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https://api.github.com/repos/huggingface/datasets/issues/7036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7036/comments | https://api.github.com/repos/huggingface/datasets/issues/7036/events | https://github.com/huggingface/datasets/pull/7036 | 2,400,035,672 | PR_kwDODunzps507bZk | 7,036 | Fix doc generation when NamedSplit is used as parameter default value | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7036). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-07-10T07:58:46Z | 2024-07-26T07:58:00Z | 2024-07-26T07:51:52Z | MEMBER | null | null | null | Fix doc generation when `NamedSplit` is used as parameter default value.
Fix #7035. | {
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https://api.github.com/repos/huggingface/datasets/issues/7524 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7524/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7524/comments | https://api.github.com/repos/huggingface/datasets/issues/7524/events | https://github.com/huggingface/datasets/pull/7524 | 3,002,067,826 | PR_kwDODunzps6S99KB | 7,524 | correct use with polars example | {
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https://api.github.com/repos/huggingface/datasets/issues/5727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5727/comments | https://api.github.com/repos/huggingface/datasets/issues/5727/events | https://github.com/huggingface/datasets/issues/5727 | 1,661,536,363 | I_kwDODunzps5jCQhr | 5,727 | load_dataset fails with FileNotFound error on Windows | {
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"Hi! Can you please paste the entire error stack trace, not only the last few lines?",
"`----> 1 dataset = datasets.load_dataset(\"glue\", \"ax\")\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1767, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, ... | 2023-04-10T23:21:12Z | 2023-07-21T14:08:20Z | 2023-07-21T14:08:19Z | NONE | null | null | {
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} | ### Describe the bug
Although I can import and run the datasets library in a Colab environment, I cannot successfully load any data on my own machine (Windows 10) despite following the install steps:
(1) create conda environment
(2) activate environment
(3) install with: ``conda` install -c huggingface -c conda-forge datasets`
Then
```
from datasets import load_dataset
# this or any other example from the website fails with the FileNotFoundError
glue = load_dataset("glue", "ax")
```
**Below I have pasted the error omitting the full path**:
```
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at C:\Users\...\glue\glue.py or any data file in the same directory. Couldn't find 'glue' on the Hugging Face Hub either: FileNotFoundError: [WinError 3] The system cannot find the path specified:
'C:\\Users\\...\\.cache\\huggingface'
```
### Steps to reproduce the bug
On Windows 10
1) create a minimal conda environment (with just Python)
(2) activate environment
(3) install datasets with: ``conda` install -c huggingface -c conda-forge datasets`
(4) import load_dataset and follow example usage from any dataset card.
### Expected behavior
The expected behavior is to load the file into the Python session running on my machine without error.
### Environment info
```
# Name Version Build Channel
aiohttp 3.8.4 py311ha68e1ae_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
arrow-cpp 11.0.0 h57928b3_13_cpu conda-forge
async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge
attrs 22.2.0 pyh71513ae_0 conda-forge
aws-c-auth 0.6.26 h1262f0c_1 conda-forge
aws-c-cal 0.5.21 h7cda486_2 conda-forge
aws-c-common 0.8.14 hcfcfb64_0 conda-forge
aws-c-compression 0.2.16 h8a79959_5 conda-forge
aws-c-event-stream 0.2.20 h5f78564_4 conda-forge
aws-c-http 0.7.6 h2545be9_0 conda-forge
aws-c-io 0.13.19 h0d2781e_3 conda-forge
aws-c-mqtt 0.8.6 hd211e0c_12 conda-forge
aws-c-s3 0.2.7 h8113e7b_1 conda-forge
aws-c-sdkutils 0.1.8 h8a79959_0 conda-forge
aws-checksums 0.1.14 h8a79959_5 conda-forge
aws-crt-cpp 0.19.8 he6d3b81_12 conda-forge
aws-sdk-cpp 1.10.57 h64004b3_8 conda-forge
brotlipy 0.7.0 py311ha68e1ae_1005 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
c-ares 1.19.0 h2bbff1b_0
ca-certificates 2023.01.10 haa95532_0
certifi 2022.12.7 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py311h7d9ee11_3 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
cryptography 40.0.1 py311h28e9c30_0 conda-forge
dataclasses 0.8 pyhc8e2a94_3 conda-forge
datasets 2.11.0 py_0 huggingface
dill 0.3.6 pyhd8ed1ab_1 conda-forge
filelock 3.11.0 pyhd8ed1ab_0 conda-forge
frozenlist 1.3.3 py311ha68e1ae_0 conda-forge
fsspec 2023.4.0 pyh1a96a4e_0 conda-forge
gflags 2.2.2 ha925a31_1004 conda-forge
glog 0.6.0 h4797de2_0 conda-forge
huggingface_hub 0.13.4 py_0 huggingface
idna 3.4 pyhd8ed1ab_0 conda-forge
importlib-metadata 6.3.0 pyha770c72_0 conda-forge
importlib_metadata 6.3.0 hd8ed1ab_0 conda-forge
intel-openmp 2023.0.0 h57928b3_25922 conda-forge
krb5 1.20.1 heb0366b_0 conda-forge
libabseil 20230125.0 cxx17_h63175ca_1 conda-forge
libarrow 11.0.0 h04c43f8_13_cpu conda-forge
libblas 3.9.0 16_win64_mkl conda-forge
libbrotlicommon 1.0.9 hcfcfb64_8 conda-forge
libbrotlidec 1.0.9 hcfcfb64_8 conda-forge
libbrotlienc 1.0.9 hcfcfb64_8 conda-forge
libcblas 3.9.0 16_win64_mkl conda-forge
libcrc32c 1.1.2 h0e60522_0 conda-forge
libcurl 7.88.1 h68f0423_1 conda-forge
libexpat 2.5.0 h63175ca_1 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libgoogle-cloud 2.8.0 hf2ff781_1 conda-forge
libgrpc 1.52.1 h32da247_1 conda-forge
libhwloc 2.9.0 h51c2c0f_0 conda-forge
libiconv 1.17 h8ffe710_0 conda-forge
liblapack 3.9.0 16_win64_mkl conda-forge
libprotobuf 3.21.12 h12be248_0 conda-forge
libsqlite 3.40.0 hcfcfb64_0 conda-forge
libssh2 1.10.0 h9a1e1f7_3 conda-forge
libthrift 0.18.1 h9ce19ad_0 conda-forge
libutf8proc 2.8.0 h82a8f57_0 conda-forge
libxml2 2.10.3 hc3477c8_6 conda-forge
libzlib 1.2.13 hcfcfb64_4 conda-forge
lz4-c 1.9.4 hcfcfb64_0 conda-forge
mkl 2022.1.0 h6a75c08_874 conda-forge
multidict 6.0.4 py311ha68e1ae_0 conda-forge
multiprocess 0.70.14 py311ha68e1ae_3 conda-forge
numpy 1.24.2 py311h0b4df5a_0 conda-forge
openssl 3.1.0 hcfcfb64_0 conda-forge
orc 1.8.3 hada7b9e_0 conda-forge
packaging 23.0 pyhd8ed1ab_0 conda-forge
pandas 2.0.0 py311hf63dbb6_0 conda-forge
parquet-cpp 1.5.1 2 conda-forge
pip 23.0.1 pyhd8ed1ab_0 conda-forge
pthreads-win32 2.9.1 hfa6e2cd_3 conda-forge
pyarrow 11.0.0 py311h6a6099b_13_cpu conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pyopenssl 23.1.1 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyh0701188_6 conda-forge
python 3.11.3 h2628c8c_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge
python-xxhash 3.2.0 py311ha68e1ae_0 conda-forge
python_abi 3.11 3_cp311 conda-forge
pytz 2023.3 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py311ha68e1ae_5 conda-forge
re2 2023.02.02 h63175ca_0 conda-forge
requests 2.28.2 pyhd8ed1ab_1 conda-forge
setuptools 67.6.1 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 hfb803bf_0 conda-forge
tbb 2021.8.0 h91493d7_0 conda-forge
tk 8.6.12 h8ffe710_0 conda-forge
tqdm 4.65.0 pyhd8ed1ab_1 conda-forge
typing-extensions 4.5.0 hd8ed1ab_0 conda-forge
typing_extensions 4.5.0 pyha770c72_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
ucrt 10.0.22621.0 h57928b3_0 conda-forge
urllib3 1.26.15 pyhd8ed1ab_0 conda-forge
vc 14.3 hb6edc58_10 conda-forge
vs2015_runtime 14.34.31931 h4c5c07a_10 conda-forge
wheel 0.40.0 pyhd8ed1ab_0 conda-forge
win_inet_pton 1.1.0 pyhd8ed1ab_6 conda-forge
xxhash 0.8.1 hcfcfb64_0 conda-forge
xz 5.2.10 h8cc25b3_1
yaml 0.2.5 h8ffe710_2 conda-forge
yarl 1.8.2 py311ha68e1ae_0 conda-forge
zipp 3.15.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hcfcfb64_4 conda-forge
zstd 1.5.4 hd43e919_0
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https://api.github.com/repos/huggingface/datasets/issues/5052 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5052/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5052/comments | https://api.github.com/repos/huggingface/datasets/issues/5052/events | https://github.com/huggingface/datasets/pull/5052 | 1,393,076,765 | PR_kwDODunzps4_-PZw | 5,052 | added from_generator method to IterableDataset class. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I added a test and moved the `streaming` param from `read` to `__init_`. Then, I also decided to update the `read` method of the rest of the packaged modules to account for this param. \r\n\r\n@hamid-vakilzadeh Are you OK with these ... | 2022-09-30T22:14:05Z | 2022-10-05T12:51:48Z | 2022-10-05T12:10:48Z | CONTRIBUTOR | null | null | null | Hello,
This resolves issues #4988.
I added a method `from_generator` to class `IterableDataset`.
I modified the `read` method of input stream generator to also return Iterable_dataset.
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https://api.github.com/repos/huggingface/datasets/issues/5717 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5717/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5717/comments | https://api.github.com/repos/huggingface/datasets/issues/5717/events | https://github.com/huggingface/datasets/issues/5717 | 1,658,729,866 | I_kwDODunzps5i3jWK | 5,717 | Errror when saving to disk a dataset of images | {
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... | null | [
"Looks like as long as the number of shards makes a batch lower than 1000 images it works. In my training set I have 40K images. If I use `num_shards=40` (batch of 1000 images) I get the error, but if I update it to `num_shards=50` (batch of 800 images) it works.\r\n\r\nI will be happy to share my dataset privately... | 2023-04-07T11:59:17Z | 2025-02-09T09:35:25Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Describe the bug
Hello!
I have an issue when I try to save on disk my dataset of images. The error I get is:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1442, in save_to_disk
for job_id, done, content in Dataset._save_to_disk_single(**kwargs):
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1473, in _save_to_disk_single
writer.write_table(pa_table)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_writer.py", line 570, in write_table
pa_table = embed_table_storage(pa_table)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2268, in embed_table_storage
arrays = [
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2269, in <listcomp>
embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 1817, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 1817, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2142, in embed_array_storage
return feature.embed_storage(array)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/features/image.py", line 269, in embed_storage
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
File "pyarrow/array.pxi", line 2766, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/array.pxi", line 2961, in pyarrow.lib.c_mask_inverted_from_obj
TypeError: Mask must be a pyarrow.Array of type boolean
```
My dataset is around 50K images, is this error might be due to a bad image?
Thanks for the help.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="/path/to/dataset")
dataset["train"].save_to_disk("./myds", num_shards=40)
```
### Expected behavior
Having my dataset properly saved to disk.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.10
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 2.0.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5469/comments | https://api.github.com/repos/huggingface/datasets/issues/5469/events | https://github.com/huggingface/datasets/pull/5469 | 1,558,346,906 | PR_kwDODunzps5Imhk2 | 5,469 | Remove deprecated `shard_size` arg from `.push_to_hub()` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-26T15:40:56Z | 2023-01-26T17:37:51Z | 2023-01-26T17:30:59Z | CONTRIBUTOR | null | null | null | The docstrings say that it was supposed to be deprecated since version 2.4.0, can we remove it? | {
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https://api.github.com/repos/huggingface/datasets/issues/7463 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7463/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7463/comments | https://api.github.com/repos/huggingface/datasets/issues/7463/events | https://github.com/huggingface/datasets/pull/7463 | 2,925,924,452 | PR_kwDODunzps6O-I6K | 7,463 | Adds EXR format to store depth images in float32 | {
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"Hi ! I'mn wondering if this shouldn't this be an `Image()` type and decoded as a `PIL.Image` ?\r\n\r\nThis would make it easier to integrate with the rest of the HF ecosystem, and you could still get a numpy array using `ds = ds.with_format(\"numpy\")` which sets all the images to be formatted as numpy arrays",
... | 2025-03-17T17:42:40Z | 2025-04-02T12:33:39Z | null | NONE | null | null | null | This PR adds the EXR feature to store depth images (or can be normals, etc) in float32.
It relies on [openexr_numpy](https://github.com/martinResearch/openexr_numpy/tree/main) to manipulate EXR images.
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/5690 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5690/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5690/comments | https://api.github.com/repos/huggingface/datasets/issues/5690/events | https://github.com/huggingface/datasets/issues/5690 | 1,649,289,883 | I_kwDODunzps5iTiqb | 5,690 | raise AttributeError(f"No {package_name} attribute {name}") AttributeError: No huggingface_hub attribute hf_api | {
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"Hi @wccccp, thanks for reporting. \r\nThat's weird since `huggingface_hub` _has_ a module called `hf_api` and you are using a recent version of it. \r\n\r\nWhich version of `datasets` are you using? And is it a bug that you experienced only recently? (cc @lhoestq can it be somehow related to the recent release of ... | 2023-03-31T08:22:22Z | 2023-07-21T14:21:57Z | 2023-07-21T14:21:57Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
rta.sh
Traceback (most recent call last):
File "run.py", line 7, in <module>
import datasets
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/builder.py", line 44, in <module>
from .data_files import DataFilesDict, _sanitize_patterns
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/data_files.py", line 120, in <module>
dataset_info: huggingface_hub.hf_api.DatasetInfo,
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/huggingface_hub/__init__.py", line 290, in __getattr__
raise AttributeError(f"No {package_name} attribute {name}")
AttributeError: No huggingface_hub attribute hf_api
### Reproduction
_No response_
### Logs
```shell
Traceback (most recent call last):
File "run.py", line 7, in <module>
import datasets
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/builder.py", line 44, in <module>
from .data_files import DataFilesDict, _sanitize_patterns
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/data_files.py", line 120, in <module>
dataset_info: huggingface_hub.hf_api.DatasetInfo,
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/huggingface_hub/__init__.py", line 290, in __getattr__
raise AttributeError(f"No {package_name} attribute {name}")
AttributeError: No huggingface_hub attribute hf_api
```
### System info
```shell
- huggingface_hub version: 0.13.2
- Platform: Linux-5.4.0-144-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /home/appuser/.cache/huggingface/token
- Has saved token ?: False
- Configured git credential helpers:
- FastAI: N/A
- Tensorflow: N/A
- Torch: 1.7.1
- Jinja2: N/A
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.3.0
- hf_transfer: N/A
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /home/appuser/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /home/appuser/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/appuser/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
```
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https://api.github.com/repos/huggingface/datasets/issues/6663 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6663/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6663/comments | https://api.github.com/repos/huggingface/datasets/issues/6663/events | https://github.com/huggingface/datasets/issues/6663 | 2,135,480,811 | I_kwDODunzps5_SNnr | 6,663 | `write_examples_on_file` and `write_batch` are broken in `ArrowWriter` | {
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"Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.",
"> Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.\r\n\r\nI feel that'd be good, but it'd be great to release a hotfix ASAP (a re... | 2024-02-15T01:43:27Z | 2024-02-16T09:25:00Z | 2024-02-16T09:25:00Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
`write_examples_on_file` and `write_batch` are broken in `ArrowWriter` since #6636. The order between the columns and the schema is not preserved anymore. So these functions don't work anymore unless the order happens to align well.
### Steps to reproduce the bug
Try to do `write_batch` with anything that has many columns, and it's likely to break.
### Expected behavior
I expect these functions to work, instead of it trying to cast a column to its incorrect type.
### Environment info
- `datasets` version: 2.17.0
- Platform: Linux-5.15.0-1040-aws-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.19.4
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4540 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4540/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4540/comments | https://api.github.com/repos/huggingface/datasets/issues/4540/events | https://github.com/huggingface/datasets/issues/4540 | 1,280,142,942 | I_kwDODunzps5MTW5e | 4,540 | Avoid splitting by` .py` for the file. | {
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"Hi @espoirMur, thanks for reporting.\r\n\r\nYou are right: that code line could be improved and made more generically valid.\r\n\r\nOn the other hand, I would suggest using `os.path.splitext` instead.\r\n\r\nAre you willing to open a PR? :)",
"I will have a look.. \r\n\r\nThis weekend .. ",
"@albertvillanova ... | 2022-06-22T13:26:55Z | 2022-07-07T13:17:44Z | 2022-07-07T13:17:44Z | NONE | null | null | {
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} | https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272
Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.
Step to reproduce.
- If you have a home folder which ends with `.py`
- load a module with a local folder
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed
A possible workaround would be to use pathlib at the mentioned line
` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .
Let me what are your thought on this and I can try to fix it by A PR.
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https://api.github.com/repos/huggingface/datasets/issues/6033 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6033/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6033/comments | https://api.github.com/repos/huggingface/datasets/issues/6033/events | https://github.com/huggingface/datasets/issues/6033 | 1,804,482,051 | I_kwDODunzps5rjjYD | 6,033 | `map` function doesn't fully utilize `input_columns`. | {
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} | ### Describe the bug
I wanted to select only some columns of data.
And I thought that's why the argument `input_columns` exists.
What I expected is like this:
If there are ["a", "b", "c", "d"] columns, and if I set `input_columns=["a", "d"]`, the data will have only ["a", "d"] columns.
But it doesn't select columns.
It preserves existing columns.
The main cause is `update` function of `dictionary` type `transformed_batch`.
https://github.com/huggingface/datasets/blob/682d21e94ab1e64c11b583de39dc4c93f0101c5a/src/datasets/iterable_dataset.py#L687-L691
`transformed_batch` gets all the columns by `transformed_batch = dict(batch)`.
Even `function_args` selects `input_columns`, `update` preserves columns other than `input_columns`.
I think it should take a new dictionary with columns in `input_columns` like this:
```
# transformed_batch = dict(batch)
# transformed_batch.update(self.function(*function_args, **self.fn_kwargs)
# This is what I think correct.
transformed_batch = self.function(*function_args, **self.fn_kwargs)
```
Let me know how to use `input_columns`.
### Steps to reproduce the bug
Described all above.
### Expected behavior
Described all above.
### Environment info
datasets: 2.12
python: 3.8 | {
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https://api.github.com/repos/huggingface/datasets/issues/7510 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7510/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7510/comments | https://api.github.com/repos/huggingface/datasets/issues/7510/events | https://github.com/huggingface/datasets/issues/7510 | 2,992,131,117 | I_kwDODunzps6yWEwt | 7,510 | Incompatibile dill version (0.3.9) in datasets 2.18.0 - 3.5.0 | {
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"Hi ! We can bump `dill` to 0.3.9 if we make sure it's deterministic and doesn't break the caching mechanism in `datasets`.\n\nWould you be interested in opening a PR ? Then we can run the CI to see if it works",
"Hi!. Yeah I can do it. Should I make any changes besides dill versions?",
"There are probably some... | 2025-04-14T07:22:44Z | 2025-04-24T19:49:31Z | null | NONE | null | null | {
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} | ### Describe the bug
Datasets 2.18.0 - 3.5.0 has a dependency on dill < 0.3.9. This causes errors with dill >= 0.3.9.
Could you please take a look into it and make it compatible?
### Steps to reproduce the bug
1. Install setuptools >= 2.18.0
2. Install dill >=0.3.9
3. Run pip check
4. Output:
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
datasets 2.18.0 requires dill<0.3.9,>=0.3.0, but you have dill 0.3.9 which is incompatible.
### Expected behavior
Pip install both libraries without any errors
### Environment info
Datasets version: 2.18 - 3.5
Python: 3.11 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7044 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7044/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7044/comments | https://api.github.com/repos/huggingface/datasets/issues/7044/events | https://github.com/huggingface/datasets/pull/7044 | 2,405,002,987 | PR_kwDODunzps51MLbh | 7,044 | Mark tests that require librosa | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7044). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-07-12T08:06:59Z | 2024-07-12T09:06:32Z | 2024-07-12T09:00:09Z | MEMBER | null | null | null | Mark tests that require `librosa`.
Note that `librosa` is an optional dependency (installed with `audio` option) and we should be able to test environments without that library installed. This is the case if we want to test Numpy 2.0, which is currently incompatible with `librosa` due to its dependency on `soxr`:
- https://github.com/dofuuz/python-soxr/issues/28 | {
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https://api.github.com/repos/huggingface/datasets/issues/6256 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6256/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6256/comments | https://api.github.com/repos/huggingface/datasets/issues/6256/events | https://github.com/huggingface/datasets/issues/6256 | 1,910,275,199 | I_kwDODunzps5x3Hx_ | 6,256 | load_dataset() function's cache_dir does not seems to work | {
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"Can you share the error message?\r\n\r\nAlso, it would help if you could check whether `huggingface_hub`'s download behaves the same:\r\n```python\r\nfrom huggingface_hub import snapshot_download\r\nsnapshot_download(\"trec\", repo_type=\"dataset\", cache_dir='/path/to/my/dir)\r\n```\r\n\r\nIn the next major relea... | 2023-09-24T15:34:06Z | 2024-10-08T15:45:19Z | 2024-10-08T15:45:18Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
datasets version: 2.14.5
when trying to run the following command
trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir')
I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine.
It seems the cache_dir parameter cannot change the dataset saving directory from the default
what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work
### Steps to reproduce the bug
datasets version: 2.14.5
when trying to run the following command
trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir')
I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine.
It seems the cache_dir parameter cannot change the dataset saving directory from the default
what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work
### Expected behavior
the dataset should be saved to the cache_dir points to
### Environment info
datasets version: 2.14.5
macos X: Ventura 13.4.1 (c) | {
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https://api.github.com/repos/huggingface/datasets/issues/6790 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6790/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6790/comments | https://api.github.com/repos/huggingface/datasets/issues/6790/events | https://github.com/huggingface/datasets/issues/6790 | 2,229,915,236 | I_kwDODunzps6E6c5k | 6,790 | PyArrow 'Memory mapping file failed: Cannot allocate memory' bug | {
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"Thanks for a very clean explanation. This happened to me too, and I don't have sudo access to update the value. I wonder if there might be another workaround.",
"One option is to just have more data in each file - /proc/sys/vm/max_map_count limits the maximum number of concurrently open files, but I don't know i... | 2024-04-07T19:25:39Z | 2024-09-06T19:09:37Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Hello,
I've been struggling with a problem using Huggingface datasets caused by PyArrow memory allocation. I finally managed to solve it, and thought to document it since similar issues have been raised here before (https://github.com/huggingface/datasets/issues/5710, https://github.com/huggingface/datasets/issues/6176).
In my case, I was trying to load ~70k dataset files from disk using `datasets.load_from_disk(data_path)` (meaning 70k repeated calls to load_from_disk). This triggered an (uninformative) exception around 64k loaded files:
```
File "pyarrow/io.pxi", line 1053, in pyarrow.lib.memory_map
File "pyarrow/io.pxi", line 1000, in pyarrow.lib.MemoryMappedFile._open
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
OSError: Memory mapping file failed: Cannot allocate memory
```
Despite system RAM usage being very low. After a lot of digging around, I discovered that my Ubuntu machine had a limit on the maximum number of memory mapped files in `/proc/sys/vm/max_map_count` set to 65530, which was causing my data loader to crash. Increasing the limit in the file (`echo <new_mmap_size> | sudo tee /proc/sys/vm/max_map_count`) made the issue go away.
While this isn't a bug as such in either Datasets or PyArrow, this behavior can be very confusing to users. Maybe this should be mentioned in documentation? I suspect the other issues raised here about memory mapping OOM errors could actually be consequence of system configuration.
Br,
Lauri
### Steps to reproduce the bug
```
import numpy as np
import pyarrow as pa
import tqdm
# Write some data to disk
arr = pa.array(np.arange(100))
schema = pa.schema([
pa.field('nums', arr.type)
])
with pa.OSFile('arraydata.arrow', 'wb') as sink:
with pa.ipc.new_file(sink, schema=schema) as writer:
batch = pa.record_batch([arr], schema=schema)
writer.write(batch)
# Number of times to open the memory map
nums = 70000
# Read the data back
arrays = [pa.memory_map('arraydata.arrow', 'r') for _ in tqdm.tqdm(range(nums))]
```
### Expected behavior
No errors.
### Environment info
datasets: 2.18.0
pyarrow: 15.0.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4882 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4882/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4882/comments | https://api.github.com/repos/huggingface/datasets/issues/4882/events | https://github.com/huggingface/datasets/pull/4882 | 1,348,913,665 | PR_kwDODunzps49sRtv | 4,882 | Fix language tags resource file | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4882). All of your documentation changes will be reflected on that endpoint."
] | 2022-08-24T06:06:01Z | 2022-08-24T13:58:33Z | 2022-08-24T13:58:30Z | MEMBER | null | null | null | This PR fixes/updates/adds ALL language tags from IANA (as of 2022-08-08).
This PR also removes all BCP47 suffixes (the languages file only contains language subtags, i.e. ISO 639 1 or 2 codes; no script/region/variant suffixes). See:
- #4753 | {
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https://api.github.com/repos/huggingface/datasets/issues/6659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6659/comments | https://api.github.com/repos/huggingface/datasets/issues/6659/events | https://github.com/huggingface/datasets/pull/6659 | 2,129,229,810 | PR_kwDODunzps5mlmmo | 6,659 | Change default compression argument for JsonDatasetWriter | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6659). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Can someone check this out?",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArr... | 2024-02-11T23:49:07Z | 2024-03-01T17:51:50Z | 2024-03-01T17:44:55Z | CONTRIBUTOR | null | null | null | Change default compression type from `None` to "infer", to align with pandas' defaults.
Documentation asks the user to supply `to_json_kwargs` with arguments suitable for pandas' `to_json` method. At the same time, while pandas' by default uses ["infer"](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) for compression, datasets enforce `None` as default. This, likely, confuses user, as they expect the same behaviour, i.e they expect that if they name their output file as "dataset.jsonl.zst" then the compression would be inferred as "zstd" and file will be compressed before writing.
Moreover, while it is probably outside of the scope of this pull request, `compression` argument needs to be capable of taking `dict` as input (along with `str`), as it does in pandas, in order to allow user to specify compression parameters. Current implementation will likely fail with `NotImplementedError`, as it expects either `None` or `str` specifying compression algo. | {
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https://api.github.com/repos/huggingface/datasets/issues/6703 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6703/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6703/comments | https://api.github.com/repos/huggingface/datasets/issues/6703/events | https://github.com/huggingface/datasets/issues/6703 | 2,163,250,590 | I_kwDODunzps6A8JWe | 6,703 | Unable to load dataset that was saved with `save_to_disk` | {
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"`save_to_disk` uses a special serialization that can only be read using `load_from_disk`.\r\n\r\nContrary to `load_dataset`, `load_from_disk` directly loads Arrow files and uses the dataset directory as cache.\r\n\r\nOn the other hand `load_dataset` does a conversion step to get Arrow files from the raw data files... | 2024-03-01T11:59:56Z | 2024-03-04T13:46:20Z | 2024-03-04T13:46:20Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I get the following error message: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.
### Steps to reproduce the bug
1. Save a dataset with `save_to_disk`
2. Try to load it with `load_datasets`
### Expected behavior
I am able to load the dataset again with `load_datasets` which most packages uses over `load_from_disk`. I want to have a workaround that allows me to create the same indexing that `push_to_hub` creates for you before using `save_to_disk` - how can that be achieved?
### Environment info
datasets 2.17.1, python 3.10 | {
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"(Sorry, meant to open this against my own fork. I'm attempting to debug this issue (https://github.com/astral-sh/uv/issues/1921#issuecomment-2058056192) reported by `huggingface/datasets` on the uv repo.)"
] | 2024-04-16T01:12:36Z | 2024-04-16T01:14:16Z | 2024-04-16T01:12:41Z | NONE | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6021 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6021/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6021/comments | https://api.github.com/repos/huggingface/datasets/issues/6021/events | https://github.com/huggingface/datasets/pull/6021 | 1,799,785,904 | PR_kwDODunzps5VP11Q | 6,021 | [docs] Update return statement of index search | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-07-11T21:33:32Z | 2023-07-12T17:13:02Z | 2023-07-12T17:03:00Z | MEMBER | null | null | null | Clarifies in the return statement of the docstring that the retrieval score is `IndexFlatL2` by default (see [PR](https://github.com/huggingface/transformers/issues/24739) and internal Slack [convo](https://huggingface.slack.com/archives/C01229B19EX/p1689105179711689)), and fixes the formatting because multiple return values are not supported. | {
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https://api.github.com/repos/huggingface/datasets/issues/6609 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6609/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6609/comments | https://api.github.com/repos/huggingface/datasets/issues/6609/events | https://github.com/huggingface/datasets/issues/6609 | 2,095,085,650 | I_kwDODunzps584HhS | 6,609 | Wrong path for cache directory in offline mode | {
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"+1",
"same error in 2.16.1",
"@kongjiellx any luck with the issue?",
"I opened https://github.com/huggingface/datasets/pull/6632 to fix this issue. Once it's merged we'll do a new release of `datasets`",
"Thanks @lhoestq !"
] | 2024-01-23T01:47:19Z | 2024-02-06T17:21:25Z | 2024-02-06T17:21:25Z | NONE | null | null | {
"completed": 0,
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"total": 0
} | ### Describe the bug
Dear huggingfacers,
I'm trying to use a subset of the-stack dataset. When I run the command the first time
```
dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )
```
It downloads the files and caches them normally.
Nevertheless, since my compute nodes are not online (`HF_DATASETS_OFFLINE=1`) . Whenever I try to run the command again, the library is passing the wrong cache path:
`Cache directory for the-stack doesn't exist at /Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data%2Ffortran-data_dir=data%2Ffortran`
when the right path is:
`'/Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data\%2Ffortran`
Not sure why those redundancies are included in the path. If I try adding the correct path through the the cache_dir argument it throws an error:
ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'bigcode/the-stack': Offline mode is enabled.
Your help with this issue is greatly appreciated. Thanks a lot for the great work.
### Steps to reproduce the bug
1:
`dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )`
2:
`HF_DATASETS_OFFLINE=1`
3:
`dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )`
### Expected behavior
being able to use the cached data
### Environment info
several different systems | {
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https://api.github.com/repos/huggingface/datasets/issues/4574 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4574/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4574/comments | https://api.github.com/repos/huggingface/datasets/issues/4574/events | https://github.com/huggingface/datasets/pull/4574 | 1,285,380,616 | PR_kwDODunzps46ZOpZ | 4,574 | Support streaming mlsum dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"After unpinning `s3fs` and pinning `fsspec[http]>=2021.11.1`, the CI installs\r\n- `fsspec-2022.1.0`\r\n- `s3fs-0.5.1`\r\n\r\nand raises the following error:\r\n```\r\n ImportError while loading conftest '/home/runner/work/datasets/d... | 2022-06-27T07:37:03Z | 2022-07-21T13:37:30Z | 2022-07-21T12:40:00Z | MEMBER | null | null | null | Support streaming mlsum dataset.
This PR:
- pins `fsspec` min version with fixed BlockSizeError: `fsspec[http]>=2021.11.1`
- https://github.com/fsspec/filesystem_spec/pull/830
- unpins `s3fs==2021.08.1` to align it with `fsspec` requirement: `s3fs>=2021.11.1`
> s3fs 2021.8.1 requires fsspec==2021.08.1
- see discussion: https://github.com/huggingface/datasets/pull/2858/files#r700027326
- updates the following requirements to be compatible with the previous ones and one with each other:
- `aiobotocore==1.4.2` to `aiobotocore>=2.0.1` (required by s3fs>=2021.11.1)
- `boto3==1.17.106` to `boto3>=1.19.8` (to be compatible with aiobotocore>=2.0.1)
- `botocore==1.20.106` to `botocore>=1.22.8` (to be compatible with aiobotocore and boto3)
Fix #4572. | {
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https://api.github.com/repos/huggingface/datasets/issues/4721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4721/comments | https://api.github.com/repos/huggingface/datasets/issues/4721/events | https://github.com/huggingface/datasets/issues/4721 | 1,310,253,552 | I_kwDODunzps5OGOHw | 4,721 | PyArrow Dataset error when calling `load_dataset` | {
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"Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nTo achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.\r\n\r\nDo you have a minimum reproducible example that we can... | 2022-07-20T01:16:03Z | 2022-07-22T14:11:47Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ## Describe the bug
I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error:
```sh
File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main
raw_datasets = load_dataset(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split
for key, table in logging.tqdm(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1309, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
## Steps to reproduce the bug
I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`.
When creating the dataset, I do something like this:
```python
import json
from datasets import Dataset, Audio
# manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text
for line in manifest_lines:
line = line.strip()
if line:
line_dict = json.loads(line)
manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}")
manifest_dict["duration"].append(line_dict["duration"])
manifest_dict["transcription"].append(line_dict["text"])
# Create a HF dataset
dataset = Dataset.from_dict(manifest_dict).cast_column(
"audio", Audio(sampling_rate=16_000),
)
# From the docs for saving to disk
# https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk
def read_audio_file(example):
with open(example["audio"]["path"], "rb") as f:
return {"audio": {"bytes": f.read()}}
dataset = dataset.map(read_audio_file, num_proc=70)
dataset.save_to_disk(f"/audio-data/hf/{artifact_name}")
dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True)
```
Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace.
I am able to load the dataset fine if I use `load_from_disk()`.
## Expected results
`load_dataset()` should behave just like `load_from_disk()` and not cause any errors.
## Actual results
See above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I am using the `huggingface/transformers-pytorch-gpu:latest` image
- `datasets` version: 2.3.0
- Platform: Docker/Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 8.0.0
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https://api.github.com/repos/huggingface/datasets/issues/7536 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7536/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7536/comments | https://api.github.com/repos/huggingface/datasets/issues/7536/events | https://github.com/huggingface/datasets/issues/7536 | 3,018,425,549 | I_kwDODunzps6z6YTN | 7,536 | [Errno 13] Permission denied: on `.incomplete` file | {
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"It must be an issue with umask being used by multiple threads indeed. Maybe we can try to make a thread safe function to apply the umask (using filelock for example)"
] | 2025-04-24T20:52:45Z | 2025-04-26T12:40:25Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When downloading a dataset, we frequently hit the below Permission Denied error. This looks to happen (at least) across datasets in from HF, S3, and GCS.
It looks like the `temp_file` being passed [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L412) can sometimes be created with `000` permissions leading to the permission denied error (the user running the code is still the owner of the file). Deleting that particular file and re-running the code with 0 changes will usually succeed.
Is there some race condition happening with the [umask](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L416), which is process global, and the [file creation](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L404)?
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv/lib/python3.12/site-packages/datasets/load.py:2084: in load_dataset
builder_instance.download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:925: in download_and_prepare
self._download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:1649: in _download_and_prepare
super()._download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:979: in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
.venv/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:120: in _split_generators
downloaded_files = dl_manager.download(files)
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:159: in download
downloaded_path_or_paths = map_nested(
.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py:514: in map_nested
_single_map_nested((function, obj, batched, batch_size, types, None, True, None))
.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py:382: in _single_map_nested
return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:206: in _download_batched
return thread_map(
.venv/lib/python3.12/site-packages/tqdm/contrib/concurrent.py:69: in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
.venv/lib/python3.12/site-packages/tqdm/contrib/concurrent.py:51: in _executor_map
return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))
.venv/lib/python3.12/site-packages/tqdm/std.py:1181: in __iter__
for obj in iterable:
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:619: in result_iterator
yield _result_or_cancel(fs.pop())
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:317: in _result_or_cancel
return fut.result(timeout)
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:449: in result
return self.__get_result()
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:401: in __get_result
raise self._exception
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/thread.py:59: in run
result = self.fn(*self.args, **self.kwargs)
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:229: in _download_single
out = cached_path(url_or_filename, download_config=download_config)
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:206: in cached_path
output_path = get_from_cache(
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:412: in get_from_cache
fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:331: in fsspec_get
fs.get_file(path, temp_file.name, callback=callback)
.venv/lib/python3.12/site-packages/fsspec/asyn.py:118: in wrapper
return sync(self.loop, func, *args, **kwargs)
.venv/lib/python3.12/site-packages/fsspec/asyn.py:103: in sync
raise return_result
.venv/lib/python3.12/site-packages/fsspec/asyn.py:56: in _runner
result[0] = await coro
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <s3fs.core.S3FileSystem object at 0x7f27c18b2e70>
rpath = '<my-bucket>/<my-prefix>/img_1.jpg'
lpath = '/home/runner/_work/_temp/hf_cache/downloads/6c97983efa4e24e534557724655df8247a0bd04326cdfc4a95b638c11e78222d.incomplete'
callback = <datasets.utils.file_utils.TqdmCallback object at 0x7f27c00cdbe0>
version_id = None, kwargs = {}
_open_file = <function S3FileSystem._get_file.<locals>._open_file at 0x7f27628d1120>
body = <StreamingBody at 0x7f276344fa80 for ClientResponse at 0x7f27c015fce0>
content_length = 521923, failed_reads = 0, bytes_read = 0
async def _get_file(
self, rpath, lpath, callback=_DEFAULT_CALLBACK, version_id=None, **kwargs
):
if os.path.isdir(lpath):
return
bucket, key, vers = self.split_path(rpath)
async def _open_file(range: int):
kw = self.req_kw.copy()
if range:
kw["Range"] = f"bytes={range}-"
resp = await self._call_s3(
"get_object",
Bucket=bucket,
Key=key,
**version_id_kw(version_id or vers),
**kw,
)
return resp["Body"], resp.get("ContentLength", None)
body, content_length = await _open_file(range=0)
callback.set_size(content_length)
failed_reads = 0
bytes_read = 0
try:
> with open(lpath, "wb") as f0:
E PermissionError: [Errno 13] Permission denied: '/home/runner/_work/_temp/hf_cache/downloads/6c97983efa4e24e534557724655df8247a0bd04326cdfc4a95b638c11e78222d.incomplete'
.venv/lib/python3.12/site-packages/s3fs/core.py:1355: PermissionError
```
### Steps to reproduce the bug
I believe this is a race condition and cannot reliably re-produce it, but it happens fairly frequently in our GitHub Actions tests and can also be re-produced (with lesser frequency) on cloud VMs.
### Expected behavior
The dataset loads properly with no permission denied error.
### Environment info
- `datasets` version: 3.5.0
- Platform: Linux-5.10.0-34-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.12.10
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5329 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5329/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5329/comments | https://api.github.com/repos/huggingface/datasets/issues/5329/events | https://github.com/huggingface/datasets/pull/5329 | 1,471,999,125 | PR_kwDODunzps5EGK3y | 5,329 | Clarify imagefolder is for small datasets | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I think it's also reasonable to add the same note to the AudioFolder decription",
"Thank you ! I think \"regular\" is more appropriate than \"small\". It can easily scale to a few thousands of images - just not millions x)",
"Rep... | 2022-12-01T21:47:29Z | 2022-12-06T17:20:04Z | 2022-12-06T17:16:53Z | MEMBER | null | null | null | Based on feedback from [here](https://github.com/huggingface/datasets/issues/5317#issuecomment-1334108824), this PR adds a note to the `imagefolder` loading and creating docs that `imagefolder` is designed for small scale image datasets. | {
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https://api.github.com/repos/huggingface/datasets/issues/6345 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6345/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6345/comments | https://api.github.com/repos/huggingface/datasets/issues/6345/events | https://github.com/huggingface/datasets/issues/6345 | 1,957,707,870 | I_kwDODunzps50sEBe | 6,345 | support squad structure datasets using a YAML parameter | {
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] | open | false | null | [] | null | [] | 2023-10-23T17:55:37Z | 2023-10-23T17:55:37Z | null | NONE | null | null | {
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} | ### Feature request
Since the squad structure is widely used, I think it could be beneficial to support it using a YAML parameter.
could you implement automatic data loading of squad-like data using squad JSON format, to read it from JSON files and view it in the correct squad structure.
The dataset structure should be like this:
https://huggingface.co/datasets/squad
Columns:id,title,context,question,answers
### Motivation
Dataset repo requires arbitrary Python code execution
### Your contribution
The dataset structure should be like this:
https://huggingface.co/datasets/squad
Columns:id,title,context,question,answers
train and dev sets in squad structure JSON files | null | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6925/comments | https://api.github.com/repos/huggingface/datasets/issues/6925/events | https://github.com/huggingface/datasets/pull/6925 | 2,321,084,967 | PR_kwDODunzps5wxDRE | 6,925 | Fix NonMatchingSplitsSizesError/ExpectedMoreSplits when passing data_dir/data_files in no-code Hub datasets | {
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"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",
... | 2024-05-28T13:33:38Z | 2024-11-07T20:41:58Z | 2024-05-31T17:10:37Z | MEMBER | null | null | null | 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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https://api.github.com/repos/huggingface/datasets/issues/6209 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6209/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6209/comments | https://api.github.com/repos/huggingface/datasets/issues/6209/events | https://github.com/huggingface/datasets/issues/6209 | 1,879,622,000 | I_kwDODunzps5wCMFw | 6,209 | CI is broken with AssertionError: 3 failed, 12 errors | {
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See: https://github.com/huggingface/datasets/actions/runs/6069947111/job/16465138041
```
=========================== short test summary info ============================
FAILED tests/test_load.py::ModuleFactoryTest::test_LocalDatasetModuleFactoryWithoutScript_with_data_dir - AssertionError: assert ({NamedSplit('train'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'], NamedSplit('test'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt']} is not None and 2 == 1)
+ where 2 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'])
FAILED tests/test_load.py::test_load_dataset_arrow[False] - AssertionError: assert 20 == 10
+ where 20 = Dataset({\n features: ['col_1'],\n num_rows: 20\n}).num_rows
FAILED tests/test_load.py::test_load_dataset_arrow[True] - assert 20 == 10
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv'])
= 3 failed, 2383 passed, 26 skipped, 9 warnings, 12 errors in 280.79s (0:04:40) =
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"Having looked into this further it seems the core of the issue is with two different formats in the same repo.\r\n\r\nWhen the `parquet` config is first, the `WebDataset`s are loaded as `parquet`, if the `WebDataset` configs are first, the `parquet` is loaded as `WebDataset`.\r\n\r\nA workaround in my case would b... | 2024-08-14T18:12:25Z | 2024-08-18T10:33:38Z | null | NONE | null | null | {
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} | Following [documentation](https://huggingface.co/docs/datasets/repository_structure#define-your-splits-and-subsets-in-yaml) I had defined different configs for [`Dataception`](https://huggingface.co/datasets/bigdata-pw/Dataception), a dataset of datasets:
```yaml
configs:
- config_name: dataception
data_files:
- path: dataception.parquet
split: train
default: true
- config_name: dataset_5423
data_files:
- path: datasets/5423.tar
split: train
...
- config_name: dataset_721736
data_files:
- path: datasets/721736.tar
split: train
```
The intent was for metadata to be browsable via Dataset Viewer, in addition to each individual dataset, and to allow datasets to be loaded by specifying the config/name to `load_dataset`.
While testing `load_dataset` I encountered the following error:
```python
>>> dataset = load_dataset("bigdata-pw/Dataception", "dataset_7691")
Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 467k/467k [00:00<00:00, 1.99MB/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.0M/71.0M [00:02<00:00, 26.8MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "datasets\load.py", line 2145, in load_dataset
builder_instance.download_and_prepare(
File "datasets\builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "datasets\builder.py", line 1100, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "datasets\packaged_modules\parquet\parquet.py", line 58, in _split_generators
self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
^^^^^^^^^^^^^^^^^
File "pyarrow\parquet\core.py", line 2325, in read_schema
file = ParquetFile(
^^^^^^^^^^^^
File "pyarrow\parquet\core.py", line 318, in __init__
self.reader.open(
File "pyarrow\_parquet.pyx", line 1470, in pyarrow._parquet.ParquetReader.open
File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
The correct file is downloaded, however the incorrect builder type is detected; `parquet` due to other content of the repository. It would appear that the config needs to be taken into account.
Note that I have removed the additional configs from the repository because of this issue and there is a limit of 3000 configs anyway so the Dataset Viewer doesn't work as I intended. I'll add them back in if it assists with testing.
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https://api.github.com/repos/huggingface/datasets/issues/4636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4636/comments | https://api.github.com/repos/huggingface/datasets/issues/4636/events | https://github.com/huggingface/datasets/issues/4636 | 1,294,547,836 | I_kwDODunzps5NKTt8 | 4,636 | Add info in docs about behavior of download_config.num_proc | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | null | [] | 2022-07-05T17:01:00Z | 2022-07-28T10:40:32Z | 2022-07-28T10:40:32Z | CONTRIBUTOR | null | null | {
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} | **Is your feature request related to a problem? Please describe.**
I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it.
**Describe the solution you'd like**
- Add note about how the default number of workers is 16. Related code:
https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302
- Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing.
**Describe alternatives you've considered**
maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`.
**Additional context**
...
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https://api.github.com/repos/huggingface/datasets/issues/5495 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5495/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5495/comments | https://api.github.com/repos/huggingface/datasets/issues/5495/events | https://github.com/huggingface/datasets/issues/5495 | 1,566,803,452 | I_kwDODunzps5dY4X8 | 5,495 | to_tf_dataset fails with datetime UTC columns even if not included in columns argument | {
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"descript... | closed | false | null | [] | null | [
"Hi! This is indeed a bug in our zero-copy logic.\r\n\r\nTo fix it, instead of the line:\r\nhttps://github.com/huggingface/datasets/blob/7cfac43b980ab9e4a69c2328f085770996323005/src/datasets/features/features.py#L702\r\n\r\nwe should have:\r\n```python\r\nreturn pa.types.is_primitive(pa_type) and not (pa.types.is_b... | 2023-02-01T20:47:33Z | 2023-02-08T14:33:19Z | 2023-02-08T14:33:19Z | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
There appears to be some eager behavior in `to_tf_dataset` that runs against every column in a dataset even if they aren't included in the columns argument. This is problematic with datetime UTC columns due to them not working with zero copy. If I don't have UTC information in my datetime column, then everything works as expected.
### Steps to reproduce the bug
```python
import numpy as np
import pandas as pd
from datasets import Dataset
df = pd.DataFrame(np.random.rand(2, 1), columns=["x"])
# df["dt"] = pd.to_datetime(["2023-01-01", "2023-01-01"]) # works fine
df["dt"] = pd.to_datetime(["2023-01-01 00:00:00.00000+00:00", "2023-01-01 00:00:00.00000+00:00"])
df.to_parquet("test.pq")
ds = Dataset.from_parquet("test.pq")
tf_ds = ds.to_tf_dataset(columns=["x"], batch_size=2, shuffle=True)
```
```
ArrowInvalid Traceback (most recent call last)
Cell In[1], line 12
8 df.to_parquet("test.pq")
11 ds = Dataset.from_parquet("test.pq")
---> 12 tf_ds = ds.to_tf_dataset(columns=["r"], batch_size=2, shuffle=True)
File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:411, in TensorflowDatasetMixin.to_tf_dataset(self, batch_size, columns, shuffle, collate_fn, drop_remainder, collate_fn_args, label_cols, prefetch, num_workers)
407 dataset = self
409 # TODO(Matt, QL): deprecate the retention of label_ids and label
--> 411 output_signature, columns_to_np_types = dataset._get_output_signature(
412 dataset,
413 collate_fn=collate_fn,
414 collate_fn_args=collate_fn_args,
415 cols_to_retain=cols_to_retain,
416 batch_size=batch_size if drop_remainder else None,
417 )
419 if "labels" in output_signature:
420 if ("label_ids" in columns or "label" in columns) and "labels" not in columns:
File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:254, in TensorflowDatasetMixin._get_output_signature(dataset, collate_fn, collate_fn_args, cols_to_retain, batch_size, num_test_batches)
252 for _ in range(num_test_batches):
253 indices = sample(range(len(dataset)), test_batch_size)
--> 254 test_batch = dataset[indices]
255 if cols_to_retain is not None:
256 test_batch = {key: value for key, value in test_batch.items() if key in cols_to_retain}
File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2590, in Dataset.__getitem__(self, key)
2588 def __getitem__(self, key): # noqa: F811
2589 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2590 return self._getitem(
2591 key,
2592 )
File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2575, in Dataset._getitem(self, key, **kwargs)
2573 formatter = get_formatter(format_type, features=self.features, **format_kwargs)
2574 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2575 formatted_output = format_table(
2576 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2577 )
2578 return formatted_output
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:634, in format_table(table, key, formatter, format_columns, output_all_columns)
632 python_formatter = PythonFormatter(features=None)
633 if format_columns is None:
--> 634 return formatter(pa_table, query_type=query_type)
635 elif query_type == "column":
636 if key in format_columns:
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:410, in Formatter.__call__(self, pa_table, query_type)
408 return self.format_column(pa_table)
409 elif query_type == "batch":
--> 410 return self.format_batch(pa_table)
File ~/venv/lib/python3.8/site-packages/datasets/formatting/np_formatter.py:78, in NumpyFormatter.format_batch(self, pa_table)
77 def format_batch(self, pa_table: pa.Table) -> Mapping:
---> 78 batch = self.numpy_arrow_extractor().extract_batch(pa_table)
79 batch = self.python_features_decoder.decode_batch(batch)
80 batch = self.recursive_tensorize(batch)
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:164, in NumpyArrowExtractor.extract_batch(self, pa_table)
163 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 164 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:164, in <dictcomp>(.0)
163 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 164 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:185, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array)
181 else:
182 zero_copy_only = _is_zero_copy_only(pa_array.type) and all(
183 not _is_array_with_nulls(chunk) for chunk in pa_array.chunks
184 )
--> 185 array: List = [
186 row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only)
187 ]
188 else:
189 if isinstance(pa_array.type, _ArrayXDExtensionType):
190 # don't call to_pylist() to preserve dtype of the fixed-size array
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:186, in <listcomp>(.0)
181 else:
182 zero_copy_only = _is_zero_copy_only(pa_array.type) and all(
183 not _is_array_with_nulls(chunk) for chunk in pa_array.chunks
184 )
185 array: List = [
--> 186 row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only)
187 ]
188 else:
189 if isinstance(pa_array.type, _ArrayXDExtensionType):
190 # don't call to_pylist() to preserve dtype of the fixed-size array
File ~/venv/lib/python3.8/site-packages/pyarrow/array.pxi:1475, in pyarrow.lib.Array.to_numpy()
File ~/venv/lib/python3.8/site-packages/pyarrow/error.pxi:100, in pyarrow.lib.check_status()
ArrowInvalid: Needed to copy 1 chunks with 0 nulls, but zero_copy_only was True
```
### Expected behavior
I think there are two potential issues/fixes
1. Proper handling of datetime UTC columns (perhaps there is something incorrect with zero copy handling here)
2. Not eagerly running against every column in a dataset when the columns argument of `to_tf_dataset` specifies a subset of columns (although I'm not sure if this is unavoidable)
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-13.2-x86_64-i386-64bit
- Python version: 3.8.12
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/4551 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4551/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4551/comments | https://api.github.com/repos/huggingface/datasets/issues/4551/events | https://github.com/huggingface/datasets/pull/4551 | 1,282,534,807 | PR_kwDODunzps46QAV- | 4,551 | Perform hidden file check on relative data file path | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I'm aware of this behavior, which is tricky to solve due to fsspec's hidden file handling (see https://github.com/huggingface/datasets/issues/4115#issuecomment-1108819538). I've tested some regex patterns to address this, and they se... | 2022-06-23T14:49:11Z | 2022-06-30T14:49:20Z | 2022-06-30T14:38:18Z | COLLABORATOR | null | null | null | Fix #4549 | {
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https://api.github.com/repos/huggingface/datasets/issues/4722 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4722/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4722/comments | https://api.github.com/repos/huggingface/datasets/issues/4722/events | https://github.com/huggingface/datasets/pull/4722 | 1,310,785,916 | PR_kwDODunzps47t_HJ | 4,722 | Docs: Fix same-page haslinks | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-20T10:04:37Z | 2022-07-20T17:02:33Z | 2022-07-20T16:49:36Z | NONE | null | null | null | `href="/docs/datasets/quickstart#audio"` implicitly goes to `href="/docs/datasets/{$LATEST_STABLE_VERSION}/quickstart#audio"`. Therefore, https://huggingface.co/docs/datasets/quickstart#audio #audio hashlink does not work since the new docs were not added to v2.3.2 (LATEST_STABLE_VERSION)
to preserve the version, it should be just `href="#audio"`, which will implicilty go to curren_page + #audio element | {
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https://api.github.com/repos/huggingface/datasets/issues/5345 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5345/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5345/comments | https://api.github.com/repos/huggingface/datasets/issues/5345/events | https://github.com/huggingface/datasets/issues/5345 | 1,486,555,384 | I_kwDODunzps5Ymwj4 | 5,345 | Wrong dtype for array in audio features | {
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"After some more investigation, this is due to [this line of code](https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L279). The function `sf.read(file)` should be updated to `sf.read(file, dtype=\"float32\")`\r\n\r\nIndeed, the default value in soundfile is `float64` ([see here](https... | 2022-12-09T11:05:11Z | 2023-02-10T14:39:28Z | null | NONE | null | null | {
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} | ### Describe the bug
When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datasets cannot be merged.
### Steps to reproduce the bug
For example, for `facebook/voxpopuli` and `mozilla-foundation/common_voice_11_0`:
```
from datasets import load_dataset, interleave_datasets
covost = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="train", streaming=True)
voxpopuli = datasets.load_dataset("facebook/voxpopuli", "nl", split="train", streaming=True)
sample_cv, = covost.take(1)
sample_vp, = voxpopuli.take(1)
assert sample_cv["audio"]["array"].dtype == sample_vp["audio"]["array"].dtype
# Fails
dataset = interleave_datasets([covost, voxpopuli])
# ValueError: The features can't be aligned because the key audio of features {'audio_id': Value(dtype='string', id=None), 'language': Value(dtype='int64', id=None), 'audio': {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)}, 'normalized_text': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'speaker_id': Value(dtype='string', id=None), 'is_gold_transcript': Value(dtype='bool', id=None), 'accent': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None)} has unexpected type - {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)} (expected either Audio(sampling_rate=16000, mono=True, decode=True, id=None) or Value("null").
```
### Expected behavior
The audio should be loaded to arrays with a unique dtype (I guess `float32`)
### Environment info
```
- `datasets` version: 2.7.1.dev0
- Platform: Linux-4.18.0-425.3.1.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.15
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
``` | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-02-20T16:50:09Z | 2023-02-21T13:20:42Z | 2023-02-21T13:13:05Z | COLLABORATOR | null | null | null | Fix for https://discord.com/channels/879548962464493619/1075729627546406912/1075729627546406912
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https://api.github.com/repos/huggingface/datasets/issues/4638 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4638/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4638/comments | https://api.github.com/repos/huggingface/datasets/issues/4638/events | https://github.com/huggingface/datasets/pull/4638 | 1,295,233,315 | PR_kwDODunzps4656H9 | 4,638 | The speechocean762 dataset | {
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"CircleCL reported two errors, but I didn't find the reason. The error message:\r\n```\r\n_________________ ERROR collecting tests/test_dataset_cards.py _________________\r\ntests/test_dataset_cards.py:53: in <module>\r\n @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\ntests/test_d... | 2022-07-06T06:17:30Z | 2022-10-03T09:34:36Z | 2022-10-03T09:34:36Z | NONE | null | null | null | [speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use.
I believe it will be easier to use if it could be available on Hugging Face. | {
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https://api.github.com/repos/huggingface/datasets/issues/5612 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5612/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5612/comments | https://api.github.com/repos/huggingface/datasets/issues/5612/events | https://github.com/huggingface/datasets/issues/5612 | 1,611,262,510 | I_kwDODunzps5gCeou | 5,612 | Arrow map type in parquet files unsupported | {
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"I'm attaching a minimal reproducible example:\r\n```python\r\nfrom datasets import load_dataset\r\nimport pyarrow as pa\r\nimport pyarrow.parquet as pq\r\n\r\ntable_with_map = pa.Table.from_pydict(\r\n {\"a\": [1, 2], \"b\": [[(\"a\", 2)], [(\"b\", 4)]]},\r\n schema=pa.schema({\"a\": pa.int32(), \"b\": pa.ma... | 2023-03-06T12:03:24Z | 2024-03-15T18:56:12Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When I try to load parquet files that were processed with Spark, I get the following issue:
`ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.`
Strangely, loading the dataset with `streaming=True` solves the issue.
### Steps to reproduce the bug
The dataset is private, but this can be reproduced with any dataset that has Arrow maps.
### Expected behavior
Loading the dataset no matter whether streaming is True or not.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31
- Python version: 3.10.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7069 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7069/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7069/comments | https://api.github.com/repos/huggingface/datasets/issues/7069/events | https://github.com/huggingface/datasets/pull/7069 | 2,429,281,339 | PR_kwDODunzps52betB | 7,069 | Fix push_to_hub by not calling create_branch if PR branch | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7069). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"cc @Wauplin maybe it's a `huggingface_hub` bug ?\r\n\r\nEDIT: ah actually the issue is ... | 2024-07-25T07:50:04Z | 2024-07-31T07:10:07Z | 2024-07-30T10:51:01Z | MEMBER | null | null | null | Fix push_to_hub by not calling create_branch if PR branch (e.g. `refs/pr/1`).
Note that currently create_branch raises a 400 Bad Request error if the user passes a PR branch (e.g. `refs/pr/1`).
EDIT:
~~Fix push_to_hub by not calling create_branch if branch exists.~~
Note that currently create_branch raises a 403 Forbidden error even if all these conditions are met:
- exist_ok is passed
- the branch already exists
- the user does not have WRITE permission
Fix #7067.
Related issue:
- https://github.com/huggingface/huggingface_hub/issues/2419 | {
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https://api.github.com/repos/huggingface/datasets/issues/6576 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6576/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6576/comments | https://api.github.com/repos/huggingface/datasets/issues/6576/events | https://github.com/huggingface/datasets/issues/6576 | 2,073,710,124 | I_kwDODunzps57mk4s | 6,576 | document page 404 not found after redirection | {
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"Thanks for reporting! I've opened a PR with a fix."
] | 2024-01-10T06:48:14Z | 2024-01-17T14:01:31Z | 2024-01-17T14:01:31Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
The redirected page encountered 404 not found.
### Steps to reproduce the bug
1. In this tutorial: https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt
original md: https://github.com/huggingface/course/blob/2c733c2246b8b7e0e6f19a9e5d15bb12df43b2a3/chapters/en/chapter5/4.mdx#L49
```
By default, 🤗 Datasets will decompress the files needed to load a dataset. If you want to preserve hard drive space, you can pass `DownloadConfig(delete_extracted=True)` to the `download_config` argument of `load_dataset()`. See the [documentation](https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig) for more details.
```
The documentation points to `https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig`
it shows `The documentation page PACKAGE_REFERENCE/BUILDER_CLASSES.HTML doesn’t exist in v2.16.1, but exists on the main version. Click [here](https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html) to redirect to the main version of the documentation.`
But the redirected website `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html` is 404 not found.
### Expected behavior
I Guess the redirected webisite should be
`https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes` (without `.html`)
or `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes#datasets.DownloadConfig`.
### Environment info
Datasets main | {
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https://api.github.com/repos/huggingface/datasets/issues/5235 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5235/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5235/comments | https://api.github.com/repos/huggingface/datasets/issues/5235/events | https://github.com/huggingface/datasets/pull/5235 | 1,448,052,660 | PR_kwDODunzps5C1pjc | 5,235 | Pin `typer` version in tests to <0.5 to fix Windows CI | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-14T13:17:02Z | 2022-11-14T15:43:01Z | 2022-11-14T13:41:12Z | CONTRIBUTOR | null | null | null | Otherwise `click` fails on Windows:
```
Traceback (most recent call last):
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\__main__.py", line 4, in <module>
setup_cli()
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\cli\_util.py", line 71, in setup_cli
command(prog_name=COMMAND)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\click\core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 785, in main
**extra,
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 190, in _main
args = click.utils._expand_args(args)
AttributeError: module 'click.utils' has no attribute '_expand_args'
```
See https://github.com/tiangolo/typer/issues/427 | {
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https://api.github.com/repos/huggingface/datasets/issues/7455 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7455/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7455/comments | https://api.github.com/repos/huggingface/datasets/issues/7455/events | https://github.com/huggingface/datasets/issues/7455 | 2,921,933,250 | I_kwDODunzps6uKSnC | 7,455 | Problems with local dataset after upgrade from 3.3.2 to 3.4.0 | {
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} | [] | open | false | null | [] | null | [
"Hi ! I just released 3.4.1 with a fix, let me know if it's working now !"
] | 2025-03-15T09:22:50Z | 2025-03-17T16:20:43Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I was not able to open a local saved dataset anymore that was created using an older datasets version after the upgrade yesterday from datasets 3.3.2 to 3.4.0
The traceback is
```
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 67, in _generate_tables
batches = pa.ipc.open_stream(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 190, in open_stream
return RecordBatchStreamReader(source, options=options,
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 52, in __init__
self._open(source, options=options, memory_pool=memory_pool)
File "pyarrow/ipc.pxi", line 1006, in pyarrow.lib._RecordBatchStreamReader._open
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 538970747 metadata bytes, but only read 2126
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 1855, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 69, in _generate_tables
reader = pa.ipc.open_file(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 234, in open_file
return RecordBatchFileReader(
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 110, in __init__
self._open(source, footer_offset=footer_offset,
File "pyarrow/ipc.pxi", line 1090, in pyarrow.lib._RecordBatchFileReader._open
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Not an Arrow file
```
### Steps to reproduce the bug
Load a dataset from a local folder with
```
dataset = load_dataset(
args.train_data_dir,
cache_dir=args.cache_dir,
)
```
as it is done for example in the training script for SD3 controlnet.
This is the minimal script to test it:
```
from datasets import load_dataset
def main():
dataset = load_dataset(
"local_dataset",
)
print(dataset)
print("Sample data:", dataset["train"][0])
if __name__ == "__main__":
main()
````
### Expected behavior
Work in 3.4.0 like in 3.3.2
### Environment info
- `datasets` version: 3.4.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7253 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7253/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7253/comments | https://api.github.com/repos/huggingface/datasets/issues/7253/events | https://github.com/huggingface/datasets/issues/7253 | 2,615,862,202 | I_kwDODunzps6b6uO6 | 7,253 | Unable to upload a large dataset zip either from command line or UI | {
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} | [] | open | false | null | [] | null | [] | 2024-10-26T13:17:06Z | 2024-10-26T13:17:06Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Unable to upload a large dataset zip from command line or UI. UI simply says error. I am trying to a upload a tar.gz file of 17GB.
<img width="550" alt="image" src="https://github.com/user-attachments/assets/f9d29024-06c8-49c4-a109-0492cff79d34">
<img width="755" alt="image" src="https://github.com/user-attachments/assets/a8d4acda-7f02-4279-9c2d-b2e0282b4faa">
### Steps to reproduce the bug
Upload a large file
### Expected behavior
The file should upload without any issue.
### Environment info
None | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5912/comments | https://api.github.com/repos/huggingface/datasets/issues/5912/events | https://github.com/huggingface/datasets/issues/5912 | 1,730,299,852 | I_kwDODunzps5nIkfM | 5,912 | Missing elements in `map` a batched dataset | {
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"Hi ! in your code batching is **only used within** `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\r\n```"
] | 2023-05-29T08:09:19Z | 2023-07-26T15:48:15Z | 2023-07-26T15:48:15Z | NONE | null | null | {
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} | ### Describe the bug
As outlined [here](https://discuss.huggingface.co/t/length-error-using-map-with-datasets/40969/3?u=sachin), the following collate function drops 5 out of possible 6 elements in the batch (it is 6 because out of the eight, two are bad links in laion). A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
The weirdest part is when inspecting the sizes of the tensors as shown below, both `tokenized_captions["input_ids"]` and `image_features` show the correct shapes. Simply the output only has one element (with the batch dimension squeezed out).
```python
class CollateFn:
def get_image(self, url):
try:
response = requests.get(url)
return Image.open(io.BytesIO(response.content)).convert("RGB")
except PIL.UnidentifiedImageError:
logger.info(f"Reading error: Could not transform f{url}")
return None
except requests.exceptions.ConnectionError:
logger.info(f"Connection error: Could not transform f{url}")
return None
def __call__(self, batch):
images = [self.get_image(url) for url in batch["url"]]
captions = [caption for caption, image in zip(batch["caption"], images) if image is not None]
images = [image for image in images if image is not None]
tokenized_captions = tokenizer(
captions,
padding="max_length",
truncation=True,
max_length=tokenizer.model_max_length,
return_tensors="pt",
)
image_features = torch.stack([torch.Tensor(feature_extractor(image)["pixel_values"][0]) for image in images])
# import pdb; pdb.set_trace()
return {"input_ids": tokenized_captions["input_ids"], "images": image_features}
collate_fn = CollateFn()
laion_ds = datasets.load_dataset("laion/laion400m", split="train", streaming=True)
laion_ds_batched = laion_ds.map(collate_fn, batched=True, batch_size=8, remove_columns=next(iter(laion_ds)).keys())
```
### Steps to reproduce the bug
A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
### Expected behavior
Would expect `next(iter(laion_ds_batched))` to produce two tensors of shape `(batch_size, 77)` and `batch_size, image_shape`.
### Environment info
datasets==2.12.0
python==3.10 | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-11-13T09:15:39Z | 2023-11-14T10:29:48Z | 2023-11-14T10:23:29Z | MEMBER | null | null | null | Support `pyarrow` 14.0.1 and fix vulnerability [CVE-2023-47248](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm).
Fix #6396. | {
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https://api.github.com/repos/huggingface/datasets/issues/5487 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5487/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5487/comments | https://api.github.com/repos/huggingface/datasets/issues/5487/events | https://github.com/huggingface/datasets/issues/5487 | 1,564,480,121 | I_kwDODunzps5dQBJ5 | 5,487 | Incorrect filepath for dill module | {
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"Hi! The correct path is still `dill._dill.XXXX` in the latest release. What do you get when you run `python -c \"import dill; print(dill.__version__)\"` in your environment?",
"`0.3.6` I feel like that's bad news, because it's probably not the issue.\r\n\r\nMy mistake, about the wrong path guess. I think I did... | 2023-01-31T15:01:08Z | 2023-02-24T16:18:36Z | 2023-02-24T16:18:36Z | NONE | null | null | {
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} | ### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import datasets
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module>
from .arrow_dataset import Dataset
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 66, in <module>
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 27, in <module>
from .features import Features, Image, Value
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/__init__.py", line 17, in <module>
from .audio import Audio
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/audio.py", line 12, in <module>
from ..download.streaming_download_manager import xopen
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/__init__.py", line 9, in <module>
from .download_manager import DownloadManager, DownloadMode
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/download_manager.py", line 36, in <module>
from ..utils.py_utils import NestedDataStructure, map_nested, size_str
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 602, in <module>
class Pickler(dill.Pickler):
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 605, in Pickler
dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())
AttributeError: module 'dill' has no attribute '_dill'
```
Looking at the github source code for dill, it appears that `datasets` has a bug or is not compatible with the latest `dill`. Specifically, rather than `dill._dill.XXXX` it should be `dill.dill._dill.XXXX`. But given the popularity of `datasets` I feel confused about me being the first person to have this issue, so it makes me wonder if I'm misdiagnosing the issue.
### Steps to reproduce the bug
Install `dill` and `datasets` packages and then `import datasets`
### Expected behavior
I expect `datasets` to import.
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.13
- PyArrow version: 11.0.0
- Pandas version: 1.4.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/5849 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5849/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5849/comments | https://api.github.com/repos/huggingface/datasets/issues/5849/events | https://github.com/huggingface/datasets/issues/5849 | 1,707,551,511 | I_kwDODunzps5lxysX | 5,849 | CSV datasets should only read the CSV data files in the repo | {
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} | When a no-script dataset has many CSV files and a JPG file, the library infers to use the Csv builder, but tries to read as CSV all files in the repo, also the JPG file.
I think the Csv builder should filter out non-CSV files when reading.
An analogue solution should be implemented for other packaged builders.
Related to:
- https://huggingface.co/datasets/abidlabs/img2text/discussions/1
- https://github.com/gradio-app/gradio/pull/3973#issuecomment-1545409061
CC: @abidlabs @severo | {
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"related: https://github.com/huggingface/datasets/issues/3504",
"another file not found:\r\n```\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 417, in _info\r\n await _file_info(\r\n File ... | 2023-08-11T19:05:25Z | 2023-08-14T23:28:38Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
can't use or download the nih exporter pile data.
```
15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()
16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights
17 column_names = next(iter(dataset)).keys()
18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__
19 for key, example in ex_iterable:
20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__
21 yield from self.generate_examples_fn(**self.kwargs)
22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples
23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f:
24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper
25 return function(*args, download_config=download_config, **kwargs)
26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open
29 return self.__enter__()
30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__
31 f = self.fs.open(self.path, mode=mode)
32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open
33 f = self._open(
34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open
35 size = size or self.info(path, **kwargs)["size"]
36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper
37 return sync(self.loop, func, *args, **kwargs)
38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync
39 raise return_result
40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner
41 result[0] = await coro
42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info
43 raise FileNotFoundError(url) from exc
44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst
```
### Steps to reproduce the bug
run this:
```
from datasets import load_dataset
path, name = 'EleutherAI/pile', 'nih_exporter'
# -- Get data set
dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch")
batch = dataset.take(512)
print(f'{batch=}')
```
### Expected behavior
print the batch
### Environment info
```
(beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.14.4
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
``` | null | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6959/comments | https://api.github.com/repos/huggingface/datasets/issues/6959/events | https://github.com/huggingface/datasets/pull/6959 | 2,340,229,908 | PR_kwDODunzps5xyVt6 | 6,959 | Better error handling in `dataset_module_factory` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6959). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Test should be fixed by https://github.com/huggingface/datasets/pull/6959/commits/ef8f7... | 2024-06-07T11:24:15Z | 2024-06-10T07:33:53Z | 2024-06-10T07:27:43Z | CONTRIBUTOR | null | null | null | cc @cakiki who reported it on [slack](https://huggingface.slack.com/archives/C039P47V1L5/p1717754405578539) (private link)
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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https://api.github.com/repos/huggingface/datasets/issues/7491 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7491/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7491/comments | https://api.github.com/repos/huggingface/datasets/issues/7491/events | https://github.com/huggingface/datasets/pull/7491 | 2,959,085,647 | PR_kwDODunzps6QtBsD | 7,491 | docs: update cache.mdx to include HF_DATASETS_CACHE documentation | {
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} | [] | closed | false | null | [] | null | [
"Already included HF_DATASETS_CACHE"
] | 2025-03-30T20:35:03Z | 2025-03-30T20:36:40Z | 2025-03-30T20:36:40Z | NONE | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7387 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7387/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7387/comments | https://api.github.com/repos/huggingface/datasets/issues/7387/events | https://github.com/huggingface/datasets/issues/7387 | 2,841,228,048 | I_kwDODunzps6pWbMQ | 7,387 | Dynamic adjusting dataloader sampling weight | {
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"You mean based on a condition that has to be checked on-the-fly during training ? Otherwise if you know in advance after how many samples you need to change the sampling you can simply concatenate the two mixes",
"Yes, like during training, if one data sample's prediction is consistently wrong, its sampling weig... | 2025-02-10T03:18:47Z | 2025-03-07T14:06:54Z | null | NONE | null | null | {
"completed": 0,
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} | Hi,
Thanks for your wonderful work! I'm wondering is there a way to dynamically adjust the sampling weight of each data in the dataset during training? Looking forward to your reply, thanks again. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5675 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5675/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5675/comments | https://api.github.com/repos/huggingface/datasets/issues/5675/events | https://github.com/huggingface/datasets/issues/5675 | 1,641,763,478 | I_kwDODunzps5h21KW | 5,675 | Filter datasets by language code | {
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"The dataset still can be found, if instead of using the search form you just enter the language code in the url, like https://huggingface.co/datasets?language=language:myv. \r\n\r\nBut of course having a more complete list of languages in the search form (or just a fallback to the language codes, if they are missi... | 2023-03-27T09:42:28Z | 2023-03-30T08:08:15Z | 2023-03-30T08:08:15Z | NONE | null | null | {
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} | Hi! I use the language search field on https://huggingface.co/datasets
However, some of the datasets tagged by ISO language code are not accessible by this search form.
For example, [myv_ru_2022](https://huggingface.co/datasets/slone/myv_ru_2022) is has `myv` language tag but it is not included in Languages search form.
I've also noticed the same problem with `mhr` (see https://huggingface.co/datasets/AigizK/mari-russian-parallel-corpora) | {
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https://api.github.com/repos/huggingface/datasets/issues/5493 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5493/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5493/comments | https://api.github.com/repos/huggingface/datasets/issues/5493/events | https://github.com/huggingface/datasets/pull/5493 | 1,566,637,806 | PR_kwDODunzps5JCSAZ | 5,493 | Remove unused `load_from_cache_file` arg from `Dataset.shard()` docstring | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5493). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n... | 2023-02-01T18:57:48Z | 2023-02-08T15:10:46Z | 2023-02-08T15:03:50Z | CONTRIBUTOR | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5072 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5072/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5072/comments | https://api.github.com/repos/huggingface/datasets/issues/5072/events | https://github.com/huggingface/datasets/pull/5072 | 1,397,765,531 | PR_kwDODunzps5ANoo5 | 5,072 | Image & Audio formatting for numpy/torch/tf/jax | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I just added a consolidation step so that numpy arrays or tensors of images are stacked together if the shapes match, instead of having lists of tensors\r\n\r\nFeel free to review @mariosasko :)",
"I added a few lines in the docs a... | 2022-10-05T13:07:03Z | 2022-10-10T13:24:10Z | 2022-10-10T13:21:32Z | MEMBER | null | null | null | Added support for image and audio formatting for numpy, torch, tf and jax.
For images, the dtype used is the one of the image (the one returned by PIL.Image), e.g. uint8
I also added support for string, binary and None types. In particular for torch and jax, strings are kept unchanged (previously it was returning an error because you can't create a tensor of strings) | {
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https://api.github.com/repos/huggingface/datasets/issues/6524 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6524/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6524/comments | https://api.github.com/repos/huggingface/datasets/issues/6524/events | https://github.com/huggingface/datasets/issues/6524 | 2,053,076,311 | I_kwDODunzps56X3VX | 6,524 | Streaming the Pile: Missing Files | {
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"Hello @FelixLabelle,\r\n\r\nAs you can see in the Community tab of the corresponding dataset, it is a known issue: https://huggingface.co/datasets/EleutherAI/pile/discussions/15\r\n\r\nThe data has been taken down due to reported copyright infringement.\r\n\r\nFeel free to continue the discussion there."
] | 2023-12-21T21:25:09Z | 2023-12-22T09:17:05Z | 2023-12-22T09:17:05Z | NONE | null | null | {
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} | ### Describe the bug
The pile does not stream, a "File not Found error" is returned. It looks like the Pile's files have been moved.
### Steps to reproduce the bug
To reproduce run the following code:
```
from datasets import load_dataset
dataset = load_dataset('EleutherAI/pile', 'en', split='train', streaming=True)
next(iter(dataset))
```
I get the following error:
`FileNotFoundError: https://the-eye.eu/public/AI/pile/train/00.jsonl.zst`
### Expected behavior
Return the data in a stream.
### Environment info
- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.5
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5293 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5293/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5293/comments | https://api.github.com/repos/huggingface/datasets/issues/5293/events | https://github.com/huggingface/datasets/issues/5293 | 1,463,669,201 | I_kwDODunzps5XPdHR | 5,293 | Support streaming datasets with pathlib.Path.with_suffix | {
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} | Extend support for streaming datasets that use `pathlib.Path.with_suffix`.
This feature will be useful e.g. for datasets containing text files and annotated files with the same name but different extension. | {
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https://api.github.com/repos/huggingface/datasets/issues/4989 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4989/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4989/comments | https://api.github.com/repos/huggingface/datasets/issues/4989/events | https://github.com/huggingface/datasets/issues/4989 | 1,376,832,233 | I_kwDODunzps5SEMrp | 4,989 | Running add_column() seems to corrupt existing sequence-type column info | {
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"Nevermind, I was incorrect."
] | 2022-09-17T17:42:05Z | 2022-09-19T12:54:54Z | 2022-09-19T12:54:54Z | NONE | null | null | {
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} | I have a dataset that contains a column ("foo") that is a sequence type of length 4. So when I run .to_pandas() on it, the resulting dataframe correctly contains 4 columns - foo_0, foo_1, foo_2, foo_3. So the 1st row of the dataframe might look like:
ds = load_dataset(...)
df = ds.to_pandas()
df:
foo_0 | foo_1 | foo_2 | foo_3
0.0 | 1.0 | 2.0 | 3.0
If I run .add_column("new_col", data) on the dataset, and then .to_pandas() on the resulting new dataset, the resulting dataframe contains only 2 columns - foo, new_col. The values in column foo are lists of length 4, the 4 elements that should have been split into separate columns. Dataframe 1st row would be:
ds = load_dataset(...)
new_ds = ds.add_column("new_col", data)
df = new_ds.to_pandas()
df:
foo | new_col
[0.0, 1.0, 2.0, 3.0] | new_val
I've explored the 2 datasets in a debugger and haven't noticed any changes to any attributes related to the foo column, but I can't determine why the dataframes are so different. | {
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https://api.github.com/repos/huggingface/datasets/issues/6065 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6065/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6065/comments | https://api.github.com/repos/huggingface/datasets/issues/6065/events | https://github.com/huggingface/datasets/pull/6065 | 1,819,334,932 | PR_kwDODunzps5WR8jI | 6,065 | Add column type guessing from map return function | {
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"Thanks for working on this. However, having thought about this issue a bit more, supporting this doesn't seem like a good idea - it's better to be explicit than implicit, according to the Zen of Python 🙂. Also, I don't think many users would use this, so this raises the question of whether this is something we wa... | 2023-07-25T00:34:17Z | 2023-07-26T15:13:45Z | 2023-07-26T15:13:44Z | NONE | null | null | null | As discussed [here](https://github.com/huggingface/datasets/issues/5965), there are some cases where datasets is unable to automatically promote columns during mapping. The fix is to explicitly provide a `features` definition so pyarrow can configure itself with the right column types from the outset.
This PR provides an alternative approach, which is functionally equivalent to specifying features but a bit cleaner within a larger mapping pipeline. It allows clients to typehint the return variable coming from the mapper function - if we find one of these type annotations specified, and no explicit features have been passed in, we'll try to convert it into a Features map. If the map function runs and casting is unable to succeed, it will raise a DatasetTransformationNotAllowedError that indicates the typehint may be to blame. It works for batched and non-batched mapping functions.
Currently supported column types:
- builtins primitives: string, int, float, bool
- dictionaries, lists (nested and one-deep)
- Optional types and None-Unions (synonymous with optional types)
It's used like:
```python
class DatasetTyped(TypedDict):
texts: list[str]
def dataset_typed_map(batch) -> DatasetTyped:
return {"texts": [text.split() for text in batch["raw_text"]]}
dataset = {"raw_text": ["", "This is a test", "This is another test"]}
with Dataset.from_dict(dataset) as dset:
new_dataset = dset.map(
dataset_typed_map,
batched=True,
batch_size=1,
num_proc=1,
)
```
Open questions:
- Should logging indicate we have automatically guessed these types? Or proceed quietly until we hit an error (as is the current implementation). | {
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https://api.github.com/repos/huggingface/datasets/issues/4699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4699/comments | https://api.github.com/repos/huggingface/datasets/issues/4699/events | https://github.com/huggingface/datasets/pull/4699 | 1,307,555,592 | PR_kwDODunzps47jA6Z | 4,699 | Fix Authentification Error while streaming | {
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"Hi, thanks for working on this, but the fix for this has already been merged in https://github.com/huggingface/datasets/pull/4608."
] | 2022-07-18T08:03:41Z | 2022-07-20T13:10:44Z | 2022-07-20T13:10:43Z | NONE | null | null | null | I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub.
```
from datasets import load_dataset
dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True)
for d in dataset['train']:
print(d)
break # this is for checking
```
This code is an example for streaming private datasets.
when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this,
```
/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)
1007 status=self.status,
1008 message=self.reason,
→ 1009 headers=self.headers,
1010 )
1011
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet')
```
(this is an example on the dataset has `parquet` extenstion)
It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~".
so I add three lines.
With this change, there is no error anymore(but this code is ad-hoc). | {
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https://api.github.com/repos/huggingface/datasets/issues/6104 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6104/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6104/comments | https://api.github.com/repos/huggingface/datasets/issues/6104/events | https://github.com/huggingface/datasets/issues/6104 | 1,828,959,107 | I_kwDODunzps5tA7OD | 6,104 | HF Datasets data access is extremely slow even when in memory | {
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"Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462"
] | 2023-07-31T11:12:19Z | 2023-08-01T11:22:43Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Doing a simple `some_dataset[:10]` can take more than a minute.
Profiling it:
<img width="1280" alt="image" src="https://github.com/huggingface/datasets/assets/36224762/e641fb95-ff02-4072-9016-5416a65f75ab">
`some_dataset` is completely in memory with no disk cache.
This is proving fatal to my usage of HF Datasets. Is there a way I can forgo the arrow format and store the dataset as PyTorch tensors so that `_tensorize` is not needed? And is `_consolidate` supposed to take this long?
It's faster to produce the dataset from scratch than to access it from HF Datasets!
### Steps to reproduce the bug
I have uploaded the dataset that causes this problem [here](https://huggingface.co/datasets/NightMachinery/hf_datasets_bug1).
```python
#!/usr/bin/env python3
import sys
import time
import torch
from datasets import load_dataset
def main(dataset_name):
# Start the timer
start_time = time.time()
# Load the dataset from Hugging Face Hub
dataset = load_dataset(dataset_name)
# Set the dataset format as torch
dataset.set_format(type="torch")
# Perform an identity map
dataset = dataset.map(lambda example: example, batched=True, batch_size=20)
# End the timer
end_time = time.time()
# Print the time taken
print(f"Time taken: {end_time - start_time:.2f} seconds")
if __name__ == "__main__":
dataset_name = "NightMachinery/hf_datasets_bug1"
print(f"dataset_name: {dataset_name}")
main(dataset_name)
```
### Expected behavior
_
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7409 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7409/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7409/comments | https://api.github.com/repos/huggingface/datasets/issues/7409/events | https://github.com/huggingface/datasets/pull/7409 | 2,858,079,508 | PR_kwDODunzps6LeOpY | 7,409 | Release: 3.3.1 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7409). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-02-17T14:52:12Z | 2025-02-17T14:54:32Z | 2025-02-17T14:53:13Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6482/comments | https://api.github.com/repos/huggingface/datasets/issues/6482/events | https://github.com/huggingface/datasets/pull/6482 | 2,032,675,918 | PR_kwDODunzps5hhl23 | 6,482 | Fix max lock length on unix | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6482). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I'm getting `AttributeError: module 'os' has no attribute 'statvfs'` on windows - rever... | 2023-12-08T13:39:30Z | 2023-12-12T11:53:32Z | 2023-12-12T11:47:27Z | MEMBER | null | null | null | reported in https://github.com/huggingface/datasets/pull/6482 | {
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https://api.github.com/repos/huggingface/datasets/issues/6882 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6882/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6882/comments | https://api.github.com/repos/huggingface/datasets/issues/6882/events | https://github.com/huggingface/datasets/issues/6882 | 2,284,803,158 | I_kwDODunzps6IL1RW | 6,882 | Connection Error When Using By-pass Proxies | {
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"Changing the supplier of the proxy will solve this problem, or you can visit and follow the instructions in https://hf-mirror.com "
] | 2024-05-08T06:40:14Z | 2024-05-17T06:38:30Z | null | NONE | null | null | {
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} | ### Describe the bug
I'm currently using Clash for Windows as my proxy tunnel, after exporting HTTP_PROXY and HTTPS_PROXY to the port that clash provides🤔, it runs into a connection error saying "Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f969d391870>: Failed to establish a new connection: [Errno 111] Connection refused'))")))"
I have already read the documentation provided on the hugginface, but I think I didn't see the detailed instruction on how to set up proxies for this library.
### Steps to reproduce the bug
1. Turn on any proxy software like Clash / ShadosocksR etc.
2. export system varibles to the port provided by your proxy software in wsl (It's ok for other applications to use proxy expect dataset-library)
3. load any dataset from hugginface online
### Expected behavior
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
Cell In[33], [line 3](vscode-notebook-cell:?execution_count=33&line=3)
[1](vscode-notebook-cell:?execution_count=33&line=1) from datasets import load_metric
----> [3](vscode-notebook-cell:?execution_count=33&line=3) metric = load_metric("seqeval")
File ~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:46, in deprecated.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
[44](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:44) warnings.warn(warning_msg, category=FutureWarning, stacklevel=2)
[45](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:45) _emitted_deprecation_warnings.add(func_hash)
---> [46](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:46) return deprecated_function(*args, **kwargs)
File ~/.local/lib/python3.10/site-packages/datasets/load.py:2104, in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, trust_remote_code, **metric_init_kwargs)
[2101](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2101) warnings.filterwarnings("ignore", message=".*https://huggingface.co/docs/evaluate$", category=FutureWarning)
[2103](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2103) download_mode = DownloadMode(download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS)
-> [2104](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2104) metric_module = metric_module_factory(
[2105](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2105) path,
[2106](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2106) revision=revision,
[2107](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2107) download_config=download_config,
[2108](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2108) download_mode=download_mode,
[2109](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2109) trust_remote_code=trust_remote_code,
[2110](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2110) ).module_path
[2111](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2111) metric_cls = import_main_class(metric_module, dataset=False)
[2112](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2112) metric = metric_cls(
[2113](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2113) config_name=config_name,
[2114](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2114) process_id=process_id,
...
--> [633](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:633) raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
[634](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:634) elif response is not None:
[635](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:635) raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))")))
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4580/comments | https://api.github.com/repos/huggingface/datasets/issues/4580/events | https://github.com/huggingface/datasets/issues/4580 | 1,286,312,912 | I_kwDODunzps5Mq5PQ | 4,580 | Dataset Viewer issue for multi_news | {
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"Thanks for reporting, @lewtun.\r\n\r\nI forced the refreshing of the preview and it worked OK for train and validation splits.\r\n\r\nI guess the error has to do with the data files being hosted at Google Drive: this gives errors when requested automatically using scripts.\r\nWe should host them to fix the error. ... | 2022-06-27T20:25:25Z | 2022-06-28T14:08:48Z | 2022-06-28T14:08:48Z | MEMBER | null | null | {
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} | ### Link
https://huggingface.co/datasets/multi_news
### Description
Not sure what the index error is referring to here:
```
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No | {
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https://api.github.com/repos/huggingface/datasets/issues/5353 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5353/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5353/comments | https://api.github.com/repos/huggingface/datasets/issues/5353/events | https://github.com/huggingface/datasets/issues/5353 | 1,491,880,500 | I_kwDODunzps5Y7Eo0 | 5,353 | Support remote file systems for `Audio` | {
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"Just seen https://github.com/huggingface/datasets/issues/5281"
] | 2022-12-12T13:22:13Z | 2022-12-12T13:37:14Z | 2022-12-12T13:37:14Z | NONE | null | null | {
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} | ### Feature request
Hi there!
It would be super cool if `Audio()`, and potentially other features, could read files from a remote file system.
### Motivation
Large amounts of data is often stored in buckets. `load_from_disk` is able to retrieve data from cloud storage but to my knowledge actually copies the datasets across first, so if you're working off a system with smaller disk specs (like a VM), you can run out of space very quickly.
### Your contribution
Something like this (for Google Cloud Platform in this instance):
```python
from datasets import Dataset, Audio
import gcsfs
fs = gcsfs.GCSFileSystem()
list_of_audio_fp = {'audio': ['1', '2', '3']}
ds = Dataset.from_dict(list_of_audio_fp)
ds = ds.cast_column("audio", Audio(sampling_rate=16000, fs=fs))
```
Under the hood:
```python
import librosa
from io import BytesIO
def load_audio(fp, sampling_rate=None, fs=None):
if fs is not None:
with fs.open(fp, 'rb') as f:
arr, sr = librosa.load(BytesIO(f), sr=sampling_rate)
else:
# Perform existing io operations
```
Written from memory so some things could be wrong. | {
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https://api.github.com/repos/huggingface/datasets/issues/6389 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6389/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6389/comments | https://api.github.com/repos/huggingface/datasets/issues/6389/events | https://github.com/huggingface/datasets/issues/6389 | 1,983,545,744 | I_kwDODunzps52OoGQ | 6,389 | Index 339 out of range for dataset of size 339 <-- save_to_file() | {
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"Hi! Can you make the above reproducer self-contained by adding code that generates the data?",
"I managed a workaround eventually but I don't know what it was (I made a lot of changes to seq2seq). I'll try to include generating code in the future. (If I close, I don't know if you see it. Feel free to close; I'l... | 2023-11-08T12:52:09Z | 2023-11-24T09:14:13Z | null | NONE | null | null | {
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} | ### Describe the bug
When saving out some Audio() data.
The data is audio recordings with associated 'sentences'.
(They use the audio 'bytes' approach because they're clips within audio files).
Code is below the traceback (I can't upload the voice audio/text (it's not even me)).
```
Traceback (most recent call last):
File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 156, in <module>
create_dataset(args)
File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 138, in create_dataset
hf_dataset.save_to_disk(args.outds, max_shard_size='50MB')
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1531, in save_to_disk
for kwargs in kwargs_per_job:
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1508, in <genexpr>
"shard": self.shard(num_shards=num_shards, index=shard_idx, contiguous=True),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 4609, in shard
return self.select(
^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3797, in select
return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3857, in _select_contiguous
_check_valid_indices_value(start, len(self))
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 648, in _check_valid_indices_value
raise IndexError(f"Index {index} out of range for dataset of size {size}.")
IndexError: Index 339 out of range for dataset of size 339.
```
### Steps to reproduce the bug
(I had to set the default max batch size down due to a different bug... or maybe it's related: https://github.com/huggingface/datasets/issues/5717)
```python3
#!/usr/bin/env python3
import argparse
import os
from pathlib import Path
import soundfile as sf
import datasets
datasets.config.DEFAULT_MAX_BATCH_SIZE=35
from datasets import Features, Array2D, Value, Dataset, Sequence, Audio
import numpy as np
import librosa
import sys
import soundfile as sf
import io
import logging
logging.basicConfig(level=logging.DEBUG, filename='debug.log', filemode='w',
format='%(name)s - %(levelname)s - %(message)s')
# Define the arguments for the command-line interface
def parse_args():
parser = argparse.ArgumentParser(description="Create a Huggingface dataset from labeled audio files.")
parser.add_argument("--indir_labeled", action="append", help="Directory containing labeled audio files.", required=True)
parser.add_argument("--outds", help="Path to save the dataset file.", required=True)
parser.add_argument("--max_clips", type=int, help="Max count of audio samples to add to the dataset.", default=None)
parser.add_argument("-r", "--sr", type=int, help="Sample rate for the audio files.", default=16000)
parser.add_argument("--no-resample", action="store_true", help="Disable resampling of the audio files.")
parser.add_argument("--max_clip_secs", type=float, help="Max length of audio clips in seconds.", default=3.0)
parser.add_argument("-v", "--verbose", action='count', default=1, help="Increase verbosity")
return parser.parse_args()
# Convert the NumPy arrays to audio bytes in WAV format
def numpy_to_bytes(audio_array, sampling_rate=16000):
with io.BytesIO() as bytes_io:
sf.write(bytes_io, audio_array, samplerate=sampling_rate,
format='wav', subtype='FLOAT') # float32
return bytes_io.getvalue()
# Function to find audio and label files in a directory
def find_audio_label_pairs(indir_labeled):
audio_label_pairs = []
for root, _, files in os.walk(indir_labeled):
for file in files:
if file.endswith(('.mp3', '.wav', '.aac', '.flac')):
audio_path = Path(root) / file
if args.verbose>1:
print(f'File: {audio_path}')
label_path = audio_path.with_suffix('.labels.txt')
if label_path.exists():
if args.verbose>0:
print(f' Pair: {audio_path}')
audio_label_pairs.append((audio_path, label_path))
return audio_label_pairs
def process_audio_label_pair(audio_path, label_path, sampling_rate, no_resample, max_clip_secs):
# Read the label file
with open(label_path, 'r') as label_file:
labels = label_file.readlines()
# Load the full audio file
full_audio, current_sr = sf.read(audio_path)
if not no_resample and current_sr != sampling_rate:
# You can use librosa.resample here if librosa is available
full_audio = librosa.resample(full_audio, orig_sr=current_sr, target_sr=sampling_rate)
audio_segments = []
sentences = []
# Process each label
for label in labels:
start_secs, end_secs, label_text = label.strip().split('\t')
start_sample = int(float(start_secs) * sampling_rate)
end_sample = int(float(end_secs) * sampling_rate)
# Extract segment and truncate or pad to max_clip_secs
audio_segment = full_audio[start_sample:end_sample]
max_samples = int(max_clip_secs * sampling_rate)
if len(audio_segment) > max_samples: # Truncate
audio_segment = audio_segment[:max_samples]
elif len(audio_segment) < max_samples: # Pad
padding = np.zeros(max_samples - len(audio_segment), dtype=audio_segment.dtype)
audio_segment = np.concatenate((audio_segment, padding))
audio_segment = numpy_to_bytes(audio_segment)
audio_data = {
'path': str(audio_path),
'bytes': audio_segment,
}
audio_segments.append(audio_data)
sentences.append(label_text)
return audio_segments, sentences
# Main function to create the dataset
def create_dataset(args):
audio_label_pairs = []
for indir in args.indir_labeled:
audio_label_pairs.extend(find_audio_label_pairs(indir))
# Initialize our dataset data
dataset_data = {
'path': [], # This will be a list of strings
'audio': [], # This will be a list of dictionaries
'sentence': [], # This will be a list of strings
}
# Process each audio-label pair and add the data to the dataset
for audio_path, label_path in audio_label_pairs[:args.max_clips]:
audio_segments, sentences = process_audio_label_pair(audio_path, label_path, args.sr, args.no_resample, args.max_clip_secs)
if audio_segments and sentences:
for audio_data, sentence in zip(audio_segments, sentences):
if args.verbose>1:
print(f'Appending {audio_data["path"]}')
dataset_data['path'].append(audio_data['path'])
dataset_data['audio'].append({
'path': audio_data['path'],
'bytes': audio_data['bytes'],
})
dataset_data['sentence'].append(sentence)
features = Features({
'path': Value('string'), # Path is redundant in common voice set also
'audio': Audio(sampling_rate=16000),
'sentence': Value('string'),
})
hf_dataset = Dataset.from_dict(dataset_data, features=features)
for key in dataset_data:
for i, item in enumerate(dataset_data[key]):
if item is None or (isinstance(item, bytes) and len(item) == 0):
logging.error(f"Invalid {key} at index {i}: {item}")
import ipdb; ipdb.set_trace(context=16); pass
hf_dataset.save_to_disk(args.outds, max_shard_size='50MB')
# try:
# hf_dataset.save_to_disk(args.outds)
# except TypeError as e:
# # If there's a TypeError, log the exception and the dataset data that might have caused it
# logging.exception("An error occurred while saving the dataset.")
# import ipdb; ipdb.set_trace(context=16); pass
# for key in dataset_data:
# logging.debug(f"{key} length: {len(dataset_data[key])}")
# if key == 'audio':
# # Log the first 100 bytes of the audio data to avoid huge log files
# for i, audio in enumerate(dataset_data[key]):
# logging.debug(f"Audio {i}: {audio['bytes'][:100]}")
# raise
# Run the script
if __name__ == "__main__":
args = parse_args()
create_dataset(args)
```
### Expected behavior
It shouldn't fail.
### Environment info
- `datasets` version: 2.14.7.dev0
- Platform: Linux-6.1.0-13-amd64-x86_64-with-glibc2.36
- Python version: 3.11.2
- `huggingface_hub` version: 0.17.3
- PyArrow version: 13.0.0
- Pandas version: 2.1.2
- `fsspec` version: 2023.9.2
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https://api.github.com/repos/huggingface/datasets/issues/6592 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6592/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6592/comments | https://api.github.com/repos/huggingface/datasets/issues/6592/events | https://github.com/huggingface/datasets/issues/6592 | 2,082,410,257 | I_kwDODunzps58Hw8R | 6,592 | Logs are delayed when doing .map when `docker logs` | {
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"Hi! `tqdm` doesn't work well in non-interactive environments, so there isn't much we can do about this. It's best to [disable it](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/utilities#datasets.disable_progress_bars) in such environments and instead use logging to track progress."
] | 2024-01-15T17:05:21Z | 2024-02-12T17:35:21Z | 2024-02-12T17:35:21Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When I run my SD training in a Docker image and then listen to logs like `docker logs train -f`, the progress bar is delayed.
It's updating every few percent.
When you have a large dataset that has to be mapped (like 1+ million samples), it's crucial to see the updates in real-time, not every couple hours to make sure nothing got frozen or broken
### Steps to reproduce the bug
1. Run any huge dataset processing as a Docker image
2. `docker logs image_name` to it
### Expected behavior
...
### Environment info
... | {
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https://api.github.com/repos/huggingface/datasets/issues/5563 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5563/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5563/comments | https://api.github.com/repos/huggingface/datasets/issues/5563/events | https://github.com/huggingface/datasets/pull/5563 | 1,595,049,025 | PR_kwDODunzps5KgtbL | 5,563 | Release: 2.10.0 | {
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} | [] | closed | false | null | [] | null | [
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-02-22T12:48:52Z | 2023-02-22T13:05:55Z | 2023-02-22T12:56:48Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7389 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7389/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7389/comments | https://api.github.com/repos/huggingface/datasets/issues/7389/events | https://github.com/huggingface/datasets/issues/7389 | 2,843,592,606 | I_kwDODunzps6pfcee | 7,389 | Getting statistics about filtered examples | {
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"You can actually track a running sum in map() or filter() :)\n\n```python\nnum_filtered = 0\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered += 1\n return condition\n\nds = ds.filter(f)\nprint(num_filtered)\n```\n\nand if you want to use... | 2025-02-10T20:48:29Z | 2025-02-11T20:44:15Z | 2025-02-11T20:44:13Z | NONE | null | null | {
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} | @lhoestq wondering if the team has thought about this and if there are any recommendations?
Currently when processing datasets some examples are bound to get filtered out, whether it's due to bad format, or length is too long, or any other custom filters that might be getting applied. Let's just focus on the filter by length for now, since that would be something that gets applied dynamically for each training run. Say we want to show a graph in W&B with the running total of the number of filtered examples so far.
What would be a good way to go about hooking this up? Because the map/filter operations happen before the DataLoader batches are created, at training time if we're just grabbing batches from the DataLoader then we won't know how many things have been filtered already. But there's not really a good way to include a 'num_filtered' key into the dataset itself either because dataset map/filter process examples independently and don't have a way to track a running sum.
The only approach I can kind of think of is having a 'is_filtered' key in the dataset, and then creating a custom batcher/collator that reads that and tracks the metric? | {
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https://api.github.com/repos/huggingface/datasets/issues/5524 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5524/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5524/comments | https://api.github.com/repos/huggingface/datasets/issues/5524/events | https://github.com/huggingface/datasets/pull/5524 | 1,580,219,454 | PR_kwDODunzps5JvbMw | 5,524 | [INVALID PR] | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2023-02-10T19:35:50Z | 2023-02-10T19:51:45Z | 2023-02-10T19:49:12Z | MEMBER | null | null | null | Hi to whoever is reading this! 🤗
## What's in this PR?
~~Basically, I've removed the 🤗`datasets` installation as `python -m pip install ".[quality]" in the `check_code_quality` job in `.github/workflows/ci.yaml`, as we don't need to install the whole package to run the CI, unless that's done on purpose e.g. to check that the Python package installation succeeds before running the tests over the matrix of os?~~
~~So I just wanted to check whether the time was reduced doing this (which I assume it will), plus whether this is something that can be improved, or just discarded in case you're also using that step to make sure that the package can be installed.~~
## What's missing?
~~I was just wondering whether you consider replacing `isort` and `flake8` with `ruff` (if possible), since it's way faster, more information at [`ruff`](https://github.com/charliermarsh/ruff). Before creating this PR the average time of the `check_code_quality` job was around 40s.~~
## Edit
Sorry for the inconvenience this may have caused, didn't realise that the config is defined in `setup.cfg` and `pyproject.toml`, so running those without installing the Python package leads to failure, my bad 😞
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https://api.github.com/repos/huggingface/datasets/issues/4923 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4923/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4923/comments | https://api.github.com/repos/huggingface/datasets/issues/4923/events | https://github.com/huggingface/datasets/pull/4923 | 1,357,735,287 | PR_kwDODunzps4-Jv7C | 4,923 | decode mp3 with librosa if torchaudio is > 0.12 as a temporary workaround | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks ! Should we still support torchaudio>0.12 if it works ? And if it doesn't we can explain that downgrading is the right solution, or alternatively use librosa",
"@lhoestq \r\n\r\n> Should we still support torchaudio>0.12 if i... | 2022-08-31T18:57:59Z | 2022-11-02T11:54:33Z | 2022-09-20T13:12:52Z | CONTRIBUTOR | null | null | null | `torchaudio>0.12` fails with decoding mp3 files if `ffmpeg<4`. currently we ask users to downgrade torchaudio, but sometimes it's not possible as torchaudio version is binded to torch version. as a temporary workaround we can decode mp3 with librosa (though it 60 times slower, at least it works)
another option would be to ask users to install the required version of `ffmpeg`, but is non-trivial on colab: it's not in apt packages in ubuntu 18 and `conda` is not preinstalled (with `conda` it would be easily installable)
- [x] decode with torchaudio anyway if the version of ffmpeg is correct? it's 60 times faster
- [x] tests
- [x] DO NOT FORGET to get back all the tests
see https://github.com/huggingface/datasets/issues/4776 and https://github.com/huggingface/datasets/issues/3663#issuecomment-1225797165 (there is a Colab notebook to reproduce the error) | {
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https://api.github.com/repos/huggingface/datasets/issues/4782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4782/comments | https://api.github.com/repos/huggingface/datasets/issues/4782/events | https://github.com/huggingface/datasets/issues/4782 | 1,326,247,158 | I_kwDODunzps5PDOz2 | 4,782 | pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 | {
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"Thanks for reporting @conceptofmind.\r\n\r\nCould you please give details about your environment? \r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```",
... | 2022-08-02T18:36:05Z | 2022-08-22T09:46:28Z | 2022-08-20T02:11:53Z | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5514 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5514/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5514/comments | https://api.github.com/repos/huggingface/datasets/issues/5514/events | https://github.com/huggingface/datasets/issues/5514 | 1,576,453,837 | I_kwDODunzps5d9sbN | 5,514 | Improve inconsistency of `Dataset.map` interface for `load_from_cache_file` | {
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"Hi, thanks for noticing this! We can't just remove the cache control as this allows us to control where the arrow files generated by the ops are written (cached on disk if enabled or a temporary directory if disabled). The right way to address this inconsistency would be by having `load_from_cache_file=None` by de... | 2023-02-08T16:40:44Z | 2023-02-14T14:26:44Z | 2023-02-14T14:26:44Z | CONTRIBUTOR | null | null | {
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} | ### Feature request
1. Replace the `load_from_cache_file` default value to `True`.
2. Remove or alter checks from `is_caching_enabled` logic.
### Motivation
I stumbled over an inconsistency in the `Dataset.map` interface. The documentation (and source) states for the parameter `load_from_cache_file`:
```
load_from_cache_file (`bool`, defaults to `True` if caching is enabled):
If a cache file storing the current computation from `function`
can be identified, use it instead of recomputing.
```
1. `load_from_cache_file` default value is `None`, while being annotated as `bool`
2. It is inconsistent with other method signatures like `filter`, that have the default value `True`
3. The logic is inconsistent, as the `map` method checks if caching is enabled through `is_caching_enabled`. This logic is not used for other similar methods.
### Your contribution
I am not fully aware of the logic behind caching checks. If this is just a inconsistency that historically grew, I would suggest to remove the `is_caching_enabled` logic as the "default" logic. Maybe someone can give insights, if environment variables have a higher priority than local variables or vice versa.
If this is clarified, I could adjust the source according to the "Feature request" section of this issue. | {
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https://api.github.com/repos/huggingface/datasets/issues/5071 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5071/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5071/comments | https://api.github.com/repos/huggingface/datasets/issues/5071/events | https://github.com/huggingface/datasets/pull/5071 | 1,397,301,270 | PR_kwDODunzps5AMG3g | 5,071 | Support DEFAULT_CONFIG_NAME when no BUILDER_CONFIGS | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Super, thanks a lot for adding this support, Albert!"
] | 2022-10-05T06:28:39Z | 2022-10-06T14:43:12Z | 2022-10-06T14:40:26Z | MEMBER | null | null | null | This PR supports defining a default config name, even if no predefined allowed config names are set.
Fix #5070.
CC: @stas00 | {
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https://api.github.com/repos/huggingface/datasets/issues/5840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5840/comments | https://api.github.com/repos/huggingface/datasets/issues/5840/events | https://github.com/huggingface/datasets/issues/5840 | 1,705,212,085 | I_kwDODunzps5lo3i1 | 5,840 | load model error. | {
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"Please report this in the `transformers` repo, as it's not related to `datasets`"
] | 2023-05-11T07:12:38Z | 2023-05-12T13:44:07Z | 2023-05-12T13:44:06Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
I had trained one model use deepspeed, when I load the final load I get the follow error:
OSError: Can't load tokenizer for '/XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/home/fm001/hzl/Project/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor' is the correct path to a directory containing all relevant files for a BloomTokenizerFast tokenizer.
my load code is : python chat.py --path /XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor/
### Steps to reproduce the bug
。。。
### Expected behavior
。。。
### Environment info
。。。 | {
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https://api.github.com/repos/huggingface/datasets/issues/5714 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5714/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5714/comments | https://api.github.com/repos/huggingface/datasets/issues/5714/events | https://github.com/huggingface/datasets/pull/5714 | 1,657,388,033 | PR_kwDODunzps5NxIOc | 5,714 | Fix xnumpy_load for .npz files | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-06T13:01:45Z | 2023-04-07T09:23:54Z | 2023-04-07T09:16:57Z | MEMBER | null | null | null | PR:
- #5626
implemented support for streaming `.npy` files by using `numpy.load`.
However, it introduced a bug when used with `.npz` files, within a context manager:
```
ValueError: seek of closed file
```
or in streaming mode:
```
ValueError: I/O operation on closed file.
```
This PR fixes the bug and tests for both `.npy` and `.npz` files.
Fix #5711. | {
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https://api.github.com/repos/huggingface/datasets/issues/6494 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6494/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6494/comments | https://api.github.com/repos/huggingface/datasets/issues/6494/events | https://github.com/huggingface/datasets/issues/6494 | 2,039,684,839 | I_kwDODunzps55kx7n | 6,494 | Image Data loaded Twice | {
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"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images
### Steps to reproduce the bug
from datasets import Dataset, load_dataset
dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False)
# print(dataset["train"][0]["image"] == dataset["train"][1]["image"])
print(dataset)
print(dataset["train"]["image"])
print(len(dataset["train"]["image"]))
### Expected behavior
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 8
})
})
[<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>]
8
### Environment info
- `datasets` version: 2.14.5
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.9.17
- Huggingface_hub version: 0.19.4
- PyArrow version: 13.0.0
- Pandas version: 2.0.3 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4608 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4608/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4608/comments | https://api.github.com/repos/huggingface/datasets/issues/4608/events | https://github.com/huggingface/datasets/pull/4608 | 1,290,298,002 | PR_kwDODunzps46pm9A | 4,608 | Fix xisfile, xgetsize, xisdir, xlistdir in private repo | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Added tests for xisfile, xgetsize, xlistdir and xglob for private repos, and also tests for xwalk that was untested"
] | 2022-06-30T15:23:21Z | 2022-07-06T12:45:59Z | 2022-07-06T12:34:19Z | MEMBER | null | null | null | `xisfile` is working in a private repository when passing a chained URL to a file inside an archive, e.g. `zip://a.txt::https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`. However it's not working when passing a simple file `https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`.
This is because the authentication headers are not passed correctly in this case.
This is causing dataset streaming to fail in private parquet repositories, as noted in https://github.com/huggingface/datasets/issues/4605
I fixed `xisfile` and the other functions that behave the same way: xgetsize, xisdir and xlistdir
TODO:
- [x] tests
fix https://github.com/huggingface/datasets/issues/4605 | {
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https://api.github.com/repos/huggingface/datasets/issues/5316 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5316/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5316/comments | https://api.github.com/repos/huggingface/datasets/issues/5316/events | https://github.com/huggingface/datasets/issues/5316 | 1,470,115,681 | I_kwDODunzps5XoC9h | 5,316 | Bug in sample_by="paragraph" | {
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"Thanks for reporting, @adampauls.\r\n\r\nWe are having a look at it. "
] | 2022-11-30T19:24:13Z | 2022-12-01T15:19:02Z | 2022-12-01T15:19:02Z | NONE | null | null | {
"completed": 0,
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"total": 0
} | ### Describe the bug
I think [this line](https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/text/text.py#L96) is wrong and should be `batch = f.read(self.config.chunksize)`. Otherwise it will never terminate because even when `f` is finished reading, `batch` will still be truthy from the last iteration.
### Steps to reproduce the bug
```
> cat test.txt
a b c
d e f
````
```python
>>> import datasets
>>> datasets.load_dataset("text", data_files={"train":"test.txt"}, sample_by="paragraph")
```
This will go on forever.
### Expected behavior
Terminates very quickly.
### Environment info
`version = "2.6.1"` but I think the bug is still there on main. | {
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https://api.github.com/repos/huggingface/datasets/issues/7162 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7162/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7162/comments | https://api.github.com/repos/huggingface/datasets/issues/7162/events | https://github.com/huggingface/datasets/pull/7162 | 2,542,323,382 | PR_kwDODunzps58WlX5 | 7,162 | Support JSON lines with empty struct | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7162). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-23T11:16:12Z | 2024-09-23T11:30:08Z | 2024-09-23T11:30:06Z | MEMBER | null | null | null | Support JSON lines with empty struct.
Fix #7161.
Related to:
- #7160 | {
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https://api.github.com/repos/huggingface/datasets/issues/6883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6883/comments | https://api.github.com/repos/huggingface/datasets/issues/6883/events | https://github.com/huggingface/datasets/pull/6883 | 2,284,808,399 | PR_kwDODunzps5u1sL1 | 6,883 | Require Pillow >= 9.4.0 to avoid AttributeError when loading image dataset | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6883). 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 ",
... | 2024-05-08T06:43:29Z | 2024-08-28T13:13:57Z | 2024-05-16T14:34:02Z | MEMBER | null | null | null | Require Pillow >= 9.4.0 to avoid AttributeError when loading image dataset.
The `PIL.Image.ExifTags` that we use in our code was implemented in Pillow-9.4.0: https://github.com/python-pillow/Pillow/commit/24a5405a9f7ea22f28f9c98b3e407292ea5ee1d3
The bug #6881 was introduced in datasets-2.19.0 by this PR:
- #6739
Fix #6881. | {
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https://api.github.com/repos/huggingface/datasets/issues/4995 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4995/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4995/comments | https://api.github.com/repos/huggingface/datasets/issues/4995/events | https://github.com/huggingface/datasets/issues/4995 | 1,379,108,482 | I_kwDODunzps5SM4aC | 4,995 | Get a specific Exception when the dataset has no data | {
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} | In the dataset viewer on the Hub (https://huggingface.co/datasets/glue/viewer), we would like (https://github.com/huggingface/moon-landing/issues/3882) to show a specific message when the repository lacks any data files.
In that case, instead of showing a complex traceback, we want to show a call to action to help the user upload data.
To do that, it would be very helpful to know for sure that the repository is missing any (supported) data files.
It could be done by raising a custom exception, for example, `NoDataError`. | {
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https://api.github.com/repos/huggingface/datasets/issues/5581 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5581/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5581/comments | https://api.github.com/repos/huggingface/datasets/issues/5581/events | https://github.com/huggingface/datasets/issues/5581 | 1,600,675,489 | I_kwDODunzps5faF6h | 5,581 | [DOC] Mistaken docs on set_format | {
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"Thanks for reporting!"
] | 2023-02-27T08:03:09Z | 2023-02-28T19:19:17Z | 2023-02-28T19:19:17Z | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format
<img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png">
While actually running it will result in:
<img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png">
### Steps to reproduce the bug
_
### Expected behavior
_
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | {
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"hi ! the `hf_hub_download` cache_dir is a different cache directory than the one for `datasets`.\r\n\r\n`hf_hub_download` uses the `huggingface_hub` cache which is located in by default in `~/.cache/huggingface/hub`, while `datasets` uses a different cache for Arrow files and map() results `~/.cache/huggingface/da... | 2025-04-04T22:36:04Z | 2025-04-15T13:19:15Z | null | NONE | null | null | null | When adding "cache_dir" to datasets.load_dataset, the cache_dir gets lost in the function calls, changing the cache dir to the default path. This fixes a few of these instances. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5053/comments | https://api.github.com/repos/huggingface/datasets/issues/5053/events | https://github.com/huggingface/datasets/issues/5053 | 1,393,739,882 | I_kwDODunzps5TEshq | 5,053 | Intermittent JSON parse error when streaming the Pile | {
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] | open | false | null | [] | null | [
"Maybe #2838 can help. In this PR we allow to skip bad chunks of JSON data to not crash the training\r\n\r\nDid you have warning messages before the error ?\r\n\r\nsomething like this maybe ?\r\n```\r\n03/24/2022 02:19:46 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host... | 2022-10-02T11:56:46Z | 2022-10-04T17:59:03Z | null | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
I have an intermittent error when streaming the Pile, where I get a JSON parse error which causes my program to crash.
This is intermittent - when I rerun the program with the same random seed it does not crash in the same way. The exact point this happens also varied - it happened to me 11B tokens and 4 days into a training run, and now just happened 2 minutes into one, but I can't reliably reproduce it.
I'm using a remote machine with 8 A6000 GPUs via runpod.io
## Expected results
I have a DataLoader which can iterate through the whole Pile
## Actual results
Stack trace:
```
Failed to read file 'zstd://12.jsonl::https://the-eye.eu/public/AI/pile/train/12.jsonl.zst' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0
```
I'm currently using HuggingFace accelerate, which also gave me the following stack trace, but I've also experienced this problem intermittently when using DataParallel, so I don't think it's to do with parallelisation
```
Traceback (most recent call last):
File "ddp_script.py", line 1258, in <module>
main()
File "ddp_script.py", line 1143, in main
for c, batch in tqdm.tqdm(enumerate(data_iter)):
File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 503, in __iter__
next_batch, next_batch_info, next_skip = self._fetch_batches(main_iterator)
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 454, in _fetch_batches
broadcast_object_list(batch_info)
File "/opt/conda/lib/python3.7/site-packages/accelerate/utils/operations.py", line 333, in broadcast_object_list
torch.distributed.broadcast_object_list(object_list, src=from_process)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1900, in broadcast_object_list
object_list[i] = _tensor_to_object(obj_view, obj_size)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1571, in _tensor_to_object
return _unpickler(io.BytesIO(buf)).load()
_pickle.UnpicklingError: invalid load key, '@'.
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset(
cfg["dataset_name"], streaming=True, split="train")
dataset = dataset.remove_columns("meta")
dataset = dataset.map(tokenize_and_concatenate, batched=True)
dataset = dataset.with_format(type="torch")
train_data_loader = DataLoader(
dataset, batch_size=cfg["batch_size"], num_workers=3)
for batch in train_data_loader:
continue
```
`tokenize_and_concatenate` is a custom tokenization function I defined on the GPT-NeoX tokenizer to tokenize the text, separated by endoftext tokens, and reshape to have length batch_size, I don't think this is related to tokenization:
```
import numpy as np
import einops
import torch
def tokenize_and_concatenate(examples):
texts = examples["text"]
full_text = tokenizer.eos_token.join(texts)
div = 20
length = len(full_text) // div
text_list = [full_text[i * length: (i + 1) * length]
for i in range(div)]
tokens = tokenizer(text_list, return_tensors="np", padding=True)[
"input_ids"
].flatten()
tokens = tokens[tokens != tokenizer.pad_token_id]
n = len(tokens)
curr_batch_size = n // (seq_len - 1)
tokens = tokens[: (seq_len - 1) * curr_batch_size]
tokens = einops.rearrange(
tokens,
"(batch_size seq) -> batch_size seq",
batch_size=curr_batch_size,
seq=seq_len - 1,
)
prefix = np.ones((curr_batch_size, 1), dtype=np.int64) * \
tokenizer.bos_token_id
return {
"text": np.concatenate([prefix, tokens], axis=1)
}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-105-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
ZStandard data:
Version: 0.18.0
Summary: Zstandard bindings for Python
Home-page: https://github.com/indygreg/python-zstandard
Author: Gregory Szorc
Author-email: gregory.szorc@gmail.com
License: BSD
Location: /opt/conda/lib/python3.7/site-packages
Requires:
Required-by: | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6168 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6168/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6168/comments | https://api.github.com/repos/huggingface/datasets/issues/6168/events | https://github.com/huggingface/datasets/pull/6168 | 1,861,867,274 | PR_kwDODunzps5YhT7Y | 6,168 | Fix ArrayXD YAML conversion | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6168). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-08-22T17:02:54Z | 2023-12-12T15:06:59Z | 2023-12-12T15:00:43Z | COLLABORATOR | null | null | null | Replace the `shape` tuple with a list in the `ArrayXD` YAML conversion.
Fix #6112 | {
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https://api.github.com/repos/huggingface/datasets/issues/4786 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4786/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4786/comments | https://api.github.com/repos/huggingface/datasets/issues/4786/events | https://github.com/huggingface/datasets/issues/4786 | 1,327,340,828 | I_kwDODunzps5PHZ0c | 4,786 | .save_to_disk('path', fs=s3) TypeError | {
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"completed": 0,
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} | The following code:
```python
import datasets
train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)
train_dataset.save_to_disk("s3://datasets/", fs=s3)
```
produces following traceback:
```shell
File "C:\Users\Hong Knop\AppData\Local\Programs\Python\Python310\lib\site-packages\botocore\auth.py", line 374, in scope
return '/'.join(scope)
```
I invoke print(scope) in <auth.py> (line 373) and find this:
```python
[('4VA08VLL3VTKQJKCAI8M',), '20220803', 'us-east-1', 's3', 'aws4_request']
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6474 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6474/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6474/comments | https://api.github.com/repos/huggingface/datasets/issues/6474/events | https://github.com/huggingface/datasets/pull/6474 | 2,027,006,715 | PR_kwDODunzps5hONZc | 6,474 | Deprecate Beam API and download from HF GCS bucket | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6474). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2023-12-05T19:51:33Z | 2024-03-12T14:56:25Z | 2024-03-12T14:50:12Z | COLLABORATOR | null | null | null | Deprecate the Beam API and download from the HF GCS bucked.
TODO:
- [x] Convert the Beam-based [`wikipedia`](https://huggingface.co/datasets/wikipedia) to an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wikipedia/discussions/19))
- [x] Make [`natural_questions`](https://huggingface.co/datasets/natural_questions) a no-code dataset ([Hub PR](https://huggingface.co/datasets/natural_questions/discussions/7))
- [x] Make [`wiki40b`](https://huggingface.co/datasets/wiki40b) a no-code dataset ([Hub PR](https://huggingface.co/datasets/wiki40b/discussions/5))
- [x] Make [`wiki_dpr`](https://huggingface.co/datasets/wiki_dpr) an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wiki_dpr/discussions/14)) | {
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https://api.github.com/repos/huggingface/datasets/issues/5442 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5442/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5442/comments | https://api.github.com/repos/huggingface/datasets/issues/5442/events | https://github.com/huggingface/datasets/issues/5442 | 1,550,084,450 | I_kwDODunzps5cZGli | 5,442 | OneDrive Integrations with HF Datasets | {
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"Hi! \r\n\r\nWe use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).\r\n\r\n[`gdrivefs`](https://githu... | 2023-01-19T23:12:08Z | 2023-02-24T16:17:51Z | 2023-02-24T16:17:51Z | NONE | null | null | {
"completed": 0,
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} | ### Feature request
First of all , I would like to thank all community who are developed DataSet storage and make it free available
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For example, if I have **50GB** on my **Onedrive** account and I want to move between drive and Hugging face repo or vis versa
### Motivation
make the dataset section more flexible with other possible storage
like the integration between Google Collab and Google drive the storage
### Your contribution
Can be done using Hugging face CLI | {
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https://api.github.com/repos/huggingface/datasets/issues/5376 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5376/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5376/comments | https://api.github.com/repos/huggingface/datasets/issues/5376/events | https://github.com/huggingface/datasets/pull/5376 | 1,502,730,559 | PR_kwDODunzps5FxWkM | 5,376 | set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5376). All of your documentation changes will be reflected on that endpoint."
] | 2022-12-19T10:56:56Z | 2022-12-19T11:01:55Z | 2022-12-19T10:57:16Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6196 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6196/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6196/comments | https://api.github.com/repos/huggingface/datasets/issues/6196/events | https://github.com/huggingface/datasets/issues/6196 | 1,875,070,972 | I_kwDODunzps5vw0_8 | 6,196 | Split order is not preserved | {
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} | I have noticed that in some cases the split order is not preserved.
For example, consider a no-script dataset with configs:
```yaml
configs:
- config_name: default
data_files:
- split: train
path: train.csv
- split: test
path: test.csv
```
- Note the defined split order is [train, test]
Once the dataset is loaded, the split order is not preserved:
```python
In [16]: ds
Out[16]:
DatasetDict({
test: Dataset({
features: ['text', 'label'],
num_rows: 1
})
train: Dataset({
features: ['text', 'label'],
num_rows: 2
})
})
```
- Note the obtained split order is [test, train] | {
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https://api.github.com/repos/huggingface/datasets/issues/5281 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5281/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5281/comments | https://api.github.com/repos/huggingface/datasets/issues/5281/events | https://github.com/huggingface/datasets/issues/5281 | 1,459,930,271 | I_kwDODunzps5XBMSf | 5,281 | Support cloud storage in load_dataset | {
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"Or for example an archive on GitHub releases! Before I added support for JXL (locally only, PR still pending) I was considering hosting my files on GitHub instead...",
"+1 to this. I would like to use 'audiofolder' with a data_dir that's on S3, for example. I don't want to upload my dataset to the Hub, but I wo... | 2022-11-22T14:00:10Z | 2024-11-15T15:03:41Z | null | MEMBER | null | null | {
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} | Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been requested several times already. Some users want to use their data from private cloud storage to train models
related:
https://github.com/huggingface/datasets/issues/3490
https://github.com/huggingface/datasets/issues/5244
[forum](https://discuss.huggingface.co/t/how-to-use-s3-path-with-load-dataset-with-streaming-true/25739/2) | {
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https://api.github.com/repos/huggingface/datasets/issues/7531 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7531/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7531/comments | https://api.github.com/repos/huggingface/datasets/issues/7531/events | https://github.com/huggingface/datasets/issues/7531 | 3,008,914,887 | I_kwDODunzps6zWGXH | 7,531 | Deepspeed reward training hangs at end of training with Dataset.from_list | {
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} | There seems to be a weird interaction between Deepspeed, the Dataset.from_list method and trl's RewardTrainer. On a multi-GPU setup (10 A100s), training always hangs at the very end of training until it times out. The training itself works fine until the end of training and running the same script with Deepspeed on a single GPU works without hangig. The issue persisted across a wide range of Deepspeed configs and training arguments. The issue went away when storing the exact same dataset as a JSON and using `dataset = load_dataset("json", ...)`. Here is my training script:
```python
import pickle
import os
import random
import warnings
import torch
from datasets import load_dataset, Dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from trl import RewardConfig, RewardTrainer, ModelConfig
####################################### Reward model #################################################
# Explicitly set arguments
model_name_or_path = "Qwen/Qwen2.5-1.5B"
output_dir = "Qwen2-0.5B-Reward-LoRA"
per_device_train_batch_size = 2
num_train_epochs = 5
gradient_checkpointing = True
learning_rate = 1.0e-4
logging_steps = 25
eval_strategy = "steps"
eval_steps = 50
max_length = 2048
torch_dtype = "auto"
trust_remote_code = False
model_args = ModelConfig(
model_name_or_path=model_name_or_path,
model_revision=None,
trust_remote_code=trust_remote_code,
torch_dtype=torch_dtype,
lora_task_type="SEQ_CLS", # Make sure task type is seq_cls
)
training_args = RewardConfig(
output_dir=output_dir,
per_device_train_batch_size=per_device_train_batch_size,
num_train_epochs=num_train_epochs,
gradient_checkpointing=gradient_checkpointing,
learning_rate=learning_rate,
logging_steps=logging_steps,
eval_strategy=eval_strategy,
eval_steps=eval_steps,
max_length=max_length,
gradient_checkpointing_kwargs=dict(use_reentrant=False),
center_rewards_coefficient = 0.01,
fp16=False,
bf16=True,
save_strategy="no",
dataloader_num_workers=0,
# deepspeed="./configs/deepspeed_config.json",
)
################
# Model & Tokenizer
################
model_kwargs = dict(
revision=model_args.model_revision,
use_cache=False if training_args.gradient_checkpointing else True,
torch_dtype=model_args.torch_dtype,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, use_fast=True
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path, num_labels=1, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
# Align padding tokens between tokenizer and model
model.config.pad_token_id = tokenizer.pad_token_id
# If post-training a base model, use ChatML as the default template
if tokenizer.chat_template is None:
model, tokenizer = setup_chat_format(model, tokenizer)
if model_args.use_peft and model_args.lora_task_type != "SEQ_CLS":
warnings.warn(
"You are using a `task_type` that is different than `SEQ_CLS` for PEFT. This will lead to silent bugs"
" Make sure to pass --lora_task_type SEQ_CLS when using this script with PEFT.",
UserWarning,
)
##############
# Load dataset
##############
with open('./prefs.pkl', 'rb') as fh:
loaded_data = pickle.load(fh)
random.shuffle(loaded_data)
dataset = []
for a_wins, a, b in loaded_data:
if a_wins == 0:
a, b = b, a
dataset.append({'chosen': a, 'rejected': b})
dataset = Dataset.from_list(dataset)
# Split the dataset into training and evaluation sets
train_eval_split = dataset.train_test_split(test_size=0.15, shuffle=True, seed=42)
# Access the training and evaluation datasets
train_dataset = train_eval_split['train']
eval_dataset = train_eval_split['test']
##########
# Training
##########
trainer = RewardTrainer(
model=model,
processing_class=tokenizer,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
trainer.train()
```
Replacing `dataset = Dataset.from_list(dataset)` with
```python
with open('./prefs.json', 'w') as fh:
json.dump(dataset, fh)
dataset = load_dataset("json", data_files="./prefs.json", split='train')
```
resolves the issue. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5459 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5459/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5459/comments | https://api.github.com/repos/huggingface/datasets/issues/5459/events | https://github.com/huggingface/datasets/pull/5459 | 1,555,367,504 | PR_kwDODunzps5Icjwe | 5,459 | Disable aiohttp requoting of redirection URL | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Comment by @lhoestq:\r\n> Do you think we need this in `datasets` if it's fixed on the moon landing side ? In the aiohttp doc they consider those symbols as \"non-safe\" ",
"The lib `requests` does not perform that requote on redir... | 2023-01-24T17:18:59Z | 2024-09-01T18:08:31Z | 2023-01-31T08:37:54Z | MEMBER | null | null | null | The library `aiohttp` performs a requoting of redirection URLs that unquotes the single quotation mark character: `%27` => `'`
This is a problem for our Hugging Face Hub, which requires exact URL from location header.
Specifically, in the query component of the URL (`https://netloc/path?query`), the value for `response-content-disposition` contains `%27`:
```
response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B
```
and after the requoting, the `%27` characters get unquoted to `'`:
```
response-content-disposition=attachment%3B+filename*%3DUTF-8''sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B
```
This PR disables the `aiohttp` requoting of redirection URLs. | {
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https://api.github.com/repos/huggingface/datasets/issues/6165 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6165/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6165/comments | https://api.github.com/repos/huggingface/datasets/issues/6165/events | https://github.com/huggingface/datasets/pull/6165 | 1,861,124,284 | PR_kwDODunzps5YexBL | 6,165 | Fix multiprocessing with spawn in iterable datasets | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq \r\nA test is failing, but I don't think it is due to my changes",
"Good catch ! Could you add a test to make sure transformed IterableDataset objects are still picklable ?\r\n\r\nSomething like `test_pickle_after_many_tra... | 2023-08-22T10:07:23Z | 2023-08-29T13:27:14Z | 2023-08-29T13:18:11Z | CONTRIBUTOR | null | null | null | The "Spawn" method is preferred when multiprocessing on macOS or Windows systems, instead of the "Fork" method on linux systems.
This causes some methods of Iterable Datasets to break when using a dataloader with more than 0 workers.
I fixed the issue by replacing lambda and local methods which are not pickle-able.
See the example below:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
if __name__ == "__main__":
dataset = load_dataset("lhoestq/demo1", split="train")
dataset = dataset.to_iterable_dataset(num_shards=3)
dataset = dataset.remove_columns(["package_name"])
dataset = dataset.rename_columns({
"review": "review1"
})
dataset = dataset.rename_column("date", "date1")
for sample in DataLoader(dataset, batch_size=None, num_workers=3):
print(sample)
```
To notice the fix on a linux system, adding these lines should do the trick:
```python
import multiprocessing
multiprocessing.set_start_method('spawn')
```
I also removed what looks like code duplication between rename_colums and rename_column
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https://api.github.com/repos/huggingface/datasets/issues/6715 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6715/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6715/comments | https://api.github.com/repos/huggingface/datasets/issues/6715/events | https://github.com/huggingface/datasets/pull/6715 | 2,167,747,095 | PR_kwDODunzps5oo36i | 6,715 | Fix sliced ConcatenationTable pickling with mixed schemas vertically | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6715). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-03-04T21:02:07Z | 2024-03-05T11:23:05Z | 2024-03-05T11:17:04Z | MEMBER | null | null | null | A sliced + pickled ConcatenationTable could end up with a different schema than the original schema, if the slice only contains blocks with only a subset of the columns.
This can lead to issues when saving datasets from a concatenation of datasets with mixed schemas
Reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595 | {
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https://api.github.com/repos/huggingface/datasets/issues/6310 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6310/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6310/comments | https://api.github.com/repos/huggingface/datasets/issues/6310/events | https://github.com/huggingface/datasets/pull/6310 | 1,947,457,988 | PR_kwDODunzps5dBPnY | 6,310 | Add return_file_name in load_dataset | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6310). All of your documentation changes will be reflected on that endpoint.",
"> Thanks for the change !\r\n> \r\n> Since `return` in python often refers to what is actually returned by the function (here `load_dataset`), I th... | 2023-10-17T13:36:57Z | 2024-08-09T11:51:55Z | 2024-07-31T13:56:50Z | NONE | null | null | null | Proposition to fix #5806.
Added an optional parameter `return_file_name` in the dataset builder config. When set to `True`, the function will include the file name corresponding to the sample in the returned output.
There is a difference between arrow-based and folder-based datasets to return the file name:
- for arrow-based: a column is concatenated after the table is cast.
- for folder-based: `dataset.info.features` has the entry `file_name` and the original file name is passed to the `sample_metadata` dictionary.
The difference in behavior might be a concern, also I do not know whether the `file_name` should return the original file path or the downloaded one for folder-based datasets.
I added some tests for the datasets that already had a test file. | {
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https://api.github.com/repos/huggingface/datasets/issues/7318 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7318/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7318/comments | https://api.github.com/repos/huggingface/datasets/issues/7318/events | https://github.com/huggingface/datasets/issues/7318 | 2,730,676,278 | I_kwDODunzps6iwtA2 | 7,318 | Introduce support for PDFs | {
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"#self-assign",
"Awesome ! Let me know if you have any question or if I can help :)\r\n\r\ncc @AndreaFrancis as well for viz",
"Other candidates libraries for the Pdf type: PyMuPDF pypdf and pdfplumber\r\n\r\nEDIT: Pymupdf looks like a good choice when it comes to maturity + performance + versatility BUT the li... | 2024-12-10T16:59:48Z | 2024-12-12T18:38:13Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Feature request
The idea (discussed in the Discord server with @lhoestq ) is to have a Pdf type like Image/Audio/Video. For example [Video](https://github.com/huggingface/datasets/blob/main/src/datasets/features/video.py) was recently added and contains how to decode a video file encoded in a dictionary like {"path": ..., "bytes": ...} as a VideoReader using decord. We want to do the same with pdf and get a [pypdfium2.PdfDocument](https://pypdfium2.readthedocs.io/en/stable/_modules/pypdfium2/_helpers/document.html#PdfDocument).
### Motivation
In many cases PDFs contain very valuable information beyond text (e.g. images, figures). Support for PDFs would help create datasets where all the information is preserved.
### Your contribution
I can start the implementation of the Pdf type :) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6780 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6780/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6780/comments | https://api.github.com/repos/huggingface/datasets/issues/6780/events | https://github.com/huggingface/datasets/pull/6780 | 2,226,160,096 | PR_kwDODunzps5rvkyj | 6,780 | Fix CI | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6780). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-04-04T17:45:04Z | 2024-04-04T18:46:04Z | 2024-04-04T18:23:34Z | COLLABORATOR | null | null | null | Updates the `wmt_t2t` test to pin the `revision` to the version with a loading script (cc @albertvillanova).
Additionally, it replaces the occurrences of the `lhoestq/test` repo id with `hf-internal-testing/dataset_with_script` and re-enables logging checks in the `Dataset.from_sql` tests. | {
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https://api.github.com/repos/huggingface/datasets/issues/7266 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7266/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7266/comments | https://api.github.com/repos/huggingface/datasets/issues/7266/events | https://github.com/huggingface/datasets/issues/7266 | 2,624,666,087 | I_kwDODunzps6ccTnn | 7,266 | The dataset viewer should be available soon. Please retry later. | {
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"Waiting is all you need. 10 hours later, it works."
] | 2024-10-30T16:32:00Z | 2024-10-31T03:48:11Z | 2024-10-31T03:48:10Z | NONE | null | null | {
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} | ### Describe the bug
After waiting for 2 hours, it still presents ``The dataset viewer should be available soon. Please retry later.''
### Steps to reproduce the bug
dataset link: https://huggingface.co/datasets/BryanW/HI_EDIT
### Expected behavior
Present the dataset viewer.
### Environment info
NA | {
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https://api.github.com/repos/huggingface/datasets/issues/7508 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7508/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7508/comments | https://api.github.com/repos/huggingface/datasets/issues/7508/events | https://github.com/huggingface/datasets/issues/7508 | 2,986,612,934 | I_kwDODunzps6yBBjG | 7,508 | Iterating over Image feature columns is extremely slow | {
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"Hi ! Could it be because the `Image()` type in dataset does `image = Image.open(image_path)` and also `image.load()` which actually loads the image data in memory ? This is needed to avoid too many open files issues, see https://github.com/huggingface/datasets/issues/3985",
"Yes, that seems to be it. For my pur... | 2025-04-10T19:00:54Z | 2025-04-15T17:57:08Z | null | NONE | null | null | {
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} | We are trying to load datasets where the image column stores `PIL.PngImagePlugin.PngImageFile` images. However, iterating over these datasets is extremely slow.
What I have found:
1. It is the presence of the image column that causes the slowdown. Removing the column from the dataset results in blazingly fast (as expected) times
2. It is ~2x faster to iterate when the column contains a single image as opposed to a list of images i.e., the feature is a Sequence of Image objects. We often need multiple images per sample, so we need to work with a list of images
3. It is ~17x faster to store paths to PNG files and load them using `PIL.Image.open`, as opposed to iterating over a `Dataset` with an Image column, and ~30x faster compared to `Sequence` of `Image`s. See a simple script below with an openly available dataset.
It would be great to understand the standard practices for storing and loading multimodal datasets (image + text).
https://huggingface.co/docs/datasets/en/image_load seems a bit underdeveloped? (e.g., `dataset.decode` only works with `IterableDataset`, but it's not clear from the doc)
Thanks!
```python
from datasets import load_dataset, load_from_disk
from PIL import Image
from pathlib import Path
ds = load_dataset("getomni-ai/ocr-benchmark")
for idx, sample in enumerate(ds["test"]):
image = sample["image"]
image.save(f"/tmp/ds_files/images/image_{idx}.png")
ds.save_to_disk("/tmp/ds_columns")
# Remove the 'image' column
ds["test"] = ds["test"].remove_columns(["image"])
# Create image paths for each sample
image_paths = [f"images/image_{idx}.png" for idx in range(len(ds["test"]))]
# Add the 'image_path' column to the dataset
ds["test"] = ds["test"].add_column("image_path", image_paths)
# Save the updated dataset
ds.save_to_disk("/tmp/ds_files")
files_path = Path("/tmp/ds_files")
column_path = Path("/tmp/ds_columns")
# load and benchmark
ds_file = load_from_disk(files_path)
ds_column = load_from_disk(column_path)
import time
images_files = []
start = time.time()
for idx in range(len(ds_file["test"])):
image_path = files_path / ds_file["test"][idx]["image_path"]
image = Image.open(image_path)
images_files.append(image)
end = time.time()
print(f"Time taken to load images from files: {end - start} seconds")
# Time taken to load images from files: 1.2364635467529297 seconds
images_column = []
start = time.time()
for idx in range(len(ds_column["test"])):
images_column.append(ds_column["test"][idx]["image"])
end = time.time()
print(f"Time taken to load images from columns: {end - start} seconds")
# Time taken to load images from columns: 20.49347186088562 seconds
``` | null | {
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