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2,379,588,676 | https://api.github.com/repos/huggingface/datasets/issues/7007 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7007/events | [] | null | 2024-06-28T05:31:21Z | [] | https://github.com/huggingface/datasets/pull/7007 | MEMBER | null | false | null | [
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | Fix CI by temporarily pinning ruff < 0.5.0 | {
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} | 2024-06-28T05:09:17Z | https://api.github.com/repos/huggingface/datasets/issues/7007/comments | As a hotfix for CI, temporarily pin ruff upper version < 0.5.0.
Fix #7006.
Revert once root cause is fixed. | {
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See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks | {
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2,378,424,349 | https://api.github.com/repos/huggingface/datasets/issues/7005 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7005/events | [] | null | 2024-06-28T09:56:19Z | [] | https://github.com/huggingface/datasets/issues/7005 | NONE | completed | null | null | [
"Hi ! `data_dir=` is for directories, can you try using `data_files=` instead ?",
"If you are trying to load your image dataset from a local folder, you should replace \"data_dir=path/to/jsonl/metadata.jsonl\" with the real folder path in your computer.\r\n\r\nhttps://huggingface.co/docs/datasets/en/image_load#im... | EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files | {
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} | I_kwDODunzps6Nw-Ad | null | 2024-06-27T15:08:26Z | https://api.github.com/repos/huggingface/datasets/issues/7005/comments | ### Describe the bug
while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files"
### Steps to reproduce the bug
This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file.
Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub).
````
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl")
````
error:
````
EmptyDatasetError Traceback (most recent call last)
Cell In[18], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("imagefolder",
4 data_dir="path/to/jsonl/file/metadata.jsonl")
5 dataset[0]["objects"]
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2589 verification_mode = VerificationMode(
2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2591 )
2593 # Create a dataset builder
-> 2594 builder_instance = load_dataset_builder(
2595 path=path,
2596 name=name,
2597 data_dir=data_dir,
2598 data_files=data_files,
2599 cache_dir=cache_dir,
2600 features=features,
2601 download_config=download_config,
2602 download_mode=download_mode,
2603 revision=revision,
2604 token=token,
2605 storage_options=storage_options,
2606 trust_remote_code=trust_remote_code,
2607 _require_default_config_name=name is None,
2608 **config_kwargs,
2609 )
2611 # Return iterable dataset in case of streaming
2612 if streaming:
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
2264 download_config = download_config.copy() if download_config else DownloadConfig()
2265 download_config.storage_options.update(storage_options)
-> 2266 dataset_module = dataset_module_factory(
2267 path,
2268 revision=revision,
2269 download_config=download_config,
2270 download_mode=download_mode,
2271 data_dir=data_dir,
2272 data_files=data_files,
2273 cache_dir=cache_dir,
2274 trust_remote_code=trust_remote_code,
2275 _require_default_config_name=_require_default_config_name,
2276 _require_custom_configs=bool(config_kwargs),
2277 )
2278 # Get dataset builder class from the processing script
2279 builder_kwargs = dataset_module.builder_kwargs
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1782 # We have several ways to get a dataset builder:
1783 #
1784 # - if path is the name of a packaged dataset module
(...)
1796
1797 # Try packaged
1798 if path in _PACKAGED_DATASETS_MODULES:
1799 return PackagedDatasetModuleFactory(
1800 path,
1801 data_dir=data_dir,
1802 data_files=data_files,
1803 download_config=download_config,
1804 download_mode=download_mode,
-> 1805 ).get_module()
1806 # Try locally
1807 elif path.endswith(filename):
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self)
1135 def get_module(self) -> DatasetModule:
1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
1137 patterns = (
1138 sanitize_patterns(self.data_files)
1139 if self.data_files is not None
-> 1140 else get_data_patterns(base_path, download_config=self.download_config)
1141 )
1142 data_files = DataFilesDict.from_patterns(
1143 patterns,
1144 download_config=self.download_config,
1145 base_path=base_path,
1146 )
1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config)
501 return _get_data_files_patterns(resolver)
502 except FileNotFoundError:
--> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files`
```
### Expected behavior
It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files."
### Environment info
I am using conda environment. | {
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2,376,064,264 | https://api.github.com/repos/huggingface/datasets/issues/7004 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7004/events | [] | null | 2024-06-29T00:15:49Z | [] | https://github.com/huggingface/datasets/pull/7004 | CONTRIBUTOR | null | false | null | [
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} | 2024-06-26T18:58:05Z | https://api.github.com/repos/huggingface/datasets/issues/7004/comments | Fixes: https://github.com/huggingface/datasets/issues/6900
Not sure if this needs any addition stuff before merging | {
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"Ok it seems the solution is to use the directory string without the trailing \"/\" which in my case as: \r\n\r\n`parquet_dir = \"~/data/Parquet\" `\r\n\r\nStill i think this is a weird behavior... "
] | Datasetbuilder Local Download FileNotFoundError | {
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} | I_kwDODunzps6NcA0_ | null | 2024-06-25T15:02:34Z | https://api.github.com/repos/huggingface/datasets/issues/7001/comments | ### Describe the bug
So I was trying to download a dataset and save it as parquet and I follow the [tutorial](https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage) of Huggingface. However, during the excution I face a FileNotFoundError.
I debug the code and it seems there is a bug there:
So first it creates a .incomplete folder and before moving its contents the following code deletes the directory
[Code](https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/builder.py#L984)
hence as a result I face with:
``` FileNotFoundError: [Errno 2] No such file or directory: '~/data/Parquet/.incomplete '```
### Steps to reproduce the bug
```
from datasets import load_dataset_builder
from pathlib import Path
parquet_dir = "~/data/Parquet/"
Path(parquet_dir).mkdir(parents=True, exist_ok=True)
builder = load_dataset_builder(
"rotten_tomatoes",
)
builder.download_and_prepare(parquet_dir, file_format="parquet")
```
### Expected behavior
Downloads the files and saves as parquet
### Environment info
Ubuntu,
Python 3.10
```
datasets 2.19.1
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"@lhoestq Thank you for merging #6607, but unfortunately the issue persists for `IterableDataset` :pensive: ",
"Hi ! I opened https://github.com/huggingface/datasets/pull/7002 to fix this bug",
"Amazing, thank you so much @lhoestq! :pray:"
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`IterableDataset.from_generator` crashes when using BFloat16:
```
File "/usr/local/lib/python3.11/site-packages/datasets/utils/_dill.py", line 169, in _save_torchTensor
args = (obj.detach().cpu().numpy(),)
^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Got unsupported ScalarType BFloat16
```
### Steps to reproduce the bug
```python
import torch
from datasets import IterableDataset
def demo(x):
yield {"x": x}
x = torch.tensor([1.], dtype=torch.bfloat16)
dataset = IterableDataset.from_generator(
demo,
gen_kwargs=dict(x=x),
)
example = next(iter(dataset))
print(example)
```
### Expected behavior
Code sample should print:
```python
{'x': tensor([1.], dtype=torch.bfloat16)}
```
### Environment info
```
datasets==2.20.0
torch==2.2.2
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Fix #6997. | {
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```
FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other']
Right contains one more item: 'other'
Full diff:
[
'clean',
- 'other',
]
FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None
```
Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a | {
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} | 2024-06-25T06:54:40Z | https://api.github.com/repos/huggingface/datasets/issues/6996/comments | Remove deprecated code, as part of the 3.0 release.
First merge:
- [x] #6983
- [x] #6987
- [ ] #6999 | {
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"What is the version of your installed `huggingface-hub`:\r\n```python\r\nimport huggingface_hub\r\nprint(huggingface_hub.__version__)\r\n```\r\n\r\nIt seems you have a very old version of `huggingface-hub`, where `CommitInfo` was not still implemented. You need to update it:\r\n```\r\npip install -U huggingface-hu... | ImportError when importing datasets.load_dataset | {
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} | I_kwDODunzps6NTjeD | null | 2024-06-24T17:07:22Z | https://api.github.com/repos/huggingface/datasets/issues/6995/comments | ### Describe the bug
I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'.
### Steps to reproduce the bug
1. pip install git+https://github.com/huggingface/datasets
2. from datasets import load_dataset
### Expected behavior
ImportError Traceback (most recent call last)
Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1)
----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
[3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train")
[4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test")
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7
1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
[2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) #
[3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License");
(...)
[12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and
[13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License.
[15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0"
---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset
[18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction
[19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63
[61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc
[62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs
---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import (
[64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo,
[65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd,
...
[70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) )
[71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile
[72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Environment info
Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub
$ datasets-cli env
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module>
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module>
from .arrow_dataset import Dataset
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module>
from huggingface_hub import (
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
(CS224S) | {
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2,370,491,689 | https://api.github.com/repos/huggingface/datasets/issues/6994 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6994/events | [] | null | 2024-06-26T04:37:35Z | [] | https://github.com/huggingface/datasets/pull/6994 | CONTRIBUTOR | null | false | null | [
"Sure~",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6994). 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>... | Fix incorrect rank value in data splitting | {
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} | 2024-06-24T15:07:47Z | https://api.github.com/repos/huggingface/datasets/issues/6994/comments | Fix #6990. | {
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2,370,444,104 | 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/events | [] | null | 2024-07-08T13:10:53Z | [] | https://github.com/huggingface/datasets/pull/6993 | MEMBER | null | false | null | [
"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>... | less script docs | {
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} | 2024-06-24T14:45:28Z | https://api.github.com/repos/huggingface/datasets/issues/6993/comments | + mark as legacy in some parts of the docs since we'll not build new features for script datasets | {
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2,367,890,622 | 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/events | [] | null | 2024-06-25T15:43:05Z | [] | https://github.com/huggingface/datasets/issues/6992 | NONE | null | null | null | [
"Hi ! can you try updating `datasets` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U datasets huggingface_hub\r\n```"
] | Dataset with streaming doesn't work with proxy | {
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} | I_kwDODunzps6NIyS- | null | 2024-06-22T16:12:08Z | https://api.github.com/repos/huggingface/datasets/issues/6992/comments | ### 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
### Environment info
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_pytorch_select 2.0 cuda_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
abseil-cpp 20220623.0 h9888cd1_6 conda-forge
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aiofiles 23.2.1 pyhd8ed1ab_0 conda-forge
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attrs 23.1.0 py311h6ffa863_0
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c-ares 1.19.1 hf118e41_0
ca-certificates 2024.6.2 h0f6029e_0 conda-forge
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cffi 1.15.1 py311hf118e41_3
charset-normalizer 2.0.4 pyhd3eb1b0_0
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contourpy 1.0.5 py311h25e6d66_0
cryptography 41.0.3 py311hb0e80e7_0
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cycler 0.11.0 pyhd3eb1b0_0
datasets 2.12.0 py311h6ffa863_0
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distro 1.9.0 pyhd8ed1ab_0 conda-forge
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scipy 1.11.1 py311hd69e9bb_0 https://ftp.osuosl.org/pub/open-ce/1.10.0
sentencepiece 0.1.97 h1e74c73_py311_pb4.21.12_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
setuptools 68.0.0 py311h6ffa863_0
six 1.16.0 pyhd3eb1b0_1
snappy 1.1.9 h29c3540_0
sqlite 3.41.2 hf118e41_0
sympy 1.12.1 pypyh2585a3b_103 conda-forge
tabulate 0.8.10 pyhd8ed1ab_0 conda-forge
tensorboard 2.13.0 pyhab0730d_pb4.21.12_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tensorboard-data-server 0.7.0 pyh6f84499_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tensorboard-plugin-wit 1.6.0 pyh9f0ad1d_0 conda-forge
tk 8.6.13 hd4bbf49_0 conda-forge
tokenizers 0.13.3 py311h3d4f45a_0
torchdata 0.6.0 py311_2 https://ftp.osuosl.org/pub/open-ce/1.10.0
torchsnapshot 0.1.0 pyhd8ed1ab_0 conda-forge
torchtext-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
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torchvision-base 0.15.2 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0
tornado 6.3.3 py311hf118e41_0
tqdm 4.65.0 py311h7837921_0
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truststore 0.8.0 py311h6ffa863_0
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tzdata 2023c h04d1e81_0
urllib3 1.26.18 py311h6ffa863_0
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zstandard 0.19.0 py311hf118e41_0
zstd 1.5.5 h57e4825_0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6991). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@albertvillanova Any chance we could get this in before the next release? Everything d... | Unblock NumPy 2.0 | {
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} | 2024-06-22T09:19:53Z | https://api.github.com/repos/huggingface/datasets/issues/6991/comments | Fixes https://github.com/huggingface/datasets/issues/6980 | {
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2,366,660,785 | https://api.github.com/repos/huggingface/datasets/issues/6990 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6990/events | [] | null | 2024-06-25T16:19:19Z | [] | https://github.com/huggingface/datasets/issues/6990 | CONTRIBUTOR | completed | null | null | [
"ah yes good catch ! feel free to open a PR with your suggested fix"
] | Problematic rank after calling `split_dataset_by_node` twice | {
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} | I_kwDODunzps6NEGCx | null | 2024-06-21T14:25:26Z | https://api.github.com/repos/huggingface/datasets/issues/6990/comments | ### Describe the bug
I'm trying to split `IterableDataset` by `split_dataset_by_node`.
But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`.
### Steps to reproduce the bug
Here is the minimal code for reproduction:
```py
>>> from datasets import load_dataset
>>> from datasets.distributed import split_dataset_by_node
>>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True)
>>> dataset = split_dataset_by_node(dataset, 1, 32)
>>> dataset._distributed
DistributedConfig(rank=1, world_size=32)
>>> dataset = split_dataset_by_node(dataset, 1, 15)
>>> dataset._distributed
DistributedConfig(rank=481, world_size=480)
```
As you can see, the second rank 481 > 480, which is problematic.
### Expected behavior
I think this error comes from this line @lhoestq
https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944
We may need to obtain the rank first. Then the above code gives
```py
>>> dataset._distributed
DistributedConfig(rank=16, world_size=480)
```
### Environment info
datasets==2.20.0 | {
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2,365,556,449 | https://api.github.com/repos/huggingface/datasets/issues/6989 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6989/events | [] | null | 2024-06-21T02:12:55Z | [] | https://github.com/huggingface/datasets/issues/6989 | NONE | null | null | null | [] | cache in nfs error | {
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} | I_kwDODunzps6M_4bh | null | 2024-06-21T02:09:22Z | https://api.github.com/repos/huggingface/datasets/issues/6989/comments | ### Describe the bug
- When reading dataset, a cache will be generated to the ~/. cache/huggingface/datasets directory
- When using .map and .filter operations, runtime cache will be generated to the /tmp/hf_datasets-* directory
- The default is to use the path of tempfile.tempdir
- If I modify this path to the NFS disk, an error will be reported, but the program will continue to run
- https://github.com/huggingface/datasets/blob/main/src/datasets/config.py#L257
```
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs000000038330a012000030b4'
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs0000000400064d4a000030e5'
```
### Steps to reproduce the bug
```
import os
import time
import tempfile
from datasets import load_dataset
def add_column(sample):
# print(type(sample))
# time.sleep(0.1)
sample['__ds__stats__'] = {'data': 123}
return sample
def filt_column(sample):
# print(type(sample))
if len(sample['content']) > 10:
return True
else:
return False
if __name__ == '__main__':
input_dir = '/mnt/temp/CN/small' # some json dataset
dataset = load_dataset('json', data_dir=input_dir)
temp_dir = '/media/release/release/temp/temp' # a nfs folder
os.makedirs(temp_dir, exist_ok=True)
# change huggingface-datasets runtime cache in nfs（default in /tmp）
tempfile.tempdir = temp_dir
aa = dataset.map(add_column, num_proc=64)
aa = aa.filter(filt_column, num_proc=64)
print(aa)
```
### Expected behavior
no error occur
### Environment info
datasets==2.18.0
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6988). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"`Dataset` objects are not made to be subclassed, so I don't think going in that directi... | [`feat`] Move dataset card creation to method for easier overriding | {
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} | 2024-06-20T10:47:57Z | https://api.github.com/repos/huggingface/datasets/issues/6988/comments | Hello!
## Pull Request overview
* Move dataset card creation to method for easier overriding
## Details
It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co/datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either:
1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time.
2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card.
So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case:
````python
import json
from typing import Any, Dict, Optional
from datasets import Dataset, load_dataset
from datasets.info import DatasetInfosDict, DatasetInfo
from datasets.utils.metadata import MetadataConfigs
from huggingface_hub import DatasetCardData, DatasetCard
TEMPLATE = r"""---
{dataset_card_data}
---
# Dataset Card for {source_dataset_name} with mined hard negatives
This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co/datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.
## Mining Parameters
The negative samples have been mined using the following parameters:
- `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples
- `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples
- `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer
- `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives
- `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair
## Dataset Format
- Columns: {column_one}, {column_two}, negative
- Column types: str, str, str
- Example:
```python
{example}
```
"""
class HNMDataset(Dataset):
@classmethod
def from_dict(cls, *args, mining_kwargs: Dict[str, Any], **kwargs) -> "HNMDataset":
dataset = super().from_dict(*args, **kwargs)
dataset.mining_kwargs = mining_kwargs
return dataset
def _create_dataset_card(
self,
dataset_card_data: DatasetCardData,
dataset_card: Optional[DatasetCard],
config_name: str,
info_to_dump: DatasetInfo,
metadata_config_to_dump: MetadataConfigs,
) -> DatasetCard:
if dataset_card:
return dataset_card
DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)
MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)
dataset_card_data.tags = ["sentence-transformers"]
dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name
# Very messy, just as an example:
dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0]
content = TEMPLATE.format(**{
"dataset_card_data": str(dataset_card_data),
"source_dataset_name": dataset_name,
"source_dataset_id": dataset_id,
"range_min": self.mining_kwargs["range_min"],
"range_max": self.mining_kwargs["range_max"],
"margin": self.mining_kwargs["margin"],
"sampling_strategy": self.mining_kwargs["sampling_strategy"],
"num_negatives": self.mining_kwargs["num_negatives"],
"column_one": self.column_names[0],
"column_two": self.column_names[1],
"example": json.dumps(self[0], indent=4),
})
return DatasetCard(content)
source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]")
dataset = HNMDataset.from_dict({
"query": source_dataset["question"],
"answer": source_dataset["answer"],
# "negative": ... <- In my case, this column would be 'mined' automatically with these parameters
}, mining_kwargs={
"range_min": 10,
"range_max": 20,
"max_score": 0.9,
"margin": 0.1,
"sampling_strategy": "random",
"num_negatives": 3,
"source_dataset": source_dataset,
})
dataset.push_to_hub("tomaarsen/mining_demo", private=True)
````
In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co/datasets/tomaarsen/mining_demo
This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card.
## Note
I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :)
cc @albertvillanova @lhoestq
cc @LysandreJik
- Tom Aarsen | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | Remove beam | {
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"@albertvillanova @KennethEnevoldsen"
] | Add large_list type support in string_to_arrow | {
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} | 2024-06-19T14:54:25Z | https://api.github.com/repos/huggingface/datasets/issues/6986/comments | add large_list type support in string_to_arrow() and _arrow_to_datasets_dtype() in features.py
Fix #6984
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"Please note that the error is raised just at import:\r\n```python\r\nimport pyarrow.parquet as pq\r\n```\r\n\r\nTherefore it must be caused by some problem with your pyarrow installation. I would recommend you uninstall and install pyarrow again.\r\n\r\nI also see that it seems you use conda to install pyarrow. Pl... | AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType' | {
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} | I_kwDODunzps6Mzwgk | null | 2024-06-19T13:22:28Z | https://api.github.com/repos/huggingface/datasets/issues/6985/comments | ### Describe the bug
I have been struggling with this for two days, any help would be appreciated. Python 3.10
```
from setfit import SetFitModel
from huggingface_hub import login
access_token_read = "cccxxxccc"
# Authenticate with the Hugging Face Hub
login(token=access_token_read)
# Load the models from the Hugging Face Hub
trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance")
trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust")
trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent")
trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic")
```
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 from setfit import SetFitModel
2 from huggingface_hub import login
4 access_token_read = "ccsddsds"
File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7
4 import os
5 import warnings
----> 7 from .data import get_templated_dataset, sample_dataset
8 from .model_card import SetFitModelCardData
9 from .modeling import SetFitHead, SetFitModel
File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5
3 import pandas as pd
4 import torch
----> 5 from datasets import Dataset, DatasetDict, load_dataset
6 from torch.utils.data import Dataset as TorchDataset
8 from . import logging
File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18
1 # ruff: noqa
2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
3 #
(...)
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
16 __version__ = "2.19.0"
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76
73 from tqdm.contrib.concurrent import thread_map
75 from . import config
---> 76 from .arrow_reader import ArrowReader
77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
78 from .data_files import sanitize_patterns
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29
26 from typing import TYPE_CHECKING, List, Optional, Union
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
32 from .download.download_config import DownloadConfig
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20
1 # Licensed to the Apache Software Foundation (ASF) under one
2 # or more contributor license agreements. See the NOTICE file
3 # distributed with this work for additional information
(...)
17
18 # flake8: noqa
---> 20 from .core import *
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33
30 import pyarrow as pa
32 try:
---> 33 import pyarrow._parquet as _parquet
34 except ImportError as exc:
35 raise ImportError(
36 "The pyarrow installation is not built with support "
37 f"for the Parquet file format ({str(exc)})"
38 ) from None
File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()
AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
```
setfit: 1.0.3
transformers: 4.41.2
lingua-language-detector: 2.0.2
polars: 0.20.31
lightning: None
google-cloud-bigquery: 3.24.0
shapely: 2.0.4
pyarrow: 16.0.0
### Steps to reproduce the bug
I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have.
### Expected behavior
Just ron normally.
### Environment info
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"Hi ! Thanks for reporting :)\r\n\r\nWe don't support `large_list` yet, though it should be added to `Sequence` IMO (maybe with a parameter `large=True` ?)"
] | Convert polars DataFrame back to datasets | {
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} | I_kwDODunzps6My3NC | null | 2024-06-19T11:38:48Z | https://api.github.com/repos/huggingface/datasets/issues/6984/comments | ### Feature request
This returns error.
```python
from datasets import Dataset
dsdf = Dataset.from_dict({"x": [[1, 2], [3, 4, 5]], "y": ["a", "b"]})
Dataset.from_polars(dsdf.to_polars())
```
ValueError: Arrow type large_list<item: int64> does not have a datasets dtype equivalent.
### Motivation
When datasets contain Sequence data type, it will be converted to Arrow type large_list. However, the reverse (from large_list to Sequence) does not work.
### Your contribution
No | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | Remove metrics | {
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} | 2024-06-19T09:08:55Z | https://api.github.com/repos/huggingface/datasets/issues/6983/comments | Remove all metrics, as part of the 3.0 release.
Note they are deprecated since 2.5.0 version. | {
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"it seems the bug will happened in all windows system, I tried it in windows8.1, 10, 11 and all of them failed. But it won't happened in the Linux(Ubuntu and Centos7) and Mac (both my virtual and physical machine). I still don't know what the problem is. May be related to the path? I cannot run the split file in m... | cannot split dataset when using load_dataset | {
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} | I_kwDODunzps6MxBgd | null | 2024-06-19T08:07:16Z | https://api.github.com/repos/huggingface/datasets/issues/6982/comments | ### Describe the bug
when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document,
This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generating train split and validation split but bug happen again in test split.
### Steps to reproduce the bug
from datasets import load_dataset, load_metric, Audio
common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True)
### Expected behavior
```
{
"name": "ValueError",
"message": "Instruction \"train\" corresponds to no data!",
"stack": "---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 3
1 from datasets import load_dataset, load_metric, Audio
----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True)
4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2622 # Build dataset for splits
2623 keep_in_memory = (
2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2625 )
-> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2627 # Rename and cast features to match task schema
2628 if task is not None:
2629 # To avoid issuing the same warning twice
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1265 # Create a dataset for each of the given splits
-> 1266 datasets = map_nested(
1267 partial(
1268 self._build_single_dataset,
1269 run_post_process=run_post_process,
1270 verification_mode=verification_mode,
1271 in_memory=in_memory,
1272 ),
1273 split,
1274 map_tuple=True,
1275 disable_tqdm=True,
1276 )
1277 if isinstance(datasets, dict):
1278 datasets = DatasetDict(datasets)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)
482 if batched:
483 data_struct = [data_struct]
--> 484 mapped = function(data_struct)
485 if batched:
486 mapped = mapped[0]
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory)
1293 split = Split(split)
1295 # Build base dataset
-> 1296 ds = self._as_dataset(
1297 split=split,
1298 in_memory=in_memory,
1299 )
1300 if run_post_process:
1301 for resource_file_name in self._post_processing_resources(split).values():
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory)
1368 if self._check_legacy_cache():
1369 dataset_name = self.name
-> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read(
1371 name=dataset_name,
1372 instructions=split,
1373 split_infos=self.info.splits.values(),
1374 in_memory=in_memory,
1375 )
1376 fingerprint = self._get_dataset_fingerprint(split)
1377 return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory)
254 msg = f'Instruction \"{instructions}\" corresponds to no data!'
255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!'
--> 256 raise ValueError(msg)
257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
ValueError: Instruction \"train\" corresponds to no data!"
}
```
### Environment info
Environment:
python 3.9
windows 11 pro
VScode+jupyter | {
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The docs needed to be updated due to this PR:
- #6954 | {
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} | I_kwDODunzps6MuKBq | null | 2024-06-18T23:30:22Z | https://api.github.com/repos/huggingface/datasets/issues/6980/comments | ### Feature request
Support NumPy 2.0.
### Motivation
NumPy introduces the Array API, which bridges the gap between machine learning libraries. Many clients of HuggingFace are eager to start using the Array API.
Besides that, NumPy 2 provides a cleaner interface than NumPy 1.
### Tasks
NumPy 2.0 was released for testing so that libraries could ensure compatibility [since mid-March](https://github.com/numpy/numpy/issues/24300#issuecomment-1986815755). What needs to be done for HuggingFace to support Numpy 2?
- [x] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976
- [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975): https://github.com/huggingface/datasets/pull/6991 | {
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"Hello,\r\n\r\nHave you tried loading the dataset in streaming mode? [Documentation](https://huggingface.co/docs/datasets/v2.20.0/stream)\r\n\r\nThis way you wouldn't have to load it all. Also, let's be nice to Parquet, it's a really nice technology and we don't need to be mean :)",
"I have downloaded part of it,... | How can I load partial parquet files only? | {
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} | I_kwDODunzps6MrWsD | null | 2024-06-18T15:44:16Z | https://api.github.com/repos/huggingface/datasets/issues/6979/comments | I have a HUGE dataset about 14TB, I unable to download all parquet all. I just take about 100 from it.
dataset = load_dataset("xx/", data_files="data/train-001*-of-00314.parquet")
How can I just using 000 - 100 from a 00314 from all partially?
I search whole net didn't found a solution, **this is stupid if they didn't support it, and I swear I wont using stupid parquet any more**
| {
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2,359,511,469 | https://api.github.com/repos/huggingface/datasets/issues/6978 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6978/events | [] | null | 2024-06-19T06:23:24Z | [] | https://github.com/huggingface/datasets/pull/6978 | MEMBER | null | false | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6978). 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>... | Fix regression for pandas < 2.0.0 in JSON loader | {
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} | 2024-06-18T10:26:34Z | https://api.github.com/repos/huggingface/datasets/issues/6978/comments | A regression was introduced for pandas < 2.0.0 in PR:
- #6914
As described in pandas docs, the `dtype_backend` parameter was first added in pandas 2.0.0: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html
This PR fixes the regression by passing (or not) the `dtype_backend` parameter depending on pandas version.
Maybe, in a future 3.0 `datasets` release, we could just require pandas > 2.0.
Reported by:
- #6977 | {
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2,359,295,045 | https://api.github.com/repos/huggingface/datasets/issues/6977 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6977/events | [] | null | 2024-06-18T10:06:10Z | [
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | https://github.com/huggingface/datasets/issues/6977 | NONE | completed | null | null | [
"Thanks for reporting, @xiaoyaolangzhi.\r\n\r\nIndeed, we are currently requiring `pandas` >= 2.0.0.\r\n\r\nYou will need to update pandas in your local environment:\r\n```\r\npip install -U pandas\r\n``` ",
"Thank you very much."
] | load json file error with v2.20.0 | {
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} | I_kwDODunzps6Mn_xF | null | 2024-06-18T08:41:01Z | https://api.github.com/repos/huggingface/datasets/issues/6977/comments | ### Describe the bug
```
load_dataset(path="json", data_files="./test.json")
```
```
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 132, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1997, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 155, in _generate_tables
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/t1.py", line 11, in <module>
load_dataset(path=data_path, data_files="./t2.json")
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2616, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 2040, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
```
```
import pandas as pd
with open("./test.json", "r") as f:
df = pd.read_json(f, dtype_backend="pyarrow")
```
```
Traceback (most recent call last):
File "/app/t3.py", line 3, in <module>
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
```
datasets 2.20.0
pandas 1.5.3
``` | {
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2,357,107,203 | https://api.github.com/repos/huggingface/datasets/issues/6976 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6976/events | [] | null | 2024-06-19T14:30:32Z | [] | https://github.com/huggingface/datasets/pull/6976 | CONTRIBUTOR | null | false | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6976). 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>... | Ensure compatibility with numpy 2.0.0 | {
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} | 2024-06-17T11:29:22Z | https://api.github.com/repos/huggingface/datasets/issues/6976/comments | Following the conversion guide, copy=False is no longer required and will result in an error: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword.
The following fix should resolve the issue.
error found during testing on the MTEB repository e.g. [here](https://github.com/embeddings-benchmark/mteb/pull/938) | {
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2,357,003,959 | https://api.github.com/repos/huggingface/datasets/issues/6975 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6975/events | [] | null | 2024-06-17T12:49:53Z | [] | https://github.com/huggingface/datasets/pull/6975 | MEMBER | null | false | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6975). 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>... | Set temporary numpy upper version < 2.0.0 to fix CI | {
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} | 2024-06-17T10:36:54Z | https://api.github.com/repos/huggingface/datasets/issues/6975/comments | Set temporary numpy upper version < 2.0.0 to fix CI. See: https://github.com/huggingface/datasets/actions/runs/9546031216/job/26308072017
```
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
``` | {
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2,355,517,362 | https://api.github.com/repos/huggingface/datasets/issues/6973 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6973/events | [] | null | 2024-07-01T11:25:40Z | [] | https://github.com/huggingface/datasets/issues/6973 | NONE | completed | null | null | [
"add remove_unused_columns=False to training_args\r\nhttps://github.com/huggingface/datasets/issues/6535#issuecomment-1874024704",
"Closing this issue because it was a reported and fixed in transformers."
] | IndexError during training with Squad dataset and T5-small model | {
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} | I_kwDODunzps6MZley | null | 2024-06-16T07:53:54Z | https://api.github.com/repos/huggingface/datasets/issues/6973/comments | ### Describe the bug
I am encountering an IndexError while training a T5-small model on the Squad dataset using the transformers and datasets libraries. The error occurs even with a minimal reproducible example, suggesting a potential bug or incompatibility.
### Steps to reproduce the bug
1.Install the required libraries: !pip install transformers datasets
2.Run the following code:
!pip install transformers datasets
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer, DataCollatorWithPadding
# Load a small, publicly available dataset
from datasets import load_dataset
dataset = load_dataset("squad", split="train[:100]") # Use a small subset for testing
# Load a pre-trained model and tokenizer
model_name = "t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Define a basic data collator
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
per_device_train_batch_size=2,
num_train_epochs=1,
)
# Create a trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
data_collator=data_collator,
)
# Train the model
trainer.train()
### Expected behavior
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
[<ipython-input-23-f13a4b23c001>](https://localhost:8080/#) in <cell line: 34>()
32
33 # Train the model
---> 34 trainer.train()
10 frames
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size)
427 if isinstance(key, int):
428 if (key < 0 and key + size < 0) or (key >= size):
--> 429 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
430 return
431 elif isinstance(key, slice):
IndexError: Invalid key: 42 is out of bounds for size 0
### Environment info
transformers version:4.41.2
datasets version:1.18.4
Python version:3.10.12
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This is important to make streaming datasets compatible with multiprocessing e.g. for parallel data loading | {
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The changes are backward-compatible meaning that:
- `HF_DATASETS_OFFLINE` environment is still taken into account, though not documented
- `datasets.config.HF_DATASETS_OFFLINE` still exists, though it is not used anymore (in favor of `datasets.config.HF_HUB_OFFLINE`)
**Note:** it might break things in downstream libraries if they were monkeypatching `datasets.config.HF_DATASETS_OFFLINE` in their CI tests (for instance). Not much of a problem IMO. | {
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} | I_kwDODunzps6MBSEe | null | 2024-06-12T16:04:04Z | https://api.github.com/repos/huggingface/datasets/issues/6967/comments | ### Feature request
Large datasets like Laion400m are provided as embeddings. The provided methods in load_dataset are not straightforward for loading embedding files, i.e. img_emb_XX.npy ; XX = 0 to 99
### Motivation
The trial and experimentation is the key pivot of HF. It would be great if HF can load embeddings files s,ealessly.
### Your contribution
I cam write the loader with some help. | {
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## After:
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} | 2024-06-12T12:30:27Z | https://api.github.com/repos/huggingface/datasets/issues/6965/comments | set the right behavior of skip/take depending on whether it's called after or before shuffle/split_by_node | {
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2,344,269,477 | https://api.github.com/repos/huggingface/datasets/issues/6963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6963/events | [] | null | 2024-06-28T09:53:11Z | [] | https://github.com/huggingface/datasets/pull/6963 | MEMBER | null | false | null | [
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"ci failures are r-unrelated to this PR, merging",
"<details>\n<summary>Show benchmark... | [Streaming] retry on requests errors | {
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} | 2024-06-10T15:51:56Z | https://api.github.com/repos/huggingface/datasets/issues/6963/comments | reported in https://discuss.huggingface.co/t/speeding-up-streaming-of-large-datasets-fineweb/90714/6 when training using a streaming a dataloader
cc @Wauplin it looks like the retries from `hfh` are not always enough. In this PR I let `datasets` do additional retries (that users can configure in `datasets.config`) since I couldn't find an easy way to increase the max_retries for `hfh` users in general. | {
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2,343,394,378 | https://api.github.com/repos/huggingface/datasets/issues/6962 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6962/events | [] | null | 2024-06-11T08:31:52Z | [] | https://github.com/huggingface/datasets/pull/6962 | MEMBER | null | false | null | [
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} | 2024-06-10T09:28:02Z | https://api.github.com/repos/huggingface/datasets/issues/6962/comments | ### What does this PR do?
Remove unnecessary permissions granted to the actions workflow.
Sorry for the mishap. | {
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2,342,022,418 | https://api.github.com/repos/huggingface/datasets/issues/6961 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6961/events | [
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"We're unlikely to add more features/support for datasets with python loading scripts, which include datasets with manual download. Sorry for the inconvenience"
] | Manual downloads should count as downloads | {
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} | I_kwDODunzps6LmG0S | null | 2024-06-09T04:52:06Z | https://api.github.com/repos/huggingface/datasets/issues/6961/comments | ### Feature request
I would like to request that manual downloads of data files from Hugging Face dataset repositories count as downloads of a dataset. According to the documentation for the Hugging Face Hub, that is currently not the case: https://huggingface.co/docs/hub/en/datasets-download-stats
### Motivation
This would ensure that downloads are accurately reported to end users.
### Your contribution
N/A | {
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2,340,791,685 | https://api.github.com/repos/huggingface/datasets/issues/6960 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6960/events | [] | null | 2024-06-08T14:58:27Z | [] | https://github.com/huggingface/datasets/pull/6960 | MEMBER | null | false | null | [
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"Yes!",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\... | feat(ci): add trufflehog secrets detection | {
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} | 2024-06-07T16:18:23Z | https://api.github.com/repos/huggingface/datasets/issues/6960/comments | ### What does this PR do?
Adding a GH action to scan for leaked secrets on each commit.
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2,340,229,908 | https://api.github.com/repos/huggingface/datasets/issues/6959 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6959/events | [] | null | 2024-06-10T07:33:53Z | [] | https://github.com/huggingface/datasets/pull/6959 | CONTRIBUTOR | null | false | null | [
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"Test should be fixed by https://github.com/huggingface/datasets/pull/6959/commits/ef8f7... | Better error handling in `dataset_module_factory` | {
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This PR updates how errors are handled in `dataset_module_factory` when the `dataset_info` cannot be accessed:
1. Use multiple `except ... as e` instead of using `isinstance(e, ...)`
2. Always raise `DatasetNotFoundError` with `from e` so that the initial error is explicitly logged in the stacktrace.
3. Differentiate `RepoNotFoundError` / `GatedRepoError` / `RevisionNotFoundError` cases | {
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2,337,476,383 | https://api.github.com/repos/huggingface/datasets/issues/6958 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6958/events | [] | null | 2024-07-01T11:27:46Z | [] | https://github.com/huggingface/datasets/issues/6958 | NONE | completed | null | null | [
"I can load public dataset, but for my private dataset it fails",
"https://huggingface.co/docs/datasets/upload_dataset",
"I have checked the API HTTP link. Repository Not Found for url: https://huggingface.co/api/datasets/xxx/xxx.\r\n\r\n![image](https://github.com/huggingface/datasets/assets/39621324/4aceef59-... | My Private Dataset doesn't exist on the Hub or cannot be accessed | {
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} | I_kwDODunzps6LUw8f | null | 2024-06-06T06:52:19Z | https://api.github.com/repos/huggingface/datasets/issues/6958/comments | ### Describe the bug
```
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1852, in dataset_module_factory
raise DatasetNotFoundError(msg + f" at revision '{revision}'" if revision else msg)
datasets.exceptions.DatasetNotFoundError: Dataset 'xxx' doesn't exist on the Hub or cannot be accessed
>>> dataset = load_dataset("xxxx", token=True)
404 error 404 Client Error. (Request ID: Root=xxxx)
Repository Not Found for url: https://huggingface.co/api/datasets/xxx/xxx.
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.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 2593, in load_dataset
builder_instance = load_dataset_builder(
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 2265, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory
raise e1 from None
File "/root/miniconda3/envs/gino_conda/lib/python3.9/site-packages/datasets/load.py", line 1852, in dataset_module_factory
raise DatasetNotFoundError(msg + f" at revision '{revision}'" if revision else msg)
datasets.exceptions.DatasetNotFoundError: Dataset 'xxx' doesn't exist on the Hub or cannot be accessed
```
### Steps to reproduce the bug
123
### Expected behavior
123
### Environment info
123 | {
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- #6956
Typos:
- `comparisions` => `comparisons`
- two consecutive sentences both ending in colon
- split one sentence into two
Sorry, I did not have time to review that PR.
CC: @lhoestq | {
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- [x] fix tests | {
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Additionally rename some of them, deprecate the former ones, and make the deprecation backward compatible (by making the new errors inherit from the former ones). | {
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"@xianbaoqian ",
"Feel free to open a PR in `m-a-p/COIG-CQIA` to define a default subset. Currently there is no default.\r\n\r\nYou can find some documentation at https://huggingface.co/docs/hub/datasets-manual-configuration#multiple-configurations",
"@lhoestq \r\n\r\nWhilst having a default subset readily avai... | load_dataset() should load all subsets, if no specific subset is specified | {
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} | I_kwDODunzps6LEkfC | null | 2024-06-04T11:02:33Z | https://api.github.com/repos/huggingface/datasets/issues/6951/comments | ### Feature request
Currently load_dataset() is forcing users to specify a subset. Example
`from datasets import load_dataset
dataset = load_dataset("m-a-p/COIG-CQIA")`
```---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-10-c0cb49385da6>](https://localhost:8080/#) in <cell line: 2>()
1 from datasets import load_dataset
----> 2 dataset = load_dataset("m-a-p/COIG-CQIA")
3 frames
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _create_builder_config(self, config_name, custom_features, **config_kwargs)
582 if not config_kwargs:
583 example_of_usage = f"load_dataset('{self.dataset_name}', '{self.BUILDER_CONFIGS[0].name}')"
--> 584 raise ValueError(
585 "Config name is missing."
586 f"\nPlease pick one among the available configs: {list(self.builder_configs.keys())}"
ValueError: Config name is missing.
Please pick one among the available configs: ['chinese_traditional', 'coig_pc', 'exam', 'finance', 'douban', 'human_value', 'logi_qa', 'ruozhiba', 'segmentfault', 'wiki', 'wikihow', 'xhs', 'zhihu']
Example of usage:
`load_dataset('coig-cqia', 'chinese_traditional')`
```
This means a dataset cannot contain all the subsets at the same time. I guess one workaround is to manually specify the subset files like in [here](https://huggingface.co/datasets/m-a-p/COIG-CQIA/discussions/1#658698b44bb41498f75c5622), which is clumsy.
### Motivation
Ideally, if not subset is specified, the API should just try to load all subsets. This makes it much easier to handle datasets w/ subsets.
### Your contribution
Not sure since I'm not familiar w/ the lib src. | {
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2,333,005,974 | https://api.github.com/repos/huggingface/datasets/issues/6950 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6950/events | [
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"Hi ! It seems the documentation was outdated in this paragraph\r\n\r\nI fixed it here: https://github.com/huggingface/datasets/pull/6956",
"Fixed."
] | `Dataset.with_format` behaves inconsistently with documentation | {
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} | I_kwDODunzps6LDtiW | null | 2024-06-04T09:18:32Z | https://api.github.com/repos/huggingface/datasets/issues/6950/comments | ### Describe the bug
The actual behavior of the interface `Dataset.with_format` is inconsistent with the documentation.
https://huggingface.co/docs/datasets/use_with_pytorch#n-dimensional-arrays
https://huggingface.co/docs/datasets/v2.19.0/en/use_with_tensorflow#n-dimensional-arrays
> If your dataset consists of N-dimensional arrays, you will see that by default they are considered as nested lists.
> In particular, a PyTorch formatted dataset outputs nested lists instead of a single tensor.
> A TensorFlow formatted dataset outputs a RaggedTensor instead of a single tensor.
But I get a single tensor by default, which is inconsistent with the description.
Actually the current behavior seems more reasonable to me. Therefore, the document needs to be modified.
### Steps to reproduce the bug
```python
>>> from datasets import Dataset
>>> data = [[[1, 2],[3, 4]],[[5, 6],[7, 8]]]
>>> ds = Dataset.from_dict({"data": data})
>>> ds = ds.with_format("torch")
>>> ds[0]
{'data': tensor([[1, 2],
[3, 4]])}
>>> ds = ds.with_format("tf")
>>> ds[0]
{'data': <tf.Tensor: shape=(2, 2), dtype=int64, numpy=
array([[1, 2],
[3, 4]])>}
```
### Expected behavior
```python
>>> from datasets import Dataset
>>> data = [[[1, 2],[3, 4]],[[5, 6],[7, 8]]]
>>> ds = Dataset.from_dict({"data": data})
>>> ds = ds.with_format("torch")
>>> ds[0]
{'data': [tensor([1, 2]), tensor([3, 4])]}
>>> ds = ds.with_format("tf")
>>> ds[0]
{'data': <tf.RaggedTensor [[1, 2], [3, 4]]>}
```
### Environment info
datasets==2.19.1
torch==2.1.0
tensorflow==2.13.1 | {
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2,332,336,573 | https://api.github.com/repos/huggingface/datasets/issues/6949 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6949/events | [] | null | 2024-07-01T11:33:46Z | [] | https://github.com/huggingface/datasets/issues/6949 | NONE | completed | null | null | [
"Hi, @lion-ops.\r\n\r\nIn our Continuous Integration we have many tests on loading JSON files and all of them work properly.\r\n\r\nCould you please share your \"train.json\" file, so that we can try to reproduce the issue you have? ",
"> Hi, @lion-ops.\r\n> \r\n> In our Continuous Integration we have many tests ... | load_dataset error | {
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} | I_kwDODunzps6LBKG9 | null | 2024-06-04T01:24:45Z | https://api.github.com/repos/huggingface/datasets/issues/6949/comments | ### Describe the bug
Why does the program get stuck when I use load_dataset method, and it still gets stuck after loading for several hours? In fact, my json file is only 21m, and I can load it in one go using open('', 'r').
### Steps to reproduce the bug
1. pip install datasets==2.19.2
2. from datasets import Dataset, DatasetDict, NamedSplit, Split, load_dataset
3. data = load_dataset('json', data_files='train.json')
### Expected behavior
It is able to load my json correctly
### Environment info
datasets==2.19.2 | {
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2,331,758,300 | https://api.github.com/repos/huggingface/datasets/issues/6948 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6948/events | [] | null | 2024-06-03T18:10:57Z | [] | https://github.com/huggingface/datasets/issues/6948 | NONE | null | null | null | [] | to_tf_dataset: Visible devices cannot be modified after being initialized | {
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} | I_kwDODunzps6K-87c | null | 2024-06-03T18:10:57Z | https://api.github.com/repos/huggingface/datasets/issues/6948/comments | ### Describe the bug
When trying to use to_tf_dataset with a custom data_loader collate_fn when I use parallelism I am met with the following error as many times as number of workers there were in ``num_workers``.
File "/opt/miniconda/envs/env/lib/python3.11/site-packages/multiprocess/process.py", line 314, in _bootstrap
self.run()
File "/opt/miniconda/envs/env/lib/python3.11/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/opt/miniconda/envs/env/lib/python3.11/site-packages/datasets/utils/tf_utils.py", line 438, in worker_loop
tf.config.set_visible_devices([], "GPU") # Make sure workers don't try to allocate GPU memory
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda/envs/env/lib/python3.11/site-packages/tensorflow/python/framework/config.py", line 566, in set_visible_devices
context.context().set_visible_devices(devices, device_type)
File "/opt/miniconda/envs/env/lib/python3.11/site-packages/tensorflow/python/eager/context.py", line 1737, in set_visible_devices
raise RuntimeError(
RuntimeError: Visible devices cannot be modified after being initialized
### Steps to reproduce the bug
1. Download a dataset using HuggingFace load_dataset
2. Define a function that transforms the data in some way to be used in the collate_fn argument
3. Provide a ``batch_size`` and ``num_workers`` value in the ``to_tf_dataset`` function
4. Either retrieve directly or use tfds benchmark to test the dataset
``` python
from datasets import load_datasets
import tensorflow_datasets as tfds
from keras_cv.layers import Resizing
def data_loader(examples):
x = Resizing(examples[0]['image'], 256, 256, crop_to_aspect_ratio=True)
return {X[0]: x}
ds = load_datasets("logasja/FDF", split="test")
ds = ds.to_tf_dataset(collate_fn=data_loader, batch_size=16, num_workers=2)
tfds.benchmark(ds)
```
### Expected behavior
Use multiple processes to apply transformations from the collate_fn to the tf dataset on the CPU.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-1023-oracle-x86_64-with-glibc2.35
- Python version: 3.11.8
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0 | {
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"same problem here",
"Hello,\r\n\r\nAre you sure you are really using datasets version 2.19.2? We just made the patch release yesterday specifically to fix this issue:\r\n- #6925\r\n\r\nI can't reproduce the error:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset('allenai... | FileNotFoundError：error when loading C4 dataset | {
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} | I_kwDODunzps6K8fpH | null | 2024-06-03T13:06:33Z | https://api.github.com/repos/huggingface/datasets/issues/6947/comments | ### Describe the bug
can't load c4 datasets
When I replace the datasets package to 2.12.2 I get raise datasets.utils.info_utils.ExpectedMoreSplits: {'train'}
How can I fix this？
### Steps to reproduce the bug
1.from datasets import load_dataset
2.dataset = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation')
3. raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at local_path/c4_val/allenai/c4/c4.py or any data file in the same directory. Couldn't find 'allenai/c4' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/allenai/c4@1588ec454efa1a09f29cd18ddd04fe05fc8653a2/en/c4-validation.00003-of-00008.json.gz' 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']
### Expected behavior
The data was successfully imported
### Environment info
python version 3.9
datasets version 2.19.2 | {
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} | 2024-06-03T06:24:47Z | https://api.github.com/repos/huggingface/datasets/issues/6946/comments | Re-enable import sorting that was wrongly disabled by `flake8: noqa` directive after switching to `ruff` linter in datasets-2.10.0 PR:
- #5519
Note that after the linter switch, we wrongly replaced `flake8: noqa` with `ruff: noqa` in datasets-2.17.0 PR:
- #6619
That replacement was wrong because we kept the `isort: skip` directives although they were indeed disabled by `flake8: noqa` first and by `ruff: noqa` afterwards. See for example `__init__.py` file after the linter switch:
- We kept the `flake8: noqa` directive
https://github.com/huggingface/datasets/blob/06ae3f678651bfbb3ca7dd3274ee2f38e0e0237e/src/datasets/__init__.py#L1
- Whereas we also kept the `isort: skip` directives (that were disabled)
https://github.com/huggingface/datasets/blob/06ae3f678651bfbb3ca7dd3274ee2f38e0e0237e/src/datasets/__init__.py#L82-L84
Fix #6942. | {
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Version 2.32.1 was yanked: https://pypi.org/project/requests/2.32.1/ | {
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- #5519
import sorting was disabled in all files containing the `# flake8: noqa` directive
- https://github.com/astral-sh/ruff/issues/11679
We should re-enable import sorting on those files. | {
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} | I_kwDODunzps6K0Kd1 | null | 2024-06-01T05:34:52Z | https://api.github.com/repos/huggingface/datasets/issues/6941/comments | ### Feature request
Supporting FFCV, https://github.com/libffcv/ffcv
### Motivation
According to the benchmark, FFCV seems to be fastest image loading method.
### Your contribution
no | {
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} | I_kwDODunzps6KzDGH | null | 2024-05-31T21:55:50Z | https://api.github.com/repos/huggingface/datasets/issues/6940/comments | ### Feature request
Add an option when sharding a dataset to have all shards the same size. Will be good to provide both an option of duplication, and by truncation.
### Motivation
Currently the behavior of sharding is "If n % i == l, then the first l shards will have length (n // i) + 1, and the remaining shards will have length (n // i).". However, when using FSDP we want the shards to have the same size. This requires the user to manually handle this situation, but it will be nice if we had an option to shard the dataset into equally sized shards.
### Your contribution
For now just a PR. I can also add code that does what is needed, but probably not efficient.
Shard to equal size by duplication:
```
remainder = len(dataset) % num_shards
num_missing_examples = num_shards - remainder
duplicated = dataset.select(list(range(num_missing_examples)))
dataset = concatenate_datasets([dataset, duplicated])
shard = dataset.shard(num_shards, shard_idx)
```
Or by truncation:
```
shard = dataset.shard(num_shards, shard_idx)
num_examples_per_shard = len(dataset) // num_shards
shard = shard.select(list(range(num_examples_per_shard)))
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} | I_kwDODunzps6Kw136 | null | 2024-05-31T15:08:42Z | https://api.github.com/repos/huggingface/datasets/issues/6939/comments | As reported by @regisss, an `ExpectedMoreSplits` error is raised when passing `data_dir`:
```python
from datasets import load_dataset
dataset = load_dataset(
"lvwerra/stack-exchange-paired",
split="train",
cache_dir=None,
data_dir="data/rl",
)
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2609, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1140, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/info_utils.py", line 92, in verify_splits
raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits)))
datasets.utils.info_utils.ExpectedMoreSplits: {'test'}
``` | {
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2,327,568,281 | https://api.github.com/repos/huggingface/datasets/issues/6938 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6938/events | [] | null | 2024-05-31T15:28:03Z | [] | https://github.com/huggingface/datasets/pull/6938 | MEMBER | null | false | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6938). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"fix is included in https://github.com/huggingface/datasets/pull/6925"
] | Fix expected splits when passing data_files or dir | {
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} | 2024-05-31T11:04:22Z | https://api.github.com/repos/huggingface/datasets/issues/6938/comments | reported on slack:
The following code snippet gives an error with v2.19 but not with v2.18:
from datasets import load_dataset
```
dataset = load_dataset(
"lvwerra/stack-exchange-paired",
split="train",
cache_dir=None,
data_dir="data/rl",
)
```
and the error is:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2609, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1140, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/info_utils.py", line 92, in verify_splits
raise ExpectedMoreSplits(str(set(expected_splits) - set(recorded_splits)))
datasets.utils.info_utils.ExpectedMoreSplits: {'test'}
```
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2,327,212,611 | https://api.github.com/repos/huggingface/datasets/issues/6937 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6937/events | [
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | https://github.com/huggingface/datasets/issues/6937 | MEMBER | null | null | null | [] | JSON loader implicitly coerces floats to integers | {
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} | I_kwDODunzps6KtnJD | null | 2024-05-31T08:09:12Z | https://api.github.com/repos/huggingface/datasets/issues/6937/comments | The JSON loader implicitly coerces floats to integers.
The column values `[0.0, 1.0, 2.0]` are coerced to `[0, 1, 2]`.
See CI error in dataset-viewer: https://github.com/huggingface/dataset-viewer/actions/runs/9290164936/job/25576926446
```
=================================== FAILURES ===================================
___________________________ test_statistics_endpoint ___________________________
normal_user_public_json_dataset = 'DVUser/tmp-dataset-17170199043860'
def test_statistics_endpoint(normal_user_public_json_dataset: str) -> None:
dataset = normal_user_public_json_dataset
config, split = get_default_config_split()
statistics_response = poll_until_ready_and_assert(
relative_url=f"/statistics?dataset={dataset}&config={config}&split={split}",
check_x_revision=True,
dataset=dataset,
)
content = statistics_response.json()
assert len(content) == 3
assert sorted(content) == ["num_examples", "partial", "statistics"], statistics_response
statistics = content["statistics"]
num_examples = content["num_examples"]
partial = content["partial"]
assert isinstance(statistics, list), statistics
assert len(statistics) == 6
assert num_examples == 4
assert partial is False
string_label_column = statistics[0]
assert "column_name" in string_label_column
assert "column_statistics" in string_label_column
assert "column_type" in string_label_column
assert string_label_column["column_name"] == "col_1"
assert string_label_column["column_type"] == "string_label" # 4 unique values -> label
assert isinstance(string_label_column["column_statistics"], dict)
assert string_label_column["column_statistics"] == {
"nan_count": 0,
"nan_proportion": 0.0,
"no_label_count": 0,
"no_label_proportion": 0.0,
"n_unique": 4,
"frequencies": {
"There goes another one.": 1,
"Vader turns round and round in circles as his ship spins into space.": 1,
"We count thirty Rebel ships, Lord Vader.": 1,
"The wingman spots the pirateship coming at him and warns the Dark Lord": 1,
},
}
int_column = statistics[1]
assert "column_name" in int_column
assert "column_statistics" in int_column
assert "column_type" in int_column
assert int_column["column_name"] == "col_2"
assert int_column["column_type"] == "int"
assert isinstance(int_column["column_statistics"], dict)
assert int_column["column_statistics"] == {
"histogram": {"bin_edges": [0, 1, 2, 3, 3], "hist": [1, 1, 1, 1]},
"max": 3,
"mean": 1.5,
"median": 1.5,
"min": 0,
"nan_count": 0,
"nan_proportion": 0.0,
"std": 1.29099,
}
float_column = statistics[2]
assert "column_name" in float_column
assert "column_statistics" in float_column
assert "column_type" in float_column
assert float_column["column_name"] == "col_3"
> assert float_column["column_type"] == "float"
E AssertionError: assert 'int' == 'float'
E - float
E + int
tests/test_14_statistics.py:72: AssertionError
=========================== short test summary info ============================
FAILED tests/test_14_statistics.py::test_statistics_endpoint - AssertionError: assert 'int' == 'float'
- float
+ int
```
This bug was introduced after:
- #6914
We have reported the issue to pandas:
- https://github.com/pandas-dev/pandas/issues/58866 | {
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2,326,119,853 | 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/events | [] | null | 2024-07-22T23:08:42Z | [] | https://github.com/huggingface/datasets/issues/6936 | NONE | null | null | null | [
"I got the same issue. Any updates so far for this issue?"
] | save_to_disk() freezes when saving on s3 bucket with multiprocessing | {
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} | I_kwDODunzps6KpcWt | null | 2024-05-30T16:48:39Z | https://api.github.com/repos/huggingface/datasets/issues/6936/comments | ### 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` | {
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2,325,612,022 | https://api.github.com/repos/huggingface/datasets/issues/6935 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6935/events | [] | null | 2024-05-30T12:53:36Z | [] | https://github.com/huggingface/datasets/issues/6935 | NONE | null | null | null | [] | Support for pathlib.Path in datasets 2.19.0 | {
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} | I_kwDODunzps6KngX2 | null | 2024-05-30T12:53:36Z | https://api.github.com/repos/huggingface/datasets/issues/6935/comments | ### Describe the bug
After the recent update of `datasets`, Dataset.save_to_disk does not accept a pathlib.Path anymore. It was supported in 2.18.0 and previous versions. Is this intentional? Was it supported before only because of a Python dusk-typing miracle?
### Steps to reproduce the bug
```
from datasets import Dataset
import pathlib
path = pathlib.Path("./my_out_path")
Dataset.from_dict(
{"text": ["hello world"], "label": [777], "split": ["train"]}
.save_to_disk(path)
```
This results in an error when using datasets 2.19:
```
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/Users/jb/scratch/venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 1515, in save_to_disk
fs, _ = url_to_fs(dataset_path, **(storage_options or {}))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/jb/scratch/venv/lib/python3.11/site-packages/fsspec/core.py", line 383, in url_to_fs
chain = _un_chain(url, kwargs)
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/jb/scratch/venv/lib/python3.11/site-packages/fsspec/core.py", line 323, in _un_chain
if "::" in path
^^^^^^^^^^^^
TypeError: argument of type 'PosixPath' is not iterable
```
Converting to str works, however.
```
Dataset.from_dict(
{"text": ["hello world"], "label": [777], "split": ["train"]}
).save_to_disk(str(path))
```
### Expected behavior
My dataset gets saved to disk without an error.
### Environment info
aiohttp==3.9.5
aiosignal==1.3.1
attrs==23.2.0
certifi==2024.2.2
charset-normalizer==3.3.2
datasets==2.19.0
dill==0.3.8
filelock==3.14.0
frozenlist==1.4.1
fsspec==2024.3.1
huggingface-hub==0.23.2
idna==3.7
multidict==6.0.5
multiprocess==0.70.16
numpy==1.26.4
packaging==24.0
pandas==2.2.2
pyarrow==16.1.0
pyarrow-hotfix==0.6
python-dateutil==2.9.0.post0
pytz==2024.1
PyYAML==6.0.1
requests==2.32.3
six==1.16.0
tqdm==4.66.4
typing_extensions==4.12.0
tzdata==2024.1
urllib3==2.2.1
xxhash==3.4.1
yarl==1.9.4 | {
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2,325,341,717 | https://api.github.com/repos/huggingface/datasets/issues/6934 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6934/events | [] | null | 2024-05-31T10:25:08Z | [] | https://github.com/huggingface/datasets/pull/6934 | MEMBER | null | false | null | [
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | Revert ci user | {
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2,325,300,800 | 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/events | [] | null | 2024-05-30T10:30:54Z | [] | https://github.com/huggingface/datasets/pull/6933 | MEMBER | null | false | null | [
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | update ci user | {
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2,324,729,267 | https://api.github.com/repos/huggingface/datasets/issues/6932 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6932/events | [] | null | 2024-06-04T12:56:20Z | [] | https://github.com/huggingface/datasets/pull/6932 | CONTRIBUTOR | null | false | null | [
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2,323,457,525 | https://api.github.com/repos/huggingface/datasets/issues/6931 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6931/events | [] | null | 2024-05-29T16:33:18Z | [] | https://github.com/huggingface/datasets/pull/6931 | MEMBER | null | false | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6931). 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>... | [WebDataset] Support compressed files | {
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2,323,225,922 | https://api.github.com/repos/huggingface/datasets/issues/6930 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6930/events | [] | null | 2024-07-23T06:25:24Z | [] | https://github.com/huggingface/datasets/issues/6930 | NONE | null | null | null | [
"How do you solve it ?\r\n",
"> How do you solve it ?\r\n\r\nPlease check your Python environment and dataset version. I have just resolved the issue, which was caused by a Python environment switching error\r\n"
] | ValueError: Couldn't infer the same data file format for all splits. Got {'train': ('json', {}), 'validation': (None, {})} | {
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} | I_kwDODunzps6KeZ1C | null | 2024-05-29T12:40:05Z | https://api.github.com/repos/huggingface/datasets/issues/6930/comments | ### Describe the bug
When I run the code en = load_dataset("allenai/c4", "en", streaming=True), I encounter an error: raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}") ValueError: Couldn't infer the same data file format for all splits. Got {'train': ('json', {}), 'validation': (None, {})}.
However, running dataset = load_dataset('allenai/c4', streaming=True, data_files={'validation': 'en/c4-validation.00003-of-00008.json.gz'}, split='validation') works fine. What is the issue here?
### Steps to reproduce the bug
run code：
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
from datasets import load_dataset
en = load_dataset("allenai/c4", "en", streaming=True)
### Expected behavior
Successfully loaded the dataset.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-28-generic-x86_64-with-glibc2.17
- Python version: 3.8.19
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.0.3
- `fsspec` version: 2024.2.0
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2,322,980,077 | https://api.github.com/repos/huggingface/datasets/issues/6929 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6929/events | [
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"you're right, we're tackling this here: https://github.com/huggingface/dataset-viewer/issues/2757",
"@severo : great !"
] | Avoid downloading the whole dataset when only README.me has been touched on hub. | {
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} | I_kwDODunzps6Kddzt | null | 2024-05-29T10:36:06Z | https://api.github.com/repos/huggingface/datasets/issues/6929/comments | ### Feature request
`datasets.load_dataset()` triggers a new download of the **whole dataset** when the README.md file has been touched on huggingface hub, even if data files / parquet files are the exact same.
I think the current behaviour of the load_dataset function is triggered whenever a change of the hash of latest commit on huggingface hub, but is there a clever way to only download again the dataset **if and only if** data is modified ?
### Motivation
The current behaviour is a waste of network bandwidth / disk space / research time.
### Your contribution
I don't have time to submit a PR, but I hope a simple solution will emerge from this issue ! | {
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2,321,084,967 | https://api.github.com/repos/huggingface/datasets/issues/6925 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6925/events | [] | null | 2024-06-02T14:11:13Z | [] | https://github.com/huggingface/datasets/pull/6925 | MEMBER | null | false | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6925). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Do you think this is worth making a patch release for?\r\nCC: @huggingface/datasets",
... | Fix NonMatchingSplitsSizesError/ExpectedMoreSplits when passing data_dir/data_files in no-code Hub datasets | {
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} | 2024-05-28T13:33:38Z | https://api.github.com/repos/huggingface/datasets/issues/6925/comments | Fix `NonMatchingSplitsSizesError` or `ExpectedMoreSplits` error for no-code Hub datasets if the user passes:
- `data_dir`
- `data_files`
The proposed solution is to avoid using exported dataset info (from Parquet exports) in these cases.
Additionally, also if the user passes `revision` other than "main" (so that no network requests are made).
This PR fixes a bug introduced by:
- #6714
Fix #6918, fix #6939. | {
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2,320,531,015 | https://api.github.com/repos/huggingface/datasets/issues/6924 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6924/events | [] | null | 2024-05-28T09:07:41Z | [] | https://github.com/huggingface/datasets/issues/6924 | NONE | null | null | null | [] | Caching map result of DatasetDict. | {
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} | I_kwDODunzps6KUH5H | null | 2024-05-28T09:07:41Z | https://api.github.com/repos/huggingface/datasets/issues/6924/comments | Hi!
I'm currenty using the map function to tokenize a somewhat large dataset, so I need to use the cache to save ~25 mins.
Changing num_proc incduces the recomputation of the map, I'm not sure why and if this is excepted behavior?
here it says, that cached files are loaded sequentially:
https://github.com/huggingface/datasets/blob/bb2664cf540d5ce4b066365e7c8b26e7f1ca4743/src/datasets/arrow_dataset.py#L3005-L3006
it seems like I can pass in a fingerprint, and load it directly:
https://github.com/huggingface/datasets/blob/bb2664cf540d5ce4b066365e7c8b26e7f1ca4743/src/datasets/arrow_dataset.py#L3108-L3125
**Environment Setup:**
- Python 3.11.9
- datasets 2.19.1 conda-forge
- Linux 6.1.83-1.el9.elrepo.x86_64
**MRE**
```python
fixed raw_datasets
fixed tokenize_function
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=9,
remove_columns=['text'],
load_from_cache_file= True,
desc="Running tokenizer on dataset line_by_line",
)
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=5,
remove_columns=['text'],
load_from_cache_file= True,
desc="Running tokenizer on dataset line_by_line",
)
``` | {
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2,319,292,872 | https://api.github.com/repos/huggingface/datasets/issues/6923 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6923/events | [] | null | 2024-05-27T14:27:57Z | [] | https://github.com/huggingface/datasets/issues/6923 | NONE | null | null | null | [] | Export Parquet Tablet Audio-Set is null bytes in Arrow | {
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} | I_kwDODunzps6KPZnI | null | 2024-05-27T14:27:57Z | https://api.github.com/repos/huggingface/datasets/issues/6923/comments | ### Describe the bug
Exporting the processed audio inside the table with the dataset.to_parquet function, the object pyarrow {bytes: null, path: "Some/Path"}
At the same time, the same dataset uploaded to the hub has bit arrays
### Steps to reproduce the bug
1.Get dataset from audio and cast it
2.Export and push dataset
3.It’s scary to be indignant at the difference in the uploaded dataset and the fact that it was saved locally
```py
from datasets import Dataset, Audio
df = Dataset.from_csv("./datasets.csv")
df = df.cast_column("audio", Audio(16000))
df.to_parquet("./datasets.parquet")
df.push_to_hub(repo_id="************", token="**********************")
```
You can use "try replicate case" for this
[replicate_packet.zip](https://github.com/huggingface/datasets/files/15457114/replicate_packet.zip)
### Expected behavior
Two parquet tables identical in content. It is obvious?
### Environment info
Python 3.11+ (I try did it in 3.12 and got same result ) | {
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Follow-up on:
- #5573 | {
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2,315,618,993 | https://api.github.com/repos/huggingface/datasets/issues/6919 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6919/events | [] | null | 2024-05-24T14:59:45Z | [] | https://github.com/huggingface/datasets/issues/6919 | NONE | null | null | null | [] | Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> | {
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} | I_kwDODunzps6KBYqx | null | 2024-05-24T14:59:45Z | https://api.github.com/repos/huggingface/datasets/issues/6919/comments | ### Describe the bug
I wrote a notebook to load an existing dataset, process it, and upload as a private dataset using `dataset.push_to_hub(...)` at the end. The push to hub is failing with:
```
ValueError: Invalid metadata in README.md.
- Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python[/tuple](http://192.168.1.128:8888/tuple)> (50:11)
47 | - 4
48 | - 4
49 | - 8
50 | - !!binary |
----------------^
51 | TwAAAA==
52 | '1': !!python[/object/apply](http://192.168.1.128:8888/object/apply):nump ...
```
My dataset has a `train` and `validation` dataset. These are the features:
```
{'c1': Value(dtype='string', id=None),
'c2': Value(dtype='string', id=None),
'c3': [{'value': Value(dtype='string', id=None),
'start': Value(dtype='int64', id=None),
'end': Value(dtype='int64', id=None),
'label': Value(dtype='string', id=None)}],
'c4': Value(dtype='string', id=None),
'c5': Value(dtype='string', id=None),
'c6': Value(dtype='string', id=None),
'c7': Value(dtype='string', id=None),
'c8': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),
'c9': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
'c10': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),
'labels': Sequence(feature=ClassLabel(names=['O', 'B-ABC', 'I-ABC', ...], id=None), length=-1, id=None),
'c12': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
```
This used to work until I decided to cast the `labels` feature to a `Sequence(ClassLabel(...))` type with:
```
ds['train'] = ds['train'].cast_column("labels", Sequence(ClassLabel(names=list(labels))))
ds['validation'] = ds['validation'].cast_column("labels", Sequence(ClassLabel(names=list(labels))))
```
### Steps to reproduce the bug
1. Start with any token classification dataset.
2. Add a `labels` column with data such as `[0,0,0,12,13,13,13,0,0]`.
3. Cast the label column from `Sequence` to `Sequence(ClassLabel))` with:
```
labels = ['O', 'B-TEST', 'I-TEST']
ds = ds.cast_column("labels", Sequence(ClassLabel(names=labels)))
```
4. Push to hub with `ds.push_to_hub("me/awesome-stuff-dataset")`
### Expected behavior
I expected `push_to_hub` to successfully push my dataset to the hub without error.
### Environment info
Python 3.11.9
datasets==2.19.1
transformers==4.41.1
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2,315,322,738 | https://api.github.com/repos/huggingface/datasets/issues/6918 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6918/events | [
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | https://github.com/huggingface/datasets/issues/6918 | NONE | completed | null | null | [
"Thanks for reporting, @srehaag.\r\n\r\nWe are investigating this issue.",
"I confirm there is a bug for data-based Hub datasets when the user passes `data_dir`, which was introduced by PR:\r\n- #6714"
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} | I_kwDODunzps6KAQVy | null | 2024-05-24T12:43:39Z | https://api.github.com/repos/huggingface/datasets/issues/6918/comments | ### Describe the bug
Loading a dataset from with a data_dir argument generates a NonMatchingSplitsSizesError if there are multiple directories in the dataset.
This appears to happen because the expected split is calculated based on the data in all the directories whereas the recorded split is calculated based on the data in the directory specified using the data_dir argument.
This is recent behavior. Until the past few weeks loading using the data_dir argument worked without any issue.
### Steps to reproduce the bug
Simple test dataset available here: https://huggingface.co/datasets/srehaag/hf-bug-temp
The dataset contains two directories "data1" and "data2", each with a file called "train.parquet" with a 2 x 5 table.
from datasets import load_dataset
dataset = load_dataset("srehaag/hf-bug-temp", data_dir = "data1")
Generates:
---------------------------------------------------------------------------
NonMatchingSplitsSizesError Traceback (most recent call last)
Cell In[3], <a href='vscode-notebook-cell:?execution_count=3&line=2'>line 2</a>
<a href='vscode-notebook-cell:?execution_count=3&line=1'>1</a> from datasets import load_dataset
----> <a href='vscode-notebook-cell:?execution_count=3&line=2'>2</a> dataset = load_dataset("srehaag/hf-bug-temp", data_dir = "data1")
File ~/.python/current/lib/python3.10/site-packages/datasets/load.py:2609, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2606'>2606</a> return builder_instance.as_streaming_dataset(split=split)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2608'>2608</a> # Download and prepare data
-> <a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2609'>2609</a> builder_instance.download_and_prepare(
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2610'>2610</a> download_config=download_config,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2611'>2611</a> download_mode=download_mode,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2612'>2612</a> verification_mode=verification_mode,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2613'>2613</a> num_proc=num_proc,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2614'>2614</a> storage_options=storage_options,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2615'>2615</a> )
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2617'>2617</a> # Build dataset for splits
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2618'>2618</a> keep_in_memory = (
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2619'>2619</a> keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/load.py:2620'>2620</a> )
File ~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1025'>1025</a> if num_proc is not None:
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1026'>1026</a> prepare_split_kwargs["num_proc"] = num_proc
-> <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1027'>1027</a> self._download_and_prepare(
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1028'>1028</a> dl_manager=dl_manager,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1029'>1029</a> verification_mode=verification_mode,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1030'>1030</a> **prepare_split_kwargs,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1031'>1031</a> **download_and_prepare_kwargs,
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1032'>1032</a> )
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1033'>1033</a> # Sync info
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1034'>1034</a> self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1140, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1137'>1137</a> dl_manager.manage_extracted_files()
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1139'>1139</a> if verification_mode == VerificationMode.BASIC_CHECKS or verification_mode == VerificationMode.ALL_CHECKS:
-> <a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1140'>1140</a> verify_splits(self.info.splits, split_dict)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1142'>1142</a> # Update the info object with the splits.
<a href='~/.python/current/lib/python3.10/site-packages/datasets/builder.py:1143'>1143</a> self.info.splits = split_dict
File ~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:101, in verify_splits(expected_splits, recorded_splits)
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:95'>95</a> bad_splits = [
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:96'>96</a> {"expected": expected_splits[name], "recorded": recorded_splits[name]}
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:97'>97</a> for name in expected_splits
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:98'>98</a> if expected_splits[name].num_examples != recorded_splits[name].num_examples
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:99'>99</a> ]
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:100'>100</a> if len(bad_splits) > 0:
--> <a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:101'>101</a> raise NonMatchingSplitsSizesError(str(bad_splits))
<a href='~/.python/current/lib/python3.10/site-packages/datasets/utils/info_utils.py:102'>102</a> logger.info("All the splits matched successfully.")
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=212, num_examples=10, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=106, num_examples=5, shard_lengths=None, dataset_name='hf-bug-temp')}]
__________
By contrast, this loads the data from both data1/train.parquet and data2/train.parquet without any error message:
from datasets import load_dataset
dataset = load_dataset("srehaag/hf-bug-temp")
### Expected behavior
Should load the 5 x 2 table from data1/train.parquet without error message.
### Environment info
Used Codespaces to simplify environment (see details below), but bug is present across various configurations.
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-1021-azure-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.1
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1 | {
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2,314,683,663 | https://api.github.com/repos/huggingface/datasets/issues/6917 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6917/events | [] | null | 2024-05-24T07:54:51Z | [] | https://github.com/huggingface/datasets/issues/6917 | NONE | null | null | null | [] | WinError 32 The process cannot access the file during load_dataset | {
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} | I_kwDODunzps6J90UP | null | 2024-05-24T07:54:51Z | https://api.github.com/repos/huggingface/datasets/issues/6917/comments | ### Describe the bug
When I try to load the opus_book from hugging face (following the [guide on the website](https://huggingface.co/docs/transformers/main/en/tasks/translation))
```python
from datasets import load_dataset, Dataset
dataset = load_dataset("Helsinki-NLP/opus_books", "en-fr", features=["id", "translation"])
```
I get an error:
`PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:/Users/Me/.cache/huggingface/datasets/Helsinki-NLP___parquet/ca-de-a39f1ef185b9b73b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec.incomplete\\parquet-train-00000-00000-of-NNNNN.arrow'
`
<details><summary>Full stacktrace</summary>
<p>
```python
AttributeError Traceback (most recent call last)
File c:\Users\Me\.conda\envs\ia\lib\site-packages\datasets\builder.py:1858, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
[1857](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1857) _time = time.time()
-> [1858](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1858) for _, table in generator:
[1859](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1859) if max_shard_size is not None and writer._num_bytes > max_shard_size:
File c:\Users\Me\.conda\envs\ia\lib\site-packages\datasets\packaged_modules\parquet\parquet.py:59, in Parquet._generate_tables(self, files)
[58](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/packaged_modules/parquet/parquet.py:58) def _generate_tables(self, files):
---> [59](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/packaged_modules/parquet/parquet.py:59) schema = self.config.features.arrow_schema if self.config.features is not None else None
[60](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/packaged_modules/parquet/parquet.py:60) if self.config.features is not None and self.config.columns is not None:
AttributeError: 'list' object has no attribute 'arrow_schema'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
File c:\Users\Me\.conda\envs\ia\lib\site-packages\datasets\builder.py:1882, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
[1881](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1881) num_shards = shard_id + 1
-> [1882](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1882) num_examples, num_bytes = writer.finalize()
[1883](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/builder.py:1883) writer.close()
File c:\Users\Me\.conda\envs\ia\lib\site-packages\datasets\arrow_writer.py:584, in ArrowWriter.finalize(self, close_stream)
[583](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/arrow_writer.py:583) # If schema is known, infer features even if no examples were written
--> [584](file:///C:/Users/Me/.conda/envs/ia/lib/site-packages/datasets/arrow_writer.py:584) if self.pa_writer is None and self.schema:
...
--> [627](file:///C:/Users/Me/.conda/envs/ia/lib/shutil.py:627) os.unlink(fullname)
[628](file:///C:/Users/Me/.conda/envs/ia/lib/shutil.py:628) except OSError:
[629](file:///C:/Users/Me/.conda/envs/ia/lib/shutil.py:629) onerror(os.unlink, fullname, sys.exc_info())
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:/Users/Me/.cache/huggingface/datasets/Helsinki-NLP___parquet/ca-de-a39f1ef185b9b73b/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec.incomplete\\parquet-train-00000-00000-of-NNNNN.arrow'
```
</p>
</details>
### Steps to reproduce the bug
Steps to reproduce:
Just execute these lines
```python
from datasets import load_dataset, Dataset
dataset = load_dataset("Helsinki-NLP/opus_books", "en-fr", features=["id", "translation"])
```
### Expected behavior
I expect the dataset to be loaded without any errors.
### Environment info
| Package| Version|
|--------|--------|
| transformers| 4.37.2|
| python| 3.9.19|
| pytorch| 2.3.0|
| datasets|2.12.0 |
| arrow | 1.2.3|
I am using Conda on Windows 11. | {
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} | I_kwDODunzps6JyV6s | null | 2024-05-22T23:52:15Z | https://api.github.com/repos/huggingface/datasets/issues/6916/comments | ### Describe the bug
I currently have a dataset which has not been splited. When pushing the dataset to my hugging face dataset repository, it is split into a testing and training set. How can I prevent the split from happening?
### Steps to reproduce the bug
1. Have a unsplit dataset
```python
Dataset({ features: ['input', 'output', 'Attack', '__index_level_0__'], num_rows: 944685 })
```
2. Push it to huggingface
```python
dataset.push_to_hub(dataset_name)
```
3. On the hugging face dataset repo, the dataset then appears to be splited:
4. Indeed, when loading the dataset from this repo, the dataset is split in two testing and training set.
```python
from datasets import load_dataset, Dataset
dataset = load_dataset("Jetlime/NF-CSE-CIC-IDS2018-v2", streaming=True)
dataset
```
output:
```
IterableDatasetDict({
train: IterableDataset({
features: ['input', 'output', 'Attack', '__index_level_0__'],
n_shards: 2
})
test: IterableDataset({
features: ['input', 'output', 'Attack', '__index_level_0__'],
n_shards: 1
})
```
### Expected behavior
The dataset shall not be splited, as not requested.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
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Note that their parent `BuilderConfig` validates its attributes `name` and `data_files` in its `__post_init__` method: https://github.com/huggingface/datasets/blob/60d21efbc01e15d0b596ac1072750cbecd91548a/src/datasets/builder.py#L128-L137
This PR makes the derived config classes call their parent `__post_init__` method to validate their `name` and `data_files` attributes. | {
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Additionally, support JSON file with a list of strings field.
Fix #6913. | {
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For example, when loading a JSON files with a list of objects, each with the following ordered keys:
- [ID, Language, Topic],
the resulting dataset may have columns:
- [ID, Topic, Language], or
- [Topic, Language, ID], or
- [Topic, ID, Language],...
This issue is caused by the use of a Python set (which does not preserve the order):
https://github.com/huggingface/datasets/blob/60d21efbc01e15d0b596ac1072750cbecd91548a/src/datasets/packaged_modules/json/json.py#L168
introduced in
- #5772 | {
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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... | Add MedImg for streaming | {
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} | I_kwDODunzps6JpiDJ | null | 2024-05-22T00:55:30Z | https://api.github.com/repos/huggingface/datasets/issues/6912/comments | ### 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/#). | {
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Since the merge of this PR:
- #2986
the builders' variable self.config.data_files is always a dict, which makes the condition on (str, list, tuple) dead code. | {
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- #6493 | {
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2,304,958,116 | https://api.github.com/repos/huggingface/datasets/issues/6908 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6908/events | [] | null | 2024-05-24T10:58:09Z | [
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"I am not able to reproduce the error with datasets 2.19.1:\r\n```python\r\nIn [1]: from datasets import load_dataset; ds = load_dataset(\"stas/c4-en-10k\", streaming=True); item = next(iter(ds[\"train\"])); item\r\nOut[1]: {'text': 'Beginners BBQ Class Taking Place in Missoula!\\nDo you want to get better at makin... | Fail to load "stas/c4-en-10k" dataset since 2.16 version | {
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} | I_kwDODunzps6JYt6k | null | 2024-05-20T02:43:59Z | https://api.github.com/repos/huggingface/datasets/issues/6908/comments | ### Describe the bug
When update datasets library to version 2.16+ ( I test it on 2.16, 2.19.0 and 2.19.1), using the following code to load stas/c4-en-10k dataset
```python
from datasets import load_dataset, Dataset
dataset = load_dataset('stas/c4-en-10k')
```
and then it raise UnicodeDecodeError like
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 2523, in load_dataset
builder_instance = load_dataset_builder(
File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 2195, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 1846, in dataset_module_factory
raise e1 from None
File "/home/*/conda3/envs/watermark/lib/python3.10/site-packages/datasets/load.py", line 1798, in dataset_module_factory
can_load_config_from_parquet_export = "DEFAULT_CONFIG_NAME" not in f.read()
File "/home/*/conda3/envs/watermark/lib/python3.10/codecs.py", line 322, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8b in position 1: invalid start byte
```
I found that fs.open loads a gzip file and parses it like plain text using utf-8 encoder.
```python
fs = HfFileSystem('https://huggingface.co')
fs.open("datasets/stas/c4-en-10k/c4-en-10k.py", "rb")
data = fs.read() # data is gzip bytes begin with b'\x1f\x8b\x08\x00\x00\tn\x88\x00...'
data2 = unzip_gzip_bytes(data) # data2 is what we want: '# coding=utf-8\n# Copyright 2020 The HuggingFace Datasets...'
```
### Steps to reproduce the bug
1. Install datasets between version 2.16 and 2.19
2. Use `datasets.load_dataset` method to load `stas/c4-en-10k` dataset.
### Expected behavior
Load dataset normally.
### Environment info
Platform = Linux-5.4.0-159-generic-x86_64-with-glibc2.35
Python = 3.10.14
Datasets = 2.19 | {
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2,303,855,833 | https://api.github.com/repos/huggingface/datasets/issues/6907 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6907/events | [
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"Update: I ended up deciding to go back to use lines of dictionaries instead of arrays, not because of this issue as my users would be capable of downloading my corpus without `datasets`, but the speed and storage savings are not currently worth breaking my API and harming the backwards compatibility of each new re... | Support the deserialization of json lines files comprised of lists | {
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} | I_kwDODunzps6JUgzZ | null | 2024-05-18T05:07:23Z | https://api.github.com/repos/huggingface/datasets/issues/6907/comments | ### Feature request
I manage a somewhat large and popular Hugging Face dataset known as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus). I recently updated my corpus to be stored in a json lines file where each line is an array and each element represents a value at a particular column. Previously, my corpus was stored as a json lines file where each line was a dictionary and the keys were the fields.
Essentially, a line in my json lines file used to look like this:
```json
{"version_id":"","type":"","jurisdiction":"","source":"","citation":"","url":"","when_scraped":"","text":""}
```
And now it looks like this:
```json
["","","","","","","",""]
```
This saves 65 bytes per document and allows me very quickly serialise and deserialise documents via `msgspec`.
After making this change, I found that `datasets` was incapable of deserialising my Corpus without a custom loading script, even if I ensured that the `dataset_info` field in my dataset card contained the desired names of my features.
I would like to request that functionality be added to support this format which is more memory-efficent and faster than using dictionaries.
### Motivation
The [documentation](https://huggingface.co/docs/datasets/en/dataset_script) for creating dataset loading scripts asserts that:
> In the next major release, the new safety features of 🤗 Datasets will disable running dataset loading scripts by default, and you will have to pass trust_remote_code=True to load datasets that require running a dataset script.
I would rather not require my users to pass `trust_remote_code=True` which means that I will need built-in support for this format.
### Your contribution
I would be happy to submit a PR for this if this is something you would incorporate into `datasets` and if I can be pointed to where the code would need to go. | {
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