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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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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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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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https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB | {
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https://api.github.com/repos/huggingface/datasets/issues/1369 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1369/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1369/comments | https://api.github.com/repos/huggingface/datasets/issues/1369/events | https://github.com/huggingface/datasets/pull/1369 | 760,227,776 | MDExOlB1bGxSZXF1ZXN0NTM1MDk0NDk1 | 1,369 | Use passed --cache_dir for modules cache | {
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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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https://api.github.com/repos/huggingface/datasets/issues/1367 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1367/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1367/comments | https://api.github.com/repos/huggingface/datasets/issues/1367/events | https://github.com/huggingface/datasets/pull/1367 | 760,208,191 | MDExOlB1bGxSZXF1ZXN0NTM1MDc4MTAx | 1,367 | adding covid-tweets-japanese | {
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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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} | `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 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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} | Add additional instructions for how to generate dataset_infos.json for manual download datasets. Information courtesy of `Taimur Ibrahim` from the slack channel | {
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} | `craigslist_bargains` data set from [here](https://worksheets.codalab.org/worksheets/0x453913e76b65495d8b9730d41c7e0a0c/)
(Cleaned up version of #1278) | {
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Ready for review! | {
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https://api.github.com/repos/huggingface/datasets/issues/1337 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1337/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1337/comments | https://api.github.com/repos/huggingface/datasets/issues/1337/events | https://github.com/huggingface/datasets/pull/1337 | 759,710,482 | MDExOlB1bGxSZXF1ZXN0NTM0NjY3NDUz | 1,337 | Add spanish billion words | {
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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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} | 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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