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] | open | false | [] | null | [] | 2026-03-06T01:23:08 | 2026-03-06T11:57:06 | null | null | NONE | null | null | null | null | ### Feature request
In `datasets` library we have options to load `image` and `audio` datasets which can be viewed in the Dataset Viewer.
LIKE:
```bash
from datasets import load_dataset, Image
from datasets import load_dataset, Audio
```
> https://huggingface.co/docs/datasets/en/image_load & https://huggingface.co/docs/datasets/en/audio_process
---
I guess there's a need for supporting 3D datasets as well for *storing* & *loading* 3D object files like (`.glb, .ply, .obj` etc...) maybe with something like:
```bash
from datasets import load_dataset, Mesh # or `_3D` or whatever is naming convention
```
### Motivation
Multi-Modal datasets are rapidly increasing with modalites like 3D, Video etc, `datasets` already support experimental `Video` import.
I have a dataset where I've stored everything as dict so it atleast look balanced across all those 4 columns and 4 modalites It have (Text, Image, Audio, 3D Mesh) but it don't look that good since RAW binary bytes are visible. And Needs to be decoded for that specific field containing the binary data.
### Your contribution
Perhaps I could have made a PR for this, but since things might not get much attention here, I shouldn't...
Contributors can try to support it so the the huggingface server is not at heavy duty of loading preview images for each mesh in the dataset viewer that can crash. So a cool svg Icon saying 3D or Mesh inplace of preview can be good. OR they can still try to render an image from those 3D data and use it in preview place just like Image/Video.
Edit:
SO Working on PR for this, gonna push soon...
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} | When using `Dataset.map()` with `num_proc > 1`, shards containing only empty lists get their features inferred as `List(Value('null'))`, which is incompatible with `List(Struct({...}))` from shards with actual data.
The existing `_check_if_features_can_be_aligned` and `_align_features` only handled top-level `Value('null')` compatibility. This commit adds a `_is_null_feature()` helper that recursively checks for null types inside container features (`List, LargeList, Sequence`), and uses it in both alignment functions.
Fixes #8046 | null | {
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"\n### Root cause\n\nIn `src/datasets/features/features.py`, both `_check_if_features_can_be_aligned` and `_align_features` use:\n\n```python\nisinstance(v, Value) and v.dtype == \"null\"\n```\n\nto check for null compatibility. However, `List(Value('null'))` is **not** an instance of `Value` — it's an instance of ... | 2026-03-05T20:12:13 | 2026-03-05T20:12:35 | null | null | NONE | null | null | null | null | ### Describe the bug
When using `Dataset.map()` with `num_proc > 1`, if the map function produces a list column where some shards contain only empty lists `[]` and other shards contain non-empty lists of structs, the feature type inference is inconsistent across shards:
- Shards with only empty lists → `List(Value('null'))`
- Shards with non-empty lists → `List(Struct({...}))`
During shard concatenation, `_check_if_features_can_be_aligned` raises a `ValueError` because it only handles top-level `Value("null")` compatibility but does **not** handle nested null types like `List(Value('null'))`.
Setting `num_proc=1` works correctly because all data is processed in a single shard.
### Steps to reproduce the bug
```python
import datasets
print(datasets.__version__) # 4.6.1
data = {
"id": [f"item_{i}" for i in range(8)],
"labels": [
[{"type": "A", "word": "x", "start": 0, "end": 5}], # only first item has data
[], [], [], [], [], [], [] # rest are empty
]
}
ds = datasets.Dataset.from_dict(data)
# Works fine with num_proc=1
result = ds.map(lambda x: {"label": x["labels"]}, num_proc=1)
print(result.features) # OK
# Fails with num_proc=2
result = ds.map(lambda x: {"label": x["labels"]}, num_proc=2)
# ValueError: The features can't be aligned because the key label of features
# ... has unexpected type - List(Value('null'))
# (expected either List({'end': Value('int64'), ...}) or Value("null").
```
### Expected behavior
`List(Value('null'))` should be treated as compatible with any `List(...)` type during feature alignment, similar to how `Value('null')` is already treated as compatible with any other type.
### Environment info
- `datasets` version: 4.6.1 (also reproduced on 4.1.1)
- Python version: 3.12.0
- OS: Linux | null | {
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"I've tested this change along with the changes from my other PRs (https://github.com/HaukurPall/datasets/tree/all-fixes) and this change **dramatically** reduces the memory requirement per process.\r\n\r\nContext:\r\n\r\nI'm working with audio datasets, the audio is embedded in the parquet. When loading a 1GB shar... | 2026-03-05T15:13:53 | 2026-03-06T11:47:00 | null | null | NONE | null | null | false | {
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} | `IterableDataset.to_parquet()` materialized the entire shard into memory via
`pa.concat_tables(list(...))` before writing. For datasets with large shards and multiprocessing (push_to_hub with num_proc > 0) the memory requirements can be really large.
This PR writes parquet incrementally by iterating Arrow batches and writing each
directly to `pq.ParquetWriter`, reducing the memory requirement from shard size to something around row group size (with lots of caveats).
**Note**
A thing to note is that this code path is only executed when `features` are set on the `IterableDataset`, in f.ex. `map` or the dataset is `cast` before `push_to_hub`.
Changes:
- Extend `ParquetDatasetWriter` to accept both `Dataset` and `IterableDataset`.
- `IterableDataset.to_parquet()` now delegates directly to `ParquetDatasetWriter`
- Use `pq.ParquetWriter` as a context manager for proper cleanup on errors
- Falls back to materialization only when `features` is `None` (schema required upfront)
That being said, I'm not entirely convinced that extending the ParquetDatasetWriter to support IterableDataset is the correct approach or even if there are other footguns related to CDC or other stuff. | null | {
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} | ## Summary
When `num_proc` exceeds the number of output shards in `Dataset.push_to_hub`, workers
without assigned output shards silently drop their data. The metadata still reports the
correct dataset length (using `len(self[split])`), masking the data loss.
This fix caps `num_jobs` at `num_shards` in `_push_parquet_shards_to_hub`, matching
what the streaming path already does.
## Reproduction
1. Have a dataset with a small number of shards (e.g. 2)
2. Call `push_to_hub` with `num_proc > num_shards` (e.g. 6)
3. Only a fraction of the samples are actually written to the hub | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8042 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8042/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8042/comments | https://api.github.com/repos/huggingface/datasets/issues/8042/events | https://github.com/huggingface/datasets/pull/8042 | 4,027,903,841 | PR_kwDODunzps7ILwT5 | 8,042 | Fix schema enforcement in streaming _convert_to_arrow | {
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} | When the iter_arrow chain is broken (e.g. after .map() without with_format("arrow")), _convert_to_arrow falls back to pa.Table.from_pylist() which infers types from values. If early batches contain None or empty lists, Arrow infers null/list<null> types that conflict with later batches containing real data, causing ArrowInvalid schema mismatch errors.
Add an optional features parameter to _convert_to_arrow that applies cast_table_to_features to each produced table, mirroring what ArrowWriter.write_table does in the map-style path. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8041 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8041/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8041/comments | https://api.github.com/repos/huggingface/datasets/issues/8041/events | https://github.com/huggingface/datasets/pull/8041 | 4,027,152,089 | PR_kwDODunzps7IJPKo | 8,041 | Use num_examples instead of len(self) for iterable_dataset's SplitInfo | {
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https://api.github.com/repos/huggingface/datasets/issues/8040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8040/comments | https://api.github.com/repos/huggingface/datasets/issues/8040/events | https://github.com/huggingface/datasets/pull/8040 | 4,025,912,223 | PR_kwDODunzps7IFFnG | 8,040 | Fix the logic for allowed extensions when creating build configurations (#8034) | {
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https://api.github.com/repos/huggingface/datasets/issues/8039 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8039/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8039/comments | https://api.github.com/repos/huggingface/datasets/issues/8039/events | https://github.com/huggingface/datasets/pull/8039 | 4,025,811,123 | PR_kwDODunzps7IEv9Z | 8,039 | Fix non-deterministic by sorting metadata extensions (#8034) | {
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https://api.github.com/repos/huggingface/datasets/issues/8038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8038/comments | https://api.github.com/repos/huggingface/datasets/issues/8038/events | https://github.com/huggingface/datasets/issues/8038 | 4,020,184,168 | I_kwDODunzps7vnyRo | 8,038 | Typo in iterable_dataset.py | {
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} | [] | open | false | [] | null | [] | 2026-03-04T05:37:15 | 2026-03-04T05:37:15 | null | null | NONE | null | null | null | null | ### Describe the bug
`streaming=True**kwargs,` should be `streaming=True, **kwargs,`
https://github.com/huggingface/datasets/blob/81027be09d5cd9f06a6d64ef1a8a3e9ebd0f86fd/src/datasets/iterable_dataset.py#L2950
### Steps to reproduce the bug
```
from Datasets import Dataset
IterableDataset.from_csv("file.csv")
```
### Expected behavior
Load the csv file as an iterable dataset.
### Environment info
- `datasets` version: 4.6.1
- Platform: Linux-6.14.0-37-generic-x86_64-with-glibc2.39
- Python version: 3.13.11
- `huggingface_hub` version: 1.5.0
- PyArrow version: 23.0.1
- Pandas version: 3.0.1
- `fsspec` version: 2026.2.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8037/comments | https://api.github.com/repos/huggingface/datasets/issues/8037/events | https://github.com/huggingface/datasets/issues/8037 | 4,008,928,790 | I_kwDODunzps7u82YW | 8,037 | IterableDataset.filter() chaining fails due to features being lost when is_typed=True | {
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} | [] | open | false | [] | null | [] | 2026-03-02T02:30:24 | 2026-03-02T02:30:24 | null | null | NONE | null | null | null | null | ### Describe the bug
Chaining multiple .filter() calls on an IterableDataset raises a TypeError: 'NoneType' object is not a mapping on the second .filter() call.
In `IterableDataset.filter()`, the internal `ex_iterable` is wrapped with FormattedExamplesIterable:
```python
ex_iterable = FormattedExamplesIterable(
ex_iterable,
formatting=self._formatting,
features=None if ex_iterable.is_typed else self._info.features,
token_per_repo_id=self._token_per_repo_id,
)
```
After the first .filter(), the resulting ex_iterable becomes typed (is_typed=True) because a ===MASK=== column is added. On the second .filter(), since is_typed=True, features=None is passed to FormattedExamplesIterable. This causes .features to return None, and FilteredExamplesIterable.__init__ fails when trying to unpack it.
The fix should preserve the existing features when is_typed=True, e.g.:
```python
features=ex_iterable.features if ex_iterable.is_typed else self._info.features
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-v2", "Scala", split="train", streaming=True)
ds = ds.filter(lambda row: row["src_encoding"] == "UTF-8")
ds = ds.filter(lambda row: row["branch_name"] == "refs/heads/master") # TypeError here
```
## Error traceback
```
File ".../datasets/iterable_dataset.py", line 2975, in filter
ex_iterable = FilteredExamplesIterable(
File ".../datasets/iterable_dataset.py", line 1606, in __init__
features = Features({**ex_iterable.features, self.mask_column_name: Value("bool")})
TypeError: 'NoneType' object is not a mapping
```
### Expected behavior
The error should not raised in this context.
### Environment info
datasets: 4.5.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8036/comments | https://api.github.com/repos/huggingface/datasets/issues/8036/events | https://github.com/huggingface/datasets/pull/8036 | 4,006,914,206 | PR_kwDODunzps7HHMKn | 8,036 | follow `cache_dir` in download option when loading datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/8035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8035/comments | https://api.github.com/repos/huggingface/datasets/issues/8035/events | https://github.com/huggingface/datasets/pull/8035 | 4,005,673,675 | PR_kwDODunzps7HDPtk | 8,035 | Update links in README to use markdown format | {
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https://api.github.com/repos/huggingface/datasets/issues/8034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8034/comments | https://api.github.com/repos/huggingface/datasets/issues/8034/events | https://github.com/huggingface/datasets/issues/8034 | 4,004,188,523 | I_kwDODunzps7uqxFr | 8,034 | [BUG] load_datasets() cannot use the generated Arrow cache correctly | {
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"PYTHONHASHSEED=1 uv run python -c\n \"\nfrom datasets.load import LocalDatasetModuleFactory\nmod = LocalDatasetModuleFactory('/cache/datasets/imagenet-1k')\ndm = mod.get_module()\nprint('Hash:', dm.hash)\n\"\nResolving data files: 100%|█████████| 294/294 [00:00<00:00, 20584.68it/s]\nResolving data files: 100%|████... | 2026-02-28T08:11:23 | 2026-03-04T13:50:08 | null | null | NONE | null | null | null | null | ### Describe the bug
The datasets library cannot use the generated Arrow cache correctly, seemingly due to a flaw in the internal hash symbol calculation logic.
The following code provides verification.
I am trying to locate the specific code position, and if there are further developments, I will update here.
### Steps to reproduce the bug
python -c "
from datasets.load import LocalDatasetModuleFactory
mod = LocalDatasetModuleFactory('/cache/datasets/imagenet-1k')
dm = mod.get_module()
print('Hash:', dm.hash)
"
Resolving data files: 100%|█████████| 294/294 [00:00<00:00, 23895.46it/s]
Resolving data files: 100%|██████████| 28/28 [00:00<00:00, 221168.57it/s]
Hash: 9e9925e0f7d48775
python -c "
from datasets.load import LocalDatasetModuleFactory
mod = LocalDatasetModuleFactory('/cache/datasets/imagenet-1k')
dm = mod.get_module()
print('Hash:', dm.hash)
"
Resolving data files: 100%|█████████| 294/294 [00:00<00:00, 26252.91it/s]
Resolving data files: 100%|██████████| 28/28 [00:00<00:00, 188205.95it/s]
Hash: 9af23f3d5d488660
### Expected behavior
The expected behavior is that the above code should give the same hash value.
### Environment info
- `datasets` version: 4.6.1
- Platform: Linux-6.8.0-100-generic-x86_64-with-glibc2.39
- Python version: 3.12.12
- `huggingface_hub` version: 1.5.0
- PyArrow version: 23.0.1
- Pandas version: 3.0.1
- `fsspec` version: 2026.2.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8033 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8033/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8033/comments | https://api.github.com/repos/huggingface/datasets/issues/8033/events | https://github.com/huggingface/datasets/pull/8033 | 4,003,917,950 | PR_kwDODunzps7G-QP1 | 8,033 | perf: use deque for async map task/index queues | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8033). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Friendly ping — CI is green and this is ready for review. Happy to address any feedback... | 2026-02-28T05:07:00 | 2026-02-28T21:32:44 | null | null | NONE | null | null | false | {
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} | ## Problem
`arrow_dataset.py` and `iterable_dataset.py` drain `indices` and `tasks` lists front-to-back via `.pop(0)` during parallel async map operations. With `MAX_NUM_RUNNING_ASYNC_MAP_FUNCTIONS_IN_PARALLEL` items in flight, each `.pop(0)` is **O(n)** making the drain loop **O(n²)**.
## Solution
Switch both `indices` and `tasks` to `collections.deque` with `.popleft()` for **O(1)** front removal.
## Changes
- `src/datasets/arrow_dataset.py`: Import `deque`, type `tasks` and `indices` as deque, use `.popleft()`
- `src/datasets/iterable_dataset.py`: Import `deque`, type `tasks`, `indices`, and `_owned_loops_and_tasks` entry as deque, use `.popleft()`
## Testing
- Syntax verified via `ast.parse()` on both files | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8030/comments | https://api.github.com/repos/huggingface/datasets/issues/8030/events | https://github.com/huggingface/datasets/pull/8030 | 4,003,238,887 | PR_kwDODunzps7G8F4x | 8,030 | Remove tmp file in push to hub | {
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"Actually, I used debugger to step into the code of `datasets`, and found `load_dataset` calls into `cached_path`, where it calls into `hf_hub_download` method for `hf://` links:\nhttps://github.com/huggingface/datasets/blob/6e8eaeed4d202b72eff44ba1a9ec5b7d81d2d3e6/src/datasets/utils/file_utils.py#L182-L195\n\nSome... | 2026-02-27T16:05:30 | 2026-02-27T23:34:28 | null | null | NONE | null | null | null | null | ### Describe the bug
Downloaded files still go to `~/.cache/huggingface/hub/` even if I specified `cache_dir` option in `download_config` in `load_dataset`.
### Steps to reproduce the bug
Run my freshly written script and found that downloaded files did not go where I want.
```python
'''
下载OpenWebText数据集，允许使用代理
'''
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Download TikToken Files')
parser.add_argument('--output-path', required=True, metavar='PATH', help='输出目录')
parser.add_argument('--mirror', required=False, metavar='URL', help='HF镜像网址，例如：https://hf-mirror.com')
parser.add_argument('--proxy', required=False, metavar='URL', help='代理网址')
args = parser.parse_args()
else: args = None
import os
import shutil
from pathlib import Path
from typing import cast
if __name__ == '__main__':
assert args is not None
output_path = Path(args.output_path).resolve()
proxy_url = None if args.proxy is None else str(args.proxy)
mirror_url = None if args.mirror is None else str(args.mirror)
output_path.mkdir(parents=True, exist_ok=True)
download_cache_dir = output_path / 'download_cache'
read_cache_dir = output_path / 'read_cache'
save_dir = output_path / 'saved'
complete_mark = output_path / 'completed'
def clear_cache():
shutil.rmtree(download_cache_dir)
shutil.rmtree(read_cache_dir)
def download_and_save():
if mirror_url is not None:
os.environ["HF_ENDPOINT"] = mirror_url
from datasets import DownloadConfig, load_dataset
if proxy_url is not None: proxy_dict = { "http": proxy_url, "https": proxy_url }
else: proxy_dict = None
dataset = load_dataset(
'openwebtext',
cache_dir=str(read_cache_dir),
download_config=DownloadConfig(cache_dir=download_cache_dir, proxies=proxy_dict)
)
dataset.save_to_disk(save_dir)
if complete_mark.is_file():
print('OpenWebText is already downloaded')
clear_cache()
else:
download_and_save()
complete_mark.touch(exist_ok=True)
clear_cache()
```
### Expected behavior
Downloaded files goes to where I specified in `download_config`.
### Environment info
```
> uv run datasets-cli env
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 4.6.0
- Platform: Windows-11-10.0.26200-SP0
- Python version: 3.14.3
- `huggingface_hub` version: 1.5.0
- PyArrow version: 23.0.1
- Pandas version: 3.0.1
- `fsspec` version: 2026.2.0
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8028/comments | https://api.github.com/repos/huggingface/datasets/issues/8028/events | https://github.com/huggingface/datasets/pull/8028 | 4,000,313,779 | PR_kwDODunzps7GybfA | 8,028 | Fix torchcodec audio decoding to respect 'num_channels' | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8028). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-27T10:28:46 | 2026-02-28T10:46:48 | null | null | NONE | null | null | false | {
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} | Fixes torchcodec audio decoding when `num_channels` is set on `Audio`.
Before this change, `AudioDecoder["array"]` reduced multi-channel audio to mono by averaging channels, so the requested channel behavior was not respected.
With this PR:
- multi-channel decoded arrays are preserved by default;
- mono output is returned only when `num_channels == 1` is explicitly requested.
#### Previous behavior
`ds_stereo = ds.cast_column("audio", Audio(num_channels={...}))` - None, 1, 2
Original file: '(16000, 2)' - stereo ✓
'Audio(num_channels=None)': '(16000,)' - mono ✗
'Audio(num_channels=2)': '(16000,)' - mono ✗
'Audio(num_channels=1)': '(16000,)' - mono ✓
#### New behavior
'num_channels=None' preserves the original number of channels from the source file.
'num_channels=2' preserves/converts to stereo output with shape '(2, num_samples)'.
'num_channels=1' downmixes to mono with shape '(num_samples,)'.
#### Results
**Original file shape** (via soundfile): (16000, 2)
HF datasets shape with num_channels=None: (2, 16000)
HF datasets shape with num_channels=1: (16000,)
HF datasets shape with num_channels=2: (2, 16000)
Fixes #8005. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8027 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8027/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8027/comments | https://api.github.com/repos/huggingface/datasets/issues/8027/events | https://github.com/huggingface/datasets/pull/8027 | 3,997,153,282 | PR_kwDODunzps7GoGjP | 8,027 | Add `Json()` type | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8027). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-26T18:38:45 | 2026-02-28T15:34:54 | null | null | MEMBER | null | null | false | {
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} | `Json()` type is needed when some fields don't have fixed types, e.g. "tools" and "tool_calls" types in conversation + tool calling datasets like [dataclaw](https://huggingface.co/datasets?other=dataclaw) datasets. Cc @peteromallet and @woctordho @Nanbeige for viz
Examples of supported tool-calling / dataclaw datasets:
```python
ds = load_dataset("peteromallet/dataclaw-peteromallet") # happens to not need Json() since tool types are fixed
ds = load_dataset("woctordho/dataclaw")
ds = load_dataset("Nanbeige/ToolMind")
```
The `Json()` type is auto-applied when loading JSON files when mixed types are found. The `Json()`type is set to the list containing objects with mixed types, to end up with a `List(Json())` type for "tools" and "tool_calls" columns.
It is also possible to define a Dataset with a `Json()` type like this:
```python
>>> from datasets import load_dataset, Dataset, Features, Json, List
>>> example = {"a": [{"key": 0}, {"another-key": "another-type"}]}
>>> features = Features({"a": List(Json())})
>>> ds = Dataset.from_list([example], features=features)
>>> ds[0]
{'a': [{'key': 0}, {'another-key': 'another-type'}]}
```
close https://github.com/huggingface/datasets/issues/7869
close https://github.com/huggingface/datasets/issues/4120
close https://github.com/huggingface/datasets/issues/5827
related to https://github.com/huggingface/datasets/issues/7092
related to https://github.com/huggingface/datasets/issues/6162
related to https://huggingface.co/datasets/PatoFlamejanteTV/LocalLLaMA/discussions/1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8026 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8026/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8026/comments | https://api.github.com/repos/huggingface/datasets/issues/8026/events | https://github.com/huggingface/datasets/pull/8026 | 3,989,139,119 | PR_kwDODunzps7GNnFC | 8,026 | set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8026). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-25T12:18:15 | 2026-02-25T12:35:43 | 2026-02-25T12:18:34 | null | MEMBER | null | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/8025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8025/comments | https://api.github.com/repos/huggingface/datasets/issues/8025/events | https://github.com/huggingface/datasets/pull/8025 | 3,989,040,394 | PR_kwDODunzps7GNSUF | 8,025 | release: 4.6.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8025). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-25T11:57:08 | 2026-02-25T11:59:59 | 2026-02-25T11:58:17 | null | MEMBER | null | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/8024 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8024/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8024/comments | https://api.github.com/repos/huggingface/datasets/issues/8024/events | https://github.com/huggingface/datasets/pull/8024 | 3,983,877,502 | PR_kwDODunzps7F8QeB | 8,024 | Allow import polars in map() | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8024). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-24T13:57:57 | 2026-02-24T14:08:17 | 2026-02-24T14:08:15 | null | MEMBER | null | null | false | {
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} | close https://github.com/huggingface/datasets/issues/7988 | {
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https://api.github.com/repos/huggingface/datasets/issues/8023 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8023/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8023/comments | https://api.github.com/repos/huggingface/datasets/issues/8023/events | https://github.com/huggingface/datasets/pull/8023 | 3,983,829,205 | PR_kwDODunzps7F8GUT | 8,023 | Support empty shard in from_generator | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8023). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-24T13:47:07 | 2026-02-24T13:58:13 | 2026-02-24T13:58:11 | null | MEMBER | null | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/8022 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8022/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8022/comments | https://api.github.com/repos/huggingface/datasets/issues/8022/events | https://github.com/huggingface/datasets/pull/8022 | 3,983,057,516 | PR_kwDODunzps7F5eYs | 8,022 | fix: resolve base_path conflict between builder_kwargs and config_kwargs | {
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} | ## What does this PR fix?
This PR fixes a bug where passing `base_path` to `load_dataset_builder()` or `load_dataset()` causes a TypeError because both `builder_kwargs` and `config_kwargs` can contain the same key.
## Problem
When users call:
```python
from datasets import load_dataset
load_dataset("rotten_tomatoes", base_path="./sample_data")
```
They get:
```
TypeError: type object got multiple values for keyword argument "base_path"
```
This happens because `base_path` can exist in both `builder_kwargs` (from the dataset module) and `config_kwargs` (from user input), causing a conflict when both are unpacked.
## Solution
- Pop `base_path` from `builder_kwargs` before merging with `config_kwargs`
- Explicitly pass `base_path` to the builder constructor
## Testing
The fix should resolve the issue demonstrated in #4910:
```python
from datasets import load_dataset
load_dataset("rotten_tomatoes", base_path="./sample_data") # Now works!
```
Fixes #4910 | {
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https://api.github.com/repos/huggingface/datasets/issues/8021 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8021/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8021/comments | https://api.github.com/repos/huggingface/datasets/issues/8021/events | https://github.com/huggingface/datasets/pull/8021 | 3,981,426,772 | PR_kwDODunzps7F0FmV | 8,021 | progress bar optional manual control | {
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"Hi @lhoestq ,\r\nCan you please trigger the CI CD pipeline ?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8021). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq I have appli... | 2026-02-24T04:22:17 | 2026-03-02T05:08:37 | null | null | CONTRIBUTOR | null | null | false | {
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} | Fix: https://github.com/huggingface/datasets/issues/7939
Summary
The default behavior for the progress bar has been adjusted based on dataset size. It remains enabled for small datasets and is automatically disabled for larger datasets to improve performance. Users retain full control and can explicitly enable or disable the progress bar using the progress_bar parameter.
Changes
Modified the logic to disable the progress bar when the number of files exceeds 16. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8020 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8020/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8020/comments | https://api.github.com/repos/huggingface/datasets/issues/8020/events | https://github.com/huggingface/datasets/pull/8020 | 3,977,114,662 | PR_kwDODunzps7Fl8mj | 8,020 | feat: add return_file_name support to Parquet packaged builder | {
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} | Part of #5806. Extends the `return_file_name` feature (implemented for JSON in #7948 and CSV in #8019) to the Parquet packaged builder. When `return_file_name=True`, a `file_name` column containing the source file basename is appended to each batch. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8019 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8019/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8019/comments | https://api.github.com/repos/huggingface/datasets/issues/8019/events | https://github.com/huggingface/datasets/pull/8019 | 3,977,073,546 | PR_kwDODunzps7Flz15 | 8,019 | feat: add return_file_name support to CSV packaged builder | {
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} | ## Summary
- Adds an optional `return_file_name: bool = False` parameter to `CsvConfig`
- When `return_file_name=True`, a `file_name` column is appended to every batch in `_generate_tables`, containing the basename of the source CSV file for each row
- Default is `False`, preserving full backward compatibility
## Motivation
Part of #5806. Extends the `file_name` feature (already implemented for the JSON packaged builder in #7948) to the CSV packaged builder. This enables use cases such as resuming training from checkpoints by identifying which data shards have already been consumed.
## Changes
- `src/datasets/packaged_modules/csv/csv.py`: Add `return_file_name: bool = False` field to `CsvConfig`; in `_generate_tables`, append a `file_name` column when the flag is `True`
- `tests/packaged_modules/test_csv.py`: Add three tests covering default behavior (no column), enabled behavior (column present), and correct column values
## Test plan
- [ ] `test_csv_no_file_name_by_default` — verifies `file_name` column is absent by default
- [ ] `test_csv_return_file_name_enabled` — verifies `file_name` column is present when `return_file_name=True`
- [ ] `test_csv_file_name_values` — verifies each row's `file_name` value equals the source file basename
All three tests pass locally.
🤖 Generated with [Claude Code](https://claude.com/claude-code) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8017 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8017/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8017/comments | https://api.github.com/repos/huggingface/datasets/issues/8017/events | https://github.com/huggingface/datasets/pull/8017 | 3,975,274,092 | PR_kwDODunzps7FgERR | 8,017 | Improve error message for deprecated dataset scripts with migration guidance | {
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"Hi team! I’m just checking in on this PR to see if there is any additional information or documentation I can provide to help with the review process. Thanks for your time!"
] | 2026-02-22T19:31:34 | 2026-02-28T14:24:11 | null | null | NONE | null | null | false | {
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} | Summary
This PR improves the RuntimeError message raised when deprecated local dataset scripts are detected.
Previously, the error message:
"Dataset scripts are no longer supported"
did not provide actionable migration guidance.
Improvements
- Clarifies the new architecture
- Provides explicit migration steps
- Includes link to official documentation
- Adds a focused unit test to validate error clarity
This should reduce confusion for contributors migrating legacy dataset scripts. | null | {
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} | ## Summary
Replace all remaining `:obj:`, `:class:`, `:func:`, and `:meth:` Sphinx cross-reference roles with plain backtick references, aligning with the project's current documentation conventions.
**14 files, 71 replacements. No logic changes.**
## Context
As noted in #5324, the codebase had a mix of old Sphinx syntax and the modern backtick-only format used by HuggingFace's doc builder. @stevhliu [mentioned](https://github.com/huggingface/datasets/issues/5324#issuecomment-1525157369) that the user-facing APIs had been cleaned up, but old syntax was still lingering in non-public APIs. This PR catches the remaining occurrences.
## What changed
| Old syntax | New syntax | Count |
|---|---|---|
| `:obj:\`bool\`` | `` `bool` `` | 18 |
| `:class:\`DatasetInfo\`` | `` `DatasetInfo` `` | 15 |
| `:func:\`fsspec.open\`` | `` `fsspec.open` `` | 9 |
| `:meth:\`...\`` | `` `...` `` | 2 |
| `:obj:\`~pathlib.Path\`` | `` `Path` `` | 8 |
| `:class:\`~download.DownloadConfig\`` | `` `DownloadConfig` `` | 3 |
| *other* | — | 16 |
Tilde references (`~module.Class`) are resolved to their short name (`Class`), matching the existing convention in the codebase.
## Files touched
```
src/datasets/arrow_dataset.py
src/datasets/arrow_writer.py
src/datasets/dataset_dict.py
src/datasets/features/features.py
src/datasets/filesystems/compression.py
src/datasets/fingerprint.py
src/datasets/formatting/formatting.py
src/datasets/inspect.py
src/datasets/iterable_dataset.py
src/datasets/load.py
src/datasets/streaming.py
src/datasets/table.py
src/datasets/utils/deprecation_utils.py
src/datasets/utils/file_utils.py
```
Relates to #5324 | null | {
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"Hi, reshuffling is actually pretty easy:\n\n```python\n\"\"\" shuffle.py \"\"\"\nfrom datasets import load_dataset\n\nnum_proc = 8 # tweak it here depending on your machine\nseed = 42\nds = load_dataset(\"jackyhate/text-to-image-2M\", num_proc=num_proc)\nds = ds.shuffle(seed=seed)\nds.push_to_hub(\"my-username/te... | 2026-02-22T11:34:06 | 2026-02-26T18:09:27 | null | null | NONE | null | null | null | null | ### Describe the bug
For training to work properly it is considered good practice in machine learning to sample the dataset randomly (uniformly). I am not sure how the user is expected to do it correctly when the creator of the dataset did a bad job.
https://huggingface.co/docs/hub/datasets-webdataset
```
**Shuffle**
Generally, datasets in WebDataset formats are already shuffled and ready to feed to a DataLoader. But you can still reshuffle the data with WebDataset’s approximate shuffling.
In addition to shuffling the list of shards, WebDataset uses a buffer to shuffle a dataset without any cost to speed:
```
Let me expose the general problem with this specific dataset.
https://huggingface.co/datasets/jackyhate/text-to-image-2M
I'll only consider the single node, single worker case to highlight the problem, which is somewhat hidden and attenuated when training using multiple machines.
Here is the recommended way to load it on the main page
```
# copy pasted from https://huggingface.co/datasets/jackyhate/text-to-image-2M
num_shards = 46 # Number of webdataset tar files
urls = [base_url.format(i=i) for i in range(num_shards)]
dataset = load_dataset("webdataset", data_files={"train": urls}, split="train", streaming=True)
# Example of iterating through the dataset
for image in dataset:
print(image) # single image in row with associated columns
break
```
The dataset is composed of 46 shards from multiple sources : Some shards have data generated with flux, and other have data generated from dall-e. There are usually 50000 images per file except for data_000034.tar and data_000046.tar which have less files because they are the end files of some version of the dataset.
Initially I thought you were using the [webdataset](https://github.com/webdataset/webdataset) library which has at least tried to think about the problem of shuffling the dataset (although poorly documented and with bad default behavior : See https://github.com/webdataset/webdataset/blob/e0953f9bba17b416d5792d5a263b171c266e78be/src/webdataset/mix.py )
But it seems that you are just iterating the shards in order and then iterating the examples of each shard in order.
https://github.com/huggingface/datasets/blob/fe7353a478c1bd25873fad210ac4bdd4bb0c63cc/src/datasets/packaged_modules/webdataset/webdataset.py#L111-L130
If I understand correctly the recommended way to shuffle the data as described in https://huggingface.co/docs/datasets/loading
`ds = ds.shuffle(seed=42, buffer_size=10_000) # shuffles the shards order + uses a shuffle buffer`
or shuffle with a buffer in the dataloader.
This is problematic because the dataset **has not been initially shuffled** by the creator of the dataset, which means that samples are highly correlated, for examples samples coming from the flux shard have a lot more details than those coming from the dall-e shard, and they are not mixed in the shuffle buffer, which result in the loss function having a periodic pattern by epoch typical of bad shuffling behavior.
Of course we could just reshuffle the datasets beforehand but these are huge and we would need to do it for every versions of the dataset.
The random mixing and round robin mixing which can be used in the webdataset library, can also be problematic when the shards are of various sizes : When we stopped at the expiration of the first shard, we never visit all the examples from the later portions of other shards. Also if some shards provenance dataset are not uniformly balanced, we have some period of time where the shard is not sampled, for example when the smaller shard has been exhausted, samples from its class won't be seen until the next epoch.
The proper way being sampling from shards so that they are all exhausted roughly at the same time, aka using a smaller sampling probability for smaller shards, but that necessitate to know the sizes of the shard beforehand, and if you also do per shard shuffling like webdataset library do, it needs to open all shards simultaneously and therefore require a lot of memory to load nshards*buffer_size.
Hopefully I have been clear enough, and the problem which occurs with webdataset and probably also occurs with every other streamable format that you handle can be made to work in a standardized, robust and bug-free way, as the types of bugs these generate are usually of the silent kind where training works better but it generalizes less well because the training used some correlation from the sequence of the dataset.
### Steps to reproduce the bug
Observe periodic behavior of loss function when training a simple neural network on the dataset
### Expected behavior
A behavior somewhat similar to a standard uniform shuffle
### Environment info
not relevant | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8014). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"> lgtm ! can you run `make style` to fix the CI before we merge ?\r\n\r\ndone. All good... | 2026-02-21T14:11:40 | 2026-02-25T20:07:50 | null | null | NONE | null | null | false | {
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} | This PR speeds up `get_data_patterns` for local paths.
### Problem
For `load_dataset("imagefolder", data_dir=...)`, `get_data_patterns` was repeatedly scanning the same local directory tree for many split patterns (`train`, `test`, etc.).
With large folders, this became very slow. This has also been reported before in earlier performance discussions/issues.
### Change
In `get_data_patterns` (local paths only):
- scan files once (`resolve_pattern("**", ...)`)
- then match split patterns in memory
Remote paths keep the old behavior.
No API changes.
### My Env
- Mac mini (M4), 16 GB RAM
- Python 3.13
- Datasets version: 4.5.0
### Benchmarks
`imagefolder` with local `.jpg` files
`data_dir_only`:
- 10k: `4.35s -> 1.40s` (3.10x)
- 100k: `33.48s -> 9.77s` (3.43x)
- 300k: `160.20s -> 35.81s` (4.47x)
- 1M: `1877.70s -> 164.17s` (11.44x) :fire:
`explicit_data_files`:
- 10k: `0.75s -> 0.79s`
- 100k: `7.23s -> 7.29s`
- 300k: `25.44s -> 24.26s`
- 1M: `115.85s -> 112.66s`
As expected, the improvement is on `data_dir_only` (auto pattern detection path).
Memory usage did not show a consistent regression in these runs and stayed within normal run-to-run variance for this benchmark setup. | null | {
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The `operation.To` part would actually render as a hyperlink when printed to Slack. | null | {
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"This is not a bug. This is intended behaviour. The only ways to resolve this are\n1. use the old `datasets` versions\n2. load the dataset and upload it to your own repo without a script\n3. raise an issue in the dataset and wait for the owner to update it."
] | 2026-02-19T16:27:51 | 2026-02-20T11:58:20 | 2026-02-20T11:58:20 | null | NONE | null | null | null | null | ### Describe the bug
It looks like the current documentation suggests loading the dataset like this:
`load_dataset("Hello-SimpleAI/HC3", split="train")
`
But with datasets>=3.x, this raises:
RuntimeError: Dataset scripts are no longer supported, but found HC3.py
The dataset loads correctly if we specify the parquet export branch:
```
load_dataset(
"Hello-SimpleAI/HC3",
revision="refs/convert/parquet",
split="train"
)
```
Since dataset scripts are no longer supported in newer versions of datasets, it might be better to update the documentation to reflect the parquet-based loading method .
Just wanted to flag this so others don’t run into the same confusion.
### Steps to reproduce the bug
### Steps to reproduce
Using `datasets>=3.x`:
```python
from datasets import load_dataset
ds = load_dataset("Hello-SimpleAI/HC3", split="train")
```
### Error
```
RuntimeError: Dataset scripts are no longer supported, but found HC3.py
```
This appears to happen because the repository still contains a legacy loading script (`HC3.py`), and recent versions of `datasets` no longer support dataset scripts.
### Working alternative
The dataset loads correctly when specifying the parquet export revision:
```python
from datasets import load_dataset
ds = load_dataset(
"Hello-SimpleAI/HC3",
revision="refs/convert/parquet",
split="train"
)
print(ds)
```
This successfully downloads and loads the dataset.
### Expected behavior
### Expected behavior
The dataset should load successfully using the example shown in the documentation:
```python
load_dataset("Hello-SimpleAI/HC3", split="train")
```
without requiring a specific `revision` parameter.
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-6.6.105+-x86_64-with-glibc2.35
- Python version: 3.12.12
- `huggingface_hub` version: 1.4.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2025.3.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/8011 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8011/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8011/comments | https://api.github.com/repos/huggingface/datasets/issues/8011/events | https://github.com/huggingface/datasets/pull/8011 | 3,963,479,811 | PR_kwDODunzps7E59Pn | 8,011 | fix: condition for file_name feature check | {
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"Hi @lhoestq, have proposed a fix for [#8010](https://github.com/huggingface/datasets/issues/8010)"
] | 2026-02-19T15:12:31 | 2026-02-19T15:29:29 | 2026-02-19T15:29:29 | null | NONE | null | null | false | {
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} | ## Description
Accept `Value("large_string")` in addition to `Value("string")` for the `file_name` (or `*_file_name`) metadata key in folder-based builders (e.g. `audiofolder`).
## Problem
When loading a dataset with `load_dataset("audiofolder", data_dir=...)` and a `metadata.csv` file, the metadata schema is inferred via pandas to Arrow. For string columns, Arrow often uses the `large_string` type, so `Features.from_arrow_schema()` yields `Value("large_string")` for the `file_name` column. The builder only checks for `Value("string")`, so validation fails with:
`ValueError: file_name or *_file_name must be present as dictionary key (with type string) in metadata files`
Users then have to convert metadata to JSONL (which produces Arrow `string`) or work around the loader.
## Solution
Treat `Value("large_string")` as valid for the `file_name` / `*_file_name` key, since it is still a string type and behaves the same for resolution of audio/image paths. This matches the fact that when metadata is loaded from CSV (via pandas and then converted to Arrow), string columns are often inferred as Arrow `large_string`, so accepting it keeps CSV metadata working without format changes. | {
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https://api.github.com/repos/huggingface/datasets/issues/8010 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8010/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8010/comments | https://api.github.com/repos/huggingface/datasets/issues/8010/events | https://github.com/huggingface/datasets/issues/8010 | 3,963,448,558 | I_kwDODunzps7sPWzu | 8,010 | audiofolder / folder-based loader rejects metadata.csv when file_name column is inferred as large_string | {
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"Proposing a PR for this [here](https://github.com/huggingface/datasets/pull/8011)\n\nPS: I didn't find a contributing documentation. Please recommend any changes in issue or PR if any. Thank you!"
] | 2026-02-19T15:06:09 | 2026-02-19T15:29:41 | 2026-02-19T15:29:41 | null | NONE | null | null | null | null | ### Describe the bug
When loading a dataset with load_dataset("audiofolder", data_dir=...) and a metadata.csv (with a file_name column), loading can fail with:
`ValueError: file_name or *_file_name must be present as dictionary key (with type string) in metadata files`
even when the CSV clearly has a file_name column with string values.
**Cause**
In folder_based_builder.py, the metadata schema is inferred from the CSV via pandas to Arrow. For string columns, Arrow often uses the large_string type. The check only accepts Value("string") and does not accept Value("large_string"), so validation fails even though the column is a valid string column.
### Steps to reproduce the bug
1. Create a folder with:
- `metadata.csv` with columns: `file_name`, `transcript`, `duration` (and audio `.wav` files referenced by `file_name`).
2. Run:
```
from datasets import load_dataset
ds = load_dataset("audiofolder", data_dir="/path/to/folder")
```
### Expected behavior
Metadata CSV with a `file_name` column (inferred as either `string` or `large_string`) should be accepted, since both represent string data.
### Environment info
- `datasets` version: 4.5.0
- Platform: Linux-6.8.0-100-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.36.0
- PyArrow version: 23.0.0
- Pandas version: 3.0.0
- `fsspec` version: 2025.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/8009 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8009/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8009/comments | https://api.github.com/repos/huggingface/datasets/issues/8009/events | https://github.com/huggingface/datasets/pull/8009 | 3,958,228,652 | PR_kwDODunzps7Eo7KV | 8,009 | More IterableDataset.from_x methods and docs and polars.Lazyframe support | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8009). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-18T14:48:39 | 2026-02-18T15:18:17 | 2026-02-18T15:18:14 | null | MEMBER | null | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/8008 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8008/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8008/comments | https://api.github.com/repos/huggingface/datasets/issues/8008/events | https://github.com/huggingface/datasets/pull/8008 | 3,948,857,401 | PR_kwDODunzps7EJ_oM | 8,008 | fix: prevent duplicate keywords in load_dataset_builder (#4910) | {
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https://api.github.com/repos/huggingface/datasets/issues/8007 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8007/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8007/comments | https://api.github.com/repos/huggingface/datasets/issues/8007/events | https://github.com/huggingface/datasets/issues/8007 | 3,946,695,329 | I_kwDODunzps7rPcqh | 8,007 | Add option for loading audio with video | {
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"Hi,\nI’m interested in working on this issue.\n\nFrom what I understand, audio decoding would involve an AudioDecoder, and previous discussions have suggested handling it separately. I’d like to explore whether integrating audio extraction in relation to the current video decoding workflow would be appropriate, or... | 2026-02-16T09:11:02 | 2026-02-16T15:56:21 | null | null | NONE | null | null | null | null | ### Describe the bug
Currently, `torchcodec` don't allow extracting `Audio` from `Video` https://github.com/meta-pytorch/torchcodec/issues/1158, but when I upload videos with audio to hub using `videofolder`, then this is not possible to retrieve audio from it. Probably `VideoDecoder` can be extended with `audio` parameter to retrieve this information
### Steps to reproduce the bug
```python
from datasets import load_dataset
test_ds = load_dataset("videofolder", data_dir="/path/to/video")
# uploaded version
# test_ds = load_dataset("Samoed/testds")
test_ds["train"][1]["video"]
# <torchcodec.decoders._video_decoder.VideoDecoder
```
### Expected behavior
Some option to retrieve audio information.
### Environment info
```
datasets==4.5.0
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8006 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8006/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8006/comments | https://api.github.com/repos/huggingface/datasets/issues/8006/events | https://github.com/huggingface/datasets/issues/8006 | 3,944,394,074 | I_kwDODunzps7rGq1a | 8,006 | Regression: from_generator crashes if one generator call returns no results | {
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} | [] | closed | false | [] | null | [] | 2026-02-15T16:16:48 | 2026-02-24T13:58:12 | 2026-02-24T13:58:12 | null | CONTRIBUTOR | null | null | null | null | ### Describe the bug
Dataset.from_generator splits any list kwarg to the generator and makes a separate call to the generator for each member of the list even in the num_proc=1 case.
I have a generator that processes a number of files, filtering them and producing examples. It used to be the case that things worked fine if one of the files produced no examples.
It doesn't work any more.
I believe that commit 2ed6f72d88c0f37b75751cd0cd41a485439e16c9 is responsible.
What ends up happening is that the code tries to update input shard lengths but the list of shard lengths is still empty.
### Steps to reproduce the bug
```python
import datasets
def gen_examples(num_examples, fingerprint):
assert len(num_examples) == 1
for x in range(num_examples[0]):
yield dict(feature=x)
ds = datasets.Dataset.from_generator(gen_examples, gen_kwargs=dict(num_examples=[0,1],
fingerprint=str(id([]))))
```
Traceback looks like
```
Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.13/dist-packages/datasets/builder.py", line 1598, in _prepare_split_single
original_shard_lengths[original_shard_id] += 1
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/srv/models/zendegi_ai/sexpositive-sft/ai-tools/training_tools/foo.py", line 10, in <module>
ds = datasets.Dataset.from_generator(gen_examples, gen_kwargs=dict(num_examples=[0,1],
fingerprint=str(id([]))))
File "/usr/local/lib/python3.13/dist-packages/datasets/arrow_dataset.py", line 1204, in from_generator
).read()
~~~~^^
File "/usr/local/lib/python3.13/dist-packages/datasets/io/generator.py", line 52, in read
self.builder.download_and_prepare(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
download_config=download_config,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
...<3 lines>...
num_proc=self.num_proc,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.13/dist-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
dl_manager=dl_manager,
^^^^^^^^^^^^^^^^^^^^^^
...<2 lines>...
**download_and_prepare_kwargs,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.13/dist-packages/datasets/builder.py", line 1634, in _download_and_prepare
super()._download_and_prepare(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
dl_manager,
^^^^^^^^^^^
verification_mode,
^^^^^^^^^^^^^^^^^^
**prepare_splits_kwargs,
^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.13/dist-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.13/dist-packages/datasets/builder.py", line 1438, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.13/dist-packages/datasets/builder.py", line 1617, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
I expect a dataset with one example.
In my case it's fairly easy to work around this. Ideally this behavior would be supported.
If it's not going to be supported, catching the situation and returning a "generators must always return at least one example" error would have saved me hours of debugging.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 4.5.0
- Platform: Linux-6.18.5+deb14-amd64-x86_64-with-glibc2.41
- Python version: 3.13.5
- `huggingface_hub` version: 1.4.1
- PyArrow version: 23.0.0
- Pandas version: 3.0.0
- `fsspec` version: 2025.10.0
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https://api.github.com/repos/huggingface/datasets/issues/8005 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8005/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8005/comments | https://api.github.com/repos/huggingface/datasets/issues/8005/events | https://github.com/huggingface/datasets/issues/8005 | 3,941,908,297 | I_kwDODunzps7q9L9J | 8,005 | Multi-channel audio is automatically cast to mono, num_channels is ignored | {
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"**Workaround**\nDirectly load audio using torchcodec, this is what datasets does under the hood (but doesn't maintain multi-channel)\n\n```python\nimport torchcodec\n\ndecoder = torchcodec.decoders.AudioDecoder(audio[\"bytes\"])\naudio_samples = decoder.get_all_samples()\n\naudio = audio_samples.data.numpy()\nsamp... | 2026-02-14T17:28:03 | 2026-02-25T15:17:18 | null | null | NONE | null | null | null | null | ### Describe the bug
The `num_channels` parameter in `datasets.Audio()` is documented to preserve stereo channels when set to `None` (preserve original) or `2` (explicit stereo), but it currently downmixes all audio to mono regardless of this setting.
### Steps to reproduce the bug
```python
import numpy as np
import soundfile as sf
import tempfile
from datasets import Dataset, Audio
# Create a stereo audio file
sample_rate = 16000
duration = 1.0
num_samples = int(sample_rate * duration)
left_channel = np.sin(2 * np.pi * 440 * np.linspace(0, duration, num_samples))
right_channel = np.sin(2 * np.pi * 880 * np.linspace(0, duration, num_samples))
stereo_audio = np.stack([left_channel, right_channel], axis=1).astype(np.float32)
temp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
sf.write(temp_file.name, stereo_audio, sample_rate)
# Create HuggingFace dataset
dataset_dict = {"audio": [temp_file.name]}
ds = Dataset.from_dict(dataset_dict)
# Test with num_channels=2
ds_stereo = ds.cast_column("audio", Audio(num_channels=2))
audio_data = ds_stereo[0]["audio"]
print(f"Original file shape (via soundfile): {sf.read(temp_file.name)[0].shape}")
# Output: (16000, 2) ✓ Stereo
print(f"HF datasets shape with num_channels=2: {audio_data['array'].shape}")
# Output: (16000,) ✗ Mono (should be (2, 16000))
```
**Result:**
- Original file: `(16000, 2)` - stereo ✓
- `Audio(num_channels=None)`: `(16000,)` - mono ✗
- `Audio(num_channels=2)`: `(16000,)` - mono ✗
- `Audio(num_channels=1)`: `(16000,)` - mono ✓
### Expected behavior
According to the documentation, `Audio` decoding should return samples with shape `(num_channels, num_samples)`:
- `num_channels=None` should preserve the original number of channels from the source file
- `num_channels=2` should preserve/convert to stereo output with shape `(2, num_samples)`
- `num_channels=1` should downmix to mono with shape `(num_samples,)`
**Actual Behavior**
All `num_channels` settings produce mono output with shape `(num_samples,)`, even when the source audio file is stereo.
### Environment info
OS: macOS / Linux
Python 3.10.19
```
datasets 4.4.2
torchcodec 0.10.0
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8004 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8004/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8004/comments | https://api.github.com/repos/huggingface/datasets/issues/8004/events | https://github.com/huggingface/datasets/pull/8004 | 3,939,675,475 | PR_kwDODunzps7DsCBM | 8,004 | fix save_to_disk/load_from_disk with pathlib.Path input | {
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} | Since #6704, `save_to_disk` and `load_from_disk` use `fsspec.core.url_to_fs` which expects a `str`, but both methods accept `PathLike` (which includes `pathlib.Path`). Passing a `Path` object raises a `TypeError` because `url_to_fs` can't handle it.
Fixed by converting the path argument with `os.fspath()` before handing it off to `url_to_fs`. This affects all five call sites across `Dataset`, `DatasetDict`, and the standalone `load_from_disk` function.
Fixes #6829 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8003 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8003/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8003/comments | https://api.github.com/repos/huggingface/datasets/issues/8003/events | https://github.com/huggingface/datasets/pull/8003 | 3,938,501,557 | PR_kwDODunzps7DoBmp | 8,003 | very basic support for more hf urls | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_8003). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-13T18:30:56 | 2026-02-13T18:47:58 | 2026-02-13T18:47:57 | null | MEMBER | null | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/8002 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8002/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8002/comments | https://api.github.com/repos/huggingface/datasets/issues/8002/events | https://github.com/huggingface/datasets/issues/8002 | 3,937,013,814 | I_kwDODunzps7qqhA2 | 8,002 | The `Sequence` class in features do not have "dtype" | {
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} | [] | open | false | [] | null | [] | 2026-02-13T12:45:19 | 2026-02-13T12:52:36 | null | null | NONE | null | null | null | null | ### Describe the bug
**I'm not sure if this is a bug.**
I see that a `FeatureType` object contains an attribute called `self.dtype` that is not covered when this feature is a `Sequence` or a `List`.
When I try to run a multilabel classification with this example script from the transformers library:
https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_classification.py#L442
I get this error on the linked line:
```shell
AttributeError: 'List' object has no attribute 'dtype'. Did you mean: '_type'?
```
Looking at the check that the script is attempting to perform, could we perhaps add a `self.dtype="list"` attribute for this `FeatureType` 's: `Sequence`, `List`, etc.?
### Steps to reproduce the bug
For example, this code works for me:
```python
from datasets import ClassLabel, Features, Sequence, Value
features = {'text': Value('string'), 'label': ClassLabel(names=['No', 'Yes'])}
print(features["text"].dtype)
print(features["label"].dtype)
```
```output
'string'
'int64'
```
and this code does not work for me:
```python
from datasets import ClassLabel, Features, Sequence, Value
features = {'text': Value('string'), 'label': Sequence(ClassLabel(names=['No', 'Yes']))}
print(features["label"].dtype) # it could be equal to "list"?
```
```output
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'List' object has no attribute 'dtype'. Did you mean: '_type'?
```
### Expected behavior
The attribute `dtype` equal to `"list"` when using objects of type `Sequence`.
```python
from datasets import ClassLabel, Features, Sequence, Value
features = {'text': Value('string'), 'label': Sequence(ClassLabel(names=['No', 'Yes']))}
print(features["label"].dtype)
```
```output
'list'
```
### Environment info
I have installed `datasets==4.5.0`. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/8000 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/8000/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/8000/comments | https://api.github.com/repos/huggingface/datasets/issues/8000/events | https://github.com/huggingface/datasets/pull/8000 | 3,919,504,253 | PR_kwDODunzps7CpK9i | 8,000 | Fix: make environment variable naming consistent (issue #7998) | {
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"Hi @lhoestq ,\r\nI’ve addressed the inconsistent environment variable name HF_DATASETS_DISABLE_PROGRESS_BARS in docstrings and warning messages. Please review the changes and let me know if there’s anything else I should update to complete this issue fix."
] | 2026-02-10T05:19:53 | 2026-02-16T15:29:49 | 2026-02-16T15:29:49 | null | CONTRIBUTOR | null | null | false | {
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} | Fix: https://github.com/huggingface/datasets/issues/7998
**Summary**
Addressed the inconsistent environment variable name HF_DATASETS_DISABLE_PROGRESS_BARS used for toggling progress bars, particularly in docstrings and warning messages.
**Changes**
Updated function docstrings to use the correct environment variable name
Corrected warning messages in tqdm.py to ensure consistency | {
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https://api.github.com/repos/huggingface/datasets/issues/7999 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7999/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7999/comments | https://api.github.com/repos/huggingface/datasets/issues/7999/events | https://github.com/huggingface/datasets/issues/7999 | 3,915,367,642 | I_kwDODunzps7pX8Ta | 7,999 | Too many dataloader workers: 4 (max is dataset.num_shards=3). Stopping 1 dataloader workers. | {
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"Hi, thanks for the clear question and code snippet!\n\nFrom my understanding, **hf_dataset.num_shards** represents the number of actual iterable partitions that the streaming dataset can be read from in parallel. This is not the same as the number of underlying Parquet files—many files can be grouped into a much s... | 2026-02-09T09:26:37 | 2026-02-15T14:25:56 | null | null | NONE | null | null | null | null | Hi !
I’m working on training with a large-scale dataset (100+ Parquet files) using lazy loading, and I’m struggling to understand/optimize the num_shards setting— in the lerobot repo: streaming_datasets.py:
```
from datasets import load_dataset
self.hf_dataset: datasets.IterableDataset = load_dataset(
self.repo_id if not self.streaming_from_local else str(self.root),
split="train",
streaming=self.streaming,
data_files="data/*/*.parquet",
revision=self.revision,
)
self.num_shards = min(self.hf_dataset.num_shards, max_num_shards)
```
```
dataloader = torch.utils.data.DataLoader(
datasets[sub_idx],
num_workers=datasets[sub_idx].num_shards, #cfg.num_workers,
batch_size=cfg.batch_size,
shuffle=shuffle and not cfg.dataset.streaming,
sampler=sampler,
collate_fn=FlowerDataCollator(),
pin_memory=device.type == "cuda",
drop_last=True,
prefetch_factor=2 if cfg.num_workers > 0 else None,
)
```
What exactly does hf_dataset.**num_shards** represent? Is it safe to manually override/edit num_shards?
My batch loading is slower than expected (2-3s per batch) despite num_worker cannot be bigger with warning: Too many dataloader workers: 4 (max is dataset.num_shards=3). Stopping 1 dataloader workers.
Even use num_workers=datasets[sub_idx].num_shards, the waring is still exist! (my num_worker is 4 and hf_dataset.num_shards is 100+, so the datasets[sub_idx].num_shards=4)
Why does the "too many workers" warning persist even when num_workers equals dataset.num_shards—and how do I fix this?
Thanks so much for any insights or help with this! Really appreciate your time and expertise 😊
| null | {
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https://github.com/huggingface/datasets/blob/025593f2f0722f31fc136e0ae45da4ff44d4416a/src/datasets/config.py#L221-L226
Docstrings and warnings report env var name `HF_DATASETS_DISABLE_PROGRESS_BAR` without the ending `S`.
https://github.com/huggingface/datasets/blob/025593f2f0722f31fc136e0ae45da4ff44d4416a/src/datasets/utils/tqdm.py#L61-L73
https://github.com/huggingface/datasets/blob/025593f2f0722f31fc136e0ae45da4ff44d4416a/src/datasets/utils/tqdm.py#L78-L90
https://github.com/huggingface/datasets/blob/025593f2f0722f31fc136e0ae45da4ff44d4416a/src/datasets/utils/tqdm.py#L95-L100
This affects doc webpages as well, e.g., see https://huggingface.co/docs/datasets/en/package_reference/utilities#datasets.enable_progress_bars. | {
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"Closing in favor of #7996 which addresses the same issue with the same fix."
] | 2026-02-08T07:02:00 | 2026-02-11T08:23:06 | 2026-02-11T08:23:06 | null | NONE | null | null | false | {
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} | Fixes #7990
## Summary
When Dataset.map is called and the first examples processed return None, the writer is never properly initialized, causing a ValueError.
## Changes
- Modified _map_single to initialize the writer early if the first batch returns empty results
- Ensures writer is set before the first call to writer.write_batch
## Test Plan
- Added test case that reproduces the bug
- Verified the fix resolves the issue
- Existing tests still pass | {
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"Friendly ping - any chance someone could take a look at this when they get a chance? Happy to make any changes if needed."
] | 2026-02-08T05:52:45 | 2026-02-19T04:58:58 | null | null | NONE | null | null | false | {
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} | ## Summary
Fixes #7990
This PR fixes a bug in `Dataset.map()` where the writer initialization was incorrectly tied to the index being 0, causing crashes when the map function returns `None` for the first few examples and later returns a dict.
### Changes
- **Non-batched mode** (line 3676): Changed from `if i == 0:` to `if writer is None:`
- **Batched mode** (line 3701): Changed from `if i and i[0] == 0:` to `if writer is None:`
### Why This Fix Works
The original code assumed that `update_data` would always be determined by the time the first example (i=0) was processed. However, `update_data` is set lazily after processing each example - it becomes `True` when the function first returns a non-None value.
If a function returns `None` for early examples and a dict for later ones:
1. At i=0, the function returns `None`, so `update_data` remains `None`
2. Writer is NOT initialized (because we're not updating data)
3. At i=2, the function returns a dict, so `update_data` becomes `True`
4. **Old code**: Tries to use `writer` (still None) because i != 0 → crash
5. **New code**: Checks `if writer is None` and initializes it → works correctly
### Test Plan
The fix can be verified with this minimal test case from the issue:
```python
from datasets import Dataset
ds = Dataset.from_dict({"x": [1, 2, 3]})
def fn(example, idx):
if idx < 2:
return None
return {"x": [example["x"] * 10]}
# Should work without errors
result = list(ds.map(fn, with_indices=True))
print(result) # [{'x': 1}, {'x': 2}, {'x': [30]}]
```
**Before this fix**: Crashes with `AttributeError: 'NoneType' object has no attribute 'write'`
**After this fix**: Works correctly
### Related
This fix ensures the writer is initialized the first time a non-None value is returned, regardless of which example index that occurs at. This makes the code more robust to different map function behaviors. | null | {
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"Apologize for the ping @lhoestq but I wanted to know whether this is something you could look into? And more generally whether you have a policy to regularly bump fsspec? Thanks 🙏",
"thanks for opening a PR, I'm triggering the CI and I'll merge if it's all green :)",
"The docs for this PR live [here](https://... | 2026-02-08T05:43:15 | 2026-02-16T15:28:59 | 2026-02-16T15:28:59 | null | CONTRIBUTOR | null | null | false | {
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I saw you had some API compat issues in the past (https://github.com/huggingface/datasets/issues/7326) and I understand the need for an upper bound.
But I wonder if you think your CI and tests are a good proxy to catch fsspec API breakage? If that's the case then triggering dataset CI with the latest version of fsspec should tell us whether it's all good right?
Happy to open a PR if needed. | {
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} | This PR introduces a new dataset builder, SparseCsv, designed to handle "wide" tabular datasets (e.g., 100k+ columns common in transcriptomics, sparse NLP features, or recommender systems) that are typically too large to load into memory as dense Arrow tables.
It also adds a utility function, `sparse_collate_fn`, to seamlessly convert these sparse examples into `torch.sparse` or `scipy.sparse` matrices during training.
This PR should fix #7377 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7992 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7992/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7992/comments | https://api.github.com/repos/huggingface/datasets/issues/7992/events | https://github.com/huggingface/datasets/pull/7992 | 3,897,848,157 | PR_kwDODunzps7BiJps | 7,992 | Add `IterableDataset.reshard()` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7992). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-04T18:24:41 | 2026-02-04T18:55:38 | 2026-02-04T18:55:35 | null | MEMBER | null | null | false | {
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} | To increase the number of shards of a dataset, you can use [`IterableDataset.reshard`]:
```py
>>> dataset
IterableDataset({
features: ['label', 'title', 'content'],
num_shards: 4
})
>>> dataset.reshard()
IterableDataset({
features: ['label', 'title', 'content'],
num_shards: 3600
})
```
The resharding mechanism depends on the dataset file format.
For example for Parquet, it reshards using row groups instead of having one file per shard.
We can implement other formats later (e.g. JSON Lines, CSV can be split by recovering line boundaries from arbitrary locations)
Other details:
* fixed concatenate after shuffling, now it correctly shuffles the shards: close https://github.com/huggingface/datasets/issues/7196
* fixed interleave after split_by_node: close https://github.com/huggingface/datasets/issues/7868
* changed a bit how the effective seed works in multi node/worker/epoch situations
related to https://github.com/huggingface/datasets/issues/7917 | {
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} | [] | closed | false | [] | null | [] | 2026-02-04T14:39:46 | 2026-02-05T10:30:09 | 2026-02-05T10:30:09 | null | NONE | null | null | null | null | i am using the python api wrapper to list all datasets available on hugging face. This is for research, and i need all datasets to determine which % have language tags and other related questions requiring the total list. However, the following code that worked a few months ago:
```
from huggingface_hub import HfApi
api = HfApi(token=token)
datasets = list(api.list_datasets())
```
now gives a JSONDecodeError when reaching 2000 results. My understanding is that this is a pagination issue, as there is no cursor exposed to the API and so it doesnt know where to read from after the limit.
Is there any way to find all datasets available and combine them into a list?
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} | [] | open | false | [] | null | [] | 2026-02-04T10:43:20 | 2026-02-04T10:43:20 | null | null | NONE | null | null | null | null | ### Describe the bug
I detected a serious [bug from datasets/arrow_dataset.py](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L3676)
---
**Description of the bug**
`Dataset.map` crashes with `writer is None` when the map function returns `None` for the first few examples and a dictionary (or `pa.Table` / DataFrame) for later examples. This happens because the internal writer is initialized only when `i == 0` (or `i[0] == 0` in batched mode), but `update_data` is determined lazily after processing the first example/batch.
**Steps to reproduce**
```python
from datasets import Dataset
ds = Dataset.from_dict({"x": [1, 2, 3]})
def fn(example, idx):
if idx < 2:
return None
return {"x": [example["x"] * 10]}
list(ds.map(fn, with_indices=True))
```
**Expected behavior**
* The function should work regardless of when `update_data` becomes `True`.
* Writer should be initialized the first time a non-`None` return occurs, not tied to the first index.
**Environment info**
* `datasets` version: <insert your version>
* Python version: 3.12
* OS: <insert your OS>
**Suggested fix**
Replace `if i == 0` / `if i[0] == 0` checks with `if writer is None` when initializing the writer.
---
### Steps to reproduce the bug
Here's a ready-to-use version you can paste into that section:
---
### Steps to reproduce the bug
```python
from datasets import Dataset
# Create a minimal dataset
ds = Dataset.from_dict({"x": [1, 2, 3]})
# Define a map function that returns None for first examples, dict later
def fn(example, idx):
if idx < 2:
return None
return {"x": [example["x"] * 10]}
# Apply map with indices
list(ds.map(fn, with_indices=True))
```
**Expected:** function executes without errors.
**Observed:** crashes with `AttributeError: 'NoneType' object has no attribute 'write'` because the internal writer is not initialized when the first non-None return happens after i > 0.
---
This is minimal and clearly demonstrates the exact failure condition (`None` early, `dict` later).
### Expected behavior
---
**Expected behavior**
The `Dataset.map` function should handle map functions that return `None` for some examples and a dictionary (or `pa.Table` / DataFrame) for later examples. In this case, the internal writer should be initialized when the first non-`None` value is returned, so that the dataset can be updated without crashing. The code should run successfully for all examples and return the updated dataset.
---
### Environment info
- python3.12
- datasets==3.6.0 [but the latest version still has this problem]
- transformers==4.55.2
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7989). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-04T09:42:49 | 2026-02-04T15:20:04 | 2026-02-04T15:20:02 | null | CONTRIBUTOR | null | null | false | {
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} | This PR removes workaround for pre-release `transformers v5.*` / `huggingface_hub v1.*` in `test_py314_future` job since they are now officially released.
cc @Wauplin just for viz since you introduced this in https://github.com/huggingface/datasets/pull/7783. | {
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https://api.github.com/repos/huggingface/datasets/issues/7988 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7988/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7988/comments | https://api.github.com/repos/huggingface/datasets/issues/7988/events | https://github.com/huggingface/datasets/issues/7988 | 3,895,353,435 | I_kwDODunzps7oLmBb | 7,988 | `Dataset.map()` breaks when `function` calls `import polars as pl` and `num_proc`>1: "UnboundLocalError: cannot access local variable 'pl' where it is not associated with a value" | {
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} | [] | closed | false | [] | null | [] | 2026-02-04T08:42:23 | 2026-02-24T14:08:16 | 2026-02-24T14:08:16 | null | NONE | null | null | null | null | ### Describe the bug
# Repro
These two conditions seem to consistently reproduce the issue:
- function passed to `Dataset.map()` explicitly or implicitly calls `import polars as pl`
- `num_proc` > 1
# Trace
```
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "c:\Users\{redacted}\.venv\Lib\site-packages\multiprocess\pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "c:\Users\{redacted}\.venv\Lib\site-packages\datasets\utils\py_utils.py", line 586, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\Users\{redacted}\.venv\Lib\site-packages\datasets\arrow_dataset.py", line 3687, in _map_single
and isinstance(example, pl.DataFrame)
^^
UnboundLocalError: cannot access local variable 'pl' where it is not associated with a value
"""
The above exception was the direct cause of the following exception:
UnboundLocalError Traceback (most recent call last)
Cell In[2], [line 9](vscode-notebook-cell:?execution_count=2&line=9)
6 import polars as pl
7 return {'squared': sample['n'] ** 2}
----> [9](vscode-notebook-cell:?execution_count=2&line=9) ds.map(square, num_proc=2)
File c:\Users\{redacted}\.venv\Lib\site-packages\datasets\arrow_dataset.py:562, in transmit_format.<locals>.wrapper(*args, **kwargs)
555 self_format = {
556 "type": self._format_type,
557 "format_kwargs": self._format_kwargs,
558 "columns": self._format_columns,
559 "output_all_columns": self._output_all_columns,
560 }
561 # apply actual function
--> [562](file:///C:/Users/{redacted}/.venv/Lib/site-packages/datasets/arrow_dataset.py:562) out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
563 datasets: list["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
564 # re-apply format to the output
File c:\Users\{redacted}\.venv\Lib\site-packages\datasets\arrow_dataset.py:3334, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc, try_original_type)
3331 os.environ = prev_env
3332 logger.info(f"Spawning {num_proc} processes")
-> [3334](file:///C:/Users/{redacted}/.venv/Lib/site-packages/datasets/arrow_dataset.py:3334) for rank, done, content in iflatmap_unordered(
3335 pool, Dataset._map_single, kwargs_iterable=unprocessed_kwargs_per_job
3336 ):
3337 check_if_shard_done(rank, done, content)
3339 pool.close()
File c:\Users\{redacted}\.venv\Lib\site-packages\datasets\utils\py_utils.py:626, in iflatmap_unordered(pool, func, kwargs_iterable)
623 finally:
624 if not pool_changed:
625 # we get the result in case there's an error to raise
--> [626](file:///C:/Users/{redacted}/.venv/Lib/site-packages/datasets/utils/py_utils.py:626) [async_result.get(timeout=0.05) for async_result in async_results]
File c:\Users\{redacted}\.venv\Lib\site-packages\multiprocess\pool.py:774, in ApplyResult.get(self, timeout)
772 return self._value
773 else:
--> [774](file:///C:/Users/{redacted}/.venv/Lib/site-packages/multiprocess/pool.py:774) raise self._value
UnboundLocalError: cannot access local variable 'pl' where it is not associated with a value
```
# Why `import polars` in a worker function?
To my knowledge `Dataset.map()` doesn't support a worker init function, and objects useful inside a function aren't always pickable, so I commonly use this pattern to essentially construct the unpickable object in a worker process:
```python
def func(example, **kwargs):
if 'my_unpickable_object' not in globals():
from my_module import MyClass
my_unpickable_object = MyClass(**kwargs)
return {'newcol': my_unpickable_object.calculate_something(example['n'])}
ds = Dataset.load_from_disk(...)
ds.map(func, num_proc=2, ...)
```
and here `from my_module import MyClass` may implicitly call `import polars as pl` e.g. when `my_module.py` has that line, or when it imports some other module containing `import polars as pl`.
# A workaround
Things seem to work ok if I don't do any import inside `func`, but instead pass a class already imported outside `func` as a contructor, like below. Although I'm unsure how many scenarios this workaround can cover.
```python
from my_module import MyClass
def func(example, constructor=MyClass, **kwargs):
if 'my_unpickable_object' not in globals():
my_unpickable_object = constructor(**kwargs)
return {'newcol': my_unpickable_object.calculate_something(example['n'])}
ds = Dataset.load_from_disk(...)
ds.map(func, num_proc=2, ...)
```
# My speculations
Before the crash point, on line 3656 of `arrow_dataset.py` it reads:
```python
if config.POLARS_AVAILABLE and "polars" in sys.modules:
import polars as pl
````
My guess is these conditions are somehow messed up in a worker process, and it ended up not entering the `if` block.
### Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({'n': list(range(10_000))})
def square(sample):
import polars as pl
return {'squared': sample['n'] ** 2}
ds.map(square, num_proc=2)
```
### Expected behavior
```
Dataset({
features: ['n', 'squared'],
num_rows: 10000
})
```
### Environment info
- `datasets` version: 4.5.0
- Platform: Windows-11-10.0.26200-SP0
- Python version: 3.12.10
- `huggingface_hub` version: 0.31.2
- PyArrow version: 21.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7987 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7987/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7987/comments | https://api.github.com/repos/huggingface/datasets/issues/7987/events | https://github.com/huggingface/datasets/pull/7987 | 3,894,713,494 | PR_kwDODunzps7BX0pY | 7,987 | Fix index out of bound error with original_shard_lengths. | {
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} | [] | open | false | [] | null | [] | 2026-02-04T05:20:43 | 2026-02-04T05:20:43 | null | null | NONE | null | null | false | {
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} | I have gotten the following error
```
original_shard_lengths[original_shard_id] += 1
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
```
Not sure what causes it, but this fixes the error. This may not be the proper fix for the root cause though. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7986 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7986/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7986/comments | https://api.github.com/repos/huggingface/datasets/issues/7986/events | https://github.com/huggingface/datasets/issues/7986 | 3,892,776,651 | I_kwDODunzps7oBw7L | 7,986 | `Dataset.map()` causes cache miss/fingerprint change when closure captures self containing non-deterministic state. | {
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"I suggest metion this in docs specifically for attention with use, tell users explicitly to pass arguments with `fn_kwargs` param or using `functools.partial` to create a pure funcion."
] | 2026-02-03T19:16:49 | 2026-02-06T08:38:13 | null | null | NONE | null | null | null | null | ### Describe the bug
When using `.map()` with a function defined inside a **class (of which has any non-deterministic states)** method (a closure), if that function captures `self` to access a configuration variable (e.g., self.foo), the fingerprint mechanism serializes the entire class instance state.
If the class instance contains any non-deterministic state (such as random seeds, loggers, or distinct object IDs—in my case, PyTorch Lightning's `LightningDataModule`), the fingerprint changes on every run, rendering the cache useless.
While this may be intended behavior for `dill`, it is a significant "gotcha" for users migrating code into classes, as unrelated state changes cause massive re-processing overhead.
Real world "cache explosion" screenshot caused by the fingerprint mismatch:
<img width="942" height="382" alt="Image" src="https://github.com/user-attachments/assets/2fb0acba-ac07-4f00-bf30-c1ac932c9072" />
### Steps to reproduce the bug
Minimal reproduction code block:
```python3
import datasets
import uuid
# Prevent logging spam
datasets.logging.set_verbosity_error()
class ReproduceIssue:
def __init__(self):
# This is the variable we actually care about in the map function
self.foo = 32
# This simulates "dirty" internal state often found in framework classes
# (e.g., unique IDs, pointers to loggers, thread locks, or random seeds)
self.hidden_state = uuid.uuid4()
self.dataset = datasets.Dataset.from_dict({"strokes": [1, 2, 3]})
def setup(self):
# Closure captures 'self' to access 'self.foo'
def preprocess(batch):
# Accessing self binds the function to the specific instance state
_ = self.foo
return {"foo": batch["bar"]}
return self.dataset.map(preprocess, batched=True)
print("--- Run 1 ---")
inst1 = ReproduceIssue()
ds1 = inst1.setup()
print(f"Fingerprint 1: {ds1._fingerprint}")
print("\n--- Run 2 (New Instance) ---")
inst2 = ReproduceIssue()
ds2 = inst2.setup()
print(f"Fingerprint 2: {ds2._fingerprint}")
if ds1._fingerprint != ds2._fingerprint:
print("\n❌ ISSUE REPRODUCED: Fingerprints differ (Cache Miss).")
else:
print("\n✅ Fingerprints match.")
```
Result:
```
--- Run 1 ---
Mapping: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 2025.26 examples/s]
Fingerprint 1: 1ce6104f9e97912a
--- Run 2 (New Instance) ---
Mapping: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 2300.77 examples/s]
Fingerprint 2: c0fc011ff86ea571
--- Result ---
❌ CACHE MISS: Fingerprints are different!
```
### Expected behavior
The fingerprint should ideally depend **only on the bytecode of the function and the values of the variables actually accessed** (`self.foo`), rather than the state of the whole object self.
### Environment info
datasets version: 4.5.0, platform: any, python version: 3.13.
This was encountered while subclassing torch lightning's `LightningDataModule`. These objects inherently **contain internal state that differs per instance**. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7985 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7985/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7985/comments | https://api.github.com/repos/huggingface/datasets/issues/7985/events | https://github.com/huggingface/datasets/pull/7985 | 3,892,480,150 | PR_kwDODunzps7BQaGn | 7,985 | Remove unused data files optims | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7985). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-02-03T17:58:30 | 2026-02-03T18:30:30 | 2026-02-03T18:30:28 | null | MEMBER | null | null | false | {
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} | this fixes module inference when there are many metadata files
e.g. the lance dataset at https://huggingface.co/datasets/davanstrien/encyclopaedia-britannica-lance has > 200 metadata files
those optims are not used anymore, they come from a time we were dealing with slow data files iterators instead of lists | {
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"Hi! It looks like the GitHub Actions check suites for this PR are in `action_required` (no workflows actually ran). This is usually due to fork workflow approval.\n\nCould a maintainer please approve/run the workflows so CI can execute? Happy to address anything CI flags once it runs.",
"The docs for this PR liv... | 2026-02-03T00:06:46 | 2026-03-03T09:39:30 | null | null | NONE | null | null | false | {
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} | Add initial Zarr streaming support (POC).
This introduces a `zarr` packaged module and docs/tests to validate basic loading.
Note: I pushed a follow-up commit to fix an accidental duplication in `benchmarks/benchmark_zarr_streaming.py` (file now contains a single benchmark script). | null | {
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"Hi! It looks like the GitHub Actions check suites for this PR are in `action_required` (no workflows actually ran). This is usually due to fork workflow approval.\n\nCould a maintainer please approve/run the workflows so CI can execute? Happy to address anything CI flags once it runs.",
"Quick ping on this one. ... | 2026-02-02T23:34:51 | 2026-03-03T09:39:40 | null | null | NONE | null | null | false | {
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} | Fix unstable dataset fingerprinting when hashing `PreTrainedTokenizerFast`.
Some tokenizers backed by `tokenizers.Tokenizer` mutate runtime settings (padding/truncation) when called, which can change the serialized state and make dataset fingerprints unstable. That prevents `.map(load_from_cache_file=True)` from reusing cache files.
Fix: when hashing, temporarily disable backend padding/truncation so runtime settings don’t affect the fingerprint, then restore the original settings.
Includes a regression test showing `Hasher.hash(tokenizer)` stays stable after calling the tokenizer. | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7981). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7979). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"And they are never deleted?",
"Just to be clear @lhoestq, by not deleting the files o... | 2026-02-02T16:10:38 | 2026-02-27T23:28:51 | 2026-02-02T16:26:14 | null | MEMBER | null | null | false | {
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this is enabled for for datasets loaded using streaming=True/False | {
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} | Fix #4910 : Merge duplicate kwargs in `load_dataset_builder()`
Problem: load_dataset("dataset", base_path="./data")` gives TypeError("multiple values for keyword 'base_path')
Fix: {**builder_kwargs, **config_kwargs} to user kwargs override dataset defaults
Repro : python
Before: TypeError
load_dataset("rotten_tomatoes", base_path="./sample_data")
After: Works
| null | {
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} | When a dataset is cached and accessed in offline mode, get_dataset_config_names was returning default instead of the actual cached config names. This happened because CachedDatasetModuleFactory.get_module returned a DatasetModule without builder_configs_parameters, causing the fallback to default in get_dataset_config_names.
The fix reads config_name from each dataset_info file in the cache directory and includes them as builder_configs_parameters in the returned DatasetModule. Invalid or missing dataset_info.json files are handled.
**Testing:**
1. Download a dataset in online mode so it gets cached
2. Switch to offline mode and call get_dataset_config_names
3. Verify it returns the cached config names instead of ['default']
**Example:**
- HF_DATASETS_OFFLINE=0 HF_HOME="/tmp/hftemp" python -c "import datasets; datasets.load_dataset('cais/mmlu', 'all')"
- HF_DATASETS_OFFLINE=1 HF_HOME="/tmp/hftemp" python -c "import datasets; print(datasets.get_dataset_config_names('cais/mmlu'))"
- -> Expected output: ['all']
Fixes https://github.com/huggingface/datasets/issues/7947 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7976). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-01-31T11:49:39 | 2026-02-03T20:03:48 | 2026-01-31T11:50:33 | null | MEMBER | null | null | false | {
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"Quick ping on this one. Happy to update anything if you want changes."
] | 2026-01-31T07:00:43 | 2026-03-03T09:39:47 | null | null | NONE | null | null | false | {
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} | Docs: add a minimal `Dataset.from_dict` example.
This helps new users discover the most direct way to build a small dataset from in-memory Python data.
Docs-only change. | null | {
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"Quick ping on this one. Happy to update anything if you want changes."
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} | Avoid passing duplicate keyword arguments to `load_dataset_builder`.
Some module factories provide values in `builder_kwargs` (e.g. `base_path`), and users can also pass the same keys via `config_kwargs`, which raises:
`TypeError: ... got multiple values for keyword argument ...`.
Fix: if `config_kwargs` is provided, drop overlapping keys from `builder_kwargs` (keep the user-provided values).
Includes a regression test. | null | {
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"Hi! It looks like the GitHub Actions check suites for this PR are in `action_required` (no workflows actually ran). This is usually due to fork workflow approval.\n\nCould a maintainer please approve/run the workflows so CI can execute? Happy to address anything CI flags once it runs.",
"The docs for this PR liv... | 2026-01-31T06:04:51 | 2026-03-03T09:39:33 | null | null | NONE | null | null | false | {
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} | Fix `resolve_pattern` for *local symlinked files*.
Problem: on the local `file://` filesystem, `fsspec` can report symlinks as `type=="other"` and omit the `islink` flag, so symlinked files are skipped.
Fix: when `protocol=="file"`, treat `os.path.islink(filepath)` as a link candidate and include it if it resolves to a regular file.
Includes a regression test in `tests/test_data_files.py`. | null | {
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} | This commit optimizes streaming operations by implementing [_iter_arrow](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:377:4-391:57) for [SkipExamplesIterable](cci:2://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:1798:0-1892:42), [TakeExamplesIterable](cci:2://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:1944:0-2048:42), and [StepExamplesIterable](cci:2://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:667:0-743:42).
### Key Changes:
- **Fast Batch Processing**: Enabled batch-level slicing for `.skip(n)` and `.take(n)` on streaming datasets, bypassing slow row-by-row iteration.
- **Optimized Sharding**: Updated [StepExamplesIterable](cci:2://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:667:0-743:42) (used in distributed training) to use Arrow's `.take()` to extract multiple records from a batch simultaneously.
- **State Preservation**: Reinforced [_init_state_dict](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:505:4-514:31) and [load_state_dict](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:2382:4-2430:46) to support flawless checkpointing and resumption while using Arrow iteration.
### Performance Impact:
Users will experience significant performance gains when skipping or taking examples in streaming mode. By staying in the "Arrow path" and avoiding Python dictionary conversions, data loading overhead is drastically reduced, especially for large-scale training jobs.
### Testing:
Integrated 6 new unit tests into [tests/test_iterable_dataset.py](cci:7://file:///c:/Users/ASUS/Desktop/datasets/tests/test_iterable_dataset.py:0:0-0:0) to verify:
- Functional correctness for [skip](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:3149:4-3191:9), [take](cci:1://file:///c:/Users/ASUS/Desktop/datasets/src/datasets/iterable_dataset.py:3236:4-3271:9), and `step` using Arrow iteration.
- Reliable state checkpointing and resumption after partial iteration. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7971 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7971/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7971/comments | https://api.github.com/repos/huggingface/datasets/issues/7971/events | https://github.com/huggingface/datasets/pull/7971 | 3,871,984,311 | PR_kwDODunzps7AMzLl | 7,971 | push_to_hub() for videos | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7971). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-01-29T18:16:58 | 2026-01-31T11:50:25 | 2026-01-29T18:56:04 | null | MEMBER | null | null | false | {
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} | possible now that row group sizes are auto-determined based on the content size after https://github.com/huggingface/datasets/pull/7589
Videos are uploaded as PLAIN in Parquet to make sure they can be seeked remotely and with random access to frames in https://github.com/huggingface/datasets/pull/7976
In the future it could be cool to have the same behavior as when videos are separate files, i.e. lazily load them instead of downloading them completely in streaming mode.
Right now there is this discrepency:
- `load_dataset("username/my-folder-of-videos", streaming=True)` -> videos are lazy loaded one by one when iterating, and only actually downloaded when accessing frames in `torchcodec`
- `load_dataset("username/my-video-dataset-in-parquet", streaming=True)` -> videos are downloaded one by one when iterating, even if no frame is accessed in `torchcodec`
close https://github.com/huggingface/datasets/issues/7493 | {
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https://api.github.com/repos/huggingface/datasets/issues/7970 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7970/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7970/comments | https://api.github.com/repos/huggingface/datasets/issues/7970/events | https://github.com/huggingface/datasets/issues/7970 | 3,869,700,866 | I_kwDODunzps7mpvMC | 7,970 | cast_column(..., Audio) fails with load_dataset("csv",) | {
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"The following code *does* work:\n```py\nfrom datasets import load_dataset,Audio,Features\n\ndataset = load_dataset(\"csv\",data_files=\"audio.csv\",features=Features({\"audio\": Audio()}))\nprint(dataset[\"train\"][0][\"audio\"])\n```",
"Thanks for reporing ! Are you using pandas v3 by any chance ? The CSV loade... | 2026-01-29T09:33:35 | 2026-01-29T22:24:14 | null | null | NONE | null | null | null | null | ### Describe the bug
Attempt to load a dataset from a csv with a single `audio` column with a single row with a path to an audio file fails when casting the column to Audio, but the exact same dataset created from a dictionary succeeds.
### Steps to reproduce the bug
1. Have any valid audio file `audio.wav`
2. Have a csv file named `audio.csv` with the following content:
```csv
"audio"
"audio.wav"
```
3. Attempt to execute the following python code:
```py
from datasets import load_dataset,Audio,Dataset
dataset = Dataset.from_dict({"audio": ["audio.wav"]})
dataset = dataset.cast_column("audio", Audio())
print(dataset[0]["audio"])
# ^^ succeeds with output: <datasets.features._torchcodec.AudioDecoder object at 0x7a32b341a3c0>
dataset = load_dataset("csv", data_files="audio.csv")
dataset = dataset.cast_column("audio", Audio())
# ^^ errors and terminates
print(dataset[0]["audio"])
```
The error is:
```pytb
Traceback (most recent call last):
File "~/datasets-bug/explore.py", line 8, in <module>
dataset = dataset.cast_column("audio", Audio(sampling_rate=24000))
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/dataset_dict.py", line 337, in cast_column
return DatasetDict({k: dataset.cast_column(column=column, feature=feature) for k, dataset in self.items()})
~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/fingerprint.py", line 468, in wrapper
out = func(dataset, *args, **kwargs)
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/arrow_dataset.py", line 2201, in cast_column
dataset._data = dataset._data.cast(dataset.features.arrow_schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1124, in cast
return MemoryMappedTable(table_cast(self.table, *args, **kwargs), self.path, replays)
~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
return feature.cast_storage(array)
~~~~~~~~~~~~~~~~~~~~^^^^^^^
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/features/audio.py", line 272, in cast_storage
return array_cast(storage, self.pa_type)
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
File "~/datasets-bug/.venv/lib/python3.14/site-packages/datasets/table.py", line 1949, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1147, in pyarrow.lib.Array.cast
File "~/datasets-bug/.venv/lib/python3.14/site-packages/pyarrow/compute.py", line 412, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from large_string to struct using function cast_struct
```
### Expected behavior
The audio column with file paths loaded from a csv can be converted to AudioDecoder objects the same as an identical dataset created from a dict.
### Environment info
datasets 4.3.0 and 4.5.0, Ubuntu 24.04 amd64, python 3.13.11 and 3.14.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7969 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7969/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7969/comments | https://api.github.com/repos/huggingface/datasets/issues/7969/events | https://github.com/huggingface/datasets/pull/7969 | 3,865,100,307 | PR_kwDODunzps6_1p9H | 7,969 | Count examples in lance | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7969). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-01-28T12:00:37 | 2026-01-28T13:00:26 | 2026-01-28T13:00:23 | null | MEMBER | null | null | false | {
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} | ```python
In [1]: from datasets import load_dataset_builder, StreamingDownloadManager
In [2]: b = load_dataset_builder("lance-format/openvid-lance")
Resolving data files: 100%|█| 240/240 [00:00<00:00, 42675.64it/s
In [3]: b.count_examples(StreamingDownloadManager())
Out[3]: {'train': 937957}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7968 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7968/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7968/comments | https://api.github.com/repos/huggingface/datasets/issues/7968/events | https://github.com/huggingface/datasets/issues/7968 | 3,864,988,355 | I_kwDODunzps7mXwrD | 7,968 | Potential conflicting type checks and dead code in `/src/datasets/table.py` | {
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"ConcatenationTable is a subclass of datasets.table.Table but not pa.Table, so it should be fine"
] | 2026-01-28T11:34:53 | 2026-01-28T13:05:28 | null | null | NONE | null | null | null | null | When statically analyzing and manually reviewing the code, I noticed a potential logic conflicting in `/src/datasets/table.py` as follows:
```
def to_blocks(table: Union[pa.Table, Table]) -> list[list[TableBlock]]:
if isinstance(table, pa.Table):
return [[InMemoryTable(table)]]
elif isinstance(table, ConcatenationTable): # dead code
return copy.deepcopy(table.blocks)
else:
return [[table]]
```
Within the function, the condition `isinstance(table, ConcatenationTable)` at line 4 will never be True because the previous condition `isinstance(table, pa.Table)` at line 2 would have already caught all instances of `ConcatenationTable` (since `ConcatenationTable` is a subtype of `pa.Table`). This creates a logical conflict in the type checking flow.
Please verify if this logic is intentional or it is an issue warranting a refactoring or fixing. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7967 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7967/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7967/comments | https://api.github.com/repos/huggingface/datasets/issues/7967/events | https://github.com/huggingface/datasets/pull/7967 | 3,863,579,646 | PR_kwDODunzps6_wnm8 | 7,967 | Issue 7756 Fix - multiprocessing hang issue with start method check | {
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} | Added a fix to prevent multiprocessing hangs by checking the start method.
Detects bad multiprocessing start_method, fallback happens.
https://github.com/huggingface/datasets/issues/7756 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7966 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7966/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7966/comments | https://api.github.com/repos/huggingface/datasets/issues/7966/events | https://github.com/huggingface/datasets/pull/7966 | 3,861,774,379 | PR_kwDODunzps6_qp_e | 7,966 | Infer types from lance blobs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7966). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-01-27T18:00:25 | 2026-01-28T13:02:25 | 2026-01-28T13:02:23 | null | MEMBER | null | null | false | {
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} | Ex: infer Video() type in https://huggingface.co/datasets/lance-format/openvid-lance and Image() type in https://huggingface.co/datasets/lance-format/laion-1m
```python
from datasets import load_dataset
ds = load_dataset("lance-format/laion-1m", streaming=True, split="train")
print(ds.feature["image"])
# Image()
ds = load_dataset("lance-format/openvid-lance", streaming=True, split="train")
print(ds.feature["video_blob"])
# Video()
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7965 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7965/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7965/comments | https://api.github.com/repos/huggingface/datasets/issues/7965/events | https://github.com/huggingface/datasets/issues/7965 | 3,858,483,549 | I_kwDODunzps7l-8ld | 7,965 | `huggingface_hub.errors.HfHubHTTPError: 404 Client Error: Not Found for url` when fetching a dataset with `datasets.load_dataset` | {
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"Hi ! Yes you should use `cornell-movie-review-data/rotten_tomatoes` instead of `rotten_tomatoes`, which is the legacy name. Those datasets have been moved under their actual owners accounts some time ago (but we were keeping the old names as aliases)\n\nSome other impacted names are:\n- `imdb` -> `stanfordnlp/imdb... | 2026-01-27T02:20:31 | 2026-01-28T15:14:50 | 2026-01-28T15:14:50 | null | NONE | null | null | null | null | Not a bug but a question. We started getting the following error:
https://github.com/mlflow/mlflow/actions/runs/21368603305/job/61506951617
```
ests/data/test_huggingface_dataset_and_source.py::test_from_huggingface_dataset_constructs_expected_dataset_with_revision - huggingface_hub.errors.HfHubHTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/datasets/rotten_tomatoes/revision/aa13bc287fa6fcab6daf52f0dfb9994269ffea28 (Request ID: Root=1-6977aeca-35bc2b5b605884926a9224d0;aa2391f3-26e8-4975-b9bb-114b2fa40223)
```
Adding a user id fixed the issue (https://github.com/mlflow/mlflow/pull/20350). `https://huggingface.co/api/datasets` no longer accepts a name-only path like `rotten_tomatoes`? Just wondering what changed. Thanks! | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7964). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-01-26T22:56:24 | 2026-01-26T22:59:18 | 2026-01-26T22:56:38 | null | MEMBER | null | null | false | {
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} | following #7913 | {
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https://api.github.com/repos/huggingface/datasets/issues/7963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7963/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7963/comments | https://api.github.com/repos/huggingface/datasets/issues/7963/events | https://github.com/huggingface/datasets/pull/7963 | 3,856,921,322 | PR_kwDODunzps6_ajTD | 7,963 | Support null in json string cols | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7963). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-01-26T17:31:55 | 2026-01-26T17:48:46 | 2026-01-26T17:48:44 | null | MEMBER | null | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/7962 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7962/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7962/comments | https://api.github.com/repos/huggingface/datasets/issues/7962/events | https://github.com/huggingface/datasets/pull/7962 | 3,856,811,005 | PR_kwDODunzps6_aM71 | 7,962 | Use Sequence instead of list in Dataset.from_parquet type hints | {
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"Thank you for the review!\r\n\r\nI’ve updated the implementation to:\r\n- Use Sequence from collections.abc as per project conventions\r\n- Restore backward compatibility with Union[PathLike, Sequence[PathLike]]\r\n- Keep the columns annotation as Optional[Sequence[str]]\r\n\r\nThe fixes are pushed. Please let me ... | 2026-01-26T17:01:47 | 2026-02-04T06:47:52 | null | null | NONE | null | null | false | {
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} | This PR updates type annotations in Dataset.from_parquet to use Sequence instead of list to avoid mypy invariant type issues, as discussed in issue #5354.
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https://api.github.com/repos/huggingface/datasets/issues/7961 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7961/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7961/comments | https://api.github.com/repos/huggingface/datasets/issues/7961/events | https://github.com/huggingface/datasets/pull/7961 | 3,847,883,164 | PR_kwDODunzps6-9GHz | 7,961 | Revert "feat: avoid some copies in torch formatter (#7787)" | {
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] | 2026-01-23T15:13:01 | 2026-01-23T15:16:01 | 2026-01-23T15:15:33 | null | MEMBER | null | null | false | {
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} | This reverts commit c412a6f5a50955e141c5169bf7abe005d10228d2 (I assume it was ai generated which makes it hard for me to review and make sure it doesn't have bad edge cases, but lmk if it wasn't, anyways it didn't take into account the torch kwargs which are responsible for sending the data to the correct device) | {
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} | This PR improves the documentation in
src/datasets/splits.py
by:
Fixing pluralization/grammar errors in docstrings (Lines 62, 73, 403).
Adding Python type hints to the
NamedSplit
constructor for better code quality.
Verified with ruff format and ruff check. | {
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"@lhoestq tagging you here as you were on the previous issue, hope that's fine. ",
"Thanks for reporting, let me take a look",
"I reverted this change which caused the issue #7961 , I'll do a new release soon but in the meantime feel free to install `datasets` from source",
"@lhoestq thanks a lot. I am actual... | 2026-01-23T12:06:48 | 2026-03-04T13:17:28 | 2026-03-04T13:17:28 | null | NONE | null | null | null | null | ### Describe the bug
The problem is that when calling:
```ds.set_format(type="torch", columns = ["input", "labels"], device="cuda")```
The device type of the individual datapoints is now: `cpu` as opposed to `cuda:0`.
With `v4.0.0` it still works. With `v4.5.0` it doesn't work anymore.
Related Issue:
https://github.com/huggingface/datasets/issues/1762
### Steps to reproduce the bug
Steps to reproduce the bug:
```
ds.set_format(type="torch", columns = ["input", "labels"], device="cuda")
print(ds["train"][0]["input"].device) #outputs cpu
```
The above should be `cuda:0`.
### Expected behavior
`set_format` should be able to move data to GPU.
### Environment info
datasets==4.5.0 | {
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"Hi maintainers 👋 \r\nThis PR fixes `all_exhausted_without_replacement` to ensure all samples are emitted exactly once, \r\nand adds a regression test reproducing the reported issue.\r\n\r\nHappy to adjust the implementation or split changes if you’d prefer.\r\nThanks!\r\n",
"We just merged https://github.com/... | 2026-01-21T18:47:32 | 2026-01-23T16:09:28 | 2026-01-23T16:09:28 | null | NONE | null | null | false | {
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} | Fix interleave_datasets "all_exhausted_without_replacement" stopping strategy
- Corrected logic to ensure each sample is picked exactly once when using
stopping_strategy="all_exhausted_without_replacement".
- Adjusted boolean stopping condition to properly track dataset exhaustion.
- Added test to verify the last element is included as expected.
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"Yes, that's a hard limit. Can you split your files into different folders? Or we'll probably have a new repo type in the near to mid future that will relax this limit a bit. ",
"Thanks! Working around this with a different sharding parameter to have fewer overall fragments (and therefore fewer files in `*.lance/... | 2026-01-21T16:37:38 | 2026-01-22T09:38:38 | null | null | NONE | null | null | null | null | ### Feature request
Can the hard limit of 10k files per folder be extended with acceptable loss in performance?
### Motivation
I'm uploading a lance dataset to huggingface hub and have a folder inside lance internals (`data/*.lance/_transactions`) that has > 20k atomic transaction records and my commits are being rejected.
```
Bad request for commit endpoint:
Your push was rejected because it contains too many files per directory. Each directory in your git repo can only contain up to 10000 files. Offending reference: refs/heads/main Offending directories: /data/expression.lance/_transactions/
```
### Your contribution
Open to suggestions for how to make a PR | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7955). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2026-01-21T08:10:45 | 2026-01-24T02:55:56 | 2026-01-23T16:08:39 | null | CONTRIBUTOR | null | null | false | {
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} | When using interleave_datasets with stopping_strategy="all_exhausted_without_replacement" and probabilities=None, the function was incorrectly falling into the undersampling branch, causing it to stop at min(lengths) instead of continuing until all datasets were exhausted.
This fix adds a specific branch to handle the all_exhausted_without_replacement case when probabilities=None. The new logic cycles through all datasets round by round, adding elements from each dataset until all are exhausted, ensuring each element appears exactly once.
Example fix:
- Input: d1=[0,1,2], d2=[10,11,12,13], d3=[20,21,22]
- Before: [0, 10, 20, 1, 11, 21, 2, 12, 22]
- After: [0, 10, 20, 1, 11, 21, 2, 12, 22, 13]
🤖 Generated with [Claude Code](https://claude.com/claude-code) | {
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"https://github.com/huggingface/datasets/pull/7955"
] | 2026-01-21T07:50:31 | 2026-01-21T08:13:00 | null | null | CONTRIBUTOR | null | null | null | null | ### Describe the bug
>>> from datasets import Dataset, interleave_datasets
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted_without_replacement")
>>> dataset["a"][:100]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
expected output: [0, 10, 20, 1, 11, 21, 2, 12, 22,13]
datasets version 4.5.0
### Steps to reproduce the bug
>>> from datasets import Dataset, interleave_datasets
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted_without_replacement")
>>> dataset["a"][:100]
### Expected behavior
[0, 10, 20, 1, 11, 21, 2, 12, 22,13]
### Environment info
datasets version 4.5.0 | null | {
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} | \This PR replaces `list` type hints with `Sequence` in `from_parquet` to improve type checking.
Note: Local pytest errors on Python 3.13 due to removal of `distutils` are unrelated to this change.
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} | This PR replaces `list` type hints with `Sequence` in `from_parquet` to improve type checking.
Note: Local pytest errors on Python 3.13 due to removal of `distutils` are unrelated to this change.
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} | ## Summary
Add native support for loading GenBank (.gb, .gbk, .genbank) files, a standard format for biological sequence data with annotations maintained by NCBI.
## Changes
- Add `genbank` packaged module with pure Python state machine parser
- Register GenBank extensions in `_PACKAGED_DATASETS_MODULES` and `_EXTENSION_TO_MODULE`
- Add comprehensive test suite (28 tests)
## Features
- **Metadata parsing**: LOCUS, DEFINITION, ACCESSION, VERSION, KEYWORDS, ORGANISM, taxonomy
- **Feature parsing**: Structured JSON output with location parsing (complement, join)
- **Sequence parsing**: ORIGIN section with automatic length calculation
- **Compression support**: gzip, bz2, xz via magic bytes detection
- **Memory efficiency**: Dual-threshold batching (batch_size + max_batch_bytes)
- **Large sequences**: Uses `large_string` Arrow type for sequences/features
## Usage
```python
from datasets import load_dataset
# Load GenBank files
ds = load_dataset("genbank", data_files="sequences.gb")
# With options
ds = load_dataset("genbank", data_files="*.gbk",
columns=["sequence", "organism", "features"],
parse_features=True)
```
## Test plan
- [x] All 28 unit tests pass
- [x] Tests cover: basic loading, multi-record, compression, feature parsing, column filtering, batching, schema types | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7950). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Hi @prrao87 ! All the main examples with pylance should be in their dedicated documenta... | 2026-01-17T19:33:31 | 2026-01-23T16:40:20 | 2026-01-23T16:16:43 | null | CONTRIBUTOR | null | null | false | {
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} | ## Summary
Updated the Lance integration docs to match the official dataset cards and expand coverage of multimodal workflows in `use_with_lance.mdx`.
## Details
- Added a Lance format-focused guidance page for multimodal examples:
- Stream from the Hub via `datasets` API
- Use Lance's dataset API to pass in a `hf://...` path specifier,
- Scan, vector search, schema evolution examples
- "Export to Lance" example for materializing filtered subsets locally
- Aligned all dataset names/paths to existing datasets so users can immediately test it out: `lance-format/laion-1m` (image) and the `lance-format/openvid-lance` (video)
Depending on the questions we get from users, we may add more descriptive examples as we along, but this should be a good start! | {
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"This PR clarifies the documentation build instructions by improving wording and adding clarity for local builds."
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} | This PR adds an optional `return_file_name` parameter to the JSON dataset loader.
When enabled, a new `file_name` column is added containing the source file name
for each row. Default behavior is unchanged.
Changes:
- Add `return_file_name` to JsonConfig
- Append file name during JSON table generation
- Add tests covering default and enabled behavior, and ensures other functions are not affected
Motivation:
This helps resume training from checkpoints by identifying already-consumed data shards.
Fixes #5806
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