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I'll make a new PR. | {
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https://arxiv.org/abs/2011.03088 | {
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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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http://opus.nlpl.eu/UNPC.php | {
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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`. | {
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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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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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} | # 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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} | Adding UnMulti
http://www.euromatrixplus.net/multi-un/ | {
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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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} | 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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} | This PR adds a new dataset of YouTube captions, error and corrections. This dataset was created in just the last week, as inspired by this sprint! | {
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} | This PR adds the StackOverflow StackSample dataset from Kaggle: https://www.kaggle.com/stackoverflow/stacksample
Ran through all of the steps. However, since my dataset requires manually downloading the data, I was unable to run the pytest on the real dataset (the dummy data pytest passed). | {
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} | This PR adds the TweetQA dataset, the first dataset for QA on social media data by leveraging news media and crowdsourcing.
Paper: https://arxiv.org/abs/1907.06292
Repository: https://tweetqa.github.io/ | {
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(Cleaned up version of #1278) | {
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Ready for review! | {
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} | Add an unannotated corpus of the Spanish language of nearly 1.5 billion words, compiled from different resources from the web.
The dataset needs 10 GB (download: 1.89 GiB, generated: 8.34 GiB, post-processed: Unknown size, total: 10.22 GiB), the test using dummy data pass but my laptop isn't able to run it on the real data (I left it running for over 8 hours and it didn't finish). | {
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Contains loading script as well as dataset card including YAML tags. | {
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https://api.github.com/repos/huggingface/datasets/issues/1335 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1335/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1335/comments | https://api.github.com/repos/huggingface/datasets/issues/1335/events | https://github.com/huggingface/datasets/pull/1335 | 759,705,835 | MDExOlB1bGxSZXF1ZXN0NTM0NjYzNzQ2 | 1,335 | Added Bianet dataset | {
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} | Hi :hugs:, This is a PR for [Bianet: A parallel news corpus in Turkish, Kurdish and English; Source](http://opus.nlpl.eu/Bianet.php) dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/1332 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1332/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1332/comments | https://api.github.com/repos/huggingface/datasets/issues/1332/events | https://github.com/huggingface/datasets/pull/1332 | 759,679,135 | MDExOlB1bGxSZXF1ZXN0NTM0NjQxOTE5 | 1,332 | Add Open Subtitles Dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/1331 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1331/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1331/comments | https://api.github.com/repos/huggingface/datasets/issues/1331/events | https://github.com/huggingface/datasets/pull/1331 | 759,677,189 | MDExOlB1bGxSZXF1ZXN0NTM0NjQwMzc5 | 1,331 | First version of the new dataset hausa_voa_topics | {
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} | Contains loading script as well as dataset card including YAML tags.
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https://api.github.com/repos/huggingface/datasets/issues/1330 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1330/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1330/comments | https://api.github.com/repos/huggingface/datasets/issues/1330/events | https://github.com/huggingface/datasets/pull/1330 | 759,657,324 | MDExOlB1bGxSZXF1ZXN0NTM0NjI0MzMx | 1,330 | added un_ga dataset | {
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} | Hi :hugs:, This is a PR for [United nations general assembly resolutions: A six-language parallel corpus](http://opus.nlpl.eu/UN.php) dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/1329 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1329/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1329/comments | https://api.github.com/repos/huggingface/datasets/issues/1329/events | https://github.com/huggingface/datasets/pull/1329 | 759,654,174 | MDExOlB1bGxSZXF1ZXN0NTM0NjIxNzg0 | 1,329 | Add yoruba ner corpus | {
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Paper: https://arxiv.org/abs/2010.11574
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks | {
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more info : http://opus.nlpl.eu/TEP.php | {
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] | open | false | null | [] | null | [] | 2020-12-08T16:25:58Z | 2020-12-22T07:50:56Z | null | NONE | null | null | null | 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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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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} | 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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Paper: https://arxiv.org/abs/1907.00409
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks | {
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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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https://api.github.com/repos/huggingface/datasets/issues/1317 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1317/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1317/comments | https://api.github.com/repos/huggingface/datasets/issues/1317/events | https://github.com/huggingface/datasets/pull/1317 | 759,553,495 | MDExOlB1bGxSZXF1ZXN0NTM0NTM5NTQ5 | 1,317 | add 10k German News Article Dataset | {
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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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I included the dataset card. | {
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- **Homepage:** http://zil.ipipan.waw.pl/HateSpeech
- **Paper:** http://www.qualitativesociologyreview.org/PL/Volume38/PSJ_13_2_Troszynski_Wawer.pdf | {
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https://api.github.com/repos/huggingface/datasets/issues/1310 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1310/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1310/comments | https://api.github.com/repos/huggingface/datasets/issues/1310/events | https://github.com/huggingface/datasets/pull/1310 | 759,508,921 | MDExOlB1bGxSZXF1ZXN0NTM0NTAyNzE5 | 1,310 | Add OffensEval-TR 2020 Dataset | {
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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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} | 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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} | 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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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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