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dataset link: https://www.kaggle.com/Cornell-University/arxiv | {
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As some tests did not clear! 👎🏼 | {
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|
https://api.github.com/repos/huggingface/datasets/issues/1294 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1294/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1294/comments | https://api.github.com/repos/huggingface/datasets/issues/1294/events | https://github.com/huggingface/datasets/pull/1294 | 759,365,246 | MDExOlB1bGxSZXF1ZXN0NTM0MzgzMjg5 | 1,294 | 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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https://api.github.com/repos/huggingface/datasets/issues/1292 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1292/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1292/comments | https://api.github.com/repos/huggingface/datasets/issues/1292/events | https://github.com/huggingface/datasets/pull/1292 | 759,354,627 | MDExOlB1bGxSZXF1ZXN0NTM0Mzc0MzQ3 | 1,292 | arXiv dataset added | {
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https://api.github.com/repos/huggingface/datasets/issues/1291 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1291/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1291/comments | https://api.github.com/repos/huggingface/datasets/issues/1291/events | https://github.com/huggingface/datasets/pull/1291 | 759,352,810 | MDExOlB1bGxSZXF1ZXN0NTM0MzcyNzk2 | 1,291 | adding pubmed_qa dataset | {
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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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https://api.github.com/repos/huggingface/datasets/issues/1290 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1290/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1290/comments | https://api.github.com/repos/huggingface/datasets/issues/1290/events | https://github.com/huggingface/datasets/issues/1290 | 759,339,989 | MDU6SXNzdWU3NTkzMzk5ODk= | 1,290 | imdb dataset cannot be downloaded | {
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} | [] | closed | false | null | [] | null | [] | 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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https://api.github.com/repos/huggingface/datasets/issues/1289 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1289/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1289/comments | https://api.github.com/repos/huggingface/datasets/issues/1289/events | https://github.com/huggingface/datasets/pull/1289 | 759,333,684 | MDExOlB1bGxSZXF1ZXN0NTM0MzU2ODUw | 1,289 | Jigsaw toxicity classification dataset added | {
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} | The dataset requires manually downloading data from Kaggle. | {
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https://api.github.com/repos/huggingface/datasets/issues/1288 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1288/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1288/comments | https://api.github.com/repos/huggingface/datasets/issues/1288/events | https://github.com/huggingface/datasets/pull/1288 | 759,309,457 | MDExOlB1bGxSZXF1ZXN0NTM0MzM2Mzgz | 1,288 | Add CodeSearchNet corpus dataset | {
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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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https://api.github.com/repos/huggingface/datasets/issues/1287 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1287/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1287/comments | https://api.github.com/repos/huggingface/datasets/issues/1287/events | https://github.com/huggingface/datasets/issues/1287 | 759,300,992 | MDU6SXNzdWU3NTkzMDA5OTI= | 1,287 | 'iwslt2017-ro-nl', cannot be downloaded | {
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] | null | [] | 2020-12-08T09:56:55Z | 2022-06-13T10:41:33Z | 2022-06-13T10:41:33Z | 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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https://api.github.com/repos/huggingface/datasets/issues/1286 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1286/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1286/comments | https://api.github.com/repos/huggingface/datasets/issues/1286/events | https://github.com/huggingface/datasets/issues/1286 | 759,291,509 | MDU6SXNzdWU3NTkyOTE1MDk= | 1,286 | [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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} | [] | closed | false | null | [] | null | [] | 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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https://api.github.com/repos/huggingface/datasets/issues/1285 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1285/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1285/comments | https://api.github.com/repos/huggingface/datasets/issues/1285/events | https://github.com/huggingface/datasets/issues/1285 | 759,278,758 | MDU6SXNzdWU3NTkyNzg3NTg= | 1,285 | boolq does not work | {
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} | [] | closed | false | null | [] | null | [] | 2020-12-08T09:28:47Z | 2020-12-08T09:47:10Z | 2020-12-08T09:47:10Z | CONTRIBUTOR | null | null | null | 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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} | - 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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} | 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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https://github.com/wenhuchen/HybridQA | {
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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` dataset from [here](https://worksheets.codalab.org/worksheets/0x453913e76b65495d8b9730d41c7e0a0c/) | {
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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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} | I just added Yoruba GV NER dataset from this paper https://www.aclweb.org/anthology/2020.lrec-1.335/ | {
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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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} | Hi :) I added this [SMS Spam Dataset](http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection) | {
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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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} | 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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Link to the paper: https://arxiv.org/pdf/2004.11339.pdf
Link to the homepage: https://covidqa.ai | {
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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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} | 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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https://github.com/ilmgut/limit_dataset | {
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Paper: https://arxiv.org/abs/1907.00409
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks | {
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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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} | 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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Once complete this will add the [Doc2dial](https://doc2dial.github.io/data.html) dataset from the generic data sets list. | {
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More info : http://opus.nlpl.eu/Elhuyar.php | {
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https://github.com/google-research-datasets/poem-sentiment | {
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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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More info : http://opus.nlpl.eu/Finlex.php | {
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} | Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki | {
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} | Adding the [NFH: Numeric Fused Head](https://nlp.biu.ac.il/~lazary/fh/) dataset.
Everything looks sensible and I've included both the identification and resolution tasks. I haven't personally used this dataset in my research so am unable to specify what the default configuration / supervised keys should be.
I've filled out the basic info on the model card to the best of my knowledge but it's a little tricky to understand exactly what the fields represent.
Dataset author: @yanaiela | {
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} | This PR adds the [Hong Kong Cantonese Corpus](http://compling.hss.ntu.edu.sg/hkcancor/), by [Luke and Wong 2015](http://compling.hss.ntu.edu.sg/hkcancor/data/LukeWong_Hong-Kong-Cantonese-Corpus.pdf).
The dummy data included here was manually created, as the original dataset uses a xml-like format (see a copy hosted [here](https://github.com/fcbond/hkcancor/blob/master/sample/d1_v.txt) for example) that requires a few processing steps. | {
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https://api.github.com/repos/huggingface/datasets/issues/1219 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1219/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1219/comments | https://api.github.com/repos/huggingface/datasets/issues/1219/events | https://github.com/huggingface/datasets/pull/1219 | 758,013,368 | MDExOlB1bGxSZXF1ZXN0NTMzMjU5NzMw | 1,219 | Add Korean NER dataset | {
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} | Supersedes #1177
> This PR adds the [Korean named entity recognition dataset](https://github.com/kmounlp/NER). This dataset has been used in many downstream tasks, such as training [KoBERT](https://github.com/SKTBrain/KoBERT) for NER, as seen in this [KoBERT-CRF implementation](https://github.com/eagle705/pytorch-bert-crf-ner). | {
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} | 3 tasks for the WMT 20 MLQE shared tasks -> 3 different datasets
(I re-created #1137 because it was too messy).
Note that in L199 `task3.py`, I used `logging.warning` to print some missing data in the train set. | {
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} | Adding the data from: https://datacommons.org/factcheck/
Had to cheat a bit with the dummy data as the test doesn't recognize `.txt.gz`: had to rename uncompressed files with the `.gz` extension manually without actually compressing | {
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} | This PR adds [LiMiT](https://github.com/ilmgut/limit_dataset), a dataset for literal motion classification/extraction by [Manotas et al., 2020](https://www.aclweb.org/anthology/2020.findings-emnlp.88.pdf). | {
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} | added files for irc disentanglement dataset
was unable to test dummy data as a result of vpn/proxy issues | {
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https://api.github.com/repos/huggingface/datasets/issues/1214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1214/comments | https://api.github.com/repos/huggingface/datasets/issues/1214/events | https://github.com/huggingface/datasets/pull/1214 | 758,002,786 | MDExOlB1bGxSZXF1ZXN0NTMzMjUyNTgx | 1,214 | adding medical-questions-pairs dataset | {
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} | This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors.
Dataset : https://github.com/curai/medical-question-pair-dataset
Paper : https://drive.google.com/file/d/1CHPGBXkvZuZc8hpr46HeHU6U6jnVze-s/view | {
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https://api.github.com/repos/huggingface/datasets/issues/1213 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1213/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1213/comments | https://api.github.com/repos/huggingface/datasets/issues/1213/events | https://github.com/huggingface/datasets/pull/1213 | 757,983,884 | MDExOlB1bGxSZXF1ZXN0NTMzMjM4NzEz | 1,213 | add taskmaster3 | {
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} | Adding Taskmaster-3 dataset
https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020.
The dataset structure almost same as original dataset with these two changes
1. In original dataset, each `apis` has a `args` filed which is a `dict` with variable keys, which represent the name and value of the args. Here converted that to a `list` of `dict` with keys `arg_name` and `arg_value`. For ex.
```python
args = {"name.movie": "Mulan", "name.theater": ": "Mountain AMC 16"}
```
becomes
```python
[
{
"arg_name": "name.movie",
"arg_value": "Mulan"
},
{
"arg_name": "name.theater",
"arg_value": "Mountain AMC 16"
}
]
```
2. Each `apis` has a `response` which is also a `dict` with variable keys representing response name/type and it's value. As above converted it to `list` of `dict` with keys `response_name` and `response_value`.
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} | Adds a collection of Spanish corpora that can be useful for pretraining language models.
Following a nice suggestion from @yjernite we provide the user with three main ways to preprocess / load either
* the whole corpus (17GB!)
* one specific sub-corpus
* the whole corpus, but return a single split. this is useful if you want to cache the whole preprocessing step once and interact with individual sub-corpora
See the dataset card for more details.
Ready for review! | {
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This PR improves the config's description a bit by linking to the paper. | {
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https://api.github.com/repos/huggingface/datasets/issues/1205 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1205/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1205/comments | https://api.github.com/repos/huggingface/datasets/issues/1205/events | https://github.com/huggingface/datasets/pull/1205 | 757,942,403 | MDExOlB1bGxSZXF1ZXN0NTMzMjA4NDI1 | 1,205 | add lst20 with manual download | {
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} | passed on local:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_lst20
```
Not sure how to test:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lst20
```
```
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
``` | {
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