mengta666/huggingface_github huggingface dataset:
Issue: Cannot download dataset_info.json

I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:

```
ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/text/default-53ee3045f07ba8ca/0.0.0/dataset_info.json
```

I tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?

Versions:
Python version 3.7.3
PyTorch version 1.6.0
TensorFlow version 2.3.0
datasets version: 1.0.1 


Comments:
Thanks for reporting !
We should add support for servers without internet connection indeed
I'll do that early next week

Thanks, @lhoestq !
Please let me know when it is available. 

Right now the recommended way is to create the dataset on a server with internet connection and then to save it and copy the serialized dataset to the server without internet connection.

#652 should allow you to load text/json/csv/pandas datasets without an internet connection **IF** you've the dataset script locally.

Example: 
If you have `datasets/text/text.py` locally, then you can do `load_dataset("./datasets/text", data_files=...)`
Issue: GLUE/QQP dataset: NonMatchingChecksumError

Hi @lhoestq , I know you are busy and there are also other important issues. But if this is easy to be fixed, I am shamelessly wondering if you can give me some help , so I can evaluate my models and restart with my developing cycle asap. 😚

datasets version: editable install of master at 9/17

`datasets.load_dataset('glue','qqp', cache_dir='./datasets')`

```
Downloading and preparing dataset glue/qqp (download: 57.73 MiB, generated: 107.02 MiB, post-processed: Unknown size, total: 164.75 MiB) to ./datasets/glue/qqp/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4...
---------------------------------------------------------------------------
NonMatchingChecksumError                  Traceback (most recent call last)
 in 
----> 1 datasets.load_dataset('glue','qqp', cache_dir='./datasets')

~/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
    609         download_config=download_config,
    610         download_mode=download_mode,
--> 611         ignore_verifications=ignore_verifications,
    612     )
    613 

~/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
    467                     if not downloaded_from_gcs:
    468                         self._download_and_prepare(
--> 469                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
    470                         )
    471                     # Sync info

~/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
    527         if verify_infos:
    528             verify_checksums(
--> 529                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
    530             )
    531 

~/datasets/src/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
     37     if len(bad_urls) > 0:
     38         error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))
     40     logger.info("All the checksums matched successfully" + for_verification_name)
     41 

NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip']
```

Comments:
Hi ! Sure I'll take a look
Issue: straddling object straddles two block boundaries

I am trying to read json data (it's an array with lots of dictionaries) and getting block boundaries issue as below : 

I tried calling read_json with readOptions but no luck .

```
table = json.read_json(fn)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pyarrow/_json.pyx", line 246, in pyarrow._json.read_json
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```


Comments:
sorry it's an apache arrow issue.
Issue: Mistakes in MLQA features names

I think the following features in MLQA shouldn't be named the way they are:
1. `questions` (should be `question`)
2. `ids` (should be `id`)
3. `start` (should be `answer_start`)

The reasons I'm suggesting these features be renamed are:
* To make them consistent with other QA datasets like SQuAD, XQuAD, TyDiQA etc. and hence make it easier to concatenate multiple QA datasets.
* The features names are not the same as the ones provided in the original MLQA datasets (it uses the names I suggested).

I know these columns can be renamed using  using `Dataset.rename_column_`, `questions` and `ids` can be easily renamed but `start` on the other hand is annoying to rename since it's nested inside the feature `answers`.


Comments:
Indeed you're right ! Thanks for reporting that

Could you open a PR to fix the features names ?
Issue: Don't use the old NYU GLUE dataset URLs

NYU is switching dataset hosting from Google to FB. Initial changes to `datasets` are in https://github.com/jeswan/nlp/commit/b7d4a071d432592ded971e30ef73330529de25ce. What tests do you suggest I run before opening a PR?

See: https://github.com/jiant-dev/jiant/issues/161 and https://github.com/nyu-mll/jiant/pull/1112

Comments:
Feel free to open the PR ;)
Thanks for updating the dataset_info.json file !
Issue: Indices incorrect with multiprocessing

When `num_proc` > 1, the indices argument passed to the map function is incorrect:

```python
d = load_dataset('imdb', split='test[:1%]')

def fn(x, inds):
    print(inds)
    return x

d.select(range(10)).map(fn, with_indices=True, batched=True)
# [0, 1]
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)
# [0, 1]
# [0, 1]
# [0, 1, 2, 3, 4]
# [0, 1, 2, 3, 4]
```

As you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`.

Comments:
I fixed a bug that could cause this issue earlier today. Could you pull the latest version and try again ?

Still the case on master.
I guess we should have an offset in the multi-procs indeed (hopefully it's enough).

Also, side note is that we should add some logging before the "test" to say we are testing the function otherwise its confusing for the user to see two outputs I think. Proposal (see the "Testing the mapped function outputs:" lines):
```
>>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)
Done writing 10 indices in 80 bytes .
Done writing 5 indices in 41 bytes .
Done writing 5 indices in 41 bytes .
Spawning 2 processes
Testing the mapped function outputs:
inds: [0, 1]
inds: [0, 1]
Testing finished, running the mapped function on the dataset:
#0:   0%|                                                                                                                                                                    | 0/1 [00:00<?, ?ba/s]
inds: [0, 1, 2, 3, 4]                                                                                                                                                                             inds: [0, 1, 2, 3, 4]                                                                                                                                                         | 0/1 [00:00<?, ?ba/s]
#0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1321.04ba/s]
#1: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1841.22ba/s]
Concatenating 2 shards from multiprocessing
Dataset(features: {'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None)}, num_rows: 10)
```
Issue: `Dataset`/`DatasetDict` has no attribute 'save_to_disk'

Hi,

As the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method.  While the file [`arrow_dataset.py`](https://github.com/huggingface/nlp/blob/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1/src/nlp/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https://github.com/huggingface/nlp.git ` and still no luck. Do I need to install the library in another way?

Comments:
`pip install git+https://github.com/huggingface/nlp.git` should have done the job.

Did you uninstall `nlp` before installing from github ?

> Did you uninstall `nlp` before installing from github ?

I did not. I created a new environment and installed `nlp` directly from `github` and it worked!

Thanks.
Issue: The process cannot access the file because it is being used by another process (windows)

Hi, I consistently get the following error when developing in my PC (windows 10):

```
    train_dataset = train_dataset.map(convert_to_features, batched=True)
  File "C:\Users\saareliad\AppData\Local\Continuum\miniconda3\envs\py38\lib\site-packages\nlp\arrow_dataset.py", line 970, in map
    shutil.move(tmp_file.name, cache_file_name)
  File "C:\Users\saareliad\AppData\Local\Continuum\miniconda3\envs\py38\lib\shutil.py", line 803, in move
    os.unlink(src)
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\Users\\saareliad\\.cache\\huggingface\\datasets\\squad\\plain_text\\1.0.0\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\tmpsau1bep1'

```

Comments:
Hi, which version of `nlp` are you using?

By the way we'll be releasing today a significant update fixing many issues (but also comprising a few breaking changes).
You can see more informations here #545 and try it by installing from source from the master branch.

I'm using version 0.4.0.



Ok, it's probably fixed on master. Otherwise if you can give me a fully self-contained exemple to reproduce the error, I can try to investigate.

I get the same behavior, on Windows, when `map`ping a function to a loaded dataset. 
The error doesn't occur if I re-run the cell a second time though! 
I'm on version 1.0.1.

This is going to be fixed by #644 

@saareliad I got the same issue that troubled me quite a while. Unfortunately, there are no good answers to this issue online, I tried it on Linux and that's absolutely fine. After hacking the source code, I solved this problem as follows.

In the source code file: arrow_dataset.py -> _map_single(...)

change
```python
if update_data and tmp_file is not None:
    shutil.move(tmp_file.name, cache_file_name)
```
to
```python
tmp_file.close()
if update_data and tmp_file is not None:
    shutil.move(tmp_file.name, cache_file_name)
```

Then it works without needing multiple times runs to avoid the permission error.
I know this solution is unusual since it changes the source code. Hopefully, the lib's contributors can have better solutions in the future.


@wangcongcong123 thanks  for sharing.
(BTW I also solved it locally on windows by putting the problematic line under try except and not using cache... On windows I just needed 1% of the dataset anyway)
Issue: nlp re-creates already-there caches when using a script, but not within a shell

`nlp` keeps creating new caches for the same file when launching `filter` from a script, and behaves correctly from within the shell.

Example: try running

```
import nlp

hans_easy_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 0)
hans_hard_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 1)
```

twice. If launched from a `file.py` script, the cache will be re-created the second time. If launched as 3 shell/`ipython` commands, `nlp` will correctly re-use the cache.
As observed with @lhoestq.

Comments:
Couln't reproduce on my side :/ 
let me know if you manage to reproduce on another env (colab for example)

Fixed with a clean re-install!
Issue: `metric.compute` throws `ArrowInvalid` error

I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0`

```
  File "/home/beltagy/trainer.py", line 92, in validation_step
    rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL'])
  File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 224, in compute
    self.finalize(timeout=timeout)
  File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 213, in finalize
    self.data = Dataset(**reader.read_files(node_files))
  File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 217, in read_files
    dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions)
  File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 162, in _read_files
    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
  File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 276, in _get_dataset_from_filename
    f = pa.ipc.open_stream(mmap)
  File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 173, in open_stream
    return RecordBatchStreamReader(source)
  File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 64, in __init__
    self._open(source)
  File "pyarrow/ipc.pxi", line 469, in pyarrow.lib._RecordBatchStreamReader._open
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0
```

Comments:
Hmm might be related to what we are solving in #564

Could you try to update to `datasets>=1.0.0` (we changed the name of the library) and try again ?
If is was related to the distributed setup settings it must be fixed.
If it was related to empty metric inputs it's going to be fixed in #654 

Closing this one as it was fixed in #654 
Feel free to re-open if you have other questions
Issue: No module named 'nlp.logging'

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'
```

Comments:
Thanks for reporting.

Apparently this is a versioning issue: the lib downloaded the `bleurt` script from the master branch where we did this change recently. We'll fix that in a new release this week or early next week. Cc @thomwolf 

Until that, I'd suggest you to download the right bleurt folder from github ([this one](https://github.com/huggingface/nlp/tree/0.4.0/metrics/bleurt)) and do

```python
from nlp import load_metric

bleurt = load_metric("path/to/bleurt/folder")
```

To download it you can either clone the repo or download the `bleurt.py` file and place it in a folder named `bleurt` 

Actually we can fix this on our side, this script didn't had to be updated. I'll do it in a few minutes
Issue: New release coming up for this library

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

Comments:
Update: release is planed mid-next week.
Issue: nlp.load_dataset is not safe for multi processes when loading from local files

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.

Comments:
I'll take a look!
Issue: Best practices for training tokenizers with nlp

Hi, thank you for developing this library. 

What do you think are the best practices for training tokenizers using `nlp`?  In the document and examples, I could only find pre-trained tokenizers used.

Comments:
Docs that explain how to train a tokenizer with `datasets` are available here: https://huggingface.co/docs/tokenizers/training_from_memory#using-the-datasets-library
Issue: [Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data

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 

Comments:
Hi @gaguilar 

If you want to take care of this, it very simple, you just need to regenerate the `dataset_infos.json` file as indicated [in the doc](https://huggingface.co/nlp/share_dataset.html#adding-metadata) by [installing from source](https://huggingface.co/nlp/installation.html#installing-from-source) and running the following command from the root of the repo:
```bash
python nlp-cli test ./datasets/lince --save_infos --all_configs
```
And then you can open a pull-request with the updated json file.

Otherwise we'll do it sometime this week.

Hi @thomwolf 

Thanks for the details! I just created a PR with the updated `dataset_infos.json` file (#550).

Thanks for updating the json file. Closing this one
Issue: `list_datasets()` is broken.

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'
```

Comments:
Thanks for reporting !
This has been fixed in #475 and the fix will be available in the next release

What you can do instead to get the list of the datasets is call

```python
print([dataset.id for dataset in nlp.list_datasets()])
```

Thanks @lhoestq .
Issue: wmt download speed example

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 )




Comments:
Thanks for creating the issue :)
The download link for wmt-en-de raw looks like a mirror. We should use that instead of the current url.
Is this mirror official ?

Also it looks like for `ro-en` it tried to download other languages. If we manage to only download the one that is asked it'd be cool

Also cc @patrickvonplaten 

Mirror is not official.

Shall we host the files ourselves or it is fine to use this mirror in your opinion ?

Should we add an argument in `load_dataset` to override some URL with a custom URL (e.g. mirror) or a local path?

This could also be used to provide local files instead of the original files as requested by some users (e.g. when you made a dataset with the same format than SQuAD and what to use it instead of the official dataset files).

@lhoestq I think we should host it ourselves. I'll put the subset of wmt (without preprocessed files) that we need on s3 and post a link over the weekend.

Is there a solution yet? The download speed is still too slow. 60-70kbps download for wmt16 and around 100kbps for wmt19. @sshleifer 

I'm working on mirror links which will provide high download speed :)
See https://github.com/huggingface/datasets/issues/1892

Resolved via https://github.com/huggingface/datasets/pull/1912
Issue: [BUG] Metrics throwing new error on master since 0.4.0

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))
```

Comments:
Update - maybe this is only failing on bleu because I was not tokenizing inputs to the metric

Closing - seems to be just forgetting to tokenize. And found the helpful discussion in huggingface/evaluate#105
Issue: add MLDoc dataset

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

Comments:
Any updates on this?

This request is still an open issue waiting to be addressed by any community member, @GuillemGSubies.
Issue: Version of numpy to use the library

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.

Comments:
Seems like this method was added in 1.17. I'll add a requirement on this.

Thank you so much. After upgrading the numpy library, it worked.
Issue: Converting TensorFlow dataset example

Hi,
I want to use TensorFlow datasets with this repo, I noticed you made some conversion script,
can you give a simple example of using it?

Thanks


Comments:
Do you want to convert a dataset script to the tfds format ?
If so, we currently have a comversion script nlp/commands/convert.py but it is a conversion script that goes from tfds to nlp.
I think it shouldn't be too hard to do the changes in reverse (at some manual adjustments).
If you manage to make it work in reverse, feel free to open a PR to share it with the community :)

In our docs: [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html).
Issue: TypeError: Receiver() takes no arguments

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.

Comments:
Which version of Apache Beam do you have (can you copy your full environment info here)?

apache-beam==2.23.0
nlp==0.4.0

For me this was resolved by running the same python script on Linux (or really WSL). 

Do you manage to run a dummy beam pipeline with python on windows ? 
You can test a dummy pipeline with [this code](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/wordcount_minimal.py)

If you get the same error, it means that the issue comes from apache beam.
Otherwise we'll investigate what went wrong here

Still, same error, so I guess it is on apache beam then. 
Thanks for the investigation.

Thanks for trying
Let us know if you find clues of what caused this issue, or if you find a fix
Issue: Errors when I use 

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



Comments:
Looks like an issue with 3.0.2 transformers version. Works fine when I use "master" version of transformers.
Issue: Caching doesn't work for map (non-deterministic)

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?

Comments:
Thanks for reporting !

To store the cache file, we compute a hash of the function given in `.map`, using our own hashing function.
The hash doesn't seem to stay the same over sessions for the tokenizer.
Apparently this is because of the regex at `tokenizer.pat` is not well supported by our hashing function.

I'm working on a fix

Thanks everyone. Works great now.

Hi. I believe the fix was for the nlp library. Is there a solution to handle compiled regex expressions in .map() with the caching. I want to run a simple regex pattern on a big dataset, but I am running into the issue of compiled expression not being cached. 

Instead of opening a new issue, I thought I would put my query here. Let me know if a new issue would be more suitable. Thanks

Hi @MaveriQ! This fix is also included in the `datasets` library. Can you provide a reproducer?
Issue: nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema

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)
```


Comments:
In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.
Could you try to update `nlp` ?

Also, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.

Or maybe the assertion comes from elsewhere ?

I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.

Since `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in features.

I'm doing a refactor of type inference in #363 . Both text fields should match after that

By default nullable will be set to True

It should be good now. I was able to run

```python
>>> from nlp import concatenate_datasets, load_dataset
>>>
>>> bookcorpus = load_dataset("bookcorpus", split="train")
>>> wiki = load_dataset("wikipedia", "20200501.en", split="train")
>>> wiki.remove_columns_("title")  # only keep the text
>>>
>>> assert bookcorpus.features.type == wiki.features.type
>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])
```

Thanks!
Issue: No 0.4.0 release on GitHub

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.

Comments:
I did the release on github, and updated the doc :)
Sorry for the delay

Thanks!
Issue: rotten tomatoes movie review dataset taken down

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.

Comments:
found a mirror: https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz

fixed in #484 

Closing this one. Thanks again @jxmorris12 for taking care of this :)
Issue: Export TFRecord to GCP bucket

Previously, I was writing TFRecords manually to GCP bucket with : `with tf.io.TFRecordWriter('gs://my_bucket/x.tfrecord')`

Since `0.4.0` is out with the `export()` function, I tried it. But it seems TFRecords cannot be directly written to GCP bucket.

`dataset.export('local.tfrecord')` works fine,  
but `dataset.export('gs://my_bucket/x.tfrecord')` does not work. 

There is no error message, I just can't find the file on my bucket...

---

Looking at the code, `nlp` is using `tf.data.experimental.TFRecordWriter`, while I was using `tf.io.TFRecordWriter`.  

**What's the difference between those 2 ? How can I write TFRecords files directly to GCP bucket ?**

@jarednielsen @lhoestq 

Comments:
Nevermind, I restarted my python session and it worked fine...

---

I had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)
Issue: Overview.ipynb throws exceptions with nlp 0.4.0

with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-5-48907f2ad433> in <module>
----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
      2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
      3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)

<ipython-input-5-48907f2ad433> in <dictcomp>(.0)
----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
      2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
      3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)

AttributeError: 'numpy.ndarray' object has no attribute 'to_tensor'

Comments:
Thanks for reporting this issue

There was a bug where numpy arrays would get returned instead of tensorflow tensors.
This is fixed on master.

I tried to re-run the colab and encountered this error instead:

```
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'to_tensor'
```

This is because the dataset returns a Tensor and not a RaggedTensor.
But I think we should always return a RaggedTensor unless the length of the sequence is fixed (it that case they can be stack into a Tensor).

Hi, I got another error (on Colab):

```python
# You can read a few attributes of the datasets before loading them (they are python dataclasses)
from dataclasses import asdict

for key, value in asdict(datasets[6]).items():
    print('👉 ' + key + ': ' + str(value))

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-6-b8ace6c227a2> in <module>()
      2 from dataclasses import asdict
      3 
----> 4 for key, value in asdict(datasets[6]).items():
      5     print('👉 ' + key + ': ' + str(value))

/usr/local/lib/python3.6/dist-packages/dataclasses.py in asdict(obj, dict_factory)
   1008     """
   1009     if not _is_dataclass_instance(obj):
-> 1010         raise TypeError("asdict() should be called on dataclass instances")
   1011     return _asdict_inner(obj, dict_factory)
   1012 

TypeError: asdict() should be called on dataclass instances
```

Indeed we'll update the cola with the new release coming up this week.
Issue: test_load_real_dataset when config has BUILDER_CONFIGS that matter

It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.
I think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https://github.com/huggingface/nlp/blob/master/tests/test_dataset_common.py#L200)). This causes [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`.

For an example, you can try running the test for lince:
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince`
which yields
> E           TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column'

Comments:
The `data_dir` parameter has been removed. Now the error is `ValueError: Config name is missing`

As mentioned in #470 I think we can have one test with the first config of BUILDER_CONFIGS, and another test that runs all of the configs in BUILDER_CONFIGS

This was fixed in #527 

Closing this one, but feel free to re-open if you have other questions
Issue: DEFAULT_TOKENIZER import error in sacrebleu

Latest Version 0.3.0

When loading the metric "sacrebleu" there is an import error due to the wrong path
![image](https://user-images.githubusercontent.com/5303103/88633063-2c5e5f00-d0bd-11ea-8ca8-4704dc975433.png)


Comments:
This issue was resolved by #447
Issue: Keep loading old file even I specify a new file in load_dataset

I used load a file called 'a.csv' by 
```
dataset = load_dataset('csv', data_file='./a.csv')
```
And after a while, I tried to load another csv called 'b.csv'
```
dataset = load_dataset('csv', data_file='./b.csv')
```
However, the new dataset seems to remain the old 'a.csv' and not loading new csv file.

Even worse, after I load a.csv, the load_dataset function keeps loading the 'a.csv' afterward. 

Is this a cache problem?


Comments:
Same here !

This is the only fix I could come up with without touching the repo's code.
```python
from nlp.builder import FORCE_REDOWNLOAD
dataset = load_dataset('csv', data_file='./a.csv', download_mode=FORCE_REDOWNLOAD, version='0.0.1')
```
You'll have to change the version each time you want to load a different csv file.
If you're willing to add a ```print```, you can go to ```nlp.load``` and add ```print(builder_instance.cache_dir)``` right before the ```return ds``` in the ```load_dataset``` method. It'll print the cache folder, and you'll just have to erase it (and then you won't need the change here above).