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fix(json): defensively recover invalid Arrow tables in JSON loader (#5531)
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## 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-arrivin...
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Take the image dtype from Arrow storage in Image.cast_storage
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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(0, 256...
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Return numpy scalars from numpy formatting and keep duration dtypes
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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 unhasha...
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Validate features when a cast only changes schema metadata
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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 kept a...
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fix(tsfile): version-aware guard for the optional Apache TsFile SDK
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## Summary - Raise a **version-aware** `ImportError` when the Apache TsFile SDK is missing: point at `pip install 'datasets[tsfile]'` only on Python 3.14+, and explain the pyarrow conflict on 3.10–3.13. - Declare optional extra `tsfile>=2.3.0; python_version >= '3.14'` (mirrored in test/dev extras) and install it in t...
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fix(datasets): preserve dtype in Image.cast_storage
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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) without...
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Fix offline cache lookup with custom data files
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## 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 unresolved p...
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fix(tsfile): guard optional Apache TsFile SDK import
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## 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 `pyarrow>...
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Fix typos in docstrings, comments and an error message
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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 simulatenous ca...
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Fix dead PDF processing link in README
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## 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 maintena...
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8,511
`Image.cast_storage` warns about downcasting int64 to uint8 for storage that is already uint8
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2026-08-24T09:23:22
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### 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<item: uin...
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8,509
Add return_file_name parameter to JSON dataset loader (Fixes #5806)
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2026-08-24T06:22:02
2026-08-24T06:22:02
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## 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` for back...
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8,508
Validate ArrayXD dimensions during Arrow conversion
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2026-08-23T18:49:53
2026-09-14T15:37:29
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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 after c...
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8,507
ArrayXD silently reshapes inputs with incompatible nested dimensions
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2026-08-23T18:42:56
2026-08-23T18:43:58
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### 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 silen...
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8,505
Request to correct language name for ISO code 'blk' from 'Pa'o Karen' to 'Pa'O'
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2026-08-23T12:05:19
2026-08-23T12:05:19
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### 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 is inco...
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8,504
Preserve special NumPy scalar types in NumPy formatting
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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 boolea...
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8,503
Preserve NumPy temporal and boolean scalars
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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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8,502
Add support for datasets backed by vortex file format
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2026-08-21T16:23:30
2026-08-27T17:02:22
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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. As req...
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Format bool and temporal values as numpy scalars and keep the duration dtype in numpy format
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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 dtype only...
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with_format("numpy") returns bool/datetime values as 0-d unhashable arrays and casts duration columns to int64
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### 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 silen...
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8,499
TsFile format filter uses an inconsistent URL and is not available initially
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2026-08-19T11:35:39
2026-09-01T04:16:37
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## 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. Inconsistent...
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fix: bound Dataset.take range to prevent IndexError when n > len(dataset)
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## 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/datase...
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8,496
Preserve storage options across URL hops
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## 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 the l...
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Validate class labels when casting an integer column to ClassLabel
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`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 write p...
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cast_column to ClassLabel silently accepts out-of-range label indices
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### 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 Dataset, ...
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Don't mask the original error when finalizing the writer fails in Dataset.map
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## 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 closed ...
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Let Dataset.take(n) return what it has when n > len(dataset)
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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: ['a'], n...
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`Dataset.map(num_proc=N)` worker crashes with `ValueError: I/O operation on closed file` when `finalize()` is interrupted
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### 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.jsonl --...
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Fix archive path check allowing sibling-directory escapes
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## 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(base, p...
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Add integration test for loading renamed dataset repositories
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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 under the...
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Skip Spark tests when PySpark is unavailable
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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. Vali...
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Use the active Python interpreter in fingerprint test
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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 `python3` m...
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Fix PandasArrayExtensionArray.take coercing fill_value when not filling
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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 values tha...
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Fix Dataset.take raising IndexError when n exceeds dataset length
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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 request ...
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8,484
Validate each element in ClassLabel.int2str
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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 to inde...
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Dataset.take(n) raises IndexError when n > len(dataset), but IterableDataset.take(n) returns what it has
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### 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_dict(...
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Fix Dataset.skip(n) raising IndexError when n == len(dataset)
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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. >>> ds.skip...
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ClassLabel.str2int and int2str do not validate the elements of an iterable
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### 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 labels rou...
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tests(utils): add unit tests for is_documented_by in doc_utils
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tests(utils): add unit tests for is_documented_by in doc_utils
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Handle null values when batching by column
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## 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, "B"] ...
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Reject non-positive batch sizes in batch()
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## 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 @LeSingh1 in ...
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Fix OverflowError in Dataset.select on in-range negative indices
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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())`, wh...
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`Dataset.select` raises a raw OverflowError on in-range negative indices that its own validation allows
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### Describe the bug `Dataset.select` validates indices with [`_check_valid_indices_value`](https://github.com/huggingface/datasets/blob/836b82e0544cabf6474b25ade131b4d21e570373/src/datasets/arrow_dataset.py#L636-L638), which implements Python-style negative indexing: it only raises for a negative index when `index + ...
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8,474
Fix negative percent split boundary anchoring to the start when it rounds to 0
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Fixes #8473. ### What this fixes With the default `closest` rounding, a negative percent split boundary whose absolute value rounds to 0 loses its sign, so "relative to the end" silently becomes "relative to the start": ```python # on a 30-example split train[-1%:] -> (0, 30) all 30 rows, while train[99%:] corre...
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8,473
Negative percent split boundary silently anchors to the start when it rounds to 0 (`train[-1%:]` returns the whole split)
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### Describe the bug With the default `closest` rounding, a negative percent split boundary whose absolute value rounds to 0 loses its sign, so "relative to the end" silently becomes "relative to the start": - `train[-1%:]` on a 30-example split returns **all 30 rows** instead of the last 1% (~0 rows, as `train[99%:]...
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8,472
batch(0) silently returns one batch with the whole dataset instead of raising
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2026-08-12T07:52:28
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### Describe the bug `Dataset.batch()` and `IterableDataset.batch()` accept `batch_size=0` and negative sizes, and quietly return **one batch holding the whole dataset**. Asking for a zero-sized batch produces the largest possible batch, which is as close to the opposite of the request as the API can get. ### Steps o...
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8,471
Stop `Dataset.sort()` emitting a pyarrow FutureWarning
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## The bug Every `Dataset.sort()` call emits a pyarrow `FutureWarning` on pyarrow >= 25: ``` FutureWarning: Specifying null_placement in SortOptions is deprecated as of 25.0.0. Specify null_placement per sort_key instead. ``` It comes from `Dataset.sort` itself, not from user code: ```python indices = pc.sort_indic...
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Keep the dtype of an empty column in the numpy formatter
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Fixes #8469. ## The bug Under `numpy` format, a column holding zero rows comes back as `float32` regardless of its declared dtype — while the same column keeps its dtype as soon as it holds one row, and `pandas`/`arrow` keep it either way. ```python feats = Features({"a": Value("int64")}) ds = Dataset.from_dict({"a"...
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Empty column loses its dtype under numpy format (always float32)
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### Describe the bug Under `numpy` format, a column that holds zero rows comes back as `float32` no matter what dtype it was declared with. The same column keeps its dtype as soon as it holds a single row, and the `pandas` and `arrow` formatters keep it in both cases. This is easy to hit in normal code: any `filter` ...
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Implement `construct_from_string` on `PandasArrayExtensionDtype`
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Fixes #8467. ## The bug `PandasArrayExtensionDtype` never implements `construct_from_string`, so it inherits the base `ExtensionDtype` version: ```python @classmethod def construct_from_string(cls, string): assert isinstance(cls.name, str), (cls, type(cls.name)) ``` `name` is an instance `@property` here, becau...
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PandasArrayExtensionDtype has no construct_from_string, so comparing the dtype to any string raises AssertionError
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### Describe the bug `PandasArrayExtensionDtype` does not implement `construct_from_string`, so it inherits the base `ExtensionDtype` version, which opens with: ```python @classmethod def construct_from_string(cls, string): assert isinstance(cls.name, str), (cls, type(cls.name)) ``` `name` is an instance `@prope...
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Raise a clear error for a deeply nested mesh file
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Fixes #8246. ## The bug A GLB file carries its scene description as an embedded JSON chunk, and that JSON can be nested arbitrarily deeply. Parsing it exhausts the interpreter stack, so `Mesh.decode_example` propagates a bare ``` RecursionError: maximum recursion depth exceeded while decoding a JSON object from a un...
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8,465
PandasArrayExtensionArray.take coerces fill_value before checking whether anything is filled, breaking boolean masks on integer array columns
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### Describe the bug `PandasArrayExtensionArray.take` coerces `fill_value` to the array's value type **before** checking whether any element will actually be filled: https://github.com/huggingface/datasets/blob/main/src/datasets/features/features.py#L965-L972 ```python if allow_fill: fill_value = ( self....
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Make `PandasArrayExtensionDtype._metadata` a tuple
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Fixes #8375. ## The bug `PandasArrayExtensionDtype._metadata` was the plain string `"value_type"`. Pandas expects a **tuple of attribute names** and iterates over it in `ExtensionDtype.__eq__` and `ExtensionDtype.__hash__`: ```python all(getattr(self, attr) == getattr(other, attr) for attr in self._metadata) ``` A ...
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very bad quality images in most of them
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### Describe the bug very bad quality images in most of them ### Steps to reproduce the bug . ### Expected behavior . ### Environment info .
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Fix info from data_dir/data_files or malformed yaml
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- don't reuse the info from a config if data_files or data_dir are passed - ignore malformed split info in yaml cc @anton-l
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Add test for tokenizer stride with Dataset.map (issue #4769)
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## Problem When processing SQuAD v1.1 datasets with `max_seq_length=128` and `doc_stride=96` using transformers' QA pipeline, users encountered an assertion failure: ``` thread '<unnamed>' panicked at 'assertion failed: stride < max_len' ``` This prevented legitimate use of the stride parameter in question-answering p...
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Fix Audio encoding for path rows with null arrays
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## Summary - treat a null `array` value as absent in `Audio.encode_example()`, so path-backed rows reach the existing path branch - cover the null-array case in the existing `encode_example` and `cast_to_audio_features` parametrize lists ## Problem Casting a struct column to `Audio()` fails when a row is path-backed...
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Fix Dataset.to_json() truncating/corrupting non-millisecond temporal columns
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Fixes #8390 ## Problem `Dataset.to_json()` writes every timestamp/duration column through `pandas.DataFrame.to_json()`, which only supports a single global `date_unit` for the entire call (defaulting to milliseconds). This ignores each column's actual Arrow unit (`s`/`ms`/`us`/`ns`), causing a `to_json()` → `f...
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Replace httpx with httpx2
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### Feature request Replace the httpx library with the [httpx2](https://github.com/pydantic/httpx2) fork, maintained by Pydantic. ### Motivation Httpx has not been released since December 2024, and has closed all public access to Issues / Discussions / PRs, this raises questions about the actual state of maintenance...
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fix(types): expose PyTorch dataset compatibility
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## What does this PR do? Make `Dataset` and `IterableDataset` visible to static type checkers as compatible with PyTorch's dataset classes, so Pyright accepts both map-style and iterable datasets as `torch.utils.data.DataLoader` inputs, including `dataset.with_format(torch)`. This addresses the typing gap described in...
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Fix Dataset.map(batched=True, drop_last_batch=True) returning unchanged input when dataset is smaller than batch_size
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Fixes #8386 ## Bug When `drop_last_batch=True` and the dataset has fewer rows than `batch_size`, `Dataset.map(batched=True, ...)` was returning the **input dataset unchanged** instead of an empty dataset. Every batch is incomplete in this situation, so the mapped function is never invoked and `update_data` stays fals...
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close input stream in _in_memory_arrow_table_from_file to prevent file descriptor leaks
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In `src/datasets/table.py`, `_in_memory_arrow_table_from_file` opens a `pa.input_stream(filename)` without a `with` context manager block. When exceptions occur during stream parsing or when exception tracebacks retain frame variables in memory (e.g., in logging handlers or data loader error queues), local `in_memory...
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Coerce ClassLabel.names to plain str to fix invalid YAML in README.md
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Coerce ClassLabel.names to plain str to fix invalid YAML in README.md Fixes #6919 ## Problem `push_to_hub` can fail with: ``` ValueError: Invalid metadata in README.md. - Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> (50:11) ``` This happens whenever a `ClassLabel`'s `names` list contain...
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Reject features with different columns in IterableDataset.cast
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`IterableDataset.cast()` assigns the given features directly: ```python features = _fix_for_backward_compatible_features(features) info = self._info.copy() info.features = features ``` There is no check that the columns match the dataset's, which `Dataset.cast()` does perform. The worst case is an **extra** column: i...
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Let a dataset with an Audio column be flattened
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`Dataset.flatten()` fails on any dataset that holds a decodable `Audio` column: ```python from datasets import Dataset, Features, Audio, Value features = Features({"audio": Audio(), "text": Value("string")}) ds = Dataset.from_dict({"audio": [None], "text": ["a"]}, features=features) ds.flatten() ``` ``` ValueError: ...
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8,451
Use the documented default batch_size in the batched IterableDataset exports
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`IterableDataset.to_dict()`, `to_pandas()` and `to_polars()` all document `batch_size` as: > batch_size (`int`, *optional*): The size (number of rows) of the batches if `batched` is `True`. Defaults to `datasets.config.DEFAULT_MAX_BATCH_SIZE`. but the default is never applied. `batch_size` (`None`) is passed straight...
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Raise a clear error for values that cannot be encoded as an image
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Putting an unsupported value in an `Image()` column fails with an error from deep inside the encoder rather than a message about the image: ```python from datasets import Dataset, Features, Image Dataset.from_dict({"im": [123]}, features=Features({"im": Image()})) ``` ``` AttributeError: 'int' object has no attribut...
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Check the per-split arguments of DatasetDict
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`DatasetDict.map`, `filter`, `flatten_indices`, `sort` and `shuffle` take per-split arguments — `cache_file_names`, `indices_cache_file_names`, `seeds`, `generators` — documented as *"You have to provide one ... per dataset in the dataset dictionary"*. None of them checks that, they just index the dict: ```python from...
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Reject an unknown column in IterableDataset.cast_column
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`IterableDataset.cast_column()` writes the feature straight into the features dict: ```python feature = _fix_for_backward_compatible_features(feature) info = self._info.copy() info.features[column] = feature ``` There is no check that `column` is actually a column of the dataset, so a typo doesn't fail — it **invents...
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Reject an invalid axis in concatenate_datasets
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`concatenate_datasets()` documents `axis` as `{0, 1}`, but does not check it. Any other value silently returns **only the first dataset**: ```python from datasets import Dataset, concatenate_datasets a = Dataset.from_dict({"a": [1, 2, 3]}) b = Dataset.from_dict({"b": [4, 5, 6]}) concatenate_datasets([a, b], axis=2)....
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Reject a non-positive batch_size in the Dataset export methods
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All the `Dataset.to_*` exports batch with `range(0, len(self), batch_size)`. A negative `batch_size` makes that range empty, so the data is silently dropped — including when writing to a file: ```python from datasets import Dataset ds = Dataset.from_dict({"col_1": [0, 1, 2]}) len(list(ds.to_dict(batch_size=-1, batch...
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Fix jax formatting of temporal and decimal columns
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Temporal columns are either rejected or **silently corrupted** by the jax format. ### 1. Timestamps, dates, times and decimals raise ```python import datetime from datasets import Dataset ds = Dataset.from_dict({"t": [datetime.datetime(2020, 1, 1)]}) ds.with_format("jax")[0]["t"] ``` ``` TypeError: Error interpreti...
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8,444
Fix StopIteration when the JSON data files are empty
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Feature inference in `json.py` did `next(iter(...))` catching only `FullReadDisallowed`, so an empty `.jsonl` leaked a bare `StopIteration` out of `load_dataset` (the PEP 479 hazard). This brings `json.py` in line with its neighbours rather than adding a special case: `arrow.py` and `parquet.py` already infer with a `...
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Validate by_column and fix the error message in Dataset.batch
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Two small problems in `Dataset.batch()`. **1. The error names the wrong class.** ```python ds.batch() # ValueError: IterableDataset.batch() misses `batch_size` or `by_column` argument. ``` `IterableDataset.batch()` has its own copy of the same check, so this occurrence in `arrow_dataset.py` is just a copy-paste that...
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Accept numpy integers as train_size / test_size in train_test_split
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`Dataset.train_test_split()` uses `isinstance(..., int)` / `isinstance(..., float)` to tell "a number of rows" from "a fraction", and rejects everything else: ```python if train_size is not None and not isinstance(train_size, (int, float)): raise ValueError(f"Invalid value for train_size: {train_size} of type {typ...
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Reject a non-positive buffer_size in IterableDataset.shuffle
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`IterableDataset.shuffle()` doesn't check `buffer_size`, and the two bad values fail in very different ways. ## A negative buffer_size buffers the whole dataset `BufferShuffledExamplesIterable.__iter__` fills the buffer with an equality test: ```python for x in self.ex_iterable: if len(mem_buffer) == buffer_size...
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Don't crash when formatting temporal and decimal columns to torch
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A dataset with a temporal or decimal column cannot be used with the torch format at all: ```python import datetime from datasets import Dataset ds = Dataset.from_dict({"t": [datetime.datetime(2020, 1, 1)]}) ds.with_format("torch")[0]["t"] ``` ``` TypeError: can't convert np.ndarray of type numpy.datetime64. The only...
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Don't include writer_batch_size in the fingerprint
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`writer_batch_size` is documented as > Number of rows per write operation for the cache file writer. This value is a good trade-off between memory usage during the processing, and processing speed. It cannot change the data of the resulting dataset — only how it is written. Yet it is part of the fingerprint of `map`,...
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Validate rank and world_size in split_dataset_by_node for IterableDataset
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`split_dataset_by_node()` behaves differently on the two dataset types for invalid `rank` / `world_size`. The map-style version delegates to `Dataset.shard()`, which rejects them: ```python def _split_by_node_map_style_dataset(dataset, rank, world_size): return dataset.shard(num_shards=world_size, index=rank, cont...
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`Dataset.filter(..., batched=True)` builds the kept indices with a `zip`: ```python indices_array = [i for i, to_keep in zip(indices, mask) if to_keep] ``` `zip` stops at the shorter of its arguments, so a `function` that does not return exactly one value per example is not an error — it silently changes the result: ...
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Validate seed and generator in Dataset.train_test_split
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`Dataset.shuffle()` validates its two randomness parameters: ```python if seed is not None and generator is not None: raise ValueError("Both `seed` and `generator` were provided. Please specify just one of them.") if generator is not None and not isinstance(generator, np.random.Generator): raise ValueError("T...
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Validate num_shards in Dataset.to_iterable_dataset
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`Dataset.to_iterable_dataset(num_shards=0)` (or any negative value) returns an `IterableDataset` that silently yields **nothing**: ```python from datasets import Dataset ds = Dataset.from_dict({"col_1": [1, 2, 3]}) list(ds.to_iterable_dataset(num_shards=0)) # [] -- no error list(ds.to_iterable_dataset(num_shards=...
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Fix exporting an IterableDataset that has no examples
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Every conversion/export method of `IterableDataset` materializes the dataset with `pa.concat_tables()` over its Arrow batches: ```python table = pa.concat_tables(list(self.with_format("arrow").iter(batch_size=1000))) return Dataset(table, fingerprint="unset").to_list() ``` If the dataset doesn't yield any example the...
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Validate num_shards in Dataset.save_to_disk
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`Dataset.save_to_disk()` accepts `num_shards`, but never checks that it is positive. With `num_shards=0` (or a negative value) it reports success while writing **no data file at all**: ```python from datasets import Dataset, load_from_disk ds = Dataset.from_dict({"col_1": [1, 2, 3]}) ds.save_to_disk("dir", num_shards...
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Fix numpy formatting of a list column whose first row is null
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Formatting a list column to numpy or torch crashes when the **first** row is null: ```python from datasets import Dataset, Features, List, Value ds = Dataset.from_dict({"c": [None, [3, 4]]}, features=Features({"c": List(Value("int64"))})) ds.with_format("numpy")[:]["c"] ``` ``` AttributeError: 'NoneType' object has ...
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Fix the archive member path check against a sibling output directory
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Fix Dataset.flatten with max_depth <= 1
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`Dataset.flatten()` accepts a `max_depth` argument (also exposed through `DatasetDict.flatten()`), but any value `<= 1` crashes: ```python from datasets import Dataset Dataset.from_dict({"a": [{"b": {"c": 1}}]}).flatten(max_depth=1) # UnboundLocalError: cannot access local variable 'depth' where it is not associated ...
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Reject a non-positive batch_size in Dataset.iter
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`Dataset.iter(0)` never returns on a dataset without an indices mapping: ```python from datasets import Dataset ds = Dataset.from_dict({"i": list(range(5))}) next(ds.iter(0)) # hangs forever ``` The fast path calls `table_iter(self.data, batch_size=0)`, which asks pyarrow for chunks of `max_chunksize=0`; every chu...
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Keep the column order in Features.flatten()
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`Features.flatten()` and `Dataset.flatten()` disagree on the resulting column order: ```python from datasets import Dataset, Features, Value features = Features({"a": Value("int64"), "s": {"x": Value("string")}, "l": Value("int64")}) ds = Dataset.from_dict({"a": [1], "s": [{"x": "q"}], "l": [2]}, features=features) ...
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Validate the arguments of IterableDataset.shard
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`IterableDataset.shard()` doesn't validate its arguments, so invalid calls either return the wrong data silently or crash with a low-level error from an internal helper. `Dataset.shard()` already validates the index. ```python ds = Dataset.from_dict({"a": range(20)}).to_iterable_dataset(num_shards=4) ds.shard(num_sha...
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Fix casting a chunked array that has no chunk
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A batched `map` on an arrow-formatted dataset crashes when the function filters out every row of a batch and the output features are given explicitly: ```python import pyarrow.compute as pc from datasets import Dataset, Features, Value ds = Dataset.from_dict({"a": [1, 2, 3, 4]}).with_format("arrow") ds.map( lambd...
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Return a new Dataset when transforming a dataset with no rows
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`Dataset.map`, `.filter`, `.select`, `.sort`, `.shuffle` and `.train_test_split` short-circuit when the dataset has no rows and return `self` — the very same object. `train_test_split` even returns that same object as *both* splits. Because `set_format` / `set_transform` / `reset_format` / `add_faiss_index` mutate a d...
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Encode the column in Dataset.add_column when a feature is given
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`Dataset.add_column()` takes a `feature` argument documented as the "column datatype", but it is passed directly as the arrow schema of `InMemoryTable.from_pydict()`. That only works when the raw Python values already match the feature's *storage* type, so any feature that encodes its values fails: ```python from data...
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Fix keep_in_memory=True in Dataset.sort and Dataset.train_test_split
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`Dataset.sort()` and `Dataset.train_test_split()` raise as soon as `keep_in_memory=True` is used on a dataset that has cache files — i.e. on anything returned by `load_dataset()` or `load_from_disk()`: ```python from datasets import Dataset Dataset.from_dict({"col_1": [3, 1, 2]}).save_to_disk("ds") ds = Dataset.load_...
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Fix to_pandas() on a dataset with no rows and an ArrayXD column
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Converting a dataset that has no rows and an `ArrayXD` column to pandas raises: ```python import datasets from datasets import Dataset, Features, Array2D features = Features({"foo": Array2D(shape=(2, 2), dtype="float32")}) ds = Dataset.from_dict({"foo": []}, features=features) ds.to_pandas() ``` ``` pyarrow.lib.Arro...
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Validate the columns in IterableDataset.rename_columns
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`IterableDataset.rename_columns()` doesn't check the column names it is given, so when `info.features` is known it can return a dataset whose `features` contradict the examples it yields. The error is only raised much later, by `_rename_columns_fn()`, when the dataset is iterated. ```python ds = Dataset.from_dict({"a"...
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Keep the dtype of an empty column when formatting to numpy or torch
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An empty column silently changes dtype when formatted to numpy or torch: ```python from datasets import Dataset ds = Dataset.from_dict({"a": [1, 2, 3]}).with_format("torch") print(ds[:]["a"].dtype) # torch.int64 print(ds.filter(lambda x: False)[:]["a"].dtype) # torch.float32 <-- expected to...
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Fix label inference for folder-based builders on archives
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`imagefolder` / `audiofolder` / `videofolder` datasets whose media files live inside a ZIP archive get the wrong labels, and usually fail to build at all. ## The bug In `_split_generators()` the entries of `files` have two different shapes: ```python # loose files: (original_file, None, downloaded_file) files = tup...
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https://api.github.com/repos/huggingface/datasets/issues/8419/timeline
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