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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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"Please could you post the whole script? I can't reproduce your issue. After updating the feature names/labels to match with the data, everything works fine for me. Try to update datasets/transformers to the newest version.",
"Actually, the problem was how `tokenize` function was defined. This was completely my side mistake, so there are really no needs in this issue anymore"
] | 2020-12-09T22:05:42 | 2020-12-17T18:13:40 | 2020-12-17T18:13:40 | NONE | 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 | [] | 2020-12-09T21:54:29 | 2020-12-11T13:34:04 | 2020-12-11T13:34:04 | CONTRIBUTOR | null | Contains loading script as well as dataset card including YAML tags. | {
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} | [] | closed | false | null | [] | null | [] | 2020-12-09T21:08:33 | 2020-12-11T14:02:31 | 2020-12-11T14:02:31 | CONTRIBUTOR | null | 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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} | [] | closed | false | null | [] | null | [] | 2020-12-09T21:06:07 | 2020-12-17T09:40:56 | 2020-12-17T09:40:56 | CONTRIBUTOR | null | Data loading script and dataset card for Dialectal Arabic Resources dataset.
Fixed git issues from PR #976 | {
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} | [] | closed | false | null | [] | null | [] | 2020-12-09T20:38:35 | 2020-12-11T11:23:31 | 2020-12-11T11:23:31 | CONTRIBUTOR | null | Add Shrinked Turkish NER from [Kaggle](https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar). | {
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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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link: https://huggingface.co/datasets/card-creator/ | {
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> Couldn't find file locally at blah/blah.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1/metrics/blah/blah.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/met
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If the dataset was added recently, you may need to to pass script_version="master" to find the loading script on the master branch. | {
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Testing with dummy data is not working (the file is said to not exist). Sorry for the incomplete PR. | {
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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 | [] | 2020-12-09T07:41:30 | 2020-12-10T14:28:41 | 2020-12-10T14:28:41 | CONTRIBUTOR | null | `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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Dataset website: https://yale-lily.github.io/spider
Paper link: https://www.aclweb.org/anthology/D18-1425/ | {
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} | [] | closed | false | null | [] | null | [] | 2020-12-09T05:52:34 | 2020-12-15T18:12:56 | 2020-12-15T18:12:56 | CONTRIBUTOR | null | 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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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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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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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/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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} | [] | closed | false | null | [] | null | [] | 2020-12-08T17:58:38 | 2020-12-14T17:52:34 | 2020-12-14T17:52:34 | CONTRIBUTOR | null | 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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