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https://api.github.com/repos/huggingface/datasets/issues/7700 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7700/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7700/comments | https://api.github.com/repos/huggingface/datasets/issues/7700/events | https://github.com/huggingface/datasets/issues/7700 | 3,263,922,255 | I_kwDODunzps7Ci4BP | 7,700 | [doc] map.num_proc needs clarification | {
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```
num_proc (int, optional, defaults to None) — Max number of processes when generating cache. Already cached
shards are loaded sequentially.
```
for batch:
```
num_proc (int, optional, defaults to None): The number of processes to use for multiprocessing. If None, no
multiprocessing is used. This can significantly speed up batching for large datasets.
```
So what happens to `map.num_proc` - is it the same behavior as `batch.num_proc` - so only if `num_proc=None` then no multiprocessing is used?
Let's update the doc to be unambiguous.
**bonus**: we could make all of these behave similarly to `DataLoader.num_workers` - where `num_workers==0` implies no multiprocessing. I think that's the most intuitive, IMHO. 0 workers - the main process has to do all the work. `None` could be the same as `0`.
context: debugging a failing `map`
Thank you! | null | {
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"body": " Mario’s Patch (in PR #6832):\r\n```\r\ndef _make_split_generators_kwargs(self, prepare_split_kwargs):\r\n # Pass `pipeline` into `_split_generators()` from `prepare_split_kwargs` if\r\n # it's in the call signature of `_split_generators()`.\r\n # Thi... | 2025-07-28T19:40:40Z | 2025-07-29T09:25:12Z | null | CONTRIBUTOR | null | null | null | (revival of #6832)
https://github.com/huggingface/datasets/pull/7648#issuecomment-3084050130
Close https://github.com/huggingface/datasets/issues/4101, and more
---
### PR under work!!!!
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https://api.github.com/repos/huggingface/datasets/issues/7709 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7709/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7709/comments | https://api.github.com/repos/huggingface/datasets/issues/7709/events | https://github.com/huggingface/datasets/issues/7709 | 3,276,677,990 | I_kwDODunzps7DTiNm | 7,709 | Release 4.0.0 breaks usage patterns of with_format | {
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"body": "This is a breaking change with 4.0 which introduced `Column` objects. To get the numpy array from a `Column` you can `col[i]`, `col[i:j]` or even `col[:]` if you want the full column as a numpy array:\n\n```python\nfrom datasets import load_dataset\ndataset = load_... | 2025-07-30T11:34:53Z | 2025-07-30T15:41:59Z | null | NONE | null | null | {
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} | ### Describe the bug
Previously it was possible to access a whole column that was e.g. in numpy format via `with_format` by indexing the column. Now this possibility seems to be gone with the new Column() class. As far as I see, this makes working on a whole column (in-memory) more complex, i.e. normalizing an in-memory dataset for which iterating would be too slow. Is this intended behaviour? I couldn't find much documentation on the intended usage of the new Column class yet.
### Steps to reproduce the bug
Steps to reproduce:
```
from datasets import load_dataset
dataset = load_dataset("lhoestq/demo1")
dataset = dataset.with_format("numpy")
print(dataset["star"].ndim)
```
### Expected behavior
Working on whole columns should be possible.
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-6.8.0-63-generic-x86_64-with-glibc2.36
- Python version: 3.12.11
- `huggingface_hub` version: 0.34.3
- PyArrow version: 21.0.0
- Pandas version: 2.3.1
- `fsspec` version: 2025.3.0 | null | {
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{
"author_association": "NONE",
"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7708). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-07-29T13:17:07Z",
... | 2025-07-29T13:14:30Z | 2025-07-30T15:55:00Z | null | MEMBER | null | null | null | Retry the step that (download + update + upload) the README.md using `create_commit(..., parent_commit=...)` if there was a commit in the meantime. This should enable concurrent `push_to_hub()` since it won't overwrite the README.md metadata anymore.
DO NOT MERGE FOR NOW since it seems there is one bug that prevents this logic from working:
I'm using parent_commit to enable concurrent push_to_hub() in datasets for a retry mechanism, but for some reason I always run into a weird situation.
Sometimes create_commit(.., parent_commit=...) returns error 500 but the commit did happen on the Hub side without respecting parent_commit
e.g. request id
```
huggingface_hub.errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/lhoestq/tmp/commit/main (Request ID: Root=1-6888d8af-2ce517bc60c69cb378b51526;d1b17993-c5d0-4ccd-9926-060c45f9ed61)
```
fix coming in [internal](https://github.com/huggingface-internal/moon-landing/pull/14617)
close https://github.com/huggingface/datasets/issues/7600 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7705 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7705/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7705/comments | https://api.github.com/repos/huggingface/datasets/issues/7705/events | https://github.com/huggingface/datasets/issues/7705 | 3,269,070,499 | I_kwDODunzps7C2g6j | 7,705 | Can Not read installed dataset in dataset.load(.) | {
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{
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"body": "You can download the dataset locally using [huggingface_hub.snapshot_download](https://huggingface.co/docs/huggingface_hub/v0.34.3/en/package_reference/file_download#huggingface_hub.snapshot_download) and then do\n\n```python\ndataset = load_dataset(local_directory... | 2025-07-28T09:43:54Z | 2025-07-30T15:44:26Z | null | NONE | null | null | {
"completed": 0,
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} | Hi, folks, I'm newbie in huggingface dataset api.
As title, i'm facing the issue that the dataset.load api can not connect to the installed dataset.
code snippet :
<img width="572" height="253" alt="Image" src="https://github.com/user-attachments/assets/10f48aaf-d6ca-4239-b1cf-145d74f125d1" />
data path :
"/xxx/joseph/llava_ds/vlm_ds"
it contains all video clips i want!
<img width="1398" height="261" alt="Image" src="https://github.com/user-attachments/assets/bf213b66-e344-4311-97e7-bc209677ae77" />
i run the py script by
<img width="1042" height="38" alt="Image" src="https://github.com/user-attachments/assets/8b3fcee4-e1a6-41b8-bee1-91567b00d9d2" />
But bad happended, even i provide dataset path by "HF_HUB_CACHE", it still attempt to download data from remote side :
<img width="1697" height="813" alt="Image" src="https://github.com/user-attachments/assets/baa6cff1-a724-4710-a8c4-4805459deffb" />
Any suggestion will be appreciated!! | null | {
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{
"author_association": "NONE",
"body": "Hi @lhoestq, just a gentle follow-up on this doc fix PR (#7704). Let me know if any changes are needed — happy to update.\r\nHope this improvement helps users run the example without confusion!",
"created_at": "2025-08-01T13:48:35Z",
"html_url": "https://gith... | 2025-07-26T14:18:17Z | 2025-07-26T14:18:17Z | null | NONE | null | null | null | ## Problem
The current datasets.Dataset.map() example in the documentation demonstrates batched processing using a tokenizer object without defining or importing it. This causes a NameError when users copy and run the example as-is, breaking the expected seamless experience.
## Correction
This PR fixes the issue by explicitly importing and initializing the tokenizer using the Transformers library (AutoTokenizer.from_pretrained("bert-base-uncased")), making the example self-contained and runnable without errors.
This will help new users understand the workflow and apply the method correctly.
Closes #7703
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https://api.github.com/repos/huggingface/datasets/issues/7701 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7701/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7701/comments | https://api.github.com/repos/huggingface/datasets/issues/7701/events | https://github.com/huggingface/datasets/pull/7701 | 3,265,236,296 | PR_kwDODunzps6gvJ83 | 7,701 | Update fsspec max version to current release 2025.7.0 | {
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{
"author_association": "CONTRIBUTOR",
"body": "@lhoestq I ran the test suite locally and while some tests were failing those failures are present on the main branch too. Could you please review and trigger the CI?",
"created_at": "2025-07-26T08:02:37Z",
"html_url": "https://github.com/huggingface/d... | 2025-07-26T06:47:59Z | 2025-07-28T11:58:11Z | 2025-07-28T11:58:11Z | CONTRIBUTOR | null | null | null | Diffusers currently asks for a max fsspec version of `2025.3.0`. This change updates it to the current latest version. This change is mainly required to resolve conflicts with other packages in an environment. In my particular case, `aider-chat` which is a part of my environment installs `2025.5.1` which is incompatible with `datasets`. | {
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https://api.github.com/repos/huggingface/datasets/issues/7703 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7703/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7703/comments | https://api.github.com/repos/huggingface/datasets/issues/7703/events | https://github.com/huggingface/datasets/issues/7703 | 3,265,648,942 | I_kwDODunzps7Cpdku | 7,703 | [Docs] map() example uses undefined `tokenizer` — causes NameError | {
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{
"author_association": "NONE",
"body": "I've submitted PR #7704 which adds documentation to clarify the behavior of `map()` when returning `None`.",
"created_at": "2025-07-27T05:39:18Z",
"html_url": "https://github.com/huggingface/datasets/issues/7703#issuecomment-3124002704",
"id": 3124002704,... | 2025-07-26T13:35:11Z | 2025-07-27T09:44:35Z | null | NONE | null | null | {
"completed": 0,
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} | ## Description
The current documentation example for `datasets.Dataset.map()` demonstrates batched processing but uses a `tokenizer` object without defining or importing it. This causes an error every time it's copied.
Here is the problematic line:
```python
# process a batch of examples
>>> ds = ds.map(lambda example: tokenizer(example["text"]), batched=True)
```
This assumes the user has already set up a tokenizer, which contradicts the goal of having self-contained, copy-paste-friendly examples.
## Problem
Users who copy and run the example as-is will encounter:
```python
NameError: name 'tokenizer' is not defined
```
This breaks the flow for users and violates HuggingFace's documentation principle that examples should "work as expected" when copied directly.
## Proposal
Update the example to include the required tokenizer setup using the Transformers library, like so:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
ds_tokenized = ds.map(lambda example: tokenizer(example["text"]), batched=True)
```
This will help new users understand the workflow and apply the method correctly.
## Note
This PR complements ongoing improvements like #7700, which clarifies multiprocessing in .map(). My change focuses on undefined tokenizer — causes NameError
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/7707 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7707/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7707/comments | https://api.github.com/repos/huggingface/datasets/issues/7707/events | https://github.com/huggingface/datasets/issues/7707 | 3,271,867,998 | I_kwDODunzps7DBL5e | 7,707 | load_dataset() in 4.0.0 failed when decoding audio | {
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{
"author_association": "NONE",
"body": "Hi @lhoestq . Would you please have a look at it? I use the official NV Docker ([NV official docker image](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch): `nvcr.io/nvidia/pytorch:25.06-py3`) on A100 and encountered this issue, but I don't know how to ... | 2025-07-29T03:25:03Z | 2025-07-31T03:01:09Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Cannot decode audio data.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
print(dataset[0]["audio"]["array"])
```
1st round run, got
```
File "/usr/local/lib/python3.12/dist-packages/datasets/features/audio.py", line 172, in decode_example
raise ImportError("To support decoding audio data, please install 'torchcodec'.")
ImportError: To support decoding audio data, please install 'torchcodec'.
```
After `pip install torchcodec` and run, got
```
File "/usr/local/lib/python3.12/dist-packages/torchcodec/_core/_metadata.py", line 16, in <module>
from torchcodec._core.ops import (
File "/usr/local/lib/python3.12/dist-packages/torchcodec/_core/ops.py", line 84, in <module>
load_torchcodec_shared_libraries()
File "/usr/local/lib/python3.12/dist-packages/torchcodec/_core/ops.py", line 69, in load_torchcodec_shared_libraries
raise RuntimeError(
RuntimeError: Could not load libtorchcodec. Likely causes:
1. FFmpeg is not properly installed in your environment. We support
versions 4, 5, 6 and 7.
2. The PyTorch version (2.8.0a0+5228986c39.nv25.06) is not compatible with
this version of TorchCodec. Refer to the version compatibility
table:
https://github.com/pytorch/torchcodec?tab=readme-ov-file#installing-torchcodec.
3. Another runtime dependency; see exceptions below.
The following exceptions were raised as we tried to load libtorchcodec:
[start of libtorchcodec loading traceback]
FFmpeg version 7: libavutil.so.59: cannot open shared object file: No such file or directory
FFmpeg version 6: libavutil.so.58: cannot open shared object file: No such file or directory
FFmpeg version 5: libavutil.so.57: cannot open shared object file: No such file or directory
FFmpeg version 4: libavutil.so.56: cannot open shared object file: No such file or directory
[end of libtorchcodec loading traceback].
```
After `apt update && apt install ffmpeg -y`, got
```
Traceback (most recent call last):
File "/workspace/jiqing/test_datasets.py", line 4, in <module>
print(dataset[0]["audio"]["array"])
~~~~~~~^^^
File "/usr/local/lib/python3.12/dist-packages/datasets/arrow_dataset.py", line 2859, in __getitem__
return self._getitem(key)
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/datasets/arrow_dataset.py", line 2841, in _getitem
formatted_output = format_table(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/datasets/formatting/formatting.py", line 657, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/datasets/formatting/formatting.py", line 410, in __call__
return self.format_row(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/datasets/formatting/formatting.py", line 459, in format_row
row = self.python_features_decoder.decode_row(row)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-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 "/usr/local/lib/python3.12/dist-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 "/usr/local/lib/python3.12/dist-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 "/usr/local/lib/python3.12/dist-packages/datasets/features/audio.py", line 198, in decode_example
audio = AudioDecoder(bytes, stream_index=self.stream_index, sample_rate=self.sampling_rate)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torchcodec/decoders/_audio_decoder.py", line 62, in __init__
self._decoder = create_decoder(source=source, seek_mode="approximate")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torchcodec/decoders/_decoder_utils.py", line 33, in create_decoder
return core.create_from_bytes(source, seek_mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torchcodec/_core/ops.py", line 144, in create_from_bytes
return create_from_tensor(buffer, seek_mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 756, in __call__
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
NotImplementedError: Could not run 'torchcodec_ns::create_from_tensor' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchcodec_ns::create_from_tensor' is only available for these backends: [Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMTIA, AutogradMAIA, AutogradMeta, Tracer, AutocastCPU, AutocastMTIA, AutocastMAIA, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
Meta: registered at /dev/null:214 [kernel]
BackendSelect: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /__w/torchcodec/torchcodec/pytorch/torchcodec/src/torchcodec/_core/custom_ops.cpp:694 [kernel]
FuncTorchDynamicLayerBackMode: registered at /opt/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:479 [backend fallback]
Functionalize: registered at /opt/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:349 [backend fallback]
Named: registered at /opt/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /opt/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at /opt/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /opt/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:104 [backend fallback]
AutogradOther: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:63 [backend fallback]
AutogradCPU: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:67 [backend fallback]
AutogradCUDA: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:75 [backend fallback]
AutogradXLA: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:87 [backend fallback]
AutogradMPS: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:95 [backend fallback]
AutogradXPU: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:71 [backend fallback]
AutogradHPU: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:108 [backend fallback]
AutogradLazy: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:91 [backend fallback]
AutogradMTIA: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:79 [backend fallback]
AutogradMAIA: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:83 [backend fallback]
AutogradMeta: registered at /opt/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:99 [backend fallback]
Tracer: registered at /opt/pytorch/pytorch/torch/csrc/autograd/TraceTypeManual.cpp:294 [backend fallback]
AutocastCPU: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:322 [backend fallback]
AutocastMTIA: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:466 [backend fallback]
AutocastMAIA: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:504 [backend fallback]
AutocastXPU: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:542 [backend fallback]
AutocastMPS: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:209 [backend fallback]
AutocastCUDA: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:165 [backend fallback]
FuncTorchBatched: registered at /opt/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:731 [backend fallback]
BatchedNestedTensor: registered at /opt/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:758 [backend fallback]
FuncTorchVmapMode: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:27 [backend fallback]
Batched: registered at /opt/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at /opt/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at /opt/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:208 [backend fallback]
PythonTLSSnapshot: registered at /opt/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:202 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at /opt/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:475 [backend fallback]
PreDispatch: registered at /opt/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:206 [backend fallback]
PythonDispatcher: registered at /opt/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:198 [backend fallback]
```
### Expected behavior
The result is
```
[0.00238037 0.0020752 0.00198364 ... 0.00042725 0.00057983 0.0010376 ]
```
on `datasets==3.6.0`
### Environment info
[NV official docker image](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch): `nvcr.io/nvidia/pytorch:25.06-py3`
```
- `datasets` version: 4.0.0
- Platform: Linux-5.4.292-1.el8.elrepo.x86_64-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.34.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0
``` | null | {
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{
"author_association": "MEMBER",
"body": "I think we can support num_proc=0 and make it equivalent to `None` to make it simpler",
"created_at": "2025-07-30T15:46:03Z",
"html_url": "https://github.com/huggingface/datasets/pull/7702#issuecomment-3136895555",
"id": 3136895555,
"issue_url": "ht... | 2025-07-26T08:19:39Z | 2025-07-31T05:22:35Z | null | NONE | null | null | null | Fixes issue #7700
This PR makes num_proc=0 behave like None in Dataset.map(), disabling multiprocessing.
It improves UX by aligning with DataLoader(num_workers=0) behavior.
The num_proc docstring is also updated to clearly explain valid values and behavior.
@SunMarc
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https://api.github.com/repos/huggingface/datasets/issues/7697 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7697/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7697/comments | https://api.github.com/repos/huggingface/datasets/issues/7697/events | https://github.com/huggingface/datasets/issues/7697 | 3,254,526,399 | I_kwDODunzps7B_CG_ | 7,697 | - | {
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https://api.github.com/repos/huggingface/datasets/issues/7698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7698/comments | https://api.github.com/repos/huggingface/datasets/issues/7698/events | https://github.com/huggingface/datasets/issues/7698 | 3,255,350,916 | I_kwDODunzps7CCLaE | 7,698 | NotImplementedError when using streaming=True in Google Colab environment | {
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{
"author_association": "CONTRIBUTOR",
"body": "Hi, @Aniket17200, try upgrading datasets using '!pip install -U datasets'. I hope this will resolve your issue.",
"created_at": "2025-07-23T13:46:33Z",
"html_url": "https://github.com/huggingface/datasets/issues/7698#issuecomment-3108643682",
"id":... | 2025-07-23T08:04:53Z | 2025-07-23T15:06:23Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
When attempting to load a large dataset (like tiiuae/falcon-refinedweb or allenai/c4) using streaming=True in a standard Google Colab notebook, the process fails with a NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet. This issue persists even after upgrading datasets and huggingface_hub and restarting the session.
### Steps to reproduce the bug
Open a new Google Colab notebook.
(Optional but recommended) Run !pip install --upgrade datasets huggingface_hub and restart the runtime.
Run the following code:
Python
from datasets import load_dataset
try:
print("Attempting to load a stream...")
streaming_dataset = load_dataset('tiiuae/falcon-refinedweb', streaming=True)
print("Success!")
except Exception as e:
print(e)
### Expected behavior
The load_dataset command should return a StreamingDataset object without raising an error, allowing iteration over the dataset.
Actual Behavior
The code fails and prints the following error traceback:
[PASTE THE FULL ERROR TRACEBACK HERE]
(Note: Copy the entire error message you received, from Traceback... to the final error line, and paste it in this section.)
### Environment info
Platform: Google Colab
datasets version: [Run !pip show datasets in Colab and paste the version here]
huggingface_hub version: [Run !pip show huggingface_hub and paste the version here]
Python version: [Run !python --version and paste the version here] | null | {
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{
"author_association": "NONE",
"body": "It seems the error occurs right here, as it tries to infer the Features: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/webdataset/webdataset.py#L78-L90",
"created_at": "2025-07-19T18:44:34Z",
"html_url": "https://github.com/h... | 2025-07-19T18:40:27Z | 2025-07-25T08:51:10Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I am creating a large WebDataset-format dataset for sign language processing research, and a number of the videos are over 2GB. The instant I hit one of the shards with one of those videos, I get a ArrowCapacityError, even with streaming.
I made a config for the dataset that specifically includes just one problem shard, and the error triggers the instant you even run load_dataset(), even with streaming=True
```
ds = load_dataset("bible-nlp/sign-bibles", "ase_chronological_bible_translation_in_american_sign_language_119_introductions_and_passages_debugging_problem_shard", streaming=True, split="train")
```
This gives:
```
File "/opt/home/cleong/projects/semantic_and_visual_similarity/sign-bibles-dataset/sign_bibles_dataset/tasks/test_iteration.py", line 13, in iterate_keys
ds = load_dataset("bible-nlp/sign-bibles", language_subset, streaming=True, split="train")
File "/opt/home/cleong/envs/sign-bibles-dataset/lib/python3.13/site-packages/datasets/load.py", line 1409, in load_dataset
return builder_instance.as_streaming_dataset(split=split)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/opt/home/cleong/envs/sign-bibles-dataset/lib/python3.13/site-packages/datasets/builder.py", line 1225, in as_streaming_dataset
splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/opt/home/cleong/envs/sign-bibles-dataset/lib/python3.13/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators
pa.Table.from_pylist(cast_to_python_objects([example], only_1d_for_numpy=True))
~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 2046, in pyarrow.lib._Tabular.from_pylist
File "pyarrow/table.pxi", line 6431, in pyarrow.lib._from_pylist
File "pyarrow/table.pxi", line 4893, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 1607, in pyarrow.lib._sanitize_arrays
File "pyarrow/table.pxi", line 1588, in pyarrow.lib._schema_from_arrays
File "pyarrow/array.pxi", line 375, in pyarrow.lib.array
File "pyarrow/array.pxi", line 45, in pyarrow.lib._sequence_to_array
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.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 3980158992
```
### Steps to reproduce the bug
```python
#!/usr/bin/env python
import argparse
from datasets import get_dataset_config_names, load_dataset
from tqdm import tqdm
from pyarrow.lib import ArrowCapacityError, ArrowInvalid
def iterate_keys(language_subset: str) -> None:
"""Iterate over all samples in the Sign Bibles dataset and print idx and sample key."""
# https://huggingface.co/docs/datasets/v4.0.0/en/package_reference/loading_methods#datasets.load_dataset
ds = load_dataset("bible-nlp/sign-bibles", language_subset, streaming=True, split="train")
print(f"\n==> Loaded dataset config '{language_subset}'")
idx = 0
estimated_shard_index = 0
samples_per_shard = 5
with tqdm(desc=f"{language_subset} samples") as pbar:
iterator = iter(ds)
while True:
try:
if idx % samples_per_shard == 0 and idx > 0: # 5 samples per shard: 0, 1, 2, 3, 4
print(f"Estimated Shard idx (starting at 0, {samples_per_shard}/shard): {estimated_shard_index}")
estimated_shard_index += 1
sample = next(iterator)
sample_key = sample.get("__key__", "missing-key")
print(f"[{language_subset}] idx={idx}, key={sample_key}")
idx += 1
pbar.update(1)
except StopIteration:
print(f"Finished iterating through {idx} samples of {language_subset}")
break
except (ArrowCapacityError, ArrowInvalid) as e:
print(f"PyArrow error on idx={idx}, config={language_subset}: {e}")
idx += 1
pbar.update(1)
continue
except KeyError as e:
print(f"Missing key error on idx={idx}, config={language_subset}: {e}")
idx += 1
pbar.update(1)
continue
def main():
configs = get_dataset_config_names("bible-nlp/sign-bibles")
print(f"Available configs: {configs}")
configs = [
"ase_chronological_bible_translation_in_american_sign_language_119_introductions_and_passages_debugging_problem_shard"
]
for language_subset in configs:
print(f"TESTING CONFIG {language_subset}")
iterate_keys(language_subset)
# try:
# except (ArrowCapacityError, ArrowInvalid) as e:
# print(f"PyArrow error at config level for {language_subset}: {e}")
# continue
# except RuntimeError as e:
# print(f"RuntimeError at config level for {language_subset}: {e}")
# continue
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Iterate through Sign Bibles dataset and print sample keys.")
args = parser.parse_args()
main()
```
### Expected behavior
I expect, when I load with streaming=True, that there should not be any data loaded or anything like that.
https://huggingface.co/docs/datasets/main/en/package_reference/loading_methods#datasets.load_dataset says that with streaming=true,
I did expect to have some trouble with large files, but that the streaming mode would not actually try to load them unless requested, e.g. with sample["mp4"]
>In the streaming case:
> Don’t download or cache anything. Instead, the dataset is lazily loaded and will be streamed on-the-fly when iterating on it.
### Environment info
Local setup: Conda environment on Ubuntu, pip list includes the following
datasets 4.0.0
pyarrow 20.0.0
Verified on Colab: https://colab.research.google.com/drive/1HdN8stlROWrLSYXUoNeV0vQ9pClhIVM8?usp=sharing, though there it crashes by using up all available RAM
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https://api.github.com/repos/huggingface/datasets/issues/7699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7699/comments | https://api.github.com/repos/huggingface/datasets/issues/7699/events | https://github.com/huggingface/datasets/issues/7699 | 3,261,053,171 | I_kwDODunzps7CX7jz | 7,699 | Broken link in documentation for "Create a video dataset" | {
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} | [] | open | false | null | [] | null | [
{
"author_association": "MEMBER",
"body": "The URL is ok but it seems the webdataset website is down. There seems to be a related issue here: https://github.com/webdataset/webdataset/issues/155\n\nFeel free to ask the authors there for an update. Otherwise happy to witch the link to the mirror shared in tha... | 2025-07-24T19:46:28Z | 2025-07-25T15:27:47Z | null | NONE | null | null | {
"completed": 0,
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} | The link to "the [WebDataset documentation](https://webdataset.github.io/webdataset)." is broken.
https://huggingface.co/docs/datasets/main/en/video_dataset#webdataset
<img width="2048" height="264" alt="Image" src="https://github.com/user-attachments/assets/975dd10c-aad8-42fc-9fbc-de0e2747a326" /> | null | {
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{
"author_association": "MEMBER",
"body": "Hi ! it would be cool to convert this dataset to Parquet. This will make it work for `datasets>=4.0`, enable the Dataset Viewer and make it more reliable to load/stream (currently it uses a loading script in python and those are known for having issues sometimes)\n... | 2025-07-20T11:08:20Z | 2025-07-25T14:38:54Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I am trying to load YODAS2 dataset with datasets==3.6.0
```
from datasets import load_dataset
next(iter(load_dataset('espnet/yodas2', name='ru000', split='train', streaming=True, trust_remote_code=True)))
```
And get `UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa8 in position 1: invalid start byte`
The cause of the error is the following:
```
from datasets.utils.file_utils import xopen
filepath = 'https://huggingface.co/datasets/espnet/yodas2/resolve/c9674490249665d658f527e2684848377108d82c/data/ru000/text/00000000.json'
xopen(filepath, 'r').read()
>>> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa8 in position 1: invalid start byte
```
And the cause of this is the following:
```
import fsspec
fsspec.open(
'hf://datasets/espnet/yodas2@c9674490249665d658f527e2684848377108d82c/data/ru000/text/00000000.json',
mode='r',
hf={'token': None, 'endpoint': 'https://huggingface.co'},
).open().read()
>>> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa8 in position 1: invalid start byte
```
Is it true that streaming=True loading is not supported anymore for trust_remote_code=True, even with datasets==3.6.0? This breaks backward compatibility.
### Steps to reproduce the bug
```
from datasets import load_dataset
next(iter(load_dataset('espnet/yodas2', name='ru000', split='train', streaming=True)))
```
### Expected behavior
No errors expected
### Environment info
datasets==3.6.0, ubuntu 24.04 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7694 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7694/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7694/comments | https://api.github.com/repos/huggingface/datasets/issues/7694/events | https://github.com/huggingface/datasets/issues/7694 | 3,247,600,408 | I_kwDODunzps7BknMY | 7,694 | Dataset.to_json consumes excessive memory, appears to not be a streaming operation | {
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{
"author_association": "MEMBER",
"body": "Hi ! to_json is memory efficient and writes the data by batch:\n\nhttps://github.com/huggingface/datasets/blob/d9861d86be222884dabbd534a2db770c70c9b558/src/datasets/io/json.py#L153-L159\n\nWhat memory are you mesuring ? If you are mesuring RSS, it is likely that it... | 2025-07-21T07:51:25Z | 2025-07-25T14:42:21Z | null | NONE | null | null | {
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} | ### Describe the bug
When exporting a Dataset object to a JSON Lines file using the .to_json(lines=True) method, the process consumes a very large amount of memory. The memory usage is proportional to the size of the entire Dataset object being saved, rather than being a low, constant memory operation.
This behavior is unexpected, as the JSONL format is line-oriented and ideally suited for streaming writes. This issue can easily lead to Out-of-Memory (OOM) errors when exporting large datasets, especially in memory-constrained environments like Docker containers.
<img width="1343" height="329" alt="Image" src="https://github.com/user-attachments/assets/518b4263-ad12-422d-9672-28ffe97240ce" />
### Steps to reproduce the bug
```
import os
from datasets import load_dataset, Dataset
from loguru import logger
# A public dataset to test with
REPO_ID = "adam89/TinyStoriesChinese"
SUBSET = "default"
SPLIT = "train"
NUM_ROWS_TO_LOAD = 10 # Use a reasonably large number to see the memory spike
def run_test():
"""Loads data into memory and then saves it, triggering the memory issue."""
logger.info("Step 1: Loading data into an in-memory Dataset object...")
# Create an in-memory Dataset object from a stream
# This simulates having a processed dataset ready to be saved
iterable_dataset = load_dataset(REPO_ID, name=SUBSET, split=SPLIT, streaming=True)
limited_stream = iterable_dataset.take(NUM_ROWS_TO_LOAD)
in_memory_dataset = Dataset.from_generator(limited_stream.__iter__)
logger.info(f"Dataset with {len(in_memory_dataset)} rows created in memory.")
output_path = "./test_output.jsonl"
logger.info(f"Step 2: Saving the dataset to {output_path} using .to_json()...")
logger.info("Please monitor memory usage during this step.")
# This is the step that causes the massive memory allocation
in_memory_dataset.to_json(output_path, force_ascii=False)
logger.info("Save operation complete.")
os.remove(output_path)
if __name__ == "__main__":
# To see the memory usage clearly, run this script with a memory profiler:
# python -m memray run your_script_name.py
# python -m memray tree xxx.bin
run_test()
```
### Expected behavior
I would expect the .to_json(lines=True) method to be a memory-efficient, streaming operation. The memory usage should remain low and relatively constant, as data is converted and written to the file line-by-line or in small batches. The memory footprint should not be proportional to the total number of rows in the in_memory_dataset.
### Environment info
datasets version:3.6.0
Python version:3.9.18
os:macOS 15.3.1 (arm64) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7695 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7695/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7695/comments | https://api.github.com/repos/huggingface/datasets/issues/7695/events | https://github.com/huggingface/datasets/pull/7695 | 3,251,904,843 | PR_kwDODunzps6gB7jS | 7,695 | Support downloading specific splits in load_dataset | {
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{
"author_association": "CONTRIBUTOR",
"body": "I’ve completed the following steps to continue the partial split download support (from PR #6832):\r\n\r\nI did changes on top of what has been done by mario. Here are some of those changes: \r\n- Restored support for writing multiple split shards:\r\n\r\n- In... | 2025-07-22T09:33:54Z | 2025-07-28T17:33:30Z | 2025-07-28T17:15:45Z | CONTRIBUTOR | null | null | null | This PR builds on #6832 by @mariosasko.
May close - #4101, #2538
Discussion - https://github.com/huggingface/datasets/pull/7648#issuecomment-3084050130
---
### Note - This PR is under work and frequent changes will be pushed. | {
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https://api.github.com/repos/huggingface/datasets/issues/7690 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7690/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7690/comments | https://api.github.com/repos/huggingface/datasets/issues/7690/events | https://github.com/huggingface/datasets/pull/7690 | 3,244,380,691 | PR_kwDODunzps6fozag | 7,690 | HDF5 support | {
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{
"author_association": "NONE",
"body": "A few to-dos which I think can be left for future PRs (which I am happy to do/help with -- just this one is already huge 😄 ):\r\n- [Enum types](https://docs.h5py.org/en/stable/special.html#enumerated-types)\r\n- HDF5 [io](https://github.com/huggingface/datasets/tree... | 2025-07-18T21:09:41Z | 2025-07-28T21:32:12Z | null | NONE | null | null | null | This PR adds support for tabular HDF5 file(s) by converting each row to an Arrow table. It supports columns with the usual dtypes including up to 5-dimensional arrays as well as support for complex/compound types by splitting them into several columns. All datasets within the HDF5 file should have rows on the first dimension (groups/subgroups are still allowed). Closes #3113.
Replaces #7625 which only supports a relatively small subset of HDF5. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7693 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7693/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7693/comments | https://api.github.com/repos/huggingface/datasets/issues/7693/events | https://github.com/huggingface/datasets/issues/7693 | 3,246,369,678 | I_kwDODunzps7Bf6uO | 7,693 | Dataset scripts are no longer supported, but found superb.py | {
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{
"author_association": "NONE",
"body": "I got a pretty similar issue when I try to load bigbio/neurotrial_ner dataset. \n`Dataset scripts are no longer supported, but found neurotrial_ner.py`",
"created_at": "2025-07-21T14:10:07Z",
"html_url": "https://github.com/huggingface/datasets/issues/7693#is... | 2025-07-20T13:48:06Z | 2025-07-30T15:01:03Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Hello,
I'm trying to follow the [Hugging Face Pipelines tutorial](https://huggingface.co/docs/transformers/main_classes/pipelines) but the tutorial seems to work only on old datasets versions.
I then get the error :
```
--------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[65], [line 1](vscode-notebook-cell:?execution_count=65&line=1)
----> [1](vscode-notebook-cell:?execution_count=65&line=1) dataset = datasets.load_dataset("superb", name="asr", split="test")
3 # KeyDataset (only *pt*) will simply return the item in the dict returned by the dataset item
4 # as we're not interested in the *target* part of the dataset. For sentence pair use KeyPairDataset
5 for out in tqdm(pipe(KeyDataset(dataset, "file"))):
File ~/Desktop/debug/llm_course/.venv/lib/python3.11/site-packages/datasets/load.py:1392, 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)
1387 verification_mode = VerificationMode(
1388 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
1389 )
1391 # Create a dataset builder
-> [1392](https://file+.vscode-resource.vscode-cdn.net/home/edwin/Desktop/debug/llm_course/~/Desktop/debug/llm_course/.venv/lib/python3.11/site-packages/datasets/load.py:1392) builder_instance = load_dataset_builder(
1393 path=path,
1394 name=name,
1395 data_dir=data_dir,
1396 data_files=data_files,
1397 cache_dir=cache_dir,
1398 features=features,
1399 download_config=download_config,
1400 download_mode=download_mode,
1401 revision=revision,
1402 token=token,
1403 storage_options=storage_options,
1404 **config_kwargs,
1405 )
1407 # Return iterable dataset in case of streaming
1408 if streaming:
File ~/Desktop/debug/llm_course/.venv/lib/python3.11/site-packages/datasets/load.py:1132, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, **config_kwargs)
1130 if features is not None:
1131 features = _fix_for_backward_compatible_features(features)
-> [1132](https://file+.vscode-resource.vscode-cdn.net/home/edwin/Desktop/debug/llm_course/~/Desktop/debug/llm_course/.venv/lib/python3.11/site-packages/datasets/load.py:1132) dataset_module = dataset_module_factory(
1133 path,
1134 revision=revision,
1135 download_config=download_config,
1136 download_mode=download_mode,
1137 data_dir=data_dir,
1138 data_files=data_files,
1139 cache_dir=cache_dir,
1140 )
1141 # Get dataset builder class
1142 builder_kwargs = dataset_module.builder_kwargs
File ~/Desktop/debug/llm_course/.venv/lib/python3.11/site-packages/datasets/load.py:1031, in dataset_module_factory(path, revision, download_config, download_mode, data_dir, data_files, cache_dir, **download_kwargs)
1026 if isinstance(e1, FileNotFoundError):
1027 raise FileNotFoundError(
1028 f"Couldn't find any data file at {relative_to_absolute_path(path)}. "
1029 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1030 ) from None
-> [1031](https://file+.vscode-resource.vscode-cdn.net/home/edwin/Desktop/debug/llm_course/~/Desktop/debug/llm_course/.venv/lib/python3.11/site-packages/datasets/load.py:1031) raise e1 from None
1032 else:
1033 raise FileNotFoundError(f"Couldn't find any data file at {relative_to_absolute_path(path)}.")
File ~/Desktop/debug/llm_course/.venv/lib/python3.11/site-packages/datasets/load.py:989, in dataset_module_factory(path, revision, download_config, download_mode, data_dir, data_files, cache_dir, **download_kwargs)
981 try:
982 api.hf_hub_download(
983 repo_id=path,
984 filename=filename,
(...) 987 proxies=download_config.proxies,
988 )
--> [989](https://file+.vscode-resource.vscode-cdn.net/home/edwin/Desktop/debug/llm_course/~/Desktop/debug/llm_course/.venv/lib/python3.11/site-packages/datasets/load.py:989) raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
990 except EntryNotFoundError:
991 # Use the infos from the parquet export except in some cases:
992 if data_dir or data_files or (revision and revision != "main"):
RuntimeError: Dataset scripts are no longer supported, but found superb.py
```
NB : I tried to replace "superb" by "anton-l/superb_demo" but I get a 'torchcodec' importing error. Maybe I misunderstood something.
### Steps to reproduce the bug
```
import datasets
from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
from tqdm.auto import tqdm
pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
dataset = datasets.load_dataset("superb", name="asr", split="test")
# KeyDataset (only *pt*) will simply return the item in the dict returned by the dataset item
# as we're not interested in the *target* part of the dataset. For sentence pair use KeyPairDataset
for out in tqdm(pipe(KeyDataset(dataset, "file"))):
print(out)
# {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
# {"text": ....}
# ....
```
### Expected behavior
Get the tutorial expected results
### Environment info
--- SYSTEM INFO ---
Operating System: Ubuntu 24.10
Kernel: Linux 6.11.0-29-generic
Architecture: x86-64
--- PYTHON ---
Python 3.11.13
--- VENV INFO ----
datasets=4.0.0
transformers=4.53
tqdm=4.67.1 | null | {
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"body": "Hi ! This is because `datasets` now uses the FFmpeg-based library `torchcodec` instead of the libsndfile-based library `soundfile` to decode audio data. Those two have different decoding implementations",
"created_at": "2025-07-25T14:27:36Z",
"html_url": "h... | 2025-07-22T17:02:17Z | 2025-07-30T14:22:21Z | 2025-07-30T14:22:21Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
In datasets 4.0.0 release, `load_dataset()` returns different audio samples compared to earlier versions, this breaks integration tests that depend on consistent sample data across different environments (first and second envs specified below).
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(24000))
sample= ds[0]["audio"]["array"]
print(sample)
# sample in 3.6.0
[0.00231914 0.00245417 0.00187414 ... 0.00061956 0.00101157 0.00076325]
# sample in 4.0.0
array([0.00238037, 0.00220794, 0.00198703, ..., 0.00057983, 0.00085863,
0.00115309], dtype=float32)
```
### Expected behavior
The same dataset should load identical samples across versions to maintain reproducibility.
### Environment info
First env:
- datasets version: 3.6.0
- Platform: Windows-10-10.0.26100-SP0
- Python: 3.11.0
Second env:
- datasets version: 4.0.0
- Platform: Linux-6.1.123+-x86_64-with-glibc2.35
- Python: 3.11.13 | {
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} | https://api.github.com/repos/huggingface/datasets/issues/7696/timeline | null | completed | null | null | 2025-07-22T17:02:17Z | 2025-07-30T14:22:21Z | 7 days, 21:20:04 | false |
https://api.github.com/repos/huggingface/datasets/issues/7686 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7686/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7686/comments | https://api.github.com/repos/huggingface/datasets/issues/7686/events | https://github.com/huggingface/datasets/issues/7686 | 3,237,201,090 | I_kwDODunzps7A88TC | 7,686 | load_dataset does not check .no_exist files in the hub cache | {
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"completed": 0,
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"total": 0
} | ### Describe the bug
I'm not entirely sure if this should be submitted as a bug in the `datasets` library or the `huggingface_hub` library, given it could be fixed at different levels of the stack.
The fundamental issue is that the `load_datasets` api doesn't use the `.no_exist` files in the hub cache unlike other wrapper APIs that do. This is because the `utils.file_utils.cached_path` used directly calls `hf_hub_download` instead of using `file_download.try_to_load_from_cache` from `huggingface_hub` (see `transformers` library `utils.hub.cached_files` for one alternate example).
This results in unnecessary metadata HTTP requests occurring for files that don't exist on every call. It won't generate the .no_exist cache files, nor will it use them.
### Steps to reproduce the bug
Run the following snippet as one example (setting cache dirs to clean paths for clarity)
`env HF_HOME=~/local_hf_hub python repro.py`
```
from datasets import load_dataset
import huggingface_hub
# monkeypatch to print out metadata requests being made
original_get_hf_file_metadata = huggingface_hub.file_download.get_hf_file_metadata
def get_hf_file_metadata_wrapper(*args, **kwargs):
print("File metadata request made (get_hf_file_metadata):", args, kwargs)
return original_get_hf_file_metadata(*args, **kwargs)
# Apply the patch
huggingface_hub.file_download.get_hf_file_metadata = get_hf_file_metadata_wrapper
dataset = load_dataset(
"Salesforce/wikitext",
"wikitext-2-v1",
split="test",
trust_remote_code=True,
cache_dir="~/local_datasets",
revision="b08601e04326c79dfdd32d625aee71d232d685c3",
)
```
This may be called over and over again, and you will see the same calls for files that don't exist:
```
File metadata request made (get_hf_file_metadata): () {'url': 'https://huggingface.co/datasets/Salesforce/wikitext/resolve/b08601e04326c79dfdd32d625aee71d232d685c3/wikitext.py', 'proxies': None, 'timeout': 10, 'headers': {'user-agent': 'datasets/3.6.0; hf_hub/0.33.2; python/3.12.11; torch/2.7.0; huggingface_hub/0.33.2; pyarrow/20.0.0; jax/0.5.3'}, 'token': None}
File metadata request made (get_hf_file_metadata): () {'url': 'https://huggingface.co/datasets/Salesforce/wikitext/resolve/b08601e04326c79dfdd32d625aee71d232d685c3/.huggingface.yaml', 'proxies': None, 'timeout': 10, 'headers': {'user-agent': 'datasets/3.6.0; hf_hub/0.33.2; python/3.12.11; torch/2.7.0; huggingface_hub/0.33.2; pyarrow/20.0.0; jax/0.5.3'}, 'token': None}
File metadata request made (get_hf_file_metadata): () {'url': 'https://huggingface.co/datasets/Salesforce/wikitext/resolve/b08601e04326c79dfdd32d625aee71d232d685c3/dataset_infos.json', 'proxies': None, 'timeout': 10, 'headers': {'user-agent': 'datasets/3.6.0; hf_hub/0.33.2; python/3.12.11; torch/2.7.0; huggingface_hub/0.33.2; pyarrow/20.0.0; jax/0.5.3'}, 'token': None}
```
And you can see that the .no_exist folder is never created
```
$ ls ~/local_hf_hub/hub/datasets--Salesforce--wikitext/
blobs refs snapshots
```
### Expected behavior
The expected behavior is for the print "File metadata request made" to stop after the first call, and for .no_exist directory & files to be populated under ~/local_hf_hub/hub/datasets--Salesforce--wikitext/
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-6.5.13-65-650-4141-22041-coreweave-amd64-85c45edc-x86_64-with-glibc2.35
- Python version: 3.12.11
- `huggingface_hub` version: 0.33.2
- PyArrow version: 20.0.0
- Pandas version: 2.3.1
- `fsspec` version: 2024.9.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7688 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7688/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7688/comments | https://api.github.com/repos/huggingface/datasets/issues/7688/events | https://github.com/huggingface/datasets/issues/7688 | 3,238,851,443 | I_kwDODunzps7BDPNz | 7,688 | No module named "distributed" | {
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{
"author_association": "NONE",
"body": "The error ModuleNotFoundError: No module named 'datasets.distributed' means your installed datasets library is too old or incompatible with the version of Library you are using(in my case it was BEIR). The datasets.distributed module was removed in recent versions of... | 2025-07-17T09:32:35Z | 2025-07-25T15:14:19Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
hello, when I run the command "from datasets.distributed import split_dataset_by_node", I always met the bug "No module named 'datasets.distributed" in different version like 4.0.0, 2.21.0 and so on. How can I solve this?
### Steps to reproduce the bug
1. pip install datasets
2. from datasets.distributed import split_dataset_by_node
### Expected behavior
expecting the command "from datasets.distributed import split_dataset_by_node" can be ran successfully
### Environment info
python: 3.12 | null | {
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{
"author_association": "NONE",
"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7684). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-07-15T10:16:10Z",
... | 2025-07-15T10:13:42Z | 2025-07-15T10:24:08Z | 2025-07-15T10:24:07Z | MEMBER | null | null | null | fix https://github.com/huggingface/datasets/issues/7682 | {
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https://api.github.com/repos/huggingface/datasets/issues/7687 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7687/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7687/comments | https://api.github.com/repos/huggingface/datasets/issues/7687/events | https://github.com/huggingface/datasets/issues/7687 | 3,238,760,301 | I_kwDODunzps7BC49t | 7,687 | Datasets keeps rebuilding the dataset every time i call the python script | {
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{
"author_association": "MEMBER",
"body": "here is the code to load the dataset form the cache:\n\n```python\ns = load_dataset('databricks/databricks-dolly-15k')['train']\n```\n\nif you pass the location of a local directory it will create a new cache based on that directory content",
"created_at": "202... | 2025-07-17T09:03:38Z | 2025-07-25T15:21:31Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Every time it runs, somehow, samples increase.
This can cause a 12mb dataset to have other built versions of 400 mbs+
<img width="363" height="481" alt="Image" src="https://github.com/user-attachments/assets/766ce958-bd2b-41bc-b950-86710259bfdc" />
### Steps to reproduce the bug
`from datasets import load_dataset
s = load_dataset('~/.cache/huggingface/datasets/databricks___databricks-dolly-15k')['train']
`
1. A dataset needs to be available in the .cache folder
2. Run the code multiple times, and every time it runs, more versions are created
### Expected behavior
The number of samples increases every time the script runs
### Environment info
- `datasets` version: 3.6.0
- Platform: Windows-11-10.0.26100-SP0
- Python version: 3.13.3
- `huggingface_hub` version: 0.32.3
- PyArrow version: 20.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0
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{
"author_association": "MEMBER",
"body": "This is a weird bug, is it a range that is supposed to be satisfiable ? I mean, is it on the boundraries ?\n\nLet me know if you'r e still having the issue, in case it was just a transient bug",
"created_at": "2025-07-25T15:24:05Z",
"html_url": "https://git... | 2025-07-16T18:39:44Z | 2025-07-25T16:09:50Z | null | NONE | null | null | {
"completed": 0,
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"total": 0
} | ### Describe the bug
First off, I do apologize if this is not the correct repo for submitting this issue. Please direct me to another one if it's more appropriate elsewhere.
The datasets rest api is inconsistently giving `416 Range Not Satisfiable` when using a range request to get portions of the parquet files. More often than not, I am seeing 416, but other times for an identical request, it gives me the data along with `206 Partial Content` as expected.
### Steps to reproduce the bug
repeating this request multiple times will return either 416 or 206.
```sh
$ curl -v -L -H "Range: bytes=217875070-218006142" -o output.parquet "https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet"
```
Note: this is not limited to just the above file, I tried with many different datasets and am able to consistently reproduce issue across multiple datasets.
when the 416 is returned, I get the following headers
```
< HTTP/2 416
< content-type: text/html
< content-length: 49
< server: CloudFront
< date: Wed, 16 Jul 2025 14:58:43 GMT
< expires: Wed, 16 Jul 2025 14:58:43 GMT
< content-range: bytes */177
< x-cache: Error from cloudfront
< via: 1.1 873527676a354c5998cad133525df9c0.cloudfront.net (CloudFront)
<
```
this suggests to me that there is likely a CDN/caching/routing issue happening and the request is not getting routed properly.
Full verbose output via curl.
<details>
❯ curl -v -L -H "Range: bytes=217875070-218006142" -o output.parquet "https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet"
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0* Host huggingface.co:443 was resolved.
* IPv6: (none)
* IPv4: 18.160.102.96, 18.160.102.110, 18.160.102.4, 18.160.102.86
* Trying 18.160.102.96:443...
* Connected to huggingface.co (18.160.102.96) port 443
* ALPN: curl offers h2,http/1.1
* (304) (OUT), TLS handshake, Client hello (1):
} [319 bytes data]
* CAfile: /etc/ssl/cert.pem
* CApath: none
* (304) (IN), TLS handshake, Server hello (2):
{ [122 bytes data]
* (304) (IN), TLS handshake, Unknown (8):
{ [19 bytes data]
* (304) (IN), TLS handshake, Certificate (11):
{ [3821 bytes data]
* (304) (IN), TLS handshake, CERT verify (15):
{ [264 bytes data]
* (304) (IN), TLS handshake, Finished (20):
{ [36 bytes data]
* (304) (OUT), TLS handshake, Finished (20):
} [36 bytes data]
* SSL connection using TLSv1.3 / AEAD-AES128-GCM-SHA256 / [blank] / UNDEF
* ALPN: server accepted h2
* Server certificate:
* subject: CN=huggingface.co
* start date: Apr 13 00:00:00 2025 GMT
* expire date: May 12 23:59:59 2026 GMT
* subjectAltName: host "huggingface.co" matched cert's "huggingface.co"
* issuer: C=US; O=Amazon; CN=Amazon RSA 2048 M02
* SSL certificate verify ok.
* using HTTP/2
* [HTTP/2] [1] OPENED stream for https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet
* [HTTP/2] [1] [:method: GET]
* [HTTP/2] [1] [:scheme: https]
* [HTTP/2] [1] [:authority: huggingface.co]
* [HTTP/2] [1] [:path: /api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet]
* [HTTP/2] [1] [user-agent: curl/8.7.1]
* [HTTP/2] [1] [accept: */*]
* [HTTP/2] [1] [range: bytes=217875070-218006142]
> GET /api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet HTTP/2
> Host: huggingface.co
> User-Agent: curl/8.7.1
> Accept: */*
> Range: bytes=217875070-218006142
>
* Request completely sent off
< HTTP/2 416
< content-type: text/html
< content-length: 49
< server: CloudFront
< date: Wed, 16 Jul 2025 14:58:41 GMT
< expires: Wed, 16 Jul 2025 14:58:41 GMT
< content-range: bytes */177
< x-cache: Error from cloudfront
< via: 1.1 e2f1bed2f82641d6d5439eac20a790ba.cloudfront.net (CloudFront)
< x-amz-cf-pop: MSP50-P1
< x-amz-cf-id: Mo8hn-EZLJqE_hoBday8DdhmVXhV3v9-Wg-EEHI6gX_fNlkanVIUBA==
<
{ [49 bytes data]
100 49 100 49 0 0 2215 0 --:--:-- --:--:-- --:--:-- 2227
* Connection #0 to host huggingface.co left intact
(.venv) Daft main** ≡❯ curl -v -L -H "Range: bytes=217875070-218006142" -o output.parquet "https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet"
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0* Host huggingface.co:443 was resolved.
* IPv6: (none)
* IPv4: 18.160.102.96, 18.160.102.110, 18.160.102.4, 18.160.102.86
* Trying 18.160.102.96:443...
* Connected to huggingface.co (18.160.102.96) port 443
* ALPN: curl offers h2,http/1.1
* (304) (OUT), TLS handshake, Client hello (1):
} [319 bytes data]
* CAfile: /etc/ssl/cert.pem
* CApath: none
* (304) (IN), TLS handshake, Server hello (2):
{ [122 bytes data]
* (304) (IN), TLS handshake, Unknown (8):
{ [19 bytes data]
* (304) (IN), TLS handshake, Certificate (11):
{ [3821 bytes data]
* (304) (IN), TLS handshake, CERT verify (15):
{ [264 bytes data]
* (304) (IN), TLS handshake, Finished (20):
{ [36 bytes data]
* (304) (OUT), TLS handshake, Finished (20):
} [36 bytes data]
* SSL connection using TLSv1.3 / AEAD-AES128-GCM-SHA256 / [blank] / UNDEF
* ALPN: server accepted h2
* Server certificate:
* subject: CN=huggingface.co
* start date: Apr 13 00:00:00 2025 GMT
* expire date: May 12 23:59:59 2026 GMT
* subjectAltName: host "huggingface.co" matched cert's "huggingface.co"
* issuer: C=US; O=Amazon; CN=Amazon RSA 2048 M02
* SSL certificate verify ok.
* using HTTP/2
* [HTTP/2] [1] OPENED stream for https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet
* [HTTP/2] [1] [:method: GET]
* [HTTP/2] [1] [:scheme: https]
* [HTTP/2] [1] [:authority: huggingface.co]
* [HTTP/2] [1] [:path: /api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet]
* [HTTP/2] [1] [user-agent: curl/8.7.1]
* [HTTP/2] [1] [accept: */*]
* [HTTP/2] [1] [range: bytes=217875070-218006142]
> GET /api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet HTTP/2
> Host: huggingface.co
> User-Agent: curl/8.7.1
> Accept: */*
> Range: bytes=217875070-218006142
>
* Request completely sent off
< HTTP/2 416
< content-type: text/html
< content-length: 49
< server: CloudFront
< date: Wed, 16 Jul 2025 14:58:42 GMT
< expires: Wed, 16 Jul 2025 14:58:42 GMT
< content-range: bytes */177
< x-cache: Error from cloudfront
< via: 1.1 bb352451e1eacf85f8786ee3ecd07eca.cloudfront.net (CloudFront)
< x-amz-cf-pop: MSP50-P1
< x-amz-cf-id: 9xy-CX9KvlS8Ye4eFr8jXMDobZHFkvdyvkLJGmK_qiwZQywCCwfq7Q==
<
{ [49 bytes data]
100 49 100 49 0 0 2381 0 --:--:-- --:--:-- --:--:-- 2450
* Connection #0 to host huggingface.co left intact
(.venv) Daft main** ≡❯ curl -v -L -H "Range: bytes=217875070-218006142" -o output.parquet "https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet"
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0* Host huggingface.co:443 was resolved.
* IPv6: (none)
* IPv4: 18.160.102.96, 18.160.102.110, 18.160.102.4, 18.160.102.86
* Trying 18.160.102.96:443...
* Connected to huggingface.co (18.160.102.96) port 443
* ALPN: curl offers h2,http/1.1
* (304) (OUT), TLS handshake, Client hello (1):
} [319 bytes data]
* CAfile: /etc/ssl/cert.pem
* CApath: none
* (304) (IN), TLS handshake, Server hello (2):
{ [122 bytes data]
* (304) (IN), TLS handshake, Unknown (8):
{ [19 bytes data]
* (304) (IN), TLS handshake, Certificate (11):
{ [3821 bytes data]
* (304) (IN), TLS handshake, CERT verify (15):
{ [264 bytes data]
* (304) (IN), TLS handshake, Finished (20):
{ [36 bytes data]
* (304) (OUT), TLS handshake, Finished (20):
} [36 bytes data]
* SSL connection using TLSv1.3 / AEAD-AES128-GCM-SHA256 / [blank] / UNDEF
* ALPN: server accepted h2
* Server certificate:
* subject: CN=huggingface.co
* start date: Apr 13 00:00:00 2025 GMT
* expire date: May 12 23:59:59 2026 GMT
* subjectAltName: host "huggingface.co" matched cert's "huggingface.co"
* issuer: C=US; O=Amazon; CN=Amazon RSA 2048 M02
* SSL certificate verify ok.
* using HTTP/2
* [HTTP/2] [1] OPENED stream for https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet
* [HTTP/2] [1] [:method: GET]
* [HTTP/2] [1] [:scheme: https]
* [HTTP/2] [1] [:authority: huggingface.co]
* [HTTP/2] [1] [:path: /api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet]
* [HTTP/2] [1] [user-agent: curl/8.7.1]
* [HTTP/2] [1] [accept: */*]
* [HTTP/2] [1] [range: bytes=217875070-218006142]
> GET /api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet HTTP/2
> Host: huggingface.co
> User-Agent: curl/8.7.1
> Accept: */*
> Range: bytes=217875070-218006142
>
* Request completely sent off
< HTTP/2 416
< content-type: text/html
< content-length: 49
< server: CloudFront
< date: Wed, 16 Jul 2025 14:58:43 GMT
< expires: Wed, 16 Jul 2025 14:58:43 GMT
< content-range: bytes */177
< x-cache: Error from cloudfront
< via: 1.1 873527676a354c5998cad133525df9c0.cloudfront.net (CloudFront)
< x-amz-cf-pop: MSP50-P1
< x-amz-cf-id: wtBgwY4u4YJ2pD1ovM8UV770UiJoqWfs7i7VzschDyoLv5g7swGGmw==
<
{ [49 bytes data]
100 49 100 49 0 0 2273 0 --:--:-- --:--:-- --:--:-- 2333
* Connection #0 to host huggingface.co left intact
(.venv) Daft main** ≡❯ curl -v -L -H "Range: bytes=217875070-218006142" -o output.parquet "https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet"
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0* Host huggingface.co:443 was resolved.
* IPv6: (none)
* IPv4: 18.160.102.96, 18.160.102.110, 18.160.102.4, 18.160.102.86
* Trying 18.160.102.96:443...
* Connected to huggingface.co (18.160.102.96) port 443
* ALPN: curl offers h2,http/1.1
* (304) (OUT), TLS handshake, Client hello (1):
} [319 bytes data]
* CAfile: /etc/ssl/cert.pem
* CApath: none
* (304) (IN), TLS handshake, Server hello (2):
{ [122 bytes data]
* (304) (IN), TLS handshake, Unknown (8):
{ [19 bytes data]
* (304) (IN), TLS handshake, Certificate (11):
{ [3821 bytes data]
* (304) (IN), TLS handshake, CERT verify (15):
{ [264 bytes data]
* (304) (IN), TLS handshake, Finished (20):
{ [36 bytes data]
* (304) (OUT), TLS handshake, Finished (20):
} [36 bytes data]
* SSL connection using TLSv1.3 / AEAD-AES128-GCM-SHA256 / [blank] / UNDEF
* ALPN: server accepted h2
* Server certificate:
* subject: CN=huggingface.co
* start date: Apr 13 00:00:00 2025 GMT
* expire date: May 12 23:59:59 2026 GMT
* subjectAltName: host "huggingface.co" matched cert's "huggingface.co"
* issuer: C=US; O=Amazon; CN=Amazon RSA 2048 M02
* SSL certificate verify ok.
* using HTTP/2
* [HTTP/2] [1] OPENED stream for https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet
* [HTTP/2] [1] [:method: GET]
* [HTTP/2] [1] [:scheme: https]
* [HTTP/2] [1] [:authority: huggingface.co]
* [HTTP/2] [1] [:path: /api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet]
* [HTTP/2] [1] [user-agent: curl/8.7.1]
* [HTTP/2] [1] [accept: */*]
* [HTTP/2] [1] [range: bytes=217875070-218006142]
> GET /api/datasets/HuggingFaceTB/smoltalk2/parquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0.parquet HTTP/2
> Host: huggingface.co
> User-Agent: curl/8.7.1
> Accept: */*
> Range: bytes=217875070-218006142
>
* Request completely sent off
< HTTP/2 302
< content-type: text/plain; charset=utf-8
< content-length: 177
< location: https://huggingface.co/datasets/HuggingFaceTB/smoltalk2/resolve/refs%2Fconvert%2Fparquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0000.parquet
< date: Wed, 16 Jul 2025 14:58:44 GMT
< x-powered-by: huggingface-moon
< cross-origin-opener-policy: same-origin
< referrer-policy: strict-origin-when-cross-origin
< x-request-id: Root=1-6877be24-476860f03849cb1a1570c9d8
< access-control-allow-origin: https://huggingface.co
< access-control-expose-headers: X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range,X-Linked-Size,X-Linked-ETag,X-Xet-Hash
< set-cookie: token=; Path=/; Expires=Thu, 01 Jan 1970 00:00:00 GMT; Secure; SameSite=None
< set-cookie: token=; Domain=huggingface.co; Path=/; Expires=Thu, 01 Jan 1970 00:00:00 GMT; Secure; SameSite=Lax
< x-cache: Miss from cloudfront
< via: 1.1 dd5af138aa8a11d8a70d5ef690ad1a2a.cloudfront.net (CloudFront)
< x-amz-cf-pop: MSP50-P1
< x-amz-cf-id: xuSi0X5RpH1OZqQOM8gGQLQLU8eOM6Gbkk-bgIX_qBnTTaa1VNkExA==
<
* Ignoring the response-body
100 177 100 177 0 0 2021 0 --:--:-- --:--:-- --:--:-- 2034
* Connection #0 to host huggingface.co left intact
* Issue another request to this URL: 'https://huggingface.co/datasets/HuggingFaceTB/smoltalk2/resolve/refs%2Fconvert%2Fparquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0000.parquet'
* Found bundle for host: 0x600002d54570 [can multiplex]
* Re-using existing connection with host huggingface.co
* [HTTP/2] [3] OPENED stream for https://huggingface.co/datasets/HuggingFaceTB/smoltalk2/resolve/refs%2Fconvert%2Fparquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0000.parquet
* [HTTP/2] [3] [:method: GET]
* [HTTP/2] [3] [:scheme: https]
* [HTTP/2] [3] [:authority: huggingface.co]
* [HTTP/2] [3] [:path: /datasets/HuggingFaceTB/smoltalk2/resolve/refs%2Fconvert%2Fparquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0000.parquet]
* [HTTP/2] [3] [user-agent: curl/8.7.1]
* [HTTP/2] [3] [accept: */*]
* [HTTP/2] [3] [range: bytes=217875070-218006142]
> GET /datasets/HuggingFaceTB/smoltalk2/resolve/refs%2Fconvert%2Fparquet/Mid/Llama_Nemotron_Post_Training_Dataset_reasoning_r1/0000.parquet HTTP/2
> Host: huggingface.co
> User-Agent: curl/8.7.1
> Accept: */*
> Range: bytes=217875070-218006142
>
* Request completely sent off
< HTTP/2 302
< content-type: text/plain; charset=utf-8
< content-length: 1317
< location: https://cas-bridge.xethub.hf.co/xet-bridge-us/686fc33898943c873b45c9a0/cf8a3a5665cf8b2ff667fb5236a1e5cb13c7582955f9533c88e1387997ef3af9?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=cas%2F20250716%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250716T145416Z&X-Amz-Expires=3600&X-Amz-Signature=21a15b50740d73fd8ce82d5105733ca067d2e612ada22570e09e93ebcc7f8842&X-Amz-SignedHeaders=host&X-Xet-Cas-Uid=public&response-content-disposition=inline%3B+filename*%3DUTF-8%27%270000.parquet%3B+filename%3D%220000.parquet%22%3B&x-id=GetObject&Expires=1752681256&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc1MjY4MTI1Nn19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2FzLWJyaWRnZS54ZXRodWIuaGYuY28veGV0LWJyaWRnZS11cy82ODZmYzMzODk4OTQzYzg3M2I0NWM5YTAvY2Y4YTNhNTY2NWNmOGIyZmY2NjdmYjUyMzZhMWU1Y2IxM2M3NTgyOTU1Zjk1MzNjODhlMTM4Nzk5N2VmM2FmOSoifV19&Signature=Tl3xorJ-7yaWvG6Y1AhhRlV2Wko9QpoK1tdPOfNZaRbHo%7EdaAkJRJfcLAYD5YzozfHWBZMLlJsaMPJ1MAne21nr5%7E737sE6yLfBwHdP3ZFZhgrLsN%7EvkIWK2GYX543qTg-pVsf3it92w1oWyoyYNQ9srxLfEIuG2AKV2Nu3Ejl7S%7EaAq4Gv4jNemvRTLBFGgYPdUeuavudl4OD4RGkSGTnpzh-P-OBk5WvgpdZZnbb1cRAP73tFHsPDX4%7ETfQIor109G%7E0TB3Jq0wopO9WV0sMQyQs9peZc6bxONiTxb9aHM4yNvWNbVGtlPuC6YS4c9T1e9%7EehdgU4sDOI%7EhpaCvg__&Key-Pair-Id=K2L8F4GPSG1IFC
< date: Wed, 16 Jul 2025 14:58:44 GMT
< x-powered-by: huggingface-moon
< cross-origin-opener-policy: same-origin
< referrer-policy: strict-origin-when-cross-origin
< x-request-id: Root=1-6877be24-4f628b292dc8a7a5339c41d3
< access-control-allow-origin: https://huggingface.co
< vary: Origin, Accept
< access-control-expose-headers: X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range,X-Linked-Size,X-Linked-ETag,X-Xet-Hash
< set-cookie: token=; Path=/; Expires=Thu, 01 Jan 1970 00:00:00 GMT; Secure; SameSite=None
< set-cookie: token=; Domain=huggingface.co; Path=/; Expires=Thu, 01 Jan 1970 00:00:00 GMT; Secure; SameSite=Lax
< x-repo-commit: 712df366ffbc959d9f4279bf2da579230b7ca5d8
< accept-ranges: bytes
< x-linked-size: 218006142
< x-linked-etag: "01736bf26d0046ddec4ab8900fba3f0dc6500b038314b44d0edb73a7c88dec07"
< x-xet-hash: cf8a3a5665cf8b2ff667fb5236a1e5cb13c7582955f9533c88e1387997ef3af9
< link: <https://huggingface.co/api/datasets/HuggingFaceTB/smoltalk2/xet-read-token/712df366ffbc959d9f4279bf2da579230b7ca5d8>; rel="xet-auth", <https://cas-server.xethub.hf.co/reconstruction/cf8a3a5665cf8b2ff667fb5236a1e5cb13c7582955f9533c88e1387997ef3af9>; rel="xet-reconstruction-info"
< x-cache: Miss from cloudfront
< via: 1.1 dd5af138aa8a11d8a70d5ef690ad1a2a.cloudfront.net (CloudFront)
< x-amz-cf-pop: MSP50-P1
< x-amz-cf-id: 0qXw2sJGrWCLVt7c-Vtn09uE3nu6CrJw9RmAKvNr_flG75muclvlIg==
<
* Ignoring the response-body
100 1317 100 1317 0 0 9268 0 --:--:-- --:--:-- --:--:-- 9268
* Connection #0 to host huggingface.co left intact
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</details>
### Expected behavior
always get back a `206`
### Environment info
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{
"author_association": "NONE",
"body": "Same here, for `HuggingFaceFW/fineweb`. Code that worked with no issues for the last 2 months suddenly fails today. Tried updating `datasets`, `huggingface_hub`, `fsspec` to newest versions, but the same error occurs.",
"created_at": "2025-07-18T09:34:20Z",
"... | 2025-07-18T09:30:04Z | 2025-07-18T11:59:51Z | 2025-07-18T11:52:29Z | NONE | null | null | {
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} | ### Describe the bug
Up until a couple days ago I was having no issues loading `Helsinki-NLP/europarl` and `Helsinki-NLP/un_pc`, but now suddenly I get the following error:
```
huggingface_hub.errors.BadRequestError: (Request ID: ...)
Bad request:
* Invalid input: expected array, received string * at paths * Invalid input: expected boolean, received string * at expand
✖ Invalid input: expected array, received string
→ at paths
✖ Invalid input: expected boolean, received string
→ at expand
```
I tried with both `4.0.0` and `3.5.1` since this dataset uses `trust_remote_code`, but I get the same error with both.
What can I do to load the dataset? I checked the documentation and GitHub issues here, but couldn't find a solution.
### Steps to reproduce the bug
```python
import datasets
ds = datasets.load_dataset("Helsinki-NLP/europarl", "en-fr", streaming=True, trust_remote_code=True)["train"]
```
### Expected behavior
That the dataset loads as it did a couple days ago.
### Environment info
- `datasets` version: 3.5.1
- Platform: Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.11.11
- `huggingface_hub` version: 0.30.2
- PyArrow version: 20.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1 | {
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{
"author_association": "NONE",
"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7683). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-07-15T09:40:16Z",
... | 2025-07-15T09:37:44Z | 2025-07-15T10:13:58Z | 2025-07-15T10:13:56Z | MEMBER | null | null | null | for https://huggingface.co/datasets/allenai/olmo-mix-1124 | {
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https://api.github.com/repos/huggingface/datasets/issues/7681 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7681/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7681/comments | https://api.github.com/repos/huggingface/datasets/issues/7681/events | https://github.com/huggingface/datasets/issues/7681 | 3,227,112,736 | I_kwDODunzps7AWdUg | 7,681 | Probabilistic High Memory Usage and Freeze on Python 3.10 | {
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} | ### Describe the bug
A probabilistic issue encountered when processing datasets containing PIL.Image columns using the huggingface/datasets library on Python 3.10. The process occasionally experiences a sudden and significant memory spike, reaching 100% utilization, leading to a complete freeze. During this freeze, the process becomes unresponsive, cannot be forcefully terminated, and does not throw any exceptions.
I have attempted to mitigate this issue by setting `datasets.config.IN_MEMORY_MAX_SIZE`, but it had no effect. In fact, based on the document of `load_dataset`, I suspect that setting `IN_MEMORY_MAX_SIZE` might even have a counterproductive effect.
This bug is not consistently reproducible, but its occurrence rate significantly decreases or disappears entirely when upgrading Python to version 3.11 or higher. Therefore, this issue also serves to share a potential solution for others who might encounter similar problems.
### Steps to reproduce the bug
Due to the probabilistic nature of this bug, consistent reproduction cannot be guaranteed for every run. However, in my environment, processing large datasets like timm/imagenet-1k-wds(whether reading, casting, or mapping operations) almost certainly triggers the issue at some point.
The probability of the issue occurring drastically increases when num_proc is set to a value greater than 1 during operations.
When the issue occurs, my system logs repeatedly show the following warnings:
```
WARN: very high memory utilization: 57.74GiB / 57.74GiB (100 %)
WARN: container is unhealthy: triggered memory limits (OOM)
WARN: container is unhealthy: triggered memory limits (OOM)
WARN: container is unhealthy: triggered memory limits (OOM)
```
### Expected behavior
The dataset should be read and processed normally without memory exhaustion or freezing. If an unrecoverable error occurs, an appropriate exception should be raised.
I have found that upgrading Python to version 3.11 or above completely resolves this issue. On Python 3.11, when memory usage approaches 100%, it suddenly drops before slowly increasing again. I suspect this behavior is due to an expected memory management action, possibly involving writing to disk cache, which prevents the complete freeze observed in Python 3.10.
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-5.15.0-71-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.33.4
- PyArrow version: 20.0.0
- Pandas version: 2.3.1
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{
"author_association": "MEMBER",
"body": "thanks for reporting, I opened a PR and I'll make a patch release soon ",
"created_at": "2025-07-15T10:14:37Z",
"html_url": "https://github.com/huggingface/datasets/issues/7682#issuecomment-3073027855",
"id": 3073027855,
"issue_url": "https://api.gi... | 2025-07-14T18:41:02Z | 2025-07-15T12:10:39Z | 2025-07-15T10:24:08Z | NONE | null | null | {
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} | ### Describe the bug
Casting features with Audio for numpy arrays - done here with `ds.map(gen_sine, features=features)` fails
in version 4.0.0 but not in version 3.6.0
### Steps to reproduce the bug
The following `uv script` should be able to reproduce the bug in version 4.0.0
and pass in version 3.6.0 on a macOS Sequoia 15.5
```python
# /// script
# requires-python = ">=3.13"
# dependencies = [
# "datasets[audio]==4.0.0",
# "librosa>=0.11.0",
# ]
# ///
# NAME
# create_audio_dataset.py - create an audio dataset of sine waves
#
# SYNOPSIS
# uv run create_audio_dataset.py
#
# DESCRIPTION
# Create an audio dataset using the Hugging Face [datasets] library.
# Illustrates how to create synthetic audio datasets using the [map]
# datasets function.
#
# The strategy is to first create a dataset with the input to the
# generation function, then execute the map function that generates
# the result, and finally cast the final features.
#
# BUG
# Casting features with Audio for numpy arrays -
# done here with `ds.map(gen_sine, features=features)` fails
# in version 4.0.0 but not in version 3.6.0
#
# This happens both in cases where --extra audio is provided and where is not.
# When audio is not provided i've installed the latest compatible version
# of soundfile.
#
# The error when soundfile is installed but the audio --extra is not
# indicates that the array values do not have the `.T` property,
# whilst also indicating that the value is a list instead of a numpy array.
#
# Last lines of error report when for datasets + soundfile case
# ...
#
# File "/Users/luasantilli/.cache/uv/archive-v0/tc_5IhQe7Zpw8ZXgQWpnl/lib/python3.13/site-packages/datasets/features/audio.py", line 239, in cast_storage
# storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()])
# ~~~~~~~~~~~~~~~~~~~~~~^^^
# File "/Users/luasantilli/.cache/uv/archive-v0/tc_5IhQe7Zpw8ZXgQWpnl/lib/python3.13/site-packages/datasets/features/audio.py", line 122, in encode_example
# sf.write(buffer, value["array"].T, value["sampling_rate"], format="wav")
# ^^^^^^^^^^^^^^^^
# AttributeError: 'list' object has no attribute 'T'
# ...
#
# For the case of datasets[audio] without explicit adding soundfile I get an FFmpeg
# error.
#
# Last lines of error report:
#
# ...
# RuntimeError: Could not load libtorchcodec. Likely causes:
# 1. FFmpeg is not properly installed in your environment. We support
# versions 4, 5, 6 and 7.
# 2. The PyTorch version (2.7.1) is not compatible with
# this version of TorchCodec. Refer to the version compatibility
# table:
# https://github.com/pytorch/torchcodec?tab=readme-ov-file#installing-torchcodec.
# 3. Another runtime dependency; see exceptions below.
# The following exceptions were raised as we tried to load libtorchcodec:
#
# [start of libtorchcodec loading traceback]
# FFmpeg version 7: dlopen(/Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder7.dylib, 0x0006): Library not loaded: @rpath/libavutil.59.dylib
# Referenced from: <6DB21246-F28A-31A6-910A-D8F3355D1064> /Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder7.dylib
# Reason: no LC_RPATH's found
# FFmpeg version 6: dlopen(/Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder6.dylib, 0x0006): Library not loaded: @rpath/libavutil.58.dylib
# Referenced from: <BD3B44FC-E14B-3ABF-800F-BB54B6CCA3B1> /Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder6.dylib
# Reason: no LC_RPATH's found
# FFmpeg version 5: dlopen(/Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder5.dylib, 0x0006): Library not loaded: @rpath/libavutil.57.dylib
# Referenced from: <F06EBF8A-238C-3A96-BFBB-B34E0BBDABF0> /Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder5.dylib
# Reason: no LC_RPATH's found
# FFmpeg version 4: dlopen(/Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder4.dylib, 0x0006): Library not loaded: @rpath/libavutil.56.dylib
# Referenced from: <6E59F017-C703-3AF6-A271-6277DD5F8170> /Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder4.dylib
# Reason: no LC_RPATH's found
# ...
#
# This is strange because the the same error does not happen when using version 3.6.0 with datasets[audio].
#
# The same error appears in python3.12
## Imports
import numpy as np
from datasets import Dataset, Features, Audio, Value
## Parameters
NUM_WAVES = 128
SAMPLE_RATE = 16_000
DURATION = 1.0
## Input dataset arguments
freqs = np.linspace(100, 2000, NUM_WAVES).tolist()
ds = Dataset.from_dict({"frequency": freqs})
## Features for the final dataset
features = Features(
{"frequency": Value("float32"), "audio": Audio(sampling_rate=SAMPLE_RATE)}
)
## Generate audio sine waves and cast features
def gen_sine(example):
t = np.linspace(0, DURATION, int(SAMPLE_RATE * DURATION), endpoint=False)
wav = np.sin(2 * np.pi * example["frequency"] * t)
return {
"frequency": example["frequency"],
"audio": {"array": wav, "sampling_rate": SAMPLE_RATE},
}
ds = ds.map(gen_sine, features=features)
print(ds)
print(ds.features)
```
### Expected behavior
I expect the result of version `4.0.0` to be the same of that in version `3.6.0`. Switching the value
of the script above to `3.6.0` I get the following, expected, result:
```
$ uv run bug_report.py
Map: 100%|███████████████████████████████████████████████████████| 128/128 [00:00<00:00, 204.58 examples/s]
Dataset({
features: ['frequency', 'audio'],
num_rows: 128
})
{'frequency': Value(dtype='float32', id=None), 'audio': Audio(sampling_rate=16000, mono=True, decode=True, id=None)}
```
### Environment info
- `datasets` version: 4.0.0
- Platform: macOS-15.5-arm64-arm-64bit-Mach-O
- Python version: 3.13.1
- `huggingface_hub` version: 0.33.4
- PyArrow version: 20.0.0
- Pandas version: 2.3.1
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https://api.github.com/repos/huggingface/datasets/issues/7680 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7680/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7680/comments | https://api.github.com/repos/huggingface/datasets/issues/7680/events | https://github.com/huggingface/datasets/issues/7680 | 3,224,824,151 | I_kwDODunzps7ANulX | 7,680 | Question about iterable dataset and streaming | {
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{
"author_association": "MEMBER",
"body": "> If we have already loaded the dataset, why doing to_iterable_dataset? Does it go through the dataset faster than map-style dataset?\n\nyes, it makes a faster DataLoader for example (otherwise DataLoader uses `__getitem__` which is slower than iterating)\n\n> load... | 2025-07-12T04:48:30Z | 2025-07-15T13:39:38Z | null | NONE | null | null | {
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} | In the doc, I found the following example: https://github.com/huggingface/datasets/blob/611f5a592359ebac6f858f515c776aa7d99838b2/docs/source/stream.mdx?plain=1#L65-L78
I am confused,
1. If we have already loaded the dataset, why doing `to_iterable_dataset`? Does it go through the dataset faster than map-style dataset?
2. `load_dataset(streaming=True)` is useful for huge dataset, but the speed is slow. How to make it comparable to `to_iterable_dataset` without loading the whole dataset into RAM? | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7679 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7679/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7679/comments | https://api.github.com/repos/huggingface/datasets/issues/7679/events | https://github.com/huggingface/datasets/issues/7679 | 3,220,787,371 | I_kwDODunzps6_-VCr | 7,679 | metric glue breaks with 4.0.0 | {
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{
"author_association": "MEMBER",
"body": "I released `evaluate` 0.4.5 yesterday to fix the issue - sorry for the inconvenience:\n\n```\npip install -U evaluate\n```",
"created_at": "2025-07-11T08:52:28Z",
"html_url": "https://github.com/huggingface/datasets/issues/7679#issuecomment-3061315967",
... | 2025-07-10T21:39:50Z | 2025-07-11T17:42:01Z | 2025-07-11T17:42:01Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
worked fine with 3.6.0, and with 4.0.0 `eval_metric = metric.compute()` in HF Accelerate breaks.
The code that fails is:
https://huggingface.co/spaces/evaluate-metric/glue/blob/v0.4.0/glue.py#L84
```
def simple_accuracy(preds, labels):
print(preds, labels)
print(f"{preds==labels}")
return float((preds == labels).mean())
```
data:
```
Column([1, 0, 0, 1, 1]) Column([1, 0, 0, 1, 0])
False
```
```
[rank0]: return float((preds == labels).mean())
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^
[rank0]: AttributeError: 'bool' object has no attribute 'mean'
```
Some behavior has changed in this new major release of `datasets` and requires updating HF accelerate and perhaps the glue metric code, all belong to HF.
### Environment info
datasets=4.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7678 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7678/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7678/comments | https://api.github.com/repos/huggingface/datasets/issues/7678/events | https://github.com/huggingface/datasets/issues/7678 | 3,218,625,544 | I_kwDODunzps6_2FQI | 7,678 | To support decoding audio data, please install 'torchcodec'. | {
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{
"author_association": "MEMBER",
"body": "Hi ! yes you should `!pip install -U datasets[audio]` to have the required dependencies.\n\n`datasets` 4.0 now relies on `torchcodec` for audio decoding. The `torchcodec` AudioDecoder enables streaming from HF and also allows to decode ranges of audio",
"create... | 2025-07-10T09:43:13Z | 2025-07-22T03:46:52Z | 2025-07-11T05:05:42Z | NONE | null | null | {
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In the latest version of datasets==4.0.0, i cannot print the audio data on the Colab notebook. But it works on the 3.6.0 version.
!pip install -q -U datasets huggingface_hub fsspec
from datasets import load_dataset
downloaded_dataset = load_dataset("ymoslem/MediaSpeech", "tr", split="train")
print(downloaded_dataset["audio"][0])
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
[/tmp/ipython-input-4-90623240.py](https://localhost:8080/#) in <cell line: 0>()
----> 1 downloaded_dataset["audio"][0]
10 frames
[/usr/local/lib/python3.11/dist-packages/datasets/features/audio.py](https://localhost:8080/#) in decode_example(self, value, token_per_repo_id)
170 from ._torchcodec import AudioDecoder
171 else:
--> 172 raise ImportError("To support decoding audio data, please install 'torchcodec'.")
173
174 if not self.decode:
ImportError: To support decoding audio data, please install 'torchcodec'.
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-6.1.123+-x86_64-with-glibc2.35
- Python version: 3.11.13
- `huggingface_hub` version: 0.33.2
- PyArrow version: 18.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2025.3.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7676 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7676/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7676/comments | https://api.github.com/repos/huggingface/datasets/issues/7676/events | https://github.com/huggingface/datasets/issues/7676 | 3,216,857,559 | I_kwDODunzps6_vVnX | 7,676 | Many things broken since the new 4.0.0 release | {
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{
"author_association": "MEMBER",
"body": "Happy to take a look, do you have a list of impacted datasets ?",
"created_at": "2025-07-09T23:21:18Z",
"html_url": "https://github.com/huggingface/datasets/issues/7676#issuecomment-3054410935",
"id": 3054410935,
"issue_url": "https://api.github.com... | 2025-07-09T18:59:50Z | 2025-07-21T10:38:01Z | null | NONE | null | null | {
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} | ### Describe the bug
The new changes in 4.0.0 are breaking many datasets, including those from lm-evaluation-harness.
I am trying to revert back to older versions, like 3.6.0 to make the eval work but I keep getting:
``` Python
File /venv/main/lib/python3.12/site-packages/datasets/features/features.py:1474, in generate_from_dict(obj)
1471 class_type = _FEATURE_TYPES.get(_type, None) or globals().get(_type, None)
1473 if class_type is None:
-> 1474 raise ValueError(f"Feature type '{_type}' not found. Available feature types: {list(_FEATURE_TYPES.keys())}")
1476 if class_type == LargeList:
1477 feature = obj.pop("feature")
ValueError: Feature type 'List' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'Sequence', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf']
```
### Steps to reproduce the bug
``` Python
import lm_eval
model_eval = lm_eval.models.huggingface.HFLM(pretrained=model, tokenizer=tokenizer)
lm_eval.evaluator.simple_evaluate(model_eval, tasks=["winogrande"], num_fewshot=5, batch_size=1)
```
### Expected behavior
Older `datasets` versions should work just fine as before
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-6.8.0-60-generic-x86_64-with-glibc2.39
- Python version: 3.12.11
- `huggingface_hub` version: 0.33.1
- PyArrow version: 20.0.0
- Pandas version: 2.3.1
- `fsspec` version: 2025.3.0
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https://api.github.com/repos/huggingface/datasets/issues/7675 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7675/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7675/comments | https://api.github.com/repos/huggingface/datasets/issues/7675/events | https://github.com/huggingface/datasets/issues/7675 | 3,216,699,094 | I_kwDODunzps6_uu7W | 7,675 | common_voice_11_0.py failure in dataset library | {
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{
"author_association": "MEMBER",
"body": "Hi ! This dataset is not in a supported format and `datasets` 4 doesn't support datasets that based on python scripts which are often source of errors. Feel free to ask the dataset authors to convert the dataset to a supported format at https://huggingface.co/datas... | 2025-07-09T17:47:59Z | 2025-07-22T09:35:42Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I tried to download dataset but have got this error:
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test", streaming=True)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[8], line 4
1 from datasets import load_dataset
----> 4 load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test", streaming=True)
File c:\Users\ege_g\AppData\Local\Programs\Python\Python312\Lib\site-packages\datasets\load.py:1392, 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)
1387 verification_mode = VerificationMode(
1388 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
1389 )
1391 # Create a dataset builder
-> 1392 builder_instance = load_dataset_builder(
1393 path=path,
1394 name=name,
1395 data_dir=data_dir,
1396 data_files=data_files,
1397 cache_dir=cache_dir,
1398 features=features,
1399 download_config=download_config,
1400 download_mode=download_mode,
1401 revision=revision,
1402 token=token,
1403 storage_options=storage_options,
1404 **config_kwargs,
1405 )
1407 # Return iterable dataset in case of streaming
1408 if streaming:
File c:\Users\ege_g\AppData\Local\Programs\Python\Python312\Lib\site-packages\datasets\load.py:1132, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, **config_kwargs)
1130 if features is not None:
1131 features = _fix_for_backward_compatible_features(features)
-> 1132 dataset_module = dataset_module_factory(
1133 path,
1134 revision=revision,
1135 download_config=download_config,
1136 download_mode=download_mode,
1137 data_dir=data_dir,
1138 data_files=data_files,
1139 cache_dir=cache_dir,
1140 )
1141 # Get dataset builder class
1142 builder_kwargs = dataset_module.builder_kwargs
File c:\Users\ege_g\AppData\Local\Programs\Python\Python312\Lib\site-packages\datasets\load.py:1031, in dataset_module_factory(path, revision, download_config, download_mode, data_dir, data_files, cache_dir, **download_kwargs)
1026 if isinstance(e1, FileNotFoundError):
1027 raise FileNotFoundError(
1028 f"Couldn't find any data file at {relative_to_absolute_path(path)}. "
1029 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1030 ) from None
-> 1031 raise e1 from None
1032 else:
1033 raise FileNotFoundError(f"Couldn't find any data file at {relative_to_absolute_path(path)}.")
File c:\Users\ege_g\AppData\Local\Programs\Python\Python312\Lib\site-packages\datasets\load.py:989, in dataset_module_factory(path, revision, download_config, download_mode, data_dir, data_files, cache_dir, **download_kwargs)
981 try:
982 api.hf_hub_download(
983 repo_id=path,
984 filename=filename,
(...)
987 proxies=download_config.proxies,
988 )
--> 989 raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
990 except EntryNotFoundError:
991 # Use the infos from the parquet export except in some cases:
992 if data_dir or data_files or (revision and revision != "main"):
RuntimeError: Dataset scripts are no longer supported, but found common_voice_11_0.py
### Steps to reproduce the bug
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test", streaming=True)
### Expected behavior
its supposed to download this dataset.
### Environment info
Python 3.12 , Windows 11 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7677 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7677/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7677/comments | https://api.github.com/repos/huggingface/datasets/issues/7677/events | https://github.com/huggingface/datasets/issues/7677 | 3,218,044,656 | I_kwDODunzps6_z3bw | 7,677 | Toxicity fails with datasets 4.0.0 | {
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{
"author_association": "MEMBER",
"body": "Hi ! You can fix this by upgrading `evaluate`:\n\n```\npip install -U evaluate\n```",
"created_at": "2025-07-10T14:46:48Z",
"html_url": "https://github.com/huggingface/datasets/issues/7677#issuecomment-3057782286",
"id": 3057782286,
"issue_url": "ht... | 2025-07-10T06:15:22Z | 2025-07-11T04:40:59Z | 2025-07-11T04:40:59Z | NONE | null | null | {
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} | ### Describe the bug
With the latest 4.0.0 release, huggingface toxicity evaluation module fails with error: `ValueError: text input must be of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).`
### Steps to reproduce the bug
Repro:
```
>>> toxicity.compute(predictions=["This is a response"])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/evaluate/module.py", line 467, in compute
output = self._compute(**inputs, **compute_kwargs)
File "/Users/serena.ruan/.cache/huggingface/modules/evaluate_modules/metrics/evaluate-measurement--toxicity/2390290fa0bf6d78480143547c6b08f3d4f8805b249df8c7a8e80d0ce8e3778b/toxicity.py", line 135, in _compute
scores = toxicity(predictions, self.toxic_classifier, toxic_label)
File "/Users/serena.ruan/.cache/huggingface/modules/evaluate_modules/metrics/evaluate-measurement--toxicity/2390290fa0bf6d78480143547c6b08f3d4f8805b249df8c7a8e80d0ce8e3778b/toxicity.py", line 103, in toxicity
for pred_toxic in toxic_classifier(preds):
File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/pipelines/text_classification.py", line 159, in __call__
result = super().__call__(*inputs, **kwargs)
File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1431, in __call__
return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1437, in run_single
model_inputs = self.preprocess(inputs, **preprocess_params)
File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/pipelines/text_classification.py", line 183, in preprocess
return self.tokenizer(inputs, return_tensors=return_tensors, **tokenizer_kwargs)
File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2867, in __call__
encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)
File "/Users/serena.ruan/miniconda3/envs/mlflow-310/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2927, in _call_one
raise ValueError(
ValueError: text input must be of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).
```
### Expected behavior
This works before 4.0.0 release
### Environment info
- `datasets` version: 4.0.0
- Platform: macOS-15.5-arm64-arm-64bit
- Python version: 3.10.16
- `huggingface_hub` version: 0.33.0
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0 | {
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... | 2025-07-09T09:53:39Z | 2025-07-09T09:56:29Z | 2025-07-09T09:56:28Z | MEMBER | null | null | null | ```python
>>> Features({"a": Sequence(Sequence({"c": Value("int64")}))})
{'a': List({'c': List(Value('int64'))})}
```
instead of `{'a': {'c': List(List(Value('int64')))}}` | {
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"author_association": "MEMBER",
"body": "Hi, map() always expect an output.\n\nIf you wish to filter examples, you should use filter(), in your case it could be something like this:\n\n```python\nds = ds.map(my_processing_function).filter(ignore_long_prompts)\n```",
"created_at": "2025-07-09T10:30:39Z... | 2025-07-08T17:07:47Z | 2025-07-09T12:30:32Z | 2025-07-09T12:30:32Z | NONE | null | null | {
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} | ### Describe the bug
https://github.com/huggingface/datasets/blob/8a19de052e3d79f79cea26821454bbcf0e9dcd68/src/datasets/arrow_dataset.py#L3652C29-L3652C37
Here we can see the writer is initialized on `i==0`. However, there can be cases where in the user mapping function, the first example is filtered out (length constraints, etc).
In this case, the writer would be a `None` type and the code will report `NoneType has no write function`.
A simple fix is available, simply change line 3652 from `if i == 0:` to `if writer is None:`
### Steps to reproduce the bug
Prepare a dataset
have this function
```
import datasets
def make_map_fn(split, max_prompt_tokens=3):
def process_fn(example, idx):
question = example['question']
reasoning_steps = example['reasoning_steps']
label = example['label']
answer_format = ""
for i in range(len(reasoning_steps)):
system_message = "Dummy"
all_steps_formatted = []
content = f"""Dummy"""
prompt = [
{"role": "system", "content": system_message},
{"role": "user", "content": content},
]
tokenized = tokenizer.apply_chat_template(prompt, return_tensors="pt", truncation=False)
if tokenized.shape[1] > max_prompt_tokens:
return None # skip overly long examples
data = {
"dummy": "dummy"
}
return data
return process_fn
...
# load your dataset
...
train = train.map(function=make_map_fn('train'), with_indices=True)
```
### Expected behavior
The dataset mapping shall behave even when the first example is filtered out.
### Environment info
I am using `datasets==3.6.0` but I have observed this issue in the github repo too: https://github.com/huggingface/datasets/blob/8a19de052e3d79f79cea26821454bbcf0e9dcd68/src/datasets/arrow_dataset.py#L3652C29-L3652C37 | {
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"created_at": "2025-07-07T13:07:47Z",
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https://api.github.com/repos/huggingface/datasets/issues/7669 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7669/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7669/comments | https://api.github.com/repos/huggingface/datasets/issues/7669/events | https://github.com/huggingface/datasets/issues/7669 | 3,203,541,091 | I_kwDODunzps6-8ihj | 7,669 | How can I add my custom data to huggingface datasets | {
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"body": "Hey @xiagod \n\nThe easiest way to add your custom data to Hugging Face Datasets is to use the built-in load_dataset function with your local files. Some examples include:\n\nCSV files:\nfrom datasets import load_dataset\ndataset = load_dataset(\"csv\", data_files=\"... | 2025-07-04T19:19:54Z | 2025-07-05T18:19:37Z | null | NONE | null | null | {
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"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7666). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-07-02T15:00:37Z",
... | 2025-07-02T14:58:00Z | 2025-07-02T15:00:37Z | 2025-07-02T14:59:40Z | MEMBER | null | null | null | cc @kashif | {
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https://api.github.com/repos/huggingface/datasets/issues/7668 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7668/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7668/comments | https://api.github.com/repos/huggingface/datasets/issues/7668/events | https://github.com/huggingface/datasets/issues/7668 | 3,199,039,322 | I_kwDODunzps6-rXda | 7,668 | Broken EXIF crash the whole program | {
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"body": "There are other discussions about error handling for images decoding here : https://github.com/huggingface/datasets/issues/7632 https://github.com/huggingface/datasets/issues/7612\n\nand a PR here: https://github.com/huggingface/datasets/pull/7638 (would love your ... | 2025-07-03T11:24:15Z | 2025-07-03T12:27:16Z | null | NONE | null | null | {
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} | ### Describe the bug
When parsing this image in the ImageNet1K dataset, the `datasets` crashs whole training process just because unable to parse an invalid EXIF tag.
### Steps to reproduce the bug
Use the `datasets.Image.decode_example` method to decode the aforementioned image could reproduce the bug.
The decoding function will throw an unhandled exception at the `image.getexif()` method call due to invalid utf-8 stream in EXIF tags.
```
File lib/python3.12/site-packages/datasets/features/image.py:188, in Image.decode_example(self, value, token_per_repo_id)
186 image = PIL.Image.open(BytesIO(bytes_))
187 image.load() # to avoid "Too many open files" errors
--> 188 if image.getexif().get(PIL.Image.ExifTags.Base.Orientation) is not None:
189 image = PIL.ImageOps.exif_transpose(image)
190 if self.mode and self.mode != image.mode:
File lib/python3.12/site-packages/PIL/Image.py:1542, in Image.getexif(self)
1540 xmp_tags = self.info.get("XML:com.adobe.xmp")
1541 if not xmp_tags and (xmp_tags := self.info.get("xmp")):
-> 1542 xmp_tags = xmp_tags.decode("utf-8")
1543 if xmp_tags:
1544 match = re.search(r'tiff:Orientation(="|>)([0-9])', xmp_tags)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa8 in position 4312: invalid start byte
```
### Expected behavior
The invalid EXIF tag should simply be ignored or issue a warning message, instead of crash the whole program at once.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-6.5.0-18-generic-x86_64-with-glibc2.35
- Python version: 3.12.11
- `huggingface_hub` version: 0.33.0
- PyArrow version: 20.0.0
- Pandas version: 2.3.0
- `fsspec` version: 2025.3.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7665 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7665/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7665/comments | https://api.github.com/repos/huggingface/datasets/issues/7665/events | https://github.com/huggingface/datasets/issues/7665 | 3,193,239,955 | I_kwDODunzps6-VPmT | 7,665 | Function load_dataset() misinterprets string field content as part of dataset schema when dealing with `.jsonl` files | {
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{
"author_association": "NONE",
"body": "Somehow I created the issue twice🙈 This one is an exact duplicate of #7664.",
"created_at": "2025-07-01T17:17:31Z",
"html_url": "https://github.com/huggingface/datasets/issues/7665#issuecomment-3024901004",
"id": 3024901004,
"issue_url": "https://api... | 2025-07-01T17:14:53Z | 2025-07-01T17:17:48Z | 2025-07-01T17:17:48Z | NONE | null | null | {
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} | ### Describe the bug
When loading a `.jsonl` file using `load_dataset("json", data_files="data.jsonl", split="train")`, the function misinterprets the content of a string field as if it were part of the dataset schema.
In my case there is a field `body:` with a string value
```
"### Describe the bug (...) ,action: string, datetime: timestamp[s], author: string, (...) Pandas version: 1.3.4"
```
As a result, I got an exception
```
"TypeError: Couldn't cast array of type timestamp[s] to null".
```
Full stack-trace in the attached file below.
I also attach a minimized dataset (data.json, a single entry) that reproduces the error.
**Observations**(on the minimal example):
- if I remove _all fields before_ `body`, a different error appears,
- if I remove _all fields after_ `body`, yet another error appears,
- if `body` is _the only field_, the error disappears.
So this might be one complex bug or several edge cases interacting. I haven’t dug deeper.
Also changing the file extension to `.json` or `.txt` avoids the problem. This suggests **a possible workaround** for the general case: convert `.jsonl` to `.json`. Though I haven’t verified correctness of that workaround yet.
Anyway my understanding is that `load_dataset` with first argument set to "json" should properly handle `.jsonl` files. Correct me if I'm wrong.
[stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt)
[data.json](https://github.com/user-attachments/files/21004164/data.json)
P.S.
I discovered this while going through the HuggingFace tutorial. Specifically [this part](https://huggingface.co/learn/llm-course/chapter5/5?fw=pt).I will try to inform the tutorial team about this issue, as it can be a showstopper for young 🤗 adepts.
### Steps to reproduce the bug
1. Download attached [data.json](https://github.com/user-attachments/files/21004164/data.json) file.
2. Run the following code which should work correctly:
```
from datasets import load_dataset
load_dataset("json", data_files="data.json", split="train")
```
3. Change extension of the `data` file to `.jsonl` and run:
```
from datasets import load_dataset
load_dataset("json", data_files="data.jsonl", split="train")
```
This will trigger an error like the one in the attached [stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt).
One can also try removing fields before the `body` field and after it. These actions give different errors.
### Expected behavior
Parsing data in `.jsonl` format should yield the same result as parsing the same data in `.json` format. In any case, the content of a string field should never be interpreted as part of the dataset schema.
### Environment info
datasets version: _3.6.0_
pyarrow version: _20.0.0_
Python version: _3.11.9_
platform version: _macOS-15.5-arm64-arm-64bit_ | {
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"author_association": "NONE",
"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7667). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-07-02T15:10:28Z",
... | 2025-07-02T15:07:58Z | 2025-07-02T15:10:28Z | 2025-07-02T15:08:03Z | MEMBER | null | null | null | cc @kashif | {
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{
"author_association": "NONE",
"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7663). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-07-01T13:53:17Z",
... | 2025-07-01T13:50:36Z | 2025-07-01T13:58:41Z | 2025-07-01T13:58:39Z | MEMBER | null | null | null | example: https://huggingface.co/datasets/lhoestq/overlapping-subsets-imagefolder/tree/main
To make multiple subsets for an imagefolder (one metadata file per subset), e.g.
```yaml
configs:
- config_name: default
metadata_filenames:
- metadata.csv
- config_name: other
metadata_filenames:
- metadata2.csv
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7664 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7664/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7664/comments | https://api.github.com/repos/huggingface/datasets/issues/7664/events | https://github.com/huggingface/datasets/issues/7664 | 3,193,239,035 | I_kwDODunzps6-VPX7 | 7,664 | Function load_dataset() misinterprets string field content as part of dataset schema when dealing with `.jsonl` files | {
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{
"author_association": "NONE",
"body": "Hey @zdzichukowalski, I was not able to reproduce this on python 3.11.9 and datasets 3.6.0. The contents of \"body\" are correctly parsed as a string and no other fields like timestamps are created. Could you try reproducing this in a fresh environment, or posting th... | 2025-07-01T17:14:32Z | 2025-07-09T13:14:11Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When loading a `.jsonl` file using `load_dataset("json", data_files="data.jsonl", split="train")`, the function misinterprets the content of a string field as if it were part of the dataset schema.
In my case there is a field `body:` with a string value
```
"### Describe the bug (...) ,action: string, datetime: timestamp[s], author: string, (...) Pandas version: 1.3.4"
```
As a result, I got an exception
```
"TypeError: Couldn't cast array of type timestamp[s] to null".
```
Full stack-trace in the attached file below.
I also attach a minimized dataset (data.json, a single entry) that reproduces the error.
**Observations**(on the minimal example):
- if I remove _all fields before_ `body`, a different error appears,
- if I remove _all fields after_ `body`, yet another error appears,
- if `body` is _the only field_, the error disappears.
So this might be one complex bug or several edge cases interacting. I haven’t dug deeper.
Also changing the file extension to `.json` or `.txt` avoids the problem. This suggests **a possible workaround** for the general case: convert `.jsonl` to `.json`. Though I haven’t verified correctness of that workaround yet.
Anyway my understanding is that `load_dataset` with first argument set to "json" should properly handle `.jsonl` files. Correct me if I'm wrong.
[stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt)
[data.json](https://github.com/user-attachments/files/21004164/data.json)
P.S.
I discovered this while going through the HuggingFace tutorial. Specifically [this part](https://huggingface.co/learn/llm-course/chapter5/5?fw=pt). I will try to inform the tutorial team about this issue, as it can be a showstopper for young 🤗 adepts.
### Steps to reproduce the bug
1. Download attached [data.json](https://github.com/user-attachments/files/21004164/data.json) file.
2. Run the following code which should work correctly:
```
from datasets import load_dataset
load_dataset("json", data_files="data.json", split="train")
```
3. Change extension of the `data` file to `.jsonl` and run:
```
from datasets import load_dataset
load_dataset("json", data_files="data.jsonl", split="train")
```
This will trigger an error like the one in the attached [stack_trace.txt](https://github.com/user-attachments/files/21004153/stack_trace.txt).
One can also try removing fields before the `body` field and after it. These actions give different errors.
### Expected behavior
Parsing data in `.jsonl` format should yield the same result as parsing the same data in `.json` format. In any case, the content of a string field should never be interpreted as part of the dataset schema.
### Environment info
datasets version: _3.6.0_
pyarrow version: _20.0.0_
Python version: _3.11.9_
platform version: _macOS-15.5-arm64-arm-64bit_ | null | {
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{
"author_association": "MEMBER",
"body": "Hi ! `add_column` loads the full column data in memory:\n\nhttps://github.com/huggingface/datasets/blob/bfa497b1666f4c58bd231c440d8b92f9859f3a58/src/datasets/arrow_dataset.py#L6021-L6021\n\na workaround to add the new column is to include the new data in the map() ... | 2025-07-01T05:45:57Z | 2025-07-10T06:17:40Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I have a 30TB dataset. When I perform add_column and cast_column operations on it and then execute a multiprocessing map, it results in an OOM (Out of Memory) error. However, if I skip the add_column and cast_column steps and directly run the map, there is no OOM. After debugging step by step, I found that the OOM is caused at this point, and I suspect it’s because the add_column and cast_column operations are not cached, which causes the entire dataset to be loaded in each subprocess, leading to the OOM. The critical line of code is: https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/utils/py_utils.py#L607
Note num_process=1 would not cause OOM. I'm confused.
### Steps to reproduce the bug
For reproduce, you can load dataset and set cache_dir (for caching): amphion/Emilia-Dataset which is a veru large datasets that RAM can not fits.
And apply the map with multiprocessing after a transform operation (e.g. add_column, cast_column).
As long as num_process>1, it must cause OOM.
### Expected behavior
It should not cause OOM.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-5.10.134-16.101.al8.x86_64-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.33.1
- PyArrow version: 20.0.0
- Pandas version: 2.3.0
- `fsspec` version: 2024.6.1 | null | {
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{
"author_association": "NONE",
"body": "Deleting a class (**not instance**) attribute might be invalid in this case, which is `tqdm` doing in `ensure_lock`.\n\n```python\nfrom tqdm import tqdm as old_tqdm\n\nclass tqdm1(old_tqdm):\n def __delattr__(self, attr):\n try:\n super().__dela... | 2025-06-30T15:57:16Z | 2025-07-03T15:14:27Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
`AttributeError: type object 'tqdm' has no attribute '_lock'`
It occurs when I'm trying to load datasets in thread pool.
Issue https://github.com/huggingface/datasets/issues/6066 and PR https://github.com/huggingface/datasets/pull/6067 https://github.com/huggingface/datasets/pull/6068 tried to fix this.
### Steps to reproduce the bug
Will have to try several times to reproduce the error because it is concerned with threads.
1. save some datasets for test
```pythonfrom datasets import Dataset, DatasetDict
import os
os.makedirs("test_dataset_shards", exist_ok=True)
for i in range(10):
data = Dataset.from_dict({"text": [f"example {j}" for j in range(1000000)]})
data = DatasetDict({'train': data})
data.save_to_disk(f"test_dataset_shards/shard_{i}")
```
2. load them in a thread pool
```python
from datasets import load_from_disk
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor, as_completed
import glob
datas = glob.glob('test_dataset_shards/shard_*')
with ThreadPoolExecutor(max_workers=10) as pool:
futures = [pool.submit(load_from_disk, it) for it in datas]
datas = []
for future in tqdm(as_completed(futures), total=len(futures)):
datas.append(future.result())
```
### Expected behavior
no exception raised
### Environment info
datasets==2.19.0
python==3.10 | null | {
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} | [] | closed | false | null | [] | null | [] | 2025-06-30T09:58:44Z | 2025-07-07T08:38:19Z | 2025-07-01T14:01:42Z | CONTRIBUTOR | null | null | null | In the Preprocess tutorial, the to "the beans dataset" is incorrect. Fixed. | {
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"author_association": "CONTRIBUTOR",
"body": "Hi!\r\nI haven’t included a test for this change, as the fix is quite small and targeted.\r\nPlease let me know if you’d like a test for this case or if you’d prefer to handle it during review.\r\nThanks!",
"created_at": "2025-06-30T09:31:34Z",
"html_u... | 2025-06-30T09:31:12Z | 2025-07-01T16:26:30Z | 2025-07-01T16:26:12Z | CONTRIBUTOR | null | null | null | This PR fixes a bug where calling `IterableDatasetDict.map()` or `IterableDataset.map()` with the default `features=None` argument would overwrite the existing `info.features` attribute with `None`. This, in turn, caused the resulting dataset to lose its schema, breaking downstream usage of attributes like `column_names`.
Why
Previously, the code would always set `info.features = features`, even if `features` was `None`. When mapping with removal of columns or other transformations, this led to the destruction of the schema and caused failures in code that relied on the dataset schema being present.
How
We now update `info.features` only if `features` is not `None`. This preserves the original schema unless the user explicitly provides a new one.
Reference
Fixes #7568 | {
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https://api.github.com/repos/huggingface/datasets/issues/7657 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7657/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7657/comments | https://api.github.com/repos/huggingface/datasets/issues/7657/events | https://github.com/huggingface/datasets/pull/7657 | 3,186,036,016 | PR_kwDODunzps6cks2E | 7,657 | feat: add subset_name as alias for name in load_dataset | {
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} | [] | open | false | null | [] | null | [] | 2025-06-29T10:39:00Z | 2025-07-18T17:45:41Z | null | CONTRIBUTOR | null | null | null | fixes #7637
This PR introduces subset_name as a user-facing alias for the name (previously `config_name`) argument in load_dataset. It aligns terminology with the Hugging Face Hub UI (which shows “Subset”), reducing confusion for new users.
Supports `subset_name` in `load_dataset()`
Adds `.subset_name` property to DatasetBuilder
Maintains full backward compatibility
Raises clear error if name and `subset_name` conflict | null | {
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} | [] | open | false | null | [] | null | [] | 2025-06-29T07:50:13Z | 2025-06-29T07:50:13Z | null | CONTRIBUTOR | null | null | null | Fixes #7630
### Problem
When calling `.map()` on an `IterableDataset`, resuming from a checkpoint skips a large number of samples. This is because `MappedExamplesIterable` did not implement `state_dict()` or `load_state_dict()`, so checkpointing was not properly delegated to the underlying iterable.
### What This PR Does
This patch adds:
```python
def state_dict(self):
return self.ex_iterable.state_dict()
def load_state_dict(self, state):
self.ex_iterable.load_state_dict(state)
```
to MappedExamplesIterable, so the wrapped base iterable's state can be saved and restored as expected.
Result
Using .map() no longer causes sample skipping after checkpoint resume.
Let me know if a dedicated test case is required — happy to add one! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7653 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7653/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7653/comments | https://api.github.com/repos/huggingface/datasets/issues/7653/events | https://github.com/huggingface/datasets/pull/7653 | 3,184,746,093 | PR_kwDODunzps6chLmb | 7,653 | feat(load): fallback to `load_from_disk()` when loading a saved dataset directory | {
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} | [] | open | false | null | [] | null | [] | 2025-06-28T08:47:36Z | 2025-06-28T08:47:36Z | null | CONTRIBUTOR | null | null | null | ### Related Issue
Fixes #7503
Partially addresses #5044 by allowing `load_dataset()` to auto-detect and gracefully delegate to `load_from_disk()` for locally saved datasets.
---
### What does this PR do?
This PR introduces a minimal fallback mechanism in `load_dataset()` that detects when the provided `path` points to a dataset saved using `save_to_disk()`, and automatically redirects to `load_from_disk()`.
#### 🐛 Before (unexpected metadata-only rows):
```python
ds = load_dataset("/path/to/saved_dataset")
# → returns rows with only internal metadata (_data_files, _fingerprint, etc.)
````
#### ✅ After (graceful fallback):
```python
ds = load_dataset("/path/to/saved_dataset")
# → logs a warning and internally switches to load_from_disk()
```
---
### Why is this useful?
* Prevents confusion when reloading local datasets saved via `save_to_disk()`.
* Enables smoother compatibility with frameworks (e.g., TRL, `lighteval`) that rely on `load_dataset()` calls.
* Fully backward-compatible — hub-based loading, custom builders, and streaming remain untouched.
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This PR resolves a compatibility error when loading datasets via `load_dataset()` using outdated arguments like `use_auth_token`.
**What was happening:**
Users passing `use_auth_token` in `load_dataset(..., use_auth_token=...)` encountered a `ValueError`: BuilderConfig ParquetConfig(...) doesn't have a 'use_auth_token' key.
**Why:**
`use_auth_token` has been deprecated and removed from config definitions (replaced by `token`), but the `load_dataset()` function still forwarded it via `**config_kwargs` to BuilderConfigs, leading to unrecognized key errors.
**Fix:**
We now intercept and strip `use_auth_token` from `config_kwargs` inside `load_dataset`, replacing it with a warning:
```python
if "use_auth_token" in config_kwargs:
logger.warning("The 'use_auth_token' argument is deprecated. Please use 'token' instead.")
config_kwargs.pop("use_auth_token")
```
This ensures legacy compatibility while guiding users to switch to the token argument.
Let me know if you'd prefer a deprecation error instead of a warning. Thanks! | null | {
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"author_association": "NONE",
"body": "I need this feature right now. It would be great if it could automatically fill in None for non-existent keys instead of reporting an error.",
"created_at": "2025-07-03T06:52:10Z",
"html_url": "https://github.com/huggingface/datasets/pull/7652#issuecomment-30... | 2025-06-27T16:18:42Z | 2025-07-14T10:41:53Z | null | CONTRIBUTOR | null | null | null | Fixes #7594
This PR adds support for filtering specific columns when loading datasets from .json or .jsonl files — similar to how the columns=... argument works for Parquet.
As suggested, support for the `columns=...` argument (previously available for Parquet) has now been extended to **JSON and JSONL** loading via `load_dataset(...)`. You can now load only specific keys/columns and skip the rest — which should help in cases where some fields are unclean, inconsistent, or just unnecessary.
### Example:
```python
from datasets import load_dataset
dataset = load_dataset("json", data_files="your_data.jsonl", columns=["id", "title"])
print(dataset["train"].column_names)
# Output: ['id', 'title']
```
### Summary of changes:
* Added `columns: Optional[List[str]]` to `JsonConfig`
* Updated `_generate_tables()` to filter selected columns
* Forwarded `columns` argument from `load_dataset()` to the config
* Added test for validation(should be fine!)
Let me know if you'd like the same to be added for CSV or others as a follow-up — happy to help. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7651 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7651/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7651/comments | https://api.github.com/repos/huggingface/datasets/issues/7651/events | https://github.com/huggingface/datasets/pull/7651 | 3,182,792,775 | PR_kwDODunzps6cbMmg | 7,651 | fix: Extended metadata file names for folder_based_builder | {
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The metadata files generated by the `DatasetDict.save_to_file` function are not included in the folder_based_builder's metadata list, causing issues when only 1 actual data file is present, as described in issue #7650.
This PR adds these filenames to the builder, allowing correct loading. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7650 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7650/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7650/comments | https://api.github.com/repos/huggingface/datasets/issues/7650/events | https://github.com/huggingface/datasets/issues/7650 | 3,182,745,315 | I_kwDODunzps69tNbj | 7,650 | `load_dataset` defaults to json file format for datasets with 1 shard | {
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} | ### Describe the bug
I currently have multiple datasets (train+validation) saved as 50MB shards. For one dataset the validation pair is small enough to fit into a single shard and this apparently causes problems when loading the dataset. I created the datasets using a DatasetDict, saved them as 50MB arrow files for streaming and then load each dataset. I have no problem loading any of the other datasets with more than 1 arrow file/shard.
The error indicates the training set got loaded in arrow format (correct) and the validation set in json (incorrect). This seems to be because some of the metadata files are considered as dataset files.
```
Error loading /nfs/dataset_pt-uk: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('validation'): ('json', {})}
```
Concretely, there is a mismatch between the metadata created by the `DatasetDict.save_to_file` and the builder for `datasets.load_dataset`:
https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/data_files.py#L107
The `folder_based_builder` lists all files and with 1 arrow file the json files (that are actually metadata) are in the majority.
https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58
### Steps to reproduce the bug
Create a dataset with metadata and 1 arrow file in validation and multiple arrow files in the training set, following the above description. In my case, I saved the files via:
```python
dataset = DatasetDict({
'train': train_dataset,
'validation': val_dataset
})
dataset.save_to_disk(output_path, max_shard_size="50MB")
```
### Expected behavior
The dataset would get loaded.
### Environment info
- `datasets` version: 3.6.0
- Platform: Linux-6.14.0-22-generic-x86_64-with-glibc2.41
- Python version: 3.12.7
- `huggingface_hub` version: 0.31.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7647 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7647/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7647/comments | https://api.github.com/repos/huggingface/datasets/issues/7647/events | https://github.com/huggingface/datasets/issues/7647 | 3,178,952,517 | I_kwDODunzps69evdF | 7,647 | loading mozilla-foundation--common_voice_11_0 fails | {
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{
"author_association": "NONE",
"body": "@claude Could you please address this issue",
"created_at": "2025-06-27T12:27:55Z",
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"id": 3012884773,
"issue_url": "https://api.github.com/repos/huggingface/dat... | 2025-06-26T12:23:48Z | 2025-07-10T14:49:30Z | null | NONE | null | null | {
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} | ### Describe the bug
Hello everyone,
i am trying to load `mozilla-foundation--common_voice_11_0` and it fails. Reproducer
```
import datasets
datasets.load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test", streaming=True, trust_remote_code=True)
```
and it fails with
```
File ~/opt/envs/.../lib/python3.10/site-packages/datasets/utils/file_utils.py:827, in _add_retries_to_file_obj_read_method.<locals>.read_with_retries(*args, **kwargs)
825 for retry in range(1, max_retries + 1):
826 try:
--> 827 out = read(*args, **kwargs)
828 break
829 except (
830 _AiohttpClientError,
831 asyncio.TimeoutError,
832 requests.exceptions.ConnectionError,
833 requests.exceptions.Timeout,
834 ) as err:
File /usr/lib/python3.10/codecs.py:322, in BufferedIncrementalDecoder.decode(self, input, final)
319 def decode(self, input, final=False):
320 # decode input (taking the buffer into account)
321 data = self.buffer + input
--> 322 (result, consumed) = self._buffer_decode(data, self.errors, final)
323 # keep undecoded input until the next call
324 self.buffer = data[consumed:]
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte
```
When i remove streaming then everything is good but i need `streaming=True`
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test", streaming=True, trust_remote_code=True)
```
### Expected behavior
Expected that it will download dataset
### Environment info
datasets==3.6.0
python3.10
on all platforms linux/win/mac | null | {
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{
"author_association": "MEMBER",
"body": "it was already added in https://github.com/huggingface/datasets/pull/7606 actually ^^'",
"created_at": "2025-07-07T09:31:13Z",
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"issue_url": "h... | 2025-06-27T05:59:03Z | 2025-07-07T18:13:53Z | 2025-07-07T18:13:52Z | CONTRIBUTOR | null | null | null | Fixes #7591
### Add num_proc support to `push_to_hub()` for parallel shard upload
This PR adds support for parallel upload of dataset shards via the `num_proc` argument in `Dataset.push_to_hub()`.
📌 While the `num_proc` parameter was already present in the `push_to_hub()` signature and correctly passed to `_push_parquet_shards_to_hub()`, it was not being used to parallelize the upload.
🔧 This PR updates the internal `_push_parquet_shards_to_hub()` function to:
- Use `multiprocessing.Pool` and `iflatmap_unordered()` for concurrent shard upload when `num_proc > 1`
- Preserve original serial upload behavior if `num_proc` is `None` or ≤ 1
- Keep tqdm progress and commit behavior unchanged
Let me know if any test coverage or further changes are needed!
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{
"author_association": "MEMBER",
"body": "I believe there are other occurences of cases like this, like select_columns, select, filter, shard and flatten, could you also fix the docstring for them as well before we merge ?",
"created_at": "2025-07-07T15:11:04Z",
"html_url": "https://github.com/hugg... | 2025-06-27T05:27:04Z | 2025-07-28T19:42:34Z | 2025-07-17T13:14:17Z | CONTRIBUTOR | null | null | null | Fixes #7611
This PR fixes the usage example in the Dataset.add_column() docstring, which previously implied that add_column() modifies the dataset in-place.
Why:
The method returns a new dataset with the additional column, and users must assign the result to a variable to preserve the change.
This should make the behavior clearer for users.
@lhoestq @davanstrien | {
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{
"author_association": "CONTRIBUTOR",
"body": "It adds automatic grouping of files into subsets based on their root name (e.g., `train0.jsonl`, `train1.jsonl` → `\"train\"`), as discussed above. The logic is integrated into `FolderBasedBuilder` and is fully tested + documented.\r\n\r\nLet me know if any ch... | 2025-06-26T07:01:37Z | 2025-07-14T10:42:56Z | null | CONTRIBUTOR | null | null | null | Fixes #7066
This PR introduces automatic **subset-level grouping** for folder-based dataset builders by:
1. Adding a utility function `group_files_by_subset()` that clusters files by root name (ignoring digits and shard suffixes).
2. Integrating this logic into `FolderBasedBuilder._split_generators()` to yield one split per subset.
3. Adding unit tests for the grouping function.
4. Updating the documentation to describe this new behavior under `docs/source/repository_structure.mdx`.
---
### Motivation
Datasets with files like:
```
train0.jsonl
train1.jsonl
animals.jsonl
metadata.jsonl
```
will now be **automatically grouped** as:
- `"train"` subset → `train0.jsonl`, `train1.jsonl`
- `"animals"` subset → `animals.jsonl`
- `"metadata"` subset → `metadata.jsonl`
This enables structured multi-subset loading even when the dataset doesn't follow traditional `train/validation/test` split conventions.
---
### Files Changed
- `src/datasets/data_files.py`: added `group_files_by_subset()` utility
- `src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py`: grouped files before yielding splits
- `tests/test_data_files.py`: added unit test `test_group_files_by_subset`
- `docs/source/repository_structure.mdx`: documented subset grouping for maintainers and users
---
### Benefits
- More flexible and robust dataset split logic
- Enables logical grouping of user-uploaded files without nested folder structure
- Backward-compatible with all existing folder-based configs
---
Ready for review ✅ | null | {
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https://github.com/huggingface/datasets/blob/e71b0b19d79c7531f9b9bea7c09916b5f6157f42/src/datasets/features/features.py#L1126-L1129 | null | {
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... | 2025-06-25T17:07:55Z | 2025-06-25T17:10:30Z | 2025-06-25T17:08:01Z | MEMBER | null | null | null | fix error from https://github.com/huggingface/datasets/pull/7643 | {
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... | 2025-06-25T17:05:09Z | 2025-06-25T17:07:40Z | 2025-06-25T17:05:44Z | MEMBER | null | null | null | useful to still get `isinstance(Sequence(Value("int64")), Sequence)`for downstream libs like evaluate | {
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https://api.github.com/repos/huggingface/datasets/issues/7640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7640/comments | https://api.github.com/repos/huggingface/datasets/issues/7640/events | https://github.com/huggingface/datasets/pull/7640 | 3,175,914,924 | PR_kwDODunzps6cEofU | 7,640 | better features repr | {
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"created_at": "2025-06-25T14:40:16Z",
... | 2025-06-25T14:37:32Z | 2025-06-25T14:46:47Z | 2025-06-25T14:46:45Z | MEMBER | null | null | null | following the addition of List in #7634
before:
```python
In [3]: ds.features
Out[3]:
{'json': {'id': Value(dtype='string', id=None),
'metadata:transcript': [{'end': Value(dtype='float64', id=None),
'start': Value(dtype='float64', id=None),
'transcript': Value(dtype='string', id=None),
'words': [{'end': Value(dtype='float64', id=None),
'score': Value(dtype='float64', id=None),
'start': Value(dtype='float64', id=None),
'word': Value(dtype='string', id=None)}]}],
'metadata:vad': [{'end': Value(dtype='float64', id=None),
'start': Value(dtype='float64', id=None)}]},
'mp4': Value(dtype='binary', id=None),
'npz': {'boxes_and_keypoints:box': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'boxes_and_keypoints:is_valid_box': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None),
'boxes_and_keypoints:keypoints': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'movement:EmotionArousalToken': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:EmotionValenceToken': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:FAUToken': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:FAUValue': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:alignment_head_rotation': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:alignment_translation': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'movement:emotion_arousal': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:emotion_scores': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:emotion_valence': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:expression': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:frame_latent': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:gaze_encodings': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:head_encodings': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:hypernet_features': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'movement:is_valid': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'smplh:body_pose': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'smplh:global_orient': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None),
'smplh:is_valid': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None),
'smplh:left_hand_pose': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'smplh:right_hand_pose': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None),
'smplh:translation': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None)},
'wav': Audio(sampling_rate=None, mono=True, decode=True, id=None),
'__key__': Value(dtype='string', id=None),
'__url__': Value(dtype='string', id=None)}
```
after:
```python
In [3]: ds.features
Out[3]:
{'json': {'id': Value('string'),
'metadata:transcript': List({'end': Value('float64'), 'start': Value('float64'), 'transcript': Value('string'), 'words': List({'end': Value('float64'), 'score': Value('float64'), 'start': Value('float64'), 'word': Value('string')})}),
'metadata:vad': List({'end': Value('float64'), 'start': Value('float64')})},
'mp4': Value('binary'),
'npz': {'boxes_and_keypoints:box': List(List(Value('float32'))),
'boxes_and_keypoints:is_valid_box': List(Value('bool')),
'boxes_and_keypoints:keypoints': List(List(List(Value('float32')))),
'movement:EmotionArousalToken': List(List(Value('float32'))),
'movement:EmotionValenceToken': List(List(Value('float32'))),
'movement:FAUToken': List(List(Value('float32'))),
'movement:FAUValue': List(List(Value('float32'))),
'movement:alignment_head_rotation': List(List(Value('float32'))),
'movement:alignment_translation': List(List(List(Value('float32')))),
'movement:emotion_arousal': List(List(Value('float32'))),
'movement:emotion_scores': List(List(Value('float32'))),
'movement:emotion_valence': List(List(Value('float32'))),
'movement:expression': List(List(Value('float32'))),
'movement:frame_latent': List(List(Value('float32'))),
'movement:gaze_encodings': List(List(Value('float32'))),
'movement:head_encodings': List(List(Value('float32'))),
'movement:hypernet_features': List(List(Value('float32'))),
'movement:is_valid': List(List(Value('float32'))),
'smplh:body_pose': List(List(List(Value('float32')))),
'smplh:global_orient': List(List(Value('float32'))),
'smplh:is_valid': List(Value('bool')),
'smplh:left_hand_pose': List(List(List(Value('float32')))),
'smplh:right_hand_pose': List(List(List(Value('float32')))),
'smplh:translation': List(List(Value('float32')))},
'wav': Audio(sampling_rate=None, decode=True, stream_index=None),
'__key__': Value('string'),
'__url__': Value('string')}
``` | {
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} | 2025-06-25T14:37:32Z | 2025-06-25T14:46:45Z | 0:09:13 | true |
https://api.github.com/repos/huggingface/datasets/issues/7639 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7639/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7639/comments | https://api.github.com/repos/huggingface/datasets/issues/7639/events | https://github.com/huggingface/datasets/pull/7639 | 3,175,616,169 | PR_kwDODunzps6cDoAf | 7,639 | fix save_infos | {
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"created_at": "2025-06-25T13:19:33Z",
... | 2025-06-25T13:16:26Z | 2025-06-25T13:19:33Z | 2025-06-25T13:16:33Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7638 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7638/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7638/comments | https://api.github.com/repos/huggingface/datasets/issues/7638/events | https://github.com/huggingface/datasets/pull/7638 | 3,172,645,391 | PR_kwDODunzps6b5vpZ | 7,638 | Add ignore_decode_errors option to Image feature for robust decoding #7612 | {
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{
"author_association": "CONTRIBUTOR",
"body": "cc @lhoestq",
"created_at": "2025-06-24T16:48:03Z",
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"no... | 2025-06-24T16:47:51Z | 2025-07-04T07:07:30Z | null | CONTRIBUTOR | null | null | null | This PR implements support for robust image decoding in the `Image` feature, as discussed in issue #7612.
## 🔧 What was added
- A new boolean field: `ignore_decode_errors` (default: `False`)
- If set to `True`, any exceptions during decoding will be caught, and `None` will be returned instead of raising an error
```python
features = Features({
"image": Image(decode=True, ignore_decode_errors=True),
})
````
This enables robust iteration over potentially corrupted datasets — especially useful when streaming datasets like WebDataset or image-heavy public sets where sample corruption is common.
## 🧪 Behavior
* If `ignore_decode_errors=False` (default), decoding behaves exactly as before
* If `True`, decoding errors are caught, and a warning is emitted:
```
[Image.decode_example] Skipped corrupted image: ...
```
## 🧵 Linked issue
Closes #7612
Let me know if you'd like a follow-up test PR. Happy to write one! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7636/comments | https://api.github.com/repos/huggingface/datasets/issues/7636/events | https://github.com/huggingface/datasets/issues/7636 | 3,170,878,167 | I_kwDODunzps68_8LX | 7,636 | "open" in globals()["__builtins__"], an error occurs: "TypeError: argument of type 'module' is not iterable" | {
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{
"author_association": "NONE",
"body": "@kuanyan9527 Your query is indeed valid. Following could be its reasoning:\n\nQuoting from https://stackoverflow.com/a/11181607:\n\"By default, when in the `__main__` module,` __builtins__` is the built-in module `__builtin__` (note: no 's'); when in any other module... | 2025-06-24T08:09:39Z | 2025-07-10T04:13:16Z | null | NONE | null | null | {
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} | When I run the following code, an error occurs: "TypeError: argument of type 'module' is not iterable"
```python
print("open" in globals()["__builtins__"])
```
Traceback (most recent call last):
File "./main.py", line 2, in <module>
print("open" in globals()["__builtins__"])
^^^^^^^^^^^^^^^^^^^^^^
TypeError: argument of type 'module' is not iterable
But this code runs fine in datasets, I don't understand why
[src/datasets/utils/patching.py#L96](https://github.com/huggingface/datasets/blob/3.6.0/src/datasets/utils/patching.py#L96) | {
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https://api.github.com/repos/huggingface/datasets/issues/7637 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7637/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7637/comments | https://api.github.com/repos/huggingface/datasets/issues/7637/events | https://github.com/huggingface/datasets/issues/7637 | 3,171,883,522 | I_kwDODunzps69DxoC | 7,637 | Introduce subset_name as an alias of config_name | {
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"body": "I second this! When you come from the Hub, the intuitive question is \"how do I set the subset name\", and it's not easily answered from the docs: `subset_name` would answer this directly.",
"created_at": "2025-06-24T12:52:48Z",
"html_url": "https://github.co... | 2025-06-24T12:49:01Z | 2025-07-01T16:08:33Z | null | MEMBER | null | null | {
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} | ### Feature request
Add support for `subset_name` as an alias for `config_name` in the datasets library and related tools (such as loading scripts, documentation, and metadata).
### Motivation
The Hugging Face Hub dataset viewer displays a column named **"Subset"**, which refers to what is currently technically called config_name in the datasets library. This inconsistency has caused confusion for many users, especially those unfamiliar with the internal terminology.
I have repeatedly received questions from users trying to understand what "config" means, and why it doesn’t match what they see as "subset" on the Hub. Renaming everything to `subset_name` might be too disruptive, but introducing subset_name as a clear alias for config_name could significantly improve user experience without breaking backward compatibility.
This change would:
- Align terminology across the Hub UI and datasets codebase
- Reduce user confusion, especially for newcomers
- Make documentation and examples more intuitive
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https://api.github.com/repos/huggingface/datasets/issues/7635 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7635/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7635/comments | https://api.github.com/repos/huggingface/datasets/issues/7635/events | https://github.com/huggingface/datasets/pull/7635 | 3,170,486,408 | PR_kwDODunzps6bybOe | 7,635 | Fix: Preserve float columns in JSON loader when values are integer-like (e.g. 0.0, 1.0) | {
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} | [] | open | false | null | [] | null | [] | 2025-06-24T06:16:48Z | 2025-06-24T06:16:48Z | null | CONTRIBUTOR | null | null | null | This PR fixes a bug in the JSON loader where columns containing float values like `[0.0, 1.0, 2.0]` were being implicitly coerced to `int`, due to pandas or Arrow type inference.
This caused issues downstream in statistics computation (e.g., dataset-viewer) where such columns were incorrectly labeled as `"int"` instead of `"float"`.
### 🔍 What was happening:
When the JSON loader falls back to `pandas_read_json()` (after `pa.read_json()` fails), pandas/Arrow can coerce float values to integers if all values are integer-like (e.g., `0.0 == 0`).
### ✅ What this PR does:
- Adds a check in the fallback path of `_generate_tables()`
- Ensures that columns made entirely of floats are preserved as `"float64"` even if they are integer-like (e.g. `0.0`, `1.0`)
- This prevents loss of float semantics when creating the Arrow table
### 🧪 Reproducible Example:
```json
[{"col": 0.0}, {"col": 1.0}, {"col": 2.0}]
````
Previously loaded as:
* `int`
Now correctly loaded as:
* `float`
Fixes #6937
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https://api.github.com/repos/huggingface/datasets/issues/7634 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7634/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7634/comments | https://api.github.com/repos/huggingface/datasets/issues/7634/events | https://github.com/huggingface/datasets/pull/7634 | 3,169,389,653 | PR_kwDODunzps6buyij | 7,634 | Replace Sequence by List | {
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"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7634). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-06-23T20:38:32Z",
... | 2025-06-23T20:35:48Z | 2025-06-25T13:59:13Z | 2025-06-25T13:59:11Z | MEMBER | null | null | null | Sequence is just a utility that we need to keep for backward compatibility. And `[ ]` was used instead but doesn't allow passing the length of the list.
This PR removes most mentions of Sequence and usage of `[ ]` and defines a proper List type instead.
before: `Sequence(Value("int64"))` or `[Value("int64")]`
now: `List(Value("int64"))`
This PR conserves full backward compatibility. And it's a good occasion with the release of 4.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7632 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7632/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7632/comments | https://api.github.com/repos/huggingface/datasets/issues/7632/events | https://github.com/huggingface/datasets/issues/7632 | 3,168,283,589 | I_kwDODunzps682CvF | 7,632 | Graceful Error Handling for cast_column("image", Image(decode=True)) in Hugging Face Datasets | {
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"body": "Hi! This is now handled in PR #7638",
"created_at": "2025-07-04T07:08:11Z",
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} | ### Feature request
Currently, when using dataset.cast_column("image", Image(decode=True)), the pipeline throws an error and halts if any image in the dataset is invalid or corrupted (e.g., truncated files, incorrect formats, unreachable URLs). This behavior disrupts large-scale processing where a few faulty samples are common.
reference : https://discuss.huggingface.co/t/handle-errors-when-loading-images-404-corrupted-etc/50318/5
https://discuss.huggingface.co/t/handling-non-existing-url-in-image-dataset-while-cast-column/69185
Proposed Feature
Introduce a mechanism (e.g., a continue_on_error=True flag or global error handling mode) in Image(decode=True) that:
Skips invalid images and sets them as None, or
Logs the error but allows the rest of the dataset to be processed without interruption.
Example Usage
from datasets import load_dataset, Image
dataset = load_dataset("my_dataset")
dataset = dataset.cast_column("image", Image(decode=True, continue_on_error=True))
Benefits
Ensures robust large-scale image dataset processing.
Improves developer productivity by avoiding custom retry/error-handling code.
Aligns with best practices in dataset preprocessing pipelines that tolerate minor data corruption.
Potential Implementation Options
Internally wrap the decoding in a try/except block.
Return None or a placeholder on failure.
Optionally allow custom error callbacks or logging.
### Motivation
Robustness: Large-scale image datasets often contain a small fraction of corrupt files or unreachable URLs. Halting on the first error forces users to write custom workarounds or preprocess externally.
Simplicity: A built-in flag removes boilerplate try/except logic around every decode step.
Performance: Skipping invalid samples inline is more efficient than a two-pass approach (filter then decode).
### Your contribution
1. API Change
Extend datasets.features.Image(decode=True) to accept continue_on_error: bool = False.
2. Behavior
If continue_on_error=False (default), maintain current behavior: any decode error raises an exception.
If continue_on_error=True, wrap decode logic in try/except:
On success: store the decoded image.
On failure: log a warning (e.g., via logging.warning) and set the field to None (or a sentinel value).
3. Optional Enhancements
Allow a callback hook:
Image(decode=True, continue_on_error=True, on_error=lambda idx, url, exc: ...)
Emit metrics or counts of skipped images. | null | {
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} | I’m a beginner from NIT Srinagar proposing a dataset of 50 Tamil text pairs for discourse coherence (coherent/incoherent labels) to support NLP research in low-resource languages.
- Size: 50 samples
- Format: CSV with columns (text1, text2, label)
- Use case: Training NLP models for coherence
I’ll use GitHub’s web editor and Google Colab. Please confirm if this fits. | null | {
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"body": "- This PR assumes that `HfFileSystem` in `huggingface_hub` supports receiving `headers` in `storage_options`. If not, a follow-up PR can be opened to add this support to `HfFileSystem.__init__`.\r\n- No test was added for this since it’s a config passthrough. ... | 2025-06-21T14:22:25Z | 2025-06-21T14:25:28Z | null | CONTRIBUTOR | null | null | null | Fixes part of issue #6046
### Problem
The `user-agent` defined in `DownloadConfig` was not passed down to fsspec-based filesystems like `HfFileSystem`, which prevents proper identification/tracking of client requests.
### Solution
Added support for injecting the `user-agent` into `storage_options["headers"]` within `_prepare_single_hop_path_and_storage_options()` based on the `protocol`.
Now, when using `hf://`, `http://`, or `https://`, the custom user-agent is passed automatically.
### Code Location
Modified:
- `src/datasets/utils/file_utils.py`
Used `get_datasets_user_agent(...)` to ensure proper formatting and fallback logic. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7630 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7630/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7630/comments | https://api.github.com/repos/huggingface/datasets/issues/7630/events | https://github.com/huggingface/datasets/issues/7630 | 3,164,650,900 | I_kwDODunzps68oL2U | 7,630 | [bug] resume from ckpt skips samples if .map is applied | {
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{
"author_association": "CONTRIBUTOR",
"body": "Thanks for reporting this — it looks like a separate but related bug to #7538, which involved sample loss when resuming an `IterableDataset` wrapped in `FormattedExamplesIterable`. That was resolved in #7553 by re-batching the iterable to track offset correctl... | 2025-06-21T01:50:03Z | 2025-06-29T07:51:32Z | null | NONE | null | null | {
"completed": 0,
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"total": 0
} | ### Describe the bug
resume from ckpt skips samples if .map is applied
Maybe related: https://github.com/huggingface/datasets/issues/7538
### Steps to reproduce the bug
```python
from datasets import Dataset
from datasets.distributed import split_dataset_by_node
# Create dataset with map transformation
def create_dataset():
ds = Dataset.from_dict({"id": list(range(100))})
ds = ds.to_iterable_dataset(num_shards=4)
ds = ds.map(lambda x: x) #comment it out to get desired behavior
ds = split_dataset_by_node(ds, rank=0, world_size=2)
return ds
ds = create_dataset()
# Iterate and save checkpoint after 10 samples
it = iter(ds)
for idx, sample in enumerate(it):
if idx == 9: # Checkpoint after 10 samples
checkpoint = ds.state_dict()
print(f"Checkpoint saved at sample: {sample['id']}")
break
# Continue with original iterator
original_next_samples = []
for idx, sample in enumerate(it):
original_next_samples.append(sample["id"])
if idx >= 4:
break
# Resume from checkpoint
ds_new = create_dataset()
ds_new.load_state_dict(checkpoint)
# Get samples from resumed iterator
it_new = iter(ds_new)
resumed_next_samples = []
for idx, sample in enumerate(it_new):
resumed_next_samples.append(sample["id"])
if idx >= 4:
break
print(f"\nExpected next samples: {original_next_samples}")
print(f"Actual next samples: {resumed_next_samples}")
print(
f"\n❌ BUG: {resumed_next_samples[0] - original_next_samples[0]} samples were skipped!"
)
```
With map
```
Checkpoint saved at sample: 9
Expected next samples: [10, 11, 12, 13, 14]
Actual next samples: [50, 51, 52, 53, 54]
❌ BUG: 40 samples were skipped!
```
### Expected behavior
without map
```
Expected next samples: [10, 11, 12, 13, 14]
Actual next samples: [10, 11, 12, 13, 14]
❌ BUG: 0 samples were skipped!
```
### Environment info
datasets == 3.6.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7629 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7629/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7629/comments | https://api.github.com/repos/huggingface/datasets/issues/7629/events | https://github.com/huggingface/datasets/pull/7629 | 3,161,169,782 | PR_kwDODunzps6bTc7b | 7,629 | Add test for `as_iterable_dataset()` method in DatasetBuilder | {
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} | [] | open | false | null | [] | null | [] | 2025-06-19T19:23:55Z | 2025-06-19T19:23:55Z | null | CONTRIBUTOR | null | null | null | This PR adds a test for the new `as_iterable_dataset()` method introduced in PR #7628.
The test:
- Loads a builder using `load_dataset_builder("c4", "en")`
- Runs `download_and_prepare()`
- Streams examples using `builder.as_iterable_dataset(split="train[:100]")`
- Verifies streamed examples contain the "text" field
This ensures that the builder correctly streams data from cached Arrow files.
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https://api.github.com/repos/huggingface/datasets/issues/7628 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7628/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7628/comments | https://api.github.com/repos/huggingface/datasets/issues/7628/events | https://github.com/huggingface/datasets/pull/7628 | 3,161,156,461 | PR_kwDODunzps6bTaGk | 7,628 | Add `as_iterable_dataset()` method to DatasetBuilder for streaming from cached Arrow files | {
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} | [] | open | false | null | [] | null | [] | 2025-06-19T19:15:41Z | 2025-06-19T19:15:41Z | null | CONTRIBUTOR | null | null | null | This PR implements `builder.as_iterable_dataset(split=...)` as discussed in #5481.
It allows users to load an `IterableDataset` directly from cached Arrow files (using ArrowReader and ArrowExamplesIterable), without loading the full dataset into memory.
This is useful for large-scale training scenarios where memory is constrained. A test has also been added in `test_builder.py`.
Related to: #5481
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https://api.github.com/repos/huggingface/datasets/issues/7626 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7626/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7626/comments | https://api.github.com/repos/huggingface/datasets/issues/7626/events | https://github.com/huggingface/datasets/pull/7626 | 3,159,322,138 | PR_kwDODunzps6bNMuF | 7,626 | feat(map): reuse unchanged columns when input_columns specified to reduce disk usage (#6013) | {
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} | [] | closed | false | null | [] | null | [] | 2025-06-19T07:41:45Z | 2025-07-28T17:39:12Z | 2025-07-28T17:39:12Z | CONTRIBUTOR | null | null | null | ## Summary
This PR addresses [#6013](https://github.com/huggingface/datasets/issues/6013) by reusing unchanged columns from the original dataset in the `map()` method when `input_columns` is specified.
## What’s Implemented
- Injected logic at the end of `Dataset.map()` to:
- Identify untouched columns not in `input_columns` or `remove_columns`
- Select those columns from the original dataset
- Concatenate them with the transformed result using `pyarrow.concat_tables`
## Example Behavior
```python
ds = Dataset.from_dict({"a": [1, 2], "b": [3, 4]})
ds2 = ds.map(lambda x: {"c": x["a"] + 10}, input_columns=["a"], remove_columns=["a"])
print(ds2.column_names) # Output: ['b', 'c']
````
Column `b` is reused from the original dataset.
## Notes
* This keeps disk usage and caching minimal by avoiding full dataset duplication.
* Only triggered when `input_columns` is set.
---
cc @lhoestq @mariosasko for review 🙂
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{
"author_association": "NONE",
"body": "### > Update\n\nThe bottleneck, from what I understand, was making one network request per file\n\nFor 30k images, this meant 30k separate GET requests to the MinIO server through the S3 API, and that was killing the performance\n\nUsing webDataset to transform the l... | 2025-06-19T14:28:41Z | 2025-06-23T12:39:10Z | 2025-06-23T12:39:10Z | NONE | null | null | {
"completed": 0,
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} | Hi,
I’m new to HF dataset and I tried to create datasets based on data versioned in **lakeFS** _(**MinIO** S3 bucket as storage backend)_
Here I’m using ±30000 PIL image from MNIST data however it is taking around 12min to execute, which is a lot!
From what I understand, it is loading the images into cache then building the dataset.
– Please find bellow the execution screenshot –
Is there a way to optimize this or am I doing something wrong?
Thanks!
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{
"author_association": "MEMBER",
"body": "Hi ! It should follow the same order as the order of the keys in the metadata file",
"created_at": "2025-06-19T13:42:55Z",
"html_url": "https://github.com/huggingface/datasets/issues/7624#issuecomment-2988156316",
"id": 2988156316,
"issue_url": "htt... | 2025-06-18T09:25:19Z | 2025-06-20T07:46:43Z | 2025-06-20T07:46:43Z | NONE | null | null | {
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#Dataset
I’m currently uploading a dataset that includes an `"image"` column (PNG files), along with some metadata columns. The dataset is loaded from a .jsonl file. My goal is to have the "image" column appear as the first column in the dataset card preview UI on the :hugs: Hub.
However, at the moment, the `"image"` column is not the first—in fact, it appears last, which is not ideal for the presentation I’d like to achieve.
I have a couple of questions:
Is there a way to force the dataset card to display the `"image"` column first?
Is there currently any way to control or influence the column order in the dataset preview UI?
Does the order of keys in the .jsonl file or the features argument affect the display order?
Thanks again for your time and help! :blush: | {
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https://api.github.com/repos/huggingface/datasets/issues/7625 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7625/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7625/comments | https://api.github.com/repos/huggingface/datasets/issues/7625/events | https://github.com/huggingface/datasets/pull/7625 | 3,159,016,001 | PR_kwDODunzps6bMKof | 7,625 | feat: Add h5folder dataset loader for HDF5 support | {
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{
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"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7625). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-06-25T14:05:11Z",
... | 2025-06-19T05:39:10Z | 2025-06-26T05:44:26Z | null | CONTRIBUTOR | null | null | null | ### Related Issue
Closes #3113
### What does this PR do?
This PR introduces a new dataset loader module called **`h5folder`** to support loading datasets stored in **HDF5 (.h5)** format.
It allows users to do:
```python
from datasets import load_dataset
dataset = load_dataset("h5folder", data_dir="path/to/")
````
### 🧩 Design Overview
* Implemented inside `datasets/packaged_modules/h5folder/h5folder.py`
* Based on the `GeneratorBasedBuilder` API
* Uses `h5py` to read HDF5 files and yield examples
* Expects datasets such as `id`, `data`, and `label` inside `data.h5`
* Converts numpy arrays to Python types before yielding
### 🧪 Example `.h5` Structure (for local testing)
```python
import h5py
import numpy as np
with h5py.File("data.h5", "w") as f:
f.create_dataset("id", data=np.arange(100))
f.create_dataset("data", data=np.random.randn(100, 10))
f.create_dataset("label", data=np.random.randint(0, 2, size=100))
```
### ✅ Testing
- The loader logic follows the structure of existing modules like `imagefolder`
- Will rely on Hugging Face CI to validate integration
- Manually testing planned once merged or during feedback
### 📁 Files Added
* `datasets/src/datasets/packaged_modules/h5folder/h5folder.py`
### 📌 Component(s) Affected
* `area/datasets`
* `area/load`
### 📦 Release Note Classification
* `rn/feature` – Adds support for loading `.h5` datasets via `load_dataset("h5folder", ...)`
---
Let me know if any changes or improvements are needed — happy to iterate. Thanks for reviewing!
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https://api.github.com/repos/huggingface/datasets/issues/7623 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7623/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7623/comments | https://api.github.com/repos/huggingface/datasets/issues/7623/events | https://github.com/huggingface/datasets/pull/7623 | 3,154,519,684 | PR_kwDODunzps6a9Jk5 | 7,623 | fix: raise error in FolderBasedBuilder when data_dir and data_files are missing | {
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{
"author_association": "CONTRIBUTOR",
"body": "@lhoestq Moved the logic to FolderBasedBuilder._info() as discussed in previous PR (#7618). Let me know if anything else is needed — happy to update!",
"created_at": "2025-06-17T19:24:44Z",
"html_url": "https://github.com/huggingface/datasets/pull/7623... | 2025-06-17T19:16:34Z | 2025-06-18T14:18:41Z | 2025-06-18T14:18:41Z | CONTRIBUTOR | null | null | null | ### Related Issues/PRs
Fixes #6152
---
### What changes are proposed in this pull request?
This PR adds a dedicated validation check in the `_info()` method of the `FolderBasedBuilder` class to ensure that users provide either `data_dir` or `data_files` when loading folder-based datasets (such as `audiofolder`, `imagefolder`, etc.).
---
### Why this change?
Previously, when calling:
```python
load_dataset("audiofolder")
````
without specifying `data_dir` or `data_files`, the loader would silently fallback to the **current working directory**, leading to:
* Long loading times
* Unexpected behavior (e.g., scanning unrelated files)
This behavior was discussed in issue #6152. As suggested by maintainers, the fix has now been implemented directly inside the `FolderBasedBuilder._info()` method — keeping the logic localized to the specific builder instead of a generic loader function.
---
### How is this PR tested?
* ✅ Manually tested by calling `load_dataset("audiofolder")` with no `data_dir` or `data_files` → a `ValueError` is now raised early.
* ✅ Existing functionality (with valid input) remains unaffected.
---
### Does this PR require documentation update?
* [x] No
---
### Release Notes
#### Is this a user-facing change?
* [x] Yes
> Folder-based datasets now raise an explicit error if neither `data_dir` nor `data_files` are specified, preventing unintended fallback to the current working directory.
---
#### What component(s) does this PR affect?
* [x] `area/datasets`
* [x] `area/load`
---
<a name="release-note-category"></a>
#### How should the PR be classified?
* [x] `rn/bug-fix` - A user-facing bug fix
---
#### Should this be included in the next patch release?
* [x] Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/7622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7622/comments | https://api.github.com/repos/huggingface/datasets/issues/7622/events | https://github.com/huggingface/datasets/pull/7622 | 3,154,398,557 | PR_kwDODunzps6a8v6J | 7,622 | Guard against duplicate builder_kwargs/config_kwargs in load_dataset_… | {
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"author_association": "NONE",
"body": "Hi folks, this PR fixes the duplicate-kwargs edge case and includes a unit test. Would love a review when you have a moment!\r\n\r\n@zach-huggingface\r\n@SunMarc ",
"created_at": "2025-07-02T12:39:09Z",
"html_url": "https://github.com/huggingface/datasets/pul... | 2025-06-17T18:28:35Z | 2025-07-23T14:06:20Z | 2025-07-23T14:06:20Z | NONE | null | null | null | …builder (#4910 )
### What does this PR do?
Fixes edge case in `load_dataset_builder` by raising a `TypeError` if the same key exists in both `builder_kwargs` and `config_kwargs`.
### Implementation details
- Added a guard clause in `load_dataset_builder` to detect duplicate keys between `builder_kwargs` and `config_kwargs`
- Wrote a unit test in `tests/test_load_duplicate_keys.py` to verify the exception is raised correctly
### Fixes
Closes #4910
### Reviewers
@zach-huggingface
@SunMarc
Would appreciate your review if you have time - thanks! | {
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"created_at": "2025-06-17T14:50:28Z",
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{
"author_association": "NONE",
"body": "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7620). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"created_at": "2025-06-17T13:44:22Z",
... | 2025-06-17T13:41:54Z | 2025-06-17T13:58:26Z | 2025-06-17T13:58:24Z | MEMBER | null | null | null | before release 4.0
(I also did minor improvements to `features` to not show their `id=None` in their `__repr__()`) | {
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https://api.github.com/repos/huggingface/datasets/issues/7619 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7619/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7619/comments | https://api.github.com/repos/huggingface/datasets/issues/7619/events | https://github.com/huggingface/datasets/issues/7619 | 3,153,058,517 | I_kwDODunzps6779rV | 7,619 | `from_list` fails while `from_generator` works for large datasets | {
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{
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"body": "@lhoestq any thoughts on this? ",
"created_at": "2025-06-18T09:29:24Z",
"html_url": "https://github.com/huggingface/datasets/issues/7619#issuecomment-2983410342",
"id": 2983410342,
"issue_url": "https://api.github.com/repos/huggingface/datasets/issues... | 2025-06-17T10:58:55Z | 2025-06-29T16:34:44Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I am constructing a large time series dataset and observed that first constructing a list of entries and then using `Dataset.from_list` led to a crash as the number of items became large. However, this is not a problem when using `Dataset.from_generator`.
### Steps to reproduce the bug
#### Snippet A (crashes)
```py
from tqdm.auto import tqdm
import numpy as np
import datasets
def data_generator():
for i in tqdm(range(10_000_000)):
length = np.random.randint(2048)
series = np.random.rand(length)
yield {"target": series, "item_id": str(i), "start": np.datetime64("2000", "ms")}
data_list = list(data_generator())
ds = datasets.Dataset.from_list(data_list)
```
The last line crashes with
```
ArrowInvalid: Value 2147483761 too large to fit in C integer type
```
#### Snippet B (works)
```py
from tqdm.auto import tqdm
import numpy as np
import datasets
def data_generator():
for i in tqdm(range(10_000_000)):
length = np.random.randint(2048)
series = np.random.rand(length)
yield {"target": series, "item_id": str(i), "start": np.datetime64("2000", "ms")}
ds = datasets.Dataset.from_generator(data_generator)
```
### Expected behavior
I expected both the approaches to work or to fail similarly.
### Environment info
```
- `datasets` version: 3.6.0
- Platform: Linux-6.8.0-1029-aws-x86_64-with-glibc2.35
- Python version: 3.11.11
- `huggingface_hub` version: 0.32.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2025.3.0
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7617 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7617/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7617/comments | https://api.github.com/repos/huggingface/datasets/issues/7617/events | https://github.com/huggingface/datasets/issues/7617 | 3,148,102,085 | I_kwDODunzps67pDnF | 7,617 | Unwanted column padding in nested lists of dicts | {
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{
"author_association": "MEMBER",
"body": "Answer from @lhoestq:\n\n> No\n> This is because Arrow and Parquet a columnar format: they require a fixed type for each column. So if you have nested dicts, each item should have the same subfields\n\nThe way around I found is the handle it after sampling with thi... | 2025-06-15T22:06:17Z | 2025-06-16T13:43:31Z | 2025-06-16T13:43:31Z | MEMBER | null | null | {
"completed": 0,
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} | ```python
from datasets import Dataset
dataset = Dataset.from_dict({
"messages": [
[
{"a": "...",},
{"b": "...",},
],
]
})
print(dataset[0])
```
What I get:
```
{'messages': [{'a': '...', 'b': None}, {'a': None, 'b': '...'}]}
```
What I want:
```
{'messages': [{'a': '...'}, {'b': '...'}]}
```
Is there an easy way to automatically remove these auto-filled null/none values?
If not, I probably need a recursive none exclusion function, don't I?
Datasets 3.6.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7618/comments | https://api.github.com/repos/huggingface/datasets/issues/7618/events | https://github.com/huggingface/datasets/pull/7618 | 3,148,912,897 | PR_kwDODunzps6aqOnm | 7,618 | fix: raise error when folder-based datasets are loaded without data_dir or data_files | {
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{
"author_association": "MEMBER",
"body": "Great ! Since this logic is specific to one builder class maybe this check can be in the class definition ? I think you can put it in FolderBasedBuilder's `_info()` method.",
"created_at": "2025-06-16T12:13:26Z",
"html_url": "https://github.com/huggingface/... | 2025-06-16T07:43:59Z | 2025-06-16T12:13:26Z | null | CONTRIBUTOR | null | null | null |
### Related Issues/PRs
<!-- Uncomment 'Resolve' if this PR can close the linked items. -->
<!-- Resolve --> #6152
---
### What changes are proposed in this pull request?
This PR adds an early validation step for folder-based datasets (like `audiofolder`) to prevent silent fallback behavior.
**Before this fix**:
- When `data_dir` or `data_files` were not provided, the loader defaulted to the current working directory.
- This caused unexpected behavior like:
- Long loading times
- Scanning unintended local files
**Now**:
- If both `data_dir` and `data_files` are missing, a `ValueError` is raised early with a helpful message.
---
### How is this PR tested?
- [x] Manual test via `load_dataset("audiofolder")` with missing `data_dir`
- [ ] Existing unit tests (should not break any)
- [ ] New tests (if needed, maintainers can guide)
---
### Does this PR require documentation update?
- [x] No. You can skip the rest of this section.
---
### Release Notes
#### Is this a user-facing change?
- [x] Yes. Give a description of this change to be included in the release notes for users.
> Adds early error handling for folder-based datasets when neither `data_dir` nor `data_files` is specified, avoiding unintended resolution to the current directory.
#### What component(s), interfaces, languages, and integrations does this PR affect?
Components:
- [x] `area/datasets`
- [x] `area/load`
---
<a name="release-note-category"></a>
#### How should the PR be classified in the release notes? Choose one:
- [x] `rn/bug-fix` - A user-facing bug fix worth mentioning in the release notes
---
#### Should this PR be included in the next patch release?
- [x] Yes (this PR will be cherry-picked and included in the next patch release)
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https://api.github.com/repos/huggingface/datasets/issues/7616 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7616/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7616/comments | https://api.github.com/repos/huggingface/datasets/issues/7616/events | https://github.com/huggingface/datasets/pull/7616 | 3,144,506,665 | PR_kwDODunzps6acSW7 | 7,616 | Torchcodec decoding | {
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{
"author_association": "CONTRIBUTOR",
"body": "@lhoestq any updates on when this will be merged? Let me know if theres anything you need from my end.",
"created_at": "2025-06-17T13:52:48Z",
"html_url": "https://github.com/huggingface/datasets/pull/7616#issuecomment-2980477096",
"id": 2980477096... | 2025-06-13T19:06:07Z | 2025-06-19T18:25:49Z | 2025-06-19T18:25:49Z | CONTRIBUTOR | null | null | null | Closes #7607
## New signatures
### Audio
```python
Audio(sampling_rate: Optional[int] = None, mono: bool = True, decode: bool = True, stream_index: Optional[int] = None)
Audio.encode_example(self, value: Union[str, bytes, bytearray, dict, "AudioDecoder"]) -> dict
Audio.decode_example(self, value: dict, token_per_repo_id: Optional[dict[str, Union[str, bool, None]]] = None) -> "AudioDecoder":
```
### Video
```python
Video(decode: bool = True, stream_index: Optional[int] = None, dimension_order: Literal['NCHW', 'NHWC'] = 'NCHW', num_ffmpeg_threads: int = 1, device: Optional[Union[str, "torch.device"]] = 'cpu', seek_mode: Literal['exact', 'approximate'] = 'exact')
Video.encode_example(self, value: Union[str, bytes, bytearray, Example, np.ndarray, "VideoDecoder"]) -> Example:
Video.decode_example(self, value: Union[str, Example], token_per_repo_id: Optional[dict[str, Union[bool, str]]] = None, ) -> "VideoDecoder":
```
## Notes
Audio features constructor takes in 1 new optional param stream_index which is passed to the AudioDecoder constructor to select the stream index of a file.
Audio feature can now take in torchcodec.decoders.AudioDecoder as input to encode_example()
Audio feature decode_example() returns torchcodec.decoders.AudioDecoder
Video feature constructor takes in 5 new optional params stream_index, dimension_order, num_ffmpeg_threads, device, seek_mode all of which are passed to VideoDecoder constructor
Video feature decode_example() returns torchcodec.decoders.VideoDecoder
Video feature can now take in torchcodec.decoders.VideoDecoder as input to encode_example()
All test cases have been updated to reflect these changes
All documentation has also been updated to reflect these changes.
Both VideoDecoder and AudioDecoder when formatted with (np_formatter, tf_formatter, etc) will ignore the type and return themselves. Formatting test cases were updated accordingly to reflect this. (Pretty simple to make this not the case if we want though)
## Errors
This test case from `tests/packaged_modules/test_audiofolder.py`
```python
@require_librosa
@require_sndfile
@pytest.mark.parametrize("streaming", [False, True])
def test_data_files_with_metadata_and_archives(streaming, cache_dir, data_files_with_zip_archives):
audiofolder = AudioFolder(data_files=data_files_with_zip_archives, cache_dir=cache_dir)
audiofolder.download_and_prepare()
datasets = audiofolder.as_streaming_dataset() if streaming else audiofolder.as_dataset()
for split, data_files in data_files_with_zip_archives.items():
num_of_archives = len(data_files) # the metadata file is inside the archive
expected_num_of_audios = 2 * num_of_archives
assert split in datasets
dataset = list(datasets[split])
assert len(dataset) == expected_num_of_audios
# make sure each sample has its own audio (all arrays are different) and metadata
assert (
sum(np.array_equal(dataset[0]["audio"].get_all_samples().data.numpy(), example["audio"].get_all_samples().data.numpy()) for example in dataset[1:])
== 0
)
assert len({example["text"] for example in dataset}) == expected_num_of_audios
assert all(example["text"] is not None for example in dataset)
```
Fails now because AudioDecoder needs to access the files after the lines below are run, but there seems to be some context issues. The file the decoder is trying to read is closed before the decoder gets the chance to decode it.
```python
audiofolder.download_and_prepare()
datasets = audiofolder.as_streaming_dataset() if streaming else audiofolder.as_dataset()
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https://api.github.com/repos/huggingface/datasets/issues/7615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7615/comments | https://api.github.com/repos/huggingface/datasets/issues/7615/events | https://github.com/huggingface/datasets/pull/7615 | 3,143,443,498 | PR_kwDODunzps6aYp18 | 7,615 | remove unused code | {
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https://api.github.com/repos/huggingface/datasets/issues/7614 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7614/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7614/comments | https://api.github.com/repos/huggingface/datasets/issues/7614/events | https://github.com/huggingface/datasets/pull/7614 | 3,143,381,638 | PR_kwDODunzps6aYcbH | 7,614 | Lazy column | {
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"created_at": "2025-06-13T12:15:37Z",
... | 2025-06-13T12:12:57Z | 2025-06-17T13:08:51Z | 2025-06-17T13:08:49Z | MEMBER | null | null | null | Same as https://github.com/huggingface/datasets/pull/7564 but for `Dataset`, cc @TopCoder2K FYI
e.g. `ds[col]` now returns a lazy Column instead of a list
This way calling `ds[col][idx]` only loads the required data in memory
(bonus: also supports subfields access with `ds[col][subcol][idx]`)
the breaking change will be for the next major release, which also includes removal of dataset scripts support
close https://github.com/huggingface/datasets/issues/4180 | {
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https://api.github.com/repos/huggingface/datasets/issues/7612 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7612/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7612/comments | https://api.github.com/repos/huggingface/datasets/issues/7612/events | https://github.com/huggingface/datasets/issues/7612 | 3,141,905,049 | I_kwDODunzps67RaqZ | 7,612 | Provide an option of robust dataset iterator with error handling | {
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{
"author_association": "MEMBER",
"body": "Hi ! Maybe we can add a parameter to the Image() type to make it to return `None` instead of raising an error in case of corruption ? Would that help ?",
"created_at": "2025-06-19T13:47:12Z",
"html_url": "https://github.com/huggingface/datasets/issues/7612#... | 2025-06-13T00:40:48Z | 2025-06-24T16:52:30Z | null | NONE | null | null | {
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} | ### Feature request
Adding an option to skip corrupted data samples. Currently the datasets behavior is throwing errors if the data sample if corrupted and let user aware and handle the data corruption. When I tried to try-catch the error at user level, the iterator will raise StopIteration when I called next() again.
The way I try to do error handling is: (This doesn't work, unfortunately)
```
# Load the dataset with streaming enabled
dataset = load_dataset(
"pixparse/cc12m-wds", split="train", streaming=True
)
# Get an iterator from the dataset
iterator = iter(dataset)
while True:
try:
# Try to get the next example
example = next(iterator)
# Try to access and process the image
image = example["jpg"]
pil_image = Image.fromarray(np.array(image))
pil_image.verify() # Verify it's a valid image file
except StopIteration: # Code path 1
print("\nStopIteration was raised! Reach the end of dataset")
raise StopIteration
except Exception as e: # Code path 2
errors += 1
print("Error! Skip this sample")
cotinue
else:
successful += 1
```
This is because the `IterableDataset` already throws an error (reaches Code path 2). And if I continue call next(), it will hit Code path 1. This is because the inner iterator of `IterableDataset`([code](https://github.com/huggingface/datasets/blob/89bd1f971402acb62805ef110bc1059c38b1c8c6/src/datasets/iterable_dataset.py#L2242)) as been stopped, so calling next() on it will raise StopIteration.
So I can not skip the corrupted data sample in this way. Would also love to hear any suggestions about creating a robust dataloader.
Thanks for your help in advance!
### Motivation
## Public dataset corruption might be common
A lot of users would use public dataset, and the public dataset might contains some corrupted data, especially for dataset with image / video etc. I totally understand it's dataset owner and user's responsibility to ensure the data integrity / run data cleaning or preprocessing, but it would be easier for developers who would use the dataset
## Use cases
For example, a robust dataloader would be easy for users who want to try quick tests on different dataset, and chose one dataset which fits their needs. So user could use IterableDataloader with `stream=True` to use the dataset easily without downloading and removing corrupted data samples from the dataset.
### Your contribution
The error handling might not trivial and might need more careful design. | null | {
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"created_at": "2025-06-13T09:06:16Z",
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https://api.github.com/repos/huggingface/datasets/issues/7611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7611/comments | https://api.github.com/repos/huggingface/datasets/issues/7611/events | https://github.com/huggingface/datasets/issues/7611 | 3,141,383,940 | I_kwDODunzps67PbcE | 7,611 | Code example for dataset.add_column() does not reflect correct way to use function | {
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{
"author_association": "CONTRIBUTOR",
"body": "Hi @shaily99 \n\nThanks for pointing this out — you're absolutely right!\n\nThe current example in the docstring for add_column() implies in-place modification, which is misleading since add_column() actually returns a new dataset.",
"created_at": "2025-06... | 2025-06-12T19:42:29Z | 2025-07-17T13:14:18Z | 2025-07-17T13:14:18Z | NONE | null | null | {
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} | https://github.com/huggingface/datasets/blame/38d4d0e11e22fdbc4acf373d2421d25abeb43439/src/datasets/arrow_dataset.py#L5925C10-L5925C10
The example seems to suggest that dataset.add_column() can add column inplace, however, this is wrong -- it cannot. It returns a new dataset with the column added to it. | {
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https://api.github.com/repos/huggingface/datasets/issues/7610 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7610/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7610/comments | https://api.github.com/repos/huggingface/datasets/issues/7610/events | https://github.com/huggingface/datasets/issues/7610 | 3,141,281,560 | I_kwDODunzps67PCcY | 7,610 | i cant confirm email | {
"avatar_url": "https://avatars.githubusercontent.com/u/187984415?v=4",
"events_url": "https://api.github.com/users/lykamspam/events{/privacy}",
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"id": 187984415,
"login": "lykamspam",
"node_id": "U_kgDOCzRqHw",
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"received_events_url": "https://api.github.com/users/lykamspam/received_events",
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"type": "User",
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{
"author_association": "CONTRIBUTOR",
"body": "Will you please clarify the issue by some screenshots or more in-depth explanation?",
"created_at": "2025-06-27T05:10:06Z",
"html_url": "https://github.com/huggingface/datasets/issues/7610#issuecomment-3011691722",
"id": 3011691722,
"issue_url"... | 2025-06-12T18:58:49Z | 2025-06-27T14:36:47Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
This is dificult, I cant confirm email because I'm not get any email!
I cant post forum because I cant confirm email!
I can send help desk because... no exist on web page.
paragraph 44
### Steps to reproduce the bug
rthjrtrt
### Expected behavior
ewtgfwetgf
### Environment info
sdgfswdegfwe | null | {
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"url": "https://api.github.com/repos/huggingface/datasets/issues/7610/reactions"
} | https://api.github.com/repos/huggingface/datasets/issues/7610/timeline | null | null | null | null | 2025-06-12T18:58:49Z | null | null | false |
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