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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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} | https://huggingface.co/docs/datasets/v4.0.0/en/package_reference/main_classes#datasets.Dataset.map.num_proc
```
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... | 2025-07-28T19:40:40 | 2025-07-29T09:25:12 | 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_... | 2025-07-30T11:34:53 | 2025-07-30T15:41:59 | 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",
... | 2025-07-29T13:14:30 | 2025-07-30T15:55:00 | 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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{
"author_association": "MEMBER",
"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... | 2025-07-28T09:43:54 | 2025-07-30T15:44:26 | 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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https://api.github.com/repos/huggingface/datasets/issues/7704 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7704/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7704/comments | https://api.github.com/repos/huggingface/datasets/issues/7704/events | https://github.com/huggingface/datasets/pull/7704 | 3,265,730,177 | PR_kwDODunzps6gwtb8 | 7,704 | Fix map() example in datasets documentation: define tokenizer before use | {
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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... | 2025-07-26T14:18:17 | 2025-07-26T14:18:17 | 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... | 2025-07-26T06:47:59 | 2025-07-28T11:58:11 | 2025-07-28T11:58:11 | 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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} | 2025-07-26T06:47:59 | 2025-07-28T11:58:11 | 2 days, 5:10:12 | true |
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,... | 2025-07-26T13:35:11 | 2025-07-27T09:44:35 | 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 ... | 2025-07-29T03:25:03 | 2025-07-31T03:01:09 | 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... | 2025-07-26T08:19:39 | 2025-07-31T05:22:35 | 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":... | 2025-07-23T08:04:53 | 2025-07-23T15:06:23 | null | NONE | null | null | {
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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... | 2025-07-19T18:40:27 | 2025-07-25T08:51:10 | null | NONE | null | null | {
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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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{
"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... | 2025-07-24T19:46:28 | 2025-07-25T15:27:47 | 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... | 2025-07-20T11:08:20 | 2025-07-25T14:38:54 | 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... | 2025-07-21T07:51:25 | 2025-07-25T14:42:21 | 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... | 2025-07-22T09:33:54 | 2025-07-28T17:33:30 | 2025-07-28T17:15:45 | 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... | 2025-07-18T21:09:41 | 2025-07-28T21:32:12 | 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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{
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"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... | 2025-07-20T13:48:06 | 2025-07-30T15:01:03 | 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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https://api.github.com/repos/huggingface/datasets/issues/7696 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7696/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7696/comments | https://api.github.com/repos/huggingface/datasets/issues/7696/events | https://github.com/huggingface/datasets/issues/7696 | 3,253,433,350 | I_kwDODunzps7B63QG | 7,696 | load_dataset() in 4.0.0 returns different audio samples compared to earlier versions breaking reproducibility | {
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{
"author_association": "MEMBER",
"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... | 2025-07-22T17:02:17 | 2025-07-30T14:22:21 | 2025-07-30T14:22:21 | NONE | null | null | {
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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 | 2025-07-22T17:02:17 | 2025-07-30T14:22:21 | 7 days, 21:20:04 | false |
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