mengta666/huggingface_github huggingface dataset:
Issue: Cannot unpickle saved .pt dataset with torch.save()/load()

Saving a formatted torch dataset to file using `torch.save()`. Loading the same file fails during unpickling:

```python
>>> import torch
>>> import nlp

>>> squad = nlp.load_dataset("squad.py", split="train")
>>> squad
Dataset(features: {'source_text': Value(dtype='string', id=None), 'target_text': Value(dtype='string', id=None)}, num_rows: 87599)
>>> squad = squad.map(create_features, batched=True)
>>> squad.set_format(type="torch", columns=["source_ids", "target_ids", "attention_mask"])
>>> torch.save(squad, "squad.pt")

>>> squad_pt = torch.load("squad.pt")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 593, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/torch/serialization.py", line 773, in _legacy_load
    result = unpickler.load()
  File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/splits.py", line 493, in __setitem__
    raise ValueError("Cannot add elem. Use .add() instead.")
ValueError: Cannot add elem. Use .add() instead.
```
where `create_features` is a function that tokenizes the data using `batch_encode_plus` and returns a Dict with `input_ids`, `target_ids` and `attention_mask`. 
```python
def create_features(batch):
    source_text_encoding = tokenizer.batch_encode_plus(
        batch["source_text"],
        max_length=max_source_length,
        pad_to_max_length=True,
        truncation=True)

    target_text_encoding = tokenizer.batch_encode_plus(
        batch["target_text"],
        max_length=max_target_length,
        pad_to_max_length=True,
        truncation=True)

    features = {
        "source_ids": source_text_encoding["input_ids"],
        "target_ids": target_text_encoding["input_ids"],
        "attention_mask": source_text_encoding["attention_mask"]
    }

    return features
```

I found a similar issue in [issue 5267 in the huggingface/transformers repo](https://github.com/huggingface/transformers/issues/5267) which was solved by downgrading to `nlp==0.2.0`. That did not solve this problem, however. 

Comments:
This seems to be fixed in a non-released version. 

Installing nlp from source
```
git clone https://github.com/huggingface/nlp
cd nlp
pip install .
```
solves the issue.
Issue: Issues: Adding a FAISS or Elastic Search index to a Dataset

It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https://huggingface.co/nlp/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab.  Is  there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ?

Comments:
`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.

Right now you can experiment with it by installing `transformers` from the master branch.
You can also check the docs of DPR [here](https://huggingface.co/transformers/master/model_doc/dpr.html).

Moreover all the indexing features will also be available in the next release of `nlp`.

@lhoestq Thanks for the info 

@lhoestq  I tried installing transformer from the master branch. Python imports for DPR again didnt' work.  Anyways, Looking forward to trying it in the next release of nlp 

@nsankar have you tried with the latest version of the library?

@yjernite it worked. Thanks
Issue: New Datasets: IWSLT15+, ITTB

**Links:**
[iwslt](https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/datasets/iwslt.html)
Don't know if that link is up to date.

[ittb](http://www.cfilt.iitb.ac.in/iitb_parallel/)
**Motivation**: replicate mbart finetuning results (table below)
![image](https://user-images.githubusercontent.com/6045025/88490093-0c1c8c00-cf67-11ea-960d-8dcaad2aa8eb.png)


For future readers, we already have the following language pairs in the wmt namespaces:

```
wmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en']
wmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en']
wmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']
wmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en']
wmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en']
wmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']
```

Comments:
Thanks Sam, we now have a very detailed tutorial and template on how to add a new dataset to the library. It typically take 1-2 hours to add one. Do you want to give it a try ?
The tutorial on writing a new dataset loading script is here: https://huggingface.co/nlp/add_dataset.html
And the part on how to share a new dataset is here: https://huggingface.co/nlp/share_dataset.html

Hi @sshleifer, I'm trying to add IWSLT using the link you provided but the download urls are not working. Only `[en, de]` pair is working. For others language pairs it throws a `404` error.
Issue: Google Colab - load_dataset - PyArrow exception

With latest PyArrow 1.0.0 installed, I get the following exception   . Restarting colab has the same issue

ImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.

The error goes only when I install version 0.16.0 
i.e.  !pip install pyarrow==0.16.0

Comments:
Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq 

+1! this is the reason our tests are failing at [TextAttack](https://github.com/QData/TextAttack) 

(Though it's worth noting if we fixed the version number of pyarrow to 0.16.0 that would fix our problem too. But in this case we'll just wait for you all to update)

Came to raise this issue, great to see other already have and it's being fixed so soon!

As an aside, since no one wrote this already, it seems like the version check only looks at the second part of the version number making sure it is >16, but pyarrow newest version is 1.0.0 so the second past is 0!

> Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq

Yes definitely

please fix this on pypi! @lhoestq 

Is this issue fixed ?

We’ll release the new version later today. Apologies for the delay.

I just pushed the new version on pypi :)

Thanks for the update.
Issue: ImportWarning for pyarrow 1.0.0

The following PR raised ImportWarning at `pyarrow ==1.0.0` https://github.com/huggingface/nlp/pull/265/files

Comments:
This was fixed in #434 
We'll do a release later this week to include this fix.
Thanks for reporting

I dont know if the fix was made but the problem is still present : 
Instaled with pip : NLP 0.3.0 // pyarrow 1.0.0 
OS : archlinux with kernel zen 5.8.5

Yes it was fixed in `nlp>=0.4.0`
You can update with pip

Sorry, I didn't got the updated version, all is now working perfectly thanks
Issue: [FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter

It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process/thread/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together?

Comments:
Yes that's definitely something we plan to add ^^

Yes, that would be nice. We could take a look at what tensorflow `tf.data` does under the hood for instance.

So `tf.data.Dataset.map()` returns a `ParallelMapDataset` if `num_parallel_calls is not None` [link](https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/python/data/ops/dataset_ops.py#L1623).

There, `num_parallel_calls` is turned into a tensor and and fed to `gen_dataset_ops.parallel_map_dataset` where it looks like tensorflow takes over.

We could start with something simple like a thread or process pool that `imap`s over some shards.
 

Multiprocessing was added in #552 . You can set the number of processes with `.map(..., num_proc=...)`. It also works for `filter`

Closing this one, but feel free to reo-open if you have other questions

@lhoestq Great feature implemented! Do you have plans to add it to official tutorials [Processing data in a Dataset](https://huggingface.co/docs/datasets/processing.html?highlight=save#augmenting-the-dataset)? It took me sometime to find this parallel processing api.

Thanks for the heads up !

I just added a paragraph about multiprocessing:
https://huggingface.co/docs/datasets/master/processing.html#multiprocessing
Issue: from_dict delete?

AttributeError: type object 'Dataset' has no attribute 'from_dict'

Comments:
`from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.
Right now if you want to use `from_dict` you have to install the package from the master branch
```
pip install git+https://github.com/huggingface/nlp.git
```

> `from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.
> Right now if you want to use `from_dict` you have to install the package from the master branch
> 
> ```
> pip install git+https://github.com/huggingface/nlp.git
> ```
OK, thank you.
Issue: Is there a way to download only NQ dev?

Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)? 
As we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data. 

I tried
```
dataset = nlp.load_dataset('natural_questions', split="validation", beam_runner="DirectRunner")
```
But this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits / slicing options only available after downloading?

Thanks!

Comments:
Unfortunately it's not possible to download only the dev set of NQ.

I think we could add a way to download only the test set by adding a custom configuration to the processing script though.

Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially also others. 
For us, it will in this case make the difference of using the library or keeping the old downloads of the raw dev datasets.  
However, I don't know if that fits into your plans with the library and can also understand if you don't want to support this.

I don't think we could force this behavior generally since the dataset script authors are free to organize the file download as they want (sometimes the mapping between split and files can be very much nontrivial) but we can add an additional configuration for Natural Question indeed as @lhoestq indicate.
Issue: train_test_split error: 'dict' object has no attribute 'deepcopy'

`train_test_split` is giving me an error when I try and call it:

`'dict' object has no attribute 'deepcopy'`

## To reproduce

```
dataset = load_dataset('glue', 'mrpc', split='train')
dataset = dataset.train_test_split(test_size=0.2)
```

## Full Stacktrace
```
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-12-feb740dbec9a> in <module>
      1 dataset = load_dataset('glue', 'mrpc', split='train')
----> 2 dataset = dataset.train_test_split(test_size=0.2)

~/anaconda3/envs/fastai2_me/lib/python3.7/site-packages/nlp/arrow_dataset.py in train_test_split(self, test_size, train_size, shuffle, seed, generator, keep_in_memory, load_from_cache_file, train_cache_file_name, test_cache_file_name, writer_batch_size)
   1032                     "writer_batch_size": writer_batch_size,
   1033                 }
-> 1034                 train_kwargs = cache_kwargs.deepcopy()
   1035                 train_kwargs["split"] = "train"
   1036                 test_kwargs = cache_kwargs.deepcopy()

AttributeError: 'dict' object has no attribute 'deepcopy'
```

Comments:
It was fixed in 2ddd18d139d3047c9c3abe96e1e7d05bb360132c.
Could you pull the latest changes from master @morganmcg1 ?

Thanks @lhoestq, works fine now!
Issue: MissingBeamOptions for Wikipedia 20200501.en

There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):

```
nlp.load_dataset('wikipedia', "20200501.en", split='train')
```

And now, having pulled master, I get:

```
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to /home/hltcoe/mgordon/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...
Traceback (most recent call last):
  File "scripts/download.py", line 11, in <module>
    fire.Fire(download_pretrain)
  File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 138, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 468, in _Fire
    target=component.__name__)
  File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 672, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "scripts/download.py", line 6, in download_pretrain
    nlp.load_dataset('wikipedia', "20200501.en", split='train')
  File "/exp/mgordon/nlp/src/nlp/load.py", line 534, in load_dataset
    save_infos=save_infos,
  File "/exp/mgordon/nlp/src/nlp/builder.py", line 460, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/exp/mgordon/nlp/src/nlp/builder.py", line 870, in _download_and_prepare
    "\n\t`{}`".format(usage_example)
nlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S
park, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
        `load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`
```

Comments:
Fixed. Could you try again @mitchellgordon95 ?
It was due a file not being updated on S3.

We need to make sure all the datasets scripts get updated properly @julien-c 

Works for me! Thanks.

I found the same issue with almost any language other than English. (For English, it works). Will someone need to update the file on S3 again?

This is because only some languages are already preprocessed (en, de, fr, it) and stored on our google storage.
We plan to have a systematic way to preprocess more wikipedia languages in the future.

For the other languages you have to process them on your side using apache beam. That's why the lib asks for a Beam runner.
Issue: 🐛 [Dataset] Cannot download wmt14, wmt15 and wmt17

1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:
```
nlp.load_dataset('wmt14','de-en')
nlp.load_dataset('wmt15','de-en')
nlp.load_dataset('wmt17','de-en')
nlp.load_dataset('wmt19','de-en')
```
The code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`

2. When trying to download `wmt17 zh-en`, I got the following error:
> ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz

Comments:
similar slow download speed here for nlp.load_dataset('wmt14', 'fr-en')
`
Downloading: 100%|██████████████████████████████████████████████████████████| 658M/658M [1:00:42<00:00, 181kB/s]
Downloading: 100%|██████████████████████████████████████████████████████████| 918M/918M [1:39:38<00:00, 154kB/s]
Downloading:   2%|▉                                                       | 40.9M/2.37G [04:48<5:03:06, 128kB/s]
`
Could we just download a specific subdataset in 'wmt14', such as 'newstest14'? 

> The code runs but the download speed is extremely slow, the same behaviour is not observed on wmt16 and wmt18

The original source for the files may provide slow download speeds.
We can probably host these files ourselves.

> When trying to download wmt17 zh-en, I got the following error:
> ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz

Looks like the file`UNv1.0.en-zh.tar.gz` is missing, or the url changed. We need to fix that

> Could we just download a specific subdataset in 'wmt14', such as 'newstest14'?

Right now I don't think it's possible. Maybe @patrickvonplaten knows more about it


Yeah, the download speed is sadly always extremely slow :-/. 
I will try to check out the `wmt17 zh-en` bug :-) 

Maybe this can be used - https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 

These issues seem to be fixed now.
Issue: Conversion through to_pandas output numpy arrays for lists instead of python objects

In a related question, the conversion through to_pandas output numpy arrays for the lists instead of python objects.

Here is an example:
```python
>>> dataset._data.slice(key, 1).to_pandas().to_dict("list")
{'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [array([  101,  7277,  2180,  5303,  4806,  1117,  1711,   117,  2292,
        1119,  1270,   107,  1103,  7737,   107,   117,  1104,  9938,
        4267, 12223, 21811,  1117,  2554,   119,   102])], 'token_type_ids': [array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0])], 'attention_mask': [array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1])]}
>>> type(dataset._data.slice(key, 1).to_pandas().to_dict("list")['input_ids'][0])
<class 'numpy.ndarray'>
>>> dataset._data.slice(key, 1).to_pydict()
{'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [[101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
```

Comments:
To convert from arrow type we have three options: to_numpy, to_pandas and to_pydict/to_pylist.

- to_numpy and to_pandas return numpy arrays instead of lists but are very fast.
- to_pydict/to_pylist can be 100x slower and become the bottleneck for reading data, but at least they return lists.

Maybe we can have to_pydict/to_pylist as the default and use to_numpy or to_pandas when the format (set by `set_format`) is 'numpy' or 'pandas'
Issue: [dataset] Structure of MLQA seems unecessary nested

The features of the MLQA dataset comprise several nested dictionaries with a single element inside (for `questions` and `ids`): https://github.com/huggingface/nlp/blob/master/datasets/mlqa/mlqa.py#L90-L97

Should we keep this @mariamabarham @patrickvonplaten? Was this added for compatibility with tfds?

```python
            features=nlp.Features(
                {
                    "context": nlp.Value("string"),
                    "questions": nlp.features.Sequence({"question": nlp.Value("string")}),
                    "answers": nlp.features.Sequence(
                        {"text": nlp.Value("string"), "answer_start": nlp.Value("int32"),}
                    ),
                    "ids": nlp.features.Sequence({"idx": nlp.Value("string")})
```

Comments:
Same for the RACE dataset: https://github.com/huggingface/nlp/blob/master/datasets/race/race.py

Should we scan all the datasets to remove this pattern of un-necessary nesting?

You're right, I think we don't need to use the nested dictionary.
Issue: TypeError when computing bertscore

Hi, 

I installed nlp 0.3.0 via pip, and my python version is 3.7.
When I tried to compute bertscore with the code:
```
import nlp 
bertscore = nlp.load_metric('bertscore')  
# load hyps and refs 
...
print (bertscore.compute(hyps, refs, lang='en'))
```

I got the following error.
```
Traceback (most recent call last):
  File "bert_score_evaluate.py", line 16, in <module>
    print (bertscore.compute(hyps, refs, lang='en'))
  File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metric.py", line 200, in compute
    output = self._compute(predictions=predictions, references=references, **metrics_kwargs)
  File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metrics/bertscore/fb176889831bf0ce995ed197edc94b2e9a83f647a869bb8c9477dbb2d04d0f08/bertscore.py", line 105, in _compute
    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)
TypeError: get_hash() takes 3 positional arguments but 4 were given
```

It seems like there is something wrong with get_hash() function?

Comments:
I am not able to reproduce this issue on my side.
Could you give us more details about the inputs you used ?

I do get another error though:
```
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/bert_score/utils.py in bert_cos_score_idf(model, refs, hyps, tokenizer, idf_dict, verbose, batch_size, device, all_layers)
    371         return sorted(list(set(l)), key=lambda x: len(x.split(" ")))
    372 
--> 373     sentences = dedup_and_sort(refs + hyps)
    374     embs = []
    375     iter_range = range(0, len(sentences), batch_size)

ValueError: operands could not be broadcast together with shapes (0,) (2,)
```
That's because it gets numpy arrays as input and not lists. See #387 

The other issue was fixed by #403 

Do you still get this issue @willywsm1013 ?
Issue: can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries

Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB):
```
dataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: ["./path/to/file.json"]})
```
causes
```
Traceback (most recent call last):
  File "dataloader.py", line 9, in <module>
    ["./path/to/file.json"]})
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/load.py", line 524, in load_dataset
    save_infos=save_infos,
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 483, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 719, in _prepare_split
    for key, table in utils.tqdm(generator, unit=" tables", leave=False):
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/tqdm/std.py", line 1129, in __iter__
    for obj in iterable:
  File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/datasets/json/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b/json.py", line 53, in _generate_tables
    file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options,
  File "pyarrow/_json.pyx", line 191, in pyarrow._json.read_json
  File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```

I haven't been able to find any reports of this specific pyarrow error here or elsewhere. 

Comments:
I am able to reproduce this with the official SQuAD `train-v2.0.json` file downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/

I am facing this issue in transformers library 3.0.2 while reading a csv using datasets.
Is this fixed in latest version? 
I updated the latest version 4.0.1 but still getting this error. What could cause this error?
Issue: load_metric can't acquire lock anymore

I can't load metric (glue) anymore after an error in a previous run. I even removed the whole cache folder `/home/XXX/.cache/huggingface/`, and the issue persisted. What are the steps to fix this?

    Traceback (most recent call last):
      File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 101, in __init__
        self.filelock.acquire(timeout=1)
      File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/filelock.py", line 278, in acquire
        raise Timeout(self._lock_file)
    filelock.Timeout: The file lock '/home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock' could not be acquired.

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last):
      File "examples_huggingface_nlp.py", line 268, in <module>
        main()
      File "examples_huggingface_nlp.py", line 242, in main
        dataset, metric = get_dataset_metric(glue_task)
      File "examples_huggingface_nlp.py", line 77, in get_dataset_metric
        metric = nlp.load_metric('glue', glue_config, experiment_id=1)
      File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/load.py", line 440, in load_metric
        **metric_init_kwargs,
      File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 104, in __init__
        "Cannot acquire lock, caching file might be used by another process, "
    ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id' for this run.
    I0709 15:54:41.008838 139854118430464 filelock.py:318] Lock 139852058030936 released on /home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock


Comments:
I found that, in the same process (or the same interactive session), if I do

import nlp

m1 = nlp.load_metric('glue', 'mrpc')
m2 = nlp.load_metric('glue', 'sst2')

I will get the same error `ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id'`.
Issue: How to augment data ?

Is there any clean way to augment data ?

For now my work-around is to use batched map, like this :

```python
def aug(samples):
    # Simply copy the existing data to have x2 amount of data
    for k, v in samples.items():
        samples[k].extend(v)
    return samples

dataset = dataset.map(aug, batched=True)
```

Comments:
Using batched map is probably the easiest way at the moment.
What kind of augmentation would you like to do ?

Some samples in the dataset are too long, I want to divide them in several samples.

Using batched map is the way to go then.
We'll make it clearer in the docs that map could be used for augmentation.

Let me know if you think there should be another way to do it. Or feel free to close the issue otherwise.

It just feels awkward to use map to augment data. Also it means it's not possible to augment data in a non-batched way.

But to be honest I have no idea of a good API...

Or for non-batched samples, how about returning a tuple ?

```python
def aug(sample):
    # Simply copy the existing data to have x2 amount of data
    return sample, sample

dataset = dataset.map(aug)
```

It feels really natural and easy, but :

* it means the behavior with batched data is different
* I don't know how doable it is backend-wise

@lhoestq 

As we're working with arrow's columnar format we prefer to play with batches that are dictionaries instead of tuples.
If we have tuple it implies to re-format the data each time we want to write to arrow, which can lower the speed of map for example.

It's also a matter of coherence, as we don't want users to be confused whether they have to return dictionaries for some functions and tuples for others when they're doing batches.
Issue: 🐛 [Metrics] ROUGE is non-deterministic

If I run the ROUGE metric 2 times, with same predictions / references, the scores are slightly different.

Refer to [this Colab notebook](https://colab.research.google.com/drive/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.

Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :

> ['0.3350', '0.1470', '0.2329']
['0.3358', '0.1451', '0.2332']

---

Why ROUGE is not deterministic ?

Comments:
Hi, can you give a full self-contained example to reproduce this behavior?

> Hi, can you give a full self-contained example to reproduce this behavior?

There is a notebook in the post ;)

> If I run the ROUGE metric 2 times, with same predictions / references, the scores are slightly different.
> 
> Refer to [this Colab notebook](https://colab.research.google.com/drive/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.
> 
> Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :
> 
> > ['0.3350', '0.1470', '0.2329']
> > ['0.3358', '0.1451', '0.2332']
> 
> Why ROUGE is not deterministic ?

This is because of rouge's `BootstrapAggregator` that uses sampling to get confidence intervals (low, mid, high).
You can get deterministic scores per sentence pair by using
```python
score = rouge.compute(rouge_types=["rouge1", "rouge2", "rougeL"], use_aggregator=False)
```
Or you can set numpy's random seed if you still want to use the aggregator.

Maybe we can set all the random seeds of numpy/torch etc. while running `metric.compute` ?

We should probably indeed!

Now if you re-run the notebook, the two printed results are the same @colanim
```
['0.3356', '0.1466', '0.2318']
['0.3356', '0.1466', '0.2318']
```
However across sessions, the results may change (as numpy's random seed can be different). You can prevent that by setting your seed:
```python
rouge = nlp.load_metric('rouge', seed=42)
```

> 

Minor nit: Note that "aggregator" is misspelled in this command. Should be `use_aggregator=False`.  

Thanks, I fixed the code snippet
Issue: can't load SNLI dataset

`nlp` seems to load `snli` from some URL based on nlp.stanford.edu. This subdomain is frequently down -- including right now, when I'd like to load `snli` in a Colab notebook, but can't.

Is there a plan to move these datasets to huggingface servers for a more stable solution?

Btw, here's the stack trace:

```
File "/content/nlp/src/nlp/builder.py", line 432, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/content/nlp/src/nlp/builder.py", line 466, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/content/nlp/src/nlp/datasets/snli/e417f6f2e16254938d977a17ed32f3998f5b23e4fcab0f6eb1d28784f23ea60d/snli.py", line 76, in _split_generators
    dl_dir = dl_manager.download_and_extract(_DATA_URL)
  File "/content/nlp/src/nlp/utils/download_manager.py", line 217, in download_and_extract
    return self.extract(self.download(url_or_urls))
  File "/content/nlp/src/nlp/utils/download_manager.py", line 156, in download
    lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,
  File "/content/nlp/src/nlp/utils/py_utils.py", line 190, in map_nested
    return function(data_struct)
  File "/content/nlp/src/nlp/utils/download_manager.py", line 156, in <lambda>
    lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,
  File "/content/nlp/src/nlp/utils/file_utils.py", line 198, in cached_path
    local_files_only=download_config.local_files_only,
  File "/content/nlp/src/nlp/utils/file_utils.py", line 356, in get_from_cache
    raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://nlp.stanford.edu/projects/snli/snli_1.0.zip
```

Comments:
I just added the processed files of `snli` on our google storage, so that when you do `load_dataset` it can download the processed files from there :)

We are thinking about having available those processed files for more datasets in the future, because sometimes files aren't available (like for `snli`), or the download speed is too slow, or sometimes the files take time to be processed.

Closing this one. Feel free to re-open if you have other questions :)

Thank you!
Issue: Blog Authorship Corpus, Non Matching Splits Sizes Error, nlp viewer

Selecting `blog_authorship_corpus` in the nlp viewer throws the following error: 

```
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]
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 172, 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 132, 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
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 488, in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 70, in verify_splits
    raise NonMatchingSplitsSizesError(str(bad_splits))
```
@srush @lhoestq 

Comments:
I wonder if this means downloading failed? That corpus has a really slow server.

This dataset seems to have a decoding problem that results in inconsistencies in the number of generated examples.
See #215.
That's why we end up with a `NonMatchingSplitsSizesError `.
Issue: Nested sequences with dicts

Am pretty much finished [adding a dataset](https://github.com/ghomasHudson/nlp/blob/DocRED/datasets/docred/docred.py) for [DocRED](https://github.com/thunlp/DocRED), but am getting an error when trying to add a nested `nlp.features.sequence(nlp.features.sequence({key:value,...}))`. 

The original data is in this format:
```python
{
  'title': "Title of wiki page",
  'vertexSet': [
                  [
                    { 'name': "mention_name", 
                      'sent_id': "mention in which sentence", 
                      'pos': ["postion of mention in a sentence"], 
                      'type': "NER_type"},
                    {another mention}
                  ], 
                  [another entity]
                ]
    ...
}
```
So to represent this I've attempted to write:
```
...
features=nlp.Features({
    "title": nlp.Value("string"),
    "vertexSet": nlp.features.Sequence(nlp.features.Sequence({
        "name": nlp.Value("string"),
        "sent_id": nlp.Value("int32"),
        "pos": nlp.features.Sequence(nlp.Value("int32")),
        "type": nlp.Value("string"),
    })),
    ...
    }),
...
```
This is giving me the error:
```
pyarrow.lib.ArrowTypeError: Could not convert [{'pos': [[0,2], [2,4], [3,5]], "type": ["ORG", "ORG", "ORG"], "name": ["Lark Force", "Lark Force", "Lark Force", "sent_id": [0, 3, 4]}..... with type list: was not a dict, tuple, or recognized null value for conversion to struct type
```
Do we expect the pyarrow stuff to break when doing this deeper nesting? I've checked that it still works when you do `nlp.features.Sequence(nlp.features.Sequence(nlp.Value("string"))` or `nlp.features.Sequence({key:value,...})` just not nested sequences with a dict.

If it's not possible, I can always convert it to a shallower structure. I'd rather not change the DocRED authors' structure if I don't have to though.

Comments:
Oh yes, this is a backward compatibility feature with tensorflow_dataset in which a `Sequence` or `dict` is converted in a `dict` of `lists`, unfortunately it is not very intuitive, see here: https://github.com/huggingface/nlp/blob/master/src/nlp/features.py#L409

To avoid this behavior, you can just define the list in the feature with a simple list or a tuple (which is also simpler to write).
In your case, the features could be as follow:
``` python
...
features=nlp.Features({
    "title": nlp.Value("string"),
    "vertexSet": [[{
        "name": nlp.Value("string"),
        "sent_id": nlp.Value("int32"),
        "pos": nlp.features.Sequence(nlp.Value("int32")),
        "type": nlp.Value("string"),
    }]],
    ...
    }),
...
```
Issue: Adding a dataset with multiple subtasks

I intent to add the datasets of the MT Quality Estimation shared tasks to `nlp`. However, they have different subtasks -- such as word-level, sentence-level and document-level quality estimation, each of which having different language pairs, and some of the data reused in different subtasks.

For example, in [QE 2019,](http://www.statmt.org/wmt19/qe-task.html) we had the same English-Russian and English-German data for word-level and sentence-level QE. 

I suppose these datasets could have both their word and sentence-level labels inside `nlp.Features`; but what about other subtasks? Should they be considered a different dataset altogether?

I read the discussion on #217 but the case of QE seems a lot simpler.

Comments:
For one dataset you can have different configurations that each have their own `nlp.Features`.
We imagine having one configuration per subtask for example.
They are loaded with `nlp.load_dataset("my_dataset", "my_config")`.

For example the `glue` dataset has many configurations. It is a bit different from your case though because each configuration is a dataset by itself (sst2, mnli).
Another example is `wikipedia` that has one configuration per language.
Issue: [Feature request] Add `shard()` method to dataset

Currently, to shard a dataset into 10 pieces on different ranks, you can run

```python
rank = 3 # for example
size = 10
dataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f"train[{rank*10}%:{(rank+1)*10}%]")
```

However, this breaks down if you have a number of ranks that doesn't divide cleanly into 100, such as 64 ranks. Is there interest in adding a method shard() that looks like this?

```python
rank = 3
size = 64
dataset = nlp.load_dataset("wikitext", "wikitext-2-raw-v1", split="train").shard(rank=rank, size=size)
```

TensorFlow has a similar API: https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard. I'd be happy to contribute this code.

Comments:
Hi Jared,
Interesting, thanks for raising this question. You can also do that after loading with `dataset.select()` or `dataset.filter()` which let you keep only a specific subset of rows in a dataset.
What is your use-case for sharding?

Thanks for the pointer to those functions! It's still a little more verbose since you have to manually calculate which ids each rank would keep, but definitely works.

My use case is multi-node, multi-GPU training and avoiding global batches of duplicate elements. I'm using horovod. You can shuffle indices, or set random seeds, but explicitly sharding the dataset up front is the safest and clearest way I've found to do so.
Issue: Question - Sign Language Datasets

An emerging field in NLP is SLP - sign language processing.

I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.

So, what do you think about (me, or others) adding datasets here?


An example dataset would be [RWTH-PHOENIX-Weather 2014 T](https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/)
For every item in the dataset, the data object includes:
1. video_path - path to mp4 file
2. pose_path - a path to `.pose` file with human pose landmarks
3. openpose_path - a path to a `.json` file with human pose landmarks
4. gloss - string
5. text - string
6. video_metadata - height, width, frames, framerate


------

To make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so.

Comments:
Even more complicating - 

As I see it, datasets can have "addons".
For example, the WebNLG dataset is a dataset for data-to-text. However, a work of mine and other works enriched this dataset with text plans / underlying text structures. In that case, I see a need to load the dataset "WebNLG" with "plans" addon.

Same for sign language - if there is a dataset of videos, one addon can be to run OpenPose, another to run ARKit4 pose estimation, and another to run PoseNet, or even just a video embedding addon. (which are expensive to run individually for everyone who wants to use these data)

This is something I dabbled with my own implementation to a [research datasets library](https://github.com/AmitMY/meta-scholar/) and I love to get the discussion going on these topics.

This is a really cool idea !
The example for data objects you gave for the RWTH-PHOENIX-Weather 2014 T dataset can totally fit inside the library.

For your point about formats like `ilex`, `eaf`, or `srt`, it is possible to use any library in your dataset script.
However most user probably won't need these libraries, as most datasets don't need them, and therefore it's unlikely that we will have them in the minimum requirements to use `nlp` (we want to keep it as light-weight as possible). If a user wants to load your dataset and doesn't have the libraries you need, an error is raised asking the user to install them.

More generally, we plan to have something like a `requirements.txt` per dataset. This could also be a place for addons as you said. What do you think ?

Thanks, Quentin, I think a `requirements.txt` per dataset will be a good thing.
I will work on adding this dataset next week, and once we sort all of the kinks, I'll add more.
Issue: Setting cache_dir gives error on wikipedia download

First of all thank you for a super handy library! I'd like to download large files to a specific drive so I set `cache_dir=my_path`. This works fine with e.g. imdb and squad. But on wikipedia I get an error:
```
nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=my_path)
```
```
OSError                                   Traceback (most recent call last)
<ipython-input-2-23551344d7bc> in <module>
      1 import nlp
----> 2 nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=path)

~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
    522         download_mode=download_mode,
    523         ignore_verifications=ignore_verifications,
--> 524         save_infos=save_infos,
    525     )
    526 

~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
    385                     with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir):
    386                         reader = ArrowReader(self._cache_dir, self.info)
--> 387                         reader.download_from_hf_gcs(self._cache_dir, self._relative_data_dir(with_version=True))
    388                         downloaded_info = DatasetInfo.from_directory(self._cache_dir)
    389                         self.info.update(downloaded_info)

~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/arrow_reader.py in download_from_hf_gcs(self, cache_dir, relative_data_dir)
    231             remote_dataset_info = os.path.join(remote_cache_dir, "dataset_info.json")
    232             downloaded_dataset_info = cached_path(remote_dataset_info)
--> 233             os.rename(downloaded_dataset_info, os.path.join(cache_dir, "dataset_info.json"))
    234             if self._info is not None:
    235                 self._info.update(self._info.from_directory(cache_dir))

OSError: [Errno 18] Invalid cross-device link: '/home/local/NTU/nn/.cache/huggingface/datasets/025fa4fd4f04aaafc9e939260fbc8f0bb190ce14c61310c8ae1ddd1dcb31f88c.9637f367b6711a79ca478be55fe6989b8aea4941b7ef7adc67b89ff403020947' -> '/data/nn/nlp/wikipedia/20200501.de/1.0.0.incomplete/dataset_info.json'
```

Comments:
Whoops didn't mean to close this one.
I did some changes, could you try to run it from the master branch ?

Now it works, thanks!
Issue: Error in Demo for Specific Datasets

Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.

![image](https://user-images.githubusercontent.com/60150701/85347842-ac861900-b4ae-11ea-98c4-a53a00934783.png)


Comments:
Thanks for reporting these errors :)

I can actually see two issues here.

First, datasets like `natural_questions` require apache_beam to be processed. Right now the import is not at the right place so we have this error message. However, even the imports are fixed, the nlp viewer doesn't actually have the resources to process NQ right now so we'll have to wait until we have a version that we've already processed on our google storage (that's what we've done for wikipedia for example).

Second, datasets like `newsroom` require manual downloads as we're not allowed to redistribute the data ourselves (if I'm not wrong). An error message should be displayed saying that we're not allowed to show the dataset.

I can fix the first issue with the imports but for the second one I think we'll have to see with @srush to show a message for datasets that require manual downloads (it can be checked whether a dataset requires manual downloads if `dataset_builder_instance.manual_download_instructions is not None`).



I added apache-beam to the viewer. We can think about how to add newsroom. 

We don't plan to host the source files of newsroom ourselves for now.
You can still get the dataset if you follow the download instructions given by `dataset = load_dataset('newsroom')` though.
The viewer also shows the instructions now.

Closing this one. If you have other questions, feel free to re-open :)