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https://api.github.com/repos/huggingface/datasets/issues/7766
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I_kwDODunzps7LWQ4d
| 7,766
|
cast columns to Image/Audio/Video with `storage_options`
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### Feature request
Allow `storage_options` to be passed in
1. `cast` related operations (e.g., `cast_columns, cast`)
2. `info` related reading (e.g., `from_dict, from_pandas, from_polars`) together with `info.features`
```python3
import datasets
image_path = "s3://bucket/sample.png"
dataset = datasets.Dataset.from_dict({"image_path": [image_path]})
# dataset = dataset.cast_column("image_path", datasets.Image()) # now works without `storage_options`
# expected behavior
dataset = dataset.cast_column("image_path", datasets.Image(), storage_options={"anon": True})
```
### Motivation
I'm using my own registered fsspec filesystem (s3 with customized local cache support). I need to pass cache folder paths `cache_dirs: list[str]` to the filesystem when I read the remote images (cast from file_paths).
### Your contribution
Could help with a PR at weekends
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I_kwDODunzps7LWDga
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polars dataset cannot cast column to Image/Audio/Video
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### Describe the bug
`from_polars` dataset cannot cast column to Image/Audio/Video, while it works on `from_pandas` and `from_dict`
### Steps to reproduce the bug
```python3
import datasets
import pandas as pd
import polars as pl
image_path = "./sample.png"
# polars
df = pl.DataFrame({"image_path": [image_path]})
dataset = datasets.Dataset.from_polars(df)
dataset = dataset.cast_column("image_path", datasets.Image())
# # raises Error
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from large_string to struct using function cast_struct
# pandas
df = pd.DataFrame({"image_path": [image_path]})
dataset = datasets.Dataset.from_pandas(df)
dataset = dataset.cast_column("image_path", datasets.Image())
# # pass
{'image_path': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=338x277 at 0x7FBA719D4050>}
# dict
dataset = datasets.Dataset.from_dict({"image_path": [image_path]})
dataset = dataset.cast_column("image_path", datasets.Image())
# # pass
{'image_path': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=338x277 at 0x7FBA719D4050>}
```
### Expected behavior
`from_polars` case shouldn't raise error and have the same outputs as `from_pandas` and `from_dict`
### Environment info
```
# Name Version Build Channel
datasets 4.0.0 pypi_0 pypi
pandas 2.3.1 pypi_0 pypi
polars 1.32.3 pypi_0 pypi
```
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update torchcodec in ci
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MEMBER
| null | null | null | null |
before the release, to make sure everything works fine
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Bump dill to 0.4.0
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2025-09-15 08:37:48+00:00
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CONTRIBUTOR
| null | null | null | null |
This bumps `dill` to 0.3.9 and closes #7510
It turns out the only thing required to make the tests pass was to extend the version checks to include 0.3.9.
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Parquet: use data page v2 for efficient page pruning
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MEMBER
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This is needed to enable page pruning with DataFusion, which will be useful for the Dataset Viewer.
Indeed page pruning with DataFusion allows to download only certain pages of a row group, reducing the I/O required to read just a few rows.
But while data page v1 generally works, it's not easy with DataFusion to do page pruning on datasets with nested data. This is because rows can span multiple pages in v1, contrary to v2.
cc @severo for viz
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PR_kwDODunzps6n1bls
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Audio: use TorchCodec instead of Soundfile for encoding
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MEMBER
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this removes the dependency on Soundfile completely
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Hugging Face Hub Dataset Upload CAS Error
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2025-09-16 20:01:36+00:00
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### Describe the bug
Experiencing persistent 401 Unauthorized errors when attempting to upload datasets to Hugging Face Hub using the `datasets` library. The error occurs specifically with the CAS (Content Addressable Storage) service during the upload process. Tried using HF_HUB_DISABLE_XET=1. It seems to work for smaller files.
Exact error message :
```
Processing Files (0 / 0) : | | 0.00B / 0.00B 2025-09-10T09:44:35.657565Z ERROR Fatal Error: "cas::upload_xorb" api call failed (request id 01b[...]XXX): HTTP status client error (401 Unauthorized) for url (https://cas-server.xethub.hf.co/xorb/default/7f3abdc[...]XXX)
at /home/runner/work/xet-core/xet-core/cas_client/src/retry_wrapper.rs:113
Processing Files (0 / 0) : 0%| | 0.00B / 184kB, 0.00B/s
New Data Upload : 0%| | 0.00B / 184kB, 0.00B/s
❌ Failed to push some_dataset: Data processing error: CAS service error : Reqwest Error: HTTP status client error (401 Unauthorized), domain: https://cas-server.xethub.hf.co/xorb/default/7f3abdc[...]XXX
```
Workaround Attempts
1. **Disabled XET**: Set `HF_HUB_DISABLE_XET=1` environment variable
2. **Updated hf-xet**: Use `hf-xet==1.1.9` rather than latest
3. **Verified Authentication**: Confirmed HF token is valid and has write permissions
4. **Tested with Smaller Datasets**:
- 100 samples: ✅ **SUCCESS** (uploaded successfully)
- 10,000 samples: ❌ **FAILS** (401 Unauthorized)
### Steps to reproduce the bug
```python
from datasets import Dataset, DatasetDict
# Create dataset (example with 10,000 samples)
dataset = Dataset.from_dict({
"question": questions,
"answer": answers,
# ... other fields
})
# Split into train/test
dataset_dict = dataset.train_test_split(test_size=0.1)
# Upload to Hub
dataset_dict.push_to_hub("Org/some-dataset")
```
### Expected behavior
## Expected Behavior
- Dataset should upload successfully to Hugging Face Hub
- Progress bars should complete without authentication errors
- Dataset should be accessible at the specified repository URL
## Actual Behavior
- Upload fails consistently with 401 Unauthorized error
- Error occurs specifically during CAS service interaction
- No progress is made on the upload (0% completion)
- Dataset is created on Hugging Face Hub with no data folder
### Environment info
- **Platform**: SageMaker (AWS)
- **Python Version**: 3.12
- **Libraries**:
- `datasets` library (latest version)
- `hf-xet==1.1.9` (attempted fix)
- **Authentication**: Hugging Face token configured
- **Dataset Size**: ~10,000 samples, works for smaller sizes (e.g. 100)
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Comment/feature request: Huggingface 502s from GHA
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2025-09-09 11:59:20+00:00
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This is no longer a pressing issue, but for completeness I am reporting that in August 26th, GET requests to `https://datasets-server.huggingface.co/info\?dataset\=livebench/math` were returning 502s when invoked from [github actions](https://github.com/UKGovernmentBEIS/inspect_evals/actions/runs/17241892475/job/48921123754) (that link will expire eventually, [here are the logs](https://github.com/user-attachments/files/22233578/logs_44225296943.zip)).
When invoked from actions, it appeared to be consistently failing for ~6 hours. However, these 502s never occurred when the request was invoked from my local machine in that same time period.
I suspect that this is related to how the requests are routed with github actions versus locally.
Its not clear to me if the request even reached huggingface servers or if its the github proxy that stopped it from going through, but I wanted to report it nonetheless in case this is helpful information. I'm curious if huggingface can do anything on their end to confirm cause.
And a feature request for if this happens in the future (assuming huggingface has visibilty on it): A "datasets status" page highlighting if 502s occur for specific individual datasets could be useful for people debugging on the other end of this!
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|
Option for Anonymous Dataset link
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### Feature request
Allow for anonymized viewing of datasets. For instance, something similar to [Anonymous GitHub](https://anonymous.4open.science/).
### Motivation
We generally publish our data through Hugging Face. This has worked out very well as it's both our repository and archive (thanks to the DOI feature!). However, we have an increasing challenge when it comes to sharing our datasets for paper (both conference and journal) submissions. Due to the need to share data anonymously, we can't use the Hugging Face URLs, but datasets tend to be too large for inclusion as a zip. Being able to have an anonymous link would be great since we can't be double-publishing the data.
### Your contribution
Sorry, I don't have a contribution to make to the implementation of this. Perhaps it would be possible to work off the [Anonymous GitHub](https://github.com/tdurieux/anonymous_github) code to generate something analogous with pointers to the data still on Hugging Face's servers (instead of the duplication of data required for the GitHub version)?
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| 7,757
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Add support for `.conll` file format in datasets
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### Feature request
I’d like to request native support in the Hugging Face datasets library for reading .conll files (CoNLL format). This format is widely used in NLP tasks, especially for Named Entity Recognition (NER), POS tagging, and other token classification problems.
Right now `.conll` datasets need to be manually parsed or preprocessed before being loaded into datasets. Having built in support would save time and make workflows smoother for researchers and practitioners.
I propose -
Add a conll dataset builder or file parser to datasets that can:
- Read `.conll` files with customizable delimiters (space, tab).
- Handle sentence/document boundaries (typically indicated by empty lines).
- Support common CoNLL variants (e.g., CoNLL-2000 chunking, CoNLL-2003 NER).
- Output a dataset where each example contains:
- tokens: list of strings
- tags (or similar): list of labels aligned with tokens
Given a .conll snippet like:
```
EU NNP B-ORG
rejects VBZ O
German JJ B-MISC
call NN O
. . O
```
The dataset should load as:
```
{
"tokens": ["EU", "rejects", "German", "call", "."],
"tags": ["B-ORG", "O", "B-MISC", "O", "O"]
}
```
### Motivation
- CoNLL files are a standard benchmark format in NLP (e.g., CoNLL-2003, CoNLL-2000).
- Many users train NER or sequence labeling models (like BERT for token classification) directly on `.conll`
- Right now you have to write your own parsing scripts. Built in support would unify this process and would be much more convenient
### Your contribution
I’d be happy to contribute by implementing this feature. My plan is to-
- Add a new dataset script (conll.py) to handle .conll files.
- Implement parsing logic that supports sentence/document boundaries and token-label alignment.
- Write unit tests with small `.conll` examples to ensure correctness.
- Add documentation and usage examples so new users can easily load `.conll` datasets.
This would be my first open source contribution, so I’ll follow the `CONTRIBUTING.md` guidelines closely and adjust based on feedback from the maintainers.
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datasets.map(f, num_proc=N) hangs with N>1 when run on import
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2025-09-05 10:32:01+00:00
|
2025-09-05 10:32:01+00:00
|
NaT
|
NONE
| null | null |
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### Describe the bug
If you `import` a module that runs `datasets.map(f, num_proc=N)` at the top-level, Python hangs.
### Steps to reproduce the bug
1. Create a file that runs datasets.map at the top-level:
```bash
cat <<EOF > import_me.py
import datasets
the_dataset = datasets.load_dataset("openai/openai_humaneval")
the_dataset = the_dataset.map(lambda item: item, num_proc=2)
EOF
```
2. Start Python REPL:
```bash
uv run --python 3.12.3 --with "datasets==4.0.0" python3
Python 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
```
3. Import the file:
```python
import import_me
````
Observe hang.
### Expected behavior
Ideally would not hang, or would fallback to num_proc=1 with a warning.
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-6.14.0-29-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.34.4
- PyArrow version: 21.0.0
- Pandas version: 2.3.2
- `fsspec` version: 2025.3.0
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PR_kwDODunzps6m-MTU
| 7,755
|
Support pathlib.Path for feature input
|
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2025-09-05 02:38:07+00:00
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2025-09-10 15:19:35+00:00
|
2025-09-10 15:19:35+00:00
|
CONTRIBUTOR
| null | null | null | null |
This PR adds support for specifying image, video, audio, and pdf features using `pathlib.Path`.
|
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PR_kwDODunzps6m6qRo
| 7,754
|
Add columns support to JSON loader
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2025-09-04 18:21:26+00:00
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2025-09-04 18:21:26+00:00
|
NaT
|
CONTRIBUTOR
| null | null | null | null |
New fix to #7594
This PR adds support for the columns argument in the JSON dataset builder.
Added columns parameter to JsonConfig.
Applied column filtering after table creation, filling missing columns with None.
Extended tests to cover:
- Selecting a subset of columns
- Handling missing requested columns
- Column selection on list-of-strings case
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| 3,381,831,487
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I_kwDODunzps7Jkqc_
| 7,753
|
datasets massively slows data reads, even in memory
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2025-09-04 01:45:24+00:00
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2025-09-18 22:08:51+00:00
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NONE
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### Describe the bug
Loading image data in a huggingface dataset results in very slow read speeds, approximately 1000 times longer than reading the same data from a pytorch dataset. This applies even when the dataset is loaded into RAM using a `keep_in_memory=True` flag.
The following script reproduces the result with random data, but it applies equally to datasets that are loaded from the hub.
### Steps to reproduce the bug
The following script should reproduce the behavior
```
import torch
import time
from datasets import Dataset
images = torch.randint(0, 255, (1000, 3, 224, 224), dtype=torch.uint8)
labels = torch.randint(0, 200, (1000,), dtype=torch.uint8)
pt_dataset = torch.utils.data.TensorDataset(images, labels)
hf_dataset = Dataset.from_dict({'image': images, 'label':labels})
hf_dataset.set_format('torch', dtype=torch.uint8)
hf_in_memory = hf_dataset.map(lambda x: x, keep_in_memory=True)
# measure access speeds
def time_access(dataset, img_col):
start_time = time.time()
for i in range(1000):
_ = dataset[i][img_col].shape
end_time = time.time()
return end_time - start_time
print(f"In-memory Tensor access: {time_access(pt_dataset, 0):.4f} seconds")
print(f"HF Dataset access: {time_access(hf_dataset, 'image'):.4f} seconds")
print(f"In-memory HF Dataset access: {time_access(hf_in_memory, 'image'):.4f} seconds")
```
### Expected behavior
For me, the above script produces
```
In-memory Tensor access: 0.0025 seconds
HF Dataset access: 2.9317 seconds
In-memory HF Dataset access: 2.8082 seconds
```
I think that this difference is larger than expected.
### Environment info
- `datasets` version: 4.0.0
- Platform: macOS-14.7.7-arm64-arm-64bit
- Python version: 3.12.11
- `huggingface_hub` version: 0.34.3
- PyArrow version: 18.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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PR_kwDODunzps6ljQLy
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|
Fix: Update Dill Version in Setup py
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2025-08-27 07:39:51+00:00
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2025-11-03 14:52:58+00:00
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2025-11-03 14:52:58+00:00
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NONE
| null | null | null | null |
Currently the DIll version is less than 3.9 and now major libraries like Multiprocess, gepa requires Dill version as 0.4.0 and this is making a conflict in installation. So added this small PR to update the DIll.
|
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I_kwDODunzps7ILKi4
| 7,751
|
Dill version update
|
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2025-08-27 07:38:30+00:00
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2025-09-10 14:24:02+00:00
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NaT
|
NONE
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### Describe the bug
Why the datasets is not updating the dill ?
Just want to know if I update the dill version in dill what will be the repucssion.
For now in multiplaces I have to update the library like process requirequire dill 0.4.0 so why not datasets.
Adding a pr too.
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
.
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PR_kwDODunzps6lfwcx
| 7,750
|
Refactor: use unpacking in load.py for time and memory improvement
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2025-08-26 22:13:11+00:00
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2025-08-26 22:13:11+00:00
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NaT
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CONTRIBUTOR
| null | null | null | null |
In `src/datasets/load.py`, we can use unpacking rather than concatenating two lists for improved time and memory performance. It’s a small improvement in absolute terms, but a consistent and measurable one:
```diff
- ALL_ALLOWED_EXTENSIONS = list(_EXTENSION_TO_MODULE.keys()) + [".zip"]
+ ALL_ALLOWED_EXTENSIONS = [*_EXTENSION_TO_MODULE.keys(), ".zip"]
```
Benchmarking shows approximately 32.3% time improvement and 30.6% memory improvement.
Example benchmarking script:
```python
#!/usr/bin/env python3
"""
Benchmark script to test performance of list(_EXTENSION_TO_MODULE.keys()) vs [*_EXTENSION_TO_MODULE.keys()]
"""
import time
import tracemalloc
from statistics import mean, stdev
# Simulate _EXTENSION_TO_MODULE - based on actual size from datasets
_EXTENSION_TO_MODULE = {
f".ext{i}": f"module{i}" for i in range(20) # Realistic size
}
def method_old():
"""Current implementation using list()"""
return list(_EXTENSION_TO_MODULE.keys()) + [".zip"]
def method_new():
"""Proposed implementation using unpacking"""
return [*_EXTENSION_TO_MODULE.keys(), ".zip"]
def benchmark_time(func, iterations=100000):
"""Benchmark execution time"""
times = []
for _ in range(10): # Multiple runs for accuracy
start = time.perf_counter()
for _ in range(iterations):
func()
end = time.perf_counter()
times.append((end - start) / iterations * 1_000_000) # microseconds
return mean(times), stdev(times)
def benchmark_memory(func):
"""Benchmark peak memory usage"""
tracemalloc.start()
func()
current, peak = tracemalloc.get_traced_memory()
tracemalloc.stop()
return peak
if __name__ == "__main__":
print("Benchmarking list() vs unpacking performance...\n")
# Time benchmarks
old_time, old_std = benchmark_time(method_old)
new_time, new_std = benchmark_time(method_new)
print(f"Time Performance (µs per operation):")
print(f" list() approach: {old_time:.3f} ± {old_std:.3f}")
print(f" unpacking approach: {new_time:.3f} ± {new_std:.3f}")
print(f" Improvement: {((old_time - new_time) / old_time * 100):.1f}% faster")
# Memory benchmarks
old_mem = benchmark_memory(method_old)
new_mem = benchmark_memory(method_new)
print(f"\nMemory Usage (bytes):")
print(f" list() approach: {old_mem}")
print(f" unpacking approach: {new_mem}")
print(f" Reduction: {old_mem - new_mem} bytes ({((old_mem - new_mem) / old_mem * 100):.1f}% less)")
# Verify identical results
assert method_old() == method_new(), "Results should be identical!"
print(f"\n✓ Both methods produce identical results")
```
Results:
```
Benchmarking list() vs unpacking performance...
Time Performance (µs per operation):
list() approach: 0.213 ± 0.020
unpacking approach: 0.144 ± 0.002
Improvement: 32.3% faster
Memory Usage (bytes):
list() approach: 392
unpacking approach: 272
Reduction: 120 bytes (30.6% less)
✓ Both methods produce identical results
```
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PR_kwDODunzps6lddDW
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Fix typo in error message for cache directory deletion
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2025-08-26 17:47:22+00:00
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2025-09-12 15:43:08+00:00
|
2025-09-12 13:22:18+00:00
|
CONTRIBUTOR
| null | null | null | null |
This PR fixes a small typo in an error message in `src/datasets/fingerprint.py`:
https://github.com/huggingface/datasets/blob/910fab20606893f69b4fccac5fcc883dddf5a14d/src/datasets/fingerprint.py#L63
```diff
- occured
+ occurred
```
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docs: Streaming best practices
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2025-08-23 00:18:43+00:00
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2025-09-07 02:33:36+00:00
|
NaT
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NONE
| null | null | null | null |
Add a new 'Streaming best practices' page with practical patterns and pitfalls for large-scale/production use of IterableDataset. Includes examples for batched map with remove_columns, deterministic shuffling with set_epoch, multi-worker sharding, checkpoint/resume, and persistence to Parquet/Hub. Linked from How-to > General usage, next to Stream.
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PR_kwDODunzps6k-Rtd
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|
Add wikipedia-2023-redirects dataset
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2025-08-22 23:49:53+00:00
|
2025-09-12 13:23:34+00:00
|
NaT
|
NONE
| null | null | null | null |
Title: Add wikipedia-2023-redirects dataset (redirect resolution + pageviews)
Summary
- New dataset loader: wikipedia_2023_redirects
- Canonical Wikipedia pages enriched with:
- redirects (aliases pointing to the page)
- 2023 pageviews (aggregated)
- Streaming support; robust parsing; license notes included
- Tests with tiny dummy data (XML + TSVs); covers streaming
Motivation
RAG/retrieval often benefits from:
- Query expansion via redirect aliases
- Popularity prior via pageviews
This loader offers a practical, maintenance-light way to access canonical pages alongside their redirect aliases and 2023 pageview totals.
Features
- id: string
- title: string
- url: string
- text: string
- redirects: list[string]
- pageviews_2023: int32
- timestamp: string
Licensing
- Wikipedia text: CC BY-SA 3.0 (attribution and share-alike apply)
- Pageviews: public domain
The PR docs mention both, and the module docstring cites sources.
Notes
- The URLs in _get_urls_for_config are wired to dummy files for tests. In production, these would point to Wikimedia dumps:
- XML page dumps: https://dumps.wikimedia.org/
- Pageviews: https://dumps.wikimedia.org/other/pageviews/
- The schema is intentionally simple and stable. Pageview aggregation is per-title sum across 2023.
Testing
- make style && make quality
- pytest -q tests/test_dataset_wikipedia_2023_redirects.py
Example
```python
from datasets import load_dataset
ds = load_dataset("wikipedia_2023_redirects", split="train")
print(ds[0]["title"], ds[0]["redirects"][:5], ds[0]["pageviews_2023"])
```
Acknowledgements
- Wikipedia/Wikimedia Foundation for the source data
- Hugging Face Datasets for the dataset infrastructure
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I_kwDODunzps7HZp5r
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Fix: Canonical 'multi_news' dataset is broken and should be updated to a Parquet version
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2025-08-22 12:52:03+00:00
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2025-08-27 20:23:35+00:00
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Hi,
The canonical `multi_news` dataset is currently broken and fails to load. This is because it points to the [alexfabri/multi_news](https://huggingface.co/datasets/alexfabbri/multi_news) repository, which contains a legacy loading script (`multi_news.py`) that requires the now-removed `trust_remote_code` parameter.
The original maintainer's GitHub and Hugging Face repositories appear to be inactive, so a community-led fix is needed.
I have created a working fix by converting the dataset to the modern Parquet format, which does not require a loading script. The fixed version is available here and loads correctly:
**[Awesome075/multi_news_parquet](https://huggingface.co/datasets/Awesome075/multi_news_parquet)**
Could the maintainers please guide me or themselves update the official `multi_news` dataset to use this working Parquet version? This would involve updating the canonical pointer for "multi_news" to resolve to the new repository.
This action would fix the dataset for all users and ensure its continued availability.
Thank you!
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I_kwDODunzps7HZQZ1
| 7,745
|
Audio mono argument no longer supported, despite class documentation
|
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2025-08-22 12:15:41+00:00
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2025-08-24 18:22:41+00:00
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### Describe the bug
Either update the documentation, or re-introduce the flag (and corresponding logic to convert the audio to mono)
### Steps to reproduce the bug
Audio(sampling_rate=16000, mono=True) raises the error
TypeError: Audio.__init__() got an unexpected keyword argument 'mono'
However, in the class documentation, is says:
Args:
sampling_rate (`int`, *optional*):
Target sampling rate. If `None`, the native sampling rate is used.
mono (`bool`, defaults to `True`):
Whether to convert the audio signal to mono by averaging samples across
channels.
[...]
### Expected behavior
The above call should either work, or the documentation within the Audio class should be updated
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-5.15.0-124-generic-x86_64-with-glibc2.35
- Python version: 3.12.11
- `huggingface_hub` version: 0.34.4
- PyArrow version: 21.0.0
- Pandas version: 2.3.2
- `fsspec` version: 2025.3.0
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I_kwDODunzps7HSeye
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dtype: ClassLabel is not parsed correctly in `features.py`
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2025-08-21 23:28:50+00:00
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2025-09-10 15:23:41+00:00
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2025-09-10 15:23:41+00:00
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`dtype: ClassLabel` in the README.md yaml metadata is parsed incorrectly and causes the data viewer to fail.
This yaml in my metadata ([source](https://huggingface.co/datasets/BrentLab/yeast_genome_resources/blob/main/README.md), though i changed `ClassLabel` to `string` to using different dtype in order to avoid the error):
```yaml
license: mit
pretty_name: BrentLab Yeast Genome Resources
size_categories:
- 1K<n<10K
language:
- en
dataset_info:
features:
- name: start
dtype: int32
description: Start coordinate (1-based, **inclusive**)
- name: end
dtype: int32
description: End coordinate (1-based, **inclusive**)
- name: strand
dtype: ClassLabel
...
```
is producing the following error in the data viewer:
```
Error code: ConfigNamesError
Exception: ValueError
Message: Feature type 'Classlabel' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'List', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
return HubDatasetModuleFactory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 605, in get_module
dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 386, in from_dataset_card_data
dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 317, in _from_yaml_dict
yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2027, in _from_yaml_list
return cls.from_dict(from_yaml_inner(yaml_data))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1872, in from_dict
obj = generate_from_dict(dic)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1465, in generate_from_dict
raise ValueError(f"Feature type '{_type}' not found. Available feature types: {list(_FEATURE_TYPES.keys())}")
ValueError: Feature type 'Classlabel' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'LargeList', 'List', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image', 'Video', 'Pdf']
```
I think that this is caused by this line
https://github.com/huggingface/datasets/blob/896616c6cb03d92a33248c3529b0796cda27e955/src/datasets/features/features.py#L2013
Reproducible example from [naming.py](https://github.com/huggingface/datasets/blob/896616c6cb03d92a33248c3529b0796cda27e955/src/datasets/naming.py)
```python
import itertools
import os
import re
_uppercase_uppercase_re = re.compile(r"([A-Z]+)([A-Z][a-z])")
_lowercase_uppercase_re = re.compile(r"([a-z\d])([A-Z])")
_single_underscore_re = re.compile(r"(?<!_)_(?!_)")
_multiple_underscores_re = re.compile(r"(_{2,})")
_split_re = r"^\w+(\.\w+)*$"
def snakecase_to_camelcase(name):
"""Convert snake-case string to camel-case string."""
name = _single_underscore_re.split(name)
name = [_multiple_underscores_re.split(n) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(name) if n != "")
snakecase_to_camelcase("ClassLabel")
```
Result:
```raw
'Classlabel'
```
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| 3,342,611,297
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PR_kwDODunzps6ku8Jw
| 7,743
|
Refactor HDF5 and preserve tree structure
|
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2025-08-21 17:28:17+00:00
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2025-08-26 15:28:05+00:00
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2025-08-26 15:28:05+00:00
|
CONTRIBUTOR
| null | null | null | null |
Closes #7741. Followup to #7690
- Recursive parsing and feature inference, to preserve the tree structure of the file. Note this means we now visit all links in the file. It also means we have to call` combine_chunks` on any large non-root datasets.
- Support for `complex64` (two `float32`s, used to be converted to two `float64`s)
- Support for ndim complex, compound, more field types for compound (due to reusing the main parser, compound types are treated like groups)
- Cleaned up varlen support
- Always do feature inference and always cast to features (used to cast to schema)
- Updated tests to use `load_dataset` instead of internal APIs
- Removed `columns` in config. Have to give Features (i.e., must specify types) if filtering
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module 'pyarrow' has no attribute 'PyExtensionType'
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2025-08-20 06:14:33+00:00
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2025-09-09 02:51:46+00:00
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NaT
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### Describe the bug
When importing certain libraries, users will encounter the following error which can be traced back to the datasets library.
module 'pyarrow' has no attribute 'PyExtensionType'.
Example issue: https://github.com/explodinggradients/ragas/issues/2170
The issue occurs due to the following. I will proceed to submit a PR with the below fix:
**Issue Reason**
The issue is that PyArrow version 21.0.0 doesn’t have PyExtensionType. This was changed in newer versions of PyArrow. The
PyExtensionType class was renamed to ExtensionType in PyArrow 13.0.0 and later versions.
** Issue Solution**
Making the following changes to the following lib files should temporarily resolve the issue.
I will submit a PR to the dataets library in the meantime.
env_name/lib/python3.10/site-packages/datasets/features/features.py:
```
> 521 self.shape = tuple(shape)
522 self.value_type = dtype
523 self.storage_dtype = self._generate_dtype(self.value_type)
524 - pa.PyExtensionType.__init__(self, self.storage_dtype)
524 + pa.ExtensionType.__init__(self, self.storage_dtype)
525
526 def __reduce__(self):
527 return self.__class__, (
```
Updated venv_name/lib/python3.10/site-packages/datasets/features/features.py:
```
510 _type: str = field(default=“Array5D”, init=False, repr=False)
511
512
513 - class _ArrayXDExtensionType(pa.PyExtensionType):
513 + class _ArrayXDExtensionType(pa.ExtensionType):
514 ndims: Optional[int] = None
515
516 def __init__(self, shape: tuple, dtype: str):
```
### Steps to reproduce the bug
Ragas version: 0.3.1
Python version: 3.11
**Code to Reproduce**
_**In notebook:**_
!pip install ragas
from ragas import evaluate
### Expected behavior
The required package installs without issue.
### Environment info
In Jupyter Notebook.
venv
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I_kwDODunzps7GxcCQ
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|
Preserve tree structure when loading HDF5
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2025-08-26 15:28:06+00:00
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CONTRIBUTOR
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### Feature request
https://github.com/huggingface/datasets/pull/7740#discussion_r2285605374
### Motivation
`datasets` has the `Features` class for representing nested features. HDF5 files have groups of datasets which are nested, though in #7690 the keys are flattened. We should preserve that structure for the user.
### Your contribution
I'll open a PR (#7743)
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PR_kwDODunzps6kUMKM
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|
Document HDF5 support
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2025-09-24 14:51:11+00:00
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2025-09-24 14:51:11+00:00
|
CONTRIBUTOR
| null | null | null | null |
I think these are at least the main places where we should put content. Ideally it is not just repeated in the final version
ref #7690
- [x] Wait for #7743 to land
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I_kwDODunzps7Gkzti
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Replacement of "Sequence" feature with "List" breaks backward compatibility
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2025-08-18 17:28:38+00:00
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2025-09-10 14:17:50+00:00
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PR #7634 replaced the Sequence feature with List in 4.0.0, so datasets saved with version 4.0.0 with that feature cannot be loaded by earlier versions. There is no clear option in 4.0.0 to use the legacy feature type to preserve backward compatibility.
Why is this a problem? I have a complex preprocessing and training pipeline dependent on 3.6.0; we manage a very large number of separate datasets that get concatenated during training. If just one of those datasets is saved with 4.0.0, they become unusable, and we have no way of "fixing" them. I can load them in 4.0.0 but I can't re-save with the legacy feature type, and I can't load it in 3.6.0 for obvious reasons.
Perhaps I'm missing something here, since the PR says that backward compatibility is preserved; if so, it's not obvious to me how.
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I_kwDODunzps7Ga7nS
| 7,738
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Allow saving multi-dimensional ndarray with dynamic shapes
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### Feature request
I propose adding a dedicated feature to the datasets library that allows for the efficient storage and retrieval of multi-dimensional ndarray with dynamic shapes. Similar to how Image columns handle variable-sized images, this feature would provide a structured way to store array data where the dimensions are not fixed.
A possible implementation could be a new Array or Tensor feature type that stores the data in a structured format, for example,
```python
{
"shape": (5, 224, 224),
"dtype": "uint8",
"data": [...]
}
```
This would allow the datasets library to handle heterogeneous array sizes within a single column without requiring a fixed shape definition in the feature schema.
### Motivation
I am currently trying to upload data from astronomical telescopes, specifically FITS files, to the Hugging Face Hub. This type of data is very similar to images but often has more than three dimensions. For example, data from the SDSS project contains five channels (u, g, r, i, z), and the pixel values can exceed 255, making the Pillow based Image feature unsuitable.
The current datasets library requires a fixed shape to be defined in the feature schema for multi-dimensional arrays, which is a major roadblock. This prevents me from saving my data, as the dimensions of the arrays can vary across different FITS files.
https://github.com/huggingface/datasets/blob/985c9bee6bfc345787a8b9dd316e1d4f3b930503/src/datasets/features/features.py#L613-L614
A feature that supports dynamic shapes would be incredibly beneficial for the astronomy community and other fields dealing with similar high-dimensional, variable-sized data (e.g., medical imaging, scientific simulations).
### Your contribution
I am willing to create a PR to help implement this feature if the proposal is accepted.
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docs: Add column overwrite example to batch mapping guide
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2025-08-13 14:20:19+00:00
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2025-09-04 11:11:37+00:00
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2025-09-04 11:11:37+00:00
|
CONTRIBUTOR
| null | null | null | null |
This PR adds a complementary example showing the **column-overwriting** pattern, which is both more direct and more flexible for many transformations.
### Proposed Change
The original `remove_columns` example remains untouched. Below it, this PR introduces an alternative approach that overwrites an existing column during batch mapping.
This teaches users a core `.map()` capability for in-place transformations without extra intermediate steps.
**New Example:**
> ```python
> >>> from datasets import Dataset
> >>> dataset = Dataset.from_dict({"a": [0, 1, 2]})
> # Overwrite "a" directly to duplicate each value
> >>> duplicated_dataset = dataset.map(
> ... lambda batch: {"a": [x for x in batch["a"] for _ in range(2)]},
> ... batched=True
> ... )
> >>> duplicated_dataset
> Dataset({
> features: ['a'],
> num_rows: 6
> })
> >>> duplicated_dataset["a"]
> [0, 0, 1, 1, 2, 2]
> ```
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Fix type hint `train_test_split`
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fix largelist repr
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PR_kwDODunzps6i4pmA
| 7,734
|
Fixing __getitem__ of datasets which behaves inconsistent to documentation when setting _format_type to None
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2025-08-09 15:52:54+00:00
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2025-08-17 07:23:00+00:00
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2025-08-17 07:23:00+00:00
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NONE
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Setting _format_type to None, should return plain python object but as of 4.0.0 returns Column. This fails in libs such as sentencetransformers (such as in generation of hard negatives) where plain python is expected.
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I_kwDODunzps7E_ftj
| 7,733
|
Dataset Repo Paths to Locally Stored Images Not Being Appended to Image Path
|
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2025-08-08 19:10:58+00:00
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2025-10-07 04:47:36+00:00
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2025-10-07 04:32:48+00:00
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### Describe the bug
I’m not sure if this is a bug or a feature and I just don’t fully understand how dataset loading is to work, but it appears there may be a bug with how locally stored Image() are being accessed. I’ve uploaded a new dataset to hugging face (rmdig/rocky_mountain_snowpack) but I’ve come into a ton of trouble trying to have the images handled properly (at least in the way I’d expect them to be handled).
I find that I cannot use relative paths for loading images remotely from the Hugging Face repo or from a local repository. Any time I do it always simply appends my current working directory to the dataset. As a result to use the datasets library with my dataset I have to change my working directory to the dataset folder or abandon the dataset object structure, which I cannot imagine you intended. As a result I have to use URL’s since an absolute path on my system obviously wouldn’t work for others. The URL works ok, but despite me having it locally downloaded, it appears to be redownloading the dataset every time I train my snowGAN model on it (and often times I’m coming into HTTPS errors for over requesting the data).
Or maybe image relative paths aren't intended to be loaded directly through your datasets library as images and should be kept as strings for the user to handle? If so I feel like you’re missing out on some pretty seamless functionality
### Steps to reproduce the bug
1. Download a local copy of the dataset (rmdig/rocky_mountain_snowpack) through git or whatever you prefer.
2. Alter the README.md YAML for file_path (the relative path to each image) to be type Image instead of type string
`
---
dataset_info:
features:
- name: image
dtype: Image
- name: file_path
dtype: Image
`
3. Initialize the dataset locally, make sure your working directory is not the dataset directory root
`dataset = datasets.load_dataset(‘path/to/local/rocky_mountain_snowpack/‘)`
4. Call to one of the samples and you’ll get an error that the image was not found in current/working/directory/preprocessed/cores/image_1.png. Showing that it’s simply looking in the current working directory + relative path
`
>>> dataset['train'][0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 2859, in __getitem__
return self._getitem(key)
^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 2841, in _getitem
formatted_output = format_table(
^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 657, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 410, in __call__
return self.format_row(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 459, in format_row
row = self.python_features_decoder.decode_row(row)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 223, in decode_row
return self.features.decode_example(row, token_per_repo_id=self.token_per_repo_id) if self.features else row
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/features/features.py", line 2093, in decode_example
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/features/features.py", line 1405, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/datasets/features/image.py", line 171, in decode_example
image = PIL.Image.open(path)
^^^^^^^^^^^^^^^^^^^^
File "/Users/dennyschaedig/miniconda3/lib/python3.12/site-packages/PIL/Image.py", line 3277, in open
fp = builtins.open(filename, "rb")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/Users/dennyschaedig/Datasets/preprocessed/cores/image_1.png'
`
### Expected behavior
I expect the datasets and Image() to load the locally hosted data using path/to/local/rocky_mountain_snowpack/ (that I pass in with my datasets.load_dataset() or the you all handle on the backend) call + relative path.
Instead it appears to load from my current working directory + relative path.
### Environment info
Tested on…
Windows 11, Ubuntu Linux 22.04 and Mac Sequoia 15.5 Silicone M2
datasets version 4.0.0
Python 3.12 and 3.13
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I_kwDODunzps7E-VBn
| 7,732
|
webdataset: key errors when `field_name` has upper case characters
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2025-08-08 16:56:42+00:00
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2025-08-08 16:56:42+00:00
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NaT
|
CONTRIBUTOR
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### Describe the bug
When using a webdataset each sample can be a collection of different "fields"
like this:
```
images17/image194.left.jpg
images17/image194.right.jpg
images17/image194.json
images17/image12.left.jpg
images17/image12.right.jpg
images17/image12.json
```
if the field_name contains upper case characters, the HF webdataset integration throws a key error when trying to load the dataset:
e.g. from a dataset (now updated so that it doesn't throw this error)
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[1], line 2
1 from datasets import load_dataset
----> 2 ds = load_dataset("commaai/comma2k19", data_files={'train': ['data-00000.tar.gz']}, num_proc=1)
File ~/xx/.venv/lib/python3.11/site-packages/datasets/load.py:1412, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, **config_kwargs)
1409 return builder_instance.as_streaming_dataset(split=split)
1411 # Download and prepare data
-> 1412 builder_instance.download_and_prepare(
1413 download_config=download_config,
1414 download_mode=download_mode,
1415 verification_mode=verification_mode,
1416 num_proc=num_proc,
1417 storage_options=storage_options,
1418 )
1420 # Build dataset for splits
1421 keep_in_memory = (
1422 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1423 )
File ~/xx/.venv/lib/python3.11/site-packages/datasets/builder.py:894, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
892 if num_proc is not None:
893 prepare_split_kwargs["num_proc"] = num_proc
--> 894 self._download_and_prepare(
895 dl_manager=dl_manager,
896 verification_mode=verification_mode,
897 **prepare_split_kwargs,
898 **download_and_prepare_kwargs,
899 )
900 # Sync info
901 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/xx/.venv/lib/python3.11/site-packages/datasets/builder.py:1609, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)
1608 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
-> 1609 super()._download_and_prepare(
1610 dl_manager,
1611 verification_mode,
1612 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS
1613 or verification_mode == VerificationMode.ALL_CHECKS,
1614 **prepare_splits_kwargs,
1615 )
File ~/xx/.venv/lib/python3.11/site-packages/datasets/builder.py:948, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
946 split_dict = SplitDict(dataset_name=self.dataset_name)
947 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 948 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
950 # Checksums verification
951 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File ~/xx/.venv/lib/python3.11/site-packages/datasets/packaged_modules/webdataset/webdataset.py:81, in WebDataset._split_generators(self, dl_manager)
78 if not self.info.features:
79 # Get one example to get the feature types
80 pipeline = self._get_pipeline_from_tar(tar_paths[0], tar_iterators[0])
---> 81 first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
82 if any(example.keys() != first_examples[0].keys() for example in first_examples):
83 raise ValueError(
84 "The TAR archives of the dataset should be in WebDataset format, "
85 "but the files in the archive don't share the same prefix or the same types."
86 )
File ~/xx/.venv/lib/python3.11/site-packages/datasets/packaged_modules/webdataset/webdataset.py:55, in WebDataset._get_pipeline_from_tar(cls, tar_path, tar_iterator)
53 data_extension = field_name.split(".")[-1]
54 if data_extension in cls.DECODERS:
---> 55 current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
56 if current_example:
57 yield current_example
KeyError: 'processed_log_IMU_magnetometer_value.npy'
```
### Steps to reproduce the bug
unit test was added in: https://github.com/huggingface/datasets/pull/7726
it fails without the fixed proposed in the same PR
### Expected behavior
Not throwing a key error.
### Environment info
```
- `datasets` version: 4.0.0
- Platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.39
- Python version: 3.11.4
- `huggingface_hub` version: 0.33.4
- PyArrow version: 21.0.0
- Pandas version: 2.3.1
- `fsspec` version: 2025.7.0
```
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I_kwDODunzps7E6YBT
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Add the possibility of a backend for audio decoding
|
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### Feature request
Add the possibility of a backend for audio decoding. Before version 4.0.0, soundfile was used, and now torchcodec is used, but the problem is that torchcodec requires ffmpeg, which is problematic to install on the same colab. Therefore, I suggest adding a decoder selection when loading the dataset.
### Motivation
I use a service for training models in which ffmpeg cannot be installed.
### Your contribution
I use a service for training models in which ffmpeg cannot be installed.
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| 3,301,907,242
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PR_kwDODunzps6iqTZI
| 7,730
|
Grammar fix: correct "showed" to "shown" in fingerprint.py
|
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2025-08-07 21:22:56+00:00
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2025-08-13 18:34:30+00:00
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2025-08-13 13:12:56+00:00
|
CONTRIBUTOR
| null | null | null | null |
This PR corrects a small grammatical issue in the outputs of fingerprint.py:
```diff
- "This warning is only showed once. Subsequent hashing failures won't be showed."
+ "This warning is only shown once. Subsequent hashing failures won't be shown."
```
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I_kwDODunzps7EvEW6
| 7,729
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OSError: libcudart.so.11.0: cannot open shared object file: No such file or directory
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> Hi is there any solution for that eror i try to install this one
pip install torch==1.12.1+cpu torchaudio==0.12.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
this is working fine but tell me how to install pytorch version that is fit for gpu
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I_kwDODunzps7EoIf4
| 7,728
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NonMatchingSplitsSizesError and ExpectedMoreSplitsError
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### Describe the bug
When loading dataset, the info specified by `data_files` did not overwrite the original info.
### Steps to reproduce the bug
```python
from datasets import load_dataset
traindata = load_dataset(
"allenai/c4",
"en",
data_files={"train": "en/c4-train.00000-of-01024.json.gz",
"validation": "en/c4-validation.00000-of-00008.json.gz"},
)
```
```log
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=828589180707, num_examples=364868892, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=809262831, num_examples=356317, shard_lengths=[223006, 133311], dataset_name='c4')}, {'expected': SplitInfo(name='validation', num_bytes=825767266, num_examples=364608, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='validation', num_bytes=102199431, num_examples=45576, shard_lengths=None, dataset_name='c4')}]
```
```python
from datasets import load_dataset
traindata = load_dataset(
"allenai/c4",
"en",
data_files={"train": "en/c4-train.00000-of-01024.json.gz"},
split="train"
)
```
```log
ExpectedMoreSplitsError: {'validation'}
```
### Expected behavior
No error
### Environment info
datasets 4.0.0
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I_kwDODunzps7EcKyy
| 7,727
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config paths that start with ./ are not valid as hf:// accessed repos, but are valid when accessed locally
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2025-08-06 08:21:37+00:00
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2025-08-06 08:21:37+00:00
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### Describe the bug
```
- config_name: some_config
data_files:
- split: train
path:
- images/xyz/*.jpg
```
will correctly download but
```
- config_name: some_config
data_files:
- split: train
path:
- ./images/xyz/*.jpg
```
will error with `FileNotFoundError` due to improper url joining. `load_dataset` on the same directory locally works fine.
### Steps to reproduce the bug
1. create a README.md with the front matter of the form
```
- config_name: some_config
data_files:
- split: train
path:
- ./images/xyz/*.jpg
```
2. `touch ./images/xyz/1.jpg`
3. Observe this directory loads with `load_dataset("filesystem_path", "some_config")` correctly.
4. Observe exceptions when you load this with `load_dataset("repoid/filesystem_path", "some_config")`
### Expected behavior
`./` prefix should be interpreted correctly
### Environment info
datasets 4.0.0
datasets 3.4.0
reproduce
| null |
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PR_kwDODunzps6iO_oF
| 7,726
|
fix(webdataset): don't .lower() field_name
|
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[] | null | 3
|
2025-08-05 16:57:09+00:00
|
2025-08-20 16:35:55+00:00
|
2025-08-20 16:35:55+00:00
|
CONTRIBUTOR
| null | null | null | null |
This fixes cases where keys have upper case identifiers
|
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I_kwDODunzps7EPL5p
| 7,724
|
Can not stepinto load_dataset.py?
|
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2025-08-05 09:28:51+00:00
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2025-08-05 09:28:51+00:00
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NaT
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I set a breakpoint in "load_dataset.py" and try to debug my data load codes, but it does not stop at any breakpoints, so "load_dataset.py" can not be stepped into ?
<!-- Failed to upload "截图 2025-08-05 17-25-18.png" -->
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| 3,289,943,261
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I_kwDODunzps7EGIzd
| 7,723
|
Don't remove `trust_remote_code` arg!!!
|
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### Feature request
defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!
Add `trust_remote_code` arg back please!
### Motivation
defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!
### Your contribution
defaulting it to False is nice balance. we need manully setting it to True in certain scenarios!
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I_kwDODunzps7EFXcI
| 7,722
|
Out of memory even though using load_dataset(..., streaming=True)
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### Describe the bug
I am iterating over a large dataset that I load using streaming=True to avoid running out of memory. Unfortunately, I am observing that memory usage increases over time and I'm finally running in an oom.
### Steps to reproduce the bug
```
ds = load_dataset("openslr/librispeech_asr", split="train.clean.360", streaming=True)
for i,sample in enumerate(tqdm(ds)):
target_file = os.path.join(NSFW_TARGET_FOLDER, f'audio{i}.wav')
try:
sf.write(target_file, sample['audio']['array'], samplerate=sample['audio']['sampling_rate'])
except Exception as e:
print(f"Could not write audio {i} in ds: {e}")
```
### Expected behavior
I'd expect to have a small memory footprint and memory being freed after each iteration of the for loop. Instead the memory usage is increasing. I tried to remove the logic to write the sound file and just print the sample but the issue remains the same.
### Environment info
Python 3.12.11
Ubuntu 24
datasets 4.0.0 and 3.6.0
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I_kwDODunzps7EEKi4
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|
Bad split error message when using percentages
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### Describe the bug
Hi, I'm trying to download a dataset. To not load the entire dataset in memory, I split it as described [here](https://huggingface.co/docs/datasets/v4.0.0/loading#slice-splits) in 10% steps.
When doing so, the library returns this error:
raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
ValueError: Bad split: train[0%:10%]. Available splits: ['train']
Edit: Same happens with a split like _train[:90000]_
### Steps to reproduce the bug
```
for split in range(10):
split_str = f"train[{split*10}%:{(split+1)*10}%]"
print(f"Processing split {split_str}...")
ds = load_dataset("user/dataset", split=split_str, streaming=True)
```
### Expected behavior
I'd expect the library to split my dataset in 10% steps.
### Environment info
python 3.12.11
ubuntu 24
dataset 4.0.0
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I_kwDODunzps7D7e-x
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|
Datasets 4.0 map function causing column not found
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### Describe the bug
Column returned after mapping is not found in new instance of the dataset.
### Steps to reproduce the bug
Code for reproduction. After running get_total_audio_length, it is errored out due to `data` not having `duration`
```
def compute_duration(x):
return {"duration": len(x["audio"]["array"]) / x["audio"]["sampling_rate"]}
def get_total_audio_length(dataset):
data = dataset.map(compute_duration, num_proc=NUM_PROC)
print(data)
durations=data["duration"]
total_seconds = sum(durations)
return total_seconds
```
### Expected behavior
New datasets.Dataset instance should have new columns attached.
### Environment info
- `datasets` version: 4.0.0
- Platform: Linux-5.4.0-124-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.33.2
- PyArrow version: 20.0.0
- Pandas version: 2.3.0
- `fsspec` version: 2023.12.2
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I_kwDODunzps7D20or
| 7,719
|
Specify dataset columns types in typehint
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### Feature request
Make dataset optionaly generic to datasets usage with type annotations like it was done in `torch.Dataloader` https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/utils/data/dataloader.py#L131
### Motivation
In MTEB we're using a lot of datasets objects, but they're a bit poor in typehints. E.g. we can specify this for dataloder
```python
from typing import TypedDict
from torch.utils.data import DataLoader
class CorpusInput(TypedDict):
title: list[str]
body: list[str]
class QueryInput(TypedDict):
query: list[str]
instruction: list[str]
def queries_loader() -> DataLoader[QueryInput]:
...
def corpus_loader() -> DataLoader[CorpusInput]:
...
```
But for datasets we can only specify columns in type in comments
```python
from datasets import Dataset
QueryDataset = Dataset
"""Query dataset should have `query` and `instructions` columns as `str` """
```
### Your contribution
I can create draft implementation
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add support for pyarrow string view in features
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CONTRIBUTOR
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Cached dataset is not used when explicitly passing the cache_dir parameter
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### Describe the bug
Hi, we are pre-downloading a dataset using snapshot_download(). When loading this exact dataset with load_dataset() the cached snapshot is not used. In both calls, I provide the cache_dir parameter.
### Steps to reproduce the bug
```
from datasets import load_dataset, concatenate_datasets
from huggingface_hub import snapshot_download
def download_ds(name: str):
snapshot_download(repo_id=name, repo_type="dataset", cache_dir="G:/Datasets/cache")
def prepare_ds():
audio_ds = load_dataset("openslr/librispeech_asr", num_proc=4, cache_dir="G:/Datasets/cache")
print(sfw_ds.features)
if __name__ == '__main__':
download_ds("openslr/librispeech_asr")
prepare_ds()
```
### Expected behavior
I'd expect that the cached version of the dataset is used. Instead, the same dataset is downloaded again to the default cache directory.
### Environment info
Windows 11
datasets==4.0.0
Python 3.12.11
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typo
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Docs: Use Image(mode="F") for PNG/JPEG depth maps
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PR_kwDODunzps6hkfHj
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fix num_proc=1 ci test
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PR_kwDODunzps6hjik2
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Update cli.mdx to refer to the new "hf" CLI
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CONTRIBUTOR
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Update to refer to `hf auth login`
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Retry intermediate commits too
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PR_kwDODunzps6hiXm0
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Update dataset_dict push_to_hub
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MEMBER
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following https://github.com/huggingface/datasets/pull/7708
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PR_kwDODunzps6hgXxW
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|
Concurrent IterableDataset push_to_hub
|
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2025-07-31 10:12:52+00:00
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Same as https://github.com/huggingface/datasets/pull/7708 but for `IterableDataset`
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I_kwDODunzps7DTiNm
| 7,709
|
Release 4.0.0 breaks usage patterns of with_format
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2025-07-30 11:34:53+00:00
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2025-08-07 08:27:18+00:00
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2025-08-07 08:27:18+00:00
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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
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PR_kwDODunzps6hLVip
| 7,708
|
Concurrent push_to_hub
|
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2025-07-29 13:14:30+00:00
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2025-07-31 10:00:50+00:00
|
2025-07-31 10:00:49+00:00
|
MEMBER
| null | 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.
Note: we fixed an issue server side to make this work:
<details>
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)
</details>
close https://github.com/huggingface/datasets/issues/7600
|
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|
load_dataset() in 4.0.0 failed when decoding audio
|
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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
```
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| 3,271,129,240
|
PR_kwDODunzps6hC5uD
| 7,706
|
Reimplemented partial split download support (revival of #6832)
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2025-07-28 19:40:40+00:00
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2025-10-29 10:20:22+00:00
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NaT
|
CONTRIBUTOR
| null | 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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| 3,269,070,499
|
I_kwDODunzps7C2g6j
| 7,705
|
Can Not read installed dataset in dataset.load(.)
|
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2025-07-28 09:43:54+00:00
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2025-08-05 01:24:32+00:00
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NaT
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NONE
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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!!
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PR_kwDODunzps6gwtb8
| 7,704
|
Fix map() example in datasets documentation: define tokenizer before use
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2025-08-13 13:23:18+00:00
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2025-08-13 13:06:37+00:00
|
CONTRIBUTOR
| null | 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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|
[Docs] map() example uses undefined `tokenizer` — causes NameError
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NaT
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CONTRIBUTOR
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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
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PR_kwDODunzps6gvdYC
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|
num_proc=0 behave like None, num_proc=1 uses one worker (not main process) and clarify num_proc documentation
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2025-07-31 14:52:33+00:00
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2025-07-31 14:52:33+00:00
|
CONTRIBUTOR
| null | 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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Update fsspec max version to current release 2025.7.0
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2025-07-26 06:47:59+00:00
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2025-08-13 17:32:07+00:00
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2025-07-28 11:58:11+00:00
|
CONTRIBUTOR
| null | 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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[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!
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Broken link in documentation for "Create a video dataset"
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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" />
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NotImplementedError when using streaming=True in Google Colab environment
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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]
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I_kwDODunzps7B63QG
| 7,696
|
load_dataset() in 4.0.0 returns different audio samples compared to earlier versions breaking reproducibility
|
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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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PR_kwDODunzps6gB7jS
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|
Support downloading specific splits in load_dataset
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2025-07-22 09:33:54+00:00
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2025-07-28 17:33:30+00:00
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2025-07-28 17:15:45+00:00
|
CONTRIBUTOR
| null | 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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|
Dataset.to_json consumes excessive memory, appears to not be a streaming operation
|
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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)
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Dataset scripts are no longer supported, but found superb.py
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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
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I_kwDODunzps7BfiDb
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xopen: invalid start byte for streaming dataset with trust_remote_code=True
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2025-07-20 11:08:20+00:00
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2025-07-25 14:38:54+00:00
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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
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I_kwDODunzps7Bcx6i
| 7,691
|
Large WebDataset: pyarrow.lib.ArrowCapacityError on load() even with streaming
|
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2025-07-19 18:40:27+00:00
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2025-07-25 08:51:10+00:00
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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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PR_kwDODunzps6fozag
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HDF5 support
|
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2025-07-18 21:09:41+00:00
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2025-08-19 15:18:58+00:00
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2025-08-19 13:28:53+00:00
|
CONTRIBUTOR
| null | 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 using `Features(dict)`. 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.
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BadRequestError for loading dataset?
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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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No module named "distributed"
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2025-07-17 09:32:35+00:00
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2025-07-25 15:14:19+00:00
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NaT
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### 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
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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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2025-07-17 09:03:38+00:00
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2025-07-25 15:21:31+00:00
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NaT
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### 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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| 3,237,201,090
|
I_kwDODunzps7A88TC
| 7,686
|
load_dataset does not check .no_exist files in the hub cache
|
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2025-07-16 20:04:00+00:00
|
2025-07-16 20:04:00+00:00
|
NaT
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### 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
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| 3,236,979,340
|
I_kwDODunzps7A8GKM
| 7,685
|
Inconsistent range request behavior for parquet REST api
|
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2025-07-16 18:39:44+00:00
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2025-08-11 08:16:54+00:00
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NaT
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### 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
* Issue another request to this URL: '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'
* Host cas-bridge.xethub.hf.co:443 was resolved.
* IPv6: (none)
* IPv4: 18.160.181.55, 18.160.181.54, 18.160.181.52, 18.160.181.88
* Trying 18.160.181.55:443...
* Connected to cas-bridge.xethub.hf.co (18.160.181.55) port 443
* ALPN: curl offers h2,http/1.1
* (304) (OUT), TLS handshake, Client hello (1):
} [328 bytes data]
* (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):
{ [3818 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=cas-bridge.xethub.hf.co
* start date: Jun 4 00:00:00 2025 GMT
* expire date: Jul 3 23:59:59 2026 GMT
* subjectAltName: host "cas-bridge.xethub.hf.co" matched cert's "cas-bridge.xethub.hf.co"
* issuer: C=US; O=Amazon; CN=Amazon RSA 2048 M04
* SSL certificate verify ok.
* using HTTP/2
* [HTTP/2] [1] OPENED stream for 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
* [HTTP/2] [1] [:method: GET]
* [HTTP/2] [1] [:scheme: https]
* [HTTP/2] [1] [:authority: cas-bridge.xethub.hf.co]
* [HTTP/2] [1] [:path: /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]
* [HTTP/2] [1] [user-agent: curl/8.7.1]
* [HTTP/2] [1] [accept: */*]
* [HTTP/2] [1] [range: bytes=217875070-218006142]
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### Expected behavior
always get back a `206`
### Environment info
n/a
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PR_kwDODunzps6e9SjQ
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|
fix audio cast storage from array + sampling_rate
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MEMBER
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fix https://github.com/huggingface/datasets/issues/7682
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PR_kwDODunzps6e82iW
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Convert to string when needed + faster .zstd
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MEMBER
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for https://huggingface.co/datasets/allenai/olmo-mix-1124
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I_kwDODunzps7AgR3V
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Fail to cast Audio feature for numpy arrays in datasets 4.0.0
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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
- `fsspec` version: 2025.3.0
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I_kwDODunzps7AWdUg
| 7,681
|
Probabilistic High Memory Usage and Freeze on Python 3.10
|
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2025-07-14 01:57:16+00:00
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2025-07-14 01:57:16+00:00
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NaT
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NONE
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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
- `fsspec` version: 2025.3.0
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| 3,224,824,151
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I_kwDODunzps7ANulX
| 7,680
|
Question about iterable dataset and streaming
|
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2025-07-12 04:48:30+00:00
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2025-08-01 13:01:48+00:00
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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?
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| 3,220,787,371
|
I_kwDODunzps6_-VCr
| 7,679
|
metric glue breaks with 4.0.0
|
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2025-07-10 21:39:50+00:00
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2025-07-11 17:42:01+00:00
|
2025-07-11 17:42:01+00:00
|
CONTRIBUTOR
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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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I_kwDODunzps6_2FQI
| 7,678
|
To support decoding audio data, please install 'torchcodec'.
|
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2025-07-10 09:43:13+00:00
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2025-07-22 03:46:52+00:00
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2025-07-11 05:05:42+00:00
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NONE
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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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I_kwDODunzps6_z3bw
| 7,677
|
Toxicity fails with datasets 4.0.0
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2025-07-10 06:15:22+00:00
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2025-07-11 04:40:59+00:00
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2025-07-11 04:40:59+00:00
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NONE
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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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I_kwDODunzps6_vVnX
| 7,676
|
Many things broken since the new 4.0.0 release
|
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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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| 3,216,699,094
|
I_kwDODunzps6_uu7W
| 7,675
|
common_voice_11_0.py failure in dataset library
|
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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
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PR_kwDODunzps6eJGo5
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set dev version
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Release: 4.0.0
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|
Fix double sequence
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```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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I_kwDODunzps6_hefO
| 7,671
|
Mapping function not working if the first example is returned as None
|
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2025-07-08 17:07:47+00:00
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2025-07-09 12:30:32+00:00
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2025-07-09 12:30:32+00:00
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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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PR_kwDODunzps6dwgOc
| 7,670
|
Fix audio bytes
|
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2025-07-07 13:05:33+00:00
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MEMBER
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I_kwDODunzps6-8ihj
| 7,669
|
How can I add my custom data to huggingface datasets
|
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2025-07-04 19:19:54+00:00
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2025-07-05 18:19:37+00:00
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NaT
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NONE
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I want to add my custom dataset in huggingface dataset. Please guide me how to achieve that.
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I_kwDODunzps6-rXda
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|
Broken EXIF crash the whole program
|
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2025-07-03 11:24:15+00:00
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2025-07-03 12:27:16+00:00
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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
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PR_kwDODunzps6dGmm8
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Fix infer list of images
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2025-07-02 15:10:28+00:00
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2025-07-02 15:08:03+00:00
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MEMBER
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cc @kashif
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Backward compat list feature
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"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"gravatar_id": "",
"html_url": "https://github.com/lhoestq",
"id": 42851186,
"login": "lhoestq",
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"site_admin": false,
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"type": "User",
"url": "https://api.github.com/users/lhoestq",
"user_view_type": "public"
}
|
[] |
closed
| false
| null |
[] | null | 1
|
2025-07-02 14:58:00+00:00
|
2025-07-02 15:00:37+00:00
|
2025-07-02 14:59:40+00:00
|
MEMBER
| null | null | null | null |
cc @kashif
|
{
"avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4",
"events_url": "https://api.github.com/users/lhoestq/events{/privacy}",
"followers_url": "https://api.github.com/users/lhoestq/followers",
"following_url": "https://api.github.com/users/lhoestq/following{/other_user}",
"gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}",
"gravatar_id": "",
"html_url": "https://github.com/lhoestq",
"id": 42851186,
"login": "lhoestq",
"node_id": "MDQ6VXNlcjQyODUxMTg2",
"organizations_url": "https://api.github.com/users/lhoestq/orgs",
"received_events_url": "https://api.github.com/users/lhoestq/received_events",
"repos_url": "https://api.github.com/users/lhoestq/repos",
"site_admin": false,
"starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions",
"type": "User",
"url": "https://api.github.com/users/lhoestq",
"user_view_type": "public"
}
|
{
"+1": 0,
"-1": 0,
"confused": 0,
"eyes": 0,
"heart": 1,
"hooray": 0,
"laugh": 0,
"rocket": 0,
"total_count": 1,
"url": "https://api.github.com/repos/huggingface/datasets/issues/7666/reactions"
}
|
https://api.github.com/repos/huggingface/datasets/issues/7666/timeline
| null | null | 0
|
{
"diff_url": "https://github.com/huggingface/datasets/pull/7666.diff",
"html_url": "https://github.com/huggingface/datasets/pull/7666",
"merged_at": "2025-07-02T14:59:40Z",
"patch_url": "https://github.com/huggingface/datasets/pull/7666.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/7666"
}
| true
|
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