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https://api.github.com/repos/huggingface/datasets/issues/1458 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1458/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1458/comments | https://api.github.com/repos/huggingface/datasets/issues/1458/events | https://github.com/huggingface/datasets/pull/1458 | 761,235,962 | MDExOlB1bGxSZXF1ZXN0NTM1OTMyMTA1 | 1,458 | Add id_nergrit_corpus | {
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} | Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis.
Recently my PR for id_nergrit_ner has been accepted and merged to the main branch. The id_nergrit_ner has only one dataset (NER), and this new PR renamed the dataset from id_nergrit_ner to id_nergrit_corpus and added 2 other remaining datasets (Statement Extraction, and Sentiment Analysis.) | {
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} | [] | closed | false | null | [] | null | 2 | 2020-12-10 10:16:55+00:00 | 2020-12-10 17:49:55+00:00 | 2020-12-10 17:49:55+00:00 | NONE | null | null | null | null | ```
import datasets
nli_data = datasets.load_dataset("snli")
train_data = nli_data['train']
train_labels = train_data['label']
label_set = set(train_labels)
print(label_set)
```
**Output:**
`{0, 1, 2, -1}` | {
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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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} | Thai Toxicity Tweet Corpus contains 3,300 tweets (506 tweets with texts missing) annotated by humans with guidelines including a 44-word dictionary. The author obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing target, and word sense ambiguity.
Notes from data cleaner: The data is included into [huggingface/datasets](https://www.github.com/huggingface/datasets) in Dec 2020. By this time, 506 of the tweets are not available publicly anymore. We denote these by `TWEET_NOT_FOUND` in `tweet_text`.
Processing can be found at [this PR](https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/pull/1). | {
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!pip install datasets
import datasets as ds
corpus = ds.load_dataset('large_spanish_corpus')
```
gives the error
> FileNotFoundError: Couldn't find file locally at large_spanish_corpus/large_spanish_corpus.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/large_spanish_corpus/large_spanish_corpus.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/large_spanish_corpus/large_spanish_corpus.py
not just `large_spanish_corpus`, `zest` too, but `squad` is available.
this was using colab and localy | {
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} | While I was adding one XML dataset, I noticed that all the dataset was loaded in memory during the dummy data generation process (using nearly all my laptop RAM).
Looking at the code, I have found that the origin is the use of `ET.parse()`. This method loads **all the file content in memory**.
In order to fix this, I have refactorized the code and use `ET.iterparse()` instead, which **parses the file content incrementally**.
I have also implemented a test. | {
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} | This PR adds a dataset of 200k English plaintext Jokes from three sources: Reddit, Stupidstuff, and Wocka.
Link: https://github.com/taivop/joke-dataset
This is my second PR.
First was: [#1269 ](https://github.com/huggingface/datasets/pull/1269) | {
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3 separate configs are available with min. 1K and max. 211.3k examples | {
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} | This PR adds the FreebaseQA dataset: A Trivia-type QA Data Set over the Freebase Knowledge Graph
Repo: https://github.com/kelvin-jiang/FreebaseQA
Paper: https://www.aclweb.org/anthology/N19-1028.pdf
## TODO: create dummy data
Error encountered when running `python datasets-cli dummy_data datasets/freebase_qa --auto_generate`
```
f"Couldn't parse columns {list(json_data.keys())}. "
ValueError: Couldn't parse columns ['Dataset', 'Version', 'Questions']. Maybe specify which json field must be used to read the data with --json_field <my_field>.
```
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} | [] | closed | false | null | [] | null | 1 | 2020-12-10 02:15:02+00:00 | 2020-12-17 09:59:54+00:00 | 2020-12-17 09:59:54+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | This is my first dataset to be added to HF. | {
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} | Adding ArCOV-19 dataset. ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes over 1M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and-liked). The propagation networks include both retweets and conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others. | {
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https://api.github.com/repos/huggingface/datasets/issues/1423 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1423/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1423/comments | https://api.github.com/repos/huggingface/datasets/issues/1423/events | https://github.com/huggingface/datasets/pull/1423 | 760,712,421 | MDExOlB1bGxSZXF1ZXN0NTM1NDk3OTk5 | 1,423 | Imppres | {
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https://api.github.com/repos/huggingface/datasets/issues/1422 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1422/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1422/comments | https://api.github.com/repos/huggingface/datasets/issues/1422/events | https://github.com/huggingface/datasets/issues/1422 | 760,707,113 | MDU6SXNzdWU3NjA3MDcxMTM= | 1,422 | Can't map dataset (loaded from csv) | {
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} | [] | closed | false | null | [] | null | 2 | 2020-12-09 22:05:42+00:00 | 2020-12-17 18:13:40+00:00 | 2020-12-17 18:13:40+00:00 | NONE | null | null | null | null | Hello! I am trying to load single csv file with two columns: ('label': str, 'text' str), where is label is str of two possible classes.
Below steps are similar with [this notebook](https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing), where bert model and tokenizer are used to classify lmdb loaded dataset. Only one difference it is the dataset loaded from .csv file.
Here is how I load it:
```python
data_path = 'data.csv'
data = pd.read_csv(data_path)
# process class name to indices
classes = ['neg', 'pos']
class_to_idx = { cl: i for i, cl in enumerate(classes) }
# now data is like {'label': int, 'text' str}
data['label'] = data['label'].apply(lambda x: class_to_idx[x])
# load dataset and map it with defined `tokenize` function
features = Features({
target: ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None),
feature: Value(dtype='string', id=None),
})
dataset = Dataset.from_pandas(data, features=features)
dataset.map(tokenize, batched=True, batch_size=len(dataset))
```
It ruins on the last line with following error:
```
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-112-32b6275ce418> in <module>()
9 })
10 dataset = Dataset.from_pandas(data, features=features)
---> 11 dataset.map(tokenizer, batched=True, batch_size=len(dataset))
2 frames
/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1237 test_inputs = self[:2] if batched else self[0]
1238 test_indices = [0, 1] if batched else 0
-> 1239 update_data = does_function_return_dict(test_inputs, test_indices)
1240 logger.info("Testing finished, running the mapping function on the dataset")
1241
/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in does_function_return_dict(inputs, indices)
1208 fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]
1209 processed_inputs = (
-> 1210 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1211 )
1212 does_return_dict = isinstance(processed_inputs, Mapping)
/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)
2281 )
2282 ), (
-> 2283 "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
2284 "or `List[List[str]]` (batch of pretokenized examples)."
2285 )
AssertionError: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).
```
which I think is not expected. I also tried the same steps using `Dataset.from_csv` which resulted in the same error.
For reproducing this, I used [this dataset from kaggle](https://www.kaggle.com/team-ai/spam-text-message-classification) | {
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} | [] | closed | false | null | [] | null | 1 | 2020-12-09 21:54:29+00:00 | 2020-12-11 13:34:04+00:00 | 2020-12-11 13:34:04+00:00 | CONTRIBUTOR | null | null | 0 | {
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Fixed git issues from PR #976 | {
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But I'm having a hard time generating dummy data since the original dataset extion is `.arff` and the [_create_dummy_data function](https://github.com/huggingface/datasets/blob/a4aeaf911240057286a01bff1b1d75a89aedd57b/src/datasets/commands/dummy_data.py#L185) doesn't allow it. | {
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} | [] | closed | false | null | [] | null | 2 | 2020-12-09 18:42:58+00:00 | 2020-12-13 19:11:18+00:00 | 2020-12-13 19:11:18+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | I had mistaken use git rebase, I was so hurried to fix it. However, I didn't fully consider the use of git reset , so I unintendedly stopped PR (#1367) altogether. Sorry about that.
I'll make a new PR. | {
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https://arxiv.org/abs/2011.03088 | {
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} | [] | closed | false | null | [] | null | 2 | 2020-12-09 14:16:55+00:00 | 2020-12-14 18:06:43+00:00 | 2020-12-14 16:23:59+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | Add LIAR dataset from [“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection](https://www.aclweb.org/anthology/P17-2067/). | {
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} | `best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by [NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). The test set answers are not provided publicly. | {
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} | Adding Scielo: Parallel corpus of full-text articles in Portuguese, English and Spanish from SciELO
https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB | {
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} | When passed `--cache_dir` arg:
```shell
python datasets-cli test datasets/<my-dataset-folder> --save_infos --all_configs --cache_dir <my-cache-dir>
```
it is not used for caching the modules, which are cached in the default location at `.cache/huggingface/modules`.
With this fix, the modules will be cached at `<my-cache-dir>/modules`. | null | {
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} | [] | closed | false | null | [] | null | 2 | 2020-12-09 10:34:01+00:00 | 2020-12-09 17:25:14+00:00 | 2020-12-09 17:25:14+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | Adding COVID-19 Japanese Tweets Dataset as part of the sprint.
Testing with dummy data is not working (the file is said to not exist). Sorry for the incomplete PR. | {
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} | [] | closed | false | null | [] | null | 1 | 2020-12-09 10:30:23+00:00 | 2020-12-14 14:27:57+00:00 | 2020-12-14 14:27:57+00:00 | CONTRIBUTOR | null | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/1365 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1365/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1365/comments | https://api.github.com/repos/huggingface/datasets/issues/1365/events | https://github.com/huggingface/datasets/pull/1365 | 760,188,457 | MDExOlB1bGxSZXF1ZXN0NTM1MDYxNTI2 | 1,365 | Add Mkqa dataset | {
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} | [] | closed | false | null | [] | null | 2 | 2020-12-09 10:06:33+00:00 | 2020-12-10 15:37:56+00:00 | 2020-12-10 15:37:56+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | # MKQA: Multilingual Knowledge Questions & Answers Dataset
Adding the [MKQA](https://github.com/apple/ml-mkqa) dataset as part of the sprint 🎉
There is no official data splits so I added just a `train` split.
differently from the original:
- answer:type field is a ClassLabel (I thought it might be possible to train on this as a label for categorizing questions)
- answer:entity field has a default value of empty string '' (since this key is not available for all in original)
- answer:alias has default value of []
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
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} | [] | closed | false | null | [] | null | 3 | 2020-12-09 09:33:59+00:00 | 2021-01-25 15:31:51+00:00 | 2021-01-25 15:31:31+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | Narrative QA with manual download for stories. | {
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https://api.github.com/repos/huggingface/datasets/issues/1363 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1363/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1363/comments | https://api.github.com/repos/huggingface/datasets/issues/1363/events | https://github.com/huggingface/datasets/pull/1363 | 760,160,944 | MDExOlB1bGxSZXF1ZXN0NTM1MDM4NjM0 | 1,363 | Adding OPUS MultiUN | {
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} | [] | closed | false | null | [] | null | 0 | 2020-12-09 09:29:01+00:00 | 2020-12-09 17:54:20+00:00 | 2020-12-09 17:54:20+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | Adding UnMulti
http://www.euromatrixplus.net/multi-un/ | {
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https://api.github.com/repos/huggingface/datasets/issues/1362 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1362/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1362/comments | https://api.github.com/repos/huggingface/datasets/issues/1362/events | https://github.com/huggingface/datasets/pull/1362 | 760,138,233 | MDExOlB1bGxSZXF1ZXN0NTM1MDIwMDAz | 1,362 | adding opus_infopankki | {
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} | [] | closed | false | null | [] | null | 1 | 2020-12-09 08:57:10+00:00 | 2020-12-09 18:16:20+00:00 | 2020-12-09 18:13:48+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | Adding opus_infopankki
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} | [] | closed | false | null | [] | null | 2 | 2020-12-09 08:02:45+00:00 | 2020-12-16 17:04:44+00:00 | 2020-12-16 17:04:44+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | [] | closed | false | null | [] | null | 0 | 2020-12-09 07:41:30+00:00 | 2020-12-10 14:28:41+00:00 | 2020-12-10 14:28:41+00:00 | CONTRIBUTOR | null | null | 0 | {
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} | `wisesight1000` contains Thai social media texts randomly drawn from the full `wisesight-sentiment`, tokenized by human annotators. Out of the labels `neg` (negative), `neu` (neutral), `pos` (positive), `q` (question), 250 samples each. Some texts are removed because they look like spam.Because these samples are representative of real world content, we believe having these annotaed samples will allow the community to robustly evaluate tokenization algorithms. | {
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https://api.github.com/repos/huggingface/datasets/issues/1358 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1358/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1358/comments | https://api.github.com/repos/huggingface/datasets/issues/1358/events | https://github.com/huggingface/datasets/pull/1358 | 760,031,131 | MDExOlB1bGxSZXF1ZXN0NTM0OTI5ODIx | 1,358 | Add spider dataset | {
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} | This PR adds the Spider dataset, a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students. The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases.
Dataset website: https://yale-lily.github.io/spider
Paper link: https://www.aclweb.org/anthology/D18-1425/ | {
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