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I_kwDODunzps6nEbxM

Allow pushing config version to hub

open

NONE

### Feature request Currently, when datasets are created, they can be versioned by passing the `version` argument to `load_dataset(...)`. For example creating `outcomes.csv` on the command line ``` echo "id,value\n1,0\n2,0\n3,1\n4,1\n" > outcomes.csv ``` and creating it ``` import datasets dataset = datasets.load_dataset( "csv", data_files ="outcomes.csv", keep_in_memory = True, version = '1.0.0') ``` The version info is stored in the `info` and can be accessed e.g. by `next(iter(dataset.values())).info.version` This dataset can be uploaded to the hub with `dataset.push_to_hub(repo_id = "maomlab/example_dataset")`. This will create a dataset on the hub with the following in the `README.md`, but it doesn't upload the version information: ``` --- dataset_info: features: - name: id dtype: int64 - name: value dtype: int64 splits: - name: train num_bytes: 64 num_examples: 4 download_size: 1332 dataset_size: 64 configs: - config_name: default data_files: - split: train path: data/train-* --- ``` However, when I download from the hub, the version information is missing: ``` dataset_from_hub_no_version = datasets.load_dataset("maomlab/example_dataset") next(iter(dataset.values())).info.version ``` I can add the version information manually to the hub, by appending it to the end of config section: ``` ... configs: - config_name: default data_files: - split: train path: data/train-* version: 1.0.0 --- ``` And then when I download it, the version information is correct. ### Motivation ### Why adding version information for each config makes sense 1. The version information is already recorded in the dataset config info data structure and is able to parse it correctly, so it makes sense to sync it with `push_to_hub`. 2. Keeping the version info in at the config level is different from version info at the branch level. As the former relates to the version of the specific dataset the config refers to rather than the version of the dataset curation itself. ## A explanation for the current behavior: In [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1 ), the `_INCLUDED_INFO_IN_YAML` variable doesn't include `"version"`. If my reading of the code is right, adding `"version"` to `_INCLUDED_INFO_IN_YAML`, would allow the version information to be uploaded to the hub. ### Your contribution Request: add `"version"` to `_INCLUDE_INFO_IN_YAML` in [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1 )

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I_kwDODunzps6nDinV

Support for sparse arrays with the Arrow Sparse Tensor format?

open

NONE

### Feature request AI in biology is becoming a big thing. One thing that would be a huge benefit to the field that Huggingface Datasets doesn't currently have is native support for **sparse arrays**. Arrow has support for sparse tensors. https://arrow.apache.org/docs/format/Other.html#sparse-tensor It would be a big deal if Hugging Face Datasets supported sparse tensors as a feature type, natively. ### Motivation This is important for example in the field of transcriptomics (modeling and understanding gene expression), because a large fraction of the genes are not expressed (zero). More generally, in science, sparse arrays are very common, so adding support for them would be very benefitial, it would make just using Hugging Face Dataset objects a lot more straightforward and clean. ### Your contribution We can discuss this further once the team comments of what they think about the feature, and if there were previous attempts at making it work, and understanding their evaluation of how hard it would be. My intuition is that it should be fairly straightforward, as the Arrow backend already supports it.

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PR_kwDODunzps6IiO9j

[docs] uv install

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MEMBER

Proposes adding uv to installation docs (see Slack thread [here](https://huggingface.slack.com/archives/C01N44FJDHT/p1737377177709279) for more context) if you're interested!

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I_kwDODunzps6m7efC

vllm批量推理报错

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NONE

### Describe the bug  ### Steps to reproduce the bug  ### Expected behavior  ### Environment info 

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PR_kwDODunzps6IC66n

Remove .h5 from imagefolder extensions

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MEMBER

the format is not relevant for imagefolder, and makes the viewer fail to process datasets on HF (so many that the viewer takes more time to process new datasets)

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I_kwDODunzps6mfWqT

Excessive RAM Usage After Dataset Concatenation concatenate_datasets

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NONE

### Describe the bug When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption. https://github.com/huggingface/datasets/issues/2276 ### Steps to reproduce the bug ``` rom datasets import DatasetDict, concatenate_datasets dataset = DatasetDict.load_from_disk("data") ... ... combined_dataset = concatenate_datasets( [dataset[split] for split in dataset] ) #start SentenceTransformer training ``` ### Expected behavior I would not expect RAM utilization to increase after concatenation. Removing the concatenation step resolves the issue ### Environment info sentence-transformers==3.1.1 datasets==3.2.0 python3.10

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I_kwDODunzps6mZuRI

Inconsistent Behavior Between `load_dataset` and `load_from_disk` When Loading Sharded Datasets

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NONE

### Description I encountered an inconsistency in behavior between `load_dataset` and `load_from_disk` when loading sharded datasets. Here is a minimal example to reproduce the issue: #### Code 1: Using `load_dataset` ```python from datasets import Dataset, load_dataset # First save with max_shard_size=10 Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10) # Second save with max_shard_size=10 Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10) # Load the DatasetDict loaded_datasetdict = load_dataset("my_sharded_datasetdict") print(loaded_datasetdict) ``` **Output**: - `train` has 1350 samples. - `test` has 150 samples. #### Code 2: Using `load_from_disk` ```python from datasets import Dataset, load_from_disk # First save with max_shard_size=10 Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10) # Second save with max_shard_size=10 Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10) # Load the DatasetDict loaded_datasetdict = load_from_disk("my_sharded_datasetdict") print(loaded_datasetdict) ``` **Output**: - `train` has 450 samples. - `test` has 50 samples. ### Expected Behavior I expected both `load_dataset` and `load_from_disk` to load the same dataset, as they are pointing to the same directory. However, the results differ significantly: - `load_dataset` seems to merge all shards, resulting in a combined dataset. - `load_from_disk` only loads the last saved dataset, ignoring previous shards. ### Questions 1. Is this behavior intentional? If so, could you clarify the difference between `load_dataset` and `load_from_disk` in the documentation? 2. If this is not intentional, could this be considered a bug? 3. What is the recommended way to handle cases where multiple datasets are saved to the same directory? Thank you for your time and effort in maintaining this great library! I look forward to your feedback.

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I_kwDODunzps6mVGmB

500 Server error with pushing a dataset

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NONE

### Describe the bug Suddenly, I started getting this error message saying it was an internal error. `Error creating/pushing dataset: 500 Server Error: Internal Server Error for url: https://huggingface.co./api/datasets/ll4ma-lab/grasp-dataset/commit/main (Request ID: Root=1-6787f0b7-66d5bd45413e481c4c2fb22d;670d04ff-65f5-4741-a353-2eacc47a3928) Internal Error - We're working hard to fix this as soon as possible! Traceback (most recent call last): File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status response.raise_for_status() File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/requests/models.py", line 1024, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co./api/datasets/ll4ma-lab/grasp-dataset/commit/main The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/uufs/chpc.utah.edu/common/home/u1295595/grasp_dataset_converter/src/grasp_dataset_converter/main.py", line 142, in main subset_train.push_to_hub(dataset_name, split='train') File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5624, in push_to_hub commit_info = api.create_commit( File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 1518, in _inner return fn(self, *args, **kwargs) File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 4087, in create_commit hf_raise_for_status(commit_resp, endpoint_name="commit") File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/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: 500 Server Error: Internal Server Error for url: https://huggingface.co./api/datasets/ll4ma-lab/grasp-dataset/commit/main (Request ID: Root=1-6787f0b7-66d5bd45413e481c4c2fb22d;670d04ff-65f5-4741-a353-2eacc47a3928) Internal Error - We're working hard to fix this as soon as possible!` ### Steps to reproduce the bug I am pushing a Dataset in a loop via push_to_hub API ### Expected behavior It worked fine until it stopped working suddenly. Expected behavior: It should start working again ### Environment info - `datasets` version: 3.2.0 - Platform: Linux-4.18.0-477.15.1.el8_8.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.0 - `huggingface_hub` version: 0.27.1 - PyArrow version: 18.1.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.9.0

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PR_kwDODunzps6HwAu7

Support faster processing using pandas or polars functions in `IterableDataset.map()`

open

MEMBER

Allow super fast processing using pandas or polars functions in `IterableDataset.map()` by adding support to pandas and polars formatting in `IterableDataset` ```python import polars as pl from datasets import Dataset ds = Dataset.from_dict({"i": range(10)}).to_iterable_dataset() ds = ds.with_format("polars") ds = ds.map(lambda df: df.with_columns(pl.col("i"), pl.col("i").add(1).alias("i+1")), batched=True) ds = ds.with_format(None) print(next(iter(ds))) # {'i': 0, 'i+1': 1} ``` It leverages arrow's zero-copy features from/to pandas and polars. related to https://github.com/huggingface/datasets/issues/3444 #6762

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I_kwDODunzps6mITGW

Importing dataset gives unhelpful error message when filenames in metadata.csv are not found in the directory

open

NONE

### Describe the bug While importing an audiofolder dataset, where the names of the audiofiles don't correspond to the filenames in the metadata.csv, we get an unclear error message that is not helpful for the debugging, i.e. ``` ValueError: Instruction "train" corresponds to no data! ``` ### Steps to reproduce the bug Assume an audiofolder with audiofiles, filename1.mp3, filename2.mp3 etc and a file metadata.csv which contains the columns file_name and sentence. The file_names are formatted like filename1.mp3, filename2.mp3 etc. Load the audio ``` from datasets import load_dataset load_dataset("audiofolder", data_dir='/path/to/audiofolder') ``` When the file_names in the csv are not in sync with the filenames in the audiofolder, then we get an Error message: ``` File /opt/conda/lib/python3.12/site-packages/datasets/arrow_reader.py:251, in BaseReader.read(self, name, instructions, split_infos, in_memory) 249 if not files: 250 msg = f'Instruction "{instructions}" corresponds to no data!' --> 251 raise ValueError(msg) 252 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ValueError: Instruction "train" corresponds to no data! ``` load_dataset has a default value for the argument split = 'train'. ### Expected behavior It would be better to get an error report something like: ``` The metadata.csv file has different filenames than the files in the datadirectory. ``` It would have saved me 4 hours of debugging. ### Environment info - `datasets` version: 3.2.0 - Platform: Linux-5.14.0-427.40.1.el9_4.x86_64-x86_64-with-glibc2.39 - Python version: 3.12.8 - `huggingface_hub` version: 0.27.0 - PyArrow version: 18.1.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.9.0

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PR_kwDODunzps6HjE97

Add with_split to DatasetDict.map

open

NONE

#7356

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I_kwDODunzps6lyqvO

Dataset.from_dict() can't handle large dict

open

NONE

### Describe the bug I have 26,000,000 3-tuples. When I use Dataset.from_dict() to load, neither. py nor Jupiter notebook can run successfully. This is my code: ``` # len(example_data) is 26,000,000, 'diff' is a text diff1_list = [example_data[i].texts[0] for i in range(len(example_data))] diff2_list = [example_data[i].texts[1] for i in range(len(example_data))] label_list = [example_data[i].label for i in range(len(example_data))] embedding_dataset = Dataset.from_dict({ "diff1": diff1_list, "diff2": diff2_list, "label": label_list }) ``` ### Steps to reproduce the bug 1. Initialize a large 3-tuple, e.g. 26,000,000 2. Use Dataset.from_dict() to load ### Expected behavior Dataset.from_dict() run successfully ### Environment info sentence-transformers 3.3.1

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I_kwDODunzps6ltruH

A parameter is specified but not used in datasets.arrow_dataset.Dataset.from_pandas()

open

NONE

### Describe the bug I am interested in creating train, test and eval splits from a pandas Dataframe, therefore I was looking at the possibilities I can follow. I noticed the split parameter and was hopeful to use it in order to generate the 3 at once, however, while trying to understand the code, i noticed that it has no added value (correct me if I am wrong or misunderstood the code). from_pandas function code : ```python if info is not None and features is not None and info.features != features: raise ValueError( f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}" ) features = features if features is not None else info.features if info is not None else None if info is None: info = DatasetInfo() info.features = features table = InMemoryTable.from_pandas( df=df, preserve_index=preserve_index, ) if features is not None: # more expensive cast than InMemoryTable.from_pandas(..., schema=features.arrow_schema) # needed to support the str to Audio conversion for instance table = table.cast(features.arrow_schema) return cls(table, info=info, split=split) ``` ### Steps to reproduce the bug ```python from datasets import Dataset # Filling the split parameter with whatever causes no harm at all data = Dataset.from_pandas(self.raw_data, split='egiojegoierjgoiejgrefiergiuorenvuirgurthgi') ``` ### Expected behavior Would be great if there is no split parameter (if it isn't working), or to add a concrete example of how it can be used. ### Environment info - `datasets` version: 3.2.0 - Platform: Linux-5.15.0-127-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.27.1 - PyArrow version: 18.1.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.9.0

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I_kwDODunzps6lhJP0

API endpoints for gated dataset access requests

closed

NONE

### Feature request I would like a programatic way of requesting access to gated datasets. The current solution to gain access forces me to visit a website and physically click an "agreement" button (as per the [documentation](https://huggingface.co./docs/hub/en/datasets-gated#access-gated-datasets-as-a-user)). An ideal approach would be HF API download methods that negotiate access on my behalf based on information from my CLI login and/or token. I realise that may be naive given the various types of access semantics available to dataset authors (automatic versus manual approval, for example) and complexities it might add to existing methods, but something along those lines would be nice. Perhaps using the `*_access_request` methods available to dataset authors can be a precedent; see [`reject_access_request`](https://huggingface.co./docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.reject_access_request) for example. ### Motivation When trying to download files from a gated dataset, I'm met with a `GatedRepoError` and instructed to visit the repository's website to gain access: ``` Cannot access gated repo for url https://huggingface.co./datasets/open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details/resolve/main/meta-llama__Meta-Llama-3.1-70B-Instruct/samples_leaderboard_math_precalculus_hard_2024-07-19T18-47-29.522341.jsonl. Access to dataset open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details is restricted and you are not in the authorized list. Visit https://huggingface.co./datasets/open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details to ask for access. ``` This makes task automation extremely difficult. For example, I'm interested in studying sample-level responses of models on the LLM leaderboard -- how they answered particular questions on a given evaluation framework. As I come across more and more participants that gate their data, it's becoming unwieldy to continue my work (there over 2,000 participants, so in the worst case that's the number of website visits I'd need to manually undertake). One approach is use Selenium to react to the `GatedRepoError`, but that seems like overkill; and a potential violation HF terms of service (?). As mentioned in the previous section, there seems to be an [API for gated dataset owners](https://huggingface.co./docs/hub/en/datasets-gated#via-the-api) to managed access requests, and thus some appetite for allowing automated management of gating. This feature request is to extend that to dataset users. ### Your contribution Whether I can help depends on a few things; one being the complexity of the underlying gated access design. If this feature request is accepted I am open to being involved in discussions and testing, and even development under the right time-outcome tradeoff.

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I_kwDODunzps6lWUEc

ImportError: To support decoding images, please install 'Pillow'.

open

NONE

### Describe the bug Following this tutorial locally using a macboko and VSCode: https://huggingface.co./docs/diffusers/en/tutorials/basic_training This line of code: for i, image in enumerate(dataset[:4]["image"]): throws: ImportError: To support decoding images, please install 'Pillow'. Pillow is installed. ### Steps to reproduce the bug Run the tutorial ### Expected behavior Images should be rendered ### Environment info MacBook, VSCode

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I_kwDODunzps6lU8n1

HuggingFace CLI dataset download raises error

closed

NONE

### Describe the bug Trying to download Hugging Face datasets using Hugging Face CLI raises error. This error only started after December 27th, 2024. For example: ``` huggingface-cli download --repo-type dataset gboleda/wikicorpus Traceback (most recent call last): File "/home/ubuntu/test_venv/bin/huggingface-cli", line 8, in <module> sys.exit(main()) File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/huggingface_cli.py", line 51, in main service.run() File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/download.py", line 146, in run print(self._download()) # Print path to downloaded files File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/download.py", line 180, in _download return snapshot_download( File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/_snapshot_download.py", line 164, in snapshot_download repo_info = api.repo_info(repo_id=repo_id, repo_type=repo_type, revision=revision, token=token) File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2491, in repo_info return method( File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2366, in dataset_info return DatasetInfo(**data) File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 799, in __init__ self.tags = kwargs.pop("tags") KeyError: 'tags' ``` ### Steps to reproduce the bug ``` 1. huggingface-cli download --repo-type dataset gboleda/wikicorpus ``` ### Expected behavior There should be no error. ### Environment info - `datasets` version: 2.19.1 - Platform: Linux-6.8.0-1015-aws-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.23.5 - PyArrow version: 18.1.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.3.1

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PR_kwDODunzps6G4t2p

Fix lock permission

open

NONE

All files except lock file have proper permission obeying `ACL` property if it is set. If the cache directory has `ACL` property, it should be respected instead of just using `umask` for permission. To fix it, just create a lock file and pass the created `mode`. By creating a lock file with `touch()` before `FileLock` create it with `mode`, - if `ACL` is not set, same as before - if `ACL` is set, `ACL` is respected If it is acceptable, it should be also applied to [`huggingface_hub`](https://github.com/huggingface/huggingface_hub/blob/2702ec2a2bd0124cc1fddfd72ccb1297b2478148/src/huggingface_hub/utils/_fixes.py#L95) I guess.

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I_kwDODunzps6lNZHu

error when loading dataset in Hugging Face: NoneType error is not callable

open

NONE

### Describe the bug I met an error when running a notebook provide by Hugging Face, and met the error. ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[2], line 5 3 # Load the enhancers dataset from the InstaDeep Hugging Face ressources 4 dataset_name = "enhancers_types" ----> 5 train_dataset_enhancers = load_dataset( 6 "InstaDeepAI/nucleotide_transformer_downstream_tasks_revised", 7 dataset_name, 8 split="train", 9 streaming= False, 10 ) 11 test_dataset_enhancers = load_dataset( 12 "InstaDeepAI/nucleotide_transformer_downstream_tasks_revised", 13 dataset_name, 14 split="test", 15 streaming= False, 16 ) File /public/home/hhl/miniconda3/envs/transformer/lib/python3.9/site-packages/datasets/load.py:2129, 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) 2124 verification_mode = VerificationMode( 2125 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS 2126 ) 2128 # Create a dataset builder -> 2129 builder_instance = load_dataset_builder( 2130 path=path, 2131 name=name, 2132 data_dir=data_dir, 2133 data_files=data_files, 2134 cache_dir=cache_dir, 2135 features=features, 2136 download_config=download_config, 2137 download_mode=download_mode, 2138 revision=revision, 2139 token=token, 2140 storage_options=storage_options, 2141 trust_remote_code=trust_remote_code, 2142 _require_default_config_name=name is None, 2143 **config_kwargs, 2144 ) 2146 # Return iterable dataset in case of streaming 2147 if streaming: File /public/home/hhl/miniconda3/envs/transformer/lib/python3.9/site-packages/datasets/load.py:1886, 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) 1884 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name) 1885 # Instantiate the dataset builder -> 1886 builder_instance: DatasetBuilder = builder_cls( 1887 cache_dir=cache_dir, 1888 dataset_name=dataset_name, 1889 config_name=config_name, 1890 data_dir=data_dir, 1891 data_files=data_files, 1892 hash=dataset_module.hash, 1893 info=info, 1894 features=features, 1895 token=token, 1896 storage_options=storage_options, 1897 **builder_kwargs, 1898 **config_kwargs, 1899 ) 1900 builder_instance._use_legacy_cache_dir_if_possible(dataset_module) 1902 return builder_instance TypeError: 'NoneType' object is not callable ``` I have checked my internet, it worked well. And the dataset name was just copied from the Hugging Face. Totally no idea what is wrong! ### Steps to reproduce the bug To reproduce the bug you may run ``` from datasets import load_dataset, Dataset # Load the enhancers dataset from the InstaDeep Hugging Face ressources dataset_name = "enhancers_types" train_dataset_enhancers = load_dataset( "InstaDeepAI/nucleotide_transformer_downstream_tasks_revised", dataset_name, split="train", streaming= False, ) test_dataset_enhancers = load_dataset( "InstaDeepAI/nucleotide_transformer_downstream_tasks_revised", dataset_name, split="test", streaming= False, ) ``` ### Expected behavior 1. what may be the reasons of the error 2. how can I fine which reason lead to the error 3. how can I save the problem ### Environment info ``` - `datasets` version: 3.2.0 - Platform: Linux-5.15.0-117-generic-x86_64-with-glibc2.31 - Python version: 3.9.21 - `huggingface_hub` version: 0.27.0 - PyArrow version: 18.1.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.9.0 ```

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I_kwDODunzps6lLDUy

There are multiple 'mteb/arguana' configurations in the cache: default, corpus, queries with HF_HUB_OFFLINE=1

open

NONE

### Describe the bug Hey folks, I am trying to run this code - ```python from datasets import load_dataset, get_dataset_config_names ds = load_dataset("mteb/arguana") ``` with HF_HUB_OFFLINE=1 But I get the following error - ```python Using the latest cached version of the dataset since mteb/arguana couldn't be found on the Hugging Face Hub (offline mode is enabled). --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[2], line 1 ----> 1 ds = load_dataset("mteb/arguana") File ~/env/lib/python3.10/site-packages/datasets/load.py:2129, 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) 2124 verification_mode = VerificationMode( 2125 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS 2126 ) 2128 # Create a dataset builder -> 2129 builder_instance = load_dataset_builder( 2130 path=path, 2131 name=name, 2132 data_dir=data_dir, 2133 data_files=data_files, 2134 cache_dir=cache_dir, 2135 features=features, 2136 download_config=download_config, 2137 download_mode=download_mode, 2138 revision=revision, 2139 token=token, 2140 storage_options=storage_options, 2141 trust_remote_code=trust_remote_code, 2142 _require_default_config_name=name is None, 2143 **config_kwargs, 2144 ) 2146 # Return iterable dataset in case of streaming 2147 if streaming: File ~/env/lib/python3.10/site-packages/datasets/load.py:1886, 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) 1884 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name) 1885 # Instantiate the dataset builder -> 1886 builder_instance: DatasetBuilder = builder_cls( 1887 cache_dir=cache_dir, 1888 dataset_name=dataset_name, 1889 config_name=config_name, 1890 data_dir=data_dir, 1891 data_files=data_files, 1892 hash=dataset_module.hash, 1893 info=info, 1894 features=features, 1895 token=token, 1896 storage_options=storage_options, 1897 **builder_kwargs, 1898 **config_kwargs, 1899 ) 1900 builder_instance._use_legacy_cache_dir_if_possible(dataset_module) 1902 return builder_instance File ~/env/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py:124, in Cache.__init__(self, cache_dir, dataset_name, config_name, version, hash, base_path, info, features, token, repo_id, data_files, data_dir, storage_options, writer_batch_size, **config_kwargs) 122 config_kwargs["data_dir"] = data_dir 123 if hash == "auto" and version == "auto": --> 124 config_name, version, hash = _find_hash_in_cache( 125 dataset_name=repo_id or dataset_name, 126 config_name=config_name, 127 cache_dir=cache_dir, 128 config_kwargs=config_kwargs, 129 custom_features=features, 130 ) 131 elif hash == "auto" or version == "auto": 132 raise NotImplementedError("Pass both hash='auto' and version='auto' instead") File ~/env/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py:84, in _find_hash_in_cache(dataset_name, config_name, cache_dir, config_kwargs, custom_features) 72 other_configs = [ 73 Path(_cached_directory_path).parts[-3] 74 for _cached_directory_path in glob.glob(os.path.join(cached_datasets_directory_path_root, "*", version, hash)) (...) 81 ) 82 ] 83 if not config_id and len(other_configs) > 1: ---> 84 raise ValueError( 85 f"There are multiple '{dataset_name}' configurations in the cache: {', '.join(other_configs)}" 86 f"\nPlease specify which configuration to reload from the cache, e.g." 87 f"\n\tload_dataset('{dataset_name}', '{other_configs[0]}')" 88 ) 89 config_name = cached_directory_path.parts[-3] 90 warning_msg = ( 91 f"Found the latest cached dataset configuration '{config_name}' at {cached_directory_path} " 92 f"(last modified on {time.ctime(_get_modification_time(cached_directory_path))})." 93 ) ValueError: There are multiple 'mteb/arguana' configurations in the cache: queries, corpus, default Please specify which configuration to reload from the cache, e.g. load_dataset('mteb/arguana', 'queries') ``` It works when I run the same code with HF_HUB_OFFLINE=0, but after the data is downloaded, I turn off the HF hub cache with HF_HUB_OFFLINE=1, and then this error appears. Are there some files I am missing with hub disabled? ### Steps to reproduce the bug from datasets import load_dataset, get_dataset_config_names ds = load_dataset("mteb/arguana") with HF_HUB_OFFLINE=1 (after already running it with HF_HUB_OFFLINE=0 and populating the datasets cache) ### Expected behavior Dataset loaded successfully as it does with HF_HUB_OFFLINE=1 ### Environment info - `datasets` version: 3.2.0 - Platform: Linux-5.15.148.2-2.cm2-x86_64-with-glibc2.35 - Python version: 3.10.14 - `huggingface_hub` version: 0.27.0 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1

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PR_kwDODunzps6G1kka

Fix remove_columns in the formatted case

open

MEMBER

`remove_columns` had no effect when running a function in `.map()` on dataset that is formatted This aligns the logic of `map()` with the non formatted case and also with with https://github.com/huggingface/datasets/pull/7353

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I_kwDODunzps6lIc4_

Python process aborded with GIL issue when using image dataset

open

NONE

### Describe the bug The issue is visible only with the latest `datasets==3.2.0`. When using image dataset the Python process gets aborted right before the exit with the following error: ``` Fatal Python error: PyGILState_Release: thread state 0x7fa1f409ade0 must be current when releasing Python runtime state: finalizing (tstate=0x0000000000ad2958) Thread 0x00007fa33d157740 (most recent call first): <no Python frame> Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._boun ded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.ts libs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.t slibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._l ibs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pan das._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, charset_normalizer.md, requests.pa ckages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, markupsafe._speedups, PIL._imaging, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards , torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, sentencepiece._sentencepiece, sklearn.__check_build._check_build, psutil._psut il_linux, psutil._psutil_posix, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.l inalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_up date, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flo w, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial ._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, s cipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, sc ipy.optimize._zeros, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.l inalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integr ate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._bspl, scipy.interpolate._ppoly, scipy.interpolate.interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._r gi_cython, scipy.special.cython_special, scipy.stats._stats, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy._lib._uarray._uarray, scipy.stats._ansari_swilk_statis tics, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.stats._unuran.unuran_wrapper, scipy.ndimage._nd_image, _ni_label, scipy.ndimage._ni_label, sklearn.utils._isf inite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.p reprocessing._target_encoder_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._bas e, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distanc es_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, s klearn.metrics._pairwise_fast, PIL._imagingft, google._upb._message, h5py._errors, h5py.defs, h5py._objects, h5py.h5, h5py.utils, h5py.h5t, h5py.h5s, h5py.h5ac, h5py.h5p, h5py.h5r, h5py._proxy, h5py._conv, h5py.h5z, h5py.h5a, h5py.h5d, h5py.h5ds, h5py.h5g, h5py.h5i, h5py.h5o, h5py.h5f, h5py.h5fd, h5py.h5pl, h5py.h5l, h5py._selector, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs , pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, propcache._helpers_c, yarl._quoting_c, aiohttp._helpers, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket, frozenlist._frozenlist, xxhash ._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, regex._regex, scipy .io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, PIL._imagingmath, PIL._webp (total: 236) Aborted (core dumped) ```an ### Steps to reproduce the bug Install `datasets==3.2.0` Run the following script: ```python import datasets DATASET_NAME = "phiyodr/InpaintCOCO" NUM_SAMPLES = 10 def preprocess_fn(example): return { "prompts": example["inpaint_caption"], "images": example["coco_image"], "masks": example["mask"], } default_dataset = datasets.load_dataset( DATASET_NAME, split="test", streaming=True ).filter(lambda example: example["inpaint_caption"] != "").take(NUM_SAMPLES) test_data = default_dataset.map( lambda x: preprocess_fn(x), remove_columns=default_dataset.column_names ) for data in test_data: print(data["prompts"]) `` ### Expected behavior The script should not hang or crash. ### Environment info - `datasets` version: 3.2.0 - Platform: Linux-5.15.0-50-generic-x86_64-with-glibc2.31 - Python version: 3.11.0 - `huggingface_hub` version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.2.0

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I_kwDODunzps6lHEv_

How about adding a feature to pass the key when performing map on DatasetDict?

open

NONE

### Feature request Add a feature to pass the key of the DatasetDict when performing map ### Motivation I often preprocess using map on DatasetDict. Sometimes, I need to preprocess train and valid data differently depending on the task. So, I thought it would be nice to pass the key (like train, valid) when performing map on DatasetDict. What do you think? ### Your contribution I can submit a pull request to add the feature to pass the key of the DatasetDict when performing map.

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I_kwDODunzps6lCvMD

Not available datasets[audio] on python 3.13

open

NONE

### Describe the bug This is the error I got, it seems numba package does not support python 3.13 PS C:\Users\sergi\Documents> pip install datasets[audio] Defaulting to user installation because normal site-packages is not writeable Collecting datasets[audio] Using cached datasets-3.2.0-py3-none-any.whl.metadata (20 kB) ... (OTHER PACKAGES) Collecting numba>=0.51.0 (from librosa->datasets[audio]) Downloading numba-0.60.0.tar.gz (2.7 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.7/2.7 MB 44.1 MB/s eta 0:00:00 Installing build dependencies ... done Getting requirements to build wheel ... error error: subprocess-exited-with-error × Getting requirements to build wheel did not run successfully. │ exit code: 1 ╰─> [24 lines of output] Traceback (most recent call last): File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module> main() ~~~~^^ File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main json_out['return_val'] = hook(**hook_input['kwargs']) ~~~~^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel return hook(config_settings) File "C:\Users\sergi\AppData\Local\Temp\pip-build-env-yauns_qh\overlay\Lib\site-packages\setuptools\build_meta.py", line 334, in get_requires_for_build_wheel return self._get_build_requires(config_settings, requirements=[]) ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\sergi\AppData\Local\Temp\pip-build-env-yauns_qh\overlay\Lib\site-packages\setuptools\build_meta.py", line 304, in _get_build_requires self.run_setup() ~~~~~~~~~~~~~~^^ RuntimeError: Cannot install on Python version 3.13.1; only versions >=3.9,<3.13 are supported. [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: subprocess-exited-with-error × Getting requirements to build wheel did not run successfully. │ exit code: 1 ╰─> See above for output. ### Steps to reproduce the bug 1. install python >=3.13 2. !pip install datasets[audio] ### Expected behavior I needed datasets[audio] in the python 3.13 ### Environment info python 3.13.1

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I_kwDODunzps6lCuoN

A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

closed

NONE

### Describe the bug Following this tutorial: https://huggingface.co./docs/diffusers/en/tutorials/basic_training and running it locally using VSCode on my MacBook. The first line in the tutorial fails: from datasets import load_dataset dataset = load_dataset('huggan/smithsonian_butterflies_subset', split="train"). with this error: A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. and ImportError: numpy.core.multiarray failed to import. Does from datasets import load_dataset really use NumPy 1.x? ### Steps to reproduce the bug Open VSCode. create a new venv. Create a new ipynb file. Import pip install diffusers[training] try to run this line of code: from datasets import load_dataset ### Expected behavior data is loaded ### Environment info ran this: datasets-cli env and got A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2.

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PR_kwDODunzps6Gtd6K

changes to MappedExamplesIterable to resolve #7345

closed

CONTRIBUTOR

modified `MappedExamplesIterable` and `test_iterable_dataset.py::test_mapped_examples_iterable_with_indices` fix #7345 @lhoestq

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PR_kwDODunzps6GrBB5

fsspec 2024.12.0

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MEMBER

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PR_kwDODunzps6Gq6Bf

Bump hfh to 0.24 to fix ci

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PR_kwDODunzps6GqseO

Webdataset special columns in last position

closed

MEMBER

Place columns "__key__" and "__url__" in last position in the Dataset Viewer since they are not the main content before: <img width="1012" alt="image" src="https://github.com/user-attachments/assets/b556c1fe-2674-4ba0-9643-c074aa9716fd" />

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PR_kwDODunzps6Gldcy

Catch OSError for arrow

closed

MEMBER

fixes https://github.com/huggingface/datasets/issues/7346 (also updated `ruff` and appleid style changes)

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I_kwDODunzps6khpDj

Converting Arrow to WebDataset TAR Format for Offline Use

closed

NONE

### 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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I_kwDODunzps6kbzd2

OSError: Invalid flatbuffers message.

closed

NONE

### 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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I_kwDODunzps6kbK1t

Different behaviour of IterableDataset.map vs Dataset.map with remove_columns

closed

CONTRIBUTOR

### 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

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I_kwDODunzps6kMe9P

HfHubHTTPError: 429 Client Error: Too Many Requests for URL when trying to access SlimPajama-627B or c4 on TPUs

closed

NONE

### 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

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I_kwDODunzps6j8bF_

[Bug] Inconsistent behavior of data_files and data_dir in load_dataset method.

closed

NONE

### 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

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PR_kwDODunzps6FvgcK

Update LICENSE

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NONE

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PR_kwDODunzps6FiGlt

minor video docs on how to install

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PR_kwDODunzps6FhdR2

don't import soundfile in tests

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PR_kwDODunzps6FhaTl

Update CONTRIBUTING.md

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I_kwDODunzps6jm4IB

One or several metadata.jsonl were found, but not in the same directory or in a parent directory of

open

NONE

### 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

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I_kwDODunzps6jmYHY

Clarify documentation or Create DatasetCard

open

NONE

### 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

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I_kwDODunzps6jhYfM

Too many open files: '/root/.cache/huggingface/token'

open

NONE

### 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.

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I_kwDODunzps6jVSYH

TypeError: Value.__init__() missing 1 required positional argument: 'dtype'

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### 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

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PR_kwDODunzps6FKK13

Fix typo in arrow_dataset

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I_kwDODunzps6jOmvd

.map() is not caching and ram goes OOM

open

NONE

### 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

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I_kwDODunzps6jNXJm

Remove upper bound for fsspec

open

NONE

### 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

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PR_kwDODunzps6FDpMp

Introduce pdf support (#7318)

open

NONE

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

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I_kwDODunzps6jFC36

Unexpected cache behaviour using load_dataset

open

NONE

### 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

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I_kwDODunzps6i2uaU

ArrowInvalid: JSON parse error: Column() changed from object to array in row 0

open

NONE

### 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 ```

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I_kwDODunzps6i0VEI

ImportError: cannot import name 'set_caching_enabled' from 'datasets'

open

NONE

### 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

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I_kwDODunzps6iyXak

ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['label']

closed

NONE

### 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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PR_kwDODunzps6EvHBp

set dev version

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I_kwDODunzps6iwtA2

Introduce support for PDFs

open

NONE

### 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 :)

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PR_kwDODunzps6EvC5Q

Release: 3.2.0

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PR_kwDODunzps6Etc0U

More docs to from_dict to mention that the result lives in RAM

closed

MEMBER

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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PR_kwDODunzps6EkCi5

Resolved for empty datafiles

open

NONE

Resolved for Issue#6152

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I_kwDODunzps6ifyF6

Cannot create a dataset with relative audio path

open

NONE

### 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

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PR_kwDODunzps6EbwNN

[Audio Features - DO NOT MERGE] PoC for adding an offset+sliced reading to audio file.

open

NONE

This is a proof of concept for #7310 . The idea is to enable the access to others column of the dataset row when loading an audio file into a table. This is to allow sliced reading. As stated in the issue, many people have very long audio files and use start and stop slicing in this audio file. 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.

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I_kwDODunzps6ibD2G

How to get the original dataset name with username?

open

CONTRIBUTOR

### 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.

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I_kwDODunzps6iaZ2L

Enable the Audio Feature to decode / read with an offset + duration

open

NONE

### 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.

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I_kwDODunzps6itILT

Allow manual configuration of Dataset Viewer for datasets not created with the `datasets` library

open

NONE

#### **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.

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PR_kwDODunzps6EW77b

Faster parquet streaming + filters with predicate pushdown

closed

MEMBER

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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PR_kwDODunzps6ELKcR

refactor: remove unnecessary else

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I_kwDODunzps6iHPfo

Creating new dataset from list loses information. (Audio Information Lost - either Datatype or Values).

open

NONE

### 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

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I_kwDODunzps6h4XTD

Build Documentation Test Fails Due to "Bad Credentials" Error

open

CONTRIBUTOR

### 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 ```

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https://github.com/huggingface/datasets/pull/7304

PR_kwDODunzps6D5saw

Update iterable_dataset.py

closed

MEMBER

close https://github.com/huggingface/datasets/issues/7297

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