kardwalker/my_dataset huggingface dataset:
GitHub issue #562: [Reproductibility] Allow to pin versions of datasets/metrics
State: closed | Created: 2020-09-02T10:30:13Z

Repurpose the `version` attribute in datasets and metrics to let the user pin a specific version of datasets and metric scripts:
```
dataset = nlp.load_dataset('squad', version='1.0.0')
metric = nlp.load_metric('squad', version='1.0.0')
```

Notes:
- version number are the release version of the library
- currently only possible for canonical datasets/metrics, ie. integrated in the GitHub repo of the library
GitHub issue #560: Using custom DownloadConfig results in an error
State: closed | Created: 2020-09-01T22:23:02Z

## Version / Environment

Ubuntu 18.04
Python 3.6.8
nlp 0.4.0

## Description

Loading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.

## How to reproduce

### Example without DownloadConfig --> works

```python
import os

os.environ["HF_HOME"] = "/data/hf-test-without-dl-config-01/"

import logging
import nlp

logging.basicConfig(level=logging.INFO)

if __name__ == "__main__":
    imdb = nlp.load_dataset(path="imdb")
```

### Example with DownloadConfig --> doesn't work

```python
import os

os.environ["HF_HOME"] = "/data/hf-test-with-dl-config-01/"

import logging
import nlp
from nlp.utils import DownloadConfig

logging.basicConfig(level=logging.INFO)

if __name__ == "__main__":
    download_config = DownloadConfig()
    imdb = nlp.load_dataset(path="imdb", download_config=download_config)
```

Error traceback:

```
Traceback (most recent call last):
  File "/.../example_with_dl_config.py", line 13, in <module>
    imdb = nlp.load_dataset(path="imdb", download_config=download_config)
  File "/.../python3.6/python3.6/site-packages/nlp/load.py", line 549, in load_dataset
    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
  File "/.../python3.6/python3.6/site-packages/nlp/builder.py", line 463, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/.../python3.6/python3.6/site-packages/nlp/builder.py", line 518, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/.../python3.6/python3.6/site-packages/nlp/datasets/imdb/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743/imdb.py", line 86, in _split_generators
    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
  File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 220, in download_and_extract
    return self.extract(self.download(url_or_urls))
  File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 158, in download
    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
  File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 108, in _record_sizes_checksums
    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)
  File "/.../python3.6/python3.6/site-packages/nlp/utils/info_utils.py", line 79, in get_size_checksum_dict
    with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/data/hf-test-with-dl-config-01/datasets/extracted/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'
```
GitHub issue #559: Adding the KILT knowledge source and tasks
State: closed | Created: 2020-09-01T20:05:13Z

This adds Wikipedia pre-processed for KILT, as well as the task data. Only the question IDs are provided for TriviaQA, but they can easily be mapped back with:
```
import nlp

kilt_wikipedia = nlp.load_dataset('kilt_wikipedia')

kilt_tasks = nlp.load_dataset('kilt_tasks')
triviaqa = nlp.load_dataset('trivia_qa', 'unfiltered.nocontext')
triviaqa_map = {}
for k in ['train', 'validation', 'test']:
    triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(triviaqa[k]['question_id'])])
    kilt_tasks[k + '_triviaqa'] = kilt_tasks[k + '_triviaqa'].filter(lambda x: x['id'] in triviaqa_map)
    kilt_tasks[k + '_triviaqa'].map(lambda x: {'input': triviaqa[split][triviaqa_map[x['id']]]['question']})
```

It would be great to have the dataset by Monday, which is when the paper should land on Arxiv and @fabiopetroni is planning on tweeting about the paper and `facebookresearch` repository for the datasett
GitHub issue #554: nlp downloads to its module path
State: closed | Created: 2020-09-01T14:06:14Z

I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:

```>>> import nlp
>>> squad_dataset = nlp.load_dataset('squad')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/load.py", line 530, in load_dataset
    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)
  File "/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/load.py", line 329, in prepare_module
    os.makedirs(main_folder_path, exist_ok=True)
  File "/nix/store/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5/lib/python3.8/os.py", line 223, in makedirs
    mkdir(name, mode)
OSError: [Errno 30] Read-only file system: '/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/datasets/squad'
```

Do you have any suggested workaround for this issue?

Perhaps overriding the default value for `force_local_path` of `prepare_module`?
GitHub issue #552: Add multiprocessing
State: closed | Created: 2020-09-01T11:56:17Z

Adding multiprocessing to `.map`

It works in 3 steps:
- shard the dataset in `num_proc` shards
- spawn one process per shard and call `map` on them
- concatenate the resulting datasets

Example of usage:

```python
from nlp import load_dataset

dataset = load_dataset("squad", split="train")

def function(x):
    return {"lowered": x.lower()}

processed = d.map(
    function,
    input_columns=["context"],
    num_proc=4,
    cache_file_name="playground/tmp.arrow",
    load_from_cache_file=False
)
```

Here it writes 4 files  depending on the process rank:
- `playground/tmp_00000_of_00004.arrow`
- `playground/tmp_00001_of_00004.arrow`
- `playground/tmp_00002_of_00004.arrow`
- `playground/tmp_00003_of_00004.arrow`

The suffix format can be specified by the user.

If the `cache_file_name` is not specified, it writes into separated files depending on the fingerprint, as usual.

I still need to:
- write tests for this
- try to improve the logging (currently it shows 4 progress bars, but if one finishes before the others, then the following messages are written over the progress bars)
GitHub issue #550: [BUGFIX] Solving mismatched checksum issue for the LinCE dataset (#539)
State: closed | Created: 2020-09-01T03:27:03Z

Hi,

I have added the updated `dataset_infos.json` file for the LinCE benchmark. This update is to fix the mismatched checksum bug #539 for one of the datasets in the LinCE benchmark. To update the file, I run this command from the nlp root directory:

```
python nlp-cli test ./datasets/lince --save_infos --all_configs
```

**NOTE**: I needed to  change [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/commands/dummy_data.py#L8) from: `from .utils.logging import get_logger` to `from nlp.utils.logging import get_logger`, otherwise the script was not able to import `get_logger`. However, I did not include that in this PR since that could have been just my environment (and another PR could be fixing this already if it is actually an issue).
GitHub issue #549: Fix bleurt logging import
State: closed | Created: 2020-09-01T03:01:25Z

Bleurt started throwing an error in some code we have.
This looks like the fix but...

It's also unnerving that even a prebuilt docker image with pinned versions can be working 1 day and then fail the next (especially for production systems).

Any way for us to pin your metrics code so that they are guaranteed not to to change and possibly fail on repository changes?

Thanks (and also for your continued work on the lib...)
GitHub issue #548: [Breaking] Switch text loading to multi-threaded PyArrow loading
State: closed | Created: 2020-08-31T15:15:41Z

Test if we can get better performances for large-scale text datasets by using multi-threaded text file loading based on Apache Arrow multi-threaded CSV loader.

If it works ok, it would fix #546.

**Breaking change**:
The text lines now do not include final line-breaks anymore.
GitHub issue #546: Very slow data loading on large dataset
State: closed | Created: 2020-08-31T12:57:23Z

I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.
It has been 8 hours and still, it is on the loading steps.
It does work when the text dataset size is small about  1 GB, but it doesn't scale.
It also uses a single thread during the data loading step.

```
train_files = glob.glob("xxx/*.txt",recursive=True)
random.shuffle(train_files)

print(train_files)

dataset = nlp.load_dataset('text', 
                           data_files=train_files,
                           name="customDataset",
                           version="1.0.0",
                           cache_dir="xxx/nlp")
```

Is there something that I am missing ?
GitHub issue #545: New release coming up for this library
State: closed | Created: 2020-08-31T11:37:38Z

Hi all,
A few words on the roadmap for this library.

The next release will be a big one and is planed at the end of this week.

In addition to the support for indexed datasets (useful for non-parametric models like REALM, RAG, DPR, knn-LM and many other fast dataset retrieval technics), it will:
- have support for multi-modal datasets
- include various significant improvements on speed for standard processing (map, shuffling, ...)
- have a better support for metrics (better caching, and a robust API) and a bigger focus on reproductibility
- change the name to the final name (voted by the community): `datasets`
- be the 1.0.0 release as we think the API will be mostly stabilized from now on
GitHub issue #543: nlp.load_dataset is not safe for multi processes when loading from local files
State: closed | Created: 2020-08-30T03:20:34Z

Loading from local files, e.g., `dataset = nlp.load_dataset('csv', data_files=['file_1.csv', 'file_2.csv'])`
concurrently from multiple processes, will raise `FileExistsError` from builder's line 430, https://github.com/huggingface/nlp/blob/6655008c738cb613c522deb3bd18e35a67b2a7e5/src/nlp/builder.py#L423-L438

Likely because multiple processes step into download_and_prepare, https://github.com/huggingface/nlp/blob/6655008c738cb613c522deb3bd18e35a67b2a7e5/src/nlp/load.py#L550-L554

This can happen when launching distributed training with commands like `python -m torch.distributed.launch --nproc_per_node 4` on a new collection of files never loaded before.

I can create a PR that puts in some file locks. It would be helpful if I can be informed of the convention for naming and placement of the lock.
GitHub issue #540: [BUGFIX] Fix Race Dataset Checksum bug
State: closed | Created: 2020-08-29T07:00:10Z

In #537 I noticed that there was a bug in checksum checking when I have tried to download the race dataset. The reason for this is that the current preprocessing was just considering the `high school` data and it was ignoring the `middle` one. This PR just fixes it :)

Moreover, I have added some descriptions.
GitHub issue #539: [Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data
State: closed | Created: 2020-08-28T19:55:51Z

Hi,

There is a `NonMatchingChecksumError` error for the `lid_msaea` (language identification for Modern Standard Arabic - Egyptian Arabic) dataset from the LinCE benchmark due to a minor update on that dataset. 

How can I update the checksum of the library to solve this issue? The error is below and it also appears in the [nlp viewer](https://huggingface.co/nlp/viewer/?dataset=lince&config=lid_msaea):

```python
import nlp
nlp.load_dataset('lince', 'lid_msaea')
```

Output:
```
NonMatchingChecksumError: ['https://ritual.uh.edu/lince/libaccess/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9/lid_msaea.zip']
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
    exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 196, in <module>
    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 591, in wrapped_func
    return get_or_create_cached_value()
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 575, in get_or_create_cached_value
    return_value = func(*args, **kwargs)
File "/home/sasha/nlp-viewer/run.py", line 150, in get
    builder_instance.download_and_prepare()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare
    download_config.force_download = download_mode == FORCE_REDOWNLOAD
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 469, in _download_and_prepare
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 36, in verify_checksums
    raise NonMatchingChecksumError(str(bad_urls))
```

Thank you in advance!

@lhoestq
GitHub issue #538: [logging] Add centralized logging - Bump-up cache loads to warnings
State: closed | Created: 2020-08-28T11:42:29Z

Add a `nlp.logging` module to set the global logging level easily. The verbosity level also controls the tqdm bars (disabled when set higher than INFO).

You can use:
```
nlp.logging.set_verbosity(verbosity: int)
nlp.logging.set_verbosity_info()
nlp.logging.set_verbosity_warning()
nlp.logging.set_verbosity_debug()
nlp.logging.set_verbosity_error()
nlp.logging.get_verbosity() -> int
```
And use the levels:
```
nlp.logging.CRITICAL
nlp.logging.DEBUG
nlp.logging.ERROR
nlp.logging.FATAL
nlp.logging.INFO
nlp.logging.NOTSET
nlp.logging.WARN
nlp.logging.WARNING
```
GitHub issue #537: [Dataset] RACE dataset Checksums error
State: closed | Created: 2020-08-27T23:58:16Z

Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:

```
dataset = nlp.load_dataset("race")
len(dataset["train"]), len(dataset["validation"])
```

But then I got the following error:

```
---------------------------------------------------------------------------
NonMatchingChecksumError                  Traceback (most recent call last)
<ipython-input-15-8bf7603ce0ed> in <module>
----> 1 dataset = nlp.load_dataset("race")
      2 len(dataset["train"]), len(dataset["validation"])

~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
    546 
    547     # Download and prepare data
--> 548     builder_instance.download_and_prepare(
    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
    550     )

~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
    460                         logger.info("Dataset not on Hf google storage. Downloading and preparing it from source")
    461                 if not downloaded_from_gcs:
--> 462                     self._download_and_prepare(
    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
    464                     )

~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
    519         # Checksums verification
    520         if verify_infos:
--> 521             verify_checksums(
    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
    523             )

~/miniconda3/envs/masters/lib/python3.8/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
     36     if len(bad_urls) > 0:
     37         error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))
     39     logger.info("All the checksums matched successfully" + for_verification_name)
     40 

NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz']
```
GitHub issue #536: Fingerprint
State: closed | Created: 2020-08-27T16:27:09Z

This PR is a continuation of #513 , in which many in-place functions were introduced or updated (cast_, flatten_) etc.
However the caching didn't handle these changes. Indeed the caching took into account only the previous cache file name of the table, and not the possible in-place transforms of the table.

To fix that, I added the concept of dataset fingerprint, that is updated after each transform (in place or not), and stored inside the table metadata.

When a dataset is created, an initial fingerprint is computed. If the dataset is memory-mapped, then the fingerprint generator doesn't read the table and only looks at the filename. However if the table is in-memory, then the fingerprint generator reads the content of the table using a batched non-crypto hashing.

I added a utility class to compute hashes of arbitrary python objects in `fingerprint.py` : `Hasher`. The API is close to standard hashing tools (`.update`, `.hexdigest`). It also supports custom hashing functions depending on object types using a registry like pickle. I added a custom hashing function to hash a `pa.Table` in a batched way, and also for `nlp.DatasetInfo` to leverage its json serialization feature.

Note about this PR:
This is a draft PR because #513 needs to be merged first.
The diff that is shown is for branches fingerprint -> indices (and not master, for now)
GitHub issue #535: Benchmarks
State: closed | Created: 2020-08-26T11:21:26Z

Adding some benchmarks with DVC/CML

To add a new tracked benchmark:
- create a new python benchmarking script in `./benchmarks/`. The script can use the utilities in `./benchmarks/utils.py` and should output a JSON file with results in `./benchmarks/results/`.
- add a new pipeline stage in [dvc.yaml](./dvc.yaml) with the name of your new benchmark.

That's it
GitHub issue #534: `list_datasets()` is broken.
State: closed | Created: 2020-08-26T08:19:01Z

version = '0.4.0'

`list_datasets()` is broken. It results in the following error : 

```
In [3]: nlp.list_datasets()
Out[3]: ---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
    700                 type_pprinters=self.type_printers,
    701                 deferred_pprinters=self.deferred_printers)
--> 702             printer.pretty(obj)
    703             printer.flush()
    704             return stream.getvalue()

~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in pretty(self, obj)
    375                 if cls in self.type_pprinters:
    376                     # printer registered in self.type_pprinters
--> 377                     return self.type_pprinters[cls](obj, self, cycle)
    378                 else:
    379                     # deferred printer

~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in inner(obj, p, cycle)
    553                 p.text(',')
    554                 p.breakable()
--> 555             p.pretty(x)
    556         if len(obj) == 1 and type(obj) is tuple:
    557             # Special case for 1-item tuples.

~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in pretty(self, obj)
    392                         if cls is not object \
    393                                 and callable(cls.__dict__.get('__repr__')):
--> 394                             return _repr_pprint(obj, self, cycle)
    395
    396             return _default_pprint(obj, self, cycle)

~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
    698     """A pprint that just redirects to the normal repr function."""
    699     # Find newlines and replace them with p.break_()
--> 700     output = repr(obj)
    701     lines = output.splitlines()
    702     with p.group():

~/.virtualenvs/san-lgUCsFg_/lib/python3.8/site-packages/nlp/hf_api.py in __repr__(self)
    110
    111     def __repr__(self):
--> 112         single_line_description = self.description.replace("\n", "")
    113         return f"nlp.ObjectInfo(id='{self.id}', description='{single_line_description}', files={self.siblings})"
    114

AttributeError: 'NoneType' object has no attribute 'replace'
```
GitHub issue #530: use ragged tensor by default
State: closed | Created: 2020-08-24T17:06:15Z

I think it's better if it's clear whether the returned tensor is ragged or not when the type is set to tensorflow.
Previously it was a tensor (not ragged) if numpy could stack the output (which can change depending on the batch of example you take), which make things difficult to handle, as it may sometimes return a ragged tensor and sometimes not.

Therefore I reverted this behavior to always return a ragged tensor as we used to do.
GitHub issue #529: Add MLSUM
State: closed | Created: 2020-08-24T16:18:35Z

Hello (again :) !), 

So, I started a new branch because of a [rebase issue](https://github.com/huggingface/nlp/pull/463), sorry for the mess. 

However, the command `pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_mlsum` still fails because there is no default language dataset : the script throws an error as a specific config language is necessary. 

I think that setting a default language would be a bad workaround for this so I kept it as it is. Putting all the train files across languages together would also be a bad idea because of the size. 

Thanks for your help, 

Rachel
GitHub issue #527: Fix config used for slow test on real dataset
State: closed | Created: 2020-08-24T12:39:34Z

As noticed in #470, #474, #476, #504 , the slow test `test_load_real_dataset` couldn't run on datasets that require config parameters.

To fix that I replaced it with one test with the first config of BUILDER_CONFIGS `test_load_real_dataset`, and another test that runs all of the configs in BUILDER_CONFIGS `test_load_real_dataset_all_configs`
GitHub issue #526: Returning None instead of "python" if dataset is unformatted
State: closed | Created: 2020-08-24T12:10:35Z

Following the discussion on Slack, this small fix ensures that calling `dataset.set_format(type=dataset.format["type"])` works properly. Slightly breaking as calling `dataset.format` when the dataset is unformatted will return `None` instead of `python`.
GitHub issue #525: wmt download speed example
State: closed | Created: 2020-08-21T23:29:06Z

Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.

```
import nlp
nlp.load_dataset('wmt16', 'de-en')
```
Downloads at 49.1 KB/S

Whereas 
```
pip install gdown # download from google drive
!gdown https://drive.google.com/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj
```
Downloads at 127 MB/s. (The file is a copy of wmt-en-de raw).


```
nlp.load_dataset('wmt16', 'ro-en')
```
goes at 27 MB/s, much faster. 

if we wget the same data from s3 is the same download speed, but ¼ the file size:
```
wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro_packed_200_rand.tgz
```

Finally,
```
nlp.load_dataset('wmt19', 'zh-en')
```
Starts fast, but broken. (duplicate of #493 )
GitHub issue #524: Some docs are missing parameter names
State: closed | Created: 2020-08-21T16:47:34Z

See https://huggingface.co/nlp/master/package_reference/main_classes.html#nlp.Dataset.map. I believe this is because the parameter names are enclosed in backticks in the docstrings, maybe it's an old docstring format that doesn't work with the current Sphinx version.
GitHub issue #523: Speed up Tokenization by optimizing cast_to_python_objects
State: closed | Created: 2020-08-20T09:42:02Z

I changed how `cast_to_python_objects` works to make it faster.
It is used to cast numpy/pytorch/tensorflow/pandas objects to python lists, and it works recursively.

To avoid iterating over possibly long lists, it first checks if the first element that is not None has to be casted.
If the first element needs to be casted, then all the elements of the list will be casted, otherwise they'll stay the same.
This trick allows to cast objects that contain tokenizers outputs without iterating over every single token for example.

Speed improvement:


```python
import transformers
import nlp

tok = transformers.BertTokenizerFast.from_pretrained("bert-base-uncased")
txt = ["a " * 512] * 1000
dataset = nlp.Dataset.from_dict({"txt": txt})

# Tokenization using .map is now faster. Previously it was taking 3.5s
%time _ = dataset.map(lambda x: tok(x["txt"]), batched=True, load_from_cache_file=False)
# 450ms

# for comparison
%time _ = tok(txt)
# 280ms

```
GitHub issue #520: Transform references for sacrebleu
State: closed | Created: 2020-08-20T00:26:55Z

Currently it is impossible to use sacrebleu when len(predictions) != the number of references per prediction (very uncommon), due to a strange format expected by sacrebleu. If one passes in the data to `nlp.metric.compute()` in sacrebleu format, `nlp` throws an error due to mismatching lengths between predictions and references. If one uses a more standard format where predictions and references are lists of the same length, sacrebleu throws an error.

This PR transforms reference data in a more standard format into the [unusual format](https://github.com/mjpost/sacreBLEU#using-sacrebleu-from-python) expected by sacrebleu.
GitHub issue #519: [BUG] Metrics throwing new error on master since 0.4.0
State: closed | Created: 2020-08-19T21:29:15Z

The following error occurs when passing in references of type `List[List[str]]` to metrics like bleu.
Wasn't happening on 0.4.0 but happening now on master.

```
  File "/usr/local/lib/python3.7/site-packages/nlp/metric.py", line 226, in compute
    self.add_batch(predictions=predictions, references=references)
  File "/usr/local/lib/python3.7/site-packages/nlp/metric.py", line 242, in add_batch
    batch = self.info.features.encode_batch(batch)
  File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 527, in encode_batch
    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]
  File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 527, in <listcomp>
    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]
  File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 456, in encode_nested_example
    raise ValueError("Got a string but expected a list instead: '{}'".format(obj))
```
GitHub issue #518: [METRICS, breaking] Refactor caching behavior, pickle/cloudpickle metrics and dataset, add tests on metrics
State: closed | Created: 2020-08-19T19:43:08Z

Move the acquisition of the filelock at a later stage during metrics processing so it can be pickled/cloudpickled after instantiation.

Also add some tests on pickling, concurrent but separate metric instances and concurrent and distributed metric instances.

Changes significantly the caching behavior for the metrics:
- if the metric is used in a non-distributed setup (most common case) we try to find a free cache file using UUID instead of asking for an `experiment_id` if we can't lock the cache file this allows to use several instances of the same metrics in parallel.
- if the metrics is used in a distributed setup we ask for an `experiment_id` if we can't lock the cache file (because all the nodes need to have related cache file names for the final sync.
- after the computation, we free the locks and delete all the cache files.

Breaking: Some arguments for Metrics initialization have been removed for simplicity (`version`...) and some have been renamed for consistency with the rest of the library (`in_memory` => `keep_in_memory`).

Also remove the `_has_transformers` detection in utils to avoid importing transformers everytime during loading.
GitHub issue #517: add MLDoc dataset
State: open | Created: 2020-08-19T14:41:59Z

Hi,

I am recommending that someone add MLDoc, a multilingual news topic classification dataset.

- Here's a link to the Github: https://github.com/facebookresearch/MLDoc
- and the paper: http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf

Looks like the dataset contains news stories in multiple languages that can be classified into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets). There are 13 languages: Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish
GitHub issue #515: Fix batched map for formatted dataset
State: closed | Created: 2020-08-19T13:34:50Z

If you had a dataset formatted as numpy for example, and tried to do a batched map, then it would crash because one of the elements from the inputs was missing for unchanged columns (ex: batch of length 999 instead of 1000).
The happened during the creation of the `pa.Table`, since columns had different lengths.
GitHub issue #514: dataset.shuffle(keep_in_memory=True) is never allowed
State: closed | Created: 2020-08-18T18:47:40Z

As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`

The commit added the lines
```python
# lines 994-996 in src/nlp/arrow_dataset.py
       assert (
            not keep_in_memory or cache_file_name is None
        ), "Please use either `keep_in_memory` or `cache_file_name` but not both."
```

This affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. 

I'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.
GitHub issue #513: [speedup] Use indices mappings instead of deepcopy for all the samples reordering methods
State: closed | Created: 2020-08-18T17:36:02Z

Use an indices mapping instead of rewriting the dataset for all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`).

Added a `flatten_indices` method which copy the dataset to a new table to remove the indices mapping with tests.

All the samples re-ordering/selection methods should be a lot faster. The downside is that iterating on very large batch of the dataset might be a little slower when we have changed the order of the samples since with in these case we use `pyarrow.Table.take` instead of `pyarrow.Table.slice`. There is no free lunch but the speed of iterating over the dataset is rarely the bottleneck.

*Backward breaking change*: the `cache_file_name` argument in all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`) is now called `indices_cache_file_name` on purpose to make it explicit to the user that this caching file is used for caching the indices mapping and not the dataset itself.
GitHub issue #511: dataset.shuffle() and select() resets format. Intended?
State: closed | Created: 2020-08-18T13:46:01Z

Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?

When working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save("dataset.pt")`. Later loading the dataset object using `torch.load("dataset.pt")`, which conserves the defined format before saving. 
I do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset. 

The obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.

_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_

####  How to reproduce:

```python
import nlp
from transformers import T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained("t5-base")
def create_features(batch):
    context_encoding = tokenizer.batch_encode_plus(batch["context"])
    return {"input_ids": context_encoding["input_ids"]}

dataset = nlp.load_dataset("cosmos_qa", split="train")
dataset = dataset.map(create_features, batched=True)
dataset.set_format(type="torch", columns=["input_ids"])
dataset[0]
# {'input_ids': tensor([ 1804,  3525,  1602,  ...   0,     0])}

dataset = dataset.shuffle()
dataset[0]
# {'id': '3Q9(...)20', 'context': "Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}

```
GitHub issue #510: Version of numpy to use the library
State: closed | Created: 2020-08-18T08:59:13Z

Thank you so much for your excellent work! I would like to use nlp library in my project. While importing nlp, I am receiving the following error `AttributeError: module 'numpy.random' has no attribute 'Generator'` Numpy version in my project is 1.16.0. May I learn which numpy version is used for the nlp library.

Thanks in advance.
GitHub issue #508: TypeError: Receiver() takes no arguments
State: closed | Created: 2020-08-16T07:18:16Z

I am trying to load a wikipedia data set

```
import nlp
from nlp import load_dataset

dataset = load_dataset("wikipedia", "20200501.en", split="train", cache_dir=data_path, beam_runner='DirectRunner')
#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')
```

This fails in the apache beam runner. 

```
Traceback (most recent call last):
  File "D:/ML/wikiembedding/gpt2_sv.py", line 36, in <module>
    dataset = load_dataset("wikipedia", "20200501.en", split="train", cache_dir=my_cache_dir, beam_runner='DirectRunner')
  File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\load.py", line 548, in load_dataset
    builder_instance.download_and_prepare(
  File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\builder.py", line 462, in download_and_prepare
    self._download_and_prepare(
  File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\builder.py", line 969, in _download_and_prepare
    pipeline_results = pipeline.run()
  File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\pipeline.py", line 534, in run
    return self.runner.run_pipeline(self, self._options)
....
  File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 218, in process_encoded
    self.output(decoded_value)
  File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\runners\worker\operations.py", line 332, in output
    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)
  File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\Cython\Shadow.py", line 167, in cast
    return type(*args)
TypeError: Receiver() takes no arguments

```

This is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump.
GitHub issue #507: Errors when I use 
State: closed | Created: 2020-08-14T21:03:57Z

I tried the following example code from https://huggingface.co/deepset/roberta-base-squad2 and got errors 
I am using **transformers 3.0.2** code .


from transformers.pipelines import pipeline
from transformers.modeling_auto import AutoModelForQuestionAnswering
from transformers.tokenization_auto import AutoTokenizer

model_name = "deepset/roberta-base-squad2"

nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Why is model conversion important?',
    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)

The errors are :

res = nlp(QA_input)
  File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in __call__
    for s, e, score in zip(starts, ends, scores)
  File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in <listcomp>
    for s, e, score in zip(starts, ends, scores)
KeyError: 0
GitHub issue #504: Added downloading to Hyperpartisan news detection
State: closed | Created: 2020-08-13T21:53:46Z

Following the discussion on Slack and #349, I've updated the hyperpartisan dataset to pull directly from Zenodo rather than manual install, which should make this dataset much more accessible. Many thanks to @johanneskiesel !

Currently doesn't pass `test_load_real_dataset` - I'm using `self.config.name` which is `default` in this test. Might be related to #474
GitHub issue #502: Fix tokenizers caching
State: closed | Created: 2020-08-13T15:53:37Z

I've found some cases where the caching didn't work properly for tokenizers:

1. if a tokenizer has a regex pattern, then the caching would be inconsistent across sessions
2. if a tokenizer has a cache attribute that changes after some calls, the the caching would not work after cache updates
3. if a tokenizer is used inside a function, the caching of this function would result in the same cache file for different tokenizers
4. if `unique_no_split_tokens`'s attribute is not the same across sessions (after loading a tokenizer) then the caching could be inconsistent

To fix that, this is what I did:

1. register a specific `save_regex` function for pickle that makes regex dumps deterministic
2. ignore cache attribute of some tokenizers before dumping
3. enable recursive dump by default for all dumps
4. make `unique_no_split_tokens` deterministic in https://github.com/huggingface/transformers/pull/6461

I also added tests to make sure that tokenizers hashing works as expected.
In the future we should find a way to test if hashing also works across session (maybe using two CI jobs ? or by hardcoding a tokenizer's hash ?)
GitHub issue #501: Caching doesn't work for map (non-deterministic)
State: closed | Created: 2020-08-12T20:20:07Z

The caching functionality doesn't work reliably when tokenizing a dataset. Here's a small example to reproduce it. 

```python
import nlp
import transformers

def main():
    ds = nlp.load_dataset("reddit", split="train[:500]")

    tokenizer = transformers.AutoTokenizer.from_pretrained("gpt2")

    def convert_to_features(example_batch):
        input_str = example_batch["body"]
        encodings = tokenizer(input_str, add_special_tokens=True, truncation=True)
        return encodings

    ds = ds.map(convert_to_features, batched=True)

if __name__ == "__main__":
    main()
```

Roughly 3/10 times, this example recomputes the tokenization.

Is this expected behaviour?
GitHub issue #499: Narrativeqa (with full text)
State: closed | Created: 2020-08-12T13:49:43Z

Following the uploading of the full text data in #309, I've added the full text to the narrativeqa dataset.

Few notes:
- Had some encoding issues using the default `open` so am using `open(encoding="latin-1"...` which seems to fix it. Looks fine.
-  Can't get the dummy data to work. Currently putting stuff at: 
    ```
    dummy
    |---- 0.0.0
            |- dummy_data.zip
                |-master.zip
                |   |- narrativeqa-master
                |       |- documents.csv
                |       |- qaps.csv
                |       |- third_party ......
                | 
                | - narrativeqa_full_text.zip
                |       | - 001.content
                |       | - ....
    ```
    Not sure what I'm messing up here (probably something obvious).
GitHub issue #497: skip header in PAWS-X
State: closed | Created: 2020-08-11T17:26:25Z

This should fix #485 

I also updated the `dataset_infos.json` file that is used to verify the integrity of the generated splits (the number of examples was reduced by one).

Note that there are new fields in `dataset_infos.json` introduced in the latest release 0.4.0 corresponding to post processing info. I removed them in this case when I ran `nlp-cli ./datasets/xtreme --save_infos` to keep backward compatibility (versions 0.3.0 can't load these fields).

I think I'll change the logic so that `nlp-cli test` doesn't create these fields for dataset with no post processing
GitHub issue #496: fix bad type in overflow check
State: closed | Created: 2020-08-11T16:24:58Z

When writing an arrow file and inferring the features, the overflow check could fail if the first example had a `null` field.
This is because we were not using the inferred features to do this check, and we could end up with arrays that don't match because of a type mismatch (`null` vs `string` for example).

This should fix #482
GitHub issue #495: stack vectors in pytorch and tensorflow
State: closed | Created: 2020-08-11T15:12:53Z

When the format of a dataset is set to pytorch or tensorflow, and if the dataset has vectors in it, they were not stacked together as tensors when calling `dataset[i:i + batch_size][column]` or `dataset[column]`.

I added support for stacked tensors for both pytorch and tensorflow.
For ragged tensors, they are stacked only for tensorflow as pytorch doesn't support ragged tensors.
GitHub issue #494: Fix numpy stacking
State: closed | Created: 2020-08-11T13:40:30Z

When getting items using a column name as a key, numpy arrays were not stacked.
I fixed that and added some tests.

There is another issue that still needs to be fixed though: when getting items using a column name as a key, pytorch tensors are not stacked (it outputs a list of tensors). This PR should help with the to fix this issue.
GitHub issue #492: nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema
State: closed | Created: 2020-08-11T00:27:46Z

Here's the code I'm trying to run:

```python
dset_wikipedia = nlp.load_dataset("wikipedia", "20200501.en", split="train", cache_dir=args.cache_dir)
dset_wikipedia.drop(columns=["title"])
dset_wikipedia.features.pop("title")
dset_books = nlp.load_dataset("bookcorpus", split="train", cache_dir=args.cache_dir)
dset = nlp.concatenate_datasets([dset_wikipedia, dset_books])
```

This fails because they have different schemas, despite having identical features.

```python
assert dset_wikipedia.features == dset_books.features # True
assert dset_wikipedia._data.schema == dset_books._data.schema # False
```

The Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.

```python
dset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)
```
GitHub issue #491: No 0.4.0 release on GitHub
State: closed | Created: 2020-08-10T23:59:57Z

0.4.0 was released on PyPi, but not on GitHub. This means [the documentation](https://huggingface.co/nlp/) is still displaying from 0.3.0, and that there's no tag to easily clone the 0.4.0 version of the repo.
GitHub issue #490: Loading preprocessed Wikipedia dataset requires apache_beam
State: closed | Created: 2020-08-10T23:46:50Z

Running 

`nlp.load_dataset("wikipedia", "20200501.en", split="train", dir="/tmp/wikipedia")`

gives an error if apache_beam is not installed, stemming from

https://github.com/huggingface/nlp/blob/38eb2413de54ee804b0be81781bd65ac4a748ced/src/nlp/builder.py#L981-L988

This succeeded without the dependency in version 0.3.0. This seems like an unnecessary dependency to process some dataset info if you're using the already-preprocessed version. Could it be removed?
GitHub issue #486: Bookcorpus data contains pretokenized text
State: closed | Created: 2020-08-09T06:53:24Z

It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, "didn't" becomes "did" + "n't", and double quotes are changed to `` and '' for start and end quotes, respectively.

On my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https://github.com/nltk/nltk/pull/2575
GitHub issue #485: PAWS dataset first item is header
State: closed | Created: 2020-08-08T22:05:25Z

```
import nlp
dataset = nlp.load_dataset('xtreme', 'PAWS-X.en')
dataset['test'][0]
```

prints the following

```
{'label': 'label', 'sentence1': 'sentence1', 'sentence2': 'sentence2'}
```

dataset['test'][0] should probably be the first item in the dataset, not just a dictionary mapping the column names to themselves. Probably just need to ignore the first row in the dataset by default or something like that.
GitHub issue #483: rotten tomatoes movie review dataset taken down
State: closed | Created: 2020-08-07T15:12:01Z

In an interesting twist of events, the individual who created the movie review seems to have left Cornell, and their webpage has been removed, along with the movie review dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz). It's not downloadable anymore.