Kokolipa/github-issues huggingface dataset:
Issue: CLI test command throws NonMatchingSplitsSizesError when saving infos
State: closed
Labels: name: bug

When trying to generate a datset JSON metadata, a `NonMatchingSplitsSizesError` is thrown:
```
$ datasets-cli test datasets/arabic_billion_words --save_infos --all_configs
Testing builder 'Alittihad' (1/10)
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: Unknown size, post-processed: Unknown size, total: 332.13 MiB) to .cache\arabic_billion_words\Alittihad\1.1.0\8175ff1c9714c6d5d15b1141b6042e5edf048276bb81a9c14e35e149a7a62ae4...
Traceback (most recent call last):
  File "path\huggingface\datasets\.venv\Scripts\datasets-cli-script.py", line 33, in <module>
    sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
  File "path\huggingface\datasets\src\datasets\commands\datasets_cli.py", line 33, in main
    service.run()
  File "path\huggingface\datasets\src\datasets\commands\test.py", line 144, in run
    builder.download_and_prepare(
  File "path\huggingface\datasets\src\datasets\builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "path\huggingface\datasets\src\datasets\builder.py", line 709, in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
  File "path\huggingface\datasets\src\datasets\utils\info_utils.py", line 74, in verify_splits
    raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words')}]
```

This is due because a previous run generated a wrong `dataset_info.json`.

This error can be avoided by passing `--ignore_verifications`, but I think this should be assumed when passing `--save_infos`.
Issue: [when Image type will exist] provide a way to get the data as binary + filename
State: closed
Labels: name: enhancement

name: dataset-viewer

**Is your feature request related to a problem? Please describe.**

When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.

Note: this issue would apply exactly the same for the `Audio` type.

**Describe the solution you'd like**

If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:

```python
  filename: str
  data: bytes
```

**Describe alternatives you've considered**

A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.


@severo, maybe somehow related to this PR ?
- #3129

@severo I'll keep that in mind.

You can track progress on the Image feature in #3163 (still in the early stage).  

Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after all

Fixed with https://github.com/huggingface/datasets/pull/3163
Issue: Infer the features if missing
State: closed
Labels: name: enhancement

name: dataset-viewer

**Is your feature request related to a problem? Please describe.**

Some datasets, in particular community datasets, have no info file, thus no features.

**Describe the solution you'd like**

If a dataset has no features, the first loaded data (5-10 rows) could be used to infer the type.

Related: `datasets` would provide a way to load the data, and get the rows AND the features as the result.

**Describe alternatives you've considered**

The HF hub could also provide some UI to help the dataset maintainers to explicit the types of their rows, or automatically infer them as an initial proposal.

Done by @lhoestq here: https://github.com/huggingface/datasets/pull/4500 (https://github.com/huggingface/datasets/pull/4500/files#diff-02930e1d966f4b41f9ddf15d961f16f5466d9bee583138657018c7329f71aa43R1255 in particular)
Issue: Provide a way to check if the features (in info) match with the data of a split
State: open
Labels: name: enhancement

name: dataset-viewer

**Is your feature request related to a problem? Please describe.**

I understand that currently the data loaded has not always the type described in the info features

**Describe the solution you'd like**

Provide a way to check if the rows have the type described by info features

**Describe alternatives you've considered**

Always check it, and raise an error when loading the data if their type doesn't match the features.


Related: #3144
Issue: Provide a way to write a streamed dataset to the disk
State: open
Labels: name: enhancement

name: dataset-viewer

**Is your feature request related to a problem? Please describe.**

The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.

**Describe the solution you'd like**

Provide a way to write the streamed rows of a dataset on the disk, and to load from it later.

**Describe alternatives you've considered**

Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data. 


Yes, I agree this feature is much needed. We could do something similar to what TF does (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache). 

Ideally, if the entire streamed dataset is consumed/cached, the generated cache should be reusable for the Arrow dataset.

@mariosasko Hi big brother,any update on this? It's 2024 with large streamed dataset loading consume too much time(exp.  2day..), really need this feature for what TF does
Issue: Fix file/directory deletion on Windows  
State: open
Labels: name: bug

Currently, on Windows, some attempts to delete a dataset file/directory will fail with the `PerimissionError`. 

Examples:
- download a dataset, then force redownload it in the same session while keeping a reference to the downloaded dataset
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset = load_dataset("sst", split="train", download_mode="force_redownload")
```
- try to clean up the cache files while keeping a reference to those files (via the mapped dataset):
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset_mapped = dset.map(lambda _: {"dummy_col": 1})
dset.cleanup_cache_files()
```
We should fix those.
Issue: More fine-grained taxonomy of error types
State: open
Labels: name: enhancement

name: dataset-viewer

**Is your feature request related to a problem? Please describe.**

Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did

**Describe the solution you'd like**

Give a specific exception type for every group of similar errors

**Describe alternatives you've considered**

Rely on the error message, using regex


related: #4995
Issue: Make inspect.get_dataset_config_names always return a non-empty list of configs
State: closed
Labels: name: enhancement

name: dataset-viewer

**Is your feature request related to a problem? Please describe.**

Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to

**Describe the solution you'd like**

In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`).

https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161


Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?

Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:
- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).
- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check/region_1`)
Issue: Add ADE20k
State: closed
Labels: name: dataset request

name: vision

## Adding a Dataset
- **Name:** ADE20k (actually it's called the MIT Scene Parsing Benchmark, it's actually a subset of ADE20k but a lot of authors still call it ADE20k)
- **Description:** A semantic segmentation dataset, consisting of 150 classes.
- **Paper:** http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf
- **Data:** http://sceneparsing.csail.mit.edu/
- **Motivation:** I am currently adding Transformer-based semantic segmentation models that achieve SOTA on this dataset. It would be great to directly access this dataset using HuggingFace Datasets, in order to make example scripts in HuggingFace Transformers.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


I think we can close this issue since PR [#3607](https://github.com/huggingface/datasets/pull/3607) solves this.
Issue: datasets-cli: convertion of a tfds dataset to a huggingface one.
State: open

### Discussed in https://github.com/huggingface/datasets/discussions/3079

<div type='discussions-op-text'>

<sup>Originally posted by **vitalyshalumov** October 14, 2021</sup>
I'm trying to convert a tfds dataset to a huggingface one.

I've tried:

1.       datasets-cli convert  --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/mnist/3.0.1/

2.       datasets-cli convert  --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/


and other permutations.
The script appears to be running and finishing without an error but when looking in the  huggingface/datasets/ folder nothing is created.


</div>

Hi,

the MNIST dataset is already available on the Hub. You can use it as follows:
```python
import datasets
dataset_dict = datasets.load_dataset("mnist")
```

As for the conversion of TFDS datasets to HF datasets, we will be working on it in the coming months, so stay tuned.
Issue: "arabic_billion_words" dataset does not create the full dataset
State: closed
Labels: name: bug

## Describe the bug
When running: 
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
the correct dataset file is pulled from the url.
But, the generated dataset includes just a small portion of the data included in the file.
This is true for all other portions of the "arabic_billion_words" dataset ('Almasryalyoum',.....)

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
raw_dataset = load_dataset('arabic_billion_words','Alittihad')

#The screen message
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB) 

## Expected results
over 100K sentences

## Actual results
only 11K sentences

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1



Thanks for reporting, @vitalyshalumov.

Apparently the script to parse the data has a bug, and does not generate the entire dataset.

I'm fixing it.
Issue: Segmentation fault when loading datasets from file
State: closed
Labels: name: bug

## Describe the bug
Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/

## Steps to reproduce the bug

Download an example file:
```
wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl
```
Then in Python:
```
import datasets
tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000)
```

## Expected results
a `tiny_kelm` functional dataset

## Actual results
☠️ `Segmentation fault (core dumped)` ☠️

## Environment info
- `datasets` version: 1.14.0
- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0

Hi ! I created an issue on Arrow's JIRA after making a minimum reproducible example

https://issues.apache.org/jira/browse/ARROW-14439

```python
import io

import pyarrow.json as paj

batch = b'{"a": [], "b": 1}\n{"b": 1}'
block_size = 12

paj.read_json(
    io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
```

I don't see a way to workaround this properly now without hurting the performance of the JSON loader significantly though

The issue has been fixed in pyarrow 6.0.0, please update pyarrow :)

The issue was due to missing fields in the JSON data of type list. Now it's working fine and missing list fields are replaced with empty lists
Issue: Add OpenSLR 83 - Crowdsourced high-quality UK and Ireland English Dialect speech
State: closed
Labels: name: dataset request

## Adding a Dataset
- **Name:** *openslr**
- **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.*
- **Paper:** *https://www.openslr.org/resources/83/about.html*
- **Data:** *Eleven separate data files can be found via https://www.openslr.org/resources/83/*
- **Motivation:** *Increase english ASR data with UK and Irish dialects*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

The *openslr* dataset already exists, this will add additional subset, *SLR83*.

Ugh. The index files for SLR83 are CSV, not TSV. I need to add logic to process these index files.
Issue: CI error at each release commit
State: closed
Labels: name: bug

After 1.12.0, there is a recurrent CI error at each release commit: https://app.circleci.com/pipelines/github/huggingface/datasets/8289/workflows/665d954d-e409-4602-8202-e678594d2946/jobs/51110

```
____________________ LoadTest.test_load_dataset_canonical _____________________
[gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe

self = <tests.test_load.LoadTest testMethod=test_load_dataset_canonical>

    def test_load_dataset_canonical(self):
        scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION)
        with self.assertRaises(FileNotFoundError) as context:
            datasets.load_dataset("_dummy")
        self.assertIn(
            f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py",
>           str(context.exception),
        )
E       AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/1.14.0/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at C:\\Users\\circleci\\datasets\\_dummy\\_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"

tests\test_load.py:358: AssertionError
```
Issue: Loading Data from HDF files
State: closed
Labels: name: enhancement

name: good second issue

**Is your feature request related to a problem? Please describe.**
More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset.

**Describe the solution you'd like**
I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc).

**Describe alternatives you've considered**
Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible.

**Additional context**
HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.


I'm currently working on bringing [Ecoset](https://www.pnas.org/doi/10.1073/pnas.2011417118) to huggingface datasets and I would second this request...

I would also like this support or something similar. Geospatial datasets come in netcdf which is derived from hdf5, or zarr. I've gotten zarr stores to work with datasets and streaming, but it takes awhile to convert the data to zarr if it's not stored in that natively. 

@mariosasko , I would like to contribute on this "good second issue" . Is there anything in the works for this Issue or can I go ahead ? 


Hi @VijayKalmath! As far as I know, nobody is working on it, so feel free to take over. Also, before you start, I suggest you comment `#self-assign` on this issue to assign it to yourself.

#self-assign

Hey @mariosasko can you assign this issue to me !!

So basically, we just need to load HDF5 files to Parquet?

e.g. Like this? https://stackoverflow.com/questions/46157709/converting-hdf5-to-parquet-without-loading-into-memory

#self-assign
Hi! I'd love to take a shot at implementing this feature to allow loading HDF5 datasets via Hugging Face Datasets. Will keep you updated here!

Hey @mariosasko,
I've opened a PR to address this: #7625
It adds a new h5folder loader to support .h5 files via load_dataset("h5folder", data_dir=...).
Feedback welcome!
Issue: concatenate_datasets removes ClassLabel typing.
State: closed
Labels: name: bug

## Describe the bug

When concatenating two datasets, we lose typing of ClassLabel columns.

I can work on this if this is a legitimate bug,

## Steps to reproduce the bug
```python
import datasets
from datasets import Dataset, ClassLabel, Value, concatenate_datasets

DS_LEN = 100
my_dataset = Dataset.from_dict(
    {
        "sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)],
        "label": [i % 2 for i in range(DS_LEN)]
    }
)
my_predictions = Dataset.from_dict(
    {
        "pred": [(i + 1) % 2 for i in range(DS_LEN)]
    }
)

my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])}))
print("Original")
print(my_dataset)
print(my_dataset.features)


concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1)
print("Concatenated")
print(concat_ds)
print(concat_ds.features)


```

## Expected results
The features of `concat_ds` should contain ClassLabel.

## Actual results

On master, I get: 
```
{'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)}
```

## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1



Something like this would fix it I think: https://github.com/huggingface/datasets/compare/master...Dref360:HF-3111/concatenate_types?expand=1
Issue: download_mode=`force_redownload` does not work on removed datasets
State: open
Labels: name: bug

name: dataset-viewer

## Describe the bug

If a cached dataset is removed from the library, I don't see how to delete it programmatically. I thought that using `force_redownload` would try to refresh the cache, then raise an exception, but it reuses the cache instead.

## Steps to reproduce the bug

_requires to already have `wit` in the cache_: see https://github.com/huggingface/datasets/pull/2981

```python
import datasets as ds
dataset = ds.load_dataset("wit", split="train", download_mode='force_redownload')
```

## Expected results

It should raise an exception, since the dataset does not exist anymore.

## Actual results

It uses the cached result

```
Using the latest cached version of the module from /home/slesage/.cache/huggingface/modules/datasets_modules/datasets/wit/107afbffd48e058b19101bddc47fbee25fa68eb6d50a733e262875f1285a5171 (last modified on Wed Sep 29 08:21:10 2021) since it couldn't be found locally at wit, or remotely on the Hugging Face Hub.
```

## Environment info

- `datasets` version: 1.13.4.dev0
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
Issue: Missing Zenodo 1.13.3 release
State: closed
Labels: name: bug

After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305

TODO:
- [x] Contact Zenodo support
- [x] Check it is fixed

Zenodo has fixed on their side the 1.13.3 release: https://zenodo.org/record/5589150
Issue: `ModuleNotFoundError: No module named 'fsspec.exceptions'`
State: closed
Labels: name: bug

## Describe the bug
I keep runnig into a fsspec ModuleNotFound error

## Steps to reproduce the bug
```python
>>> from datasets import get_dataset_infos
2021-10-15 15:25:37.863206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-10-15 15:25:37.863252: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/__init__.py", line 37, in <module>
    from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
  File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 56, in <module>
    from .utils.streaming_download_manager import StreamingDownloadManager
  File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 11, in <module>
    from fsspec.exceptions import FSTimeoutError
ModuleNotFoundError: No module named 'fsspec.exceptions'
```

Yet, I do have `fsspec`:
```bash
hf@victor-scale:~/dev/promptsource$ pip show fsspec
Name: fsspec
Version: 2021.5.0
Summary: File-system specification
Home-page: http://github.com/intake/filesystem_spec
Author: None
Author-email: None
License: BSD
Location: /home/hf/dev/promptsource/.venv/lib/python3.7/site-packages
Requires: 
Required-by: datasets
```

With the same version of fsspec and `datasets==1.9.0`, I don't see this problem....

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I can't even run `datasets-cli env` actually.., but here's my env:
- `datasets` version: 1.13.3
- Platform: Ubuntu 18.04
- Python version: 3.7.10
- PyArrow version: 3.0.0


Thanks for reporting, @VictorSanh.

I'm fixing it.
Issue: `cast_column` makes audio decoding fail
State: closed
Labels: name: bug

## Describe the bug

After changing the sampling rate automatic decoding fails.

## Steps to reproduce the bug
```python
from datasets import load_dataset
import datasets

ds = load_dataset("common_voice", "ab", split="train")

ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000))

print(ds[0]["audio"])  # <- this fails currently
```

yields:

```
TypeError: forward() takes 2 positional arguments but 4 were given
```

## Expected results
no failure

## Actual results
Specify the actual results or traceback.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Copy-and-paste the text below in your GitHub issue.

- `datasets` version: 1.13.2 (master)
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0


cc @anton-l @albertvillanova 

Thanks for reporting, @patrickvonplaten.

I think the issue is related to mp3 resampling, not to `cast_column`.

You can check that `cast_column` works OK with non-mp3 audio files:
```python
from datasets import load_dataset
import datasets
ds = load_dataset("arabic_speech_corpus", split="train")
ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000))
print(ds[0]["audio"])
```

I'm fixing it.
Issue: Support loading a dataset from SQLite files
State: closed
Labels: name: enhancement

name: good second issue

As requested by @julien-c, we could eventually support loading a dataset from SQLite files, like it is the case for JSON/CSV files.

for reference Kaggle has a good number of open source datasets stored in sqlite

Alternatively a tutorial or tool on how to convert from sqlite to parquet would be cool too

Hello, could we leverage [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) for this? 

This would be basically the same as [`CSVBuilder`](https://github.com/huggingface/datasets/blob/7380140accf522a4363bb56c0b77a4190f49bed6/src/datasets/packaged_modules/csv/csv.py#L127)
, but uses `pandas.read_sql(..., chunksize=1)` instead of `pandas.read_csv(..., iterator=True)`

I'm happy to work on this :) 

self-assign
Issue: VisibleDeprecationWarning when using `set_format("numpy")`
State: closed
Labels: name: bug

Code to reproduce:

```
from datasets import load_dataset
dataset = load_dataset("glue", "mnli")

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased')

def tokenize_function(dataset):
    return tokenizer(dataset['premise'])

tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset['train'].features)

tokenized_datasets.set_format("numpy")

tokenized_datasets['train'][5:8]
```
Outputs:

```
python3.9/site-packages/datasets/formatting/formatting.py:167: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  return np.array(array, copy=False, **self.np_array_kwargs)
```

I just opened a PR and I verified that the code you provided doesn't show any deprecation warning :)
Issue: Datasets with Audio feature raise error when loaded from cache due to _resampler parameter
State: closed
Labels: name: bug

## Describe the bug
As reported by @patrickvonplaten, when loaded from the cache, datasets containing the Audio feature raise TypeError.

## Steps to reproduce the bug
```python
from datasets import load_dataset

# load first time works
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") 

# load from cache breaks
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") 
```

## Actual results
```
TypeError: __init__() got an unexpected keyword argument '_resampler'
```
Issue: Error related to timeout keyword argument
State: closed
Labels: name: bug

## Describe the bug
As reported by @patrickvonplaten, a TypeError is raised when trying to load a dataset.

## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean") 
```

## Actual results
```
TypeError: dataset_info() got an unexpected keyword argument 'timeout'
```
Issue: Error when loading a metric
State: closed
Labels: name: bug

## Describe the bug
As reported by @sgugger, after last release, exception is thrown when loading a metric.

## Steps to reproduce the bug
```python
from datasets import load_metric

metric = load_metric("squad_v2")
```

## Actual results
```
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-1-e612a8cab787> in <module>
      1 from datasets import load_metric
----> 2 metric = load_metric("squad_v2")

d:\projects\huggingface\datasets\src\datasets\load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, script_version, **metric_init_kwargs)
   1336         )
   1337         revision = script_version
-> 1338     metric_module = metric_module_factory(
   1339         path, revision=revision, download_config=download_config, download_mode=download_mode
   1340     ).module_path

d:\projects\huggingface\datasets\src\datasets\load.py in metric_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, **download_kwargs)
   1237                 if not isinstance(e1, FileNotFoundError):
   1238                     raise e1 from None
-> 1239                 raise FileNotFoundError(
   1240                     f"Couldn't find a metric script at {relative_to_absolute_path(combined_path)}. "
   1241                     f"Metric '{path}' doesn't exist on the Hugging Face Hub either."

FileNotFoundError: Couldn't find a metric script at D:\projects\huggingface\datasets\squad_v2\squad_v2.py. Metric 'squad_v2' doesn't exist on the Hugging Face Hub either.
```
Issue: Custom plain text dataset, plain json dataset and plain csv dataset are remove from datasets template folder
State: closed
Labels: name: dataset request

## Adding a Dataset
- **Name:** text, json, csv
- **Description:** I am developing a customized dataset loading script. The problem is  mainly about my custom dataset is seperate into many files and I only find a dataset loading template in [https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py](https://github.com/huggingface/datasets/blob/1.2.1/datasets/json/json.py) that can handle my circumstance. I'm afraid these templates are too old to use. Could you re-add these three templates to current master branch?



Hi @zixiliuUSC, 

As explained in the documentation (https://huggingface.co/docs/datasets/loading.html#json), we support loading any dataset in JSON (as well as CSV, text, Parquet) format:
```python
ds = load_dataset('json', data_files='my_file.json')
```
Issue: Make `interleave_datasets` more robust
State: open
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**
Right now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration. 
It creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch.

**Describe the solution you'd like**
For `interleave_datasets` module, 
- [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`.
- [ ] Internal list variable `iter_cnt` that explains how many times (in steps/epochs) each dataset iterates at a given point.
- [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete  `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator`

Note: I'm new to `datasets` api. May be these features are already there in the datasets. 

Since multitask training is the latest trends, I believe this feature would make the `datasets` api more popular.

@lhoestq 

Hi @lhoestq  Any response on this issue?

Hi ! Sorry for the late response

I agree `interleave_datasets` would benefit a lot from having more flexibility. If I understand correctly it would be nice to be able to define stopping strategies like `stop="first_exhausted"` (default) or `stop="all_exhausted"`. If you'd like to contribute this feature I'd be happy to give you some pointers :)

Also one can already set the max number of iterations per dataset by doing `dataset.take(n)` on the dataset that should only have `n` samples.

Regarding the `iter_cnt` counter, I think this requires a bit more thoughts, since we might have to be able to backpropagate the the counter if `map` or other transforms have been applied after `interleave_datasets`. 

@sbmaruf I just notice that (1)`interleave_datasets` only samples indices once and reuse for all epochs, and (2) it's limited by the smallest dataset. Do you figure out an alternative way to achieve the same purpose?
Issue: Windows CI is unable to test streaming properly because of SSL issues
State: closed
Labels: name: streaming

In https://github.com/huggingface/datasets/pull/3041 the windows tests were skipped because of SSL issues with moon-staging.huggingface.co:443

The issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works.

to reproduce on windows:
```python
import fsspec

# use any URL to a file in a dataset repo
url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes"

fsspec.open(url).open()
```
raises
```python
FileNotFoundError: https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes
```
because of
```python
aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]
```

I think this problem is already fixed:
```python
In [4]: import fsspec
   ...:
   ...: url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes"
   ...:
   ...: fsspec.open(url).open()
Out[4]: <File-like object HTTPFileSystem, https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattribu
```

No I'm still having this issue on my windows, and so does the CI
Issue: Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?) 
State: open
Labels: name: enhancement

**A clear and concise description of what you want to happen.**

It would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.

**Describe alternatives you've considered**

By the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?

**Additional context**

Furthering tqdm integration #2374 and huggingface/transformers#11797 solutioned by huggingface/transformers#12226 provided with tqdm description as `desc=`

@sgugger @bhavitvyamalik

@lhoestq, @albertvillanova can we have `**tqdm_kwargs` in `map`? If there are any fields that are important to our tqdm (like iterable or unit), we can pop them before initialising the tqdm object so as to avoid duplicity.

Hi ! Sounds like a good idea :)

Also I think it would be better to have this as an actual parameters instead of kwargs to make it clearer
Issue: Error in per class precision computation 
State: closed
Labels: name: bug

## Describe the bug
When trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar`

## Steps to reproduce the bug
```python
from datasets import load_dataset, load_metric
precision_metric = load_metric("precision")
predictions = [0, 2, 1, 0, 0, 1]
references = [0, 1, 2, 0, 1, 2]
results = precision_metric.compute(predictions=predictions, references=references, average=None)
```

## Expected results
`  {'precision': array([0.66666667, 0.        , 0.        ])}`
as per https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py

## Actual results
```
   output = self._compute(predictions=predictions, references=references, **kwargs)
  File "~/.cache/huggingface/modules/datasets_modules/metrics/precision/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690/precision.py", line 110, in _compute
    sample_weight=sample_weight,
ValueError: can only convert an array of size 1 to a Python scalar
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.6.9
- PyArrow version: 5.0.0


Hi @tidhamecha2, thanks for reporting.

Indeed, we fixed this issue just one week ago: #3008

The fix will be included in our next version release.

In the meantime, you can incorporate the fix by installing `datasets` from the master branch:
```
pip install -U git+ssh://git@github.com/huggingface/datasets.git@master#egg=datasest
```
or
```
pip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets
```
Issue: load_dataset('the_pile_openwebtext2') produces ArrowInvalid, value too large to fit in C integer type
State: closed
Labels: name: bug

## Describe the bug
When loading `the_pile_openwebtext2`, we get the error `pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type`

## Steps to reproduce the bug
```python
import datasets
ds = datasets.load_dataset('the_pile_openwebtext2')
```

## Expected results
Should download the dataset, convert it to an arrow file, and return a working Dataset object.

## Actual results
The download works, but conversion to the arrow file fails as follows:

```
>>> ds = datasets.load_dataset('the_pile_openwebtext2')
Downloading and preparing dataset openwebtext2/plain_text (download: 27.33 GiB, generated: 63.86 GiB
, post-processed: Unknown size, total: 91.19 GiB) to /home/davidbau/.cache/huggingface/datasets/open
webtext2/plain_text/1.0.0/c48ec73ba3483bac673463f48f67e9a4fd8cb49a9d6ec4fb957f0b424b97cf25...
Traceback (most recent call last):
  File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/builder.py", line 1133,
 in _prepare_split
    writer.write(example, key)
  File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
366, in write
    self.write_examples_on_file()
  File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
311, in write_examples_on_file
    pa_array = pa.array(typed_sequence)
  File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
  File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
115, in __arrow_array__
    out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)
  File "pyarrow/array.pxi", line 305, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
```

- Platform: Ubuntu 20.04
- Python version: python 3.9
- PyArrow version: 3.0.0


I encountered the same bug using different datasets.
any suggestions?

+1, can reproduce here!

I get the same error
Platform: Windows 10
Python: python 3.8.8
PyArrow: 5.0

I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.

I worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes).

Addressed in https://huggingface.co/datasets/the_pile_openwebtext2/discussions/4
Issue: load_dataset cannot download the data and hangs on forever if cache dir specified
State: closed
Labels: name: bug

## Describe the bug
After updating datasets, a code that ran just fine for ages began to fail. Specifying _datasets.load_dataset_'s _cache_dir_ optional argument on Windows 10 machine results in data download to hang on forever. Same call without cache_dir works just fine. Surprisingly exact same code just runs perfectly fine on Linux docker instance running in cloud.

Unfortunately I updated Windows also at the same time and I can't remember which version of datasets was running in my conda environment prior to the update otherwise I would have tried both to check this out.  :(

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
cache_dir = 'c:/data/datasets'
dataset = load_dataset('wikipedia', '20200501.en', split='train',cache_dir=cache_dir) 
```
Note that exact same code without specifying _cache_dir_ argument works perfectly fine.
```
cache_dir = 'c:/data/datasets'
dataset = load_dataset('wikipedia', '20200501.en', split='train') 
```

## Expected results
Downloads the dataset and cache is handled in the _cache_dir_ directory
## Actual results
Data download keeps hanging on forever, **NO TRACEBACK**!

## Environment info
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.8.11
- PyArrow version: 3.0.0


Issue was environment inconsistency, updating packages did the trick

`conda install -c huggingface -c conda-forge datasets`

> Collecting package metadata (current_repodata.json): done
> Solving environment: |
> The environment is inconsistent, please check the package plan carefully
> The following packages are causing the inconsistency:
> 
>   - conda-forge/noarch::datasets==1.12.1=pyhd8ed1ab_1
>   - conda-forge/win-64::multiprocess==0.70.12.2=py38h294d835_0
> done
> 
> Package Plan
> 
>   environment location: C:\xxx\anaconda3\envs\UnBias-94-1
> 
>   added / updated specs:
>     - datasets
> 
> 
> The following NEW packages will be INSTALLED:
> 
>   dill               conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0
> 
> The following packages will be UPDATED:
> 
>   ca-certificates    pkgs/main::ca-certificates-2021.9.30-~ --> conda-forge::ca-certificates-2021.10.8-h5b45459_0
>   certifi            pkgs/main::certifi-2021.5.30-py38haa9~ --> conda-forge::certifi-2021.10.8-py38haa244fe_0
> 
> The following packages will be SUPERSEDED by a higher-priority channel:
>
Issue: Non-Matching Checksum Error with crd3 dataset
State: closed
Labels: name: bug

## Describe the bug
When I try loading the crd3 dataset (https://huggingface.co/datasets/crd3), an error is thrown.

## Steps to reproduce the bug
```python
dataset = load_dataset('crd3', split='train')
```

## Expected results
I expect no error to be thrown.


## Actual results
A non-matching checksum error is thrown.

```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/RevanthRameshkumar/CRD3/archive/master.zip']
```

## Environment info

- `datasets` version: 1.12.1
- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial
- Python version: 3.6.10
- PyArrow version: 5.0.0



I got the same error for another dataset (`multi_woz_v22`):

```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']
```

I'm seeing the same issue as @RylanSchaeffer:
Python 3.7.11, macOs 11.4
datasets==1.14.0

fails on:
```python
dataset = datasets.load_dataset("multi_woz_v22")
```
Issue: Identify which shard data belongs to
State: open
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**

I'm training on a large dataset made of multiple sub-datasets.
During training I can observe some jumps in loss which may correspond to different shards.

![image](https://user-images.githubusercontent.com/715491/136668758-521263aa-a9b2-4ad2-8d22-060b6bf86a1c.png)

My suspicion is that either:
* some of the sub-datasets are harder for the model than others
* some of the sub-datasets are not formatted properly

I'd like to identify which shards correspond to those jumps.

**Describe the solution you'd like**

It would be nice to have a key associated to each data sample or data batch containing details on where the data comes from (shard idx + item idx within the shard).
This should be supported both in local and streaming mode.

**Describe alternatives you've considered**
A fix would be for me to add myself details (shard id, sample id) as part of each data sample.
The inconvenient is that it requires users to process/reupload every dataset when they need this feature.

Independently of this I think it raises the need to allow multiprocessing during streaming so that we get samples from multiple shards in one batch.
Issue: Add PASS dataset
State: closed
Labels: name: dataset request

name: vision

## Adding a Dataset
- **Name:** PASS
- **Description:** An ImageNet replacement for self-supervised pretraining without humans
- **Data:** https://www.robots.ox.ac.uk/~vgg/research/pass/ https://github.com/yukimasano/PASS

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Issue: [save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset
State: closed
Labels: name: bug

## Describe the bug

When only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.

## Steps to reproduce the bug

E.g. run the following:

```python
from datasets import load_dataset, save_to_disk

nlp = load_dataset("glue", "mnli", split="train")
nlp.save_to_disk("full")

nlp = nlp.select(range(100))
nlp.save_to_disk("dummy")
```

Now one can see that both `"dummy"` and `"full"` have the same size. This shouldn't be the case IMO.

## Expected results

IMO `"dummy"` should be much smaller so that one can easily play around with the dataset on the hub.

## Actual results
Specify the actual results or traceback.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 5.0.0



Hi,

the `save_to_disk` docstring explains that `flatten_indices` has to be called on a dataset before saving it to save only the shard/slice of the dataset.

That works! Thansk!

Might be worth doing that automatically actually in case the `save_to_disk` is called on a dataset that has an indices mapping :-)

I agree with @patrickvonplaten: this issue is reported recurrently, so better if we implement the `.flatten_indices()` automatically?

That would be great indeed - I don't really see a use case where one would not like to call `.flatten_indices()` before calling `save_to_disk`

+1 on this !