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} | TODO:
Fix feature format issue
Create dataset_info.json file
Run pytests
Make Style | {
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} | Hi, I need to update the link to the dataset. The link in the previous PR was to a small test dataset. Thanks | {
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} | StereoSet is a dataset that measures stereotype bias in language models. StereoSet consists of 17,000 sentences that measures model preferences across gender, race, religion, and profession. | {
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} | https://parl.ai/projects/md_gender/
Mostly has the ABOUT dimension since the others are inferred from other datasets in most cases.
I tried to keep the dummy data small but one of the configs has 140 splits ( > 56KB data) | {
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In redoing the dummy data a few times, I ended up adding a load of files to git. Hopefully this should work. | {
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https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_utils.py#L51-L61
While in many datasets metrics they are the ground truth labels:
https://github.com/huggingface/datasets/blob/c3f53792a744ede18d748a1133b6597fdd2d8d18/metrics/accuracy/accuracy.py#L31-L40
Do you think predictions & references should be swapped? I'd be willing to do some refactoring here if you agree. | {
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} | See #1442.
The statement `json.load()` loads **all the file content in memory**.
In order to avoid this, file content should be parsed **iteratively**, by using the library `ijson` e.g.
I have refactorized the code into a function `_create_json_dummy_data` and I have added some tests. | {
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} | Hello @lhoestq ,
I am opening a fresh pull request as advised in my original PR https://github.com/huggingface/datasets/pull/1308
Thanks | {
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} | Updated PR to counter messed up history of previous one (https://github.com/huggingface/datasets/pull/1235) due to rebase.
Removed manual downloading of dataset. | {
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} | The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers. The size of uncompressed 500K json files (newspapers-json.tgz) is around 2.2GB. | {
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They contain original Japanese, simplified Japanese, and original English (the original text is gotten from en-ja translation corpus). Hence, it can be used not only for Japanese simplification but also for en-ja translation.
- http://www.jnlp.org/SNOW/T15
- http://www.jnlp.org/SNOW/T23 | {
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} | This PR adds the **Turkish News Categories Dataset (270K)** dataset which is a text classification dataset by me and @yavuzKomecoglu. Turkish news dataset consisting of **273601 news in 17 categories**, compiled from printed media and news websites between 2010 and 2017 by the [Interpress](https://www.interpress.com/) media monitoring company.
**Note**: Resubmitted as a clean version of the previous Pull Request(#1419). @SBrandeis @lhoestq | {
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Dataset link- https://raw.githubusercontent.com/taivop/joke-dataset/master/reddit_jokes.json | {
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} | This PR adds features of the enriched_web_nlg dataset that were not present yet (most notably sorted rdf triplet sets), and deals with some xml issues that led to returning no data in cases where surgery could be performed to salvage it. | {
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} | The original PR -> https://github.com/huggingface/datasets/pull/1383
Reason for creating again -
The reason I had to create the PR again was due to the master rebasing issue. After rebasing the changes, all the previous commits got added to the branch. | {
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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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} | HEAD-QA is a multi-choice HEAlthcare Dataset, the questions come from exams to access a specialized position in the
Spanish healthcare system. | {
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https://api.github.com/repos/huggingface/datasets/issues/1453 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1453/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1453/comments | https://api.github.com/repos/huggingface/datasets/issues/1453/events | https://github.com/huggingface/datasets/pull/1453 | 761,188,657 | MDExOlB1bGxSZXF1ZXN0NTM1ODkyNTM5 | 1,453 | Adding ethos dataset clean | {
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} | I addressed the comments on the PR1318 | {
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https://api.github.com/repos/huggingface/datasets/issues/1452 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1452/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1452/comments | https://api.github.com/repos/huggingface/datasets/issues/1452/events | https://github.com/huggingface/datasets/issues/1452 | 761,104,924 | MDU6SXNzdWU3NjExMDQ5MjQ= | 1,452 | SNLI dataset contains labels with value -1 | {
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} | [] | closed | false | null | [] | null | [] | 2020-12-10T10:16:55Z | 2020-12-10T17:49:55Z | 2020-12-10T17:49:55Z | NONE | 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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} | ECDC-TM homepage: https://ec.europa.eu/jrc/en/language-technologies/ecdc-translation-memory | {
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https://api.github.com/repos/huggingface/datasets/issues/1450 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1450/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1450/comments | https://api.github.com/repos/huggingface/datasets/issues/1450/events | https://github.com/huggingface/datasets/pull/1450 | 761,102,429 | MDExOlB1bGxSZXF1ZXN0NTM1ODIwNjg0 | 1,450 | Fix version in bible_para | {
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|
https://api.github.com/repos/huggingface/datasets/issues/1449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1449/comments | https://api.github.com/repos/huggingface/datasets/issues/1449/events | https://github.com/huggingface/datasets/pull/1449 | 761,083,210 | MDExOlB1bGxSZXF1ZXN0NTM1ODA0MzEy | 1,449 | add W&I + LOCNESS dataset (BEA-2019 workshop shared task on GEC) [PROPER] | {
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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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https://api.github.com/repos/huggingface/datasets/issues/1448 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1448/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1448/comments | https://api.github.com/repos/huggingface/datasets/issues/1448/events | https://github.com/huggingface/datasets/pull/1448 | 761,080,776 | MDExOlB1bGxSZXF1ZXN0NTM1ODAyNDM3 | 1,448 | add thai_toxicity_tweet | {
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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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} | Update step-by-step guide for windows to give an alternative to `make style`. | {
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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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} | [] | closed | false | null | [] | null | [] | 2020-12-10T07:04:17Z | 2020-12-13T05:22:00Z | 2020-12-12T05:55:43Z | CONTRIBUTOR | null | false | {
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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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} | 1k-10k -> 1k-1M
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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} | 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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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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} | This PR adds the Turkish News Categories Dataset (270K) dataset which is a text classification dataset by me and @yavuzKomecoglu. Turkish news dataset consisting of **273601 news** in **17 categories**, compiled from printed media and news websites between 2010 and 2017 by the [Interpress](https://www.interpress.com/) media monitoring company.
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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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