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1,380,589,402
I_kwDODunzps5SSh9a
5,002
Dataset Viewer issue for loubnabnl/humaneval-x
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null
2
2022-09-21T09:06:17
2022-09-21T11:49:49
2022-09-21T11:49:49
NONE
null
### Link https://huggingface.co/datasets/loubnabnl/humaneval-x/viewer/ ### Description The dataset has subsets but the viewer gets stuck in the default subset even when I select another one (the data loading of the subsets works fine) ### Owner Yes
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I_kwDODunzps5SPLHW
5,000
Dataset Viewer issue for asapp/slue
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2022-09-20T16:45:45
2022-09-27T07:04:03
2022-09-21T07:24:07
NONE
null
### Link https://huggingface.co/datasets/asapp/slue/viewer/ ### Description Hi, I wonder how to get the dataset viewer of our slue dataset to work. Best, Felix ### Owner Yes
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1,379,345,161
I_kwDODunzps5SNyMJ
4,996
Dataset Viewer issue for Jean-Baptiste/wikiner_fr
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2022-09-20T12:32:07
2022-09-27T12:35:44
2022-09-27T12:35:44
CONTRIBUTOR
null
### Link https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr ### Description ``` Error code: StreamingRowsError Exception: FileNotFoundError Message: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json' Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token) File "/src/services/worker/src/worker/utils.py", line 123, in decorator return func(*args, **kwargs) File "/src/services/worker/src/worker/responses/first_rows.py", line 77, in get_rows rows_plus_one = list(itertools.islice(ds, rows_max_number + 1)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__ for key, example in self._iter(): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter yield from ex_iterable File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__ yield from self.generate_examples_fn(**self.kwargs) File "/tmp/modules-cache/datasets_modules/datasets/Jean-Baptiste--wikiner_fr/683a580ba6ec769d508f7dfc603a651667b0ed3817b1ae5bfd45f97cc024923f/wikiner_fr.py", line 165, in _generate_examples dataset = Dataset.load_from_disk(filepath) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1210, in load_from_disk with open(Path(dataset_path, config.DATASET_STATE_JSON_FILENAME).as_posix(), encoding="utf-8") as state_file: FileNotFoundError: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json' ``` Is it an error with the dataset script, or the data itself, @huggingface/datasets? https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr/tree/main ### Owner No
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4,995
Get a specific Exception when the dataset has no data
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null
0
2022-09-20T09:31:59
2022-09-21T12:21:25
2022-09-21T12:21:25
CONTRIBUTOR
null
In the dataset viewer on the Hub (https://huggingface.co/datasets/glue/viewer), we would like (https://github.com/huggingface/moon-landing/issues/3882) to show a specific message when the repository lacks any data files. In that case, instead of showing a complex traceback, we want to show a call to action to help the user upload data. To do that, it would be very helpful to know for sure that the repository is missing any (supported) data files. It could be done by raising a custom exception, for example, `NoDataError`.
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4,994
delete the hardcoded license list in `datasets`
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null
0
2022-09-20T09:14:41
2022-09-22T11:45:47
2022-09-22T11:45:47
MEMBER
null
> Feel free to delete the license list in `datasets` [...] > > Also FYI in #4926 I also removed all the validation steps anyway (language, license, types etc.) _Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238401662_ > [...], in my opinion we can just delete this file from `datasets`, the validation is happening hub-side anyways now? _Originally posted by @julien-c in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238390659_
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4,990
"no-token" is passed to `huggingface_hub` when token is `None`
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null
6
2022-09-19T15:14:40
2022-09-30T09:16:00
2022-09-30T09:16:00
CONTRIBUTOR
null
## Describe the bug In the 2 lines listed below, a token is passed to `huggingface_hub` to get information from a dataset. If no token is provided, a "no-token" string is passed. What is the purpose of it ? If no real, I would prefer if the `None` value could be sent directly to be handle by `huggingface_hub`. I feel that here it is working because we assume the token will never be validated. https://github.com/huggingface/datasets/blob/5b23f58535f14cc4dd7649485bce1ccc836e7bca/src/datasets/load.py#L753 https://github.com/huggingface/datasets/blob/5b23f58535f14cc4dd7649485bce1ccc836e7bca/src/datasets/load.py#L1121 ## Expected results Pass `token=None` to `huggingface_hub`. ## Actual results `token="no-token"` is passed. ## Environment info `huggingface_hub v0.10.0dev`
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1,376,832,233
I_kwDODunzps5SEMrp
4,989
Running add_column() seems to corrupt existing sequence-type column info
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2022-09-17T17:42:05
2022-09-19T12:54:54
2022-09-19T12:54:54
NONE
null
I have a dataset that contains a column ("foo") that is a sequence type of length 4. So when I run .to_pandas() on it, the resulting dataframe correctly contains 4 columns - foo_0, foo_1, foo_2, foo_3. So the 1st row of the dataframe might look like: ds = load_dataset(...) df = ds.to_pandas() df: foo_0 | foo_1 | foo_2 | foo_3 0.0 | 1.0 | 2.0 | 3.0 If I run .add_column("new_col", data) on the dataset, and then .to_pandas() on the resulting new dataset, the resulting dataframe contains only 2 columns - foo, new_col. The values in column foo are lists of length 4, the 4 elements that should have been split into separate columns. Dataframe 1st row would be: ds = load_dataset(...) new_ds = ds.add_column("new_col", data) df = new_ds.to_pandas() df: foo | new_col [0.0, 1.0, 2.0, 3.0] | new_val I've explored the 2 datasets in a debugger and haven't noticed any changes to any attributes related to the foo column, but I can't determine why the dataframes are so different.
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1,376,096,584
I_kwDODunzps5SBZFI
4,988
Add `IterableDataset.from_generator` to the API
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null
3
2022-09-16T15:19:41
2022-10-05T12:10:49
2022-10-05T12:10:49
CONTRIBUTOR
null
We've just added `Dataset.from_generator` to the API. It would also be cool to add `IterableDataset.from_generator` to support creating an iterable dataset from a generator. cc @lhoestq
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I_kwDODunzps5R_wXG
4,983
How to convert torch.utils.data.Dataset to huggingface dataset?
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2022-09-16T09:15:10
2022-09-20T11:23:43
2022-09-20T11:23:43
NONE
null
I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below: ```python from datasets import Dataset data = [[1, 2],[3, 4]] ds = Dataset.from_dict({"data": data}) ds = ds.with_format("torch") ds[0] ds[:2] ``` So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert? Thanks.
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1,375,604,693
I_kwDODunzps5R_g_V
4,982
Create dataset_infos.json with VALIDATION and TEST splits
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2022-09-16T08:21:19
2022-09-28T07:59:39
2022-09-28T07:59:39
NONE
null
The problem is described in that [issue](https://github.com/huggingface/datasets/issues/4895#issuecomment-1247975569). > When I try to create data_infos.json using datasets-cli test Peter.py --save_infos --all_configs I get an error: > ValueError: Unknown split "test". Should be one of ['train']. > > The data_infos.json is created perfectly fine when I use only one split - datasets.Split.TRAIN > > You can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch) I tried to clear the cache folder, than I got an another error. I run: ``` git clone https://huggingface.co/datasets/sberbank-ai/Peter cd Peter git checkout add_splits # switch to a add_splits branch rm dataset_infos.json # remove local dataset_infos.json rm -r ~/.cache/huggingface # remove cached dataset_infos.json datasets-cli test Peter.py --save_infos --all_configs # trying to create new dataset_infos.json ``` The error message: ``` Using custom data configuration default Testing builder 'default' (1/1) Downloading and preparing dataset peter/default to /Users/kalinin/.cache/huggingface/datasets/peter/default/0.0.0/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d... Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 5160.63it/s] Extracting data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last): File "/usr/local/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/usr/local/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/usr/local/lib/python3.9/site-packages/datasets/commands/test.py", line 137, in run builder.download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 771, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/kalinin/.cache/huggingface/modules/datasets_modules/datasets/Peter/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d/Peter.py", line 23, in _split_generators data_files = dl_manager.download_and_extract(_URLS) File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 431, in download_and_extract return self.extract(self.download(url_or_urls)) File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 403, in extract extracted_paths = map_nested( File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 393, in map_nested mapped = [ File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 394, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 330, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 213, in cached_path output_path = ExtractManager(cache_dir=download_config.cache_dir).extract( File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 46, in extract self.extractor.extract(input_path, output_path, extractor_format) File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 263, in extract with FileLock(lock_path): File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 399, in __init__ max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax FileNotFoundError: [Errno 2] No such file or directory: '' Exception ignored in: <function BaseFileLock.__del__ at 0x11caeec10> Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 328, in __del__ self.release(force=True) File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 303, in release with self._thread_lock: AttributeError: 'UnixFileLock' object has no attribute '_thread_lock' Extracting data files: 0%| | 0/4 [00:00<?, ?it/s] ``` Can you help me please? ## Environment info - `datasets` version: 2.4.0 - Platform: macOS-12.5.1-x86_64-i386-64bit - Python version: 3.9.5 - PyArrow version: 9.0.0 - Pandas version: 1.2.4
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Can't create a dataset with `float16` features
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2022-09-15T21:03:24
2022-09-26T09:34:50
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CONTRIBUTOR
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## Describe the bug I can't create a dataset with `float16` features. I understand from the traceback that this is a `pyarrow` error, but I don't see anywhere in the `datasets` documentation about how to successfully do this. Is it actually supported? I've tried older versions of `pyarrow` as well with the same exact error. The bug seems to arise from `datasets` casting the values to `double` and then `pyarrow` doesn't know how to convert those back to `float16`... does that sound right? Is there a way to bypass this since it's not necessary in the `numpy` and `torch` cases? Thanks! ## Steps to reproduce the bug All of the following raise the following error with the same exact (as far as I can tell) traceback: ```python ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float ``` ```python from datasets import Dataset, Features, Value Dataset.from_dict({"x": [0.0, 1.0, 2.0]}, features=Features(x=Value("float16"))) import numpy as np Dataset.from_dict({"x": np.arange(3, dtype=np.float16)}, features=Features(x=Value("float16"))) import torch Dataset.from_dict({"x": torch.arange(3).to(torch.float16)}, features=Features(x=Value("float16"))) ``` ## Expected results A dataset with `float16` features is successfully created. ## Actual results ```python --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) Cell In [14], line 1 ----> 1 Dataset.from_dict({"x": [1.0, 2.0, 3.0]}, features=Features(x=Value("float16"))) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py:870, in Dataset.from_dict(cls, mapping, features, info, split) 865 mapping = features.encode_batch(mapping) 866 mapping = { 867 col: OptimizedTypedSequence(data, type=features[col] if features is not None else None, col=col) 868 for col, data in mapping.items() 869 } --> 870 pa_table = InMemoryTable.from_pydict(mapping=mapping) 871 if info.features is None: 872 info.features = Features({col: ts.get_inferred_type() for col, ts in mapping.items()}) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:750, in InMemoryTable.from_pydict(cls, *args, **kwargs) 734 @classmethod 735 def from_pydict(cls, *args, **kwargs): 736 """ 737 Construct a Table from Arrow arrays or columns 738 (...) 748 :class:`datasets.table.Table`: 749 """ --> 750 return cls(pa.Table.from_pydict(*args, **kwargs)) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:3648, in pyarrow.lib.Table.from_pydict() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:5174, in pyarrow.lib._from_pydict() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:343, in pyarrow.lib.asarray() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:231, in pyarrow.lib.array() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py:197, in TypedSequence.__arrow_array__(self, type) 192 # otherwise we can finally use the user's type 193 elif type is not None: 194 # We use cast_array_to_feature to support casting to custom types like Audio and Image 195 # Also, when trying type "string", we don't want to convert integers or floats to "string". 196 # We only do it if trying_type is False - since this is what the user asks for. --> 197 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 198 return out 199 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs) 1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1682 else: -> 1683 return func(array, *args, **kwargs) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1853, in cast_array_to_feature(array, feature, allow_number_to_str) 1851 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str) 1852 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1853 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) 1854 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs) 1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1682 else: -> 1683 return func(array, *args, **kwargs) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1762, in array_cast(array, pa_type, allow_number_to_str) 1760 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type): 1761 raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") -> 1762 return array.cast(pa_type) 1763 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}") File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:919, in pyarrow.lib.Array.cast() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/compute.py:389, in cast(arr, target_type, safe, options) 387 else: 388 options = CastOptions.safe(target_type) --> 389 return call_function("cast", [arr], options) File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:560, in pyarrow._compute.call_function() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:355, in pyarrow._compute.Function.call() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float ``` ## Environment info - `datasets` version: 2.4.0 - Platform: macOS-12.5.1-arm64-arm-64bit - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.4
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Make `pyarrow` optional
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2022-09-15T17:38:03
2022-09-16T17:23:47
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**Is your feature request related to a problem? Please describe.** Is `pyarrow` really needed for every dataset? **Describe the solution you'd like** It is made optional. **Describe alternatives you've considered** Likely, no.
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Providing dataset size
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2022-09-14T13:09:27
2022-09-15T16:03:58
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**Is your feature request related to a problem? Please describe.** Especially for big datasets like [LAION](https://huggingface.co/datasets/laion/laion2B-en/), it's hard to know exactly the downloaded size (because there are many files and you don't have their exact size when downloaded). **Describe the solution you'd like** Auto-populating the downloaded dataset size on the dataset page would be really useful, including that of each split (when there are some). **Describe alternatives you've considered** People should be adding this to dataset cards, but I don't think that is systematically the case :slightly_smiling_face: **Additional context** Mentioned to @lhoestq
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Hope to adapt Python3.9 as soon as possible
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2022-09-26T16:32:35
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**Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context about the feature request here.
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[Apple M1] MemoryError: Cannot allocate write+execute memory for ffi.callback()
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2022-11-18T23:45:02
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## Describe the bug I'm trying to run `cast_column("audio", Audio())` on Apple M1 Pro, but it seems that it doesn't work. ## Steps to reproduce the bug ```python import datasets dataset = load_dataset("csv", data_files="./train.csv")["train"] dataset = dataset.map(lambda x: {"audio": str(DATA_DIR / "audio" / x["audio"])}) dataset = dataset.cast_column("audio", Audio()) dataset[0] ``` ## Expected results ``` {'audio': {'bytes': None, 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav'}, 'english_transcription': 'I would like to set up a joint account with my partner', 'intent_class': 11, 'lang_id': 4, 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav', 'transcription': 'I would like to set up a joint account with my partner'} ``` ## Actual results ````--------------------------------------------------------------------------- MemoryError Traceback (most recent call last) Input In [6], in <cell line: 1>() ----> 1 dataset[0] File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/arrow_dataset.py:2165, in Dataset.__getitem__(self, key) 2163 def __getitem__(self, key): # noqa: F811 2164 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2165 return self._getitem( 2166 key, 2167 ) File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/arrow_dataset.py:2150, in Dataset._getitem(self, key, decoded, **kwargs) 2148 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 2149 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 2150 formatted_output = format_table( 2151 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 2152 ) 2153 return formatted_output File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row) 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1647, in Features.decode_example(self, example, token_per_repo_id) 1634 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None): 1635 """Decode example with custom feature decoding. 1636 1637 Args: (...) 1644 :obj:`dict[str, Any]` 1645 """ -> 1647 return { 1648 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) 1649 if self._column_requires_decoding[column_name] 1650 else value 1651 for column_name, (feature, value) in zip_dict( 1652 {key: value for key, value in self.items() if key in example}, example 1653 ) 1654 } File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1648, in <dictcomp>(.0) 1634 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None): 1635 """Decode example with custom feature decoding. 1636 1637 Args: (...) 1644 :obj:`dict[str, Any]` 1645 """ 1647 return { -> 1648 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) 1649 if self._column_requires_decoding[column_name] 1650 else value 1651 for column_name, (feature, value) in zip_dict( 1652 {key: value for key, value in self.items() if key in example}, example 1653 ) 1654 } File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1260, in decode_nested_example(schema, obj, token_per_repo_id) 1257 # Object with special decoding: 1258 elif isinstance(schema, (Audio, Image)): 1259 # we pass the token to read and decode files from private repositories in streaming mode -> 1260 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None 1261 return obj File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/audio.py:156, in Audio.decode_example(self, value, token_per_repo_id) 154 array, sampling_rate = self._decode_non_mp3_file_like(file) 155 else: --> 156 array, sampling_rate = self._decode_non_mp3_path_like(path, token_per_repo_id=token_per_repo_id) 157 return {"path": path, "array": array, "sampling_rate": sampling_rate} File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/audio.py:257, in Audio._decode_non_mp3_path_like(self, path, format, token_per_repo_id) 254 use_auth_token = None 256 with xopen(path, "rb", use_auth_token=use_auth_token) as f: --> 257 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 258 return array, sampling_rate File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/util/decorators.py:88, in deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f(*args, **kwargs) 86 extra_args = len(args) - len(all_args) 87 if extra_args <= 0: ---> 88 return f(*args, **kwargs) 90 # extra_args > 0 91 args_msg = [ 92 "{}={}".format(name, arg) 93 for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:]) 94 ] File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/core/audio.py:164, in load(path, sr, mono, offset, duration, dtype, res_type) 161 else: 162 # Otherwise try soundfile first, and then fall back if necessary 163 try: --> 164 y, sr_native = __soundfile_load(path, offset, duration, dtype) 166 except RuntimeError as exc: 167 # If soundfile failed, try audioread instead 168 if isinstance(path, (str, pathlib.PurePath)): File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/core/audio.py:195, in __soundfile_load(path, offset, duration, dtype) 192 context = path 193 else: 194 # Otherwise, create the soundfile object --> 195 context = sf.SoundFile(path) 197 with context as sf_desc: 198 sr_native = sf_desc.samplerate File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:629, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd) 626 self._mode = mode 627 self._info = _create_info_struct(file, mode, samplerate, channels, 628 format, subtype, endian) --> 629 self._file = self._open(file, mode_int, closefd) 630 if set(mode).issuperset('r+') and self.seekable(): 631 # Move write position to 0 (like in Python file objects) 632 self.seek(0) File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:1179, in SoundFile._open(self, file, mode_int, closefd) 1177 file_ptr = _snd.sf_open_fd(file, mode_int, self._info, closefd) 1178 elif _has_virtual_io_attrs(file, mode_int): -> 1179 file_ptr = _snd.sf_open_virtual(self._init_virtual_io(file), 1180 mode_int, self._info, _ffi.NULL) 1181 else: 1182 raise TypeError("Invalid file: {0!r}".format(self.name)) File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:1197, in SoundFile._init_virtual_io(self, file) 1194 def _init_virtual_io(self, file): 1195 """Initialize callback functions for sf_open_virtual().""" 1196 @_ffi.callback("sf_vio_get_filelen") -> 1197 def vio_get_filelen(user_data): 1198 curr = file.tell() 1199 file.seek(0, SEEK_END) MemoryError: Cannot allocate write+execute memory for ffi.callback(). You might be running on a system that prevents this. For more information, see https://cffi.readthedocs.io/en/latest/using.html#callbacks ``` ## Environment info - `datasets` version: 2.4.0 - Platform: macOS-12.5.1-arm64-arm-64bit - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.4
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1,368,617,322
I_kwDODunzps5Rk3Fq
4,964
Column of arrays (2D+) are using unreasonably high memory
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2022-09-10T13:07:22
2022-09-22T18:29:22
null
CONTRIBUTOR
null
## Describe the bug When trying to store `Array2D, Array3D, etc` as column values in a dataset, accessing that column (or creating depending on how you create it, see code below) will cause more than 10 fold of memory usage. ## Steps to reproduce the bug ```python from datasets import Dataset, Features, Array2D, Array3D import numpy as np column_name = "a" array_shape = (64, 64, 3) data = np.random.random((10000,) + array_shape) dataset = Dataset.from_dict({column_name: data}, features=Features({column_name: Array3D(shape=array_shape, dtype="float64")})) ``` the code above will use about 10Gb of RAM while constructing the `dataset` object. The code below will use roughly the same amount of memory (and time) when trying to actually access the data itself of that column. ```python from datasets import Dataset import numpy as np column_name = "a" array_shape = (64, 64, 3) data = np.random.random((10000,) + array_shape) dataset = Dataset.from_dict({column_name: data}) dataset[column_name] ``` ## Expected results Some memory overhead, but not like as it is now and certainly not an overhead of such runtime that is currently happening. ## Actual results Enormous memory- and runtime overhead. ## Environment info - `datasets` version: 2.3.2 - Platform: macOS-12.5.1-arm64-arm-64bit - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.4
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Dataset without script does not support regular JSON data file
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2022-09-09T18:45:33
2022-09-20T15:40:07
2022-09-20T15:40:07
MEMBER
null
### Link https://huggingface.co/datasets/julien-c/label-studio-my-dogs ### Description <img width="1115" alt="image" src="https://user-images.githubusercontent.com/326577/189422048-7e9c390f-bea7-4521-a232-43f049ccbd1f.png"> ### Owner Yes
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fsspec 2022.8.2 breaks xopen in streaming mode
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2022-09-09T17:26:55
2022-09-12T17:45:50
2022-09-12T14:32:05
NONE
null
## Describe the bug When fsspec 2022.8.2 is installed in your environment, xopen will prematurely close files, making streaming mode inoperable. ## Steps to reproduce the bug ```python import datasets data = datasets.load_dataset('MLCommons/ml_spoken_words', 'id_wav', split='train', streaming=True) ``` ## Expected results Dataset should load as iterator. ## Actual results ``` [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1737 # Return iterable dataset in case of streaming 1738 if streaming: -> 1739 return builder_instance.as_streaming_dataset(split=split) 1740 1741 # Some datasets are already processed on the HF google storage [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path) 1023 ) 1024 self._check_manual_download(dl_manager) -> 1025 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)} 1026 # By default, return all splits 1027 if split is None: [~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _split_generators(self, dl_manager) 182 name=datasets.Split.TRAIN, 183 gen_kwargs={ --> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages], 185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in 186 self.config.languages] if not dl_manager.is_streaming else None, [~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in <listcomp>(.0) 182 name=datasets.Split.TRAIN, 183 gen_kwargs={ --> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages], 185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in 186 self.config.languages] if not dl_manager.is_streaming else None, [~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives(dl_manager, lang, format, split) 267 # for streaming case 268 def _download_audio_archives(dl_manager, lang, format, split): --> 269 archives_paths = _download_audio_archives_paths(dl_manager, lang, format, split) 270 return [dl_manager.iter_archive(archive_path) for archive_path in archives_paths] [~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives_paths(dl_manager, lang, format, split) 251 n_files_path = dl_manager.download(n_files_url) 252 --> 253 with open(n_files_path, "r", encoding="utf-8") as file: 254 n_files = int(file.read().strip()) # the file contains a number of archives 255 ValueError: I/O operation on closed file. ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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BioASQ AttributeError: 'BuilderConfig' object has no attribute 'schema'
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2022-09-09T16:06:43
2022-09-13T08:51:03
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NONE
null
## Describe the bug I am trying to load a dataset from drive and running into an error. ## Steps to reproduce the bug ```python data_dir = "/Users/dlituiev/repos/datasets/bioasq/BioASQ-training9b" bioasq_task_b = load_dataset("aps/bioasq_task_b", data_dir=data_dir) ``` ## Actual results `AttributeError: 'BuilderConfig' object has no attribute 'schema'` <details> ``` Using custom data configuration default-a1ca3e05be5abf2f --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Input In [8], in <cell line: 2>() 1 data_dir = "/Users/dlituiev/repos/datasets/bioasq/BioASQ-training9b" ----> 2 bioasq_task_b = load_dataset("aps/bioasq_task_b", data_dir=data_dir) File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/load.py:1723, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1720 ignore_verifications = ignore_verifications or save_infos 1722 # Create a dataset builder -> 1723 builder_instance = load_dataset_builder( 1724 path=path, 1725 name=name, 1726 data_dir=data_dir, 1727 data_files=data_files, 1728 cache_dir=cache_dir, 1729 features=features, 1730 download_config=download_config, 1731 download_mode=download_mode, 1732 revision=revision, 1733 use_auth_token=use_auth_token, 1734 **config_kwargs, 1735 ) 1737 # Return iterable dataset in case of streaming 1738 if streaming: File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/load.py:1526, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1523 raise ValueError(error_msg) 1525 # Instantiate the dataset builder -> 1526 builder_instance: DatasetBuilder = builder_cls( 1527 cache_dir=cache_dir, 1528 config_name=config_name, 1529 data_dir=data_dir, 1530 data_files=data_files, 1531 hash=hash, 1532 features=features, 1533 use_auth_token=use_auth_token, 1534 **builder_kwargs, 1535 **config_kwargs, 1536 ) 1538 return builder_instance File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/builder.py:1154, in GeneratorBasedBuilder.__init__(self, writer_batch_size, *args, **kwargs) 1153 def __init__(self, *args, writer_batch_size=None, **kwargs): -> 1154 super().__init__(*args, **kwargs) 1155 # Batch size used by the ArrowWriter 1156 # It defines the number of samples that are kept in memory before writing them 1157 # and also the length of the arrow chunks 1158 # None means that the ArrowWriter will use its default value 1159 self._writer_batch_size = writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/builder.py:307, in DatasetBuilder.__init__(self, cache_dir, config_name, hash, base_path, info, features, use_auth_token, repo_id, data_files, data_dir, name, **config_kwargs) 305 if info is None: 306 info = self.get_exported_dataset_info() --> 307 info.update(self._info()) 308 info.builder_name = self.name 309 info.config_name = self.config.name File ~/.cache/huggingface/modules/datasets_modules/datasets/aps--bioasq_task_b/3d54b1213f7e8001eef755af92877f9efa44161ee83c2a70d5d649defa95759e/bioasq_task_b.py:477, in BioasqTaskBDataset._info(self) 474 def _info(self): 475 476 # BioASQ Task B source schema --> 477 if self.config.schema == "source": 478 features = datasets.Features( 479 { 480 "id": datasets.Value("string"), (...) 504 } 505 ) 506 # simplified schema for QA tasks AttributeError: 'BuilderConfig' object has no attribute 'schema' ``` </details> ## Environment info - `datasets` version: 2.4.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.4 - PyArrow version: 9.0.0 - Pandas version: 1.4.3
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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.4.0/datasets/jsonl/jsonl.py
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2022-09-09T11:29:55
2022-09-09T11:38:44
2022-09-09T11:38:44
NONE
null
Hi, When I use load_dataset from local jsonl files, below error happens, and I type the link into the browser prompting me `404: Not Found`. I download the other `.py` files using the same method and it works. It seems that the server is missing the appropriate file, or it is a problem with the code version. ``` ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x2b08342004c0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))"))) ```
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1,366,382,314
I_kwDODunzps5RcVbq
4,955
Raise a more precise error when the URL is unreachable in streaming mode
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2022-09-08T13:52:37
2022-09-08T13:53:36
null
CONTRIBUTOR
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See for example: - https://github.com/huggingface/datasets/issues/3191 - https://github.com/huggingface/datasets/issues/3186 It would help provide clearer information on the Hub and help the dataset maintainer solve the issue by themselves quicker. Currently: - https://huggingface.co/datasets/compguesswhat <img width="1029" alt="Capture d’écran 2022-09-08 à 15 51 37" src="https://user-images.githubusercontent.com/1676121/189139946-6deffb91-f21b-4281-8825-a98026c69740.png"> - https://huggingface.co/datasets/nli_tr <img width="1032" alt="Capture d’écran 2022-09-08 à 15 51 44" src="https://user-images.githubusercontent.com/1676121/189139963-d26490ed-ad23-48ea-9cfc-1ab9c4d08d0c.png"> cc @albertvillanova
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CI test of TensorFlow is failing
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2022-09-08T13:39:29
2022-09-08T15:14:45
2022-09-08T15:14:45
MEMBER
null
## Describe the bug The following CI test fails: https://github.com/huggingface/datasets/runs/8246722693?check_suite_focus=true ``` FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - AssertionError: ``` Details: ``` _________________________ TempSeedTest.test_tensorflow _________________________ [gw0] linux -- Python 3.7.13 /opt/hostedtoolcache/Python/3.7.13/x64/bin/python self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow> @require_tf def test_tensorflow(self): import tensorflow as tf from tensorflow.keras import layers def gen_random_output(): model = layers.Dense(2) x = tf.random.uniform((1, 3)) return model(x).numpy() with temp_seed(42, set_tensorflow=True): out1 = gen_random_output() with temp_seed(42, set_tensorflow=True): out2 = gen_random_output() out3 = gen_random_output() > np.testing.assert_equal(out1, out2) E AssertionError: E Arrays are not equal E E Mismatched elements: 2 / 2 (100%) E Max absolute difference: 0.84619296 E Max relative difference: 16.083529 E x: array([[-0.793581, 0.333286]], dtype=float32) E y: array([[0.052612, 0.539708]], dtype=float32) tests/test_py_utils.py:149: AssertionError ```
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1,364,691,096
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4,945
Push to hub can push splits that do not respect the regex
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2022-09-07T13:45:17
2022-09-13T10:16:35
2022-09-13T10:16:35
MEMBER
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## Describe the bug The `push_to_hub` method can push splits that do not respect the regex check that is used for downloads. Therefore, splits may be pushed but never re-used, which can be painful if the split was done after runtime preprocessing. ## Steps to reproduce the bug ```python >>> from datasets import Dataset, DatasetDict, load_dataset >>> d = Dataset.from_dict({'x': [1,2,3], 'y': [1,2,3]}) >>> di = DatasetDict() >>> di['identifier-with-column'] = d >>> di.push_to_hub('open-source-metrics/test') Pushing split identifier-with-column to the Hub. Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:04<00:00, 4.40s/it] ``` Loading it afterwards: ```python >>> load_dataset('open-source-metrics/test') Downloading: 100%|██████████| 610/610 [00:00<00:00, 432kB/s] Using custom data configuration open-source-metrics--test-28b63ec7cde80488 Downloading and preparing dataset None/None (download: 950 bytes, generated: 48 bytes, post-processed: Unknown size, total: 998 bytes) to /home/lysandre/.cache/huggingface/datasets/open-source-metrics___parquet/open-source-metrics--test-28b63ec7cde80488/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec... Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 100%|██████████| 950/950 [00:00<00:00, 1.01MB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.48s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 2291.97it/s] Traceback (most recent call last): File "/home/lysandre/.pyenv/versions/3.10.6/lib/python3.10/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/load.py", line 1746, in load_dataset builder_instance.download_and_prepare( File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 771, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 48, in _split_generators splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files})) File "<string>", line 5, in __init__ File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 599, in __post_init__ NamedSplit(self.name) # check that it's a valid split name File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 346, in __init__ raise ValueError(f"Split name should match '{_split_re}' but got '{split_name}'.") ValueError: Split name should match '^\w+(\.\w+)*$' but got 'identifier-with-column'. ``` ## Expected results I would expect `push_to_hub` to stop me in my tracks if trying to upload a split that will not be working afterwards. ## Actual results See above ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.64-1-lts-x86_64-with-glibc2.36 - Python version: 3.10.6 - PyArrow version: 9.0.0 - Pandas version: 1.4.4
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larger dataset, larger GPU memory in the training phase? Is that correct?
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2022-09-07T08:46:30
2022-09-07T12:34:58
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from datasets import set_caching_enabled set_caching_enabled(False) for ds_name in ["squad","newsqa","nqopen","narrativeqa"]: train_ds = load_from_disk("../../../dall/downstream/processedproqa/{}-train.hf".format(ds_name)) break train_ds = concatenate_datasets([train_ds,train_ds,train_ds,train_ds]) #operation 1 trainer = QuestionAnsweringTrainer( #huggingface trainer model=model, args=training_args, train_dataset=train_ds, eval_dataset= None, eval_examples=None, answer_column_name=answer_column, dataset_name="squad", tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics if training_args.predict_with_generate else None, ) with operation 1, the GPU memory increases from 16G to 23G
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Trec Dataset has incorrect labels
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null
1
2022-09-06T22:13:40
2022-09-08T11:12:03
2022-09-08T11:12:03
NONE
null
## Describe the bug Both coarse and fine labels seem to be out of line. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = "trec" raw_datasets = load_dataset(dataset) df = pd.DataFrame(raw_datasets["test"]) df.head() ``` ## Expected results text (string) | coarse_label (class label) | fine_label (class label) -- | -- | -- How far is it from Denver to Aspen ? | 5 (NUM) | 40 (NUM:dist) What county is Modesto , California in ? | 4 (LOC) | 32 (LOC:city) Who was Galileo ? | 3 (HUM) | 31 (HUM:desc) What is an atom ? | 2 (DESC) | 24 (DESC:def) When did Hawaii become a state ? | 5 (NUM) | 39 (NUM:date) ## Actual results index | label-coarse |label-fine | text -- |-- | -- | -- 0 | 4 | 40 | How far is it from Denver to Aspen ? 1 | 5 | 21 | What county is Modesto , California in ? 2 | 3 | 12 | Who was Galileo ? 3 | 0 | 7 | What is an atom ? 4 | 4 | 8 | When did Hawaii become a state ? ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-1086-azure-x86_64-with-glibc2.27 - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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1,363,274,907
I_kwDODunzps5RQeyb
4,936
vivos (Vietnamese speech corpus) dataset not accessible
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null
3
2022-09-06T13:17:55
2022-09-21T06:06:02
2022-09-12T07:14:20
CONTRIBUTOR
null
## Describe the bug VIVOS data is not accessible anymore, neither of these links work (at least from France): * https://ailab.hcmus.edu.vn/assets/vivos.tar.gz (data) * https://ailab.hcmus.edu.vn/vivos (dataset page) Therefore `load_dataset` doesn't work. ## Steps to reproduce the bug ```python ds = load_dataset("vivos") ``` ## Expected results dataset loaded ## Actual results ``` ConnectionError: Couldn't reach https://ailab.hcmus.edu.vn/assets/vivos.tar.gz (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='ailab.hcmus.edu.vn', port=443): Max retries exceeded with url: /assets/vivos.tar.gz (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f9d8a27d190>: Failed to establish a new connection: [Errno -5] No address associated with hostname'))"))) ``` Will try to contact the authors, as we wanted to use Vivos as an example in documentation on how to create scripts for audio datasets (https://github.com/huggingface/datasets/pull/4872), because it's small and straightforward and uses tar archives.
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I_kwDODunzps5RQTBw
4,935
Dataset Viewer issue for ubuntu_dialogs_corpus
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2022-09-06T12:41:50
2022-09-06T12:51:25
2022-09-06T12:51:25
NONE
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### Link _No response_ ### Description _No response_ ### Owner _No response_
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4,934
Dataset Viewer issue for indonesian-nlp/librivox-indonesia
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6
2022-09-06T10:03:23
2022-09-06T12:46:40
2022-09-06T12:46:40
CONTRIBUTOR
null
### Link https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia ### Description I created a new speech dataset https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia, but the dataset preview doesn't work with following error message: ``` Server error Status code: 400 Exception: TypeError Message: unsupported operand type(s) for +: 'NoneType' and 'str' ``` Please help, I am not sure what the problem here is. Thanks a lot. ### Owner Yes
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Dataset/DatasetDict.filter() cannot have `batched=True` due to `mask` (numpy array?) being non-iterable.
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2022-09-06T09:47:48
2022-09-06T11:44:27
2022-09-06T11:44:27
CONTRIBUTOR
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## Describe the bug `Dataset/DatasetDict.filter()` cannot have `batched=True` due to `mask` (numpy array?) being non-iterable. ## Steps to reproduce the bug (In a python 3.7.12 env, I've tried 2.4.0 and 2.3.2 with both `pyarraw==9.0.0` and `pyarrow==8.0.0`.) ```python from datasets import load_dataset ds_mc4_ja = load_dataset("mc4", "ja") # This will take 6+ hours... perhaps test it with a toy dataset instead? ds_mc4_ja_2020 = ds_mc4_ja.filter( lambda example: example["timestamp"][:4] == "2020", batched=True, ) ``` ## Expected results No error ## Actual results ```python --------------------------------------------------------------------------- RemoteTraceback Traceback (most recent call last) RemoteTraceback: """ Traceback (most recent call last): File "/opt/conda/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker result = (True, func(*args, **kwds)) File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 524, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py", line 480, in wrapper out = func(self, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2779, in _map_single offset=offset, File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2655, in apply_function_on_filtered_inputs processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2347, in decorated result = f(decorated_item, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 4946, in get_indices_from_mask_function indices_array = [i for i, to_keep in zip(indices, mask) if to_keep] TypeError: zip argument #2 must support iteration """ The above exception was the direct cause of the following exception: TypeError Traceback (most recent call last) /tmp/ipykernel_51348/2345782281.py in <module> 7 batched=True, 8 # batch_size=10_000, ----> 9 num_proc=111, 10 ) 11 # ds_mc4_ja_clean_2020 = ds_mc4_ja.filter( /opt/conda/lib/python3.7/site-packages/datasets/dataset_dict.py in filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, fn_kwargs, num_proc, desc) 878 desc=desc, 879 ) --> 880 for k, dataset in self.items() 881 } 882 ) /opt/conda/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 878 desc=desc, 879 ) --> 880 for k, dataset in self.items() 881 } 882 ) /opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 522 } 523 # apply actual function --> 524 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 525 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 526 # re-apply format to the output /opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 478 # Call actual function 479 --> 480 out = func(self, *args, **kwargs) 481 482 # Update fingerprint of in-place transforms + update in-place history of transforms /opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2920 new_fingerprint=new_fingerprint, 2921 input_columns=input_columns, -> 2922 desc=desc, 2923 ) 2924 new_dataset = copy.deepcopy(self) /opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2498 2499 for index, async_result in results.items(): -> 2500 transformed_shards[index] = async_result.get() 2501 2502 assert ( /opt/conda/lib/python3.7/site-packages/multiprocess/pool.py in get(self, timeout) 655 return self._value 656 else: --> 657 raise self._value 658 659 def _set(self, i, obj): TypeError: zip argument #2 must support iteration ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.5 (I've tried 2.4.0 and 2.3.2 with both `pyarraw==9.0.0` and `pyarrow==8.0.0`.)
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1,362,522,423
I_kwDODunzps5RNnE3
4,932
Dataset Viewer issue for bigscience-biomedical/biosses
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2022-09-05T22:40:32
2022-09-06T14:24:56
2022-09-06T14:24:56
NONE
null
### Link https://huggingface.co/datasets/bigscience-biomedical/biosses ### Description I've just been working on adding the dataset loader script to this dataset and working with the relative imports. I'm not sure how to interpret the error below (show where the dataset preview used to be) . ``` Status code: 400 Exception: ModuleNotFoundError Message: No module named 'datasets_modules.datasets.bigscience-biomedical--biosses.ddbd5893bf6c2f4db06f407665eaeac619520ba41f69d94ead28f7cc5b674056.bigbiohub' ``` ### Owner Yes
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1,358,611,513
I_kwDODunzps5Q-sQ5
4,924
Concatenate_datasets loads everything into RAM
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2022-09-01T10:25:17
2022-09-01T11:50:54
2022-09-01T11:50:54
NONE
null
## Describe the bug When loading the datasets seperately and saving them on disk, I want to concatenate them. But `concatenate_datasets` is filling up my RAM and the process gets killed. Is there a way to prevent this from happening or is this intended behaviour? Thanks in advance ## Steps to reproduce the bug ```python gcs = gcsfs.GCSFileSystem(project='project') datasets = [load_from_disk(f'path/to/slice/of/data/{i}', fs=gcs, keep_in_memory=False) for i in range(10)] dataset = concatenate_datasets(datasets) ``` ## Expected results A concatenated dataset which is stored on my disk. ## Actual results Concatenated dataset gets loaded into RAM and overflows it which gets the process killed. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 8.0.1 - Pandas version: 1.4.3
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4,922
I/O error on Google Colab in streaming mode
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2022-08-31T18:08:26
2022-08-31T18:15:48
2022-08-31T18:15:48
NONE
null
## Describe the bug When trying to load a streaming dataset in Google Colab the loading fails with an I/O error ## Steps to reproduce the bug ```python import datasets from datasets import load_dataset hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION) list(hf_ds.take(5)) ``` ## Expected results It should load five data points ## Actual results ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-13-7b5b8b1e7e58>](https://localhost:8080/#) in <module> 2 from datasets import load_dataset 3 hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION) ----> 4 list(hf_ds.take(5)) 6 frames [/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self) 716 717 def __iter__(self): --> 718 for key, example in self._iter(): 719 if self.features: 720 # `IterableDataset` automatically fills missing columns with None. [/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in _iter(self) 706 else: 707 ex_iterable = self._ex_iterable --> 708 yield from ex_iterable 709 710 def _iter_shard(self, shard_idx: int): [/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self) 582 583 def __iter__(self): --> 584 yield from islice(self.ex_iterable, self.n) 585 586 def shuffle_data_sources(self, generator: np.random.Generator) -> "TakeExamplesIterable": [/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self) 110 111 def __iter__(self): --> 112 yield from self.generate_examples_fn(**self.kwargs) 113 114 def shuffle_data_sources(self, generator: np.random.Generator) -> "ExamplesIterable": [~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _generate_examples(self, split_subsets, extraction_map, with_translation) 845 raise ValueError("Invalid number of files: %d" % len(files)) 846 --> 847 for sub_key, ex in sub_generator(*sub_generator_args): 848 if not all(ex.values()): 849 continue [~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _parse_parallel_sentences(f1, f2, filename1, filename2) 923 l2_sentences, l2 = parse_file(f2_i, filename2) 924 --> 925 for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)): 926 key = f"{f_id}/{line_id}" 927 yield key, {l1: s1, l2: s2} [~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in gen() 895 896 def gen(): --> 897 with open(path, encoding="utf-8") as f: 898 for line in f: 899 seg_match = re.match(seg_re, line) ValueError: I/O operation on closed file. ``` ## Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.4.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 9.0.0. (the same error happened with PyArrow version 6.0.0) - Pandas version: 1.3.5
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Unable to load local tsv files through load_dataset method
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2022-08-31T16:13:39
2022-09-01T05:31:30
2022-09-01T05:31:30
NONE
null
## Describe the bug Unable to load local tsv files through load_dataset method. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug data_files = { 'train': 'train.tsv', 'test': 'test.tsv' } raw_datasets = load_dataset('tsv', data_files=data_files) ## Expected results I am pretty sure the data files exist in the current directory. The above code should load them as Datasets, but threw exceptions. ## Actual results --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [<ipython-input-9-24207899c1af>](https://localhost:8080/#) in <module> ----> 1 raw_datasets = load_dataset('tsv', data_files='train.tsv') 2 frames [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1244 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1246 ) from None 1247 raise e1 from None 1248 else: FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/tsv/tsv.py ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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4,918
Dataset Viewer issue for pysentimiento/spanish-targeted-sentiment-headlines
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null
2
2022-08-31T12:09:07
2022-09-05T21:36:34
2022-09-05T16:32:44
NONE
null
### Link https://huggingface.co/datasets/pysentimiento/spanish-targeted-sentiment-headlines ### Description After moving the dataset from my user (`finiteautomata`) to the `pysentimiento` organization, the dataset viewer says that it doesn't exist. ### Owner _No response_
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I_kwDODunzps5Q5SJx
4,917
Keys mismatch: make error message more informative
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4
2022-08-31T11:24:34
2022-09-05T08:43:38
2022-09-05T08:43:38
CONTRIBUTOR
null
**Is your feature request related to a problem? Please describe.** When loading a dataset from disk with a defect in its `dataset_info.json` describing its features (I don’t know when/why/how this happens but it deserves its own issue), you will get an error message like: `ValueError: Keys mismatch: between {'bar': Value(dtype='int64', id=None)} and {'foo': Value(dtype='int64', id=None)}` Which is fine when you have only a few features like in the example but it gets very hard to read when you have a lot of features in your dataset. **Describe the solution you'd like** The error message should give the difference between the features (what keys are in A but missing in B and vice-versa). It should also tell which keys are inferred from `dataset.arrow` and which come from `dataset_info.json`. Willing to help :)
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4,916
Apache Beam unable to write the downloaded wikipedia dataset
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2022-08-31T09:39:25
2022-08-31T10:53:19
2022-08-31T10:53:19
NONE
null
## Describe the bug Hi, I am currently trying to download wikipedia dataset using load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download. It downloads the file but while saving it in hugging face cache it fails to write. This happens for any available date of any language in wikipedia dump. I had raised another issue earlier #4915 but probably was not that clear and the solution provider misunderstood my problem. Hence raising one more issue. Any help is appreciated. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner') ``` ## Expected results to load the dataset ## Actual results I am pasting the error trace here: Downloading builder script: 35.9kB [00:00, ?B/s] Downloading metadata: 30.4kB [00:00, 1.94MB/s] Using custom data configuration 20220401.aa-date=20220401,language=aa Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559... Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s] Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it] Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s] Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s] Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it] Traceback (most recent call last): File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process self.writer = self.sink.open_writer(init_result, str(uuid.uuid4())) File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f return fnc(self, *args, **kwargs) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer return FileBasedSinkWriter(self, writer_path) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init self.temp_handle = self.sink.open(temp_shard_path) File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open self._file_handle = super().open(temp_path) File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f return fnc(self, *args, **kwargs) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open temp_path, self.mime_type, self.compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create return filesystem.create(path, mime_type, compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create return self._path_open(path, 'wb', mime_type, compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open raw_file = io.open(path, mode) FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "G:/abc/temp.py", line 32, in beam_runner='DirectRunner') File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset use_auth_token=use_auth_token, File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare pipeline_results = pipeline.run() File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run return self.runner.run_pipeline(self, self._options) File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline return runner.run_pipeline(pipeline, options) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline options) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api return self.run_stages(stage_context, stages) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages runner_execution_context, bundle_context_manager, bundle_input) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle bundle_manager)) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle data_input, data_output, input_timers, expected_timer_output) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle result_future = self._worker_handler.control_conn.push(process_bundle_req) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push response = self.worker.do_instruction(request) File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction getattr(request, request_type), request.instruction_id) File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle bundle_processor.process_bundle(instruction_id)) File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle element.data) File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded self.output(decoded_value) File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 240, in 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"apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1507, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process self.writer = self.sink.open_writer(init_result, str(uuid.uuid4())) File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f return fnc(self, *args, **kwargs) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer return FileBasedSinkWriter(self, writer_path) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init self.temp_handle = self.sink.open(temp_shard_path) File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open self._file_handle = super().open(temp_path) File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f return fnc(self, *args, **kwargs) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open temp_path, self.mime_type, self.compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create return filesystem.create(path, mime_type, compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create return self._path_open(path, 'wb', mime_type, compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open raw_file = io.open(path, mode) RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles'] ## Environment info Python: 3.7.6 Windows 10 Pro datasets :2.4.0 apache_beam: 2.41.0 mwparserfromhell: 0.6.4
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FileNotFoundError while downloading wikipedia dataset for any language
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2022-08-30T16:15:46
2022-12-04T22:20:33
null
NONE
null
## Describe the bug Hi, I am currently trying to download wikipedia dataset using load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download. Environment: ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner') ``` ## Expected results to load the dataset ## Actual results I am pasting the error trace here: Downloading builder script: 35.9kB [00:00, ?B/s] Downloading metadata: 30.4kB [00:00, 1.94MB/s] Using custom data configuration 20220401.aa-date=20220401,language=aa Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559... Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s] Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it] Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s] Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s] Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it] Traceback (most recent call last): File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process self.writer = self.sink.open_writer(init_result, str(uuid.uuid4())) File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f return fnc(self, *args, **kwargs) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer return FileBasedSinkWriter(self, writer_path) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__ self.temp_handle = self.sink.open(temp_shard_path) File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open self._file_handle = super().open(temp_path) File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f return fnc(self, *args, **kwargs) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open temp_path, self.mime_type, self.compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create return filesystem.create(path, mime_type, compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create return self._path_open(path, 'wb', mime_type, compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open raw_file = io.open(path, mode) FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "G:/abc/temp.py", line 32, in <module> beam_runner='DirectRunner') File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset use_auth_token=use_auth_token, File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare pipeline_results = pipeline.run() File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run return self.runner.run_pipeline(self, self._options) File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline return runner.run_pipeline(pipeline, options) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline options) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api return self.run_stages(stage_context, stages) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages runner_execution_context, bundle_context_manager, bundle_input) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle bundle_manager)) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle data_input, data_output, input_timers, expected_timer_output) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle result_future = self._worker_handler.control_conn.push(process_bundle_req) File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push response = self.worker.do_instruction(request) File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction getattr(request, request_type), request.instruction_id) File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle bundle_processor.process_bundle(instruction_id)) File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle element.data) File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded self.output(decoded_value) File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag File "apache_beam\runners\worker\operations.py", line 324, in apache_beam.runners.worker.operations.GeneralPurposeConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 905, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 1507, in apache_beam.runners.common.DoFnRunner._reraise_augmented File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process self.writer = self.sink.open_writer(init_result, str(uuid.uuid4())) File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f return fnc(self, *args, **kwargs) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer return FileBasedSinkWriter(self, writer_path) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__ self.temp_handle = self.sink.open(temp_shard_path) File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open self._file_handle = super().open(temp_path) File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f return fnc(self, *args, **kwargs) File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open temp_path, self.mime_type, self.compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create return filesystem.create(path, mime_type, compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create return self._path_open(path, 'wb', mime_type, compression_type) File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open raw_file = io.open(path, mode) RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles'] ## Environment info Python: 3.7.6 Windows 10 Pro datasets :2.4.0 apache_beam: 2.41.0 mwparserfromhell: 0.6.4
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1,355,078,864
I_kwDODunzps5QxNzQ
4,912
datasets map() handles all data at a stroke and takes long time
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2022-08-30T02:25:56
2022-09-06T09:23:35
2022-09-06T09:23:35
NONE
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**1. Background** Huggingface datasets package advises using `map()` to process data in batches. In the example code on pretraining masked language model, they use `map()` to tokenize all data at a stroke before the train loop. The corresponding code: ``` with accelerator.main_process_first(): tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on every text in dataset" ) ``` **2. The problem** Thus, when I try the same pertaining code with a much larger corpus, it takes quite a long time to tokenize. Also, we can choose to tokenize data in `data-collator`. In this way, the program only tokenizes one batch in the next training step and avoids getting stuck in tokenization. **3. My question** As described above, my questions are: * **Which is better? Process in `map()` or in `data-collator`** * **Why huggingface advises `map()` function?** There should be some advantages to using `map()` Thanks for your answers!
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I_kwDODunzps5Quupi
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[Tests] Ensure `datasets` supports renamed repositories
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2022-08-29T14:46:14
2022-08-29T15:31:03
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On https://hf.co/datasets you can rename a dataset (or sometimes move it to another user/org). The website handles redirections correctly and AFAIK `datasets` does as well. However it would be nice to have an integration test to make sure we don't break support for renamed datasets. To implement this we can use the /api/repos/move endpoint on hub-ci to rename/move a repo (it is documented at https://huggingface.co/docs/hub/api)
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I_kwDODunzps5Quhy4
4,910
Identical keywords in build_kwargs and config_kwargs lead to TypeError in load_dataset_builder()
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2022-08-29T14:11:48
2022-09-13T11:58:46
null
NONE
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## Describe the bug In `load_dataset_builder()`, `build_kwargs` and `config_kwargs` can contain the same keywords leading to a TypeError("type object got multiple values for keyword argument "xyz"). I ran into this problem with the keyword: `base_path`. It might happen with other kwargs as well. I think a quickfix would be ```python builder_cls = import_main_class(dataset_module.module_path) builder_kwargs = dataset_module.builder_kwargs data_files = builder_kwargs.pop("data_files", data_files) config_name = builder_kwargs.pop("config_name", name) hash = builder_kwargs.pop("hash") base_path = builder_kwargs.pop("base_path") ``` and then pass base_path into `builder_cls`. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("rotten_tomatoes", base_path="./sample_data") ``` ## Expected results The docs state: `**config_kwargs` — Keyword arguments to be passed to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.BuilderConfig) and used in the [DatasetBuilder](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.DatasetBuilder). So I would expect to be able to pass the base_path into `load_dataset()`. ## Actual results TypeError("type object got multiple values for keyword argument "base_path"). ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: macOS-12.5-arm64-arm-64bit - Python version: 3.8.9 - PyArrow version: 9.0.0
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4,907
None Type error for swda datasets
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2022-08-29T07:05:20
2022-08-30T14:43:41
2022-08-30T14:43:41
NONE
null
## Describe the bug I got `'NoneType' object is not callable` error while calling the swda datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("swda") ``` ## Expected results Run without error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Python version: 3.8.10
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Can't import datasets AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)
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2022-08-28T02:23:24
2023-01-12T23:23:41
2022-10-03T12:22:50
NONE
null
## Describe the bug A clear and concise description of what the bug is. Not able to import datasets ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import os os.environ["WANDB_API_KEY"] = "0" ## to silence warning import numpy as np import random import sklearn import matplotlib.pyplot as plt import pandas as pd import sys import tensorflow as tf import plotly.express as px import transformers import tokenizers import nlp as nlp import utils import datasets ``` ## Expected results A clear and concise description of the expected results. import should work normal ## Actual results Specify the actual results or traceback. --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-21-b3b5b0b62103> in <module> 13 import nlp as nlp 14 import utils ---> 15 import datasets ~\anaconda3\lib\site-packages\datasets\__init__.py in <module> 44 from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled 45 from .info import DatasetInfo, MetricInfo ---> 46 from .inspect import ( 47 get_dataset_config_info, 48 get_dataset_config_names, ~\anaconda3\lib\site-packages\datasets\inspect.py in <module> 28 from .download.streaming_download_manager import StreamingDownloadManager 29 from .info import DatasetInfo ---> 30 from .load import dataset_module_factory, import_main_class, load_dataset_builder, metric_module_factory 31 from .utils.file_utils import relative_to_absolute_path 32 from .utils.logging import get_logger ~\anaconda3\lib\site-packages\datasets\load.py in <module> 53 from .iterable_dataset import IterableDataset 54 from .metric import Metric ---> 55 from .packaged_modules import ( 56 _EXTENSION_TO_MODULE, 57 _MODULE_SUPPORTS_METADATA, ~\anaconda3\lib\site-packages\datasets\packaged_modules\__init__.py in <module> 4 from typing import List 5 ----> 6 from .csv import csv 7 from .imagefolder import imagefolder 8 from .json import json ~\anaconda3\lib\site-packages\datasets\packaged_modules\csv\csv.py in <module> 13 14 ---> 15 logger = datasets.utils.logging.get_logger(__name__) 16 17 _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS = ["names", "prefix"] AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.4.0 - Platform: Windows-10-10.0.22000-SP0 - Python version: 3.8.8 - PyArrow version: 9.0.0 - Pandas version: 1.2.4
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Name the default config `default`
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2022-08-26T16:16:22
2022-08-26T16:16:38
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CONTRIBUTOR
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Currently, if a dataset has no configuration, a default configuration is created from the dataset name. For example, for a dataset loaded from the hub repository, such as https://huggingface.co/datasets/user/dataset (repo id is `user/dataset`), the default configuration will be `user--dataset`. It might be easier to handle to set it to `default`, or another reserved word.
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1,352,405,855
I_kwDODunzps5QnBNf
4,900
Dataset Viewer issue for asaxena1990/Dummy_dataset
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2022-08-26T15:15:44
2022-08-26T16:48:11
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NONE
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### Link _No response_ ### Description _No response_ ### Owner _No response_
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1,351,851,254
I_kwDODunzps5Qk5z2
4,898
Dataset Viewer issue for timit_asr
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2022-08-26T07:12:05
2022-10-03T12:40:28
2022-10-03T12:40:27
NONE
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### Link _No response_ ### Description _No response_ ### Owner _No response_
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1,351,784,727
I_kwDODunzps5QkpkX
4,897
datasets generate large arrow file
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2022-08-26T05:51:16
2022-09-18T05:07:52
2022-09-18T05:07:52
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Checking the large file in disk, and found the large cache file in the cifar10 data directory: ![image](https://user-images.githubusercontent.com/18533904/186830449-ba96cdeb-0fe8-4543-994d-2abe7145933f.png) As we know, the size of cifar10 dataset is ~130MB, but the cache file has almost 30GB size, there may be some problems here.
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I_kwDODunzps5Qg4y_
4,895
load_dataset method returns Unknown split "validation" even if this dir exists
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2022-08-25T12:11:00
2022-10-06T17:49:28
2022-09-29T08:07:50
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## Describe the bug The `datasets.load_dataset` returns a `ValueError: Unknown split "validation". Should be one of ['train', 'test'].` when running `load_dataset(local_data_dir_path, split="validation")` even if the `validation` sub-directory exists in the local data path. The data directories are as follows and attached to this issue: ``` test_data1 |_ train |_ 1012.png |_ metadata.jsonl ... |_ test ... |_ validation |_ 234.png |_ metadata.jsonl ... test_data2 |_ train |_ train_1012.png |_ metadata.jsonl ... |_ test ... |_ validation |_ val_234.png |_ metadata.jsonl ... ``` They contain the same image files and `metadata.jsonl` but the images in `test_data2` have the split names prepended i.e. `train_1012.png, val_234.png` and the images in `test_data1` do not have the split names prepended to the image names i.e. `1012.png, 234.png` I actually saw in another issue `val` was not recognized as a split name but here I would expect the files to take the split from the parent directory name i.e. val should become part of the validation split? ## Steps to reproduce the bug ```python import datasets datasets.logging.set_verbosity_error() from datasets import load_dataset, get_dataset_split_names # the following only finds train, validation and test splits correctly path = "./test_data1" print("######################", get_dataset_split_names(path), "######################") dataset_list = [] for spt in ["train", "test", "validation"]: dataset = load_dataset(path, split=spt) dataset_list.append(dataset) # the following only finds train and test splits path = "./test_data2" print("######################", get_dataset_split_names(path), "######################") dataset_list = [] for spt in ["train", "test", "validation"]: dataset = load_dataset(path, split=spt) dataset_list.append(dataset) ``` ## Expected results ``` ###################### ['train', 'test', 'validation'] ###################### ###################### ['train', 'test', 'validation'] ###################### ``` ## Actual results ``` Traceback (most recent call last): File "test_data_loader.py", line 11, in <module> dataset = load_dataset(path, split=spt) File "/home/venv/lib/python3.8/site-packages/datasets/load.py", line 1758, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 893, in as_dataset datasets = map_nested( File "/home/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 385, in map_nested return function(data_struct) File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 924, in _build_single_dataset ds = self._as_dataset( File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 993, in _as_dataset dataset_kwargs = ArrowReader(self._cache_dir, self.info).read( File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 211, in read files = self.get_file_instructions(name, instructions, split_infos) File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 184, in get_file_instructions file_instructions = make_file_instructions( File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 107, in make_file_instructions absolute_instructions = instruction.to_absolute(name2len) File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in to_absolute return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions] File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in <listcomp> return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions] File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 433, in _rel_to_abs_instr raise ValueError(f'Unknown split "{split}". Should be one of {list(name2len)}.') ValueError: Unknown split "validation". Should be one of ['train', 'test']. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux Ubuntu 18.04 - Python version: 3.8.12 - PyArrow version: 9.0.0 Data files [test_data1.zip](https://github.com/huggingface/datasets/files/9424463/test_data1.zip) [test_data2.zip](https://github.com/huggingface/datasets/files/9424468/test_data2.zip)
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I_kwDODunzps5QgV66
4,893
Oversampling strategy for iterable datasets in `interleave_datasets`
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2022-08-25T10:06:55
2022-10-03T12:37:46
2022-10-03T12:37:46
MEMBER
null
In https://github.com/huggingface/datasets/pull/4831 @ylacombe added an oversampling strategy for `interleave_datasets`. However right now it doesn't work for datasets loaded using `load_dataset(..., streaming=True)`, which are `IterableDataset` objects. It would be nice to expand `interleave_datasets` for iterable datasets as well to support this oversampling strategy ```python >>> from datasets.iterable_dataset import IterableDataset, ExamplesIterable >>> d1 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [0, 1, 2]], {})) >>> d2 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [10, 11, 12, 13]], {})) >>> d3 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [20, 21, 22, 23, 24]], {})) >>> dataset = interleave_datasets([d1, d2, d3]) # is supported >>> [x["a"] for x in dataset] [0, 10, 20, 1, 11, 21, 2, 12, 22] >>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") # is not supported yet >>> [x["a"] for x in dataset] [0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24] ``` This can be implemented by adding the strategy to both `CyclingMultiSourcesExamplesIterable` and `RandomlyCyclingMultiSourcesExamplesIterable` used in `_interleave_iterable_datasets` in `iterable_dataset.py` I would be happy to share some guidance if anyone would like to give it a shot :)
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torchaudio 11.0 yields different results than torchaudio 12.1 when loading MP3
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2022-08-24T16:54:43
2022-10-05T13:54:04
null
MEMBER
null
## Describe the bug When loading Common Voice with torchaudio 0.11.0 the results are different to 0.12.1 which leads to problems in transformers see: https://github.com/huggingface/transformers/pull/18749 ## Steps to reproduce the bug If you run the following code once with `torchaudio==0.11.0+cu102` and `torchaudio==0.12.1+cu102` you can see that the tensors differ. This is a pretty big breaking change and makes some integration tests fail in Transformers. ```python #!/usr/bin/env python3 from datasets import load_dataset import datasets import numpy as np import torch import torchaudio print("torch vesion", torch.__version__) print("torchaudio vesion", torchaudio.__version__) save_audio = True load_audios = False if save_audio: ds = load_dataset("common_voice", "en", split="train", streaming=True) ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000)) ds_iter = iter(ds) sample = next(ds_iter) np.save(f"audio_sample_{torch.__version__}", sample["audio"]["array"]) print(sample["audio"]["array"]) if load_audios: array_torch_11 = np.load("/home/patrick/audio_sample_1.11.0+cu102.npy") print("Array 11 Shape", array_torch_11.shape) print("Array 11 abs sum", np.sum(np.abs(array_torch_11))) array_torch_12 = np.load("/home/patrick/audio_sample_1.12.1+cu102.npy") print("Array 12 Shape", array_torch_12.shape) print("Array 12 abs sum", np.sum(np.abs(array_torch_12))) ``` Having saved the tensors the print output yields: ``` torch vesion 1.12.1+cu102 torchaudio vesion 0.12.1+cu102 Array 11 Shape (122880,) Array 11 abs sum 1396.4988 Array 12 Shape (123264,) Array 12 abs sum 1396.5193 ``` ## Expected results torchaudio 11.0 and 12.1 should yield same results. ## Actual results See above. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.1.1.dev0 - Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.34 - Python version: 3.9.7 - PyArrow version: 6.0.1 - Pandas version: 1.4.2
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Dataset Viewer issue for subjqa
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2022-08-24T13:26:20
2022-09-08T08:23:42
2022-09-08T08:23:42
MEMBER
null
### Link https://huggingface.co/datasets/subjqa ### Description Getting the following error for this dataset: ``` Status code: 500 Exception: Status500Error Message: 2 or more items returned, instead of 1 ``` Not sure what's causing it though 🤔 ### Owner Yes
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Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid
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2022-08-24T11:24:21
2023-02-02T02:40:53
null
NONE
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## Describe the bug Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('huggan/CelebA-HQ') ``` ## Expected results See https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing#scrollTo=N3ml_7f8kzDd ## Actual results ``` File "/home/jean/projects/cold_diffusion/celebA.py", line 4, in <module> dataset = load_dataset('huggan/CelebA-HQ') File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/load.py", line 1793, in load_dataset builder_instance.download_and_prepare( File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 1274, in _prepare_split for key, table in logging.tqdm( File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables parquet_file = pq.ParquetFile(f) File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/pyarrow/parquet/__init__.py", line 286, in __init__ self.reader.open( File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets-2.4.1.dev0 - Platform: Ubuntu 18.04 - Python version: 3.10 - PyArrow version: pyarrow 9.0.0
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4,885
Create dataset from list of dicts
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2022-08-24T10:01:24
2022-09-08T16:02:52
2022-09-08T16:02:52
CONTRIBUTOR
null
I often find myself with data from a variety of sources, and a list of dicts is very common among these. However, converting this to a Dataset is a little awkward, requiring either ```Dataset.from_pandas(pd.DataFrame(formatted_training_data))``` Which can error out on some more exotic values as 2-d arrays for reasons that are not entirely clear > ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column labels with type object') Alternatively: ```Dataset.from_dict({k: [s[k] for s in formatted_training_data] for k in formatted_training_data[0].keys()})``` Which works, but is a little ugly. **Describe the solution you'd like** Either `.from_dict` accepting a list of dicts, or a `.from_records` function accepting such. I am happy to PR this, just wanted to check you are happy to accept this I haven't missed something obvious, and which of the solutions would be prefered.
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1,349,083,235
I_kwDODunzps5QaWBj
4,883
With dataloader RSS memory consumed by HF datasets monotonically increases
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2022-08-24T08:42:54
2022-09-29T16:16:31
null
MEMBER
null
## Describe the bug When the HF datasets is used in conjunction with PyTorch Dataloader, the RSS memory of the process keeps on increasing when it should stay constant. ## Steps to reproduce the bug Run and observe the output of this snippet which logs RSS memory. ```python import psutil import os from transformers import BertTokenizer from datasets import load_dataset from torch.utils.data import DataLoader BATCH_SIZE = 32 NUM_TRIES = 10 tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") def transform(x): x.update(tokenizer(x["text"], return_tensors="pt", max_length=64, padding="max_length", truncation=True)) x.pop("text") x.pop("label") return x dataset = load_dataset("imdb", split="train") dataset.set_transform(transform) train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4) mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024) count = 0 while count < NUM_TRIES: for idx, batch in enumerate(train_loader): mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024) print(count, idx, mem_after - mem_before) count += 1 ``` ## Expected results Memory should not increase after initial setup and loading of the dataset ## Actual results Memory continuously increases as can be seen in the log. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0
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Language names and language codes: connecting to a big database (rather than slow enrichment of custom list)
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48
2022-08-23T20:14:24
2023-01-03T08:32:35
null
NONE
null
**The problem:** Language diversity is an important dimension of the diversity of datasets. To find one's way around datasets, being able to search by language name and by standardized codes appears crucial. Currently the list of language codes is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json), right? At about 1,500 entries, it is roughly at 1/4th of the world's diversity of extant languages. (Probably less, as the list of 1,418 contains variants that are linguistically very close: 108 varieties of English, for instance.) Looking forward to ever increasing coverage, how will the list of language names and language codes improve over time? Enrichment of the custom list by HFT contributors (like [here](https://github.com/huggingface/datasets/pull/4880)) has several issues: * progress is likely to be slow: ![image](https://user-images.githubusercontent.com/6072524/186253353-62f42168-3d31-4105-be1c-5eb1f818d528.png) (input required from reviewers, etc.) * the more contributors, the less consistency can be expected among contributions. No need to elaborate on how much confusion is likely to ensue as datasets accumulate. * there is no information on which language relates with which: no encoding of the special closeness between the languages of the Northwestern Germanic branch (English+Dutch+German etc.), for instance. Information on phylogenetic closeness can be relevant to run experiments on transfer of technology from one language to its close relatives. **A solution that seems desirable:** Connecting to an established database that (i) aims at full coverage of the world's languages and (ii) has information on higher-level groupings, alternative names, etc. It takes a lot of hard work to do such databases. Two important initiatives are [Ethnologue](https://www.ethnologue.com/) (ISO standard) and [Glottolog](https://glottolog.org/). Both have pros and cons. Glottolog contains references to Ethnologue identifiers, so adopting Glottolog entails getting the advantages of both sets of language codes. Both seem technically accessible & 'developer-friendly'. Glottolog has a [GitHub repo](https://github.com/glottolog/glottolog). For Ethnologue, harvesting tools have been devised (see [here](https://github.com/lyy1994/ethnologue); I did not try it out). In case a conversation with linguists seemed in order here, I'd be happy to participate ('pro bono', of course), & to rustle up more colleagues as useful, to help this useful development happen. With appreciation of HFT,
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[not really a bug] `identical_ok` is deprecated in huggingface-hub's `upload_file`
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2022-08-23T17:09:55
2022-09-13T14:00:06
2022-09-13T14:00:05
CONTRIBUTOR
null
In the huggingface-hub dependency, the `identical_ok` argument has no effect in `upload_file` (and it will be removed soon) See https://github.com/huggingface/huggingface_hub/blob/43499582b19df1ed081a5b2bd7a364e9cacdc91d/src/huggingface_hub/hf_api.py#L2164-L2169 It's used here: https://github.com/huggingface/datasets/blob/fcfcc951a73efbc677f9def9a8707d0af93d5890/src/datasets/dataset_dict.py#L1373-L1381 https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4354-L4362 https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4197-L4213 We should remove it. Maybe the third code sample has an unexpected behavior since it uses the non-default value `identical_ok = False`, but the argument is ignored.
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Move DatasetInfo from `datasets_infos.json` to the YAML tags in `README.md`
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2022-08-23T16:16:41
2022-10-03T09:11:13
2022-10-03T09:11:13
MEMBER
null
Currently there are two places to find metadata for datasets: - datasets_infos.json, which contains **per dataset config** - description - citation - license - splits and sizes - checksums of the data files - feature types - and more - YAML tags, which contain - license - language - train-eval-index - and more It would be nice to have a single place instead. We can rely on the YAML tags more than the JSON file for consistency with models. And it would all be indexed by our back-end directly, which is nice to have. One way would be to move everything to the YAML tags except the checksums (there can be tens of thousands of them). The description/citation is already in the dataset card so we probably don't need to have them in the YAML card, it would be redundant. Here is an example for SQuAD ```yaml download_size: 35142551 dataset_size: 89789763 version: 1.0.0 splits: - name: train num_examples: 87599 num_bytes: 79317110 - name: validation num_examples: 10570 num_bytes: 10472653 features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: text list: dtype: string - name: answer_start list: dtype: int32 ``` Since there is only one configuration for SQuAD, this structure is ok. For datasets with several configs we can see in a second step, but IMO it would be ok to have these fields per config using another syntax ```yaml configs: - config: unlabeled splits: - name: train num_examples: 10000 features: - name: text dtype: string - config: labeled splits: - name: train num_examples: 100 features: - name: text dtype: string - name: label dtype: ClassLabel names: - negative - positive ``` So in the end you could specify a YAML tag either at the top level (for all configs) or per config in the `configs` field Alternatively we could keep config specific stuff in the `dataset_infos.json` as it it today Not sure yet what's the best approach here but cc @julien-c @mariosasko @albertvillanova @polinaeterna for feedback :)
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`_resolve_features` ignores the token
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11
2022-08-23T14:57:36
2022-10-17T13:45:47
null
CONTRIBUTOR
null
## Describe the bug When calling [`_resolve_features()`](https://github.com/huggingface/datasets/blob/54b532a8a2f5353fdb0207578162153f7b2da2ec/src/datasets/iterable_dataset.py#L1255) on a gated dataset, ie. a dataset which requires a token to be loaded, the token seems to be ignored even if it has been provided to `load_dataset` before. ## Steps to reproduce the bug ```python import os os.environ["HF_ENDPOINT"] = "https://hub-ci.huggingface.co/" hf_token = "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" from datasets import load_dataset # public dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654226756" config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654226756" split_name = "train" iterable_dataset = load_dataset( dataset_name, name=config_name, split=split_name, streaming=True, use_auth_token=hf_token, ) iterable_dataset = iterable_dataset._resolve_features() print(iterable_dataset.features) # gated dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654317644" config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654317644" split_name = "train" iterable_dataset = load_dataset( dataset_name, name=config_name, split=split_name, streaming=True, use_auth_token=hf_token, ) try: iterable_dataset = iterable_dataset._resolve_features() except FileNotFoundError as e: print("FAILS") ``` ## Expected results I expect to have the same result on a public dataset and on a gated (or private) dataset, if the token has been provided. ## Actual results An exception is thrown on gated datasets. ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-1017-aws-x86_64-with-glibc2.35 - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.4.2
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Multiple dataloader memory error
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2022-08-23T08:59:50
2023-01-26T02:01:11
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For the use of multiple datasets and tasks, we use around more than 200+ dataloaders, then pass it into `dataloader1, dataloader2, ..., dataloader200=accelerate.prepare(dataloader1, dataloader2, ..., dataloader200)` It causes the memory error when generating batches. Any solutions to it? ```bash File "/home/xxx/my_code/src/utils/data_utils.py", line 54, in generate_batch x = next(iterator) File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 301, in __iter__ for batch in super().__iter__(): File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 28, in fetch data.append(next(self.dataset_iter)) File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 249, in __iter__ for element in self.dataset: File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 503, in __iter__ for key, example in self._iter(): File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 500, in _iter yield from ex_iterable File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 231, in __iter__ new_key = "_".join(str(key) for key in keys) MemoryError ```
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Dataset Viewer issue for MoritzLaurer/multilingual_nli
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null
4
2022-08-19T14:55:20
2022-08-22T14:47:14
2022-08-22T06:13:20
NONE
null
### Link _No response_ ### Description I've just uploaded a new dataset to the hub and the viewer does not work for some reason, see here: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli It displays the error: ``` Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` Weirdly enough the dataviewer works for an earlier version of the same dataset. The only difference is that it is smaller, but I'm not aware of other changes I have made: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli_test Do you know why the dataviewer is not working? ### Owner _No response_
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1,344,410,043
I_kwDODunzps5QIhG7
4,864
Allow pathlib PoxisPath in Dataset.read_json
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2022-08-19T12:59:17
2022-08-19T12:59:17
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** ``` from pathlib import Path from datasets import Dataset ds = Dataset.read_json(Path('data.json')) ``` causes an error ``` AttributeError: 'PosixPath' object has no attribute 'decode' ``` **Describe the solution you'd like** It should be able to accept PosixPath and read the json from inside.
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TFDS wiki_dialog dataset to Huggingface dataset
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2022-08-18T23:06:30
2022-08-22T09:41:45
2022-08-22T05:18:53
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null
## Adding a Dataset - **Name:** *Wiki_dialog* - **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A - **Paper: https://arxiv.org/abs/2205.09073 - **Data: https://github.com/google-research/dialog-inpainting - **Motivation:** *Research and Development on biggest corpus of dialog data* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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Got "AttributeError: 'xPath' object has no attribute 'read'" when loading an excel dataset with my own code
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5
2022-08-18T18:36:14
2022-08-31T09:25:08
2022-08-31T09:25:08
NONE
null
## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug # The dataset function is as follows: from pathlib import Path from typing import Dict, List, Tuple import datasets import pandas as pd _CITATION = """\ """ _DATASETNAME = "jadi_ide" _DESCRIPTION = """\ """ _HOMEPAGE = "" _LICENSE = "Unknown" _URLS = { _DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx", } _SOURCE_VERSION = "1.0.0" class JaDi_Ide(datasets.GeneratorBasedBuilder): SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BUILDER_CONFIGS = [ NusantaraConfig( name="jadi_ide_source", version=SOURCE_VERSION, description="JaDi-Ide source schema", schema="source", subset_id="jadi_ide", ), ] DEFAULT_CONFIG_NAME = "source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string") } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" # Dataset does not have predetermined split, putting all as TRAIN urls = _URLS[_DATASETNAME] base_dir = Path(dl_manager.download_and_extract(urls)) data_files = {"train": base_dir} return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_files["train"], "split": "train", }, ), ] def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" df = pd.read_excel(filepath, engine='openpyxl') df.columns = ["id", "text", "label"] if self.config.schema == "source": for row in df.itertuples(): ex = { "id": str(row.id), "text": row.text, "label": row.label, } yield row.id, ex ``` ## Expected results Expecting to load the dataset smoothly. ## Actual results File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset use_auth_token=use_auth_token, File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split desc=f"Generating {split_info.name} split", File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples df = pd.read_excel(filepath, engine='openpyxl') File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs) AttributeError: 'xPath' object has no attribute 'read' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid - Python version: 3.7.4 - PyArrow version: 9.0.0 - Pandas version: 0.25.1
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Using disk for memory with the method `from_dict`
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2022-08-18T15:18:18
2023-01-26T18:36:28
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MEMBER
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**Is your feature request related to a problem? Please describe.** I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM error. **Describe the solution you'd like** The method `from_dict` loads the data in RAM. It could be good to add an option to use the disk instead. **Describe alternatives you've considered** To solve the problem, I have to do an intermediate step where I save the new datasets at each iteration with `save_to_disk`. Once it's done, I open them all and concatenate them.
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4,859
can't install using conda on Windows 10
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## Describe the bug I wanted to install using conda or Anaconda navigator. That didn't work, so I had to install using pip. ## Steps to reproduce the bug conda install -c huggingface -c conda-forge datasets ## Expected results Should have indicated successful installation. ## Actual results Solving environment: failed with initial frozen solve. Retrying with flexible solve. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. ... took forever, so I cancelled it with ctrl-c ## Environment info - `datasets` version: 2.4.0 # after installing with pip - Platform: Windows-10-10.0.19044-SP0 - Python version: 3.9.12 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 - conda version: 4.13.0 conda info active environment : base active env location : G:\anaconda2022 shell level : 1 user config file : C:\Users\michael\.condarc populated config files : C:\Users\michael\.condarc conda version : 4.13.0 conda-build version : 3.21.8 python version : 3.9.12.final.0 virtual packages : __cuda=11.1=0 __win=0=0 __archspec=1=x86_64 base environment : G:\anaconda2022 (writable) conda av data dir : G:\anaconda2022\etc\conda conda av metadata url : None channel URLs : https://conda.anaconda.org/pytorch/win-64 https://conda.anaconda.org/pytorch/noarch https://conda.anaconda.org/huggingface/win-64 https://conda.anaconda.org/huggingface/noarch https://conda.anaconda.org/conda-forge/win-64 https://conda.anaconda.org/conda-forge/noarch https://conda.anaconda.org/anaconda-fusion/win-64 https://conda.anaconda.org/anaconda-fusion/noarch https://repo.anaconda.com/pkgs/main/win-64 https://repo.anaconda.com/pkgs/main/noarch https://repo.anaconda.com/pkgs/r/win-64 https://repo.anaconda.com/pkgs/r/noarch https://repo.anaconda.com/pkgs/msys2/win-64 https://repo.anaconda.com/pkgs/msys2/noarch package cache : G:\anaconda2022\pkgs C:\Users\michael\.conda\pkgs C:\Users\michael\AppData\Local\conda\conda\pkgs envs directories : G:\anaconda2022\envs C:\Users\michael\.conda\envs C:\Users\michael\AppData\Local\conda\conda\envs platform : win-64 user-agent : conda/4.13.0 requests/2.27.1 CPython/3.9.12 Windows/10 Windows/10.0.19044 administrator : False netrc file : None offline mode : False
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1,340,859,853
I_kwDODunzps5P6-XN
4,858
map() function removes columns when input_columns is not None
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2022-08-16T20:42:30
2022-09-22T13:55:24
2022-09-22T13:55:24
NONE
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## Describe the bug The map function, removes features from the dataset that are not present in the _input_columns_ list of columns, despite the columns being removed not mentioned in the _remove_columns_ argument. ## Steps to reproduce the bug ```python from datasets import Dataset ds = Dataset.from_dict({"a" : [1,2,3],"b" : [0,1,0], "c" : [2,4,5]}) def double(x,y): x = x*2 y = y*2 return {"d" : x, "e" : y} ds.map(double, input_columns=["a","c"]) ``` ## Expected results ``` Dataset({ features: ['a', 'b', 'c', 'd', 'e'], num_rows: 3 }) ``` ## Actual results ``` Dataset({ features: ['a', 'c', 'd', 'e'], num_rows: 3 }) ``` In this specific example feature **b** should not be removed. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: linux (colab) - Python version: 3.7.13 - PyArrow version: 6.0.1
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1,340,397,153
I_kwDODunzps5P5NZh
4,857
No preprocessed wikipedia is working on huggingface/datasets
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2022-08-16T13:55:33
2022-08-17T13:35:08
2022-08-17T13:35:08
NONE
null
## Describe the bug 20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface https://huggingface.co/datasets/wikipedia https://dumps.wikimedia.org/enwiki/
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1,339,779,957
I_kwDODunzps5P22t1
4,856
file missing when load_dataset with openwebtext on windows
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2022-08-16T04:04:22
2023-01-04T03:39:12
2023-01-04T03:39:12
NONE
null
## Describe the bug 0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz with 7-zip. ## Steps to reproduce the bug ```sh python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base ``` or ```python from datasets import load_dataset load_dataset("openwebtext", None, cache_dir=None, use_auth_token=None) ``` ## Expected results Loading is successful ## Actual results Traceback (most recent call last): File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 704, in download_and_prepare self._download_and_prepare( File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 795, in _download_and_prepare raise OSError( OSError: Cannot find data file. Original error: [Errno 22] Invalid argument: 'F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa/0015896-b1054262f7da52a0518521e29c8e352c.txt' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: windows - Python version: 3.8.5 - PyArrow version: 9.0.0
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4,855
Dataset Viewer issue for super_glue
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2022-08-16T01:34:56
2022-08-22T10:08:01
2022-08-22T10:07:45
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### Link https://huggingface.co/datasets/super_glue ### Description can't view super_glue dataset on the web page ### Owner _No response_
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1,339,450,991
I_kwDODunzps5P1mZv
4,852
Bug in multilingual_with_para config of exams dataset and checksums error
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2022-08-15T20:14:52
2022-09-16T09:50:55
2022-08-16T06:29:07
MEMBER
null
## Describe the bug There is a bug for "multilingual_with_para" config in exams dataset: ```python ds = load_dataset("./datasets/exams", split="train") ``` raises: ``` KeyError: 'choices' ``` Moreover, there is a NonMatchingChecksumError: ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_vi_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_vi_with_para.jsonl.tar.gz'] ``` CC: @thesofakillers
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I_kwDODunzps5PtjrQ
4,840
Dataset Viewer issue for darragh/demo_data_raw3
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2022-08-12T15:22:58
2022-09-08T07:55:44
null
CONTRIBUTOR
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### Link https://huggingface.co/datasets/darragh/demo_data_raw3 ### Description ``` Exception: ValueError Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent. ``` reported by @NielsRogge ### Owner No
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1,337,206,377
I_kwDODunzps5PtCZp
4,839
ImageFolder dataset builder does not read the validation data set if it is named as "val"
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1
2022-08-12T13:26:00
2022-08-30T10:14:55
2022-08-30T10:14:55
CONTRIBUTOR
null
**Is your feature request related to a problem? Please describe.** Currently, the `'imagefolder'` data set builder in [`load_dataset()`](https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/load.py#L1541] ) only [supports](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) the following names as the validation data set directory name: `["validation", "valid", "dev"]`. When the validation directory is named as `'val'`, the Data set will not have a validation split. I expected this to be a trivial task but ended up spending a lot of time before knowing that only the above names are supported. Here's a minimal example of `val` not being recognized: ```python import os import numpy as np import cv2 from datasets import load_dataset # creating a dummy data set with the following structure: # ROOT # | -- train # | ---- class_1 # | ---- class_2 # | -- val # | ---- class_1 # | ---- class_2 ROOT = "data" for which in ["train", "val"]: for class_name in ["class_1", "class_2"]: dir_name = os.path.join(ROOT, which, class_name) if not os.path.exists(dir_name): os.makedirs(dir_name) for i in range(10): cv2.imwrite( os.path.join(dir_name, f"{i}.png"), np.random.random((224, 224)) ) # trying to create a data set dataset = load_dataset( "imagefolder", data_dir=ROOT ) >> dataset DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 20 }) }) # ^ note how the dataset only has a 'train' subset ``` **Describe the solution you'd like** The suggestion is to include `"val"` to [that list ](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) as that's a commonly used phrase to name the validation directory. Also, In the documentation, explicitly mention that only such directory names are supported as train/val/test directories to avoid confusion. **Describe alternatives you've considered** In the documentation, explicitly mention that only such directory names are supported as train/val/test directories without adding `val` to the above list. **Additional context** A question asked in the forum: [ Loading an imagenet-style image dataset with train/val directories](https://discuss.huggingface.co/t/loading-an-imagenet-style-image-dataset-with-train-val-directories/21554)
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1,337,067,632
I_kwDODunzps5Psghw
4,836
Is it possible to pass multiple links to a split in load script?
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2022-08-12T11:06:11
2022-08-12T11:06:11
null
NONE
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**Is your feature request related to a problem? Please describe.** I wanted to use a python loading script in hugging face datasets that use different sources of text (it's somehow a compilation of multiple datasets + my own dataset) based on how `load_dataset` [works](https://huggingface.co/docs/datasets/loading) I assumed I could do something like bellow in my loading script: ```python ... _URL = "MY_DATASET_URL/resolve/main/data/" _URLS = { "train": [ "FIRST_URL_TO.txt", _URL + "train-00000-of-00001-676bfebbc8742592.parquet" ] } ... ``` but when loading the dataset it raises the following error: ```python File ~/.local/lib/python3.8/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs ... 668 if isinstance(a, str): 669 # Force-cast str subclasses to str (issue #21127) 670 parts.append(str(a)) TypeError: expected str, bytes or os.PathLike object, not list ``` **Describe the solution you'd like** I believe since it's possible for `load_dataset` to get list of URLs instead of just a URL for `train` split it can be possible here too. **Describe alternatives you've considered** An alternative solution would be to download all needed datasets locally and `push_to_hub` them all, but since the datasets I'm talking about are huge it's not among my options. **Additional context** I think loading `text` beside the `parquet` is completely a different issue but I believe I can figure it out by proposing a config for my dataset to load each entry of `_URLS['train']` separately either by `load_dataset("text", ...` or `load_dataset("parquet", ...`.
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4,829
Misalignment between card tag validation and docs
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2022-08-11T14:44:45
2022-08-11T14:46:35
null
MEMBER
null
## Describe the bug As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284 the validation of the dataset card tags is not aligned with its documentation: e.g. - implementation: `license: List[str]` - docs: `license: Union[str, List[str]]` They should be aligned. CC: @julien-c
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I_kwDODunzps5PnMnY
4,822
Moving dataset between namespaces breaks dataset viewer
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2022-08-11T09:05:30
2022-09-16T20:03:09
null
CONTRIBUTOR
null
## Describe the bug I moved a dataset from my own namespace to an org and that broke the dataset viewer. To fix it I had to manually edit the `dataset_info.json` file and change the first key in the json from `username--datasetname` to `orgname--datasetname` ## Steps to reproduce the bug What I did was: 1- Upload a dataset to my own namespace using `push_to_hub` 2- Move the dataset from my namespace to an org using the web interface. ## Expected results For the file to be changed accordingly. ## Actual results Broken dataset viewer. ## Environment info - `datasets` version: 2.3.3.dev0 - Platform: Linux-4.15.0-189-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.5 - PyArrow version: 7.0.0 - Pandas version: 1.3.5
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1,335,117,132
I_kwDODunzps5PlEVM
4,820
Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.
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null
1
2022-08-10T19:42:33
2022-08-10T19:53:10
2022-08-10T19:53:10
NONE
null
Hi, when i try to run prepare_dataset function in [fine tuning ASR tutorial 4](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) , i got this error. I got this error Terminating: fork() called from a process already using GNU OpenMP, this is unsafe. There is no other logs available, so i have no clue what is the cause of it. ``` def prepare_dataset(batch): audio = batch["path"] # batched output is "un-batched" batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] batch["input_length"] = len(batch["input_values"]) with processor.as_target_processor(): batch["labels"] = processor(batch["text"]).input_ids return batch data = data.map(prepare_dataset, remove_columns=data.column_names["train"], num_proc=4) ``` Specify the actual results or traceback. There is no traceback except `Terminating: fork() called from a process already using GNU OpenMP, this is unsafe.` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-43-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.4.3
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1,334,572,163
I_kwDODunzps5Pi_SD
4,817
Outdated Link for mkqa Dataset
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null
1
2022-08-10T12:45:45
2022-08-11T09:37:52
2022-08-11T09:37:52
NONE
null
## Describe the bug The URL used to download the mkqa dataset is outdated. It seems the URL to download the dataset is currently https://github.com/apple/ml-mkqa/blob/main/dataset/mkqa.jsonl.gz instead of https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz (master branch has been renamed to main). ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mkqa") ``` ## Expected results downloads the dataset ## Actual results ```python Downloading builder script: 4.79k/? [00:00<00:00, 201kB/s] Downloading metadata: 13.2k/? [00:00<00:00, 504kB/s] Downloading and preparing dataset mkqa/mkqa (download: 11.35 MiB, generated: 34.29 MiB, post-processed: Unknown size, total: 45.65 MiB) to /home/lhr/.cache/huggingface/datasets/mkqa/mkqa/1.0.0/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d... Downloading data files: 0% 0/1 [00:00<?, ?it/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Input In [3], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("mkqa") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/load.py:1746, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1745 # Download and prepare data -> 1746 builder_instance.download_and_prepare( 1747 download_config=download_config, 1748 download_mode=download_mode, 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, 1751 use_auth_token=use_auth_token, 1752 ) 1754 # Build dataset for splits 1755 keep_in_memory = ( 1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1757 ) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mkqa/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d/mkqa.py:130, in Mkqa._split_generators(self, dl_manager) 128 # download and extract URLs 129 urls_to_download = _URLS --> 130 downloaded_files = dl_manager.download_and_extract(urls_to_download) 132 return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})] File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls) 415 def download_and_extract(self, url_or_urls): 416 """Download and extract given url_or_urls. 417 418 Is roughly equivalent to: (...) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:309, in DownloadManager.download(self, url_or_urls) 306 download_func = partial(self._download, download_config=download_config) 308 start_time = datetime.now() --> 309 downloaded_path_or_paths = map_nested( 310 download_func, 311 url_or_urls, 312 map_tuple=True, 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time 318 logger.info(f"Downloading took {duration.total_seconds() // 60} min") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:393, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: --> 393 mapped = [ 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:394, in <listcomp>(.0) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: 393 mapped = [ --> 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:330, in _single_map_nested(args) 328 # Singleton first to spare some computation 329 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 330 return function(data_struct) 332 # Reduce logging to keep things readable in multiprocessing with tqdm 333 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:335, in DownloadManager._download(self, url_or_filename, download_config) 332 if is_relative_path(url_or_filename): 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:185, in cached_path(url_or_filename, download_config, **download_kwargs) 181 url_or_filename = str(url_or_filename) 183 if is_remote_url(url_or_filename): 184 # URL, so get it from the cache (downloading if necessary) --> 185 output_path = get_from_cache( 186 url_or_filename, 187 cache_dir=cache_dir, 188 force_download=download_config.force_download, 189 proxies=download_config.proxies, 190 resume_download=download_config.resume_download, 191 user_agent=download_config.user_agent, 192 local_files_only=download_config.local_files_only, 193 use_etag=download_config.use_etag, 194 max_retries=download_config.max_retries, 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): 200 # File, and it exists. 201 output_path = url_or_filename File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:530, in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc) 525 raise FileNotFoundError( 526 f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" 527 " disabled. To enable file online look-ups, set 'local_files_only' to False." 528 ) 529 elif response is not None and response.status_code == 404: --> 530 raise FileNotFoundError(f"Couldn't find file at {url}") 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 9.0.0 - Pandas version: 1.4.2
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I_kwDODunzps5PhGtf
4,815
Outdated loading script for OPUS ParaCrawl dataset
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0
2022-08-10T05:12:34
2022-08-12T14:17:57
2022-08-12T14:17:57
MEMBER
null
## Describe the bug Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.
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1,333,356,230
I_kwDODunzps5PeWbG
4,814
Support CSV as metadata file format in AudioFolder/ImageFolder
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2022-08-09T14:36:49
2022-08-31T11:59:08
2022-08-31T11:59:08
CONTRIBUTOR
null
Requested here: https://discuss.huggingface.co/t/how-to-structure-an-image-dataset-repo-using-the-image-folder-approach/21004. CSV is also used in AutoTrain for specifying metadata in image datasets.
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I_kwDODunzps5PdKDd
4,811
Bug in function validate_type for Python >= 3.9
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2022-08-09T10:25:21
2022-08-12T13:27:05
2022-08-12T13:27:05
MEMBER
null
## Describe the bug The function `validate_type` assumes that the type `typing.Optional[str]` is automatically transformed to `typing.Union[str, NoneType]`. ```python In [4]: typing.Optional[str] Out[4]: typing.Union[str, NoneType] ``` However, this is not the case for Python 3.9: ```python In [3]: typing.Optional[str] Out[3]: typing.Optional[str] ```
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Add more information to the dataset card of mlqa dataset
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2022-08-09T07:35:42
2022-08-09T13:33:23
2022-08-09T13:33:23
CONTRIBUTOR
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4,805
Wrong example in opus_gnome dataset card
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2022-08-09T03:21:27
2022-08-09T11:52:05
2022-08-09T11:52:05
CONTRIBUTOR
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## Describe the bug I found that [the example on opus_gone dataset ](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary) doesn't work. ## Steps to reproduce the bug ```python load_dataset("gnome", lang1="it", lang2="pl") ``` `"gnome"` should be `"opus_gnome"` ## Expected results ```bash 100% 1/1 [00:00<00:00, 42.09it/s] DatasetDict({ train: Dataset({ features: ['id', 'translation'], num_rows: 8368 }) }) ``` ## Actual results ```bash Couldn't find 'gnome' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/gnome/gnome.py ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.27 - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3
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1,332,630,358
I_kwDODunzps5PblNW
4,804
streaming dataset with concatenating splits raises an error
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2022-08-09T02:41:56
2022-08-17T12:42:36
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NONE
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## Describe the bug streaming dataset with concatenating splits raises an error ## Steps to reproduce the bug ```python from datasets import load_dataset # no error repo = "nateraw/ade20k-tiny" dataset = load_dataset(repo, split="train+validation") ``` ```python from datasets import load_dataset # error repo = "nateraw/ade20k-tiny" dataset = load_dataset(repo, split="train+validation", streaming=True) ``` ```sh --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>() 3 # error 4 repo = "nateraw/ade20k-tiny" ----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True) 1 frames [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path) 1030 splits_generator = splits_generators[split] 1031 else: -> 1032 raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}") 1033 1034 # Create a dataset for each of the given splits ValueError: Bad split: train+validation. Available splits: ['validation', 'train'] ``` [Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing) ## Expected results load successfully or throws an error saying it is not supported. ## Actual results above ## Environment info - `datasets` version: 2.4.0 - Platform: Windows-10-10.0.22000-SP0 (windows11 x64) - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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1,332,079,562
I_kwDODunzps5PZevK
4,803
Support `pipeline` argument in inspect.py functions
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2022-08-08T16:01:24
2022-08-08T16:01:24
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** The `wikipedia` dataset requires a `pipeline` argument to build the list of splits: https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py#L937 But this is currently not supported in `get_dataset_config_info`: https://github.com/huggingface/datasets/blob/main/src/datasets/inspect.py#L373-L375 which is called by other functions, e.g. `get_dataset_split_names`. **Additional context** The dataset viewer is not working out-of-the-box on `wikipedia` for this reason: https://huggingface.co/datasets/wikipedia/viewer <img width="637" alt="Capture d’écran 2022-08-08 à 12 01 16" src="https://user-images.githubusercontent.com/1676121/183461838-5330783b-0269-4ba7-a999-314cde2023d8.png">
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`with_format` behavior is inconsistent on different datasets
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2022-08-08T10:41:34
2022-08-09T16:49:09
null
CONTRIBUTOR
null
## Describe the bug I found a case where `with_format` does not transform the dataset to the requested format. ## Steps to reproduce the bug Run: ```python from transformers import AutoTokenizer, AutoFeatureExtractor from datasets import load_dataset raw = load_dataset("glue", "sst2", split="train") raw = raw.select(range(100)) tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled") def preprocess_func(examples): return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True) data = raw.map(preprocess_func, batched=True) print(type(data[0]["input_ids"])) data = data.with_format("torch", columns=["input_ids"]) print(type(data[0]["input_ids"])) ``` printing as expected: ```python <class 'list'> <class 'torch.Tensor'> ``` Then run: ```python raw = load_dataset("beans", split="train") raw = raw.select(range(100)) preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans") def preprocess_func(examples): imgs = [img.convert("RGB") for img in examples["image"]] return preprocessor(imgs) data = raw.map(preprocess_func, batched=True) print(type(data[0]["pixel_values"])) data = data.with_format("torch", columns=["pixel_values"]) print(type(data[0]["pixel_values"])) ``` Printing, unexpectedly ```python <class 'list'> <class 'list'> ``` ## Expected results `with_format` should transform into the requested format; it's not the case. ## Actual results `type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979 - Platform: Linux - Python version: 3.9.12 - PyArrow version: 7.0.0
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4,799
video dataset loader/parser
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2022-08-07T01:54:12
2022-08-09T16:42:51
2022-08-09T16:42:51
CONTRIBUTOR
null
you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)? could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem.
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1,329,887,810
I_kwDODunzps5PRHpC
4,796
ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB when adding image to Dataset
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6
2022-08-05T12:41:19
2022-11-23T12:18:00
null
CONTRIBUTOR
null
## Describe the bug When adding a Pillow image to an existing Dataset on the hub, `add_item` fails due to the Pillow image not being automatically converted into the Image feature. ## Steps to reproduce the bug ```python from datasets import load_dataset from PIL import Image dataset = load_dataset("hf-internal-testing/example-documents") # load any random Pillow image image = Image.open("/content/cord_example.png").convert("RGB") new_image = {'image': image} dataset['test'] = dataset['test'].add_item(new_image) ``` ## Expected results The image should be automatically casted to the Image feature when using `add_item`. For now, this can be fixed by using `encode_example`: ``` import datasets feature = datasets.Image(decode=False) new_image = {'image': feature.encode_example(image)} dataset['test'] = dataset['test'].add_item(new_image) ``` ## Actual results ``` ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=576x864 at 0x7F7CCC4589D0> with type Image: did not recognize Python value type when inferring an Arrow data type ```
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1,329,525,732
I_kwDODunzps5PPvPk
4,795
Missing MBPP splits
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2022-08-05T06:51:01
2022-09-13T12:27:24
2022-09-13T12:27:24
NONE
null
(@albertvillanova) The [MBPP dataset on the Hub](https://huggingface.co/datasets/mbpp) has only a test split for both its "full" and its "sanitized" subset, while the [paper](https://arxiv.org/abs/2108.07732) states in subsection 2.1 regarding the full split: > In the experiments described later in the paper, we hold out 10 problems for **few-shot prompting**, another 500 as our **test** dataset (which is used to evaluate both few-shot inference and fine-tuned models), 374 problems for **fine-tuning**, and the rest for **validation**. If the dataset on the Hub should reproduce most closely what the original authors use, I guess this four-way split should be reflected. The paper doesn't explicitly state the task_id ranges of the splits, but the [GitHub readme](https://github.com/google-research/google-research/tree/master/mbpp) referenced in the paper specifies exact task_id ranges, although it misstates the total number of samples: > We specify a train and test split to use for evaluation. Specifically: > > * Task IDs 11-510 are used for evaluation. > * Task IDs 1-10 and 511-1000 are used for training and/or prompting. We typically used 1-10 for few-shot prompting, although you can feel free to use any of the training examples. I.e. the few-shot, train and validation splits are combined into one split, with a soft suggestion of using the first ten for few-shot prompting. It is not explicitly stated whether the 374 fine-tuning samples mentioned in the paper have task_id 511 to 784 or 601 to 974 or are randomly sampled from task_id 511 to 974. Regarding the "sanitized" split the paper states the following: > For evaluations involving the edited dataset, we perform comparisons with 100 problems that appear in both the original and edited dataset, using the same held out 10 problems for few-shot prompting and 374 problems for fine-tuning. The statement doesn't appear to be very precise, as among the 10 few-shot problems, those with task_id 1, 5 and 10 are not even part of the sanitized variant, and many from the task_id range from 511 to 974 are missing (e.g. task_id 511 to 553). I suppose the idea the task_id ranges for each split remain the same, even if some of the task_ids are not present. That would result in 7 few-shot, 257 test, 141 train and 22 validation examples in the sanitized split.
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Add DocVQA
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2022-08-04T13:07:26
2022-08-08T05:31:20
null
CONTRIBUTOR
null
## Adding a Dataset - **Name:** DocVQA - **Description:** Document Visual Question Answering (DocVQA) seeks to inspire a “purpose-driven” point of view in Document Analysis and Recognition research, where the document content is extracted and used to respond to high-level tasks defined by the human consumers of this information. - **Paper:** https://arxiv.org/abs/2007.00398 - **Data:** https://www.docvqa.org/datasets/docvqa - **Motivation:** Models like LayoutLM and Donut in the Transformers library are fine-tuned on DocVQA. Would be very handy to directly load this dataset from the hub. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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1,328,571,064
I_kwDODunzps5PMGK4
4,791
Dataset Viewer issue for Team-PIXEL/rendered-wikipedia-english
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1
2022-08-04T12:49:16
2022-08-04T13:43:16
2022-08-04T13:43:16
NONE
null
### Link https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train ### Description The dataset can be loaded fine but the viewer shows this error: ``` Server Error Status code: 400 Exception: Status400Error Message: The dataset does not exist. ``` I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed? ### Owner Yes
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Issue with fine classes in trec dataset
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2022-08-04T12:28:51
2022-08-22T16:14:16
2022-08-22T16:14:16
MEMBER
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## Describe the bug According to their paper, the TREC dataset contains 2 kinds of classes: - 6 coarse classes: TREC-6 - 50 fine classes: TREC-50 However, our implementation only has 47 (instead of 50) fine classes. The reason for this is that we only considered the last segment of the label, which is repeated for several coarse classes: - We have one `desc` fine label instead of 2: - `DESC:desc` - `HUM:desc` - We have one `other` fine label instead of 3: - `ENTY:other` - `LOC:other` - `NUM:other` From their paper: > We define a two-layered taxonomy, which represents a natural semantic classification for typical answers in the TREC task. The hierarchy contains 6 coarse classes and 50 fine classes, > Each coarse class contains a non-overlapping set of fine classes.
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NonMatchingChecksumError in mbpp dataset
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2022-08-04T08:15:51
2022-08-04T17:21:01
2022-08-04T17:21:01
MEMBER
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## Describe the bug As reported on the Hub [Fix Checksum Mismatch](https://huggingface.co/datasets/mbpp/discussions/1), there is a `NonMatchingChecksumError` when loading mbpp dataset ## Steps to reproduce the bug ```python ds = load_dataset("mbpp", "full") ``` ## Expected results Loading of the dataset without any exception raised. ## Actual results ``` NonMatchingChecksumError Traceback (most recent call last) <ipython-input-1-a3fbdd3ed82e> in <module> ----> 1 ds = load_dataset("mbpp", "full") .../huggingface/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1791 1792 # Download and prepare data -> 1793 builder_instance.download_and_prepare( 1794 download_config=download_config, 1795 download_mode=download_mode, .../huggingface/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) .../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1225 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1228 1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: .../huggingface/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: --> 775 verify_checksums( 776 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 777 ) .../huggingface/datasets/src/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://raw.githubusercontent.com/google-research/google-research/master/mbpp/mbpp.jsonl'] ```
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.save_to_disk('path', fs=s3) TypeError
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2022-08-03T14:49:29
2022-08-03T15:23:00
2022-08-03T15:23:00
NONE
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The following code: ```python import datasets train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"]) s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key) train_dataset.save_to_disk("s3://datasets/", fs=s3) ``` produces following traceback: ```shell File "C:\Users\Hong Knop\AppData\Local\Programs\Python\Python310\lib\site-packages\botocore\auth.py", line 374, in scope return '/'.join(scope) ``` I invoke print(scope) in <auth.py> (line 373) and find this: ```python [('4VA08VLL3VTKQJKCAI8M',), '20220803', 'us-east-1', 's3', 'aws4_request'] ```
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4,784
Add Multiface dataset
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2022-08-02T21:00:22
2022-08-08T14:42:36
null
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## Adding a Dataset - **Name:** Multiface dataset - **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps - **Data:** https://github.com/facebookresearch/multiface The whole dataset is 65TB though, so I'm not sure Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
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2022-08-02T18:36:05
2022-08-22T09:46:28
2022-08-20T02:11:53
NONE
null
## Describe the bug Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets. ## Steps to reproduce the bug ```python dataset_name = "the_pile" ds = load_dataset(dataset_name, split="train") ds = ds.map(preprocess, num_proc=num_workers) uniques = set(ds.unique("hash")) ``` Gists for minimum reproducible example: https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2 ## Expected results Chunking and writing out a deduplicated dataset. ## Actual results ``` return dataset._data.column(column).unique().to_pylist() File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648 ```
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4,779
Loading natural_questions requires apache_beam even with existing preprocessed data
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2022-08-02T15:06:57
2022-08-02T16:03:18
2022-08-02T16:03:18
MEMBER
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## Describe the bug When loading "natural_questions", the package "apache_beam" is required: ``` ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam. Please install it using 'pip install apache_beam' for instance' ``` This requirement is unnecessary, once there exists preprocessed data and the script just needs to download it. ## Steps to reproduce the bug ```python load_dataset("natural_questions", "dev", split="validation", revision="main") ``` ## Expected results No ImportError raised. ## Actual results ``` ImportError Traceback (most recent call last) [<ipython-input-3-c938e7c05d02>](https://localhost:8080/#) in <module>() ----> 1 from datasets import load_dataset; ds = load_dataset("natural_questions", "dev", split="validation", revision="main") [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1732 revision=revision, 1733 use_auth_token=use_auth_token, -> 1734 **config_kwargs, 1735 ) 1736 [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1504 download_mode=download_mode, 1505 data_dir=data_dir, -> 1506 data_files=data_files, 1507 ) 1508 [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1246 ) from None -> 1247 raise e1 from None 1248 else: 1249 raise FileNotFoundError( [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1180 download_config=download_config, 1181 download_mode=download_mode, -> 1182 dynamic_modules_path=dynamic_modules_path, 1183 ).get_module() 1184 elif path.count("/") == 1: # community dataset on the Hub [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 490 base_path=hf_github_url(path=self.name, name="", revision=revision), 491 imports=imports, --> 492 download_config=self.download_config, 493 ) 494 additional_files = [(config.DATASETDICT_INFOS_FILENAME, dataset_infos_path)] if dataset_infos_path else [] [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in _download_additional_modules(name, base_path, imports, download_config) 214 _them_str = "them" if len(needs_to_be_installed) > 1 else "it" 215 raise ImportError( --> 216 f"To be able to use {name}, you need to install the following {_depencencies_str}: " 217 f"{', '.join(needs_to_be_installed)}.\nPlease install {_them_str} using 'pip install " 218 f"{' '.join(needs_to_be_installed.values())}' for instance'" ImportError: To be able to use natural_questions, you need to install the following dependency: apache_beam. Please install it using 'pip install apache_beam' for instance' ``` ## Environment info Colab notebook.
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RuntimeError when using torchaudio 0.12.0 to load MP3 audio file
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2022-08-01T14:11:23
2022-08-24T16:55:43
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MEMBER
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Current version of `torchaudio` (0.12.0) raises a RuntimeError when trying to use `sox_io` backend but non-Python dependency `sox` is not installed: https://github.com/pytorch/audio/blob/2e1388401c434011e9f044b40bc8374f2ddfc414/torchaudio/backend/sox_io_backend.py#L21-L29 ```python def _fail_load( filepath: str, frame_offset: int = 0, num_frames: int = -1, normalize: bool = True, channels_first: bool = True, format: Optional[str] = None, ) -> Tuple[torch.Tensor, int]: raise RuntimeError("Failed to load audio from {}".format(filepath)) ``` Maybe we should raise a more actionable error message so that the user knows how to fix it. UPDATE: - this is an incompatibility of latest torchaudio (0.12.0) and the sox backend TODO: - [x] as a temporary solution, we should recommend installing torchaudio<0.12.0 - #4777 - #4785 - [ ] however, a stable solution must be found for torchaudio>=0.12.0 Related to: - https://github.com/huggingface/transformers/issues/18379
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Streaming not supported in Theivaprakasham/wildreceipt
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2022-08-01T09:46:17
2022-08-01T10:30:29
2022-08-01T10:30:29
NONE
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### Link _No response_ ### Description _No response_ ### Owner _No response_
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Training hangs at the end of epoch, with set_transform/with_transform+multiple workers
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2022-07-31T06:32:28
2022-07-31T06:36:43
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## Describe the bug I use load_dataset() (I tried with [wiki](https://huggingface.co/datasets/wikipedia) and my own json data) and use set_transform/with_transform for preprocessing. But it hangs at the end of the 1st epoch if dataloader_num_workers>=1. No problem with single worker. ## Steps to reproduce the bug ```python train_dataset = datasets.load_dataset("wikipedia", "20220301.en", split='train', cache_dir=model_args.cache_dir, streaming=False) train_dataset.set_transform(psg_parse_fn) train_dataloader = DataLoader( train_dataset, batch_size=args.train_batch_size, sampler=DistributedSampler(train_dataset), collate_fn=data_collator, drop_last=args.dataloader_drop_last, num_workers=args.dataloader_num_workers, ) ``` ## Expected results ## Actual results It simply hangs. The ending step is num_example/batch_size (one epoch). ## Environment info - `datasets` version: 2.4.1.dev0 - Platform: Linux-5.4.170+-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 8.0.0 - Pandas version: 1.4.1
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AssertionError when using label_cols in to_tf_dataset
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2022-07-29T21:32:12
2022-09-12T11:24:46
2022-09-12T11:24:46
NONE
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## Describe the bug An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`. The assertion is in this line: https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475 ## Steps to reproduce the bug ```python from datasets import load_dataset from transformers import DefaultDataCollator dataset = load_dataset('glue', 'mrpc', split='train') tf_dataset = dataset.to_tf_dataset( columns=["sentence1", "sentence2", "idx"], label_cols=["label"], batch_size=16, collate_fn=DefaultDataCollator(return_tensors="tf"), ) ``` ## Expected results No assertion error. ## Actual results ``` AssertionError: in user code: File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels * assert set(features.keys()).union(labels.keys()) == set(input_batch.keys()) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.3
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Fail to process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96.
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## Describe the bug datasets fail to process SQuADv1.1 with max_seq_length=128, doc_stride=96 when calling datasets["train"].train_dataset.map(). ## Steps to reproduce the bug I used huggingface[ TF2 question-answering examples](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering). And my scripts are as follows: ``` python run_qa.py \ --model_name_or_path $BERT_DIR \ --dataset_name $SQUAD_DIR \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 128 \ --doc_stride 96 \ --output_dir $OUTPUT \ --save_steps 10000 \ --overwrite_cache \ --overwrite_output_dir \ ``` ## Expected results Normally process SQuADv1.1 datasets with max_seq_length=128, doc_stride=96. ## Actual results ``` INFO:__main__:Padding all batches to max length because argument was set or we're on TPU. WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_train_features at 0x7f15bc2d07a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. 0%| | 0/88 [00:00<?, ?ba/s]thread '<unnamed>' panicked at 'assertion failed: stride < max_len', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/encoding.rs:311:9 note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace 0%| | 0/88 [00:00<?, ?ba/s] Traceback (most recent call last): File "run_qa.py", line 743, in <module> main() File "run_qa.py", line 485, in main load_from_cache_file=not data_args.overwrite_cache, File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2394, in map desc=desc, File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 551, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 518, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/anaconda3/envs/py37/lib/python3.7/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2768, in _map_single offset=offset, File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2644, in apply_function_on_filtered_inputs processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) File "anaconda3/envs/py37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2336, in decorated result = f(decorated_item, *args, **kwargs) File "run_qa.py", line 410, in prepare_train_features padding=padding, File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2512, in __call__ **kwargs, File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2703, in batch_encode_plus **kwargs, File "anaconda3/envs/py37/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 429, in _batch_encode_plus is_pretokenized=is_split_into_words, pyo3_runtime.PanicException: assertion failed: stride < max_len Traceback (most recent call last): File "./data/SQuADv1.1/evaluate-v1.1.py", line 92, in <module> with open(args.prediction_file) as prediction_file: FileNotFoundError: [Errno 2] No such file or directory: './output/bert_base_squadv1.1_tf2/eval_predictions.json' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.3.2 - Platform: Ubuntu, pytorch=1.11.0, tensorflow-gpu=2.9.1 - Python version: 2.7 - PyArrow version: 8.0.0
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