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https://api.github.com/repos/huggingface/datasets/issues/7347 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7347/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7347/comments | https://api.github.com/repos/huggingface/datasets/issues/7347/events | https://github.com/huggingface/datasets/issues/7347 | 2,760,282,339 | I_kwDODunzps6khpDj | 7,347 | Converting Arrow to WebDataset TAR Format for Offline Use | {
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"Hi,\r\n\r\nI've downloaded an Arrow-formatted dataset offline using the hugggingface's datasets library by:\r\n\r\nimport json\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"pixparse/cc3m-wds\")\r\ndataset.save_to_disk(\"./cc3m_1\")\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\nIs there a straightforward method to achieve this conversion without an internet connection? Can I simply convert it by\r\n\r\ntar -cvf\r\n\r\n\r\nbtw, when I tried:\r\n\r\nimport webdataset as wds\r\nfrom huggingface_hub import get_token\r\nfrom torch.utils.data import DataLoader\r\n\r\nhf_token = get_token()\r\nurl = \"https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar\"\r\nurl = f\"pipe:curl -s -L {url} -H 'Authorization:Bearer {hf_token}'\"\r\ndataset = wds.WebDataset(url).decode()\r\ndataset.save_to_disk(\"./cc3m_webdataset\")\r\n\r\n\r\nerror occured:\r\n\r\nAttributeError: 'WebDataset' object has no attribute 'save_to_disk'\r\n\r\n\r\nThanks a lot!\r\n\r\nMotivation\r\n\r\nConverting Arrow to WebDataset TAR Format\r\n\r\nYour contribution\r\n\r\nNo clue yet\r\n\r\n\r\nاحصل على Outlook لـ iOS<https://aka.ms/o0ukef>\r\n________________________________\r\nمن: katie312 ***@***.***>\r\nتم الإرسال: Friday, December 27, 2024 4:41:21 AM\r\nإلى: huggingface/datasets ***@***.***>\r\nنسخة: Subscribed ***@***.***>\r\nالموضوع: [huggingface/datasets] Converting Arrow to WebDataset TAR Format for Offline Use (Issue #7347)\r\n\r\n\r\nFeature request\r\n\r\nHi,\r\n\r\nI've downloaded an Arrow-formatted dataset offline using the hugggingface's datasets library by:\r\n\r\nimport json\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"pixparse/cc3m-wds\")\r\ndataset.save_to_disk(\"./cc3m_1\")\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\nIs there a straightforward method to achieve this conversion without an internet connection? Can I simply convert it by\r\n\r\ntar -cvf\r\n\r\n\r\nbtw, when I tried:\r\n\r\nimport webdataset as wds\r\nfrom huggingface_hub import get_token\r\nfrom torch.utils.data import DataLoader\r\n\r\nhf_token = get_token()\r\nurl = \"https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar\"\r\nurl = f\"pipe:curl -s -L {url} -H 'Authorization:Bearer {hf_token}'\"\r\ndataset = wds.WebDataset(url).decode()\r\ndataset.save_to_disk(\"./cc3m_webdataset\")\r\n\r\n\r\nerror occured:\r\n\r\nAttributeError: 'WebDataset' object has no attribute 'save_to_disk'\r\n\r\n\r\nThanks a lot!\r\n\r\nMotivation\r\n\r\nConverting Arrow to WebDataset TAR Format\r\n\r\nYour contribution\r\n\r\nNo clue yet\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/7347>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AQJDZ2X2RUIIULBJEF5R2HL2HSV4DAVCNFSM6AAAAABUH5QSLCVHI2DSMVQWIX3LMV43ASLTON2WKOZSG43DAMRYGIZTGOI>.\r\nYou are receiving this because you are subscribed to this thread.Message ID: ***@***.***>\r\n",
"> now I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n\r\nyou can directly download the .TAR files from HF using e.g. `huggingface-cli download` and load them in webdataset :)",
"الفله سنه والطبقه يوم\r\n\r\nاحصل على Outlook لـ iOS<https://aka.ms/o0ukef>\r\n________________________________\r\nمن: Quentin Lhoest ***@***.***>\r\nتم الإرسال: Friday, December 27, 2024 4:14:43 PM\r\nإلى: huggingface/datasets ***@***.***>\r\nنسخة: hamad350 ***@***.***>; Comment ***@***.***>\r\nالموضوع: Re: [huggingface/datasets] Converting Arrow to WebDataset TAR Format for Offline Use (Issue #7347)\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n\r\nyou can directly download the .TAR files from HF using e.g. huggingface-cli download and load them in webdataset :)\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/7347#issuecomment-2563691570>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AQJDZ2R5M3Z7L2MZZYARYID2HVHEHAVCNFSM6AAAAABUH5QSLCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKNRTGY4TCNJXGA>.\r\nYou are receiving this because you commented.Message ID: ***@***.***>\r\n",
"> > now I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n> \r\n> you can directly download the .TAR files from HF using e.g. `huggingface-cli download` and load them in webdataset :)\r\n\r\nThanks a lot! I completely forgot to use Hugging Face-CLI download. Thanks for the reminding!"
] | 1,735 | 1,735 | 1,735 | NONE | null | ### Feature request
Hi,
I've downloaded an Arrow-formatted dataset offline using the hugggingface's datasets library by:
```
import json
from datasets import load_dataset
dataset = load_dataset("pixparse/cc3m-wds")
dataset.save_to_disk("./cc3m_1")
```
now I need to convert it to WebDataset's TAR format for offline data ingestion.
Is there a straightforward method to achieve this conversion without an internet connection? Can I simply convert it by
```
tar -cvf
```
btw, when I tried:
```
import webdataset as wds
from huggingface_hub import get_token
from torch.utils.data import DataLoader
hf_token = get_token()
url = "https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar"
url = f"pipe:curl -s -L {url} -H 'Authorization:Bearer {hf_token}'"
dataset = wds.WebDataset(url).decode()
dataset.save_to_disk("./cc3m_webdataset")
```
error occured:
```
AttributeError: 'WebDataset' object has no attribute 'save_to_disk'
```
Thanks a lot!
### Motivation
Converting Arrow to WebDataset TAR Format
### Your contribution
No clue yet | {
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https://api.github.com/repos/huggingface/datasets/issues/7346 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7346/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7346/comments | https://api.github.com/repos/huggingface/datasets/issues/7346/events | https://github.com/huggingface/datasets/issues/7346 | 2,758,752,118 | I_kwDODunzps6kbzd2 | 7,346 | OSError: Invalid flatbuffers message. | {
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} | [] | open | false | null | [] | null | [] | 1,735 | 1,735 | null | NONE | null | ### Describe the bug
When loading a large 2D data (1000 × 1152) with a large number of (2,000 data in this case) in `load_dataset`, the error message `OSError: Invalid flatbuffers message` is reported.
When only 300 pieces of data of this size (1000 × 1152) are stored, they can be loaded correctly.
When 2,000 2D arrays are stored in each file, about 100 files are generated, each with a file size of about 5-6GB. But when 300 2D arrays are stored in each file, **about 600 files are generated, which is too many files**.
### Steps to reproduce the bug
error:
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
Cell In[2], line 4
1 from datasets import Dataset
2 from datasets import load_dataset
----> 4 real_dataset = load_dataset("arrow", data_files='tensorData/real_ResidueTensor/*', split="train")#.with_format("torch") # , split="train"
5 # sim_dataset = load_dataset("arrow", data_files='tensorData/sim_ResidueTensor/*', split="train").with_format("torch")
6 real_dataset
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/load.py:2151](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/load.py#line=2150), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2148 return builder_instance.as_streaming_dataset(split=split)
2150 # Download and prepare data
-> 2151 builder_instance.download_and_prepare(
2152 download_config=download_config,
2153 download_mode=download_mode,
2154 verification_mode=verification_mode,
2155 num_proc=num_proc,
2156 storage_options=storage_options,
2157 )
2159 # Build dataset for splits
2160 keep_in_memory = (
2161 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2162 )
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py:924](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py#line=923), in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
922 if num_proc is not None:
923 prepare_split_kwargs["num_proc"] = num_proc
--> 924 self._download_and_prepare(
925 dl_manager=dl_manager,
926 verification_mode=verification_mode,
927 **prepare_split_kwargs,
928 **download_and_prepare_kwargs,
929 )
930 # Sync info
931 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py:978](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py#line=977), in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
976 split_dict = SplitDict(dataset_name=self.dataset_name)
977 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 978 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
980 # Checksums verification
981 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py:47](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py#line=46), in Arrow._split_generators(self, dl_manager)
45 with open(file, "rb") as f:
46 try:
---> 47 reader = pa.ipc.open_stream(f)
48 except pa.lib.ArrowInvalid:
49 reader = pa.ipc.open_file(f)
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py:190](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py#line=189), in open_stream(source, options, memory_pool)
171 def open_stream(source, *, options=None, memory_pool=None):
172 """
173 Create reader for Arrow streaming format.
174
(...)
188 A reader for the given source
189 """
--> 190 return RecordBatchStreamReader(source, options=options,
191 memory_pool=memory_pool)
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py:52](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py#line=51), in RecordBatchStreamReader.__init__(self, source, options, memory_pool)
50 def __init__(self, source, *, options=None, memory_pool=None):
51 options = _ensure_default_ipc_read_options(options)
---> 52 self._open(source, options=options, memory_pool=memory_pool)
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.pxi:1006](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.pxi#line=1005), in pyarrow.lib._RecordBatchStreamReader._open()
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi:155](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi#line=154), in pyarrow.lib.pyarrow_internal_check_status()
File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi:92](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi#line=91), in pyarrow.lib.check_status()
OSError: Invalid flatbuffers message.
```
reproduce:Here is just an example result, the real 2D matrix is the output of the ESM large model, and the matrix size is approximate
```python
import numpy as np
import pyarrow as pa
random_arrays_list = [np.random.rand(1000, 1152) for _ in range(2000)]
table = pa.Table.from_pydict({
'tensor': [tensor.tolist() for tensor in random_arrays_list]
})
import pyarrow.feather as feather
feather.write_feather(table, 'test.arrow')
from datasets import load_dataset
dataset = load_dataset("arrow", data_files='test.arrow', split="train")
```
### Expected behavior
`load_dataset` load the dataset as normal as `feather.read_feather`
```python
import pyarrow.feather as feather
feather.read_feather('tensorData/real_ResidueTensor/real_tensor_1.arrow')
```
Plus `load_dataset("parquet", data_files='test.arrow', split="train")` works fine
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-49-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.26.5
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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} | [] | open | false | null | [] | null | [] | 1,735 | 1,735 | null | NONE | null | ### Describe the bug
The following code
```python
import datasets as hf
ds1 = hf.Dataset.from_list([{'i': i} for i in [0,1]])
#ds1 = ds1.to_iterable_dataset()
ds2 = ds1.map(
lambda i: {'i': i+1},
input_columns = ['i'],
remove_columns = ['i']
)
list(ds2)
```
produces
```python
[{'i': 1}, {'i': 2}]
```
as expected. If the line that converts `ds1` to iterable is uncommented so that the `ds2` is a map of an `IterableDataset`, the result is
```python
[{},{}]
```
I expected the output to be the same as before. It seems that in the second case the removed column is not added back into the output.
The issue seems to be [here](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L1093): the columns are removed after the mapping which is not what we want (or what the [documentation says](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L2370)) because we want the columns removed from the transformed example but then added if the map produced them.
This is `datasets==3.2.0` and `python==3.10`
### Steps to reproduce the bug
see above
### Expected behavior
see above
### Environment info
see above | null | {
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} | [] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Describe the bug
I am trying to run some trainings on Google's TPUs using Huggingface's DataLoader on [SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [c4](https://huggingface.co/datasets/allenai/c4), but I end up running into `429 Client Error: Too Many Requests for URL` error when I call `load_dataset`. The even odder part is that I am able to sucessfully run trainings with the [wikitext dataset](https://huggingface.co/datasets/Salesforce/wikitext). Is there something I need to setup to specifically train with SlimPajama or C4 with TPUs because I am not clear why I am getting these errors.
### Steps to reproduce the bug
These are the commands you could run to produce the error below but you will require a ClearML account (you can create one [here](https://app.clear.ml/login?redirect=%2Fdashboard)) with a queue setup to run on Google TPUs
```bash
git clone https://github.com/clankur/muGPT.git
cd muGPT
python -m train --config-name=slim_v4-32_84m.yaml +training.queue={NAME_OF_CLEARML_QUEUE}
```
The error I see:
```
Traceback (most recent call last):
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/clearml/binding/hydra_bind.py", line 230, in _patched_task_function
return task_function(a_config, *a_args, **a_kwargs)
File "/home/clankur/.clearml/venvs-builds/3.10/task_repository/muGPT.git/train.py", line 1037, in main
main_contained(config, logger)
File "/home/clankur/.clearml/venvs-builds/3.10/task_repository/muGPT.git/train.py", line 840, in main_contained
loader = get_loader("train", config.training_data, config.training.tokens)
File "/home/clankur/.clearml/venvs-builds/3.10/task_repository/muGPT.git/input_loader.py", line 549, in get_loader
return HuggingFaceDataLoader(split, config, token_batch_params)
File "/home/clankur/.clearml/venvs-builds/3.10/task_repository/muGPT.git/input_loader.py", line 395, in __init__
self.dataset = load_dataset(
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset
builder_instance = load_dataset_builder(
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 1495, in dataset_module_factory
raise e1 from None
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 1479, in dataset_module_factory
).get_module()
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/load.py", line 1034, in get_module
else get_data_patterns(base_path, download_config=self.download_config)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/data_files.py", line 457, in get_data_patterns
return _get_data_files_patterns(resolver)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/data_files.py", line 248, in _get_data_files_patterns
data_files = pattern_resolver(pattern)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern
for filepath, info in fs.glob(pattern, detail=True).items()
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 409, in glob
return super().glob(path, **kwargs)
File "/home/clankur/.clearml/venvs-builds/3.10/lib/python3.10/site-packages/fsspec/spec.py", line 602, in glob
allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 429, in find
out = self._ls_tree(path, recursive=True, refresh=refresh, revision=resolved_path.revision, **kwargs)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 358, in _ls_tree
self._ls_tree(
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 375, in _ls_tree
for path_info in tree:
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3080, in list_repo_tree
for path_info in paginate(path=tree_url, headers=headers, params={"recursive": recursive, "expand": expand}):
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/utils/_pagination.py", line 46, in paginate
hf_raise_for_status(r)
File "/home/clankur/conda/envs/jax/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/cerebras/SlimPajama-627B/tree/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543?recursive=True&expand=True&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBaWE4wTDJOb2RXNXJNUzlsZUdGdGNHeGxYMmh2YkdSdmRYUmZPVFEzTG1wemIyNXNMbnB6ZENKOTo2MjUw (Request ID: Root=1-67673de9-1413900606ede7712b08ef2c;1304c09c-3e69-4222-be14-f10ee709d49c)
maximum queue size reached
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
### Expected behavior
I'd expect the DataLoader to load from the SlimPajama-627B and c4 dataset without issue.
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.8.0-1035-gcp-x86_64-with-glibc2.31
- Python version: 3.10.16
- Huggingface_hub version: 0.26.5
- PyArrow version: 18.1.0
- Pandas version: 2.2.3 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7343 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7343/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7343/comments | https://api.github.com/repos/huggingface/datasets/issues/7343/events | https://github.com/huggingface/datasets/issues/7343 | 2,750,525,823 | I_kwDODunzps6j8bF_ | 7,343 | [Bug] Inconsistent behavior of data_files and data_dir in load_dataset method. | {
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} | [] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Describe the bug
Inconsistent operation of data_files and data_dir in load_dataset method.
### Steps to reproduce the bug
# First
I have three files, named 'train.json', 'val.json', 'test.json'.
Each one has a simple dict `{text:'aaa'}`.
Their path are `/data/train.json`, `/data/val.json`, `/data/test.json`
I load dataset with `data_files` argument:
```py
files = [os.path.join('./data',file) for file in os.listdir('./data')]
ds = load_dataset(
path='json',
data_files=files,)
```
And I get:
```py
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 3
})
})
```
However, If I load dataset with `data_dir` argument:
```py
ds = load_dataset(
path='json',
data_dir='./data',)
```
And I get:
```py
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 1
})
validation: Dataset({
features: ['text'],
num_rows: 1
})
test: Dataset({
features: ['text'],
num_rows: 1
})
})
```
Two results are not the same. Their behaviors are not equal, even if the statement [here](https://github.com/huggingface/datasets/blob/d0c152a979d91cc34b605c0298aebc650ab7dd27/src/datasets/load.py#L1790) said that their behaviors are equal.
# Second
If some filename include 'test' while others do not, `load_dataset` only return `test` dataset and others files are **abandoned**.
Given two files named `test.json` and `1.json`
Each one has a simple dict `{text:'aaa'}`.
I load the dataset using:
```py
ds = load_dataset(
path='json',
data_dir='./data',)
```
Only `test` is returned, `1.json` is missing:
```py
DatasetDict({
test: Dataset({
features: ['text'],
num_rows: 1
})
})
```
Things do not change even I manually set `split='train'`
### Expected behavior
1. Fix the above bugs.
2. Although the document says that load_dataset method will `Find which file goes into which split (e.g. train/test) based on file and directory names or on the YAML configuration`, I hope I can manually decide whether to do so. Sometimes users may accidentally put a `test` string in the filename but they just want a single `train` dataset. If the number of files in `data_dir` is huge, it's not easy to find out what cause the second situation metioned above.
### Environment info
datasets==3.2.0
Ubuntu18.84 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7340 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7340/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7340/comments | https://api.github.com/repos/huggingface/datasets/issues/7340/events | https://github.com/huggingface/datasets/pull/7340 | 2,745,473,274 | PR_kwDODunzps6FhdR2 | 7,340 | don't import soundfile in tests | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7340). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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https://api.github.com/repos/huggingface/datasets/issues/7339 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7339/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7339/comments | https://api.github.com/repos/huggingface/datasets/issues/7339/events | https://github.com/huggingface/datasets/pull/7339 | 2,745,460,060 | PR_kwDODunzps6FhaTl | 7,339 | Update CONTRIBUTING.md | {
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https://api.github.com/repos/huggingface/datasets/issues/7337 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7337/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7337/comments | https://api.github.com/repos/huggingface/datasets/issues/7337/events | https://github.com/huggingface/datasets/issues/7337 | 2,744,877,569 | I_kwDODunzps6jm4IB | 7,337 | One or several metadata.jsonl were found, but not in the same directory or in a parent directory of | {
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} | [] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Describe the bug
ImageFolder with metadata.jsonl error. I downloaded liuhaotian/LLaVA-CC3M-Pretrain-595K locally from Hugging Face. According to the tutorial in https://huggingface.co/docs/datasets/image_dataset#image-captioning, only put images.zip and metadata.jsonl containing information in the same folder. However, after loading, an error was reported: One or several metadata.jsonl were found, but not in the same directory or in a parent directory of.
The data in my jsonl file is as follows:
> {"id": "GCC_train_002448550", "file_name": "GCC_train_002448550.jpg", "conversations": [{"from": "human", "value": "<image>\nProvide a brief description of the given image."}, {"from": "gpt", "value": "a view of a city , where the flyover was proposed to reduce the increasing traffic on thursday ."}]}
### Steps to reproduce the bug
from datasets import load_dataset
image = load_dataset("imagefolder",data_dir='data/opensource_data')
### Expected behavior
success
### Environment info
datasets==3.2.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7336 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7336/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7336/comments | https://api.github.com/repos/huggingface/datasets/issues/7336/events | https://github.com/huggingface/datasets/issues/7336 | 2,744,746,456 | I_kwDODunzps6jmYHY | 7,336 | Clarify documentation or Create DatasetCard | {
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"description": "New feature or request"
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] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Feature request
I noticed that you can use a Model Card instead of a Dataset Card when pushing a dataset to the Hub, but this isn’t clearly mentioned in [the docs.](https://huggingface.co/docs/datasets/dataset_card)
- Update the docs to clarify that a Model Card can work for datasets too.
- It might be worth creating a dedicated DatasetCard module, similar to the ModelCard module, for consistency and better support.
Not sure if this belongs here or on the [Hub repo](https://github.com/huggingface/huggingface_hub), but thought I’d bring it up!
### Motivation
I just spent an hour like on [this issue](https://github.com/huggingface/trl/pull/2491) trying to create a `DatasetCard` for a script.
### Your contribution
might later | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7335 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7335/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7335/comments | https://api.github.com/repos/huggingface/datasets/issues/7335/events | https://github.com/huggingface/datasets/issues/7335 | 2,743,437,260 | I_kwDODunzps6jhYfM | 7,335 | Too many open files: '/root/.cache/huggingface/token' | {
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} | [] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Describe the bug
I ran this code:
```
from datasets import load_dataset
dataset = load_dataset("common-canvas/commoncatalog-cc-by", cache_dir="/datadrive/datasets/cc", num_proc=1000)
```
And got this error.
Before it was some other file though (lie something...incomplete)
runnting
```
ulimit -n 8192
```
did not help at all.
### Steps to reproduce the bug
Run the code i sent
### Expected behavior
Should be no errors
### Environment info
linux, jupyter lab. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7334 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7334/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7334/comments | https://api.github.com/repos/huggingface/datasets/issues/7334/events | https://github.com/huggingface/datasets/issues/7334 | 2,740,266,503 | I_kwDODunzps6jVSYH | 7,334 | TypeError: Value.__init__() missing 1 required positional argument: 'dtype' | {
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} | [] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Describe the bug
ds = load_dataset(
"./xxx.py",
name="default",
split="train",
)
The datasets does not support debugging locally anymore...
### Steps to reproduce the bug
```
from datasets import load_dataset
ds = load_dataset(
"./repo.py",
name="default",
split="train",
)
for item in ds:
print(item)
```
It works fine for "username/repo", but it does not work for "./repo.py" when debugging locally...
Running above code template will report TypeError: Value.__init__() missing 1 required positional argument: 'dtype'
### Expected behavior
fix this bug
### Environment info
python 3.10 datasets==2.21 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7328 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7328/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7328/comments | https://api.github.com/repos/huggingface/datasets/issues/7328/events | https://github.com/huggingface/datasets/pull/7328 | 2,738,626,593 | PR_kwDODunzps6FKK13 | 7,328 | Fix typo in arrow_dataset | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7328). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,734 | 1,734 | 1,734 | CONTRIBUTOR | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7327 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7327/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7327/comments | https://api.github.com/repos/huggingface/datasets/issues/7327/events | https://github.com/huggingface/datasets/issues/7327 | 2,738,514,909 | I_kwDODunzps6jOmvd | 7,327 | .map() is not caching and ram goes OOM | {
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} | [] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Describe the bug
Im trying to run a fairly simple map that is converting a dataset into numpy arrays. however, it just piles up on memory and doesnt write to disk. Ive tried multiple cache techniques such as specifying the cache dir, setting max mem, +++ but none seem to work. What am I missing here?
### Steps to reproduce the bug
```
from pydub import AudioSegment
import io
import base64
import numpy as np
import os
CACHE_PATH = "/mnt/extdisk/cache" # "/root/.cache/huggingface/"#
os.environ["HF_HOME"] = CACHE_PATH
import datasets
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# Create a handler for Jupyter notebook
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
#datasets.config.IN_MEMORY_MAX_SIZE= 1000#*(2**30) #50 gb
print(datasets.config.HF_CACHE_HOME)
print(datasets.config.HF_DATASETS_CACHE)
# Decode the base64 string into bytes
def convert_mp3_to_audio_segment(example):
"""
example = ds['train'][0]
"""
try:
audio_data_bytes = base64.b64decode(example['audio'])
# Use pydub to load the MP3 audio from the decoded bytes
audio_segment = AudioSegment.from_file(io.BytesIO(audio_data_bytes), format="mp3")
# Resample to 24_000
audio_segment = audio_segment.set_frame_rate(24_000)
audio = {'sampling_rate' : audio_segment.frame_rate,
'array' : np.array(audio_segment.get_array_of_samples(), dtype="float")}
del audio_segment
duration = len(audio['array']) / audio['sampling_rate']
except Exception as e:
logger.warning(f"Failed to convert audio for {example['id']}. Error: {e}")
audio = {'sampling_rate' : 0,
'array' : np.array([]), duration : 0}
return {'audio' : audio, 'duration' : duration}
ds = datasets.load_dataset("NbAiLab/nb_distil_speech_noconcat_stortinget", cache_dir=CACHE_PATH, keep_in_memory=False)
#%%
num_proc=32
ds_processed = (
ds
#.select(range(10))
.map(convert_mp3_to_audio_segment, num_proc=num_proc, desc="Converting mp3 to audio segment") #, cache_file_name=f"{CACHE_PATH}/stortinget_audio" # , cache_file_name="test"
)
```
### Expected behavior
the map should write to disk
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.39
- Python version: 3.12.7
- `huggingface_hub` version: 0.26.3
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7326 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7326/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7326/comments | https://api.github.com/repos/huggingface/datasets/issues/7326/events | https://github.com/huggingface/datasets/issues/7326 | 2,738,188,902 | I_kwDODunzps6jNXJm | 7,326 | Remove upper bound for fsspec | {
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} | [] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Describe the bug
As also raised by @cyyever in https://github.com/huggingface/datasets/pull/7296 and @NeilGirdhar in https://github.com/huggingface/datasets/commit/d5468836fe94e8be1ae093397dd43d4a2503b926#commitcomment-140952162 , `datasets` has a problematic version constraint on `fsspec`.
In our case this causes (unnecessary?) troubles due to a race condition bug in that version of the corresponding `gcsfs` plugin, that causes deadlocks: https://github.com/fsspec/gcsfs/pull/643
We just use a version override to ignore the constraint from `datasets`, but imho the version constraint could just be removed in the first place?
The last few PRs bumping the upper bound were basically uneventful:
* https://github.com/huggingface/datasets/pull/7219
* https://github.com/huggingface/datasets/pull/6921
* https://github.com/huggingface/datasets/pull/6747
### Steps to reproduce the bug
-
### Expected behavior
Installing `fsspec>=2024.10.0` along `datasets` should be possible without overwriting constraints.
### Environment info
All recent datasets versions | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7325 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7325/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7325/comments | https://api.github.com/repos/huggingface/datasets/issues/7325/events | https://github.com/huggingface/datasets/pull/7325 | 2,736,618,054 | PR_kwDODunzps6FDpMp | 7,325 | Introduce pdf support (#7318) | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7325). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Hi @AndreaFrancis and @lhoestq ! Thanks for looking at the code and for all the changes and suggestions. I have worked on all your suggestions. I need to work a bit more on the tests, but I created a first version and uploaded a simple pdf to use for testing. The pdf has a couple of pages some of which have images, figures, and tables (this is exactly the pdfs for which I think keeping the pdf format is very interesting because they have multiple types of content). Will try to finish the tests as soon as possible."
] | 1,734 | 1,734 | null | NONE | null | First implementation of the Pdf feature to support pdfs (#7318) . Using [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library) as the default library to work with pdfs.
@lhoestq and @AndreaFrancis | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7323 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7323/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7323/comments | https://api.github.com/repos/huggingface/datasets/issues/7323/events | https://github.com/huggingface/datasets/issues/7323 | 2,736,008,698 | I_kwDODunzps6jFC36 | 7,323 | Unexpected cache behaviour using load_dataset | {
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} | [] | open | false | null | [] | null | [] | 1,734 | 1,734 | null | NONE | null | ### Describe the bug
Following the (Cache management)[https://huggingface.co/docs/datasets/en/cache] docu and previous behaviour from datasets version 2.18.0, one is able to change the cache directory. Previously, all downloaded/extracted/etc files were found in this folder. As i have recently update to the latest version this is not the case anymore. Downloaded files are stored in `~/.cache/huggingface/hub`.
Providing the `cache_dir` argument in `load_dataset` the cache directory is created and there are some files but the bulk is still in `~/.cache/huggingface/hub`.
I believe this could be solved by adding the cache_dir argument [here](https://github.com/huggingface/datasets/blob/fdda5585ab18ea1292547f36c969d12c408ab842/src/datasets/utils/file_utils.py#L188)
### Steps to reproduce the bug
For example using https://huggingface.co/datasets/ashraq/esc50:
```python
from datasets import load_dataset
ds = load_dataset("ashraq/esc50", "default", cache_dir="~/custom/cache/path/esc50")
```
### Expected behavior
I would expect the bulk of files related to the dataset to be stored somewhere in `~/custom/cache/path/esc50`, but it seems they are in `~/.cache/huggingface/hub/datasets--ashraq--esc50`.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.14.0-503.15.1.el9_5.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.14
- `huggingface_hub` version: 0.26.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7322 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7322/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7322/comments | https://api.github.com/repos/huggingface/datasets/issues/7322/events | https://github.com/huggingface/datasets/issues/7322 | 2,732,254,868 | I_kwDODunzps6i2uaU | 7,322 | ArrowInvalid: JSON parse error: Column() changed from object to array in row 0 | {
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} | [] | open | false | null | [] | null | [] | 1,733 | 1,733 | null | NONE | null | ### Describe the bug
Encountering an error while loading the ```liuhaotian/LLaVA-Instruct-150K dataset```.
### Steps to reproduce the bug
```
from datasets import load_dataset
fw =load_dataset("liuhaotian/LLaVA-Instruct-150K")
```
Error:
```
ArrowInvalid Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py](https://localhost:8080/#) in _generate_tables(self, files)
136 try:
--> 137 pa_table = paj.read_json(
138 io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
20 frames
ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
ArrowTypeError Traceback (most recent call last)
ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column id with type object')
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1895 if isinstance(e, DatasetGenerationError):
1896 raise
-> 1897 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1898
1899 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
I have tried loading the dataset both on my own server and on Colab, and encountered errors in both instances.
### Environment info
```
- `datasets` version: 3.2.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.3
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.9.0
```
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/7321 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7321/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7321/comments | https://api.github.com/repos/huggingface/datasets/issues/7321/events | https://github.com/huggingface/datasets/issues/7321 | 2,731,626,760 | I_kwDODunzps6i0VEI | 7,321 | ImportError: cannot import name 'set_caching_enabled' from 'datasets' | {
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"pip install datasets==2.18.0",
"Hi ! I think you need to update axolotl"
] | 1,733 | 1,733 | null | NONE | null | ### Describe the bug
Traceback (most recent call last):
File "/usr/local/lib/python3.10/runpy.py", line 187, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "/usr/local/lib/python3.10/runpy.py", line 110, in _get_module_details
__import__(pkg_name)
File "/home/Medusa/axolotl/src/axolotl/cli/__init__.py", line 23, in <module>
from axolotl.train import TrainDatasetMeta
File "/home/Medusa/axolotl/src/axolotl/train.py", line 23, in <module>
from axolotl.utils.trainer import setup_trainer
File "/home/Medusa/axolotl/src/axolotl/utils/trainer.py", line 13, in <module>
from datasets import set_caching_enabled
ImportError: cannot import name 'set_caching_enabled' from 'datasets' (/usr/local/lib/python3.10/site-packages/datasets/__init__.py)
### Steps to reproduce the bug
1、axolotl
2、accelerate launch -m axolotl.cli.train examples/medusa/qwen_lora_stage1.yml
### Expected behavior
enable datasets
### Environment info
python3.10 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7320 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7320/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7320/comments | https://api.github.com/repos/huggingface/datasets/issues/7320/events | https://github.com/huggingface/datasets/issues/7320 | 2,731,112,100 | I_kwDODunzps6iyXak | 7,320 | ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['label'] | {
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"Now i have other error"
] | 1,733 | 1,733 | 1,733 | NONE | null | ### Describe the bug
I am trying to create a PEFT model from DISTILBERT model, and run a training loop. However, the trainer.train() is giving me this error: ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['label']
Here is my code:
### Steps to reproduce the bug
#Creating a PEFT Config
from peft import LoraConfig
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import get_peft_model
lora_config = LoraConfig(
task_type="SEQ_CLASS",
r=8,
lora_alpha=32,
target_modules=["q_lin", "k_lin", "v_lin"],
lora_dropout=0.01,
)
#Converting a Transformers Model into a PEFT Model
model = AutoModelForSequenceClassification.from_pretrained(
"distilbert-base-uncased",
num_labels=2, #Binary classification, 1 = positive, 0 = negative
)
lora_model = get_peft_model(model, lora_config)
print(lora_model)
Tokenize data set
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
# Load the train and test splits dataset
dataset = load_dataset("fancyzhx/amazon_polarity")
#create a smaller subset for train and test
subset_size = 5000
small_train_dataset = dataset["train"].shuffle(seed=42).select(range(subset_size))
small_test_dataset = dataset["test"].shuffle(seed=42).select(range(subset_size))
#Tokenize data
def tokenize_function(example):
return tokenizer(example["content"], padding="max_length", truncation=True)
tokenized_train_dataset = small_train_dataset.map(tokenize_function, batched=True)
tokenized_test_dataset = small_test_dataset.map(tokenize_function, batched=True)
train_lora = tokenized_train_dataset.rename_column('label', 'labels')
test_lora = tokenized_test_dataset.rename_column('label', 'labels')
print(tokenized_train_dataset.column_names)
print(tokenized_test_dataset.column_names)
#Train the PEFT model
import numpy as np
from transformers import Trainer, TrainingArguments, default_data_collator, DataCollatorWithPadding
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
def compute_metrics(eval_pred):
predictions, labels = eval_pred
predictions = np.argmax(predictions, axis=1)
return {"accuracy": (predictions == labels).mean()}
trainer = Trainer(
model=lora_model,
args=TrainingArguments(
output_dir=".",
learning_rate=2e-3,
# Reduce the batch size if you don't have enough memory
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
num_train_epochs=3,
weight_decay=0.01,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
),
train_dataset=tokenized_train_dataset,
eval_dataset=tokenized_test_dataset,
tokenizer=tokenizer,
data_collator=DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt"),
compute_metrics=compute_metrics,
)
trainer.train()
### Expected behavior
Example of output:
[558/558 01:04, Epoch XX]
Epoch | Training Loss | Validation Loss | Accuracy
-- | -- | -- | --
1 | No log | 0.046478 | 0.988341
2 | 0.052800 | 0.048840 | 0.988341
### Environment info
Using python and jupyter notbook | {
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https://api.github.com/repos/huggingface/datasets/issues/7319 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7319/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7319/comments | https://api.github.com/repos/huggingface/datasets/issues/7319/events | https://github.com/huggingface/datasets/pull/7319 | 2,730,679,980 | PR_kwDODunzps6EvHBp | 7,319 | set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7319). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,733 | 1,733 | 1,733 | MEMBER | null | null | {
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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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] | open | false | null | [] | null | [
"#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 license is maybe an issue, and pypdf, pypdfium2 or pdfplumber are good options imo",
"Related to https://github.com/huggingface/datasets/issues/7058",
"PyMuPDF is AGPL licensed, so we can't use it. I will move forward with [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library).",
"Hi both! I have made a pull request with a first basic implementation of the Pdf feature. I followed closely what I saw on the Video and Image features. It is my first time contributing so any comments are very welcomed. I think it would be useful to outline together what additional things we can implement (e.g. enabling parsing of the pdf). Thanks :) "
] | 1,733 | 1,734 | null | NONE | null | ### 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/7317 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7317/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7317/comments | https://api.github.com/repos/huggingface/datasets/issues/7317/events | https://github.com/huggingface/datasets/pull/7317 | 2,730,661,237 | PR_kwDODunzps6EvC5Q | 7,317 | Release: 3.2.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7317). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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https://api.github.com/repos/huggingface/datasets/issues/7316 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7316/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7316/comments | https://api.github.com/repos/huggingface/datasets/issues/7316/events | https://github.com/huggingface/datasets/pull/7316 | 2,730,196,085 | PR_kwDODunzps6Etc0U | 7,316 | More docs to from_dict to mention that the result lives in RAM | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7316). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,733 | 1,733 | 1,733 | MEMBER | null | following discussions at https://discuss.huggingface.co/t/how-to-load-this-simple-audio-data-set-and-use-dataset-map-without-memory-issues/17722/14 | {
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https://api.github.com/repos/huggingface/datasets/issues/7314 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7314/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7314/comments | https://api.github.com/repos/huggingface/datasets/issues/7314/events | https://github.com/huggingface/datasets/pull/7314 | 2,727,502,630 | PR_kwDODunzps6EkCi5 | 7,314 | Resolved for empty datafiles | {
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"Closes #6152 ",
"@mariosasko I hope this resolves #6152 "
] | 1,733 | 1,735 | null | NONE | null | Resolved for Issue#6152 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7313 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7313/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7313/comments | https://api.github.com/repos/huggingface/datasets/issues/7313/events | https://github.com/huggingface/datasets/issues/7313 | 2,726,240,634 | I_kwDODunzps6ifyF6 | 7,313 | Cannot create a dataset with relative audio path | {
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"Hello ! when you `cast_column` you need the paths to be absolute paths or relative paths to your working directory, not the original dataset directory.\r\n\r\nThough I'd recommend structuring your dataset as an AudioFolder which automatically links a metadata.jsonl or csv to the audio files via relative paths **within** the dataset repository: https://huggingface.co/docs/datasets/v3.2.0/en/audio_load#audiofolder",
"@lhoestq thank you, but there are two problems with using AudioFolder:\r\n1. It is said that AudioFolder requires metadata.csv. However, my datset is too large and contains nested and np.ndarray fields, so I can't use csv.\r\n2. It is said that I need to load the dataset with `load_dataset(\"audiofolder\", ...)`. However, if possible, I want my dataset to be loaded as usual with `load_dataset(dataset_name)` after I upload if to HF.",
"You can use metadata.jsonl if you have nested data :)\r\n\r\nAnd actually if you have a dataset structured as an AudioFolder then `load_dataset(dataset_name)` does work after uploading to HF"
] | 1,733 | 1,734 | null | NONE | null | ### Describe the bug
Hello! I want to create a dataset of parquet files, with audios stored as separate .mp3 files. However, it says "No such file or directory" (see the reproducing code).
### Steps to reproduce the bug
Creating a dataset
```
from pathlib import Path
from datasets import Dataset, load_dataset, Audio
Path('my_dataset/audio').mkdir(parents=True, exist_ok=True)
Path('my_dataset/audio/file.mp3').touch(exist_ok=True)
Dataset.from_list(
[{'audio': {'path': 'audio/file.mp3'}}]
).to_parquet('my_dataset/data.parquet')
```
Result:
```
# my_dataset
# ├── audio
# │ └── file.mp3
# └── data.parquet
```
Trying to load the dataset
```
dataset = (
load_dataset('my_dataset', split='train')
.cast_column('audio', Audio(sampling_rate=16_000))
)
dataset[0]
>>> FileNotFoundError: [Errno 2] No such file or directory: 'audio/file.mp3'
```
### Expected behavior
I expect the dataset to load correctly.
I've found 2 workarounds, but they are not very good:
1. I can specify an absolute path to the audio, however, when I move the folder or upload to HF it will stop working.
2. I can set `'path': 'file.mp3'`, and load with `load_dataset('my_dataset', data_dir='audio')` - it seems to work, but does this mean that anyone from Hugging Face who wants to use this dataset should also pass the `data_dir` argument, otherwise it won't work?
### Environment info
datasets 3.1.0, Ubuntu 24.04.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7312 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7312/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7312/comments | https://api.github.com/repos/huggingface/datasets/issues/7312/events | https://github.com/huggingface/datasets/pull/7312 | 2,725,103,094 | PR_kwDODunzps6EbwNN | 7,312 | [Audio Features - DO NOT MERGE] PoC for adding an offset+sliced reading to audio file. | {
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Right now, this code work as a PoC on my dataset. However, this is **just to illustrate** the idea. Many things are messed up, the first being that the shards have wildly varying sizes.
Could be of interest to @lhoestq and @sanchit-gandhi ?
Happy to test better ideas locally. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7311 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7311/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7311/comments | https://api.github.com/repos/huggingface/datasets/issues/7311/events | https://github.com/huggingface/datasets/issues/7311 | 2,725,002,630 | I_kwDODunzps6ibD2G | 7,311 | How to get the original dataset name with username? | {
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] | open | false | null | [] | null | [] | 1,733 | 1,733 | null | CONTRIBUTOR | null | ### Feature request
The issue is related to ray data https://github.com/ray-project/ray/issues/49008 which it requires to check if the dataset is the original one just after `load_dataset` and parquet files are already available on hf hub.
The solution used now is to get the dataset name, config and split, then `load_dataset` again and check the fingerprint. But it's unable to get the correct dataset name if it contains username. So how to get the dataset name with username prefix, or is there another way to query if a dataset is the original one with parquet available?
@lhoestq
### Motivation
https://github.com/ray-project/ray/issues/49008
### Your contribution
Would like to fix that. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7310 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7310/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7310/comments | https://api.github.com/repos/huggingface/datasets/issues/7310/events | https://github.com/huggingface/datasets/issues/7310 | 2,724,830,603 | I_kwDODunzps6iaZ2L | 7,310 | Enable the Audio Feature to decode / read with an offset + duration | {
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"Hi ! What about having audio + start + duration columns and enable something like this ?\r\n\r\n```python\r\nfor example in ds:\r\n array = example[\"audio\"].read(start=example[\"start\"], frames=example[\"duration\"])\r\n```",
"Hi @lhoestq, this would work with a file-based dataset but would be terrible for a sharded one as it would duplicate the large audio file many times. Also, very long audio files are not embedded very well in the parquet file, even with large_binary(). It crashed a few times for me until I switched to one sample == one file :-( "
] | 1,733 | 1,733 | null | NONE | null | ### Feature request
For most large speech dataset, we do not wish to generate hundreds of millions of small audio samples. Instead, it is quite common to provide larger audio files with frame offset (soundfile start and stop arguments). We should be able to pass these arguments to Audio() (column ID corresponding in the dataset row).
### Motivation
I am currently generating a fairly big dataset to .parquet(). Unfortunately, it does not work because all existing functions load the whole .wav file corresponding to the row. All my attempts at bypassing this failed. We should be able to put in the Table only the bytes corresponding to what soundfile reads with an offset (and subset of the audio file).
### Your contribution
I can totally test whatever code on my large dataset creation script. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7315 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7315/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7315/comments | https://api.github.com/repos/huggingface/datasets/issues/7315/events | https://github.com/huggingface/datasets/issues/7315 | 2,729,738,963 | I_kwDODunzps6itILT | 7,315 | Allow manual configuration of Dataset Viewer for datasets not created with the `datasets` library | {
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"Hi @diarray-hub , thanks for opening the issue :) Let me ping @lhoestq and @severo from the dataset viewer team :hugs: ",
"amazing :)",
"Hi ! why not modify the manifest.json file directly ? this way users see in the viewer the dataset as is instead which makes it easier to use using e.g. the `datasets` library",
"Can I create and push the dataset with the dataset library while also pushing the dataset directory, mainting its structure and all the files as with git? ",
"(I transferred to the issue to the `datasets` repo as it's not related to `huggingface_hub`)",
"> Can I create and push the dataset with the dataset library while also pushing the dataset directory, mainting its structure and all the files as with git?\r\n\r\nyes push_to_hub simply uploads Parquet files in a directory named \"data\" in the git repository\r\n",
"That's the problem actually, I need that the data stays in the same format and the directory they are in keep the same structure in order to go quick with Nemo training so users of Nvidia's Nemo framework don't need to write any preprocessing code before starting training. That's why I used git instead of push_to_hub so me and other users working with Nemo can just:\r\n1. git clone\r\n2. asr_model.setup_training_data(train_data_config={'manifest_filepath': training_manifest_filepath})\r\n\r\nAnd start training already. It may be not very kind of me to prioritize users of a specific framework but I noticed that it take much more code to convert an huggingFace dataset with the parquet file to Nemo manifest format than the inverse :haha: ",
"Happy to help if you think the Nemo dataset format should be supported in `datasets` (and therefore in the HF Viewer that is based on `datasets`). Maybe the Nemo team could help as well\r\n\r\nThough I'm not sure if there is only one but actually many formats/structure in Nemo depending on the task ?",
"Yeah, you're right Quentin, it depends of the task. This one is for ASR. And, yes maybe they can help. I noticed that they already share their models through HF. Maybe someone in your teams already have a contact point there. Anyway it's not really a big issues since people can easily understand the dataset and its format with the dataset card but it's a little annoying for those who wanna visually explore each features with the viewer as for regular HF datasets",
"In that case I'd recommend you to upload the dataset in Nemo format and \r\n1) add the \"nemo\" tag\r\n2) add how to use the dataset in Nemo in the dataset README.md\r\n\r\nThe viewer is likely to show the audio content by default but without the transcriptions. You can also configure the viewer to show the transcriptions instead (without the audio).",
"I already did, it's just a little bit \"dommage\" (Hope you'll understand, you speak french right? Cause I don't know any english word for this) that I have to choose which one the viewer displays. But it's no problem for the usability of the dataset. Thanks Quentin :+1: ",
"It's \"dommage\" for now, but feel free to ping the Nemo people if you think there is room for making this better together :)\r\n\r\nKinda related, but the `datasets` AudioFolder structure looks similar and simply asks for a `metadata.jsonl` with a field named \"file_name\" to link the transcriptions to the audio files - you could also add this file to your repository to make the viewer show audio + transcripts.\r\n\r\nAlternatively maybe we can expand the AudioFolder configuration to allow you to set the metadata file to be the \"manifest.json\" and the linking field to be \"audio_file_name\" (we just need to agree on something general - not just for Nemo)",
"Right, actually that was my idea when I opened this issues. That's what I suggested, taking my case as an exemple but you should think of a more general approach like adding a field to configure the viewer as you wish in the metadata (in the dataset card) or a config.yaml or json file. With a level of abstraction like the solution I proposed ot even higher abstraction, it would allow for more customizability :)"
] | 1,733 | 1,733 | null | NONE | null | #### **Problem Description**
Currently, the Hugging Face Dataset Viewer automatically interprets dataset fields for datasets created with the `datasets` library. However, for datasets pushed directly via `git`, the Viewer:
- Defaults to generic columns like `label` with `null` values if no explicit mapping is provided.
- Does not allow dataset creators to configure field mappings or suppress default fields unless the dataset is recreated and pushed using the `datasets` library.
This creates a limitation for creators who:
- Use custom workflows to prepare datasets (e.g., manifest files with audio-transcription mappings).
- Push large datasets directly via `git` and cannot easily restructure them to conform to the `datasets` library format.
#### **Proposed Solution**
Introduce a feature that allows dataset creators to manually configure the Dataset Viewer behavior for datasets not created with the `datasets` library. This could be achieved by:
1. **Using the YAML Metadata in `README.md`:**
- Add support for defining the dataset's field mappings directly in the `README.md` YAML section.
- Example:
```yaml
viewer:
fields:
- name: "audio"
type: "audio_path" / "text"
source: "manifest['audio']"
- name: "bambara_transcription"
type: "text"
source: "manifest['bambara']"
- name: "french_translation"
type: "text"
source: "manifest['french']"
```
With manifest being a csv or json like format file in the repository so that the viewer understands that it should look for the values of each field in that file.
#### **Benefits**
- Improves flexibility for dataset creators who push datasets via `git`.
- Enhances dataset discoverability and usability on the Hugging Face Hub by allowing creators to present meaningful field mappings without restructuring their data.
- Reduces overhead for creators of large or complex datasets.
#### **Examples of Use Case**
- An audio dataset with transcriptions in multiple languages stored in a `manifest.json` file, where the user wants the Viewer to:
- Display the `audio` column and Explicitly map features that he defined such as `bambara_transcription` and `french_translation` from the manifest. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7309 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7309/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7309/comments | https://api.github.com/repos/huggingface/datasets/issues/7309/events | https://github.com/huggingface/datasets/pull/7309 | 2,723,636,931 | PR_kwDODunzps6EW77b | 7,309 | Faster parquet streaming + filters with predicate pushdown | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7309). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,733 | 1,733 | 1,733 | MEMBER | null | ParquetFragment.to_batches uses a buffered stream to read parquet data, which makes streaming faster (x2 on my laptop).
I also added the `filters` config parameter to support filtering with predicate pushdown, e.g.
```python
from datasets import load_dataset
filters = [('problem_source', '==', 'math')]
ds = load_dataset("nvidia/OpenMathInstruct-2", streaming=True, filters=filters)
first_example = next(iter(ds["train"]))
print(first_example["problem_source"])
# 'math'
```
cc @allisonwang-db this is a nice plus for usage in spark | {
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https://api.github.com/repos/huggingface/datasets/issues/7307 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7307/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7307/comments | https://api.github.com/repos/huggingface/datasets/issues/7307/events | https://github.com/huggingface/datasets/pull/7307 | 2,720,244,889 | PR_kwDODunzps6ELKcR | 7,307 | refactor: remove unnecessary else | {
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https://api.github.com/repos/huggingface/datasets/issues/7306 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7306/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7306/comments | https://api.github.com/repos/huggingface/datasets/issues/7306/events | https://github.com/huggingface/datasets/issues/7306 | 2,719,807,464 | I_kwDODunzps6iHPfo | 7,306 | Creating new dataset from list loses information. (Audio Information Lost - either Datatype or Values). | {
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} | [] | open | false | null | [] | null | [] | 1,733 | 1,733 | null | NONE | null | ### Describe the bug
When creating a dataset from a list of datapoints, information is lost of the individual items.
Specifically, when creating a dataset from a list of datapoints (from another dataset). Either the datatype is lost or the values are lost. See examples below.
-> What is the best way to create a dataset from a list of datapoints?
---
e.g.:
**When running this code:**
```python
from datasets import load_dataset, Dataset
commonvoice_data = load_dataset("mozilla-foundation/common_voice_17_0", "it", split="test", streaming=True)
datapoint = next(iter(commonvoice_data))
out = [datapoint]
new_data = Dataset.from_list(out) #this loses datatype information
new_data2= Dataset.from_list(out,features=commonvoice_data.features) #this loses value information
```
**We get the following**:
---
1. `datapoint`: (the original datapoint)
```
'audio': {'path': 'it_test_0/common_voice_it_23606167.mp3', 'array': array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
2.21619011e-05, 2.72628222e-05, 0.00000000e+00]), 'sampling_rate': 48000}
```
Original Dataset Features:
```
>>> commonvoice_data.features
'audio': Audio(sampling_rate=48000, mono=True, decode=True, id=None)
```
- Here we see column "audio", has the proper values (both `path` & and `array`) and has the correct datatype (Audio).
----
2. new_data[0]:
```
# Cannot be printed (as it prints the entire array).
```
New Dataset 1 Features:
```
>>> new_data.features
'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)}
```
- Here we see that the column "audio", has the correct values, but is not the Audio datatype anymore.
---
3. new_data2[0]:
```
'audio': {'path': None, 'array': array([0., 0., 0., ..., 0., 0., 0.]), 'sampling_rate': 48000},
```
New Dataset 2 Features:
```
>>> new_data2.features
'audio': Audio(sampling_rate=48000, mono=True, decode=True, id=None),
```
- Here we see that the column "audio", has the correct datatype, but all the array & path values were lost!
### Steps to reproduce the bug
## Run:
```python
from datasets import load_dataset, Dataset
commonvoice_data = load_dataset("mozilla-foundation/common_voice_17_0", "it", split="test", streaming=True)
datapoint = next(iter(commonvoice_data))
out = [datapoint]
new_data = Dataset.from_list(out) #this loses datatype information
new_data2= Dataset.from_list(out,features=commonvoice_data.features) #this loses value information
```
### Expected behavior
## Expected:
```datapoint == new_data[0]```
AND
```datapoint == new_data2[0]```
### Environment info
- `datasets` version: 3.1.0
- Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7305/comments | https://api.github.com/repos/huggingface/datasets/issues/7305/events | https://github.com/huggingface/datasets/issues/7305 | 2,715,907,267 | I_kwDODunzps6h4XTD | 7,305 | Build Documentation Test Fails Due to "Bad Credentials" Error | {
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} | [] | open | false | null | [] | null | [] | 1,733 | 1,733 | null | CONTRIBUTOR | null | ### Describe the bug
The `Build documentation / build / build_main_documentation (push)` job is consistently failing during the "Syncing repository" step. The error occurs when attempting to determine the default branch name, resulting in "Bad credentials" errors.
### Steps to reproduce the bug
1. Trigger the `build_main_documentation` job.
2. Observe the logs during the "Syncing repository" step.
### Expected behavior
The workflow should be able to retrieve the default branch name without encountering credential issues.
### Environment info
```plaintext
Syncing repository: huggingface/notebooks
Getting Git version info
Temporarily overriding HOME='/home/runner/work/_temp/00e62748-9940-4a4f-bbbc-eb2cda6d7ed6' before making global git config changes
Adding repository directory to the temporary git global config as a safe directory
/usr/bin/git config --global --add safe.directory /home/runner/work/datasets/datasets/notebooks
Initializing the repository
Disabling automatic garbage collection
Setting up auth
Determining the default branch
Retrieving the default branch name
Bad credentials - https://docs.github.com/rest
Waiting 20 seconds before trying again
Retrieving the default branch name
Bad credentials - https://docs.github.com/rest
Waiting 19 seconds before trying again
Retrieving the default branch name
Error: Bad credentials - https://docs.github.com/rest
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7304 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7304/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7304/comments | https://api.github.com/repos/huggingface/datasets/issues/7304/events | https://github.com/huggingface/datasets/pull/7304 | 2,715,179,811 | PR_kwDODunzps6D5saw | 7,304 | Update iterable_dataset.py | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7304). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,733 | 1,733 | 1,733 | MEMBER | null | close https://github.com/huggingface/datasets/issues/7297 | {
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https://api.github.com/repos/huggingface/datasets/issues/7303 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7303/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7303/comments | https://api.github.com/repos/huggingface/datasets/issues/7303/events | https://github.com/huggingface/datasets/issues/7303 | 2,705,729,696 | I_kwDODunzps6hRiig | 7,303 | DataFilesNotFoundError for datasets LM1B | {
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"Hi ! Can you try with a more recent version of `datasets` ? Also you might need to pass trust_remote_code=True since it's a script based dataset"
] | 1,732 | 1,733 | 1,733 | NONE | null | ### Describe the bug
Cannot load the dataset https://huggingface.co/datasets/billion-word-benchmark/lm1b
### Steps to reproduce the bug
`dataset = datasets.load_dataset('lm1b', split=split)`
### Expected behavior
`Traceback (most recent call last):
File "/home/hml/projects/DeepLearning/Generative_model/Diffusion-BERT/word_freq.py", line 13, in <module>
train_data = DiffusionLoader(tokenizer=tokenizer).my_load(task_name='lm1b', splits=['train'])[0]
File "/home/hml/projects/DeepLearning/Generative_model/Diffusion-BERT/dataloader.py", line 20, in my_load
return [self._load(task_name, name) for name in splits]
File "/home/hml/projects/DeepLearning/Generative_model/Diffusion-BERT/dataloader.py", line 20, in <listcomp>
return [self._load(task_name, name) for name in splits]
File "/home/hml/projects/DeepLearning/Generative_model/Diffusion-BERT/dataloader.py", line 13, in _load
dataset = datasets.load_dataset('lm1b', split=split)
File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 2594, in load_dataset
builder_instance = load_dataset_builder(
File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 2266, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 1827, in dataset_module_factory
).get_module()
File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 1040, in get_module
module_name, default_builder_kwargs = infer_module_for_data_files(
File "/home/hml/.conda/envs/DB/lib/python3.10/site-packages/datasets/load.py", line 598, in infer_module_for_data_files
raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in lm1b`
### Environment info
datasets: 2.20.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7302 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7302/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7302/comments | https://api.github.com/repos/huggingface/datasets/issues/7302/events | https://github.com/huggingface/datasets/pull/7302 | 2,702,626,386 | PR_kwDODunzps6DfY8G | 7,302 | Let server decide default repo visibility | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7302). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"No need for a specific version of huggingface_hub to avoid a breaking change no (it's a server-side change)"
] | 1,732 | 1,732 | 1,732 | CONTRIBUTOR | null | Until now, all repos were public by default when created without passing the `private` argument. This meant that passing `private=False` or `private=None` was strictly the same. This is not the case anymore. Enterprise Hub offers organizations to set a default visibility setting for new repos. This is useful for organizations forbidding public repos for security matters. This PR mostly updates docstrings + default values so that `private=None` is always passed when users don't set it manually.
This PR doesn't create any breaking change. The real update has been done server-side when introducing the new Enterprise Hub feature. Related to https://github.com/huggingface/huggingface_hub/pull/2679. | {
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https://api.github.com/repos/huggingface/datasets/issues/7301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7301/comments | https://api.github.com/repos/huggingface/datasets/issues/7301/events | https://github.com/huggingface/datasets/pull/7301 | 2,701,813,922 | PR_kwDODunzps6DdYLZ | 7,301 | update load_dataset doctring | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7301). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,732 | 1,732 | 1,732 | MEMBER | null | - remove canonical dataset name
- remove dataset script logic
- add streaming info
- clearer download and prepare steps | {
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https://api.github.com/repos/huggingface/datasets/issues/7300 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7300/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7300/comments | https://api.github.com/repos/huggingface/datasets/issues/7300/events | https://github.com/huggingface/datasets/pull/7300 | 2,701,424,320 | PR_kwDODunzps6Dcba8 | 7,300 | fix: update elasticsearch version | {
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"May I request a review @lhoestq",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7300). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,732 | 1,733 | 1,733 | CONTRIBUTOR | null | This should fix the `test_py311 (windows latest, deps-latest` errors.
```
=========================== short test summary info ===========================
ERROR tests/test_search.py - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead.
ERROR tests/test_search.py - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead.
===== 2822 passed, 54 skipped, 10 warnings, 2 errors in 373.36s (0:06:13) =====
Error: Process completed with exit code 1.
```
The elasticsearch version used is `elasticsearch==7.9.1`, which is 4 years old and uses the removed `numpy.float_`.
elasticsearch fixed this in [https://github.com/elastic/elasticsearch-py/pull/2551](https://github.com/elastic/elasticsearch-py/pull/2551) and released in 8.15.0 (August 2024) and 7.17.12 (September 2024).
| {
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https://api.github.com/repos/huggingface/datasets/issues/7299 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7299/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7299/comments | https://api.github.com/repos/huggingface/datasets/issues/7299/events | https://github.com/huggingface/datasets/issues/7299 | 2,695,378,251 | I_kwDODunzps6gqDVL | 7,299 | Efficient Image Augmentation in Hugging Face Datasets | {
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} | [] | open | false | null | [] | null | [] | 1,732 | 1,732 | null | NONE | null | ### Describe the bug
I'm using the Hugging Face datasets library to load images in batch and would like to apply a torchvision transform to solve the inconsistent image sizes in the dataset and apply some on the fly image augmentation. I can just think about using the collate_fn, but seems quite inefficient.
I'm new to the Hugging Face datasets library, I didn't find nothing in the documentation or the issues here on github.
Is there an existing way to add image transformations directly to the dataset loading pipeline?
### Steps to reproduce the bug
from datasets import load_dataset
from torch.utils.data import DataLoader
```python
def collate_fn(batch):
images = [item['image'] for item in batch]
texts = [item['text'] for item in batch]
return {
'images': images,
'texts': texts
}
dataset = load_dataset("Yuki20/pokemon_caption", split="train")
dataloader = DataLoader(dataset, batch_size=4, collate_fn=collate_fn)
# Output shows varying image sizes:
# [(1280, 1280), (431, 431), (789, 789), (769, 769)]
```
### Expected behavior
I'm looking for a way to resize images on-the-fly when loading the dataset, similar to PyTorch's Dataset.__getitem__ functionality. This would be more efficient than handling resizing in the collate_fn.
### Environment info
- `datasets` version: 3.1.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.11.10
- `huggingface_hub` version: 0.26.2
- PyArrow version: 18.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/7298 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7298/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7298/comments | https://api.github.com/repos/huggingface/datasets/issues/7298/events | https://github.com/huggingface/datasets/issues/7298 | 2,694,196,968 | I_kwDODunzps6gli7o | 7,298 | loading dataset issue with load_dataset() when training controlnet | {
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} | [] | open | false | null | [] | null | [] | 1,732 | 1,732 | null | NONE | null | ### Describe the bug
i'm unable to load my dataset for [controlnet training](https://github.com/huggingface/diffusers/blob/074e12358bc17e7dbe111ea4f62f05dbae8a49d5/examples/controlnet/train_controlnet.py#L606) using load_dataset(). however, load_from_disk() seems to work?
would appreciate if someone can explain why that's the case here
1. for reference here's the structure of the original training files _before_ dataset creation -
```
- dir train
- dir A (illustrations)
- dir B (SignWriting)
- prompt.json containing:
{"source": "B/file.png", "target": "A/file.png", "prompt": "..."}
```
2. here are features _after_ dataset creation -
```
"features": {
"control_image": {
"_type": "Image"
},
"image": {
"_type": "Image"
},
"caption": {
"dtype": "string",
"_type": "Value"
}
```
3. I've also attempted to upload the dataset to huggingface with the same error output
### Steps to reproduce the bug
1. [dataset creation script](https://github.com/sign-language-processing/signwriting-illustration/blob/main/signwriting_illustration/controlnet_huggingface/dataset.py)
2. controlnet [training script](examples/controlnet/train_controlnet.py) used
3. training parameters -
! accelerate launch diffusers/examples/controlnet/train_controlnet.py \
--pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5" \
--output_dir="$OUTPUT_DIR" \
--train_data_dir="$HF_DATASET_DIR" \
--conditioning_image_column=control_image \
--image_column=image \
--caption_column=caption \
--resolution=512\
--learning_rate=1e-5 \
--validation_image "./validation/0a4b3c71265bb3a726457837428dda78.png" "./validation/0a5922fe2c638e6776bd62f623145004.png" "./validation/1c9f1a53106f64c682cf5d009ee7156f.png" \
--validation_prompt "An illustration of a man with short hair" "An illustration of a woman with short hair" "An illustration of Barack Obama" \
--train_batch_size=4 \
--num_train_epochs=500 \
--tracker_project_name="sd-controlnet-signwriting-test" \
--hub_model_id="sarahahtee/signwriting-illustration-test" \
--checkpointing_steps=5000 \
--validation_steps=1000 \
--report_to wandb \
--push_to_hub
4. command -
` sbatch --export=HUGGINGFACE_TOKEN=hf_token,WANDB_API_KEY=api_key script.sh`
### Expected behavior
```
11/25/2024 17:12:18 - INFO - __main__ - Initializing controlnet weights from unet
Generating train split: 1 examples [00:00, 334.85 examples/s]
Traceback (most recent call last):
File "/data/user/user/signwriting_illustration/controlnet_huggingface/diffusers/examples/controlnet/train_controlnet.py", line 1189, in <module>
main(args)
File "/data/user/user/signwriting_illustration/controlnet_huggingface/diffusers/examples/controlnet/train_controlnet.py", line 923, in main
train_dataset = make_train_dataset(args, tokenizer, accelerator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/user/user/signwriting_illustration/controlnet_huggingface/diffusers/examples/controlnet/train_controlnet.py", line 639, in make_train_dataset
raise ValueError(
ValueError: `--image_column` value 'image' not found in dataset columns. Dataset columns are: _data_files, _fingerprint, _format_columns, _format_kwargs, _format_type, _output_all_columns, _split
```
### Environment info
accelerate 1.1.1
huggingface-hub 0.26.2
python 3.11
torch 2.5.1
transformers 4.46.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7297 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7297/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7297/comments | https://api.github.com/repos/huggingface/datasets/issues/7297/events | https://github.com/huggingface/datasets/issues/7297 | 2,683,977,430 | I_kwDODunzps6f-j7W | 7,297 | wrong return type for `IterableDataset.shard()` | {
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"Oops my bad ! thanks for reporting"
] | 1,732 | 1,733 | 1,733 | NONE | null | ### Describe the bug
`IterableDataset.shard()` has the wrong typing for its return as `"Dataset"`. It should be `"IterableDataset"`. Makes my IDE unhappy.
### Steps to reproduce the bug
look at [the source code](https://github.com/huggingface/datasets/blob/main/src/datasets/iterable_dataset.py#L2668)?
### Expected behavior
Correct return type as `"IterableDataset"`
### Environment info
datasets==3.1.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7296 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7296/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7296/comments | https://api.github.com/repos/huggingface/datasets/issues/7296/events | https://github.com/huggingface/datasets/pull/7296 | 2,675,573,974 | PR_kwDODunzps6ChJIJ | 7,296 | Remove upper version limit of fsspec[http] | {
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https://api.github.com/repos/huggingface/datasets/issues/7295 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7295/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7295/comments | https://api.github.com/repos/huggingface/datasets/issues/7295/events | https://github.com/huggingface/datasets/issues/7295 | 2,672,003,384 | I_kwDODunzps6fQ4k4 | 7,295 | [BUG]: Streaming from S3 triggers `unexpected keyword argument 'requote_redirect_url'` | {
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Note that this bug is only triggered when `streaming=True`. #5459 introduced always calling fsspec with `client_kwargs={"requote_redirect_url": False}`, which seems to have incompatibility issues even in the newest versions.
Analysis of what's happening:
1. `datasets` passes the `client_kwargs` through `fsspec`
2. `fsspec` passes the `client_kwargs` through `s3fs`
3. `s3fs` passes the `client_kwargs` to `aiobotocore` which uses `aiohttp`
```
s3creator = self.session.create_client(
"s3", config=conf, **init_kwargs, **client_kwargs
)
```
4. The `session` tries to create an `aiohttp` session but the `**kwargs` are not just kept as unfolded `**kwargs` but passed in as individual variables (`requote_redirect_url` and `trust_env`).
Error:
```
Traceback (most recent call last):
File "/Users/cxrh/Documents/GitHub/nlp_foundation/nlp_train/test.py", line 14, in <module>
batch = next(iter(ds))
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__
for key, example in ex_iterable:
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 255, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/packaged_modules/json/json.py", line 78, in _generate_tables
for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 840, in __iter__
yield from self.generator(*self.args, **self.kwargs)
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 921, in _iter_from_urlpaths
elif xisdir(urlpath, download_config=download_config):
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 305, in xisdir
return fs.isdir(inner_path)
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/spec.py", line 721, in isdir
return self.info(path)["type"] == "directory"
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/archive.py", line 38, in info
self._get_dirs()
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/datasets/filesystems/compression.py", line 64, in _get_dirs
f = {**self.file.fs.info(self.file.path), "name": self.uncompressed_name}
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/asyn.py", line 118, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/asyn.py", line 103, in sync
raise return_result
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/fsspec/asyn.py", line 56, in _runner
result[0] = await coro
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/s3fs/core.py", line 1302, in _info
out = await self._call_s3(
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/s3fs/core.py", line 341, in _call_s3
await self.set_session()
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/s3fs/core.py", line 524, in set_session
s3creator = self.session.create_client(
File "/Users/cxrh/miniconda3/envs/s3_data_loader/lib/python3.10/site-packages/aiobotocore/session.py", line 114, in create_client
return ClientCreatorContext(self._create_client(*args, **kwargs))
TypeError: AioSession._create_client() got an unexpected keyword argument 'requote_redirect_url'
```
### Steps to reproduce the bug
1. Install the necessary libraries, datasets having a requirement for being at least 2.19.0:
```
pip install s3fs fsspec aiohttp aiobotocore botocore 'datasets>=2.19.0'
```
2. Run this code:
```
from datasets import load_dataset
ds = load_dataset(
"json",
data_files="s3://your_path/*.jsonl.gz",
streaming=True,
split="train",
)
batch = next(iter(ds))
print(batch)
```
3. You get the `unexpected keyword argument 'requote_redirect_url'` error.
### Expected behavior
The datasets is able to load a batch from the dataset stored on S3, without triggering this `requote_redirect_url` error.
Fix: I could fix this by directly removing the `requote_redirect_url` and `trust_env` - then it loads properly.
<img width="1127" alt="image" src="https://github.com/user-attachments/assets/4c40efa9-8787-4919-b613-e4908c3d1ab2">
### Environment info
- `datasets` version: 3.1.0
- Platform: macOS-15.1-arm64-arm-64bit
- Python version: 3.10.15
- `huggingface_hub` version: 0.26.2
- PyArrow version: 18.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0 | null | {
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} | [] | open | false | null | [] | null | [] | 1,731 | 1,731 | null | NONE | null | The dependency is only used for catching an exception from other code. That can be done with an import guard.
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/7293 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7293/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7293/comments | https://api.github.com/repos/huggingface/datasets/issues/7293/events | https://github.com/huggingface/datasets/pull/7293 | 2,664,592,054 | PR_kwDODunzps6CIjS- | 7,293 | Updated inconsistent output in documentation examples for `ClassLabel` | {
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"Updated! 😄 ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7293). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq, can you help with this failing test please? 🙏 "
] | 1,731 | 1,733 | 1,733 | CONTRIBUTOR | null | fix #7129
@stevhliu | {
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"Hi ! If the dataset owner uses `push_to_hub()` instead of `save_to_disk()` and upload the local files it will fix the issue.\r\nRight now `datasets` sees the train/test/valid pickle files but they are not supported file formats.",
"Alternatively you can load the arrow file instead:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('OpenMol/PubChemSFT', data_files='stage1/*.arrow')\r\n```",
"Thanks! I'll have a try."
] | 1,731 | 1,731 | 1,731 | NONE | null | ### Describe the bug
Cannot load the dataset https://huggingface.co/datasets/OpenMol/PubChemSFT
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('OpenMol/PubChemSFT')
```
### Expected behavior
```
---------------------------------------------------------------------------
DataFilesNotFoundError Traceback (most recent call last)
Cell In[7], [line 2](vscode-notebook-cell:?execution_count=7&line=2)
[1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
----> [2](vscode-notebook-cell:?execution_count=7&line=2) dataset = load_dataset('OpenMol/PubChemSFT')
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2587, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
[2582](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2582) verification_mode = VerificationMode(
[2583](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2583) (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
[2584](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2584) )
[2586](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2586) # Create a dataset builder
-> [2587](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2587) builder_instance = load_dataset_builder(
[2588](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2588) path=path,
[2589](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2589) name=name,
[2590](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2590) data_dir=data_dir,
[2591](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2591) data_files=data_files,
[2592](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2592) cache_dir=cache_dir,
[2593](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2593) features=features,
[2594](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2594) download_config=download_config,
[2595](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2595) download_mode=download_mode,
[2596](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2596) revision=revision,
[2597](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2597) token=token,
[2598](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2598) storage_options=storage_options,
[2599](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2599) trust_remote_code=trust_remote_code,
[2600](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2600) _require_default_config_name=name is None,
[2601](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2601) **config_kwargs,
[2602](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2602) )
[2604](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2604) # Return iterable dataset in case of streaming
[2605](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2605) if streaming:
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2259, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
[2257](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2257) download_config = download_config.copy() if download_config else DownloadConfig()
[2258](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2258) download_config.storage_options.update(storage_options)
-> [2259](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2259) dataset_module = dataset_module_factory(
[2260](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2260) path,
[2261](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2261) revision=revision,
[2262](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2262) download_config=download_config,
[2263](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2263) download_mode=download_mode,
[2264](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2264) data_dir=data_dir,
[2265](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2265) data_files=data_files,
[2266](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2266) cache_dir=cache_dir,
[2267](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2267) trust_remote_code=trust_remote_code,
[2268](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2268) _require_default_config_name=_require_default_config_name,
[2269](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2269) _require_custom_configs=bool(config_kwargs),
[2270](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2270) )
[2271](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2271) # Get dataset builder class from the processing script
[2272](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:2272) builder_kwargs = dataset_module.builder_kwargs
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1904, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
[1902](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1902) raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None
[1903](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1903) if isinstance(e1, (DataFilesNotFoundError, DatasetNotFoundError, EmptyDatasetError)):
-> [1904](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1904) raise e1 from None
[1905](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1905) if isinstance(e1, FileNotFoundError):
[1906](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1906) raise FileNotFoundError(
[1907](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1907) f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
[1908](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1908) f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
[1909](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1909) ) from None
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1885, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
[1876](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1876) return HubDatasetModuleFactoryWithScript(
[1877](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1877) path,
[1878](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1878) revision=revision,
(...)
[1882](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1882) trust_remote_code=trust_remote_code,
[1883](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1883) ).get_module()
[1884](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1884) else:
-> [1885](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1885) return HubDatasetModuleFactoryWithoutScript(
[1886](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1886) path,
[1887](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1887) revision=revision,
[1888](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1888) data_dir=data_dir,
[1889](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1889) data_files=data_files,
[1890](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1890) download_config=download_config,
[1891](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1891) download_mode=download_mode,
[1892](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1892) ).get_module()
[1893](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1893) except Exception as e1:
[1894](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1894) # All the attempts failed, before raising the error we should check if the module is already cached
[1895](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1895) try:
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1270, in HubDatasetModuleFactoryWithoutScript.get_module(self)
[1263](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1263) patterns = get_data_patterns(base_path, download_config=self.download_config)
[1264](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1264) data_files = DataFilesDict.from_patterns(
[1265](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1265) patterns,
[1266](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1266) base_path=base_path,
[1267](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1267) allowed_extensions=ALL_ALLOWED_EXTENSIONS,
[1268](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1268) download_config=self.download_config,
[1269](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1269) )
-> [1270](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1270) module_name, default_builder_kwargs = infer_module_for_data_files(
[1271](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1271) data_files=data_files,
[1272](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1272) path=self.name,
[1273](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1273) download_config=self.download_config,
[1274](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1274) )
[1275](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1275) data_files = data_files.filter_extensions(_MODULE_TO_EXTENSIONS[module_name])
[1276](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:1276) # Collect metadata files if the module supports them
File ~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:597, in infer_module_for_data_files(data_files, path, download_config)
[595](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:595) raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
[596](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:596) if not module_name:
--> [597](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:597) raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
[598](https://file+.vscode-resource.vscode-cdn.net/home/ubuntu/Projects/notebook/~/Softwares/anaconda3/envs/pyg-dev/lib/python3.9/site-packages/datasets/load.py:598) return module_name, default_builder_kwargs
DataFilesNotFoundError: No (supported) data files found in OpenMol/PubChemSFT
```
### Environment info
```
- `datasets` version: 3.1.0
- Platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.31
- Python version: 3.9.18
- `huggingface_hub` version: 0.25.2
- PyArrow version: 18.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.9.2
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7291 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7291/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7291/comments | https://api.github.com/repos/huggingface/datasets/issues/7291/events | https://github.com/huggingface/datasets/issues/7291 | 2,662,244,643 | I_kwDODunzps6erqEj | 7,291 | Why return_tensors='pt' doesn't work? | {
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"Hi ! `datasets` uses Arrow as storage backend which is agnostic to deep learning frameworks like torch. If you want to get torch tensors back, you need to do `dataset = dataset.with_format(\"torch\")`",
"> Hi ! `datasets` uses Arrow as storage backend which is agnostic to deep learning frameworks like torch. If you want to get torch tensors back, you need to do `dataset = dataset.with_format(\"torch\")`\r\n\r\nIt does work! Thanks for your suggestion!"
] | 1,731 | 1,731 | null | NONE | null | ### Describe the bug
I tried to add input_ids to dataset with map(), and I used the return_tensors='pt', but why I got the callback with the type of List?

### Steps to reproduce the bug

### Expected behavior
Sorry for this silly question, I'm noob on using this tool. But I think it should return a tensor value as I have used the protocol?
When I tokenize only one sentence using tokenized_input=tokenizer(input, return_tensors='pt' ),it does return in tensor type. Why doesn't it work in map()?
### Environment info
transformers>=4.41.2,<=4.45.0
datasets>=2.16.0,<=2.21.0
accelerate>=0.30.1,<=0.34.2
peft>=0.11.1,<=0.12.0
trl>=0.8.6,<=0.9.6
gradio>=4.0.0
pandas>=2.0.0
scipy
einops
sentencepiece
tiktoken
protobuf
uvicorn
pydantic
fastapi
sse-starlette
matplotlib>=3.7.0
fire
packaging
pyyaml
numpy<2.0.0
| null | {
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} | [] | open | false | null | [] | null | [] | 1,731 | 1,731 | null | NONE | null | ### Describe the bug
Hi, I'm encountered a small issue when saving datasets that led to the saving taking up to multiple hours.
Specifically, [`Dataset.save_to_disk`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.save_to_disk) is a lot slower when using `num_proc>1` than when using `num_proc=1`
The documentation mentions that "Multiprocessing is disabled by default.", but there is no explanation on how to enable it.
### Steps to reproduce the bug
```
import numpy as np
from datasets import Dataset
n_samples = int(4e6)
n_tokens_sample = 100
data_dict = {
'tokens' : np.random.randint(0, 100, (n_samples, n_tokens_sample)),
}
dataset = Dataset.from_dict(data_dict)
dataset.save_to_disk('test_dataset', num_proc=1)
dataset.save_to_disk('test_dataset', num_proc=4)
dataset.save_to_disk('test_dataset', num_proc=8)
```
This results in:
```
>>> dataset.save_to_disk('test_dataset', num_proc=1)
Saving the dataset (7/7 shards): 100%|██████████████| 4000000/4000000 [00:17<00:00, 228075.15 examples/s]
>>> dataset.save_to_disk('test_dataset', num_proc=4)
Saving the dataset (7/7 shards): 100%|██████████████| 4000000/4000000 [01:49<00:00, 36583.75 examples/s]
>>> dataset.save_to_disk('test_dataset', num_proc=8)
Saving the dataset (8/8 shards): 100%|██████████████| 4000000/4000000 [02:11<00:00, 30518.43 examples/s]
```
With larger datasets it can take hours, but I didn't benchmark that for this bug report.
### Expected behavior
I would expect using `num_proc>1` to be faster instead of slower than `num_proc=1`.
### Environment info
- `datasets` version: 3.1.0
- Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.26.2
- PyArrow version: 18.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1 | null | {
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"i think this issue is more for https://github.com/huggingface/dataset-viewer"
] | 1,731 | 1,731 | 1,731 | NONE | null | ### Describe the bug
In [my dataset](https://huggingface.co/datasets/speedcell4/opus-unigram2), there is a column called `lang2`, and there are 94 different classes in total, but the viewer says there are 83 values only. This issue only arises in the `train` split. The total number of values is also 94 in the `test` and `dev` columns, viewer tells the correct number of them.
<img width="177" alt="image" src="https://github.com/user-attachments/assets/78d76ef2-fe0e-4fa3-85e0-fb2552813d1c">
### Steps to reproduce the bug
```python3
from datasets import load_dataset
ds = load_dataset('speedcell4/opus-unigram2').unique('lang2')
for key, lang2 in ds.items():
print(key, len(lang2))
```
This script returns the following and tells that the `train` split has 94 values in the `lang2` column.
```
train 94
dev 94
test 94
zero 5
```
### Expected behavior
94 in the reviewer.
### Environment info
Collecting environment information...
PyTorch version: 2.4.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: CentOS Linux release 8.2.2004 (Core) (x86_64)
GCC version: (GCC) 8.3.1 20191121 (Red Hat 8.3.1-5)
Clang version: Could not collect
CMake version: version 3.11.4
Libc version: glibc-2.28
Python version: 3.9.20 (main, Oct 3 2024, 07:27:41) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB
Nvidia driver version: 525.85.05
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 64
On-line CPU(s) list: 0-63
Thread(s) per core: 1
Core(s) per socket: 32
Socket(s): 2
NUMA node(s): 4
Vendor ID: AuthenticAMD
CPU family: 23
Model: 49
Model name: AMD EPYC 7542 32-Core Processor
Stepping: 0
CPU MHz: 3389.114
BogoMIPS: 5789.40
Virtualization: AMD-V
L1d cache: 32K
L1i cache: 32K
L2 cache: 512K
L3 cache: 16384K
NUMA node0 CPU(s): 0-15
NUMA node1 CPU(s): 16-31
NUMA node2 CPU(s): 32-47
NUMA node3 CPU(s): 48-63
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.4.1+cu121
[pip3] torchaudio==2.4.1+cu121
[pip3] torchdevice==0.1.1
[pip3] torchglyph==0.3.2
[pip3] torchmetrics==1.5.0
[pip3] torchrua==0.5.1
[pip3] torchvision==0.19.1+cu121
[pip3] triton==3.0.0
[pip3] datasets==3.0.1
[conda] numpy 1.26.4 pypi_0 pypi
[conda] torch 2.4.1+cu121 pypi_0 pypi
[conda] torchaudio 2.4.1+cu121 pypi_0 pypi
[conda] torchdevice 0.1.1 pypi_0 pypi
[conda] torchglyph 0.3.2 pypi_0 pypi
[conda] torchmetrics 1.5.0 pypi_0 pypi
[conda] torchrua 0.5.1 pypi_0 pypi
[conda] torchvision 0.19.1+cu121 pypi_0 pypi
[conda] triton 3.0.0 pypi_0 pypi | {
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"Hi ! You can already configure the README.md to have multiple sets of splits, e.g.\r\n\r\n```yaml\r\nconfigs:\r\n- config_name: my_first_set_of_split\r\n data_files:\r\n - split: train\r\n path: *.csv\r\n- config_name: my_second_set_of_split\r\n data_files:\r\n - split: train\r\n path: train-*.csv\r\n - split: test\r\n path: test-*.csv\r\n```",
"Hi - I had something slightly different in mind:\r\n\r\nCurrently the yaml splits specified like this only allow specifying which filenames to pass to each split.\r\nBut what if I have a situation where I know which individual *training examples* I want to put in each split.\r\n\r\nI could build split-specific files, however for large datasets with overlapping (e.g. multiple sets of) splits this could result in significant duplication of data.\r\n\r\nI can see that this could actually be very much intended (i.e. to discourage overlapping splits), but wondered whether some support for handling splits based on individual identifiers is something that could be considered. ",
"This is not supported right now :/ Though you can load the data in two steps like this\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nfull_dataset = load_dataset(\"username/dataset\", split=\"train\")\r\nmy_first_set_indices = load_dataset(\"username/dataset\", \"my_first_set_of_split\", split=\"train\")\r\n\r\nmy_first_set = full_dataset.select(my_first_set_indices[\"indices\"])\r\n```\r\n\r\nyou can create such a dataset by adapting this code for example\r\n```python\r\n# upload the full dataset\r\nfull_dataset.push_to_hub(\"username/dataset\")\r\n# then upload the indices for each set\r\nDatasetDict({\r\n \"train\": Dataset.from_dict({\"indices\": [0, 1, 2, 3]}),\r\n \"test\": Dataset.from_dict({\"indices\": [4, 5]}),\r\n}).push_to_hub(\"username/dataset\", \"my_first_set_of_split\")"
] | 1,731 | 1,732 | null | CONTRIBUTOR | null | ### Feature request
As far as I understand, automated construction of splits for hub datasets is currently based on either file names or directory structure ([as described here](https://huggingface.co/docs/datasets/en/repository_structure))
It would seem to be pretty useful to also allow splits to be based on identifiers of individual examples
This could be configured like
{"split_name": {"column_name": [column values in split]}}
(This in turn requires unique 'index' columns, which could be explicitly supported or just assumed to be defined appropriately by the user).
I guess a potential downside would be that shards would end up spanning different splits - is this something that can be handled somehow? Would this only affect streaming from hub?
### Motivation
The main motivation would be that all data files could be stored in a single directory, and multiple sets of splits could be generated from the same data. This is often useful for large datasets with multiple distinct sets of splits.
This could all be configured via the README.md yaml configs
### Your contribution
May be able to contribute if it seems like a good idea | null | {
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https://github.com/huggingface/datasets/pull/6464 mentions a `num_proc` param while loading dataset from disk, but can't find that in the documentation and code anywhere
### Motivation
Make loading large datasets from disk faster
### Your contribution
Happy to contribute if given pointers | {
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"@lhoestq ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7284). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,731 | 1,732 | 1,732 | CONTRIBUTOR | null | Fix for https://github.com/huggingface/datasets/issues/7220 as suggested in discussion, in preference to #7221
(only concern would be on effect on type checking with custom feature types that aren't covered by FeatureType?) | {
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Resolves #7123. | null | {
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Trying to download only the 'validation' split of my dataset; instead hit the error `datasets.exceptions.ExpectedMoreSplitsError`.
Appears to be the same undesired behavior as reported in [#6939](https://github.com/huggingface/datasets/issues/6939), but with `data_files`, not `data_dir`.
Here is the Traceback:
```
Traceback (most recent call last):
File "/home/user/app/app.py", line 12, in <module>
ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val*'}, split='validation', trust_remote_code=True)
File "/usr/local/lib/python3.10/site-packages/datasets/load.py", line 2154, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/site-packages/datasets/builder.py", line 1018, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/usr/local/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 68, in verify_splits
raise ExpectedMoreSplitsError(str(set(expected_splits) - set(recorded_splits)))
datasets.exceptions.ExpectedMoreSplitsError: {'train', 'test'}
```
Note: I am using the `data_files` argument only because I am trying to specify that I only want the 'validation' split, and the whole dataset will be downloaded even when the `split='validation'` argument is specified, unless you also specify `data_files`, as described here: https://discuss.huggingface.co/t/how-can-i-download-a-specific-split-of-a-dataset/79027
### Steps to reproduce the bug
1. Create a Space with the default blank 'gradio' SDK https://huggingface.co/new-space
2. Create a file 'app.py' that loads a dataset to only extract a 'validation' split:
`ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val*'}, split='validation', trust_remote_code=True)`
### Expected behavior
Downloading validation split.
### Environment info
Default environment for creating a new Space. Relevant to this bug, that is:
```
FROM docker.io/library/python:3.10@sha256:fd0fa50d997eb56ce560c6e5ca6a1f5cf8fdff87572a16ac07fb1f5ca01eb608
--> RUN pip install --no-cache-dir pip==22.3.1 && pip install --no-cache-dir datasets "huggingface-hub>=0.19" "hf-transfer>=0.1.4" "protobuf<4" "click<8.1"
``` | null | {
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"Link to the dataset: https://huggingface.co/datasets/MichielBontenbal/UrbanSounds "
] | 1,730 | 1,730 | null | NONE | null | ### Describe the bug
I get a FileNotFoundError:
<img width="944" alt="image" src="https://github.com/user-attachments/assets/1336bc08-06f6-4682-a3c0-071ff65efa87">
### Steps to reproduce the bug
See screenshot.
### Expected behavior
I want to load one audiofile from the dataset.
### Environment info
MacOs Intel 14.6.1 (23G93)
Python 3.10.9
Numpy 1.23
Datasets latest version | null | {
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"Hi Elisa, please share the error traceback here, and if you manage to find the location in the `datasets` code where the error occurs, feel free to open a PR to add the necessary logging / improve the error message.",
"> please share the error traceback\n\nI don't have access to it but it should be during [this exception](https://github.com/huggingface/datasets/blob/2049c00921c59cdeb835137a1c49639cf175af07/src/datasets/builder.py#L1643) which happens during the loading of a dataset. If one of the downloaded files is corrupted, the for loop will not yield correctly, and the error will come from, say, in the case of tar files, [this iterable](https://github.com/huggingface/datasets/blob/2049c00921c59cdeb835137a1c49639cf175af07/src/datasets/utils/file_utils.py#L1293) which has no explicit error handling that leaves clues as to which file has failed.\n\nI only know the case for tar files, but I consider this issue could be happening across different file types too.",
"I think having a better error handling for this tar iterable would be useful already, maybe a simple try/except in `_iter_from_urlpath` that checks for `tarfile.ReadError` and raises an error with the `urlpath` mentioned in the error ?",
"I think not just from higher calls like the `_iter_from_urlpath` but directly wherever a file is attempted to be opened would be the best case, as the traceback would simply lead to that.",
"so maybe there should be better error messages in each dataset builder definition ? e.g. in https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/webdataset/webdataset.py for webdataset TAR archives"
] | 1,730 | 1,732 | null | NONE | null | Please update error messages to include relevant information for debugging when loading datasets with `load_dataset()` that may have a few corrupted files.
Whenever downloading a full dataset, some files might be corrupted (either at the source or from downloading corruption).
However the errors often only let me know it was a tar file if `tarfile.ReadError` appears on the traceback, and I imagine similarly for other file types.
This makes it really hard to debug which file is corrupted, and when dealing with very large datasets, it shouldn't be necessary to force download everything again. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7279 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7279/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7279/comments | https://api.github.com/repos/huggingface/datasets/issues/7279/events | https://github.com/huggingface/datasets/pull/7279 | 2,635,813,932 | PR_kwDODunzps6A8pTJ | 7,279 | Feature proposal: Stacking, potentially heterogeneous, datasets | {
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} | [] | open | false | null | [] | null | [] | 1,730 | 1,730 | null | NONE | null | ### Introduction
Hello there,
I noticed that there are two ways to combine multiple datasets: Either through `datasets.concatenate_datasets` or `datasets.interleave_datasets`. However, to my knowledge (please correct me if I am wrong) both approaches require the datasets that are combined to have the same features.
I think it would be a great idea to add support for combining multiple datasets that might not follow the same schema (i.e. have different features), for example an image and text dataset. That is why I propose a third function of the `datasets.combine` module called `stack_datasets`, which can be used to combine a list of datasets with (potentially) different features. This would look as follows:
```python
>>> from datasets import stack_datasets
>>> image_dataset = ...
>>> next(iter(image_dataset))
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=555x416 at 0x313E79CD0> }
>>> text_dataset = ...
>>> next(iter(text_dataset))
{'text': "This is a test."}
>>> stacked = stack_datasets(datasets={'i_ds': image_dataset, 't_ds': text_dataset}, stopping_strategy='all_exhausted')
>>> next(iter(stacked))
{
'i_ds': {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=555x416 at 0x313E79CD0> }
't_ds': {'text': "This is a test."}
}
```
<br />
### Motivation
I motivate this by:
**A**: The fact that Pytorch offers a similar functionality under `torch.utils.data.StackDataset` ([link](https://pytorch.org/docs/stable/data.html#torch.utils.data.StackDataset)).
**B**: In settings where one would like to e.g. train a Vision-Language model using an image-text dataset, an image dataset, and a text dataset, this functionality would offer a clean and intuitive solution to create multimodal datasets. I am aware that the aforementioned is also feasible without my proposed function, but I believe this offers a nice approach that aligns with existing functionality and is directly provided within the `datasets` package.
### API
`stack_datasets` has two arguments: `datasets` and `stopping_strategy `.
<br />
`datasets` is a dictionary of either type `Dict[str, Dataset]` or `Dict[str, IterableDatasets]`, a mixture is not allowed. It contains the names of the datasets (the keys) and the datasets themselves (the values) that should be stacked. Each item returned is a dictionary with one key-value pair for each dataset. The keys are the names of the datasets as provided in the argument `datasets`, and the values are the respective examples from the datasets.
<br />
`stopping_strategy` is the same as for `interleave_datasets`. If it is `first_exhausted` we stop if the smallest dataset runs out of examples, if it is `all_exhausted` we stop if all datasets ran out of examples at least once. For `all_exhausted` that means that we may visit examples from datasets multiple times.
### Docs
I saw that there are multiple documentations and guides on the HuggingFace website that introduce `concatenate_datasets` and `interleave_datasets`, for example [here](https://huggingface.co/docs/datasets/process#concatenate). If this request is merged I would be willing to add the new functionality at the appropriate points in the documentation (if desired).
### Tests
I also added some tests to ensure correctness. Some tests I wrote in [tests/test_iterable_dataset.py](https://github.com/TimCares/datasets/blob/fadc1159debf2a65d44e40cbf7758f2bd2cc8b08/tests/test_iterable_dataset.py#L2169)
run for both `Dataset` and `IterableDataset` even though tests for `Dataset` technically do not belong in this script, but I found that this was a nice way to cover more cases with mostly the same code.
### Additional information
I tried to write the code in a way so that it is similar to that of `concatenate_datasets` and `interleave_datasets`.
I’m open to feedback and willing to make adjustments based on your suggestions, so feel free to give me your take. :)
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/7278 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7278/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7278/comments | https://api.github.com/repos/huggingface/datasets/issues/7278/events | https://github.com/huggingface/datasets/pull/7278 | 2,633,436,151 | PR_kwDODunzps6A1ORG | 7,278 | Let soundfile directly read local audio files | {
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} | [] | open | false | null | [] | null | [] | 1,730 | 1,731 | null | NONE | null | - [x] Fixes #7276 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7277 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7277/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7277/comments | https://api.github.com/repos/huggingface/datasets/issues/7277/events | https://github.com/huggingface/datasets/pull/7277 | 2,632,459,184 | PR_kwDODunzps6AyB7O | 7,277 | Add link to video dataset | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7277). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,730 | 1,730 | 1,730 | CONTRIBUTOR | null | This PR updates https://huggingface.co/docs/datasets/loading to also link to the new video loading docs.
cc @mfarre | {
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https://api.github.com/repos/huggingface/datasets/issues/7276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7276/comments | https://api.github.com/repos/huggingface/datasets/issues/7276/events | https://github.com/huggingface/datasets/issues/7276 | 2,631,917,431 | I_kwDODunzps6c3993 | 7,276 | Accessing audio dataset value throws Format not recognised error | {
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"Hi ! can you try if this works ?\r\n\r\n```python\r\nimport soundfile as sf\r\n\r\nwith open('C:\\\\Users\\\\Nawaz-Server\\\\.cache\\\\huggingface\\\\hub\\\\datasets--fawazahmed0--bug-audio\\\\snapshots\\\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\\\data\\\\Ghamadi\\\\037136.mp3', 'rb') as f:\r\n print(sf.read(f))\r\n```",
"@lhoestq Same error, here is the output:\r\n\r\n```bash\r\n(mypy) C:\\Users\\Nawaz-Server\\Documents\\ml>python myest.py\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Nawaz-Server\\Documents\\ml\\myest.py\", line 5, in <module>\r\n print(sf.read(f))\r\n ^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 285, in read\r\n with SoundFile(file, 'r', samplerate, channels,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 658, in __init__\r\n self._file = self._open(file, mode_int, closefd)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"C:\\Users\\Nawaz-Server\\.conda\\envs\\mypy\\Lib\\site-packages\\soundfile.py\", line 1216, in _open\r\n raise LibsndfileError(err, prefix=\"Error opening {0!r}: \".format(self.name))\r\nsoundfile.LibsndfileError: Error opening <_io.BufferedReader name='C:\\\\Users\\\\Nawaz-Server\\\\.cache\\\\huggingface\\\\hub\\\\datasets--fawazahmed0--bug-audio\\\\snapshots\\\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\\\data\\\\Ghamadi\\\\037136.mp3'>: Format not recognised.\r\n\r\n```",
"upstream bug: https://github.com/bastibe/python-soundfile/issues/439"
] | 1,730 | 1,731 | null | NONE | null | ### Describe the bug
Accessing audio dataset value throws `Format not recognised error`
### Steps to reproduce the bug
**code:**
```py
from datasets import load_dataset
dataset = load_dataset("fawazahmed0/bug-audio")
for data in dataset["train"]:
print(data)
```
**output:**
```bash
(mypy) C:\Users\Nawaz-Server\Documents\ml>python myest.py
[C:\vcpkg\buildtrees\mpg123\src\0d8db63f9b-3db975bc05.clean\src\libmpg123\layer3.c:INT123_do_layer3():1801] error: dequantization failed!
{'audio': {'path': 'C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037135.mp3', 'array': array([ 0.00000000e+00, -2.86519935e-22, -2.56504911e-21, ...,
-1.94239747e-02, -2.42924765e-02, -2.99104657e-02]), 'sampling_rate': 22050}, 'reciter': 'Ghamadi', 'transcription': 'الا عجوز ا في الغبرين', 'line': 3923, 'chapter': 37, 'verse': 135, 'text': 'إِلَّا عَجُوزࣰ ا فِي ٱلۡغَٰبِرِينَ'}
Traceback (most recent call last):
File "C:\Users\Nawaz-Server\Documents\ml\myest.py", line 5, in <module>
for data in dataset["train"]:
~~~~~~~^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\arrow_dataset.py", line 2372, in __iter__
formatted_output = format_table(
^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 639, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 403, in __call__
return self.format_row(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 444, in format_row
row = self.python_features_decoder.decode_row(row)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\formatting\formatting.py", line 222, in decode_row
return self.features.decode_example(row) if self.features else row
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\features.py", line 2042, in decode_example
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\features.py", line 1403, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\datasets\features\audio.py", line 184, in decode_example
array, sampling_rate = sf.read(f)
^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 285, in read
with SoundFile(file, 'r', samplerate, channels,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Nawaz-Server\.conda\envs\mypy\Lib\site-packages\soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BufferedReader name='C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037136.mp3'>: Format not recognised.
```
### Expected behavior
Everything should work fine, as loading the problematic audio file directly with soundfile package works fine
**code:**
```
import soundfile as sf
print(sf.read('C:\\Users\\Nawaz-Server\\.cache\\huggingface\\hub\\datasets--fawazahmed0--bug-audio\\snapshots\\fab1398431fed1c0a2a7bff0945465bab8b5daef\\data\\Ghamadi\\037136.mp3'))
```
**output:**
```bash
(mypy) C:\Users\Nawaz-Server\Documents\ml>python myest.py
[C:\vcpkg\buildtrees\mpg123\src\0d8db63f9b-3db975bc05.clean\src\libmpg123\layer3.c:INT123_do_layer3():1801] error: dequantization failed!
(array([ 0.00000000e+00, -8.43723821e-22, -2.45370628e-22, ...,
-7.71464454e-03, -6.90496899e-03, -8.63333419e-03]), 22050)
```
### Environment info
- `datasets` version: 3.0.2
- Platform: Windows-11-10.0.22621-SP0
- Python version: 3.12.7
- `huggingface_hub` version: 0.26.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.10.0
- soundfile: 0.12.1 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7275 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7275/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7275/comments | https://api.github.com/repos/huggingface/datasets/issues/7275/events | https://github.com/huggingface/datasets/issues/7275 | 2,631,713,397 | I_kwDODunzps6c3MJ1 | 7,275 | load_dataset | {
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I am performing two operations I see on a hugging face tutorial (Fine-tune a language model), and I am defining every aspect inside the mapped functions, also some imports of the library because it doesnt identify anything not defined outside that function where the dataset elements are being mapped:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb#scrollTo=iaAJy5Hu3l_B
`- lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
batch_size=batch_size,
num_proc=4,
)
- tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])
def tokenize_function(examples):
model_checkpoint = 'gpt2'
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
return tokenizer(examples["text"])`
### Steps to reproduce the bug
Currently handle all the imports inside the function
### Expected behavior
The code must work es expected in the notebook, but currently this is not happening.
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb#scrollTo=iaAJy5Hu3l_B
### Environment info
print(transformers.__version__)
4.46.1 | null | {
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"PTAL @lhoestq @albertvillanova ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7273). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,730 | 1,731 | 1,731 | CONTRIBUTOR | null | Raise error when `lines = False` and `batch_size < Dataset.num_rows` in `Dataset.to_json()`.
Issue: #7037
Related PRs:
#7039 #7181 | {
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"I seem to have encountered the same problem when loading non streaming datasets. load_from_disk. Causing hundreds of GB of memory, but the dataset actually only has 50GB",
"FYI when streaming parquet data, only one row group per worker is loaded in memory at a time.\r\n\r\nBtw for datasets of embeddings you can surely optimize your RAM by reading the data as torch tensors directly instead of the default python lists\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\ndataset = load_dataset(\"WaveGenAI/dataset\", streaming=True).with_format(\"torch\")\r\n\r\ndataloader = DataLoader(dataset[\"train\"], num_workers=3)\r\n```"
] | 1,730 | 1,731 | null | NONE | null | ### Describe the bug
I try to use a dataset with streaming=True, the issue I have is that the RAM usage becomes higher and higher until it is no longer sustainable.
I understand that huggingface store data in ram during the streaming, and more worker in dataloader there are, more a lot of shard will be stored in ram, but the issue I have is that the ram usage is not constant. So after each new shard loaded, the ram usage will be higher and higher.
### Steps to reproduce the bug
You can run this code and see you ram usage, after each shard of 255 examples, your ram usage will be extended.
```py
from datasets import load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("WaveGenAI/dataset", streaming=True)
dataloader = DataLoader(dataset["train"], num_workers=3)
for i, data in enumerate(dataloader):
print(i, end="\r")
```
### Expected behavior
The Ram usage should be always the same (just 3 shards loaded in the ram).
### Environment info
- `datasets` version: 3.0.1
- Platform: Linux-6.10.5-arch1-1-x86_64-with-glibc2.40
- Python version: 3.12.4
- `huggingface_hub` version: 0.26.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1 | null | {
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"Hello, It's an interesting issue here. I have the same problem, I have a local dataset and I want to push the dataset to the hub but huggingface does a copy of it.\r\n\r\n```py\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"webdataset\", data_files=\"/media/works/data/*.tar\") # copy here\r\ndataset.push_to_hub(\"WaveGenAI/audios2\")\r\n```\r\n\r\nEdit: I can use HfApi for my use case\r\n"
] | 1,730 | 1,730 | null | NONE | null | ### Describe the bug
I have data saved with save_to_disk. The data is big (700Gb). When I try loading it, the only option is load_from_disk, and this function copies the data to a tmp directory, causing me to run out of disk space. Is there an alternative solution to that?
### Steps to reproduce the bug
when trying to load data using load_From_disk after being saved using save_to_disk
### Expected behavior
run out of disk space
### Environment info
lateest version | null | {
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"I encountered the same problem on M1, a workaround I did was to simply comment out the dependency:\r\n\r\n```python\r\n...\r\n \"zstandard\",\r\n \"polars[timezone]>=0.20.0\",\r\n # \"decord==0.6.0\",\r\n]\r\n```\r\n\r\nThis worked for me as the adjustments I did to the code do not use the dependency, but I do not know if the same holds for you.\r\n\r\nI also do not think it is a good idea to rely on a dependency (I mean decord) that has not been maintained for 2 years, but I saw that even eva-decord hasn't been maintained since last year.\r\n\r\nDid you get it to work with eva-decord?"
] | 1,730 | 1,730 | null | NONE | null | ### Describe the bug
Hi,
Decord is a dev dependency not maintained since couple years.
It does not have an ARM package available rendering it uninstallable on non-intel based macs
Suggestion is to move to eva-decord (https://github.com/georgia-tech-db/eva-decord) which doesnt have this problem.
Happy to raise a PR
### Steps to reproduce the bug
Source installation as mentioned in contributinog.md
### Expected behavior
Installation without decord failing to be installed.
### Environment info
python=3.10, M3 Mac | null | {
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"Waiting is all you need. 10 hours later, it works."
] | 1,730 | 1,730 | 1,730 | NONE | null | ### 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
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7265). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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https://api.github.com/repos/huggingface/datasets/issues/7262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7262/comments | https://api.github.com/repos/huggingface/datasets/issues/7262/events | https://github.com/huggingface/datasets/pull/7262 | 2,620,879,059 | PR_kwDODunzps6AOWI8 | 7,262 | Allow video with disabeld decoding without decord | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7262). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,730 | 1,730 | 1,730 | MEMBER | null | for the viewer, this way it can use Video(decode=False) and doesn't need decord (which causes segfaults) | {
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https://api.github.com/repos/huggingface/datasets/issues/7261 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7261/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7261/comments | https://api.github.com/repos/huggingface/datasets/issues/7261/events | https://github.com/huggingface/datasets/issues/7261 | 2,620,510,840 | I_kwDODunzps6cMdJ4 | 7,261 | Cannot load the cache when mapping the dataset | {
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} | [] | open | false | null | [] | null | [] | 1,730 | 1,730 | null | NONE | null | ### Describe the bug
I'm training the flux controlnet. The train_dataset.map() takes long time to finish. However, when I killed one training process and want to restart a new training with the same dataset. I can't reuse the mapped result even I defined the cache dir for the dataset.
with accelerator.main_process_first():
from datasets.fingerprint import Hasher
# fingerprint used by the cache for the other processes to load the result
# details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
new_fingerprint = Hasher.hash(args)
train_dataset = train_dataset.map(
compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=10,
)
### Steps to reproduce the bug
train flux controlnet and start again
### Expected behavior
will not map again
### Environment info
latest diffusers
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https://api.github.com/repos/huggingface/datasets/issues/7260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7260/comments | https://api.github.com/repos/huggingface/datasets/issues/7260/events | https://github.com/huggingface/datasets/issues/7260 | 2,620,014,285 | I_kwDODunzps6cKj7N | 7,260 | cache can't cleaned or disabled | {
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"Hey I have a similar problem and found a workaround using [temporary directories](https://docs.python.org/3/library/tempfile.html):\r\n\r\n```python\r\nfrom tempfile import TemporaryDirectory\r\n\r\nwith TemporaryDirectory() as cache_dir:\r\n data = load_dataset('json', data_files=save_local_path, split='train', cache_dir=cache_dir)\r\n```\r\n\r\nBut I do agree that it would be more intuitive if `datasets` supported this directly. Especially `disable_caching` is confusing, since it basically doesn't disable caching."
] | 1,730 | 1,733 | null | NONE | null | ### Describe the bug
I tried following ways, the cache can't be disabled.
I got 2T data, but I also got more than 2T cache file. I got pressure on storage. I need to diable cache or cleaned immediately after processed. Following ways are all not working, please give some help!
```python
from datasets import disable_caching
from transformers import AutoTokenizer
disable_caching()
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
def tokenization_fn(examples):
column_name = 'text' if 'text' in examples else 'data'
tokenized_inputs = tokenizer(
examples[column_name], return_special_tokens_mask=True, truncation=False,
max_length=tokenizer.model_max_length
)
return tokenized_inputs
data = load_dataset('json', data_files=save_local_path, split='train', cache_dir=None)
data.cleanup_cache_files()
updated_dataset = data.map(tokenization_fn, load_from_cache_file=False)
updated_dataset .cleanup_cache_files()
```
### Expected behavior
no cache file generated
### Environment info
Ubuntu 20.04.6 LTS
datasets 3.0.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7259/comments | https://api.github.com/repos/huggingface/datasets/issues/7259/events | https://github.com/huggingface/datasets/pull/7259 | 2,618,909,241 | PR_kwDODunzps6AIEY- | 7,259 | Don't embed videos | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7259). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,730 | 1,730 | 1,730 | MEMBER | null | don't include video bytes when running download_and_prepare(format="parquet")
this also affects push_to_hub which will just upload the local paths of the videos though | {
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https://api.github.com/repos/huggingface/datasets/issues/7258 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7258/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7258/comments | https://api.github.com/repos/huggingface/datasets/issues/7258/events | https://github.com/huggingface/datasets/pull/7258 | 2,618,758,399 | PR_kwDODunzps6AHlK1 | 7,258 | Always set non-null writer batch size | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7258). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,730 | 1,730 | 1,730 | MEMBER | null | bug introduced in #7230, it was preventing the Viewer limit writes to work | {
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https://api.github.com/repos/huggingface/datasets/issues/7257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7257/comments | https://api.github.com/repos/huggingface/datasets/issues/7257/events | https://github.com/huggingface/datasets/pull/7257 | 2,618,602,173 | PR_kwDODunzps6AHEfy | 7,257 | fix ci for pyarrow 18 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7256). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,730 | 1,730 | 1,730 | MEMBER | null | as reported in https://github.com/huggingface/datasets/issues/6843 | {
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https://api.github.com/repos/huggingface/datasets/issues/7255 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7255/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7255/comments | https://api.github.com/repos/huggingface/datasets/issues/7255/events | https://github.com/huggingface/datasets/pull/7255 | 2,618,540,355 | PR_kwDODunzps6AG25R | 7,255 | fix decord import | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7255). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,730 | 1,730 | 1,730 | MEMBER | null | delay the import until Video() is instantiated + also import duckdb first (otherwise importing duckdb later causes a segfault) | {
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https://api.github.com/repos/huggingface/datasets/issues/7254 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7254/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7254/comments | https://api.github.com/repos/huggingface/datasets/issues/7254/events | https://github.com/huggingface/datasets/issues/7254 | 2,616,174,996 | I_kwDODunzps6b76mU | 7,254 | mismatch for datatypes when providing `Features` with `Array2D` and user specified `dtype` and using with_format("numpy") | {
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"It seems that https://github.com/huggingface/datasets/issues/5517 is exactly the same issue.\r\n\r\nIt was mentioned there that this would be fixed in version 3.x"
] | 1,729 | 1,729 | null | NONE | null | ### Describe the bug
If the user provides a `Features` type value to `datasets.Dataset` with members having `Array2D` with a value for `dtype`, it is not respected during `with_format("numpy")` which should return a `np.array` with `dtype` that the user provided for `Array2D`. It seems for floats, it will be set to `float32` and for ints it will be set to `int64`
### Steps to reproduce the bug
```python
import numpy as np
import datasets
from datasets import Dataset, Features, Array2D
print(f"datasets version: {datasets.__version__}")
data_info = {
"arr_float" : "float64",
"arr_int" : "int32"
}
sample = {key : [np.zeros([4, 5], dtype=dtype)] for key, dtype in data_info.items()}
features = {key : Array2D(shape=(None, 5), dtype=dtype) for key, dtype in data_info.items()}
features = Features(features)
dataset = Dataset.from_dict(sample, features=features)
ds = dataset.with_format("numpy")
for key in features:
print(f"{key} feature dtype: ", ds.features[key].dtype)
print(f"{key} dtype:", ds[key].dtype)
```
Output:
```bash
datasets version: 3.0.2
arr_float feature dtype: float64
arr_float dtype: float32
arr_int feature dtype: int32
arr_int dtype: int64
```
### Expected behavior
It should return a `np.array` with `dtype` that the user provided for the corresponding member in the `Features` type value
### Environment info
- `datasets` version: 3.0.2
- Platform: Linux-6.11.5-arch1-1-x86_64-with-glibc2.40
- Python version: 3.12.7
- `huggingface_hub` version: 0.26.1
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | null | {
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} | [] | open | false | null | [] | null | [] | 1,729 | 1,729 | null | NONE | null | ### 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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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7252). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,729 | 1,729 | 1,729 | MEMBER | null | Will be useful to distribute a dataset across workers (other than pytorch) like spark
I also renamed `.n_shards` -> `.num_shards` for consistency and kept the old name for backward compatibility. And a few changes in internal functions for consistency as well (rank, world_size -> num_shards, index)
Breaking change: the new default for `contiguous` in `Dataset.shard()` is `True`, but imo not a big deal since I couldn't find any usage of `contiguous=False` internally (we always do contiguous=True for map-style datasets since its more optimized) or in the wild | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7251). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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https://api.github.com/repos/huggingface/datasets/issues/7250 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7250/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7250/comments | https://api.github.com/repos/huggingface/datasets/issues/7250/events | https://github.com/huggingface/datasets/pull/7250 | 2,612,041,969 | PR_kwDODunzps5_zDPS | 7,250 | Basic XML support (mostly copy pasted from text) | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7250). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,729 | 1,729 | 1,729 | MEMBER | null | enable the viewer for datasets like https://huggingface.co/datasets/FrancophonIA/e-calm (there will be more and more apparently)
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https://api.github.com/repos/huggingface/datasets/issues/7249 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7249/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7249/comments | https://api.github.com/repos/huggingface/datasets/issues/7249/events | https://github.com/huggingface/datasets/issues/7249 | 2,610,136,636 | I_kwDODunzps6bk4Y8 | 7,249 | How to debugging | {
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} | [] | open | false | null | [] | null | [] | 1,729 | 1,729 | null | NONE | null | ### Describe the bug
I wanted to use my own script to handle the processing, and followed the tutorial documentation by rewriting the MyDatasetConfig and MyDatasetBuilder (which contains the _info,_split_generators and _generate_examples methods) classes. Testing with simple data was able to output the results of the processing, but when I wished to do more complex processing, I found that I was unable to debug (even the simple samples were inaccessible). There are no errors reported, and I am able to print the _info,_split_generators and _generate_examples messages, but I am unable to access the breakpoints.
### Steps to reproduce the bug
# my_dataset.py
import json
import datasets
class MyDatasetConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(MyDatasetConfig, self).__init__(**kwargs)
class MyDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
MyDatasetConfig(
name="default",
version=VERSION,
description="myDATASET"
),
]
def _info(self):
print("info") # breakpoints
return datasets.DatasetInfo(
description="myDATASET",
features=datasets.Features(
{
"id": datasets.Value("int32"),
"text": datasets.Value("string"),
"label": datasets.ClassLabel(names=["negative", "positive"]),
}
),
supervised_keys=("text", "label"),
)
def _split_generators(self, dl_manager):
print("generate") # breakpoints
data_file = "data.json"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file}
),
]
def _generate_examples(self, filepath):
print("example") # breakpoints
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for idx, sample in enumerate(data):
yield idx, {
"id": sample["id"],
"text": sample["text"],
"label": sample["label"],
}
#main.py
import os
os.environ["TRANSFORMERS_NO_MULTIPROCESSING"] = "1"
from datasets import load_dataset
dataset = load_dataset("my_dataset.py", split="train", cache_dir=None)
print(dataset[:5])
### Expected behavior
Pause at breakpoints while running debugging
### Environment info
pycharm
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https://api.github.com/repos/huggingface/datasets/issues/7248 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7248/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7248/comments | https://api.github.com/repos/huggingface/datasets/issues/7248/events | https://github.com/huggingface/datasets/issues/7248 | 2,609,926,089 | I_kwDODunzps6bkE_J | 7,248 | ModuleNotFoundError: No module named 'datasets.tasks' | {
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"tasks was removed in v3: #6999 \r\n\r\nI also don't see why TextClassification is imported, since it's not used after. So the fix is simple: delete this line.",
"I opened https://huggingface.co/datasets/knowledgator/events_classification_biotech/discussions/7 to remove the line, hopefully the dataset owner will merge it soon"
] | 1,729 | 1,729 | null | NONE | null | ### Describe the bug
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
[<ipython-input-9-13b5f31bd391>](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <cell line: 1>()
----> 1 dataset = load_dataset('knowledgator/events_classification_biotech')
11 frames
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2130
2131 # Create a dataset builder
-> 2132 builder_instance = load_dataset_builder(
2133 path=path,
2134 name=name,
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
1886 raise ValueError(error_msg)
1887
-> 1888 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name)
1889 # Instantiate the dataset builder
1890 builder_instance: DatasetBuilder = builder_cls(
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in get_dataset_builder_class(dataset_module, dataset_name)
246 dataset_module.importable_file_path
247 ) if dataset_module.importable_file_path else nullcontext():
--> 248 builder_cls = import_main_class(dataset_module.module_path)
249 if dataset_module.builder_configs_parameters.builder_configs:
250 dataset_name = dataset_name or dataset_module.builder_kwargs.get("dataset_name")
[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_main_class(module_path)
167 def import_main_class(module_path) -> Optional[Type[DatasetBuilder]]:
168 """Import a module at module_path and return its main class: a DatasetBuilder"""
--> 169 module = importlib.import_module(module_path)
170 # Find the main class in our imported module
171 module_main_cls = None
[/usr/lib/python3.10/importlib/__init__.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_module(name, package)
124 break
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
127
128
/usr/lib/python3.10/importlib/_bootstrap.py in _gcd_import(name, package, level)
/usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load(name, import_)
/usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)
/usr/lib/python3.10/importlib/_bootstrap.py in _load_unlocked(spec)
/usr/lib/python3.10/importlib/_bootstrap_external.py in exec_module(self, module)
/usr/lib/python3.10/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
[~/.cache/huggingface/modules/datasets_modules/datasets/knowledgator--events_classification_biotech/9c8086d498c3104de3a3c5b6640837e18ccd829dcaca49f1cdffe3eb5c4a6361/events_classification_biotech.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <module>
1 import datasets
2 from datasets import load_dataset
----> 3 from datasets.tasks import TextClassification
4
5 DESCRIPTION = """
ModuleNotFoundError: No module named 'datasets.tasks'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
### Steps to reproduce the bug
!pip install datasets
from datasets import load_dataset
dataset = load_dataset('knowledgator/events_classification_biotech')
### Expected behavior
no ModuleNotFoundError
### Environment info
google colab | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7247 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7247/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7247/comments | https://api.github.com/repos/huggingface/datasets/issues/7247/events | https://github.com/huggingface/datasets/issues/7247 | 2,606,230,029 | I_kwDODunzps6bV-oN | 7,247 | Adding column with dict struction when mapping lead to wrong order | {
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} | [] | open | false | null | [] | null | [] | 1,729 | 1,729 | null | NONE | null | ### Describe the bug
in `map()` function, I want to add a new column with a dict structure.
```
def map_fn(example):
example['text'] = {'user': ..., 'assistant': ...}
return example
```
However this leads to a wrong order `{'assistant':..., 'user':...}` in the dataset.
Thus I can't concatenate two datasets due to the different feature structures.
[Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example
This seems an issue in low level pyarrow library instead of datasets, however, I think datasets should allow concatenate two datasets actually in the same structure.
### Steps to reproduce the bug
[Here](https://colab.research.google.com/drive/1zeaWq9Ith4DKWP_EiBNyLfc8S8I68LyY?usp=sharing) is a minimal reproducible example
### Expected behavior
two datasets could be concatenated.
### Environment info
N/A | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7246). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,729 | 1,729 | 1,729 | MEMBER | null | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7245). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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https://api.github.com/repos/huggingface/datasets/issues/7244 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7244/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7244/comments | https://api.github.com/repos/huggingface/datasets/issues/7244/events | https://github.com/huggingface/datasets/pull/7244 | 2,605,461,515 | PR_kwDODunzps5_dqWP | 7,244 | use huggingface_hub offline mode | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7244). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,729 | 1,729 | 1,729 | MEMBER | null | and better handling of LocalEntryNotfoundError cc @Wauplin
follow up to #7234 | {
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https://api.github.com/repos/huggingface/datasets/issues/7243 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7243/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7243/comments | https://api.github.com/repos/huggingface/datasets/issues/7243/events | https://github.com/huggingface/datasets/issues/7243 | 2,602,853,172 | I_kwDODunzps6bJGM0 | 7,243 | ArrayXD with None as leading dim incompatible with DatasetCardData | {
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"It looks like `CardData` in `huggingface_hub` removes None values where it shouldn't. Indeed it calls `_remove_none` on the return of `to_dict()`:\r\n\r\n```python\r\n def to_dict(self) -> Dict[str, Any]:\r\n \"\"\"Converts CardData to a dict.\r\n\r\n Returns:\r\n `dict`: CardData represented as a dictionary ready to be dumped to a YAML\r\n block for inclusion in a README.md file.\r\n \"\"\"\r\n\r\n data_dict = copy.deepcopy(self.__dict__)\r\n self._to_dict(data_dict)\r\n return _remove_none(data_dict)\r\n```\r\n\r\nWould it be ok to remove `list()` from being scanned in `_remove_none` ? it could also be a specific behavior to DatasetCardData if necessary @Wauplin ",
"I have actually no idea why none values are removed in model and dataset card data... :see_no_evil:\r\nLooks like `_remove_none` has been introduced at the same time as the entire repocard module (see https://github.com/huggingface/huggingface_hub/pull/940). I would be tempted to remove `_remove_none` entirely actually and only remove \"top-level\" None values (i.e. if something like `pipeline_tag=None` due to a default value in kwargs => we remove it). Hard to tell what could be the side effects but I'm not against trying.\r\n\r\n\r\nHowever, I'm not really in favor in making an exception only for lists. It would mean that tuples, sets and dicts are filtered but not lists, which is pretty inconsistent.",
"let's do it for top level attributes yes",
"I opened https://github.com/huggingface/huggingface_hub/pull/2626 to address it :)",
"thanks !"
] | 1,729 | 1,729 | null | CONTRIBUTOR | null | ### Describe the bug
Creating a dataset with ArrayXD features leads to errors when downloading from hub due to DatasetCardData removing the Nones
@lhoestq
### Steps to reproduce the bug
```python
import numpy as np
from datasets import Array2D, Dataset, Features, load_dataset
def examples_generator():
for i in range(4):
yield {
"array_1d": np.zeros((10,1), dtype="uint16"),
"array_2d": np.zeros((10, 1), dtype="uint16"),
}
features = Features(array_1d=Array2D((None,1), "uint16"), array_2d=Array2D((None, 1), "uint16"))
dataset = Dataset.from_generator(examples_generator, features=features)
dataset.push_to_hub("alex-hh/test_array_1d2d")
ds = load_dataset("alex-hh/test_array_1d2d")
```
Source of error appears to be DatasetCardData.to_dict invoking DatasetCardData._remove_none
```python
from huggingface_hub import DatasetCardData
from datasets.info import DatasetInfosDict
dataset_card_data = DatasetCardData()
DatasetInfosDict({"default": dataset.info.copy()}).to_dataset_card_data(dataset_card_data)
print(dataset_card_data.to_dict()) # removes Nones in shape
```
### Expected behavior
Should be possible to load datasets saved with shape None in leading dimension
### Environment info
3.0.2 and latest huggingface_hub | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7241 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7241/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7241/comments | https://api.github.com/repos/huggingface/datasets/issues/7241/events | https://github.com/huggingface/datasets/issues/7241 | 2,599,899,156 | I_kwDODunzps6a91AU | 7,241 | `push_to_hub` overwrite argument | {
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"Hi ! Do you mean deleting all the files ? or erasing the repository git history before push_to_hub ?",
"Hi! I meant the latter.",
"I don't think there is a `huggingface_hub` utility to erase the git history, cc @Wauplin maybe ?",
"What is the goal exactly of deleting all the git history without deleting the repo? ",
"You can use [`super_squash_commit`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.super_squash_history) to squash all the commits into a single one, hence deleting the git history. This is not exactly what you asked for since it squashes the commits for a specific revision (example: \"all commits on main\"). This means that if other branches exists, they are kept the same. Also if some PRs are already opened on the repo, they will become unmergeable since the commits will have diverted.",
"So the solution is:\r\n\r\n```python\r\nfrom huggingface_hub import HfApi\r\nrepo_id = \"username/dataset_name\"\r\nds.push_to_hub(repo_id)\r\nHfApi().super_squash_commit(repo_id)\r\n```\r\n\r\nThis way you erase previous git history to end up with only 1 commit containing your dataset.\r\nStill, I'd be curious why it's important in your case. Is it to save storage space ? or to disallow loading old versions of the data ?",
"Thanks, everyone! I am building a new dataset and playing around with column names, splits, etc. Sometimes I push to the hub to share it with other teammates, I don't want those variations to be part of the repo. Deleting the repo from the website takes a little time, but it also loses repo settings that I have set, since I always set it to public with manually approved requests.\r\n\r\nBTW, I had to write `HfApi().super_squash_history(repo_id, repo_type=\"dataset\")`, but otherwise it works.",
"@ceferisbarov just to let you know, recreating a gated repo + granting access to your teammates is something that you can automate with something like this (not fully tested but should work):\r\n\r\n```py\r\nfrom huggingface_hub import HfApi\r\n\r\napi = HfApi()\r\napi.delete_repo(repo_id, repo_type=\"dataset\", missing_ok=True)\r\napi.create_repo(repo_id, repo_type=\"dataset\", private=False)\r\napi.update_repo_settings(repo_id, repo_type=\"dataset\", gated=\"manual\")\r\nfor user in [\"user1\", \"user2\"] # list of teammates\r\n api.grant_access(repo_id, user, repo_type=\"dataset\")\r\n```\r\n\r\nI think it'd be a better solution than squashing commits (which is more of a hack), typically if you are using the dataset viewer.",
"This is great, @Wauplin. If we can achieve this with HfApi, then we probably don't need to add another parameter to push_to_hub. I am closing the issue."
] | 1,729 | 1,729 | 1,729 | NONE | null | ### Feature request
Add an `overwrite` argument to the `push_to_hub` method.
### Motivation
I want to overwrite a repo without deleting it on Hugging Face. Is this possible? I couldn't find anything in the documentation or tutorials.
### Your contribution
I can create a PR. | {
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https://api.github.com/repos/huggingface/datasets/issues/7240 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7240/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7240/comments | https://api.github.com/repos/huggingface/datasets/issues/7240/events | https://github.com/huggingface/datasets/pull/7240 | 2,598,980,027 | PR_kwDODunzps5_KxSL | 7,240 | Feature Request: Add functionality to pass split types like train, test in DatasetDict.map | {
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We often encounter situations where we need to preprocess data differently depending on split types such as train, valid, and test.
However, while DatasetDict.map has features to pass rank or index, there's no functionality to pass split types.
Therefore, I propose adding a 'with_splits' parameter to DatasetDict, which would allow passing the split type through fn_kwargs. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7238 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7238/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7238/comments | https://api.github.com/repos/huggingface/datasets/issues/7238/events | https://github.com/huggingface/datasets/issues/7238 | 2,598,409,993 | I_kwDODunzps6a4JcJ | 7,238 | incompatibily issue when using load_dataset with datasets==3.0.1 | {
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"Hi! I'm also getting the same issue - have you been able to find a solution to this? ",
"From what I remember, I stayed at the \"downgraded\" version of dataset (2.21.0)"
] | 1,729 | 1,733 | null | NONE | null | ### Describe the bug
There is a bug when using load_dataset with dataset version at 3.0.1 .
Please see below in the "steps to reproduce the bug".
To resolve the bug, I had to downgrade to version 2.21.0
OS: Ubuntu 24 (AWS instance)
Python: same bug under 3.12 and 3.10
The error I had was:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/load.py", line 2096, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
super()._download_and_prepare(
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/builder.py", line 977, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_0/cb17afd34f5799f97e8f48398748f83006335b702bd785f9880797838d541b81/common_voice_6_0.py", line 159, in _split_generators
archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_manager.py", line 150, in download
download_config = self.download_config.copy()
File "/home/ubuntu/miniconda3/envs/maxence_env/lib/python3.10/site-packages/datasets/download/download_config.py", line 73, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
TypeError: DownloadConfig.__init__() got an unexpected keyword argument 'ignore_url_params'
### Steps to reproduce the bug
1. install dataset with ```pip install datasets --upgrade```
2. launch python; from datasets import loaad_dataset
3. run load_dataset("mozilla-foundation/common_voice_6_0")
4. exit python
5. uninstall datasets; then ```pip install datasets==2.21.0```
6. launch python; from datasets import loaad_dataset
7. run load_dataset("mozilla-foundation/common_voice_6_0")
8. Everything runs great now
### Expected behavior
Be able to download a dataset without error
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 3.0.1
- Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.26.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1 | null | {
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