NyssaW/github-issues huggingface dataset:
Bump dill to 0.4.0

This bumps `dill` to 0.3.9 and closes #7510 

It turns out the only thing required to make the tests pass was to extend the version checks to include 0.3.9.

Comments:
Have you tried to run `pytest tests/test_fingerprint.py` ? It seems dill 0.3.9 breaks a lot of tests

```
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_tokenizer - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_tokenizer_with_cache - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: 'c48ebfacf8768f50' != '27e49d047c02c83b'
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: '65edc6b6d425a8e9' != '9f364fe298fb286a'
FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::test_move_script_doesnt_change_hash - AssertionError: assert b'93072ca404a697db\n' == b'cf89a7e497a97e32\n'
```

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7763). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Hi @lhoestq! Yes, I did. It's not really `dill` that breaks things. Rather the shims that `datasets` has in place did not include the next version. 

FYI: I also tested it with `dill-0.4.0` and the changes would need to be analogous, but I wanted to be conservative in this PR. 

The NameError is fixed in your PR since it defines the right `log()` function for 0.3.9.

But I'm less sure about the AssertionError that may be related to deterministic hashing or ipython/shell function hashing. We would need to solve these

EDIT: ah actually it does ! cool ! let me update the branch and re-run the CI
Parquet: use data page v2 for efficient page pruning

This is needed to enable page pruning with DataFusion, which will be useful for the Dataset Viewer.

Indeed page pruning with DataFusion allows to download only certain pages of a row group, reducing the I/O required to read just a few rows.

But while data page v1 generally works, it's not easy with DataFusion to do page pruning on datasets with nested data. This is because rows can span multiple pages in v1, contrary to v2.

cc @severo for viz

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7762). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Closing this since it looks like the page offset index is enough :)
Comment/feature request: Huggingface 502s from GHA

This is no longer a pressing issue, but for completeness I am reporting that in August 26th, GET requests to `https://datasets-server.huggingface.co/info\?dataset\=livebench/math` were returning 502s when invoked from [github actions](https://github.com/UKGovernmentBEIS/inspect_evals/actions/runs/17241892475/job/48921123754) (that link will expire eventually, [here are the logs](https://github.com/user-attachments/files/22233578/logs_44225296943.zip)). 

When invoked from actions, it appeared to be consistently failing for ~6 hours. However, these 502s never occurred when the request was invoked from my local machine in that same time period.

I suspect that this is related to how the requests are routed with github actions versus locally. 

Its not clear to me if the request even reached huggingface servers or if its the github proxy that stopped it from going through, but I wanted to report it nonetheless in case this is helpful information. I'm curious if huggingface can do anything on their end to confirm cause. 

And a feature request for if this happens in the future (assuming huggingface has visibilty on it):  A "datasets status" page highlighting if 502s occur for  specific individual datasets could be useful for people debugging on the other end of this!




Comments:
Option for Anonymous Dataset link

### Feature request

Allow for anonymized viewing of datasets. For instance, something similar to [Anonymous GitHub](https://anonymous.4open.science/).

### Motivation

We generally publish our data through Hugging Face. This has worked out very well as it's both our repository and archive (thanks to the DOI feature!). However, we have an increasing challenge when it comes to sharing our datasets for paper (both conference and journal) submissions. Due to the need to share data anonymously, we can't use the Hugging Face URLs, but datasets tend to be too large for inclusion as a zip. Being able to have an anonymous link would be great since we can't be double-publishing the data.

### Your contribution

Sorry, I don't have a contribution to make to the implementation of this. Perhaps it would be possible to work off the [Anonymous GitHub](https://github.com/tdurieux/anonymous_github) code to generate something analogous with pointers to the data still on Hugging Face's servers (instead of the duplication of data required for the GitHub version)?

Comments:
Add support for `.conll` file format in datasets

### Feature request

I’d like to request native support in the Hugging Face datasets library for reading .conll files (CoNLL format). This format is widely used in NLP tasks, especially for Named Entity Recognition (NER), POS tagging, and other token classification problems.

Right now `.conll` datasets need to be manually parsed or preprocessed before being loaded into datasets. Having built in support would save time and make workflows smoother for researchers and practitioners.

I propose - 
Add a conll dataset builder or file parser to datasets that can:
- Read `.conll` files with customizable delimiters (space, tab).
- Handle sentence/document boundaries (typically indicated by empty lines).
- Support common CoNLL variants (e.g., CoNLL-2000 chunking, CoNLL-2003 NER).
- Output a dataset where each example contains:
- tokens: list of strings
- tags (or similar): list of labels aligned with tokens

Given a .conll snippet like:
```
EU NNP B-ORG
rejects VBZ O
German JJ B-MISC
call NN O
. . O
```


The dataset should load as:
```
{
  "tokens": ["EU", "rejects", "German", "call", "."],
  "tags": ["B-ORG", "O", "B-MISC", "O", "O"]
}
```




### Motivation

- CoNLL files are a standard benchmark format in NLP (e.g., CoNLL-2003, CoNLL-2000).
- Many users train NER or sequence labeling models (like BERT for token classification) directly on `.conll` 
- Right now you have to write your own parsing scripts. Built in support would unify this process and would be much more convenient 

### Your contribution

I’d be happy to contribute by implementing this feature. My plan is to- 
- Add a new dataset script (conll.py) to handle .conll files.
- Implement parsing logic that supports sentence/document boundaries and token-label alignment.
- Write unit tests with small `.conll` examples to ensure correctness.
- Add documentation and usage examples so new users can easily load `.conll` datasets.

This would be my first open source contribution, so I’ll follow the `CONTRIBUTING.md` guidelines closely and adjust based on feedback from the maintainers.

Comments:
That would be cool ! feel free to ping me if I can help reviewing a PR
datasets.map(f, num_proc=N) hangs with N>1 when run on import

### Describe the bug

If you `import` a module that runs `datasets.map(f, num_proc=N)` at the top-level, Python hangs.


### Steps to reproduce the bug

1. Create a file that runs datasets.map at the top-level:

```bash
cat <<EOF > import_me.py
import datasets

the_dataset = datasets.load_dataset("openai/openai_humaneval")
the_dataset = the_dataset.map(lambda item: item, num_proc=2)
EOF
```

2. Start Python REPL:

```bash
uv run --python 3.12.3 --with "datasets==4.0.0"  python3
Python 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
```

3. Import the file:

```python
import import_me
````

Observe hang.

### Expected behavior

Ideally would not hang, or would fallback to num_proc=1 with a warning.

### Environment info


- `datasets` version: 4.0.0
- Platform: Linux-6.14.0-29-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.34.4
- PyArrow version: 21.0.0
- Pandas version: 2.3.2
- `fsspec` version: 2025.3.0


Comments:
Hii !! Frosh from India taking issue #7756. 

Plan - Add try/except multiprocessing. Start Method, Context +  Fallback to num_proc = 1. 

Forked, coding now... PR in a few
Fix: Update Dill Version in Setup py

Currently the DIll version is less than 3.9 and now major libraries like Multiprocess, gepa requires Dill version as 0.4.0 and this is making a conflict in installation. So added this small PR to update the DIll. 

Comments:
https://github.com/huggingface/datasets/issues/7751

same as https://github.com/huggingface/datasets/pull/7763: some tests need to be fixed to support 0.4.0

fixed by #7763
Dill version update

### Describe the bug

Why the datasets is not updating the dill ? 
Just want to know if I update the dill version in dill what will be the repucssion. 
For now in multiplaces I have to update the library like process requirequire dill 0.4.0 so why not datasets. 
Adding a pr too. 

### Steps to reproduce the bug

.

### Expected behavior

.

### Environment info

.

Comments:
#7752 

related: #7510
Add wikipedia-2023-redirects dataset

Title: Add wikipedia-2023-redirects dataset (redirect resolution + pageviews)

Summary
- New dataset loader: wikipedia_2023_redirects
- Canonical Wikipedia pages enriched with:
  - redirects (aliases pointing to the page)
  - 2023 pageviews (aggregated)
- Streaming support; robust parsing; license notes included
- Tests with tiny dummy data (XML + TSVs); covers streaming

Motivation
RAG/retrieval often benefits from:
- Query expansion via redirect aliases
- Popularity prior via pageviews
This loader offers a practical, maintenance-light way to access canonical pages alongside their redirect aliases and 2023 pageview totals.

Features
- id: string
- title: string
- url: string
- text: string
- redirects: list[string]
- pageviews_2023: int32
- timestamp: string

Licensing
- Wikipedia text: CC BY-SA 3.0 (attribution and share-alike apply)
- Pageviews: public domain
The PR docs mention both, and the module docstring cites sources.

Notes
- The URLs in _get_urls_for_config are wired to dummy files for tests. In production, these would point to Wikimedia dumps:
  - XML page dumps: https://dumps.wikimedia.org/
  - Pageviews: https://dumps.wikimedia.org/other/pageviews/
- The schema is intentionally simple and stable. Pageview aggregation is per-title sum across 2023.

Testing
- make style && make quality
- pytest -q tests/test_dataset_wikipedia_2023_redirects.py

Example
```python
from datasets import load_dataset
ds = load_dataset("wikipedia_2023_redirects", split="train")
print(ds[0]["title"], ds[0]["redirects"][:5], ds[0]["pageviews_2023"])
```

Acknowledgements
- Wikipedia/Wikimedia Foundation for the source data
- Hugging Face Datasets for the dataset infrastructure


Comments:
you should host this dataset on HF with `ds.push_to_hub()` ! we stopped using dataset scripts some time ago
Fix: Canonical 'multi_news' dataset is broken and should be updated to a Parquet version

Hi, 

The canonical `multi_news` dataset is currently broken and fails to load. This is because it points to the [alexfabri/multi_news](https://huggingface.co/datasets/alexfabbri/multi_news) repository, which contains a legacy loading script (`multi_news.py`) that requires the now-removed `trust_remote_code` parameter.

The original maintainer's GitHub and Hugging Face repositories appear to be inactive, so a community-led fix is needed.

I have created a working fix by converting the dataset to the modern Parquet format, which does not require a loading script. The fixed version is available here and loads correctly:

**[Awesome075/multi_news_parquet](https://huggingface.co/datasets/Awesome075/multi_news_parquet)**

Could the maintainers please guide me or themselves update the official `multi_news` dataset to use this working Parquet version? This would involve updating the canonical pointer for "multi_news" to resolve to the new repository.

This action would fix the dataset for all users and ensure its continued availability.

Thank you!

Comments:
@sayakpaul @a-r-r-o-w could you verify this issue then i can contribute to solve this issue!😊
Audio mono argument no longer supported, despite class documentation

### Describe the bug

Either update the documentation, or re-introduce the flag (and corresponding logic to convert the audio to mono)

### Steps to reproduce the bug

Audio(sampling_rate=16000, mono=True) raises the error 
TypeError: Audio.__init__() got an unexpected keyword argument 'mono'

However, in the class documentation, is says:
Args:
        sampling_rate (`int`, *optional*):
            Target sampling rate. If `None`, the native sampling rate is used.
        mono (`bool`, defaults to `True`):
            Whether to convert the audio signal to mono by averaging samples across
            channels.
        [...]

### Expected behavior

The above call should either work, or the documentation within the Audio class should be updated

### Environment info

- `datasets` version: 4.0.0
- Platform: Linux-5.15.0-124-generic-x86_64-with-glibc2.35
- Python version: 3.12.11
- `huggingface_hub` version: 0.34.4
- PyArrow version: 21.0.0
- Pandas version: 2.3.2
- `fsspec` version: 2025.3.0

Comments:
I want to solve this problem can you please assign it to me
and also can you please guide whether the mono parameter is required to be re-added or the documentation needs an update?
Refactor HDF5 and preserve tree structure

Closes #7741. Followup to #7690

- Recursive parsing and feature inference, to preserve the tree structure of the file. Note this means we now visit all links in the file. It also means we have to call` combine_chunks` on any large non-root datasets.
- Support for `complex64` (two `float32`s, used to be converted to two `float64`s)
- Support for ndim complex, compound, more field types for compound (due to reusing the main parser, compound types are treated like groups)
- Cleaned up varlen support
- Always do feature inference and always cast to features (used to cast to schema)
- Updated tests to use `load_dataset` instead of internal APIs
- Removed `columns` in config. Have to give Features (i.e., must specify types) if filtering

Comments:
@lhoestq this is ready for you now!

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7743). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
module 'pyarrow' has no attribute 'PyExtensionType'

### Describe the bug

When importing certain libraries, users will encounter the following error which can be traced back to the datasets library.

module 'pyarrow' has no attribute 'PyExtensionType'.

Example issue: https://github.com/explodinggradients/ragas/issues/2170

The issue occurs due to the following. I will proceed to submit a PR with the below fix:

**Issue Reason**
The issue is that PyArrow version 21.0.0 doesn’t have PyExtensionType. This was changed in newer versions of PyArrow. The

PyExtensionType class was renamed to ExtensionType in PyArrow 13.0.0 and later versions.


** Issue Solution**
Making the following changes to the following lib files should temporarily resolve the issue.

I will submit a PR to the dataets library in the meantime.

env_name/lib/python3.10/site-packages/datasets/features/features.py:

```
> 521            self.shape = tuple(shape)
       522            self.value_type = dtype
       523            self.storage_dtype = self._generate_dtype(self.value_type)
       524 -          pa.PyExtensionType.__init__(self, self.storage_dtype)
       524 +          pa.ExtensionType.__init__(self, self.storage_dtype)
       525
       526        def __reduce__(self):
       527            return self.__class__, (
```

Updated venv_name/lib/python3.10/site-packages/datasets/features/features.py:
```      
 510        _type: str = field(default=“Array5D”, init=False, repr=False)
       511
       512
       513 -  class _ArrayXDExtensionType(pa.PyExtensionType):
       513 +  class _ArrayXDExtensionType(pa.ExtensionType):
       514        ndims: Optional[int] = None
       515
       516        def __init__(self, shape: tuple, dtype: str):
```

### Steps to reproduce the bug

Ragas version: 0.3.1
Python version: 3.11

**Code to Reproduce**

_**In notebook:**_

!pip install ragas
from ragas import evaluate


### Expected behavior

The required package installs without issue.

### Environment info

In Jupyter Notebook.
venv

Comments:
Just checked out the files and thishad already been addressed

For others who find this issue: 

`pip install --upgrade "datasets>=2.20.0"` 

from https://github.com/explodinggradients/ragas/issues/2170#issuecomment-3204393672 can fix it.
Preserve tree structure when loading HDF5

### Feature request

https://github.com/huggingface/datasets/pull/7740#discussion_r2285605374

### Motivation

`datasets` has the `Features` class for representing nested features. HDF5 files have groups of datasets which are nested, though in #7690 the keys are flattened. We should preserve that structure for the user.

### Your contribution

I'll open a PR (#7743)

Comments:
Replacement of "Sequence" feature with "List" breaks backward compatibility

PR #7634 replaced the Sequence feature with List in 4.0.0, so datasets saved with version 4.0.0 with that feature cannot be loaded by earlier versions. There is no clear option in 4.0.0 to use the legacy feature type to preserve backward compatibility.

Why is this a problem? I have a complex preprocessing and training pipeline dependent on 3.6.0; we manage a very large number of separate datasets that get concatenated during training. If just one of those datasets is saved with 4.0.0, they become unusable, and we have no way of "fixing" them. I can load them in 4.0.0 but I can't re-save with the legacy feature type, and I can't load it in 3.6.0 for obvious reasons.

Perhaps I'm missing something here, since the PR says that backward compatibility is preserved; if so, it's not obvious to me how.

Comments:
Backward compatibility here means 4.0.0 can load datasets saved with older versions.

You will need 4.0.0 to load datasets saved with 4.0.0
Allow saving multi-dimensional ndarray with dynamic shapes

### Feature request

I propose adding a dedicated feature to the datasets library that allows for the efficient storage and retrieval of multi-dimensional ndarray with dynamic shapes. Similar to how Image columns handle variable-sized images, this feature would provide a structured way to store array data where the dimensions are not fixed.

A possible implementation could be a new Array or Tensor feature type that stores the data in a structured format, for example, 

```python
{
  "shape": (5, 224, 224), 
  "dtype": "uint8", 
  "data": [...]
}
```

This would allow the datasets library to handle heterogeneous array sizes within a single column without requiring a fixed shape definition in the feature schema.

### Motivation

I am currently trying to upload data from astronomical telescopes, specifically FITS files, to the Hugging Face Hub. This type of data is very similar to images but often has more than three dimensions. For example, data from the SDSS project contains five channels (u, g, r, i, z), and the pixel values can exceed 255, making the Pillow based Image feature unsuitable.

The current datasets library requires a fixed shape to be defined in the feature schema for multi-dimensional arrays, which is a major roadblock. This prevents me from saving my data, as the dimensions of the arrays can vary across different FITS files.

https://github.com/huggingface/datasets/blob/985c9bee6bfc345787a8b9dd316e1d4f3b930503/src/datasets/features/features.py#L613-L614

A feature that supports dynamic shapes would be incredibly beneficial for the astronomy community and other fields dealing with similar high-dimensional, variable-sized data (e.g., medical imaging, scientific simulations).

### Your contribution

I am willing to create a PR to help implement this feature if the proposal is accepted.

Comments:
I agree this would be super valuable.

It looks like this was discussed a few years ago in https://github.com/huggingface/datasets/issues/5272#issuecomment-1550200824 but there were some issues. Those PRs are merged now and it looks like Arrow [officially supports](https://arrow.apache.org/docs/format/CanonicalExtensions.html#variable-shape-tensor) this so it's a good time to re-evaluate!

Happy to help with this, maybe we can think of adding a new type `Tensor` (instead of Array2D, 3D etc. which imply a fixed number of dims - we can keep them for backward compat anyways) that uses VariableShapeTensor (or FixedShapeTensor if the shape is provided maybe ? happy to discuss this)

An update here [VariableShapeTensor C++ implementation](https://github.com/apache/arrow/issues/38007) was merged in Arrow and the relatively trivial [Python wrapper](https://github.com/apache/arrow/pull/40354) will likely make it into the next release (April-ish).

Wow exciting ! thanks @rok
docs: Add column overwrite example to batch mapping guide

This PR adds a complementary example showing the **column-overwriting** pattern, which is both more direct and more flexible for many transformations.

### Proposed Change

The original `remove_columns` example remains untouched. Below it, this PR introduces an alternative approach that overwrites an existing column during batch mapping.

This teaches users a core `.map()` capability for in-place transformations without extra intermediate steps.

**New Example:**

> ```python
> >>> from datasets import Dataset
> >>> dataset = Dataset.from_dict({"a": [0, 1, 2]})
> # Overwrite "a" directly to duplicate each value
> >>> duplicated_dataset = dataset.map(
> ...     lambda batch: {"a": [x for x in batch["a"] for _ in range(2)]},
> ...     batched=True
> ... )
> >>> duplicated_dataset
> Dataset({
>     features: ['a'],
>     num_rows: 6
> })
> >>> duplicated_dataset["a"]
> [0, 0, 1, 1, 2, 2]
> ```


Comments:
Hi @lhoestq, just a gentle follow-up on this PR.
Fixing __getitem__ of datasets which behaves inconsistent to documentation when setting _format_type to None

Setting _format_type to None, should return plain python object but as of 4.0.0 returns Column. This fails in libs such as sentencetransformers (such as in generation of hard negatives) where plain python is expected.

Comments:
this breaking change is actually expected, happy to help with a fix in sentencetransformers to account for this

Thank you for the context. I thought this was a mismatch do the documentation. Good to know it was intentional. No worries, can add a PR to sentence transformers.
Add the possibility of a backend for audio decoding

### Feature request

Add the possibility of a backend for audio decoding. Before version 4.0.0, soundfile was used, and now torchcodec is used, but the problem is that torchcodec requires ffmpeg, which is problematic to install on the same colab. Therefore, I suggest adding a decoder selection when loading the dataset.

### Motivation

I use a service for training models in which ffmpeg cannot be installed.

### Your contribution

I use a service for training models in which ffmpeg cannot be installed.

Comments:
is there a work around im stuck

never mind just downgraded
OSError: libcudart.so.11.0: cannot open shared object file: No such file or directory

> Hi  is there any solution for that eror i try to install this one 
pip install torch==1.12.1+cpu torchaudio==0.12.1+cpu -f https://download.pytorch.org/whl/torch_stable.html  
this is working fine but tell me how to install pytorch version that is fit for gpu 

Comments:
Is this related to the "datasets" library? @SaleemMalikAI
NonMatchingSplitsSizesError and ExpectedMoreSplitsError

### Describe the bug

When loading dataset, the info specified by `data_files` did not overwrite the original info.


### Steps to reproduce the bug

```python
from datasets import load_dataset

traindata = load_dataset(
            "allenai/c4",
            "en",
            data_files={"train": "en/c4-train.00000-of-01024.json.gz", 
                        "validation": "en/c4-validation.00000-of-00008.json.gz"},
        )
```


```log
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=828589180707, num_examples=364868892, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=809262831, num_examples=356317, shard_lengths=[223006, 133311], dataset_name='c4')}, {'expected': SplitInfo(name='validation', num_bytes=825767266, num_examples=364608, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='validation', num_bytes=102199431, num_examples=45576, shard_lengths=None, dataset_name='c4')}]
```

```python
from datasets import load_dataset

traindata = load_dataset(
            "allenai/c4",
            "en",
            data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
            split="train"
        )
```

```log
ExpectedMoreSplitsError: {'validation'}
```

### Expected behavior

No error

### Environment info

datasets 4.0.0

Comments:
To load just one shard without errors, you should use data_files directly with split set to "train", but don’t specify "allenai/c4", since that points to the full dataset with all shards.

Instead, do this:
```
from datasets import load_dataset
from datasets import load_dataset

# Load only one shard of C4
traindata = load_dataset(
    "json",   # <-- use "json" since you’re directly passing JSON files
    data_files={"train": "https://huggingface.co/datasets/allenai/c4/resolve/main/en/c4-train.00000-of-01024.json.gz"},
    split="train"
)

print(traindata)
```
If you want both train and validation but only a subset of shards, do:
```
traindata = load_dataset(
    "json",
    data_files={
        "train": "https://huggingface.co/datasets/allenai/c4/resolve/main/en/c4-train.00000-of-01024.json.gz",
        "validation": "https://huggingface.co/datasets/allenai/c4/resolve/main/en/c4-validation.00000-of-00008.json.gz"
    }
)

print(traindata)
```

I just want to load a few files from allenai/c4.
If I do not specify allenai/c4, where will the files be loaded from?

My apologies, I’ve modified my previous answer.
You just need to specify the full path, for example:

https://huggingface.co/datasets/allenai/c4/resolve/main/en/c4-train.00000-of-01024.json.gz

<img width="1843" height="633" alt="Image" src="https://github.com/user-attachments/assets/b2922958-9d87-4b62-a00e-c5ca02e31c27" />

I hope this updated answer is helpful.
config paths that start with ./ are not valid as hf:// accessed repos, but are valid when accessed locally

### Describe the bug

```
- config_name: some_config
  data_files:
  - split: train
    path:
    - images/xyz/*.jpg
```
will correctly download but

```
- config_name: some_config
  data_files:
  - split: train
    path:
    - ./images/xyz/*.jpg
```
will error with `FileNotFoundError` due to improper url joining. `load_dataset` on the same directory locally works fine.

### Steps to reproduce the bug

1. create a README.md with the front matter of the form

```
- config_name: some_config
  data_files:
  - split: train
    path:
    - ./images/xyz/*.jpg
```

2. `touch ./images/xyz/1.jpg`
3. Observe this directory loads with `load_dataset("filesystem_path", "some_config")` correctly.
4. Observe exceptions when you load this with `load_dataset("repoid/filesystem_path", "some_config")`

### Expected behavior

`./` prefix should be interpreted correctly

### Environment info

datasets 4.0.0
datasets 3.4.0

reproduce

Comments:
fix(webdataset): don't .lower() field_name

This fixes cases where keys have upper case identifiers

Comments:
fixes: https://github.com/huggingface/datasets/issues/7732

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7726). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

CI failures are unrelated, merging :)
Don't remove `trust_remote_code` arg!!!

### Feature request

defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!

Add `trust_remote_code` arg back please!

### Motivation

defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!


### Your contribution

defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!


Comments:
Out of memory even though using load_dataset(..., streaming=True)

### Describe the bug

I am iterating over a large dataset that I load using streaming=True to avoid running out of memory. Unfortunately, I am observing that memory usage increases over time and I'm finally running in an oom.

### Steps to reproduce the bug

```
  ds = load_dataset("openslr/librispeech_asr", split="train.clean.360", streaming=True)
  for i,sample in enumerate(tqdm(ds)):
    target_file = os.path.join(NSFW_TARGET_FOLDER, f'audio{i}.wav')
    try:
      sf.write(target_file, sample['audio']['array'], samplerate=sample['audio']['sampling_rate'])
    except Exception as e:
      print(f"Could not write audio {i} in ds: {e}")
```


### Expected behavior

I'd expect to have a small memory footprint and memory being freed after each iteration of the for loop. Instead the memory usage is increasing. I tried to remove the logic to write the sound file and just print the sample but the issue remains the same.

### Environment info

Python 3.12.11
Ubuntu 24
datasets 4.0.0 and 3.6.0

Comments:
Bad split error message when using percentages

### Describe the bug

Hi, I'm trying to download a dataset. To not load the entire dataset in memory, I split it as described [here](https://huggingface.co/docs/datasets/v4.0.0/loading#slice-splits) in 10% steps.

When doing so, the library returns this error:
    raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
ValueError: Bad split: train[0%:10%]. Available splits: ['train']

Edit: Same happens with a split like _train[:90000]_
### Steps to reproduce the bug

```
  for split in range(10):
    split_str = f"train[{split*10}%:{(split+1)*10}%]"
    print(f"Processing split {split_str}...")
    ds = load_dataset("user/dataset", split=split_str, streaming=True)
```


### Expected behavior

I'd expect the library to split my dataset in 10% steps.

### Environment info

python 3.12.11
ubuntu 24
dataset 4.0.0

Comments:
I'd like to work on this: add clearer validation/messages for percent-based splits + tests

The most basic example is this code:
`load_dataset("openslr/librispeech_asr", split="train[10%:20%]")`

This results in this ValueError:
```
    raise ValueError(f'Unknown split "{split}". Should be one of {list(name2len)}.')
ValueError: Unknown split "train". Should be one of ['test.clean', 'test.other', 'train.clean.100', 'train.clean.360', 'train.other.500', 'validation.clean', 'validation.other'].
```
Datasets 4.0 map function causing column not found

### Describe the bug

Column returned after mapping is not found in new instance of the dataset.

### Steps to reproduce the bug

Code for reproduction. After running get_total_audio_length, it is errored out due to `data` not having `duration`
```
def compute_duration(x):
    return {"duration": len(x["audio"]["array"]) / x["audio"]["sampling_rate"]}


def get_total_audio_length(dataset):
    data = dataset.map(compute_duration, num_proc=NUM_PROC)
    print(data)
    durations=data["duration"]
    total_seconds = sum(durations)
    return total_seconds
```

### Expected behavior

New datasets.Dataset instance should have new columns attached.

### Environment info

- `datasets` version: 4.0.0
- Platform: Linux-5.4.0-124-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.33.2
- PyArrow version: 20.0.0
- Pandas version: 2.3.0
- `fsspec` version: 2023.12.2

Comments:
Hi, I tried to reproduce this issue on the latest `main` branch but it seems to be working correctly now. My test script (which creates a dummy dataset and applies the `.map()` function) successfully creates and accesses the new column without a `KeyError`.

It's possible this was fixed by a recent commit. The maintainers might want to consider closing this issue.

Hi, have you tried on a large dataset (200GB+) perhaps? I will try my best to do a rerun with main branch when I have the time.

I ran it on a small dataset, maybe that’s why I didn’t hit the issue. If it still shows up on your side with the latest main, let me know. I can try it on a bigger set too.
Specify dataset columns types in typehint

### Feature request

Make dataset optionaly generic to datasets usage with type annotations like it was done in `torch.Dataloader` https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/utils/data/dataloader.py#L131

### Motivation

In MTEB we're using a lot of datasets objects, but they're a bit poor in typehints. E.g. we can specify this for dataloder

```python
from typing import TypedDict
from torch.utils.data import DataLoader

class CorpusInput(TypedDict):
    title: list[str]
    body: list[str]


class QueryInput(TypedDict):
    query: list[str]
    instruction: list[str]

def queries_loader() -> DataLoader[QueryInput]:
    ...

def corpus_loader() -> DataLoader[CorpusInput]:
   ...
```
But for datasets we can only specify columns in type in comments 
```python
from datasets import Dataset

QueryDataset = Dataset
"""Query dataset should have `query` and `instructions` columns as `str` """
```

### Your contribution

I can create draft implementation

Comments:
add support for pyarrow string view in features


Comments:
@lhoestq who do you think would be the best to have a look at this? Any pointers would be appreciated, thanks!

Hi ! what's the rationale for supporting string view ? I'm afraid it can complexify the typing logic without much value

Hi @lhoestq ! I mainly want to be able to create features by using `Features.from_arrow_schema(dataset_schema)` on an arrow dataset with string view columns, currently there is no easy way to do this, and string_view is becoming an increasingly common data type for string columns in arrow. Thanks for having a look!

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7718). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Cached dataset is not used when explicitly passing the cache_dir parameter

### Describe the bug

Hi, we are pre-downloading a dataset using snapshot_download(). When loading this exact dataset with load_dataset() the cached snapshot is not used. In both calls, I provide the cache_dir parameter.

### Steps to reproduce the bug

```
from datasets import load_dataset, concatenate_datasets
from huggingface_hub import snapshot_download
    
def download_ds(name: str):
    snapshot_download(repo_id=name, repo_type="dataset", cache_dir="G:/Datasets/cache")

def prepare_ds():
    audio_ds = load_dataset("openslr/librispeech_asr", num_proc=4, cache_dir="G:/Datasets/cache")
    print(sfw_ds.features)

if __name__ == '__main__':
    download_ds("openslr/librispeech_asr")
    prepare_ds()
```

### Expected behavior

I'd expect that the cached version of the dataset is used. Instead, the same dataset is downloaded again to the default cache directory.

### Environment info

Windows 11
datasets==4.0.0
Python 3.12.11

Comments:
Hi, I've investigated this issue and can confirm the bug. Here are my findings:

**1. Reproduction:**
I was able to reproduce the issue on the latest `main` branch. Using the provided code snippet, `snapshot_download` correctly populates the custom `cache_dir`, but `load_dataset` with the same `cache_dir` triggers a full re-download and re-processing of the dataset, ignoring the existing cache.

**2. Investigation:**
I traced the `cache_dir` parameter from `load_dataset` down to the `DatasetBuilder` class in `src/datasets/builder.py`. The root cause seems to be a mismatch between the cache path structure created by `snapshot_download` and the path structure expected by the `DatasetBuilder`.

Specifically, the `_relative_data_dir` method in `DatasetBuilder` constructs a path using `namespace___dataset_name` (with three underscores), while the cache from `snapshot_download` appears to use a `repo_id` based format like `datasets--namespace--dataset_name` (with double hyphens).

**3. Attempted Fix & Result:**
I attempted a fix by modifying the `_relative_data_dir` method to replace the path separator "/" in `self.repo_id` with "--", to align it with the `snapshot_download` structure.

This partially worked: `load_dataset` no longer re-downloads the files. However, it still re-processes them every time (triggering "Generating train split...", etc.) instead of loading the already processed Arrow files from the cache.

This suggests the issue is deeper than just the directory name and might be related to how the builder verifies the integrity or presence of the processed cache files.

I hope these findings are helpful for whoever picks up this issue.
Concurrent IterableDataset push_to_hub

Same as https://github.com/huggingface/datasets/pull/7708 but for `IterableDataset`

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7710). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Release 4.0.0 breaks usage patterns of with_format

### Describe the bug

Previously it was possible to access a whole column that was e.g. in numpy format via `with_format` by indexing the column. Now this possibility seems to be gone with the new Column() class. As far as I see, this makes working on a whole column (in-memory) more complex, i.e. normalizing an in-memory dataset for which iterating would be too slow. Is this intended behaviour? I couldn't find much documentation on the intended usage of the new Column class yet.

### Steps to reproduce the bug

Steps to reproduce:
```
from datasets import load_dataset
dataset = load_dataset("lhoestq/demo1")
dataset = dataset.with_format("numpy")
print(dataset["star"].ndim)
```

### Expected behavior

Working on whole columns should be possible.

### Environment info

- `datasets` version: 4.0.0
- Platform: Linux-6.8.0-63-generic-x86_64-with-glibc2.36
- Python version: 3.12.11
- `huggingface_hub` version: 0.34.3
- PyArrow version: 21.0.0
- Pandas version: 2.3.1
- `fsspec` version: 2025.3.0

Comments:
This is a breaking change with 4.0 which introduced `Column` objects. To get the numpy array from a `Column` you can `col[i]`, `col[i:j]` or even `col[:]` if you want the full column as a numpy array:

```python
from datasets import load_dataset
dataset = load_dataset(...)
dataset = dataset.with_format("numpy")
print(dataset["star"][:].ndim)
```

Ah perfect, thanks for clearing this up. I would close this ticket then.
Concurrent push_to_hub

Retry the step that (download + update + upload) the README.md using `create_commit(..., parent_commit=...)` if there was a commit in the meantime. This should enable concurrent `push_to_hub()` since it won't overwrite the README.md metadata anymore.

Note: we fixed an issue server side to make this work:

<details>

DO NOT MERGE FOR NOW since it seems there is one bug that prevents this logic from working:

I'm using parent_commit to enable concurrent push_to_hub() in datasets for a retry mechanism, but for some reason I always run into a weird situation.
Sometimes create_commit(.., parent_commit=...) returns error 500 but the commit did happen on the Hub side without respecting parent_commit

e.g. request id
```
huggingface_hub.errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/lhoestq/tmp/commit/main (Request ID: Root=1-6888d8af-2ce517bc60c69cb378b51526;d1b17993-c5d0-4ccd-9926-060c45f9ed61)
```

fix coming in [internal](https://github.com/huggingface-internal/moon-landing/pull/14617)

</details>

close https://github.com/huggingface/datasets/issues/7600

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7708). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Reimplemented partial split download support (revival of #6832)

(revival of #6832)

https://github.com/huggingface/datasets/pull/7648#issuecomment-3084050130

Close https://github.com/huggingface/datasets/issues/4101, and more

---
### PR under work!!!!


Comments:
 Mario’s Patch (in PR #6832):
```
def _make_split_generators_kwargs(self, prepare_split_kwargs):
    # Pass `pipeline` into `_split_generators()` from `prepare_split_kwargs` if
    # it's in the call signature of `_split_generators()`.
    # This allows for global preprocessing in beam.
    split_generators_kwargs = {}
    if "pipeline" in inspect.signature(self._split_generators).parameters:
        split_generators_kwargs["pipeline"] = prepare_split_kwargs["pipeline"]
    split_generators_kwargs.update(super()._make_split_generators_kwargs(prepare_split_kwargs))
    return split_generators_kwargs
```

In the latest main(in my fork and og repo's main):
```
def _make_split_generators_kwargs(self, prepare_split_kwargs):
    """Get kwargs for `self._split_generators()` from `prepare_split_kwargs`."""
    splits = prepare_split_kwargs.pop("splits", None)
    if self._supports_partial_generation():
        return {"splits": splits}
    return {}
```
It enables passing splits into _split_generators() only for builders that support it(if i am not wrong..). So ignored Beam logic for now!

Awesome ! btw we can modify the GeneratorBasedBuilder and ArrowBasedBuilder if needed now that custom loading scripts are not supported anymore :)

I'll review this in a bit

@lhoestq @ArjunJagdale is this still work in progress or is just a review missing? Anything I can help with here? This would indeed be a cool feature

I did a preliminary pass and it looks good but we should check the CI, could you run `make style` @ArjunJagdale so we can run the CI ?

Done! Also some parts may be incomplete because I had to focus on important exams and semester activities so couldn’t finish the work fully. I will still try my best.
Can Not read installed dataset in dataset.load(.)

Hi, folks, I'm newbie in huggingface dataset api.
As title, i'm facing the issue that the dataset.load api can not connect to the installed dataset.

code snippet :
<img width="572" height="253" alt="Image" src="https://github.com/user-attachments/assets/10f48aaf-d6ca-4239-b1cf-145d74f125d1" />

data path : 
"/xxx/joseph/llava_ds/vlm_ds"
it contains all video clips i want!
<img width="1398" height="261" alt="Image" src="https://github.com/user-attachments/assets/bf213b66-e344-4311-97e7-bc209677ae77" />

i run the py script by 
<img width="1042" height="38" alt="Image" src="https://github.com/user-attachments/assets/8b3fcee4-e1a6-41b8-bee1-91567b00d9d2" />

But bad happended, even i provide dataset path by "HF_HUB_CACHE", it still attempt to download data from remote side : 

<img width="1697" height="813" alt="Image" src="https://github.com/user-attachments/assets/baa6cff1-a724-4710-a8c4-4805459deffb" />

Any suggestion will be appreciated!!

Comments:
You can download the dataset locally using [huggingface_hub.snapshot_download](https://huggingface.co/docs/huggingface_hub/v0.34.3/en/package_reference/file_download#huggingface_hub.snapshot_download) and then do

```python
dataset = load_dataset(local_directory_path)
```

> You can download the dataset locally using [huggingface_hub.snapshot_download](https://huggingface.co/docs/huggingface_hub/v0.34.3/en/package_reference/file_download#huggingface_hub.snapshot_download) and then do
> 
> dataset = load_dataset(local_directory_path)

It's good suggestion, but my server env is network restriction. It can not directly fetch data from huggingface. I spent lot of time to download and transfer it to the server.
So, I attempt to make load_dataset connect to my local dataset.  

Just Solved it few day before. Will post solution later...
also thanks folks quick reply..
Fix map() example in datasets documentation: define tokenizer before use

## Problem
The current datasets.Dataset.map() example in the documentation demonstrates batched processing using a tokenizer object without defining or importing it. This causes a NameError when users copy and run the example as-is, breaking the expected seamless experience.

## Correction
This PR fixes the issue by explicitly importing and initializing the tokenizer using the Transformers library (AutoTokenizer.from_pretrained("bert-base-uncased")), making the example self-contained and runnable without errors.
This will help new users understand the workflow and apply the method correctly.

Closes #7703 



Comments:
Hi @lhoestq, just a gentle follow-up on this doc fix PR (#7704). Let me know if any changes are needed — happy to update.
Hope this improvement helps users run the example without confusion!

the modified file is the readme of the docs, not about map() specifically
[Docs] map() example uses undefined `tokenizer` — causes NameError

## Description

The current documentation example for `datasets.Dataset.map()` demonstrates batched processing but uses a `tokenizer` object without defining or importing it. This causes an error every time it's copied.

Here is the problematic line:

```python
# process a batch of examples
>>> ds = ds.map(lambda example: tokenizer(example["text"]), batched=True)
```
This assumes the user has already set up a tokenizer, which contradicts the goal of having self-contained, copy-paste-friendly examples. 

## Problem
Users who copy and run the example as-is will encounter:

```python
NameError: name 'tokenizer' is not defined
```

This breaks the flow for users and violates HuggingFace's documentation principle that examples should "work as expected" when copied directly. 

## Proposal
Update the example to include the required tokenizer setup using the Transformers library, like so:

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
ds_tokenized = ds.map(lambda example: tokenizer(example["text"]), batched=True)
```
This will help new users understand the workflow and apply the method correctly.

## Note
This PR complements ongoing improvements like #7700, which clarifies multiprocessing in .map(). My change focuses on undefined tokenizer — causes NameError






Comments:
I've submitted PR #7704 which adds documentation to clarify the behavior of `map()` when returning `None`.
num_proc=0 behave like None, num_proc=1 uses one worker (not main process) and clarify num_proc documentation

Fixes issue #7700

This PR makes num_proc=0 behave like None in Dataset.map(), disabling multiprocessing.
It improves UX by aligning with DataLoader(num_workers=0) behavior.
The num_proc docstring is also updated to clearly explain valid values and behavior.

@SunMarc


Comments:
I think we can support num_proc=0 and make it equivalent to `None` to make it simpler

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7702). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

> I think we can support num_proc=0 and make it equivalent to `None` to make it simpler

Thank you @lhoestq for reviewing it. Please let me know if anything needs to be updated further.