devopsmarc/my-issues-dataset huggingface dataset:
MemoryError when loading German Wikipedia 
 Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)

I'm trying to download the German Wikipedia dataset as follows:

```
wiki = nlp.load_dataset("wikipedia", "20200501.de", split="train")
```

However, when I do so, I get the following error:

```
Downloading and preparing dataset wikipedia/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/ubuntu/.cache/huggingface/datasets/wikipedia/20200501.de/1.0.0...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
    save_infos=save_infos,
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 433, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 824, in _download_and_prepare
    "\n\t`{}`".format(usage_example)
nlp.builder.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 run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). 
Example of usage: 
	`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`
```

So, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.

This isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?

My nlp version is 0.2.1.

Thank you! 
 Adding them is definitely in our short term objectives. I'll be working on this early next week :)

Although if you have an apache beam runtime feel free to specify the beam runner. You can find more info [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md) on how to make it work on Dataflow but you can adapt it for Spark or any other beam runtime (by changing the `runner`).

However if you don't have a beam runtime and even if you have enough memory, I discourage you to use the `DirectRunner` on the german or italian wikipedia. According to Apache Beam documentation it was made for testing purposes and therefore it is memory-inefficient.
MemoryError when loading German Wikipedia 
 Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)

I'm trying to download the German Wikipedia dataset as follows:

```
wiki = nlp.load_dataset("wikipedia", "20200501.de", split="train")
```

However, when I do so, I get the following error:

```
Downloading and preparing dataset wikipedia/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/ubuntu/.cache/huggingface/datasets/wikipedia/20200501.de/1.0.0...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
    save_infos=save_infos,
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 433, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 824, in _download_and_prepare
    "\n\t`{}`".format(usage_example)
nlp.builder.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 run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). 
Example of usage: 
	`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`
```

So, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.

This isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?

My nlp version is 0.2.1.

Thank you! 
 I added the German and the Italian Wikipedia to our google cloud storage:
First update the `nlp` package to 0.3.0:
```bash
pip install nlp --upgrade
```
and then
```python
from nlp import load_dataset
wiki_de = load_dataset("wikipedia", "20200501.de")
wiki_it = load_dataset("wikipedia", "20200501.it")
```
The datasets are downloaded and directly ready to use (no processing).
MemoryError when loading German Wikipedia 
 Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)

I'm trying to download the German Wikipedia dataset as follows:

```
wiki = nlp.load_dataset("wikipedia", "20200501.de", split="train")
```

However, when I do so, I get the following error:

```
Downloading and preparing dataset wikipedia/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/ubuntu/.cache/huggingface/datasets/wikipedia/20200501.de/1.0.0...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
    save_infos=save_infos,
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 433, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 824, in _download_and_prepare
    "\n\t`{}`".format(usage_example)
nlp.builder.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 run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). 
Example of usage: 
	`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`
```

So, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.

This isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?

My nlp version is 0.2.1.

Thank you! 
 Hi @lhoestq 

Wow, thanks so much, that's **really** incredible! I was considering looking at creating my own Beam Dataset, as per the doc you linked, but instead opted to process the data myself using `wikiextractor`. However, now that this is available, I'll definitely switch across and use it.

Thanks so much for the incredible work, this really helps out our team considerably!

Have a great (and well-deserved ;) weekend ahead!

P.S. I'm not sure if I should close the issue here - if so I'm happy to do so.
Empty samples in glue/qqp 
 ```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}
```
Notice that question 2 is empty string. 
BTW, I have checked and these two are the only naughty ones in all splits of qqp. 
 We are only wrapping the original dataset.

Maybe try to ask on the GLUE mailing list or reach out to the original authors?
Empty samples in glue/qqp 
 ```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}
```
Notice that question 2 is empty string. 
BTW, I have checked and these two are the only naughty ones in all splits of qqp. 
 Tanks for the suggestion, I'll try to ask GLUE benchmark.
I'll first close the issue,  post the following up here afterwards, and reopen the issue if needed.
NonMatchingChecksumError when loading pubmed dataset 
 I get this error when i run `nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')`.
The error is:

```
---------------------------------------------------------------------------
NonMatchingChecksumError                  Traceback (most recent call last)
<ipython-input-2-7742dea167d0> in <module>()
----> 1 df = nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')
      2 df = pd.DataFrame(df)
      3 gc.collect()

3 frames
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
    518         download_mode=download_mode,
    519         ignore_verifications=ignore_verifications,
--> 520         save_infos=save_infos,
    521     )
    522 

/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
    431                 verify_infos = not save_infos and not ignore_verifications
    432                 self._download_and_prepare(
--> 433                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
    434                 )
    435                 # Sync info

/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
    468         # Checksums verification
    469         if verify_infos:
--> 470             verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
    471         for split_generator in split_generators:
    472             if str(split_generator.split_info.name).lower() == "all":

/usr/local/lib/python3.6/dist-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
     34     bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
     35     if len(bad_urls) > 0:
---> 36         raise NonMatchingChecksumError(str(bad_urls))
     37     logger.info("All the checksums matched successfully.")
     38 

NonMatchingChecksumError: ['https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download']
```
I'm currently working on google colab.

That is quite strange because yesterday it was fine.
 
 For some reason the files are not available for unauthenticated users right now (like the download service of this package). Instead of downloading the right files, it downloads the html of the error.
According to the error it should be back again in 24h.

![image](https://user-images.githubusercontent.com/42851186/84751599-096c6580-afbd-11ea-97f3-ee4aef791711.png)
PG-19 
 Hi, and thanks for all your open-sourced work, as always!

I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling. 
 Got around to this today, and so far so good, I'm able to download and load pg19 locally. However, I think there may be an issue with the dummy data, and testing in general.

The problem lies in the fact that each book from pg19 actually resides as its own text file in a google cloud folder that denotes the split, where the book id is the name of the text file. https://console.cloud.google.com/storage/browser/deepmind-gutenberg/train/ I don't believe there's anywhere else (even in the supplied metadata), where the mapping of id -> split can be found.

Therefore I end up making a network call `tf.io.gfile.listdir` to get all the files within each of the split directories. https://github.com/lucidrains/nlp/commit/adbacbd85decc80db2347d0882e7dab4faa6fd03#diff-cece8f166a85dd927caf574ba303d39bR78

Does this network call need to be eventually stubbed out for testing?
PG-19 
 Hi, and thanks for all your open-sourced work, as always!

I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling. 
 Ohh nevermind, I think I can use `download_custom` here with `listdir` as the custom function. Ok, I'll keep trying to make the dummy data work!
documentation missing how to split a dataset 
 I am trying to understand how to split a dataset ( as arrow_dataset). 
I know I can do something like this to access a split which is already in the original dataset : 

`ds_test = nlp.load_dataset('imdb, split='test') `

But how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?
I guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: 

> See the  [guide on splits](https://github.com/huggingface/nlp/tree/master/docs/splits.md)  for more information.

But the guide seems to be missing.

To clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https://www.tensorflow.org/datasets/splits). But to come back to the example above: I cannot simply split the testset doing this: 
`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `
`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`

because the imdb test data is sorted by class (probably not a good idea anyway)
 
 this seems to work for my specific problem:

`self.train_ds, self.test_ds, self.val_ds = map(_prepare_ds, ('train', 'test[:25%]+test[50%:75%]', 'test[75%:]'))`
documentation missing how to split a dataset 
 I am trying to understand how to split a dataset ( as arrow_dataset). 
I know I can do something like this to access a split which is already in the original dataset : 

`ds_test = nlp.load_dataset('imdb, split='test') `

But how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?
I guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: 

> See the  [guide on splits](https://github.com/huggingface/nlp/tree/master/docs/splits.md)  for more information.

But the guide seems to be missing.

To clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https://www.tensorflow.org/datasets/splits). But to come back to the example above: I cannot simply split the testset doing this: 
`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `
`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`

because the imdb test data is sorted by class (probably not a good idea anyway)
 
 Currently you can indeed split a dataset using `ds_test = nlp.load_dataset('imdb, split='test[:5000]')` (works also with percentages).

However right now we don't have a way to shuffle a dataset but we are thinking about it in the discussion in #166. Feel free to share your thoughts about it.

One trick that you can do until we have a better solution is to shuffle and split the indices of your dataset:
```python
import nlp
from sklearn.model_selection import train_test_split

imdb = nlp.load_dataset('imbd', split='test')
test_indices, val_indices = train_test_split(range(len(imdb)))
```

and then to iterate each split:
```python
for i in test_indices:
    example = imdb[i]
   ...
```
documentation missing how to split a dataset 
 I am trying to understand how to split a dataset ( as arrow_dataset). 
I know I can do something like this to access a split which is already in the original dataset : 

`ds_test = nlp.load_dataset('imdb, split='test') `

But how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?
I guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: 

> See the  [guide on splits](https://github.com/huggingface/nlp/tree/master/docs/splits.md)  for more information.

But the guide seems to be missing.

To clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https://www.tensorflow.org/datasets/splits). But to come back to the example above: I cannot simply split the testset doing this: 
`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `
`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`

because the imdb test data is sorted by class (probably not a good idea anyway)
 
 I added a small guide [here](https://github.com/huggingface/nlp/tree/master/docs/splits.md) that explains how to split a dataset. It is very similar to the tensorflow datasets guide, as we kept the same logic.
documentation missing how to split a dataset 
 I am trying to understand how to split a dataset ( as arrow_dataset). 
I know I can do something like this to access a split which is already in the original dataset : 

`ds_test = nlp.load_dataset('imdb, split='test') `

But how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?
I guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: 

> See the  [guide on splits](https://github.com/huggingface/nlp/tree/master/docs/splits.md)  for more information.

But the guide seems to be missing.

To clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https://www.tensorflow.org/datasets/splits). But to come back to the example above: I cannot simply split the testset doing this: 
`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `
`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`

because the imdb test data is sorted by class (probably not a good idea anyway)
 
 Thanks a lot, the new explanation is very helpful!

About using train_test_split from sklearn: I stumbled across the [same error message as this user ](https://github.com/huggingface/nlp/issues/147 )and thought it can't be used at the moment in this context. Will check it out again.

One of the problems is how to shuffle very large datasets, which don't fit into the memory. Well, one strategy could be shuffling data in sections. But in a case where the data is sorted by the labels you have to swap larger sections first.
documentation missing how to split a dataset 
 I am trying to understand how to split a dataset ( as arrow_dataset). 
I know I can do something like this to access a split which is already in the original dataset : 

`ds_test = nlp.load_dataset('imdb, split='test') `

But how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?
I guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: 

> See the  [guide on splits](https://github.com/huggingface/nlp/tree/master/docs/splits.md)  for more information.

But the guide seems to be missing.

To clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https://www.tensorflow.org/datasets/splits). But to come back to the example above: I cannot simply split the testset doing this: 
`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `
`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`

because the imdb test data is sorted by class (probably not a good idea anyway)
 
 We added a way to shuffle datasets (shuffle the indices and then reorder to make a new dataset).
You can do `shuffled_dset = dataset.shuffle(seed=my_seed)`. It shuffles the whole dataset.
There is also `dataset.train_test_split()` which if very handy (with the same signature as sklearn).

Closing this issue as we added the docs for splits and tools to split datasets. Thanks again for your feedback !
Why is dataset after tokenization far more larger than the orginal one ? 
 I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
    return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow")
```
and when I see their size
```
ls -l --block-size=M
17460M  wikipedia-train.arrow
47511M  tokenized_wiki.arrow
```
The tokenized one is over 2x size of original one.
Is there something I did wrong ? 
 Hi ! This is because `.map` added the new column `input_ids` to the dataset, and so all the other columns were kept. Therefore the dataset size increased a lot.
 If you want to only keep the `input_ids` column, you can stash the other ones by specifying `remove_columns=["title", "text"]` in the arguments of `.map`
Why is dataset after tokenization far more larger than the orginal one ? 
 I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
    return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow")
```
and when I see their size
```
ls -l --block-size=M
17460M  wikipedia-train.arrow
47511M  tokenized_wiki.arrow
```
The tokenized one is over 2x size of original one.
Is there something I did wrong ? 
 Hi ! Thanks for your reply.

But since size of `input_ids` < size of `text`, I am wondering why
size of `input_ids` + `text` > 2x the size of `text`  🤔
Why is dataset after tokenization far more larger than the orginal one ? 
 I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
    return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow")
```
and when I see their size
```
ls -l --block-size=M
17460M  wikipedia-train.arrow
47511M  tokenized_wiki.arrow
```
The tokenized one is over 2x size of original one.
Is there something I did wrong ? 
 Hard to tell... This is probably related to the way apache arrow compresses lists of integers, that may be different from the compression of strings.
Why is dataset after tokenization far more larger than the orginal one ? 
 I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
    return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow")
```
and when I see their size
```
ls -l --block-size=M
17460M  wikipedia-train.arrow
47511M  tokenized_wiki.arrow
```
The tokenized one is over 2x size of original one.
Is there something I did wrong ? 
 Thanks for your point. 😀, It might be answer.
Since this is hard to know, I'll close this issue.
But if somebody knows more details, please comment below ~ 😁
[Feature request] Add a feature to dataset 
 Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? 
 Do you have an example of what you would like to do? (you can just add a field in the output of the unction you give to map and this will add this field in the output table)
[Feature request] Add a feature to dataset 
 Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? 
 Given another source of data loaded in, I want to pre-add it to the dataset so that it aligns with the indices of the arrow dataset prior to performing map.

E.g. 
```
new_info = list of length dataset['train']

dataset['train'] = dataset['train'].map(lambda x: some_function(x, new_info[index of x]))

def some_function(x, new_info_x):
    # adds new_info[index of x] as a field to x
    x['new_info'] = new_info_x
    return x
```
I was thinking to instead create a new field in the arrow dataset so that instance x contains all the necessary information when map function is applied (since I don't have index information to pass to map function).
[Feature request] Add a feature to dataset 
 Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? 
 This is what I have so far: 

```
import pyarrow as pa
from nlp.arrow_dataset import Dataset

aug_dataset = dataset['train'][:]
aug_dataset['new_info'] = new_info

#reformat as arrow-table
schema = dataset['train'].schema

# this line doesn't work:
schema.append(pa.field('new_info', pa.int32()))

table = pa.Table.from_pydict(
    aug_dataset,
    schema=schema
)
dataset['train'] = Dataset(table) 
```
[Feature request] Add a feature to dataset 
 Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? 
 Maybe you can use `with_indices`?

```python
new_info = list of length dataset['train']

def some_function(indice, x):
    # adds new_info[index of x] as a field to x
    x['new_info'] = new_info_x[indice]
    return x

dataset['train'] = dataset['train'].map(some_function, with_indices=True)
```
[Feature request] Be able to remove a specific sample of the dataset 
 As mentioned in #117, it's currently not possible to remove a sample of the dataset.

But it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the dataset, or at least mark them as `removed` so when iterating the dataset, we don't iterate these samples.

I think it should be a feature. What do you think ?

---

Any work-around in the meantime ? 
 Oh yes you can now do that with the `dataset.filter()` method that was added in #214
NonMatchingSplitsSizesError error when reading the IMDB dataset 
 Hi!

I am trying to load the `imdb` dataset with this line:

`dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')`

but I am getting the following error:

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/load.py", line 517, in load_dataset
    save_infos=save_infos,
  File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 363, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 421, in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
  File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 70, in verify_splits
    raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
```

Am I overlooking something? Thanks! 
 I just tried on my side and I didn't encounter your problem.
Apparently the script doesn't generate all the examples on your side.

Can you provide the version of `nlp` you're using ?
Can you try to clear your cache and re-run the code ?
NonMatchingSplitsSizesError error when reading the IMDB dataset 
 Hi!

I am trying to load the `imdb` dataset with this line:

`dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')`

but I am getting the following error:

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/load.py", line 517, in load_dataset
    save_infos=save_infos,
  File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 363, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 421, in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
  File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 70, in verify_splits
    raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
```

Am I overlooking something? Thanks! 
 Hi ! The cache is at ~/.cache/huggingface
You can just delete this folder if needed :)
What is the best way to cache a dataset?  
 For example if I want to use streamlit with a nlp dataset:

```
@st.cache
def load_data():
    return nlp.load_dataset('squad')
```
This code raises the error "uncachable object"

Right now I just fixed with a constant for my specific case:
```
    @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})
```
But I was curious to know what is the best way in general

 
 Everything is already cached by default in 🤗nlp (in particular dataset
loading and all the “map()” operations) so I don’t think you need to do any
specific caching in streamlit.

Tell us if you feel like it’s not the case.

On Sat, 6 Jun 2020 at 13:02, Fabrizio Milo <notifications@github.com> wrote:

> For example if I want to use streamlit with a nlp dataset:
>
> @st.cache
> def load_data():
>     return nlp.load_dataset('squad')
>
> This code raises the error "uncachable object"
>
> Right now I just fixed with a constant for my specific case:
>
>     @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})
>
> But I was curious to know what is the best way in general
>
> —
> You are receiving this because you are subscribed to this thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/nlp/issues/246>, or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ABYDIHKAKO7CWGX2QY55UXLRVIO3ZANCNFSM4NV333RQ>
> .
>
UnicodeDecodeError when downloading GLUE-MNLI 
 When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
   1128         try:
-> 1129             for obj in iterable:
   1130                 yield obj

~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\glue.py in _generate_examples(self, data_file, split, mrpc_files)
    529 
--> 530                 for n, row in enumerate(reader):
    531                     if is_cola_non_test:

~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
    110             self.fieldnames
--> 111         row = next(self.reader)
    112         self.line_num = self.reader.line_num

~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
     22     def decode(self, input, final=False):
---> 23         return codecs.charmap_decode(input,self.errors,decoding_table)[0]
     24 

UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>
```
Anyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay. 
 It should be good now, thanks for noticing and fixing it ! I would say that it was because you are on windows but not 100% sure
UnicodeDecodeError when downloading GLUE-MNLI 
 When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
   1128         try:
-> 1129             for obj in iterable:
   1130                 yield obj

~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\glue.py in _generate_examples(self, data_file, split, mrpc_files)
    529 
--> 530                 for n, row in enumerate(reader):
    531                     if is_cola_non_test:

~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
    110             self.fieldnames
--> 111         row = next(self.reader)
    112         self.line_num = self.reader.line_num

~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
     22     def decode(self, input, final=False):
---> 23         return codecs.charmap_decode(input,self.errors,decoding_table)[0]
     24 

UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>
```
Anyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay. 
 On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts would always set the encoding='utf-8' in calls to open explicitly. 
In the meantime: since Python 3.7 Windows users can set the default encoding for everything including open() to Unicode by setting this environment variable: set PYTHONUTF8=1 (details can be found in [PEP 540](https://www.python.org/dev/peps/pep-0540/))

For me this fixed the problem described by the OP.
Deterministic dataset loading 
 When calling:
```python 
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```

the resulting dataset is not deterministic over different google colabs. 
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:

https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/datasets/trivia_qa/trivia_qa.py#L180

which seems to return an ordering of files that depends on the filesystem:
https://stackoverflow.com/questions/6773584/how-is-pythons-glob-glob-ordered

I think we should go through all the dataset scripts and make sure to have deterministic behavior.

A simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name. 

What do you think @lhoestq? 
 I think using `sorted(glob.glob())` would actually solve this problem. Can you think of other reasons why dataset loading might not be deterministic? @mariamabarham @yjernite @lhoestq @thomwolf . 

I can do a sweep through the dataset scripts and fix the glob.glob() if you guys are ok with it
Deterministic dataset loading 
 When calling:
```python 
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```

the resulting dataset is not deterministic over different google colabs. 
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:

https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/datasets/trivia_qa/trivia_qa.py#L180

which seems to return an ordering of files that depends on the filesystem:
https://stackoverflow.com/questions/6773584/how-is-pythons-glob-glob-ordered

I think we should go through all the dataset scripts and make sure to have deterministic behavior.

A simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name. 

What do you think @lhoestq? 
 I'm pretty sure it would solve the problem too.

The only other dataset that is not deterministic right now is `blog_authorship_corpus` (see #215) but this is a problem related to string encodings.
[Creating new dataset] Not found dataset_info.json 
 Hi, I am trying to create Toronto Book Corpus. #131 

I ran
`~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs`
but this doesn't create `dataset_info.json` and try to use it
```
INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py for additional imports.
INFO:filelock:Lock 139795325778640 acquired on datasets/bookcorpus/bookcorpus.py.lock
INFO:nlp.load:Found main folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus
INFO:nlp.load:Found specific version folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9
INFO:nlp.load:Found script file from datasets/bookcorpus/bookcorpus.py to /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.py
INFO:nlp.load:Couldn't find dataset infos file at datasets/bookcorpus/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.json
INFO:filelock:Lock 139795325778640 released on datasets/bookcorpus/bookcorpus.py.lock
INFO:nlp.builder:Overwrite dataset info from restored data version.
INFO:nlp.info:Loading Dataset info from /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0
Traceback (most recent call last):
  File "nlp-cli", line 37, in <module>
    service.run()
  File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/commands/test.py", line 78, in run
    builders.append(builder_cls(name=config.name, data_dir=self._data_dir))
  File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__
    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
  File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__
    self.info = DatasetInfo.from_directory(self._cache_dir)
  File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory
    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/dataset_info.json'
```
btw, `ls /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/` show me nothing is in the directory.

I have also pushed the script to my fork [bookcorpus.py](https://github.com/richardyy1188/nlp/blob/bookcorpusdev/datasets/bookcorpus/bookcorpus.py).
 
 @lhoestq - this seems to happen quite often (already the 2nd issue). Can we maybe delete this automatically?
[Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0. 
 When running BERT-Score, I'm meeting this warning :

> Warning: Empty candidate sentence; Setting recall to be 0.

Code :

```
import nlp
metric = nlp.load_metric("bertscore")
scores = metric.compute(["swag", "swags"], ["swags", "totally something different"], lang="en", device=0)
```

---

**What am I doing wrong / How can I hide this warning ?** 
 This print statement comes from the official implementation of bert_score (see [here](https://github.com/Tiiiger/bert_score/blob/master/bert_score/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.
Right now we want to only use official code for metrics to have fair evaluations, so I'm not sure we can do anything about it. Maybe you can try to create an issue on their [repo](https://github.com/Tiiiger/bert_score) ?
Huggingface NLP, Uploading custom dataset 
 Hello,

Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.

Thank you! 
 What do you mean 'custom' ? You may want to elaborate on it when ask a question.

Anyway, there are two things you may interested
`nlp.Dataset.from_file` and `load_dataset(..., cache_dir=)`
Huggingface NLP, Uploading custom dataset 
 Hello,

Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.

Thank you! 
 To load a dataset you need to have a script that defines the format of the examples, the splits and the way to generate examples. As your dataset has the same format of squad, you can just copy the squad script (see the [datasets](https://github.com/huggingface/nlp/tree/master/datasets) forlder) and just replace the url to load the data to your local or remote path.

Then what you can do is `load_dataset(<path/to/your/script>)`
Huggingface NLP, Uploading custom dataset 
 Hello,

Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.

Thank you! 
 Also if you want to upload your script, you should be able to use the `nlp-cli`.

Unfortunately the upload feature was not shipped in the latest version 0.2.0. so right now you can either clone the repo to use it or wait for the next release. We will add some docs to explain how to upload datasets.
Huggingface NLP, Uploading custom dataset 
 Hello,

Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.

Thank you! 
 Since the latest release 0.2.1 you can use 
```bash
nlp-cli upload_dataset <path/to/dataset>
```
where `<path/to/dataset>` is a path to a folder containing your script (ex: `squad.py`).
This will upload the script under your namespace on our S3.

Optionally the folder can also contain `dataset_infos.json` generated using
```bash
nlp-cli test <path/to/dataset> --all_configs --save_infos
```

Then you should be able to do
```python
nlp.load_dataset("my_namespace/dataset_name")
```
Fail to download c4 english corpus 
 i run following code to download c4 English corpus.

```
dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'
, data_dir='/mypath')
```

and i met failure as follows

```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/adam/.cache/huggingface/datasets/c4/en/2.3.0...
Traceback (most recent call last):
  File "download_corpus.py", line 38, in <module>
    , data_dir='/home/adam/data/corpus/en/c4')
  File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
    save_infos=save_infos,
  File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 420, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 816, in _download_and_prepare
    dl_manager, verify_infos=False, pipeline=pipeline,
  File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 457, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/datasets/c4/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc/c4.py", line 175, in _split_generators
    dl_manager.download_checksums(_CHECKSUMS_URL)
AttributeError: 'DownloadManager' object has no attribute 'download_checksums

```
can i get any advice? 
 Hello ! Thanks for noticing this bug, let me fix that.

Also for information, as specified in the changelog of the latest release, C4 currently needs to have a runtime for apache beam to work on. Apache beam is used to process this very big dataset and it can work on dataflow, spark, flink, apex, etc. You can find more info on beam datasets [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md).

Our goal in the future is to make available an already-processed version of C4 (as we do for wikipedia for example) so that users without apache beam runtimes can load it.