificial", "pqa_labeled", "pqa_unlabeled"], "dataset_info": [{"config_name": "pqa_artificial", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}, {"name": "final_decision", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 443501057, "num_examples": 211269}], "download_size": 233411194, "dataset_size": 443501057}, {"config_name": "pqa_labeled", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}, {"name": "reasoning_required_pred", "dtype": "string"}, {"name": "reasoning_free_pred", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}, {"name": "final_decision", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2088898, "num_examples": 1000}], "download_size": 1075513, "dataset_size": 2088898}, {"config_name": "pqa_unlabeled", "features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 125922964, "num_examples": 61249}], "download_size": 66010017, "dataset_size": 125922964}], "configs": [{"config_name": "pqa_artificial", "data_files": [{"split": "train", "path": "pqa_artificial/train-*"}]}, {"config_name": "pqa_labeled", "data_files": [{"split": "train", "path": "pqa_labeled/train-*"}]}, {"config_name": "pqa_unlabeled", "data_files": [{"split": "train", "path": "pqa_unlabeled/train-*"}]}]} | false | null | 2024-03-06T01:50:16 | 199 | 5 | false | 9001f2853fb87cab8d220904e0de81ac6973b318 |
Dataset Card for [Dataset Name]
Dataset Summary
The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.
Supported Tasks and Leaderboards
The official leaderboard is available at: https://pubmedqa.github.io/.
500 questions in the pqa_labeled are used as the test set. They can be found at… See the full description on the dataset page: https://huggingface.co/datasets/qiaojin/PubMedQA. | 13,408 | 415,389 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1909.06146",
"region:us"
] | 2022-03-02T23:29:22 | pubmedqa | null |
6564cf8ec9611f7e11423ff4 | b3x0m/Chinese-H-Novels | b3x0m | {"language": ["zh"], "size_categories": ["1B<n<10B"], "task_categories": ["text-classification", "summarization", "token-classification", "text2text-generation", "question-answering", "text-generation", "fill-mask", "sentence-similarity"], "pretty_name": "H-novel-corpus", "tags": ["art"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 95784400372, "num_examples": 934354429}], "download_size": 60873072258, "dataset_size": 95784400372}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-07-12T02:32:57 | 209 | 5 | false | 16258fb735f019d2d0100960ec739b6dabc3db77 | Update 12/07/2024: convert to parquet to download easier.
Chinese 18+ novels corpus, use at your own risk, you and only you are responsible for every choice you make.
(͡ ° ͜ʖ ͡ °)
tags: socks, garter belt, foot fetish, ntr, netori.....
Thanks Moleys/Numeron for the dataset donation.
| 1,603 | 8,880 | [
"task_categories:text-classification",
"task_categories:summarization",
"task_categories:token-classification",
"task_categories:text2text-generation",
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:sentence-similarity",
"language:zh",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"art"
] | 2023-11-27T17:19:10 | null | null |
65af4645d0a5cc99d51642da | 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-*"}]}]} | false | null | 2024-12-08T22:21:49 | 135 | 5 | false | 2b6d039ed471f2ba5fd2acb718bf33b0a7e5598e | Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset.
This dataset mainly includes reviews (ratings, text) and item metadata (desc-
riptions, category information, price, brand, and images). Compared to the pre-
vious versions, the 2023 version features larger size, newer reviews (up to Sep
2023), richer and cleaner meta data, and finer-grained timestamps (from day to
milli-second). | 44,961 | 404,595 | [
"language:en",
"size_categories:10B<n<100B",
"arxiv:2403.03952",
"region:us",
"recommendation",
"reviews"
] | 2024-01-23T04:53:25 | null | null |
660e7b9b4636ce2b0e77b699 | mozilla-foundation/common_voice_17_0 | mozilla-foundation | {"pretty_name": "Common Voice Corpus 17.0", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "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", "fy", "ga", "gl", "gn", "ha", "he", "hi", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lij", "lo", "lt", "ltg", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan", "ne", "nhi", "nl", "nn", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quy", "rm", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yi", "yo", "yue", "zgh", "zh", "zu", "zza"], "language_bcp47": ["zh-CN", "zh-HK", "zh-TW", "sv-SE", "rm-sursilv", "rm-vallader", "pa-IN", "nn-NO", "ne-NP", "nan-tw", "hy-AM", "ga-IE", "fy-NL"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "source_datasets": ["extended|common_voice"], "paperswithcode_id": "common-voice", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."} | false | null | 2024-06-16T13:50:23 | 247 | 5 | false | b10d53980ef166bc24ce3358471c1970d7e6b5ec |
Dataset Card for Common Voice Corpus 17.0
Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 31175 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 20408 validated hours in 124 languages, but more voices and languages are always added.
Take a look at the Languages page to… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0. | 39,017 | 458,388 | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"language:ab",
"language:af",
"language:am",
"language:ar",
"language:as",
"language:ast",
"language:az",
"language:ba",
"language:bas",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:ckb",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:dv",
"language:dyu",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:gn",
"language:ha",
"language:he",
"language:hi",
"language:hsb",
"language:ht",
"language:hu",
"language:hy",
"language:ia",
"language:id",
"language:ig",
"language:is",
"language:it",
"language:ja",
"language:ka",
"language:kab",
"language:kk",
"language:kmr",
"language:ko",
"language:ky",
"language:lg",
"language:lij",
"language:lo",
"language:lt",
"language:ltg",
"language:lv",
"language:mdf",
"language:mhr",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:mrj",
"language:mt",
"language:myv",
"language:nan",
"language:ne",
"language:nhi",
"language:nl",
"language:nn",
"language:nso",
"language:oc",
"language:or",
"language:os",
"language:pa",
"language:pl",
"language:ps",
"language:pt",
"language:quy",
"language:rm",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sat",
"language:sc",
"language:sk",
"language:skr",
"language:sl",
"language:sq",
"language:sr",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:th",
"language:ti",
"language:tig",
"language:tk",
"language:tok",
"language:tr",
"language:tt",
"language:tw",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:vot",
"language:yi",
"language:yo",
"language:yue",
"language:zgh",
"language:zh",
"language:zu",
"language:zza",
"license:cc0-1.0",
"size_categories:10M<n<100M",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:1912.06670",
"region:us"
] | 2024-04-04T10:06:19 | common-voice | @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
} |
6649d353babc0b33565e1a4a | HumanLLMs/Human-Like-DPO-Dataset | HumanLLMs | {"language": ["en"], "license": "llama3", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.json"}]}]} | false | null | 2025-01-12T21:01:07 | 216 | 5 | false | dd82ab6a284a15765964149e6a6603ff8ed7d672 |
Enhancing Human-Like Responses in Large Language Models
🤗 Models | 📊 Dataset | 📄 Paper
Human-Like-DPO-Dataset
This dataset was created as part of research aimed at improving conversational fluency and engagement in large language models. It is suitable for formats like Direct Preference Optimization (DPO) to guide models toward generating more human-like responses.
The dataset includes 10,884 samples across 256 topics, including:
Technology
Daily Life
Science… See the full description on the dataset page: https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset. | 5,933 | 12,428 | [
"language:en",
"license:llama3",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2501.05032",
"region:us"
] | 2024-05-19T10:24:19 | null | null |
6655eb19d17e141dcb546ed5 | HuggingFaceFW/fineweb-edu | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb-Edu", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}], "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dump", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}, {"name": "token_count", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-38/*"}]}, {"config_name": "CC-MAIN-2024-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-33/*"}]}, {"config_name": "CC-MAIN-2024-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-30/*"}]}, {"config_name": "CC-MAIN-2024-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-26/*"}]}, {"config_name": "CC-MAIN-2024-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-22/*"}]}, {"config_name": "CC-MAIN-2024-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-18/*"}]}, {"config_name": "CC-MAIN-2024-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-10/*"}]}, {"config_name": "CC-MAIN-2023-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-50/*"}]}, {"config_name": "CC-MAIN-2023-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-40/*"}]}, {"config_name": "CC-MAIN-2023-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-23/*"}]}, {"config_name": "CC-MAIN-2023-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-14/*"}]}, {"config_name": "CC-MAIN-2023-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-06/*"}]}, {"config_name": "CC-MAIN-2022-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-49/*"}]}, {"config_name": "CC-MAIN-2022-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-40/*"}]}, {"config_name": "CC-MAIN-2022-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-33/*"}]}, {"config_name": "CC-MAIN-2022-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-27/*"}]}, {"config_name": "CC-MAIN-2022-21", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-21/*"}]}, {"config_name": "CC-MAIN-2022-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-05/*"}]}, {"config_name": "CC-MAIN-2021-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-49/*"}]}, {"config_name": "CC-MAIN-2021-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-43/*"}]}, {"config_name": "CC-MAIN-2021-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-39/*"}]}, {"config_name": "CC-MAIN-2021-31", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-31/*"}]}, {"config_name": "CC-MAIN-2021-25", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-25/*"}]}, {"config_name": "CC-MAIN-2021-21", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-21/*"}]}, {"config_name": "CC-MAIN-2021-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-17/*"}]}, {"config_name": "CC-MAIN-2021-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-10/*"}]}, {"config_name": "CC-MAIN-2021-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-04/*"}]}, {"config_name": "CC-MAIN-2020-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-50/*"}]}, {"config_name": "CC-MAIN-2020-45", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-45/*"}]}, {"config_name": "CC-MAIN-2020-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-40/*"}]}, {"config_name": "CC-MAIN-2020-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-34/*"}]}, {"config_name": "CC-MAIN-2020-29", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-29/*"}]}, {"config_name": "CC-MAIN-2020-24", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-24/*"}]}, {"config_name": "CC-MAIN-2020-16", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-16/*"}]}, {"config_name": "CC-MAIN-2020-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-10/*"}]}, {"config_name": "CC-MAIN-2020-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-05/*"}]}, {"config_name": "CC-MAIN-2019-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-51/*"}]}, {"config_name": "CC-MAIN-2019-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-47/*"}]}, {"config_name": "CC-MAIN-2019-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-43/*"}]}, {"config_name": "CC-MAIN-2019-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-39/*"}]}, {"config_name": "CC-MAIN-2019-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-35/*"}]}, {"config_name": "CC-MAIN-2019-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-30/*"}]}, {"config_name": "CC-MAIN-2019-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-26/*"}]}, {"config_name": "CC-MAIN-2019-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-22/*"}]}, {"config_name": "CC-MAIN-2019-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-18/*"}]}, {"config_name": "CC-MAIN-2019-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-13/*"}]}, {"config_name": "CC-MAIN-2019-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-09/*"}]}, {"config_name": "CC-MAIN-2019-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-04/*"}]}, {"config_name": "CC-MAIN-2018-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-51/*"}]}, {"config_name": "CC-MAIN-2018-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-47/*"}]}, {"config_name": "CC-MAIN-2018-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-43/*"}]}, {"config_name": "CC-MAIN-2018-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-39/*"}]}, {"config_name": "CC-MAIN-2018-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-34/*"}]}, {"config_name": "CC-MAIN-2018-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-30/*"}]}, {"config_name": "CC-MAIN-2018-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-26/*"}]}, {"config_name": "CC-MAIN-2018-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-22/*"}]}, {"config_name": "CC-MAIN-2018-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-17/*"}]}, {"config_name": "CC-MAIN-2018-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-13/*"}]}, {"config_name": "CC-MAIN-2018-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-09/*"}]}, {"config_name": "CC-MAIN-2018-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-05/*"}]}, {"config_name": "CC-MAIN-2017-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-51/*"}]}, {"config_name": "CC-MAIN-2017-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-47/*"}]}, {"config_name": "CC-MAIN-2017-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-43/*"}]}, {"config_name": "CC-MAIN-2017-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-39/*"}]}, {"config_name": "CC-MAIN-2017-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-34/*"}]}, {"config_name": "CC-MAIN-2017-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-30/*"}]}, {"config_name": "CC-MAIN-2017-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-26/*"}]}, {"config_name": "CC-MAIN-2017-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-22/*"}]}, {"config_name": "CC-MAIN-2017-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-17/*"}]}, {"config_name": "CC-MAIN-2017-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-13/*"}]}, {"config_name": "CC-MAIN-2017-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-09/*"}]}, {"config_name": "CC-MAIN-2017-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-04/*"}]}, {"config_name": "CC-MAIN-2016-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-50/*"}]}, {"config_name": "CC-MAIN-2016-44", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-44/*"}]}, {"config_name": "CC-MAIN-2016-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-40/*"}]}, {"config_name": "CC-MAIN-2016-36", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-36/*"}]}, {"config_name": "CC-MAIN-2016-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-30/*"}]}, {"config_name": "CC-MAIN-2016-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-26/*"}]}, {"config_name": "CC-MAIN-2016-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-22/*"}]}, {"config_name": "CC-MAIN-2016-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-18/*"}]}, {"config_name": "CC-MAIN-2016-07", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-07/*"}]}, {"config_name": "CC-MAIN-2015-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-48/*"}]}, {"config_name": "CC-MAIN-2015-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-40/*"}]}, {"config_name": "CC-MAIN-2015-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-35/*"}]}, {"config_name": "CC-MAIN-2015-32", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-32/*"}]}, {"config_name": "CC-MAIN-2015-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-27/*"}]}, {"config_name": "CC-MAIN-2015-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-22/*"}]}, {"config_name": "CC-MAIN-2015-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-18/*"}]}, {"config_name": "CC-MAIN-2015-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-14/*"}]}, {"config_name": "CC-MAIN-2015-11", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-11/*"}]}, {"config_name": "CC-MAIN-2015-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-06/*"}]}, {"config_name": "CC-MAIN-2014-52", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-52/*"}]}, {"config_name": "CC-MAIN-2014-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-49/*"}]}, {"config_name": "CC-MAIN-2014-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-42/*"}]}, {"config_name": "CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/*"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]} | false | null | 2025-01-31T15:56:54 | 658 | 5 | false | 4863ab07d7520451e6f73e2912ad8bfee7d97c11 |
📚 FineWeb-Edu
1.3 trillion tokens of the finest educational data the 🌐 web has to offer
Paper: https://arxiv.org/abs/2406.17557
What is it?
📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version.
To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu. | 316,529 | 3,265,548 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2406.17557",
"arxiv:2404.14219",
"arxiv:2401.10020",
"arxiv:2109.07445",
"doi:10.57967/hf/2497",
"region:us"
] | 2024-05-28T14:32:57 | null | null |
66bc06dc6da7aec8413d35ba | NousResearch/hermes-function-calling-v1 | NousResearch | {"license": "apache-2.0", "task_categories": ["text-generation", "question-answering", "feature-extraction"], "language": ["en"], "configs": [{"config_name": "func_calling_singleturn", "data_files": "func-calling-singleturn.json", "default": true}, {"config_name": "func_calling", "data_files": "func-calling.json"}, {"config_name": "glaive_func_calling", "data_files": "glaive-function-calling-5k.json"}, {"config_name": "json_mode_agentic", "data_files": "json-mode-agentic.json"}, {"config_name": "json_mode_singleturn", "data_files": "json-mode-singleturn.json"}]} | false | null | 2024-08-30T06:07:08 | 280 | 5 | false | 8f025148382537ba84cd325e1834b706e1461692 |
Hermes Function-Calling V1
This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models.
This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational… See the full description on the dataset page: https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1. | 2,115 | 11,455 | [
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:feature-extraction",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-08-14T01:22:36 | null | null |
66eb894483591125987548f7 | google/frames-benchmark | google | {"license": "apache-2.0", "language": ["en"], "tags": ["rag", "long-context", "llm-search", "reasoning", "factuality", "retrieval", "question-answering", "iterative-search"], "task_categories": ["text-classification", "token-classification", "table-question-answering", "question-answering"], "pretty_name": "Who are I or you", "size_categories": ["n>1T"]} | false | null | 2024-10-15T18:18:24 | 195 | 5 | false | 58d9fb6330f3ab1316d1eca12e5e8ef23dcc22ef |
FRAMES: Factuality, Retrieval, And reasoning MEasurement Set
FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning.
Our paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941.
Dataset Overview
824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles
Questions span diverse… See the full description on the dataset page: https://huggingface.co/datasets/google/frames-benchmark. | 1,868 | 10,966 | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2409.12941",
"region:us",
"rag",
"long-context",
"llm-search",
"reasoning",
"factuality",
"retrieval",
"question-answering",
"iterative-search"
] | 2024-09-19T02:15:32 | null | null |
66f65526a395d5e5ede5a36c | weizhiwang/Open-Qwen2VL-Data | weizhiwang | {"task_categories": ["image-text-to-text"]} | false | null | 2025-04-03T02:23:45 | 6 | 5 | false | 9fab69b323e5a518b4168fe91876edc493b3e69c | This repository contains the data for Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources.
Project page: https://victorwz.github.io/Open-Qwen2VL
Code: https://github.com/Victorwz/Open-Qwen2VL
| 2,423 | 5,753 | [
"task_categories:image-text-to-text",
"size_categories:10M<n<100M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.00595",
"region:us"
] | 2024-09-27T06:48:06 | null | null |
678618439d6c198fe89d87c1 | simplescaling/s1K | simplescaling | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "solution", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "cot_type", "dtype": "string"}, {"name": "source_type", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "cot", "dtype": "null"}, {"name": "thinking_trajectories", "sequence": "string"}, {"name": "attempt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14361402.861518776, "num_examples": 1000}], "download_size": 6884025, "dataset_size": 14361402.861518776}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-02-11T01:14:45 | 204 | 5 | false | 278d72baaa2b887a7e76a70a0ae254a5a45536e4 |
Dataset Card for s1K
Dataset Summary
s1K is a dataset of 1,000 examples of diverse, high-quality & difficult questions with distilled reasoning traces & solutions from Gemini Thining. Refer to the s1 paper for more details.
Usage
# pip install -q datasets
from datasets import load_dataset
ds = load_dataset("simplescaling/s1K")["train"]
ds[0]
Dataset Structure
Data Instances
An example looks as follows:
{
'solution': '1. **Rewrite… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/s1K. | 2,393 | 9,071 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2501.19393",
"region:us"
] | 2025-01-14T07:54:43 | null | null |
67aa021ced8d8663d42505cc | open-r1/OpenR1-Math-220k | open-r1 | {"license": "apache-2.0", "language": ["en"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "extended", "data_files": [{"split": "train", "path": "extended/train-*"}]}], "dataset_info": [{"config_name": "all", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9734110026, "num_examples": 225129}], "download_size": 4221672067, "dataset_size": 9734110026}, {"config_name": "default", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4964543659, "num_examples": 93733}], "download_size": 2149897914, "dataset_size": 4964543659}, {"config_name": "extended", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4769566550, "num_examples": 131396}], "download_size": 2063936457, "dataset_size": 4769566550}]} | false | null | 2025-02-18T11:45:27 | 535 | 5 | false | e4e141ec9dea9f8326f4d347be56105859b2bd68 |
OpenR1-Math-220k
Dataset description
OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5.
The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.
The dataset consists of two splits:… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k. | 45,766 | 85,738 | [
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T13:41:48 | null | null |
67aa648e91e6f5eb545e854e | allenai/olmOCR-mix-0225 | allenai | {"license": "odc-by", "configs": [{"config_name": "00_documents", "data_files": [{"split": "train_s2pdf", "path": ["train-s2pdf.parquet"]}, {"split": "eval_s2pdf", "path": ["eval-s2pdf.parquet"]}]}, {"config_name": "01_books", "data_files": [{"split": "train_iabooks", "path": ["train-iabooks.parquet"]}, {"split": "eval_iabooks", "path": ["eval-iabooks.parquet"]}]}]} | false | null | 2025-02-25T09:36:14 | 105 | 5 | false | a602926844ed47c43439627fd16d3de45b39e494 |
olmOCR-mix-0225
olmOCR-mix-0225 is a dataset of ~250,000 PDF pages which have been OCRed into plain-text in a natural reading order using gpt-4o-2024-08-06 and a special
prompting strategy that preserves any born-digital content from each page.
This dataset can be used to train, fine-tune, or evaluate your own OCR document pipeline.
Quick links:
📃 Paper
🤗 Model
🛠️ Code
🎮 Demo
Data Mix
Table 1: Training set composition by source
Source
Unique… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-mix-0225. | 3,821 | 6,182 | [
"license:odc-by",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T20:41:50 | null | null |
67b729ff5e1b74491f839a29 | hiyouga/geometry3k | hiyouga | {"dataset_info": {"features": [{"name": "images", "sequence": "image"}, {"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "choices", "sequence": "string"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43191899.912, "num_examples": 2101}, {"name": "validation", "num_bytes": 6009916, "num_examples": 300}, {"name": "test", "num_bytes": 12234557, "num_examples": 601}], "download_size": 59201452, "dataset_size": 61436372.912}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "mit", "task_categories": ["visual-question-answering"], "language": ["en"], "size_categories": ["1K<n<10K"]} | false | null | 2025-02-20T15:56:20 | 21 | 5 | false | 37e4933940dbe0c0a98f990799909ea868cf6d01 | This dataset was converted from https://github.com/lupantech/InterGPS using the following script.
import os
import json
from PIL import Image
from datasets import Dataset, DatasetDict, Sequence
from datasets import Image as ImageData
MAPPING = {"A": 0, "B": 1, "C": 2, "D": 3}
def generate_data(data_path: str):
for folder in os.listdir(data_path):
folder_path = os.path.join(data_path, folder)
image = Image.open(os.path.join(folder_path, "img_diagram.png"), "r")… See the full description on the dataset page: https://huggingface.co/datasets/hiyouga/geometry3k. | 8,005 | 10,867 | [
"task_categories:visual-question-answering",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-20T13:11:27 | null | null |
67be831c86192d5a0295ba8e | Kuugo/chinese_law_ft_dataset | Kuugo | {"license": "mit"} | false | null | 2025-02-28T10:22:26 | 7 | 5 | false | 923f896137ceb897700783a386e51d5b4797da25 | null | 255 | 283 | [
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-26T02:57:32 | null | null |
67caaec42790d92f9fe8ab3c | JingyaoLi/MoT-Code-350K | JingyaoLi | {"license": "mit", "task_categories": ["text2text-generation", "text-generation", "question-answering", "translation"], "multilinguality": ["monolingual"], "language": ["code"], "pretty_name": "MoTCode", "tags": ["python", "code-generation", "large-language-models"], "size_categories": ["10K<n<100K"], "task_ids": ["language-modeling"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 791280919, "num_examples": 312645}], "download_size": 161719737, "dataset_size": 791280919}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-17T09:52:41 | 5 | 5 | false | bf92a0c9b4282498a87b858b109aaa0dedc4790c |
🏠 MoTCode-Data
• 🤗 Data • 🤗 Model • 🐱 Code • 📃 Paper
Dataset Structure
from datasets import load_dataset
load_dataset("JingyaoLi/MoT-Code-350K")
DatasetDict({
train: Dataset({
features: ['instruction', 'output'],
num_rows: 312645
})
})
Modular-of-thought Data Creation
We provide an example python file to evolution a MoT dataset. Run the following command:
python src/generate_MoT_dataset.py \
--data_path $data_path \… See the full description on the dataset page: https://huggingface.co/datasets/JingyaoLi/MoT-Code-350K. | 50 | 50 | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:translation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"language:code",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2312.15960",
"region:us",
"python",
"code-generation",
"large-language-models"
] | 2025-03-07T08:31:00 | null | null |
67cc3ec9dbeab2e209a1c77f | inclusionAI/Ling-Coder-SyntheticQA | inclusionAI | {"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["10M<n<100M"], "task_categories": ["text-generation"], "tags": ["code", "synthetic"]} | false | null | 2025-03-27T12:39:52 | 9 | 5 | false | c10e53a2e4042685b9c1071da2503783d172624d |
🤗 Hugging Face
🤖 ModelScope
🖥️ GitHub
Ling-Coder Dataset
The Ling-Coder Dataset comprises the following components:
Ling-Coder-SFT: A subset of SFT data used for training Ling-Coder Lite, containing more than 5 million samples.
Ling-Coder-DPO: A subset of DPO data used for training Ling-Coder Lite, containing 250k samples.
Ling-Coder-SyntheticQA: A subset of synthetic data used for annealing training of Ling-Coder Lite, containing more… See the full description on the dataset page: https://huggingface.co/datasets/inclusionAI/Ling-Coder-SyntheticQA. | 1,069 | 1,069 | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.17793",
"region:us",
"code",
"synthetic"
] | 2025-03-08T12:57:45 | null | null |
67d2910dcd8b9b08780b66ed | chanhee-luke/RoboSpatial-Home | chanhee-luke | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "img", "dtype": "image"}, {"name": "depth_image", "dtype": "image"}, {"name": "mask", "dtype": "image"}], "splits": [{"name": "context", "num_bytes": 38727218, "num_examples": 122}, {"name": "compatibility", "num_bytes": 32578958, "num_examples": 105}, {"name": "configuration", "num_bytes": 37179863, "num_examples": 123}], "download_size": 34740820, "dataset_size": 108486039}, "configs": [{"config_name": "default", "data_files": [{"split": "context", "path": "data/context-*"}, {"split": "compatibility", "path": "data/compatibility-*"}, {"split": "configuration", "path": "data/configuration-*"}]}], "task_categories": ["question-answering", "visual-question-answering"], "language": ["en"], "pretty_name": "robospatial-home", "size_categories": ["n<1K"]} | false | null | 2025-03-26T07:09:42 | 5 | 5 | false | fbcdbc270e94d2b8c8b22fd58d29e82c0978fbfa |
RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics
🌐 Homepage | 📖 arXiv | 🛠️ Data Gen (TBA) | 🧪 Eval Code
🔔News
🧪[2025-03-26]: Released the official evaluation script for RoboSpatial-Home! Paper updated with new benchmark results.
🛠️[2025-03-13]: RoboSpatial-Home has been released. Note that this is an extended version of what was reported on arXiv. The paper will be updated to reflect this version of the dataset.… See the full description on the dataset page: https://huggingface.co/datasets/chanhee-luke/RoboSpatial-Home. | 324 | 324 | [
"task_categories:question-answering",
"task_categories:visual-question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2411.16537",
"region:us"
] | 2025-03-13T08:02:21 | null | null |
67d871b3e6d0edd52065eb83 | oumi-ai/oumi-anli-subset | oumi-ai | {"dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "claims", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "text label", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 34992991, "num_examples": 21076}], "download_size": 15175681, "dataset_size": 34992991}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cc-by-nc-4.0", "language": ["en"], "size_categories": ["10K<n<100K"]} | false | null | 2025-04-04T16:41:29 | 5 | 5 | false | b8589b4beec90bc11a020751ede3c72f62b0a223 |
oumi-ai/oumi-anli-subset
oumi-anli-subset is a text dataset designed to fine-tune language models for Claim Verification.
Prompts were pulled from ANLI training sets with responses created from Llama-3.1-405B-Instruct.
oumi-anli-subset was used to train HallOumi-8B, which achieves 77.2% Macro F1, outperforming SOTA models such as Claude Sonnet 3.5, OpenAI o1, etc.
Curated by: Oumi AI using Oumi inference
Language(s) (NLP): English
License: CC-BY-NC-4.0, Llama 3.1 Community… See the full description on the dataset page: https://huggingface.co/datasets/oumi-ai/oumi-anli-subset. | 38 | 38 | [
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-17T19:02:11 | null | null |
67d871c78f62a95751613b97 | oumi-ai/oumi-synthetic-document-claims | oumi-ai | {"dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "claims", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "text label", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 89833579, "num_examples": 7363}, {"name": "validation", "num_bytes": 12590277, "num_examples": 1039}], "download_size": 41507741, "dataset_size": 102423856}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "license": "llama3.1", "language": ["en"], "size_categories": ["1K<n<10K"]} | false | null | 2025-04-04T16:41:04 | 5 | 5 | false | 381afcd43e15a789e5824fd4089be0d9237df2ab |
oumi-ai/oumi-synthetic-document-claims
oumi-synthetic-document-claims is a text dataset designed to fine-tune language models for Claim Verification.
Prompts and responses were produced synthetically from Llama-3.1-405B-Instruct.
oumi-synthetic-document-claims was used to train HallOumi-8B, which achieves 77.2% Macro F1, outperforming SOTA models such as Claude Sonnet 3.5, OpenAI o1, etc.
Curated by: Oumi AI using Oumi inference
Language(s) (NLP): English
License: Llama 3.1… See the full description on the dataset page: https://huggingface.co/datasets/oumi-ai/oumi-synthetic-document-claims. | 45 | 45 | [
"language:en",
"license:llama3.1",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-17T19:02:31 | null | null |
67e156bc3aa73bdc22f5b858 | benjaminogbonna/nigerian_common_voice_dataset | benjaminogbonna | {"license": "apache-2.0", "task_categories": ["automatic-speech-recognition", "text-to-speech"], "pretty_name": "Nigerian Common Voice Dataset", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en", "ha", "ig", "yo"], "multilinguality": ["multilingual"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "size_categories": ["10K<n<100K"], "dataset_info": [{"config_name": "default", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "english_train", "num_bytes": 76891, "num_examples": 3}, {"name": "english_validation", "num_bytes": 76388, "num_examples": 3}, {"name": "english_test", "num_bytes": 44707, "num_examples": 3}, {"name": "hausa_train", "num_bytes": 87721, "num_examples": 3}, {"name": "hausa_validation", "num_bytes": 81663, "num_examples": 3}, {"name": "hausa_test", "num_bytes": 86685, "num_examples": 3}, {"name": "igbo_train", "num_bytes": 77798, "num_examples": 3}, {"name": "igbo_validation", "num_bytes": 109802, "num_examples": 3}, {"name": "igbo_test", "num_bytes": 103504, "num_examples": 3}, {"name": "yoruba_train", "num_bytes": 111252, "num_examples": 3}, {"name": "yoruba_validation", "num_bytes": 125347, "num_examples": 3}, {"name": "yoruba_test", "num_bytes": 116250, "num_examples": 3}], "download_size": 1127146, "dataset_size": 1098008}, {"config_name": "english", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 102291684.678, "num_examples": 2721}, {"name": "validation", "num_bytes": 12091603, "num_examples": 340}, {"name": "test", "num_bytes": 11585499, "num_examples": 341}], "download_size": 121504884, "dataset_size": 125968786.678}, {"config_name": "hausa", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 189263575.55, "num_examples": 7206}, {"name": "validation", "num_bytes": 23256496, "num_examples": 901}, {"name": "test", "num_bytes": 24050751, "num_examples": 901}], "download_size": 234586970, "dataset_size": 236570822.55}, {"config_name": "igbo", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 147708753.853, "num_examples": 4571}, {"name": "validation", "num_bytes": 19026693, "num_examples": 571}, {"name": "test", "num_bytes": 19092378, "num_examples": 572}], "download_size": 185986664, "dataset_size": 185827824.853}, {"config_name": "yoruba", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 124429039.456, "num_examples": 3336}, {"name": "validation", "num_bytes": 15302013, "num_examples": 417}, {"name": "test", "num_bytes": 15182108, "num_examples": 418}], "download_size": 147489914, "dataset_size": 154913160.456}], "configs": [{"config_name": "english", "data_files": [{"split": "train", "path": "english/train-*"}, {"split": "validation", "path": "english/validation-*"}, {"split": "test", "path": "english/test-*"}]}, {"config_name": "hausa", "data_files": [{"split": "train", "path": "hausa/train-*"}, {"split": "validation", "path": "hausa/validation-*"}, {"split": "test", "path": "hausa/test-*"}]}, {"config_name": "igbo", "data_files": [{"split": "train", "path": "igbo/train-*"}, {"split": "validation", "path": "igbo/validation-*"}, {"split": "test", "path": "igbo/test-*"}]}, {"config_name": "yoruba", "data_files": [{"split": "train", "path": "yoruba/train-*"}, {"split": "validation", "path": "yoruba/validation-*"}, {"split": "test", "path": "yoruba/test-*"}]}]} | false | null | 2025-03-30T16:49:15 | 9 | 5 | false | 97c7f160c0d563339d2f32d55945abc406696cf2 |
Dataset Card for Nigerian Common Voice Dataset
Dataset Summary
The Nigerian Common Voice Dataset is a comprehensive dataset consisting of 158 hours of audio recordings and corresponding transcription (sentence).
This dataset includes metadata like accent, locale that can help improve the accuracy of speech recognition engines. This dataset is specifically curated to address the gap in speech and language
datasets for African accents, making it a valuable resource for… See the full description on the dataset page: https://huggingface.co/datasets/benjaminogbonna/nigerian_common_voice_dataset. | 154 | 154 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"language:en",
"language:ha",
"language:ig",
"language:yo",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-24T12:57:32 | null | null |
67e5d34aae018bfa4a529862 | oumi-ai/oumi-groundedness-benchmark | oumi-ai | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sonnet prompt", "dtype": "string"}, {"name": "sonnet response", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "gemini_pro prompt", "dtype": "string"}, {"name": "gemini_pro response", "dtype": "string"}, {"name": "gpt_4o prompt", "dtype": "string"}, {"name": "gpt_4o response", "dtype": "string"}, {"name": "o1_preview prompt", "dtype": "string"}, {"name": "o1_preview response", "dtype": "string"}, {"name": "llama_405b prompt", "dtype": "string"}, {"name": "llama_405b response", "dtype": "string"}, {"name": "deepseek R1 prompt", "dtype": "string"}, {"name": "deepseek R1 response", "dtype": "string"}, {"name": "halloumi 8b prompt", "dtype": "string"}, {"name": "halloumi 8b response", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 89544117, "num_examples": 2089}], "download_size": 37056280, "dataset_size": 89544117}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | false | null | 2025-04-04T16:44:29 | 5 | 5 | false | 78ef26d1e6d5bd705633301e700b971e452585f6 |
oumi-ai/oumi-groundedness-benchmark
oumi-groundedness-benchmark is a text dataset designed to evaluate language models for Claim Verification / Hallucination Detection.
Prompts and responses were produced synthetically from Llama-3.1-405B-Instruct.
oumi-groundedness-benchmark was used to properly evaluate HallOumi-8B, which achieves 77.2% Macro F1, outperforming SOTA models such as Claude Sonnet 3.5, OpenAI o1, etc.
Curated by: Oumi AI using Oumi inference
Language(s) (NLP):… See the full description on the dataset page: https://huggingface.co/datasets/oumi-ai/oumi-groundedness-benchmark. | 46 | 49 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-27T22:38:02 | null | null |
67ea3f4ae9411fb8f29c6aaa | virtuoussy/Math-RLVR | virtuoussy | {"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"]} | false | null | 2025-04-02T10:31:47 | 5 | 5 | false | 2d2cada17d5fc0e2593181861e09f8ba4f7941bd | Math data for paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
we use a large-scale dataset of 773k Chinese Question Answering (QA) pairs, collected under authorized licenses from educational websites.
This dataset covers three educational levels: elementary, middle, and high school.
Unlike well-structured yet small-scale benchmarks such as MATH (Hendrycks et al., 2021b) and GSM8K (Cobbe et al., 2021b),
our reference answers are inherently free-form, often interwoven with… See the full description on the dataset page: https://huggingface.co/datasets/virtuoussy/Math-RLVR. | 107 | 107 | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.23829",
"region:us"
] | 2025-03-31T07:07:54 | null | null |
621ffdd236468d709f181dba | abisee/cnn_dailymail | abisee | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "paperswithcode_id": "cnn-daily-mail-1", "pretty_name": "CNN / Daily Mail", "dataset_info": [{"config_name": "1.0.0", "features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1261703785, "num_examples": 287113}, {"name": "validation", "num_bytes": 57732412, "num_examples": 13368}, {"name": "test", "num_bytes": 49925732, "num_examples": 11490}], "download_size": 836927248, "dataset_size": 1369361929}, {"config_name": "2.0.0", "features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1261703785, "num_examples": 287113}, {"name": "validation", "num_bytes": 57732412, "num_examples": 13368}, {"name": "test", "num_bytes": 49925732, "num_examples": 11490}], "download_size": 837094602, "dataset_size": 1369361929}, {"config_name": "3.0.0", "features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1261703785, "num_examples": 287113}, {"name": "validation", "num_bytes": 57732412, "num_examples": 13368}, {"name": "test", "num_bytes": 49925732, "num_examples": 11490}], "download_size": 837094602, "dataset_size": 1369361929}], "configs": [{"config_name": "1.0.0", "data_files": [{"split": "train", "path": "1.0.0/train-*"}, {"split": "validation", "path": "1.0.0/validation-*"}, {"split": "test", "path": "1.0.0/test-*"}]}, {"config_name": "2.0.0", "data_files": [{"split": "train", "path": "2.0.0/train-*"}, {"split": "validation", "path": "2.0.0/validation-*"}, {"split": "test", "path": "2.0.0/test-*"}]}, {"config_name": "3.0.0", "data_files": [{"split": "train", "path": "3.0.0/train-*"}, {"split": "validation", "path": "3.0.0/validation-*"}, {"split": "test", "path": "3.0.0/test-*"}]}], "train-eval-index": [{"config": "3.0.0", "task": "summarization", "task_id": "summarization", "splits": {"eval_split": "test"}, "col_mapping": {"article": "text", "highlights": "target"}}]} | false | null | 2024-01-18T15:31:34 | 257 | 4 | false | 96df5e686bee6baa90b8bee7c28b81fa3fa6223d |
Dataset Card for CNN Dailymail Dataset
Dataset Summary
The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.
Supported Tasks and Leaderboards
'summarization': Versions… See the full description on the dataset page: https://huggingface.co/datasets/abisee/cnn_dailymail. | 95,005 | 2,895,820 | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22 | cnn-daily-mail-1 | null |
621ffdd236468d709f181f09 | Skylion007/openwebtext | Skylion007 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "pretty_name": "OpenWebText", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "openwebtext", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 39769491688, "num_examples": 8013769}], "download_size": 12880189440, "dataset_size": 39769491688}} | false | null | 2024-05-17T17:56:27 | 414 | 4 | false | f3808c30e817981b845ec549c43e82bb467d8144 | An open-source replication of the WebText dataset from OpenAI. | 94,181 | 4,543,038 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"region:us"
] | 2022-03-02T23:29:22 | openwebtext | @misc{Gokaslan2019OpenWeb,
title={OpenWebText Corpus},
author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
year={2019}
} |
633a585e593f7e38374056ec | bigcode/the-stack | bigcode | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "pretty_name": "The-Stack", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": [], "extra_gated_prompt": "## Terms of Use for The Stack\n\nThe Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n2. The Stack is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset\u2019s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes.\n3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.\n ", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}} | false | null | 2023-04-13T12:15:50 | 790 | 4 | false | 349a71353fd5868fb90b593ef09e311379da498a |
Dataset Card for The Stack
Changelog
Release
Description
v1.0
Initial release of the Stack. Included 30 programming languages and 18 permissive licenses. Note: Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 3TB in size.
v1.1
The three copyleft licenses ((MPL/EPL/LGPL) were excluded and the list of permissive licenses extended to 193 licenses in total. The list of programming languages… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack. | 10,894 | 161,177 | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:other",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2211.15533",
"arxiv:2107.03374",
"arxiv:2207.14157",
"region:us"
] | 2022-10-03T03:34:54 | null | null |
649444227853dd12c3bbadd8 | Amod/mental_health_counseling_conversations | Amod | {"license": "openrail", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical"], "size_categories": ["1K<n<10K"]} | false | null | 2024-04-05T08:30:03 | 344 | 4 | false | 4672e03c7f1a7b2215eb4302b83ca50449ce2553 |
Amod/mental_health_counseling_conversations
Dataset Summary
This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice.
Supported Tasks and Leaderboards
The… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations. | 4,669 | 64,729 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:openrail",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"doi:10.57967/hf/1581",
"region:us",
"medical"
] | 2023-06-22T12:52:50 | null | null |
64e6f816edb36433c0ecd84d | corbt/all-recipes | corbt | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1569011376, "num_examples": 2147248}], "download_size": 807147913, "dataset_size": 1569011376}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2023-08-24T06:27:02 | 52 | 4 | false | 57e900079f461c85794b8b1e957cc3fd5e179b44 |
Dataset Card for "all-recipes"
More Information needed
| 586 | 7,067 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2023-08-24T06:26:30 | null | null |
64f7c6e8baa3b4ec4e37b1d8 | open-web-math/open-web-math | open-web-math | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "metadata", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 56651995057, "num_examples": 6315233}], "download_size": 16370689925, "dataset_size": 56651995057, "license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "OpenWebMath", "size_categories": ["10B<n<100B"]}} | false | null | 2023-10-17T20:14:00 | 310 | 4 | false | fde8ef8de2300f5e778f56261843dab89f230815 |
Keiran Paster*, Marco Dos Santos*, Zhangir Azerbayev, Jimmy Ba
GitHub | ArXiv
| PDF
OpenWebMath is a dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of 6.3 million documents containing a total of 14.7B tokens. OpenWebMath is intended for use in pretraining and finetuning large language models.
You can download the dataset using Hugging Face:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/open-web-math/open-web-math. | 17,448 | 84,845 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2310.06786",
"region:us"
] | 2023-09-06T00:25:12 | null | null |
6564d741cfdc8b6433bfba49 | MMMU/MMMU | MMMU | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "visual-question-answering", "multiple-choice"], "pretty_name": "mmmu", "dataset_info": [{"config_name": "Accounting", "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": 262599, "num_examples": 5}, {"name": "validation", "num_bytes": 1598285, "num_examples": 30}, {"name": "test", "num_bytes": 22135625, "num_examples": 380}], "download_size": 37363379, "dataset_size": 23996509}, {"config_name": "Agriculture", "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": 22082656, "num_examples": 5}, {"name": "validation", "num_bytes": 119217558, "num_examples": 30}, {"name": "test", "num_bytes": 993664077, "num_examples": 287}], "download_size": 1158036990, "dataset_size": 1134964291}, {"config_name": "Architecture_and_Engineering", "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": 137750, "num_examples": 5}, {"name": "validation", "num_bytes": 721378, "num_examples": 30}, {"name": "test", "num_bytes": 16054607, "num_examples": 551}], "download_size": 48763955, "dataset_size": 16913735}, {"config_name": "Art", "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": 6241184, "num_examples": 5}, {"name": "validation", "num_bytes": 29934534, "num_examples": 30}, {"name": "test", "num_bytes": 237801390, "num_examples": 231}], "download_size": 585798641, "dataset_size": 273977108}, {"config_name": "Art_Theory", "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": 7435106, "num_examples": 5}, {"name": "validation", "num_bytes": 33481558, "num_examples": 30}, {"name": "test", "num_bytes": 553174647, "num_examples": 429}], "download_size": 930525695, "dataset_size": 594091311}, {"config_name": "Basic_Medical_Science", "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": 814310, "num_examples": 5}, {"name": "validation", "num_bytes": 4125930, "num_examples": 30}, {"name": "test", "num_bytes": 48125891, "num_examples": 326}], "download_size": 84666454, "dataset_size": 53066131}, {"config_name": "Biology", "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": 574342, "num_examples": 5}, {"name": "validation", "num_bytes": 8491863, "num_examples": 30}, {"name": "test", "num_bytes": 132966151, "num_examples": 345}], "download_size": 410242502, "dataset_size": 142032356}, {"config_name": "Chemistry", "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": 262397, "num_examples": 5}, {"name": "validation", "num_bytes": 1518573, "num_examples": 30}, {"name": "test", "num_bytes": 37219529, "num_examples": 603}], "download_size": 108345562, "dataset_size": 39000499}, {"config_name": "Clinical_Medicine", "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": 1467945, "num_examples": 5}, {"name": "validation", "num_bytes": 10882484, "num_examples": 30}, {"name": "test", "num_bytes": 98201863, "num_examples": 325}], "download_size": 160611488, "dataset_size": 110552292}, {"config_name": "Computer_Science", "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": 440523, "num_examples": 5}, {"name": "validation", "num_bytes": 2072018, "num_examples": 30}, {"name": "test", "num_bytes": 32047381, "num_examples": 371}], "download_size": 55640991, "dataset_size": 34559922}, {"config_name": "Design", "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": 2259873, "num_examples": 5}, {"name": "validation", "num_bytes": 17923120, "num_examples": 30}, {"name": "test", "num_bytes": 77676331, "num_examples": 169}], "download_size": 142866617, "dataset_size": 97859324}, {"config_name": "Diagnostics_and_Laboratory_Medicine", "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": 2056117, "num_examples": 5}, {"name": "validation", "num_bytes": 37106233, "num_examples": 30}, {"name": "test", "num_bytes": 157003069, "num_examples": 162}], "download_size": 603957093, "dataset_size": 196165419}, {"config_name": "Economics", "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": 171434, "num_examples": 5}, {"name": "validation", "num_bytes": 1487048, "num_examples": 30}, {"name": "test", "num_bytes": 11852300, "num_examples": 267}], "download_size": 20777635, "dataset_size": 13510782}, {"config_name": "Electronics", "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": 123632, "num_examples": 5}, {"name": "validation", "num_bytes": 641377, "num_examples": 30}, {"name": "test", "num_bytes": 5717686, "num_examples": 256}], "download_size": 11602832, "dataset_size": 6482695}, {"config_name": "Energy_and_Power", "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": 105006, "num_examples": 5}, {"name": "validation", "num_bytes": 1641935, "num_examples": 30}, {"name": "test", "num_bytes": 14748428, "num_examples": 432}], "download_size": 35246567, "dataset_size": 16495369}, {"config_name": "Finance", "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": 296124, "num_examples": 5}, {"name": "validation", "num_bytes": 1071060, "num_examples": 30}, {"name": "test", "num_bytes": 12065803, "num_examples": 355}], "download_size": 29551521, "dataset_size": 13432987}, {"config_name": "Geography", "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": 1494060, "num_examples": 5}, {"name": "validation", "num_bytes": 6671316, "num_examples": 30}, {"name": "test", "num_bytes": 137218400, "num_examples": 565}], "download_size": 374766631, "dataset_size": 145383776}, {"config_name": "History", "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": 1444231, "num_examples": 5}, {"name": "validation", "num_bytes": 8819857, "num_examples": 30}, {"name": "test", "num_bytes": 115228815, "num_examples": 278}], "download_size": 232549641, "dataset_size": 125492903}, {"config_name": "Literature", "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": 2451201, "num_examples": 5}, {"name": "validation", "num_bytes": 14241046, "num_examples": 30}, {"name": "test", "num_bytes": 50301541, "num_examples": 112}], "download_size": 132145895, "dataset_size": 66993788}, {"config_name": "Manage", "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": 449514, "num_examples": 5}, {"name": "validation", "num_bytes": 3277436, "num_examples": 30}, {"name": "test", "num_bytes": 29963963, "num_examples": 245}], "download_size": 51186888, "dataset_size": 33690913}, {"config_name": "Marketing", "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": 116960, "num_examples": 5}, {"name": "validation", "num_bytes": 1472981, "num_examples": 30}, {"name": "test", "num_bytes": 7732976, "num_examples": 181}], "download_size": 13146078, "dataset_size": 9322917}, {"config_name": "Materials", "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": 239632, "num_examples": 5}, {"name": "validation", "num_bytes": 2305223, "num_examples": 30}, {"name": "test", "num_bytes": 25256854, "num_examples": 458}], "download_size": 105773156, "dataset_size": 27801709}, {"config_name": "Math", "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": 175839, "num_examples": 5}, {"name": "validation", "num_bytes": 1444496, "num_examples": 30}, {"name": "test", "num_bytes": 27701845, "num_examples": 505}], "download_size": 174098418, "dataset_size": 29322180}, {"config_name": "Mechanical_Engineering", "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": 152542, "num_examples": 5}, {"name": "validation", "num_bytes": 874988, "num_examples": 30}, {"name": "test", "num_bytes": 15093746, "num_examples": 429}], "download_size": 30450114, "dataset_size": 16121276}, {"config_name": "Music", "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": 1417615, "num_examples": 5}, {"name": "validation", "num_bytes": 9359372, "num_examples": 30}, {"name": "test", "num_bytes": 134096770, "num_examples": 334}], "download_size": 174725052, "dataset_size": 144873757}, {"config_name": "Pharmacy", "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": 207924, "num_examples": 5}, {"name": "validation", "num_bytes": 1656342, "num_examples": 30}, {"name": "test", "num_bytes": 31866248, "num_examples": 430}], "download_size": 62721263, "dataset_size": 33730514}, {"config_name": "Physics", "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": 233734, "num_examples": 5}, {"name": "validation", "num_bytes": 1114130, "num_examples": 30}, {"name": "test", "num_bytes": 15905705, "num_examples": 408}], "download_size": 35238571, "dataset_size": 17253569}, {"config_name": "Psychology", "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": 600864, "num_examples": 5}, {"name": "validation", "num_bytes": 4403886, "num_examples": 30}, {"name": "test", "num_bytes": 53813915, "num_examples": 305}], "download_size": 102466671, "dataset_size": 58818665}, {"config_name": "Public_Health", "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": 234781, "num_examples": 5}, {"name": "validation", "num_bytes": 1508761, "num_examples": 30}, {"name": "test", "num_bytes": 32150088, "num_examples": 509}], "download_size": 48231609, "dataset_size": 33893630}, {"config_name": "Sociology", "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": 3769220, "num_examples": 5}, {"name": "validation", "num_bytes": 18455336, "num_examples": 30}, {"name": "test", "num_bytes": 144301123, "num_examples": 252}], "download_size": 310313826, "dataset_size": 166525679}], "configs": [{"config_name": "Accounting", "data_files": [{"split": "dev", "path": "Accounting/dev-*"}, {"split": "validation", "path": "Accounting/validation-*"}, {"split": "test", "path": "Accounting/test-*"}]}, {"config_name": "Agriculture", "data_files": [{"split": "dev", "path": "Agriculture/dev-*"}, {"split": "validation", "path": "Agriculture/validation-*"}, {"split": "test", "path": "Agriculture/test-*"}]}, {"config_name": "Architecture_and_Engineering", "data_files": [{"split": "dev", "path": "Architecture_and_Engineering/dev-*"}, {"split": "validation", "path": "Architecture_and_Engineering/validation-*"}, {"split": "test", "path": "Architecture_and_Engineering/test-*"}]}, {"config_name": "Art", "data_files": [{"split": "dev", "path": "Art/dev-*"}, {"split": "validation", "path": "Art/validation-*"}, {"split": "test", "path": "Art/test-*"}]}, {"config_name": "Art_Theory", "data_files": [{"split": "dev", "path": "Art_Theory/dev-*"}, {"split": "validation", "path": "Art_Theory/validation-*"}, {"split": "test", "path": "Art_Theory/test-*"}]}, {"config_name": "Basic_Medical_Science", "data_files": [{"split": "dev", "path": "Basic_Medical_Science/dev-*"}, {"split": "validation", "path": "Basic_Medical_Science/validation-*"}, {"split": "test", "path": "Basic_Medical_Science/test-*"}]}, {"config_name": "Biology", "data_files": [{"split": "dev", "path": "Biology/dev-*"}, {"split": "validation", "path": "Biology/validation-*"}, {"split": "test", "path": "Biology/test-*"}]}, {"config_name": "Chemistry", "data_files": [{"split": "dev", "path": "Chemistry/dev-*"}, {"split": "validation", "path": "Chemistry/validation-*"}, {"split": "test", "path": "Chemistry/test-*"}]}, {"config_name": "Clinical_Medicine", "data_files": [{"split": "dev", "path": "Clinical_Medicine/dev-*"}, {"split": "validation", "path": "Clinical_Medicine/validation-*"}, {"split": "test", "path": "Clinical_Medicine/test-*"}]}, {"config_name": "Computer_Science", "data_files": [{"split": "dev", "path": "Computer_Science/dev-*"}, {"split": "validation", "path": "Computer_Science/validation-*"}, {"split": "test", "path": "Computer_Science/test-*"}]}, {"config_name": "Design", "data_files": [{"split": "dev", "path": "Design/dev-*"}, {"split": "validation", "path": "Design/validation-*"}, {"split": "test", "path": "Design/test-*"}]}, {"config_name": "Diagnostics_and_Laboratory_Medicine", "data_files": [{"split": "dev", "path": "Diagnostics_and_Laboratory_Medicine/dev-*"}, {"split": "validation", "path": "Diagnostics_and_Laboratory_Medicine/validation-*"}, {"split": "test", "path": "Diagnostics_and_Laboratory_Medicine/test-*"}]}, {"config_name": "Economics", "data_files": [{"split": "dev", "path": "Economics/dev-*"}, {"split": "validation", "path": "Economics/validation-*"}, {"split": "test", "path": "Economics/test-*"}]}, {"config_name": "Electronics", "data_files": [{"split": "dev", "path": "Electronics/dev-*"}, {"split": "validation", "path": "Electronics/validation-*"}, {"split": "test", "path": "Electronics/test-*"}]}, {"config_name": "Energy_and_Power", "data_files": [{"split": "dev", "path": "Energy_and_Power/dev-*"}, {"split": "validation", "path": "Energy_and_Power/validation-*"}, {"split": "test", "path": "Energy_and_Power/test-*"}]}, {"config_name": "Finance", "data_files": [{"split": "dev", "path": "Finance/dev-*"}, {"split": "validation", "path": "Finance/validation-*"}, {"split": "test", "path": "Finance/test-*"}]}, {"config_name": "Geography", "data_files": [{"split": "dev", "path": "Geography/dev-*"}, {"split": "validation", "path": "Geography/validation-*"}, {"split": "test", "path": "Geography/test-*"}]}, {"config_name": "History", "data_files": [{"split": "dev", "path": "History/dev-*"}, {"split": "validation", "path": "History/validation-*"}, {"split": "test", "path": "History/test-*"}]}, {"config_name": "Literature", "data_files": [{"split": "dev", "path": "Literature/dev-*"}, {"split": "validation", "path": "Literature/validation-*"}, {"split": "test", "path": "Literature/test-*"}]}, {"config_name": "Manage", "data_files": [{"split": "dev", "path": "Manage/dev-*"}, {"split": "validation", "path": "Manage/validation-*"}, {"split": "test", "path": "Manage/test-*"}]}, {"config_name": "Marketing", "data_files": [{"split": "dev", "path": "Marketing/dev-*"}, {"split": "validation", "path": "Marketing/validation-*"}, {"split": "test", "path": "Marketing/test-*"}]}, {"config_name": "Materials", "data_files": [{"split": "dev", "path": "Materials/dev-*"}, {"split": "validation", "path": "Materials/validation-*"}, {"split": "test", "path": "Materials/test-*"}]}, {"config_name": "Math", "data_files": [{"split": "dev", "path": "Math/dev-*"}, {"split": "validation", "path": "Math/validation-*"}, {"split": "test", "path": "Math/test-*"}]}, {"config_name": "Mechanical_Engineering", "data_files": [{"split": "dev", "path": "Mechanical_Engineering/dev-*"}, {"split": "validation", "path": "Mechanical_Engineering/validation-*"}, {"split": "test", "path": "Mechanical_Engineering/test-*"}]}, {"config_name": "Music", "data_files": [{"split": "dev", "path": "Music/dev-*"}, {"split": "validation", "path": "Music/validation-*"}, {"split": "test", "path": "Music/test-*"}]}, {"config_name": "Pharmacy", "data_files": [{"split": "dev", "path": "Pharmacy/dev-*"}, {"split": "validation", "path": "Pharmacy/validation-*"}, {"split": "test", "path": "Pharmacy/test-*"}]}, {"config_name": "Physics", "data_files": [{"split": "dev", "path": "Physics/dev-*"}, {"split": "validation", "path": "Physics/validation-*"}, {"split": "test", "path": "Physics/test-*"}]}, {"config_name": "Psychology", "data_files": [{"split": "dev", "path": "Psychology/dev-*"}, {"split": "validation", "path": "Psychology/validation-*"}, {"split": "test", "path": "Psychology/test-*"}]}, {"config_name": "Public_Health", "data_files": [{"split": "dev", "path": "Public_Health/dev-*"}, {"split": "validation", "path": "Public_Health/validation-*"}, {"split": "test", "path": "Public_Health/test-*"}]}, {"config_name": "Sociology", "data_files": [{"split": "dev", "path": "Sociology/dev-*"}, {"split": "validation", "path": "Sociology/validation-*"}, {"split": "test", "path": "Sociology/test-*"}]}], "tags": ["biology", "medical", "finance", "chemistry", "music", "art", "art_theory", "design", "music", "business", "accounting", "economics", "finance", "manage", "marketing", "health", "medicine", "basic_medical_science", "clinical", "pharmacy", "public_health", "humanities", "social_science", "history", "literature", "sociology", "psychology", "science", "biology", "chemistry", "geography", "math", "physics", "engineering", "agriculture", "architecture", "computer_science", "electronics", "energy_and_power", "materials", "mechanical_engineering"]} | false | null | 2024-09-19T17:11:03 | 241 | 4 | false | 171b0ef74cd1704464e6940860968009d8cdd59a |
MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)
🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 🤗 Paper | 📖 arXiv | GitHub
🔔News
🛠️[2024-05-30]: Fixed duplicate option issues in Materials dataset items (validation_Materials_25; test_Materials_17, 242) and content error in validation_Materials_25.
🛠️[2024-04-30]: Fixed missing "-" or "^" signs in Math dataset items (dev_Math_2, validation_Math_11, 12, 16;… See the full description on the dataset page: https://huggingface.co/datasets/MMMU/MMMU. | 22,453 | 1,473,117 | [
"task_categories:question-answering",
"task_categories:visual-question-answering",
"task_categories:multiple-choice",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2311.16502",
"region:us",
"biology",
"medical",
"finance",
"chemistry",
"music",
"art",
"art_theory",
"design",
"business",
"accounting",
"economics",
"manage",
"marketing",
"health",
"medicine",
"basic_medical_science",
"clinical",
"pharmacy",
"public_health",
"humanities",
"social_science",
"history",
"literature",
"sociology",
"psychology",
"science",
"geography",
"math",
"physics",
"engineering",
"agriculture",
"architecture",
"computer_science",
"electronics",
"energy_and_power",
"materials",
"mechanical_engineering"
] | 2023-11-27T17:52:01 | null | null |
6587ff94509bcae23f71024d | OpenAssistant/oasst2 | OpenAssistant | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 158850455, "num_examples": 128575}, {"name": "validation", "num_bytes": 7963122, "num_examples": 6599}], "download_size": 66674129, "dataset_size": 166813577}, "language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "tags": ["human-feedback"], "size_categories": ["100K<n<1M"], "pretty_name": "OpenAssistant Conversations Release 2"} | false | null | 2024-01-11T06:09:29 | 254 | 4 | false | 179dd21fc55192153d94adb0e0ce8f69e222bf75 |
Open Assistant Conversations Dataset Release 2 (OASST2)
Dataset Structure
This dataset contains message trees. Each message tree has an initial prompt message as the root node,
which can have multiple child messages as replies, and these child messages can have multiple replies.
All messages have a role property: this can either be "assistant" or "prompter". The roles in
conversation threads from prompt to leaf node strictly alternate between "prompter" and… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst2. | 1,941 | 42,678 | [
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:hu",
"language:eu",
"language:zh",
"language:eo",
"language:ja",
"language:ca",
"language:cs",
"language:bg",
"language:fi",
"language:pt",
"language:tr",
"language:ro",
"language:ar",
"language:uk",
"language:gl",
"language:fr",
"language:ko",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2304.07327",
"region:us",
"human-feedback"
] | 2023-12-24T09:53:24 | null | null |
65919009a78a277803ef68df | omar07ibrahim/AZERBAIJAN-ENGLISH-DATASET | omar07ibrahim | {"license": "cc-by-4.0"} | false | null | 2023-12-31T16:01:12 | 4 | 4 | false | 6985dd6cfc70886134f72eac4d0a6f1fc1b09af6 | null | 30 | 325 | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2023-12-31T16:00:09 | null | null |
65d2675495e8d86e2fe4124d | HuggingFaceTB/cosmopedia | HuggingFaceTB | {"dataset_info": [{"config_name": "auto_math_text", "features": [{"name": "prompt", "dtype": "string"}, {"name": "text_token_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8777587297.907892, "num_examples": 1949895}], "download_size": 4461401898, "dataset_size": 8777587297.907892}, {"config_name": "khanacademy", "features": [{"name": "prompt", "dtype": "string"}, {"name": "text_token_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 108591354.09210858, "num_examples": 24123}], "download_size": 49139761, "dataset_size": 108591354.09210858}, {"config_name": "openstax", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 667837450, "num_examples": 126332}], "download_size": 346992522, "dataset_size": 667837450}, {"config_name": "stanford", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6341291506, "num_examples": 1020024}], "download_size": 3302284560, "dataset_size": 6341291506}, {"config_name": "stories", "features": [{"name": "text", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "text_token_length", "dtype": "int64"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21314739648, "num_examples": 4992964}], "download_size": 11902294709, "dataset_size": 21314739648}, {"config_name": "web_samples_v1", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 69075726295, "num_examples": 12426348}], "download_size": 38978124936, "dataset_size": 69075726295}, {"config_name": "web_samples_v2", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 58711802939, "num_examples": 10345867}], "download_size": 32658254617, "dataset_size": 58711802939}, {"config_name": "wikihow", "features": [{"name": "text_token_length", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "seed_data", "dtype": "string"}, {"name": "format", "dtype": "string"}, {"name": "audience", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 892720528, "num_examples": 179191}], "download_size": 502284600, "dataset_size": 892720528}], "configs": [{"config_name": "auto_math_text", "data_files": [{"split": "train", "path": "data/auto_math_text/train-*"}]}, {"config_name": "khanacademy", "data_files": [{"split": "train", "path": "data/khanacademy/train-*"}]}, {"config_name": "openstax", "data_files": [{"split": "train", "path": "data/openstax/train-*"}]}, {"config_name": "stanford", "data_files": [{"split": "train", "path": "data/stanford/train-*"}]}, {"config_name": "stories", "data_files": [{"split": "train", "path": "data/stories/train-*"}]}, {"config_name": "web_samples_v1", "data_files": [{"split": "train", "path": "data/web_samples_v1/train-*"}]}, {"config_name": "web_samples_v2", "data_files": [{"split": "train", "path": "data/web_samples_v2/train-*"}]}, {"config_name": "wikihow", "data_files": [{"split": "train", "path": "data/wikihow/train-*"}]}], "license": "apache-2.0", "language": ["en"], "tags": ["synthetic"]} | false | null | 2024-08-12T22:05:49 | 602 | 4 | false | 0ae6ec63f91742bd2d1eaef4f02232c55d719385 |
Cosmopedia v0.1
Image generated by DALL-E, the prompt was generated by Mixtral-8x7B-Instruct-v0.1
Note: Cosmopedia v0.2 is available at smollm-corpus
User: What do you think "Cosmopedia" could mean? Hint: in our case it's not related to cosmology.
Mixtral-8x7B-Instruct-v0.1: A possible meaning for "Cosmopedia" could be an encyclopedia or collection of information about
different cultures, societies, and topics from around the world, emphasizing diversity and global… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/cosmopedia. | 40,073 | 179,614 | [
"language:en",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2309.05463",
"arxiv:2306.11644",
"region:us",
"synthetic"
] | 2024-02-18T20:23:48 | null | null |
663117fec57e46020df7e9c8 | ylacombe/expresso | ylacombe | {"dataset_info": {"config_name": "read", "features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5702432944.34, "num_examples": 11615}], "download_size": 5761373569, "dataset_size": 5702432944.34}, "configs": [{"config_name": "read", "data_files": [{"split": "train", "path": "read/train-*"}]}], "license": "cc-by-nc-4.0", "language": ["en"], "pretty_name": "The Expresso Dataset"} | false | null | 2024-04-30T16:49:14 | 49 | 4 | false | 9fb79a189698de3255eff48edd2bc0d9e487adc0 |
The Expresso Dataset
[paper] [demo samples] [Original repository]
Introduction
The Expresso dataset is a high-quality (48kHz) expressive speech dataset that includes both expressively rendered read speech (8 styles, in mono wav format) and improvised dialogues (26 styles, in stereo wav format). The dataset includes 4 speakers (2 males, 2 females), and totals 40 hours (11h read, 30h improvised). The transcriptions of the read speech are also provided.
You can… See the full description on the dataset page: https://huggingface.co/datasets/ylacombe/expresso. | 544 | 4,018 | [
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2308.05725",
"region:us"
] | 2024-04-30T16:10:38 | null | null |
66670ea06e382e809d2bca3b | linxy/LaTeX_OCR | linxy | {"license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"], "dataset_info": [{"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 392473380.05, "num_examples": 76318}], "download_size": 383401054, "dataset_size": 392473380.05}, {"config_name": "full", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 385291867, "num_examples": 76318}, {"name": "validation", "num_bytes": 43364061.55, "num_examples": 8475}, {"name": "test", "num_bytes": 47643036.303, "num_examples": 9443}], "download_size": 473618552, "dataset_size": 483485587.878}, {"config_name": "human_handwrite", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16181778, "num_examples": 1200}, {"name": "validation", "num_bytes": 962283, "num_examples": 68}, {"name": "test", "num_bytes": 906906, "num_examples": 70}], "download_size": 18056029, "dataset_size": 18050967}, {"config_name": "human_handwrite_print", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3152122.8, "num_examples": 1200}, {"name": "validation", "num_bytes": 182615, "num_examples": 68}, {"name": "test", "num_bytes": 181698, "num_examples": 70}], "download_size": 1336052, "dataset_size": 3516435.8}, {"config_name": "small", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 261296, "num_examples": 50}, {"name": "validation", "num_bytes": 156489, "num_examples": 30}, {"name": "test", "num_bytes": 156489, "num_examples": 30}], "download_size": 588907, "dataset_size": 574274}, {"config_name": "synthetic_handwrite", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 496610333.066, "num_examples": 76266}, {"name": "validation", "num_bytes": 63147351.515, "num_examples": 9565}, {"name": "test", "num_bytes": 62893132.805, "num_examples": 9593}], "download_size": 616418996, "dataset_size": 622650817.3859999}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "full/train-*"}]}, {"config_name": "full", "data_files": [{"split": "train", "path": "full/train-*"}, {"split": "validation", "path": "full/validation-*"}, {"split": "test", "path": "full/test-*"}]}, {"config_name": "human_handwrite", "data_files": [{"split": "train", "path": "human_handwrite/train-*"}, {"split": "validation", "path": "human_handwrite/validation-*"}, {"split": "test", "path": "human_handwrite/test-*"}]}, {"config_name": "human_handwrite_print", "data_files": [{"split": "train", "path": "human_handwrite_print/train-*"}, {"split": "validation", "path": "human_handwrite_print/validation-*"}, {"split": "test", "path": "human_handwrite_print/test-*"}]}, {"config_name": "small", "data_files": [{"split": "train", "path": "small/train-*"}, {"split": "validation", "path": "small/validation-*"}, {"split": "test", "path": "small/test-*"}]}, {"config_name": "synthetic_handwrite", "data_files": [{"split": "train", "path": "synthetic_handwrite/train-*"}, {"split": "validation", "path": "synthetic_handwrite/validation-*"}, {"split": "test", "path": "synthetic_handwrite/test-*"}]}], "tags": ["code"]} | false | null | 2024-12-29T15:49:06 | 64 | 4 | false | e4167ee052306f9fa006ea6703161956f8af9f6e |
LaTeX OCR 的数据仓库
本数据仓库是专为 LaTeX_OCR 及 LaTeX_OCR_PRO 制作的数据，来源于 https://zenodo.org/record/56198#.V2p0KTXT6eA 以及 https://www.isical.ac.in/~crohme/ 以及我们自己构建。
如果这个数据仓库有帮助到你的话，请点亮 ❤️like ++
后续追加新的数据也会放在这个仓库 ~~
原始数据仓库在github LinXueyuanStdio/Data-for-LaTeX_OCR.
数据集
本仓库有 5 个数据集
small 是小数据集，样本数 110 条，用于测试
full 是印刷体约 100k 的完整数据集。实际上样本数略小于 100k，因为用 LaTeX 的抽象语法树剔除了很多不能渲染的 LaTeX。
synthetic_handwrite 是手写体 100k 的完整数据集，基于 full 的公式，使用手写字体合成而来，可以视为人类在纸上的手写体。样本数实际上略小于 100k，理由同上。… See the full description on the dataset page: https://huggingface.co/datasets/linxy/LaTeX_OCR. | 935 | 4,502 | [
"task_categories:image-to-text",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"code"
] | 2024-06-10T14:33:04 | null | null |
668d2093d141a8c8555293c9 | CohereForAI/lbpp | CohereForAI | {"license": "apache-2.0"} | false | null | 2025-04-04T17:16:57 | 18 | 4 | false | a4753d1cf9b1e2a6261cf4dacaa7f197ef5cf3d2 | *Less Basic Python Programming* is a collection of 161 programming problems with accompanying unit tests.
They were created with the aim of being fresh (not leaked at the time of creation) and more difficult than similar datasets (e.g., HumanEval and MBPP).
It can serve as a drop-in replacement or enrichment of those datasets as they are structured in an equivalent way. | 189 | 1,832 | [
"license:apache-2.0",
"arxiv:2504.00698",
"region:us"
] | 2024-07-09T11:35:47 | null | @inproceedings{matton-etal-2024-leakage,
title = "On Leakage of Code Generation Evaluation Datasets",
author = "Matton, Alexandre and
Sherborne, Tom and
Aumiller, Dennis and
Tommasone, Elena and
Alizadeh, Milad and
He, Jingyi and
Ma, Raymond and
Voisin, Maxime and
Gilsenan-McMahon, Ellen and
Gall{\'e}, Matthias",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.772/",
doi = "10.18653/v1/2024.findings-emnlp.772",
pages = "13215--13223",
} |
67024c2defe7611a8b7f7016 | NeuroDonu/PortableVersions | NeuroDonu | {"license": "apache-2.0"} | false | null | 2025-04-05T14:18:07 | 7 | 4 | false | 014a4fc7b9099d4269738307fe9bc7169ed18b0f | null | 1,058 | 3,449 | [
"license:apache-2.0",
"region:us"
] | 2024-10-06T08:37:01 | null | null |
67268d67c784f47ded4f1cb5 | udell-lab/NLP4LP | udell-lab | {"license": "cc-by-nc-sa-4.0", "task_categories": ["text-classification"], "language": ["en"], "tags": ["optimization", "optimization modeling", "LP", "MILP"], "size_categories": ["1K<n<10K"]} | false | null | 2024-11-20T01:27:50 | 10 | 4 | false | ed8a080c5885b9ba930ebbaad5246cc2ded7b796 |
NLP4LP
NLP4LP is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes (The updated version will be added soon).
Contributions:
We appreciate contributions! To add new instances to the dataset, please create a pull request on this repository with your problem instances with the following structure:
data/
│ SUBDATASET/
│ │… See the full description on the dataset page: https://huggingface.co/datasets/udell-lab/NLP4LP. | 281 | 891 | [
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"optimization",
"optimization modeling",
"LP",
"MILP"
] | 2024-11-02T20:36:55 | null | null |
Subsets and Splits