helmo/github-issues huggingface dataset:
straddling object straddles two block boundaries
State: closed | Author: bharaniabhishek123 (NONE)

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.
Update GLUE URLs (now hosted on FB)
State: closed | Author: jeswan (CONTRIBUTOR)

NYU is switching dataset hosting from Google to FB. This PR closes https://github.com/huggingface/datasets/issues/608 and is necessary for https://github.com/jiant-dev/jiant/issues/161. This PR updates the data URLs based on changes made in https://github.com/nyu-mll/jiant/pull/1112.

Note: rebased on huggingface/datasets
Mistakes in MLQA features names
State: closed | Author: M-Salti (CONTRIBUTOR)

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 ?
Add CoNLL-2003 shared task dataset
State: closed | Author: vblagoje (CONTRIBUTOR)

Please consider adding CoNLL-2003 shared task dataset as it's beneficial for token classification tasks. The motivation behind this PR is the [PR](https://github.com/huggingface/transformers/pull/7041) in the transformers project. This dataset would be not only useful for the usual run-of-the-mill NER tasks but also for syntactic chunking and part-of-speech (POS) tagging. 

Comments: I think we should somewhere mention, that is the dataset in IOB2 tagging scheme, whereas the original dataset uses IOB1 :)

Indeed this is something we want to mention.

If would want to add more details about the IOB1->2 change, feel free to ignore my suggestions and edit the description + update the dataset_info

@lhoestq do you want me to update it or you'll update it. I am ok either way

The best would be to mention this change in the description and then update the dataset_info.json file.
Could you do that if you don't mind ?

Then it should be ready to merge :)

Thanks again for adding this dataset !

No problem @lhoestq I'll do the update

@lhoestq please check if 847addf is exactly what we want

Is the German task also part of this? If not, can it be accessed via the Datasets library?
Update GLUE URLs (now hosted on FB)
State: closed | Author: jeswan (CONTRIBUTOR)

NYU is switching dataset hosting from Google to FB. This PR closes https://github.com/huggingface/datasets/issues/608 and is necessary for https://github.com/jiant-dev/jiant/issues/161. This PR updates the data URLs based on changes made in https://github.com/nyu-mll/jiant/pull/1112.

Comments: Thanks for opening this PR :) 

We changed the name of the lib from nlp to datasets yesterday.
Could you rebase from master and re-generate the dataset_info.json file to fix the name changes ?

Rebased changes here: https://github.com/huggingface/datasets/pull/626
Don't use the old NYU GLUE dataset URLs
State: closed | Author: jeswan (CONTRIBUTOR)

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 !
Set scripts version to master
State: closed | Author: lhoestq (MEMBER)

By default the scripts version is master, so that if the library is installed with 
```
pip install git+http://github.com/huggingface/nlp.git
```
or
```
git clone http://github.com/huggingface/nlp.git
pip install -e ./nlp
```

will use the latest scripts, and not the ones from the previous version.
apply offset to indices in multiprocessed map
State: closed | Author: lhoestq (MEMBER)

Fix #597 

I fixed the indices by applying an offset.
I added the case to our tests to make sure it doesn't happen again.

I also added the message proposed by @thomwolf in #597 

```python
>>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2, load_from_cache_file=False)
Done writing 10 indices in 80 bytes .
Testing the mapped function outputs
[0, 1]
Testing finished, running the mapping function on the dataset
Done writing 5 indices in 41 bytes .
Done writing 5 indices in 41 bytes .
Spawning 2 processes
[0, 1, 2, 3, 4]
[5, 6, 7, 8, 9]
#0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 377.90ba/s]
#1: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 378.92ba/s]
Concatenating 2 shards from multiprocessing

# Dataset(features: {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}, num_rows: 10)
```
Add MATINF dataset
State: closed | Author: JetRunner (CONTRIBUTOR)

@lhoestq The command to create metadata failed. I guess it's because the zip is not downloaded from a remote address? How to solve that? Also the CI fails and I don't know how to fix that :(

Comments: Hi ! sorry for the late response

Could you try to rebase from master ? We changed the named of the library last week so you have to include this change in your code.

Can you give me more details about the error you get when running the cli command ?

Note that in case of a manual download you have to specify the directory where you downloaded the data with `--data_dir <path/to/the/directory>`

I fucked up the Git rebase lol. Closing it.
Indices incorrect with multiprocessing
State: closed | Author: joeddav (CONTRIBUTOR)

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)
```
[style/quality] Moving to isort 5.0.0 + style/quality on datasets and metrics
State: closed | Author: thomwolf (MEMBER)

Move the repo to isort 5.0.0.

Also start testing style/quality on datasets and metrics.

Specific rule: we allow F401 (unused imports) in metrics to be able to add imports to detect early on missing dependencies.
Maybe we could add this in datasets but while cleaning this I've seen many example of really unused imports in dataset so maybe it's better to have it as a line-by-line nova instead of a general rule like in metrics.

Comments: Ready for review @lhoestq, just updated a few 156 files here
`Dataset`/`DatasetDict` has no attribute 'save_to_disk'
State: closed | Author: sudarshan85 (NONE)

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.
GermEval 2014: new download urls
State: closed | Author: stefan-it (CONTRIBUTOR)

Hi,

unfortunately, the download links for the GermEval 2014 dataset have changed: they're now located on a Google Drive.

I changed the URLs and bump version from 1.0.0 to 2.0.0.

Comments: /cc: @vblagoje

Closing this one as #594 is merged (same changes except the dummy data hack)

Awesome @stefan-it ! @lhoestq how soon can I use the fixed GermEval dataset in HF token classification examples?

I've manually updated the script on S3, so you can actually use it right now with
```python
from nlp import load_dataset

germeval = load_dataset("germeval_14")
```

not sure if it's used in token classification examples already

Awesome. Not used yet but I am going to use it now. I've been working on an update for token classification examples and this was a missing piece. Thanks @stefan-it @lhoestq
The process cannot access the file because it is being used by another process (windows)
State: closed | Author: saareliad (NONE)

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)
Cannot use nlp.load_dataset text, AttributeError: module 'nlp.utils' has no attribute 'logging'
State: closed | Author: ksjae (NONE)


```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/load.py", line 533, in load_dataset
    builder_cls = import_main_class(module_path, dataset=True)
  File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/load.py", line 61, in import_main_class
    module = importlib.import_module(module_path)
  File "/root/anaconda3/envs/pytorch/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 728, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/datasets/text/5dc629379536c4037d9c2063e1caa829a1676cf795f8e030cd90a537eba20c08/text.py", line 9, in <module>
    logger = nlp.utils.logging.get_logger(__name__)
AttributeError: module 'nlp.utils' has no attribute 'logging'
```

Occurs on the following code, or any code including the load_dataset('text'):
```
dataset = load_dataset("text", data_files=file_path, split="train")
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
                                        truncation=True, max_length=args.block_size), batched=True)
dataset.set_format(type='torch', columns=['input_ids'])
return dataset
```
Use github versioning
State: closed | Author: lhoestq (MEMBER)

Right now dataset scripts and metrics are downloaded from S3 which is in sync with master. It means that it's not currently possible to pin the dataset/metric script version.

To fix that I changed the download url from S3 to github, and adding a `version` parameter in `load_dataset` and `load_metric` to pin a certain version of the lib, as in #562 

Comments: I noticed that datasets like `cnn_dailymail` need the `version` parameter to be passed to its `config_kwargs`.
Shall we rename the `version` paramater in `load_dataset` ? Maybe `repo_version` or `script_version` ?
ArrowIndexError on Dataset.select
State: closed | Author: lhoestq (MEMBER)

If the indices table consists in several chunks, then `dataset.select` results in an `ArrowIndexError` error for pyarrow < 1.0.0

Example:

```python
from nlp import load_dataset

mnli = load_dataset("glue", "mnli", split="train")
shuffled = mnli.shuffle(seed=42)
mnli.select(list(range(len(mnli))))
```

raises:
```python
---------------------------------------------------------------------------
ArrowIndexError                           Traceback (most recent call last)
<ipython-input-64-006a5d38d418> in <module>
----> 1 mnli.shuffle(seed=42).select(list(range(len(mnli))))

~/Desktop/hf/nlp/src/nlp/fingerprint.py in wrapper(*args, **kwargs)
    161             # Call actual function
    162 
--> 163             out = func(self, *args, **kwargs)
    164 
    165             # Update fingerprint of in-place transforms + update in-place history of transforms

~/Desktop/hf/nlp/src/nlp/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
   1653         if self._indices is not None:
   1654             if PYARROW_V0:
-> 1655                 indices_array = self._indices.column(0).chunk(0).take(indices_array)
   1656             else:
   1657                 indices_array = self._indices.column(0).take(indices_array)

~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.Array.take()

~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

ArrowIndexError: take index out of bounds
```

This is because the `take` method is only done on the first chunk which only contains 1000 elements by default (mnli has ~400 000 elements).

Shall we change that to use 
```python
pa.concat_tables(self._indices._indices.slice(i, 1) for i in indices_array)
```
instead of `take` ? @thomwolf
Allow for PathLike objects
State: closed | Author: BramVanroy (CONTRIBUTOR)

Using PathLike objects as input for `load_dataset` does not seem to work. The following will throw an error.

```python
files = list(Path(r"D:\corpora\yourcorpus").glob("*.txt"))
dataset = load_dataset("text", data_files=files)
```

Traceback:

```
Traceback (most recent call last):
  File "C:/dev/python/dutch-simplification/main.py", line 7, in <module>
    dataset = load_dataset("text", data_files=files)
  File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\load.py", line 548, in load_dataset
    builder_instance.download_and_prepare(
  File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 470, in download_and_prepare
    self._save_info()
  File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 564, in _save_info
    self.info.write_to_directory(self._cache_dir)
  File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\info.py", line 149, in write_to_directory
    self._dump_info(f)
  File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\info.py", line 156, in _dump_info
    file.write(json.dumps(asdict(self)).encode("utf-8"))
  File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\__init__.py", line 231, in dumps
    return _default_encoder.encode(obj)
  File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\encoder.py", line 199, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\encoder.py", line 257, in iterencode
    return _iterencode(o, 0)
TypeError: keys must be str, int, float, bool or None, not WindowsPath
```

We have to cast to a string explicitly to make this work. It would be nicer if we could actually use PathLike objects.

```python
files = [str(f) for f in Path(r"D:\corpora\wablieft").glob("*.txt")]
```
nlp re-creates already-there caches when using a script, but not within a shell
State: closed | Author: TevenLeScao (CONTRIBUTOR)

`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!
Add modules cache
State: closed | Author: lhoestq (MEMBER)

As discusses in #554 , we should use a module cache directory outside of the python packages directory since we may not have write permissions.

I added a new HF_MODULES_PATH directory that is added to the python path when doing `import nlp`.
In this directory, a module `nlp_modules` is created so that datasets can be added to `nlp_modules.datasets` and metrics to `nlp_modules.metrics`. `nlp_modules` doesn't exist on Pypi.

If someone using cloudpickle still wants to have the downloaded dataset/metrics scripts to be inside the nlp directory, it is still possible to change the environment variable HF_MODULES_CACHE to be a path inside the nlp lib.

Comments: All the tests pass on my side. Not sure if it is a cache issue or a pytest issue or a circleci issue.
EDIT: I have the same error on google colab. Trying to fix that

I think I fixed it (sorry didn't notice you were on it as well)
Faster caching for text dataset
State: closed | Author: lhoestq (MEMBER)

As mentioned in #546 and #548 , hashing `data_files` contents to get the cache directory name for a text dataset can take a long time.

To make it faster I changed the hashing so that it takes into account the `path` and the `last modified timestamp` of each data file, instead of iterating through the content of each file to get a hash.
Add CLUE Benchmark (11 datasets)
State: closed | Author: JetRunner (CONTRIBUTOR)

Add 11 tasks of [CLUE](https://github.com/CLUEbenchmark/CLUE).

Comments: Thanks, @lhoestq! I've addressed the comments. 
Also, I have tried to use `ClassLabel` [when possible](https://github.com/huggingface/nlp/pull/572/files#diff-1026ac7d7b78bf029cb0ebe63162c77dR297). Is there still somewhere else we can use `ClassLabel`? 

I believe CI failure is unrelated.

Great job!