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Samples are taken from ParlAI for consistency with the main users at the moment.
Supersede #945 (problem with the rebase) and adresses the issues mentioned in the review (dummy data are smaller now and code comments are fixed). | {
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The URL for `train+SQuAD` subset of MRQA points to the dev set instead of train set. It should be `https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SQuAD.jsonl.gz`. | {
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https://github.com/google-research-datasets/QED
https://arxiv.org/abs/2009.06354 | {
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} | Initial version only reading the metadata in CSV.
### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [ ] Both tests for the real data and the dummy data pass.
### TODO:
- [ ] add more metadata
- [ ] add full text
- [ ] add pre-computed document embedding | {
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```python
from datasets import Dataset, concatenate_datasets
ds = Dataset.from_dict({"_type": ["whatever"]}).map()
concatenate_datasets([ds])
# or simply
Dataset(ds._data)
```
Context: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column.
Not sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict.
Best wishes and keep up the awesome work! | {
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https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz
https://arxiv.org/abs/1604.04562 | {
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} | Add The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV)
Paper: [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830)
Homepage: http://oma-project.com/ | {
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} | ThaiSum, a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs written by journalists. We evaluate the performance of various existing summarization models on ThaiSum dataset and analyse the characteristic of the dataset to present its difficulties. | {
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} | * More info on the dataset: https://evasharma.github.io/bigpatent/
* There's another raw version of the dataset available from tfds. However, they're quite large so I don't have the resources to fully test all the configs for that version yet. We'll try to add it later. | {
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} | - **Name:** *Cyberbullying Detection Task*
- **Description:** *The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.*
- **Data:** *https://github.com/ptaszynski/cyberbullying-Polish*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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} | Mutual friends dataset
WIP
TODO:
- scenario_kbs (bug with pyarrow conversion)
- download from codalab checksums bug | {
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} | - **Name**: *cdsc (domains: cdsc-e & cdsc-r)*
- **Description**: *Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource.*
- **Data**: *http://2019.poleval.pl/index.php/tasks/*
- **Motivation**: *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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} | https://github.com/mhardalov/exams-qa
`multilingual` configs have all languages mixed together
`crosslingual` mixes the languages for test but separates them for train and dec, so I've made one config per language for train/dev data and one config with the joint test set | {
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} | Add news topic classification dataset in Myanmar / Burmese languagess
This data was collected in 2017 by Aye Hninn Khine , and published on GitHub with a GPL license
https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
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- Webpage/repo: https://github.com/google-research-datasets/KELM-corpus
- Paper: https://arxiv.org/pdf/2010.12688.pdf | {
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} | This PR adds the full set of 176 languages from the balanced train/dev/test splits of WikiANN / PAN-X from: https://github.com/afshinrahimi/mmner
Until now, only 40 of these languages were available in `datasets` as part of the XTREME benchmark
Courtesy of the dataset author, we can now download this dataset from a Dropbox URL without needing a manual download anymore 🥳, so at some point it would be worth updating the PAN-X subset of XTREME as well 😄
Link to gist with some snippets for producing dummy data: https://gist.github.com/lewtun/5b93294ab6dbcf59d1493dbe2cfd6bb9
P.S. @yjernite I think I was confused about needing to generate a set of YAML tags per config, so ended up just adding a single one in the README. | {
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} | - **Name:** *nkjp-ner*
- **Description:** *The NKJP-NER is based on a human-annotated part of NKJP. We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.*
- **Data:** *https://klejbenchmark.com/tasks/*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.*
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} | Add QUAC (Question Answering in Context)
I linearized most of the dictionnaries to lists.
Referenced to the authors' datasheet for the dataset card.
🦆🦆🦆
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- All tests passed successfully. | {
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https://api.github.com/repos/huggingface/datasets/issues/1065 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1065/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1065/comments | https://api.github.com/repos/huggingface/datasets/issues/1065/events | https://github.com/huggingface/datasets/pull/1065 | 756,383,414 | MDExOlB1bGxSZXF1ZXN0NTMxOTM2OTQ3 | 1,065 | add xquad-r dataset | {
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] | closed | false | null | [] | null | 2 | "2020-12-03T17:04:43Z" | "2021-01-14T02:51:25Z" | "2021-01-14T02:51:25Z" | CONTRIBUTOR | null | null | null | I have an issue adding this download link https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz
it might be because it is not a direct link (it returns 302 and redirects to aws that returns 403 for head requests).
```
r.head("https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz", allow_redirects=True)
# <Response [403]>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1062 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1062/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1062/comments | https://api.github.com/repos/huggingface/datasets/issues/1062/events | https://github.com/huggingface/datasets/pull/1062 | 756,373,187 | MDExOlB1bGxSZXF1ZXN0NTMxOTI4NDY5 | 1,062 | Add KorNLU dataset | {
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} | [] | closed | false | null | [] | null | 1 | "2020-12-03T16:52:39Z" | "2020-12-04T11:05:19Z" | "2020-12-04T11:05:19Z" | CONTRIBUTOR | null | 0 | {
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} | Added Korean NLU datasets. The link to the dataset can be found [here](https://github.com/kakaobrain/KorNLUDatasets) and the paper can be found [here](https://arxiv.org/abs/2004.03289)
**Note**: The MNLI tsv file is broken, so this code currently excludes the file. Please suggest other alternative if any @lhoestq
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/1061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1061/comments | https://api.github.com/repos/huggingface/datasets/issues/1061/events | https://github.com/huggingface/datasets/pull/1061 | 756,362,661 | MDExOlB1bGxSZXF1ZXN0NTMxOTE5ODA0 | 1,061 | add labr dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/1060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1060/comments | https://api.github.com/repos/huggingface/datasets/issues/1060/events | https://github.com/huggingface/datasets/pull/1060 | 756,349,001 | MDExOlB1bGxSZXF1ZXN0NTMxOTA4MTgx | 1,060 | Fix squad V2 metric script | {
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} | [] | closed | false | null | [] | null | 2 | "2020-12-03T16:23:32Z" | "2020-12-22T15:02:20Z" | "2020-12-22T15:02:19Z" | CONTRIBUTOR | null | 0 | {
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} | The current squad v2 metric doesn't work with the squad (v1 or v2) datasets. The script is copied from `squad_evaluate` in transformers that requires the labels (with multiple answers) to be like this:
```
references = [{'id': 'a', 'answers': [
{'text': 'Denver Broncos', 'answer_start': 177},
{'text': 'Denver Broncos', 'answer_start': 177}
]}]
```
while the dataset had references like this:
```
references = [{'id': 'a', 'answers':
{'text': ['Denver Broncos' 'Denver Broncos'], 'answer_start': [177, 177]}
}]
```
Using one or the other format fails with the current squad v2 metric:
```
from datasets import load_metric
metric = load_metric("squad_v2")
predictions = [{'id': 'a', 'prediction_text': 'Denver Broncos', 'no_answer_probability': 0.0}]
references = [{'id': 'a', 'answers': [
{'text': 'Denver Broncos', 'answer_start': 177},
{'text': 'Denver Broncos', 'answer_start': 177}
]}]
metric.compute(predictions=predictions, references=references)
```
fails as well as
```
from datasets import load_metric
metric = load_metric("squad_v2")
predictions = [{'id': 'a', 'prediction_text': 'Denver Broncos', 'no_answer_probability': 0.0}]
references = [{'id': 'a', 'answers':
{'text': ['Denver Broncos' 'Denver Broncos'], 'answer_start': [177, 177]}
}]
metric.compute(predictions=predictions, references=references)
```
This is because arrow reformats the references behind the scene.
With this PR (tested locally), both the snippets up there work and return proper results. | {
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} | [] | closed | false | null | [] | null | 0 | "2020-12-03T16:06:01Z" | "2020-12-04T13:46:05Z" | "2020-12-04T13:46:05Z" | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/1057 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1057/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1057/comments | https://api.github.com/repos/huggingface/datasets/issues/1057/events | https://github.com/huggingface/datasets/pull/1057 | 756,331,419 | MDExOlB1bGxSZXF1ZXN0NTMxODkzMjE4 | 1,057 | Adding TamilMixSentiment | {
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|
https://api.github.com/repos/huggingface/datasets/issues/1056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1056/comments | https://api.github.com/repos/huggingface/datasets/issues/1056/events | https://github.com/huggingface/datasets/pull/1056 | 756,309,828 | MDExOlB1bGxSZXF1ZXN0NTMxODc1MjA2 | 1,056 | Add deal_or_no_dialog | {
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} | Add deal_or_no_dialog Dataset
github: https://github.com/facebookresearch/end-to-end-negotiator
Paper: [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://arxiv.org/abs/1706.05125) | {
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https://api.github.com/repos/huggingface/datasets/issues/1055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1055/comments | https://api.github.com/repos/huggingface/datasets/issues/1055/events | https://github.com/huggingface/datasets/pull/1055 | 756,298,372 | MDExOlB1bGxSZXF1ZXN0NTMxODY1NjM4 | 1,055 | Add hebrew-sentiment | {
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} | Adding the dataset of SemEval 2014 Task 1
Found the dataset under the shared Google Sheet > Recurring Task Datasets
Task Homepage - https://alt.qcri.org/semeval2014/task1
Thank you! | {
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} | # Why I did
When I use "social_bias_frames" datasets in this library, I got 404 Errors.
So, I fixed this error and another some problems that I faced to use the dataset.
# What I did
* Modify this dataset URL
* Modify this dataset file names
* Add a "dataSource" column
Thank you! | {
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More info:
https://sharc-data.github.io/index.html | {
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} | Adds the GoEmotions dataset, a nice emotion classification dataset with 27 (multi-)label annotations on reddit comments. Includes both a large raw version and a narrowed version with predefined train/test/val splits, which I've included as separate configs with the latter as a default.
- Webpage/repo: https://github.com/google-research/google-research/tree/master/goemotions
- Paper: https://arxiv.org/abs/2005.00547 | {
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} | Added Korean NLU datasets. The link to the dataset can be found [here](https://github.com/kakaobrain/KorNLUDatasets) and the paper can be found [here](https://arxiv.org/abs/2004.03289)
**Note**: The MNLI tsv file is broken, so this code currently excludes the file. Please suggest other alternative if any @lhoestq
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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} | [] | closed | false | null | [] | null | 2 | "2020-12-03T11:43:46Z" | "2022-10-05T12:12:41Z" | "2022-10-05T12:12:41Z" | NONE | null | null | null | I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!
```import datasets
import torch
from datasets import load_dataset
print("version datasets", datasets.__version__)
dataset = load_dataset("snli", split='train[0:50]')
def tokenizer_fn(example):
# actually uses a tokenizer which does something like:
return {'input_ids': torch.tensor([[0, 1, 2]])}
print("First item in dataset:\n", dataset[0])
tokenized = tokenizer_fn(dataset[0])
print("Tokenized hyp:\n", tokenized)
dataset_tok = dataset.map(tokenizer_fn, batched=False,
remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
dataset_tok = dataset.map(tokenizer_fn, batched=False,
remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
```
The output is:
```
version datasets 1.1.3
Reusing dataset snli (/home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)
First item in dataset:
{'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}
Tokenized hyp:
{'input_ids': tensor([[0, 1, 2]])}
Loading cached processed dataset at /home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c/cache-fe38f449fe9ac46f.arrow
Tokenized using map:
{'input_ids': [[0, 1, 2]]}
<class 'torch.Tensor'> <class 'list'>
```
Or am I doing something wrong?
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} | github: https://github.com/fbougares/TSAC
paper: https://www.aclweb.org/anthology/W17-1307/ | {
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} | [] | closed | false | null | [] | null | 2 | "2020-12-03T11:07:59Z" | "2020-12-04T04:38:26Z" | "2020-12-04T04:38:26Z" | CONTRIBUTOR | null | 0 | {
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} | - More info on the dataset: https://evasharma.github.io/bigpatent/
- There's another raw version of the dataset available from tfds. However, they're quite large so I don't have the resources to fully test all the configs for that version yet. We'll try to add it later.
- ~Currently, there are no dummy data for this dataset yet as I'm facing some problems with generating them. I'm trying to add them later.~ | {
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} | Universal declaration of human rights with translations in 464 languages and dialects.
- UN page: https://www.ohchr.org/EN/UDHR/Pages/UDHRIndex.aspx
- Raw data source: https://unicode.org/udhr/index.html
Each instance of the dataset corresponds to one translation of the document. Since there's only one instance per language (and because there are 500 languages so the dummy data would be messy), I opted to just include them all under the same single config. I wasn't able to find any kind of license so I just copied the copyright notice.
I was pretty careful careful generating the language tags so they _should_ all be correct & consistent BCP-47 codes per the docs. | {
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More info:
http://qangaroo.cs.ucl.ac.uk/index.html | {
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More info:
http://qangaroo.cs.ucl.ac.uk/index.html
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https://api.github.com/repos/huggingface/datasets/issues/1034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1034/comments | https://api.github.com/repos/huggingface/datasets/issues/1034/events | https://github.com/huggingface/datasets/pull/1034 | 755,936,327 | MDExOlB1bGxSZXF1ZXN0NTMxNTY0MjA0 | 1,034 | add scb_mt_enth_2020 | {
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} | ## scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
The primary objective of our work is to build a large-scale English-Thai dataset for machine translation.
We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources,
namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents.
Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner.
We train machine translation models based on this dataset. Our models' performance are comparable to that of
Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is
included in the training data for both Thai-English and English-Thai translation.
The dataset, pre-trained models, and source code to reproduce our work are available for public use. | {
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Also support filenames with additional compression extension: ".txm.gz". | {
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more info : http://www.cfilt.iitb.ac.in/iitb_parallel/ | {
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} | This PR adds CrowS-Pairs datasets.
More info:
https://github.com/nyu-mll/crows-pairs/
https://arxiv.org/pdf/2010.00133.pdf | {
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https://api.github.com/repos/huggingface/datasets/issues/1030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1030/comments | https://api.github.com/repos/huggingface/datasets/issues/1030/events | https://github.com/huggingface/datasets/pull/1030 | 755,777,438 | MDExOlB1bGxSZXF1ZXN0NTMxNDI0MDM3 | 1,030 | allegro_reviews dataset | {
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} | [] | closed | false | null | [] | null | 0 | "2020-12-03T03:11:39Z" | "2020-12-04T10:56:29Z" | "2020-12-03T16:34:47Z" | CONTRIBUTOR | null | 0 | {
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} | - **Name:** *allegro_reviews*
- **Description:** *Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review).*
- **Data:** *https://github.com/allegro/klejbenchmark-allegroreviews*
- **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding.* | {
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https://api.github.com/repos/huggingface/datasets/issues/1029 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1029/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1029/comments | https://api.github.com/repos/huggingface/datasets/issues/1029/events | https://github.com/huggingface/datasets/pull/1029 | 755,767,616 | MDExOlB1bGxSZXF1ZXN0NTMxNDE2NzE4 | 1,029 | Add PEC | {
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} | Adding the ASSET dataset from https://github.com/facebookresearch/asset
One config for the simplification data, one for the human ratings of quality.
The README.md borrows from that written by @juand-r | {
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