josteece/github-issues huggingface dataset:
bookcorpusopen no longer works
Opened by lucadiliello (closed)

## Describe the bug

When using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...).

This did not happen with 1.4.1.
I tried also `rm -rf ~/.cache/huggingface` but did not help.
Changing python version between 3.7, 3.8 and 3.9 did not help too.

## Steps to reproduce the bug
```python
import datasets
d = datasets.load_dataset('bookcorpusopen')
```

## Expected results
A clear and concise description of the expected results.

## 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: 1.14.0
- Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1


Comments:
Hi ! Thanks for reporting :) I think #3280 should fix this

I tried with the latest changes from #3280 on google colab and it worked fine :)
We'll do a new release soon, in the meantime you can use the updated version with:
```python
load_dataset("bookcorpusopen", revision="master")
```

Fixed by #3280.
`datasets-cli test` should work with datasets without scripts
Opened by sashavor (open)

It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).

I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!


Comments:
> It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
> 
> I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!

why don't you try to share that info with people, so you can also save some days.

Hi ! You can run the command if you download the repository
```
git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest
```
and run the command
```
datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py
```

(though on my side it doesn't manage to download the data since the dataset is private ^^)

> Hi ! You can run the command if you download the repository
> 
> ```
> git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest
> ```
> 
> and run the command
> 
> ```
> datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py
> ```
> 
> (though on my side it doesn't manage to download the data since the dataset is private ^^)

Hi! Thanks for the info. 
git cannot find the repository. Do you know if they have depreciated these tests and created a new one?

I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`

> I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`

your example repo and this page `https://huggingface.co/docs/datasets/add_dataset.html` helped me to solve.. thanks a lot
Add riddle_sense dataset
Opened by ziyiwu9494 (closed)

Adding a new dataset for QA with riddles. I'm confused about the tagging process because it looks like the streamlit app loads data from the current repo, so is it something that should be done after merging or off my fork?

Comments:
@lhoestq 
I address all the comments, I think. Thanks! 


The five test fails are unrelated to this PR and fixed on master so we can ignore them
Better error msg if `len(predictions)` doesn't match `len(references)` in metrics
Opened by mariosasko (closed)

Improve the error message in `Metric.add_batch` if `len(predictions)` doesn't match `len(references)`.

cc: @BramVanroy (feel free to test this code on your examples and review this PR)

Comments:
Can't test this now but it may be a good improvement indeed.

I added a function, but it only works with the `list` type. For arrays/tensors, we delegate formatting to the frameworks.
Make inspect.get_dataset_config_names always return a non-empty list
Opened by albertvillanova (closed)

Make all named configs cases, so that no special unnamed config case needs to be handled differently.

Fix #3135.

Comments:
This PR is already working (although not very beautiful; see below): the idea was to have the `DatasetModule.builder_kwargs` accessible from the `builder_cls`, so that this can generate the default builder config (at the class level, without requiring the builder to be instantiated).

I have a plan for a follow-up refactoring (same functionality, better implementation, much nicer), but I think we could already merge this, so that @severo can test it in the datasets previewer and report any potential issues.

Yes @lhoestq you are completely right. Indeed I was exclusively using `builder_cls.kwargs` to get the community dataset `name` (nothing else): "lhoestq___demo1"

See et: https://github.com/huggingface/datasets/pull/3159/files#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R413-R415

In your example, the `name` I was getting from `builder_cls.kwargs` was:
```python
{"name": "lhoestq___demo1",...}
```

I'm going to refactor all the approach... as I only need the name for this specific case ;)

I think this makes more sense now, @lhoestq @severo 😅 

It works well, thanks!
Fix string encoding for Value type
Opened by lhoestq (closed)

Some metrics have `string` features but currently it fails if users pass integers instead. Indeed feature encoding that handles the conversion of the user's objects to the right python type is missing a case for `string`, while it already works as expected for integers, floats and booleans

Here is an example code that didn't work previously, but that works with this fix:
```python
import datasets

# Note that 'id' is an integer while the SQuAD metric uses strings
predictions = [{'prediction_text': '1976', 'id': 5}]
references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': 5}] 

squad_metric = datasets.load_metric("squad") 
squad_metric.add_batch(predictions=predictions, references=references) 
results = squad_metric.compute()
# {'exact_match': 100.0, 'f1': 100.0}
```

cc @sgugger @philschmid 

Comments:
That was fast!
Illegal instruction (core dumped) at datasets import
Opened by hacobe (closed)

## Describe the bug
I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)"

## Steps to reproduce the bug

```
conda create --prefix path/to/env
conda activate path/to/env
conda install -c huggingface -c conda-forge datasets
# exits with output "Illegal instruction (core dumped)"
python -m datasets
```

## Environment info

When I run "datasets-cli env", I also get "Illegal instruction (core dumped)"

If I run the following commands:

```
conda create --prefix path/to/another/new/env
conda activate path/to/another/new/env
conda install -c huggingface transformers
transformers-cli env
```

Then I get:

- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

Let me know what additional information you need in order to debug this issue. Thanks in advance!

Comments:
It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 processors.
Re-add faiss to windows testing suite
Opened by BramVanroy (closed)

In recent versions, `faiss-cpu` seems to be available for Windows as well. See the [PyPi page](https://pypi.org/project/faiss-cpu/#files) to confirm. We can therefore included it for Windows in the setup file.

At first tests didn't pass due to problems with permissions as caused by `NamedTemporaryFile` on Windows. This built-in library is notoriously poor in playing nice on Windows. The required change isn't pretty, but it works. First set `delete=False` to not automatically try to delete the file on `exit`. Then, manually delete the file with `unlink`. It's weird, I know, but it works.

```python
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
    # do stuff
os.unlink(tmp_file.name)
```

closes #3150 

Comments:
Faiss _is_ available on Windows
Opened by BramVanroy (closed)

In the setup file, I find the following:

https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/setup.py#L171

However, FAISS does install perfectly fine on Windows on my system. You can also confirm this on the [PyPi page](https://pypi.org/project/faiss-cpu/#files), where Windows wheels are available. Maybe this was true for older versions? For current versions, this can be removed I think.

(This isn't really a bug but didn't know how else to tag.)

If you agree I can do a quick PR and remove that line.

Comments:
Sure, feel free to open a PR.
Add CMU Hinglish DoG Dataset for MT
Opened by Ishan-Kumar2 (closed)

Address part of #2841 

Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model. 
Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two. 

The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py)
The dummy data part is not working properly, it shows 
``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ``` 
when I run without ``--auto_generate``.

Please let me know how I can fix that.
Thanks

Comments:
Hi @lhoestq, thanks a lot for the help. I have moved the part as suggested. 
Although still while running the dummy data script, I face this issue

```
Traceback (most recent call last):
  File "/home/ishan/anaconda3/bin/datasets-cli", line 8, in <module>
    sys.exit(main())
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/datasets_cli.py", line 33, in main
    service.run()
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py", line 318, in run
    self._autogenerate_dummy_data(
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py", line 363, in _autogenerate_dummy_data
    dataset_builder._prepare_split(split_generator)
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 1103, in _prepare_split
    example = self.info.features.encode_example(record)
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py", line 981, in encode_example
    return encode_nested_example(self, example)
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py", line 775, in encode_nested_example
    return {
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py", line 775, in <dictcomp>
    return {
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 99, in zip_dict
    yield key, tuple(d[key] for d in dicts)
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 99, in <genexpr>
    yield key, tuple(d[key] for d in dicts)
KeyError: 'status'
```
This KeyError is at times different from 'status' also.
when I run 
```
datasets-cli dummy_data datasets/cmu_hinglish_dog --auto_generate --json_field='history'
```
I have tried removing unnecessary feature type definition, but that didn't help. Please let me know if I am missing something, thanks!

The CI fail is unrelated to this PR and fixed on master. Merging !
Streaming with num_workers != 0
Opened by justheuristic (closed)

## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.

The code owner is likely @lhoestq 

## Steps to reproduce the bug

For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0

should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally

import torch
from datasets import load_dataset

data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)

data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)

# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
  print(row)
  if i > 10: break
print("DONE!")
```

## Expected results
The code should not freeze forever with num_workers=2

## Actual results
The code freezes forever with num_workers=2

## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0



Comments:
I can confirm that I was able to reproduce the bug. This seems odd given that #3423 reports duplicate data retrieval when `num_workers` and `streaming` are used together, which is obviously different from what is reported here. 

Any update? A possible solution is to have multiple arrow files as shards, and handle them like what webdatasets does.
![image](https://user-images.githubusercontent.com/11533479/148176637-72746b2c-c122-47aa-bbfe-224b13ee9a71.png)

Pytorch's new dataset RFC is supporting sharding now, which may helps avoid duplicate data under streaming mode. (https://github.com/pytorch/pytorch/blob/master/torch/utils/data/datapipes/iter/grouping.py#L13)


Hi ! Thanks for the insights :) Note that in streaming mode there're usually no arrow files. The data are streamed from TAR, ZIP, text, etc. files directly from the web. Though for sharded datasets we can definitely adopt a similar strategy !

fixed by #4375
CLI test command throws NonMatchingSplitsSizesError when saving infos
Opened by albertvillanova (closed)

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`.

Comments:
[when Image type will exist] provide a way to get the data as binary + filename
Opened by severo (closed)

**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.


Comments:
@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
Infer the features if missing
Opened by severo (closed)

**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.

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

**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.


Comments:
Related: #3144
Provide a way to write a streamed dataset to the disk
Opened by severo (open)

**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. 


Comments:
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
Fix file/directory deletion on Windows  
Opened by mariosasko (open)

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.


Comments:
More fine-grained taxonomy of error types
Opened by severo (open)

**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


Comments:
related: #4995
Fix numpy deprecation warning for ragged tensors
Opened by lhoestq (closed)

Numpy shows a deprecation warning when we call `np.array` on a list of ragged tensors without specifying the `dtype`. If their shapes match, the tensors can be collated together, otherwise the resulting array should have `dtype=np.object`.

Fix #3084 
cc @Rocketknight1 

Comments:
This'll be a really helpful fix, thank you!
Fix script of Arabic Billion Words dataset to return all data
Opened by albertvillanova (closed)

The script has a bug and only parses and generates a portion of the entire dataset.

This PR fixes the loading script so that is properly parses the entire dataset. 

Current implementation generates the same number of examples as reported in the [original paper](https://arxiv.org/abs/1611.04033) for all configurations except for one:
- For "Youm7" we generate more examples (1172136) than the ones reported by the paper (1025027)

|                | Number of examples | Number of examples according to the source |
|:---------------|-------------------:|-----:|
| Alittihad      |             349342 |349342 |
| Almasryalyoum  |             291723 |291723 |
| Almustaqbal    |             446873 |446873 |
| Alqabas        |             817274 |817274 |
| Echoroukonline |             139732 |139732 |
| Ryiadh         |             858188 | 858188 |
| Sabanews       |              92149 |92149 |
| SaudiYoum      |             888068 |888068 |
| Techreen       |             314597 |314597 |
| Youm7          |            1172136 |1025027 |

Fix #3126.

Comments: