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Jhonatan321

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Datasets_Jhona

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I_kwDODunzps6S9P5G

IterableDataset: cannot resolve features from list of numpy arrays

open

NONE

### Describe the bug when resolve features of `IterableDataset`, got `pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values` error. ``` Traceback (most recent call last): File "test.py", line 6 iter_ds = iter_ds._resolve_features() File "lib/python3.10/site-packages/datasets/iterable_dataset.py", line 2876, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "lib/python3.10/site-packages/datasets/iterable_dataset.py", line 63, in _infer_features_from_batch pa_table = pa.Table.from_pydict(batch) File "pyarrow/table.pxi", line 1813, in pyarrow.lib._Tabular.from_pydict File "pyarrow/table.pxi", line 5339, in pyarrow.lib._from_pydict File "pyarrow/array.pxi", line 374, in pyarrow.lib.asarray File "pyarrow/array.pxi", line 344, in pyarrow.lib.array File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values ``` ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np # create list of numpy iter_ds = Dataset.from_dict({'a': [[[1, 2, 3], [1, 2, 3]]]}).to_iterable_dataset().map(lambda x: {'a': [np.array(x['a'])]}) iter_ds = iter_ds._resolve_features() # errors here ``` ### Expected behavior features can be successfully resolved ### Environment info - `datasets` version: 2.21.0 - Platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.23.4 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0

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I_kwDODunzps6SiQ3B

Some of DownloadConfig's properties are always being overridden in load.py

open

NONE

### Describe the bug The `extract_compressed_file` and `force_extract` properties of DownloadConfig are always being set to True in the function `dataset_module_factory` in the `load.py` file. This behavior is very annoying because data extracted will just be ignored the next time the dataset is loaded. See this image below:  ### Steps to reproduce the bug 1. Have a local dataset that contains archived files (zip, tar.gz, etc) 2. Build a dataset loading script to download and extract these files 3. Run the load_dataset function with a DownloadConfig that specifically set `force_extract` to False 4. The extraction process will start no matter if the archives was extracted previously ### Expected behavior The extraction process should not run when the archives were previously extracted and `force_extract` is set to False. ### Environment info datasets==2.20.0 python3.9

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I_kwDODunzps6SS18S

Add Arabic Docs to datasets

open

NONE

### Feature request Add Arabic Docs to datasets [Datasets Arabic](https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx) ### Motivation @AhmedAlmaghz https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx ### Your contribution @AhmedAlmaghz https://github.com/AhmedAlmaghz/datasets/blob/main/docs/source/ar/index.mdx

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I_kwDODunzps6SHUx6

load_dataset with multiple jsonlines files interprets datastructure too early

open

NONE

### Describe the bug likely related to #6460 using `datasets.load_dataset("json", data_dir= ... )` with multiple `.jsonl` files will error if one of the files (maybe the first file?) contains a full column of empty data. ### Steps to reproduce the bug real world example: data is available in this [PR-branch](https://github.com/Vipitis/shadertoys-dataset/pull/3/commits/cb1e7157814f74acb09d5dc2f1be3c0a868a9933). Because my files are chunked by months, some months contain all empty data for some columns, just by chance - these are `[]`. Otherwise it's all the same structure. ```python from datasets import load_dataset ds = load_dataset("json", data_dir="./data/annotated/api") ``` you get a long error trace, where in the middle it says something like ```cs TypeError: Couldn't cast array of type struct<id: int64, src: string, ctype: string, channel: int64, sampler: struct<filter: string, wrap: string, vflip: string, srgb: string, internal: string>, published: int64> to null ``` toy example: (on request) ### Expected behavior Some suggestions 1. give a better error message to the user 2. consider all files before deciding on a data structure for a given column. 3. if you encounter a new structure, and can't cast that to null, replace the null-hypothesis. (maybe something for pyarrow) as a workaround I have lazily implemented the following (essentially step 2) ```python import os import jsonlines import datasets api_files = os.listdir("./data/annotated/api") api_files = [f"./data/annotated/api/{f}" for f in api_files] api_file_contents = [] for f in api_files: with jsonlines.open(f) as reader: for obj in reader: api_file_contents.append(obj) ds = datasets.Dataset.from_list(api_file_contents) ``` this works fine for my usecase, but is potentially slower and less memory efficient for really large datasets (where this is unlikely to happen in the first place). ### Environment info - `datasets` version: 2.20.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.4 - `huggingface_hub` version: 0.23.4 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2023.10.0

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I_kwDODunzps6SA3Ki

The test test_move_script_doesnt_change_hash fails because it runs the 'python' command while the python executable has a different name

open

NONE

### Describe the bug Tests should use the same pythin path as they are launched with, which in the case of FreeBSD is /usr/local/bin/python3.11 Failure: ``` if err_filename is not None: > raise child_exception_type(errno_num, err_msg, err_filename) E FileNotFoundError: [Errno 2] No such file or directory: 'python' ``` ### Steps to reproduce the bug regular test run using PyTest ### Expected behavior n/a ### Environment info FreeBSD 14.1

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I_kwDODunzps6SABdM

Missing pyspark dependency causes the testsuite to error out, instead of a few tests to be skipped

open

NONE

### Describe the bug see the subject ### Steps to reproduce the bug regular tests ### Expected behavior n/a ### Environment info version 2.20.0

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I_kwDODunzps6R4B2E

Disable warning when using with_format format on tensors

open

NONE

### Feature request If we write this code: ```python """Get data and define datasets.""" from enum import StrEnum from datasets import load_dataset from torch.utils.data import DataLoader from torchvision import transforms class Split(StrEnum): """Describes what type of split to use in the dataloader""" TRAIN = "train" TEST = "test" VAL = "validation" class ImageNetDataLoader(DataLoader): """Create an ImageNetDataloader""" _preprocess_transform = transforms.Compose( [ transforms.Resize(256), transforms.CenterCrop(224), ] ) def __init__(self, batch_size: int = 4, split: Split = Split.TRAIN): dataset = ( load_dataset( "imagenet-1k", split=split, trust_remote_code=True, streaming=True, ) .with_format("torch") .map(self._preprocess) ) super().__init__(dataset=dataset, batch_size=batch_size) def _preprocess(self, data): if data["image"].shape[0] < 3: data["image"] = data["image"].repeat(3, 1, 1) data["image"] = self._preprocess_transform(data["image"].float()) return data if __name__ == "__main__": dataloader = ImageNetDataLoader(batch_size=2) for batch in dataloader: print(batch["image"]) break ``` This will trigger an user warning : ```bash datasets\formatting\torch_formatter.py:85: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs}) ``` ### Motivation This happens because the the way the formatted tensor is returned in `TorchFormatter._tensorize`. This function handle values of different types, according to some tests it seems that possible value types are `int`, `numpy.ndarray` and `torch.Tensor`. In particular this warning is triggered when the value type is `torch.Tensor`, because is not the suggested Pytorch way of doing it: - https://stackoverflow.com/questions/55266154/pytorch-preferred-way-to-copy-a-tensor - https://discuss.pytorch.org/t/it-is-recommended-to-use-source-tensor-clone-detach-or-sourcetensor-clone-detach-requires-grad-true/101218#:~:text=The%20warning%20points%20to%20wrapping%20a%20tensor%20in%20torch.tensor%2C%20which%20is%20not%20recommended.%0AInstead%20of%20torch.tensor(outputs)%20use%20outputs.clone().detach()%20or%20the%20same%20with%20.requires_grad_(True)%2C%20if%20necessary. ### Your contribution A solution that I found to be working is to change the current way of doing it: ```python return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs}) ``` To: ```python if (isinstance(value, torch.Tensor)): tensor = value.clone().detach() if self.torch_tensor_kwargs.get('requires_grad', False): tensor.requires_grad_() return tensor else: return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs}) ```

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I_kwDODunzps6R3K1z

Unable to create dataset card for Lushootseed language

closed

NONE

### Feature request While I was creating the dataset which contained all documents from the Lushootseed Wikipedia, the dataset card asked me to enter which language the dataset was in. Since Lushootseed is a critically endangered language, it was not available as one of the options. Is it possible to allow entering languages that aren't available in the options? ### Motivation I'd like to add more information about my dataset in the dataset card, and the language is one of the most important pieces of information, since the entire dataset is primarily concerned collecting Lushootseed documents. ### Your contribution I can submit a pull request

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I_kwDODunzps6Rw6Ad

load_dataset ignores cached datasets and tries to hit HF Hub, resulting in API rate limit errors

open

NONE

### Describe the bug I have been running lm-eval-harness a lot which has results in an API rate limit. This seems strange, since all of the data should be cached locally. I have in fact verified this. ### Steps to reproduce the bug 1. Be Me 2. Run `load_dataset("TAUR-Lab/MuSR")` 3. Hit rate limit error 4. Dataset is in .cache/huggingface/datasets 5. ??? ### Expected behavior We should not run into API rate limits if we have cached the dataset ### Environment info datasets 2.16.0 python 3.10.4

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I_kwDODunzps6Rb5Oq

[Regression] IterableDataset is broken on 2.20.0

closed

NONE

### Describe the bug In the latest version of datasets there is a major regression, after creating an `IterableDataset` from a generator and applying a few operations (`map`, `select`), you can no longer iterate through the dataset multiple times. The issue seems to stem from the recent addition of "resumable IterableDatasets" (#6658) (@lhoestq). It seems like it's keeping state when it shouldn't. ### Steps to reproduce the bug Minimal Reproducible Example (comparing `datasets==2.17.0` and `datasets==2.20.0`) ``` #!/bin/bash # List of dataset versions to test versions=("2.17.0" "2.20.0") # Loop through each version for version in "${versions[@]}"; do # Install the specific version of the datasets library pip3 install -q datasets=="$version" 2>/dev/null # Run the Python script python3 - <<EOF from datasets import IterableDataset from datasets.features.features import Features, Value def test_gen(): yield from [{"foo": i} for i in range(10)] features = Features([("foo", Value("int64"))]) d = IterableDataset.from_generator(test_gen, features=features) mapped = d.map(lambda row: {"foo": row["foo"] * 2}) column = mapped.select_columns(["foo"]) print("Version $version - Iterate Once:", list(column)) print("Version $version - Iterate Twice:", list(column)) EOF done ``` The output looks like this: ``` Version 2.17.0 - Iterate Once: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}] Version 2.17.0 - Iterate Twice: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}] Version 2.20.0 - Iterate Once: [{'foo': 0}, {'foo': 2}, {'foo': 4}, {'foo': 6}, {'foo': 8}, {'foo': 10}, {'foo': 12}, {'foo': 14}, {'foo': 16}, {'foo': 18}] Version 2.20.0 - Iterate Twice: [] ``` ### Expected behavior The expected behavior is it version 2.20.0 should behave the same as 2.17.0. ### Environment info `datasets==2.20.0` on any platform.

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I_kwDODunzps6RaB0u

More easily support streaming local files

open

CONTRIBUTOR

### Feature request Simplify downloading and streaming datasets locally. Specifically, perhaps add an option to `load_dataset(..., streaming="download_first")` or add better support for streaming symlinked or arrow files. ### Motivation I have downloaded FineWeb-edu locally and currently trying to stream the dataset from the local files. I have both the raw parquet files using `hugginface-cli download --repo-type dataset HuggingFaceFW/fineweb-edu` and the processed arrow files using `load_dataset("HuggingFaceFW/fineweb-edu")`. Streaming the files locally does not work well for both file types for two different reasons. **Arrow files** When running `load_dataset("arrow", data_files={"train": "~/.cache/huggingface/datasets/HuggingFaceFW___fineweb-edu/default/0.0.0/5b89d1ea9319fe101b3cbdacd89a903aca1d6052/fineweb-edu-train-*.arrow"})` resolving the data files is fast, but because `arrow` is not included in the known [extensions file list](https://github.com/huggingface/datasets/blob/ce4a0c573920607bc6c814605734091b06b860e7/src/datasets/utils/file_utils.py#L738) , all files are opened and scanned to determine the compression type. Adding `arrow` to the known extension types resolves this issue. **Parquet files** When running `load_dataset("arrow", data_files={"train": "~/.cache/huggingface/hub/dataset-HuggingFaceFW___fineweb-edu/snapshots/5b89d1ea9319fe101b3cbdacd89a903aca1d6052/data/CC-MAIN-*/train-*.parquet"})` the paths do not get resolved because the parquet files are symlinked from the blobs (which contain all files in case there are different versions). This occurs because the [pattern matching](https://github.com/huggingface/datasets/blob/ce4a0c573920607bc6c814605734091b06b860e7/src/datasets/data_files.py#L389) checks if the path is a file and does not check for symlinks. Symlinks (at least on my machine) are of type "other". ### Your contribution I have created a PR for fixing arrow file streaming and symlinks. However, I have not checked locally if the tests work or new tests need to be added. IMO, the easiest option would be to add a `streaming=download_first` option, but I'm afraid that exceeds my current knowledge of how the datasets library works. https://github.com/huggingface/datasets/pull/7083

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I_kwDODunzps6RGBlQ

Generating train split takes a long time

open

NONE

### Describe the bug Loading a simple webdataset takes ~45 minutes. ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("PixArt-alpha/SAM-LLaVA-Captions10M") ``` ### Expected behavior The dataset should load immediately as it does when loaded through a normal indexed WebDataset loader. Generating splits should be optional and there should be a message showing how to disable it. ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.14 - `huggingface_hub` version: 0.24.1 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6RCi1i

HfHubHTTPError: 500 Server Error: Internal Server Error for url:

closed

NONE

### Describe the bug newly uploaded datasets, since yesterday, yields an error. old datasets, works fine. Seems like the datasets api server returns a 500 I'm getting the same error, when I invoke `load_dataset` with my dataset. Long discussion about it here, but I'm not sure anyone from huggingface have seen it. https://discuss.huggingface.co/t/hfhubhttperror-500-server-error-internal-server-error-for-url/99580/1 ### Steps to reproduce the bug this api url: https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3 respond with: ``` {"error":"Internal Error - We're working hard to fix this as soon as possible!"} ``` ### Expected behavior return no error with newer datasets. With older datasets I can load the datasets fine. ### Environment info # Browser When I access the api in the browser: https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3 ``` {"error":"Internal Error - We're working hard to fix this as soon as possible!"} ``` ### Request headers ``` Accept text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8 Accept-Encoding gzip, deflate, br, zstd Accept-Language en-US,en;q=0.5 Connection keep-alive Host huggingface.co Priority u=1 Sec-Fetch-Dest document Sec-Fetch-Mode navigate Sec-Fetch-Site cross-site Upgrade-Insecure-Requests 1 User-Agent Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:127.0) Gecko/20100101 Firefox/127.0 ``` ### Response headers ``` X-Firefox-Spdy h2 access-control-allow-origin https://huggingface.co access-control-expose-headers X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range content-length 80 content-type application/json; charset=utf-8 cross-origin-opener-policy same-origin date Fri, 26 Jul 2024 19:09:45 GMT etag W/"50-9qrwU+BNI4SD0Fe32p/nofkmv0c" referrer-policy strict-origin-when-cross-origin vary Origin via 1.1 1624c79cd07e6098196697a6a7907e4a.cloudfront.net (CloudFront) x-amz-cf-id SP8E7n5qRaP6i9c9G83dNAiOzJBU4GXSrDRAcVNTomY895K35H0nJQ== x-amz-cf-pop CPH50-C1 x-cache Error from cloudfront x-error-message Internal Error - We're working hard to fix this as soon as possible! x-powered-by huggingface-moon x-request-id Root=1-66a3f479-026417465ef42f49349fdca1 ```

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I_kwDODunzps6Q-qWR

column_names ignored by load_dataset() when loading CSV file

open

NONE

### Describe the bug load_dataset() ignores the column_names kwarg when loading a CSV file. Instead, it uses whatever values are on the first line of the file. ### Steps to reproduce the bug Call `load_dataset` to load data from a CSV file and specify `column_names` kwarg. ### Expected behavior The resulting dataset should have the specified column names **and** the first line of the file should be considered as data values. ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-5.10.0-30-cloud-amd64-x86_64-with-glibc2.31 - Python version: 3.9.2 - `huggingface_hub` version: 0.24.2 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6Q8OXI

CI is broken for convert_to_parquet: Invalid rev id: refs/pr/1 404 error causes RevisionNotFoundError

closed

MEMBER

See: https://github.com/huggingface/datasets/actions/runs/10095313567/job/27915185756 ``` FAILED tests/test_hub.py::test_convert_to_parquet - huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Root=1-66a25839-31ce7b475e70e7db1e4d44c2;b0c8870f-d5ef-4bf2-a6ff-0191f3df0f64) Revision Not Found for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1. Invalid rev id: refs/pr/1 ``` ``` /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/hub.py:86: in convert_to_parquet dataset.push_to_hub( /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/dataset_dict.py:1722: in push_to_hub split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:5511: in _push_parquet_shards_to_hub api.preupload_lfs_files( /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/hf_api.py:4231: in preupload_lfs_files _fetch_upload_modes( /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:118: in _inner_fn return fn(*args, **kwargs) /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/_commit_api.py:507: in _fetch_upload_modes hf_raise_for_status(resp) ```

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I_kwDODunzps6Q36z8

nm

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I_kwDODunzps6Q26Iz

Filter hangs

open

NONE

### Describe the bug When trying to filter my custom dataset, the process hangs, regardless of the lambda function used. It appears to be an issue with the way the Images are being handled. The dataset in question is a preprocessed version of https://huggingface.co/datasets/danaaubakirova/patfig where notably, I have converted the data to the Parquet format. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('lcolonn/patfig', split='test') ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y') ``` Eventually I ctrl+C and I obtain this stack trace: ``` >>> ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y') Filter: 0%| | 0/998 [00:00<?, ? examples/s]Filter: 0%| | 0/998 [00:35<?, ? examples/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/fingerprint.py", line 482, in wrapper out = func(dataset, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3714, in filter indices = self.map( ^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 602, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3161, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3552, in _map_single batch = apply_function_on_filtered_inputs( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3421, in apply_function_on_filtered_inputs processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 6478, in get_indices_from_mask_function num_examples = len(batch[next(iter(batch.keys()))]) ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 273, in __getitem__ value = self.format(key) ^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 376, in format return self.formatter.format_column(self.pa_table.select([key])) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 443, in format_column column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 219, in decode_column return self.features.decode_column(column, column_name) if self.features else column ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in decode_column [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in <listcomp> [decode_nested_example(self[column_name], value) if value is not None else None for value in column] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 1351, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/image.py", line 188, in decode_example image.load() # to avoid "Too many open files" errors ^^^^^^^^^^^^ File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/PIL/ImageFile.py", line 293, in load n, err_code = decoder.decode(b) ^^^^^^^^^^^^^^^^^ KeyboardInterrupt ``` Warning! This can even seem to cause some computers to crash. ### Expected behavior Should return the filtered dataset ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35 - Python version: 3.11.9 - `huggingface_hub` version: 0.24.0 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6Q2zWz

how set_transform affects batch size?

open

NONE

### Describe the bug I am trying to fine-tune w2v-bert for ASR task. Since my dataset is so big, I preferred to use the on-the-fly method with set_transform. So i change the preprocessing function to this: ``` def prepare_dataset(batch): input_features = processor(batch["audio"], sampling_rate=16000).input_features[0] input_length = len(input_features) labels = processor.tokenizer(batch["text"], padding=False).input_ids batch = { "input_features": [input_features], "input_length": [input_length], "labels": [labels] } return batch train_ds.set_transform(prepare_dataset) val_ds.set_transform(prepare_dataset) ``` After this, I also had to change the DataCollatorCTCWithPadding class like this: ``` @dataclass class DataCollatorCTCWithPadding: processor: Wav2Vec2BertProcessor padding: Union[bool, str] = True def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # Separate input_features and labels input_features = [{"input_features": feature["input_features"][0]} for feature in features] labels = [feature["labels"][0] for feature in features] # Pad input features batch = self.processor.pad( input_features, padding=self.padding, return_tensors="pt", ) # Pad and process labels label_features = self.processor.tokenizer.pad( {"input_ids": labels}, padding=self.padding, return_tensors="pt", ) labels = label_features["input_ids"] attention_mask = label_features["attention_mask"] # Replace padding with -100 to ignore these tokens during loss calculation labels = labels.masked_fill(attention_mask.ne(1), -100) batch["labels"] = labels return batch ``` But now a strange thing is happening, no matter how much I increase the batch size, the amount of V-RAM GPU usage does not change, while the number of total steps in the progress-bar (logging) changes. Is this normal or have I made a mistake? ### Steps to reproduce the bug i can share my code if needed ### Expected behavior Equal to the batch size value, the set_transform function is applied to the dataset and given to the model as a batch. ### Environment info all updated versions

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I_kwDODunzps6QkZXI

Convert_to_parquet fails for datasets with multiple configs

closed

NONE

If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error: ``` Traceback (most recent call last): File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main service.run() File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run dataset.push_to_hub( File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True) File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch hf_raise_for_status(response) File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status raise BadRequestError(message, response=response) from e huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f) Bad request: Invalid reference for a branch: refs/pr/1 ```

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I_kwDODunzps6QjHko

One subset per file in repo ?

open

MEMBER

Right now we consider all the files of a dataset to be the same data, e.g. ``` single_subset_dataset/ ├── train0.jsonl ├── train1.jsonl └── train2.jsonl ``` but in cases like this, each file is actually a different subset of the dataset and should be loaded separately ``` many_subsets_dataset/ ├── animals.jsonl ├── trees.jsonl └── metadata.jsonl ``` It would be nice to detect those subsets automatically using a simple heuristic. For example we can group files together if their paths names are the same except some digits ?

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I_kwDODunzps6QhoTp

Cannot get item after loading from disk and then converting to iterable.

open

NONE

### Describe the bug The dataset generated from local file works fine. ```py root = "/home/data/train" file_list1 = glob(os.path.join(root, "*part1.flac")) file_list2 = glob(os.path.join(root, "*part2.flac")) ds = ( Dataset.from_dict({"part1": file_list1, "part2": file_list2}) .cast_column("part1", Audio(sampling_rate=None, mono=False)) .cast_column("part2", Audio(sampling_rate=None, mono=False)) ) ids = ds.to_iterable_dataset(128) ids = ids.shuffle(buffer_size=10000, seed=42) dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True) for batch in dataloader: break ``` But after saving it to disk and then loading it from disk, I cannot get data as expected. ```py root = "/home/data/train" file_list1 = glob(os.path.join(root, "*part1.flac")) file_list2 = glob(os.path.join(root, "*part2.flac")) ds = ( Dataset.from_dict({"part1": file_list1, "part2": file_list2}) .cast_column("part1", Audio(sampling_rate=None, mono=False)) .cast_column("part2", Audio(sampling_rate=None, mono=False)) ) ds.save_to_disk("./train") ds = datasets.load_from_disk("./train") ids = ds.to_iterable_dataset(128) ids = ids.shuffle(buffer_size=10000, seed=42) dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True) for batch in dataloader: break ``` After a long time waiting, an error occurs: ``` Loading dataset from disk: 100%|█████████████████████████████████████████████████████████████████████████| 165/165 [00:00<00:00, 6422.18it/s] Traceback (most recent call last): File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data data = self._data_queue.get(timeout=timeout) File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/queues.py", line 113, in get if not self._poll(timeout): File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 257, in poll return self._poll(timeout) File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 424, in _poll r = wait([self], timeout) File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 931, in wait ready = selector.select(timeout) File "/home/hanzerui/.conda/envs/mss/lib/python3.10/selectors.py", line 416, in select fd_event_list = self._selector.poll(timeout) File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler _error_if_any_worker_fails() RuntimeError: DataLoader worker (pid 3490529) is killed by signal: Killed. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module> cli.main() File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main run() File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file runpy.run_path(target, run_name="__main__") File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path return _run_module_code(code, init_globals, run_name, File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code _run_code(code, mod_globals, init_globals, File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code exec(code, run_globals) File "/home/hanzerui/workspace/NetEase/test/test_datasets.py", line 60, in <module> for batch in dataloader: File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__ data = self._next_data() File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1329, in _next_data idx, data = self._get_data() File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1295, in _get_data success, data = self._try_get_data() File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1146, in _try_get_data raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e RuntimeError: DataLoader worker (pid(s) 3490529) exited unexpectedly ``` It seems that streaming is not supported by `laod_from_disk`, so does that mean I cannot convert it to iterable? ### Steps to reproduce the bug 1. Create a `Dataset` from local files with `from_dict` 2. Save it to disk with `save_to_disk` 3. Load it from disk with `load_from_disk` 4. Convert to iterable with `to_iterable_dataset` 5. Loop the dataset ### Expected behavior Get items faster than the original dataset generated from dict. ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35 - Python version: 3.10.14 - `huggingface_hub` version: 0.23.2 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6QgsLI

Add `batch` method to `Dataset`

closed

CONTRIBUTOR

### Feature request Add a `batch` method to the Dataset class, similar to the one recently implemented for `IterableDataset` in PR #7054. ### Motivation A batched iteration speeds up data loading significantly (see e.g. #6279) ### Your contribution I plan to open a PR to implement this.

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I_kwDODunzps6QeA2B

Custom Dataset | Still Raise Error while handling errors in _generate_examples

open

NONE

### Describe the bug I follow this [example](https://discuss.huggingface.co/t/error-handling-in-iterabledataset/72827/3) to handle errors in custom dataset. I am writing a dataset script which read jsonl files and i need to handle errors and continue reading files without raising exception and exit the execution. ``` def _generate_examples(self, filepaths): errors=[] id_ = 0 for filepath in filepaths: try: with open(filepath, 'r') as f: for line in f: json_obj = json.loads(line) yield id_, json_obj id_ += 1 except Exception as exc: logger.error(f"error occur at filepath: {filepath}") errors.append(error) ``` seems the logger.error is printed but still exception is raised the the run is exit. ``` Downloading and preparing dataset custom_dataset/default to /home/myuser/.cache/huggingface/datasets/custom_dataset/default-a14cdd566afee0a6/1.0.0/acfcc9fb9c57034b580c4252841 ERROR: datasets_modules.datasets.custom_dataset.acfcc9fb9c57034b580c4252841bb890a5617cbd28678dd4be5e52b81188ad02.custom_dataset: 2024-07-22 10:47:42,167: error occur at filepath: '/home/myuser/ds/corrupted-file.jsonl Traceback (most recent call last): File "/home/myuser/.cache/huggingface/modules/datasets_modules/datasets/custom_dataset/ac..2/custom_dataset.py", line 48, in _generate_examples json_obj = json.loads(line) File "myenv/lib/python3.8/json/__init__.py", line 357, in loads return _default_decoder.decode(s) File "myenv/lib/python3.8/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "myenv/lib/python3.8/json/decoder.py", line 353, in raw_decode obj, end = self.scan_once(s, idx) json.decoder.JSONDecodeError: Invalid control character at: line 1 column 4 (char 3) Generating train split: 0 examples [00:06, ? examples/s]> RemoteTraceback: """ Traceback (most recent call last): File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1637, in _prepare_split_single num_examples, num_bytes = writer.finalize() File "myenv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 594, in finalize raise SchemaInferenceError("Please pass `features` or at least one example when writing data") datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "myenv/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "myenv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 1353, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1646, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset """ The above exception was the direct cause of the following exception: │ │ │ myenv/lib/python3.8/site-packages/datasets/utils/py_utils. │ │ py:1377 in <listcomp> │ │ │ │ 1374 │ │ │ │ if all(async_result.ready() for async_result in async_results) and queue │ │ 1375 │ │ │ │ │ break │ │ 1376 │ │ # we get the result in case there's an error to raise │ │ ❱ 1377 │ │ [async_result.get() for async_result in async_results] │ │ 1378 │ │ │ │ ╭──────────────────────────────── locals ─────────────────────────────────╮ │ │ │ .0 = <list_iterator object at 0x7f2cc1f0ce20> │ │ │ │ async_result = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │ │ ╰─────────────────────────────────────────────────────────────────────────╯ │ │ │ │ myenv/lib/python3.8/site-packages/multiprocess/pool.py:771 │ │ in get │ │ │ │ 768 │ │ if self._success: │ │ 769 │ │ │ return self._value │ │ 770 │ │ else: │ │ ❱ 771 │ │ │ raise self._value │ │ 772 │ │ │ 773 │ def _set(self, i, obj): │ │ 774 │ │ self._success, self._value = obj │ │ │ │ ╭────────────────────────────── locals ──────────────────────────────╮ │ │ │ self = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │ │ │ timeout = None │ │ │ ╰────────────────────────────────────────────────────────────────────╯ │ DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug same as above ### Expected behavior should handle error and continue reading remaining files ### Environment info python 3.9

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I_kwDODunzps6QaWt0

None values are skipped when reading jsonl in subobjects

open

NONE

### Describe the bug I have been fighting against my machine since this morning only to find out this is some kind of a bug. When loading a dataset composed of `metadata.jsonl`, if you have nullable values (Optional[str]), they can be ignored by the parser, shifting things around. E.g., let's take this example Here are two version of a same dataset: [not-buggy.tar.gz](https://github.com/user-attachments/files/16333532/not-buggy.tar.gz) [buggy.tar.gz](https://github.com/user-attachments/files/16333553/buggy.tar.gz) ### Steps to reproduce the bug 1. Load the `buggy.tar.gz` dataset 2. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]` 3. Load the `not-buggy.tar.gz` dataset 4. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]` ### Expected behavior Both should have 4 baseline entries: 1. Buggy should have None followed by three lists 2. Non-Buggy should have four lists, and the first one should be an empty list. One does not work, 2 works. Despite accepting None in another position than the first one. ### Environment info - `datasets` version: 2.19.1 - Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.23.0 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.3.1

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I_kwDODunzps6QZVZj

New feature type: Document

open

COLLABORATOR

It would be useful for PDF. https://github.com/huggingface/dataset-viewer/issues/2991#issuecomment-2242656069

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I_kwDODunzps6QWFhb

WebDataset with different prefixes are unsupported

closed

NONE

### Describe the bug Consider a WebDataset with multiple images for each item where the number of images may vary: [example](https://huggingface.co/datasets/bigdata-pw/fashion-150k) Due to this [code](https://github.com/huggingface/datasets/blob/87f4c2088854ff33e817e724e75179e9975c1b02/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) an error is given. ``` The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types. ``` The purpose of this check is unclear because PyArrow supports different keys. Removing the check allows the dataset to be loaded and there's no issue when iterating through the dataset. ``` >>> from datasets import load_dataset >>> path = "shards/*.tar" >>> dataset = load_dataset("webdataset", data_files={"train": path}, split="train", streaming=True) Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 152/152 [00:00<00:00, 56458.93it/s] >>> dataset IterableDataset({ features: ['__key__', '__url__', '1.jpg', '2.jpg', '3.jpg', '4.jpg', 'json'], n_shards: 152 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("bigdata-pw/fashion-150k") ``` ### Expected behavior Dataset loads without error ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-5.14.0-467.el9.x86_64-x86_64-with-glibc2.34 - Python version: 3.9.19 - `huggingface_hub` version: 0.23.4 - PyArrow version: 17.0.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6QB7Nv

Datasets.datafiles resolve_pattern `TypeError: can only concatenate tuple (not "str") to tuple`

closed

NONE

### Describe the bug in data_files.py, line 332, `fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)` If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')` So, `isinstance(fs.protocol, str) == False` and `protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise `TypeError: can only concatenate tuple (not "str") to tuple`. ### Steps to reproduce the bug Steps to reproduce: 1. Run on a cloud server like AWS, 2. `import datasets.data_files as datafile` 3. datafile.resolve_pattern('path/to/dataset', '.') 4. `TypeError: can only concatenate tuple (not "str") to tuple` ### Expected behavior Should return path of the dataset, with fs.protocol at the beginning ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.19 - Huggingface_hub version: 0.23.5 - PyArrow version: 16.1.0 - Pandas version: 1.1.5

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I_kwDODunzps6Pm9LJ

How to set_epoch with interleave_datasets?

closed

NONE

Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples. I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch) Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start. How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset... Something like ``` dataset_a = load_dataset(...) dataset_b = load_dataset(...) def epoch_shuffled_dataset(ds): # How to make this maintain the number of shards in ds?? for epoch in itertools.count(): ds.set_epoch(epoch) yield from iter(ds) shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a}) interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted') ```

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I_kwDODunzps6PjwM-

Save nparray as list

closed

NONE

### Describe the bug When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision? ### Steps to reproduce the bug the map function ```python def convert_image_to_features(inst, processor, image_dir): image_file = inst["image_url"] file = image_file.split("/")[-1] image_path = os.path.join(image_dir, file) image = Image.open(image_path) image = image.convert("RGBA") inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"] return inst ``` main function ```python map_fun = partial( convert_image_to_features, processor=processor, image_dir=image_dir ) ds = ds.map(map_fun, batched=False, num_proc=20) print(type(ds[0]["pixel_values"]) ``` ### Expected behavior (type < list>) ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.11.5 - `huggingface_hub` version: 0.23.4 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

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I_kwDODunzps6Pjpp7

ImportError: numpy.core.multiarray when using `filter`

closed

NONE

### Describe the bug I can't apply the filter method on my dataset. ### Steps to reproduce the bug The following snippet generates a bug: ```python from datasets import load_dataset ami = load_dataset('kamilakesbi/ami', 'ihm') ami['train'].filter( lambda example: example["file_name"] == 'EN2001a' ) ``` I get the following error: `ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).` ### Expected behavior It should work properly! ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - `huggingface_hub` version: 0.23.4 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6PcDNs

Save Dataset as Sharded Parquet

open

NONE

### Feature request `to_parquet` currently saves the dataset as one massive, monolithic parquet file, rather than as several small parquet files. It should shard large datasets automatically. ### Motivation This default behavior makes me very sad because a program I ran for 6 hours saved its results using `to_parquet`, putting the entire billion+ row dataset into a 171 GB *single shard parquet file* which pyarrow, apache spark, etc. all cannot work with without completely exhausting the memory of my system. I was previously able to work with larger-than-memory parquet files, but not this one. I *assume* the reason why this is happening is because it is a single shard. Making sharding the default behavior puts datasets in parity with other frameworks, such as spark, which automatically shard when a large dataset is saved as parquet. ### Your contribution I could change the logic here https://github.com/huggingface/datasets/blob/bf6f41e94d9b2f1c620cf937a2e85e5754a8b960/src/datasets/io/parquet.py#L109-L158 to use `pyarrow.dataset.write_dataset`, which seems to support sharding, or periodically open new files. We would only shard if the user passed in a path rather than file handle.

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I_kwDODunzps6PUusm

`sort` after `filter` unreasonably slow

open

NONE

### Describe the bug as the tittle says ... ### Steps to reproduce the bug `sort` seems to be normal. ```python from datasets import Dataset import random nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)] ds = Dataset.from_list(nums) print("start sort") ds = ds.sort("k") print("finish sort") ``` but `sort` after `filter` is extremely slow. ```python from datasets import Dataset import random nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)] ds = Dataset.from_list(nums) ds = ds.filter(lambda x:x > 100, input_columns="k") print("start sort") ds = ds.sort("k") print("finish sort") ``` ### Expected behavior Is this a bug, or is it a misuse of the `sort` function? ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.13 - `huggingface_hub` version: 0.23.4 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2023.10.0

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I_kwDODunzps6POZ-_

load `streaming=True` dataset with downloaded cache

open

NONE

### Describe the bug We build a dataset which contains several hdf5 files and write a script using `h5py` to generate the dataset. The hdf5 files are large and the processed dataset cache takes more disk space. So we hope to try streaming iterable dataset. Unfortunately, `h5py` can't convert a remote URL into a hdf5 file descriptor. So we use `fsspec` as an interface like below: ```python def _generate_examples(self, filepath, split): for file in filepath: with fsspec.open(file, "rb") as fs: with h5py.File(fs, "r") as fp: # for event_id in sorted(list(fp.keys())): event_ids = list(fp.keys()) ...... ``` ### Steps to reproduce the bug The `fsspec` works, but it takes 10+ min to print the first 10 examples, which is even longer than the downloading time. I'm not sure if it just caches the whole hdf5 file and generates the examples. ### Expected behavior So does the following make sense so far? 1. download the files ```python dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True) ``` 2. load the iterable dataset faster (using the raw file cache at path `.cache/huggingface/datasets/downloads`) ```python dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True, streaming=true) ``` I made some tests, but the code above can't get the expected result. I'm not sure if this is supported. I also find the issue #6327 . It seemed similar to mine, but I couldn't find a solution. ### Environment info - `datasets` = 2.18.0 - `h5py` = 3.10.0 - `fsspec` = 2023.10.0

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I_kwDODunzps6PEAej

A bug of Dataset.to_json() function

open

NONE

### Describe the bug When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again. The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size). ### Steps to reproduce the bug try this code: ```python from datasets import load_dataset import json train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"] output_path = "./harmless-base_hftojs.json" print(len(train_dataset)) train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2) with open(output_path, encoding="utf-8") as f: data = json.loads(f.read()) ``` it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709) Extra square brackets have appeared here: <img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc"> ### Expected behavior The code runs normally. ### Environment info datasets=2.20.0

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I_kwDODunzps6PDWrp

Docs are not generated when a parameter defaults to a NamedSplit value

closed

MEMBER

While generating the docs, we get an error when some parameter defaults to a `NamedSplit` value, like: ```python def call_function(split=Split.TRAIN): ... ``` The error is: ValueError: Equality not supported between split train and <class 'inspect._empty'> See: https://github.com/huggingface/datasets/actions/runs/9869660902/job/27254359863?pr=7015 ``` Building the MDX files: 97%|█████████▋| 58/60 [00:00<00:00, 91.94it/s] Traceback (most recent call last): File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 197, in build_mdx_files content, new_anchors, source_files, errors = resolve_autodoc( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 123, in resolve_autodoc doc = autodoc( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 499, in autodoc method_doc, check = document_object( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 395, in document_object signature = format_signature(obj) File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 126, in format_signature if param.default != inspect._empty: File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 136, in __ne__ return not self.__eq__(other) File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 379, in __eq__ raise ValueError(f"Equality not supported between split {self} and {other}") ValueError: Equality not supported between split train and <class 'inspect._empty'> The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/runner/work/datasets/datasets/.venv/bin/doc-builder", line 8, in <module> sys.exit(main()) File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main args.func(args) File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/build.py", line 102, in build_command build_doc( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 367, in build_doc anchors_mapping, source_files_mapping = build_mdx_files( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 230, in build_mdx_files raise type(e)(f"There was an error when converting {file} to the MDX format.\n" + e.args[0]) from e ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format. Equality not supported between split train and <class 'inspect._empty'> ```

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I_kwDODunzps6O5bj4

`from_generator` does not allow to specify the split name

closed

CONTRIBUTOR

### Describe the bug I'm building train, dev, and test using `from_generator`; however, in all three cases, the logger prints `Generating train split:` It's not possible to change the split name since it seems to be hardcoded: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/generator/generator.py ### Steps to reproduce the bug ``` In [1]: from datasets import Dataset In [2]: def gen(): ...: yield {"pokemon": "bulbasaur", "type": "grass"} ...: In [3]: ds = Dataset.from_generator(gen) Generating train split: 1 examples [00:00, 133.89 examples/s] ``` ### Expected behavior It should be possible to specify any split name ### Environment info - `datasets` version: 2.19.2 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.5 - `huggingface_hub` version: 0.23.3 - PyArrow version: 15.0.0 - Pandas version: 2.0.3 - `fsspec` version: 2023.10.0

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I_kwDODunzps6Oxu3c

CI quality is broken: use ruff check instead

closed

MEMBER

CI quality is broken: https://github.com/huggingface/datasets/actions/runs/9838873879/job/27159697027 ``` error: `ruff <path>` has been removed. Use `ruff check <path>` instead. ```

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I_kwDODunzps6OqJAv

Add option to disable progress bar when reading a dataset ("Loading dataset from disk")

closed

NONE

### Feature request Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16. ### Motivation I am reading a lot of datasets that it creates lots of logs. <img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a"> ### Your contribution Seems like an easy fix to make. I can create a PR if necessary.

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I_kwDODunzps6OiVwo

load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error

open

NONE

### Describe the bug I'm using AWS lambda to run a python application. I run the `load_dataset` function with cache_dir="/tmp" and is still throws the OSError(30, 'Read-only file system') error. Is even updated all the HF envs to point to /tmp dir but the issue still persists. I can confirm that the I can write to /tmp directory. ### Steps to reproduce the bug ```python d = load_dataset( path=hugging_face_link, split=split, token=token, cache_dir="/tmp/hugging_face_cache", ) ``` ### Expected behavior Everything written to the file system as part of the load_datasets function should be in the /tmp directory. ### Environment info datasets version: 2.16.1 Platform: Linux-5.10.216-225.855.amzn2.x86_64-x86_64-with-glibc2.26 Python version: 3.11.9 huggingface_hub version: 0.19.4 PyArrow version: 16.1.0 Pandas version: 2.2.2 fsspec version: 2023.10.0

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I_kwDODunzps6Odqq6

Streaming dataset not returning data

open

NONE

### Describe the bug I'm deciding to post here because I'm still not sure what the issue is, or if I am using IterableDatasets wrongly. I'm following the guide on here https://huggingface.co/learn/cookbook/en/fine_tuning_code_llm_on_single_gpu pretty much to a tee and have verified that it works when I'm fine-tuning on the provided dataset. However, I'm doing some data preprocessing steps (filtering out entries), when I try to swap out the dataset for mine, it fails to train. However, I eventually fixed this by simply setting `stream=False` in `load_dataset`. Coud this be some sort of network / firewall issue I'm facing? ### Steps to reproduce the bug I made a post with greater description about how I reproduced this problem before I found my workaround: https://discuss.huggingface.co/t/problem-with-custom-iterator-of-streaming-dataset-not-returning-anything/94551 Here is the problematic dataset snippet, which works when streaming=False (and with buffer keyword removed from shuffle) ``` commitpackft = load_dataset( "chargoddard/commitpack-ft-instruct", split="train", streaming=True ).filter(lambda example: example["language"] == "Python") def form_template(example): """Forms a template for each example following the alpaca format for CommitPack""" example["content"] = ( "### Human: " + example["instruction"] + " " + example["input"] + " ### Assistant: " + example["output"] ) return example dataset = commitpackft.map( form_template, remove_columns=["id", "language", "license", "instruction", "input", "output"], ).shuffle( seed=42, buffer_size=10000 ) # remove everything since its all inside "content" now validation_data = dataset.take(4000) train_data = dataset.skip(4000) ``` The annoying part about this is that it only fails during training and I don't know when it will fail, except that it always fails during evaluation. ### Expected behavior The expected behavior is that I should be able to get something from the iterator when called instead of getting nothing / stuck in a loop somewhere. ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.31 - Python version: 3.11.7 - `huggingface_hub` version: 0.23.4 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6OVvmK

There is dead code after we require pyarrow >= 15.0.0

closed

MEMBER

There are code lines specific for pyarrow versions < 15.0.0. However, we require pyarrow >= 15.0.0 since the merge of PR: - #6892 Those code lines are now dead code and should be removed.

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I_kwDODunzps6OVRZ-

Casting list array to fixed size list raises error

closed

MEMBER

When trying to cast a list array to fixed size list, an AttributeError is raised: > AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length' Steps to reproduce the bug: ```python import pyarrow as pa from datasets.table import array_cast arr = pa.array([[0, 1]]) array_cast(arr, pa.list_(pa.int64(), 2)) ``` Stack trace: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-12-6cb90a1d8216> in <module> 3 4 arr = pa.array([[0, 1]]) ----> 5 array_cast(arr, pa.list_(pa.int64(), 2)) ~/huggingface/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs) 1802 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1803 else: -> 1804 return func(array, *args, **kwargs) 1805 1806 return wrapper ~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str) 1920 else: 1921 array_values = array.values[ -> 1922 array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length 1923 ] 1924 return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size) AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length' ```

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I_kwDODunzps6OFGE-

`load_dataset` fails to load dataset saved by `save_to_disk`

open

NONE

### Describe the bug This code fails to load the dataset it just saved: ```python from datasets import load_dataset from transformers import AutoTokenizer MODEL = "google-bert/bert-base-cased" tokenizer = AutoTokenizer.from_pretrained(MODEL) dataset = load_dataset("yelp_review_full") def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", truncation=True) tokenized_datasets = dataset.map(tokenize_function, batched=True) tokenized_datasets.save_to_disk("dataset") tokenized_datasets = load_dataset("dataset/") # raises ``` It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`. I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON: ```shell $ ls -l dataset/test -rw-r--r-- 1 sliedes sliedes 191498784 Jul 1 13:55 data-00000-of-00001.arrow -rw-r--r-- 1 sliedes sliedes 1730 Jul 1 13:55 dataset_info.json -rw-r--r-- 1 sliedes sliedes 249 Jul 1 13:55 state.json ``` ### Steps to reproduce the bug Execute the code above. ### Expected behavior The dataset is loaded successfully. ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39 - Python version: 3.12.4 - `huggingface_hub` version: 0.23.4 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0

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I_kwDODunzps6ODbOQ

`drop_duplicates` method

open

NONE

### Feature request `drop_duplicates` method for huggingface datasets (similiar in simplicity to the `pandas` one) ### Motivation Ease of use ### Your contribution I don't think i am good enough to help

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I_kwDODunzps6OCVbi

CI is broken for faiss tests on Windows: node down: Not properly terminated

closed

MEMBER

Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached. See: https://github.com/huggingface/datasets/actions/runs/9712659783 ``` test (integration, windows-latest, deps-minimum) The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes. test (integration, windows-latest, deps-latest) The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes. ``` ``` ____________________________ tests/test_search.py _____________________________ [gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index' ____________________________ tests/test_search.py _____________________________ [gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index' ``` ``` tests/test_search.py::IndexableDatasetTest::test_add_faiss_index [gw0] node down: Not properly terminated [gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index replacing crashed worker gw0 tests/test_search.py::IndexableDatasetTest::test_add_faiss_index [gw1] node down: Not properly terminated [gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index replacing crashed worker gw1 tests/test_search.py::IndexableDatasetTest::test_add_faiss_index [gw2] node down: Not properly terminated [gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index replacing crashed worker gw2 ```

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I_kwDODunzps6N2IXI

Re-enable raising error from huggingface-hub FutureWarning in CI

closed

MEMBER

Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR: - #6876 Note that this can only be done once transformers releases the fix: - https://github.com/huggingface/transformers/pull/31007

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I_kwDODunzps6N1a3l

Support ruff 0.5.0 in CI

closed

MEMBER

Support ruff 0.5.0 in CI. Also revert: - #7007

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I_kwDODunzps6N1Yhn

CI is broken after ruff-0.5.0: E721

closed

MEMBER

After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule. See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983 > src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks

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I_kwDODunzps6Nw-Ad

EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files

closed

NONE

### Describe the bug while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files" ### Steps to reproduce the bug This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file. Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub). ```` from datasets import load_dataset dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl") ```` error: ```` EmptyDatasetError Traceback (most recent call last) Cell In[18], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("imagefolder", 4 data_dir="path/to/jsonl/file/metadata.jsonl") 5 dataset[0]["objects"] File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2589 verification_mode = VerificationMode( 2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS 2591 ) 2593 # Create a dataset builder -> 2594 builder_instance = load_dataset_builder( 2595 path=path, 2596 name=name, 2597 data_dir=data_dir, 2598 data_files=data_files, 2599 cache_dir=cache_dir, 2600 features=features, 2601 download_config=download_config, 2602 download_mode=download_mode, 2603 revision=revision, 2604 token=token, 2605 storage_options=storage_options, 2606 trust_remote_code=trust_remote_code, 2607 _require_default_config_name=name is None, 2608 **config_kwargs, 2609 ) 2611 # Return iterable dataset in case of streaming 2612 if streaming: File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs) 2264 download_config = download_config.copy() if download_config else DownloadConfig() 2265 download_config.storage_options.update(storage_options) -> 2266 dataset_module = dataset_module_factory( 2267 path, 2268 revision=revision, 2269 download_config=download_config, 2270 download_mode=download_mode, 2271 data_dir=data_dir, 2272 data_files=data_files, 2273 cache_dir=cache_dir, 2274 trust_remote_code=trust_remote_code, 2275 _require_default_config_name=_require_default_config_name, 2276 _require_custom_configs=bool(config_kwargs), 2277 ) 2278 # Get dataset builder class from the processing script 2279 builder_kwargs = dataset_module.builder_kwargs File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs) 1782 # We have several ways to get a dataset builder: 1783 # 1784 # - if path is the name of a packaged dataset module (...) 1796 1797 # Try packaged 1798 if path in _PACKAGED_DATASETS_MODULES: 1799 return PackagedDatasetModuleFactory( 1800 path, 1801 data_dir=data_dir, 1802 data_files=data_files, 1803 download_config=download_config, 1804 download_mode=download_mode, -> 1805 ).get_module() 1806 # Try locally 1807 elif path.endswith(filename): File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self) 1135 def get_module(self) -> DatasetModule: 1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix() 1137 patterns = ( 1138 sanitize_patterns(self.data_files) 1139 if self.data_files is not None -> 1140 else get_data_patterns(base_path, download_config=self.download_config) 1141 ) 1142 data_files = DataFilesDict.from_patterns( 1143 patterns, 1144 download_config=self.download_config, 1145 base_path=base_path, 1146 ) 1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config) 501 return _get_data_files_patterns(resolver) 502 except FileNotFoundError: --> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files` ``` ### Expected behavior It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files." ### Environment info I am using conda environment.

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I_kwDODunzps6NcA0_

Datasetbuilder Local Download FileNotFoundError

open

NONE

### Describe the bug So I was trying to download a dataset and save it as parquet and I follow the [tutorial](https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage) of Huggingface. However, during the excution I face a FileNotFoundError. I debug the code and it seems there is a bug there: So first it creates a .incomplete folder and before moving its contents the following code deletes the directory [Code](https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/builder.py#L984) hence as a result I face with: ``` FileNotFoundError: [Errno 2] No such file or directory: '~/data/Parquet/.incomplete '``` ### Steps to reproduce the bug ``` from datasets import load_dataset_builder from pathlib import Path parquet_dir = "~/data/Parquet/" Path(parquet_dir).mkdir(parents=True, exist_ok=True) builder = load_dataset_builder( "rotten_tomatoes", ) builder.download_and_prepare(parquet_dir, file_format="parquet") ``` ### Expected behavior Downloads the files and saves as parquet ### Environment info Ubuntu, Python 3.10 ``` datasets 2.19.1 ```

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I_kwDODunzps6Nb2Qh

IterableDataset: Unsupported ScalarType BFloat16

closed

NONE

### Describe the bug `IterableDataset.from_generator` crashes when using BFloat16: ``` File "/usr/local/lib/python3.11/site-packages/datasets/utils/_dill.py", line 169, in _save_torchTensor args = (obj.detach().cpu().numpy(),) ^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: Got unsupported ScalarType BFloat16 ``` ### Steps to reproduce the bug ```python import torch from datasets import IterableDataset def demo(x): yield {"x": x} x = torch.tensor([1.], dtype=torch.bfloat16) dataset = IterableDataset.from_generator( demo, gen_kwargs=dict(x=x), ) example = next(iter(dataset)) print(example) ``` ### Expected behavior Code sample should print: ```python {'x': tensor([1.], dtype=torch.bfloat16)} ``` ### Environment info ``` datasets==2.20.0 torch==2.2.2 ```

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I_kwDODunzps6NYVSv

CI is broken for tests using hf-internal-testing/librispeech_asr_dummy

closed

MEMBER

CI is broken: https://github.com/huggingface/datasets/actions/runs/9657882317/job/26637998686?pr=6996 ``` FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other'] Right contains one more item: 'other' Full diff: [ 'clean', - 'other', ] FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None ``` Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a

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I_kwDODunzps6NTjeD

ImportError when importing datasets.load_dataset

closed

NONE

### Describe the bug I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'. ### Steps to reproduce the bug 1. pip install git+https://github.com/huggingface/datasets 2. from datasets import load_dataset ### Expected behavior ImportError Traceback (most recent call last) Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1) ----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset [3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train") [4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test") File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7 1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors. [2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) # [3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License"); (...) [12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and [13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License. [15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0" ---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset [18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction [19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63 [61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc [62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs ---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import ( [64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo, [65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd, ... [70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) ) [71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile [72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py) Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)... ### Environment info Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub $ datasets-cli env Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module> File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module> from .arrow_dataset import Dataset File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module> from huggingface_hub import ( ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py) (CS224S)

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I_kwDODunzps6NIyS-

Dataset with streaming doesn't work with proxy

open

NONE

### Describe the bug I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine. ### Steps to reproduce the bug use load_dataset with streaming = True in AIMOS ### Expected behavior does not hang indefinitely and loads batches to start training run ### Environment info _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge _pytorch_select 2.0 cuda_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 abseil-cpp 20220623.0 h9888cd1_6 conda-forge absl-py 1.0.0 py311h399429b_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 aiofiles 23.2.1 pyhd8ed1ab_0 conda-forge aiohttp 3.8.6 py311hf118e41_0 aiosignal 1.2.0 pyhd3eb1b0_0 archspec 0.2.3 pyhd8ed1ab_0 conda-forge arrow-cpp 11.0.0 ha3edaa6_5_cpu conda-forge async-timeout 4.0.2 py311h6ffa863_0 attrs 23.1.0 py311h6ffa863_0 av 10.0.0 py311he6153ed_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 aws-c-auth 0.6.24 hb81f6d7_5 conda-forge aws-c-cal 0.5.20 h3c2b4d9_6 conda-forge aws-c-common 0.8.11 h4194056_0 conda-forge aws-c-compression 0.2.16 ha19333d_3 conda-forge aws-c-event-stream 0.2.18 h12a9399_6 conda-forge aws-c-http 0.7.4 ha2cde00_2 conda-forge aws-c-io 0.13.17 h9189062_2 conda-forge aws-c-mqtt 0.8.6 h40d1a04_6 conda-forge aws-c-s3 0.2.4 hbdbe4f0_3 conda-forge aws-c-sdkutils 0.1.7 ha19333d_3 conda-forge aws-checksums 0.1.14 ha19333d_3 conda-forge aws-crt-cpp 0.19.7 hd018011_7 conda-forge aws-sdk-cpp 1.10.57 hb9575ba_4 conda-forge blas 1.0 openblas blinker 1.8.2 pyhd8ed1ab_0 conda-forge boltons 23.0.0 py311h6ffa863_0 boost-cpp 1.82.0 h25e6d66_2 bottleneck 1.3.5 py311h34f6284_0 brotli 1.0.9 hf118e41_7 brotli-bin 1.0.9 hf118e41_7 brotli-python 1.0.9 py311h4a02239_7 bzip2 1.0.8 h7b6447c_0 c-ares 1.19.1 hf118e41_0 ca-certificates 2024.6.2 h0f6029e_0 conda-forge cachetools 5.3.3 pyhd8ed1ab_0 conda-forge certifi 2024.6.2 pyhd8ed1ab_0 conda-forge cffi 1.15.1 py311hf118e41_3 charset-normalizer 2.0.4 pyhd3eb1b0_0 click 8.1.7 unix_pyh707e725_0 conda-forge conda 24.5.0 py311h1af927a_0 conda-forge conda-content-trust 0.2.0 py311h6ffa863_0 conda-libmamba-solver 23.11.1 py311h6ffa863_0 conda-package-handling 2.2.0 py311h6ffa863_0 conda-package-streaming 0.9.0 py311h6ffa863_0 contourpy 1.0.5 py311h25e6d66_0 cryptography 41.0.3 py311hb0e80e7_0 cudatoolkit 11.8.0 hedcfb66_13 conda-forge cudnn 8.9.2_11.8 h9ceb136_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 cycler 0.11.0 pyhd3eb1b0_0 datasets 2.12.0 py311h6ffa863_0 dill 0.3.6 py311h6ffa863_0 distro 1.9.0 pyhd8ed1ab_0 conda-forge ffmpeg 4.2.2 opence_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 filelock 3.9.0 py311h6ffa863_0 fmt 9.1.0 h25e6d66_0 fonttools 4.25.0 pyhd3eb1b0_0 freetype 2.12.1 hd23a775_0 frozendict 2.4.4 py311hb02d432_0 conda-forge frozenlist 1.4.0 py311hf118e41_0 fsspec 2023.9.2 py311h6ffa863_0 gflags 2.2.2 he6710b0_0 giflib 5.2.1 hf118e41_3 glog 0.6.0 hbe088e0_0 conda-forge gmp 6.3.0 h46f38da_0 conda-forge gmpy2 2.1.5 py311h2758da7_1 conda-forge google-auth 2.30.0 pyhff2d567_0 conda-forge google-auth-oauthlib 0.5.3 pyhd8ed1ab_0 conda-forge grpc-cpp 1.51.1 h8ba971d_1 conda-forge grpcio 1.54.3 py311h414e0d3_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 huggingface_hub 0.17.3 py311h6ffa863_0 icu 73.1 h4a02239_0 idna 3.4 py311h6ffa863_0 importlib-metadata 6.0.0 py311h6ffa863_0 jinja2 3.1.4 pyhd8ed1ab_0 conda-forge jpeg 9e hf118e41_1 jsonpatch 1.32 pyhd3eb1b0_0 jsonpointer 2.1 pyhd3eb1b0_0 kiwisolver 1.4.4 py311h4a02239_0 krb5 1.20.1 hc019ccd_1 lame 3.100 hb283c62_1003 conda-forge lcms2 2.12 h2045e0b_0 ld_impl_linux-ppc64le 2.38 hec883e6_1 lerc 3.0 h29c3540_0 leveldb 1.23 h24532b4_1 conda-forge libabseil 20220623.0 cxx17_h9235812_6 conda-forge libarchive 3.6.2 hd8ab008_2 libarrow 11.0.0 h837770b_5_cpu conda-forge libboost 1.82.0 haf51a6a_2 libbrotlicommon 1.0.9 hf118e41_7 libbrotlidec 1.0.9 hf118e41_7 libbrotlienc 1.0.9 hf118e41_7 libcrc32c 1.1.2 h3b9df90_0 conda-forge libcurl 8.4.0 h4d62439_0 libdeflate 1.17 hf118e41_1 libedit 3.1.20221030 hf118e41_0 libev 4.33 h140841e_1 libevent 2.1.10 h19c23f1_4 conda-forge libexpat 2.6.2 h46f38da_0 conda-forge libffi 3.4.4 h4a02239_0 libgcc-ng 13.2.0 h31e42bb_10 conda-forge libgfortran-ng 11.2.0 hb3889a9_1 libgfortran5 11.2.0 h1234567_1 libgomp 13.2.0 h31e42bb_10 conda-forge libgoogle-cloud 2.7.0 h11140b6_1 conda-forge libgrpc 1.51.1 h4d29a31_1 conda-forge libmamba 1.5.3 h7c6fafd_0 libmambapy 1.5.3 py311h828bf7b_0 libnghttp2 1.57.0 h44e5816_0 libnsl 2.0.1 ha17a0cc_0 conda-forge libopenblas 0.3.23 hc5a31fb_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 libopus 1.3.1 h4e0d66e_1 conda-forge libpng 1.6.39 hf118e41_0 libprotobuf 3.21.12 h1776448_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 libsolv 0.7.24 h0f529ac_0 libsqlite 3.45.3 hd4bbf49_0 conda-forge libssh2 1.10.0 h50fa78f_2 libstdcxx-ng 13.2.0 h262982c_10 conda-forge libthrift 0.18.0 h82f1162_0 conda-forge libtiff 4.5.1 h4a02239_0 libutf8proc 2.8.0 hb283c62_0 conda-forge libuuid 2.38.1 h4194056_0 conda-forge libvpx 1.13.1 h46f38da_0 conda-forge libwebp 1.3.2 h0f96ee2_0 libwebp-base 1.3.2 hf118e41_0 libxcrypt 4.4.36 ha17a0cc_1 conda-forge libxml2 2.10.4 h18e3229_1 libzlib 1.2.13 h1f2b957_6 conda-forge llvm-openmp 14.0.6 hc028133_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 lmdb 0.9.31 ha17a0cc_1 conda-forge lz4-c 1.9.4 h4a02239_0 markdown 3.4.4 pyhd8ed1ab_0 conda-forge markupsafe 2.1.5 py311h32d8acf_0 conda-forge matplotlib 3.8.0 py311h6ffa863_0 matplotlib-base 3.8.0 py311h52e1fcc_0 menuinst 2.1.1 py311h1af927a_0 conda-forge mpc 1.3.1 heaf1863_0 conda-forge mpfr 4.2.1 haad2271_1 conda-forge mpmath 1.3.0 pyhd8ed1ab_0 conda-forge multidict 6.0.2 py311hf118e41_0 multiprocess 0.70.14 py311h6ffa863_0 munkres 1.1.4 py_0 mypy_extensions 1.0.0 pyha770c72_0 conda-forge nccl 2.18.3 cuda11.8_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 ncurses 6.4 h4a02239_0 nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge networkx 2.8.8 pyhd8ed1ab_0 conda-forge nomkl 3.0 0 https://ftp.osuosl.org/pub/open-ce/1.10.0 numactl 2.0.16 hba61f60_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 numexpr 2.8.7 py311hc46fc55_0 numpy 1.24.3 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pyhd3eb1b0_0 python-tzdata 2023.3 pyhd3eb1b0_0 python-xxhash 2.0.2 py311hf118e41_1 python_abi 3.11 4_cp311 conda-forge pytorch 2.0.1 cuda11.8_py311_1 https://ftp.osuosl.org/pub/open-ce/1.10.0 pytorch-base 2.0.1 cuda11.8_py311_pb4.21.12_4 https://ftp.osuosl.org/pub/open-ce/1.10.0 pytz 2023.3.post1 py311h6ffa863_0 pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge pyyaml 6.0.1 py311hf118e41_0 re2 2023.02.01 h883269e_0 conda-forge readline 8.2 hf118e41_0 regex 2023.10.3 py311hf118e41_0 reproc 14.2.4 h29c3540_1 reproc-cpp 14.2.4 h29c3540_1 requests 2.31.0 py311h6ffa863_0 requests-oauthlib 2.0.0 pyhd8ed1ab_0 conda-forge responses 0.13.3 pyhd3eb1b0_0 rsa 4.9 pyhd8ed1ab_0 conda-forge ruamel.yaml 0.17.21 py311hf118e41_0 s2n 1.3.37 h5e47323_0 conda-forge safetensors 0.4.0 py311hda16d9e_0 scipy 1.11.1 py311hd69e9bb_0 https://ftp.osuosl.org/pub/open-ce/1.10.0 sentencepiece 0.1.97 h1e74c73_py311_pb4.21.12_2 https://ftp.osuosl.org/pub/open-ce/1.10.0 setuptools 68.0.0 py311h6ffa863_0 six 1.16.0 pyhd3eb1b0_1 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I_kwDODunzps6NEGCx

Problematic rank after calling `split_dataset_by_node` twice

closed

CONTRIBUTOR

### Describe the bug I'm trying to split `IterableDataset` by `split_dataset_by_node`. But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`. ### Steps to reproduce the bug Here is the minimal code for reproduction: ```py >>> from datasets import load_dataset >>> from datasets.distributed import split_dataset_by_node >>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True) >>> dataset = split_dataset_by_node(dataset, 1, 32) >>> dataset._distributed DistributedConfig(rank=1, world_size=32) >>> dataset = split_dataset_by_node(dataset, 1, 15) >>> dataset._distributed DistributedConfig(rank=481, world_size=480) ``` As you can see, the second rank 481 > 480, which is problematic. ### Expected behavior I think this error comes from this line @lhoestq https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944 We may need to obtain the rank first. Then the above code gives ```py >>> dataset._distributed DistributedConfig(rank=16, world_size=480) ``` ### Environment info datasets==2.20.0

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I_kwDODunzps6M_4bh

cache in nfs error

open

NONE

### Describe the bug - When reading dataset, a cache will be generated to the ~/. cache/huggingface/datasets directory - When using .map and .filter operations, runtime cache will be generated to the /tmp/hf_datasets-* directory - The default is to use the path of tempfile.tempdir - If I modify this path to the NFS disk, an error will be reported, but the program will continue to run - https://github.com/huggingface/datasets/blob/main/src/datasets/config.py#L257 ``` Traceback (most recent call last): File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap self.run() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server server.serve_forever() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever sys.exit(0) SystemExit: 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers finalizer() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__ res = self._callback(*self._args, **self._kwargs) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir rmtree(tempdir) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree _rmtree_safe_fd(fd, path, onerror) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd onerror(os.unlink, fullname, sys.exc_info()) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd os.unlink(entry.name, dir_fd=topfd) OSError: [Errno 16] Device or resource busy: '.nfs000000038330a012000030b4' Traceback (most recent call last): File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap self.run() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server server.serve_forever() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever sys.exit(0) SystemExit: 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers finalizer() File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__ res = self._callback(*self._args, **self._kwargs) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir rmtree(tempdir) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree _rmtree_safe_fd(fd, path, onerror) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd onerror(os.unlink, fullname, sys.exc_info()) File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd os.unlink(entry.name, dir_fd=topfd) OSError: [Errno 16] Device or resource busy: '.nfs0000000400064d4a000030e5' ``` ### Steps to reproduce the bug ``` import os import time import tempfile from datasets import load_dataset def add_column(sample): # print(type(sample)) # time.sleep(0.1) sample['__ds__stats__'] = {'data': 123} return sample def filt_column(sample): # print(type(sample)) if len(sample['content']) > 10: return True else: return False if __name__ == '__main__': input_dir = '/mnt/temp/CN/small' # some json dataset dataset = load_dataset('json', data_dir=input_dir) temp_dir = '/media/release/release/temp/temp' # a nfs folder os.makedirs(temp_dir, exist_ok=True) # change huggingface-datasets runtime cache in nfs（default in /tmp） tempfile.tempdir = temp_dir aa = dataset.map(add_column, num_proc=64) aa = aa.filter(filt_column, num_proc=64) print(aa) ``` ### Expected behavior no error occur ### Environment info datasets==2.18.0 ubuntu 20.04

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I_kwDODunzps6Mzwgk

AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'

closed

NONE

### Describe the bug I have been struggling with this for two days, any help would be appreciated. Python 3.10 ``` from setfit import SetFitModel from huggingface_hub import login access_token_read = "cccxxxccc" # Authenticate with the Hugging Face Hub login(token=access_token_read) # Load the models from the Hugging Face Hub trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance") trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust") trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent") trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic") ``` ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[6], line 1 ----> 1 from setfit import SetFitModel 2 from huggingface_hub import login 4 access_token_read = "ccsddsds" File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7 4 import os 5 import warnings ----> 7 from .data import get_templated_dataset, sample_dataset 8 from .model_card import SetFitModelCardData 9 from .modeling import SetFitHead, SetFitModel File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5 3 import pandas as pd 4 import torch ----> 5 from datasets import Dataset, DatasetDict, load_dataset 6 from torch.utils.data import Dataset as TorchDataset 8 from . import logging File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18 1 # ruff: noqa 2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors. 3 # (...) 13 # See the License for the specific language governing permissions and 14 # limitations under the License. 16 __version__ = "2.19.0" ---> 18 from .arrow_dataset import Dataset 19 from .arrow_reader import ReadInstruction 20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76 73 from tqdm.contrib.concurrent import thread_map 75 from . import config ---> 76 from .arrow_reader import ArrowReader 77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 78 from .data_files import sanitize_patterns File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29 26 from typing import TYPE_CHECKING, List, Optional, Union 28 import pyarrow as pa ---> 29 import pyarrow.parquet as pq 30 from tqdm.contrib.concurrent import thread_map 32 from .download.download_config import DownloadConfig File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20 1 # Licensed to the Apache Software Foundation (ASF) under one 2 # or more contributor license agreements. See the NOTICE file 3 # distributed with this work for additional information (...) 17 18 # flake8: noqa ---> 20 from .core import * File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33 30 import pyarrow as pa 32 try: ---> 33 import pyarrow._parquet as _parquet 34 except ImportError as exc: 35 raise ImportError( 36 "The pyarrow installation is not built with support " 37 f"for the Parquet file format ({str(exc)})" 38 ) from None File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet() AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType' ``` setfit: 1.0.3 transformers: 4.41.2 lingua-language-detector: 2.0.2 polars: 0.20.31 lightning: None google-cloud-bigquery: 3.24.0 shapely: 2.0.4 pyarrow: 16.0.0 ### Steps to reproduce the bug I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have. ### Expected behavior Just ron normally. ### Environment info 3.10

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I_kwDODunzps6My3NC

Convert polars DataFrame back to datasets

closed

NONE

### Feature request This returns error. ```python from datasets import Dataset dsdf = Dataset.from_dict({"x": [[1, 2], [3, 4, 5]], "y": ["a", "b"]}) Dataset.from_polars(dsdf.to_polars()) ``` ValueError: Arrow type large_list<item: int64> does not have a datasets dtype equivalent. ### Motivation When datasets contain Sequence data type, it will be converted to Arrow type large_list. However, the reverse (from large_list to Sequence) does not work. ### Your contribution No

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I_kwDODunzps6MxBgd

cannot split dataset when using load_dataset

closed

NONE

### Describe the bug when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document, This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generating train split and validation split but bug happen again in test split. ### Steps to reproduce the bug from datasets import load_dataset, load_metric, Audio common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True) ### Expected behavior ``` { "name": "ValueError", "message": "Instruction \"train\" corresponds to no data!", "stack": "--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[2], line 3 1 from datasets import load_dataset, load_metric, Audio ----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True) 4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) 2622 # Build dataset for splits 2623 keep_in_memory = ( 2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2625 ) -> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2627 # Rename and cast features to match task schema 2628 if task is not None: 2629 # To avoid issuing the same warning twice File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1265 # Create a dataset for each of the given splits -> 1266 datasets = map_nested( 1267 partial( 1268 self._build_single_dataset, 1269 run_post_process=run_post_process, 1270 verification_mode=verification_mode, 1271 in_memory=in_memory, 1272 ), 1273 split, 1274 map_tuple=True, 1275 disable_tqdm=True, 1276 ) 1277 if isinstance(datasets, dict): 1278 datasets = DatasetDict(datasets) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc) 482 if batched: 483 data_struct = [data_struct] --> 484 mapped = function(data_struct) 485 if batched: 486 mapped = mapped[0] File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory) 1293 split = Split(split) 1295 # Build base dataset -> 1296 ds = self._as_dataset( 1297 split=split, 1298 in_memory=in_memory, 1299 ) 1300 if run_post_process: 1301 for resource_file_name in self._post_processing_resources(split).values(): File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory) 1368 if self._check_legacy_cache(): 1369 dataset_name = self.name -> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read( 1371 name=dataset_name, 1372 instructions=split, 1373 split_infos=self.info.splits.values(), 1374 in_memory=in_memory, 1375 ) 1376 fingerprint = self._get_dataset_fingerprint(split) 1377 return Dataset(fingerprint=fingerprint, **dataset_kwargs) File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory) 254 msg = f'Instruction \"{instructions}\" corresponds to no data!' 255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!' --> 256 raise ValueError(msg) 257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ValueError: Instruction \"train\" corresponds to no data!" } ``` ### Environment info Environment: python 3.9 windows 11 pro VScode+jupyter

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I_kwDODunzps6MuKBq

Support NumPy 2.0

closed

CONTRIBUTOR

### Feature request Support NumPy 2.0. ### Motivation NumPy introduces the Array API, which bridges the gap between machine learning libraries. Many clients of HuggingFace are eager to start using the Array API. Besides that, NumPy 2 provides a cleaner interface than NumPy 1. ### Tasks NumPy 2.0 was released for testing so that libraries could ensure compatibility [since mid-March](https://github.com/numpy/numpy/issues/24300#issuecomment-1986815755). What needs to be done for HuggingFace to support Numpy 2? - [x] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976 - [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975): https://github.com/huggingface/datasets/pull/6991

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I_kwDODunzps6MrWsD

How can I load partial parquet files only?

closed

NONE

I have a HUGE dataset about 14TB, I unable to download all parquet all. I just take about 100 from it. dataset = load_dataset("xx/", data_files="data/train-001*-of-00314.parquet") How can I just using 000 - 100 from a 00314 from all partially? I search whole net didn't found a solution, **this is stupid if they didn't support it, and I swear I wont using stupid parquet any more**

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I_kwDODunzps6Mn_xF

load json file error with v2.20.0

closed

NONE

### Describe the bug ``` load_dataset(path="json", data_files="./test.json") ``` ``` Generating train split: 0 examples [00:00, ? examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 132, in _generate_tables pa_table = paj.read_json( File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1997, in _prepare_split_single for _, table in generator: File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 155, in _generate_tables df = pd.read_json(f, dtype_backend="pyarrow") File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper return func(*args, **kwargs) TypeError: read_json() got an unexpected keyword argument 'dtype_backend' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/app/t1.py", line 11, in <module> load_dataset(path=data_path, data_files="./t2.json") File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2616, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1029, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1124, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1884, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 2040, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset ``` ``` import pandas as pd with open("./test.json", "r") as f: df = pd.read_json(f, dtype_backend="pyarrow") ``` ``` Traceback (most recent call last): File "/app/t3.py", line 3, in <module> df = pd.read_json(f, dtype_backend="pyarrow") File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper return func(*args, **kwargs) TypeError: read_json() got an unexpected keyword argument 'dtype_backend' ``` ### Steps to reproduce the bug . ### Expected behavior . ### Environment info ``` datasets 2.20.0 pandas 1.5.3 ```

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I_kwDODunzps6MZley

IndexError during training with Squad dataset and T5-small model

closed

NONE

### Describe the bug I am encountering an IndexError while training a T5-small model on the Squad dataset using the transformers and datasets libraries. The error occurs even with a minimal reproducible example, suggesting a potential bug or incompatibility. ### Steps to reproduce the bug 1.Install the required libraries: !pip install transformers datasets 2.Run the following code: !pip install transformers datasets import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer, DataCollatorWithPadding # Load a small, publicly available dataset from datasets import load_dataset dataset = load_dataset("squad", split="train[:100]") # Use a small subset for testing # Load a pre-trained model and tokenizer model_name = "t5-small" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) # Define a basic data collator data_collator = DataCollatorWithPadding(tokenizer=tokenizer) # Define training arguments training_args = TrainingArguments( output_dir="./results", per_device_train_batch_size=2, num_train_epochs=1, ) # Create a trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset, data_collator=data_collator, ) # Train the model trainer.train() ### Expected behavior --------------------------------------------------------------------------- IndexError Traceback (most recent call last) [<ipython-input-23-f13a4b23c001>](https://localhost:8080/#) in <cell line: 34>() 32 33 # Train the model ---> 34 trainer.train() 10 frames [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size) 427 if isinstance(key, int): 428 if (key < 0 and key + size < 0) or (key >= size): --> 429 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") 430 return 431 elif isinstance(key, slice): IndexError: Invalid key: 42 is out of bounds for size 0 ### Environment info transformers version:4.41.2 datasets version:1.18.4 Python version:3.10.12

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I_kwDODunzps6MBSEe

Method to load Laion400m

open

NONE

### Feature request Large datasets like Laion400m are provided as embeddings. The provided methods in load_dataset are not straightforward for loading embedding files, i.e. img_emb_XX.npy ; XX = 0 to 99 ### Motivation The trial and experimentation is the key pivot of HF. It would be great if HF can load embeddings files s,ealessly. ### Your contribution I cam write the loader with some help.

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I_kwDODunzps6LmG0S

Manual downloads should count as downloads

open

NONE

### Feature request I would like to request that manual downloads of data files from Hugging Face dataset repositories count as downloads of a dataset. According to the documentation for the Hugging Face Hub, that is currently not the case: https://huggingface.co/docs/hub/en/datasets-download-stats ### Motivation This would ensure that downloads are accurately reported to end users. ### Your contribution N/A

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