jamesLeeeeeee/datasets-github-issues huggingface dataset:
No module named 'nlp.logging'
Author: melody-ju | Pull request: no | State: closed

Hi, I am using nlp version 0.4.0. Trying to use bleurt as an eval metric, however, the bleurt script imports nlp.logging which creates the following error. What am I missing?

```
>>> import nlp
2020-09-02 13:47:09.210310: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
>>> bleurt = nlp.load_metric("bleurt")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/load.py", line 443, in load_metric
    metric_cls = import_main_class(module_path, dataset=False)
  File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/load.py", line 61, in import_main_class
    module = importlib.import_module(module_path)
  File "/home/melody/anaconda3/envs/transformers/lib/python3.6/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 994, in _gcd_import
  File "<frozen importlib._bootstrap>", line 971, in _find_and_load
  File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 678, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/metrics/bleurt/43448cf2959ea81d3ae0e71c5c8ee31dc15eed9932f197f5f50673cbcecff2b5/bleurt.py", line 20, in <module>
    from nlp.logging import get_logger
ModuleNotFoundError: No module named 'nlp.logging'
```

Just to show once again that I can't import the logging module:

```
>>> import nlp
2020-09-02 13:48:38.190621: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
>>> nlp.__version__
'0.4.0'
>>> from nlp.logging import get_logger
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'nlp.logging'
```
[Reproductibility] Allow to pin versions of datasets/metrics
Author: thomwolf | Pull request: yes | State: closed

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
Using custom DownloadConfig results in an error
Author: ynouri | Pull request: no | State: closed

## 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'
```
Adding the KILT knowledge source and tasks
Author: yjernite | Pull request: yes | State: closed

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
nlp downloads to its module path
Author: danieldk | Pull request: no | State: closed

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`?
Add multiprocessing
Author: lhoestq | Pull request: yes | State: closed

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)
[BUGFIX] Solving mismatched checksum issue for the LinCE dataset (#539)
Author: gaguilar | Pull request: yes | State: closed

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).
Fix bleurt logging import
Author: jbragg | Pull request: yes | State: closed

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...)
Very slow data loading on large dataset
Author: agemagician | Pull request: no | State: closed

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 ?
New release coming up for this library
Author: thomwolf | Pull request: no | State: closed

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
nlp.load_dataset is not safe for multi processes when loading from local files
Author: luyug | Pull request: no | State: closed

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.
[Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data
Author: gaguilar | Pull request: no | State: closed

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
[logging] Add centralized logging - Bump-up cache loads to warnings
Author: thomwolf | Pull request: yes | State: closed

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
```
[Dataset] RACE dataset Checksums error
Author: abarbosa94 | Pull request: no | State: closed

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']
```
Fingerprint
Author: lhoestq | Pull request: yes | State: closed

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)
Benchmarks
Author: thomwolf | Pull request: yes | State: closed

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
`list_datasets()` is broken.
Author: ashutosh-dwivedi-e3502 | Pull request: no | State: closed

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'
```
use ragged tensor by default
Author: lhoestq | Pull request: yes | State: closed

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.
Add MLSUM
Author: RachelKer | Pull request: yes | State: closed

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