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value:docs(dataset): match train_test_split docstring params to the signature
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value:`Dataset.train_test_split`'s docstring documents `train_cache_file_name` and `test_cache_file_name`; the actual parameters are `train_indices_cache_file_name` and `test_indices_cache_file_name`. The two docstring entries renamed to match. Docs-only.
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value:Fix infinite repetition of empty iterable datasets
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value:Infinite repetition currently restarts an empty input indefinitely. For example, a streaming dataset filtered down to zero rows hangs in `dataset.repeat(None).take(1)` instead of finishing. The same problem affects an empty distributed shard and an Arrow input containing only zero-row tables. Stop infinite repet...
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value:Honor explicit Parquet compression and encoding options
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value:`Dataset.to_parquet(..., compression="gzip")` raises `TypeError: ParquetWriter() got multiple values for keyword argument 'compression'` on current main. Explicit `use_dictionary` and `column_encoding` options fail for the same reason: the writer supplies defaults as named arguments while leaving the caller's val...
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value:Fix async map checkpoint accounting across repeated resumes
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value:Resuming an iterable dataset with an asynchronous map function works once, but a checkpoint captured during the resumed iteration can skip additional examples on the next resume. A deterministic identity coroutine over IDs 0–9, checkpointed after two outputs and again after one more output, produces `[0, 1, 2, 5,...
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value:docs: add EvalPort ResultSet example to nested-field JSON loading
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value:### What Adds a small worked example to the "nested field" JSON section of the loading guide (`docs/source/loading.mdx`), right after the existing `field="data"` example, showing the same mechanism applied to an eval/benchmark run's results file: ```py {"version": "1.1.0", "suite_id": "image-description-suite"...
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value:Check the row count of every source when concatenating on axis=1
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value: Fixes #8572 A source with no rows still contributes its columns, so horizontal concatenation has to check its row count like any other source. The empty-source filter in `_concatenate_map_style_datasets` ran before the axis dispatch, so on `axis=1` a 0-row source was removed before the "Number of rows must matc...
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value:concatenate_datasets(axis=1) drops the columns of a source with no rows instead of raising
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value: `concatenate_datasets([ds1, ds2], axis=1)` returns a dataset without `ds2`'s columns when `ds2` has 0 rows, and raises nothing. The row-count check that rejects every other length mismatch never runs. ### Steps to reproduce ```python from datasets import Dataset, Features, Value, concatenate_datasets ds1 = Da...
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value:docs: add Simplified Chinese translation (README.zh.md)
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value:# What does this PR do? Fixes #8570 This PR adds a comprehensive and high-quality Simplified Chinese translation of the project's README (`README.zh.md`) and establishes a seamless bidirectional language switcher in both English and Chinese documentation. ## Summary of Changes - **New File `README.zh.md`**: F...
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value:[zh] Add Simplified Chinese translation for README
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value:### Motivation 🤗 Datasets is a fundamental library widely used by researchers, data scientists, and developers worldwide, with a large and active Chinese-speaking developer and research community. Providing an accurate, idiomatic Simplified Chinese translation (`README.zh.md`) along with a language switcher wi...
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value:Shard the sources when skipping a distributed IterableDataset
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value:`SkipExamplesIterable.shard_data_sources` returned `self` when `split_when_sharding` is False, so a `.skip()` applied after `split_dataset_by_node` threw the sharding away and every rank iterated the whole dataset. `TakeExamplesIterable` already handles that branch by sharding the sources and leaving `n` alone, s...
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value:skip() after split_dataset_by_node gives every rank the whole dataset
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value:### Describe the bug Calling `.skip()` on an iterable dataset that already went through `split_dataset_by_node` cancels the node split. Each rank then iterates every shard, so a distributed run sees each example `world_size` times. `SkipExamplesIterable.shard_data_sources` returns `self` when `split_when_shardi...
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value:Fix typo in guide template
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value:Fixes `repace` → `replace` in `templates/README_guide.md` (the template used to generate dataset guides, so the typo propagates). Docs-only change.
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value:Fix Pdf.decode_example raising TypeError on remote non-Hub paths
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value:`Pdf.decode_example` on a remote path outside the Hub raised before any download: ``` >>> Pdf().decode_example({"path": "https://example.com/a.pdf", "bytes": None}) TypeError: 'NoneType' object is not subscriptable # src/datasets/features/pdf.py:158 ``` `string_to_dict` returns `None` when the URL does not ma...
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value:Add a strategy argument to split_dataset_by_node for iterable datasets
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value:Closes #8253 by adding the explicit splitting-strategy argument that @lhoestq asked for in that thread. ```python split_dataset_by_node(ds, rank, world_size, strategy="auto" | "shards" | "examples") ``` - `"auto"`: unchanged. Shards are assigned when `num_shards % world_size == 0`, otherwise each node keeps one...
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value:Print the repr these feature examples actually produce
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value:### What breaks Four feature classes show a repr in their `Examples` block that they cannot print. ``` >>> ds.features["image"] Image(decode=True, id=None) ``` `Image`, `Pdf`, `Nifti` and `Video` all declare `id: Optional[str] = field(default=None, repr=False)`, so `id=None` never appears. On top of that `Imag...
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value:Raise clear errors for missing folder metadata files
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value:## Summary - validate local files referenced by folder metadata during example generation - report both the missing file and the metadata file that references it - preserve lazy access for remote files ## Testing - `pytest -q tests/packaged_modules/test_folder_based_builder.py tests/packaged_modules/test_image...
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value:Keep the declared dtype for an empty column under numpy format
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value:Fixes #8469. Under numpy format a column holding zero rows came back as float32 no matter what dtype it was declared with, while the same column kept its dtype as soon as it held one row. pandas and arrow were unaffected. ```python feats = Features({"a": Value("int64")}) ds = Dataset.from_dict({"a": [1, 2, 3]},...
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value:Bump fsspec upper bound to 2026.7.0
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value:## Summary - bump the fsspec upper bound from 2026.6.0 to 2026.7.0 - keep the existing minimum version unchanged Fixes #8560 ## Tests - python -m pytest -q tests/test_filesystem.py (8 passed, 2 skipped) - python -m pytest -q tests/test_file_utils.py tests/test_data_files.py tests/test_streaming_download_manag...
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value:Would it be possible to bump fsspec upper bound to 2026.7.0? Can open a PR!
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value:Fix the repeat docstring examples
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value:### What is wrong `Dataset.repeat`'s example calls `ds.take(2).repeat(2)`, which returns **four** rows, but the output printed under it has **six**: three distinct reviews, each twice. `IterableDataset.repeat` carries the same example, and its `load_dataset` call is missing `streaming=True`, so it builds a `Dat...
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value:Close the Arrow writer when finalize() fails
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value:Fixes #6917. ## Root cause `_prepare_split_single` finalises the writer inside a `finally` block, in both builders (`builder.py:1558-1563` for `GeneratorBasedBuilder`, `builder.py:1864-1869` for `ArrowBasedBuilder`): ```python finally: yield job_id, False, num_examples_progress_update num_shards = shar...
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value:Support JPEG XL images via pillow-jxl-plugin
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value:Fixes #8537. ## What - `.jxl` added to the `imagefolder` and `webdataset` image extensions. - `Image.decode_example` now imports the third-party Pillow plugins that are installed (`pillow_jxl` for now) before calling `PIL.Image.open`, via a small `_import_pil_plugins()` helper and a `config.PILLOW_JXL_AVAILABLE...
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value:fix: Raise ValueError for batch_size <= 0 in Dataset.batch and IterableDataset.batch
value:2026-09-02T09:30:54Z
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value:## Description `batch(0)` and `batch(-1)` silently returned the whole dataset instead of raising an error. This is dangerous because a batch_size computed at runtime that comes out as 0 (e.g., from an empty config or integer division) would silently return the entire dataset. The fix adds validation to both `Da...
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value:fix: Handle negative percent split boundaries correctly
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value:## Description When using negative percentages like `train[-1%:]`, the sign was lost after rounding. For example, -1% of 30 examples rounds to 0, so `train[-1%:]` would return the whole dataset instead of the last 1%. The fix converts negative percentages to their positive equivalents (`100 + from_%`) before ro...
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value:fix: Normalize negative indices in Dataset.select to fix OverflowError
value:2026-09-02T09:27:18Z
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value:## Description Dataset.select raised OverflowError for negative indices like -1, even though they are valid (e.g., -1 means last element). The issue was that pyarrow's uint64 array can't hold negative values. The fix normalizes negative indices to positive ones after validation but before converting to pyarrow ...
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value:fix: Add per-element validation to ClassLabel.str2int and int2str
value:2026-09-02T09:25:14Z
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value:## Description ClassLabel.str2int and int2str were validating only the container (the list), not the elements inside it. This caused silent failures: - `str2int([1])` returned `[1]` instead of raising ValueError - `int2str([1.7])` returned `['pos']` (truncated float) instead of raising ValueError ## Fix 1. **...
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value:fix: Remove double-wrapping of storage_options in xPath.glob
value:2026-09-02T09:23:23Z
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value:## Description The `xPath.glob` method was double-wrapping storage_options when calling `url_to_fs`. The `_prepare_path_and_storage_options` function already returns storage_options keyed by protocol (e.g., `{protocol: options}`), but the code was re-wrapping it as `{protocol: {protocol: options}}`, causing fs...
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value:Fix docstring argument names that don't match signatures
value:2026-09-02T05:01:42Z
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value:Three docstrings document parameters that don't exist on the function, so the rendered docs advertise arguments that raise `TypeError`. | Location | Documented | Actual signature | |---|---|---| | `Dataset.train_test_split` | `train_cache_file_name`, `test_cache_file_name` | `train_indices_cache_file_name`, `tes...
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value:docs: relabel directional classes on horizontal flips
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value:Fixes huggingface/datasets#8522 ## Summary - Add a semantic segmentation section on swapping left/right class IDs during horizontal flips - Show a NumPy-only `set_transform` recipe for the human parsing dataset - Mention AlbumentationsX as an optional path with license notes ## Test plan - [x] Docs reviewed loc...
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value:fix: use Sequence for path_or_paths type hints
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value:Fixes huggingface/datasets#5354 ## Summary - Replace `list[PathLike]` with `Sequence[PathLike]` in `from_csv`, `from_json`, `from_parquet`, and `from_text` signatures ## Test plan - [x] Type-only change; no runtime behavior change
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value:fix: accept pathlib.Path in save_to_disk and load_from_disk
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value:Fixes huggingface/datasets#6829 ## Summary - Convert path-like arguments with `os.fspath` before `url_to_fs` in Dataset, DatasetDict, and `load_from_disk` - Add regression test for `pathlib.Path` round-trip ## Test plan - [x] Added `test_save_and_load_from_disk_with_pathlib`
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value:Fixes huggingface/datasets#5805 ## Summary - Cross-link file and folder builders with the loading guide - Clarify in-memory vs file-based dataset creation paths - Link to the repository structure guide ## Test plan - [x] Docs links reviewed locally
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value:Fixes huggingface/datasets#5971 ## Summary - Add repository structure to the Tutorials section in the docs navigation - Link the guide from the docs index and quickstart ## Test plan - [x] Docs navigation and links reviewed locally Made with [Cursor](https://cursor.com)
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value:docs: expand performance guidance in cache guide
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value:Fixes huggingface/datasets#2494 ## Summary Expanded the **Improve performance** section in `docs/source/cache.mdx` with three practical guides: - **Maximize iteration speed** - in-memory loading, `num_proc`, batched `.map()`, caching - **Minimize RAM usage** - streaming, column selection, cache cleanup - **Minim...
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value:Fix double-wrapped storage options in xPath.glob
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value:Fixes #8543 ### Root cause `_prepare_path_and_storage_options` returns storage options already keyed by protocol. That contract is stated in the docstring of `_prepare_single_hop_path_and_storage_options` ("Storage options are formatted in the form {protocol: storage_options_for_protocol}") and applied just bef...
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value:xPath.glob passes double-wrapped storage options to url_to_fs
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value:### Describe the bug `xPath.glob` adds a second protocol key to the storage options before passing them to `url_to_fs`, so fsspec receives `https={'https': {...}}` instead of `https={...}`. `_prepare_path_and_storage_options` already returns them protocol keyed, and the docstring of `_prepare_single_hop_path_an...
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value:feat(features): add BioSequence and BioStructure decodable features
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value:Adds two decodable feature types that store a biological file as bytes and decode it to the biopython object the format describes. | Type | Module | `decode_example` returns | Serves | |---|---|---|---| | `BioSequence` | `features/bio_sequence.py` | `Bio.SeqRecord.SeqRecord` | FASTA #7923, FASTQ #7924, GenBank #...
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value:Actually drop malformed splits in DatasetInfo._from_yaml_dict
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value:`DatasetInfo._from_yaml_dict` logs "Ignoring part of dataset_info from the dataset card" but does not ignore it. The `except ValueError` added in #8462 leaves the raw value in `yaml_data`, and `splits` is a field name, so the next line still passes it to the constructor: ```python if yaml_data.get("splits") is n...
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value:Fix language name for ISO code 'blk' (#8505)
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value:The `blk` entry listed "Pa'o Karen; Pa'O". Per the request from a native speaker in #8505, "Karen" is an external administrative classification rather than a self-designation, so the entry now uses the self-identified name "Pa'O".
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value:Apply the bit-vs-byte suffix rule to the binary size units
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value:### Symptom `convert_file_size_to_int()` treats a lowercase trailing `b` as bits and divides by 8, but only for the decimal units. The binary units ignore the suffix entirely: ```python >>> from datasets.utils.py_utils import convert_file_size_to_int >>> convert_file_size_to_int("1Gb") # gigabit, correct 125...
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value:Fix batched JSON record export
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value:Fix Dataset.to_json(lines=False) when records are written in multiple batches.
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value:JPEG XL support
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value:It seems that the dataset don't support JXL natively in the same ways as e.g. PNG and JPEG are supported. A JPEG XL is supported by an international ISO standard, gives great compression ratios, finally supports lossless for JPEG and the browser support is now starting to get wide spread (safari has it for some t...
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value:Fix broken markdown link to audio_load guide
value:2026-08-30T10:11:32Z
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value:## What Fixed a stray closing bracket in about_mapstyle_vs_iterable.mdx that broke the markdown link to the audio_load guide. ## Why A small, objectively-verifiable improvement (broken-link) found during a daily open-source maintenance pass (2026-08-30). ## How verified Read the source line, confirmed the audio...
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value:Validate delimiter and repeated column_names in ConllConfig
value:2026-08-30T06:45:53Z
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value:### The bug Two `ConllConfig` arguments reach low-level code before anything validates them, so the resulting error never mentions the argument that is wrong. Both arrive wrapped in `DatasetGenerationError: An error occurred while generating the dataset`: ```python load_dataset("conll", data_files=path, column_...
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value:Validate on_bad_files in the parquet and vortex configs
value:2026-08-30T06:42:06Z
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value:### The bug `ParquetConfig` and `VortexConfig` declare `on_bad_files: Literal["error", "warn", "skip"]` but never check it, and the code branches as `if "error" / elif "warn" / else`. Anything unrecognised lands in the permissive `else`. So a typo in the *strict* value silently selects the *least* strict behavi...
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value:Validate sample_by in TextConfig
value:2026-08-30T06:38:59Z
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value:### The bug `Text._generate_tables` branches on `sample_by` as `if "line" / elif "paragraph" / elif "document"` with no fallback, and `TextConfig` never validates the value. An unrecognised one therefore produces no rows at all, and the load fails much later on schema inference: ```python load_dataset("text", d...
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value:Do not fail all of test_file_utils when zstandard is not installed
value:2026-08-30T06:32:43Z
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value:### The problem `tests/test_file_utils.py` imports `zstandard` at module level, so without it installed the entire file fails to collect: ``` ERROR tests/test_file_utils.py E ModuleNotFoundError: No module named 'zstandard' !!!!! Interrupted: 1 error during collection !!!!! ``` 136 tests are lost, almost non...
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value:Split the torch assertions out of test_dataset_to_iterable_dataset
value:2026-08-30T06:29:27Z
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value:### The problem `test_dataset_to_iterable_dataset` fails when torch is not installed: ``` FAILED tests/test_arrow_dataset.py::test_dataset_to_iterable_dataset E ValueError: PyTorch needs to be installed to be able to return PyTorch tensors. ``` torch is an optional dependency, so running the suite without it...
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value:Skip test_interleave_dataset_with_sharding when torch is not installed
value:2026-08-30T06:26:52Z
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value:### The problem `test_interleave_dataset_with_sharding` imports `torch.utils.data` and calls `.with_format("torch")`, but it is the only torch-using test in `tests/test_iterable_dataset.py` without `@require_torch`. Without torch installed it fails rather than skipping: ``` FAILED tests/test_iterable_dataset.py...
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value:Reject a repeated column name in Dataset.remove_columns
value:2026-08-30T06:19:24Z
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value:### The bug `Dataset.remove_columns` does not deduplicate its argument, so a repeated name deletes the same feature twice: ```python d = Dataset.from_dict({"a": [1, 2], "b": ["x", "y"]}) d.remove_columns(["a", "a"]) # KeyError: 'a' ``` The second `del dataset._info.features[column_name]` raises a bare `KeyErro...
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value:Allow swapping column names in IterableDataset.rename_columns
value:2026-08-30T06:14:41Z
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value:### The bug Two problems in `_rename_columns_fn`, both in the clash check. **Swapping two names is refused.** The check rejects any new name that is already a column, without noticing that the same mapping renames that column away: ```python it = Dataset.from_dict({"a": [1, 2], "b": ["x", "y"], "c": [1.0, 2.0]...
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value:Reject an unknown input_columns in IterableDataset.map and filter
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value:### The bug `input_columns` naming a column that does not exist is not checked on `IterableDataset`. The name is only looked up while iterating, so the call that is actually wrong succeeds and the failure arrives later, detached from its cause: ```python it = Dataset.from_dict({"a": [1, 2], "b": ["x", "y"]}).to...
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value:Reject a negative n in IterableDataset.skip and take
value:2026-08-30T06:05:59Z
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value:### The bug `IterableDataset.skip` and `take` silently treat a negative `n` as zero: ```python it = Dataset.from_dict({"a": [1, 2, 3, 4]}).to_iterable_dataset() list(it.skip(-1)) # [{'a': 1}, {'a': 2}, {'a': 3}, {'a': 4}] -- the whole dataset list(it.take(-1)) # [] -...
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value:Reject an unknown column when removing columns from an IterableDataset
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value:### The bug Removing a column that does not exist is silently ignored on `IterableDataset`, through either entry point: ```python it = Dataset.from_dict({"a": [1, 2], "b": ["x", "y"]}).to_iterable_dataset()._resolve_features() list(it.remove_columns(["nope"])) # [{'a': 1, 'b': 'x'}, ...] -- ...
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value:Return None for a null row of a dynamic-shape ArrayXD column
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value:`ArrayExtensionArray.to_pylist` builds the python values from `to_numpy`, where a null row is a bare `np.nan`. That is fine for the numpy format but not for the python one, and the guard added in #8363 only covered a fixed first dimension. So for a column like `Array2D(dtype="int32", shape=(None, 2))` with a `No...
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value:Docs proposal: relabel left/right segmentation classes during horizontal flips
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value:### Feature request Would you be open to a self-contained documentation recipe showing how to relabel directional semantic classes during a horizontal flip? It could be a separate section in [`docs/source/semantic_segmentation.mdx`](https://github.com/huggingface/datasets/blob/main/docs/source/semantic_segmentat...
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value:fix(json): defensively recover invalid Arrow tables in JSON loader (#5531)
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value:## What does this PR do? Adds defensive validation and recovery for malformed nested Arrow arrays returned by pyarrow.json.read_json(). When a batch produces invalid nested offsets, the JSON loader reparses only that batch row by row with pa.Table.from_pylist(), concatenates the rows while preserving late-a...
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value:Take the image dtype from Arrow storage in Image.cast_storage
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value:Closes #8511. `storage.to_pylist()` materializes the values as plain Python `int`s, which drops the Arrow value type. `np.array(arr)` on nested lists of Python ints then defaults to `int64`, and `encode_np_array` warns about a downcast to `uint8` that the storage never needed: ```python arr = np.random.randint(...
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value:Closes #8500. Two problems in `NumpyFormatter`, both falling out of numpy's type hierarchy. **Scalars.** `np.bool_` and `np.datetime64` are not `np.number`, so `_tensorize` did not early-return them and `_recursive_tensorize` ran them through `__array__` first. Row access handed back 0-d `ndarray`s, which are u...
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value:Closes #8494. `pa.Schema.__eq__` ignores metadata, so casting int64 storage to a `ClassLabel` leaves the arrow schema equal and `table_cast` took the `replace_schema_metadata` branch. That skips `cast_array_to_feature`, and with it the range check in `ClassLabel.cast_storage`, so an out-of-range label index was ...
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value:fix(tsfile): version-aware guard for the optional Apache TsFile SDK
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value:> **Status (2026-10-07):** draft. This description predates #8502, which raised the requirement on `main` to `pyarrow>=24.0.0`, and is therefore out of date, in particular the table and Q1. See my comment of 2026-10-07 below for the current status. ## Summary - Raise a **version-aware** `ImportError` when the A...
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value:Fixes spurious dtype downcast warning in Image.cast_storage. Bug: Image.cast_storage at image.py:266 warns about downcasting int64 to uint8 even though storage is already uint8. When storage is a nested pa.list type like list<item: list<item: list<item: uint8>>>, to_pylist returns Python ints and np.array(arr) w...
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value:Fix offline cache lookup with custom data files
value:2026-08-25T19:59:22Z
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value:## Summary Fixes #7551. Datasets loaded from the Hub with a `data_files` override could not be reloaded in offline mode. During online loading, relative data file patterns are resolved to revision-pinned Hub URLs before computing the cache configuration ID. During offline lookup, only the original unreso...
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value:fix(tsfile): guard optional Apache TsFile SDK import
value:2026-08-24T20:58:47Z
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value:## Summary - Guard every `tsfile` SDK import behind `_require_tsfile()` so missing extras raise `ImportError: ... pip install 'datasets[tsfile]'` instead of a bare `ModuleNotFoundError`. - Add a `tsfile` extra (`tsfile>=2.3.0`), install it in the `tests` extra on Python 3.14 (where it does not conflict with `py...
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value:Fix typos in docstrings, comments and an error message
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value:Typo fixes only — no identifiers renamed, no behavior change: - `filesystems/compression.py`: "compresssed" -> "compressed" - `fingerprint.py`: error message "Maximum lenth is" -> "Maximum length is" - `dataset_dict.py` (x2): "contain None valies" -> "values" - `iterable_dataset.py` (x2): "one thousand simulaten...
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value:Fix dead PDF processing link in README
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value:## What Fixed README.md link to PDF processing docs that pointed to a non-existent page (docs/datasets/pdf_process); redirected to docs/datasets/document_dataset, which contains the actual PDF processing content. ## Why A small, objectively-verifiable improvement (broken-link) found during a daily open-source ma...
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value:`Image.cast_storage` warns about downcasting int64 to uint8 for storage that is already uint8
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value:### Describe the bug Casting a nested-list column whose Arrow value type is already `uint8` to `Image()` emits a spurious warning: ``` UserWarning: Downcasting array dtype int64 to uint8 to be compatible with 'Pillow' ``` No downcast is actually needed. The Arrow storage type is `list<item: list<item: list<ite...
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value:Add return_file_name parameter to JSON dataset loader (Fixes #5806)
value:2026-08-24T06:22:02Z
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value:## Summary This PR adds a `return_file_name` parameter to the JSON dataset loader. When enabled, the dataset will include a `file_name` column containing the source file name for each row. ## Changes 1. **Added `return_file_name` parameter to `JsonConfig`** - A new boolean parameter that defaults to `False` fo...
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value:Validate ArrayXD dimensions during Arrow conversion
value:2026-08-23T18:49:53Z
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value:Closes #8507 `ArrayXD` storage uses variable-length Arrow lists for compatibility, but reading the array reshapes values to the declared feature shape. This meant malformed nested input could be accepted and later returned with different row boundaries. This PR: - checks every fixed `ArrayXD` dimension a...
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value:ArrayXD silently reshapes inputs with incompatible nested dimensions
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value:### Describe the bug `Array2D` and the other `ArrayXD` features accept nested input whose dimensions do not match the declared shape when the total number of values happens to match. For example, data shaped like `(2, 2)` can be created from rows of lengths `3` and `1`. When the value is read back, the rows are...
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value:Request to correct language name for ISO code 'blk' from 'Pa'o Karen' to 'Pa'O'
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value:### Describe the bug Dear Hugging Face Team, I am writing to request a correction regarding the language display name associated with the ISO 639-3 code `blk` on the platform. Currently, it is displayed as **"Pa'O Karen"**. However, as a native speaker and local member of the Pa'O community, this terminology i...
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value:Preserve special NumPy scalar types in NumPy formatting
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value:Fixes #8500.\n\nTreat NumPy scalar types uniformly in NumpyFormatter so boolean, datetime, and duration values remain scalar values during row formatting. Also avoid applying the integer default dtype to timedelta arrays, preserving duration units for batch and column formatting.\n\nAdded regression coverage for ...
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value:Fixes #8500\n\nSummary:\n- Preserve NumPy boolean, datetime, and timedelta scalars during formatting.\n- Avoid coercing timedelta arrays to int64.\n- Add focused formatter coverage.\n\nTests:\n- Python syntax compilation passed.\n- Pytest could not start because the local datasets package is not installed.
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value:Add support for datasets backed by vortex file format
value:2026-08-21T16:23:30Z
value:CONTRIBUTOR
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value:Vortex provides fast random access and state of the art analytical performance. Vortex files are self contained the integration logic largely resembles parquet. pyarrow minimum version has been raised in this pr to include fix for Table.nbytes error with view types https://github.com/apache/arrow/issues/47279. ...
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value:Format bool and temporal values as numpy scalars and keep the duration dtype in numpy format
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value:Fixes #8500 `NumpyFormatter._tensorize` now early-returns `np.bool_`/`np.datetime64`/`np.timedelta64` scalars like other numpy scalars, `_recursive_tensorize` excludes all `np.generic` scalars from `__array__` conversion (previously it re-wrapped bool/datetime scalars into 0-d arrays), and the int64 default dtyp...
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value:with_format("numpy") returns bool/datetime values as 0-d unhashable arrays and casts duration columns to int64
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value:### Describe the bug With numpy formatting, row access returns `np.int64`/`np.float64`/`np.str_` scalars for most dtypes, but **bool and timestamp/date values come back as 0-dimensional `ndarray`s** (`array(True)`), which are unhashable and fail `isinstance(x, np.bool_)` checks. Separately, **batch/column access...
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value:TsFile format filter uses an inconsistent URL and is not available initially
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value: ## Summary There appear to be two related issues with the `TsFile` format filter on the Hugging Face Datasets page: 1. The URL parameter for `TsFile` is inconsistent with other formats. 2. `TsFile` is not initially shown in the format filter and only appears after visiting a TsFile-specific URL. ## 1. Incons...
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value:fix: bound Dataset.take range to prevent IndexError when n > len(dataset)
value:2026-08-19T06:58:54Z
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value:## Summary Fixes #8483 Currently \Dataset.take(n)\ executes \self.select(range(n))\. When \ \ exceeds the dataset size \len(self)\, \ ange(n)\ attempts to index beyond the end of the table and raises an \IndexError\. Meanwhile, \IterableDataset.take(n)\ gracefully yields up to \ \ items. ## Changes - In \src/...
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value:Preserve storage options across URL hops
value:2026-08-18T13:56:16Z
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value:## Summary - return the accumulated storage options for every hop in chained fsspec URLs - add a regression test covering protocol-specific options on both `zip` and `https` hops ## Problem `_prepare_path_and_storage_options` builds `prepared_storage_options` while iterating over a multi-hop path, but returned...
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value:Validate class labels when casting an integer column to ClassLabel
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value:`table_cast` took a metadata-only fast path when the arrow schema was unchanged, so casting int64 storage to `ClassLabel` never ran `ClassLabel.cast_storage` and out-of-range label indices were accepted silently. Route metadata-only changes through `cast_table_to_schema` too, so the range check runs like on the w...
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value:cast_column to ClassLabel silently accepts out-of-range label indices
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value:### Describe the bug `Dataset.cast_column` / `Dataset.cast` to a `ClassLabel` column silently keeps label indices that are out of range for the target number of classes. The corruption only shows up later — e.g. on the first `int2str` call — or silently trains on wrong labels: ```python from datasets import Dat...
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value:Don't mask the original error when finalizing the writer fails in Dataset.map
value:2026-08-17T21:50:50Z
value:CONTRIBUTOR
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value:## Summary Fixes #8491. When `writer.finalize()` raises in `_map_single()`, the `except` block calls `finalize()` again on a writer whose stream is already closed. `finalize()` then hits `self.pa_writer is None and self.schema` and calls `_build_writer(self.schema)`, which raises `ValueError: I/O operation on c...
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value:Let Dataset.take(n) return what it has when n > len(dataset)
value:2026-08-17T06:20:57Z
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value:Fixes #8483 ### Problem `Dataset.take(n)` is `select(range(n))`, so asking for more elements than the dataset has raises from the out-of-range index — while the `IterableDataset` counterpart returns the elements it has: ```python >>> ds = Dataset.from_dict({"a": [1, 2, 3]}) >>> ds.take(3) Dataset({features: ['...
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value:`Dataset.map(num_proc=N)` worker crashes with `ValueError: I/O operation on closed file` when `finalize()` is interrupted
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value:### Describe the bug We hit this while running Megatron-LM training through ms-swift, which uses `datasets` under the hood for preprocessing. Our preprocessing step calls `Dataset.map()` with a fairly aggressive `num_proc=512` on a ~25k-example JSONL file: ```bash swift --dataset_num_proc 512 --dataset large.js...
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value:Fix archive path check allowing sibling-directory escapes
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value:## Bug The `safemembers` path check that guards archive extraction (the CVE-2007-4559 mitigation, duplicated across the tar/zip/rar/7z extractors) uses a plain `startswith(base)`. That also matches **sibling directories whose name starts with the output directory's name**: ```py target = resolved(os.path.join(b...
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value:Add integration test for loading renamed dataset repositories
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value:Fixes #4911. Adds `TestLoadFromHub.test_load_dataset_after_repo_rename` to `tests/test_upstream_hub.py`: 1. pushes a small dataset to a temporary repo on hub-ci 2. renames the repo with `HfApi.move_repo` (the `/api/repos/move` endpoint suggested in the issue), with a sanity assertion that the repo now lives und...
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value:Fixes #7089. The Spark test module no longer imports PySpark during collection. Its configuration tests remain active without the optional dependency, while the eight tests that actually create Spark sessions use the existing `require_pyspark` decorator and local imports already used elsewhere in the test suite....
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value:Fixes #7090. `test_move_script_doesnt_change_hash` now invokes both temporary scripts with `sys.executable` instead of relying on a `python` command being available on `PATH`. This keeps the subprocess interpreter aligned with the interpreter running pytest and fixes environments such as FreeBSD where only `pyth...
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value:Fix PandasArrayExtensionArray.take coercing fill_value when not filling
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value:Fixes #8465 ## Problem `PandasArrayExtensionArray.take` coerces `fill_value` to the pandas dtype *before* checking whether any row is actually missing. When `allow_fill=True` with a mask that contains no missing indices, `take` still needs to return a proper pandas Series — but the coercion can blow up for valu...
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value:Fix Dataset.take raising IndexError when n exceeds dataset length
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value:Fixes #8483 ## Problem `Dataset.take(n)` raises `IndexError: index ... is out of bounds for axis 0 with size ...` when `n` exceeds the number of rows, while `IterableDataset.take(n)` returns everything available (list-slice semantics). The two dataset flavors should behave consistently. ## Change Clamp the re...
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value:Validate each element in ClassLabel.int2str
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value:Partially fixes #8481 ### Problem `int2str` validates the container it is given, but never the elements inside it. Three consequences, in decreasing order of severity: ```python >>> cl = ClassLabel(names=["neg", "pos"]) >>> cl.int2str([1.7]) ['pos'] # silently truncated by the int(v) used t...
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value:Dataset.take(n) raises IndexError when n > len(dataset), but IterableDataset.take(n) returns what it has
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value:### Describe the bug `Dataset.take(n)` raises when asked for more elements than the dataset has, while `IterableDataset.take(n)` returns the elements it has. Both are documented identically as "Create a new [...] with only the first `n` elements". ```python >>> from datasets import Dataset >>> ds = Dataset.from...
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value:Fix Dataset.skip(n) raising IndexError when n == len(dataset)
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value:Skipping exactly as many elements as the dataset has raises, but skipping *more* than that works and returns an empty dataset: ```python >>> ds = Dataset.from_dict({"a": [1, 2, 3]}) >>> ds.skip(2) Dataset({features: ['a'], num_rows: 1}) >>> ds.skip(3) IndexError: Index 3 out of range for dataset of size 3. >>> d...
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value:ClassLabel.str2int and int2str do not validate the elements of an iterable
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value:### Describe the bug `ClassLabel.str2int` and `ClassLabel.int2str` both validate the *container* they're given, but never the elements inside it. Two of the resulting cases fail silently and return wrong labels rather than raising. `str2int` accepts ints and hands them straight back, so a column of integer labe...
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value:tests(utils): add unit tests for is_documented_by in doc_utils
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value:tests(utils): add unit tests for is_documented_by in doc_utils
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value:Handle null values when batching by column
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value:## What changed I encountered an error when batching a dataset by a nullable column. Rows containing null values were not split into the expected groups, and grouping by an all-null Arrow column raised an `ArrowNotImplementedError`. For example, batching this column: ```python [None, None, "A", "A", None,...
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value:Reject non-positive batch sizes in batch()
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value:## Summary - reject non-positive `batch_size` values in `Dataset.batch()` and `IterableDataset.batch()` - raise before mapping or consuming an iterable source - add focused regression tests for `0` and `-1` Fixes #8472 ## Related work This includes the small `_check_batch_size` helper also proposed by @LeSing...
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value:Fix OverflowError in Dataset.select on in-range negative indices
value:2026-08-13T03:07:14Z
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value:Fixes #8475. ### What this fixes `Dataset.select` validates indices with `_check_valid_indices_value`, which implements Python-style negative indexing (it only rejects a negative index when `index + size < 0`), but `_select_with_indices_mapping` then converts the indices with `pa.array(indices, type=pa.uint64()...
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