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https://api.github.com/repos/huggingface/datasets/issues/215 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/215/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/215/comments | https://api.github.com/repos/huggingface/datasets/issues/215/events | https://github.com/huggingface/datasets/issues/215 | 626,867,879 | MDU6SXNzdWU2MjY4Njc4Nzk= | 215 | NonMatchingSplitsSizesError when loading blog_authorship_corpus | {
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```
raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train',
num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'),
'recorded... | {
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https://api.github.com/repos/huggingface/datasets/issues/214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/214/comments | https://api.github.com/repos/huggingface/datasets/issues/214/events | https://github.com/huggingface/datasets/pull/214 | 626,641,549 | MDExOlB1bGxSZXF1ZXN0NDI0NTk1NjIx | 214 | [arrow_dataset.py] add new filter function | {
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} | null | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a ... | {
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For example:
```python
dataset = nlp.load_dataset('wikipedia', '20200501.aa')
```
Raises:
```
MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to ru... | {
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Previously the `.add` method of metrics expected a batch of examples.
Now `.add` expects one prediction/reference and `.add_batch` expects a batch.
I think it is more coherent with the way the ArrowWriter works. | {
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```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, load_from_cache_file=False)
```
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One of my dataset is small (< 25Mo) so it can be verified by Drive without asking the authorization to the user. This makes the download starts directly... | {
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Also, @lhoestq fixed small import bug introduced by beam command I think. | {
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} | null | While the new [NLP viewer](https://huggingface.co/nlp/viewer/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and smal... | {
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https://api.github.com/repos/huggingface/datasets/issues/206 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/206/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/206/comments | https://api.github.com/repos/huggingface/datasets/issues/206/events | https://github.com/huggingface/datasets/issues/206 | 625,842,989 | MDU6SXNzdWU2MjU4NDI5ODk= | 206 | [Question] Combine 2 datasets which have the same columns | {
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I am using ``nlp`` to load personal datasets. I created summarization datasets in multi-languages based on wikinews. I have one dataset for english and one for german (french is getting to be ready as well). I want to keep these datasets independent because they need different pre-processing (add different task-... | {
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https://api.github.com/repos/huggingface/datasets/issues/205 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/205/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/205/comments | https://api.github.com/repos/huggingface/datasets/issues/205/events | https://github.com/huggingface/datasets/pull/205 | 625,839,335 | MDExOlB1bGxSZXF1ZXN0NDIzOTY2ODE1 | 205 | Better arrow dataset iter | {
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} | null | I tried to play around with `tf.data.Dataset.from_generator` and I found out that the `__iter__` that we have for `nlp.arrow_dataset.Dataset` ignores the format that has been set (torch or tensorflow).
With these changes I should be able to come up with a `tf.data.Dataset` that uses lazy loading, as asked in #193. | {
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https://api.github.com/repos/huggingface/datasets/issues/204 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/204/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/204/comments | https://api.github.com/repos/huggingface/datasets/issues/204/events | https://github.com/huggingface/datasets/pull/204 | 625,655,849 | MDExOlB1bGxSZXF1ZXN0NDIzODE5MTQw | 204 | Add Dataflow support + Wikipedia + Wiki40b | {
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} | null | # Add Dataflow support + Wikipedia + Wiki40b
## Support datasets processing with Apache Beam
Some datasets are too big to be processed on a single machine, for example: wikipedia, wiki40b, etc. Apache Beam allows to process datasets on many execution engines like Dataflow, Spark, Flink, etc.
To process such da... | {
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https://api.github.com/repos/huggingface/datasets/issues/203 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/203/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/203/comments | https://api.github.com/repos/huggingface/datasets/issues/203/events | https://github.com/huggingface/datasets/pull/203 | 625,515,488 | MDExOlB1bGxSZXF1ZXN0NDIzNzEyMTQ3 | 203 | Raise an error if no config name for datasets like glue | {
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} | null | Some datasets like glue (see #130) and scientific_papers (see #197) have many configs.
For example for glue there are cola, sst2, mrpc etc.
Currently if a user does `load_dataset('glue')`, then Cola is loaded by default and it can be confusing. Instead, we should raise an error to let the user know that he has to p... | {
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https://api.github.com/repos/huggingface/datasets/issues/202 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/202/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/202/comments | https://api.github.com/repos/huggingface/datasets/issues/202/events | https://github.com/huggingface/datasets/issues/202 | 625,493,983 | MDU6SXNzdWU2MjU0OTM5ODM= | 202 | Mistaken `_KWARGS_DESCRIPTION` for XNLI metric | {
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The [`_KWARGS_DESCRIPTION`](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/xnli/xnli.py#L45) for the XNLI metric uses `Args` and `Returns` text from [BLEU](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/bleu/bleu.py#L58) metric:
... | {
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https://api.github.com/repos/huggingface/datasets/issues/201 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/201/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/201/comments | https://api.github.com/repos/huggingface/datasets/issues/201/events | https://github.com/huggingface/datasets/pull/201 | 625,235,430 | MDExOlB1bGxSZXF1ZXN0NDIzNDkzNTMw | 201 | Fix typo in README | {
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https://api.github.com/repos/huggingface/datasets/issues/200 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/200/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/200/comments | https://api.github.com/repos/huggingface/datasets/issues/200/events | https://github.com/huggingface/datasets/pull/200 | 625,226,638 | MDExOlB1bGxSZXF1ZXN0NDIzNDg2NTM0 | 200 | [ArrowWriter] Set schema at first write example | {
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} | null | Right now if the schema was not specified when instantiating `ArrowWriter`, then it could be set with the first `write_table` for example (it calls `self._build_writer()` to do so).
I noticed that it was not done if the first example is added via `.write`, so I added it for coherence. | {
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https://api.github.com/repos/huggingface/datasets/issues/199 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/199/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/199/comments | https://api.github.com/repos/huggingface/datasets/issues/199/events | https://github.com/huggingface/datasets/pull/199 | 625,217,440 | MDExOlB1bGxSZXF1ZXN0NDIzNDc4ODIx | 199 | Fix GermEval 2014 dataset infos | {
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this PR just removes the `dataset_info.json` file and adds a newly generated `dataset_infos.json` file. | {
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https://api.github.com/repos/huggingface/datasets/issues/198 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/198/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/198/comments | https://api.github.com/repos/huggingface/datasets/issues/198/events | https://github.com/huggingface/datasets/issues/198 | 625,200,627 | MDU6SXNzdWU2MjUyMDA2Mjc= | 198 | Index outside of table length | {
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} | null | The offset input box warns of numbers larger than a limit (like 2000) but then the errors start at a smaller value than that limit (like 1955).
> ValueError: Index (2000) outside of table length (2000).
> Traceback:
> File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _ru... | {
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https://api.github.com/repos/huggingface/datasets/issues/197 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/197/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/197/comments | https://api.github.com/repos/huggingface/datasets/issues/197/events | https://github.com/huggingface/datasets/issues/197 | 624,966,904 | MDU6SXNzdWU2MjQ5NjY5MDQ= | 197 | Scientific Papers only downloading Pubmed | {
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I have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following:
```
dataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.')
Downloading: 10... | {
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Bad characters are those that are not allowed for directory names on windows. | {
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```python
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']
train_tf_dataset.set_format(type='tensorflow', columns=columns)
features = {x: train_tf_dataset[x] for x in columns[:3]}
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https://api.github.com/repos/huggingface/datasets/issues/192 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/192/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/192/comments | https://api.github.com/repos/huggingface/datasets/issues/192/events | https://github.com/huggingface/datasets/issues/192 | 624,397,592 | MDU6SXNzdWU2MjQzOTc1OTI= | 192 | [Question] Create Apache Arrow dataset from raw text file | {
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} | null | Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as "Crime and punishment" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?
Is the worth of send it to you and add i... | {
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Fixes #164 | {
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} | null | Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the nu... | {
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* `WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be ... | {
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https://api.github.com/repos/huggingface/datasets/issues/186 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/186/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/186/comments | https://api.github.com/repos/huggingface/datasets/issues/186/events | https://github.com/huggingface/datasets/issues/186 | 623,595,180 | MDU6SXNzdWU2MjM1OTUxODA= | 186 | Weird-ish: Not creating unique caches for different phases | {
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```python
import nlp
dataset = nlp.load_dataset('boolq')
def func1(x):
return x
def func2(x):
return None
train_output = dataset["train"].map(func1)
valid_output = dataset["validation"].map(func1)
print()
print(len(train_output), len(valid_output))
# Output: 9427 9427
```
Th... | {
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https://api.github.com/repos/huggingface/datasets/issues/185 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/185/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/185/comments | https://api.github.com/repos/huggingface/datasets/issues/185/events | https://github.com/huggingface/datasets/pull/185 | 623,172,484 | MDExOlB1bGxSZXF1ZXN0NDIxODkxNjY2 | 185 | [Commands] In-detail instructions to create dummy data folder | {
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} | null | ### Dummy data command
This PR adds a new command `python nlp-cli dummy_data <path_to_dataset_folder>` that gives in-detail instructions on how to add the dummy data files.
It would be great if you can try it out by moving the current dummy_data folder of any dataset in `./datasets` with `mv datasets/<dataset_s... | {
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https://api.github.com/repos/huggingface/datasets/issues/184 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/184/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/184/comments | https://api.github.com/repos/huggingface/datasets/issues/184/events | https://github.com/huggingface/datasets/pull/184 | 623,120,929 | MDExOlB1bGxSZXF1ZXN0NDIxODQ5MTQ3 | 184 | Use IndexError instead of ValueError when index out of range | {
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} | null | **`default __iter__ needs IndexError`**.
When I want to create a wrapper of arrow dataset to adapt to fastai,
I don't know how to initialize it, so I didn't use inheritance but use object composition.
I wrote sth like this.
```
clas HF_dataset():
def __init__(self, arrow_dataset):
self.dset = arrow_datas... | {
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https://api.github.com/repos/huggingface/datasets/issues/183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/183/comments | https://api.github.com/repos/huggingface/datasets/issues/183/events | https://github.com/huggingface/datasets/issues/183 | 623,054,270 | MDU6SXNzdWU2MjMwNTQyNzA= | 183 | [Bug] labels of glue/ax are all -1 | {
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ax = nlp.load_dataset('glue', 'ax')
for i in range(30): print(ax['test'][i]['label'], end=', ')
```
```
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/182 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/182/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/182/comments | https://api.github.com/repos/huggingface/datasets/issues/182/events | https://github.com/huggingface/datasets/pull/182 | 622,646,770 | MDExOlB1bGxSZXF1ZXN0NDIxNDcxMjg4 | 182 | Update newsroom.py | {
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https://api.github.com/repos/huggingface/datasets/issues/181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/181/comments | https://api.github.com/repos/huggingface/datasets/issues/181/events | https://github.com/huggingface/datasets/issues/181 | 622,634,420 | MDU6SXNzdWU2MjI2MzQ0MjA= | 181 | Cannot upload my own dataset | {
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} | null | I look into `nlp-cli` and `user.py` to learn how to upload my own data.
It is supposed to work like this
- Register to get username, password at huggingface.co
- `nlp-cli login` and type username, passworld
- I have a single file to upload at `./ttc/ttc_freq_extra.csv`
- `nlp-cli upload ttc/ttc_freq_extra.csv`
... | {
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} | null | Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction.
This makes it impossible to have several training sets, which can occur when:
- A dataset corresponds to a collection of sub-datasets
- A dataset was built in stages, adding new examples at each stage
Would it be ... | {
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I guess the manual download instructions for `xsum` can also be improved. | {
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https://api.github.com/repos/huggingface/datasets/issues/176 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/176/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/176/comments | https://api.github.com/repos/huggingface/datasets/issues/176/events | https://github.com/huggingface/datasets/pull/176 | 621,934,638 | MDExOlB1bGxSZXF1ZXN0NDIwODkzNDky | 176 | [Tests] Refactor MockDownloadManager | {
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The print function was not of much help I think.
We should think about adding a command that creates the dummy folder structure for the user. | {
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https://api.github.com/repos/huggingface/datasets/issues/175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/175/comments | https://api.github.com/repos/huggingface/datasets/issues/175/events | https://github.com/huggingface/datasets/issues/175 | 621,929,428 | MDU6SXNzdWU2MjE5Mjk0Mjg= | 175 | [Manual data dir] Error message: nlp.load_dataset('xsum') -> TypeError | {
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```python
import nlp
xsum = nlp.load_dataset('xsum')
```
Issue is `dl_manager.manual_dir`is `None`
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-42-8a32f06... | {
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} | null | All the dummy data used for tests were duplicated. For each dataset, we had one zip file but also its extracted directory. I removed all these directories
Furthermore instead of extracting next to the dummy_data.zip file, we extract in the temp `cached_dir` used for tests, so that all the extracted directories get r... | {
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} | null | Cloning in a windows environment is not working because of use of special character '?' in folder name ..
Please consider changing the folder name ....
Reference to folder -
nlp/datasets/cnn_dailymail/dummy/3.0.0/3.0.0/dummy_data-zip-extracted/dummy_data/uc?export=download&id=0BwmD_VLjROrfM1BxdkxVaTY2bWs/dailymail/s... | {
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This should fix #143
I tested with
```python3
predictions = [
{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}
]
references = [
{'answers': [{'text': 'Denver Broncos'}], 'id': '56be4db0acb8001400a502ec'}
]
``` | {
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} | null | What we have now as the `anli` dataset is actually the αNLI dataset from the ART challenge dataset. This name is confusing because `anli` is also the name of adversarial NLI (see [https://github.com/facebookresearch/anli](https://github.com/facebookresearch/anli)).
I renamed the current `anli` dataset by `art`. | {
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} | null | This PR adds the qanta question answering datasets from [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) and [Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples](https://www.aclweb.org/anthology/Q19-1029/) (adversarial fold)
This part... | {
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Code to reproduce (From example notebook):
import nlp
wikitext_dataset = nlp.load_dataset('wikitext')
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most rece... | {
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https://api.github.com/repos/huggingface/datasets/issues/167 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/167/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/167/comments | https://api.github.com/repos/huggingface/datasets/issues/167/events | https://github.com/huggingface/datasets/pull/167 | 620,908,786 | MDExOlB1bGxSZXF1ZXN0NDIwMDY0NDMw | 167 | [Tests] refactor tests | {
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```python
if "/" not in dataset_name:
logging.info("Skip {} because it is a canonical dataset")
return
```
To run a `aws` test, one should now run the following command:
```python
pytest -s... | {
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} | null | Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.
Also, we could maybe have a clear indication of which method modify in-place and which methods return/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-pl... | {
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For ANLI, use https://github.com/facebookresearch/anli. As that paper says, "Our dataset is not
to be confused with abductive NLI (Bhagavatula et al., 2019), which calls itself αNLI, or ART.".
Indeed, the paper cited under what is currently called anli says in the abstract "We int... | {
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In order to cover multilingual support a little step could be adding standard Datasets used for Spanish NER and POS tasks.
I can provide it in raw and preprocessed formats. | {
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This makes sure it takes the previous files when computing the hash. | {
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https://api.github.com/repos/huggingface/datasets/issues/160 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/160/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/160/comments | https://api.github.com/repos/huggingface/datasets/issues/160/events | https://github.com/huggingface/datasets/issues/160 | 620,448,236 | MDU6SXNzdWU2MjA0NDgyMzY= | 160 | caching in map causes same result to be returned for train, validation and test | {
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} | null | hello,
I am working on a program that uses the `nlp` library with the `SST2` dataset.
The rough outline of the program is:
```
import nlp as nlp_datasets
...
parser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')
...
dataset = nlp_datasets.load_dataset(*args.... | {
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https://api.github.com/repos/huggingface/datasets/issues/158 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/158/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/158/comments | https://api.github.com/repos/huggingface/datasets/issues/158/events | https://github.com/huggingface/datasets/pull/158 | 620,396,658 | MDExOlB1bGxSZXF1ZXN0NDE5NjUyNTQy | 158 | add Toronto Books Corpus | {
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.
It on consider TMX and plain text files (Moses) defined in the table **Statistics and TMX/Moses Downloads** [here](http://opus.nlpl.eu/Books.php ) | {
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"TypeError: list_() takes exactly one argument (2 given)"
gist can be found here
https://gist.github.com/saahiluppal/c4b878f330b10b9ab9762bc0776c0a6a | {
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```
import nlp
dataset = nlp.load_dataset('wmt14')
print(dataset['train'][0])
```
```
Traceback (most recent call last):
File "/home/tom/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
exec(code_obj, self.... | {
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this PR adds the WNUT 17 dataset to `nlp`.
> Emerging and Rare entity recognition
> This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisati... | {
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{
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
}
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In Colab,
```
%pip install -q nlp
%pip install -q apache_beam mwparserfromhell
dataset = nlp.load_dataset('wikipedia')
```
get
```
Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/w... | {
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https://api.github.com/repos/huggingface/datasets/issues/147 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/147/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/147/comments | https://api.github.com/repos/huggingface/datasets/issues/147/events | https://github.com/huggingface/datasets/issues/147 | 619,581,907 | MDU6SXNzdWU2MTk1ODE5MDc= | 147 | Error with sklearn train_test_split | {
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"description": "New feature or request",
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"name": "enhancement",
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} | null | It would be nice if we could use sklearn `train_test_split` to quickly generate subsets from the dataset objects returned by `nlp.load_dataset`. At the moment the code:
```python
data = nlp.load_dataset('imdb', cache_dir=data_cache)
f_half, s_half = train_test_split(data['train'], test_size=0.5, random_state=seed)... | {
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https://api.github.com/repos/huggingface/datasets/issues/146 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/146/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/146/comments | https://api.github.com/repos/huggingface/datasets/issues/146/events | https://github.com/huggingface/datasets/pull/146 | 619,564,653 | MDExOlB1bGxSZXF1ZXN0NDE5MDI5MjUx | 146 | Add BERTScore to metrics | {
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} | null | This PR adds [BERTScore](https://arxiv.org/abs/1904.09675) to metrics.
Here is an example of how to use it.
```sh
import nlp
bertscore = nlp.load_metric('metrics/bertscore') # or simply nlp.load_metric('bertscore') after this is added to huggingface's s3 bucket
predictions = ['example', 'fruit']
references = [[... | {
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https://api.github.com/repos/huggingface/datasets/issues/145 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/145/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/145/comments | https://api.github.com/repos/huggingface/datasets/issues/145/events | https://github.com/huggingface/datasets/pull/145 | 619,480,549 | MDExOlB1bGxSZXF1ZXN0NDE4OTcxMjg0 | 145 | [AWS Tests] Follow-up PR from #144 | {
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https://api.github.com/repos/huggingface/datasets/issues/144 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/144/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/144/comments | https://api.github.com/repos/huggingface/datasets/issues/144/events | https://github.com/huggingface/datasets/pull/144 | 619,477,367 | MDExOlB1bGxSZXF1ZXN0NDE4OTY5NjA1 | 144 | [AWS tests] AWS test should not run for canonical datasets | {
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} | null | AWS tests should in general not run for canonical datasets. Only local tests will run in this case. This way a PR is able to pass when adding a new dataset.
This PR changes to logic to the following:
1) All datasets that are present in `nlp/datasets` are tested only locally. This way when one adds a canonical da... | {
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{
"color": "25b21e",
"default": false,
"description": "A bug in a metric script",
"id": 2067393914,
"name": "metric bug",
"node_id": "MDU6TGFiZWwyMDY3MzkzOTE0",
"url": "https://api.github.com/repos/huggingface/datasets/labels/metric%20bug"
}
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} | null | `squad_metric.compute` is giving following error
```
ArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type
```
This is how my predictions and references lo... | {
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https://api.github.com/repos/huggingface/datasets/issues/142 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/142/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/142/comments | https://api.github.com/repos/huggingface/datasets/issues/142/events | https://github.com/huggingface/datasets/pull/142 | 619,450,068 | MDExOlB1bGxSZXF1ZXN0NDE4OTU0OTc1 | 142 | [WMT] Add all wmt | {
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} | null | This PR adds all wmt datasets scripts. At the moment the script is **not** functional for the language pairs "cs-en", "ru-en", "hi-en" because apparently it takes up to a week to get the manual data for these datasets: see http://ufal.mff.cuni.cz/czeng.
The datasets are fully functional though for the "big" languag... | {
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https://api.github.com/repos/huggingface/datasets/issues/140 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/140/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/140/comments | https://api.github.com/repos/huggingface/datasets/issues/140/events | https://github.com/huggingface/datasets/pull/140 | 619,443,613 | MDExOlB1bGxSZXF1ZXN0NDE4OTUxMzg4 | 140 | [Tests] run local tests as default | {
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} | null | This PR also enables local tests by default
I think it's safer for now to enable both local and aws tests for every commit. The problem currently is that when we do a PR to add a dataset, the dataset is not yet on AWS on therefore not tested on the PR itself. Thus the PR will always be green even if the datasets are... | {
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https://api.github.com/repos/huggingface/datasets/issues/139 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/139/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/139/comments | https://api.github.com/repos/huggingface/datasets/issues/139/events | https://github.com/huggingface/datasets/pull/139 | 619,327,409 | MDExOlB1bGxSZXF1ZXN0NDE4ODc4NzMy | 139 | Add GermEval 2014 NER dataset | {
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this PR adds the GermEval 2014 NER dataset 😃
> The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation [1] with the following properties:
> - The data was sampled from German Wikipedia and News Corpora as a collection of citations.
> - The dataset covers over 31,000... | {
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https://api.github.com/repos/huggingface/datasets/issues/138 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/138/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/138/comments | https://api.github.com/repos/huggingface/datasets/issues/138/events | https://github.com/huggingface/datasets/issues/138 | 619,225,191 | MDU6SXNzdWU2MTkyMjUxOTE= | 138 | Consider renaming to nld | {
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Just making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.
The issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This... | {
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https://api.github.com/repos/huggingface/datasets/issues/136 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/136/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/136/comments | https://api.github.com/repos/huggingface/datasets/issues/136/events | https://github.com/huggingface/datasets/pull/136 | 619,211,018 | MDExOlB1bGxSZXF1ZXN0NDE4NzgxNzI4 | 136 | Update README.md | {
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https://api.github.com/repos/huggingface/datasets/issues/133 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/133/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/133/comments | https://api.github.com/repos/huggingface/datasets/issues/133/events | https://github.com/huggingface/datasets/issues/133 | 619,094,954 | MDU6SXNzdWU2MTkwOTQ5NTQ= | 133 | [Question] Using/adding a local dataset | {
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} | null | Users may want to either create/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.
It appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.
... | {
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More information and the download link are available [here](https://skylion007.github.io/OpenWebTextCorpus/). | {
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```python
dataset = nlp.load_dataset('glue')
```
The resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:
```python
metric = nlp.load_metric("glue")
```
which throws an error telling the user that they need to specify a task in GLUE. Should the... | {
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https://api.github.com/repos/huggingface/datasets/issues/128 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/128/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/128/comments | https://api.github.com/repos/huggingface/datasets/issues/128/events | https://github.com/huggingface/datasets/issues/128 | 618,951,117 | MDU6SXNzdWU2MTg5NTExMTc= | 128 | Some error inside nlp.load_dataset() | {
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} | null | First of all, nice work!
I am going through [this overview notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb)
In simple step `dataset = nlp.load_dataset('squad', split='validation[:10%]')`
I get an error, which is connected with some inner code, I think:
`... | {
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https://api.github.com/repos/huggingface/datasets/issues/126 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/126/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/126/comments | https://api.github.com/repos/huggingface/datasets/issues/126/events | https://github.com/huggingface/datasets/pull/126 | 618,897,499 | MDExOlB1bGxSZXF1ZXN0NDE4NTM1Mzc5 | 126 | remove webis | {
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} | null | Remove webis from dataset folder.
Our first dataset script that only lives on AWS :-) https://s3.console.aws.amazon.com/s3/buckets/datasets.huggingface.co/nlp/datasets/webis/tl_dr/?region=us-east-1 @julien-c @jplu | {
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https://api.github.com/repos/huggingface/datasets/issues/125 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/125/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/125/comments | https://api.github.com/repos/huggingface/datasets/issues/125/events | https://github.com/huggingface/datasets/pull/125 | 618,869,048 | MDExOlB1bGxSZXF1ZXN0NDE4NTExNDE0 | 125 | [Newsroom] add newsroom | {
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https://api.github.com/repos/huggingface/datasets/issues/123 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/123/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/123/comments | https://api.github.com/repos/huggingface/datasets/issues/123/events | https://github.com/huggingface/datasets/pull/123 | 618,820,140 | MDExOlB1bGxSZXF1ZXN0NDE4NDcxODU5 | 123 | [Tests] Local => aws | {
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As a default we set` aws=True`, `Local=False`, `slow=False`
### 1. RUN_AWS=1 (default)
This runs 4 tests per dataset script.
a) Does the dataset script have a valid etag / Can it be reached on AWS?
b) Can we load its `builder_class`?
c) Can we load **all** dataset c... | {
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[Here is the colab notebook I'm using.](https://colab.research.google.com/drive/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing)
```python
import nlp
dataset = nlp.load_dataset('squad', split='validation[:10%]')
def test(sample):
samp... | {
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[Colab notebook](https://colab.research.google.com/drive/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)
But I meet this error :
> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
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https://api.github.com/repos/huggingface/datasets/issues/118 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/118/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/118/comments | https://api.github.com/repos/huggingface/datasets/issues/118/events | https://github.com/huggingface/datasets/issues/118 | 618,643,088 | MDU6SXNzdWU2MTg2NDMwODg= | 118 | ❓ How to apply a map to all subsets ? | {
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} | null | I'm working with CNN/DM dataset, where I have 3 subsets : `train`, `test`, `validation`.
Should I apply my map function on the subsets one by one ?
```python
import nlp
cnn_dm = nlp.load_dataset('cnn_dailymail')
for corpus in ['train', 'test', 'validation']:
cnn_dm[corpus] = cnn_dm[corpus].map(my_f... | {
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https://api.github.com/repos/huggingface/datasets/issues/117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/117/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/117/comments | https://api.github.com/repos/huggingface/datasets/issues/117/events | https://github.com/huggingface/datasets/issues/117 | 618,632,573 | MDU6SXNzdWU2MTg2MzI1NzM= | 117 | ❓ How to remove specific rows of a dataset ? | {
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"gists_url": "htt... | [] | closed | false | null | [] | null | [] | 2020-05-15T01:25:06Z | 2022-07-15T08:36:44Z | 2020-05-15T07:04:32 | NONE | {
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} | null | I saw on the [example notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb#scrollTo=efFhDWhlvSVC) how to remove a specific column :
```python
dataset.drop('id')
```
But I didn't find how to remove a specific row.
**For example, how can I remove all sample w... | {
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https://api.github.com/repos/huggingface/datasets/issues/116 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/116/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/116/comments | https://api.github.com/repos/huggingface/datasets/issues/116/events | https://github.com/huggingface/datasets/issues/116 | 618,628,264 | MDU6SXNzdWU2MTg2MjgyNjQ= | 116 | 🐛 Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323 | {
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{
"color": "25b21e",
"default": false,
"description": "A bug in a metric script",
"id": 2067393914,
"name": "metric bug",
"node_id": "MDU6TGFiZWwyMDY3MzkzOTE0",
"url": "https://api.github.com/repos/huggingface/datasets/labels/metric%20bug"
}
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"completed": 0,
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} | null | I'm trying to use rouge metric.
I have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.
I tried :
```python
import nlp
rouge = nlp.load_metric('rouge')
with open("test.pred.tokenized") as p, open("test.gold.tokenized") as g:
for lp, lg in zip(p, g):
... | {
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https://api.github.com/repos/huggingface/datasets/issues/115 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/115/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/115/comments | https://api.github.com/repos/huggingface/datasets/issues/115/events | https://github.com/huggingface/datasets/issues/115 | 618,615,855 | MDU6SXNzdWU2MTg2MTU4NTU= | 115 | AttributeError: 'dict' object has no attribute 'info' | {
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} | null | I'm trying to access the information of CNN/DM dataset :
```python
cnn_dm = nlp.load_dataset('cnn_dailymail')
print(cnn_dm.info)
```
returns :
> AttributeError: 'dict' object has no attribute 'info' | {
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