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PR_kwDODunzps5-ttUV
| 7,230 |
Video support
|
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(wip and experimental)
adding the `Video` type based on `VideoReader` from `decord`
```python
>>>from datasets import load_dataset
>>> ds = load_dataset("path/to/videos", split="train").with_format("torch")
>>> print(ds[0]["video"])
<decord.video_reader.VideoReader object at 0x337a47910>
>>> print(ds[0]["video"][0])
tensor([[[73, 73, 73],
[73, 73, 73],
[73, 73, 73],
...,
[23, 23, 23],
[23, 23, 23],
[23, 23, 23]]], dtype=torch.uint8)
```
the storage is the same as for audio and images: `{"path": pa.string(), "bytes": pa.binary()}` and I did a small to keep the hf:// URL in the "path" field if possible, this way the viewer can link to fiels on the hub if possible
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handle config_name=None in push_to_hub
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This caught me out - thought it might be better to explicitly handle None?
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bump fsspec
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Preserve features in iterable dataset.filter
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Fixes example in #7208 - I'm not sure what other checks I should do? @lhoestq
I also haven't thought hard about the concatenate / interleaving example iterables but think this might work assuming that features are either all identical or None?
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Iterable dataset.filter should not override features
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CONTRIBUTOR
| null | null | null |
### Describe the bug
When calling filter on an iterable dataset, the features get set to None
### Steps to reproduce the bug
import numpy as np
import time
from datasets import Dataset, Features, Array3D
```python
features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")})
dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features)
ds = dataset.to_iterable_dataset()
orig_column_names = ds.column_names
ds = ds.filter(lambda x: True)
assert ds.column_names == orig_column_names
```
### Expected behavior
Filter should preserve features information
### Environment info
3.0.2
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fix ci benchmark
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we're not using the benchmarks anymore + they were not working anyway due to token permissions
I keep the code in case we ever want to re-run the benchmark manually
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PR_kwDODunzps599Hem
| 7,204 |
fix unbatched arrow map for iterable datasets
|
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Fixes the bug when applying map to an arrow-formatted iterable dataset described here:
https://github.com/huggingface/datasets/issues/6833#issuecomment-2399903885
```python
from datasets import load_dataset
ds = load_dataset("rotten_tomatoes", split="train", streaming=True)
ds = ds.with_format("arrow").map(lambda x: x)
for ex in ds:
pass
```
@lhoestq
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PR_kwDODunzps598skr
| 7,203 |
with_format docstring
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reported at https://github.com/huggingface/datasets/issues/3444
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PR_kwDODunzps59sgRd
| 7,200 |
Fix the environment variable for huggingface cache
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Resolve #6256. As far as I tested, `HF_DATASETS_CACHE` was ignored and I could not specify the cache directory at all except for the default one by this environment variable. `HF_HOME` has worked. Perhaps the recent change on file downloading by `huggingface_hub` could affect this bug.
In my testing, I could not specify the cache directory even by `load_dataset("dataset_name" cache_dir="...")`. It might be another issue. I also welcome any advice to solve this issue.
|
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I_kwDODunzps6YydVn
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datasets.exceptions.DatasetNotFoundError for private dataset
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NONE
| null | null | null |
### Describe the bug
The following Python code tries to download a private dataset and fails with the error `datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.`. Downloading a public dataset doesn't work.
``` py
from datasets import load_dataset
_ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly")
```
This seems to be just an issue with my machine config as the code above works with a colleague's machine. So far I have tried:
- logging back out and in from the Huggingface CLI using `huggingface-cli logout`
- manually removing the token cache at `/Users/kalyan/.cache/huggingface/token` (found using `huggingface-cli env`)
- manually passing a token in `load_dataset`
My output of `huggingface-cli whoami`:
```
kdutia
orgs: ClimatePolicyRadar
```
### Steps to reproduce the bug
```
python
Python 3.12.2 (main, Feb 6 2024, 20:19:44) [Clang 15.0.0 (clang-1500.1.0.2.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> _ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 2074, in load_dataset
builder_instance = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1795, in load_dataset_builder
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1659, in dataset_module_factory
raise e1 from None
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1597, in dataset_module_factory
raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e
datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.
>>>
```
### Expected behavior
The dataset downloads successfully.
### Environment info
From `huggingface-cli env`:
```
- huggingface_hub version: 0.25.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.12.2
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Running in Google Colab Enterprise ?: No
- Token path ?: /Users/kalyan/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: kdutia
- Configured git credential helpers: osxkeychain
- FastAI: N/A
- Tensorflow: N/A
- Torch: N/A
- Jinja2: 3.1.4
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: N/A
- hf_transfer: N/A
- gradio: N/A
- tensorboard: N/A
- numpy: 2.1.1
- pydantic: N/A
- aiohttp: 3.10.8
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /Users/kalyan/.cache/huggingface/hub
- HF_ASSETS_CACHE: /Users/kalyan/.cache/huggingface/assets
- HF_TOKEN_PATH: /Users/kalyan/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10
```
from `datasets-cli env`:
```
- `datasets` version: 3.0.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.12.2
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
```
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PR_kwDODunzps59VSrf
| 7,188 |
Pin multiprocess<0.70.1 to align with dill<0.3.9
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Pin multiprocess<0.70.1 to align with dill<0.3.9.
Note that multiprocess-0.70.1 requires dill-0.3.9: https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17
Fix #7186.
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I_kwDODunzps6Ym3FN
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pinning `dill<0.3.9` without pinning `multiprocess`
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NONE
| null | null | null |
### Describe the bug
The [latest `multiprocess` release](https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17) requires `dill>=0.3.9` which causes issues when installing `datasets` without backtracking during package version resolution. Is it possible to add a pin for multiprocess so something like `multiprocess<=0.70.16` so that the `dill` version is compatible?
### Steps to reproduce the bug
NA
### Expected behavior
NA
### Environment info
NA
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I_kwDODunzps6Yf77M
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CI benchmarks are broken
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MEMBER
| null | null | null |
Since Aug 30, 2024, CI benchmarks are broken: https://github.com/huggingface/datasets/actions/runs/11108421214/job/30861323975
```
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...
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PR_kwDODunzps59IPEf
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Pin dill<0.3.9 to fix CI
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Pin dill<0.3.9 to fix CI for deps-latest.
Note that dill-0.3.9 was released yesterday Sep 29, 2024:
- https://pypi.org/project/dill/0.3.9/
- https://github.com/uqfoundation/dill/releases/tag/0.3.9
Fix #7183.
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CI is broken for deps-latest
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MEMBER
| null | null | null |
See: https://github.com/huggingface/datasets/actions/runs/11106149906/job/30853879890
```
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_caching_on_disk - AssertionError: Lists differ: [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] != [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
First differing element 1:
{'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}
{'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}
[{'filename': '/tmp/tmp6xcyyjs4/dataset0.arrow'},
- {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}]
? ^^^^^ --------
+ {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
? ++++++++++ ^^ +
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AssertionError: Lists differ: [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] != [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
First differing element 0:
{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}
{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}
- [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}]
? ^^ -----------
+ [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
? +++++++++++ ^^
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: '52e56ee04ad92499' != '0a4f75cec280f634'
- 52e56ee04ad92499
+ 0a4f75cec280f634
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: 'a6bd2041ca63d6c0' != '517bf36b7eecdef5'
- a6bd2041ca63d6c0
+ 517bf36b7eecdef5
FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined
ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NameError: name 'log' is not defined
= 11 failed, 2850 passed, 3 skipped, 23 warnings, 1 error in 191.06s (0:03:11) =
```
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PR_kwDODunzps59GdLS
| 7,182 |
Support features in metadata configs
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Support features in metadata configs, like:
```
configs:
- config_name: default
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: score
dtype: float64
```
This will allow to avoid inference of data types.
Currently, we allow passing this information in the `dataset_info` (instead of `configs`) field, but this is not intuitive and it is not properly documented.
TODO:
- [ ] Document usage
|
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PR_kwDODunzps59Br4E
| 7,181 |
Fix datasets export to JSON
|
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I_kwDODunzps6YPq6O
| 7,180 |
Memory leak when wrapping datasets into PyTorch Dataset without explicit deletion
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[] | null | 1 | 2024-09-28T14:00:47 | 2024-09-30T12:07:56 | 2024-09-30T12:07:56 |
NONE
| null | null | null |
### Describe the bug
I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.
### Steps to reproduce the bug
Steps to reproduce:
Create a PyTorch Dataset wrapper for 'nebula/cc12m':
````
from torch.utils.data import Dataset
from tqdm import tqdm
from datasets import load_dataset
from torchvision import transforms
Image.MAX_IMAGE_PIXELS = None
class CC12M(Dataset):
def __init__(self, path_or_name='nebula/cc12m', split='train', transform=None, single_caption=True):
self.raw_dataset = load_dataset(path_or_name)[split]
if transform is None:
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711]
)
])
else:
self.transform = transforms.Compose(transform)
self.single_caption = single_caption
self.length = len(self.raw_dataset)
def __len__(self):
return self.length
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
# del item # Uncomment this line to prevent the memory leak
return image, caption
````
Iterate through the dataset without the del item line in __getitem__.
Observe RAM usage increasing constantly.
Add del item at the end of __getitem__:
```
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
del item # This line prevents the memory leak
return image, caption
```
Iterate through the dataset again and observe that RAM usage remains stable.
### Expected behavior
Expected behavior:
RAM usage should remain stable during iteration without needing to explicitly delete items.
Actual behavior:
RAM usage constantly increases unless items are explicitly deleted after use
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-4.18.0-513.5.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.12.4
- `huggingface_hub` version: 0.24.6
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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PR_kwDODunzps585Jcd
| 7,179 |
Support Python 3.11
|
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Support Python 3.11.
Fix #7178.
|
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Support Python 3.11
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Fix release instructions
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Fix release instructions.
During last release, I had to make this additional update.
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[FSTimeoutError] load_dataset
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NONE
| null | null | null |
### Describe the bug
When using `load_dataset`to load [HuggingFaceM4/VQAv2](https://huggingface.co/datasets/HuggingFaceM4/VQAv2), I am getting `FSTimeoutError`.
### Error
```
TimeoutError:
The above exception was the direct cause of the following exception:
FSTimeoutError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/fsspec/asyn.py](https://klh9mr78js-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240924-060116_RC00_678132060#) in sync(loop, func, timeout, *args, **kwargs)
99 if isinstance(return_result, asyncio.TimeoutError):
100 # suppress asyncio.TimeoutError, raise FSTimeoutError
--> 101 raise FSTimeoutError from return_result
102 elif isinstance(return_result, BaseException):
103 raise return_result
FSTimeoutError:
```
It usually fails around 5-6 GB.
<img width="847" alt="Screenshot 2024-09-26 at 9 10 19 PM" src="https://github.com/user-attachments/assets/ff91995a-fb55-4de6-8214-94025d6c8470">
### Steps to reproduce the bug
To reproduce it, run this in colab notebook:
```
!pip install -q -U datasets
from datasets import load_dataset
ds = load_dataset('HuggingFaceM4/VQAv2', split="train[:10%]")
```
### Expected behavior
It should download properly.
### Environment info
Using Colab Notebook.
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Set dev version
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PR_kwDODunzps58wNQ7
| 7,172 |
Add torchdata as a regular test dependency
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Add `torchdata` as a regular test dependency.
Note that previously, `torchdata` was installed from their repo and current main branch (0.10.0.dev) requires Python>=3.9.
Also note they made a recent release: 0.8.0 on Jul 31, 2024.
Fix #7171.
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I_kwDODunzps6X-e2n
| 7,171 |
CI is broken: No solution found when resolving dependencies
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See: https://github.com/huggingface/datasets/actions/runs/11046967444/job/30687294297
```
Run uv pip install --system -r additional-tests-requirements.txt --no-deps
× No solution found when resolving dependencies:
╰─▶ Because the current Python version (3.8.18) does not satisfy Python>=3.9
and torchdata==0.10.0a0+1a98f21 depends on Python>=3.9, we can conclude
that torchdata==0.10.0a0+1a98f21 cannot be used.
And because only torchdata==0.10.0a0+1a98f21 is available and
you require torchdata, we can conclude that your requirements are
unsatisfiable.
Error: Process completed with exit code 1.
```
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PR_kwDODunzps58mfF5
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Support JSON lines with missing columns
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Support JSON lines with missing columns.
Fix #7169.
The implemented test raised:
```
datasets.table.CastError: Couldn't cast
age: int64
to
{'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)}
because column names don't match
```
Related to:
- #7160
- #7162
|
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JSON lines with missing columns raise CastError
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JSON lines with missing columns raise CastError:
> CastError: Couldn't cast ... to ... because column names don't match
Related to:
- #7159
- #7161
|
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I_kwDODunzps6Xy7hn
| 7,168 |
sd1.5 diffusers controlnet training script gives new error
|
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[] | null | 3 | 2024-09-25T01:42:49 | 2024-09-30T05:24:03 | 2024-09-30T05:24:02 |
NONE
| null | null | null |
### Describe the bug
This will randomly pop up during training now
```
Traceback (most recent call last):
File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1192, in <module>
main(args)
File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1041, in main
for step, batch in enumerate(train_dataloader):
File "/usr/local/lib/python3.11/dist-packages/accelerate/data_loader.py", line 561, in __iter__
next_batch = next(dataloader_iter)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 630, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 673, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/_utils/fetch.py", line 50, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2746, in __getitems__
batch = self.__getitem__(keys)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2742, in __getitem__
return self._getitem(key)
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2727, in _getitem
formatted_output = format_table(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 639, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 407, in __call__
return self.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 521, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 228, in decode_batch
return self.features.decode_batch(batch) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2084, in decode_batch
[
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2085, in <listcomp>
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 1403, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
```
### Steps to reproduce the bug
Train on diffusers sd1.5 controlnet example script
This will pop up randomly, you can see in wandb below when i manually resume run everytime this error appears
### Expected behavior
Training to continue without above error
### Environment info
- datasets version: 3.0.0
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- huggingface_hub version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- fsspec version: 2024.6.1
Training on 4090
|
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I_kwDODunzps6Xy64u
| 7,167 |
Error Mapping on sd3, sdxl and upcoming flux controlnet training scripts in diffusers
|
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[] | null | 1 | 2024-09-25T01:39:51 | 2024-09-30T05:28:15 | 2024-09-30T05:28:04 |
NONE
| null | null | null |
### Describe the bug
```
Map: 6%|██████ | 8000/138120 [19:27<5:16:36, 6.85 examples/s]
Traceback (most recent call last):
File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1416, in <module>
main(args)
File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1132, in main
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 560, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3035, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3461, in _map_single
writer.write_batch(batch)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 567, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 579, in write_table
pa_table = pa_table.combine_chunks()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 4387, in pyarrow.lib.Table.combine_chunks
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.ArrowInvalid: offset overflow while concatenating arrays
Traceback (most recent call last):
File "/usr/local/bin/accelerate", line 8, in <module>
sys.exit(main())
^^^^^^
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/accelerate_cli.py", line 48, in main
args.func(args)
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 1174, in launch_command
simple_launcher(args)
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 769, in simple_launcher
```
### Steps to reproduce the bug
The dataset has no problem training on sd1.5 controlnet train script
### Expected behavior
Script not randomly erroing with error above
### Environment info
- `datasets` version: 3.0.0
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
training on A100
|
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| 7,166 |
fix docstring code example for distributed shuffle
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close https://github.com/huggingface/datasets/issues/7163
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| 7,165 |
fix increase_load_count
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it was failing since 3.0 and therefore not updating download counts on HF or in our dashboard
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I_kwDODunzps6XiVqS
| 7,163 |
Set explicit seed in iterable dataset ddp shuffling example
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[] | null | 1 | 2024-09-23T11:34:06 | 2024-09-24T14:40:15 | 2024-09-24T14:40:15 |
CONTRIBUTOR
| null | null | null |
### Describe the bug
In the examples section of the iterable dataset docs https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDataset
the ddp example shuffles without seeding
```python
from datasets.distributed import split_dataset_by_node
ids = ds.to_iterable_dataset(num_shards=512)
ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating
ids = split_dataset_by_node(ds, world_size=8, rank=0) # will keep only 512 / 8 = 64 shards from the shuffled lists of shards when you start iterating
dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from this node's list of shards to each worker when you start iterating
for example in ids:
pass
```
This code would - I think - raise an error due to the lack of an explicit seed:
https://github.com/huggingface/datasets/blob/2eb4edb97e1a6af2ea62738ec58afbd3812fc66e/src/datasets/iterable_dataset.py#L1707-L1711
### Steps to reproduce the bug
Run example code
### Expected behavior
Add explicit seeding to example code
### Environment info
latest datasets
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Support JSON lines with empty struct.
Fix #7161.
Related to:
- #7160
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JSON lines with empty struct raise ArrowTypeError: struct fields don't match or are in the wrong order
See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5
> ArrowTypeError: struct fields don't match or are in the wrong order: Input fields: struct<> output fields: struct<pov_count: int64, update_count: int64, citation_needed_count: int64>
Related to:
- #7159
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Support JSON lines with missing struct fields.
Fix #7159.
The implemented test raised:
```
TypeError: Couldn't cast array of type
struct<age: int64>
to
{'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)}
```
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MEMBER
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JSON lines with missing struct fields raise TypeError: Couldn't cast array of type.
See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5
One would expect that the struct missing fields are added with null values.
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Fix zero proba interleave datasets
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fix https://github.com/huggingface/datasets/issues/7147
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Dataset viewer not working! Failure due to more than 32 splits.
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NONE
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Hello guys,
I have a dataset and I didn't know I couldn't upload more than 32 splits. Now, my dataset viewer is not working. I don't have the dataset locally on my node anymore and recreating would take a week. And I have to publish the dataset coming Monday. I read about the practice, how I can resolve it and avoid this issue in the future. But, at the moment I need a hard fix for two of my datasets.
And I don't want to mess or change anything and allow everyone in public to see the dataset and interact with it. Can you please help me?
https://huggingface.co/datasets/laion/Wikipedia-X
https://huggingface.co/datasets/laion/Wikipedia-X-Full
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Support ndjson data files
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Support `ndjson` (Newline Delimited JSON) data files.
Fix #7153.
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Support data files with .ndjson extension
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MEMBER
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### Feature request
Support data files with `.ndjson` extension.
### Motivation
We already support data files with `.jsonl` extension.
### Your contribution
I am opening a PR.
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Align filename prefix splitting with WebDataset library
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Align filename prefix splitting with WebDataset library.
This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library.
Fix #7150.
Related to #7144.
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I_kwDODunzps6Wp6zn
| 7,150 |
WebDataset loader splits keys differently than WebDataset library
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MEMBER
| null | null | null |
As reported by @ragavsachdeva (see discussion here: https://github.com/huggingface/datasets/pull/7144#issuecomment-2348307792), our webdataset loader is not aligned with the `webdataset` library when splitting keys from filenames.
For example, we get a different key splitting for filename `/some/path/22.0/1.1.png`:
- datasets library: `/some/path/22` and `0/1.1.png`
- webdataset library: `/some/path/22.0/1`, `1.png`
```python
import webdataset as wds
wds.tariterators.base_plus_ext("/some/path/22.0/1.1.png")
# ('/some/path/22.0/1', '1.png')
```
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I_kwDODunzps6WeMYo
| 7,149 |
Datasets Unknown Keyword Argument Error - task_templates
|
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NONE
| null | null | null |
### Describe the bug
Issue
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
Gives error
```
TypeError: DatasetInfo.__init__() got an unexpected keyword argument 'task_templates'
```
A simple downgrade to lower `datasets v 2.21.0` solves it.
### Steps to reproduce the bug
1. `pip install datsets`
2.
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
### Expected behavior
Should load the dataset correctly.
### Environment info
- Datasets version `3.0.0`
- `transformers` version: 4.45.0.dev0
- Platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35
- Python version: 3.12.4
- Huggingface_hub version: 0.24.6
- Safetensors version: 0.4.5
- Accelerate version: 0.35.0.dev0
- Accelerate config: not found
- PyTorch version (GPU?): 2.4.1+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
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I_kwDODunzps6WbqRF
| 7,148 |
Bug: Error when downloading mteb/mtop_domain
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[] | null | 4 | 2024-09-13T04:09:39 | 2024-09-14T15:11:35 | 2024-09-14T15:11:35 |
NONE
| null | null | null |
### Describe the bug
When downloading the dataset "mteb/mtop_domain", ran into the following error:
```
Traceback (most recent call last):
File "/share/project/xzy/test/test_download.py", line 3, in <module>
data = load_dataset("mteb/mtop_domain", "en", trust_remote_code=True)
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2606, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2277, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1923, in dataset_module_factory
raise e1 from None
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1896, in dataset_module_factory
).get_module()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1507, in get_module
local_path = self.download_loading_script()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1467, in download_loading_script
return cached_path(file_path, download_config=download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 211, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 689, in get_from_cache
fsspec_get(
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 395, in fsspec_get
fs.get_file(path, temp_file.name, callback=callback)
File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 648, in get_file
http_get(
File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 578, in http_get
raise EnvironmentError(
OSError: Consistency check failed: file should be of size 2191 but has size 2190 ((…)ets/mteb/mtop_domain@main/mtop_domain.py).
We are sorry for the inconvenience. Please retry with `force_download=True`.
If the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub.
```
Try to download through HF datasets directly but got the same error as above.
```python
from datasets import load_dataset
data = load_dataset("mteb/mtop_domain", "en")
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
data = load_dataset("mteb/mtop_domain", "en", force_download=True)
```
With and without `force_download=True` both ran into the same error.
### Expected behavior
Should download the dataset successfully.
### Environment info
- datasets version: 2.21.0
- huggingface-hub version: 0.24.6
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I_kwDODunzps6WY-Z5
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IterableDataset strange deadlock
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NONE
| null | null | null |
### Describe the bug
```
import datasets
import torch.utils.data
num_shards = 1024
def gen(shards):
for shard in shards:
if shard < 25:
yield {"shard": shard}
def main():
dataset = datasets.IterableDataset.from_generator(
gen,
gen_kwargs={"shards": list(range(num_shards))},
)
dataset = dataset.shuffle(buffer_size=1)
dataset = datasets.interleave_datasets(
[dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted"
)
dataset = dataset.shuffle(buffer_size=1)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=8,
)
for i, batch in enumerate(dataloader):
print(batch)
if i >= 10:
break
print()
if __name__ == "__main__":
for _ in range(100):
main()
```
### Steps to reproduce the bug
Running the script above, at some point it will freeze.
- Changing `num_shards` from 1024 to 25 avoids the issue
- Commenting out the final shuffle avoids the issue
- Commenting out the interleave_datasets call avoids the issue
As an aside, if you comment out just the final shuffle, the output from interleave_datasets is not shuffled at all even though there's the shuffle before it. So something about that shuffle config is not being propagated to interleave_datasets.
### Expected behavior
The script should not freeze.
### Environment info
- `datasets` version: 3.0.0
- Platform: macOS-14.6.1-arm64-arm-64bit
- Python version: 3.12.5
- `huggingface_hub` version: 0.24.7
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
I observed this with 2.21.0 initially, then tried upgrading to 3.0.0 and could still repro.
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PR_kwDODunzps57KqRV
| 7,146 |
Set dev version
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PR_kwDODunzps57Kjjc
| 7,145 |
Release: 3.0.0
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PR_kwDODunzps57JLmb
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Fix key error in webdataset
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I was running into
```
example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
KeyError: 'png'
```
The issue is that a filename may have multiple "." e.g. `22.05.png`. Changing `split` to `rsplit` fixes it.
Related https://github.com/huggingface/datasets/issues/6880
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PR_kwDODunzps56xCm6
| 7,143 |
Modify add_column() to optionally accept a FeatureType as param
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Fix #7142.
**Before (Add + Cast)**:
```
from datasets import load_dataset, Value
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]
ds = ds.add_column("new_col", lst)
# Assigns int64 to new_col by default
print(ds.features)
ds = ds.cast_column("new_col", Value(dtype="uint16", id=None))
print(ds.features)
```
**Before (Numpy Workaround)**:
```
from datasets import load_dataset
import numpy as np
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]
ds = ds.add_column("new_col", np.array(lst, dtype=np.uint16))
print(ds.features)
```
**After**:
```
from datasets import load_dataset, Value
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]
val = Value(dtype="uint16", id=None))
ds = ds.add_column("new_col", lst, feature=val)
print(ds.features)
```
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| 2,512,244,938 |
I_kwDODunzps6VvdDK
| 7,142 |
Specifying datatype when adding a column to a dataset.
|
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[] | null | 1 | 2024-09-08T07:34:24 | 2024-09-17T03:46:32 | 2024-09-17T03:46:32 |
CONTRIBUTOR
| null | null | null |
### Feature request
There should be a way to specify the datatype of a column in `datasets.add_column()`.
### Motivation
To specify a custom datatype, we have to use `datasets.add_column()` followed by `datasets.cast_column()` which is slow for large datasets. Another workaround is to pass a `numpy.array()` of desired type to the `datasets.add_column()` function.
IMO this functionality should be natively supported.
https://discuss.huggingface.co/t/add-column-with-a-particular-type-in-datasets/95674
### Your contribution
I can submit a PR for this.
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I_kwDODunzps6Vp7tV
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Older datasets throwing safety errors with 2.21.0
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[] | null | 17 | 2024-09-06T16:26:30 | 2024-09-06T21:14:14 | 2024-09-06T19:09:29 |
NONE
| null | null | null |
### Describe the bug
The dataset loading was throwing some safety errors for this popular dataset `wmt14`.
[in]:
```
import datasets
# train_data = datasets.load_dataset("wmt14", "de-en", split="train")
train_data = datasets.load_dataset("wmt14", "de-en", split="train")
val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]")
```
[out]:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-9-445f0ecc4817>](https://localhost:8080/#) in <cell line: 4>()
2
3 # train_data = datasets.load_dataset("wmt14", "de-en", split="train")
----> 4 train_data = datasets.load_dataset("wmt14", "de-en", split="train")
5 val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]")
12 frames
[/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py](https://localhost:8080/#) in __init__(self, **kwargs)
636 if security is not None:
637 security = BlobSecurityInfo(
--> 638 safe=security["safe"], av_scan=security["avScan"], pickle_import_scan=security["pickleImportScan"]
639 )
640 self.security = security
KeyError: 'safe'
```
### Steps to reproduce the bug
See above.
### Expected behavior
Dataset properly loaded.
### Environment info
version: 2.21.0
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Disable implicit token in CI
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Disable implicit token in CI.
This PR allows running CI tests locally without implicitly using the local user HF token. For example, run locally the tests in:
- #7124
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PR_kwDODunzps55Y4TM
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Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport
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Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport.
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Test get_dataset_config_info with non-existing/gated/private dataset
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Test get_dataset_config_info with non-existing/gated/private dataset.
Related to:
- #7109
See also:
- https://github.com/huggingface/dataset-viewer/pull/3037: https://github.com/huggingface/dataset-viewer/pull/3037/commits/bb1a7e00c53c242088597cab6572e4fd57797ecb
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PR_kwDODunzps55Iukl
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Fix typed examples iterable state dict
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fix https://github.com/huggingface/datasets/issues/7085 as noted by @VeryLazyBoy and reported by @AjayP13
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| 2,480,674,237 |
PR_kwDODunzps55HrBy
| 7,120 |
don't mention the script if trust_remote_code=False
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See https://huggingface.co/datasets/Omega02gdfdd/bioclip-demo-zero-shot-mistakes for example. The error is:
```
FileNotFoundError: Couldn't find a dataset script at /src/services/worker/Omega02gdfdd/bioclip-demo-zero-shot-mistakes/bioclip-demo-zero-shot-mistakes.py or any data file in the same directory. Couldn't find 'Omega02gdfdd/bioclip-demo-zero-shot-mistakes' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/Omega02gdfdd/bioclip-demo-zero-shot-mistakes@12b0313ba4c3189ee5a24cb76200959e9bf7492e/data.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']
```
The issue there is that a `configs` parameter is set in the README, while the mentioned data file (`data.csv`) does not exist.
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PR_kwDODunzps54-GjY
| 7,119 |
Install transformers with numpy-2 CI
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Install transformers with numpy-2 CI.
Note that transformers no longer pins numpy < 2 since transformers-4.43.0:
- https://github.com/huggingface/transformers/pull/32018
- https://github.com/huggingface/transformers/releases/tag/v4.43.0
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PR_kwDODunzps549yu4
| 7,118 |
Allow numpy-2.1 and test it without audio extra
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Allow numpy-2.1 and test it without audio extra.
This PR reverts:
- #7114
Note that audio extra tests can be included again with numpy-2.1 once next numba-0.61.0 version is released.
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datasets cannot handle nested json if features is given.
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NONE
| null | null | null |
### Describe the bug
I have a json named temp.json.
```json
{"ref1": "ABC", "ref2": "DEF", "cuts":[{"cut1": 3, "cut2": 5}]}
```
I want to load it.
```python
ds = datasets.load_dataset('json', data_files="./temp.json", features=datasets.Features({
'ref1': datasets.Value('string'),
'ref2': datasets.Value('string'),
'cuts': datasets.Sequence({
"cut1": datasets.Value("uint16"),
"cut2": datasets.Value("uint16")
})
}))
```
The above code does not work. However, I can load it without giving features.
```python
ds = datasets.load_dataset('json', data_files="./temp.json")
```
Is it possible to load integers as uint16 to save some memory?
### Steps to reproduce the bug
As in the bug description.
### Expected behavior
The data are loaded and integers are uint16.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.21.0
- Platform: Linux-5.15.0-118-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.24.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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[] | null | 1 | 2024-08-20T11:05:44 | 2024-09-10T06:51:08 | 2024-09-10T06:51:08 |
NONE
| null | null | null |
### Describe the bug
Code:
`!pipuninstall -y pyarrow
!pip install --no-cache-dir pyarrow
!pip uninstall -y pyarrow
!pip install pyarrow --no-cache-dir
!pip install --upgrade datasets transformers pyarrow
!pip install pyarrow.parquet
! pip install pyarrow-core libparquet
!pip install pyarrow --no-cache-dir
!pip install pyarrow
!pip install transformers
!pip install --upgrade datasets
!pip install datasets
! pip install pyarrow
! pip install pyarrow.lib
! pip install pyarrow.parquet
!pip install transformers
import pyarrow as pa
print(pa.__version__)
from datasets import load_dataset
import pyarrow.parquet as pq
import pyarrow.lib as lib
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
from transformers import AutoTokenizer
! pip install pyarrow-core libparquet
# Load the dataset for content moderation
dataset = load_dataset("PolyAI/banking77") # Example dataset for customer support
# Initialize the tokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples['text'], padding="max_length", truncation=True)
# Apply tokenization to the entire dataset
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# Check the first few tokenized samples
print(tokenized_datasets['train'][0])
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
# Load the model
model = AutoModelForSequenceClassification.from_pretrained("facebook/opt-350m", num_labels=77)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
eval_strategy="epoch", #
save_strategy="epoch",
logging_dir="./logs",
learning_rate=2e-5,
)
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
)
# Train the model
trainer.train()
# Evaluate the model
trainer.evaluate()
`
AttributeError Traceback (most recent call last)
[<ipython-input-23-60bed3143a93>](https://localhost:8080/#) in <cell line: 22>()
20
21
---> 22 from datasets import load_dataset
23 import pyarrow.parquet as pq
24 import pyarrow.lib as lib
5 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
15 __version__ = "2.21.0"
16
---> 17 from .arrow_dataset import Dataset
18 from .arrow_reader import ReadInstruction
19 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
74
75 from . import config
---> 76 from .arrow_reader import ArrowReader
77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
78 from .data_files import sanitize_patterns
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module>
27
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
31
[/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/__init__.py](https://localhost:8080/#) in <module>
18 # flake8: noqa
19
---> 20 from .core import *
[/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py](https://localhost:8080/#) in <module>
31
32 try:
---> 33 import pyarrow._parquet as _parquet
34 except ImportError as exc:
35 raise ImportError(
/usr/local/lib/python3.10/dist-packages/pyarrow/_parquet.pyx in init pyarrow._parquet()
AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
### Steps to reproduce the bug
https://colab.research.google.com/drive/1HNbsg3tHxUJOHVtYIaRnNGY4T2PnLn4a?usp=sharing
### Expected behavior
Looks like there is an issue with datasets and pyarrow
### Environment info
google colab
python
huggingface
Found existing installation: pyarrow 17.0.0
Uninstalling pyarrow-17.0.0:
Successfully uninstalled pyarrow-17.0.0
Collecting pyarrow
Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)
Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.10/dist-packages (from pyarrow) (1.26.4)
Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (39.9 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 39.9/39.9 MB 188.9 MB/s eta 0:00:00
Installing collected packages: pyarrow
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.
ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.
Successfully installed pyarrow-17.0.0
WARNING: The following packages were previously imported in this runtime:
[pyarrow]
You must restart the runtime in order to use newly installed versions.
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PR_kwDODunzps5404mO
| 7,114 |
Temporarily pin numpy<2.1 to fix CI
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MEMBER
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Temporarily pin numpy<2.1 to fix CI.
Fix #7111.
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I_kwDODunzps6ThfSI
| 7,113 |
Stream dataset does not iterate if the batch size is larger than the dataset size (related to drop_last_batch)
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[] | null | 1 | 2024-08-20T08:26:40 | 2024-08-26T04:24:11 | 2024-08-26T04:24:10 |
NONE
| null | null | null |
### Describe the bug
Hi there,
I use streaming and interleaving to combine multiple datasets saved in jsonl files. The size of dataset can vary (from 100ish to 100k-ish). I use dataset.map() and a big batch size to reduce the IO cost. It was working fine with datasets-2.16.1 but this problem shows up after I upgraded to datasets-2.19.2. With 2.21.0 the problem remains.
Please see the code below to reproduce the problem.
The dataset can iterate correctly if we set either streaming=False or drop_last_batch=False.
I have to use drop_last_batch=True since it's for distributed training.
### Steps to reproduce the bug
```python
# datasets==2.21.0
import datasets
def data_prepare(examples):
print(examples["sentence1"][0])
return examples
batch_size = 101
# the size of the dataset is 100
# the dataset iterates correctly if we set either streaming=False or drop_last_batch=False
dataset = datasets.load_dataset("mteb/biosses-sts", split="test", streaming=True)
dataset = dataset.map(lambda x: data_prepare(x),
drop_last_batch=True,
batched=True, batch_size=batch_size)
for ex in dataset:
print(ex)
pass
```
### Expected behavior
The dataset iterates regardless of the batch size.
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.24.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0
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CI is broken for numpy-2: Failed to fetch wheel: llvmlite==0.34.0
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MEMBER
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Ci is broken with error `Failed to fetch wheel: llvmlite==0.34.0`: https://github.com/huggingface/datasets/actions/runs/10466825281/job/28984414269
```
Run uv pip install --system "datasets[tests_numpy2] @ ."
Resolved 150 packages in 4.42s
error: Failed to prepare distributions
Caused by: Failed to fetch wheel: llvmlite==0.34.0
Caused by: Build backend failed to build wheel through `build_wheel()` with exit status: 1
--- stdout:
running bdist_wheel
/home/runner/.cache/uv/builds-v0/.tmpcyKh8S/bin/python /home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py
LLVM version...
--- stderr:
Traceback (most recent call last):
File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 105, in main_posix
out = subprocess.check_output([llvm_config, '--version'])
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 421, in check_output
return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 503, in run
with Popen(*popenargs, **kwargs) as process:
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 971, in __init__
self._execute_child(args, executable, preexec_fn, close_fds,
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 1863, in _execute_child
raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'llvm-config'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 191, in <module>
main()
File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 181, in main
main_posix('linux', '.so')
File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 107, in main_posix
raise RuntimeError("%s failed executing, please point LLVM_CONFIG "
RuntimeError: llvm-config failed executing, please point LLVM_CONFIG to the path for llvm-config
error: command '/home/runner/.cache/uv/builds-v0/.tmpcyKh8S/bin/python' failed with exit code 1
```
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| 7,110 |
Fix ConnectionError for gated datasets and unauthenticated users
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Fix `ConnectionError` for gated datasets and unauthenticated users. See:
- https://github.com/huggingface/dataset-viewer/issues/3025
Note that a recent change in the Hub returns dataset info for gated datasets and unauthenticated users, instead of raising a `GatedRepoError` as before. See:
- https://github.com/huggingface/huggingface_hub/issues/2457
This PR adds an additional check (/auth-check) for gated datasets and raises `DatasetNotFoundError` for unauthenticated users, as it was the case before the change in the Hub.
- Fix suggested by @Pierrci (thanks @Wauplin for pointing it out).
Fix #7109.
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ConnectionError for gated datasets and unauthenticated users
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MEMBER
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Since the Hub returns dataset info for gated datasets and unauthenticated users, there is dead code: https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/load.py#L1846-L1852
We should remove the dead code and properly handle this case: currently we are raising a `ConnectionError` instead of a `DatasetNotFoundError` (as before).
See:
- https://github.com/huggingface/dataset-viewer/issues/3025
- https://github.com/huggingface/huggingface_hub/issues/2457
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I_kwDODunzps6TQ1xv
| 7,108 |
website broken: Create a new dataset repository, doesn't create a new repo in Firefox
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[] | null | 4 | 2024-08-16T17:23:00 | 2024-08-19T13:21:12 | 2024-08-19T06:52:48 |
NONE
| null | null | null |
### Describe the bug
This issue is also reported here:
https://discuss.huggingface.co/t/create-a-new-dataset-repository-broken-page/102644
This page is broken.
https://huggingface.co/new-dataset
I fill in the form with my text, and click `Create Dataset`.
Then the form gets wiped. And no repo got created. No error message visible in the developer console.
# Idea for improvement
For better UX, if the repo cannot be created, then show an error message, that something went wrong.
# Work around, that works for me
```python
from huggingface_hub import HfApi, HfFolder
repo_id = 'simon-arc-solve-fractal-v3'
api = HfApi()
username = api.whoami()['name']
repo_url = api.create_repo(repo_id=repo_id, exist_ok=True, private=True, repo_type="dataset")
```
### Steps to reproduce the bug
Go https://huggingface.co/new-dataset
Fill in the form.
Click `Create dataset`.
Now the form is cleared. And the page doesn't jump anywhere.
### Expected behavior
The moment the user clicks `Create dataset`, the repo gets created and the page jumps to the created repo.
### Environment info
Firefox 128.0.3 (64-bit)
macOS Sonoma 14.5
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load_dataset broken in 2.21.0
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[] | null | 4 | 2024-08-16T14:59:51 | 2024-08-18T09:28:43 | 2024-08-18T09:27:12 |
NONE
| null | null | null |
### Describe the bug
`eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)`
used to work till 2.20.0 but doesn't work in 2.21.0
In 2.20.0:
in 2.21.0:
### Steps to reproduce the bug
1. Spin up a new google collab
2. `pip install datasets==2.21.0`
3. `import datasets`
4. `eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval", "alpaca_eval_gpt4_baseline", trust_remote_code=True)`
5. Will throw an error.
### Expected behavior
Try steps 1-5 again but replace datasets version with 2.20.0, it will work
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.5
- PyArrow version: 17.0.0
- Pandas version: 2.1.4
- `fsspec` version: 2024.5.0
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Rename LargeList.dtype to LargeList.feature
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Rename `LargeList.dtype` to `LargeList.feature`.
Note that `dtype` is usually used for NumPy data types ("int64", "float32",...): see `Value.dtype`.
However, `LargeList` attribute (like `Sequence.feature`) expects a `FeatureType` instead.
With this renaming:
- we avoid confusion about the expected type and
- we also align `LargeList` with `Sequence`.
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PR_kwDODunzps54eZ0D
| 7,105 |
Use `huggingface_hub` cache
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- use `hf_hub_download()` from `huggingface_hub` for HF files
- `datasets` cache_dir is still used for:
- caching datasets as Arrow files (that back `Dataset` objects)
- extracted archives, uncompressed files
- files downloaded via http (datasets with scripts)
- I removed code that were made for http files (and also the dummy_data / mock_download_manager stuff that happened to rely on them and have been legacy for a while now)
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PR_kwDODunzps54dAhE
| 7,104 |
remove more script docs
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PR_kwDODunzps54clrp
| 7,103 |
Fix args of feature docstrings
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Fix Args section of feature docstrings.
Currently, some args do not appear in the docs because they are not properly parsed due to the lack of their type (between parentheses).
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PR_kwDODunzps54U7s4
| 7,099 |
Set dev version
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Release: 2.21.0
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PR_kwDODunzps535Xkr
| 7,096 |
Automatically create `cache_dir` from `cache_file_name`
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| null | false |
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You get a pretty unhelpful error message when specifying a `cache_file_name` in a directory that doesn't exist, e.g. `cache_file_name="./cache/data.map"`
```python
import datasets
cache_file_name="./cache/train.map"
dataset = datasets.load_dataset("ylecun/mnist")
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name)
```
```
FileNotFoundError: [Errno 2] No such file or directory: '/.../cache/tmp48r61siw'
```
It is simple enough to create and I was expecting that this would have been the case.
cc: @albertvillanova @lhoestq
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| 7,087 |
Unable to create dataset card for Lushootseed language
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NONE
| null | null | null |
### Feature request
While I was creating the dataset which contained all documents from the Lushootseed Wikipedia, the dataset card asked me to enter which language the dataset was in. Since Lushootseed is a critically endangered language, it was not available as one of the options. Is it possible to allow entering languages that aren't available in the options?
### Motivation
I'd like to add more information about my dataset in the dataset card, and the language is one of the most important pieces of information, since the entire dataset is primarily concerned collecting Lushootseed documents.
### Your contribution
I can submit a pull request
|
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[Regression] IterableDataset is broken on 2.20.0
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NONE
| null | null | null |
### Describe the bug
In the latest version of datasets there is a major regression, after creating an `IterableDataset` from a generator and applying a few operations (`map`, `select`), you can no longer iterate through the dataset multiple times.
The issue seems to stem from the recent addition of "resumable IterableDatasets" (#6658) (@lhoestq). It seems like it's keeping state when it shouldn't.
### Steps to reproduce the bug
Minimal Reproducible Example (comparing `datasets==2.17.0` and `datasets==2.20.0`)
```
#!/bin/bash
# List of dataset versions to test
versions=("2.17.0" "2.20.0")
# Loop through each version
for version in "${versions[@]}"; do
# Install the specific version of the datasets library
pip3 install -q datasets=="$version" 2>/dev/null
# Run the Python script
python3 - <<EOF
from datasets import IterableDataset
from datasets.features.features import Features, Value
def test_gen():
yield from [{"foo": i} for i in range(10)]
features = Features([("foo", Value("int64"))])
d = IterableDataset.from_generator(test_gen, features=features)
mapped = d.map(lambda row: {"foo": row["foo"] * 2})
column = mapped.select_columns(["foo"])
print("Version $version - Iterate Once:", list(column))
print("Version $version - Iterate Twice:", list(column))
EOF
done
```
The output looks like this:
```
Version 2.17.0 - Iterate Once: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}]
Version 2.17.0 - Iterate Twice: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}]
Version 2.20.0 - Iterate Once: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}]
Version 2.20.0 - Iterate Twice: []
```
### Expected behavior
The expected behavior is it version 2.20.0 should behave the same as 2.17.0.
### Environment info
`datasets==2.20.0` on any platform.
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fix streaming from arrow files
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| 7,082 |
Support HTTP authentication in non-streaming mode
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Support HTTP authentication in non-streaming mode, by support passing HTTP storage_options in non-streaming mode.
- Note that currently, HTTP authentication is supported only in streaming mode.
For example, this is necessary if a remote HTTP host requires authentication to download the data.
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| 7,081 |
Set load_from_disk path type as PathLike
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Set `load_from_disk` path type as `PathLike`. This way it is aligned with `save_to_disk`.
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HfHubHTTPError: 500 Server Error: Internal Server Error for url:
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NONE
| null | null | null |
### Describe the bug
newly uploaded datasets, since yesterday, yields an error.
old datasets, works fine.
Seems like the datasets api server returns a 500
I'm getting the same error, when I invoke `load_dataset` with my dataset.
Long discussion about it here, but I'm not sure anyone from huggingface have seen it.
https://discuss.huggingface.co/t/hfhubhttperror-500-server-error-internal-server-error-for-url/99580/1
### Steps to reproduce the bug
this api url:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
respond with:
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Expected behavior
return no error with newer datasets.
With older datasets I can load the datasets fine.
### Environment info
# Browser
When I access the api in the browser:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Request headers
```
Accept text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8
Accept-Encoding gzip, deflate, br, zstd
Accept-Language en-US,en;q=0.5
Connection keep-alive
Host huggingface.co
Priority u=1
Sec-Fetch-Dest document
Sec-Fetch-Mode navigate
Sec-Fetch-Site cross-site
Upgrade-Insecure-Requests 1
User-Agent Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:127.0) Gecko/20100101 Firefox/127.0
```
### Response headers
```
X-Firefox-Spdy h2
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
content-length 80
content-type application/json; charset=utf-8
cross-origin-opener-policy same-origin
date Fri, 26 Jul 2024 19:09:45 GMT
etag W/"50-9qrwU+BNI4SD0Fe32p/nofkmv0c"
referrer-policy strict-origin-when-cross-origin
vary Origin
via 1.1 1624c79cd07e6098196697a6a7907e4a.cloudfront.net (CloudFront)
x-amz-cf-id SP8E7n5qRaP6i9c9G83dNAiOzJBU4GXSrDRAcVNTomY895K35H0nJQ==
x-amz-cf-pop CPH50-C1
x-cache Error from cloudfront
x-error-message Internal Error - We're working hard to fix this as soon as possible!
x-powered-by huggingface-moon
x-request-id Root=1-66a3f479-026417465ef42f49349fdca1
```
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Fix CI test_convert_to_parquet
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Fix `test_convert_to_parquet` by patching `HfApi.preupload_lfs_files` and revert temporary fix:
- #7074
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PR_kwDODunzps52lTDe
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🧪 Do not mock create_commit
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| 2,432,027,412 |
PR_kwDODunzps52kciD
| 7,075 |
Update required soxr version from pre-release to release
|
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Update required `soxr` version from pre-release to release 0.4.0: https://github.com/dofuuz/python-soxr/releases/tag/v0.4.0
|
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PR_kwDODunzps52jkw4
| 7,074 |
Fix CI by temporarily marking test_convert_to_parquet as expected to fail
|
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As a hotfix for CI, temporarily mark test_convert_to_parquet as expected to fail.
Fix #7073.
Revert once root cause is fixed.
|
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I_kwDODunzps6Q8OXI
| 7,073 |
CI is broken for convert_to_parquet: Invalid rev id: refs/pr/1 404 error causes RevisionNotFoundError
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MEMBER
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See: https://github.com/huggingface/datasets/actions/runs/10095313567/job/27915185756
```
FAILED tests/test_hub.py::test_convert_to_parquet - huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Root=1-66a25839-31ce7b475e70e7db1e4d44c2;b0c8870f-d5ef-4bf2-a6ff-0191f3df0f64)
Revision Not Found for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.
Invalid rev id: refs/pr/1
```
```
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/hub.py:86: in convert_to_parquet
dataset.push_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/dataset_dict.py:1722: in push_to_hub
split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:5511: in _push_parquet_shards_to_hub
api.preupload_lfs_files(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/hf_api.py:4231: in preupload_lfs_files
_fetch_upload_modes(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:118: in _inner_fn
return fn(*args, **kwargs)
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/_commit_api.py:507: in _fetch_upload_modes
hf_raise_for_status(resp)
```
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nm
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NONE
| null | null | null | null |
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| 2,429,281,339 |
PR_kwDODunzps52betB
| 7,069 |
Fix push_to_hub by not calling create_branch if PR branch
|
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Fix push_to_hub by not calling create_branch if PR branch (e.g. `refs/pr/1`).
Note that currently create_branch raises a 400 Bad Request error if the user passes a PR branch (e.g. `refs/pr/1`).
EDIT:
~~Fix push_to_hub by not calling create_branch if branch exists.~~
Note that currently create_branch raises a 403 Forbidden error even if all these conditions are met:
- exist_ok is passed
- the branch already exists
- the user does not have WRITE permission
Fix #7067.
Related issue:
- https://github.com/huggingface/huggingface_hub/issues/2419
|
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| 2,426,657,434 |
PR_kwDODunzps52SwXS
| 7,068 |
Fix prepare_single_hop_path_and_storage_options
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[] | null | 2 | 2024-07-24T05:52:34 | 2024-07-29T07:02:07 | 2024-07-29T06:56:15 |
MEMBER
| null | false |
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Fix `_prepare_single_hop_path_and_storage_options`:
- Do not pass HF authentication headers and HF user-agent to non-HF HTTP URLs
- Do not overwrite passed `storage_options` nested values:
- Before, when passed
```DownloadConfig(storage_options={"https": {"client_kwargs": {"raise_for_status": True}}})```,
it was overwritten to
```{"https": {"client_kwargs": {"trust_env": True}}}```
- Now, the result combines both:
```{"https": {"client_kwargs": {"trust_env": True, "raise_for_status": True}}}```
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| 2,425,460,168 |
I_kwDODunzps6QkZXI
| 7,067 |
Convert_to_parquet fails for datasets with multiple configs
|
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NONE
| null | null | null |
If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error:
```
Traceback (most recent call last):
File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main
service.run()
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run
dataset.push_to_hub(
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub
api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
hf_raise_for_status(response)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f)
Bad request:
Invalid reference for a branch: refs/pr/1
```
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PR_kwDODunzps52Lz2-
| 7,064 |
Add `batch` method to `Dataset` class
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[] | null | 6 | 2024-07-23T08:40:43 | 2024-07-25T13:51:25 | 2024-07-25T13:45:20 |
CONTRIBUTOR
| null | false |
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This PR introduces a new `batch` method to the `Dataset` class, aligning its functionality with the `IterableDataset.batch()` method (implemented in #7054). The implementation uses as well the existing `map` method for efficient batching of examples.
Key changes:
- Add `batch` method to `Dataset` class in `arrow_dataset.py`
- Utilize `map` method for batching
Closes #7063
Once the approach is approved, i will create the tests and update the documentation.
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Add `batch` method to `Dataset`
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CONTRIBUTOR
| null | null | null |
### Feature request
Add a `batch` method to the Dataset class, similar to the one recently implemented for `IterableDataset` in PR #7054.
### Motivation
A batched iteration speeds up data loading significantly (see e.g. #6279)
### Your contribution
I plan to open a PR to implement this.
|
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PR_kwDODunzps52LUPR
| 7,062 |
Avoid calling http_head for non-HTTP URLs
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Avoid calling `http_head` for non-HTTP URLs, by adding and `else` statement.
Currently, it makes an unnecessary HTTP call (which adds latency) for non-HTTP protocols, like FTP, S3,...
I discovered this while working in an unrelated issue.
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PR_kwDODunzps52G71g
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WebDataset BuilderConfig
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NONE
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This PR adds `WebDatasetConfig`.
Closes #7055
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PR_kwDODunzps52EjGC
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Update load_hub.mdx
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I_kwDODunzps6QWFhb
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WebDataset with different prefixes are unsupported
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[] | null | 8 | 2024-07-22T01:14:19 | 2024-07-24T13:26:30 | 2024-07-23T13:28:46 |
NONE
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### Describe the bug
Consider a WebDataset with multiple images for each item where the number of images may vary: [example](https://huggingface.co/datasets/bigdata-pw/fashion-150k)
Due to this [code](https://github.com/huggingface/datasets/blob/87f4c2088854ff33e817e724e75179e9975c1b02/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) an error is given.
```
The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
```
The purpose of this check is unclear because PyArrow supports different keys.
Removing the check allows the dataset to be loaded and there's no issue when iterating through the dataset.
```
>>> from datasets import load_dataset
>>> path = "shards/*.tar"
>>> dataset = load_dataset("webdataset", data_files={"train": path}, split="train", streaming=True)
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 152/152 [00:00<00:00, 56458.93it/s]
>>> dataset
IterableDataset({
features: ['__key__', '__url__', '1.jpg', '2.jpg', '3.jpg', '4.jpg', 'json'],
n_shards: 152
})
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("bigdata-pw/fashion-150k")
```
### Expected behavior
Dataset loads without error
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.14.0-467.el9.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.19
- `huggingface_hub` version: 0.23.4
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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PR_kwDODunzps514T1f
| 7,054 |
Add batching to `IterableDataset`
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I've taken a try at implementing a batched `IterableDataset` as requested in issue #6279. This PR adds a new `BatchedExamplesIterable` class and a `.batch()` method to the `IterableDataset` class.
The main changes are:
1. A new `BatchedExamplesIterable` that groups examples into batches.
2. A `.batch()` method for `IterableDataset` to easily create batched versions.
3. Support for shuffling and sharding to work with PyTorch DataLoader and multiple workers.
I'm not sure if this is exactly what you had in mind and also have not fully tested it atm, so I'd really appreciate your feedback. Does this seem like it's heading in the right direction? I'm happy to make any changes or explore different approaches if needed.
Pinging @lhoestq
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Datasets.datafiles resolve_pattern `TypeError: can only concatenate tuple (not "str") to tuple`
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NONE
| null | null | null |
### Describe the bug
in data_files.py, line 332,
`fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)`
If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')`
So, `isinstance(fs.protocol, str) == False` and
`protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise
`TypeError: can only concatenate tuple (not "str") to tuple`.
### Steps to reproduce the bug
Steps to reproduce:
1. Run on a cloud server like AWS,
2. `import datasets.data_files as datafile`
3. datafile.resolve_pattern('path/to/dataset', '.')
4. `TypeError: can only concatenate tuple (not "str") to tuple`
### Expected behavior
Should return path of the dataset, with fs.protocol at the beginning
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.19
- Huggingface_hub version: 0.23.5
- PyArrow version: 16.1.0
- Pandas version: 1.1.5
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| 2,411,682,730 |
PR_kwDODunzps51iuop
| 7,052 |
Adding `Music` feature for symbolic music modality (MIDI, abc)
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[] | null | 0 | 2024-07-16T17:26:04 | 2024-07-29T06:47:55 | 2024-07-29T06:47:55 |
NONE
| null | true |
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⚠️ (WIP) ⚠️
### What this PR does
This PR adds a `Music` feature for the symbolic music modality, in particular [MIDI](https://en.wikipedia.org/wiki/Musical_Instrument_Digital_Interface) and [abc](https://en.wikipedia.org/wiki/ABC_notation) files.
### Motivations
These two file formats are widely used in the [Music Information Retrieval (MIR)](https://en.wikipedia.org/wiki/Music_information_retrieval) for tasks such as music generation, music transcription, music synthesis or music transcription. Having a dedicated feature in the datasets library would allow to both encourage researchers to share datasets of this modality as well as making them more easily usable for end users, benefitting from the perks of the library.
These file formats are supported by [symusic](https://github.com/Yikai-Liao/symusic), a lightweight Python library with C bindings (using nanobind) allowing to efficiently read, write and manipulate them. The library is actively developed, and can in the future also implement other file formats such as [musicXML](https://en.wikipedia.org/wiki/MusicXML). As such, this PR relies on it.
The music data can then easily be tokenized with appropriate tokenizers such as [MidiTok](https://github.com/Natooz/MidiTok) or converted to pianorolls matrices by symusic.
**Jul 16th 2024:**
* the tests for the `Music` feature are currently failing due to non-supported access to the LazyBatch in `test_dataset_with_music_feature_map` and `test_dataset_with_music_feature_map_resample_music` (see TODOs). I am a beginner with pyArrow, I'll take any advice to make this work;
* additional tests including the `Music` feature with parquet and WebDataset should be implemented. As of right now, I am waiting for your feedback before taking further steps;
* a `MusicFolder` should also be implemented to comply with the usages of the `Image` and `Audio` features, waiting for your feedback too.
CCing @lhoestq and @albertvillanova
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I_kwDODunzps6Pm9LJ
| 7,051 |
How to set_epoch with interleave_datasets?
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[] | null | 7 | 2024-07-15T18:24:52 | 2024-08-05T20:58:04 | 2024-08-05T20:58:04 |
NONE
| null | null | null |
Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples.
I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch)
Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start.
How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset...
Something like
```
dataset_a = load_dataset(...)
dataset_b = load_dataset(...)
def epoch_shuffled_dataset(ds):
# How to make this maintain the number of shards in ds??
for epoch in itertools.count():
ds.set_epoch(epoch)
yield from iter(ds)
shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a})
interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted')
```
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