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PR_kwDODunzps6ukXe9
| 7,824
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Fix batch_size default description in to_polars docstrings
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Fix batch_size default description in `to_polars` docstrings.
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PR_kwDODunzps6uRkGa
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Fix random seed on shuffle and interleave_datasets
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closes #7567
Add `shift_rngs` method to `ExamplesIterable` that is called directly after sharding. If a generator is available (not the case for all subclasses) we update the seed of the generator by shifting by the worker_id.
~This is just the fix for `shuffle`, in the corresponding issue `interleave_datasets` is mentioned as well, which won't be fixed with this approach.~
EDIT: This is a fix for `shuffle` and `interleave_datasets`. Adding recursivity to `shift_rngs` solved `interleave_datasets` as well. Not sure though if this is completely safe or if we could destroy something with that. I don't think so but could be wrong and appreciate some guidance from the maintainers. I also checked, on a single_worker we are always handing over `index=0` so that case preserves the seed the user specified.
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PR_kwDODunzps6uRKIJ
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|
Retry open hf file
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Fix this error
```python
File "/workdir/.venv/lib/python3.13/site-packages/datasets/utils/file_utils.py", line 934, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
File "/workdir/.venv/lib/python3.13/site-packages/fsspec/core.py", line 147, in open
return self.__enter__()
~~~~~~~~~~~~~~^^
File "/workdir/.venv/lib/python3.13/site-packages/fsspec/core.py", line 105, in __enter__
f = self.fs.open(self.path, mode=mode)
File "/workdir/.venv/lib/python3.13/site-packages/fsspec/spec.py", line 1338, in open
f = self._open(
path,
...<4 lines>...
**kwargs,
)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_file_system.py", line 275, in _open
return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_file_system.py", line 950, in __init__
self.resolved_path = fs.resolve_path(path, revision=revision)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_file_system.py", line 198, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_file_system.py", line 125, in _repo_and_revision_exist
self._api.repo_info(
~~~~~~~~~~~~~~~~~~~^
repo_id, revision=revision, repo_type=repo_type, timeout=constants.HF_HUB_ETAG_TIMEOUT
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_api.py", line 2864, in repo_info
return method(
repo_id,
...<4 lines>...
files_metadata=files_metadata,
)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/hf_api.py", line 2721, in dataset_info
r = get_session().get(path, headers=headers, timeout=timeout, params=params)
File "/workdir/.venv/lib/python3.13/site-packages/requests/sessions.py", line 602, in get
return self.request("GET", url, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
File "/workdir/.venv/lib/python3.13/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
File "/workdir/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_http.py", line 95, in send
return super().send(request, *args, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workdir/.venv/lib/python3.13/site-packages/requests/adapters.py", line 690, in send
raise ReadTimeout(e, request=request)
requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: e7e1ae72-54a0-4ce4-b011-144fb7a3fb06)')
```
which could also be related to
```python
File "/workdir/.venv/lib/python3.13/site-packages/datasets/utils/file_utils.py", line 1364, in _iter_from_urlpaths
raise FileNotFoundError(urlpath)
FileNotFoundError: hf://datasets/.../train-00013-of-00031.parquet
```
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| 3,520,913,195
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I_kwDODunzps7R3N8r
| 7,821
|
Building a dataset with large variable size arrays results in error ArrowInvalid: Value X too large to fit in C integer type
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open
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2025-10-16 08:45:17+00:00
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2025-10-16 08:54:14+00:00
|
NaT
|
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
I used map to store raw audio waveforms of variable lengths in a column of a dataset the `map` call fails with ArrowInvalid: Value X too large to fit in C integer type.
```
Traceback (most recent call last):
Traceback (most recent call last):
File "...lib/python3.12/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "...lib/python3.12/site-packages/datasets/utils/py_utils.py", line 678, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
^^^^^^^^^^^^^^^^^^^^^^^^^
File "...lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3526, in _map_single
writer.write_batch(batch)
File "...lib/python3.12/site-packages/datasets/arrow_writer.py", line 605, in write_batch
arrays.append(pa.array(typed_sequence))
^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 252, in pyarrow.lib.array
File "pyarrow/array.pxi", line 114, in pyarrow.lib._handle_arrow_array_protocol
File "...lib/python3.12/site-packages/datasets/arrow_writer.py", line 225, in __arrow_array__
out = list_of_np_array_to_pyarrow_listarray(data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "...lib/python3.12/site-packages/datasets/features/features.py", line 1538, in list_of_np_array_to_pyarrow_listarray
return list_of_pa_arrays_to_pyarrow_listarray(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "...lib/python3.12/site-packages/datasets/features/features.py", line 1530, in list_of_pa_arrays_to_pyarrow_listarray
offsets = pa.array(offsets, type=pa.int32())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 362, in pyarrow.lib.array
File "pyarrow/array.pxi", line 87, in pyarrow.lib._ndarray_to_array
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Value 2148479376 too large to fit in C integer type
```
### Steps to reproduce the bug
Calling map on a dataset that returns a column with long 1d numpy arrays of variable length.
Example:
```python
# %%
import logging
import datasets
import pandas as pd
import numpy as np
# %%
def process_batch(batch, rank):
res = []
for _ in batch["id"]:
res.append(np.zeros((2**30)).astype(np.uint16))
return {"audio": res}
if __name__ == "__main__":
df = pd.DataFrame(
{
"id": list(range(400)),
}
)
ds = datasets.Dataset.from_pandas(df)
try:
from multiprocess import set_start_method
set_start_method("spawn")
except RuntimeError:
print("Spawn method already set, continuing...")
mapped_ds = ds.map(
process_batch,
batched=True,
batch_size=2,
with_rank=True,
num_proc=2,
cache_file_name="path_to_cache/tmp.arrow",
writer_batch_size=200,
remove_columns=ds.column_names,
# disable_nullable=True,
)
```
### Expected behavior
I think the offsets should be pa.int64() if needed and not forced to be `pa.int32()`
in https://github.com/huggingface/datasets/blob/3e13d30823f8ec498d56adbc18c6880a5463b313/src/datasets/features/features.py#L1535
### Environment info
- `datasets` version: 3.3.1
- Platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35
- Python version: 3.12.9
- `huggingface_hub` version: 0.29.0
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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PR_kwDODunzps6t6suZ
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|
Keep hffs cache in workers when streaming
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2025-10-15 15:51:28+00:00
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2025-10-17 09:59:17+00:00
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2025-10-17 09:59:16+00:00
|
MEMBER
| null | null | 0
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(and also reorder the hffs args to improve caching)
When using `DataLoader(iterable_dataset, num_workers=...)` the dataset is pickled and passed to the worker. However previously the resulting dataset would be in a process with an empty hffs cache. By keeping the cache attached to `IterableDataset`, the cached hffs instances are pickled with the dataset and re-populates the cache in the DataLoader workers
this requires https://github.com/huggingface/huggingface_hub/pull/3443 to work effectively though, otherwise the unpickled hffs cache would start empty
cc @andimarafioti @ltmeyer
|
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| 3,517,086,110
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I_kwDODunzps7Ronme
| 7,819
|
Cannot download opus dataset
|
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2025-10-15 09:06:19+00:00
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2025-10-15 09:06:19+00:00
|
NaT
|
NONE
| null | null | null | null |
When I tried to download opus_books using:
from datasets import load_dataset
dataset = load_dataset("Helsinki-NLP/opus_books")
I got the following errors:
FileNotFoundError: Couldn't find any data file at /workspace/Helsinki-NLP/opus_books. Couldn't find 'Helsinki-NLP/opus_books' on the Hugging Face Hub either: LocalEntryNotFoundError: An error happened while trying to locate the file on the Hub and we cannot find the requested files in the local cache. Please check your connection and try again or make sure your Internet connection is on.
I also tried:
dataset = load_dataset("opus_books", "en-zh")
and the errors remain the same. However, I can download "mlabonne/FineTome-100k" successfully.
My datasets is version 4.2.0
Any clues? Big thanks.
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| 3,515,887,618
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I_kwDODunzps7RkDAC
| 7,818
|
train_test_split and stratify breaks with Numpy 2.0
|
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2025-10-15 00:01:19+00:00
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2025-10-15 06:17:32+00:00
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### Describe the bug
As stated in the title, since Numpy changed in version >2.0 with copy, the stratify parameters break.
e.g. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")` returns a Numpy error.
It works if you downgrade Numpy to a version lower than 2.0.
### Steps to reproduce the bug
1. Numpy > 2.0
2. `all_dataset.train_test_split(test_size=0.2,stratify_by_column="label")`
### Expected behavior
It returns a stratified split as per the results of Numpy < 2.0
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-6.8.0-85-generic-x86_64-with-glibc2.35
- Python version: 3.13.7
- Huggingface_hub version: 0.34.4
- PyArrow version: 19.0.0
- Pandas version: 2.3.2
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PR_kwDODunzps6tw-GG
| 7,817
|
fix: better args passthrough for `_batch_setitems()`
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2025-10-14 22:51:51+00:00
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2025-10-16 11:27:20+00:00
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NaT
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NONE
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In Python 3.14, there's a change in the signature of `_Pickler._batch_setitems`.
It's changed to:
```python
# pickle.py
def _batch_setitems(self, items, obj):
# Helper to batch up SETITEMS sequences; proto >= 1 only
save = self.save
write = self.write
```
To accomodate this, in `dill`, we have this compatibility code:
```python
if sys.hexversion < 0x30E00A1:
pickler._batch_setitems(iter(source.items()))
else:
pickler._batch_setitems(iter(source.items()), obj=obj)
```
Thus, the datasets package will emit this error
```
│ /Users/sghuang/mamba/envs/ds/lib/python3.14/site-packages/dill/_dill.py:1262 in save_module_dict │
│ │
│ 1259 │ │ if is_dill(pickler, child=False) and pickler._session: │
│ 1260 │ │ │ # we only care about session the first pass thru │
│ 1261 │ │ │ pickler._first_pass = False │
│ ❱ 1262 │ │ StockPickler.save_dict(pickler, obj) │
│ 1263 │ │ logger.trace(pickler, "# D2") │
│ 1264 │ return │
│ 1265 │
│ │
│ /Users/sghuang/mamba/envs/ds/lib/python3.14/pickle.py:1133 in save_dict │
│ │
│ 1130 │ │ print(f"Line number: {inspect.getsourcelines(method)[1]}") │
│ 1131 │ │ print(f"Full path: {inspect.getmodule(method)}") │
│ 1132 │ │ print(f"Class: {method.__qualname__}") │
│ ❱ 1133 │ │ self._batch_setitems(obj.items(), obj) │
│ 1134 │ │
│ 1135 │ dispatch[dict] = save_dict │
│ 1136 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: Pickler._batch_setitems() takes 2 positional arguments but 3 were given
[NOTE] when serializing datasets.table.InMemoryTable state
[NOTE] when serializing datasets.table.InMemoryTable object
```
To fix it, we update the signature of the `_batch_setitems` method defined in `utils/_dill.py`.
This fix should be backward compatible, since the compatibility is handled by `dill`.
This should close #7813.
Similar to https://github.com/joblib/joblib/issues/1658.
Related to https://github.com/uqfoundation/dill/pull/724.
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|
I_kwDODunzps7RWBMe
| 7,816
|
disable_progress_bar() not working as expected
|
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2025-10-14 03:25:39+00:00
|
2025-10-14 23:49:26+00:00
|
2025-10-14 23:49:26+00:00
|
NONE
| null | null | null | null |
### Describe the bug
Hi,
I'm trying to load a dataset on Kaggle TPU image. There is some known compat issue with progress bar on Kaggle, so I'm trying to disable the progress bar globally. This does not work as you can see in [here](https://www.kaggle.com/code/windmaple/hf-datasets-issue).
In contract, disabling progress bar for snapshot_download() works as expected as in [here](https://www.kaggle.com/code/windmaple/snapshot-download-error).
### Steps to reproduce the bug
See this [notebook](https://www.kaggle.com/code/windmaple/hf-datasets-issue).
There is sth. wrong with `shell_paraent`.
### Expected behavior
The downloader should disable progress bar and move forward w/ no error.
### Environment info
The latest version as I did:
!pip install -U datasets ipywidgets ipykernel
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PR_kwDODunzps6tiDIT
| 7,815
|
Add nifti support
|
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2025-10-13 20:07:32+00:00
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2025-10-14 17:52:13+00:00
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CONTRIBUTOR
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Add support for NIfTI.
supports #7804
This PR follows https://github.com/huggingface/datasets/pull/7325 very closely
I am a bit unsure what we need to add to the `document_dataset.mdx` and `document_load.mdx`. I should probably create a dataset on the hub first to create this guide instead of copy+pasting from PDF.
Open todos:
- [x] create nifti dataset on the hub
- ~[ ] update `document_dataset.mdx` and `document_load.mdx`~
EDIT:
I tested with two datasets I created on the hub:
- https://huggingface.co/datasets/TobiasPitters/test-nifti-unzipped
- https://huggingface.co/datasets/TobiasPitters/test-nifti
for zipped (file extension `.nii.gz` and unzipped `.nii`) files and both seem to work fine. Also tested loading locally and that seems to work as well.
Here is the scriptsthat I ran against the hub:
```python
from pathlib import Path
from datasets import load_dataset
import nibabel as nib
dataset = load_dataset(
"TobiasPitters/test-nifti-unzipped",
split="test" # Load as single Dataset, not DatasetDict
)
print("length dataset unzipped:", len(dataset))
for item in dataset:
isinstance(item["nifti"], nib.nifti1.Nifti1Image)
dataset = load_dataset(
"TobiasPitters/test-nifti",
split="train" # Load as single Dataset, not DatasetDict
)
print("length dataset zipped:", len(dataset))
for item in dataset:
isinstance(item["nifti"], nib.nifti1.Nifti1Image)
```
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PR_kwDODunzps6tfJCm
| 7,814
|
Allow streaming hdf5 files
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Add streaming support after https://github.com/huggingface/datasets/pull/7690, cc @klamike :)
## Details
in `datasets` loaders, `open()` is extended to work with files that are on disk but also on HF. Files on HF are streamed using HTTP range requests using the `HfFileSystem` implementation in the `huggingface_hub` library.
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I_kwDODunzps7Q0lkQ
| 7,813
|
Caching does not work when using python3.14
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2025-10-10 15:36:46+00:00
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2025-10-14 23:02:02+00:00
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NaT
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### Describe the bug
Traceback (most recent call last):
File "/workspace/ctn.py", line 8, in <module>
ds = load_dataset(f"naver-clova-ix/synthdog-{lang}") # или "synthdog-zh" для китайского
File "/workspace/.venv/lib/python3.14/site-packages/datasets/load.py", line 1397, in load_dataset
builder_instance = load_dataset_builder(
path=path,
...<10 lines>...
**config_kwargs,
)
File "/workspace/.venv/lib/python3.14/site-packages/datasets/load.py", line 1185, in load_dataset_builder
builder_instance._use_legacy_cache_dir_if_possible(dataset_module)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/builder.py", line 612, in _use_legacy_cache_dir_if_possible
self._check_legacy_cache2(dataset_module) or self._check_legacy_cache() or None
~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/builder.py", line 485, in _check_legacy_cache2
config_id = self.config.name + "-" + Hasher.hash({"data_files": self.config.data_files})
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/fingerprint.py", line 188, in hash
return cls.hash_bytes(dumps(value))
~~~~~^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/utils/_dill.py", line 120, in dumps
dump(obj, file)
~~~~^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/utils/_dill.py", line 114, in dump
Pickler(file, recurse=True).dump(obj)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/dill/_dill.py", line 428, in dump
StockPickler.dump(self, obj)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^
File "/usr/lib/python3.14/pickle.py", line 498, in dump
self.save(obj)
~~~~~~~~~^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/datasets/utils/_dill.py", line 70, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/dill/_dill.py", line 422, in save
StockPickler.save(self, obj, save_persistent_id)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.14/pickle.py", line 572, in save
f(self, obj) # Call unbound method with explicit self
~^^^^^^^^^^^
File "/workspace/.venv/lib/python3.14/site-packages/dill/_dill.py", line 1262, in save_module_dict
StockPickler.save_dict(pickler, obj)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^
File "/usr/lib/python3.14/pickle.py", line 1064, in save_dict
self._batch_setitems(obj.items(), obj)
~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
TypeError: Pickler._batch_setitems() takes 2 positional arguments but 3 were given
### Steps to reproduce the bug
ds_train = ds["train"].map(lambda x: {**x, "lang": lang})
### Expected behavior
Fixed bugs
### Environment info
- `datasets` version: 4.2.0
- Platform: Linux-6.8.0-85-generic-x86_64-with-glibc2.39
- Python version: 3.14.0
- `huggingface_hub` version: 0.35.3
- PyArrow version: 21.0.0
- Pandas version: 2.3.3
- `fsspec` version: 2025.9.0
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PR_kwDODunzps6s_New
| 7,812
|
docs: document_dataset PDFs & OCR
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2025-10-09 23:31:41+00:00
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NaT
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NONE
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Use acronyms consistently across document_dataset docs.
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| 3,500,741,658
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I_kwDODunzps7QqRQa
| 7,811
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SIGSEGV when Python exits due to near null deref
|
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2025-10-09 22:00:11+00:00
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2025-10-10 22:09:24+00:00
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NaT
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NONE
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### Describe the bug
When I run the following python script using datasets I get a segfault.
```python
from datasets import load_dataset
from tqdm import tqdm
progress_bar = tqdm(total=(1000), unit='cols', desc='cols ')
progress_bar.update(1)
```
```
% lldb -- python3 crashmin.py
(lldb) target create "python3"
Current executable set to '/Users/ian/bug/venv/bin/python3' (arm64).
(lldb) settings set -- target.run-args "crashmin.py"
(lldb) r
Process 8095 launched: '/Users/ian/bug/venv/bin/python3' (arm64)
Process 8095 stopped
* thread #2, stop reason = exec
frame #0: 0x0000000100014b30 dyld`_dyld_start
dyld`_dyld_start:
-> 0x100014b30 <+0>: mov x0, sp
0x100014b34 <+4>: and sp, x0, #0xfffffffffffffff0
0x100014b38 <+8>: mov x29, #0x0 ; =0
Target 0: (Python) stopped.
(lldb) c
Process 8095 resuming
cols : 0% 0/1000 [00:00<?, ?cols/s]Process 8095 stopped
* thread #2, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x10)
frame #0: 0x0000000101783454 _datetime.cpython-313-darwin.so`delta_new + 188
_datetime.cpython-313-darwin.so`delta_new:
-> 0x101783454 <+188>: ldr x3, [x20, #0x10]
0x101783458 <+192>: adrp x0, 10
0x10178345c <+196>: add x0, x0, #0x6fc ; "seconds"
Target 0: (Python) stopped.
(lldb) bt
* thread #2, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x10)
* frame #0: 0x0000000101783454 _datetime.cpython-313-darwin.so`delta_new + 188
frame #1: 0x0000000100704b60 Python`type_call + 96
frame #2: 0x000000010067ba34 Python`_PyObject_MakeTpCall + 120
frame #3: 0x00000001007aae3c Python`_PyEval_EvalFrameDefault + 30236
frame #4: 0x000000010067c900 Python`PyObject_CallOneArg + 112
frame #5: 0x000000010070f0a0 Python`slot_tp_finalize + 116
frame #6: 0x000000010070c3b4 Python`subtype_dealloc + 788
frame #7: 0x00000001006c378c Python`insertdict + 756
frame #8: 0x00000001006db2b0 Python`_PyModule_ClearDict + 660
frame #9: 0x000000010080a9a8 Python`finalize_modules + 1772
frame #10: 0x0000000100809a44 Python`_Py_Finalize + 264
frame #11: 0x0000000100837630 Python`Py_RunMain + 252
frame #12: 0x0000000100837ef8 Python`pymain_main + 304
frame #13: 0x0000000100837f98 Python`Py_BytesMain + 40
frame #14: 0x000000019cfcc274 dyld`start + 2840
(lldb) register read x20
x20 = 0x0000000000000000
(lldb)
```
### Steps to reproduce the bug
Run the script above, and observe the segfault.
### Expected behavior
No segfault
### Environment info
```
% pip freeze datasets | grep -i datasets
datasets==4.2.0
(venv) 0 ~/bug 14:58:06
% pip freeze tqdm | grep -i tqdm
tqdm==4.67.1
(venv) 0 ~/bug 14:58:16
% python --version
Python 3.13.7
```
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fix conda deps
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Set dev version
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release: 4.2.0
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typo
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add an s to be consistent with pandas' on_bad_lines
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PR_kwDODunzps6s6gnr
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|
Parquet: add `on_bad_file` argument to error/warn/skip bad files
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```python
from datasets import load_dataset
on_bad_file = "error" # default
# on_bad_file = "warn" # warn and skip bad file
# on_bad_file = "skip" # skip bad file
ds = load_dataset(parquet_dataset_id, on_bad_file=on_bad_file)
```
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Less api calls when resolving data_files
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There ~10 unnecessary `/api/datasets/user/dataset/revision`calls due to multithreading in data files resolution.
I disabled multithreading, which was actually not useful anymore since `HfFileSystem` has been using caching for a while now.
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I_kwDODunzps7Qh2bE
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Support scientific data formats
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2025-10-09 10:18:24+00:00
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2025-10-10 11:26:23+00:00
|
NaT
|
MEMBER
| null | null | null | null |
List of formats and libraries we can use to load the data in `datasets`:
- [ ] DICOMs: pydicom
- [ ] NIfTIs: nibabel
- [ ] WFDB: wfdb
cc @zaRizk7 for viz
Feel free to comment / suggest other formats and libs you'd like to see or to share your interest in one of the mentioned format
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More Parquet streaming docs
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| 3,497,454,119
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I_kwDODunzps7Qduon
| 7,802
|
[Docs] Missing documentation for `Dataset.from_dict`
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2025-10-09 02:54:41+00:00
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2025-10-19 16:09:33+00:00
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NaT
|
NONE
| null | null | null | null |
Documentation link: https://huggingface.co/docs/datasets/en/package_reference/main_classes
Link to method (docstring present): https://github.com/huggingface/datasets/blob/6f2502c5a026caa89839713f6f7c8b958e5e83eb/src/datasets/arrow_dataset.py#L1029
The docstring is present for the function, but seems missing from the official documentation for the `Dataset` class on HuggingFace.
The method in question:
```python
@classmethod
def from_dict(
cls,
mapping: dict,
features: Optional[Features] = None,
info: Optional[DatasetInfo] = None,
split: Optional[NamedSplit] = None,
) -> "Dataset":
"""
Convert `dict` to a `pyarrow.Table` to create a [`Dataset`].
Important: a dataset created with from_dict() lives in memory
and therefore doesn't have an associated cache directory.
This may change in the future, but in the meantime if you
want to reduce memory usage you should write it back on disk
and reload using e.g. save_to_disk / load_from_disk.
Args:
mapping (`Mapping`):
Mapping of strings to Arrays or Python lists.
features ([`Features`], *optional*):
Dataset features.
info (`DatasetInfo`, *optional*):
Dataset information, like description, citation, etc.
split (`NamedSplit`, *optional*):
Name of the dataset split.
Returns:
[`Dataset`]
"""
```
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| 7,801
|
Add parquet scan options and docs
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I added scan options, useful to control buffering and caching when streaming and docs, including how to select a subset of columns and apply filters
|
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| 3,494,747,495
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PR_kwDODunzps6sqkmT
| 7,800
|
Fix polars cast column image
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CONTRIBUTOR
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Fixes #7765
The problem here is that polars uses pyarrow large_string for images, while pandas and others just use the string type. This PR solves that and adds a test.
```python
import polars as pl
from datasets import Dataset
import pandas as pd
import pyarrow as pa
from pathlib import Path
shared_datadir = Path("tests/features/data")
image_path = str(shared_datadir / "test_image_rgb.jpg")
# Load via polars
df_polars = pl.DataFrame({"image_path": [image_path]})
dataset_polars = Dataset.from_polars(df_polars)
print("Polars DF is large string:", pa.types.is_large_string(df_polars.to_arrow().schema[0].type))
print("Polars DF is string:", pa.types.is_string(df_polars.to_arrow().schema[0].type))
# Load via pandas
df_pandas = pd.DataFrame({"image_path": [image_path]})
dataset_pandas = Dataset.from_pandas(df_pandas)
arrow_table_pd = pa.Table.from_pandas(df_pandas)
print("Pandas DF is large string", pa.types.is_large_string(arrow_table_pd.schema[0].type))
print("Pandas DF is string", pa.types.is_string(arrow_table_pd.schema[0].type))
```
Outputs:
```bash
Polars DF is large string: True
Polars DF is string: False
Pandas DF is large string False
Pandas DF is string True
```
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| 3,487,791,741
|
PR_kwDODunzps6sTJKA
| 7,799
|
Define CI future
|
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this should fix the CI which currently uses transformers on 3.9 while it's now unsupported
|
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I_kwDODunzps7PsM3-
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|
Audio dataset is not decoding on 4.1.1
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2025-10-05 06:37:50+00:00
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2025-10-06 14:07:55+00:00
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### Describe the bug
The audio column remain as non-decoded objects even when accessing them.
```python
dataset = load_dataset("MrDragonFox/Elise", split = "train")
dataset[0] # see that it doesn't show 'array' etc...
```
Works fine with `datasets==3.6.0`
Followed the docs in
- https://huggingface.co/docs/datasets/en/audio_load
### Steps to reproduce the bug
```python
dataset = load_dataset("MrDragonFox/Elise", split = "train")
dataset[0] # see that it doesn't show 'array' etc...
```
### Expected behavior
It should decode when accessing the elemenet
### Environment info
4.1.1
ubuntu 22.04
Related
- https://github.com/huggingface/datasets/issues/7707
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PR_kwDODunzps6rhtf_
| 7,797
|
Datasets: Add WMT21 & WMT22 loaders (basic TSV loaders, sample data, tests)
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2025-10-01 10:46:01+00:00
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2025-10-10 15:33:25+00:00
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2025-10-10 15:33:25+00:00
|
NONE
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- Implemented TSV-based dataset loaders:
- WMT21Dataset (local_datasets/wmt21/wmt21_dataset.py)
- WMT22Dataset (local_datasets/wmt22/wmt22_dataset.py)
These classes load source-target pairs from .tsv files for train, validation, and test splits.
- Created sample dummy data for both datasets:
- dummy_data/train.tsv, dummy_data/validation.tsv, dummy_data/test.tsv
- Includes a few realistic example lines to allow CI and local tests to pass without downloading full datasets.
- Added automated tests for robust validation:
- tests/test_wmt21.py and tests/test_wmt22.py
- Checks that all splits load correctly, empty lines are ignored, and the number of examples matches the number of lines in the .tsv files.
- Edge cases handled: empty lines, malformed lines, extra tabs.
- Added README.md files for both datasets:
- Provides dataset structure, usage instructions, and placeholders for citation & license information.
- Ensures that other developers and reviewers can understand dataset usage immediately.
- Ensured easy local testing:
- Load datasets programmatically using WMT21Dataset / WMT22Dataset.
- Verified train/validation/test splits are correctly returned as Python dictionaries of Dataset objects.
-Provides initial support for WMT21 and WMT22 NLP/translation experiments.
-Allows contributors and reviewers to test dataset loading locally or in CI without downloading large datasets.
-Serves as a template to extend to other WMT datasets in the future.
Testing instructions:
```
# Activate your environment
pytest tests/test_wmt21.py -v
pytest tests/test_wmt22.py -v
```
```
from local_datasets.wmt21.wmt21_dataset import WMT21Dataset
from local_datasets.wmt22.wmt22_dataset import WMT22Dataset
# WMT21
wmt21 = WMT21Dataset("local_datasets/wmt21/dummy_data")
ds21 = wmt21.load()
print(ds21["train"][0])
print(ds21["validation"][0])
print(ds21["test"][0])
# WMT22
wmt22 = WMT22Dataset("local_datasets/wmt22/dummy_data")
ds22 = wmt22.load()
print(ds22["train"][0])
print(ds22["validation"][0])
print(ds22["test"][0])
```
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PR_kwDODunzps6rZjrW
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|
Docs: fix typo, improve readability, add code comments
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2025-09-30 18:34:16+00:00
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2025-10-10 18:44:12+00:00
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NaT
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NONE
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What I did:
- Fixed a small typo in README to improve clarity
- Fixed repeated word "frameworks frameworks"
- Split long paragraphs into shorter sentences for readability
- Added # Example comments before code blocks for clarity
Why:
- Helps new users avoid confusion
How I tested:
- Checked locally in Markdown preview
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PR_kwDODunzps6rDEce
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|
Add pyarrow's binary view to features
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2025-09-29 09:12:55+00:00
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2025-10-10 16:04:21+00:00
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2025-10-10 16:04:21+00:00
|
CONTRIBUTOR
| null | null | 0
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Basically https://github.com/huggingface/datasets/pull/7718 just for binary view instead of string view
|
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PR_kwDODunzps6q4XyU
| 7,794
|
Fix nested data conversions error in parquet loading (fixes #7793)
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2025-09-27 22:04:13+00:00
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2025-10-01 16:56:20+00:00
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NaT
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NONE
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Fixes #7793
## Problem
Loading datasets with deeply nested structures (like `metr-evals/malt-public`) fails with:
ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
This occurs when parquet files contain nested data (lists, structs, maps) that exceed PyArrow's 16MB chunk limit.
## Root Cause
PyArrow's C++ implementation explicitly rejects nested data conversions when data is split across multiple chunks. The limitation exists in the `WrapIntoListArray` function where repetition levels cannot be reconstructed across chunk boundaries.
## Solution
- **Fallback mechanism**: Catches the specific PyArrow error and switches to non-chunked reading
- **Selective optimization**: Only combines chunks for problematic nested columns to minimize memory impact
- **Manual batching**: Maintains batching behavior even in fallback mode
- **Backward compatibility**: Zero impact on existing datasets
## Implementation Details
- Added `_is_nested_type()` helper to detect nested PyArrow types
- Added `_handle_nested_chunked_conversion()` for selective chunk combining
- Modified `_generate_tables()` to catch and handle the specific error
- Preserves all existing error handling and logging
## Testing
- [x] No regressions: Normal parquet datasets continue working
- [x] Code follows existing patterns in the datasets codebase
- [x] tested by original reporter (gated dataset access needed)
**Note**: This fix is based on thorough research of PyArrow limitations and similar issues in the ecosystem. While we cannot test with the original dataset due to access restrictions, the implementation follows established patterns for handling this PyArrow limitation.
## Request for Testing
@neevparikh Could you please test this fix with your original failing dataset? The implementation should resolve the nested data conversion error you encountered.
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I_kwDODunzps7OM7wL
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|
Cannot load dataset, fails with nested data conversions not implemented for chunked array outputs
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2025-09-27 01:03:12+00:00
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2025-09-27 21:35:31+00:00
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NaT
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NONE
| null | null | null | null |
### Describe the bug
Hi! When I load this dataset, it fails with a pyarrow error. I'm using datasets 4.1.1, though I also see this with datasets 4.1.2
To reproduce:
```
import datasets
ds = datasets.load_dataset(path="metr-evals/malt-public", name="irrelevant_detail")
```
Error:
```
Traceback (most recent call last):
File "/Users/neev/scratch/.venv/lib/python3.13/site-packages/datasets/builder.py", line 1815, in _prepare_split_single
for _, table in generator:
^^^^^^^^^
File "/Users/neev/scratch/.venv/lib/python3.13/site-packages/datasets/packaged_modules/parquet/parquet.py", line 93, in _generate_tables
for batch_idx, record_batch in enumerate(
~~~~~~~~~^
parquet_fragment.to_batches(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
...<5 lines>...
)
^
):
^
File "pyarrow/_dataset.pyx", line 3904, in _iterator
File "pyarrow/_dataset.pyx", line 3494, in pyarrow._dataset.TaggedRecordBatchIterator.__next__
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: Nested data conversions not implemented for chunked array outputs
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/neev/scratch/test_hf.py", line 3, in <module>
ds = datasets.load_dataset(path="metr-evals/malt-public", name="irrelevant_detail")
File "/Users/neev/scratch/.venv/lib/python3.13/site-packages/datasets/load.py", line 1412, in load_dataset
builder_instance.download_and_prepare(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
download_config=download_config,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
...<3 lines>...
storage_options=storage_options,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/Users/neev/scratch/.venv/lib/python3.13/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
dl_manager=dl_manager,
^^^^^^^^^^^^^^^^^^^^^^
...<2 lines>...
**download_and_prepare_kwargs,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/Users/neev/scratch/.venv/lib/python3.13/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/neev/scratch/.venv/lib/python3.13/site-packages/datasets/builder.py", line 1702, 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 "/Users/neev/scratch/.venv/lib/python3.13/site-packages/datasets/builder.py", line 1858, 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
```
### Steps to reproduce the bug
To reproduce:
```
import datasets
ds = datasets.load_dataset(path="metr-evals/malt-public", name="irrelevant_detail")
```
### Expected behavior
The dataset loads
### Environment info
Datasets: 4.1.1
Python: 3.13
Platform: Macos
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I_kwDODunzps7OCp2i
| 7,792
|
Concatenate IterableDataset instances and distribute underlying shards in a RoundRobin manner
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2025-09-26 10:05:19+00:00
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2025-10-15 18:05:23+00:00
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2025-10-15 18:05:23+00:00
|
NONE
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### Feature request
I would like to be able to concatenate multiple `IterableDataset` with possibly different features. I would like to then be able to stream the results in parallel (both using DDP and multiple workers in the pytorch DataLoader). I want the merge of datasets to be well balanced between the different processes.
### Motivation
I want to train a model on a combination of datasets, which I can convert to a single representation. This applies to converting different datasets items to the same Python class, as using a tokenizer on multiple modalities.
Assuming that my original datasets are not necessarily well balanced as they may have different size and thus different number of shards, I would like the merged dataset to be distributed evenly over the multiple processes. I don't mind if it's not perfectly balanced, and as result, some workers of the torch DataLoader do nothing, as long as the DDP is properly handled causing no deadlock.
### What I've tried
I've tried the two functions already provided in datasets, namely `interleave_datasets` and `concatenate_datasets`.
- Interleave seems to be the best approach of what I'm trying to do. However, it doesn't suit my purpose because as I understand it, it stops as soon as one of the dataset source is exhausted, or repeat the smallest source items until the largest is exhausted. I would like something in-between, similarly to what [roundrobin does](https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.roundrobin).
- Concatenate does not mix the data enough and one dataset may be overrepresented in some early batches.
Let's consider we have 3 datasets composed of different number of shards as follow [[s0_0, s0_1], [s1_0], [s2_0, s2_1, s2_3]], where s denotes the underlying shard, the first index the dataset and the second the shard number.
If we request 3 shards in the `shard_data_source` we should obtain the following:
index 0 gets s0_0 s2_0
index 1 gets s0_1 s2_1
index 2 gets s1_0 s2_3
I started implementing the following, but I'm afraid my sharding logic is incorrect.
```python
from copy import deepcopy
from itertools import chain, islice
import datasets
import numpy as np
from datasets import IterableDataset
from datasets.iterable_dataset import _BaseExamplesIterable
from more_itertools import roundrobin
class MixMultiSourcesExampleIterable(_BaseExamplesIterable):
def __init__(self, ex_iterables: list[_BaseExamplesIterable]):
super().__init__()
self.ex_iterables = ex_iterables
def _init_state_dict(self) -> dict:
self._state_dict = {
"ex_iterables": [ex_iterable._init_state_dict() for ex_iterable in self.ex_iterables],
"type": self.__class__.__name__,
}
return self._state_dict
@property
def num_shards(self) -> int:
return sum(ex_iterable.num_shards for ex_iterable in self.ex_iterables)
def __iter__(self):
yield from roundrobin(*self.ex_iterables)
def shuffle_data_sources(self, generator: np.random.Generator) -> "MixMultiSourcesExampleIterable":
"""Shuffle the list of examples iterable, as well as each underlying examples iterable."""
rng = deepcopy(generator)
ex_iterables = list(self.ex_iterables)
rng.shuffle(ex_iterables)
ex_iterables = [ex_iterable.shuffle_data_sources(generator) for ex_iterable in ex_iterables]
return MixMultiSourcesExampleIterable(ex_iterables)
def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> "MixMultiSourceExampleIterable":
"""Shard the underlying iterables in a roundrobin manner.
Let's consider we have our iterables as [[s0_0, s0_1], [s1_0], [s2_0, s2_1, s2_3]],
and we request 3 shards.
index 0 gets s0_0 s2_0
index 1 gets s0_1 s2_1
index 2 gets s1_0 s2_3
"""
return MixMultiSourcesExampleIterable(
list(
islice(
# flatten all underlying iterables
chain.from_iterable([ex_iterable.shard_data_sources(1, 0) for ex_iterable in self.ex_iterables]),
# offset the starting point by the index
index,
# take over the full list, so exhaust the iterators
None,
# step by the number of shards requested
num_shards,
)
)
)
def mix_dataset(iterable_datasets: list[datasets.IterableDataset]) -> IterableDataset:
ex_iterable = MixMultiSourcesExampleIterable([ds._ex_iterable for ds in iterable_datasets])
return IterableDataset(
ex_iterable, distributed=iterable_datasets[0]._distributed, formatting=iterable_datasets[0]._formatting
)
```
### Questions
- Am I missing something? Is there a way to use `interleave_datasets` or `concatenate_datasets` to fit my purpose?
- Would it be the right approach to spread the maximum number of underlying shards across my different processes?
### Your contribution
As much as I can.
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fix: add `num_proc` argument to `Dataset.to_sql`
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NONE
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**Task Done:**
- Resolve issue #7788 : Add the missing argument mapping in Dataset.to_sql (`src/datasets/arrow_dataset.py`)
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update tips in docs
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fix link for rotten_tomatoes dataset
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The current link leads to a 404 page.
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`Dataset.to_sql` doesn't utilize `num_proc`
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2025-09-24 20:34:47+00:00
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2025-09-24 20:35:01+00:00
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NaT
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The underlying `SqlDatasetWriter` has `num_proc` as an available argument [here](https://github.com/huggingface/datasets/blob/5dc1a179783dff868b0547c8486268cfaea1ea1f/src/datasets/io/sql.py#L63) , but `Dataset.to_sql()` does not accept it, therefore it is always using one process for the SQL conversion.
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PR_kwDODunzps6qXRo-
| 7,787
|
feat: avoid some copies in torch formatter
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## perf: reduce copies in TorchFormatter
This PR make changes the torch formatter to avoid unnecessary copies and casts when converting decoded batches to tensors.
Because many arrays are already in a torch-friendly memory layout and dtype, we can do zero‑copy conversions (`torch.from_numpy`) and only fall back to `as_tensor` when a dtype/device change is required. We also consolidate lists of same‑shape tensors with a cheap `stack` only when safe.
Why it helps
- Avoids extra materialization and dtype churn during batched map and indexing.
- Preserves API and outputs; only changes internal conversion logic.
Small benchmark script (based on https://github.com/huggingface/datasets/issues/6104)
```python
import time
from datasets import load_dataset
def main():
dataset = load_dataset("NightMachinery/hf_datasets_bug1")
dataset = dataset["train"] if "train" in dataset else dataset
t0 = time.time()
dataset.set_format(type="torch")
# identity map with small batches
dataset = dataset.map(lambda x: x, batched=True, batch_size=20)
# force materialization
data = dataset[:300]
print(len(data.keys()))
t1 = time.time()
print(f"Duration: {t1 - t0:.2f} s")
if __name__ == "__main__":
main()
```
Without changes
```bash
uv run bench.py
```
```bash
# 303
# Duration: 7.26 s
```
With changes
```bash
uv run bench.py
```
```bash
# 303
# Duration: 4.43 s
```
# Updated reproduction scripts
Below are some simple test cases using `main` and this `refactor-torch-formatter` branch. I've included the two scripts and output when running on a local machine.
```python
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "torch",
# "datasets",
# "pillow",
# ]
#
# [tool.uv.sources]
# datasets = { git = "https://github.com/huggingface/datasets.git" }
# ///
import time
import random
import numpy as np
from PIL import Image
from datasets import Dataset, load_dataset
import torch
def create_mock_images_dataset(num_samples=5000):
"""Create a deterministic mock dataset with PIL images."""
random.seed(42)
np.random.seed(42)
images = []
labels = []
for i in range(num_samples):
# Create deterministic RGB image
width, height = 64, 64
rgb_array = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
image = Image.fromarray(rgb_array)
images.append(image)
labels.append(i % 10) # 10 classes
return Dataset.from_dict({"image": images, "label": labels})
def create_mock_text_dataset(num_samples=5000):
"""Create a deterministic mock dataset with text."""
random.seed(42)
words = ["apple", "banana", "cherry", "date", "elderberry", "fig", "grape", "honeydew"]
texts = []
labels = []
for i in range(num_samples):
# Create deterministic text
text_length = 5 + (i % 20) # 5-24 words
text = " ".join(random.choices(words, k=text_length))
texts.append(text)
labels.append(i % 3) # 3 classes
return Dataset.from_dict({"text": texts, "label": labels})
def create_mock_ints_dataset(num_samples=5000):
"""Create a deterministic mock dataset with integers."""
random.seed(42)
data = []
labels = []
for i in range(num_samples):
# Create deterministic integer arrays
arr = [random.randint(0, 1000) for _ in range(50)] # 50 integers each
data.append(arr)
labels.append(i % 5) # 5 classes
return Dataset.from_dict({"data": data, "label": labels})
def create_mock_floats_dataset(num_samples=5000):
"""Create a deterministic mock dataset with floats."""
random.seed(42)
data = []
labels = []
for i in range(num_samples):
# Create deterministic float arrays
arr = [random.uniform(0.0, 100.0) for _ in range(30)] # 30 floats each
data.append(arr)
labels.append(i % 4) # 4 classes
return Dataset.from_dict({"data": data, "label": labels})
def benchmark_dataset(name, dataset, num_samples=1000):
"""Benchmark dataset access speed."""
print(f"\n=== {name} Dataset Benchmark ===")
t0 = time.time()
dataset.set_format(type="torch")
# identity map with small batches
dataset = dataset.map(lambda x: x, batched=True, batch_size=20)
# force materialization
data = dataset[:num_samples]
print(f"Keys: {list(data.keys())}")
print(f"Sample count: {len(data[list(data.keys())[0]])}")
t1 = time.time()
print(f"Duration: {t1 - t0:.2f} s")
print(f"Speed: {num_samples / (t1 - t0):.1f} samples/s")
def main():
# PIL Images benchmark
images_dataset = create_mock_images_dataset()
benchmark_dataset("PIL Images", images_dataset)
# Text benchmark
text_dataset = create_mock_text_dataset()
benchmark_dataset("Text", text_dataset)
# Integers benchmark
ints_dataset = create_mock_ints_dataset()
benchmark_dataset("Integers", ints_dataset)
# Floats benchmark
floats_dataset = create_mock_floats_dataset()
benchmark_dataset("Floats", floats_dataset)
if __name__ == "__main__":
main()
```
output
```bash
uv run --refresh example1.py
```
```text
=== PIL Images Dataset Benchmark ===
Map: 0%| | 0/5000 [00:00<?, ? examples/s]/Users/drbh/.cache/uv/environments-v2/example1-2aca1a30e84bdead/lib/python3.10/site-packages/datasets/features/image.py:352: UserWarning: Downcasting array dtype int64 to uint8 to be compatible with 'Pillow'
warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'")
Map: 100%|█████████████████████████████████████████████| 5000/5000 [00:01<00:00, 3669.15 examples/s]
Keys: ['image', 'label']
Sample count: 1000
Duration: 2.14 s
Speed: 466.5 samples/s
=== Text Dataset Benchmark ===
Map: 100%|███████████████████████████████████████████| 5000/5000 [00:00<00:00, 141327.04 examples/s]
Keys: ['text', 'label']
Sample count: 1000
Duration: 0.04 s
Speed: 27004.3 samples/s
=== Integers Dataset Benchmark ===
Map: 100%|███████████████████████████████████████████| 5000/5000 [00:00<00:00, 112904.90 examples/s]
Keys: ['data', 'label']
Sample count: 1000
Duration: 0.05 s
Speed: 21680.6 samples/s
=== Floats Dataset Benchmark ===
Map: 100%|███████████████████████████████████████████| 5000/5000 [00:00<00:00, 104084.25 examples/s]
Keys: ['data', 'label']
Sample count: 1000
Duration: 0.05 s
Speed: 20215.1 samples/s
```
and this branch specifically
```python
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "torch",
# "datasets",
# "pillow",
# ]
#
# [tool.uv.sources]
# datasets = { git = "https://github.com/huggingface/datasets.git", rev = "refactor-torch-formatter" }
# ///
import time
import random
import numpy as np
from PIL import Image
from datasets import Dataset, load_dataset
import torch
def create_mock_images_dataset(num_samples=5000):
"""Create a deterministic mock dataset with PIL images."""
random.seed(42)
np.random.seed(42)
images = []
labels = []
for i in range(num_samples):
# Create deterministic RGB image
width, height = 64, 64
rgb_array = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
image = Image.fromarray(rgb_array)
images.append(image)
labels.append(i % 10) # 10 classes
return Dataset.from_dict({"image": images, "label": labels})
def create_mock_text_dataset(num_samples=5000):
"""Create a deterministic mock dataset with text."""
random.seed(42)
words = [
"apple",
"banana",
"cherry",
"date",
"elderberry",
"fig",
"grape",
"honeydew",
]
texts = []
labels = []
for i in range(num_samples):
# Create deterministic text
text_length = 5 + (i % 20) # 5-24 words
text = " ".join(random.choices(words, k=text_length))
texts.append(text)
labels.append(i % 3) # 3 classes
return Dataset.from_dict({"text": texts, "label": labels})
def create_mock_ints_dataset(num_samples=5000):
"""Create a deterministic mock dataset with integers."""
random.seed(42)
data = []
labels = []
for i in range(num_samples):
# Create deterministic integer arrays
arr = [random.randint(0, 1000) for _ in range(50)] # 50 integers each
data.append(arr)
labels.append(i % 5) # 5 classes
return Dataset.from_dict({"data": data, "label": labels})
def create_mock_floats_dataset(num_samples=5000):
"""Create a deterministic mock dataset with floats."""
random.seed(42)
data = []
labels = []
for i in range(num_samples):
# Create deterministic float arrays
arr = [random.uniform(0.0, 100.0) for _ in range(30)] # 30 floats each
data.append(arr)
labels.append(i % 4) # 4 classes
return Dataset.from_dict({"data": data, "label": labels})
def benchmark_dataset(name, dataset, num_samples=1000):
"""Benchmark dataset access speed."""
print(f"\n=== {name} Dataset Benchmark ===")
t0 = time.time()
dataset.set_format(type="torch")
# identity map with small batches
dataset = dataset.map(lambda x: x, batched=True, batch_size=20)
# force materialization
data = dataset[:num_samples]
print(f"Keys: {list(data.keys())}")
print(f"Sample count: {len(data[list(data.keys())[0]])}")
t1 = time.time()
print(f"Duration: {t1 - t0:.2f} s")
print(f"Speed: {num_samples / (t1 - t0):.1f} samples/s")
def main():
# PIL Images benchmark
images_dataset = create_mock_images_dataset()
benchmark_dataset("PIL Images", images_dataset)
# Text benchmark
text_dataset = create_mock_text_dataset()
benchmark_dataset("Text", text_dataset)
# Integers benchmark
ints_dataset = create_mock_ints_dataset()
benchmark_dataset("Integers", ints_dataset)
# Floats benchmark
floats_dataset = create_mock_floats_dataset()
benchmark_dataset("Floats", floats_dataset)
if __name__ == "__main__":
main()
```
```bash
uv run --refresh example2.py
```
```text
Updated https://github.com/huggingface/datasets.git (2cb64d1b6503afb49d822b20979760efe4519d03)
Built datasets @ git+https://github.com/huggingface/datasets.git@2cb64d1b6503afb49d822b20979760efe
Uninstalled 1 package in 20ms
Installed 1 package in 5ms
=== PIL Images Dataset Benchmark ===
Map: 0%| | 0/5000 [00:00<?, ? examples/s]/Users/drbh/.cache/uv/environments-v2/example2-d4af608668b706ec/lib/python3.10/site-packages/datasets/features/image.py:352: UserWarning: Downcasting array dtype int64 to uint8 to be compatible with 'Pillow'
warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'")
Map: 100%|█████████████████████████████████████████████| 5000/5000 [00:01<00:00, 3645.14 examples/s]
Keys: ['image', 'label']
Sample count: 1000
Duration: 2.04 s
Speed: 491.2 samples/s
=== Text Dataset Benchmark ===
Map: 100%|████████████████████████████████████████████████████| 5000/5000 [00:00<00:00, 169877.28 examples/s]
Keys: ['text', 'label']
Sample count: 1000
Duration: 0.03 s
Speed: 32236.1 samples/s
=== Integers Dataset Benchmark ===
Map: 100%|████████████████████████████████████████████████████| 5000/5000 [00:00<00:00, 131940.33 examples/s]
Keys: ['data', 'label']
Sample count: 1000
Duration: 0.04 s
Speed: 25493.3 samples/s
=== Floats Dataset Benchmark ===
Map: 100%|████████████████████████████████████████████████████| 5000/5000 [00:00<00:00, 120621.64 examples/s]
Keys: ['data', 'label']
Sample count: 1000
Duration: 0.04 s
Speed: 23370.6 samples/s
```
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PR_kwDODunzps6qPTgs
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Sample without replacement option when interleaving datasets
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Right now, `interleave_datasets` function with probabilities will sample with replacement. The PR adds the ability to sample without replacement.
```
import datasets
# Create datasets of different sizes to test exhaustion
data_a = [{"value": i, "source": "A"} for i in range(5)]
data_b = [{"value": i, "source": "B"} for i in range(10, 15)]
ds_a = datasets.Dataset.from_list(data_a).to_iterable_dataset()
ds_b = datasets.Dataset.from_list(data_b).to_iterable_dataset()
# Interleave with probabilities
ds_interleaved = datasets.interleave_datasets(
[ds_a, ds_b],
probabilities=[0.6, 0.4],
seed=42,
stopping_strategy="all_exhausted",
sample_with_replacement=True,
)
for i, example in enumerate(ds_interleaved):
print(f"Sample:{i}: value:{example['value']:02d} source:{example['source']}")
```
In this example, `sample_with_replacement=True` and it prints:
```
Sample:0: value:10 source:B
Sample:1: value:00 source:A
Sample:2: value:11 source:B
Sample:3: value:12 source:B
Sample:4: value:01 source:A
Sample:5: value:13 source:B
Sample:6: value:14 source:B
Sample:7: value:10 source:B
Sample:8: value:02 source:A
Sample:9: value:03 source:A
Sample:10: value:04 source:A
```
Note that sample with value:10 source: B is sampled twice (Sample:0 and Sample:7)
Re-running with `sample_with_replacement=False` in prints:
```
Sample:0: value:10 source:B
Sample:1: value:00 source:A
Sample:2: value:11 source:B
Sample:3: value:12 source:B
Sample:4: value:01 source:A
Sample:5: value:13 source:B
Sample:6: value:14 source:B
Sample:7: value:02 source:A
Sample:8: value:03 source:A
Sample:9: value:04 source:A
```
Note that we don't see any repeated items.
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PR_kwDODunzps6pyTM_
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Fix Audio docstring by removing unsupported mono argument
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2025-09-23 09:57:37+00:00
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NaT
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This PR fixes issue #7745.
Who can review:
@lhoestq
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Support huggingface_hub v0.x and v1.x
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Related to https://github.com/huggingface/huggingface_hub/issues/3340.
This PR adapts `datasets` to be compatible with both huggingface_hub v0.x and v1.x.
In practice nothing else should change (I've checked the codebase). The `HfHubHTTPError` is a base error defined in `huggingface_hub` that inherits from `requests.HTTPError` in v0.x and will inherit from `httpx.HTTPError` in v1.x. It has been introduced ~2 years ago so it's fine to use it right now (i.e. no need to wait for v1.x release or bump minimal version).
Most of the changes have been around the test suite to make sure that tests are passing with both `requests` and `httpx` backends. Mid-term it would be good to completely remove the `requests` dependency from `datasets` but that's an orthogonal topic.
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set dev version
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release: 4.1.1
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BIGPATENT dataset inaccessible (deprecated script loader)
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NONE
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dataset: https://huggingface.co/datasets/NortheasternUniversity/big_patent
When I try to load it with the datasets library, it fails with:
RuntimeError: Dataset scripts are no longer supported, but found big_patent.py
Could you please publish a Parquet/Arrow export of BIGPATENT on the Hugging Face so that it can be accessed with datasets>=4.x.
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fix empty dataset to_parquet
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| 3,425,917,119
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PR_kwDODunzps6pDkX-
| 7,778
|
[FIX] force spawning pool for MacOS
|
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2025-09-17 11:38:38+00:00
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2025-09-18 17:04:45+00:00
|
NaT
|
NONE
| null | null | 1
|
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This PR gets multiprocessing to work on mac os:
```python
from datasets import load_dataset
ds = load_dataset("fka/awesome-chatgpt-prompts", split="train").take(100)
ds = ds.map(lambda x: x, num_proc=4)
ds.push_to_hub("burtenshaw/dataset-test", num_proc=4)
```
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I_kwDODunzps7MHSUC
| 7,777
|
push_to_hub not overwriting but stuck in a loop when there are existing commits
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2025-09-17 19:31:14+00:00
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2025-09-17 19:31:14+00:00
|
NONE
| null | null | null | null |
### Describe the bug
`get_deletions_and_dataset_card` stuck at error a commit has happened error since push to hub for http error 412 for tag 4.1.0. The error does not exists in 4.0.0.
### Steps to reproduce the bug
Create code to use push_to_hub, ran twice each time with different content for datasets.Dataset.
The code will stuck in time.sleep loop for `get_deletions_and_dataset_card`. If error is explicitly printed, the error is HTTP 412.
### Expected behavior
New datasets overwrite existing one on repo.
### Environment info
datasets 4.1.0
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PR_kwDODunzps6ow4yI
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[docs] Fix broken WebDataset link on “Create a video dataset” page
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2025-09-16 04:49:32+00:00
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2025-09-27 12:03:49+00:00
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|
NONE
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### What
Fix the "WebDataset documentation" link on the Create a video dataset page to point
to the correct section on the video load guide.
### Why
The link currently points to an external repo, but the Hugging Face docs
have an internal "WebDataset" section under video_load.
### How
- docs/source/video_dataset.mdx: updated link to
`https://huggingface.co/docs/datasets/main/en/video_load#webdataset`
### Issue
Fixes #7699
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PR_kwDODunzps6or2J2
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fix iterate nested field
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Set dev version
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Release: 4.1.0
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Error processing scalar columns using tensorflow.
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`datasets==4.0.0`
```
columns_to_return = ['input_ids','attention_mask', 'start_positions', 'end_positions']
train_ds.set_format(type='tf', columns=columns_to_return)
```
`train_ds`:
```
train_ds type: <class 'datasets.arrow_dataset.Dataset'>, shape: (1000, 9)
columns: ['question', 'sentences', 'answer', 'str_idx', 'end_idx', 'input_ids', 'attention_mask', 'start_positions', 'end_positions']
features:{'question': Value('string'), 'sentences': Value('string'), 'answer': Value('string'), 'str_idx': Value('int64'), 'end_idx': Value('int64'), 'input_ids': List(Value('int32')), 'attention_mask': List(Value('int8')), 'start_positions': Value('int64'), 'end_positions': Value('int64')}
```
`train_ds_tensor = train_ds['start_positions'].to_tensor(shape=(-1,1))` hits the following error:
```
AttributeError: 'Column' object has no attribute 'to_tensor'
```
`tf.reshape(train_ds['start_positions'], shape=[-1,1])` hits the following error:
```
TypeError: Scalar tensor has no `len()`
```
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Add support for arrow iterable when concatenating or interleaving
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Fixes a case when concatenating or interleaving datasets with `with_format(...)` call was slower.
Details here: https://github.com/huggingface/datasets/issues/6637
@lhoestq I tried to minimize the duplication between iter and iter_arrow methods, not sure if this is against the design, can separate those if needed.
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Fix: Correct float feature generation in `generate_examples`
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This PR fixes a bug in the `generate_examples` function where `datasets.Value` features with a `float` dtype were incorrectly generated using `np.random.randint`. This resulted in integer values being cast to float, which is not representative of true floating-point data.
**Key changes include:**
* Added explicit handling for `float` features using `np.random.rand` to generate continuous values.
* Introduced fail-fast type checks for unsupported dtypes to improve robustness.
* Added validation for sequence features to ensure `seq_shapes` is provided.
### Before Fix
Float features were generated incorrectly as integers cast to float:
```text
- Example 0:
- int_feature: 0
- float_feature: 9.0 <-- Incorrect: An integer disguised as a float
- string_feature: The small grey turtle was surprisingly fast...
- seq_feature: [0.3048 0.4291 0.4283]
```
### After Fix
Float features are now correctly generated as continuous numbers in the range [0, 1):
```text
+ Example 0:
+ int_feature: 0
+ float_feature: 0.0183 <-- Correct: A true random float
+ string_feature: The small grey turtle was surprisingly fast...
+ seq_feature: [0.9237 0.7972 0.8526]
```
#### Note: This PR is a follow-up/fix of the previously closed PR #7769 for clarity and context.
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Fix: Correct float feature generation in `generate_examples`
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This PR fixes a bug in the `generate_examples` function where `datasets.Value` features with a `float` dtype were incorrectly generated using `np.random.randint`. This resulted in integer values being cast to float, which is not representative of true floating-point data.
**Key changes include:**
1. Added explicit handling for float features using `np.random.rand` to generate continuous values.
2. Introduced fail-fast type checks for unsupported dtypes to improve robustness.
3. Added validation for sequence features to ensure `seq_shapes` is provided.
### Before Fix
Float features were generated incorrectly as integers cast to float:
```text
Example 0:
int_feature: 0
float_feature: 9.0 <-- Incorrect: An integer disguised as a float
string_feature: The small grey turtle was surprisingly fast...
seq_feature: [0.3048 0.4291 0.4283]
```
### After Fix
Float features are now correctly generated as continuous numbers in the range [0, 1):
```text
Example 0:
int_feature: 0
float_feature: 0.0183 <-- Correct: A true random float
string_feature: The small grey turtle was surprisingly fast...
seq_feature: [0.9237 0.7972 0.8526]
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Fix #7767
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2025-09-12 19:06:23+00:00
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2025-09-12 19:07:52+00:00
|
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|
NONE
| null | null | null | null |
### Feature request
https://github.com/huggingface/datasets/blob/4.0.0/src/datasets/load.py#L1411-L1418
```
def load_dataset(
...
dl_manager: Optional[DownloadManager] = None, # add this new argument
**config_kwargs,
) -> Union[DatasetDict, Dataset, IterableDatasetDict, IterableDataset]:
...
# Create a dataset builder
builder_instance = load_dataset_builder(
path=path,
name=name,
data_dir=data_dir,
data_files=data_files,
cache_dir=cache_dir,
features=features,
download_config=download_config,
download_mode=download_mode,
revision=revision,
token=token,
storage_options=storage_options,
**config_kwargs,
)
# Return iterable dataset in case of streaming
if streaming:
return builder_instance.as_streaming_dataset(split=split)
# Note: This is the revised part
if dl_manager is None:
if download_config is None:
download_config = DownloadConfig(
cache_dir=builder_instance._cache_downloaded_dir,
force_download=download_mode == DownloadMode.FORCE_REDOWNLOAD,
force_extract=download_mode == DownloadMode.FORCE_REDOWNLOAD,
use_etag=False,
num_proc=num_proc,
token=builder_instance.token,
storage_options=builder_instance.storage_options,
) # We don't use etag for data files to speed up the process
dl_manager = DownloadManager(
dataset_name=builder_instance.dataset_name,
download_config=download_config,
data_dir=builder_instance.config.data_dir,
record_checksums=(
builder_instance._record_infos or verification_mode == VerificationMode.ALL_CHECKS
),
)
# Download and prepare data
builder_instance.download_and_prepare(
download_config=download_config,
download_mode=download_mode,
verification_mode=verification_mode,
dl_manager=dl_manager, # pass the new argument
num_proc=num_proc,
storage_options=storage_options,
)
...
```
### Motivation
In my case, I'm hoping to deal with the cache files downloading manually (not using hash filenames and save to another location, or using potential existing local files).
### Your contribution
It's already implemented above. If maintainers think this should be considered, I'll open a PR.
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| 3,411,611,165
|
I_kwDODunzps7LWQ4d
| 7,766
|
cast columns to Image/Audio/Video with `storage_options`
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2025-09-12 18:51:01+00:00
|
2025-09-27 08:14:47+00:00
|
NaT
|
NONE
| null | null | null | null |
### Feature request
Allow `storage_options` to be passed in
1. `cast` related operations (e.g., `cast_columns, cast`)
2. `info` related reading (e.g., `from_dict, from_pandas, from_polars`) together with `info.features`
```python3
import datasets
image_path = "s3://bucket/sample.png"
dataset = datasets.Dataset.from_dict({"image_path": [image_path]})
# dataset = dataset.cast_column("image_path", datasets.Image()) # now works without `storage_options`
# expected behavior
dataset = dataset.cast_column("image_path", datasets.Image(), storage_options={"anon": True})
```
### Motivation
I'm using my own registered fsspec filesystem (s3 with customized local cache support). I need to pass cache folder paths `cache_dirs: list[str]` to the filesystem when I read the remote images (cast from file_paths).
### Your contribution
Could help with a PR at weekends
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I_kwDODunzps7LWDga
| 7,765
|
polars dataset cannot cast column to Image/Audio/Video
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2025-09-12 18:32:49+00:00
|
2025-10-13 14:39:48+00:00
|
2025-10-13 14:39:48+00:00
|
NONE
| null | null | null | null |
### Describe the bug
`from_polars` dataset cannot cast column to Image/Audio/Video, while it works on `from_pandas` and `from_dict`
### Steps to reproduce the bug
```python3
import datasets
import pandas as pd
import polars as pl
image_path = "./sample.png"
# polars
df = pl.DataFrame({"image_path": [image_path]})
dataset = datasets.Dataset.from_polars(df)
dataset = dataset.cast_column("image_path", datasets.Image())
# # raises Error
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from large_string to struct using function cast_struct
# pandas
df = pd.DataFrame({"image_path": [image_path]})
dataset = datasets.Dataset.from_pandas(df)
dataset = dataset.cast_column("image_path", datasets.Image())
# # pass
{'image_path': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=338x277 at 0x7FBA719D4050>}
# dict
dataset = datasets.Dataset.from_dict({"image_path": [image_path]})
dataset = dataset.cast_column("image_path", datasets.Image())
# # pass
{'image_path': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=338x277 at 0x7FBA719D4050>}
```
### Expected behavior
`from_polars` case shouldn't raise error and have the same outputs as `from_pandas` and `from_dict`
### Environment info
```
# Name Version Build Channel
datasets 4.0.0 pypi_0 pypi
pandas 2.3.1 pypi_0 pypi
polars 1.32.3 pypi_0 pypi
```
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PR_kwDODunzps6oQltc
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update torchcodec in ci
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before the release, to make sure everything works fine
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PR_kwDODunzps6oGx51
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Bump dill to 0.4.0
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This bumps `dill` to 0.3.9 and closes #7510
It turns out the only thing required to make the tests pass was to extend the version checks to include 0.3.9.
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Parquet: use data page v2 for efficient page pruning
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2025-09-11 14:42:22+00:00
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MEMBER
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This is needed to enable page pruning with DataFusion, which will be useful for the Dataset Viewer.
Indeed page pruning with DataFusion allows to download only certain pages of a row group, reducing the I/O required to read just a few rows.
But while data page v1 generally works, it's not easy with DataFusion to do page pruning on datasets with nested data. This is because rows can span multiple pages in v1, contrary to v2.
cc @severo for viz
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PR_kwDODunzps6n1bls
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Audio: use TorchCodec instead of Soundfile for encoding
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2025-09-10 14:47:07+00:00
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this removes the dependency on Soundfile completely
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I_kwDODunzps7Kw1c9
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Hugging Face Hub Dataset Upload CAS Error
|
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2025-09-10 10:01:19+00:00
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2025-09-16 20:01:36+00:00
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NaT
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NONE
| null | null | null | null |
### Describe the bug
Experiencing persistent 401 Unauthorized errors when attempting to upload datasets to Hugging Face Hub using the `datasets` library. The error occurs specifically with the CAS (Content Addressable Storage) service during the upload process. Tried using HF_HUB_DISABLE_XET=1. It seems to work for smaller files.
Exact error message :
```
Processing Files (0 / 0) : | | 0.00B / 0.00B 2025-09-10T09:44:35.657565Z ERROR Fatal Error: "cas::upload_xorb" api call failed (request id 01b[...]XXX): HTTP status client error (401 Unauthorized) for url (https://cas-server.xethub.hf.co/xorb/default/7f3abdc[...]XXX)
at /home/runner/work/xet-core/xet-core/cas_client/src/retry_wrapper.rs:113
Processing Files (0 / 0) : 0%| | 0.00B / 184kB, 0.00B/s
New Data Upload : 0%| | 0.00B / 184kB, 0.00B/s
❌ Failed to push some_dataset: Data processing error: CAS service error : Reqwest Error: HTTP status client error (401 Unauthorized), domain: https://cas-server.xethub.hf.co/xorb/default/7f3abdc[...]XXX
```
Workaround Attempts
1. **Disabled XET**: Set `HF_HUB_DISABLE_XET=1` environment variable
2. **Updated hf-xet**: Use `hf-xet==1.1.9` rather than latest
3. **Verified Authentication**: Confirmed HF token is valid and has write permissions
4. **Tested with Smaller Datasets**:
- 100 samples: ✅ **SUCCESS** (uploaded successfully)
- 10,000 samples: ❌ **FAILS** (401 Unauthorized)
### Steps to reproduce the bug
```python
from datasets import Dataset, DatasetDict
# Create dataset (example with 10,000 samples)
dataset = Dataset.from_dict({
"question": questions,
"answer": answers,
# ... other fields
})
# Split into train/test
dataset_dict = dataset.train_test_split(test_size=0.1)
# Upload to Hub
dataset_dict.push_to_hub("Org/some-dataset")
```
### Expected behavior
## Expected Behavior
- Dataset should upload successfully to Hugging Face Hub
- Progress bars should complete without authentication errors
- Dataset should be accessible at the specified repository URL
## Actual Behavior
- Upload fails consistently with 401 Unauthorized error
- Error occurs specifically during CAS service interaction
- No progress is made on the upload (0% completion)
- Dataset is created on Hugging Face Hub with no data folder
### Environment info
- **Platform**: SageMaker (AWS)
- **Python Version**: 3.12
- **Libraries**:
- `datasets` library (latest version)
- `hf-xet==1.1.9` (attempted fix)
- **Authentication**: Hugging Face token configured
- **Dataset Size**: ~10,000 samples, works for smaller sizes (e.g. 100)
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I_kwDODunzps7KiuI5
| 7,759
|
Comment/feature request: Huggingface 502s from GHA
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2025-09-09 11:59:20+00:00
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2025-09-09 13:02:28+00:00
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NaT
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NONE
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This is no longer a pressing issue, but for completeness I am reporting that in August 26th, GET requests to `https://datasets-server.huggingface.co/info\?dataset\=livebench/math` were returning 502s when invoked from [github actions](https://github.com/UKGovernmentBEIS/inspect_evals/actions/runs/17241892475/job/48921123754) (that link will expire eventually, [here are the logs](https://github.com/user-attachments/files/22233578/logs_44225296943.zip)).
When invoked from actions, it appeared to be consistently failing for ~6 hours. However, these 502s never occurred when the request was invoked from my local machine in that same time period.
I suspect that this is related to how the requests are routed with github actions versus locally.
Its not clear to me if the request even reached huggingface servers or if its the github proxy that stopped it from going through, but I wanted to report it nonetheless in case this is helpful information. I'm curious if huggingface can do anything on their end to confirm cause.
And a feature request for if this happens in the future (assuming huggingface has visibilty on it): A "datasets status" page highlighting if 502s occur for specific individual datasets could be useful for people debugging on the other end of this!
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| 7,758
|
Option for Anonymous Dataset link
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2025-09-08 20:20:10+00:00
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2025-09-08 20:20:10+00:00
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NaT
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NONE
| null | null | null | null |
### Feature request
Allow for anonymized viewing of datasets. For instance, something similar to [Anonymous GitHub](https://anonymous.4open.science/).
### Motivation
We generally publish our data through Hugging Face. This has worked out very well as it's both our repository and archive (thanks to the DOI feature!). However, we have an increasing challenge when it comes to sharing our datasets for paper (both conference and journal) submissions. Due to the need to share data anonymously, we can't use the Hugging Face URLs, but datasets tend to be too large for inclusion as a zip. Being able to have an anonymous link would be great since we can't be double-publishing the data.
### Your contribution
Sorry, I don't have a contribution to make to the implementation of this. Perhaps it would be possible to work off the [Anonymous GitHub](https://github.com/tdurieux/anonymous_github) code to generate something analogous with pointers to the data still on Hugging Face's servers (instead of the duplication of data required for the GitHub version)?
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Add support for `.conll` file format in datasets
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2025-09-06 07:25:39+00:00
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2025-09-10 14:22:48+00:00
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NaT
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NONE
| null | null | null | null |
### Feature request
I’d like to request native support in the Hugging Face datasets library for reading .conll files (CoNLL format). This format is widely used in NLP tasks, especially for Named Entity Recognition (NER), POS tagging, and other token classification problems.
Right now `.conll` datasets need to be manually parsed or preprocessed before being loaded into datasets. Having built in support would save time and make workflows smoother for researchers and practitioners.
I propose -
Add a conll dataset builder or file parser to datasets that can:
- Read `.conll` files with customizable delimiters (space, tab).
- Handle sentence/document boundaries (typically indicated by empty lines).
- Support common CoNLL variants (e.g., CoNLL-2000 chunking, CoNLL-2003 NER).
- Output a dataset where each example contains:
- tokens: list of strings
- tags (or similar): list of labels aligned with tokens
Given a .conll snippet like:
```
EU NNP B-ORG
rejects VBZ O
German JJ B-MISC
call NN O
. . O
```
The dataset should load as:
```
{
"tokens": ["EU", "rejects", "German", "call", "."],
"tags": ["B-ORG", "O", "B-MISC", "O", "O"]
}
```
### Motivation
- CoNLL files are a standard benchmark format in NLP (e.g., CoNLL-2003, CoNLL-2000).
- Many users train NER or sequence labeling models (like BERT for token classification) directly on `.conll`
- Right now you have to write your own parsing scripts. Built in support would unify this process and would be much more convenient
### Your contribution
I’d be happy to contribute by implementing this feature. My plan is to-
- Add a new dataset script (conll.py) to handle .conll files.
- Implement parsing logic that supports sentence/document boundaries and token-label alignment.
- Write unit tests with small `.conll` examples to ensure correctness.
- Add documentation and usage examples so new users can easily load `.conll` datasets.
This would be my first open source contribution, so I’ll follow the `CONTRIBUTING.md` guidelines closely and adjust based on feedback from the maintainers.
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I_kwDODunzps7J4rBV
| 7,756
|
datasets.map(f, num_proc=N) hangs with N>1 when run on import
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2025-09-05 10:32:01+00:00
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2025-09-05 10:32:01+00:00
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### Describe the bug
If you `import` a module that runs `datasets.map(f, num_proc=N)` at the top-level, Python hangs.
### Steps to reproduce the bug
1. Create a file that runs datasets.map at the top-level:
```bash
cat <<EOF > import_me.py
import datasets
the_dataset = datasets.load_dataset("openai/openai_humaneval")
the_dataset = the_dataset.map(lambda item: item, num_proc=2)
EOF
```
2. Start Python REPL:
```bash
uv run --python 3.12.3 --with "datasets==4.0.0" python3
Python 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
```
3. Import the file:
```python
import import_me
````
Observe hang.
### Expected behavior
Ideally would not hang, or would fallback to num_proc=1 with a warning.
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-6.14.0-29-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.34.4
- PyArrow version: 21.0.0
- Pandas version: 2.3.2
- `fsspec` version: 2025.3.0
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PR_kwDODunzps6m-MTU
| 7,755
|
Support pathlib.Path for feature input
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2025-09-05 02:38:07+00:00
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2025-09-10 15:19:35+00:00
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CONTRIBUTOR
| null | null | 0
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This PR adds support for specifying image, video, audio, and pdf features using `pathlib.Path`.
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PR_kwDODunzps6m6qRo
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|
Add columns support to JSON loader
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2025-09-04 18:21:26+00:00
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NaT
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CONTRIBUTOR
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New fix to #7594
This PR adds support for the columns argument in the JSON dataset builder.
Added columns parameter to JsonConfig.
Applied column filtering after table creation, filling missing columns with None.
Extended tests to cover:
- Selecting a subset of columns
- Handling missing requested columns
- Column selection on list-of-strings case
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I_kwDODunzps7Jkqc_
| 7,753
|
datasets massively slows data reads, even in memory
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2025-09-04 01:45:24+00:00
|
2025-09-18 22:08:51+00:00
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NONE
| null | null | null | null |
### Describe the bug
Loading image data in a huggingface dataset results in very slow read speeds, approximately 1000 times longer than reading the same data from a pytorch dataset. This applies even when the dataset is loaded into RAM using a `keep_in_memory=True` flag.
The following script reproduces the result with random data, but it applies equally to datasets that are loaded from the hub.
### Steps to reproduce the bug
The following script should reproduce the behavior
```
import torch
import time
from datasets import Dataset
images = torch.randint(0, 255, (1000, 3, 224, 224), dtype=torch.uint8)
labels = torch.randint(0, 200, (1000,), dtype=torch.uint8)
pt_dataset = torch.utils.data.TensorDataset(images, labels)
hf_dataset = Dataset.from_dict({'image': images, 'label':labels})
hf_dataset.set_format('torch', dtype=torch.uint8)
hf_in_memory = hf_dataset.map(lambda x: x, keep_in_memory=True)
# measure access speeds
def time_access(dataset, img_col):
start_time = time.time()
for i in range(1000):
_ = dataset[i][img_col].shape
end_time = time.time()
return end_time - start_time
print(f"In-memory Tensor access: {time_access(pt_dataset, 0):.4f} seconds")
print(f"HF Dataset access: {time_access(hf_dataset, 'image'):.4f} seconds")
print(f"In-memory HF Dataset access: {time_access(hf_in_memory, 'image'):.4f} seconds")
```
### Expected behavior
For me, the above script produces
```
In-memory Tensor access: 0.0025 seconds
HF Dataset access: 2.9317 seconds
In-memory HF Dataset access: 2.8082 seconds
```
I think that this difference is larger than expected.
### Environment info
- `datasets` version: 4.0.0
- Platform: macOS-14.7.7-arm64-arm-64bit
- Python version: 3.12.11
- `huggingface_hub` version: 0.34.3
- PyArrow version: 18.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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PR_kwDODunzps6ljQLy
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|
Fix: Update Dill Version in Setup py
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Currently the DIll version is less than 3.9 and now major libraries like Multiprocess, gepa requires Dill version as 0.4.0 and this is making a conflict in installation. So added this small PR to update the DIll.
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I_kwDODunzps7ILKi4
| 7,751
|
Dill version update
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2025-08-27 07:38:30+00:00
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2025-09-10 14:24:02+00:00
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NaT
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NONE
| null | null | null | null |
### Describe the bug
Why the datasets is not updating the dill ?
Just want to know if I update the dill version in dill what will be the repucssion.
For now in multiplaces I have to update the library like process requirequire dill 0.4.0 so why not datasets.
Adding a pr too.
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
.
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PR_kwDODunzps6lfwcx
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Refactor: use unpacking in load.py for time and memory improvement
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In `src/datasets/load.py`, we can use unpacking rather than concatenating two lists for improved time and memory performance. It’s a small improvement in absolute terms, but a consistent and measurable one:
```diff
- ALL_ALLOWED_EXTENSIONS = list(_EXTENSION_TO_MODULE.keys()) + [".zip"]
+ ALL_ALLOWED_EXTENSIONS = [*_EXTENSION_TO_MODULE.keys(), ".zip"]
```
Benchmarking shows approximately 32.3% time improvement and 30.6% memory improvement.
Example benchmarking script:
```python
#!/usr/bin/env python3
"""
Benchmark script to test performance of list(_EXTENSION_TO_MODULE.keys()) vs [*_EXTENSION_TO_MODULE.keys()]
"""
import time
import tracemalloc
from statistics import mean, stdev
# Simulate _EXTENSION_TO_MODULE - based on actual size from datasets
_EXTENSION_TO_MODULE = {
f".ext{i}": f"module{i}" for i in range(20) # Realistic size
}
def method_old():
"""Current implementation using list()"""
return list(_EXTENSION_TO_MODULE.keys()) + [".zip"]
def method_new():
"""Proposed implementation using unpacking"""
return [*_EXTENSION_TO_MODULE.keys(), ".zip"]
def benchmark_time(func, iterations=100000):
"""Benchmark execution time"""
times = []
for _ in range(10): # Multiple runs for accuracy
start = time.perf_counter()
for _ in range(iterations):
func()
end = time.perf_counter()
times.append((end - start) / iterations * 1_000_000) # microseconds
return mean(times), stdev(times)
def benchmark_memory(func):
"""Benchmark peak memory usage"""
tracemalloc.start()
func()
current, peak = tracemalloc.get_traced_memory()
tracemalloc.stop()
return peak
if __name__ == "__main__":
print("Benchmarking list() vs unpacking performance...\n")
# Time benchmarks
old_time, old_std = benchmark_time(method_old)
new_time, new_std = benchmark_time(method_new)
print(f"Time Performance (µs per operation):")
print(f" list() approach: {old_time:.3f} ± {old_std:.3f}")
print(f" unpacking approach: {new_time:.3f} ± {new_std:.3f}")
print(f" Improvement: {((old_time - new_time) / old_time * 100):.1f}% faster")
# Memory benchmarks
old_mem = benchmark_memory(method_old)
new_mem = benchmark_memory(method_new)
print(f"\nMemory Usage (bytes):")
print(f" list() approach: {old_mem}")
print(f" unpacking approach: {new_mem}")
print(f" Reduction: {old_mem - new_mem} bytes ({((old_mem - new_mem) / old_mem * 100):.1f}% less)")
# Verify identical results
assert method_old() == method_new(), "Results should be identical!"
print(f"\n✓ Both methods produce identical results")
```
Results:
```
Benchmarking list() vs unpacking performance...
Time Performance (µs per operation):
list() approach: 0.213 ± 0.020
unpacking approach: 0.144 ± 0.002
Improvement: 32.3% faster
Memory Usage (bytes):
list() approach: 392
unpacking approach: 272
Reduction: 120 bytes (30.6% less)
✓ Both methods produce identical results
```
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Fix typo in error message for cache directory deletion
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2025-08-26 17:47:22+00:00
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2025-09-12 15:43:08+00:00
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2025-09-12 13:22:18+00:00
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This PR fixes a small typo in an error message in `src/datasets/fingerprint.py`:
https://github.com/huggingface/datasets/blob/910fab20606893f69b4fccac5fcc883dddf5a14d/src/datasets/fingerprint.py#L63
```diff
- occured
+ occurred
```
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PR_kwDODunzps6k-adX
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docs: Streaming best practices
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2025-08-23 00:18:43+00:00
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2025-09-07 02:33:36+00:00
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NaT
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NONE
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Add a new 'Streaming best practices' page with practical patterns and pitfalls for large-scale/production use of IterableDataset. Includes examples for batched map with remove_columns, deterministic shuffling with set_epoch, multi-worker sharding, checkpoint/resume, and persistence to Parquet/Hub. Linked from How-to > General usage, next to Stream.
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PR_kwDODunzps6k-Rtd
| 7,747
|
Add wikipedia-2023-redirects dataset
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NONE
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Title: Add wikipedia-2023-redirects dataset (redirect resolution + pageviews)
Summary
- New dataset loader: wikipedia_2023_redirects
- Canonical Wikipedia pages enriched with:
- redirects (aliases pointing to the page)
- 2023 pageviews (aggregated)
- Streaming support; robust parsing; license notes included
- Tests with tiny dummy data (XML + TSVs); covers streaming
Motivation
RAG/retrieval often benefits from:
- Query expansion via redirect aliases
- Popularity prior via pageviews
This loader offers a practical, maintenance-light way to access canonical pages alongside their redirect aliases and 2023 pageview totals.
Features
- id: string
- title: string
- url: string
- text: string
- redirects: list[string]
- pageviews_2023: int32
- timestamp: string
Licensing
- Wikipedia text: CC BY-SA 3.0 (attribution and share-alike apply)
- Pageviews: public domain
The PR docs mention both, and the module docstring cites sources.
Notes
- The URLs in _get_urls_for_config are wired to dummy files for tests. In production, these would point to Wikimedia dumps:
- XML page dumps: https://dumps.wikimedia.org/
- Pageviews: https://dumps.wikimedia.org/other/pageviews/
- The schema is intentionally simple and stable. Pageview aggregation is per-title sum across 2023.
Testing
- make style && make quality
- pytest -q tests/test_dataset_wikipedia_2023_redirects.py
Example
```python
from datasets import load_dataset
ds = load_dataset("wikipedia_2023_redirects", split="train")
print(ds[0]["title"], ds[0]["redirects"][:5], ds[0]["pageviews_2023"])
```
Acknowledgements
- Wikipedia/Wikimedia Foundation for the source data
- Hugging Face Datasets for the dataset infrastructure
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I_kwDODunzps7HZp5r
| 7,746
|
Fix: Canonical 'multi_news' dataset is broken and should be updated to a Parquet version
|
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2025-08-22 12:52:03+00:00
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2025-08-27 20:23:35+00:00
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Hi,
The canonical `multi_news` dataset is currently broken and fails to load. This is because it points to the [alexfabri/multi_news](https://huggingface.co/datasets/alexfabbri/multi_news) repository, which contains a legacy loading script (`multi_news.py`) that requires the now-removed `trust_remote_code` parameter.
The original maintainer's GitHub and Hugging Face repositories appear to be inactive, so a community-led fix is needed.
I have created a working fix by converting the dataset to the modern Parquet format, which does not require a loading script. The fixed version is available here and loads correctly:
**[Awesome075/multi_news_parquet](https://huggingface.co/datasets/Awesome075/multi_news_parquet)**
Could the maintainers please guide me or themselves update the official `multi_news` dataset to use this working Parquet version? This would involve updating the canonical pointer for "multi_news" to resolve to the new repository.
This action would fix the dataset for all users and ensure its continued availability.
Thank you!
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I_kwDODunzps7HZQZ1
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|
Audio mono argument no longer supported, despite class documentation
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2025-08-22 12:15:41+00:00
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2025-08-24 18:22:41+00:00
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NaT
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NONE
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### Describe the bug
Either update the documentation, or re-introduce the flag (and corresponding logic to convert the audio to mono)
### Steps to reproduce the bug
Audio(sampling_rate=16000, mono=True) raises the error
TypeError: Audio.__init__() got an unexpected keyword argument 'mono'
However, in the class documentation, is says:
Args:
sampling_rate (`int`, *optional*):
Target sampling rate. If `None`, the native sampling rate is used.
mono (`bool`, defaults to `True`):
Whether to convert the audio signal to mono by averaging samples across
channels.
[...]
### Expected behavior
The above call should either work, or the documentation within the Audio class should be updated
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-5.15.0-124-generic-x86_64-with-glibc2.35
- Python version: 3.12.11
- `huggingface_hub` version: 0.34.4
- PyArrow version: 21.0.0
- Pandas version: 2.3.2
- `fsspec` version: 2025.3.0
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I_kwDODunzps7HSeye
| 7,744
|
dtype: ClassLabel is not parsed correctly in `features.py`
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2025-08-21 23:28:50+00:00
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2025-09-10 15:23:41+00:00
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2025-09-10 15:23:41+00:00
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NONE
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`dtype: ClassLabel` in the README.md yaml metadata is parsed incorrectly and causes the data viewer to fail.
This yaml in my metadata ([source](https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/README.md), though i changed `ClassLabel` to `string` to using different dtype in order to avoid the error):
```yaml
license: mit
pretty_name: BrentLab Yeast Genome Resources
size_categories:
- 1K<n<10K
language:
- en
dataset_info:
features:
- name: start
dtype: int32
description: Start coordinate (1-based, **inclusive**)
- name: end
dtype: int32
description: End coordinate (1-based, **inclusive**)
- name: strand
dtype: ClassLabel
...
```
is producing the following error in the data viewer:
```
Error code: ConfigNamesError
Exception: ValueError
Message: Feature type 'Classlabel' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'List', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
return HubDatasetModuleFactory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 605, in get_module
dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 386, in from_dataset_card_data
dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 317, in _from_yaml_dict
yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2027, in _from_yaml_list
return cls.from_dict(from_yaml_inner(yaml_data))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1872, in from_dict
obj = generate_from_dict(dic)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1465, in generate_from_dict
raise ValueError(f"Feature type '{_type}' not found. Available feature types: {list(_FEATURE_TYPES.keys())}")
ValueError: Feature type 'Classlabel' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'List', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf']
```
I think that this is caused by this line
https://github.com/huggingface/datasets/blob/896616c6cb03d92a33248c3529b0796cda27e955/src/datasets/features/features.py#L2013
Reproducible example from [naming.py](https://github.com/huggingface/datasets/blob/896616c6cb03d92a33248c3529b0796cda27e955/src/datasets/naming.py)
```python
import itertools
import os
import re
_uppercase_uppercase_re = re.compile(r"([A-Z]+)([A-Z][a-z])")
_lowercase_uppercase_re = re.compile(r"([a-z\d])([A-Z])")
_single_underscore_re = re.compile(r"(?<!_)_(?!_)")
_multiple_underscores_re = re.compile(r"(_{2,})")
_split_re = r"^\w+(\.\w+)*$"
def snakecase_to_camelcase(name):
"""Convert snake-case string to camel-case string."""
name = _single_underscore_re.split(name)
name = [_multiple_underscores_re.split(n) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(name) if n != "")
snakecase_to_camelcase("ClassLabel")
```
Result:
```raw
'Classlabel'
```
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PR_kwDODunzps6ku8Jw
| 7,743
|
Refactor HDF5 and preserve tree structure
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2025-08-21 17:28:17+00:00
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2025-08-26 15:28:05+00:00
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2025-08-26 15:28:05+00:00
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CONTRIBUTOR
| null | null | 0
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Closes #7741. Followup to #7690
- Recursive parsing and feature inference, to preserve the tree structure of the file. Note this means we now visit all links in the file. It also means we have to call` combine_chunks` on any large non-root datasets.
- Support for `complex64` (two `float32`s, used to be converted to two `float64`s)
- Support for ndim complex, compound, more field types for compound (due to reusing the main parser, compound types are treated like groups)
- Cleaned up varlen support
- Always do feature inference and always cast to features (used to cast to schema)
- Updated tests to use `load_dataset` instead of internal APIs
- Removed `columns` in config. Have to give Features (i.e., must specify types) if filtering
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module 'pyarrow' has no attribute 'PyExtensionType'
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2025-08-20 06:14:33+00:00
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2025-09-09 02:51:46+00:00
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NaT
|
NONE
| null | null | null | null |
### Describe the bug
When importing certain libraries, users will encounter the following error which can be traced back to the datasets library.
module 'pyarrow' has no attribute 'PyExtensionType'.
Example issue: https://github.com/explodinggradients/ragas/issues/2170
The issue occurs due to the following. I will proceed to submit a PR with the below fix:
**Issue Reason**
The issue is that PyArrow version 21.0.0 doesn’t have PyExtensionType. This was changed in newer versions of PyArrow. The
PyExtensionType class was renamed to ExtensionType in PyArrow 13.0.0 and later versions.
** Issue Solution**
Making the following changes to the following lib files should temporarily resolve the issue.
I will submit a PR to the dataets library in the meantime.
env_name/lib/python3.10/site-packages/datasets/features/features.py:
```
> 521 self.shape = tuple(shape)
522 self.value_type = dtype
523 self.storage_dtype = self._generate_dtype(self.value_type)
524 - pa.PyExtensionType.__init__(self, self.storage_dtype)
524 + pa.ExtensionType.__init__(self, self.storage_dtype)
525
526 def __reduce__(self):
527 return self.__class__, (
```
Updated venv_name/lib/python3.10/site-packages/datasets/features/features.py:
```
510 _type: str = field(default=“Array5D”, init=False, repr=False)
511
512
513 - class _ArrayXDExtensionType(pa.PyExtensionType):
513 + class _ArrayXDExtensionType(pa.ExtensionType):
514 ndims: Optional[int] = None
515
516 def __init__(self, shape: tuple, dtype: str):
```
### Steps to reproduce the bug
Ragas version: 0.3.1
Python version: 3.11
**Code to Reproduce**
_**In notebook:**_
!pip install ragas
from ragas import evaluate
### Expected behavior
The required package installs without issue.
### Environment info
In Jupyter Notebook.
venv
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I_kwDODunzps7GxcCQ
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|
Preserve tree structure when loading HDF5
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2025-08-19 15:42:05+00:00
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2025-08-26 15:28:06+00:00
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2025-08-26 15:28:06+00:00
|
CONTRIBUTOR
| null | null | null | null |
### Feature request
https://github.com/huggingface/datasets/pull/7740#discussion_r2285605374
### Motivation
`datasets` has the `Features` class for representing nested features. HDF5 files have groups of datasets which are nested, though in #7690 the keys are flattened. We should preserve that structure for the user.
### Your contribution
I'll open a PR (#7743)
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PR_kwDODunzps6kUMKM
| 7,740
|
Document HDF5 support
|
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CONTRIBUTOR
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I think these are at least the main places where we should put content. Ideally it is not just repeated in the final version
ref #7690
- [x] Wait for #7743 to land
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I_kwDODunzps7Gkzti
| 7,739
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Replacement of "Sequence" feature with "List" breaks backward compatibility
|
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2025-08-18 17:28:38+00:00
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2025-09-10 14:17:50+00:00
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NaT
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NONE
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PR #7634 replaced the Sequence feature with List in 4.0.0, so datasets saved with version 4.0.0 with that feature cannot be loaded by earlier versions. There is no clear option in 4.0.0 to use the legacy feature type to preserve backward compatibility.
Why is this a problem? I have a complex preprocessing and training pipeline dependent on 3.6.0; we manage a very large number of separate datasets that get concatenated during training. If just one of those datasets is saved with 4.0.0, they become unusable, and we have no way of "fixing" them. I can load them in 4.0.0 but I can't re-save with the legacy feature type, and I can't load it in 3.6.0 for obvious reasons.
Perhaps I'm missing something here, since the PR says that backward compatibility is preserved; if so, it's not obvious to me how.
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I_kwDODunzps7Ga7nS
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Allow saving multi-dimensional ndarray with dynamic shapes
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NaT
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NONE
| null | null | null | null |
### Feature request
I propose adding a dedicated feature to the datasets library that allows for the efficient storage and retrieval of multi-dimensional ndarray with dynamic shapes. Similar to how Image columns handle variable-sized images, this feature would provide a structured way to store array data where the dimensions are not fixed.
A possible implementation could be a new Array or Tensor feature type that stores the data in a structured format, for example,
```python
{
"shape": (5, 224, 224),
"dtype": "uint8",
"data": [...]
}
```
This would allow the datasets library to handle heterogeneous array sizes within a single column without requiring a fixed shape definition in the feature schema.
### Motivation
I am currently trying to upload data from astronomical telescopes, specifically FITS files, to the Hugging Face Hub. This type of data is very similar to images but often has more than three dimensions. For example, data from the SDSS project contains five channels (u, g, r, i, z), and the pixel values can exceed 255, making the Pillow based Image feature unsuitable.
The current datasets library requires a fixed shape to be defined in the feature schema for multi-dimensional arrays, which is a major roadblock. This prevents me from saving my data, as the dimensions of the arrays can vary across different FITS files.
https://github.com/huggingface/datasets/blob/985c9bee6bfc345787a8b9dd316e1d4f3b930503/src/datasets/features/features.py#L613-L614
A feature that supports dynamic shapes would be incredibly beneficial for the astronomy community and other fields dealing with similar high-dimensional, variable-sized data (e.g., medical imaging, scientific simulations).
### Your contribution
I am willing to create a PR to help implement this feature if the proposal is accepted.
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PR_kwDODunzps6jf5io
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docs: Add column overwrite example to batch mapping guide
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CONTRIBUTOR
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This PR adds a complementary example showing the **column-overwriting** pattern, which is both more direct and more flexible for many transformations.
### Proposed Change
The original `remove_columns` example remains untouched. Below it, this PR introduces an alternative approach that overwrites an existing column during batch mapping.
This teaches users a core `.map()` capability for in-place transformations without extra intermediate steps.
**New Example:**
> ```python
> >>> from datasets import Dataset
> >>> dataset = Dataset.from_dict({"a": [0, 1, 2]})
> # Overwrite "a" directly to duplicate each value
> >>> duplicated_dataset = dataset.map(
> ... lambda batch: {"a": [x for x in batch["a"] for _ in range(2)]},
> ... batched=True
> ... )
> >>> duplicated_dataset
> Dataset({
> features: ['a'],
> num_rows: 6
> })
> >>> duplicated_dataset["a"]
> [0, 0, 1, 1, 2, 2]
> ```
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Fix type hint `train_test_split`
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fix largelist repr
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PR_kwDODunzps6i4pmA
| 7,734
|
Fixing __getitem__ of datasets which behaves inconsistent to documentation when setting _format_type to None
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2025-08-09 15:52:54+00:00
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2025-08-17 07:23:00+00:00
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2025-08-17 07:23:00+00:00
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NONE
| null | null | 0
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Setting _format_type to None, should return plain python object but as of 4.0.0 returns Column. This fails in libs such as sentencetransformers (such as in generation of hard negatives) where plain python is expected.
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| 3,304,979,299
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I_kwDODunzps7E_ftj
| 7,733
|
Dataset Repo Paths to Locally Stored Images Not Being Appended to Image Path
|
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2025-08-08 19:10:58+00:00
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2025-10-07 04:47:36+00:00
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2025-10-07 04:32:48+00:00
|
NONE
| null | null | null | null |
### Describe the bug
I’m not sure if this is a bug or a feature and I just don’t fully understand how dataset loading is to work, but it appears there may be a bug with how locally stored Image() are being accessed. I’ve uploaded a new dataset to hugging face (rmdig/rocky_mountain_snowpack) but I’ve come into a ton of trouble trying to have the images handled properly (at least in the way I’d expect them to be handled).
I find that I cannot use relative paths for loading images remotely from the Hugging Face repo or from a local repository. Any time I do it always simply appends my current working directory to the dataset. As a result to use the datasets library with my dataset I have to change my working directory to the dataset folder or abandon the dataset object structure, which I cannot imagine you intended. As a result I have to use URL’s since an absolute path on my system obviously wouldn’t work for others. The URL works ok, but despite me having it locally downloaded, it appears to be redownloading the dataset every time I train my snowGAN model on it (and often times I’m coming into HTTPS errors for over requesting the data).
Or maybe image relative paths aren't intended to be loaded directly through your datasets library as images and should be kept as strings for the user to handle? If so I feel like you’re missing out on some pretty seamless functionality
### Steps to reproduce the bug
1. Download a local copy of the dataset (rmdig/rocky_mountain_snowpack) through git or whatever you prefer.
2. Alter the README.md YAML for file_path (the relative path to each image) to be type Image instead of type string
`
---
dataset_info:
features:
- name: image
dtype: Image
- name: file_path
dtype: Image
`
3. Initialize the dataset locally, make sure your working directory is not the dataset directory root
`dataset = datasets.load_dataset(‘path/to/local/rocky_mountain_snowpack/‘)`
4. Call to one of the samples and you’ll get an error that the image was not found in current/working/directory/preprocessed/cores/image_1.png. Showing that it’s simply looking in the current working directory + relative path
`
>>> dataset['train'][0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 2859, in __getitem__
return self._getitem(key)
^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 2841, in _getitem
formatted_output = format_table(
^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 657, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 410, in __call__
return self.format_row(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 459, in format_row
row = self.python_features_decoder.decode_row(row)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 223, in decode_row
return self.features.decode_example(row, token_per_repo_id=self.token_per_repo_id) if self.features else row
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/features/features.py", line 2093, in decode_example
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/features/features.py", line 1405, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/features/image.py", line 171, in decode_example
image = PIL.Image.open(path)
^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/PIL/Image.py", line 3277, in open
fp = builtins.open(filename, "rb")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/Users/dennyschaedig/Datasets/preprocessed/cores/image_1.png'
`
### Expected behavior
I expect the datasets and Image() to load the locally hosted data using path/to/local/rocky_mountain_snowpack/ (that I pass in with my datasets.load_dataset() or the you all handle on the backend) call + relative path.
Instead it appears to load from my current working directory + relative path.
### Environment info
Tested on…
Windows 11, Ubuntu Linux 22.04 and Mac Sequoia 15.5 Silicone M2
datasets version 4.0.0
Python 3.12 and 3.13
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I_kwDODunzps7E-VBn
| 7,732
|
webdataset: key errors when `field_name` has upper case characters
|
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2025-08-08 16:56:42+00:00
|
2025-08-08 16:56:42+00:00
|
NaT
|
CONTRIBUTOR
| null | null | null | null |
### Describe the bug
When using a webdataset each sample can be a collection of different "fields"
like this:
```
images17/image194.left.jpg
images17/image194.right.jpg
images17/image194.json
images17/image12.left.jpg
images17/image12.right.jpg
images17/image12.json
```
if the field_name contains upper case characters, the HF webdataset integration throws a key error when trying to load the dataset:
e.g. from a dataset (now updated so that it doesn't throw this error)
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[1], line 2
1 from datasets import load_dataset
----> 2 ds = load_dataset("commaai/comma2k19", data_files={'train': ['data-00000.tar.gz']}, num_proc=1)
File ~/xx/.venv/lib/python3.11/site-packages/datasets/load.py:1412, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, **config_kwargs)
1409 return builder_instance.as_streaming_dataset(split=split)
1411 # Download and prepare data
-> 1412 builder_instance.download_and_prepare(
1413 download_config=download_config,
1414 download_mode=download_mode,
1415 verification_mode=verification_mode,
1416 num_proc=num_proc,
1417 storage_options=storage_options,
1418 )
1420 # Build dataset for splits
1421 keep_in_memory = (
1422 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1423 )
File ~/xx/.venv/lib/python3.11/site-packages/datasets/builder.py:894, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
892 if num_proc is not None:
893 prepare_split_kwargs["num_proc"] = num_proc
--> 894 self._download_and_prepare(
895 dl_manager=dl_manager,
896 verification_mode=verification_mode,
897 **prepare_split_kwargs,
898 **download_and_prepare_kwargs,
899 )
900 # Sync info
901 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/xx/.venv/lib/python3.11/site-packages/datasets/builder.py:1609, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)
1608 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
-> 1609 super()._download_and_prepare(
1610 dl_manager,
1611 verification_mode,
1612 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS
1613 or verification_mode == VerificationMode.ALL_CHECKS,
1614 **prepare_splits_kwargs,
1615 )
File ~/xx/.venv/lib/python3.11/site-packages/datasets/builder.py:948, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
946 split_dict = SplitDict(dataset_name=self.dataset_name)
947 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 948 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
950 # Checksums verification
951 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File ~/xx/.venv/lib/python3.11/site-packages/datasets/packaged_modules/webdataset/webdataset.py:81, in WebDataset._split_generators(self, dl_manager)
78 if not self.info.features:
79 # Get one example to get the feature types
80 pipeline = self._get_pipeline_from_tar(tar_paths[0], tar_iterators[0])
---> 81 first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
82 if any(example.keys() != first_examples[0].keys() for example in first_examples):
83 raise ValueError(
84 "The TAR archives of the dataset should be in WebDataset format, "
85 "but the files in the archive don't share the same prefix or the same types."
86 )
File ~/xx/.venv/lib/python3.11/site-packages/datasets/packaged_modules/webdataset/webdataset.py:55, in WebDataset._get_pipeline_from_tar(cls, tar_path, tar_iterator)
53 data_extension = field_name.split(".")[-1]
54 if data_extension in cls.DECODERS:
---> 55 current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
56 if current_example:
57 yield current_example
KeyError: 'processed_log_IMU_magnetometer_value.npy'
```
### Steps to reproduce the bug
unit test was added in: https://github.com/huggingface/datasets/pull/7726
it fails without the fixed proposed in the same PR
### Expected behavior
Not throwing a key error.
### Environment info
```
- `datasets` version: 4.0.0
- Platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.39
- Python version: 3.11.4
- `huggingface_hub` version: 0.33.4
- PyArrow version: 21.0.0
- Pandas version: 2.3.1
- `fsspec` version: 2025.7.0
```
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I_kwDODunzps7E6YBT
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|
Add the possibility of a backend for audio decoding
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"id": 1935892871,
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open
| false
| null |
[] | null | 2
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2025-08-08 11:08:56+00:00
|
2025-08-20 16:29:33+00:00
|
NaT
|
NONE
| null | null | null | null |
### Feature request
Add the possibility of a backend for audio decoding. Before version 4.0.0, soundfile was used, and now torchcodec is used, but the problem is that torchcodec requires ffmpeg, which is problematic to install on the same colab. Therefore, I suggest adding a decoder selection when loading the dataset.
### Motivation
I use a service for training models in which ffmpeg cannot be installed.
### Your contribution
I use a service for training models in which ffmpeg cannot be installed.
| null |
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| 3,301,907,242
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PR_kwDODunzps6iqTZI
| 7,730
|
Grammar fix: correct "showed" to "shown" in fingerprint.py
|
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2025-08-07 21:22:56+00:00
|
2025-08-13 18:34:30+00:00
|
2025-08-13 13:12:56+00:00
|
CONTRIBUTOR
| null | null | 0
|
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This PR corrects a small grammatical issue in the outputs of fingerprint.py:
```diff
- "This warning is only showed once. Subsequent hashing failures won't be showed."
+ "This warning is only shown once. Subsequent hashing failures won't be shown."
```
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I_kwDODunzps7EvEW6
| 7,729
|
OSError: libcudart.so.11.0: cannot open shared object file: No such file or directory
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open
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[] | null | 1
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2025-08-07 14:07:23+00:00
|
2025-09-24 02:17:15+00:00
|
NaT
|
NONE
| null | null | null | null |
> Hi is there any solution for that eror i try to install this one
pip install torch==1.12.1+cpu torchaudio==0.12.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
this is working fine but tell me how to install pytorch version that is fit for gpu
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I_kwDODunzps7EoIf4
| 7,728
|
NonMatchingSplitsSizesError and ExpectedMoreSplitsError
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open
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2025-08-07 04:04:50+00:00
|
2025-10-06 21:08:39+00:00
|
NaT
|
NONE
| null | null | null | null |
### Describe the bug
When loading dataset, the info specified by `data_files` did not overwrite the original info.
### Steps to reproduce the bug
```python
from datasets import load_dataset
traindata = load_dataset(
"allenai/c4",
"en",
data_files={"train": "en/c4-train.00000-of-01024.json.gz",
"validation": "en/c4-validation.00000-of-00008.json.gz"},
)
```
```log
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=828589180707, num_examples=364868892, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=809262831, num_examples=356317, shard_lengths=[223006, 133311], dataset_name='c4')}, {'expected': SplitInfo(name='validation', num_bytes=825767266, num_examples=364608, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='validation', num_bytes=102199431, num_examples=45576, shard_lengths=None, dataset_name='c4')}]
```
```python
from datasets import load_dataset
traindata = load_dataset(
"allenai/c4",
"en",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train"
)
```
```log
ExpectedMoreSplitsError: {'validation'}
```
### Expected behavior
No error
### Environment info
datasets 4.0.0
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I_kwDODunzps7EcKyy
| 7,727
|
config paths that start with ./ are not valid as hf:// accessed repos, but are valid when accessed locally
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open
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[] | null | 0
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2025-08-06 08:21:37+00:00
|
2025-08-06 08:21:37+00:00
|
NaT
|
NONE
| null | null | null | null |
### Describe the bug
```
- config_name: some_config
data_files:
- split: train
path:
- images/xyz/*.jpg
```
will correctly download but
```
- config_name: some_config
data_files:
- split: train
path:
- ./images/xyz/*.jpg
```
will error with `FileNotFoundError` due to improper url joining. `load_dataset` on the same directory locally works fine.
### Steps to reproduce the bug
1. create a README.md with the front matter of the form
```
- config_name: some_config
data_files:
- split: train
path:
- ./images/xyz/*.jpg
```
2. `touch ./images/xyz/1.jpg`
3. Observe this directory loads with `load_dataset("filesystem_path", "some_config")` correctly.
4. Observe exceptions when you load this with `load_dataset("repoid/filesystem_path", "some_config")`
### Expected behavior
`./` prefix should be interpreted correctly
### Environment info
datasets 4.0.0
datasets 3.4.0
reproduce
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PR_kwDODunzps6iO_oF
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fix(webdataset): don't .lower() field_name
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|
2025-08-05 16:57:09+00:00
|
2025-08-20 16:35:55+00:00
|
2025-08-20 16:35:55+00:00
|
CONTRIBUTOR
| null | null | 0
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This fixes cases where keys have upper case identifiers
|
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I_kwDODunzps7EPL5p
| 7,724
|
Can not stepinto load_dataset.py?
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open
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2025-08-05 09:28:51+00:00
|
2025-08-05 09:28:51+00:00
|
NaT
|
NONE
| null | null | null | null |
I set a breakpoint in "load_dataset.py" and try to debug my data load codes, but it does not stop at any breakpoints, so "load_dataset.py" can not be stepped into ?
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I_kwDODunzps7EGIzd
| 7,723
|
Don't remove `trust_remote_code` arg!!!
|
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2025-08-04 15:42:07+00:00
|
2025-08-04 15:42:07+00:00
|
NaT
|
NONE
| null | null | null | null |
### Feature request
defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!
Add `trust_remote_code` arg back please!
### Motivation
defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!
### Your contribution
defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!
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