SurAyush/github_issues huggingface dataset:
Issue: Add MultiEURLEX dataset
State: closed
Author: iliaschalkidis

**Add new MultiEURLEX Dataset**

MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource). Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels); this is multi-label classification task (given the text, predict multiple labels).

Comments:
Hi @lhoestq, we have this new cool multilingual dataset coming at EMNLP 2021. It would be really nice if we could have it in Hugging Face asap. Thanks! 

Hi @lhoestq, I adopted most of your suggestions:

- Dummy data files reduced, including the 2 smallest documents per subset JSONL.
- README was updated with the publication URL and instructions on how to download and use label descriptors. Excessive newlines were deleted.

I would prefer to keep the label list in a pure format (original ids), to enable people to combine those with more information or possibly in the future explore the dataset, find inconsistencies and fix those to release a new version. 

Thanks for the changes :)

Regarding the labels:

If you use the ClassLabel feature type, the only change is that it will store the ids as integers instead of (currently) string.
The advantage is that if people want to know what id corresponds to which label name, they can use `classlabel.int2str`. It is also the format that helps automate model training for classification in `transformers`.

Let me know if that sounds good to you or if you still want to stick with the labels as they are now.

Hey @lhoestq, thanks for providing this information. This sounds great. I updated my code accordingly to use `ClassLabel`. Could you please provide a minimal example of how `classlabel.int2str` works in practice in my case, where labels are a sequence?

```python
from datasets import load_dataset
dataset = load_dataset('multi_eurlex', 'all_languages')
# Read strs from the labels (list of integers) for the 1st sample of the training split
```

I would like to include this in the README file.

Could you also provide some info on how I could define the supervized key to automate model training, as you said?

Thanks!

Thanks for the update :)

Here is an example of usage:
```python
from datasets import load_dataset
dataset = load_dataset('multi_eurlex', 'all_languages', split='train')
classlabel = dataset.features["labels"].feature
print(dataset[0]["labels"])
# [1, 20, 7, 3, 0]
print(classlabel.int2str(dataset[0]["labels"]))
# ['100160', '100155', '100158', '100147', '100149']
```

The ClassLabel is simply used to define the `id2label` dictionary of classification models, to make the ids match between the model and the dataset. There nothing more to do :p 

I think one last thing to do is just update the `dataset_infos.json` file and we'll be good !

Everything is ready! 👍
Issue: fix: 🐛 be more specific when catching exceptions
State: closed
Author: severo

The same specific exception is catched in other parts of the same
function.

Comments:
To give more context: after our discussion, if I understood properly, you are trying to fix a call to `datasets` that takes 15 minutes: https://github.com/huggingface/datasets-preview-backend/issues/17 Is this right?



Yes, that's it. And to do that I'm trying to use https://pypi.org/project/stopit/, which will raise a stopit.TimeoutException exception. But currently, if this exception is raised, it's caught and considered as a "FileNotFoundError" while it should not be caught. 

And what about passing the `timeout` parameter instead?

It might be a good idea, but I would have to add a timeout argument to several methods, I'm not sure we want that (I want to ensure all my queries in https://github.com/huggingface/datasets-preview-backend/tree/master/src/datasets_preview_backend/queries resolve in a given time, be it with an error in case of timeout, or with the successful response). The methods are `prepare_module`, `import_main_class`, *builder_cls.*`get_all_exported_dataset_infos`, `load_dataset_builder`, and `load_dataset`

I understand, you are trying to find a fix for your use case. OK.

Just note that it is also an issue for `datasets` users. Once #2859 fixed in `datasets`, you will no longer have this issue...

Closing, since 1. my problem is more #2859, and I was asking for that change in order to make a hack work on my side, 2. if we want to change how exceptions are handled, we surely want to do it on all the codebase, not only in this particular case.
Issue: Cannot download TOTTO dataset
State: closed
Author: mrm8488

Error: Couldn't find file at https://storage.googleapis.com/totto/totto_data.zip

`datasets version: 1.11.0`
# How to reproduce:

```py
from datasets import load_dataset
dataset = load_dataset('totto')
```




Comments:
Hola @mrm8488, thanks for reporting.

Apparently, the data source host changed their URL one week ago: https://github.com/google-research-datasets/ToTTo/commit/cebeb430ec2a97747e704d16a9354f7d9073ff8f

I'm fixing it.
Issue: Loading allenai/c4 in streaming mode does too many HEAD requests
State: closed
Author: lhoestq

This does 60,000+ HEAD requests to get all the ETags of all the data files:
```python
from datasets import load_dataset
load_dataset("allenai/c4", streaming=True)
```
It makes loading the dataset completely impractical.

The ETags are used to compute the config id (it must depend on the data files being used).
Instead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)

Comments:
https://github.com/huggingface/datasets/blob/6c766f9115d686182d76b1b937cb27e099c45d68/src/datasets/builder.py#L179-L186

Thanks a lot!!!
Issue: fix: 🐛 remove URL's query string only if it's ?dl=1
State: closed
Author: severo

A lot of URL use the query strings, for example
http://opus.nlpl.eu/download.php?f=Bianet/v1/moses/en-ku.txt.zip, we
must not remove it when trying to detect the protocol. We thus remove it
only in the case of the query string being ?dl=1 which occurs on dropbox
and dl.orangedox.com. Also: add unit tests.

See https://github.com/huggingface/datasets/pull/2843 for the original
discussion.

Comments:
Issue: Fix windows CI CondaError
State: closed
Author: lhoestq

From this thread: https://github.com/conda/conda/issues/6057

We can fix the conda error
```
CondaError: Cannot link a source that does not exist.
C:\Users\...\Anaconda3\Scripts\conda.exe
```

by doing
```bash
conda update conda
```

before doing any install in the windows CI

Comments:
Issue: Fix caching when moving script
State: closed
Author: lhoestq

When caching the result of a `map` function, the hash that is computed depends on many properties of this function, such as all the python objects it uses, its code and also the location of this code.

Using the full path of the python script for the location of the code makes the hash change if a script like `run_mlm.py` is moved.

I changed this by simply using the base name of the script instead of the full path.

Note that this change also affects the hash of the code used from imported modules, but I think it's fine. Indeed it hashes the code of the imported modules anyway, so the location of the python files of the imported modules doesn't matter when computing the hash.

Close https://github.com/huggingface/datasets/issues/2825

Comments:
Merging since the CI failure is unrelated to this PR
Issue: Add AMI dataset
State: closed
Author: cahya-wirawan

This is an initial commit for AMI dataset

Comments:
Hey @cahya-wirawan, 

I played around with the dataset a bit and it looks already very good to me! That's exactly how it should be constructed :-) I can help you a bit with defining the config, etc... on Monday!

@lhoestq - I think the dataset is ready to be merged :-) 

At the moment, I don't really see how the failing tests correspond to this PR:
- https://app.circleci.com/pipelines/github/huggingface/datasets/7838/workflows/932a40a2-3e11-48be-84f0-c6434510058e/jobs/48318?invite=true#step-107-18
- https://app.circleci.com/pipelines/github/huggingface/datasets/7838/workflows/932a40a2-3e11-48be-84f0-c6434510058e/jobs/48316?invite=true#step-102-136

could you maybe give it a look? :-)
Issue: Wound segmentation datasets
State: open
Author: osanseviero

## Adding a Dataset
- **Name:** Wound segmentation datasets
- **Description:** annotated wound image dataset 
- **Paper:** https://www.nature.com/articles/s41598-020-78799-w
- **Data:** https://github.com/uwm-bigdata/wound-segmentation
- **Motivation:** Interesting simple image dataset, useful for segmentation, with visibility due to http://www.miccai.org/special-interest-groups/challenges/ and https://fusc.grand-challenge.org/

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
Issue: Add Open Catalyst Project Dataset
State: open
Author: osanseviero

## Adding a Dataset
- **Name:**  Open Catalyst 2020 (OC20) Dataset
- **Website:** https://opencatalystproject.org/
- **Data:** https://github.com/Open-Catalyst-Project/ocp/blob/master/DATASET.md

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
Issue: Negative timezone
State: closed
Author: jadermcs

## Describe the bug
The load_dataset method do not accept a parquet file with a negative timezone, as it has the following regex:
```
"^(s|ms|us|ns),\s*tz=([a-zA-Z0-9/_+:]*)$"
```
So a valid timestap ```timestamp[us, tz=-03:00]``` returns an error when loading parquet files.

## Steps to reproduce the bug
```python
# Where the timestamp column has a tz of -03:00
datasets = load_dataset('parquet', data_files={'train': train_files, 'validation': validation_files,
                                        'test': test_files}, cache_dir="./cache_teste/")
```

## Expected results
The -03:00 is a valid tz so the regex should accept this without raising an error.

## Actual results
As this regex disaproves a valid tz it raises the following error:
```python
raise ValueError(
                f"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp."
                f"Examples include timestamp[us] or timestamp[us, tz=America/New_York]"
                f"See: https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp"
            )
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 5.0.0


Comments:
Fixed by #2847.
Issue: [feature request] adding easy to remember `datasets.cache_dataset()` + `datasets.is_dataset_cached()`
State: open
Author: stas00

Often, there is a need to prepare a dataset but not use it immediately, e.g. think tests suite setup, so it'd be really useful to be able to do:

``` 
if not datasets.is_dataset_cached(ds): datasets.cache_dataset(ds)
```

This can already be done with:
```
builder = load_dataset_builder(ds)
if not os.path.idsir(builder.cache_dir):
    builder.download_and_prepare()
```

but the current way is a way less intuitive and much harder to remember than the proposed API, IMHO. 

One more way is to do:

```
_ = load_dataset(ds)
```
but it wastes resources loading the dataset when it's not needed.

this has been discussed at https://huggingface.slack.com/archives/C01229B19EX/p1630021912025800

Thank you!

@lhoestq 


Comments:
Issue: Fix extraction protocol inference from urls with params
State: closed
Author: lhoestq

Previously it was unable to infer the compression protocol for files at URLs like
```
https://foo.bar/train.json.gz?dl=1
```
because of the query parameters.

I fixed that, this should allow 10+ datasets to work in streaming mode:
```
  "discovery",
  "emotion",
  "grail_qa",
  "guardian_authorship",
  "pragmeval",
  "simple_questions_v2",
  "versae/adobo",
  "w-nicole/childes_data",
  "w-nicole/childes_data_no_tags_",
  "w-nicole/childes_data_with_tags",
  "w-nicole/childes_data_with_tags_"
```

cc @severo 

Comments:
merging since the windows error is just a CircleCI issue

It works, eg https://observablehq.com/@huggingface/datasets-preview-backend-client#{%22datasetId%22%3A%22discovery%22} and https://datasets-preview.huggingface.tech/rows?dataset=discovery&config=discovery&split=train

Nice !
Issue: always requiring the username in the dataset name when there is one
State: closed
Author: stas00

Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.

So both of us started with `stas/openwebtext-10k`, somewhere along the lines lost `stas/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`

So this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w/o it.

The same in code:

```
# first run
python -c "from datasets import load_dataset; load_dataset('stas/openwebtext-10k')"
# now run immediately
python -c "from datasets import load_dataset; load_dataset('openwebtext-10k')"
# the second command should fail, but it doesn't fail now.
```

Please let me know if I explained myself clearly.

Thank you!

Comments:
From what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?

I don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:
```
# first run
python -c "from datasets import load_dataset; load_dataset('stas/openwebtext-10k')"
# now run immediately
python -c "from datasets import load_dataset; load_dataset('openwebtext-10k')"
# the second command should fail, but it doesn't fail now.
```

Moreover, if someone were to create `openwebtext-10k` w/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas/openwebtext-10k`.

And if there are 2 users with the same dataset name `foo/ds` and `bar/ds` - currently this won't work to get the correct dataset.

So really there 3 unrelated issues hiding in the current behavior.

This has been fixed now, and we'll do a new release of the library today.

Now the stas/openwebtext-10k dataset is cached at `.cache/huggingface/datasets/stas___openwebtext10k` and openwebtext-10k would be at `.cache/huggingface/datasets/openwebtext10k`. Since they are different, the cache won't fall back on loading the wrong one anymore.

Same for the python script used to generate the dataset: stas/openwebtext-10k is cached at `.cache/huggingface/modules/datasets_modules/datasets/stas___openwebtext10k` and openwebtext-10k would be at `.cache/huggingface/modules/datasets_modules/datasets/openwebtext10k`

Amazing! Thank you for adding this improvement, @lhoestq!

(can be closed?)

Yes indeed :) thanks
Issue: Adding GLUECoS Hinglish and Spanglish code-switching bemchmark
State: open
Author: yjernite

## Adding a Dataset
- **Name:** GLUECoS
- **Description:** a Microsoft Benchmark to evaluate code-switching for only two language pairs but a variety of tasks
- **Paper:** https://aclanthology.org/2020.acl-main.329/
- **Data:** https://github.com/microsoft/GLUECoS
- **Motivation:** We currently only have [one other](https://huggingface.co/datasets/lince) dataset for code-switching

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
Hi @yjernite I am interested in adding this dataset. 
In the repo they have also added a code mixed MT task from English to Hinglish [here](https://github.com/microsoft/GLUECoS#code-mixed-machine-translation-task). I think this could be a good dataset addition in itself and then I can add the rest of the GLUECoS tasks as one dataset. What do you think?
Issue: OpenWebText: NonMatchingSplitsSizesError
State: closed
Author: thomasw21

## Describe the bug

When downloading `openwebtext`, I'm getting:
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]
```

I suspect that the file we download from has changed since the size doesn't look like to match with documentation

`Downloading:   0%|          | 0.00/12.9G [00:00<?, ?B/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.

## Steps to reproduce the bug
```python
from datasets import load_dataset

load_dataset("openwebtext", download_mode="force_redownload")
```

## Expected results

Loading is successful

## Actual results

Loading throws above error.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: linux (Redhat version 8.1)
- Python version: 3.8
- PyArrow version: 4.0.1


Comments:
Thanks for reporting, I'm updating the verifications metadata

I just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.

Can you try to delete your cache ( by default at `~/.cache/huggingface/datasets`) and try again please ?
Also, on which platform are you (linux/macos/windows) ?

I'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode="force_redownload"` would bypass cache.
Sorry plateform should be linux (Redhat version 8.1)

Hi @thomasw21 , are you still having this issue after clearing your cache ?

Sorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !
Issue: prepare_module issue when loading from read-only fs
State: closed
Author: Dref360

## Describe the bug

When we use prepare_module from a readonly file system, we create a FileLock using the `local_path`.
This path is not necessarily writable.

`lock_path = local_path + ".lock"`


## Steps to reproduce the bug

Run `load_dataset` on a readonly python loader file.
```python
ds = load_dataset(
        python_loader, data_files={"train": train_path, "test": test_path}
    )
```

where `python_loader` is a path to a file located in a readonly folder.

## Expected results
This should work I think?

## Actual results

```python
    return load_dataset(
  File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 711, in load_dataset
    module_path, hash, resolved_file_path = prepare_module(
  File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 465, in prepare_module
    with FileLock(lock_path):
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 314, in __enter__
    self.acquire()
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 263, in acquire
    self._acquire()
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 378, in _acquire
    fd = os.open(self._lock_file, open_mode)
OSError: [Errno 30] Read-only file system: 'YOUR_FILE.py.lock'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 3.0.0


Comments:
Hello, I opened #2887 to fix this.
Issue: Optimize Dataset.filter to only compute the indices to keep
State: closed
Author: lhoestq

Optimize `Dataset.filter` to only compute the indices of the rows to keep, instead of creating a new Arrow table with the rows to keep. Creating a new table was an issue because it could take a lot of disk space.

This will be useful to process audio datasets for example cc @patrickvonplaten 

Comments:
Maybe worth updating the docs here as well?

Yup, will do !
Issue: Update: timit_asr - make the dataset streamable
State: closed
Author: lhoestq

The TIMIT ASR dataset had two issues that was preventing it from being streamable:

1. it was missing a call to `open` before `pd.read_csv`
2. it was using `os.path.dirname` which is not supported for streaming

I made the dataset streamable by using `open` to load the CSV, and by adding the support for `os.path.dirname` in dataset scripts to stream data

You can now do
```python
from datasets import load_dataset

timit_asr = load_dataset("timit_asr", streaming=True)
print(next(iter(timit_asr["train"])))
```
prints:

```json
{"file": "zip://data/TRAIN/DR4/MMDM0/SI681.WAV::https://data.deepai.org/timit.zip",
"phonetic_detail": {"start": [0, 1960, 2466, 3480, 4000, 5960, 7480, 7880, 9400, 9960, 10680, 13480, 15680, 15880, 16920, 18297, 18882, 19480, 21723, 22516, 24040, 25190, 27080, 28160, 28560, 30120, 31832, 33240, 34640, 35968, 37720],
"utterance": ["h#", "w", "ix", "dcl", "s", "ah", "tcl", "ch", "ix", "n", "ae", "kcl", "t", "ix", "v", "r", "ix", "f", "y", "ux", "zh", "el", "bcl", "b", "iy", "y", "ux", "s", "f", "el", "h#"],
"stop": [1960, 2466, 3480, 4000, 5960, 7480, 7880, 9400, 9960, 10680, 13480, 15680, 15880, 16920, 18297, 18882, 19480, 21723, 22516, 24040, 25190, 27080, 28160, 28560, 30120, 31832, 33240, 34640, 35968, 37720, 39920]},
"sentence_type": "SI", "id": "SI681",
"speaker_id": "MMDM0",
"dialect_region": "DR4",
"text": "Would such an act of refusal be useful?",
"word_detail": {
    "start": [1960, 4000, 9400, 10680, 15880, 18297, 27080, 30120],
    "utterance": ["would", "such", "an", "act", "of", "refusal", "be", "useful"],
    "stop": [4000, 9400, 10680, 15880, 18297, 27080, 30120, 37720]
}}
```

cc @patrickvonplaten @vrindaprabhu

Comments:
Issue: IndexError when accessing first element of a Dataset if first RecordBatch is empty
State: closed
Author: lhoestq

The computation of the offsets of the underlying Table of a Dataset has some issues if the first RecordBatch is empty.

```python
from datasets import Dataset
import pyarrow as pa

pa_table = pa.Table.from_pydict({"a": [1]})
pa_table2 = pa.Table.from_pydict({"a": []}, schema=pa_table.schema)
ds_table = pa.concat_tables([pa_table2, pa_table])

dataset = Dataset(ds_table)

print([len(b) for b in dataset.data._batches])
# [0, 1]

print(dataset.data._offsets)
# [0 0 1] (should be [0, 1])

dataset[0]
```
raises
```python
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/table.py in _interpolation_search(arr, x)
     90         else:
     91             i, j = i, k
---> 92     raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.")
     93 
     94 

IndexError: Invalid query '0' for size 1.
```

This can be fixed by ignoring empty batches when computing `table._batches` and `table._offsets`

cc @SaulLu 

Comments: