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https://api.github.com/repos/huggingface/datasets/issues/7460 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7460/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7460/comments | https://api.github.com/repos/huggingface/datasets/issues/7460/events | https://github.com/huggingface/datasets/pull/7460 | 2,925,605,865 | PR_kwDODunzps6O9Ccc | 7,460 | release: 3.4.1 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7460). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-03-17T15:58:31Z | 2025-03-17T16:01:14Z | 2025-03-17T15:59:19Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7198 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7198/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7198/comments | https://api.github.com/repos/huggingface/datasets/issues/7198/events | https://github.com/huggingface/datasets/pull/7198 | 2,566,064,849 | PR_kwDODunzps59mwgu | 7,198 | Add repeat method to datasets | {
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"@lhoestq does this look reasonable?",
"updated and added test cases!",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7198). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"thanks for ... | 2024-10-04T10:45:16Z | 2025-02-05T16:32:31Z | 2025-02-05T16:32:31Z | CONTRIBUTOR | null | null | null | Following up on discussion in #6623 and #7198 I thought this would be pretty useful for my case so had a go at implementing.
My main motivation is to be able to call iterable_dataset.repeat(None).take(samples_per_epoch) to safely avoid timeout issues in a distributed training setting. This would provide a straightforward workaround for several open issues related to this situation: https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719.
@lhoestq let me know if this looks on the right track! | {
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https://api.github.com/repos/huggingface/datasets/issues/5516 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5516/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5516/comments | https://api.github.com/repos/huggingface/datasets/issues/5516/events | https://github.com/huggingface/datasets/pull/5516 | 1,577,661,640 | PR_kwDODunzps5JmzPQ | 5,516 | Reload features from Parquet metadata | {
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"Thanks a lot for your help @lhoestq. I've simplified what turned out to be a simple fix and added the unit test.\r\n\r\nDoes this look ready to be merged or is there anything I'm still missing?",
"Cool ! I think you just need to remove the unused import in `io/parquet.py`\r\n```\r\nsrc/datasets/io/parquet.py:4:1... | 2023-02-09T10:52:15Z | 2023-02-12T16:00:00Z | 2023-02-12T15:57:01Z | CONTRIBUTOR | null | null | null | Resolves #5482.
Attaches feature metadata to parquet files serialised using `Dataset.to_parquet`.
This allows retrieving data with "rich" feature types (e.g., `datasets.features.image.Image` or `datasets.features.audio.Audio`) from parquet files without cumbersome casting (for an example, see #5482).
@lhoestq It seems that it is sufficient to attach metadata to the schema prior to serialising and features are loaded back with correct types afterwards automatically.
I used the following script to test the implementation:
```python
from pathlib import Path
import datasets
dataset_name = "Maysee/tiny-imagenet"
ds = datasets.load_dataset(dataset_name, split=datasets.Split.TRAIN)
output_directory_path = Path(__file__).parent.joinpath("example_test_outputs", dataset_name.replace("/", "_"))
output_directory_path.mkdir(exist_ok=True, parents=True)
output_filepath = output_directory_path.joinpath("ds.parquet")
ds.to_parquet(str(output_filepath))
reloaded_ds = datasets.load_dataset(str(output_directory_path), split=datasets.Split.TRAIN)
assert ds.features == reloaded_ds.features
```
Prior to the change in this PR this script raises an `AssertionError` and the `Image` features lose their type after serialisation. After the change in this PR, the assertion does not raise an error and manual inspection of the features shows type `Image` for the respective columns of `reloaded_ds `.
Some open questions:
* How/where can I best add new unit tests for this implementation?
* What dataset would I best use in the tests? I chose `Maysee/tiny-imagenet` mainly because it is small and contains an ?Image` feature that can be used to test, but I'd be happy for suggestions on a suitable data source to use.
* Currently I'm calling `datasets.arrow_writer.ArrowWriter._build_metadata` as I need the same logic. However, I'm not happy with the coupling between `datasets.io.parquet` and `datasets.arrow_writer` it leaves me with. Suggest to factor this common logic out into a helper function and reuse it from both of these. Do you agree and if yes, could you please guide me where I would best place this function?
Many thanks in advance and kind regards,
MFreidank
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https://api.github.com/repos/huggingface/datasets/issues/5019 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5019/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5019/comments | https://api.github.com/repos/huggingface/datasets/issues/5019/events | https://github.com/huggingface/datasets/pull/5019 | 1,384,673,718 | PR_kwDODunzps4_iq9b | 5,019 | Update swiss judgment prediction | {
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"Thank you very much for the detailed review @albertvillanova!\r\n\r\nI updated the PR with the requested changes. ",
"At the end, I had to manually fix the conflict, so that CI tests are launched.\r\n\r\nPLEASE NOTE: you should first pull to incorporate the previous commit\r\n```shell\r\ngit pull\r\n```",
"_Th... | 2022-09-24T13:28:57Z | 2022-09-28T07:13:39Z | 2022-09-28T05:48:50Z | CONTRIBUTOR | null | null | null | Hi,
I updated the dataset to include additional data made available recently. When I test it locally, it seems to work. However, I get the following error with the dummy data creation:
`Dummy data generation done but dummy data test failed since splits ['train', 'validation', 'test'] have 0 examples for config 'fr'`. Do you know why this could be the case?
Cheers,
Joel | {
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"Makes sense! For the last structure, we could count the path segments (delimited by \"/\" for URLs and `os.sep` for local paths) to ensure all inferred labels are on the same level. Otherwise, I think it's safe to assume they are meaningless and ignore them.\r\n"
] | 2022-10-24T13:28:18Z | 2022-11-15T16:31:10Z | 2022-11-15T16:31:09Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Describe the bug
By default FolderBasedBuilder infers labels if there is not metadata files, even if it's meaningless (for example, they are in a single directory or in the root folder, see this repo as an example: https://huggingface.co/datasets/patrickvonplaten/audios
As this is a corner case for quick exploration of images or audios on the Hub.
### Steps to reproduce the bug
If you have directory like this:
```
repo
image1.jpg
image2.jpg
image3.jpg
```
or
```
repo
data
image1.jpg
image2.jpg
image3.jpg
```
doing `ds = load_dataset(repo)` would create `label` feature:
```python
print(ds["train"][0])
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FB5326468E0>, 'label': 0}
```
Also, if you have the following structure:
```
repo
data
image1.jpg
image2.jpg
image3.jpg
image4.jpg
image5.jpg
image6.jpg
```
it will infer two labels:
```python
print(ds["train"][0])
print(ds["train"][-1])
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7FB5326468E0>, 'label': 1}
>> {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x415 at 0x7FB5326555B0>, 'label': 0}
```
### Expected behavior
We should have only one base feature (Image/Audio) in such cases.
### Environment info
all versions of `datasets` | {
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https://api.github.com/repos/huggingface/datasets/issues/6646 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6646/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6646/comments | https://api.github.com/repos/huggingface/datasets/issues/6646/events | https://github.com/huggingface/datasets/pull/6646 | 2,123,134,128 | PR_kwDODunzps5mRIma | 6,646 | Better multi-gpu example | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6646). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-02-07T14:15:01Z | 2024-02-09T17:43:32Z | 2024-02-07T14:59:11Z | MEMBER | null | null | null | Use Qwen1.5-0.5B-Chat as an easy example for multi-GPU
the previous example was using a model for translation and the way it was setup was not really the right way to use the model. | {
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https://api.github.com/repos/huggingface/datasets/issues/7475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7475/comments | https://api.github.com/repos/huggingface/datasets/issues/7475/events | https://github.com/huggingface/datasets/issues/7475 | 2,946,640,570 | I_kwDODunzps6voiq6 | 7,475 | IterableDataset's state_dict shard_example_idx is always equal to the number of samples in a shard | {
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"Hey, I’d love to work on this issue but I am a beginner, can I work it with you?",
"Hello. I'm sorry but I don't have much time to get in the details for now.\nHave you managed to reproduce the issue with the code provided ?\nIf you want to work on it, you can self-assign and ask @lhoestq for directions",
"Hi ... | 2025-03-25T13:58:07Z | 2025-04-18T00:49:37Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Describe the bug
I've noticed a strange behaviour with Iterable state_dict: the value of shard_example_idx is always equal to the amount of samples in a shard.
### Steps to reproduce the bug
I am reusing the example from the doc
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=1)
state_dict = None
# Iterate through the dataset and print examples
for idx, example in enumerate(ds):
print(example)
if idx == 2:
state_dict = ds.state_dict()
print("checkpoint")
break
print(state_dict)
```
Returns:
```
{'a': 0}
{'a': 1}
checkpoint
{'examples_iterable': {'shard_idx': 0, 'shard_example_idx': 6, 'type': 'ArrowExamplesIterable'}, 'epoch': 0}
```
### Expected behavior
shard_example_idx should be 2 instead of 6
If we run with num_shards=2, then shard_example_idx is 3 instead of 2 and so on.
### Environment info
- `datasets` version: 3.4.1
- Platform: macOS-14.6.1-arm64-arm-64bit
- Python version: 3.12.9
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6986 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6986/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6986/comments | https://api.github.com/repos/huggingface/datasets/issues/6986/events | https://github.com/huggingface/datasets/pull/6986 | 2,362,584,179 | PR_kwDODunzps5y-Zi0 | 6,986 | Add large_list type support in string_to_arrow | {
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"@albertvillanova @KennethEnevoldsen"
] | 2024-06-19T14:54:25Z | 2024-08-12T14:43:48Z | 2024-08-12T14:43:47Z | NONE | null | null | null | add large_list type support in string_to_arrow() and _arrow_to_datasets_dtype() in features.py
Fix #6984
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https://api.github.com/repos/huggingface/datasets/issues/4833 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4833/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4833/comments | https://api.github.com/repos/huggingface/datasets/issues/4833/events | https://github.com/huggingface/datasets/pull/4833 | 1,336,946,965 | PR_kwDODunzps49E_Nk | 4,833 | Fix missing tags in dataset cards | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-12T09:04:52Z | 2022-09-22T14:41:23Z | 2022-08-12T09:45:55Z | MEMBER | null | null | null | Fix missing tags in dataset cards:
- boolq
- break_data
- definite_pronoun_resolution
- emo
- kor_nli
- pg19
- quartz
- sciq
- squad_es
- wmt14
- wmt15
- wmt16
- wmt17
- wmt18
- wmt19
- wmt_t2t
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. | {
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https://api.github.com/repos/huggingface/datasets/issues/5252 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5252/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5252/comments | https://api.github.com/repos/huggingface/datasets/issues/5252/events | https://github.com/huggingface/datasets/pull/5252 | 1,451,765,838 | PR_kwDODunzps5DCI1U | 5,252 | Support for decoding Image/Audio types in map when format type is not default one | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5252). All of your documentation changes will be reflected on that endpoint.",
"Yes, if the image column is the first in the batch keys, it will ... | 2022-11-16T15:02:13Z | 2022-12-13T17:01:54Z | 2022-12-13T16:59:04Z | COLLABORATOR | null | null | null | Add support for decoding the `Image`/`Audio` types in `map` for the formats (Numpy, TF, Jax, PyTorch) other than the default one (Python).
Additional improvements:
* make `Dataset`'s "iter" API cleaner by removing `_iter` and replacing `_iter_batches` with `iter(batch_size)` (also implemented for `IterableDataset`)
* iterate over arrow tables in `map` to avoid `_getitem` calls, which are much slower than `__iter__`/`iter(batch_size)`, when the `format_type` is not Python
* fix `_iter_batches` (now named `iter`) when `drop_last_batch=True` and `pyarrow<=8.0.0` is installed
* lazily extract and decode arrow data in the default format
TODO:
* [x] update the `iter` benchmark in the docs (the `BeamBuilder` cannot load the preprocessed datasets from our bucket, so wait for this to be fixed (cc @lhoestq))
Fix https://github.com/huggingface/datasets/issues/3992, fix https://github.com/huggingface/datasets/issues/3756 | {
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https://api.github.com/repos/huggingface/datasets/issues/7434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7434/comments | https://api.github.com/repos/huggingface/datasets/issues/7434/events | https://github.com/huggingface/datasets/pull/7434 | 2,893,075,908 | PR_kwDODunzps6NP-vn | 7,434 | Refactor `Dataset.map` to reuse cache files mapped with different `num_proc` | {
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"@lhoestq please let me know what you think about this.",
"It looks like I can't change the merge target to #7435, so it will look like there is a bunch of extra stuff until #7435 is in main.",
"@lhoestq Thanks so much for reviewing #7435! Now that that's merged, I think this PR is ready!! Can you kick off CI w... | 2025-03-04T06:12:37Z | 2025-03-21T12:37:16Z | null | CONTRIBUTOR | null | null | null | Fixes #7433
This refactor unifies `num_proc is None or num_proc == 1` and `num_proc > 1`; instead of handling them completely separately where one uses a list of kwargs and shards and the other just uses a single set of kwargs and `self`, by wrapping the `num_proc == 1` case in a list and making the difference just whether or not you use a pool, you set up either case to be able to load each other's cache files just by changing `num_shards`; `num_proc == 1` can sequentially load the shards of a dataset mapped `num_shards > 1` and map any missing shards
Other than the structural refactor, the main contribution of this PR is `existing_cache_file_map`, which uses a regex of `cache_file_name` and `suffix_template` to find existing cache files, grouped by their `num_shards`; using this data structure, we can reset `num_shards` to an existing set of cache files, and load them accordingly | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5367 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5367/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5367/comments | https://api.github.com/repos/huggingface/datasets/issues/5367/events | https://github.com/huggingface/datasets/pull/5367 | 1,499,174,749 | PR_kwDODunzps5FlevK | 5,367 | Fix remove columns from lazy dict | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-12-15T22:04:12Z | 2022-12-15T22:27:53Z | 2022-12-15T22:24:50Z | MEMBER | null | null | null | This was introduced in https://github.com/huggingface/datasets/pull/5252 and causing the transformers CI to break: https://app.circleci.com/pipelines/github/huggingface/transformers/53886/workflows/522faf2e-a053-454c-94f8-a617fde33393/jobs/648597
Basically this code should return a dataset with only one column:
```python
from datasets import *
ds = Dataset.from_dict({"a": range(5)})
def f(x):
x["b"] = x["a"]
return x
ds = ds.map(f, remove_columns=["a"])
assert ds.column_names == ["b"]
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5181/comments | https://api.github.com/repos/huggingface/datasets/issues/5181/events | https://github.com/huggingface/datasets/issues/5181 | 1,431,027,102 | I_kwDODunzps5VS72e | 5,181 | Add a guide for semantic segmentation | {
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... | null | [
"Sure this sounds great! Would this be pure torchvision, albumentations, or something else?",
"I am considering `torchvision` and `albumentations`. Also [works with TensorFlow](https://github.com/deep-diver/segformer-tf-transformers/blob/main/notebooks/TFSegFormer_Finetune.ipynb). \r\n\r\nI am assigning the issue... | 2022-11-01T07:54:50Z | 2022-11-04T18:23:36Z | 2022-11-04T18:23:36Z | MEMBER | null | null | {
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} | Currently, we have these guides for object detection and image classification:
* https://huggingface.co/docs/datasets/object_detection
* https://huggingface.co/docs/datasets/image_classification
I am proposing adding a similar guide for semantic segmentation.
I am happy to contribute a PR for it.
Cc: @osanseviero @nateraw | {
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https://api.github.com/repos/huggingface/datasets/issues/6262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6262/comments | https://api.github.com/repos/huggingface/datasets/issues/6262/events | https://github.com/huggingface/datasets/pull/6262 | 1,914,895,459 | PR_kwDODunzps5bTh6H | 6,262 | Fix CI 404 errors | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-09-27T07:40:18Z | 2023-09-28T15:39:16Z | 2023-09-28T15:30:40Z | MEMBER | null | null | null | Currently our CI usually raises 404 errors when trying to delete temporary repositories. See, e.g.: https://github.com/huggingface/datasets/actions/runs/6314980985/job/17146507884
```
FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files_with_max_shard_size - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fb99-4a52c561752ece3d77eb6d57;2b61cae4-613d-4a73-bbb1-2faf9e32b02d)
Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features_audio - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fbb2-0333dd666d42f0e173c2bb68;dfdc4271-b49b-4008-8c49-f05cf7c1d53d)
Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_custom_splits - huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-6512fbca-167690694f39770a5b3a444e;baeaa905-0a57-4585-ac97-9aaae12dd47d)
Repository Not Found for url: https://hub-ci.huggingface.co/api/repos/delete.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
```
I think this can be caused by collisions in temporary repository IDs because we create them in multiprocessing:
```python
with temporary_repo(f"{CI_HUB_USER}/test-{int(time.time() * 10e3)}") as ds_name:
```
This can also be caused when there is another issue that does not allow the creation of the repository, thus making it impossible to delete it.
This PR tries to fix this issue by increasing the precision of the number on the repository ID: `10e6` instead of `10e3`.
Additionally, this PR catches RepositoryNotFoundError.
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https://api.github.com/repos/huggingface/datasets/issues/6912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6912/comments | https://api.github.com/repos/huggingface/datasets/issues/6912/events | https://github.com/huggingface/datasets/issues/6912 | 2,309,365,961 | I_kwDODunzps6JpiDJ | 6,912 | Add MedImg for streaming | {
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"@mariosasko, @lhoestq, @albertvillanova\r\nHello! Can anyone help? or can you guys suggest who can help with this?",
"Hi ! Feel free to download the dataset and create a `Dataset` object with it.\r\n\r\nThen your'll be able to use `push_to_hub()` to upload the dataset to HF in Parquet format and make it streamab... | 2024-05-22T00:55:30Z | 2024-09-05T16:53:54Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Feature request
Host the MedImg dataset (similar to Imagenet but for biomedical images).
### Motivation
There is a clear need for biomedical image foundation models and large scale biomedical datasets that are easily streamable. This would be an excellent tool for the biomedical community.
### Your contribution
MedImg can be found [here](https://www.cuilab.cn/medimg/#). | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7134 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7134/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7134/comments | https://api.github.com/repos/huggingface/datasets/issues/7134/events | https://github.com/huggingface/datasets/issues/7134 | 2,499,484,041 | I_kwDODunzps6U-xmJ | 7,134 | Attempting to return a rank 3 grayscale image from dataset.map results in extreme slowdown | {
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"completed": 0,
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} | ### Describe the bug
Background: Digital images are often represented as a (Height, Width, Channel) tensor. This is the same for huggingface datasets that contain images. These images are loaded in Pillow containers which offer, for example, the `.convert` method.
I can convert an image from a (H,W,3) shape to a grayscale (H,W) image and I have no problems with this. But when attempting to return a (H,W,1) shaped matrix from a map function, it never completes and sometimes even results in an OOM from the OS.
I've used various methods to expand a (H,W) shaped array to a (H,W,1) array. But they all resulted in extremely long map operations consuming a lot of CPU and RAM.
### Steps to reproduce the bug
Below is a minimal example using two methods to get the desired output. Both of which don't work
```py
import tensorflow as tf
import datasets
import numpy as np
ds = datasets.load_dataset("project-sloth/captcha-images")
to_gray_pillow = lambda sample: {'image': np.expand_dims(sample['image'].convert("L"), axis=-1)}
ds_gray = ds.map(to_gray_pillow)
# Alternatively
ds = datasets.load_dataset("project-sloth/captcha-images").with_format("tensorflow")
to_gray_tf = lambda sample: {'image': tf.expand_dims(tf.image.rgb_to_grayscale(sample['image']), axis=-1)}
ds_gray = ds.map(to_gray_tf)
```
### Expected behavior
I expect the map operation to complete and return a new dataset containing grayscale images in a (H,W,1) shape.
### Environment info
datasets 2.21.0
python tested with both 3.11 and 3.12
host os : linux | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7018 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7018/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7018/comments | https://api.github.com/repos/huggingface/datasets/issues/7018/events | https://github.com/huggingface/datasets/issues/7018 | 2,383,700,286 | I_kwDODunzps6OFGE- | 7,018 | `load_dataset` fails to load dataset saved by `save_to_disk` | {
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"In my case the error was:\r\n```\r\nValueError: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.\r\n```\r\nDid you try `load_from_disk`?",
"More generally, any reason there is no API consistency between save_to_disk and push_to_hub ? \r\n\r\nWould be nice... | 2024-07-01T12:19:19Z | 2024-12-03T11:26:17Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
This code fails to load the dataset it just saved:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
MODEL = "google-bert/bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
dataset = load_dataset("yelp_review_full")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets.save_to_disk("dataset")
tokenized_datasets = load_dataset("dataset/") # raises
```
It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`.
I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON:
```shell
$ ls -l dataset/test
-rw-r--r-- 1 sliedes sliedes 191498784 Jul 1 13:55 data-00000-of-00001.arrow
-rw-r--r-- 1 sliedes sliedes 1730 Jul 1 13:55 dataset_info.json
-rw-r--r-- 1 sliedes sliedes 249 Jul 1 13:55 state.json
```
### Steps to reproduce the bug
Execute the code above.
### Expected behavior
The dataset is loaded successfully.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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https://api.github.com/repos/huggingface/datasets/issues/4689 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4689/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4689/comments | https://api.github.com/repos/huggingface/datasets/issues/4689/events | https://github.com/huggingface/datasets/pull/4689 | 1,306,230,203 | PR_kwDODunzps47eyw5 | 4,689 | Test extractors for all compression formats | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-15T16:29:55Z | 2022-07-15T17:47:02Z | 2022-07-15T17:35:24Z | MEMBER | null | null | null | This PR:
- Adds all compression formats to `test_extractor`
- Tests each base extractor for all compression formats
Note that all compression formats are tested except "rar". | {
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https://api.github.com/repos/huggingface/datasets/issues/7256 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7256/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7256/comments | https://api.github.com/repos/huggingface/datasets/issues/7256/events | https://github.com/huggingface/datasets/pull/7256 | 2,618,580,188 | PR_kwDODunzps6AG_qk | 7,256 | Retry all requests timeouts | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7256). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-28T14:23:16Z | 2024-10-28T14:56:28Z | 2024-10-28T14:56:26Z | MEMBER | null | null | null | as reported in https://github.com/huggingface/datasets/issues/6843 | {
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} | 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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https://api.github.com/repos/huggingface/datasets/issues/7314 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7314/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7314/comments | https://api.github.com/repos/huggingface/datasets/issues/7314/events | https://github.com/huggingface/datasets/pull/7314 | 2,727,502,630 | PR_kwDODunzps6EkCi5 | 7,314 | Resolved for empty datafiles | {
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"Closes #6152 ",
"@mariosasko I hope this resolves #6152 "
] | 2024-12-09T15:47:22Z | 2024-12-27T18:20:21Z | null | NONE | null | null | null | Resolved for Issue#6152 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7378 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7378/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7378/comments | https://api.github.com/repos/huggingface/datasets/issues/7378/events | https://github.com/huggingface/datasets/issues/7378 | 2,802,957,388 | I_kwDODunzps6nEbxM | 7,378 | Allow pushing config version to hub | {
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] | open | false | null | [] | null | [
"Hi ! This sounds reasonable to me, feel free to open a PR :)"
] | 2025-01-21T22:35:07Z | 2025-01-30T13:56:56Z | null | NONE | null | null | {
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} | ### Feature request
Currently, when datasets are created, they can be versioned by passing the `version` argument to `load_dataset(...)`. For example creating `outcomes.csv` on the command line
```
echo "id,value\n1,0\n2,0\n3,1\n4,1\n" > outcomes.csv
```
and creating it
```
import datasets
dataset = datasets.load_dataset(
"csv",
data_files ="outcomes.csv",
keep_in_memory = True,
version = '1.0.0')
```
The version info is stored in the `info` and can be accessed e.g. by `next(iter(dataset.values())).info.version`
This dataset can be uploaded to the hub with `dataset.push_to_hub(repo_id = "maomlab/example_dataset")`. This will create a dataset on the hub with the following in the `README.md`, but it doesn't upload the version information:
```
---
dataset_info:
features:
- name: id
dtype: int64
- name: value
dtype: int64
splits:
- name: train
num_bytes: 64
num_examples: 4
download_size: 1332
dataset_size: 64
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
```
However, when I download from the hub, the version information is missing:
```
dataset_from_hub_no_version = datasets.load_dataset("maomlab/example_dataset")
next(iter(dataset.values())).info.version
```
I can add the version information manually to the hub, by appending it to the end of config section:
```
...
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
version: 1.0.0
---
```
And then when I download it, the version information is correct.
### Motivation
### Why adding version information for each config makes sense
1. The version information is already recorded in the dataset config info data structure and is able to parse it correctly, so it makes sense to sync it with `push_to_hub`.
2. Keeping the version info in at the config level is different from version info at the branch level. As the former relates to the version of the specific dataset the config refers to rather than the version of the dataset curation itself.
## A explanation for the current behavior:
In [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
), the `_INCLUDED_INFO_IN_YAML` variable doesn't include `"version"`.
If my reading of the code is right, adding `"version"` to `_INCLUDED_INFO_IN_YAML`, would allow the version information to be uploaded to the hub.
### Your contribution
Request: add `"version"` to `_INCLUDE_INFO_IN_YAML` in [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
)
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https://api.github.com/repos/huggingface/datasets/issues/6398 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6398/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6398/comments | https://api.github.com/repos/huggingface/datasets/issues/6398/events | https://github.com/huggingface/datasets/pull/6398 | 1,987,786,446 | PR_kwDODunzps5fJlP7 | 6,398 | Remove redundant condition in builders | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-11-10T14:56:43Z | 2023-11-14T10:49:15Z | 2023-11-14T10:43:00Z | MEMBER | null | null | null | Minor refactoring to remove redundant condition. | {
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https://api.github.com/repos/huggingface/datasets/issues/7182 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7182/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7182/comments | https://api.github.com/repos/huggingface/datasets/issues/7182/events | https://github.com/huggingface/datasets/pull/7182 | 2,556,333,671 | PR_kwDODunzps59GdLS | 7,182 | Support features in metadata configs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7182). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"The CI issue is unrelated:\r\n- #7183"
] | 2024-09-30T11:14:53Z | 2024-10-09T16:03:57Z | 2024-10-09T16:03:54Z | MEMBER | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/7213 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7213/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7213/comments | https://api.github.com/repos/huggingface/datasets/issues/7213/events | https://github.com/huggingface/datasets/issues/7213 | 2,578,675,565 | I_kwDODunzps6Zs3dt | 7,213 | Add with_rank to Dataset.from_generator | {
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"completed": 0,
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} | ### Feature request
Add `with_rank` to `Dataset.from_generator` similar to `Dataset.map` and `Dataset.filter`.
### Motivation
As for `Dataset.map` and `Dataset.filter`, this is useful when creating cache files using multi-GPU, where the rank can be used to select GPU IDs. For now, rank can be added in the `gen_kwars` argument; however, this, in turn, includes the rank when computing the fingerprint.
### Your contribution
Added #7199 which passes rank based on the `job_id` set by `num_proc`. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6093 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6093/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6093/comments | https://api.github.com/repos/huggingface/datasets/issues/6093/events | https://github.com/huggingface/datasets/pull/6093 | 1,826,210,490 | PR_kwDODunzps5WpLfh | 6,093 | Deprecate `download_custom` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-07-28T10:49:06Z | 2023-08-21T17:51:34Z | 2023-07-28T11:30:02Z | COLLABORATOR | null | null | null | Deprecate `DownloadManager.download_custom`. Users should use `fsspec` URLs (cacheable) or make direct requests with `fsspec`/`requests` (not cacheable) instead.
We should deprecate this method as it's not compatible with streaming, and implementing the streaming version of it is hard/impossible. There have been requests to implement the streaming version of this method on the forum, but the reason for this seems to be a tip in the docs that "promotes" this method (this PR removes it).
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https://api.github.com/repos/huggingface/datasets/issues/7316 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7316/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7316/comments | https://api.github.com/repos/huggingface/datasets/issues/7316/events | https://github.com/huggingface/datasets/pull/7316 | 2,730,196,085 | PR_kwDODunzps6Etc0U | 7,316 | More docs to from_dict to mention that the result lives in RAM | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7316). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-12-10T13:56:01Z | 2024-12-10T13:58:32Z | 2024-12-10T13:57:02Z | MEMBER | null | null | null | following discussions at https://discuss.huggingface.co/t/how-to-load-this-simple-audio-data-set-and-use-dataset-map-without-memory-issues/17722/14 | {
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https://api.github.com/repos/huggingface/datasets/issues/5950 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5950/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5950/comments | https://api.github.com/repos/huggingface/datasets/issues/5950/events | https://github.com/huggingface/datasets/issues/5950 | 1,755,197,946 | I_kwDODunzps5onjH6 | 5,950 | Support for data with instance-wise dictionary as features | {
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"Hi ! We use the Arrow columnar format under the hood, which doesn't support such dictionaries: each field must have a fixed type and exist in each sample.\r\n\r\nInstead you can restructure your data like\r\n```\r\n{\r\n \"index\": 0,\r\n \"keys\": [\"2 * x + y >= 3\"],\r\n \"values\": [[\"2 * x + y >= 3\... | 2023-06-13T15:49:00Z | 2025-04-07T13:20:37Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Feature request
I notice that when loading data instances with feature type of python dictionary, the dictionary keys would be broadcast so that every instance has the same set of keys. Please see an example in the Motivation section.
It is possible to avoid this behavior, i.e., load dictionary features as it is and do not broadcast the keys among instances? Please note that these dictionaries would have to be processed dynamically at each training iteration into strings (and tokenized).
### Motivation
I am trying to load a dataset from a json file. Each instance of the dataset has a feature that is a dictionary but its keys depend on the instance. Every two instances may have different keys. For example, imagine a dataset that contains a set of math expressions from a bunch of mutually redundant expressions:
```
{
"index": 0,
"feature": {
"2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"],
...
}
},
...
{
"index": 9999,
"feature": {
"x >= 6": ["x >= 6", "x >= 0", "x >= -1"],
...
}
},
...
```
When directly loading the dataset using `data = load_dataset("json", data_files=file_paths, split='train')`, each instance would have all the keys from other instances and None as values. That is, instance of index 0 becomes:
```
{
"index": 0,
"feature": {
"2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"],
...
"x >= 6": None, # keys from other instances
...
}
},
```
This is not desirable. Moreover, issue would be raised if I attempt to combine two such datasets using `data = concatenate_datasets(multi_datasets)`, perhaps because their dictionary features contain different keys.
A solution I can think of is to store the dictionary features as a long string, and evaluate it later. Please kindly suggest any other solution using existing methods of datasets.
### Your contribution
N/A | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5328 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5328/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5328/comments | https://api.github.com/repos/huggingface/datasets/issues/5328/events | https://github.com/huggingface/datasets/pull/5328 | 1,471,661,437 | PR_kwDODunzps5EFAyT | 5,328 | Fix docs building for main | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"EDIT\r\nAt least the docs for ~~main~~ PR branch are now built:\r\n- https://github.com/huggingface/datasets/actions/runs/3594847760/jobs/6053620813",
"Build documentation for main branch was triggered after this PR being merged: h... | 2022-12-01T17:07:45Z | 2022-12-02T16:29:00Z | 2022-12-02T16:26:00Z | MEMBER | null | null | null | This PR reverts the triggering event for building documentation introduced by:
- #5250
Fix #5326. | {
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https://api.github.com/repos/huggingface/datasets/issues/5858 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5858/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5858/comments | https://api.github.com/repos/huggingface/datasets/issues/5858/events | https://github.com/huggingface/datasets/issues/5858 | 1,709,332,632 | I_kwDODunzps5l4liY | 5,858 | Throw an error when dataset improperly indexed | {
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"Thanks for reporting, @sarahwie.\r\n\r\nPlease note that in `datasets` we do not have vectorized operation like `pandas`. Therefore, your equality comparisons above are `False`:\r\n- For example: `squad['question']` returns a `list`, and this list is not equal to `\"Who was the Norse leader?\"`\r\n\r\nThe `False` ... | 2023-05-15T05:15:53Z | 2023-05-25T16:23:19Z | 2023-05-25T16:23:19Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Pandas-style subset indexing on dataset does not throw an error, when maybe it should. Instead returns the first instance of the dataset regardless of index condition.
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. `squad = datasets.load_dataset("squad_v2", split="validation")`
2. `item = squad[squad['question'] == "Who was the Norse leader?"]`
or `it = squad[squad['id'] == '56ddde6b9a695914005b962b']`
3. returns the first item in the dataset, which does not satisfy the above conditions:
`{'id': '56ddde6b9a695914005b9628', 'title': 'Normans', 'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse ("Norman" comes from "Norseman") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.', 'question': 'In what country is Normandy located?', 'answers': {'text': ['France', 'France', 'France', 'France'], 'answer_start': [159, 159, 159, 159]}}`
### Expected behavior
Should either throw an error message, or return the dataset item that satisfies the condition.
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-13.3.1-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5194 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5194/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5194/comments | https://api.github.com/repos/huggingface/datasets/issues/5194/events | https://github.com/huggingface/datasets/pull/5194 | 1,434,206,951 | PR_kwDODunzps5CHPNY | 5,194 | Fix docs about dataset_info in YAML | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-03T07:10:23Z | 2022-11-03T13:31:27Z | 2022-11-03T13:29:21Z | MEMBER | null | null | null | This PR fixes some misalignment in the docs after we transferred the dataset_info from `dataset_infos.json` to YAML in the dataset card:
- #4926
Related to:
- #5193 | {
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https://api.github.com/repos/huggingface/datasets/issues/5472 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5472/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5472/comments | https://api.github.com/repos/huggingface/datasets/issues/5472/events | https://github.com/huggingface/datasets/pull/5472 | 1,558,662,251 | PR_kwDODunzps5Inlp8 | 5,472 | Release: 2.9.0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-26T19:29:42Z | 2023-01-26T19:40:44Z | 2023-01-26T19:33:00Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7358 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7358/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7358/comments | https://api.github.com/repos/huggingface/datasets/issues/7358/events | https://github.com/huggingface/datasets/pull/7358 | 2,770,927,769 | PR_kwDODunzps6G1kka | 7,358 | Fix remove_columns in the formatted case | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7358). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-01-06T15:44:23Z | 2025-01-06T15:46:46Z | null | MEMBER | null | null | null | `remove_columns` had no effect when running a function in `.map()` on dataset that is formatted
This aligns the logic of `map()` with the non formatted case and also with with https://github.com/huggingface/datasets/pull/7353 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6661 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6661/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6661/comments | https://api.github.com/repos/huggingface/datasets/issues/6661/events | https://github.com/huggingface/datasets/issues/6661 | 2,132,296,267 | I_kwDODunzps5_GEJL | 6,661 | Import error on Google Colab | {
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"Hi! This can happen if an incompatible `pyarrow` version (`pyarrow<12.0.0`) has been imported before the `datasets` installation and the Colab session hasn't been restarted afterward. To avoid the error, go to \"Runtime -> Restart session\" after `!pip install -U datasets` and before `import datasets`, or insert t... | 2024-02-13T13:12:40Z | 2024-02-25T16:37:54Z | 2024-02-14T08:04:47Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Cannot be imported on Google Colab, the import throws the following error:
ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject
### Steps to reproduce the bug
1. `! pip install -U datasets`
2. `import datasets`
### Expected behavior
Should be possible to use the library
### Environment info
- `datasets` version: 2.17.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4537 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4537/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4537/comments | https://api.github.com/repos/huggingface/datasets/issues/4537/events | https://github.com/huggingface/datasets/pull/4537 | 1,279,144,310 | PR_kwDODunzps46ESJn | 4,537 | Fix WMT dataset loading issue and docs update | {
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"The PR branch now has some commits unrelated to the changes, probably due to rebasing. Can you please close this PR and open a new one from a new branch? You can use `git cherry-pick` to preserve the relevant changes:\r\n```bash\r\ngit checkout master\r\ngit remote add upstream git@github.com:huggingface/datasets... | 2022-06-21T21:48:02Z | 2022-06-24T07:05:43Z | 2022-06-24T07:05:10Z | CONTRIBUTOR | null | null | null | This PR is a fix for #4354
Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets.
As I am on a M1 Mac, I am not able to create a virtual `dev` environment using `pip install -e ".[dev]"`. Issue is with `tensorflow-text` not supported on M1s and there is no supporting repo by Apple or Google. So, if I was needed to perform local testing, I am not able to do that.
Let me know, if any additional changes are required.
Thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/7096 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7096/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7096/comments | https://api.github.com/repos/huggingface/datasets/issues/7096/events | https://github.com/huggingface/datasets/pull/7096 | 2,456,929,173 | PR_kwDODunzps535Xkr | 7,096 | Automatically create `cache_dir` from `cache_file_name` | {
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"Hi @albertvillanova, is this PR looking okay to you? Anything else you'd like to see?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7096). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",... | 2024-08-09T01:34:06Z | 2024-08-15T17:25:26Z | 2024-08-15T10:13:22Z | CONTRIBUTOR | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/5531 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5531/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5531/comments | https://api.github.com/repos/huggingface/datasets/issues/5531/events | https://github.com/huggingface/datasets/issues/5531 | 1,584,387,276 | I_kwDODunzps5eb9TM | 5,531 | Invalid Arrow data from JSONL | {
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"completed": 0,
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"total": 0
} | This code fails:
```python
from datasets import Dataset
ds = Dataset.from_json(path_to_file)
ds.data.validate()
```
raises
```python
ArrowInvalid: Column 2: In chunk 1: Invalid: Struct child array #3 invalid: Invalid: Length spanned by list offsets (4064) larger than values array (length 4063)
```
This causes many issues for @TevenLeScao:
- `map` fails because it fails to rewrite invalid arrow arrays
```python
~/Desktop/hf/datasets/src/datasets/arrow_writer.py in write_examples_on_file(self)
438 if all(isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) for row in self.current_examples):
439 arrays = [row[0][col] for row in self.current_examples]
--> 440 batch_examples[col] = array_concat(arrays)
441 else:
442 batch_examples[col] = [
~/Desktop/hf/datasets/src/datasets/table.py in array_concat(arrays)
1885
1886 if not _is_extension_type(array_type):
-> 1887 return pa.concat_arrays(arrays)
1888
1889 def _offsets_concat(offsets):
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.concat_arrays()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIndexError: array slice would exceed array length
```
- `to_dict()` **segfaults** ⚠️
```python
/Users/runner/work/crossbow/crossbow/arrow/cpp/src/arrow/array/data.cc:99: Check failed: (off) <= (length) Slice offset greater
than array length
```
To reproduce: unzip the archive and run the above code using `sanity_oscar_en.jsonl`
[sanity_oscar_en.jsonl.zip](https://github.com/huggingface/datasets/files/10734124/sanity_oscar_en.jsonl.zip)
PS: reading using pandas and converting to Arrow works though (note that the dataset lives in RAM in this case):
```python
ds = Dataset.from_pandas(pd.read_json(path_to_file, lines=True))
ds.data.validate()
```
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https://api.github.com/repos/huggingface/datasets/issues/7249 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7249/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7249/comments | https://api.github.com/repos/huggingface/datasets/issues/7249/events | https://github.com/huggingface/datasets/issues/7249 | 2,610,136,636 | I_kwDODunzps6bk4Y8 | 7,249 | How to debugging | {
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} | [] | open | false | null | [] | null | [] | 2024-10-24T01:03:51Z | 2024-10-24T01:03:51Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I wanted to use my own script to handle the processing, and followed the tutorial documentation by rewriting the MyDatasetConfig and MyDatasetBuilder (which contains the _info,_split_generators and _generate_examples methods) classes. Testing with simple data was able to output the results of the processing, but when I wished to do more complex processing, I found that I was unable to debug (even the simple samples were inaccessible). There are no errors reported, and I am able to print the _info,_split_generators and _generate_examples messages, but I am unable to access the breakpoints.
### Steps to reproduce the bug
# my_dataset.py
import json
import datasets
class MyDatasetConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(MyDatasetConfig, self).__init__(**kwargs)
class MyDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
MyDatasetConfig(
name="default",
version=VERSION,
description="myDATASET"
),
]
def _info(self):
print("info") # breakpoints
return datasets.DatasetInfo(
description="myDATASET",
features=datasets.Features(
{
"id": datasets.Value("int32"),
"text": datasets.Value("string"),
"label": datasets.ClassLabel(names=["negative", "positive"]),
}
),
supervised_keys=("text", "label"),
)
def _split_generators(self, dl_manager):
print("generate") # breakpoints
data_file = "data.json"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file}
),
]
def _generate_examples(self, filepath):
print("example") # breakpoints
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for idx, sample in enumerate(data):
yield idx, {
"id": sample["id"],
"text": sample["text"],
"label": sample["label"],
}
#main.py
import os
os.environ["TRANSFORMERS_NO_MULTIPROCESSING"] = "1"
from datasets import load_dataset
dataset = load_dataset("my_dataset.py", split="train", cache_dir=None)
print(dataset[:5])
### Expected behavior
Pause at breakpoints while running debugging
### Environment info
pycharm
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https://api.github.com/repos/huggingface/datasets/issues/6742 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6742/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6742/comments | https://api.github.com/repos/huggingface/datasets/issues/6742/events | https://github.com/huggingface/datasets/pull/6742 | 2,195,134,854 | PR_kwDODunzps5qGSfG | 6,742 | Fix missing download_config in get_data_patterns | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6742). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-03-19T14:29:25Z | 2024-03-19T18:24:39Z | 2024-03-19T18:15:13Z | MEMBER | null | null | null | Reported in https://github.com/huggingface/datasets-server/issues/2607 | {
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https://api.github.com/repos/huggingface/datasets/issues/6993 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6993/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6993/comments | https://api.github.com/repos/huggingface/datasets/issues/6993/events | https://github.com/huggingface/datasets/pull/6993 | 2,370,444,104 | PR_kwDODunzps5zYN7P | 6,993 | less script docs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6993). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-06-24T14:45:28Z | 2024-07-08T13:10:53Z | 2024-06-27T09:31:21Z | MEMBER | null | null | null | + mark as legacy in some parts of the docs since we'll not build new features for script datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/4633 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4633/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4633/comments | https://api.github.com/repos/huggingface/datasets/issues/4633/events | https://github.com/huggingface/datasets/pull/4633 | 1,294,367,783 | PR_kwDODunzps462_qX | 4,633 | [data_files] Only match separated split names | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I ran a script to find affected datasets (just did it on non-private non-gated). Adding \"testing\" and \"evaluation\" fixes all of of them except one:\r\n- projecte-aina/cat_manynames:\thuman_annotated_testset.tsv\r\n\r\nLet me open... | 2022-07-05T14:18:11Z | 2022-07-18T13:20:29Z | 2022-07-18T13:07:33Z | MEMBER | null | null | null | As reported in https://github.com/huggingface/datasets/issues/4477, the current pattern matching to infer which file goes into which split is too permissive. For example a file "contest.py" would be considered part of a test split (it contains "test") and "seqeval.py" as well (it contains "eval").
In this PR I made the pattern matching more robust by only matching split names **between separators**. The supported separators are dots, dashes, spaces and underscores.
I updated the docs accordingly.
One detail about the tests: I had to update one test because it was using `PurePath.match` as a reference for globbing, but it doesn't support the `[..]` glob pattern. Therefore I added a `mock_fs` context manager that can be used to easily define a dummy filesystem with certain files in it and run pattern matching tests. Its code comes mostly from test_streaming_download_manager.py
Close https://github.com/huggingface/datasets/issues/4477 | {
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https://api.github.com/repos/huggingface/datasets/issues/7530 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7530/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7530/comments | https://api.github.com/repos/huggingface/datasets/issues/7530/events | https://github.com/huggingface/datasets/issues/7530 | 3,007,452,499 | I_kwDODunzps6zQhVT | 7,530 | How to solve "Spaces stuck in Building" problems | {
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"I'm facing the same issue—Space stuck in \"Building\" even after restart and Factory rebuild. Any fix?\n",
"> I'm facing the same issue—Space stuck in \"Building\" even after restart and Factory rebuild. Any fix?\n\nAlso see https://github.com/huggingface/huggingface_hub/issues/3019",
"I'm facing the same issu... | 2025-04-21T03:08:38Z | 2025-04-22T07:49:52Z | 2025-04-22T07:49:52Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Public spaces may stuck in Building after restarting, error log as follows:
build error
Unexpected job error
ERROR: failed to push spaces-registry.huggingface.tech/spaces/*:cpu-*-*: unexpected status from HEAD request to https://spaces-registry.huggingface.tech/v2/spaces/*/manifests/cpu-*-*: 401 Unauthorized
### Steps to reproduce the bug
Restart space / Factory rebuild cannot avoid it
### Expected behavior
Fix this problem
### Environment info
no requirements.txt can still happen
python gradio spaces | {
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https://api.github.com/repos/huggingface/datasets/issues/7275 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7275/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7275/comments | https://api.github.com/repos/huggingface/datasets/issues/7275/events | https://github.com/huggingface/datasets/issues/7275 | 2,631,713,397 | I_kwDODunzps6c3MJ1 | 7,275 | load_dataset | {
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} | [] | open | false | null | [] | null | [] | 2024-11-04T03:01:44Z | 2024-11-04T03:01:44Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I am performing two operations I see on a hugging face tutorial (Fine-tune a language model), and I am defining every aspect inside the mapped functions, also some imports of the library because it doesnt identify anything not defined outside that function where the dataset elements are being mapped:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb#scrollTo=iaAJy5Hu3l_B
`- lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
batch_size=batch_size,
num_proc=4,
)
- tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])
def tokenize_function(examples):
model_checkpoint = 'gpt2'
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
return tokenizer(examples["text"])`
### Steps to reproduce the bug
Currently handle all the imports inside the function
### Expected behavior
The code must work es expected in the notebook, but currently this is not happening.
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb#scrollTo=iaAJy5Hu3l_B
### Environment info
print(transformers.__version__)
4.46.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4832 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4832/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4832/comments | https://api.github.com/repos/huggingface/datasets/issues/4832/events | https://github.com/huggingface/datasets/pull/4832 | 1,336,727,389 | PR_kwDODunzps49EQav | 4,832 | Fix tags in dataset cards | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The non-passing tests are caused by other missing information in the dataset cards."
] | 2022-08-12T04:11:23Z | 2022-08-12T04:41:55Z | 2022-08-12T04:27:24Z | MEMBER | null | null | null | Fix wrong tags in dataset cards. | {
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https://api.github.com/repos/huggingface/datasets/issues/5535 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5535/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5535/comments | https://api.github.com/repos/huggingface/datasets/issues/5535/events | https://github.com/huggingface/datasets/pull/5535 | 1,586,520,369 | PR_kwDODunzps5KEb5L | 5,535 | Add JAX-formatting documentation | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Awesome thank you !\r\n> \r\n> Could you also explain how to use certain types like ClassLabel, Image or Audio with jax ? You can get a lot of inspiration from the \"Other feature types\" section in the [PyTorch page](https://huggi... | 2023-02-15T20:35:11Z | 2023-02-20T10:39:42Z | 2023-02-20T10:32:39Z | MEMBER | null | null | null | ## What's in this PR?
As a follow-up of #5522, I've created this entry in the documentation to explain how to use `.with_format("jax")` and why is it useful.
@lhoestq Feel free to drop any feedback and/or suggestion, as probably more useful features can be included there! | {
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https://api.github.com/repos/huggingface/datasets/issues/5451 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5451/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5451/comments | https://api.github.com/repos/huggingface/datasets/issues/5451/events | https://github.com/huggingface/datasets/issues/5451 | 1,552,336,300 | I_kwDODunzps5chsWs | 5,451 | ImageFolder BadZipFile: Bad offset for central directory | {
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"Hi ! Could you share the full stack trace ? Which dataset did you try to load ?\r\n\r\nit may be related to https://github.com/huggingface/datasets/pull/5640",
"The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`.",
"For others that find ... | 2023-01-22T23:50:12Z | 2023-05-23T10:35:48Z | 2023-02-10T16:31:36Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents │
│ │
│ 1350 │ │ # self.start_dir: Position of start of central directory │
│ 1351 │ │ self.start_dir = offset_cd + concat │
│ 1352 │ │ if self.start_dir < 0: │
│ ❱ 1353 │ │ │ raise BadZipFile("Bad offset for central directory") │
│ 1354 │ │ fp.seek(self.start_dir, 0) │
│ 1355 │ │ data = fp.read(size_cd) │
│ 1356 │ │ fp = io.BytesIO(data) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
BadZipFile: Bad offset for central directory
Extracting data files: 35%|█████████████████▊ | 38572/110812 [00:10<00:20, 3576.26it/s]
```
### Steps to reproduce the bug
```
load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
),
```
### Expected behavior
loads the dataset
### Environment info
datasets==2.8.0
Python 3.10.8
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https://api.github.com/repos/huggingface/datasets/issues/6155 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6155/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6155/comments | https://api.github.com/repos/huggingface/datasets/issues/6155/events | https://github.com/huggingface/datasets/pull/6155 | 1,854,661,682 | PR_kwDODunzps5YI8Pc | 6,155 | Raise FileNotFoundError when passing data_files that don't exist | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-08-17T09:49:48Z | 2023-08-18T13:45:58Z | 2023-08-18T13:35:13Z | MEMBER | null | null | null | e.g. when running `load_dataset("parquet", data_files="doesnt_exist.parquet")` | {
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https://api.github.com/repos/huggingface/datasets/issues/6745 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6745/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6745/comments | https://api.github.com/repos/huggingface/datasets/issues/6745/events | https://github.com/huggingface/datasets/issues/6745 | 2,198,541,732 | I_kwDODunzps6DCxWk | 6,745 | Scraping the whole of github including private repos is bad; kindly stop | {
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"It's not twitter here"
] | 2024-03-20T20:54:06Z | 2024-03-21T12:28:04Z | 2024-03-21T10:24:56Z | NONE | null | null | {
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} | ### Feature request
https://github.com/bigcode-project/opt-out-v2 - opt out is not consent. kindly quit this ridiculous nonsense.
### Motivation
[EDITED: insults not tolerated]
### Your contribution
[EDITED: insults not tolerated] | {
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https://api.github.com/repos/huggingface/datasets/issues/6992 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6992/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6992/comments | https://api.github.com/repos/huggingface/datasets/issues/6992/events | https://github.com/huggingface/datasets/issues/6992 | 2,367,890,622 | I_kwDODunzps6NIyS- | 6,992 | Dataset with streaming doesn't work with proxy | {
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"Hi ! can you try updating `datasets` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U datasets huggingface_hub\r\n```"
] | 2024-06-22T16:12:08Z | 2024-06-25T15:43:05Z | null | NONE | null | null | {
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} | ### Describe the bug
I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine.
### Steps to reproduce the bug
use load_dataset with streaming = True in AIMOS
### Expected behavior
does not hang indefinitely and loads batches to start training run
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zipp 3.11.0 py311h6ffa863_0
zlib 1.2.13 h1f2b957_6 conda-forge
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zstd 1.5.5 h57e4825_0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5456 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5456/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5456/comments | https://api.github.com/repos/huggingface/datasets/issues/5456/events | https://github.com/huggingface/datasets/pull/5456 | 1,553,905,148 | PR_kwDODunzps5IXq92 | 5,456 | feat: tqdm for `to_parquet` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-23T22:05:38Z | 2023-01-24T11:26:47Z | 2023-01-24T11:17:12Z | CONTRIBUTOR | null | null | null | As described in #5418
I noticed also that the `to_json` function supports multi-workers whereas `to_parquet`, is that not possible/not needed with Parquet or something that hasn't been implemented yet? | {
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https://api.github.com/repos/huggingface/datasets/issues/5165 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5165/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5165/comments | https://api.github.com/repos/huggingface/datasets/issues/5165/events | https://github.com/huggingface/datasets/issues/5165 | 1,423,616,677 | I_kwDODunzps5U2qql | 5,165 | Memory explosion when trying to access 4d tensors in datasets cast to torch or np | {
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"completed": 0,
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} | ### Describe the bug
When trying to access an item by index, in a datasets.Dataset cast to torch/np using `set_format` or `with_format`, we get a memory explosion if the item contains 4d (or above) tensors.
### Steps to reproduce the bug
MWE:
```python
from datasets import load_dataset
import numpy as np
def create_4d_tensor(item):
i = item["num_nodes"]
item["x_big"] = np.random.rand(i, 2*i, int(i/2), 1) + 1 # we create a big 4d tensor
return item
if __name__ == "__main__":
dataset = load_dataset(path=f"graphs-datasets/PROTEINS")
# This works
print(dataset["train"].format)
print(dataset["train"][0].keys())
dataset = dataset.map(
create_4d_tensor,
batched=False,
writer_batch_size=100,
)
# This works
print(dataset["train"].format)
print(dataset["train"][0].keys())
dataset.set_format("torch")
print(dataset["train"].format)
# This gets killed :(
print(dataset["train"][0].keys())
```
The problem likely comes from `format_table` [here](https://cs.github.com/huggingface/datasets/blob/f09f781be3278156ce3aa6ec90c1926b1846a78f/src/datasets/arrow_dataset.py#L2328)
### Expected behavior
No memory explosion when trying to access dataset items after cast.
### Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6523 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6523/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6523/comments | https://api.github.com/repos/huggingface/datasets/issues/6523/events | https://github.com/huggingface/datasets/pull/6523 | 2,052,643,484 | PR_kwDODunzps5ilV6d | 6,523 | fix tests | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6523). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2023-12-21T15:36:21Z | 2023-12-21T15:56:54Z | 2023-12-21T15:50:38Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5543 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5543/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5543/comments | https://api.github.com/repos/huggingface/datasets/issues/5543/events | https://github.com/huggingface/datasets/issues/5543 | 1,588,951,379 | I_kwDODunzps5etXlT | 5,543 | the pile datasets url seems to change back | {
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"Thanks for reporting, @wjfwzzc.\r\n\r\nI am transferring this issue to the corresponding dataset on the Hub: https://huggingface.co/datasets/bookcorpusopen/discussions/1",
"Thank you. All fixes are done:\r\n- [x] https://huggingface.co/datasets/bookcorpusopen/discussions/2\r\n- [x] https://huggingface.co/dataset... | 2023-02-17T08:40:11Z | 2023-02-21T06:37:00Z | 2023-02-20T08:41:33Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
in #3627, the host url of the pile dataset became `https://mystic.the-eye.eu`. Now the new url is broken, but `https://the-eye.eu` seems to work again.
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("bookcorpusopen")
```
shows
```python3
ConnectionError: Couldn't reach https://mystic.the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz (ProxyError(MaxRetryError("HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_pr
eliminary_components/books1.tar.gz (Caused by ProxyError('Cannot connect to proxy.', OSError('Tunnel connection failed: 504 Gateway Timeout')))")))
```
### Expected behavior
Downloading as normal.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/6333 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6333/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6333/comments | https://api.github.com/repos/huggingface/datasets/issues/6333/events | https://github.com/huggingface/datasets/issues/6333 | 1,956,714,423 | I_kwDODunzps50oRe3 | 6,333 | Support fsspec 2023.10.0 | {
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"Hi @albertvillanova @lhoestq \r\n\r\nI believe the pull request that pins the fsspec version (https://github.com/huggingface/datasets/pull/6331) was merged by mistake. Another fix for the issue was merged on the same day an hour apart. See https://github.com/huggingface/datasets/pull/6334\r\n\r\nI'm now having an ... | 2023-10-23T09:14:53Z | 2024-02-07T12:39:58Z | 2024-02-07T12:39:58Z | MEMBER | null | null | {
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} | Once root issue is fixed, remove temporary pin of fsspec < 2023.10.0 introduced by:
- #6331
Related to issue:
- #6330
As @ZachNagengast suggested, the issue might be related to:
- https://github.com/fsspec/filesystem_spec/pull/1381 | {
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https://api.github.com/repos/huggingface/datasets/issues/4916 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4916/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4916/comments | https://api.github.com/repos/huggingface/datasets/issues/4916/events | https://github.com/huggingface/datasets/issues/4916 | 1,357,076,940 | I_kwDODunzps5Q41nM | 4,916 | Apache Beam unable to write the downloaded wikipedia dataset | {
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"See:\r\n- #4915"
] | 2022-08-31T09:39:25Z | 2022-08-31T10:53:19Z | 2022-08-31T10:53:19Z | NONE | null | null | {
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} | ## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download. It downloads the file but while saving it in hugging face cache it fails to write. This happens for any available date of any language in wikipedia dump. I had raised another issue earlier #4915 but probably was not that clear and the solution provider misunderstood my problem. Hence raising one more issue. Any help is appreciated.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
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File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
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File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
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File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 324, in apache_beam.runners.worker.operations.GeneralPurposeConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 905, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
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File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/6376 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6376/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6376/comments | https://api.github.com/repos/huggingface/datasets/issues/6376/events | https://github.com/huggingface/datasets/issues/6376 | 1,973,927,468 | I_kwDODunzps51p74s | 6,376 | Caching problem when deleting a dataset | {
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"Thanks for reporting! Can you also share the error message printed in step 5?",
"I did not store it at the time but I'll try to re-do a mwe next week to get it again",
"I haven't managed to reproduce this issue using a [notebook](https://colab.research.google.com/drive/1m6eduYun7pFTkigrCJAFgw0BghlbvXIL?usp=sha... | 2023-11-02T10:15:58Z | 2023-12-04T16:53:34Z | 2023-12-04T16:53:33Z | MEMBER | null | null | {
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} | ### Describe the bug
Pushing a dataset with n + m features to a repo which was deleted, but contained n features, will fail.
### Steps to reproduce the bug
1. Create a dataset with n features per row
2. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)`
3. Go on the hub, delete the repo at `YOUR_PATH`
4. Update your local dataset to have n + m features per row
5. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` will fail because of a mismatch in features number
### Expected behavior
Step 5 should work or display a message to indicate the cache has not been cleared
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.31
- Python version: 3.10.10
- Huggingface_hub version: 0.16.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
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https://api.github.com/repos/huggingface/datasets/issues/4915 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4915/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4915/comments | https://api.github.com/repos/huggingface/datasets/issues/4915/events | https://github.com/huggingface/datasets/issues/4915 | 1,356,009,042 | I_kwDODunzps5Q0w5S | 4,915 | FileNotFoundError while downloading wikipedia dataset for any language | {
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"Hi @Shilpac20,\r\n\r\nAs explained in the Wikipedia dataset card: https://huggingface.co/datasets/wikipedia\r\n> You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).\r\n\r\nThis means that, before passing a specific date, you should first make sure it is availabl... | 2022-08-30T16:15:46Z | 2022-12-04T22:20:33Z | null | NONE | null | null | {
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} | ## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download.
Environment:
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in <module>
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
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self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4
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https://api.github.com/repos/huggingface/datasets/issues/5317 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5317/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5317/comments | https://api.github.com/repos/huggingface/datasets/issues/5317/events | https://github.com/huggingface/datasets/issues/5317 | 1,470,390,164 | I_kwDODunzps5XpF-U | 5,317 | `ImageFolder` performs poorly with large datasets | {
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"Hi ! ImageFolder is made for small scale datasets indeed. For large scale image datasets you better group your images in TAR archives or Arrow/Parquet files. This is true not just for ImageFolder loading performance, but also because having millions of files is not ideal for your filesystem or when moving the data... | 2022-12-01T00:04:21Z | 2022-12-01T21:49:26Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.
## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metadata.jsonl` file
## Performance Degradation Point 1
Degradation occurs because [`get_data_files_patterns`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L231-L243) runs the exact same scan for many different types of patterns, and there doesn't seem to be a way to easily limit this. It's controlled by the definition of [`ALL_DEFAULT_PATTERNS`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L82-L85).
One scan with 3M+ files takes about 10-15 minutes to complete on my setup, so having those extra scans really slows things down – from 10 minutes to 60+. Most of the scans return no matches, but they still take a significant amount of time to complete – hence the poor performance.
As a side effect, when this scan is run on 3M+ image files, Python also consumes up to 12 GB of RAM, which is not ideal.
## Performance Degradation Point 2
The second performance bottleneck is in [`PackagedDatasetModuleFactory.get_module`](https://github.com/huggingface/datasets/blob/d7dfbc83d68e87ba002c5eb2555f7a932e59038a/src/datasets/load.py#L707-L711), which calls `DataFilesDict.from_local_or_remote`.
It runs for a long time (60min+), consuming significant amounts of RAM – even more than the point 1 above. Based on `iostat -d 2`, it performs **zero** disk operations, which to me suggests that there is a code based bottleneck there that could be sorted out.
### Steps to reproduce the bug
```python
from datasets import load_dataset
import os
import huggingface_hub
dataset = load_dataset(
'imagefolder',
data_dir='/some/path',
# just to spell it out:
split=None,
drop_labels=True,
keep_in_memory=False
)
dataset.push_to_hub('account/dataset', private=True)
```
### Expected behavior
While it's certainly possible to write a custom loader to replace `ImageFolder` with, it'd be great if the off-the-shelf `ImageFolder` would by default have a setup that can scale to large datasets.
Or perhaps there could be a dedicated loader just for large datasets that trades off flexibility for performance? As in, maybe you have to define explicitly how you want it to work rather than it trying to guess your data structure like `_get_data_files_patterns()` does?
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-4.14.296-222.539.amzn2.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.7.10
- PyArrow version: 10.0.1
- Pandas version: 1.3.5
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https://api.github.com/repos/huggingface/datasets/issues/6008 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6008/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6008/comments | https://api.github.com/repos/huggingface/datasets/issues/6008/events | https://github.com/huggingface/datasets/issues/6008 | 1,789,869,344 | I_kwDODunzps5qrz0g | 6,008 | Dataset.from_generator consistently freezes at ~1000 rows | {
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"By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Arra... | 2023-07-05T16:06:48Z | 2023-07-10T13:46:39Z | 2023-07-10T13:46:39Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I
Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset.
I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset.
Let me know if you have ideas how to resolve it!
### Steps to reproduce the bug
```python
from datasets import Dataset
import numpy as np
def gen():
for row in range(10000):
yield {"i": np.random.rand(512, 512, 3)}
Dataset.from_generator(gen)
# -> 90% of the time gets stuck around 1000 rows
```
### Expected behavior
Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 12.0.1
- Pandas version: 1.5.1
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https://api.github.com/repos/huggingface/datasets/issues/6188 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6188/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6188/comments | https://api.github.com/repos/huggingface/datasets/issues/6188/events | https://github.com/huggingface/datasets/issues/6188 | 1,870,987,640 | I_kwDODunzps5vhQF4 | 6,188 | [Feature Request] Check the length of batch before writing so that empty batch is allowed | {
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"I think this error means you filter all examples within an (input) batch by deleting its columns. In that case, to avoid the error, you can set the column value to an empty list (`input_batch[\"col\"] = []`) instead."
] | 2023-08-29T06:37:34Z | 2023-09-19T21:55:38Z | 2023-09-19T21:55:37Z | NONE | null | null | {
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} | ### Use Case
I use `dataset.map(process_fn, batched=True)` to process the dataset, with data **augmentations or filtering**. However, when all examples within a batch is filtered out, i.e. **an empty batch is returned**, the following error will be thrown:
```
ValueError: Schema and number of arrays unequal
```
This is because the empty batch does not comply with the schema of other batches. I think an empty batch should be allowed to facilitate coding (one does not need to assign an empty list manually for all keys.)
A simple fix is to check the length of `batch` before writing:
```
if len(batch):
writer.write_batch(batch)
```
instead of
https://github.com/huggingface/datasets/blob/74d60213dcbd7c99484c62ce1d3dfd90a1df0770/src/datasets/arrow_dataset.py#L3493
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https://api.github.com/repos/huggingface/datasets/issues/4564 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4564/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4564/comments | https://api.github.com/repos/huggingface/datasets/issues/4564/events | https://github.com/huggingface/datasets/pull/4564 | 1,283,932,333 | PR_kwDODunzps46UqUN | 4,564 | Support streaming bookcorpus dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-24T16:13:39Z | 2022-07-06T09:34:48Z | 2022-07-06T09:23:04Z | MEMBER | null | null | null | Support streaming bookcorpus dataset. | {
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"You should be able to fix this by updating `huggingface_hub` with `pip install -U huggingface_hub`. We use this package under the hood to resolve the Hub's files."
] | 2024-04-01T20:17:48Z | 2024-04-11T17:31:44Z | 2024-04-11T17:31:44Z | NONE | null | null | {
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} | ### Describe the bug
`Datasets==2.18.0` is not compatible with `fsspec==2023.12.2`.
I have to downgrade fsspec to `fsspec==2023.10.0` to make `Datasets==2.18.0` work properly.
### Steps to reproduce the bug
To reproduce the bug:
1. Make sure that `Datasets==2.18.0` and `fsspec==2023.12.2`.
2. Run the following code:
```
from datasets import load_dataset
dataset = load_dataset("trec")
```
3. Then one will get the following error message:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2265, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 371, in __init__
self.config, self.config_id = self._create_builder_config(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 620, in _create_builder_config
builder_config._resolve_data_files(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 211, in _resolve_data_files
self.data_files = self.data_files.resolve(base_path, download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 799, in resolve
out[key] = data_files_patterns_list.resolve(base_path, download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 752, in resolve
resolve_pattern(
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 393, in resolve_pattern
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find 'hf://datasets/trec@65752bf53af25bc935a0dce92fb5b6c930728450/default/train/0000.parquet' 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']
```
4. Similar issue also found for the following code:
```
dataset = load_dataset("sst", "default")
```
### Expected behavior
If the dataset is loaded correctly, one will have:
```
>>> print(dataset)
DatasetDict({
train: Dataset({
features: ['text', 'coarse_label', 'fine_label'],
num_rows: 5452
})
test: Dataset({
features: ['text', 'coarse_label', 'fine_label'],
num_rows: 500
})
})
>>>
```
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.1
- Pandas version: 2.2.1
- `fsspec` version: 2023.12.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6462 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6462/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6462/comments | https://api.github.com/repos/huggingface/datasets/issues/6462/events | https://github.com/huggingface/datasets/pull/6462 | 2,019,238,388 | PR_kwDODunzps5gz68T | 6,462 | Missing DatasetNotFoundError | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-11-30T18:09:43Z | 2023-11-30T18:36:40Z | 2023-11-30T18:30:30Z | MEMBER | null | null | null | continuation of https://github.com/huggingface/datasets/pull/6431
this should fix the CI in https://github.com/huggingface/datasets/pull/6458 too | {
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https://api.github.com/repos/huggingface/datasets/issues/6159 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6159/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6159/comments | https://api.github.com/repos/huggingface/datasets/issues/6159/events | https://github.com/huggingface/datasets/issues/6159 | 1,855,691,512 | I_kwDODunzps5um5r4 | 6,159 | Add `BoundingBox` feature | {
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"My proposal would look like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom datasets.features import Sequence, BboxFeature\r\n\r\n# load images along with boxes\r\ndataset = load_dataset(\"imagefolder\", data_dir=\"/path/to/folder\", split=\"train\")\r\n\r\n# map the boxes column to the appropr... | 2023-08-17T20:49:51Z | 2024-11-18T17:58:43Z | null | COLLABORATOR | null | null | {
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} | ... to make working with object detection datasets easier. Currently, `Sequence(int_or_float, length=4)` can be used to represent this feature optimally (in the storage backend), so I only see this feature being useful if we make it work with the viewer. Also, bounding boxes usually come in 4 different formats (explained [here](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/)), so we need to decide which one to support (or maybe all of them).
cc @NielsRogge @severo | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7271 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7271/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7271/comments | https://api.github.com/repos/huggingface/datasets/issues/7271/events | https://github.com/huggingface/datasets/pull/7271 | 2,627,135,540 | PR_kwDODunzps6AiZaj | 7,271 | Set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7271). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-31T15:22:51Z | 2024-10-31T15:25:27Z | 2024-10-31T15:22:59Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5806 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5806/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5806/comments | https://api.github.com/repos/huggingface/datasets/issues/5806/events | https://github.com/huggingface/datasets/issues/5806 | 1,688,598,095 | I_kwDODunzps5kpfZP | 5,806 | Return the name of the currently loaded file in the load_dataset function. | {
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"Implementing this makes sense (e.g., `tensorflow_datasets`' imagefolder returns image filenames). Also, in Datasets 3.0, we plan only to store the bytes of an image/audio, not its path, so this feature would be useful when the path info is still needed.",
"Hey @mariosasko, Can I work on this issue, this one seem... | 2023-04-28T13:50:15Z | 2025-03-21T12:07:15Z | null | NONE | null | null | {
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} | ### Feature request
Add an optional parameter return_file_name in the load_dataset function. When it is set to True, the function will include the name of the file corresponding to the current line as a feature in the returned output.
### Motivation
When training large language models, machine problems may interrupt the training process. In such cases, it is common to load a previously saved checkpoint to resume training. I would like to be able to obtain the names of the previously trained data shards, so that I can skip these parts of the data during continued training to avoid overfitting and redundant training time.
### Your contribution
I currently use a dataset in jsonl format, so I am primarily interested in the json format. I suggest adding the file name to the returned table here https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/json/json.py#L92. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4890 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4890/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4890/comments | https://api.github.com/repos/huggingface/datasets/issues/4890/events | https://github.com/huggingface/datasets/pull/4890 | 1,350,578,029 | PR_kwDODunzps49x1YC | 4,890 | add Dataset.from_list | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@albertvillanova it seems tests fail on pyarrow 6, perhaps from_pylist is a v7 method? How do you usually handle these version differences?\r\nAdded something that at least works"
] | 2022-08-25T09:05:58Z | 2022-09-02T10:22:59Z | 2022-09-02T10:20:33Z | CONTRIBUTOR | null | null | null | As discussed in #4885
I initially added this bit at the end, thinking filling this field was necessary as it is done in from_dict.
However, it seems the constructor takes care of filling info when it is empty.
```
if info.features is None:
info.features = Features(
{
col: generate_from_arrow_type(coldata.type)
for col, coldata in zip(pa_table.column_names, pa_table.columns)
}
)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7446 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7446/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7446/comments | https://api.github.com/repos/huggingface/datasets/issues/7446/events | https://github.com/huggingface/datasets/issues/7446 | 2,913,050,552 | I_kwDODunzps6toZ-4 | 7,446 | pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int' | {
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"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
A dict with its keys are all str but get following error
```python
test_data=[{'input_ids':[1,2,3],'labels':[[Counter({2:1})]]}]
dataset = datasets.Dataset.from_list(test_data)
```
```bash
pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
datasets 3.3.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4951 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4951/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4951/comments | https://api.github.com/repos/huggingface/datasets/issues/4951/events | https://github.com/huggingface/datasets/pull/4951 | 1,365,954,814 | PR_kwDODunzps4-lDqd | 4,951 | Fix license information in qasc dataset card | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-08T10:04:39Z | 2022-09-08T14:54:47Z | 2022-09-08T14:52:05Z | MEMBER | null | null | null | This PR adds the license information to `qasc` dataset, once reported via GitHub by Tushar Khot, the dataset is licensed under CC BY 4.0:
- https://github.com/allenai/qasc/issues/5
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https://api.github.com/repos/huggingface/datasets/issues/4659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4659/comments | https://api.github.com/repos/huggingface/datasets/issues/4659/events | https://github.com/huggingface/datasets/pull/4659 | 1,297,094,140 | PR_kwDODunzps47AQo9 | 4,659 | Transfer CI to GitHub Actions | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks a lot @albertvillanova ! I hope we're finally done with flakiness on windows ^^\r\n\r\nAlso thanks for paying extra attention to billing and avoiding running unnecessary jobs. Though for certain aspects (see my comments), I th... | 2022-07-07T09:29:47Z | 2022-07-12T11:30:20Z | 2022-07-12T11:18:25Z | MEMBER | null | null | null | This PR transfers CI from CircleCI to GitHub Actions. The implementation in GitHub Actions tries to be as faithful as possible to the implementation in CircleCI and get the same output results (exceptions below).
**IMPORTANT NOTE**: The fast-fail policy (described below) is not finally implemented, so that:
- we can continue merging PRs with CI in red because of some random error returned by the Hub
- it is not annoying for maintainers to have to relaunch failed CI jobs
See comments here: https://github.com/huggingface/datasets/pull/4659#discussion_r918802348
Differences in the implementation in GitHub Actions compared to the CircleCI one:
- This PR introduces some *fail-fast* mechanisms to significantly reduce the total time CI is running, both because of environmental impact and because CI in GitHub Actions billing depends on the minutes per month running time (see [About billing for GitHub Actions](https://docs.github.com/en/billing/managing-billing-for-github-actions/about-billing-for-github-actions)):
- All tests *depend* on `check_code_quality` job: only if `check_code_quality` passes, then the other test jobs are launched
- The tests are implemented with a matrix strategy (cross-product: OS and PyArrow versions) and fail-fast: if any of the 4 processes fails, the others are cancelled
- OS dependencies for Linux (see table below)
| OS dependencies | Passed tests | Skipped tests |
| --- | ---: | ---: |
| libsndfile1-dev | 4786 | 3119 |
| libsndfile1 | 4786 | 3119 |
| libsndfile1, sox | 4788 | 3117 |
- This PR replaces `libsndfile1-dev` with `libsndfile1`: the same number of passing tests but less packages installed
- This PR adds `sox`: required by MP3 tests (2 more tests are passed: 4788 instead of 4786)
- For tests using PyArrow 6, this PR uses 6.0.1 instead of 6.0.0
TO DO:
- [ ] Remove old CircleCI CI: kept for the moment to compare stability and performance
Close #4658.
## Comparison between CircleCI and GitHub Actions
| | | CircleCI | GitHub Actions |
| --- | --- | ---: | ---: |
| Ubuntu, pyarrow-latest ||||
|| Passed tests | 4786 | 4788 |
|| Duration | 11m 0s | 10m 10s |
| Windows, pyarrow-latest ||||
|| Passed tests | 4783 | 4783 |
|| Duration | 29m 59s | 22m 56s | | {
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https://api.github.com/repos/huggingface/datasets/issues/6578 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6578/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6578/comments | https://api.github.com/repos/huggingface/datasets/issues/6578/events | https://github.com/huggingface/datasets/pull/6578 | 2,074,923,321 | PR_kwDODunzps5jtthB | 6,578 | Faster webdataset streaming | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6578). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I added faster streaming support using streaming Requests instances in `huggingface_hub... | 2024-01-10T18:18:09Z | 2024-01-30T18:46:02Z | 2024-01-30T18:39:51Z | MEMBER | null | null | null | requests.get(..., streaming=True) is faster than using HTTP range requests when streaming large TAR files
it can be enabled using block_size=0 in fsspec
cc @rwightman | {
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https://api.github.com/repos/huggingface/datasets/issues/7433 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7433/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7433/comments | https://api.github.com/repos/huggingface/datasets/issues/7433/events | https://github.com/huggingface/datasets/issues/7433 | 2,890,240,400 | I_kwDODunzps6sRZGQ | 7,433 | `Dataset.map` ignores existing caches and remaps when ran with different `num_proc` | {
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"This feels related: https://github.com/huggingface/datasets/issues/3044",
"@lhoestq This comment specifically, I agree:\n\n* https://github.com/huggingface/datasets/issues/3044#issuecomment-1239877570\n\n> Almost a year later and I'm in a similar boat. Using custom fingerprints and when using multiprocessing the... | 2025-03-03T05:51:26Z | 2025-03-04T05:55:08Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
If you `map` a dataset and save it to a specific `cache_file_name` with a specific `num_proc`, and then call map again with that same existing `cache_file_name` but a different `num_proc`, the dataset will be re-mapped.
### Steps to reproduce the bug
1. Download a dataset
```python
import datasets
dataset = datasets.load_dataset("ylecun/mnist")
```
```
Generating train split: 100%|██████████| 60000/60000 [00:00<00:00, 116429.85 examples/s]
Generating test split: 100%|██████████| 10000/10000 [00:00<00:00, 103310.27 examples/s]
```
2. `map` and cache it with a specific `num_proc`
```python
cache_file_name="./cache/train.map"
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=2)
```
```
Map (num_proc=2): 100%|██████████| 60000/60000 [00:01<00:00, 53764.03 examples/s]
```
3. `map` it with a different `num_proc` and the same `cache_file_name` as before
```python
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=3)
```
```
Map (num_proc=3): 100%|██████████| 60000/60000 [00:00<00:00, 65377.12 examples/s]
```
### Expected behavior
If I specify an existing `cache_file_name`, I don't expect using a different `num_proc` than the one that was used to generate it to cause the dataset to have be be re-mapped.
### Environment info
```console
$ datasets-cli env
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.35
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6936 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6936/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6936/comments | https://api.github.com/repos/huggingface/datasets/issues/6936/events | https://github.com/huggingface/datasets/issues/6936 | 2,326,119,853 | I_kwDODunzps6KpcWt | 6,936 | save_to_disk() freezes when saving on s3 bucket with multiprocessing | {
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"I got the same issue. Any updates so far for this issue?",
"Same here. Any updates?",
"+1, experiencing this as well"
] | 2024-05-30T16:48:39Z | 2025-02-06T22:12:52Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I'm trying to save a `Dataset` using the `save_to_disk()` function with:
- `num_proc > 1`
- `dataset_path` being a s3 bucket path e.g. "s3://{bucket_name}/{dataset_folder}/"
The hf progress bar shows up but the saving does not seem to start.
When using one processor only (`num_proc=1`), everything works fine.
When saving the dataset on local disk (as opposed to s3 bucket) with `num_proc > 1`, everything works fine.
Thank you for your help! :)
### Steps to reproduce the bug
I tried without any storage options:
```
from datasets import load_dataset
sandbox_ds = load_dataset("openai_humaneval")
sandbox_ds["test"].save_to_disk(
"s3://bucket-name/test_multiprocessing_saving/",
num_proc=4,
)
```
and with the specific s3fs storage options:
```
from datasets import load_dataset
from s3fs import S3FileSystem
def get_s3fs():
return S3FileSystem()
sandbox_ds = load_dataset("openai_humaneval")
sandbox_ds["test"].save_to_disk(
"s3://bucket-name/test_multiprocessing_saving/",
num_proc=4,
storage_options=get_s3fs().storage_options, # also tried: storage_options=S3FileSystem().storage_options
)
```
I'm guessing I might use `storage_options` parameter wrongly, but I didn't find anything online that made it work.
**NB**: Behavior is the same when trying to save the whole `DatasetDict`.
### Expected behavior
Progress bar fills in and saving is carried out.
### Environment info
`datasets==2.18.0` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6313 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6313/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6313/comments | https://api.github.com/repos/huggingface/datasets/issues/6313/events | https://github.com/huggingface/datasets/pull/6313 | 1,951,527,712 | PR_kwDODunzps5dPGmL | 6,313 | Fix commit message formatting in multi-commit uploads | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-10-19T07:53:56Z | 2023-10-20T14:06:13Z | 2023-10-20T13:57:39Z | MEMBER | null | null | null | Currently, the commit message keeps on adding:
- `Upload dataset (part 00000-of-00002)`
- `Upload dataset (part 00000-of-00002) (part 00001-of-00002)`
Introduced in https://github.com/huggingface/datasets/pull/6269
This PR fixes this issue to have
- `Upload dataset (part 00000-of-00002)`
- `Upload dataset (part 00001-of-00002)` | {
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https://api.github.com/repos/huggingface/datasets/issues/4952 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4952/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4952/comments | https://api.github.com/repos/huggingface/datasets/issues/4952/events | https://github.com/huggingface/datasets/pull/4952 | 1,366,354,604 | PR_kwDODunzps4-meM0 | 4,952 | Add test-datasets CI job | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Closing this one since the dataset scripts will be removed in https://github.com/huggingface/datasets/pull/4974"
] | 2022-09-08T13:38:30Z | 2023-09-24T10:05:57Z | 2022-09-16T13:25:48Z | MEMBER | null | null | null | To avoid having too many conflicts in the datasets and metrics dependencies I split the CI into test and test-catalog
test does the test of the core of the `datasets` lib, while test-catalog tests the datasets scripts and metrics scripts
This also makes `pip install -e .[dev]` much smaller for developers
WDYT @albertvillanova ? | {
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https://api.github.com/repos/huggingface/datasets/issues/6788 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6788/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6788/comments | https://api.github.com/repos/huggingface/datasets/issues/6788/events | https://github.com/huggingface/datasets/issues/6788 | 2,229,207,521 | I_kwDODunzps6E3wHh | 6,788 | A Question About the Map Function | {
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"All data is saved in the arrow format on disk.\r\nIf you return a tensor it gets converted to arrow before saving to disk when using map.\r\n\r\nTo get a tensor when you access data elements you can use `dataset.set_format(\"pt\")`.\r\nNote that this just changes how the data is loaded, not how it is stored.",
"... | 2024-04-06T11:45:23Z | 2024-04-11T05:29:35Z | 2024-04-11T05:29:35Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Hello,
I have a question regarding the map function in the Hugging Face datasets.
The situation is as follows: when I load a jsonl file using load_dataset(..., streaming=False), and then utilize the map function to process it, I specify that the returned example should be of type Torch.tensor. However, I noticed that after applying the map function, the datatype automatically changes to List, which leads to errors in my program.
I attempted to use load_dataset(..., streaming=True), and this issue no longer occurs. I'm not entirely clear on why this happens. Could you please provide some insights into this?
### Steps to reproduce the bug
1.dataset = load_dataset(xxx, streaming = False)
2. dataset.map(function), function will return torch.Tensor.
3. you will find the format of data in dataset is List.
### Expected behavior
I expected to receieve the format of data is torch.Tensor.
### Environment info
2.18.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5976 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5976/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5976/comments | https://api.github.com/repos/huggingface/datasets/issues/5976/events | https://github.com/huggingface/datasets/pull/5976 | 1,768,503,913 | PR_kwDODunzps5TmAFp | 5,976 | Avoid stuck map operation when subprocesses crashes | {
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"Hi ! Do you think this can be fixed at the Pool level ? Ideally it should be the Pool responsibility to handle this, not the `map` code. We could even subclass Pool if needed (at least the one from `multiprocess`)",
"@lhoestq it makes sense to me. Just pushed a refactoring creating a `class ProcessPool(multiproc... | 2023-06-21T21:18:31Z | 2023-07-10T09:58:39Z | 2023-07-10T09:50:07Z | CONTRIBUTOR | null | null | null | I've been using Dataset.map() with `num_proc=os.cpu_count()` to leverage multicore processing for my datasets, but from time to time I get stuck processes waiting forever. Apparently, when one of the subprocesses is abruptly killed (OOM killer, segfault, SIGKILL, etc), the main process keeps waiting for the async task sent to that child process to finish.
It seems to be easy to reproduce the issue with the following script:
```
import os
from datasets import Dataset, Features, Value
def do_stuck(item):
os.kill(os.getpid(), 9)
data = {
"col1": list(range(5)),
"col2": list(range(5)),
}
ds = Dataset.from_dict(
data,
features=Features({
"col1": Value("int64"),
"col2": Value("int64"),
}),
)
print(ds.map(do_stuck, num_proc=4))
```
This is an old behavior in Python, which apparently was fixed a few years ago in `concurrent.futures.ProcessPoolExecutor` ([ref](https://bugs.python.org/issue9205)), but not in `multiprocessing.pool.Pool` / `multiprocess.pool.Pool`, which is used by `Dataset.map` ([ref](https://bugs.python.org/issue22393)).
This PR is an idea to try to detect when a child process gets killed, and raises a `RuntimeError` warning the dataset.map() caller.
EDIT: Related proposal for future improvement: https://github.com/huggingface/datasets/discussions/5977 | {
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https://api.github.com/repos/huggingface/datasets/issues/5278 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5278/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5278/comments | https://api.github.com/repos/huggingface/datasets/issues/5278/events | https://github.com/huggingface/datasets/issues/5278 | 1,459,574,490 | I_kwDODunzps5W_1ba | 5,278 | load_dataset does not read jsonl metadata file properly | {
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"Can you try to remove \"drop_labels=false\" ? It may force the loader to infer the labels instead of reading the metadata",
"Hi, thanks for responding. I tried that, but it does not change anything.",
"Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4",
"Probably the issue, will ... | 2022-11-22T10:24:46Z | 2023-02-14T14:48:16Z | 2022-11-23T11:38:35Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features.
Below is code to reproduce my exact example/problem.
### Steps to reproduce the bug
```ruby
dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF"
id = dataset_link
output = 'Godardv01.zip'
gdown.download(id=id, output=output, quiet=False)
ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False)
print(ds)
```
### Expected behavior
I would expect that it returned "image" and "text" columns from the code above.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5797 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5797/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5797/comments | https://api.github.com/repos/huggingface/datasets/issues/5797/events | https://github.com/huggingface/datasets/issues/5797 | 1,685,501,199 | I_kwDODunzps5kdrUP | 5,797 | load_dataset is case sentitive? | {
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"Hi @haonan-li , thank you for the report! It seems to be a bug on the [`huggingface_hub`](https://github.com/huggingface/huggingface_hub) site, there is even no such dataset as `mbzuai/bactrian-x` on the Hub. I opened and [issue](https://github.com/huggingface/huggingface_hub/issues/1453) there.",
"I think `loa... | 2023-04-26T18:19:04Z | 2023-04-27T11:56:58Z | null | NONE | null | null | {
"completed": 0,
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"total": 0
} | ### Describe the bug
load_dataset() function is case sensitive?
### Steps to reproduce the bug
The following two code, get totally different behavior.
1. load_dataset('mbzuai/bactrian-x','en')
2. load_dataset('MBZUAI/Bactrian-X','en')
### Expected behavior
Compare 1 and 2.
1 will download all 52 subsets, shell output:
```Downloading and preparing dataset json/MBZUAI--bactrian-X to xxx```
2 will only download single subset, shell output
```Downloading and preparing dataset bactrian-x/en to xxx```
### Environment info
Python 3.10.11
datasets Version: 2.11.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6558 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6558/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6558/comments | https://api.github.com/repos/huggingface/datasets/issues/6558/events | https://github.com/huggingface/datasets/issues/6558 | 2,064,885,984 | I_kwDODunzps57E6jg | 6,558 | OSError: image file is truncated (1 bytes not processed) #28323 | {
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} | [] | closed | false | null | [] | null | [
"You can add \r\n\r\n```python\r\nfrom PIL import ImageFile\r\nImageFile.LOAD_TRUNCATED_IMAGES = True\r\n```\r\n\r\nafter the imports to be able to read truncated images."
] | 2024-01-04T02:15:13Z | 2024-02-21T00:38:12Z | 2024-02-21T00:38:12Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
```
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
Cell In[24], line 28
23 return example
25 # Filter the dataset
26 # filtered_dataset = dataset.filter(contains_number)
27 # Add the 'label' field in the dataset
---> 28 labeled_dataset = dataset.filter(contains_number).map(add_label)
29 # View the structure of the updated dataset
30 print(labeled_dataset)
File /usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py:975, in DatasetDict.filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, fn_kwargs, num_proc, desc)
972 if cache_file_names is None:
973 cache_file_names = {k: None for k in self}
974 return DatasetDict(
--> 975 {
976 k: dataset.filter(
977 function=function,
978 with_indices=with_indices,
979 input_columns=input_columns,
980 batched=batched,
981 batch_size=batch_size,
982 keep_in_memory=keep_in_memory,
983 load_from_cache_file=load_from_cache_file,
984 cache_file_name=cache_file_names[k],
985 writer_batch_size=writer_batch_size,
986 fn_kwargs=fn_kwargs,
987 num_proc=num_proc,
988 desc=desc,
989 )
990 for k, dataset in self.items()
991 }
992 )
File /usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py:976, in <dictcomp>(.0)
972 if cache_file_names is None:
973 cache_file_names = {k: None for k in self}
974 return DatasetDict(
975 {
--> 976 k: dataset.filter(
977 function=function,
978 with_indices=with_indices,
979 input_columns=input_columns,
980 batched=batched,
981 batch_size=batch_size,
982 keep_in_memory=keep_in_memory,
983 load_from_cache_file=load_from_cache_file,
984 cache_file_name=cache_file_names[k],
985 writer_batch_size=writer_batch_size,
986 fn_kwargs=fn_kwargs,
987 num_proc=num_proc,
988 desc=desc,
989 )
990 for k, dataset in self.items()
991 }
992 )
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:557, in transmit_format.<locals>.wrapper(*args, **kwargs)
550 self_format = {
551 "type": self._format_type,
552 "format_kwargs": self._format_kwargs,
553 "columns": self._format_columns,
554 "output_all_columns": self._output_all_columns,
555 }
556 # apply actual function
--> 557 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
559 # re-apply format to the output
File /usr/local/lib/python3.10/dist-packages/datasets/fingerprint.py:481, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
477 validate_fingerprint(kwargs[fingerprint_name])
479 # Call actual function
--> 481 out = func(dataset, *args, **kwargs)
483 # Update fingerprint of in-place transforms + update in-place history of transforms
485 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3623, in Dataset.filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
3620 if len(self) == 0:
3621 return self
-> 3623 indices = self.map(
3624 function=partial(
3625 get_indices_from_mask_function, function, batched, with_indices, input_columns, self._indices
3626 ),
3627 with_indices=True,
3628 features=Features({"indices": Value("uint64")}),
3629 batched=True,
3630 batch_size=batch_size,
3631 remove_columns=self.column_names,
3632 keep_in_memory=keep_in_memory,
3633 load_from_cache_file=load_from_cache_file,
3634 cache_file_name=cache_file_name,
3635 writer_batch_size=writer_batch_size,
3636 fn_kwargs=fn_kwargs,
3637 num_proc=num_proc,
3638 suffix_template=suffix_template,
3639 new_fingerprint=new_fingerprint,
3640 input_columns=input_columns,
3641 desc=desc or "Filter",
3642 )
3643 new_dataset = copy.deepcopy(self)
3644 new_dataset._indices = indices.data
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:592, in transmit_tasks.<locals>.wrapper(*args, **kwargs)
590 self: "Dataset" = kwargs.pop("self")
591 # apply actual function
--> 592 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
593 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
594 for dataset in datasets:
595 # Remove task templates if a column mapping of the template is no longer valid
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:557, in transmit_format.<locals>.wrapper(*args, **kwargs)
550 self_format = {
551 "type": self._format_type,
552 "format_kwargs": self._format_kwargs,
553 "columns": self._format_columns,
554 "output_all_columns": self._output_all_columns,
555 }
556 # apply actual function
--> 557 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
559 # re-apply format to the output
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3093, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
3087 if transformed_dataset is None:
3088 with hf_tqdm(
3089 unit=" examples",
3090 total=pbar_total,
3091 desc=desc or "Map",
3092 ) as pbar:
-> 3093 for rank, done, content in Dataset._map_single(**dataset_kwargs):
3094 if done:
3095 shards_done += 1
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3470, in Dataset._map_single(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)
3466 indices = list(
3467 range(*(slice(i, i + batch_size).indices(shard.num_rows)))
3468 ) # Something simpler?
3469 try:
-> 3470 batch = apply_function_on_filtered_inputs(
3471 batch,
3472 indices,
3473 check_same_num_examples=len(shard.list_indexes()) > 0,
3474 offset=offset,
3475 )
3476 except NumExamplesMismatchError:
3477 raise DatasetTransformationNotAllowedError(
3478 "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it."
3479 ) from None
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3349, in Dataset._map_single.<locals>.apply_function_on_filtered_inputs(pa_inputs, indices, check_same_num_examples, offset)
3347 if with_rank:
3348 additional_args += (rank,)
-> 3349 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
3350 if isinstance(processed_inputs, LazyDict):
3351 processed_inputs = {
3352 k: v for k, v in processed_inputs.data.items() if k not in processed_inputs.keys_to_format
3353 }
File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:6212, in get_indices_from_mask_function(function, batched, with_indices, input_columns, indices_mapping, *args, **fn_kwargs)
6209 if input_columns is None:
6210 # inputs only contains a batch of examples
6211 batch: dict = inputs[0]
-> 6212 num_examples = len(batch[next(iter(batch.keys()))])
6213 for i in range(num_examples):
6214 example = {key: batch[key][i] for key in batch}
File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:272, in LazyDict.__getitem__(self, key)
270 value = self.data[key]
271 if key in self.keys_to_format:
--> 272 value = self.format(key)
273 self.data[key] = value
274 self.keys_to_format.remove(key)
File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:375, in LazyBatch.format(self, key)
374 def format(self, key):
--> 375 return self.formatter.format_column(self.pa_table.select([key]))
File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:442, in PythonFormatter.format_column(self, pa_table)
440 def format_column(self, pa_table: pa.Table) -> list:
441 column = self.python_arrow_extractor().extract_column(pa_table)
--> 442 column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
443 return column
File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:218, in PythonFeaturesDecoder.decode_column(self, column, column_name)
217 def decode_column(self, column: list, column_name: str) -> list:
--> 218 return self.features.decode_column(column, column_name) if self.features else column
File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1951, in Features.decode_column(self, column, column_name)
1938 def decode_column(self, column: list, column_name: str):
1939 """Decode column with custom feature decoding.
1940
1941 Args:
(...)
1948 `list[Any]`
1949 """
1950 return (
-> 1951 [decode_nested_example(self[column_name], value) if value is not None else None for value in column]
1952 if self._column_requires_decoding[column_name]
1953 else column
1954 )
File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1951, in <listcomp>(.0)
1938 def decode_column(self, column: list, column_name: str):
1939 """Decode column with custom feature decoding.
1940
1941 Args:
(...)
1948 `list[Any]`
1949 """
1950 return (
-> 1951 [decode_nested_example(self[column_name], value) if value is not None else None for value in column]
1952 if self._column_requires_decoding[column_name]
1953 else column
1954 )
File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1339, in decode_nested_example(schema, obj, token_per_repo_id)
1336 elif isinstance(schema, (Audio, Image)):
1337 # we pass the token to read and decode files from private repositories in streaming mode
1338 if obj is not None and schema.decode:
-> 1339 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
1340 return obj
File /usr/local/lib/python3.10/dist-packages/datasets/features/image.py:185, in Image.decode_example(self, value, token_per_repo_id)
183 else:
184 image = PIL.Image.open(BytesIO(bytes_))
--> 185 image.load() # to avoid "Too many open files" errors
186 return image
File /usr/local/lib/python3.10/dist-packages/PIL/ImageFile.py:254, in ImageFile.load(self)
252 break
253 else:
--> 254 raise OSError(
255 "image file is truncated "
256 f"({len(b)} bytes not processed)"
257 )
259 b = b + s
260 n, err_code = decoder.decode(b)
OSError: image file is truncated (1 bytes not processed)
```
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("mehul7/captioned_military_aircraft")
from transformers import AutoImageProcessor
checkpoint = "microsoft/resnet-50"
image_processor = AutoImageProcessor.from_pretrained(checkpoint)
import re
from PIL import Image
import io
def contains_number(example):
try:
image = Image.open(io.BytesIO(example["image"]['bytes']))
t = image_processor(images=image, return_tensors="pt")['pixel_values']
except Exception as e:
print(f"Error processing image：{example['text']}")
return False
return bool(re.search(r'\d', example['text']))
# Define a function to add the 'label' field
def add_label(example):
lab = example['text'].split()
temp = 'NOT'
for item in lab:
if str(item[-1]).isdigit():
temp = item
break
example['label'] = temp
return example
# Filter the dataset
# filtered_dataset = dataset.filter(contains_number)
# Add the 'label' field in the dataset
labeled_dataset = dataset.filter(contains_number).map(add_label)
# View the structure of the updated dataset
print(labeled_dataset)
```
### Expected behavior
needs to form labels
same as : https://www.kaggle.com/code/jiabaowangts/dataset-air/notebook
### Environment info
Kaggle notebook P100 | {
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https://api.github.com/repos/huggingface/datasets/issues/4910 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4910/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4910/comments | https://api.github.com/repos/huggingface/datasets/issues/4910/events | https://github.com/huggingface/datasets/issues/4910 | 1,354,374,328 | I_kwDODunzps5Quhy4 | 4,910 | Identical keywords in build_kwargs and config_kwargs lead to TypeError in load_dataset_builder() | {
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"I am getting similar error - `TypeError: type object got multiple values for keyword argument 'name'` while following this [tutorial](https://huggingface.co/docs/datasets/dataset_script#create-a-dataset-loading-script). I am getting this error with the `dataset-cli test` command.\r\n\r\n`datasets` version: 2.4.0",... | 2022-08-29T14:11:48Z | 2022-09-13T11:58:46Z | null | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
In `load_dataset_builder()`, `build_kwargs` and `config_kwargs` can contain the same keywords leading to a TypeError("type object got multiple values for keyword argument "xyz").
I ran into this problem with the keyword: `base_path`. It might happen with other kwargs as well. I think a quickfix would be
```python
builder_cls = import_main_class(dataset_module.module_path)
builder_kwargs = dataset_module.builder_kwargs
data_files = builder_kwargs.pop("data_files", data_files)
config_name = builder_kwargs.pop("config_name", name)
hash = builder_kwargs.pop("hash")
base_path = builder_kwargs.pop("base_path")
```
and then pass base_path into `builder_cls`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("rotten_tomatoes", base_path="./sample_data")
```
## Expected results
The docs state: `**config_kwargs` — Keyword arguments to be passed to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.BuilderConfig) and used in the [DatasetBuilder](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.DatasetBuilder).
So I would expect to be able to pass the base_path into `load_dataset()`.
## Actual results
TypeError("type object got multiple values for keyword argument "base_path").
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.8.9
- PyArrow version: 9.0.0
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https://api.github.com/repos/huggingface/datasets/issues/6178 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6178/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6178/comments | https://api.github.com/repos/huggingface/datasets/issues/6178/events | https://github.com/huggingface/datasets/issues/6178 | 1,866,610,102 | I_kwDODunzps5vQjW2 | 6,178 | 'import datasets' throws "invalid syntax error" | {
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"This seems to be related to your environment and not the `datasets` code (e.g., this could happen when exposing the Python 3.9 site packages to a lower Python version (interpreter))"
] | 2023-08-25T08:35:14Z | 2023-09-27T17:33:39Z | 2023-09-27T17:33:39Z | NONE | null | null | {
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} | ### Describe the bug
Hi,
I have been trying to import the datasets library but I keep gtting this error.
`Traceback (most recent call last):
File /opt/local/jupyterhub/lib64/python3.9/site-packages/IPython/core/interactiveshell.py:3508 in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
Cell In[2], line 1
import datasets
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/__init__.py:22
from .arrow_dataset import Dataset
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_dataset.py:67
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_writer.py:27
from .features import Features, Image, Value
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/__init__.py:17
from .audio import Audio
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/audio.py:11
from ..download.streaming_download_manager import xopen, xsplitext
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/__init__.py:10
from .streaming_download_manager import StreamingDownloadManager
File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/streaming_download_manager.py:18
from aiohttp.client_exceptions import ClientError
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/__init__.py:7
from .connector import * # noqa
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/connector.py:12
from .client import ClientRequest
File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/client.py:144
yield from asyncio.async(resp.release(), loop=loop)
^
SyntaxError: invalid syntax`
I have simply used these commands:
`import datasets`
and
`from datasets import load_dataset`
### Environment info
The library has been installed a virtual machine on JupyterHub. Although I have used this library multiple times (on the same VM) before, to train/test an ASR or other ML models, I had never encountered this error. | {
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https://api.github.com/repos/huggingface/datasets/issues/5213 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5213/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5213/comments | https://api.github.com/repos/huggingface/datasets/issues/5213/events | https://github.com/huggingface/datasets/pull/5213 | 1,440,037,534 | PR_kwDODunzps5CalQ_ | 5,213 | Add support for different configs with `push_to_hub` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5213). All of your documentation changes will be reflected on that endpoint.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface... | 2022-11-08T11:45:47Z | 2022-12-02T16:48:23Z | 2022-12-02T16:44:07Z | CONTRIBUTOR | null | null | null | will solve #5151
@lhoestq @albertvillanova @mariosasko
This is still a super draft so please ignore code issues but I want to discuss some conceptually important things.
I suggest a way to do `.push_to_hub("repo_id", "config_name")` with pushing parquet files to directories named as `config_name` (inside `data/` dir as it is now), for example:
```
data
|__config-v1
train-00000-00002-...-.parquet
train-00001-00002-...-.parquet
...
|__config-v2
....
```
When loading a dataset, I parse these configs from repository data files (only for `"data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"` pattern that is used for parquet datasets pushed with `.push_to_hub`).
Therefore,
- when user tries to load a dataset that has configs parsed from data files dir names without providing a config (like `load_dataset("repo")` instead of `load_dataset("repo", "config-v1")`) - raise error and asks for config - to be aligned with how it works in datasets with scripts.
- for backward compatibility: if user tries to `.push_to_hub(""repo", "config_name")` to an existing parquet repo with no configurations (all parquet files are directly in `data/` dir) - raise error. My initial idea was to raise a warning and move these files to another dir with name (config) like "default" or smth but in a PR and suggest user to merge it on the Hub. But there is no support for renaming (moving) files via `HfApi` yet so it would require deleting and pushing again if I understand it right.
This parsing approach can be extended to other Hub packaged modules, and to local packaged modules and other data files patterns
(except for cases when splits are in dir names `KEYWORDS_IN_DIR_NAME_BASE_PATTERNS` because we allow for arbitrary depth of directory hierarchy).
Do you think it's reasonable? Not sure how to provide flexibility (and backward compatibility) to not parsing configs and load all the data in a single config as it is now.
I also thought about getting information about configs from Readme.md `dataset_info` ([example](https://huggingface.co/datasets/polinaeterna/test_push_two_configs/blob/main/README.md)). But that way we
are dependent on if it exists. It is created automatically with `.push_to_hub` but what if it is
accidentally deleted or smth).
Also, what I don't like is that this parsing is a part of Module/DataFiles logic, not Builder's one, which is not aligned with datasets with custom scripts. But I don't know to implement the second approach in current library's logic.
What do you think about this all? Am I missing smth?
TODO:
- [ ] save cache in the same dir for configs of the same datasets
- [ ] fix verification errors
- [ ] correctly update `dataset_infos.json` too
- [ ] ...
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https://api.github.com/repos/huggingface/datasets/issues/6253 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6253/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6253/comments | https://api.github.com/repos/huggingface/datasets/issues/6253/events | https://github.com/huggingface/datasets/pull/6253 | 1,906,618,910 | PR_kwDODunzps5a3s__ | 6,253 | Check builder cls default config name in inspect | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-09-21T10:15:32Z | 2023-09-21T14:16:44Z | 2023-09-21T14:08:00Z | MEMBER | null | null | null | Fix https://github.com/huggingface/datasets-server/issues/1812
this was causing this issue:
```ipython
In [1]: from datasets import *
In [2]: inspect.get_dataset_config_names("aakanksha/udpos")
Out[2]: ['default']
In [3]: load_dataset_builder("aakanksha/udpos").config.name
Out[3]: 'en'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6602 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6602/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6602/comments | https://api.github.com/repos/huggingface/datasets/issues/6602/events | https://github.com/huggingface/datasets/issues/6602 | 2,089,217,483 | I_kwDODunzps58hu3L | 6,602 | Index error when data is large | {
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"I'm facing this problem while doing my translation of [mteb/stackexchange-clustering](https://huggingface.co/datasets/mteb/stackexchange-clustering). each row has lots of samples (up to 100k samples), because in this dataset, each row represent multiple clusters.\nmy hack is to setting `max_shard_size` to 20Gb or ... | 2024-01-18T23:00:47Z | 2025-04-16T04:13:01Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
At `save_to_disk` step, the `max_shard_size` by default is `500MB`. However, one row of the dataset might be larger than `500MB` then the saving will throw an index error. Without looking at the source code, the bug is due to wrong calculation of number of shards which i think is
`total_size / min(max_shard_size, row_size)` which should be `total_size / max(max_shard_size, row_size)`
The fix is setting a larger `max_shard_size`
### Steps to reproduce the bug
1. create a dataset with large dense tensors per row
2. set a small `max_shard_size` say 1MB
3. `save_to_disk`
### Expected behavior
```
raise IndexError(f"Index {index} out of range for dataset of size {size}.")
IndexError: Index 10 out of range for dataset of size 10.
```
### Environment info
- `datasets` version: 2.16.0
- Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.12.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6888 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6888/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6888/comments | https://api.github.com/repos/huggingface/datasets/issues/6888/events | https://github.com/huggingface/datasets/pull/6888 | 2,287,169,676 | PR_kwDODunzps5u9omr | 6,888 | Support WebDataset containing file basenames with dots | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6888). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I think webdataset splits the file name and extension using the first dot no ?\r\n\r\nh... | 2024-05-09T08:25:30Z | 2024-05-10T13:54:06Z | 2024-05-10T13:54:06Z | MEMBER | null | null | null | Support WebDataset containing file basenames with dots.
Fix #6880. | {
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https://api.github.com/repos/huggingface/datasets/issues/5561 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5561/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5561/comments | https://api.github.com/repos/huggingface/datasets/issues/5561/events | https://github.com/huggingface/datasets/pull/5561 | 1,593,862,388 | PR_kwDODunzps5Kcxw_ | 5,561 | Add pre-commit config yaml file to enable automatic code formatting | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Better yet have someone enable pre-commit CI https://pre-commit.ci/ and it will apply the pre-commit fixes to the PR automatically as an additional commit.",
"@Skylion007 hi! I agree with @nateraw here, I'd better not force to use ... | 2023-02-21T17:35:07Z | 2023-02-28T15:37:22Z | 2023-02-23T18:23:29Z | CONTRIBUTOR | null | null | null | @huggingface/datasets do you think it would be useful? Motivation - sometimes PRs are like 30% "fix: style" commits :)
If so - I need to double check the config but for me locally it works as expected. | {
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https://api.github.com/repos/huggingface/datasets/issues/6215 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6215/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6215/comments | https://api.github.com/repos/huggingface/datasets/issues/6215/events | https://github.com/huggingface/datasets/pull/6215 | 1,882,176,970 | PR_kwDODunzps5ZlcqC | 6,215 | Fix checking patterns to infer packaged builder | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"oh wow good catch",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy af... | 2023-09-05T15:10:47Z | 2023-09-06T10:34:00Z | 2023-09-06T10:25:00Z | CONTRIBUTOR | null | null | null | Don't ignore results of pattern resolving if `self.data_files` is not None. Otherwise lines 854 and 1037 make no sense. | {
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https://api.github.com/repos/huggingface/datasets/issues/4942 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4942/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4942/comments | https://api.github.com/repos/huggingface/datasets/issues/4942/events | https://github.com/huggingface/datasets/issues/4942 | 1,363,869,421 | I_kwDODunzps5RSv7t | 4,942 | Trec Dataset has incorrect labels | {
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"Thanks for reporting, @wmpauli. \r\n\r\nIndeed we recently fixed this issue:\r\n- #4801 \r\n\r\nThe fix will be accessible after our next library release. In the meantime, you can have it by passing `revision=\"main\"` to `load_dataset`."
] | 2022-09-06T22:13:40Z | 2022-09-08T11:12:03Z | 2022-09-08T11:12:03Z | NONE | null | null | {
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} | ## Describe the bug
Both coarse and fine labels seem to be out of line.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = "trec"
raw_datasets = load_dataset(dataset)
df = pd.DataFrame(raw_datasets["test"])
df.head()
```
## Expected results
text (string) | coarse_label (class label) | fine_label (class label)
-- | -- | --
How far is it from Denver to Aspen ? | 5 (NUM) | 40 (NUM:dist)
What county is Modesto , California in ? | 4 (LOC) | 32 (LOC:city)
Who was Galileo ? | 3 (HUM) | 31 (HUM:desc)
What is an atom ? | 2 (DESC) | 24 (DESC:def)
When did Hawaii become a state ? | 5 (NUM) | 39 (NUM:date)
## Actual results
index | label-coarse |label-fine | text
-- |-- | -- | --
0 | 4 | 40 | How far is it from Denver to Aspen ?
1 | 5 | 21 | What county is Modesto , California in ?
2 | 3 | 12 | Who was Galileo ?
3 | 0 | 7 | What is an atom ?
4 | 4 | 8 | When did Hawaii become a state ?
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-1086-azure-x86_64-with-glibc2.27
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/5663 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5663/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5663/comments | https://api.github.com/repos/huggingface/datasets/issues/5663/events | https://github.com/huggingface/datasets/issues/5663 | 1,637,173,248 | I_kwDODunzps5hlUgA | 5,663 | CI is broken: ModuleNotFoundError: jax requires jaxlib to be installed | {
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```
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_in_memory - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_on_disk - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_audio - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_device - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_image - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_jnp_array_kwargs - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/features/test_features.py::CastToPythonObjectsTest::test_cast_to_python_objects_jax - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
===== 8 failed, 2147 passed, 10 skipped, 37 warnings in 228.69s (0:03:48) ======
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https://api.github.com/repos/huggingface/datasets/issues/6374 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6374/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6374/comments | https://api.github.com/repos/huggingface/datasets/issues/6374/events | https://github.com/huggingface/datasets/issues/6374 | 1,973,857,428 | I_kwDODunzps51pqyU | 6,374 | CI is broken: TypeError: Couldn't cast array | {
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```
FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type
fixed_size_list<item: int32>[3]
to
Sequence(feature=Value(dtype='int64', id=None), length=3, id=None)
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https://api.github.com/repos/huggingface/datasets/issues/5688 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5688/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5688/comments | https://api.github.com/repos/huggingface/datasets/issues/5688/events | https://github.com/huggingface/datasets/issues/5688 | 1,648,463,504 | I_kwDODunzps5iQY6Q | 5,688 | Wikipedia download_and_prepare for GCS | {
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"Hi @adrianfagerland, thanks for reporting.\r\n\r\nPlease note that \"wikipedia\" is a special dataset, with an Apache Beam builder: https://beam.apache.org/\r\nYou can find more info about Beam datasets in our docs: https://huggingface.co/docs/datasets/beam\r\n\r\nIt was implemented to be run in parallel processin... | 2023-03-30T23:43:22Z | 2024-03-15T15:59:18Z | 2024-03-15T15:59:18Z | NONE | null | null | {
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} | ### Describe the bug
I am unable to download the wikipedia dataset onto GCS.
When I run the script provided the memory firstly gets eaten up, then it crashes.
I tried running this on a VM with 128GB RAM and all I got was a two empty files: _data_builder.lock_, _data.incomplete/beam-temp-wikipedia-train-1ab2039acf3611ed87a9893475de0093_
I have troubleshot this for two straight days now, but I am just unable to get the dataset into storage.
### Steps to reproduce the bug
Run this and insert a path:
```
import datasets
builder = datasets.load_dataset_builder(
"wikipedia", language="en", date="20230320", beam_runner="DirectRunner")
builder.download_and_prepare({path}, file_format="parquet")
```
This is where the problem of it eating RAM occurs.
I have also tried several versions of this, based on the docs:
```
import gcsfs
import datasets
storage_options = {"project": "tdt4310", "token": "cloud"}
fs = gcsfs.GCSFileSystem(**storage_options)
output_dir = "gcs://wikipediadata/"
builder = datasets.load_dataset_builder(
"wikipedia", date="20230320", language="en", beam_runner="DirectRunner")
builder.download_and_prepare(
output_dir, storage_options=storage_options, file_format="parquet")
```
The error message that is received here is:
> ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://wikipediadata/wikipedia-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite']
I have ran `pip install apache-beam[gcp]`
### Expected behavior
The wikipedia data loaded into GCS
Everything worked when testing with a smaller demo dataset found somewhere in the docs
### Environment info
Newest published version of datasets. Python 3.9. Also tested with Python 3.7. 128GB RAM Google Cloud VM instance. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @mariosasko , I have added a test for the `keep_in_memory` version. I have also removed the `Compatible with temp_seed` part in the scope of `dset_shuffled`, please verify if that makes sense."
] | 2022-10-06T11:10:46Z | 2022-10-07T14:35:34Z | 2022-10-07T14:32:54Z | CONTRIBUTOR | null | null | null | Fixing #514 .
Hello @mariosasko 👋, I have implemented what you have recommanded to fix the keep in memory problem for shuffle on the issue #514 . | {
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https://api.github.com/repos/huggingface/datasets/issues/6999 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6999/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6999/comments | https://api.github.com/repos/huggingface/datasets/issues/6999/events | https://github.com/huggingface/datasets/pull/6999 | 2,372,124,589 | PR_kwDODunzps5zd-ak | 6,999 | Remove tasks | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6999). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-06-25T09:06:16Z | 2024-08-21T09:07:07Z | 2024-08-21T09:01:18Z | MEMBER | null | null | null | Remove tasks, as part of the 3.0 release. | {
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https://api.github.com/repos/huggingface/datasets/issues/5346 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5346/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5346/comments | https://api.github.com/repos/huggingface/datasets/issues/5346/events | https://github.com/huggingface/datasets/issues/5346 | 1,486,884,983 | I_kwDODunzps5YoBB3 | 5,346 | [Quick poll] Give your opinion on the future of the Hugging Face Open Source ecosystem! | {
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"As the survey is finished, can we close this issue, @LysandreJik ?",
"Yes! I'll post a public summary on the forums shortly.",
"Is the summary available? I would be interested in reading your findings."
] | 2022-12-09T14:48:02Z | 2023-06-02T20:24:44Z | 2023-01-25T19:35:40Z | MEMBER | null | null | {
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} | Thanks to all of you, Datasets is just about to pass 15k stars!
Since the last survey, a lot has happened: the [diffusers](https://github.com/huggingface/diffusers), [evaluate](https://github.com/huggingface/evaluate) and [skops](https://github.com/skops-dev/skops) libraries were born. `timm` joined the Hugging Face ecosystem. There were 25 new releases of `transformers`, 21 new releases of `datasets`, 13 new releases of `accelerate`.
If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts:
[**hf.co/oss-survey**](https://docs.google.com/forms/d/e/1FAIpQLSf4xFQKtpjr6I_l7OfNofqiR8s-WG6tcNbkchDJJf5gYD72zQ/viewform?usp=sf_link)
(please reply in the above feedback form rather than to this thread)
Thank you all on behalf of the HuggingFace team! 🤗 | {
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https://api.github.com/repos/huggingface/datasets/issues/7339 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7339/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7339/comments | https://api.github.com/repos/huggingface/datasets/issues/7339/events | https://github.com/huggingface/datasets/pull/7339 | 2,745,460,060 | PR_kwDODunzps6FhaTl | 7,339 | Update CONTRIBUTING.md | {
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] | 2024-12-17T16:45:25Z | 2024-12-17T16:51:36Z | 2024-12-17T16:46:30Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6933 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6933/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6933/comments | https://api.github.com/repos/huggingface/datasets/issues/6933/events | https://github.com/huggingface/datasets/pull/6933 | 2,325,300,800 | PR_kwDODunzps5w_cW4 | 6,933 | update ci user | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6933). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-05-30T10:23:02Z | 2024-05-30T10:30:54Z | 2024-05-30T10:23:12Z | MEMBER | null | null | null | token is ok to be public since it's only for the hub-ci | {
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https://api.github.com/repos/huggingface/datasets/issues/5042 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5042/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5042/comments | https://api.github.com/repos/huggingface/datasets/issues/5042/events | https://github.com/huggingface/datasets/pull/5042 | 1,390,762,877 | PR_kwDODunzps4_2eqa | 5,042 | Update swiss judgment prediction | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-29T12:10:02Z | 2022-09-30T07:14:00Z | 2022-09-29T14:32:02Z | CONTRIBUTOR | null | null | null | I forgot to add the new citation. | {
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https://api.github.com/repos/huggingface/datasets/issues/5873 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5873/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5873/comments | https://api.github.com/repos/huggingface/datasets/issues/5873/events | https://github.com/huggingface/datasets/issues/5873 | 1,713,269,724 | I_kwDODunzps5mHmvc | 5,873 | Allow setting the environment variable for the lock file path | {
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} | ### Feature request
Add an environment variable to replace the default lock file path.
### Motivation
Usually, dataset path is a read-only path while the lock file needs to be modified each time. It would be convenient if the path can be reset individually.
### Your contribution
```/src/datasets/utils/filelock.py
class UnixFileLock(BaseFileLock):
def __init__(self, lock_file, timeout=-1, max_filename_length=None):
#-------------------
if os.getenv('DS_TMP_PATH'):
file_name = str(lock_file).split('/')[-1]
dataset_tmp_path = os.getenv('DS_TMP_PATH')
lock_file = os.path.join(dataset_tmp_path, file_name)
#-------------------
max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax
super().__init__(lock_file, timeout=timeout, max_filename_length=max_filename_length)
```
A simple demo is as upper. Thanks. | null | {
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