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value:`Dataset.select` raises a raw OverflowError on in-range negative indices that its own validation allows
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value:### 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 `in...
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value:Fix negative percent split boundary anchoring to the start when it rounds to 0
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value: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%:]...
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value:Negative percent split boundary silently anchors to the start when it rounds to 0 (`train[-1%:]` returns the whole split)
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value:### 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...
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value:batch(0) silently returns one batch with the whole dataset instead of raising
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value:### 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. ### S...
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value:Stop `Dataset.sort()` emitting a pyarrow FutureWarning
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value:## 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...
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value:Keep the dtype of an empty column in the numpy formatter
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value: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_dic...
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value:Empty column loses its dtype under numpy format (always float32)
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value:### 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 `fi...
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value:Implement `construct_from_string` on `PandasArrayExtensionDtype`
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value: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,...
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value:PandasArrayExtensionDtype has no construct_from_string, so comparing the dtype to any string raises AssertionError
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value:### 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 `...
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value:Raise a clear error for a deeply nested mesh file
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value: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 fro...
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value:PandasArrayExtensionArray.take coerces fill_value before checking whether anything is filled, breaking boolean masks on integer array columns
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value:### 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 = ( ...
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value:Make `PandasArrayExtensionDtype._metadata` a tuple
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value: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) `...
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value:very bad quality images in most of them
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value:### Describe the bug very bad quality images in most of them ### Steps to reproduce the bug . ### Expected behavior . ### Environment info .
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value:Fix info from data_dir/data_files or malformed yaml
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value:- 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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value:Add test for tokenizer stride with Dataset.map (issue #4769)
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value:## 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-answe...
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value:Fix Audio encoding for path rows with null arrays
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value:## 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-...
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value:Fix Dataset.to_json() truncating/corrupting non-millisecond temporal columns
value:2026-08-10T23:07:21Z
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value: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()...
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value:Replace httpx with httpx2
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value:### 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 maint...
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value:fix(types): expose PyTorch dataset compatibility
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value:## 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 descri...
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value:Fix Dataset.map(batched=True, drop_last_batch=True) returning unchanged input when dataset is smaller than batch_size
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value: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` stay...
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value:close input stream in _in_memory_arrow_table_from_file to prevent file descriptor leaks
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value: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_...
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value:Coerce ClassLabel.names to plain str to fix invalid YAML in README.md
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value: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 c...
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value:`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** col...
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value:Let a dataset with an Audio column be flattened
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value:`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() ``` ``` ValueE...
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value:Use the documented default batch_size in the batched IterableDataset exports
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value:`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 st...
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value:Raise a clear error for values that cannot be encoded as an image
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value: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 at...
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value:Check the per-split arguments of DatasetDict
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value:`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: ```pytho...
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value:Reject an unknown column in IterableDataset.cast_column
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value:`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 **i...
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value:Reject an invalid axis in concatenate_datasets
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value:`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], ax...
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value:Reject a non-positive batch_size in the Dataset export methods
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value: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,...
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value:Fix jax formatting of temporal and decimal columns
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value: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 inte...
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value:Fix StopIteration when the JSON data files are empty
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value: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 wi...
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value:Validate by_column and fix the error message in Dataset.batch
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value: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-past...
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value:Accept numpy integers as train_size / test_size in train_test_split
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value:`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 typ...
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value:Reject a non-positive buffer_size in IterableDataset.shuffle
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value:`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) == buffe...
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value:Don't crash when formatting temporal and decimal columns to torch
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value: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. Th...
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value:Don't include writer_batch_size in the fingerprint
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value:`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 ...
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value:Validate rank and world_size in split_dataset_by_node for IterableDataset
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value:`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...
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value:Raise when a batched filter function returns the wrong number of values
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value:`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 re...
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value:Validate seed and generator in Dataset.train_test_split
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value:`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 ValueEr...
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value:Validate num_shards in Dataset.to_iterable_dataset
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value:`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_s...
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value:Fix exporting an IterableDataset that has no examples
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value: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 examp...
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value:Validate num_shards in Dataset.save_to_disk
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value:`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_...
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value:Fix numpy formatting of a list column whose first row is null
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value: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' objec...
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value:Fix the archive member path check against a sibling output directory
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value:The `safemembers()` guards that implement the CVE-2007-4559 mitigation compare the resolved member path to the output directory with a plain `str.startswith()`: ```python def badpath(path: str, base: str) -> bool: # joinpath will ignore base if path is absolute return not resolved(os.path.join(base, path...
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value:Fix Dataset.flatten with max_depth <= 1
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value:`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 assoc...
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value:Reject a non-positive batch_size in Dataset.iter
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value:`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`; eve...
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value:Keep the column order in Features.flatten()
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value:`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=featu...
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value:Validate the arguments of IterableDataset.shard
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value:`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(n...
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value:Fix casting a chunked array that has no chunk
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value: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( ...
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value:Return a new Dataset when transforming a dataset with no rows
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value:`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` muta...
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value:Encode the column in Dataset.add_column when a feature is given
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value:`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 fro...
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value:Fix keep_in_memory=True in Dataset.sort and Dataset.train_test_split
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value:`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...
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value:Fix to_pandas() on a dataset with no rows and an ArrayXD column
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value: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.li...
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value:Validate the columns in IterableDataset.rename_columns
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value:`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_dic...
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value:Keep the dtype of an empty column when formatting to numpy or torch
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value: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 <-- expec...
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value:`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...
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value:Fix DataFrame masking on ArrayXD columns with an integer value type
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value:Selecting rows with a boolean mask on a `DataFrame` that has an `Array2D`/`Array3D`/... column with an integer value type raises: ```python import datasets, numpy as np features = datasets.Features({"id": datasets.Value("int64"), "foo": datasets.Array2D(dtype="int32", shape=(2, 2))}) ds = datasets.Dataset.from_...
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value:Shutdown ThreadPoolExecutor in CyclingMultiSourcesExamplesIterable
value:2026-08-08T15:58:26Z
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value:Add missing executor.shutdown(wait=True) call in the finally block of _iter_arrow(), matching the behavior in __iter__ to ensure threads are shut down before cleanup and sleep.
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value:Fix cryptic TypeError from ClassLabel.int2str on a string input
value:2026-08-08T13:27:22Z
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value:`ClassLabel.int2str("1")` raises a cryptic `TypeError: '<=' not supported between instances of 'int' and 'str'` instead of a clear error. `int2str` guards its input with `isinstance(values, Iterable)`, but a `str` is iterable, so a string is not wrapped as a single value; the code then iterates over the string's...
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value:Add initial devcontainer configuration
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value:Fix offline simulation with huggingface_hub 1.27
value:2026-08-07T12:39:32Z
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value:2026-08-10T19:31:39Z
value:## Summary Surfaced by the `huggingface_hub` v1.27.0.rc0 RC test branch, where 6 tests in `tests/test_load.py` fail: [ci-test-huggingface-hub-1.27.0.rc0-release](https://github.com/huggingface/datasets/compare/main...ci-test-huggingface-hub-1.27.0.rc0-release). They're all offline-simulation tests, and the fail...
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value:Fix IndexError on negative list indices for datasets with an indices mapping
value:2026-08-06T20:02:13Z
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value:## Summary In `src/datasets/formatting/formatting.py`, `_query_table_with_indices_mapping`'s iterable branch passes each index directly to `indices.fast_slice(i, 1)`: ```python return _query_table(table, [indices.fast_slice(i, 1).column(0)[0].as_py() for i in key]) ``` `fast_slice` raises `IndexError("Offset m...
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value:Restore the _legacy_no_dict_keys_sorting branch in Pickler._batch_setitems
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value:### Root cause `Pickler._legacy_no_dict_keys_sorting` lost its only reader in #7817. That PR rewrote `_batch_setitems` to accept the argument Python 3.14 passes, and in the same hunk dropped the early return: ```diff - def _batch_setitems(self, items): - if self._legacy_no_dict_keys_sorting: - ...
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value:_legacy_no_dict_keys_sorting has no reader since #7817, so the 2.15.0 legacy cache lookup computes the wrong config_id
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value:### Describe the bug `Pickler._legacy_no_dict_keys_sorting` has a definition and a writer, but since #7817 it has no reader, so the flag does nothing. `DatasetBuilder._check_legacy_cache2` patches it on to recompute the `config_id` that 2.15.0 would have produced, because 2.15.0 hashed dicts without sorting the...
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value:docs: fix broken audio link in mapstyle vs iterable guide
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value:The "map-style vs iterable" guide links the four loading guides on one line, but the `audio` link has a stray `]` inside its target (`./audio_load]`), which makes the Markdown link malformed and breaks it. The three sibling links on the same line are correct (`./tabular_load`, `./nlp_load`, `./image_load`), and ...
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value:Save the shuffle buffer content in the state dict so resuming is exact
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value:## Overview There are two problems when resuming a shuffled iterable dataset. The known one from the warning and the discussion in #7901 is that the shuffle buffer content isn't saved in the state dict so you lose roughly `buffer_size` examples every resume. The second is you can get an example twice. The `...
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value:Optimize PyArrow block shuffling with dynamic rebatched chunk interleaving
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value:When streaming PyArrow datasets with shuffling enabled (`buffer_size > 0`), the `BufferShuffledExamplesIterable._iter_arrow()` method historically relied on a Python-level `list` to maintain the shuffle buffer, replacing elements one-by-one. In a multi-worker `DataLoader` environment, this per-row generator appro...
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value:Preserve nanosecond precision in Python dataset construction
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value:## What it is Preserves exact nanosecond precision for pandas timestamps and timedeltas when users declare `Value("timestamp[ns]")` or `Value("duration[ns]")`. Previously, `Dataset.from_dict`, `Dataset.from_list`, and `Dataset.map` reported a nanosecond feature while silently storing values truncated to microse...
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value:Match Dataset.map's on_mixed_types default in DatasetDict.map
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value:### Problem `Dataset.map` defaults `on_mixed_types` to `"use_json"`, so a mapped column with no predefined schema becomes a `Json()` feature instead of failing type inference. `DatasetDict.map` defaults the same parameter to `None` and forwards it to every split unconditionally, so wrapping the splits in a `Data...
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value:Fix broken links in documentation
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value:Several documentation pages link to URLs that now return 404. This PR updates them to their current locations. | File | Old (404) | New | |------|-----------|-----| | `object_detection.mdx` | `albumentations.ai/docs/examples/example_bboxes/` | `albumentations.ai/docs/examples/example-bboxes/` | | `image_dataset....
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value:The pandas decoder builds its transform map from all of `self.features`, but the frame has already been projected to the format's `columns`: ```python ds.with_format("pandas", columns=["label"])[0] # KeyError: "Label(s) ['image'] do not exist" ``` Scoped to pandas — `None`, `numpy`, `torch` and `arrow` all hand...
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value:Fix PolarsFeaturesDecoder.decode_row
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value:`PolarsFeaturesDecoder.decode_row` is a verbatim copy of the pandas implementation, but polars' `map_rows` takes a callable rather than a dict of column transforms: ```python ds.with_format("polars")[0] # TypeError: 'dict' object is not callable ``` So `with_format("polars")` is unusable on any dataset with an ...
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value:Allow reading a split with no examples
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value:`make_file_instructions` skips every file whose take is 0, so `BaseReader.read` receives an empty file list and raises — which means one empty split makes the entire `DatasetDict` unloadable: ```python load_dataset("json", data_files={"train": [...], "test": []}) # ValueError: Instruction "test" corresponds to n...
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value:Fix align_labels_with_mapping on a list of ClassLabel
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value:#7634 dropped `Sequence` from the `.features` import but left two references in `align_labels_with_mapping`, so every non-plain-`ClassLabel` column raises: ```python ds.align_labels_with_mapping({"O": 0, "LOC": 1, "PER": 2}, "ner_tags") # NameError: name 'Sequence' is not defined ``` That covers the token-class...
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value:With `stopping_strategy="all_exhausted"` and no `probabilities`, the indices come from `np.arange(max(lengths)) % lengths`. A zero-length input makes that a modulo by zero, which yields index 0; after the source offset is added it is either out of range or a valid index into a *different* dataset: ```python inte...
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value:Fix casting a sliced list array to a fixed size list
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value:`array_cast` and `cast_array_to_feature` build the child window for a fixed-size list as `array.values[array.offset * length : ...]`. But `.values` is the **unsliced** child array, so that arithmetic is only correct when every earlier row holds exactly `length` items. `Dataset.cast` slices into batches, so this f...
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value:Support empty input shards in ArrowBasedBuilder
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value:`ArrowBasedBuilder._prepare_split_single` assumes every input shard yields at least one table, so `original_shard_lengths` is indexed out of range when a shard yields nothing: ```python load_dataset("json", data_files=["00.jsonl", "01.jsonl"]) # 00.jsonl is empty # DatasetGenerationError # cause: IndexError:...
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value:Fix malformed audio link in mapstyle vs iterable docs
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value:# What does this PR do? Fixes a malformed link in `docs/source/about_mapstyle_vs_iterable.mdx`. The audio guide link was written as `[audio](./audio_load])` (a stray closing parenthesis inside the link target), so it resolved to a non-existent path. This PR removes the stray `)` so the link points to the actual...
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value:Forward iter_arrow, is_typed and features from RepeatExamplesIterable
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value:`RepeatExamplesIterable` never forwarded `iter_arrow`, `is_typed` or `features` to the iterable it wraps, so `.repeat()` stranded an Arrow-capable child behind a non-Arrow parent: iteration fell back to the per-example path and the dtypes were re-inferred from Python objects. This copies the three-property block...
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value:IterableDataset.repeat() drops the Arrow fast path and widens numpy dtypes
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value:### Describe the bug `RepeatExamplesIterable` is the only wrapper in `iterable_dataset.py` that does not forward `iter_arrow`, `is_typed` and `features` to the iterable it wraps. Two things follow from that: `.repeat()` drops the Arrow fast path, and under `numpy` format it silently widens the declared dtypes. ...
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value:CPU Bottleneck in `IterableDataset.state_dict()` when shuffling multiple shards
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value:**Describe the bug** There is a significant CPU bottleneck in `IterableDataset` dataloading when `buffer_size` and `max_buffer_input_shards` are greater than 1 (i.e. highly parallelized streaming with a shuffle buffer). Currently, `_BaseExamplesIterable.state_dict()` falls back to performing a full recursive `co...
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value:Add Spark Connect support for Spark datasets
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value:## What does this PR do? Adds Spark Connect support to `Dataset.from_spark` and `IterableDataset.from_spark` by replacing client-side RDD and `SparkContext` calls with DataFrame APIs supported by Spark Connect. - discovers partitions with `spark_partition_id()` for streaming datasets - runs the shared-cache pro...
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value:Nanosecond timestamps are silently truncated even when Value("timestamp[ns]") is requested
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value:### Describe the bug `Dataset.from_dict` / `from_list` / `map` silently drop sub-microsecond precision, even when the user explicitly declares a nanosecond feature type. The resulting dataset reports `timestamp[ns]` but the data has already been truncated to microseconds. ### Steps to reproduce the bug ```pyth...
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value:to_json cannot round-trip temporal columns: all timestamps written as epoch milliseconds
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value:### Describe the bug `Dataset.to_json()` serializes every temporal column as a bare epoch integer at a single fixed resolution (pandas' `date_unit="ms"` default), with no unit recorded in the output. Timestamps therefore do not survive a `to_json` → `from_json` round trip: they either come back silently wrong or...
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value:Keep the id of List and LargeList features when visiting them
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value:`_visit()` and `_visit_with_path()` rebuild `List` / `LargeList` in order to recurse into the inner feature, but they don't carry `id` over, so it is silently reset to `None`. Every other feature type is passed through untouched and keeps its `id`. `Features.__init__` runs `_visit()` on every column (to convert ...
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value:`Dataset.to_iterable_dataset()` defaults to `num_shards=1`, but the guard rejects any `num_shards > len(self)`, so it always raises on an empty dataset: ```python >>> Dataset.from_dict({"a": []}).to_iterable_dataset() ValueError: Unable to shard a dataset of size 0 into 1 shards (the number of shards exceeds the...
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value:Fix `deepcopy` bottleneck in state_dict()
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value:Fix #8393 This PR significantly resolves a major CPU bottleneck in `IterableDataset` dataloading when `buffer_size` and `max_buffer_input_shards` are > 1. **The Bug:** Currently, `_BaseExamplesIterable.state_dict()` falls back to performing a full recursive `copy.deepcopy(self._state_dict)`. In nested it...
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value:map(batched=True, drop_last_batch=True) returns the input unchanged when the dataset is smaller than batch_size
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value:### Describe the bug When `drop_last_batch=True` and the dataset is smaller than `batch_size`, `Dataset.map(batched=True, ...)` returns the **input unchanged** instead of an empty dataset. The mapped function is never called, but the original rows are passed through with the original (unbatched) schema. The ite...
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value:Fix save_to_disk writing no shard for a dataset with no rows
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value:`save_to_disk` computes the number of shards from the estimated size and then clamps it to the number of rows (added in #7912): ```python num_shards = min(len(self.data), num_shards) ``` For a dataset with no rows that gives `0` shards. No `.arrow` file is written, `state.json` gets an empty `"_data_files"`, an...
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value:Fix IterableDataset.remove_columns when a single column name is passed
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value:`IterableDataset.remove_columns` accepts either a single column name or a list, but it only ever tests `col in column_names`. When a string is passed, that becomes a substring test, so any existing column whose name is contained in the one being removed is dropped from `info.features`: ```python ds = Dataset.fro...
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value:Apply the indices mapping in Dataset.unique
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value:`Dataset.unique` only takes the indices mapping into account when it has a different number of rows than the underlying table: ```python if self._indices is not None and self._indices.num_rows != self._data.num_rows: ``` An indices mapping can also repeat some rows and drop others while keeping the same length....
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value:Fix IterableDataset.to_dict() and to_polars() returning an empty generator
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value:### The bug `IterableDataset.to_dict()` and `IterableDataset.to_polars()` each contain a bare `yield` in their `batched=True` branch. A bare `yield` anywhere in a function body makes the whole function a generator function, so both methods return a generator on every call, including the `batched=False` default. ...
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value:IterableDataset.to_dict() and to_polars() return an empty generator instead of the data
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value:### Describe the bug `IterableDataset.to_dict()` and `IterableDataset.to_polars()` each have a bare `yield` in their `batched=True` branch, which makes the whole method a generator function. Calling either one the way its docstring shows returns a generator that yields nothing, and the value the `else` branch re...
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value:perf(formatting): Python row iteration -- up to 1.43x faster
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value:## TL;DR - Read standard Arrow cells directly and reuse the stateless Python extractor, while retaining ArrayXD conversion. - This speeds up default Python `for row in ds` iteration and `ds[i]` access; `ds.map()` is unchanged. - The recorded benchmark measures iteration time on a single core with stock defaults,...
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value:feat: add return_file_name option to JSON loader
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value:## What does this PR do? Adds a `return_file_name` option to the JSON loader. When enabled, the resulting dataset includes a `file_name` column indicating the source file of each row. ## Motivation When training is interrupted and resumed, it's useful to know which file each row came from so already-trai...
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value:Fix PandasArrayExtensionDtype._metadata: must be a tuple, not a string
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value:## Summary Fixes #8375 — `PandasArrayExtensionDtype._metadata` is a plain string instead of a tuple, causing `AttributeError: 'PandasArrayExtensionDtype' object has no attribute 'v'` whenever pandas compares two instances of this dtype (e.g. during a boolean-mask filter on a DataFrame with an array-feature column...
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value:Fix PandasArrayExtensionDtype._metadata to use a tuple
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value:## What was wrong `PandasArrayExtensionDtype` set `_metadata = "value_type"` (a string), but pandas expects a tuple of attribute names, e.g. `("value_type",)`. When pandas compares dtypes (during things like `df[mask]`), it loops over `_metadata` and looks up each entry as an attribute. With a string, it iterat...
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value:`PandasArrayExtensionDtype._metadata` should be a tuple, not a string
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value:### Describe the bug There's a small typo in `PandasArrayExtensionDtype` (`src/datasets/features/features.py`): ```python _metadata = "value_type" ``` Pandas expects `_metadata` to be a tuple of attribute names, like `("value_type",)`, not a plain string ([API documentation ref](https://pandas.pydata.org/docs...
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value:feat: Qdrant Search Index
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value:2026-10-07T17:37:42Z
value:Resolves #5698 ## Description This PR adds Qdrant as a vector search backend alongside FAISS and Elasticsearch. Associated documentation and tests are also included. The Qdrant client dependency is marked optional and installed on demand. [Qdrant](https://qdrant.tech/) is an open-source vector search en...
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