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Hugging Face/Datasets/4.0.0

4.0.0

July 9, 2025DatasetsView original ↗
$npx -y @buildinternet/releases show rel_NLmKOkWvV98CCUyozeZj8

New Features

  • Add IterableDataset.push_to_hub() by @lhoestq in https://github.com/huggingface/datasets/pull/7595

    # Build streaming data pipelines in a few lines of code !
from datasets import load_dataset

ds = load_dataset(..., streaming=True)
ds = ds.map(...).filter(...)
ds.push_to_hub(...)

  • Add num_proc= to .push_to_hub() (Dataset and IterableDataset) by @lhoestq in https://github.com/huggingface/datasets/pull/7606

    # Faster push to Hub ! Available for both Dataset and IterableDataset
ds.push_to_hub(..., num_proc=8)

  • New Column object

    # Syntax:
ds["column_name"]  # datasets.Column([...]) or datasets.IterableColumn(...)

# Iterate on a column:
for text in ds["text"]:
    ...

# Load one cell without bringing the full column in memory
first_text = ds["text"][0]  # equivalent to ds[0]["text"]

  • Torchcodec decoding by @TyTodd in https://github.com/huggingface/datasets/pull/7616

    • Enables streaming only the ranges you need !
    # Don't download full audios/videos when it's not necessary
# Now with torchcodec it only streams the required ranges/frames:
from datasets import load_dataset

ds = load_dataset(..., streaming=True)
for example in ds:
    video = example["video"]
    frames = video.get_frames_in_range(start=0, stop=6, step=1)  # only stream certain frames
    • Requires torch>=2.7.0 and FFmpeg >= 4
    • Not available for Windows yet but it is coming soon - in the meantime please use datasets<4.0
    • Load audio data with AudioDecoder:
    audio = dataset[0]["audio"]  # <datasets.features._torchcodec.AudioDecoder object at 0x11642b6a0>
samples = audio.get_all_samples()  # or use get_samples_played_in_range(...)
samples.data  # tensor([[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  ...,  2.3447e-06, -1.9127e-04, -5.3330e-05]]
samples.sample_rate  # 16000

# old syntax is still supported
array, sr = audio["array"], audio["sampling_rate"]
    • Load video data with VideoDecoder:
    video = dataset[0]["video"] <torchcodec.decoders._video_decoder.VideoDecoder object at 0x14a61d5a0>
first_frame = video.get_frame_at(0)
first_frame.data.shape  # (3, 240, 320)
first_frame.pts_seconds  # 0.0
frames = video.get_frames_in_range(0, 6, 1)
frames.data.shape  # torch.Size([5, 3, 240, 320])

Breaking changes

  • Remove scripts altogether by @lhoestq in https://github.com/huggingface/datasets/pull/7592

    • trust_remote_code is no longer supported

  • Torchcodec decoding by @TyTodd in https://github.com/huggingface/datasets/pull/7616

    • torchcodec replaces soundfile for audio decoding
    • torchcodec replaces decord for video decoding

  • Replace Sequence by List by @lhoestq in https://github.com/huggingface/datasets/pull/7634

    • Introduction of the List type
    from datasets import Features, List, Value

features = Features({
    "texts": List(Value("string")),
    "four_paragraphs": List(Value("string"), length=4)
})
    • Sequence was a legacy type from tensorflow datasets which converted list of dicts to dicts of lists. It is no longer a type but it becomes a utility that returns a List or a dict depending on the subfeature
    from datasets import Sequence

Sequence(Value("string"))  # List(Value("string"))
Sequence({"texts": Value("string")})  # {"texts": List(Value("string"))}

Other improvements and bug fixes

New Contributors

Full Changelog: https://github.com/huggingface/datasets/compare/3.6.0...4.0.0

Fetched April 7, 2026