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Added the NewsPH Raw dataset and corresponding dataset card
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This PR adds the original NewsPH dataset which is used to autogenerate the NewsPH-NLI dataset. Reopened a new PR as the previous one had problems.
Paper: https://arxiv.org/abs/2010.11574
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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TEP: Tehran English-Persian parallel corpus
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TEP: Tehran English-Persian parallel corpus
more info : http://opus.nlpl.eu/TEP.php
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Add humicroedit dataset
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Pull request for adding humicroedit dataset
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❓ Sharing ElasticSearch indexed dataset
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[] | null | 3 | 2020-12-08T16:25:58Z | 2020-12-22T07:50:56Z | null |
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Hi there,
First of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.
**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering
- how can I know where it has been saved?
- how can I share the indexed dataset with others?
I tried to dig into the docs, but could not find anything about that.
Thank you very much for your help.
Best,
Pietro
Edit: apologies for the wrong label
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Add CC-News dataset of English language articles
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Adds [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/) dataset. It contains 708241 English language news articles. Although each article has a language field these tags are not reliable. I've used Spacy language detection [pipeline](https://spacy.io/universe/project/spacy-langdetect) to confirm that the article language is indeed English.
The prepared dataset is temporarily hosted on my private Google Storage [bucket](https://storage.googleapis.com/hf_datasets/cc_news.tar.gz). We can move it to HF storage and update this PR before merging.
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add indonlu benchmark datasets
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The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for the Indonesian language. There are 12 datasets in IndoNLU.
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The Dutch social media tweets dataset. Which has a total of more than 210k tweets in dutch language. These tweets have been machine annotated with sentiment scores (`label` feature) and `industry` and `hisco_codes`
It can be used for sentiment analysis, multi-label classification and entity tagging
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Added the WikiText-TL39 dataset and corresponding card
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This PR adds the WikiText-TL-39 Filipino Language Modeling dataset. Restarted a new pull request since there were problems with the earlier one.
Paper: https://arxiv.org/abs/1907.00409
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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adding wili-2018 language identification dataset
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ethos first commit
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Ethos passed all the tests except from this one:
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your-dataset-name>
with this error:
E OSError: Cannot find data file.
E Original error:
E [Errno 2] No such file or directory:
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add 10k German News Article Dataset
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Allow GitHub releases as dataset source
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CONTRIBUTOR
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# Summary
Providing a GitHub release URL to `DownloadManager.download()` currently throws a `ConnectionError: Couldn't reach [DOWNLOAD_URL]`. This PR fixes this problem by adding an exception for GitHub releases in `datasets.utils.file_utils.get_from_cache()`.
# Reproduce
```
import datasets
url = 'http://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz'
result = datasets.utils.file_utils.get_from_cache(url)
# Returns: ConnectionError: Couldn't reach http://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz
```
# Cause
GitHub releases returns a HTTP status 403 (FOUND), indicating that the request is being redirected (to AWS S3, in this case). `get_from_cache()` checks whether the status is 200 (OK) or if it is part of two exceptions (Google Drive or Firebase), otherwise the mentioned error is thrown.
# Solution
Just like the exceptions for Google Drive and Firebase, add a condition for GitHub releases URLs that return the HTTP status 403. If this is the case, continue normally.
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add yelp_review_full
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This corresponds to the Yelp-5 requested in https://github.com/huggingface/datasets/issues/353
I included the dataset card.
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Add snips built in intents 2016 12
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This PR proposes to add the Snips.ai built in intents dataset. The first configuration added is for the intent labels only, but the dataset includes entity slots that may in future be added as alternate configurations.
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Add HateSpeech Corpus for Polish
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CONTRIBUTOR
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This PR adds a HateSpeech Corpus for Polish, containing offensive language examples.
- **Homepage:** http://zil.ipipan.waw.pl/HateSpeech
- **Paper:** http://www.qualitativesociologyreview.org/PL/Volume38/PSJ_13_2_Troszynski_Wawer.pdf
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Requires manually downloading data from Kaggle.
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Add OPUS Bible Corpus (102 Languages)
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Add OffensEval-TR 2020 Dataset
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CONTRIBUTOR
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This PR adds the OffensEval-TR 2020 dataset which is a Turkish offensive language corpus by me and @basakbuluz. The corpus consist of randomly sampled tweets and annotated in a similar way to [OffensEval](https://sites.google.com/site/offensevalsharedtask/) and [GermEval](https://projects.fzai.h-da.de/iggsa/).
- **Homepage:** [offensive-turkish](https://coltekin.github.io/offensive-turkish/)
- **Paper:** [A Corpus of Turkish Offensive Language on Social Media](https://coltekin.github.io/offensive-turkish/troff.pdf)
- **Point of Contact:** [Çağrı Çöltekin]([email protected])
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Add SAMSum Corpus dataset
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Did not spent much time writing README, might update later.
Copied description and some stuff from tensorflow_datasets
https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/summarization/samsum.py
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Add Wiki Lingua Dataset
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Hello,
This is my first PR.
I have added Wiki Lingua Dataset along with dataset card to the best of my knowledge.
There was one hiccup though. I was unable to create dummy data because the data is in pkl format.
From the document, I see that:
```At the moment it supports data files in the following format: txt, csv, tsv, jsonl, json, xml```
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Adding Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES
https://sites.google.com/view/felipe-soares/datasets#h.p_kxOR6EhHm2a6
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- **Name:** W&I + LOCNESS dataset (from the BEA-2019 workshop shared task on GEC)
- **Description:** https://www.cl.cam.ac.uk/research/nl/bea2019st/#data
- **Paper:** https://www.aclweb.org/anthology/W19-4406/
- **Motivation:** This is a recent dataset (actually two in one) for grammatical error correction and is used for benchmarking in this field of NLP.
### Checkbox
- [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 : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
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[README] Added Windows command to enable slow tests
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The Windows command to run slow tests has caused issues, so this adds a functional Windows command.
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Adding EiTB-ParCC: Parallel Corpus of Comparable News
http://opus.nlpl.eu/EiTB-ParCC.php
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Adding Opus OpenOffice: http://opus.nlpl.eu/OpenOffice.php
8 languages, 28 bitexts
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**adding arXiv dataset**: arXiv dataset and metadata of 1.7M+ scholarly papers across STEM
dataset link: https://www.kaggle.com/Cornell-University/arxiv
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WIP
As some tests did not clear! 👎🏼
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NONE
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FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition/german_legal_entity_recognition.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/german_legal_entity_recognition/german_legal_entity_recognition.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/german_legal_entity_recognition/german_legal_entity_recognition.py
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Add OPUS Ted Talks 2013
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OPUS Ted Talks 2013
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The Snips Built In Intents 2016 dataset.
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This PR proposes to add the Snips.ai built in intents dataset. The first configuration added is for the intent labels only, but the dataset includes entity slots that may in future be added as alternate configurations.
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adding opus_euconst
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Adding EUconst, a parallel corpus collected from the European Constitution.
21 languages, 210 bitexts
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Pubmed QA dataset:
PQA-L(abeled) 1k
PQA-U(labeled) 61.2k
PQA-A(rtifical labeled) 211.3k
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| 1,290 |
imdb dataset cannot be downloaded
|
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[] | null | 3 | 2020-12-08T10:47:36Z | 2020-12-24T17:38:09Z | 2020-12-24T17:38:09Z |
CONTRIBUTOR
| null | null | null |
hi
please find error below getting imdb train spli:
thanks
`
datasets.load_dataset>>> datasets.load_dataset("imdb", split="train")`
errors
```
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets/downloads
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 558, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 73, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=7486451, num_examples=5628, dataset_name='imdb')}]
```
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Jigsaw toxicity classification dataset added
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[] | null | 0 | 2020-12-08T10:38:51Z | 2020-12-08T15:17:48Z | 2020-12-08T15:17:48Z |
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The dataset requires manually downloading data from Kaggle.
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| 759,309,457 |
MDExOlB1bGxSZXF1ZXN0NTM0MzM2Mzgz
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Add CodeSearchNet corpus dataset
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[] | null | 1 | 2020-12-08T10:07:50Z | 2020-12-09T17:05:28Z | 2020-12-09T17:05:28Z |
CONTRIBUTOR
| null | 0 |
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This PR adds the CodeSearchNet corpus proxy dataset for semantic code search: https://github.com/github/CodeSearchNet
I have had a few issues, mentioned below. Would appreciate some help on how to solve them.
## Issues generating dataset card
Is there something wrong with my declaration of the dataset features ?
```
features=datasets.Features(
{
"repository_name": datasets.Value("string"),
"func_path_in_repository": datasets.Value("string"),
"func_name": datasets.Value("string"),
"whole_func_string": datasets.Value("string"),
"language": datasets.Value("string"),
"func_code_string": datasets.Value("string"),
"func_code_tokens": datasets.Sequence(datasets.Value("string")),
"func_documentation_string": datasets.Value("string"),
"func_documentation_tokens": datasets.Sequence(datasets.Value("string")),
"split_name": datasets.Value("string"),
"func_code_url": datasets.Value("string"),
# TODO - add licensing info in the examples
}
),
```
When running the streamlite app for tagging the dataset on my machine, I get the following error :
## Issues with dummy data
Due to the unusual structure of the data, I have been unable to generate dummy data automatically.
I tried to generate it manually, but pytests fail when using the manually-generated dummy data ! Pytests work fine when using the real data.
```
============================================================================================== test session starts ==============================================================================================
platform linux -- Python 3.7.9, pytest-6.1.2, py-1.9.0, pluggy-0.13.1
plugins: xdist-2.1.0, forked-1.3.0
collected 1 item
tests/test_dataset_common.py F [100%]
=================================================================================================== FAILURES ====================================================================================================
________________________________________________________________________ LocalDatasetTest.test_load_dataset_all_configs_code_search_net _________________________________________________________________________
self = <tests.test_dataset_common.LocalDatasetTest testMethod=test_load_dataset_all_configs_code_search_net>, dataset_name = 'code_search_net'
@slow
def test_load_dataset_all_configs(self, dataset_name):
configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)
> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True)
tests/test_dataset_common.py:237:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_dataset_common.py:198: in check_load_dataset
self.parent.assertTrue(len(dataset[split]) > 0)
E AssertionError: False is not true
--------------------------------------------------------------------------------------------- Captured stdout call ----------------------------------------------------------------------------------------------
Downloading and preparing dataset code_search_net/all (download: 1.00 MiB, generated: 1.00 MiB, post-processed: Unknown size, total: 2.00 MiB) to /tmp/tmppx78sj24/code_search_net/all/1.0.0...
Dataset code_search_net downloaded and prepared to /tmp/tmppx78sj24/code_search_net/all/1.0.0. Subsequent calls will reuse this data.
--------------------------------------------------------------------------------------------- Captured stderr call ----------------------------------------------------------------------------------------------
... (irrelevant info - Deprecation warnings)
============================================================================================ short test summary info ============================================================================================
FAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_code_search_net - AssertionError: False is not true
========================================================================================= 1 failed, 4 warnings in 3.00s ========================================================================================
```
## Note : Data structure in S3
The data is stored on S3, and organized by programming languages.
It is stored in the following repository structure:
```
.
├── <language_name> # e.g. python
│ └── final
│ └── jsonl
│ ├── test
│ │ └── <language_name>_test_0.jsonl.gz
│ ├── train
│ │ ├── <language_name>_train_0.jsonl.gz
│ │ ├── <language_name>_train_1.jsonl.gz
│ │ ├── ...
│ │ └── <language_name>_train_n.jsonl.gz
│ └── valid
│ └── <language_name>_valid_0.jsonl.gz
├── <language_name>_dedupe_definitions_v2.pkl
└── <language_name>_licenses.pkl
```
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'iwslt2017-ro-nl', cannot be downloaded
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CONTRIBUTOR
| null | null | null |
Hi
I am trying
`>>> datasets.load_dataset("iwslt2017", 'iwslt2017-ro-nl', split="train")`
getting this error thank you for your help
```
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
Downloading and preparing dataset iwsl_t217/iwslt2017-ro-nl (download: 314.07 MiB, generated: 39.92 MiB, post-processed: Unknown size, total: 354.00 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/iwsl_t217/iwslt2017-ro-nl/1.0.0/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd...
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets/downloads
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/iwslt2017/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd/iwslt2017.py", line 118, in _split_generators
dl_dir = dl_manager.download_and_extract(MULTI_URL)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
return function(data_struct)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 477, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz
```
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[libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0): terminate called after throwing an instance of 'google::protobuf::FatalException' what(): CHECK failed: (index) >= (0): Aborted
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[] | null | 6 | 2020-12-08T09:44:15Z | 2020-12-12T19:36:22Z | 2020-12-12T16:22:36Z |
CONTRIBUTOR
| null | null | null |
Hi
I am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help
{'epoch': 20.0}
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:16<00:00, 1.22it/s]
12/08/2020 10:41:19 - INFO - seq2seq.trainers.trainer - Saving model checkpoint to outputs/experiment/joint/finetune/lr-2e-5
12/08/2020 10:41:24 - INFO - __main__ - {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}
12/08/2020 10:41:24 - INFO - __main__ - *** Evaluate ***
12/08/2020 10:41:24 - INFO - seq2seq.utils.utils - using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - ***** Running Evaluation *****
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - Num examples = 1998
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - Batch size = 64
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:37<00:00, 1.19s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0):
terminate called after throwing an instance of 'google::protobuf::FatalException'
what(): CHECK failed: (index) >= (0):
Aborted
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MDU6SXNzdWU3NTkyNzg3NTg=
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boolq does not work
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[] | null | 3 | 2020-12-08T09:28:47Z | 2020-12-08T09:47:10Z | 2020-12-08T09:47:10Z |
CONTRIBUTOR
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Hi
I am getting this error when trying to load boolq, thanks for your help
ts_boolq_default_0.1.0_2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11.lock
Traceback (most recent call last):
File "finetune_t5_trainer.py", line 274, in <module>
main()
File "finetune_t5_trainer.py", line 147, in main
for task in data_args.tasks]
File "finetune_t5_trainer.py", line 147, in <listcomp>
for task in data_args.tasks]
File "/remote/idiap.svm/user.active/rkarimi/dev/ruse/seq2seq/tasks/tasks.py", line 58, in get_dataset
dataset = self.load_dataset(split=split)
File "/remote/idiap.svm/user.active/rkarimi/dev/ruse/seq2seq/tasks/tasks.py", line 54, in load_dataset
return datasets.load_dataset(self.task.name, split=split)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 149, in download_custom
custom_download(url, path)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py", line 516, in copy_v2
compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)
tensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists
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Update coqa dataset url
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`datasets.stanford.edu` is invalid.
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| 759,251,457 |
MDExOlB1bGxSZXF1ZXN0NTM0Mjg4MDg2
| 1,283 |
Add dutch book review dataset
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[] | null | 1 | 2020-12-08T08:50:48Z | 2020-12-09T20:21:58Z | 2020-12-09T17:25:25Z |
CONTRIBUTOR
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- Name: Dutch Book Review Dataset (DBRD)
- Description: The DBRD (pronounced dee-bird) dataset contains over 110k book reviews along with associated binary sentiment polarity labels and is intended as a benchmark for sentiment classification in Dutch.
- Paper: https://arxiv.org/abs/1910.00896
- Data: https://github.com/benjaminvdb/DBRD
- Motivation: A large (real-life) dataset of Dutch book reviews and sentiment polarity (positive/negative), based on the associated rating.
Checks
- [x] Create the dataset script /datasets/dbrd/dbrd.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _info(), _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 : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
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add thaiqa_squad
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[] | null | 0 | 2020-12-08T08:14:38Z | 2020-12-08T18:36:18Z | 2020-12-08T18:36:18Z |
CONTRIBUTOR
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Example format is a little different from SQuAD since `thaiqa` always have one answer per question so I added a check to convert answers to lists if they are not already one to future-proof additional questions that might have multiple answers.
`thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from Wikipedia articles and adapted to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format by [PyThaiNLP](https://github.com/PyThaiNLP/).
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adding hybrid_qa
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MEMBER
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Adding HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
https://github.com/wenhuchen/HybridQA
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disaster response messages dataset
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[] | null | 2 | 2020-12-08T07:27:16Z | 2020-12-09T16:21:57Z | 2020-12-09T16:21:57Z |
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MDExOlB1bGxSZXF1ZXN0NTM0MTU4OTY5
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added para_pat
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[] | null | 2 | 2020-12-08T06:28:47Z | 2020-12-14T13:41:17Z | 2020-12-14T13:41:17Z |
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Dataset link : https://figshare.com/articles/ParaPat_The_Multi-Million_Sentences_Parallel_Corpus_of_Patents_Abstracts/12627632
Working on README.md currently
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Craigslist bargains
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[] | null | 2 | 2020-12-08T01:45:55Z | 2020-12-09T00:46:15Z | 2020-12-09T00:46:15Z |
CONTRIBUTOR
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`craigslist_bargains` dataset from [here](https://worksheets.codalab.org/worksheets/0x453913e76b65495d8b9730d41c7e0a0c/)
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add One Million Posts Corpus
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[] | null | 1 | 2020-12-08T00:50:08Z | 2020-12-11T18:28:18Z | 2020-12-11T18:28:18Z |
CONTRIBUTOR
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- **Name:** One Million Posts Corpus
- **Description:** The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).
- **Paper:** https://dl.acm.org/doi/10.1145/3077136.3080711
- **Data:** https://github.com/OFAI/million-post-corpus
- **Motivation:** Big German (real-life) dataset containing different annotations around forum moderation with expert annotations.
### Checkbox
- [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 : fill the tags and the various paragraphs
- [X] Both tests for the real data and the dummy data pass.
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Yoruba GV NER added
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[] | null | 1 | 2020-12-08T00:31:38Z | 2020-12-08T23:25:28Z | 2020-12-08T23:25:28Z |
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I just added Yoruba GV NER dataset from this paper https://www.aclweb.org/anthology/2020.lrec-1.335/
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oclar-dataset
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[] | null | 1 | 2020-12-07T23:56:45Z | 2020-12-09T15:36:08Z | 2020-12-09T15:36:08Z |
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Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on reviews, including hotels, restaurants, shops, and others. : [homepage](http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#)
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Created wiki_movies dataset.
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[] | null | 5 | 2020-12-07T23:38:54Z | 2020-12-14T13:56:49Z | 2020-12-14T13:56:49Z |
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First PR (ever). Hopefully this movies dataset is useful to others!
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[] | null | 0 | 2020-12-07T23:19:36Z | 2020-12-07T23:48:05Z | 2020-12-07T23:47:48Z |
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Hi :) I added this [SMS Spam Dataset](http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection)
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add DFKI SmartData Corpus
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- **Name:** DFKI SmartData Corpus
- **Description:** DFKI SmartData Corpus is a dataset of 2598 German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types.
- **Paper:** https://www.dfki.de/fileadmin/user_upload/import/9427_lrec_smartdata_corpus.pdf
- **Data:** https://github.com/DFKI-NLP/smartdata-corpus
- **Motivation:** Contains fine-grained NER labels for German.
### Checkbox
- [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 : fill the tags and the various paragraphs
- [X] Both tests for the real data and the dummy data pass.
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Adding OneStopEnglish corpus dataset
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[] | null | 1 | 2020-12-07T22:05:11Z | 2020-12-09T18:43:38Z | 2020-12-09T15:33:53Z |
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This PR adds OneStopEnglish Corpus containing texts classified into reading levels (elementary, intermediate, advance) for automatic readability assessment and text simplification.
Link to the paper: https://www.aclweb.org/anthology/W18-0535.pdf
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new pr for Turkish NER
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Has part
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removing unzipped hansards dummy data
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which were added by mistake
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Add CovidQA dataset
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This PR adds CovidQA, a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle’s COVID-19 Open Research Dataset Challenge.
Link to the paper: https://arxiv.org/pdf/2004.11339.pdf
Link to the homepage: https://covidqa.ai
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enriched webnlg dataset rebase
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Rebase of #1206 !
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Manual Download of a kaggle dataset. Mostly followed process as ms_terms.
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Add Google Sentence Compression dataset
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For more information: https://www.aclweb.org/anthology/D13-1155.pdf
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NONE
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Added the raw version of the NewsPH dataset, which was used to automatically generate the NewsPH-NLI corpus. Dataset of news articles in Filipino from mainstream Philippine news sites on the internet. Can be used as a language modeling dataset or to reproduce the NewsPH-NLI dataset.
Paper: https://arxiv.org/abs/2010.11574
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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Add KorQPair dataset
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This PR adds a [Korean paired question dataset](https://github.com/songys/Question_pair) containing labels indicating whether two questions in a given pair are semantically identical. This dataset was used to evaluate the performance of [KoGPT2](https://github.com/SKT-AI/KoGPT2#subtask-evaluations) on a phrase detection downstream task.
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Adding LiMiT: The Literal Motion in Text Dataset
https://github.com/ilmgut/limit_dataset
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[doc] nlp/viewer ➡️datasets/viewer
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cc @srush
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[] | null | 1 | 2020-12-07T13:43:48Z | 2020-12-08T16:00:58Z | 2020-12-08T16:00:58Z |
NONE
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This PR adds the WikiText-TL-39 Filipino Language Modeling dataset.
Paper: https://arxiv.org/abs/1907.00409
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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MDExOlB1bGxSZXF1ZXN0NTMzNjc4MDE1
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add thainer
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ThaiNER (v1.3) is a 6,456-sentence named entity recognition dataset created from expanding the 2,258-sentence
[unnamed dataset](http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) by
[Tirasaroj and Aroonmanakun (2012)](http://pioneer.chula.ac.th/~awirote/publications/).
It is used to train NER taggers in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp).
The NER tags are annotated by [Tirasaroj and Aroonmanakun (2012)]((http://pioneer.chula.ac.th/~awirote/publications/))
for 2,258 sentences and the rest by [@wannaphong](https://github.com/wannaphong/).
The POS tags are done by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)'s `perceptron` engine trained on `orchid_ud`.
[@wannaphong](https://github.com/wannaphong/) is now the only maintainer of this dataset.
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Add Naver sentiment movie corpus
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Supersedes #1168
> This PR adds the [Naver sentiment movie corpus](https://github.com/e9t/nsmc), a dataset containing Korean movie reviews from Naver, the most commonly used search engine in Korea. This dataset is often used to benchmark models on Korean NLP tasks, as seen in [this paper](https://www.aclweb.org/anthology/2020.lrec-1.199.pdf).
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MDExOlB1bGxSZXF1ZXN0NTMzNjY2NTg2
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Add Wiki Atomic Edits Dataset (43M edits)
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Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis. This PR is only for the Named Entity Recognition.
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Add doc2dial dataset
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### Doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset v0.9
Once complete this will add the [Doc2dial](https://doc2dial.github.io/data.html) dataset from the generic data sets list.
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IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia. It contains 12 datasets.
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Opus elhuyar dataset for MT task having languages pair in Spanish to Basque
More info : http://opus.nlpl.eu/Elhuyar.php
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This corresponds to the Yelp-5 requested in https://github.com/huggingface/datasets/issues/353
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Adding poem_sentiment dataset.
https://github.com/google-research-datasets/poem-sentiment
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Add AmbigQA dataset
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# AmbigQA: Answering Ambiguous Open-domain Questions Dataset
Adding the [AmbigQA](https://nlp.cs.washington.edu/ambigqa/) dataset as part of the sprint 🎉 (from Open dataset list for Dataset sprint)
Added both the light and full versions (as seen on the dataset homepage)
The json format changes based on the value of one 'type' field, so I set the unavailable field to an empty list. This is explained in the README -> Data Fields
```py
train_light_dataset = load_dataset('./datasets/ambig_qa',"light",split="train")
val_light_dataset = load_dataset('./datasets/ambig_qa',"light",split="validation")
train_full_dataset = load_dataset('./datasets/ambig_qa',"full",split="train")
val_full_dataset = load_dataset('./datasets/ambig_qa',"full",split="validation")
for example in train_light_dataset:
for i,t in enumerate(example['annotations']['type']):
if t =='singleAnswer':
# use the example['annotations']['answer'][i]
# example['annotations']['qaPairs'][i] - > is []
print(example['annotations']['answer'][i])
else:
# use the example['annotations']['qaPairs'][i]
# example['annotations']['answer'][i] - > is []
print(example['annotations']['qaPairs'][i])
```
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
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Opus finlex dataset of language pair Finnish and Swedish
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Added Opus_finlex dataset of language pair Finnish and Swedish
More info : http://opus.nlpl.eu/Finlex.php
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Wino bias
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The PR will fail circleCi tests because of the requirement of manual loading of data. Fresh PR because of messed up history of the previous one.
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Adverse Drug Reaction Data: ADE-Corpus-V2 dataset added configs for different tasks with given data
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Add Curiosity Dialogs Dataset
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Add Facebook [Curiosity Dialogs](https://github.com/facebookresearch/curiosity) Dataset.
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| 758,180,669 |
MDExOlB1bGxSZXF1ZXN0NTMzMzkyNTc0
| 1,232 |
Add Grail QA dataset
|
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[] | null | 0 | 2020-12-07T05:46:45Z | 2020-12-08T13:03:19Z | 2020-12-08T13:03:19Z |
CONTRIBUTOR
| null | 0 |
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For more information: https://dki-lab.github.io/GrailQA/
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| 758,121,398 |
MDExOlB1bGxSZXF1ZXN0NTMzMzQzMzAz
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Add Urdu Sentiment Corpus (USC)
|
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[] | null | 0 | 2020-12-07T03:25:20Z | 2020-12-07T18:05:16Z | 2020-12-07T16:43:23Z |
CONTRIBUTOR
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@lhoestq opened a clean PR containing only relevant files.
old PR #1140
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Add Urdu fake news dataset
|
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[] | null | 1 | 2020-12-07T03:19:50Z | 2020-12-07T18:04:55Z | 2020-12-07T16:57:54Z |
CONTRIBUTOR
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@lhoestq opened a clean PR containing only relevant files.
old PR #1125
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Muchocine - Spanish movie reviews dataset
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[] | null | 4 | 2020-12-07T02:23:29Z | 2020-12-21T10:09:09Z | 2020-12-21T10:09:09Z |
CONTRIBUTOR
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add opus_100 dataset
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[] | null | 1 | 2020-12-06T23:17:24Z | 2020-12-09T14:54:00Z | 2020-12-09T14:54:00Z |
CONTRIBUTOR
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This PR will add [opus100 dataset](http://opus.nlpl.eu/opus-100.php).
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