konrud/llm_course_chapter5_own_dataset_github_issues huggingface dataset:
Adding Qanta (Quizbowl) Dataset

This PR adds the qanta question answering datasets from [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) and [Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples](https://www.aclweb.org/anthology/Q19-1029/) (adversarial fold)

This partially continues a discussion around fixing dummy data from https://github.com/huggingface/nlp/issues/161

I ran the following code to double check that it works and did some sanity checks on the output. The majority of the code itself is from our `allennlp` version of the dataset reader.

```python
import nlp
# Default is full question
data = nlp.load_dataset('./datasets/qanta') 
# Four configs
# Primarily useful for training
data = nlp.load_dataset('./datasets/qanta', 'mode=sentences,char_skip=25')  
# Primarily used in evaluation
data = nlp.load_dataset('./datasets/qanta', 'mode=first,char_skip=25')  
data = nlp.load_dataset('./datasets/qanta', 'mode=full,char_skip=25')  
# Primarily useful in evaluation and "live" play
data = nlp.load_dataset('./datasets/qanta', 'mode=runs,char_skip=25')  
```

Hi @EntilZha - sorry for waiting so long until taking action here. We created a new command and a new recipe of how to add dummy_data. Can you maybe rebase to `master` as explained in 7. of https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-contribute-to-nlp and check that your dummy data is correct following the instructions here: https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset ? 

If the tests described in 5. of https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset pass we can merge the PR :-) 

I updated to the most recent master and followed the steps, but still having the similar error where it can't find the correct file since the path to the directory is given, rather than the individual files within them. This still something wrong about how I'm inputting the data or how the tests are reading it?

It's the dummy_data structure. You actually have to call the dummy data file name `dummy_data` (not .json anything). So there should not be a `dummy_data` folder but for each config only a `dummy_data` which contains your json dummy data. Can you maybe try once more - if it doesn't work I do it for you :-). 

Would that work if there are multiple files? In my case, I'm including something similar to squad 1.0/2.0 where we have the main dataset + an additional challenge set in different files. Would I have the zip decompress to two files in that case?

This dataset was actually a special case. It helped us improve the  dummy data instructions :-), see #195 .Close this PR and merge #194.
[Tests] refactor tests

This PR separates AWS and Local tests to remove these ugly statements in the script:
```python
        if "/" not in dataset_name:
            logging.info("Skip {} because it is a canonical dataset")
            return
```

To run a `aws` test, one should now run the following command: 

```python 
pytest -s tests/test_dataset_common.py::AWSDatasetTest::test_builder_class_wmt14
```

The same `local` test, can be run with:
```python 
pytest -s tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_wmt14
```

Nice !
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?

+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.
Add Spanish POR and NER Datasets

Hi guys,
In order to cover multilingual support a little step could be adding standard Datasets used for Spanish NER and POS tasks.
I can provide it in raw and preprocessed formats.

Hello @mrm8488, are these datasets official datasets published in an NLP/CL/ML venue?

What about this one: https://github.com/ccasimiro88/TranslateAlignRetrieve?
[Feature request] Add cos-e v1.0

I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https://www.aclweb.org/anthology/P19-1487/), and v1.11 has noted [annotation](https://github.com/salesforce/cos-e/issues/2) [issues](https://arxiv.org/pdf/2004.14546.pdf).

Sounds good, @mariamabarham do you want to give a look?
I think we should have two configurations so we can allow either version of the dataset to be loaded with the `1.0` version being the default maybe.

Cc some authors of the great cos-e: @nazneenrajani @bmccann

cos_e v1.0 is related to CQA v1.0 but only CQA v1.11 dataset is available on their website. Indeed their is lots of ids in cos_e v1, which are  not in CQA v1.11  or the other way around.
@sarahwie, @thomwolf, @nazneenrajani,  @bmccann  do you know where I can  find CQA v1.0


@mariamabarham I'm also not sure where to find CQA 1.0. Perhaps it's not possible to include this version of the dataset. I'll close the issue if that's the case.

I do have a copy of the dataset. I can upload it to our repo.

Great @nazneenrajani. let me know once done.
Thanks

@mariamabarham @sarahwie I added them to the cos-e repo https://github.com/salesforce/cos-e/tree/master/data/v1.0

You can now do
```python
from nlp import load_dataset
cos_e = load_dataset("cos_e", "v1.0")
```
Thanks @mariamabarham !

Thanks!

@mariamabarham Just wanted to note that default behavior `cos_e = load_dataset("cos_e")` now loads `v1.0`. Not sure if this is intentional (but the flag specification does work as intended). 

> @mariamabarham Just wanted to note that default behavior `cos_e = load_dataset("cos_e")` now loads `v1.0`. Not sure if this is intentional (but the flag specification does work as intended).

In the new version of `nlp`, if you try `cos_e = load_dataset("cos_e")` it throws this error:
```
ValueError: Config name is missing.
Please pick one among the available configs: ['v1.0', 'v1.11']
Example of usage:
	`load_dataset('cos_e', 'v1.0')`
```
For datasets with at least two configurations, we now force the user to pick one (no default)
nlp.load_dataset() gives "TypeError: list_() takes exactly one argument (2 given)"

I'm trying to load datasets from nlp but there seems to have error saying 
"TypeError: list_() takes exactly one argument (2 given)"

gist can be found here
https://gist.github.com/saahiluppal/c4b878f330b10b9ab9762bc0776c0a6a

You can just run: 
`val = nlp.load_dataset('squad')` 

if you want to have just the validation script you can also do:

`val = nlp.load_dataset('squad', split="validation")`

If you want to load a local dataset, make sure you include a `./` before the folder name. 

This happens by just doing run all cells on colab ... I assumed the colab example is broken. 

Oh I see you might have a wrong version of pyarrow install on the colab -> could you try the following. Add these lines to the beginning of your notebook, restart the runtime and run it again:
```
!pip uninstall -y -qq pyarrow
!pip uninstall -y -qq nlp
!pip install -qq git+https://github.com/huggingface/nlp.git
```

> Oh I see you might have a wrong version of pyarrow install on the colab -> could you try the following. Add these lines to the beginning of your notebook, restart the runtime and run it again:
> 
> ```
> !pip uninstall -y -qq pyarrow
> !pip uninstall -y -qq nlp
> !pip install -qq git+https://github.com/huggingface/nlp.git
> ```

Tried, having the same error.

Can you post a link here of your colab? I'll make a copy of it and see what's wrong

This should be fixed in the current version of the notebook. You can try it again

Also see: https://github.com/huggingface/nlp/issues/222

I am getting this error when running this command
```
val = nlp.load_dataset('squad', split="validation")
```

FileNotFoundError: [Errno 2] No such file or directory: '/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/dataset_info.json'

Can anybody help?

It seems like your download was corrupted :-/ Can you run the following command: 

```
rm -r /root/.cache/huggingface/datasets
```

to delete the cache completely and rerun the download? 

I tried the notebook again today and it worked without barfing. 👌
Meta-datasets (GLUE/XTREME/...) – Special care to attributions and citations

Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation/specific homepage/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.

Let's take GLUE as an example:

The configuration has the citation for each dataset included (e.g. [here](https://github.com/huggingface/nlp/blob/master/datasets/glue/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded.

As @yoavgo suggested, there should be the possibility to call a function like  nlp.bib that outputs all bibtex ref from the datasets and models actually used and eventually nlp.bib.forreadme that would output the same info + versions numbers so they can be included in a readme.md file.

Actually, double checking with @mariamabarham, we already have this feature I think.

It's like this currently:
```python
>>> from nlp import load_dataset
>>> 
>>> dataset = load_dataset('glue', 'cola', split='train')
>>> print(dataset.info.citation)
@article{warstadt2018neural,
  title={Neural Network Acceptability Judgments},
  author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1805.12471},
  year={2018}
}
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}

Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```

What do you think @dseddah?

Looks good but why would there be a difference between the ref in the source and the one to be printed? 

Yes, I think we should remove this warning @mariamabarham.

It's probably a relic of tfds which didn't have the same way to access citations.
[Feature request] Add Ubuntu Dialogue Corpus dataset

https://github.com/rkadlec/ubuntu-ranking-dataset-creator or http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/

@AlphaMycelium the Ubuntu Dialogue Corpus [version 2]( https://github.com/rkadlec/ubuntu-ranking-dataset-creator) is added. Note that it requires a manual download by following the download instructions in the [repos]( https://github.com/rkadlec/ubuntu-ranking-dataset-creator).
Maybe we can close this issue for now?
_download_and_prepare() got an unexpected keyword argument 'verify_infos'

# Reproduce
In Colab,
```
%pip install -q  nlp
%pip install -q apache_beam mwparserfromhell

dataset = nlp.load_dataset('wikipedia')
```
get
```
Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wikipedia/20200501.aa/1.0.0...

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-6-52471d2a0088> in <module>()
----> 1 dataset = nlp.load_dataset('wikipedia')

1 frames

/usr/local/lib/python3.6/dist-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)
    515         download_mode=download_mode,
    516         ignore_verifications=ignore_verifications,
--> 517         save_infos=save_infos,
    518     )
    519 

/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
    361                 verify_infos = not save_infos and not ignore_verifications
    362                 self._download_and_prepare(
--> 363                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
    364                 )
    365                 # Sync info

TypeError: _download_and_prepare() got an unexpected keyword argument 'verify_infos'
```

Same error for dataset 'wiki40b'

Should be fixed on master :)
Error with sklearn train_test_split

It would be nice if we could use sklearn `train_test_split` to quickly generate subsets from the dataset objects returned by `nlp.load_dataset`. At the moment the code:

```python
data = nlp.load_dataset('imdb', cache_dir=data_cache)
f_half, s_half = train_test_split(data['train'], test_size=0.5, random_state=seed)
```
throws:
```
ValueError: Can only get row(s) (int or slice) or columns (string).
```
It's not a big deal, since there are other ways to split the data, but it would be a cool thing to have.

Indeed. Probably we will want to have a similar method directly in the library

Related: #166
ArrowTypeError in squad metrics

`squad_metric.compute` is giving following error
```
ArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type
```

This is how my predictions and references look like
```
predictions[0]
# {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}
```

```
references[0]
# {'answers': [{'text': 'Denver Broncos'},
  {'text': 'Denver Broncos'},
  {'text': 'Denver Broncos'}],
 'id': '56be4db0acb8001400a502ec'}
```

These are structured as per the `squad_metric.compute` help string.

There was an issue in the format, thanks.
Now you can do
```python3
squad_dset = nlp.load_dataset("squad")
squad_metric = nlp.load_metric("/Users/quentinlhoest/Desktop/hf/nlp-bis/metrics/squad")
predictions = [
    {"id": v["id"], "prediction_text": v["answers"]["text"][0]}  # take first possible answer
    for v in squad_dset["validation"]
]
squad_metric.compute(predictions, squad_dset["validation"])
```

and the expected format is 
```
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict {'text': list of possible texts for the answer, as a list of strings}
```
[Tests] run local tests as default

This PR also enables local tests by default

I think it's safer for now to enable both local and aws tests for every commit. The problem currently is that when we do a PR to add a dataset, the dataset is not yet on AWS on therefore not tested on the PR itself. Thus the PR will always be green even if the datasets are not correct. This PR aims at fixing this.

## Suggestion on how to commit to the repo from now on:
Now since the repo is "online", I think we should adopt a couple of best practices:
1) - No direct committing to the repo anymore. Every change should be opened in a PR and be well documented so that we can find it later
2) - Every PR has to be reviewed by at least x people (I guess @thomwolf you should decide here) because we now have to be much more careful when doing changes to the API for backward compatibility, etc...


You are right and I think those are usual best practice :) I'm 100% fine with this^^

Merging this for now to unblock other PRs.
[Question] Using/adding a local dataset

Users may want to either create/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.

It appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.

A notebook/example script demonstrating this would be very helpful.

Hi @zphang,

So you can just give the local path to a dataset script file and it should work.

Here is an example:
- you can download one of the scripts in the `datasets` folder of the present repo (or clone the repo)
- then you can load it with `load_dataset('PATH/TO/YOUR/LOCAL/SCRIPT.py')`

Does it make sense?

Could you give a more concrete example, please? 

I looked up wikitext dataset script from the repo. Should I just overwrite the `data_file` on line 98 to point to the local dataset directory? Would it work for different configurations of wikitext (wikitext2, wikitext103 etc.)?

Or maybe we can use DownloadManager to specify local dataset location? In that case, where do we use DownloadManager instance?

Thanks

Hi @MaveriQ , although what I am doing is to commit a new dataset, but I think looking at imdb script might help.
You may want to use `dl_manager.download_custom`, give it a url(arbitrary string), a custom_download(arbitrary function) and return a path, and finally use _get sample to fetch a sample.

The download manager supports local directories. You can specify a local directory instead of a url and it should work.

Closing this one.
Feel free to re-open if you have other questions :)
[Feature Request] Add the OpenWebText dataset

The OpenWebText dataset is an open clone of OpenAI's WebText dataset. It can be used to train ELECTRA as is specified in the [README](https://www.github.com/google-research/electra).

More information and the download link are available [here](https://skylion007.github.io/OpenWebTextCorpus/).

We're experimenting with hosting the OpenWebText corpus on Zenodo for easier downloading. https://zenodo.org/record/3834942#.Xs1w8i-z2J8

Closing since it's been added in #660
[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.

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 .😉
Loading GLUE dataset loads CoLA by default

If I run:

```python
dataset = nlp.load_dataset('glue')
```
The resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:

```python
metric = nlp.load_metric("glue")
```
which throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets?

As a follow-up to this: It looks like the actual GLUE task name is supplied as the `name` argument. Is there a way to check what `name`s/sub-datasets are available under a grouping like GLUE? That information doesn't seem to be readily available in info from `nlp.list_datasets()`.

Edit: I found the info under `Glue.BUILDER_CONFIGS`

Yes so the first config is loaded by default when no `name` is supplied but for GLUE this should probably throw an error indeed.

We can probably just add an `__init__` at the top of the `class Glue(nlp.GeneratorBasedBuilder)` in the `glue.py` script which does this check:
```
class Glue(nlp.GeneratorBasedBuilder):
    def __init__(self, *args, **kwargs):
        assert 'name' in kwargs and kwargs[name] is not None, "Glue has to be called with a configuration name"
        super(Glue, self).__init__(*args, **kwargs)
```

An error is raised if the sub-dataset is not specified :)
```
ValueError: Config name is missing.
Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']
Example of usage:
	`load_dataset('glue', 'cola')`
```
[Feature request] Add Google Natural Question dataset

Would be great to have https://github.com/google-research-datasets/natural-questions as an alternative to SQuAD.

Indeed, I think this one is almost ready cc @lhoestq 

I'm doing the latest adjustments to make the processing of the dataset run on Dataflow

Is there an update to this? It will be very beneficial for the QA community!

Still work in progress :)
The idea is to have the dataset already processed somewhere so that the user only have to download the processed files. I'm also doing it for wikipedia.

Super appreciate your hard work !!
I'll cross my fingers and hope easily loadable wikipedia dataset will come soon. 

Quick update on NQ: due to some limitations I met using apache beam + parquet I was not able to use the dataset in a nested parquet structure in python to convert it to our Apache Arrow format yet.
However we had planned to change this conversion step anyways so we'll make just sure that it enables to process and convert the NQ dataset to arrow.

NQ was added in #427 🎉
🐛 `map` not working

I'm trying to run a basic example (mapping function to add a prefix).  
[Here is the colab notebook I'm using.](https://colab.research.google.com/drive/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing)

```python
import nlp

dataset = nlp.load_dataset('squad', split='validation[:10%]')

def test(sample):
    sample['title'] = "test prefix @@@ " + sample["title"]
    return sample

print(dataset[0]['title'])
dataset.map(test)
print(dataset[0]['title'])
```
Output :
> Super_Bowl_50
Super_Bowl_50

Expected output :
> Super_Bowl_50
test prefix @@@ Super_Bowl_50

I didn't assign the output 🤦‍♂️

```python
dataset.map(test)
```

should be :

```python
dataset = dataset.map(test)
```
🐛 Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'

I'm trying to load CNN/DM dataset on Colab.

[Colab notebook](https://colab.research.google.com/drive/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)

But I meet this error :

> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'


It's strange, after installing `nlp` on Colab, the `pyarrow` version seems fine from `pip` but not from python :

```python
import pyarrow

!pip show pyarrow
print("version = {}".format(pyarrow.__version__))
```

> Name: pyarrow
Version: 0.17.0
Summary: Python library for Apache Arrow
Home-page: https://arrow.apache.org/
Author: None
Author-email: None
License: Apache License, Version 2.0
Location: /usr/local/lib/python3.6/dist-packages
Requires: numpy
Required-by: nlp, feather-format
> 
> version = 0.14.1

Ok I just had to restart the runtime after installing `nlp`. After restarting, the version of `pyarrow` is fine.
❓ How to apply a map to all subsets ?

I'm working with CNN/DM dataset, where I have 3 subsets : `train`, `test`, `validation`.

Should I apply my map function on the subsets one by one ?

```python
import nlp

cnn_dm = nlp.load_dataset('cnn_dailymail')
for corpus in ['train', 'test', 'validation']:
         cnn_dm[corpus] = cnn_dm[corpus].map(my_func)
```

Or is there a better way to do this ?

That's the way!
❓ How to remove specific rows of a dataset ?

I saw on the [example notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb#scrollTo=efFhDWhlvSVC) how to remove a specific column :

```python
dataset.drop('id')
```

But I didn't find how to remove a specific row. 

**For example, how can I remove all sample with `id` < 10 ?**

Hi, you can't do that at the moment.

Can you do it by now? Coz it would be awfully helpful!

you can convert dataset object to pandas and remove a feature and convert back to dataset .

That's what I ended up doing too. but it feels like a workaround to a feature that should be added to the datasets class.
🐛 Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323

I'm trying to use rouge metric.

I have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.  
I tried :

```python
import nlp

rouge = nlp.load_metric('rouge')
with open("test.pred.tokenized") as p, open("test.gold.tokenized") as g:
    for lp, lg in zip(p, g):
            rouge.add(lp, lg)
```

But I meet following error :

> pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323

---

Full stack-trace :

```
Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
  File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/metric.py", line 224, in add
    self.writer.write_batch(batch)
  File "/home/me/.venv/transformers/lib/python3.6/site-packages/nlp/arrow_writer.py", line 148, in write_batch
    pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)
  File "pyarrow/table.pxi", line 1550, in pyarrow.lib.Table.from_pydict
  File "pyarrow/table.pxi", line 1503, in pyarrow.lib.Table.from_arrays
  File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table
  File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323
```

(`nlp` installed from source)

Can you share your data files or a minimally reproducible example?

Sure, [here is a Colab notebook](https://colab.research.google.com/drive/1uiS89fnHMG7HV_cYxp3r-_LqJQvNNKs9?usp=sharing) reproducing the error.

> ArrowInvalid: Column 1 named references expected length 36 but got length 56

This is because `add` takes as input a batch of elements and you provided only one. I think we should have `add` for one prediction/reference and `add_batch` for a batch of predictions/references. This would make it more coherent with the way we use Arrow.

Let me do this change

Thanks for noticing though. I was mainly used to do `.compute` directly ^^

Thanks @lhoestq it works :)
AttributeError: 'dict' object has no attribute 'info'

I'm trying to access the information of CNN/DM dataset :

```python
cnn_dm = nlp.load_dataset('cnn_dailymail')
print(cnn_dm.info)
```

returns :

> AttributeError: 'dict' object has no attribute 'info'

I could access the info by first accessing the different splits :

```python
import nlp

cnn_dm = nlp.load_dataset('cnn_dailymail')
print(cnn_dm['train'].info)
```

Information seems to be duplicated between the subsets :

```python
print(cnn_dm["train"].info == cnn_dm["test"].info == cnn_dm["validation"].info)
# True
```

Is it expected ?

Good point @Colanim ! What happens under the hood when running:

```python
import nlp

cnn_dm = nlp.load_dataset('cnn_dailymail')
```

is that for every split in `cnn_dailymail`, a different dataset object (which all holds the same info) is created. This has the advantages that the datasets are easily separable in a training setup. 
Also note that you can load e.g. only the `train` split of the dataset via:

```python
cnn_dm_train = nlp.load_dataset('cnn_dailymail', split="train")
print(cnn_dm_train.info)
```

I think we should make the `info` object slightly different when creating the dataset for each split - at the moment it contains for example the variable `splits` which should maybe be renamed to `split` and contain only one `SplitInfo` object ...
Couldn't reach CNN/DM dataset

I can't get CNN / DailyMail dataset.

```python
import nlp

assert "cnn_dailymail" in [dataset.id for dataset in nlp.list_datasets()]
cnn_dm = nlp.load_dataset('cnn_dailymail')
```

[Colab notebook](https://colab.research.google.com/drive/1zQ3bYAVzm1h0mw0yWPqKAg_4EUlSx5Ex?usp=sharing)

gives following error :

```
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/cnn_dailymail/cnn_dailymail.py
```

Installing from source (instead of Pypi package) solved the problem.
[Manual downloads] add logic proposal for manual downloads and add wikihow

Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset. 

The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.

The dataset can then be loaded via:

```python
import nlp
nlp.load_dataset("wikihow", data_dir="~/wikihow/manual_dir")
```

I added/changed so that there are explicit error messages when using manually downloaded files.



> Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset.
> 
> The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.
> 
> The dataset can then be loaded via:
> 
> ```python
> import nlp
> nlp.load_dataset("wikihow", data_dir="~/wikihow/manual_dir")
> ```
> 
> I added/changed so that there are explicit error messages when using manually downloaded files.

wouldn't be nicer if we can have `manual_dir/wikihow`? 

> > Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset.
> > The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.
> > The dataset can then be loaded via:
> > ```python
> > import nlp
> > nlp.load_dataset("wikihow", data_dir="~/wikihow/manual_dir")
> > ```
> > 
> > 
> > I added/changed so that there are explicit error messages when using manually downloaded files.
> 
> wouldn't be nicer if we can have `manual_dir/wikihow`?

Sure, I mean the user can decide whatever he likes best :-) The path one puts in `data_dir` will be used as the path to the manual dir. `nlp.load_dataset("wikihow", data_dir="~/manual_dir/wikihow")` would work as well as any other path ;-)  

Perfect! You can merge!
Run save infos

I replaced the old checksum file with the new `dataset_infos.json` by running the script on almost all the datasets we have. The only one that is still running on my side is the cornell dialog

Haha that cornell dialogue dataset - that ran for 3h on my computer as well. The `generate_examples` method in this script is one of the most inefficient code samples I've ever seen :D 

Indeed it's been 3 hours already
```73111 examples [3:07:48,  2.40 examples/s]```
Add per type scores in seqeval metric

This PR add a bit more detail in the seqeval metric. Now the usage and output are:

```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
predictions =  [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
met.compute(predictions, references)

#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8}
```

It is also possible to compute scores for non IOB notations, POS tagging for example hasn't this kind of notation. Add `suffix` parameter:

```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
predictions =  [['O', 'O', 'MISC', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
met.compute(predictions, references, metrics_kwargs={"suffix": True})

#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.9}
```

LGTM :-) Some small suggestions to shorten the code a bit :-) 

Can you put the kwargs as normal kwargs instead of a dict? (And add them to the kwargs description As well)

@thom Is-it what you meant?

Yes and there is a dynamically generated doc string in the metric script KWARGS DESCRIPTION
Webis tl-dr

Add the Webid TL:DR dataset.

Should that rather be in an organization scope, @thomwolf @patrickvonplaten ?

> Should that rather be in an organization scope, @thomwolf @patrickvonplaten ?

I'm a bit indifferent - both would be fine for me!

@jplu - if creating the dummy_data is too tedious, I can do it as well :-) 

There is dummy_data here, no ?

Yeah I think naming it webis/tl_dr would be best @jplu if that works for you

No problem at all!! On it^^

> There is dummy_data here, no ?

Some paths were wrong - the structure is really confusing and the error messages don't really help either - I have to think about how to make this easier to understand!

Hope it was ok that I fiddled with your PR !

> Some paths were wrong - the structure is really confusing and the error message don't really help either - I have to think about how to make this easier to understand!

Oh ok! I haven't noticed that sorry :(

> Hope it was ok that I fiddled with your PR !

Of course it was ok :)

@julien-c Looks like what you have in mind?

```python
import nlp
nlp.load_dataset("datasets/webis", "tl_dr")

#Output: Downloading and preparing dataset webis/tl_dr (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/jplu/.cache/huggingface/datasets/webis/tl_dr/1.0.0...
```

Merging this for now. Maybe we can see whether to rename it in a different PR @julien-c ? 


Hi, 
Author here of the webis-tldr corpus. Any plans on integrating this dataset into the hub? I remember we could access it in the previous versions of the library. If there is a particular issue that I can help with, do let me know.

Thanks!

Hi @shahbazsyed, this dataset _is_ inside the hub but it's namespaced by the organization name `webis`.

You can load it following the steps described in https://huggingface.co/datasets/webis/tl_dr

Here's a Colab showcasing that it works: https://colab.research.google.com/drive/11IrzRVpnMLJZ8_UFFHLR8FhiajjAHRUU?usp=sharing

The reason the code is in S3 and not in this repo is that the dataset is namespaced under the `webis` organization. We don't have a lot of namespaced datasets yet but this should become the main way we add more datasets in the future.
Let us know if that's an issue for you. Thank you!
Replace checksums files by Dataset infos json

### Better verifications when loading a dataset

I replaced the `urls_checksums` directory that used to contain `checksums.txt` and `cached_sizes.txt`, by a single file `dataset_infos.json`. It's just a dict `config_name` -> `DatasetInfo`.

It simplifies and improves how verifications of checksums and splits sizes are done, as they're all stored in `DatasetInfo` (one per config). Also, having already access to `DatasetInfo` enables to check disk space before running `download_and_prepare` for a given config.

The dataset infos json file is user readable, you can take a look at the squad one that I generated in this PR.

### Renaming

According to these changes, I did some renaming:
`save_checksums` -> `save_infos`
`ignore_checksums` -> `ignore_verifications`

for example, when you are creating a dataset you have to run
```nlp-cli test path/to/my/dataset --save_infos --all_configs```
instead of
```nlp-cli test path/to/my/dataset --save_checksums --all_configs```

### And now, the fun part

We'll have to rerun the `nlp-cli test ... --save_infos --all_configs` for all the datasets

-----------------

feedback appreciated !

Great! LGTM :-) 

> Ok, really clean!
> I like the logic (not a huge fan of using `_asdict_inner` but it makes sense).
> I think it's a nice improvement!
> 
> How should we update the files in the repo? Run a big job on a server or on somebody's computer who has most of the datasets already downloaded?

Maybe we can split the updates among us...IMO most datasets run very quickly. 
I think I've downloaded 50 datasets and 80% are loaded in <5min,  15% in <1h and then `wmt` which is still downloading (since 12h). 
I deleted my cache because the `wmt` downloads require quite a lot of space, so I only have parts of the `wmt` datasets on my computer. 

@mariamabarham I guess you have downloaded most of the datasets no?
Add nbytes + nexamples check

### Save size and number of examples
Now when you do `save_checksums`, it also create `cached_sizes.txt` right next to the checksum file.
This new file stores the bytes sizes and the number of examples of each split that has been prepared and stored in the cache. Example:

```
# Cached sizes: <full_config_name> <num_bytes> <num_examples>
hansards/house/1.0.0/test 22906629 122290
hansards/house/1.0.0/train 191459584 947969
hansards/senate/1.0.0/test 5711686 25553
hansards/senate/1.0.0/train 40324278 182135
```

### Check processing output 

If there is a `caches_sizes.txt`, then each time we run `download_and_prepare` it will make sure that the sizes match. You can set `ignore_checksums=True` if you don't want that to happen.

### Fill Dataset Info

All the split infos and the checksums are now stored correctly in DatasetInfo after `download_and_prepare`

### Check space on disk before running `download_and_prepare`

Check if the space is lower than the sum of the sizes of the files in `checksums.txt` and `cached_files.txt`. This is not ideal though as it considers the files for all configs.

TODO:
A better way to do it would be to have save the `DatasetInfo` instead of the `checksums.txt` and `cached_sizes.txt`, in order to have one file per dataset config (and therefore consider only the sizes of the files for one config and not all of them). It can also be the occasion to factorize all the `download_and_prepare` verifications. Maybe next PR ?

Looks good to me! Should we hard code those numbers in the config classes and make sure that when loading a dataset that the numbers match?
WIP adding metrics

Adding the following metrics as identified by @mariamabarham:

1. BLEU:  BiLingual Evaluation Understudy: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py,  https://github.com/chakki-works/sumeval/blob/master/sumeval/metrics/bleu.py (multilingual)
2. GLEU:  Google-BLEU:  https://github.com/cnap/gec-ranking/blob/master/scripts/compute_gleu
3. Sacrebleu: https://pypi.org/project/sacrebleu/1.4.8/ (pypi package), https://github.com/mjpost/sacrebleu (github implementation)
4. ROUGE: Recall-Oriented Understudy for Gisting Evaluation: https://github.com/google-research/google-research/tree/master/rouge, https://github.com/chakki-works/sumeval/blob/master/sumeval/metrics/rouge.py (multilingual)
5. Seqeval: https://github.com/chakki-works/seqeval (github implementation), https://pypi.org/project/seqeval/0.0.12/ (pypi package)
6. Coval:  coreference evaluation package for the CoNLL and ARRAU datasets https://github.com/ns-moosavi/coval
7. SQuAD v1 evaluation script
8. SQuAD V2 evaluation script: https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/
9. GLUE
10. XNLI


Not now:
1. Perplexity: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/perplexity.py
2. Spearman: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/spearman_correlation.py
3. F1_measure: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/f1_measure.py
4. Pearson_corelation: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/pearson_correlation.py
5. AUC: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/auc.py 
6. Entropy: https://github.com/allenai/allennlp/blob/master/allennlp/training/metrics/entropy.py

It's all about my metric stuff so I'll probably merge it unless you want to have a look.

Took the occasion to remove the old doc and requirements.txt
JSON script

Add a JSONS script to read JSON datasets from files.

The tests for the Wikipedia dataset do not pass anymore with the error:
```
To be able to use this dataset, you need to install the following dependencies ['mwparserfromhell'] using 'pip install mwparserfromhell' for instance'
```

This was an issue on master. You can just rebase from master.

Perfect! Indeed, it worked^^ Thanks @lhoestq 

Currently the dummy_data tests are always green because in a PR the dataset is not yet synchronized with aws. This PR fixes this: https://github.com/huggingface/nlp/pull/140 . 

Could you test `json` locally or wait until the PR: https://github.com/huggingface/nlp/pull/140 is merged ? :-) 

Ok, I will wait #140 to be merged and then rebase :)
Fix arrow writer for big datasets using writer_batch_size

This PR fixes Yacine's bug.
According to [this](https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations), it is not recommended to have pyarrow arrays bigger than 2Go.

Therefore I set a default batch size of 100 000 examples per batch. In general it shouldn't exceed 2Go. If it does, I reduce the batch_size on the fly, and I notify the user with a warning.

After a quick chat with Yacine : the 2Go test may not be sufficient actually, as I'm looking at the size of the array and not the size of the current_rows. If the test doesn't do the job I think I'll remove it and lower the batch size a bit to be sure that it never exceeds 2Go. I'll do more tests later
[Tests] Test files locally

This PR adds a `aws` and a `local` decorator to the tests so that tests now run on the local datasets. 

By default, the `aws` is deactivated and `local` is activated and `slow` is deactivated, so that only 1 test per dataset runs on circle ci. 

**When local is activated all folders in `./datasets` are tested.**

**Important** When adding a dataset, we should no longer upload it to AWS. The steps are:
1. Open a PR
2. Add a dataset as described in `datasets/README.md`
3. If all tests pass, push to master

Currently we have 49 functional datasets in our code base. 

We have 6 datasets "under-construction" that don't pass the tests - so I put them in a folder "datasets_under_construction" - it would be nice to open a PR to fix them and put them in the `datasets` folder.

**Important** when running tests locally, the datasets are cached so to rerun them delete your local cache via:
`rm -r ~/.cache/huggingface/datasets/*` 

@thomwolf @mariamabarham @lhoestq 

Super nice, good job @patrickvonplaten!
Update to simplify some datasets conversion

This PR updates the encoding of `Values` like `integers`, `boolean` and `float` to use python casting and avoid having to cast in the dataset scripts, as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r420176626

We could also change (not included in this PR yet):
- `supervized_keys` to make them a NamedTuple instead of a dataclass, and
- handle specifically the `Translation` features.
as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r421740236

@patrickvonplaten @mariamabarham tell me if you want these two last changes as well.

Awesome! 

Also we should convert `tf.io.gfile.exists` into `os.path.exists` , `tf.io.gfile.listdir`into `os.listdir` and `tf.io.gfile.glob`  into `glob.glob` (will need to add `import glob`)

> Also we should convert `tf.io.gfile.exists` into `os.path.exists` , `tf.io.gfile.listdir`into `os.listdir` and `tf.io.gfile.glob` into `glob.glob` (will need to add `import glob`)

We should probably open a new PR about this

I think it might be a good idea to both change the supervised keys to a named tuple and also handle the translation features specifically.

Just noticed that `pyarrow` apparently does not have a `is_boolean` function. Or do I have the wrong `pyarrow` version? 

Ah, it was a typo `pa.types.is_boolean` is the correct name. Will fix in: https://github.com/huggingface/nlp/pull/59
Better cached path

### Changes:
- The `cached_path` no longer returns None if the file is missing/the url doesn't work. Instead, it can raise `FileNotFoundError` (missing file), `ConnectionError` (no cache and unreachable url) or `ValueError` (parsing error)
- Fix requests to firebase API that doesn't handle HEAD requests...
- Allow custom download in datasets script: it allows to use `tf.io.gfile.copy` for example, to download from google storage. I added an example: the `boolq` script

I should have read this PR before doing my own: https://github.com/huggingface/nlp/pull/62 :D 
will close mine. Looks great :-) 

> Awesome, this is really nice!
> 
> By the way, we should improve the `cached_path` method of the `transformers` repo similarly, don't you think (@patrickvonplaten in particular).

Yeah, we should do the same in `transformers` I think - will note it down.
[Testing] Improved testing structure

This PR refactors the test design a bit and puts the mock download manager in the `utils` files as it is just a test helper class.

as @mariamabarham pointed out, creating a dummy folder structure can be quite hard to grasp.
This PR tries to change that to some extent.

It follows the following logic for the `dummy` folder structure now:
1.) The data bulider has no config -> the  `dummy` folder structure is:
`dummy/<version>/dummy_data.zip`
2) The data builder has >= 1 configs -> the `dummy` folder structure is: 
`dummy/<config_name_1>/<version>/dummy_data.zip`
`dummy/<config_name_2>/<version>/dummy_data.zip`

Now, the difficult part is how to create the `dummy_data.zip` file. There are two cases:
A) The `data_urs` parameter inserted into the `download_and_extract` fn is a **string**:
-> the `dummy_data.zip` file zips the folder: 
`dummy_data/<relative_path_of_folder_structure_of_url>`
B) The `data_urs` parameter inserted into the `download_and_extract` fn is a **dict**:
-> the `dummy_data.zip` file zips the folder: 
`dummy_data/<relative_path_of_folder_structure_of_url_behind _key_1>`
`dummy_data/<relative_path_of_folder_structure_of_url_behind _key_2>`

By relative folder structure I mean `url_path.split('./')[-1]`. As an example the dataset **xquad** by deepmind has the following url path behind the key `de`: `https://github.com/deepmind/xquad/blob/master/xquad.de.json` 
-> This means that the relative url path should be `xquad.de.json`.


@mariamabarham B) is a change from how is was before and I think is makes more sense. 
While before the `dummy_data.zip` file for xquad with config `de` looked like:
`dummy_data/de` it would now look like `dummy_data/xquad.de.json`. I think this is better and easier to understand. 

Therefore there are currently 6 tests that would have to have changed their dummy folder structure, but which can easily be done (30min). 

I also added a function: `print_dummy_data_folder_structure` that prints out the expected structures when testing which should be quite helpful.

Awesome!
Let's have this in the doc at the end :-)