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chore(deps): update dependency datasets to v4.8.3#105

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chore(deps): update dependency datasets to v4.8.3#105
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This PR contains the following updates:

Package Change Age Confidence
datasets ==4.0.0 -> ==4.8.3 age confidence

Release Notes

huggingface/datasets (datasets)

v4.8.3

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What's Changed

Full Changelog: huggingface/datasets@4.8.2...4.8.3

v4.8.2

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What's Changed

Full Changelog: huggingface/datasets@4.8.1...4.8.2

v4.8.1

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What's Changed

Full Changelog: huggingface/datasets@4.8.0...4.8.1

v4.8.0

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Dataset Features

  • Read (and write) from HF Storage Buckets: load raw data, process and save to Dataset Repos by @​lhoestq in #​8064

    from datasets import load_dataset
# load raw data from a Storage Bucket on HF
ds = load_dataset("buckets/username/data-bucket", data_files=["*.jsonl"])
# or manually, using hf:// paths
ds = load_dataset("json", data_files=["hf://buckets/username/data-bucket/*.jsonl"])
# process, filter
ds = ds.map(...).filter(...)
# publish the AI-ready dataset
ds.push_to_hub("username/my-dataset-ready-for-training")

    This also fixes multiprocessed push_to_hub on macos that was causing segfault (now it uses spawn instead of fork).
    And it bumps dill and multiprocess versions to support python 3.14

  • Datasets streaming iterable packaged improvements and fixes by @​Michael-RDev in #​8068

    • added max_shard_size to IterableDataset.push_to_hub (but requires iterating twice to know the full dataset twice - improvements are welcome)
    • more arrow-native iterable operations for IterableDataset
    • better support of glob patterns in archives, e.g. zip://*.jsonl::hf://datasets/username/dataset-name/data.zip
    • fixes for to_pandas, videofolder, load_dataset_builder kwargs

What's Changed

New Contributors

Full Changelog: huggingface/datasets@4.7.0...4.8.0

v4.7.0

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Datasets Features

  • Add Json() type by @​lhoestq in #​8027
    • JSON Lines files that contain arbitrary JSON objects like tool calling datasets are now supported. When there is a field or subfield containing mixed types (e.g. mix of str/int/float/dict/list or dictionaries with arbitrary keys), the Json()type is used to store such data that would normally not be supported in Arrow/Parquet
    • Use the Json() type in Features() for any dataset, it is supported in any functions that accepts features=like load_dataset(), .map(), .cast(), .from_dict(), .from_list()
    • Use on_mixed_types="use_json" to automatically set the Json() type on mixed types in .from_dict(), .from_list() and .map()

Examples:

You can use on_mixed_types="use_json" or specify features= with a [Json] type:

>>> ds = Dataset.from_dict({"a": [0, "foo", {"subfield": "bar"}]})
Traceback (most recent call last):
  ...
  File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Could not convert 'foo' with type str: tried to convert to int64

>>> features = Features({"a": Json()})
>>> ds = Dataset.from_dict({"a": [0, "foo", {"subfield": "bar"}]}, features=features)
>>> ds.features
{'a': Json()}
>>> list(ds["a"])
[0, "foo", {"subfield": "bar"}]

This is also useful for lists of dictionaries with arbitrary keys and values, to avoid filling missing fields with None:

>>> ds = Dataset.from_dict({"a": [[{"b": 0}, {"c": 0}]]})
>>> ds.features
{'a': List({'b': Value('int64'), 'c': Value('int64')})}
>>> list(ds["a"])
[[{'b': 0, 'c': None}, {'b': None, 'c': 0}]]  # missing fields are filled with None

>>> features = Features({"a": List(Json())})
>>> ds = Dataset.from_dict({"a": [[{"b": 0}, {"c": 0}]]}, features=features)
>>> ds.features
{'a': List(Json())}
>>> list(ds["a"])
[[{'b': 0}, {'c': 0}]]  # OK

Another example with tool calling data and the on_mixed_types="use_json" argument (useful to not have to specify features= manually):

>>> messages = [
...     {"role": "user", "content": "Turn on the living room lights and play my electronic music playlist."},
...     {"role": "assistant", "tool_calls": [
...         {"type": "function", "function": {
...             "name": "control_light",
...             "arguments": {"room": "living room", "state": "on"}
...         }},
...         {"type": "function", "function": {
...             "name": "play_music",
...             "arguments": {"playlist": "electronic"}  # mixed-type here since keys ["playlist"] and ["room", "state"] are different
...         }}]
...     },
...     {"role": "tool", "name": "control_light", "content": "The lights in the living room are now on."},
...     {"role": "tool", "name": "play_music", "content": "The music is now playing."},
...     {"role": "assistant", "content": "Done!"}
... ]
>>> ds = Dataset.from_dict({"messages": [messages]}, on_mixed_types="use_json")
>>> ds.features
{'messages': List({'role': Value('string'), 'content': Value('string'), 'tool_calls': List(Json()), 'name': Value('string')})}
>>> ds[0][1]["tool_calls"][0]["function"]["arguments"]
{"room": "living room", "state": "on"}

What's Changed

New Contributors

Full Changelog: huggingface/datasets@4.6.1...4.7.0

v4.6.1

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Bug fix

Full Changelog: huggingface/datasets@4.6.0...4.6.1

v4.6.0

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Dataset Features

  • Support Image, Video and Audio types in Lance datasets

    >>> from datasets import load_dataset
>>> ds = load_dataset("lance-format/Openvid-1M", streaming=True, split="train")
>>> ds.features
{'video_blob': Video(),
 'video_path': Value('string'),
 'caption': Value('string'),
 'aesthetic_score': Value('float64'),
 'motion_score': Value('float64'),
 'temporal_consistency_score': Value('float64'),
 'camera_motion': Value('string'),
 'frame': Value('int64'),
 'fps': Value('float64'),
 'seconds': Value('float64'),
 'embedding': List(Value('float32'), length=1024)}

  • Push to hub now supports Video types

     >>> from datasets import Dataset, Video
>>> ds = Dataset.from_dict({"video": ["path/to/video.mp4"]})
>>> ds = ds.cast_column("video", Video())
>>> ds.push_to_hub("username/my-video-dataset")

  • Write image/audio/video blobs as is in parquet (PLAIN) in push_to_hub() by @​lhoestq in #​7976

    • this enables cross-format Xet deduplication for image/audio/video, e.g. deduplicate videos between Lance, WebDataset, Parquet files and plain video files and make downloads and uploads faster to Hugging Face
    • E.g. if you convert a Lance video dataset to a Parquet video dataset on Hugging Face, the upload will be much faster since videos don't need to be reuploaded. Under the hood, the Xet storage reuses the binary chunks from the videos in Lance format for the videos in Parquet format
    • See more info here: https://huggingface.co/docs/hub/en/xet/deduplication

image

  • Add IterableDataset.reshard() by @​lhoestq in #​7992

    Reshard the dataset if possible, i.e. split the current shards further into more shards.
    This increases the number of shards and the resulting dataset has num_shards >= previous_num_shards.
    Equality may happen if no shard can be split further.

    The resharding mechanism depends on the dataset file format:

    • Parquet: shard per row group instead of per file
    • Other: not implemented yet (contributions are welcome !)
    >>> from datasets import load_dataset
>>> ds = load_dataset("fancyzhx/amazon_polarity", split="train", streaming=True)
>>> ds
IterableDataset({
    features: ['label', 'title', 'content'],
    num_shards: 4
})
>>> ds.reshard()
IterableDataset({
    features: ['label', 'title', 'content'],
    num_shards: 3600
})

What's Changed

New Contributors

Full Changelog: huggingface/datasets@4.5.0...4.6.0

v4.5.0

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Dataset Features

  • Add lance format support by @​eddyxu in #​7913

    • Support for both Lance dataset (including metadata / manifests) and standalone .lance files
    • e.g. with lance-format/fineweb-edu
    from datasets import load_dataset

ds = load_dataset("lance-format/fineweb-edu", streaming=True)
for example in ds["train"]:
    ...

What's Changed

New Contributors

Full Changelog: huggingface/datasets@4.4.2...4.5.0

v4.4.2

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Bug fixes

Minor additions

New Contributors

Full Changelog: huggingface/datasets@4.4.1...4.4.2

v4.4.1

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Bug fixes and improvements

Full Changelog: huggingface/datasets@4.4.0...4.4.1

v4.4.0

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Dataset Features

  • Add nifti support by @​CloseChoice in #​7815

    • Load medical imaging datasets from Hugging Face:
    ds = load_dataset("username/my_nifti_dataset")
ds["train"][0]  # {"nifti": <nibabel.nifti1.Nifti1Image>}
    • Load medical imaging datasets from your disk:
    files = ["/path/to/scan_001.nii.gz", "/path/to/scan_002.nii.gz"]
ds = Dataset.from_dict({"nifti": files}).cast_column("nifti", Nifti())
ds["train"][0]  # {"nifti": <nibabel.nifti1.Nifti1Image>}

  • Add num channels to audio by @​CloseChoice in #​7840

# samples have shape (num_channels, num_samples)
ds = ds.cast_column("audio", Audio())  # default, use all channels
ds = ds.cast_column("audio", Audio(num_channels=2))  # use stereo
ds = ds.cast_column("audio", Audio(num_channels=1))  # use mono

What's Changed

New Contributors

Full Changelog: huggingface/datasets@4.3.0...4.4.0

v4.3.0

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Dataset Features

Enable large scale distributed dataset streaming:

These improvements require huggingface_hub>=1.1.0 to take full effect

What's Changed

New Contributors

Full Changelog: huggingface/datasets@4.2.0...4.3.0

v4.2.0

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Dataset Features

  • Sample without replacement option when interleaving datasets by @​radulescupetru in #​7786

    ds = interleave_datasets(datasets, stopping_strategy="all_exhausted_without_replacement")

  • Parquet: add on_bad_files argument to error/warn/skip bad files by @​lhoestq in #​7806

    ds = load_dataset(parquet_dataset_id, on_bad_files="warn")

  • Add parquet scan options and docs by @​lhoestq in #​7801

    • docs to select columns and filter data efficiently
    ds = load_dataset(parquet_dataset_id, columns=["col_0", "col_1"])
ds = load_dataset(parquet_dataset_id, filters=[("col_0", "==", 0)])
    • new argument to control buffering and caching when streaming
    fragment_scan_options = pyarrow.dataset.ParquetFragmentScanOptions(cache_options=pyarrow.CacheOptions(prefetch_limit=1, range_size_limit=128 << 20))
ds = load_dataset(parquet_dataset_id, streaming=True, fragment_scan_options=fragment_scan_options)
What's Changed
New Contributors

Full Changelog: huggingface/datasets@4.1.1...4.2.0

v4.1.1

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What's Changed
New Contributors

Full Changelog: huggingface/datasets@4.1.0...4.1.1

v4.1.0

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Dataset Features

  • feat: use content defined chunking by @​kszucs in #​7589

    • Parquet datasets are now Optimized Parquet !
      image

    • internally uses use_content_defined_chunking=True when writing Parquet files

    • this enables fast deduped uploads to Hugging Face !

    # Now faster thanks to content defined chunking
ds.push_to_hub("username/dataset_name")
    • this optimizes Parquet for Xet, the dedupe-based storage backend of Hugging Face. It allows to not have to upload data that already exist somewhere on HF (on an other file / version for example). Parquet content defined chunking defines Parquet pages boundaries based on the content of the data, in order to detect duplicate data easily.
    • with this change, the new default row group size for Parquet is set to 100MB
    • write_page_index=True is also used to enable fast random access for the Dataset Viewer and tools that need it

  • Concurrent push_to_hub by @​lhoestq in #​7708

  • Concurrent IterableDataset push_to_hub by @​lhoestq in #​7710

  • HDF5 support by @​klamike in #​7690

    • load HDF5 datasets in one line of code
    ds = load_dataset("username/dataset-with-hdf5-files")
    • each (possibly nested) field in the HDF5 file is parsed a a column, with the first dimension used for rows
Other improvements and bug fixes
New Contributors

Full Changelog: huggingface/datasets@4.0.0...4.1.0

Configuration

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♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

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Documentation

Find out how to configure dependency updates in MintMaker documentation or see all available configuration options in Renovate documentation.

@red-hat-konflux red-hat-konflux bot force-pushed the konflux/mintmaker/konflux-poc-1/datasets-4.x branch from a47ba2e to 1cc25ae Compare February 28, 2026 01:42
@red-hat-konflux red-hat-konflux bot changed the title chore(deps): update dependency datasets to v4.6.0 chore(deps): update dependency datasets to v4.6.1 Feb 28, 2026
@red-hat-konflux red-hat-konflux bot force-pushed the konflux/mintmaker/konflux-poc-1/datasets-4.x branch from 1cc25ae to 1fbe726 Compare March 10, 2026 02:04
@red-hat-konflux red-hat-konflux bot changed the title chore(deps): update dependency datasets to v4.6.1 chore(deps): update dependency datasets to v4.7.0 Mar 10, 2026
@red-hat-konflux red-hat-konflux bot force-pushed the konflux/mintmaker/konflux-poc-1/datasets-4.x branch from 1fbe726 to 7a9504e Compare March 17, 2026 05:56
@red-hat-konflux red-hat-konflux bot changed the title chore(deps): update dependency datasets to v4.7.0 chore(deps): update dependency datasets to v4.8.2 Mar 17, 2026
@red-hat-konflux
chore(deps): update dependency datasets to v4.8.3
2e5bde0 
Signed-off-by: red-hat-konflux <126015336+red-hat-konflux[bot]@users.noreply.github.com>
@red-hat-konflux red-hat-konflux bot force-pushed the konflux/mintmaker/konflux-poc-1/datasets-4.x branch from 7a9504e to 2e5bde0 Compare March 19, 2026 17:56
@red-hat-konflux red-hat-konflux bot changed the title chore(deps): update dependency datasets to v4.8.2 chore(deps): update dependency datasets to v4.8.3 Mar 19, 2026
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