noamaanMulla-03/datasets-issues huggingface dataset:
Fix manual download instructions

This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`. 

Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs.

After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed.

Also this PR should unblock solves a bug with `wmt16 - ro-en` 
@sshleifer from this branch you should be able to succesfully run

```python 
import nlp 
ds = nlp.load_dataset('./datasets/wmt16', 'ro-en')
```

and once this PR is merged S3 should be synched so that 

```python
import nlp
ds = nlp.load_dataset("wmt16", "ro-en")
```

works as well.

**Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility.


Verified that this works, thanks!

But I get
```python
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py
```
When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).


Both machines can run
```bash
aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/
```
but it seems one must be in the nlp directory to run the command?

(I ran `pip install -e . ` on this branch in both situations.)




`https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py` looks very weird.

(Also, S3 is not a file-system, it's a flat key-value store)

Good to merge I think @lhoestq 

> But I get
> 
> ```python
> ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py
> ```
> 
> When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).
> 
> Both machines can run
> 
> ```shell
> aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/
> ```
> 
> but it seems one must be in the nlp directory to run the command?
> 
> (I ran `pip install -e . ` on this branch in both situations.)

As soon as it is on master, the dataset script wmt16.py will be synced on S3 and you'll be able to do `load_dataset("wmt16")`
Error with SquadV2 Metrics

I can't seem to import squad v2 metrics. 

**squad_metric = nlp.load_metric('squad_v2')**

**This throws me an error.:**


```
ImportError                               Traceback (most recent call last)
<ipython-input-8-170b6a170555> in <module>
----> 1 squad_metric = nlp.load_metric('squad_v2')

~/env/lib64/python3.6/site-packages/nlp/load.py in load_metric(path, name, process_id, num_process, data_dir, experiment_id, in_memory, download_config, **metric_init_kwargs)
    426     """
    427     module_path = prepare_module(path, download_config=download_config, dataset=False)
--> 428     metric_cls = import_main_class(module_path, dataset=False)
    429     metric = metric_cls(
    430         name=name,

~/env/lib64/python3.6/site-packages/nlp/load.py in import_main_class(module_path, dataset)
     55     """
     56     importlib.invalidate_caches()
---> 57     module = importlib.import_module(module_path)
     58 
     59     if dataset:

/usr/lib64/python3.6/importlib/__init__.py in import_module(name, package)
    124                 break
    125             level += 1
--> 126     return _bootstrap._gcd_import(name[level:], package, level)
    127 
    128 

/usr/lib64/python3.6/importlib/_bootstrap.py in _gcd_import(name, package, level)

/usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load(name, import_)

/usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)

/usr/lib64/python3.6/importlib/_bootstrap.py in _load_unlocked(spec)

/usr/lib64/python3.6/importlib/_bootstrap_external.py in exec_module(self, module)

/usr/lib64/python3.6/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)

~/env/lib64/python3.6/site-packages/nlp/metrics/squad_v2/a15e787c76889174874386d3def75321f0284c11730d2a57e28fe1352c9b5c7a/squad_v2.py in <module>
     16 
     17 import nlp
---> 18 from .evaluate import evaluate
     19 
     20 _CITATION = """\

ImportError: cannot import name 'evaluate'
```
Private/sensitive data

Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch. 

Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. 
Is there support/a plan to support such data with NLP, e.g. by reading it from local sources?

Use case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive/private data without the need to rethink data processing pipelines. 

Many thanks for your responses ahead of time and kind regards,
MFreidank


Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)

We're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road.

Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV/TSV and JSON are supported. To load a dataset in JSON format:

```
nlp.load_dataset(path="json", data_files={nlp.Split.TRAIN: ["path/to/train.json"], nlp.Split.TEST: ["path/to/test.json"]})
```

For CSV/TSV datasets, you have to replace `json` by `csv`.

Hi @julien-c @jplu,
Thanks for sharing this solution with me, it helps, this is what I was looking for. 
If not already there and only missed by me, this could be a great addition in the docs.

Closing my issue as resolved, thanks again.
ConnectionError - Eli5 dataset download

Hi, I have a problem with downloading Eli5 dataset. When typing `nlp.load_dataset('eli5')`, I get ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/eli5/LFQA_reddit/1.0.0/explain_like_im_five-train_eli5.arrow

I would appreciate if you could help me with this issue.


It should ne fixed now, thanks for reporting this one :)
It was an issue on our google storage.

Let me now if you're still facing this issue.

It works now, thanks for prompt help!
Empty samples in glue/qqp

```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}
```
Notice that question 2 is empty string. 
BTW, I have checked and these two are the only naughty ones in all splits of qqp.


We are only wrapping the original dataset.

Maybe try to ask on the GLUE mailing list or reach out to the original authors?

Tanks for the suggestion, I'll try to ask GLUE benchmark.
I'll first close the issue,  post the following up here afterwards, and reopen the issue if needed.
update xsum

This PR makes the following update to the xsum dataset:

- Manual download is not required anymore

- dataset can be loaded as follow: `nlp.load_dataset('xsum')`


**Important** 
Instead of using on outdated url to download the data:  "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json" 

a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used
, so that the user does not need to manually download the data anymore. 
There might be slight breaking changes here for xsum. 


Looks cool!
@mariamabarham can you add a detailed description here what exactly is changed and how the user can load xsum now?

And a rebase should solve the conflicts

This is a super useful PR :-) @sshleifer - maybe you can take a look at the updated version of xsum if you can use it for your use case. Now, one should be able to just load it with:

```python 
nlp.load_datasets("xsum", ....)  # no manual dir required anymore
```
Update metadata for x_stance dataset

Thank you for featuring the x_stance dataset in your library. This PR updates some metadata:
- Citation: Replace preprint with proceedings
- URL: Use a URL with long-term availability



Great! Thanks @jvamvas for these updates.


I have fixed a warning. The remaining test failure is due to an unrelated dataset.

We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ?
snli -1 labels

I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```



@jxmorris12  , we use `-1` to label examples for which `gold label`  is missing (`gold label = -` in the original dataset). 

Thanks @mariamabarham! so the original dataset is missing some labels? That is weird. Is standard practice just to discard those examples training/eval?

Yes the original dataset is missing some labels maybe  @sleepinyourhat , @gangeli can correct me if I'm wrong 
For my personal opinion at least if you want your model to learn to predict no answer (-1) you can leave it their but otherwise you can discard them. 

thanks @mariamabarham  :)
Improve input warning for evaluation metrics

Hi, 

I am the author of `bert_score`. Recently, we received [ an issue ](https://github.com/Tiiiger/bert_score/issues/62) reporting a problem in using `bert_score` from the `nlp` package (also see #238 in this repo).  After looking into this, I realized that the problem arises from the format `nlp.Metric` takes input. 

Here is a minimal example:
```python
import nlp

scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
    for lp, lg in zip(p, g):
        scorer.add(lp, lg)
score = scorer.compute(lang="en")
```

The problem in the above code is that `scorer.add()` expects a list of strings as input for the references. As a result, the `scorer` here would take a list of characters in `lg` to be the references. The correct implementation would be calling
```python
scorer.add(lp, [lg])
```

I just want to raise this issue to you to prevent future user errors of a similar kind. I assume some simple type checking can prevent this from happening?

Thanks!
Fix bertscore references

I added some type checking for metrics. There was an issue where a metric could interpret a string a a list. A `ValueError` is raised if a string is given instead of a list.

Moreover I added support for both strings and lists of strings for `references` in `bertscore`, as it is the case in the original code.

Both ways work:
```
import nlp

scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
    for lp, lg in zip(p, g):
        scorer.add(lp, [lg])
score = scorer.compute(lang="en")
```

```
import nlp

scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
    for lp, lg in zip(p, g):
        scorer.add(lp, lg)
score = scorer.compute(lang="en")
```

This should fix #295 and #238
Add a method to shuffle a dataset

Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.

Also, we could maybe have a clear indication of which method modify in-place and which methods return/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think?

Labels: name: generic discussion

+1 for the naming convention

About the `shuffle` method, from my understanding it should be done in `Dataloader` (better separation between dataset processing - usage)

+1 for shuffle in `Dataloader`. 
Some `Dataloader` just store idxs of dataset and just shuffle those idxs, which might(?) be faster than do shuffle in dataset, especially when doing shuffle every epoch.

Also +1 for the naming convention.

As you might already know the issue of dataset shuffling came up in the nlp code [walkthrough](https://youtu.be/G3pOvrKkFuk?t=3204) by Yannic Kilcher


We added the `.shuffle` method :)

Closing this one.
break statement not required



I guess,test failing due to connection error?

We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ?

If I'm not wrong this function returns None if no main class was found.
I think it makes things less clear not to have a return at the end of the function.
I guess we can have one return in the for loop instead of the break statement, AND one return at the end to explicitly return None.
What do you think ?
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'
```


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!
add wikisql

Adding the [WikiSQL](https://github.com/salesforce/WikiSQL) dataset.

Interesting things to note:
- Have copied the function (`_convert_to_human_readable`) which converts the SQL query to a human-readable (string) format as this is what most people will want when actually using this dataset for NLP applications.
- `conds` was originally a tuple but is converted to a dictionary to support differing types.

Would be nice to add the logical_form metrics too at some point.


That's great work @ghomasHudson !
[Feature request] Add Toronto BookCorpus dataset

I know the copyright/distribution of this one is complex, but it would be great to have! That, combined with the existing `wikitext`, would provide a complete dataset for pretraining models like BERT.

Labels: name: dataset request

As far as I understand, `wikitext` is refer to `WikiText-103` and `WikiText-2` that created by researchers in Salesforce, and mostly used in traditional language modeling.

You might want to say `wikipedia`, a dump from wikimedia foundation.

Also I would like to have Toronto BookCorpus too ! Though it involves copyright problem...

Hi, @lhoestq, just a reminder that this is solved by #248 .😉
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 

Labels: name: nlp-viewer

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 `.
[Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0.

When running BERT-Score, I'm meeting this warning :

> Warning: Empty candidate sentence; Setting recall to be 0.

Code :

```
import nlp
metric = nlp.load_metric("bertscore")
scores = metric.compute(["swag", "swags"], ["swags", "totally something different"], lang="en", device=0)
```

---

**What am I doing wrong / How can I hide this warning ?**

Labels: name: metric bug

This print statement comes from the official implementation of bert_score (see [here](https://github.com/Tiiiger/bert_score/blob/master/bert_score/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.
Right now we want to only use official code for metrics to have fair evaluations, so I'm not sure we can do anything about it. Maybe you can try to create an issue on their [repo](https://github.com/Tiiiger/bert_score) ?
Add MWSC

Adding the [Modified Winograd Schema Challenge](https://github.com/salesforce/decaNLP/blob/master/local_data/schema.txt) dataset which formed part of the [decaNLP](http://decanlp.com/) benchmark. Not sure how much use people would find for it it outside of the benchmark, but it is general purpose.

Code is heavily borrowed from the [decaNLP repo](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L773-L877).

There's a few (possibly overly opinionated) design choices I made:

- I used the train/test/dev split [buried in the decaNLP code](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L852-L855)
- I split out each example into the 2 alternatives. Originally the data uses the format:
    ```
    The city councilmen refused the demonstrators a permit because they [feared/advocated] violence. 
    Who [feared/advocated] violence? 
    councilmen/demonstrators
    ```
    I split into the 2 variants:
    ```
    The city councilmen refused the demonstrators a permit because they feared violence. 
    Who feared violence? 
    councilmen/demonstrators
    
    The city councilmen refused the demonstrators a permit because they advocated violence. 
    Who advocated violence? 
    councilmen/demonstrators
    ```
    I can't see any use for having the options combined into a single example (splitting them is [the way decaNLP processes](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L846-L850)) them. You can't train on both versions with them combined, and splitting the examples later would be a pain to do. I think [winogrande.py](https://github.com/huggingface/nlp/blob/master/datasets/winogrande/winogrande.py) presents the data in this way?

- I've not used the decaNLP framing (appending the options to the question e.g. `Who feared violence? 
 -- councilmen or demonstrators?`) but left it more generic by adding the options as a new key: `"options":["councilmen","demonstrators"]` This should be an easy thing to change using `map` if needed by a specific application.

Dataset is working as-is but if anyone has any thoughts/preferences on the design decisions here I'm definitely open to different choices.


Looks good to me
output nested dict in get_nearest_examples

As we are using a columnar format like arrow as the backend for datasets, we expect to have a dictionary of columns when we slice a dataset like in this example:
```python
my_examples = dataset[0:10]
print(type(my_examples))
# >>> dict
print(my_examples["my_column"][0]
# >>> this is the first element of the column 'my_column'
```

Therefore I wanted to keep this logic when calling `get_nearest_examples` that returns the top 10 nearest examples:
```python
dataset.add_faiss_index(column="embeddings")
scores, examples = dataset.get_nearest_examples("embeddings", query=my_numpy_embedding)
print(type(examples))
# >>> dict
```

Previously it was returning a list[dict]. It was the only place that was using this output format.

To make it work I had to implement `__getitem__(key)` where `key` is a list.
This is different from `.select` because `.select` is a dataset transform (it returns a new dataset object) while `__getitem__` is an extraction method (it returns python dictionaries).