konrud/llm_course_chapter5_own_dataset_github_issues huggingface dataset:
Dataset Viewer issue for vadis/sv-ident

### Link

https://huggingface.co/datasets/vadis/sv-ident

### Description

The dataset preview does not work:
```
Server Error

Status code:   400
Exception:     Status400Error
Message:       The dataset does not exist.
```

However, the dataset is streamable and works locally:
```python
In [1]: from datasets import load_dataset; ds = load_dataset("sv-ident.py", split="train", streaming=True); item = next(iter(ds)); item
Using custom data configuration default
Out[1]: 
{'sentence': 'Our point, however, is that so long as downward (favorable) comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.',
 'is_variable': 1,
 'variable': ['exploredata-ZA5400_VarV66', 'exploredata-ZA5400_VarV53'],
 'research_data': ['ZA5400'],
 'doc_id': '73106',
 'uuid': 'b9fbb80f-3492-4b42-b9d5-0254cc33ac10',
 'lang': 'en'}
```

CC: @e-tornike

### Owner

No

Fixed, thanks!
![Uploading Capture d’écran 2022-06-21 à 18.42.40.png…]()
split cache used when processing different split

## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through

```
class myDataModule:

    def train_dataloader(self):
         ds = load_dataset('squad', split='train')
         ds = ds.map(some_function)
         return [ds]

    def val_dataloader(self):
         ds = load_dataset('squad', split="validation")
         ds = ds.map(some_function)
         return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.

If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first!

I was not able to reproduce this behavior (I tried without using pytorch lightning though, since I don't know what code you ran in pytorch lightning to get this).

If you can provide a MWE that would be perfect ! :)

Hi, I think the issue happened because I was loading datasets under an `if` ... `else` statement and the condition would change the dataset I would need to load but instead the cached one was always returned. However, I believe that is expected behaviour, if so I'll close the issue.

Otherwise I will try to provide a MWE
Datasets method `.map` not hashing

## Describe the bug
Datasets method `.map` not hashing, even with an empty no-op function

## Steps to reproduce the bug
```python
from datasets import load_dataset

# download 9MB dummy dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")

def prepare_dataset(batch):
     return(batch)

ds = ds.map(
    prepare_dataset,
    num_proc=1,
    desc="preprocess train dataset",
)
```

## Expected results
Hashed and cached dataset preprocessing

## Actual results
Does not hash properly:
```
Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2

cc @lhoestq 


Fix posted: https://github.com/huggingface/datasets/issues/4506#issuecomment-1157417219

Didn't realize it's a bug when I asked the question yesterday! Free free to post an answer if you are sure the cause has been addressed.

https://stackoverflow.com/questions/72664827/can-pickle-dill-foo-but-not-lambda-x-foox

Thank @nalzok . That works for me:

`pip install "dill<0.3.5"`
Create new sections for audio and vision in guides

This PR creates separate sections in the guides for audio, vision, text, and general usage so it is easier for users to find loading, processing, or sharing guides specific to the dataset type they're working with. It'll also allow us to scale the docs to additional dataset types - like time series, tabular, etc. - while keeping our docs information architecture. 

Some other changes include:

- ~Experimented with decorating text with some CSS to highlight guides specific to each modality. Hopefully, it'll be easier for users to find and realize that these different docs exist!~ Will experiment with this in a different PR.
- Added deprecation warning for Metrics and redirect to Evaluate.
- Updated `set_format` section to recommend using the new `to_tf_dataset` function if you need to convert to a TensorFlow dataset.
- Reorganized `toctree` to nest general usage, audio, vision, and text sections under the how-to guides.
- A quick review and edit to the Load and Process docs for clarity.

_The documentation is not available anymore as the PR was closed or merged._

Ready for review!

The `toctree` is a bit longer now with the sections. I think if we keep the audio/vision/text/dataset repository sections collapsed by default, and keep the general usage expanded, it may look a little cleaner and not as overwhelming. Let me know what you think! 😄
Add tags for task_ids:summarization-* and task_categories:summarization*

yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json
separate Pull Request will modify dataset_infos.json to add these tags

The Enron dataset (dataset id aeslc) is only tagged with:

    arxiv:1906.03497'
    languages:en
    pretty_name:AESLC

Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance):

    'task_ids:summarization'
    'task_ids:summarization-other-conversations-summarization'
    "task_ids:other-other-query-based-multi-document-summarization"
    'task_ids:summarization-other-aspect-based-summarization'
    'task_ids:summarization--other-headline-generation'

The subject might also be used for the task_category "task_categories:summarization"

E-mail chains might be used for the task category "task_categories:dialogue-system"

Associated community discussion is [here](https://huggingface.co/datasets/aeslc/discussions/1).
Paper referenced in the `dataset_infos.json` is [here](https://arxiv.org/pdf/1906.03497.pdf). It mentions the  _email-subject-generation_ task, which is not a tag mentioned in any other dataset so it was not added in this pull request. The _summarization_ task is mentioned as a related task.

_The documentation is not available anymore as the PR was closed or merged._
Fix hashing for python 3.9

In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function.

Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9

To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic.

Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now.

Fix https://github.com/huggingface/datasets/issues/4506

_The documentation is not available anymore as the PR was closed or merged._

What do you think @albertvillanova ?
Add uppercased versions of image file extensions for automatic module inference

Adds the uppercased versions of the image file extensions to the supported extensions. 

Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain)

Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too.

Fix #4514.  

_The documentation is not available anymore as the PR was closed or merged._
Allow .JPEG as a file extension

## Describe the bug
When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed.

## Steps to reproduce the bug
```python
# use bash to create 2 sham datasets with jpeg and JPEG ext
!mkdir dataset_a
!mkdir dataset_b
!wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg
!cp example_img.jpeg ./dataset_a/
!mv example_img.jpeg ./dataset_b/example_img.JPEG

from datasets import load_dataset

# working
df1 = load_dataset("./dataset_a", ignore_verifications=True)

#not working
df2 = load_dataset("./dataset_b", ignore_verifications=True)

# show
print(df1, df2)
```

## Expected results
```
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 1
    })
}) DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 1
    })
})
```

## Actual results
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```

I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.

Hi, thanks for reporting!  I've opened a PR with the fix.

Wow, that was quick! Thank you very much 🙏
Update Google Cloud Storage documentation and add Azure Blob Storage example

While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included.

Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage".

Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems.

And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers.

Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs:

_The documentation is not available anymore as the PR was closed or merged._

Hi @stevhliu, I've kept the `>>>` before all the in-line code comments as it was done like that in the default S3 example that was already there, I assume that it's done like that just for readiness, let me know whether we should remove the `>>>` in the Python blocks before the in-line code comments or keep them.

![image](https://user-images.githubusercontent.com/36760800/174254663-b68d28d2-eae1-40f3-8695-dc4b0c3b479a.png)


Comments are ignored by doctest, so I think we can remove the `>>>` :)

Cool I'll remove those now 👍🏻

Sure @lhoestq, I just kept that structure as that was the more similar one to the one that was already there, but we can go with that approach, just let me know whether I should change the headers so as to leave all those providers in the same level (`h2`). Thanks!
Support all negative values in ClassLabel

We usually use -1 to represent a missing label, but we should also support any negative values (some users use -100 for example). This is a regression from `datasets` 2.3

Fix https://github.com/huggingface/datasets/issues/4508

_The documentation is not available anymore as the PR was closed or merged._

Thanks for this fix! I'm not sure what the release timeline is, but FYI #4508 is a breaking issue for transformer token classification using Trainer and PyTorch. PyTorch defaults to -100 as the ignored label for [negative log loss](https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html?highlight=nllloss#torch.nn.NLLLoss), so switching labels to -1 leads to index errors using Trainer defaults.

As a workaround, I'm using master branch directly (`pip install git+https://github.com/huggingface/datasets.git@master` for anyone who needs to do the same) until this gets released.

The new release `2.4` fixes the issue, feel free to update `datasets` :) 
```
pip install -U datasets
```
Add regression test for `ArrowWriter.write_batch` when batch is empty

As spotted by @cccntu in #4502, there's a logic bug in `ArrowWriter.write_batch` as the if-statement to handle the empty batches as detailed in the docstrings of the function ("Ignores the batch if it appears to be empty, preventing a potential schema update of unknown types."), the current if-statement is not handling properly `writer.write_batch({})` as an error is triggered.

Also, if we add a regression test in `test_arrow_writer.py::test_write_batch` before applying the fix, the test will fail as when trying to write an empty batch as follows:

```
=================================================================================== short test summary info ===================================================================================
FAILED tests/test_arrow_writer.py::test_write_batch[None-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[None-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[None-10] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-10] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-10] - ValueError: Schema and number of arrays unequal
======================================================================== 9 failed, 73 deselected, 7 warnings in 0.81s =========================================================================
```

So the batch is not ignored when empty, as `batch_examples={}` won't match the condition `if batch_examples: ...`.

_The documentation is not available anymore as the PR was closed or merged._

As mentioned by @lhoestq, the current behavior is correct and we should not expect batches with different columns, in that case, the if should fail, as the values of the batch can be empty, but not the actual `batch_examples` value.
Support skipping Parquet to Arrow conversion when using Beam


The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4509). All of your documentation changes will be reflected on that endpoint.

When #4724 is merged, we can just pass `file_format="parquet"` to `download_and_prepare` and it will output parquet fiels without converting to arrow

I think we can close this one
cast_storage method from datasets.features

## Describe the bug
A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets.

## Steps to reproduce the bug
Steps are:
- load whatever datset
- write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification
- map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features

# Sample code to reproduce the bug
def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length")

    labels = []
    for i, label in enumerate(examples[f"labels"]):
        word_ids = tokenized_inputs.word_ids(batch_index=i)  # Map tokens to their respective word.
        previous_word_idx = None
        label_ids = []
        for word_idx in word_ids:  # Set the special tokens to -100.
            if word_idx is None:
                label_ids.append(-100)
            elif word_idx != previous_word_idx:  # Only label the first token of a given word.
                label_ids.append(label[word_idx])
            else:
                label_ids.append(-100)
            previous_word_idx = word_idx
        labels.append(label_ids)

    tokenized_inputs["labels"] = labels
    return tokenized_inputs

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
dt = dataset.map(tokenize_and_align_labels, batched=True)

## Expected results
New dataset objects should load and do on older versions.

## Actual results
"ValueError: Class label -100 less than -1" from cast_storage method from datasets.features

## Environment info
everything works fine on older installations of datasets/transformers

Issue arises when installing datasets on google collab under python3.7
I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2


Hi! We've recently added a check to the `ClassLabel` type to ensure the values are in the valid label range `-1, 0, ..., num_classes-1` (-1 is used for missing values). The error in your case happens only if the `labels` column is of type `Sequence(ClassLabel(...))` before the `map` call and can be avoided by calling `dataset = dataset.cast_column("labels", Sequence(Value("int")))` beforehand. The token-classification examples in Transformers introduce a new `labels` column, so their type is also `Sequence(Value("int"))`, which doesn't lead to an error as this type unbounded. 

I'm fine with re-adding support for all negative values for unknown/missing labels @mariosasko, wdyt ?
How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script

If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair.

Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`?

Many thanks for any help.

Hi @liyucheng09.

Users can pass the `split` parameter to `load_dataset`. For example, if your split name is "train",
```python
ds = load_dataset("dataset_name", split="train")
```
will return a `Dataset` instance.

@albertvillanova Thanks! I can't believe I didn't know this feature till now.
Fix double dots in data files

As mentioned in https://github.com/huggingface/transformers/pull/17715 `data_files` can't find a file if the path contains double dots `/../`. This has been introduced in https://github.com/huggingface/datasets/pull/4412, by trying to ignore hidden files and directories (i.e. if they start with a dot)

I fixed this and added a test

cc @sgugger @ydshieh 

_The documentation is not available anymore as the PR was closed or merged._

The CI fails are unrelated to this PR (apparently something related to `seqeval` on windows) - merging :)
Refactor and add metadata to fever dataset

Related to: #4452 and #3792.

_The documentation is not available anymore as the PR was closed or merged._

But this is somehow fever v3 dataset (see this link https://fever.ai/ under the dropdown menu called Datasets). Our fever dataset already contains v1 and v2 configs. Then, I added this as if v3 config (but named feverous instead of v3 to align with the original naming by data owners).

In any case, if you really think this should be a new dataset, then I would propose to create it on the Hub instead, as "fever/feverous".

> In any case, if you really think this should be a new dataset, then I would propose to create it on the Hub instead, as "fever/feverous".

Yea makes sense ! thanks :) let's push more datasets on the hub rather than on github from now on

I have added "feverous" dataset to the Hub: https://huggingface.co/datasets/fever/feverous

I change the name of this PR accordingly, as now it only:
- Refactors code and include for both Fever v1.0 and v2.0 specific:
  - Descriptions
  - Citations
  - Homepages
- Updates documentation card aligned with above:
  - It was missing v2.0 description and citation.
- Update metadata JSON
Add `concatenate_datasets` for iterable datasets

`concatenate_datasets` currently only supports lists of `datasets.Dataset`, not lists of `datasets.IterableDataset` like `interleave_datasets`

Fix https://github.com/huggingface/datasets/issues/2564

I also moved `_interleave_map_style_datasets` from combine.py to arrow_dataset.py, since the logic depends a lot on the `Dataset` object internals

And I moved `concatenate_datasets` from arrow_dataset.py to combine.py to have it with `interleave_datasets` (though it's also copied in arrow_dataset module for backward compatibility for now)

_The documentation is not available anymore as the PR was closed or merged._

Thanks ! I addressed your comments :)

> There is a slight difference in concatenate_datasets between the version for map-style datasets and the one for iterable datasets

Indeed, here is what I did to fix this:

- axis 0: fill missing columns with None.
(I first iterate over the input datasets to infer their columns from the first examples, then I set the features of the resulting dataset to be the merged features)
This is consistent with non-streaming concatenation

- axis 1: **fill the missing rows with None**, for consistency with axis 0
(but let me know what you think, I can still revert this behavior and raise an error when one of the dataset runs out of examples)
We might have to align the non-streaming concatenation with this behavior though, for consistency. What do you think ?

Added more comments as suggested, and some typing

While factorizing _apply_features_types for both IterableDataset and TypedExamplesIterable, I  fixed a missing `token_per_repo_id` that was not passed to TypedExamplesIteable

Let me know what you think now @mariosasko
WER and CER > 1

## Describe the bug
It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd.

If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py#L105) line to
```python
return min(incorrect / total, 1.0)
```

## Steps to reproduce the bug
```python
from datasets import load_metric
wer = load_metric("wer")
wer_value = wer.compute(predictions=["Hi World vka"], references=["Hello"])
print(wer_value)
```

## Expected results
```
1.0
```

## Actual results
```
3.0
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5

WER can have values bigger than 1.0, this is expected when there are too many insertions

From [wikipedia](https://en.wikipedia.org/wiki/Word_error_rate):
> Note that since N is the number of words in the reference, the word error rate can be larger than 1.0
Re-add download_manager module in utils

https://github.com/huggingface/datasets/pull/4384 moved `datasets.utils.download_manager` to `datasets.download.download_manager`

This breaks `evaluate` which imports `DownloadMode` from `datasets.utils.download_manager` 

This PR re-adds `datasets.utils.download_manager` without circular imports.

We could also show a message that says that accessing it is deprecated, but I think we can do this in a subsequent PR, and just focus on doing a patch release for now

_The documentation is not available anymore as the PR was closed or merged._

Thanks for the fix.

I'm wondering how this fixes backward compatibility...

Executing this code:
```python
from datasets.utils.download_manager import DownloadMode
```
we will have
```python
DownloadMode = None
```

If afterwards we use something like:
```python
if download_mode == DownloadMode.FORCE_REDOWNLOAD
```
that will raise an exception.

It works fine on my side:
```python
>>> from datasets.utils.download_manager import DownloadMode
>>> DownloadMode is not None
True
```

As reported in https://github.com/huggingface/evaluate/pull/143
```python
from datasets.utils import DownloadConfig
```
is also missing, I'm re-adding it

Took the liberty of merging this one, to do a patch release soon. If we think of a better approach we can improve it later
Replace `assertEqual` with `assertTupleEqual` in unit tests for verbosity

As detailed in #4419 and as suggested by @mariosasko, we could replace the `assertEqual` assertions with `assertTupleEqual` when the assertion is between Tuples, in order to make the tests more verbose.

_The documentation is not available anymore as the PR was closed or merged._

FYI I used the following regex to look for the `assertEqual` statements where the assertion was being done over a Tuple: `self.assertEqual(.*, \(.*,)(\)\))$`, hope this is useful!
Fix patching module that doesn't exist

Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true

When trying to patch `scipy.io.loadmat`:

```python
ModuleNotFoundError: No module named 'scipy'
```

Instead it shouldn't raise an error and do nothing

Bug introduced by #4375

Fix https://github.com/huggingface/datasets/issues/4494

_The documentation is not available anymore as the PR was closed or merged._
Add `@transmit_format` in `flatten`

As suggested by @mariosasko in https://github.com/huggingface/datasets/pull/4411, we should include the `@transmit_format` decorator to `flatten`, `rename_column`, and `rename_columns` so as to ensure that the value of `_format_columns` in an `ArrowDataset` is properly updated.

**Edit**: according to @mariosasko comment below, the decorator `@transmit_format` doesn't handle column renaming, so it's done manually for those instead.

@mariosasko please let me know whether we need to include some sort of tests to make sure that the decorator is working as expected. Thanks! 🤗 

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4493). All of your documentation changes will be reflected on that endpoint.

Hi, thanks for working on this! Yes, please add (simple) tests so we can avoid any unexpected behavior in the future.

`@transmit_format` doesn't handle column renaming, so I removed it from `rename_column` and `rename_columns` and added a comment to explain this.

Oops, I thought this PR was already merged and deleted from the source repository, I'll be creating a new branch out of `main` so as to re-create this PR... My bad :weary:
Dataset Viewer issue for Pavithree/test

### Link

https://huggingface.co/datasets/Pavithree/test

### Description

I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using function cast_null error. Is there anything missing from my end? Kindly help.

### Owner

_No response_

This issue can be resolved according to this post https://stackoverflow.com/questions/70566660/parquet-with-null-columns-on-pyarrow. It looks like first data entry in the json file must not have any null values as pyarrow uses this first file to infer schema for entire dataset.
Use `torch.nested_tensor` for arrays of varying length in torch formatter

Use `torch.nested_tensor` for arrays of varying length in `TorchFormatter`.

The PyTorch API of nested tensors is in the prototype stage, so wait for it to become more mature.

What's the current behavior?

Currently, we return a list of Torch tensors if their shapes don't match. If they do, we consolidate them into a single Torch tensor.
Add SV-Ident dataset


Hi @e-tornike, thanks a lot for adding this interesting dataset.

Recently at Hugging Face, we have decided to give priority to adding datasets directly on the Hub. Would you mind to transfer your loading script to the Hub? You could create a dedicated org namespace, so that your dataset would be accessible using `vadis/sv_ident` or `sdproc/sv_ident` or `coling/sv_ident` (as you prefer).

You have an example here: https://huggingface.co/datasets/projecte-aina/catalan_textual_corpus

Additionally, please feel free to ping us if you need assistance/help in creating this dataset.

You could put the link to your Hub dataset here in this Issue discussion page, so that we can follow the progress. :)

Hi @albertvillanova, thanks for the feedback! Uploading via the Hub is a lot easier! 

I've uploaded the dataset here: https://huggingface.co/datasets/vadis/sv-ident, but it's reporting a "Status400Error". Is there any way to see the logs of the dataset script and what is causing the error?

Hi @e-tornike, good job at https://huggingface.co/datasets/vadis/sv-ident.

Normally, you can run locally the loading of the dataset by passing `streaming=True` (as the previewer does):
```python
ds = load_dataset("path/to/sv_ident.py, split="train", streaming=True)
item = next(iter(ds))
item
```

Let me have a look and open a discussion on your Hub repo! ;)

I've opened an Issue: 
- #4527
Update PASS dataset version

Update the PASS dataset to version v3 (the newest one) from the [version history](https://github.com/yukimasano/PASS/blob/main/version_history.txt).

PS: The older versions are not exposed as configs in the script because v1 was removed from Zenodo, and the same thing will probably happen to v2.

_The documentation is not available anymore as the PR was closed or merged._
Add CCAgT dataset

As described in #4075 

I could not generate the dummy data. Also, on the data repository isn't provided the split IDs, but I copy the functions that provide the correct data split. In summary, to have a better distribution, the data in this dataset should be separated based on the amount of NORs in each image.

_The documentation is not available anymore as the PR was closed or merged._

Hi! Excellent job @johnnv1! There were typos/missing words in the card, so I took the liberty to rewrite some parts to make them easier to understand. Let me know if you are ok with the changes. Also, feel free to add some info under the `Who are the annotators?` section.

Additionally, I fixed the issue with streaming and renamed the `digits` feature to `objects`.

@lhoestq Are you ok with skipping the dummy data test here as it's tricky to generate it due to the splits separation logic?

I think I can also add instance segmentation: by exposing the segment of each instance, so it will be similar with object detection:

- `instances`: a dictionary containing bounding boxes, segments, and labels of the cell objects 
  - `bbox`: a list of bounding boxes
  - `segment`: a list of segments in format of `[polygon]`, where each polygon is `[x0, y0, ..., xn, yn]`
  - `label`: a list of integers representing the category

Do you think it would be ok?

Don't you think it makes sense to keep the same category IDs for all approaches? 

Now we have:
 - nucleus category ID equals 0 for object detection and instance segmentation
 - background category ID equals 0 (on the masks) for semantic segmentation

I find it weird to have a dummy label in object detection just to align the mapping with semantic segmentation. Instead, let's explain in the card (under Data Fields -> annotation) what the pixel values mean (background + object detection labels)

Ok, I can do that in the next few days. I will create a `lapix` organization, and I will add this dataset and also #4565

So, I think we can close this PR? I have already moved these files there.

The link of CCAgT dataset is: https://huggingface.co/datasets/lapix/CCAgT

🤗 

Woohoo awesome !

closing this PR :)
Fix cast to null

It currently fails with `ArrowNotImplementedError` instead of `TypeError` when one tries to cast integer to null type.

Because if this, type inference breaks when one replaces null values with integers in `map` (it first tries to cast to the previous type before inferring the new type).

Fix https://github.com/huggingface/datasets/issues/4483

_The documentation is not available anymore as the PR was closed or merged._
Better ImportError message when a dataset script dependency is missing

When a depenency is missing for a dataset script, an ImportError message is shown, with a tip to install the missing dependencies. This message is not ideal at the moment: it may show duplicate dependencies, and is not very readable.

I improved it from
```
ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance'
```
to
```
ImportError: To be able to use bigbench, you need to install the following dependency: bigbench.
Please install it using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"' for instance'
```

_The documentation is not available anymore as the PR was closed or merged._

Discussed offline with @mariosasko, merging :)

Fwiw, i think this same issue is occurring on the datasets website page, where preview isn't available due to the `bigbench` import error

For the preview of BigBench datasets, we're just waiting for bigbench to have a stable version on PyPI, instead of the one hosted on GCS ;)
Test that TensorFlow is not imported on startup

TF takes some time to be imported, and also uses some GPU memory.

I just added a test to make sure that in the future it's never imported by default when
```python
import datasets
```
is called.

Right now this fails because `huggingface_hub` does import tensorflow (though this is fixed now on their `main` branch)

I'll mark this PR as ready for review once `huggingface_hub` has a new release

_The documentation is not available anymore as the PR was closed or merged._

Should we close this PR?

I'm closing this PR. Feel free to reopen it if necessary.
Fix iwslt2017

The files were moved to google drive, I hosted them on the Hub instead (ok according to the license)

I also updated the `datasets_infos.json`

_The documentation is not available anymore as the PR was closed or merged._

CI fails are just abut missing tags in the dataset card, merging !

FYI, 

The checksums have not been edited from the changes of .tgz to .zip files, and as a result a `ExpectedMoreDownloadedFiles` error occurs. Updating them in the `dataset_infos.json` should fix the error. 

Thanks for reporting and sorry for the delay, I opened https://huggingface.co/datasets/iwslt2017/discussions/2 to fix this
Include entity positions as feature in ReCoRD

https://huggingface.co/datasets/super_glue/viewer/record/validation

TLDR: We need to record entity positions, which are included in the source data but excluded by the loading script, to enable efficient and effective training for ReCoRD.

Currently, the loading script ignores the entity positions ("entity_start", "entity_end") and only records entity text. This might be because the training method of the official baseline is to make n training instance from a datapoint by replacing \"\@ placeholder\" in query with each entity individually.

But it increases the already heavy computation by multiple folds. So DeBERTa uses a method that take entity embeddings by their positions in the passage, and thus makes one training instance from one data point. It is way more efficient and proved effective for the ReCoRD task.

Can anybody help me with the dataset card rendering error? Maybe @lhoestq ?

_The documentation is not available anymore as the PR was closed or merged._

Thanks for the reply @lhoestq !

I have sucessed on `datasets-cli test ./datasets/super_glue --name record --save_infos`,
But as you can see, the check ran into `FAILED tests/test_dataset_cards.py::test_changed_dataset_card[super_glue] - V...`.
How can we solve it?

That would be neat! Let me implement it.
Dataset Viewer issue for fgrezes/WIESP2022-NER

### Link

_No response_

### Description

_No response_

### Owner

_No response_

https://huggingface.co/datasets/fgrezes/WIESP2022-NER

The error:

```
Message:       Couldn't find a dataset script at /src/services/worker/fgrezes/WIESP2022-NER/WIESP2022-NER.py or any data file in the same directory. Couldn't find 'fgrezes/WIESP2022-NER' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**test*', '**eval*'] in dataset repository fgrezes/WIESP2022-NER with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```

I understand the issue is not related to the dataset viewer in itself, but with the autodetection of the data files without a loading script in the datasets library. cc @lhoestq @albertvillanova @mariosasko 

Apparently it finds `scoring-scripts/compute_seqeval.py` which matches `**eval*`, a regex that detects a test split. We should probably improve the regex because it's not supposed to catch this kind of files. It must also only check for files with supported extensions: txt, csv, png etc.
`to_pandas` doesn't take into account format.

**Is your feature request related to a problem? Please describe.**

I have a large dataset that I need to convert part of to pandas to do some further analysis. Calling `to_pandas` directly on it is expensive. So I thought I could simply select the columns that I want and then call `to_pandas`.

**Describe the solution you'd like**

```python
from datasets import Dataset

ds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]})
pandas_df = ds.with_format(columns=['a', 'b']).to_pandas()

# I would expect `pandas_df` to only include a,b as column.
```


**Describe alternatives you've considered**
I could remove all columns that I don't want? But I don't know all of them in advance. 

**Additional context**
I can probably make a PR with some pointers.


Thanks for opening a discussion :)

Note that you can use `.remove_columns(...)` to keep only the ones you're interested in before calling `.to_pandas()`

Yes I can do that thank you!

Do you think that conceptually my example should work? If not, I'm happy to close this issue. 

If yes, I can start working on it.

Hi! Instead of `with_format(columns=['a', 'b']).to_pandas()`, use `with_format("pandas", columns=["a", "b"])` for easy conversion of the parts of the dataset to pandas via indexing/slicing.

The full code:
```python
from datasets import Dataset

ds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]})
pandas_df = ds.with_format("pandas", columns=['a', 'b'])[:]
```

Ahhhh Thank you!

closing then :)
Improve error message for missing pacakges from inside dataset script

Improve the error message for missing packages from inside a dataset script:

With this change, the error message for missing packages for `bigbench` looks as follows:
```
ImportError: To be able to use bigbench, you need to install the following dependencies:
        - 'bigbench' using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"'
```

And this is how it looked before:
```
ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance'
```

_The documentation is not available anymore as the PR was closed or merged._

I opened a PR before I noticed yours ^^' You can find it here: https://github.com/huggingface/datasets/pull/4484

The only comment I have regarding your message is that it possibly shows several `pip install` commands, whereas one can run one single `pip install` command with the list of missing dependencies, which is maybe simpler.

Let me know which one your prefer

Closing in favor of #4484.
[Docs] How to use with PyTorch page

Currently the docs about PyTorch are scattered around different pages, and we were missing a place to explain more in depth how to use and optimize a dataset for PyTorch. This PR is related to #4457 which is the TF counterpart :)

cc @Rocketknight1 we can try to align both documentations contents now I think

cc @stevhliu let me know what you think !

_The documentation is not available anymore as the PR was closed or merged._
Add SST-2 dataset

Add SST-2 dataset.

Currently it is part of GLUE benchmark.

This PR adds it as a standalone dataset.

CC: @julien-c 

_The documentation is not available anymore as the PR was closed or merged._

on the hub this dataset is referenced as `sst-2` not `sst2` – is there a canonical orthography? If not, could we name it `sst-2`?

@julien-c, we normally do not use hyphens for dataset names: whenever the original dataset name contains a hyphen, we usually:
- either suppress it: CoNLL-2000 (`conll2000`), CORD-19 (`cord19`)
- or replace it with underscore: CC-News (`cc_news`), SQuAD-es (`squad_es`)

There are some exceptions though... (I wonder why)

I think, the reason is there was a 1-to-1 relation with the corresponding Python module name.

I personally find confusing not having a rule and using both hyphens and underscores indistinctly: you never know which is the right orthography.

Whichever the decision we make, I would prefer to be applied consistently.

Also note that we already implemented this dataset as part of GLUE: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py#L163
- dataset name: `glue`
- config name: `sst2`

On the other hand, let's see how other libraries name it:
- torchtext: `SST2` https://pytorch.org/text/stable/datasets.html#sst2
- OpenAI CLIP: `rendered-sst2` https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md
- Kaggle: `SST2` https://www.kaggle.com/datasets/atulanandjha/stanford-sentiment-treebank-v2-sst2/version/22
- TensorFlow Datasets: `glue/sst2` https://www.tensorflow.org/datasets/catalog/glue#gluesst2

Ok, another option is to open PRs against the models in https://huggingface.co/models?datasets=sst-2 to change their dataset reference to `sst2`

(BTW some models refer to `sst2` already – but they're less popular: https://huggingface.co/models?datasets=sst2)

OK, I'm taking care of the subsequent PRs on models to align with this dataset name.
CI error with repo lhoestq/_dummy

## Describe the bug
CI is failing because of repo "lhoestq/_dummy". See: https://app.circleci.com/pipelines/github/huggingface/datasets/12461/workflows/1b040b45-9578-4ab9-8c44-c643c4eb8691/jobs/74269
```
requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/api/datasets/lhoestq/_dummy?full=true
```

The repo seems to no longer exist: https://huggingface.co/api/datasets/lhoestq/_dummy
```
error: "Repository not found"
```

CC: @lhoestq 

fixed by https://github.com/huggingface/datasets/pull/4472
Generalize tutorials for audio and vision

This PR updates the tutorials to be more generalizable to all modalities. After reading the tutorials, a user should be able to load any type of dataset, know how to index into and slice a dataset, and do the most basic/common type of preprocessing (tokenization, resampling, applying transforms) depending on their dataset.

Other changes include:

- Removed the sections about a dataset's metadata, features, and columns because we cover this in an earlier tutorial about inspecting the `DatasetInfo` through the dataset builder.
- Separate the sharing dataset tutorial into two sections: (1) uploading via the web interface and (2) using the `huggingface_hub` library.
- Renamed some tutorials in the TOC to be more clear and specific.
- Added more text to nudge users towards joining the community and asking questions on the forums.
- If it's okay with everyone, I'd also like to remove the section about loading and using metrics since we have the `evaluate` docs now.


_The documentation is not available anymore as the PR was closed or merged._