rIsHu009/github-issues-updated huggingface dataset:
Title: Add `IterableDataset.push_to_hub()`
Author: lhoestq (MEMBER)
State: closed
Created: 2025-06-05T15:29:32Z

Basic implementation, which writes one shard per input dataset shard.
This is to be improved later.

Close https://github.com/huggingface/datasets/issues/5665

PS: for image/audio datasets structured as actual image/audio files (not parquet), you can sometimes speed it up with `ds.decode(num_threads=...).push_to_hub(...)`

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7595). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Add option to ignore keys/columns when loading a dataset from jsonl(or any other data format)
Author: avishaiElmakies (NONE)
State: open
Created: 2025-06-05T11:12:45Z
Labels: enhancement

### Feature request

Hi, I would like the option to ignore keys/columns when loading a dataset from files (e.g. jsonl).

### Motivation

I am working on a dataset which is built on jsonl. It seems the dataset is unclean and a column has different types in each row. I can't clean this or remove the column (It is not my data and it is too big for me to clean and save on my own hardware). 
I would like the option to just ignore this column when using `load_dataset`, since i don't need it.
I tried to look if this is already possible but couldn't find a solution. if there is I would love some help. If it is not currently possible, I would love this feature

### Your contribution

I don't think I can help this time, unfortunately.

Comments:
Good point, I'd be in favor of having the `columns` argument in `JsonConfig` (and the others) to align with `ParquetConfig` to let users choose which columns to load and ignore the rest

Is it possible to ignore columns when using parquet? 

Yes, you can pass `columns=...` to load_dataset to select which columns to load, and it is passed to `ParquetConfig` :)

Ok, i didn't know that. 
Anyway, it would be good to add this to others
Title: Fix broken link to albumentations
Author: ternaus (CONTRIBUTOR)
State: closed
Created: 2025-06-04T19:00:13Z

A few months back I rewrote all docs at [https://albumentations.ai/docs](https://albumentations.ai/docs), and some pages changed their links.

In this PR fixed link to the most recent doc in Albumentations about bounding boxes and it's format. 

Fix a few typos in the doc as well.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7593). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@lhoestq ping
Title: Remove scripts altogether
Author: lhoestq (MEMBER)
State: closed
Created: 2025-06-04T15:14:11Z

TODO:
- [x] remplace fixtures based on script with no-script fixtures
- [x] windaube

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7592). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Add num_proc parameter to push_to_hub
Author: SwayStar123 (NONE)
State: open
Created: 2025-06-04T13:19:15Z
Labels: enhancement

### Feature request

A number of processes parameter to the dataset.push_to_hub method

### Motivation

Shards are currently uploaded serially which makes it slow for many shards, uploading can be done in parallel and much faster

Comments:
Title: feat: use content defined chunking 
Author: kszucs (COLLABORATOR)
State: open
Created: 2025-05-29T18:19:41Z

WIP:
- [x] set the parameters in `io.parquet.ParquetDatasetReader`
- [x] set the parameters in `arrow_writer.ParquetWriter`

It requires a new pyarrow pin ">=21.0.0" which is not yet released.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7589). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Need to set `DEFAULT_MAX_BATCH_SIZE = 1024 * 1024`
Title: ValueError: Invalid pattern: '**' can only be an entire path component [Colab]
Author: wkambale (NONE)
State: closed
Created: 2025-05-27T13:46:05Z

### Describe the bug

I have a dataset on HF [here](https://huggingface.co/datasets/kambale/luganda-english-parallel-corpus) that i've previously used to train a translation model [here](https://huggingface.co/kambale/pearl-11m-translate).

now i changed a few hyperparameters to increase number of tokens for the model, increase Transformer layers, and all

however, when i try to load the dataset, this error keeps coming up.. i have tried everything.. i have re-written the code a hundred times, and this keep coming up

### Steps to reproduce the bug

Imports:

```bash
!pip install datasets huggingface_hub fsspec
```

Python code:

```python
from datasets import load_dataset

HF_DATASET_NAME = "kambale/luganda-english-parallel-corpus"

# Load the dataset
try:
    if not HF_DATASET_NAME or HF_DATASET_NAME == "YOUR_HF_DATASET_NAME":
        raise ValueError(
            "Please provide a valid Hugging Face dataset name."
        )

    dataset = load_dataset(HF_DATASET_NAME)

# Omitted code as the error happens on the line above 

except ValueError as ve:
    print(f"Configuration Error: {ve}")
    raise
except Exception as e:
    print(f"An error occurred while loading the dataset '{HF_DATASET_NAME}': {e}")
    raise e
```

now, i have tried going through this [issue](https://github.com/huggingface/datasets/issues/6737) and nothing helps 

### Expected behavior

loading the dataset successfully and perform splits (train, test, validation)

### Environment info

from the imports, i do not install specific versions of these libraries, so the latest or available version is installed

* `datasets` version: latest
* `Platform`: Google Colab
* `Hardware`: NVIDIA A100 GPU
* `Python` version: latest
* `huggingface_hub` version: latest
* `fsspec` version: latest

Comments:
Could you please run the following code snippet in your environment and share the exact output? This will help check for any compatibility issues within the env itself. 

```
import datasets
import huggingface_hub
import fsspec

print("datasets version:", datasets.__version__)
print("huggingface_hub version:", huggingface_hub.__version__)
print("fsspec version:", fsspec.__version__)
```

```bash
datasets version: 2.14.4
huggingface_hub version: 0.31.4
fsspec version: 2025.3.2
```

Version 2.14.4 is not the latest version available, in fact it is from August 08, 2023 (you can check here: https://pypi.org/project/datasets/#history)

Use pip install datasets==3.6.0 to install a more recent version (from May 7, 2025)

I also had the same problem with Colab, after updating to the latest version it was solved.

I hope it helps

thank you @CleitonOERocha. it sure did help.

updating `datasets` to v3.6.0 and keeping `fsspec` on v2025.3.2 eliminates the issue.

Very helpful, thank you!
Title: load_dataset splits typing
Author: lhoestq (MEMBER)
State: closed
Created: 2025-05-26T18:28:40Z

close https://github.com/huggingface/datasets/issues/7583

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7587). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: help is appreciated
Author: rajasekarnp1 (NONE)
State: open
Created: 2025-05-26T14:00:42Z
Labels: enhancement

### Feature request

https://github.com/rajasekarnp1/neural-audio-upscaler/tree/main

### Motivation

 ai model develpment and audio 

### Your contribution

   ai model develpment and audio

Comments:
how is this related to this repository ?
Title: Avoid multiple default config names
Author: albertvillanova (MEMBER)
State: closed
Created: 2025-05-26T13:27:59Z

Fix duplicating default config names.

Currently, when calling `push_to_hub(set_default=True` with 2 different config names, both are set as default. 

Moreover, this will generate an error next time we try to push another default config name, raised by `MetadataConfigs.get_default_config_name`:
https://github.com/huggingface/datasets/blob/da1db8a5b89fc0badaa0f571b36e122e52ae8c61/src/datasets/arrow_dataset.py#L5757
https://github.com/huggingface/datasets/blob/da1db8a5b89fc0badaa0f571b36e122e52ae8c61/src/datasets/utils/metadata.py#L186-L188

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7585). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Add LMDB format support
Author: trotsky1997 (NONE)
State: open
Created: 2025-05-26T07:10:13Z
Labels: enhancement

### Feature request

Add LMDB format support for large memory-mapping files

### Motivation

Add LMDB format support for large memory-mapping files

### Your contribution

I'm trying to add it

Comments:
Hi ! Can you explain what's your use case ? Is it about converting LMDB to Dataset objects (i.e. converting to Arrow) ?
Title: load_dataset type stubs reject List[str] for split parameter, but runtime supports it
Author: hierr (NONE)
State: closed
Created: 2025-05-25T02:33:18Z

### Describe the bug

The [load_dataset](https://huggingface.co/docs/datasets/v3.6.0/en/package_reference/loading_methods#datasets.load_dataset) method accepts a `List[str]` as the split parameter at runtime, however, the current type stubs restrict the split parameter to `Union[str, Split, None]`. This causes type checkers like Pylance to raise `reportArgumentType` errors when passing a list of strings, even though it works as intended at runtime.

### Steps to reproduce the bug

1. Use load_dataset with multiple splits e.g.:
```
from datasets import load_dataset

ds_train, ds_val, ds_test = load_dataset(
    "Silly-Machine/TuPyE-Dataset",
    "binary",
    split=["train[:75%]", "train[75%:]", "test"]
)
```
2. Observe that code executes correctly at runtime and Pylance raises `Argument of type "List[str]" cannot be assigned to parameter "split" of type "str | Split | None"`

### Expected behavior

The type stubs for [load_dataset](https://huggingface.co/docs/datasets/v3.6.0/en/package_reference/loading_methods#datasets.load_dataset) should accept `Union[str, Split, List[str], None]` or more specific overloads for the split parameter to correctly represent runtime behavior. 

### Environment info

- `datasets` version: 3.6.0
- Platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39
- Python version: 3.12.7
- `huggingface_hub` version: 0.32.0
- PyArrow version: 20.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0

Comments:
Title: fix: Add embed_storage in Pdf feature
Author: AndreaFrancis (CONTRIBUTOR)
State: closed
Created: 2025-05-22T14:06:29Z

Add missing `embed_storage` method in Pdf feature (Same as in Audio and Image)

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7582). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Requesting a specific split (eg: test) still downloads all (train, test, val) data when streaming=False.
Author: s3pi (NONE)
State: open
Created: 2025-05-22T11:08:16Z

### Describe the bug

When using load_dataset() from the datasets library (in load.py), specifying a particular split (e.g., split="train") still results in downloading data for all splits when streaming=False. This happens during the builder_instance.download_and_prepare() call.
This behavior leads to unnecessary bandwidth usage and longer download times, especially for large datasets, even if the user only intends to use a single split.

### Steps to reproduce the bug

dataset_name = "skbose/indian-english-nptel-v0"
dataset = load_dataset(dataset_name, token=hf_token, split="test")


### Expected behavior

Optimize the download logic so that only the required split is downloaded when streaming=False when a specific split is provided.

### Environment info

Dataset: skbose/indian-english-nptel-v0
Platform: M1 Apple Silicon
Python verison: 3.12.9
datasets>=3.5.0

Comments:
Hi ! There was a PR open to improve this: https://github.com/huggingface/datasets/pull/6832 
but it hasn't been continued so far.

It would be a cool improvement though !
Title: Fix typos in PDF and Video documentation
Author: AndreaFrancis (CONTRIBUTOR)
State: closed
Created: 2025-05-22T02:27:40Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7579). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: arrow_schema is not compatible with list
Author: jonathanshen-upwork (NONE)
State: closed
Created: 2025-05-21T16:37:01Z

### Describe the bug

```
import datasets
f = datasets.Features({'x': list[datasets.Value(dtype='int32')]})
f.arrow_schema

Traceback (most recent call last):
  File "datasets/features/features.py", line 1826, in arrow_schema
    return pa.schema(self.type).with_metadata({"huggingface": json.dumps(hf_metadata)})
                     ^^^^^^^^^
  File "datasets/features/features.py", line 1815, in type
    return get_nested_type(self)
           ^^^^^^^^^^^^^^^^^^^^^
  File "datasets/features/features.py", line 1252, in get_nested_type
    return pa.struct(
           ^^^^^^^^^^
  File "pyarrow/types.pxi", line 5406, in pyarrow.lib.struct
  File "pyarrow/types.pxi", line 3890, in pyarrow.lib.field
  File "pyarrow/types.pxi", line 5918, in pyarrow.lib.ensure_type
TypeError: DataType expected, got <class 'list'>
```

The following works
```
f = datasets.Features({'x': datasets.LargeList(datasets.Value(dtype='int32'))})
```

### Expected behavior

according to https://github.com/huggingface/datasets/blob/458f45a22c3cc9aea5f442f6f519333dcfeae9b9/src/datasets/features/features.py#L1765 python list should be a valid type specification for features

### Environment info

- `datasets` version: 3.5.1
- Platform: macOS-15.5-arm64-arm-64bit
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0

Comments:
Thanks for reporting, I'll look into it

Actually it looks like you just forgot parenthesis:

```diff
- f = datasets.Features({'x': list[datasets.Value(dtype='int32')]})
+ f = datasets.Features({'x': list([datasets.Value(dtype='int32')])})
```

or simply using the `[ ]` syntax:

```python
f = datasets.Features({'x':[datasets.Value(dtype='int32')]})
```

I'm closing this issue if you don't mind

Ah is that what the syntax is? I don't think I was able to find an actual example of it so I assumed it was in the same way that you specify types eg. `list[int]`. This is good to know, thanks.
Title: Fix regex library warnings
Author: emmanuel-ferdman (CONTRIBUTOR)
State: closed
Created: 2025-05-21T14:31:58Z

# PR Summary
This small PR resolves the regex library warnings showing starting Python3.11:
```python
DeprecationWarning: 'count' is passed as positional argument
```

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7576). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Missing multilingual directions in IWSLT2017 dataset's processing script
Author: andy-joy-25 (NONE)
State: open
Created: 2025-05-21T09:53:17Z

### Describe the bug

Hi, 

Upon using `iwslt2017.py` in `IWSLT/iwslt2017` on the Hub for loading the datasets, I am unable to obtain the datasets for the language pairs `de-it`, `de-ro`, `de-nl`, `it-de`, `nl-de`, and `ro-de` using it. These 6 pairs do not show up when using `get_dataset_config_names()` to obtain the list of all the configs present in `IWSLT/iwslt2017`. This should not be the case since as mentioned in their original paper (please see https://aclanthology.org/2017.iwslt-1.1.pdf), the authors specify that "_this year we proposed the multilingual translation between any pair of languages from {Dutch, English, German, Italian, Romanian}..._" and because these datasets are indeed present in `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip`.

Best Regards,
Anand

### Steps to reproduce the bug

Check the output of `get_dataset_config_names("IWSLT/iwslt2017", trust_remote_code=True)`: only 24 language pairs are present and the following 6 config names are absent: `iwslt2017-de-it`, `iwslt2017-de-ro`, `iwslt2017-de-nl`, `iwslt2017-it-de`, `iwslt2017-nl-de`, and `iwslt2017-ro-de`.

### Expected behavior

The aforementioned 6 language pairs should also be present and hence, all these 6 language pairs' IWSLT2017 datasets must also be available for further use. 

I would suggest removing `de` from the `BI_LANGUAGES` list and moving it over to the `MULTI_LANGUAGES` list instead in `iwslt2017.py` to account for all the 6 missing language pairs (the same `de-en` dataset is present in both `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip` and `data/2017-01-trnted/texts/de/en/de-en.zip` but the `de-ro`, `de-nl`, `it-de`, `nl-de`, and `ro-de` datasets are only present in  `data/2017-01-trnmted/texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.zip`: so, its unclear why the following comment: _`# XXX: Artificially removed DE from here, as it also exists within bilingual data`_ has been added as `L71` in `iwslt2017.py`). The `README.md` file in `IWSLT/iwslt2017`must then be re-created using `datasets-cli test path/to/iwslt2017.py --save_info --all_configs` to pass all split size verification checks for the 6 new language pairs which were previously non-existent. 

### Environment info

- `datasets` version: 3.5.0
- Platform: Linux-6.8.0-56-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.30.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0

Comments:
I have opened 2 PRs on the Hub: `https://huggingface.co/datasets/IWSLT/iwslt2017/discussions/7` and `https://huggingface.co/datasets/IWSLT/iwslt2017/discussions/8` to resolve this issue

cool ! I pinged the owners of the dataset on HF to merge your PRs :)
Title: No Samsum dataset
Author: IgorKasianenko (NONE)
State: closed
Created: 2025-05-20T09:54:35Z

### Describe the bug

https://huggingface.co/datasets/Samsung/samsum dataset not found error 404

Originated from https://github.com/meta-llama/llama-cookbook/issues/948

### Steps to reproduce the bug

go to website https://huggingface.co/datasets/Samsung/samsum
see the error

also downloading it with python throws 
```
Couldn't find 'Samsung/samsum' on the Hugging Face Hub either: FileNotFoundError: Samsung/samsum@f00baf5a7d4abfec6820415493bcb52c587788e6/samsum.py (repository not found)
```

### Expected behavior

Dataset exists

### Environment info

```
- `datasets` version: 3.2.0
- Platform: macOS-15.4.1-arm64-arm-64bit
- Python version: 3.12.2
- `huggingface_hub` version: 0.26.5
- PyArrow version: 16.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
```

Comments:
According to the following https://huggingface.co/posts/seawolf2357/424129432408590, as of now the dataset seems to be inaccessible.

@IgorKasianenko, would https://huggingface.co/datasets/knkarthick/samsum suffice for your purpose?


Thanks @SP1029 for the update!
That will work for now, using it as replacement. Is there a officially recommended way to maintain the CC licensed dataset under the organization account? 
Feel free to close this issue

> Is there an officially recommended way to maintain a CC-licensed dataset under an organizational account?

@IgorKasianenko, apologies, this is not my area of expertise.

> Please feel free to close this issue.

I have limited access and may not be able to do that. Since you opened it, you would be able to close it.
Title: Fixed typos
Author: TopCoder2K (CONTRIBUTOR)
State: closed
Created: 2025-05-19T17:16:59Z

More info: [comment](https://github.com/huggingface/datasets/pull/7564#issuecomment-2863391781).

Comments:
@lhoestq, mentioning in case you haven't seen this PR. The contribution is very small and easy to check :)
Title: fix string_to_dict test
Author: lhoestq (MEMBER)
State: closed
Created: 2025-05-19T14:49:23Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7571). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: add check if repo exists for dataset uploading
Author: Samoed (NONE)
State: open
Created: 2025-05-09T10:27:00Z

Currently, I'm reuploading datasets for [`MTEB`](https://github.com/embeddings-benchmark/mteb/). Some of them have many splits (more than 20), and I'm encountering the error:
`Too many requests for https://huggingface.co/datasets/repo/create`.

It seems that this issue occurs because the dataset tries to recreate itself every time a split is uploaded. To resolve this, I've added a check to ensure that if the dataset already exists, it won't attempt to recreate it.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7565). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@lhoestq Can you review, please? I don't think that errors in CI are related to my changes
Title: Implementation of iteration over values of a column in an IterableDataset object
Author: TopCoder2K (CONTRIBUTOR)
State: closed
Created: 2025-05-08T14:59:22Z

Refers to [this issue](https://github.com/huggingface/datasets/issues/7381).

Comments:
A couple of questions:
1. I've noticed two strange things: 1) "Around 80% of the final dataset is made of the `en_dataset`" in https://huggingface.co/docs/datasets/stream, 2) "Click on "Pull request" to send your to the project maintainers" in https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md Are `en_dataset` and "your [???]" typos? If so, I can fix them in this PR.
2. Should I update https://huggingface.co/docs/datasets/stream or https://huggingface.co/docs/datasets/access#iterabledataset to include the new feature?

Great ! and chained indexing was easy indeed, thanks :)

regarding your questions:

> I've noticed two strange things: 1) "Around 80% of the final dataset is made of the en_dataset" in https://huggingface.co/docs/datasets/stream, 2) "Click on "Pull request" to send your to the project maintainers" in https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md Are en_dataset and "your [???]" typos? If so, I can fix them in this PR.

Oh good catch, both should be fixed indeed. Feel free to open a new PR for those docs fixes

> Should I update https://huggingface.co/docs/datasets/stream or https://huggingface.co/docs/datasets/access#iterabledataset to include the new feature?

Yep good idea, I think in both places, since /stream is supposed to be exhaustive, and /access already mentions accessing a specific column for `Dataset`

@lhoestq, thank you for the answers!

> Yep good idea, I think in both places, since /stream is supposed to be exhaustive, and /access already mentions accessing a specific column for Dataset

👍, I'll try to add something.

By the way, do you have any ideas about why the CI pipelines have failed? Essentially, I've already encountered these problems [here](https://github.com/huggingface/datasets/issues/7381#issuecomment-2863421974).
I think `check_code_quality` has failed due to the usage of `pre-commit`. The problem seems to be the old version of the ruff hook. I've tried `v0.11.8` (the one that was installed with `pip install -e ".[quality]"`) and `pre-commit` seems to work like `make style` now. However, I don't have any ideas about `pyav` since I don't know what it is...

I've updated /stream and /access, please check the style and clarity. By the way, I would like to add `IterableDataset.skip` near `IterableDataset.take` to mimic [slicing](https://huggingface.co/docs/datasets/access/#slicing). What do you think?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7564). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: release: 3.6.0
Author: lhoestq (MEMBER)
State: closed
Created: 2025-05-07T15:15:13Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7562). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: NotImplementedError: <class 'datasets.iterable_dataset.RepeatExamplesIterable'> doesn't implement num_shards yet
Author: cyanic-selkie (NONE)
State: closed
Created: 2025-05-07T15:05:42Z

### Describe the bug

When using `.repeat()` on an `IterableDataset`, this error gets thrown. There is [this thread](https://discuss.huggingface.co/t/making-an-infinite-iterabledataset/146192/5) that seems to imply the fix is trivial, but I don't know anything about this codebase, so I'm opening this issue rather than attempting to open a PR.

### Steps to reproduce the bug

1. Create an `IterableDataset`.
2. Call `.repeat(None)` on it.
3. Wrap it in a pytorch `DataLoader`
4. Iterate over it.

### Expected behavior

This should work normally.

### Environment info

datasets: 3.5.0

Comments:
Title: fix decoding tests
Author: lhoestq (MEMBER)
State: closed
Created: 2025-05-07T14:56:14Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7560). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: fix aiohttp import
Author: lhoestq (MEMBER)
State: closed
Created: 2025-05-07T14:31:32Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7559). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: fix regression
Author: lhoestq (MEMBER)
State: closed
Created: 2025-05-07T13:56:03Z

reported in https://github.com/huggingface/datasets/pull/7557 (I just reorganized the condition)

wanted to apply this change to the original PR but github didn't let me apply it directly - merging this one instead

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7558). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: check for empty _formatting
Author: winglian (CONTRIBUTOR)
State: closed
Created: 2025-05-07T13:22:37Z

Fixes a regression from #7553 breaking shuffling of iterable datasets 
<img width="884" alt="Screenshot 2025-05-07 at 9 16 52 AM" src="https://github.com/user-attachments/assets/d2f43c5f-4092-4efe-ac31-a32cbd025fe3" />

Comments:
Thanks for reporting and for the fix ! I tried to reorganize the condition in your PR but didn't get the right permission so. I ended up merging https://github.com/huggingface/datasets/pull/7558 directly so I can make a release today - I hope you don't mind
Title: Add `--merge-pull-request` option for `convert_to_parquet`
Author: klamike (NONE)
State: open
Created: 2025-05-06T18:05:05Z

Closes #7527 

Note that this implementation **will only merge the last PR in the case that they get split up by `push_to_hub`**. See https://github.com/huggingface/datasets/discussions/7555 for more details.

Comments:
This is ready for a review, happy to make any changes. The main question for maintainers is how this should interact with #7555. If my suggestion there is accepted, this PR can be kept as is. If not, more changes are required to merge all the PR parts.
Title: datasets downloads and generates all splits, even though a single split is requested (for dataset with loading script)
Author: sei-eschwartz (NONE)
State: closed
Created: 2025-05-06T14:43:38Z

### Describe the bug

`datasets` downloads and generates all splits, even though a single split is requested.  [This](https://huggingface.co/datasets/jordiae/exebench) is the dataset in question.  It uses a loading script.  I am not 100% sure that this is a bug, because maybe with loading scripts `datasets` must actually process all the splits?  But I thought loading scripts were designed to avoid this.

### Steps to reproduce the bug

See [this notebook](https://colab.research.google.com/drive/14kcXp_hgcdj-kIzK0bCG6taE-CLZPVvq?usp=sharing)

Or:
```python
from datasets import load_dataset
dataset = load_dataset('jordiae/exebench', split='test_synth', trust_remote_code=True)
```

### Expected behavior

I expected only the `test_synth` split to be downloaded and processed.

### Environment info

- `datasets` version: 3.5.1
- Platform: Linux-6.1.123+-x86_64-with-glibc2.35
- Python version: 3.11.12
- `huggingface_hub` version: 0.30.2
- PyArrow version: 18.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2025.3.0

Comments:
Hi ! there has been some effort on allowing to download only a subset of splits in https://github.com/huggingface/datasets/pull/6832 but no one has been continuing this work so far. This would be a welcomed contribution though

Also note that loading script are often unoptimized, and we recommend using datasets in standard formats like Parquet instead.

Btw there is a CLI tool to convert a loading script to parquet:

```
datasets-cli convert_to_parquet <dataset-name> --trust_remote_code
```

Closing in favor of #6832
Title: Rebatch arrow iterables before formatted iterable
Author: lhoestq (MEMBER)
State: closed
Created: 2025-05-06T13:59:58Z

close https://github.com/huggingface/datasets/issues/7538 and https://github.com/huggingface/datasets/issues/7475

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7553). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@lhoestq Our CI found an issue with this changeset causing a regression with shuffling iterable datasets 
<img width="884" alt="Screenshot 2025-05-07 at 9 16 52 AM" src="https://github.com/user-attachments/assets/bf7d9c7e-cc14-47da-8da6-d1a345992d7c" />
Title: Enable xet in push to hub
Author: lhoestq (MEMBER)
State: closed
Created: 2025-05-05T17:02:09Z

follows https://github.com/huggingface/huggingface_hub/pull/3035

related to https://github.com/huggingface/datasets/issues/7526

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7552). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Issue with offline mode and partial dataset cached
Author: nrv (NONE)
State: open
Created: 2025-05-04T16:49:37Z

### Describe the bug

Hi, 

a issue related to #4760 here when loading a single file from a dataset, unable to access it in offline mode afterwards

### Steps to reproduce the bug

```python
import os
# os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["HF_TOKEN"] = "xxxxxxxxxxxxxx"

import datasets

dataset_name = "uonlp/CulturaX"
data_files = "fr/fr_part_00038.parquet"

ds = datasets.load_dataset(dataset_name, split='train', data_files=data_files)
print(f"Dataset loaded   : {ds}")
```
Once the file has been cached, I rerun with the HF_HUB_OFFLINE activated an get this error : 
```
ValueError: Couldn't find cache for uonlp/CulturaX for config 'default-1e725f978350254e'
Available configs in the cache: ['default-2935e8cdcc21c613']
```

### Expected behavior

Should be able to access the previously cached files

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-5.4.0-215-generic-x86_64-with-glibc2.31
- Python version: 3.12.0
- `huggingface_hub` version: 0.27.0
- PyArrow version: 19.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1

Comments:
It seems the problem comes from builder.py / create_config_id()

On the first call, when the cache is empty we have
```
config_kwargs = {'data_files': {'train': ['hf://datasets/uonlp/CulturaX@6a8734bc69fefcbb7735f4f9250f43e4cd7a442e/fr/fr_part_00038.parquet']}}
```
leading to config_id beeing 'default-2935e8cdcc21c613'

then, on the second call, 
```
config_kwargs = {'data_files': 'fr/fr_part_00038.parquet'}
```
thus explaining why the hash is not the same, despite having the same parameter when calling load_dataset : data_files="fr/fr_part_00038.parquet"

Same behavior with version 3.5.1

Same issue when loading `google/IndicGenBench_flores_in` with `dataset==2.21.0` and `dataset==3.6.0` .




> It seems the problem comes from builder.py / create_config_id()
> 
> On the first call, when the cache is empty we have
> 
> ```
> config_kwargs = {'data_files': {'train': ['hf://datasets/uonlp/CulturaX@6a8734bc69fefcbb7735f4f9250f43e4cd7a442e/fr/fr_part_00038.parquet']}}
> ```
> 
> leading to config_id beeing 'default-2935e8cdcc21c613'
> 
> then, on the second call,
> 
> ```
> config_kwargs = {'data_files': 'fr/fr_part_00038.parquet'}
> ```
> 
> thus explaining why the hash is not the same, despite having the same parameter when calling load_dataset : data_files="fr/fr_part_00038.parquet"


I have identified that the issue indeed lies in the `data_files` within `config_kwargs`. 
The format and prefix of `data_files` differ depending on whether `HF_HUB_OFFLINE` is set, leading to different final `config_id` values. 
When I use other datasets without passing the `data_files` parameter, this issue does not occur.

A possible solution might be to standardize the formatting of `data_files` within the `create_config_id` function.
Title: Avoid global umask for setting file mode.
Author: ryan-clancy (CONTRIBUTOR)
State: closed
Created: 2025-05-01T22:24:24Z

This PR updates the method for setting the permissions on `cache_path` after calling `shutil.move`. The call to `shutil.move` may not preserve permissions if the source and destination are on different filesystems. Reading and resetting umask can cause race conditions, so directly read what permissions were set for the `temp_file` instead.

This fixes https://github.com/huggingface/datasets/issues/7536.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7547). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Large memory use when loading large datasets to a ZFS pool
Author: FredHaa (NONE)
State: closed
Created: 2025-05-01T14:43:47Z

### Describe the bug

When I load large parquet based datasets from the hub like `MLCommons/peoples_speech` using `load_dataset`, all my memory (500GB) is used and isn't released after loading, meaning that the process is terminated by the kernel if I try to load an additional dataset. This makes it impossible to train models using multiple large datasets.

### Steps to reproduce the bug

`uv run --with datasets==3.5.1 python`

```python
from datasets import load_dataset
load_dataset('MLCommons/peoples_speech', 'clean')
load_dataset('mozilla-foundation/common_voice_17_0', 'en')
```

### Expected behavior

I would expect that a lot less than 500GB of RAM would be required to load the dataset, or at least that the RAM usage would be cleared as soon as the dataset is loaded (and thus reside as a memory mapped file) such that other datasets can be loaded.

### Environment info

I am currently using the latest datasets==3.5.1 but I have had the same problem with multiple other versions.

Comments:
Hi ! datasets are memory mapped from disk, so they don't fill out your RAM. Not sure what's the source of your memory issue.

What kind of system are you using ? and what kind of disk ?

Well, the fact of the matter is that my RAM is getting filled out by running the given example, as shown in [this video](https://streamable.com/usb0ql).

My system is a GPU server running Ubuntu. The disk is a SATA SSD attached to the server using a backplane. It is formatted with ZFS, mounted in /cache, and my HF_HOME is set to /cache/hf

I really need this fixed, so I am more than willing to test out various suggestions you might have, or write a PR if we can figure out what is going on.

I'm not super familiar with ZFS, but it looks like it loads the data in memory when the files are memory mapped, which is an issue.

Maybe it's a caching mechanism ? Since `datasets` accesses every memory mapped file to read a small part (the metadata of the arrow record batches), maybe ZFS brings the whole files in memory for quicker subsequent reads. This is an antipattern when it comes to lazy loading datasets of that size though

This is the answer.

I tried changing my HF_HOME to an NFS share, and no RAM is then consumed loading the dataset.

I will try to see if I can find a way to configure the ZFS pool to not cache the files (disabling the ARC/primary cache didn't work), and if I do write the solution in this issue. If I can't I guess I have to reformat my cache drive.
Title: Networked Pull Through Cache
Author: wrmedford (NONE)
State: open
Created: 2025-04-30T15:16:33Z
Labels: enhancement

### Feature request

Introduce a HF_DATASET_CACHE_NETWORK_LOCATION configuration (e.g. an environment variable) together with a companion network cache service.

Enable a three-tier cache lookup for datasets:

1. Local on-disk cache
2. Configurable network cache proxy
3. Official Hugging Face Hub

### Motivation

- Distributed training & ephemeral jobs: In high-performance or containerized clusters, relying solely on a local disk cache either becomes a streaming bottleneck or incurs a heavy cold-start penalty as each job must re-download datasets.
- Traffic & cost reduction: A pull-through network cache lets multiple consumers share a common cache layer, reducing duplicate downloads from the Hub and lowering egress costs.
- Better streaming adoption: By offloading repeat dataset pulls to a locally managed cache proxy, streaming workloads can achieve higher throughput and more predictable latency.
- Proven pattern: Similar proxy-cache solutions (e.g. Harbor’s Proxy Cache for Docker images) have demonstrated reliability and performance at scale: https://goharbor.io/docs/2.1.0/administration/configure-proxy-cache/

### Your contribution

I’m happy to draft the initial PR for adding HF_DATASET_CACHE_NETWORK_LOCATION support in datasets and sketch out a minimal cache-service prototype.

I have limited bandwidth so I would be looking for collaborators if anyone else is interested.

Comments:
Title: Add try_original_type to DatasetDict.map
Author: yoshitomo-matsubara (CONTRIBUTOR)
State: closed
Created: 2025-04-29T04:39:44Z

This PR resolves #7472 for DatasetDict 

The previously merged PR #7483 added `try_original_type` to ArrowDataset, but DatasetDict misses `try_original_type`

Cc: @lhoestq

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7544). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Sure! I just committed the changes

@lhoestq 
Let me know if there are other things to do before merge or other places to add `try_original_type` argument
Title: The memory-disk mapping failure issue of the map function（resolved, but there are some suggestions.）
Author: jxma20 (NONE)
State: closed
Created: 2025-04-29T03:04:59Z

### Describe the bug

## bug
When the map function processes a large dataset, it temporarily stores the data in a cache file on the disk. After the data is stored, the memory occupied by it is released. Therefore, when using the map function to process a large-scale dataset, only a dataset space of the size of `writer_batch_size` will be occupied in memory.

However, I found that the map function does not actually reduce memory usage when I used it. At first, I thought there was a bug in the program, causing a memory leak—meaning the memory was not released after the data was stored in the cache. But later, I used a Linux command to check for recently modified files during program execution and found that no new files were created or modified. This indicates that the program did not store the dataset in the disk cache.
## bug solved
After modifying the parameters of the map function multiple times, I discovered the `cache_file_name` parameter. By changing it, the cache file can be stored in the specified directory. After making this change, I noticed that the cache file appeared. Initially, I found this quite incredible, but then I wondered if the cache file might have failed to be stored in a certain folder. This could be related to the fact that I don't have root privileges.

So, I delved into the source code of the map function to find out where the cache file would be stored by default. Eventually, I found the function `def _get_cache_file_path(self, fingerprint):`, which automatically generates the storage path for the cache file. The output was as follows: `/tmp/hf_datasets-j5qco9ug/cache-f2830487643b9cc2.arrow`. My hypothesis was confirmed: the lack of root privileges indeed prevented the cache file from being stored, which in turn prevented the release of memory. Therefore, changing the storage location to a folder where I have write access resolved the issue.

### Steps to reproduce the bug

my code
`train_data = train_data.map(process_fun, remove_columns=['image_name', 'question_type', 'concern', 'question', 'candidate_answers', 'answer'])`

### Expected behavior

Although my bug has been resolved, it still took me nearly a week to search for relevant information and debug the program. However, if a warning or error message about insufficient cache file write permissions could be provided during program execution, I might have been able to identify the cause more quickly. Therefore, I hope this aspect can be improved. I am documenting this bug here so that friends who encounter similar issues can solve their problems in a timely manner.

### Environment info

python: 3.10.15
datasets: 3.5.0

Comments:
Title: release: 3.5.1
Author: lhoestq (MEMBER)
State: closed
Created: 2025-04-28T14:00:59Z


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7541). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: support pyarrow 20
Author: lhoestq (MEMBER)
State: closed
Created: 2025-04-28T13:01:11Z

fix

```
TypeError: ArrayExtensionArray.to_pylist() got an unexpected keyword argument 'maps_as_pydicts'
```

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7540). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Fix IterableDataset state_dict shard_example_idx counting
Author: Harry-Yang0518 (CONTRIBUTOR)
State: closed
Created: 2025-04-27T20:41:18Z

# Fix IterableDataset's state_dict shard_example_idx reporting

## Description

This PR fixes issue #7475 where the `shard_example_idx` value in `IterableDataset`'s `state_dict()` always equals the number of samples in a shard, even if only a few examples have been consumed.

The issue is in the `_iter_arrow` method of the `ArrowExamplesIterable` class where it updates the `shard_example_idx` state by the full length of the batch (`len(pa_table)`) even when we're only partway through processing the examples. 

## Changes

Modified the `_iter_arrow` method of `ArrowExamplesIterable` to:
1. Track the actual number of examples processed
2. Only increment the `shard_example_idx` by the number of examples actually yielded
3. Handle partial batches correctly

## How to Test

I've included a simple test case that demonstrates the fix:

```python
from datasets import Dataset

# Create a test dataset
ds = Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=1)

# Iterate through part of the dataset
for idx, example in enumerate(ds):
    print(example)
    if idx == 2:  # Stop after 3 examples (0, 1, 2)
        state_dict = ds.state_dict()
        print("Checkpoint state_dict:", state_dict)
        break

# Before the fix, the output would show shard_example_idx: 6
# After the fix, it shows shard_example_idx: 3, correctly reflecting the 3 processed examples
```

## Implementation Details

1. Added logic to track the number of examples actually seen in the current shard
2. Modified the state update to only count examples actually yielded
3. Improved handling of partial batches and skipped examples

This fix ensures that checkpointing and resuming works correctly with exactly the expected number of examples, rather than skipping ahead to the end of the batch.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7539). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Hi ! FYI I made a PR to fix https://github.com/huggingface/datasets/issues/7538 and it also fixed https://github.com/huggingface/datasets/issues/7475, so if I'm not mistaken this PR is not needed anymore
Title: `IterableDataset` drops samples when resuming from a checkpoint
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2025-04-27T19:34:49Z
Labels: bug

When resuming from a checkpoint, `IterableDataset` will drop samples if `num_shards % world_size == 0` and the underlying example supports `iter_arrow` and needs to be formatted. 

In that case, the `FormattedExamplesIterable` fetches a batch of samples from the child iterable's `iter_arrow` and yields them one by one (after formatting). However, the child increments the `shard_example_idx` counter (in its `iter_arrow`) before returning the batch for the whole batch size, which leads to a portion of samples being skipped if the iteration (of the parent iterable) is stopped mid-batch. 

Perhaps one way to avoid this would be by signalling the child iterable which samples (within the chunk) are processed by the parent and which are not, so that it can adjust the `shard_example_idx` counter accordingly. This would also mean the chunk needs to be sliced when resuming, but this is straightforward to implement.

The following is a minimal reproducer of the bug:
```python
from datasets import Dataset
from datasets.distributed import split_dataset_by_node

ds = Dataset.from_dict({"n": list(range(24))})
ds = ds.to_iterable_dataset(num_shards=4)

world_size = 4
rank = 0    
ds_rank = split_dataset_by_node(ds, rank, world_size)

it = iter(ds_rank)
examples = []
for idx, example in enumerate(it):
    examples.append(example)
    if idx == 2:
        state_dict = ds_rank.state_dict()
        break

ds_rank.load_state_dict(state_dict)
it_resumed = iter(ds_rank)
examples_resumed = examples[:]

for example in it:
    examples.append(example)

for example in it_resumed:
    examples_resumed.append(example)

print("ORIGINAL ITER EXAMPLES:", examples)
print("RESUMED ITER EXAMPLES:", examples_resumed)
```

Comments:
Thanks for reporting ! I fixed the issue using RebatchedArrowExamplesIterable before the formatted iterable