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fix(deps): update dependency datasets to v5 by dreadnode-renovate-bot[bot] · Pull Request #357 · dreadnode/sdk · GitHub

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fix(deps): update dependency datasets to v5 - #357

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fix(deps): update dependency datasets to v5#357
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This PR contains the following updates:

Package Change Age Confidence
datasets >=4.0.0,<5.0.0 → >=5.0.1,<5.1.0

Release Notes

huggingface/datasets (datasets)

v5.0.1

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Bug fixes

Docs

New Contributors

Full Changelog: huggingface/datasets@5.0.0...5.0.1

v5.0.0

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Datasets Features

Agent traces

  • Parse Agent traces messages for SFT using teich by @​lhoestq in #​8232

    • Agent traces from claude_code/pi/codex and others can now be loaded with load_dataset
    • Using the teich library (new optional dependency), traces are parsed to messages to enable training on traces using e.g. trl
    • Load the data:
    >>> from datasets import load_dataset
>>> ds = load_dataset("lhoestq/agent-traces-example", split="train")
>>> ds[0]["messages"]
[{'role': 'user', 'content': 'Download a random dataset from Hugging Face, use DuckDB to inspect it, and come back with a short report about it. Be concise and include: dataset name, what files/format you found, row count or rough size if you can determine it,...'
 ...]
    • Train on agent traces:
    trl sft --dataset-name lhoestq/agent-traces-example ...
Next-level shuffling in streaming mode

  • Use multiple input shards for shuffle buffer by @​lhoestq in #​8194

    ds = load_dataset(..., streaming=True)
ds = ds.shuffle(seed=42)
# or configure local buffer shuffling manually, default is:
ds = ds.shuffle(seed=42, buffer_size=1000, max_buffer_input_shards=10)

    before👎:

    after✨:

    toy example comparison

    from datasets import IterableDataset

ds = IterableDataset.from_dict({"i": range(123_456_789)}, num_shards=1024)
ds = ds.shuffle(seed=42)

print("Cold start ids:")
print(list(ds.take(10)["i"]))
print("Nominal regime ids:")
print(list(ds.skip(10_000).take(10)["i"]))

    before👎:

    Cold start ids:
[6148853, 6149537, 6149418, 6149202, 6149197, 6149622, 6148849, 6149461, 6148965, 6148858]
Nominal regime ids:
[6149537, 6149418, 6149202, 6149197, 6149622, 6148849, 6149461, 6148965, 6148858, 6149290]

    after✨:

    Cold start ids:
[7836668, 9283505, 95847927, 482299, 9283471, 482341, 112003312, 59920157, 43764666, 95847871]
Nominal regime ids:
[9283505, 95847927, 482299, 9283471, 482341, 112003312, 59920157, 43764666, 95847871, 16758448]

    Note: ds.state_dict() and ds.load_state_dict() are still supported for this improved shuffling :) enabling dataset checkpointing

    Note 2: it uses threads to fetch the first examples in parallel from the input shards

    Note 3: This is a BREAKING CHANGE: the default shuffling mechanism now uses multiple input shards. You can get the old mechanism by passing max_buffer_input_shards=1 to IterableDataset.shuffle()

New batching features for robotics datasets

  • Add batch(by_column=...) by @​lhoestq in #​8172

    from datasets import Dataset

ds = Dataset.from_dict({"episode": [0] * 10 + [1] * 10, "frame": list(range(10)) * 2})
# ds = ds.to_iterable_dataset()
ds = ds.batch(by_column="episode")
for x in ds:
    print(x)
# {'episode': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'frame': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]}
# {'episode': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'frame': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]}
New supported formats

Other improvements and bug fixes

New Contributors

Full Changelog: huggingface/datasets@4.8.5...5.0.0

v4.8.5

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Main bug fixes

Other improvements and bug fixes

New Contributors

Full Changelog: huggingface/datasets@4.8.4...4.8.5

v4.8.4

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What's Changed

Full Changelog: huggingface/datasets@4.8.3...4.8.4

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

  • 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

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This PR has been generated by Mend Renovate CLI.

dreadnode-renovate-bot Bot added area/python Changes to Python package configuration and dependencies type/digest Dependency digest updates labels Jun 7, 2026
fix(deps): update dependency datasets to v5
7ca227b 
| datasource | package  | from  | to    |
| ---------- | -------- | ----- | ----- |
| pypi       | datasets | 4.4.1 | 5.0.1 |
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