helmo/github-issues huggingface dataset:
[dateset subset missing]  xtreme paws-x
State: closed | Author: cosmeowpawlitan (CONTRIBUTOR)

I tried nlp.load_dataset('xtreme', 'PAWS-X.es') but get the value error
It turns out that the subset for Spanish is missing
https://github.com/google-research-datasets/paws/tree/master/pawsx

Comments: You're right, thanks for pointing it out. We will update it
🐛 [Metrics] ROUGE is non-deterministic
State: closed | Author: astariul (NONE)

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
Starting to add some real doc
State: closed | Author: thomwolf (MEMBER)

Adding a lot of documentation for:
- load a dataset
- explore the dataset object
- process data with the dataset
- add a new dataset script
- share a dataset script
- full package reference

This version of the doc can be explored here: https://2219-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html

Also:
- fix a bug in `train_test_split`
- update the `csv` script
- add a verbose argument to the dataset processing methods

Still missing:
- doc for the metrics
- how to directly upload a community provided dataset with the CLI
- clean up more docstrings
- add the `features` argument to `load_dataset` (should be another PR)

Comments: Ok this is starting to be really big so it's probably good to merge this first version of the doc and continue in another PR :)

This first version of the doc can be explored here: https://2219-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html
Add hashes to cnn_dailymail
State: closed | Author: jbragg (CONTRIBUTOR)

The URL hashes are helpful for comparing results from other sources.

Comments: Looks you to me :)

Could you also update the json file that goes with the dataset script by doing 
```
nlp-cli test ./datasets/cnn_dailymail --save_infos --all_configs
```
It will update the features metadata and the size of the dataset with your changes.

@lhoestq I ran that command.

Thanks for the helpful repository!
Add text dataset
State: closed | Author: jarednielsen (CONTRIBUTOR)

Usage:

```python
from nlp import load_dataset
dset = load_dataset("text", data_files="/path/to/file.txt")["train"]
```


I created a dummy_data.zip which contains three files: `train.txt`, `test.txt`, `dev.txt`. Each of these contains two lines. It passes

```bash
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_text
```

but I would like a second set of eyes to ensure I did it right.
can't load SNLI dataset
State: closed | Author: jxmorris12 (CONTRIBUTOR)

`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!
More faiss control
State: closed | Author: lhoestq (MEMBER)

Allow users to specify a faiss index they created themselves, as sometimes indexes can be composite for examples

Comments: > Ok, so we're getting rid of the `FaissGpuOptions`?

We support `device=...` because it's simple, but faiss GPU options can be used in so many ways (you can set different gpu options for the different parts of your index for example) that it's probably better to let the user create and configure its index and then use `custom_index=...`
add from_pandas and from_dict
State: closed | Author: lhoestq (MEMBER)

I added two new methods to the `Dataset` class:
- `from_pandas()` to create a dataset from a pandas dataframe
- `from_dict()` to create a dataset from a dictionary (keys = columns)

It uses the `pa.Table.from_pandas` and `pa.Table.from_pydict` funcitons to do so.
It is also possible to specify the features types via `features=...` if there are ambiguities (null/nan values), otherwise the arrow schema is infered from the data automatically by pyarrow.

One question that I have right now:
+ Should we also add a `save()` method that would write the dataset on the disk ? Right now if we create a `Dataset` using those two new methods, the data are kept in RAM. Then to reload it we can call the `from_file()` method.
Hyperpartisan news detection
State: closed | Author: ghomasHudson (CONTRIBUTOR)

Adding the hyperpartisan news detection dataset from PAN. This contains news article text, labelled with whether they're hyper-partisan and why kinds of biases they display.

Implementation notes:
- As with many PAN tasks, the data is hosted on [Zenodo](https://zenodo.org/record/1489920) and must be requested before use. I've used the manual download stuff for this, although the dataset is provided under a Creative Commons Attribution 4.0 International License, so we could host a version if we wanted to?
- The 'bias' attribute doesn't exist for the 'byarticle' configuration. I've added an empty string to the class labels to deal with this. Is there a more standard value for empty data?
- Should we always subclass `nlp.BuilderConfig`?



Comments: Thank you so much for working on this! This is awesome!

How much would it help you if we would remove the manual request?

We are naturally interested in getting some broad idea of how many people and who are using our dataset. But if you consider hosting the dataset yourself, I would rather remove this small barrier on our side (so that we then still get the download count from your library).

This is an interesting aspect indeed!
Do you want to send me an email (see my homepage) and I'll invite you on our slack channel to talk about that?
@ghomasHudson wanna reach out to me as well? I tried to find your email to invite you without success.
Search qa
State: closed | Author: mariamabarham (CONTRIBUTOR)

This PR adds the Search QA dataset used in **SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine**. The dataset has the following config name:

- raw_jeopardy: raw data

- train_test_val:  which is the splitted version

#336 

Comments: Could you rebase from master just to make sure we won't break anything for `fever` pls @mariamabarham ?
Fix nested tensorflow format
State: closed | Author: lhoestq (MEMBER)

In #339 and #337 we are thinking about adding a way to export datasets to tfrecords.

However I noticed that it was not possible to do `dset.set_format("tensorflow")` on datasets with nested features like `squad`. I fixed that using a nested map operations to convert features to `tf.ragged.constant`.

I also added tests on the `set_format` function.
[Feature request] Export Arrow dataset to TFRecords
State: closed | Author: jarednielsen (CONTRIBUTOR)

The TFRecord generation process is error-prone and requires complex separate Python scripts to download and preprocess the data. I propose to combine the user-friendly features of `nlp` with the speed and efficiency of TFRecords. Sample API:

```python
# use these existing methods
ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
ds = ds.map(lambda ex: tokenizer(ex))
ds.set_format("tensorflow", columns=["input_ids", "token_type_ids", "attention_mask"])
# then add this method
ds.export(folder="/my/tfrecords", prefix="myrecord", num_shards=8, format="tfrecord")
```
which would create files like so:
```bash
/my/tfrecords/myrecord_1.tfrecord
/my/tfrecords/myrecord_2.tfrecord
...
```

I would be happy to contribute this method. We could use a similar approach for PyTorch. Thoughts?
[Dataset requests] New datasets for Open Question Answering
State: closed | Author: thomwolf (MEMBER)
Labels: name: help wanted

name: dataset request

We are still a few datasets missing for Open-Question Answering which is currently a field in strong development.

Namely, it would be really nice to add:
- WebQuestions (Berant et al., 2013) [done]
- CuratedTrec (Baudis et al. 2015) [not open-source]
- MS-MARCO (NGuyen et al. 2016) [done]
- SearchQA (Dunn et al. 2017) [done]
- FEVER (Thorne et al. 2018) - [ done]

 

All these datasets are cited in http://arxiv.org/abs/2005.11401
fix variable name typo
State: closed | Author: stas00 (CONTRIBUTOR)


Comments: Good catch :)
I think there is another occurence that needs to be fixed in the second gist (line 4924 of the notebook file):
```python
bleu = nlp.load_metric(...)
```

Was fixed in e16f79b5f7fc12a6a30c777722be46897a272e6f
Closing it.
Add wiki_dpr
State: closed | Author: lhoestq (MEMBER)

Presented in the [Dense Passage Retrieval paper](https://arxiv.org/pdf/2004.04906.pdf), this dataset consists in 21M passages from the english wikipedia along with their 768-dim embeddings computed using DPR's context encoder.

Note on the implementation:
- There are two configs: with and without the embeddings (73GB vs 14GB)
- I used a non-fixed-size sequence of floats to describe the feature format of the embeddings. I wanted to use fixed-size sequences but I had issues with reading the arrow file afterwards (for example `dataset[0]` was crashing)
- I added the case for lists of urls as input of the download_manager

Comments: The two configurations don't have the same sizes, I may change that so that they both have 21015300 examples for convenience, even though it's supposed to have 21015324 examples in total.

One configuration only has 21015300 examples because it seems that the embeddings of the last 24 examples are missing.

It's ok to merge now imo. I'll make another PR if we find a way to have the missing embeddings
Doc red
State: closed | Author: ghomasHudson (CONTRIBUTOR)

Adding [DocRED](https://github.com/thunlp/DocRED) - a relation extraction dataset which tests document-level RE. A few implementation notes:

- There are 2 separate versions of the training set - *annotated* and *distant*. Instead of `nlp.Split.Train` I've used the splits `"train_annotated"` and `"train_distant"` to reflect this.
- As well as the relation id, the full relation name is mapped from `rel_info.json`
- I renamed the 'h', 'r', 't' keys to 'head', 'relation' and 'tail' to make them more readable.
- Used the fix from #319 to allow nested sequences of dicts.
Add package path to sys when downloading package as github archive
State: closed | Author: yjernite (MEMBER)

This fixes the `coval.py` metric so that imports within the downloaded module work correctly. We can use a similar trick to add the BLEURT metric (@ankparikh)

@thomwolf not sure how you feel about adding to the `PYTHONPATH` from the script. This is the only way I could make it work with my understanding of `importlib` but there might be a more elegant method.

This PR fixes https://github.com/huggingface/nlp/issues/305

Comments: Sorry for the long diff, everything after the imports comes from `black` for code quality :/ 

 I think it's fine and I can't think of another way to make the import work anyways.

Maybe we can have the `sys.path` behavior inside `prepare_module` instead ? Currently it seems to come out of nowhere in the code ^^'
We could check if external imports have a `__init__.py` and if it is the case then we can add to directory to the `PYTHONPATH`
output nested dict in get_nearest_examples
State: closed | Author: lhoestq (MEMBER)

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).
Blog Authorship Corpus, Non Matching Splits Sizes Error, nlp viewer
State: closed | Author: mariamabarham (CONTRIBUTOR)
Labels: name: 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 `.
Nested sequences with dicts
State: closed | Author: ghomasHudson (CONTRIBUTOR)

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"),
    }]],
    ...
    }),
...
```
Adding a dataset with multiple subtasks
State: closed | Author: erickrf (NONE)

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.