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PR_kwDODunzps5dijxt

Release: 2.14.6

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PR_kwDODunzps5dhGpW

Release 2.14.5

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(wrong release number - I was continuing the 2.14 branch but 2.14.5 was released from `main`)

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PR_kwDODunzps5dhFfg

minor release step improvement

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PR_kwDODunzps5dg_sb

pin fsspec before it switches to glob.glob

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PR_kwDODunzps5dg9Uu

Pin supported upper version of fsspec

closed

MEMBER

Pin upper version of `fsspec` to avoid disruptions introduced by breaking changes (and the need of urgent patch releases with hotfixes) on each release on their side. See: - #6331 - #6210 - #5731 - #5617 - #5447 I propose that we explicitly test, introduce fixes and support each new `fsspec` version release. CC: @LysandreJik

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PR_kwDODunzps5dgy0w

unpin-fsspec

closed

MEMBER

Close #6333.

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PR_kwDODunzps5dggIV

Support fsspec 2023.10.0

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Fix #6333.

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PR_kwDODunzps5dgbpR

datasets.filesystems: fix is_remote_filesystems

closed

CONTRIBUTOR

Close #6330, close #6333. `fsspec.implementations.LocalFilesystem.protocol` was changed from `str` "file" to `tuple[str,...]` ("file", "local") in `fsspec>=2023.10.0` This commit supports both styles.

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I_kwDODunzps50oRe3

Support fsspec 2023.10.0

closed

MEMBER

Once root issue is fixed, remove temporary pin of fsspec < 2023.10.0 introduced by: - #6331 Related to issue: - #6330 As @ZachNagengast suggested, the issue might be related to: - https://github.com/fsspec/filesystem_spec/pull/1381

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https://github.com/huggingface/datasets/pull/6332

PR_kwDODunzps5dgW3w

Replace deprecated license_file in setup.cfg

closed

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Replace deprecated license_file in `setup.cfg`. See: https://github.com/huggingface/datasets/actions/runs/6610930650/job/17953825724?pr=6331 ``` /tmp/pip-build-env-a51hls20/overlay/lib/python3.8/site-packages/setuptools/config/setupcfg.py:293: _DeprecatedConfig: Deprecated config in `setup.cfg` !! ******************************************************************************** The license_file parameter is deprecated, use license_files instead. By 2023-Oct-30, you need to update your project and remove deprecated calls or your builds will no longer be supported. See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details. ******************************************************************************** !! ```

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PR_kwDODunzps5dgRQt

Temporarily pin fsspec < 2023.10.0

closed

MEMBER

Temporarily pin fsspec < 2023.10.0 until permanent solution is found. Hot fix #6330. See: https://github.com/huggingface/datasets/actions/runs/6610904287/job/17953774987 ``` ... ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported. = 373 failed, 2055 passed, 17 skipped, 8 warnings, 6 errors in 228.14s (0:03:48) = ```

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I_kwDODunzps50lwEu

Latest fsspec==2023.10.0 issue with streaming datasets

closed

CONTRIBUTOR

### Describe the bug Loading a streaming dataset with this version of fsspec fails with the following error: `NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet.` I suspect the issue is with this PR https://github.com/fsspec/filesystem_spec/pull/1381 ### Steps to reproduce the bug 1. Upgrade fsspec to version `2023.10.0` 2. Attempt to load a streaming dataset e.g. `load_dataset("laion/gpt4v-emotion-dataset", split="train", streaming=True)` 3. Observe the following exception: ``` File "/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/datasets/load.py", line 2146, in load_dataset return builder_instance.as_streaming_dataset(split=split) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/datasets/builder.py", line 1318, in as_streaming_dataset raise NotImplementedError( NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet. ``` ### Expected behavior Should stream the dataset as normal. ### Environment info datasets@main fsspec==2023.10.0

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I_kwDODunzps50lAZk

شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

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شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

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I_kwDODunzps50lAXw

شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

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I_kwDODunzps50jh2j

FileNotFoundError when trying to load the downloaded dataset with `load_dataset(..., streaming=True)`

closed

NONE

### Describe the bug Hi, I'm trying to load the dataset `togethercomputer/RedPajama-Data-1T-Sample` with `load_dataset` in streaming mode, i.e., `streaming=True`, but `FileNotFoundError` occurs. ### Steps to reproduce the bug I've downloaded the dataset and save it to the cache dir in advance. My hope is loading the files in offline environment and without taking too much hours to prepross the entire data before running into the training process. So I try the following code to load the files streamingly ```py dataset = load_dataset('togethercomputer/RedPajama-Data-1T-Sample', streaming=True) print(next(iter(dataset['train']))) ``` Sadly, it raises the following: ``` FileNotFoundError: [Errno 2] No such file or directory: 'CURRENT_CODE_PATH/arxiv_sample.jsonl' ``` I've noticed that the dataset can be properly found in the begining ``` Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/togethercomputer--RedPajama-Data-1T-Sample/6ea3bc8ec2e84ec6d2df1930942e9028ace8c5b9d9143823cf911c50bbd92039 (last modified on Sat Oct 21 20:12:57 2023) since it couldn't be found locally at togethercomputer/RedPajama-Data-1T-Sample., or remotely on the Hugging Face Hub. ``` But it seems that the paths couldn't be properly parsed when loading iteratively. How should I fix this error. I've tried specifying `data_files` or `data_dir` as `.../arxiv_sample.jsonl` but none of them works. Thanks. ### Expected behavior Properly load the dataset. ### Environment info `datasets==2.14.5`

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PR_kwDODunzps5dcSRa

Create battery_analysis.py

closed

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PR_kwDODunzps5dcSM3

Create battery_analysis.py

closed

NONE

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I_kwDODunzps50iN2f

Conversion to Arrow fails due to wrong type heuristic

closed

NONE

### Describe the bug I have a list of dictionaries with valid/JSON-serializable values. One key is the denominator for a paragraph. In 99.9% of cases its a number, but there are some occurences of '1a', '2b' and so on. If trying to convert this list to a dataset with `Dataset.from_list()`, I always get `ArrowInvalid: Could not convert '1' with type str: tried to convert to int64`, presumably because pyarrow tries to convert the keys to integers. Is there any way to circumvent this and fix dtypes? I didn't find anything in the documentation. ### Steps to reproduce the bug * create a list of dicts with one key being a string of an integer for the first few thousand occurences and try to convert to dataset. ### Expected behavior There shouldn't be an error (e.g. some flag to turn off automatic str to numeric conversion). ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.35 - Python version: 3.9.18 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1

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I_kwDODunzps50e21c

Loading dataset from large GCS bucket very slow since 2.14

open

NONE

### Describe the bug Since updating to >2.14 we have very slow access to our parquet files on GCS when loading a dataset (>30 min vs 3s). Our GCS bucket has many objects and resolving globs is very slow. I could track down the problem to this change: https://github.com/huggingface/datasets/blame/bade7af74437347a760830466eb74f7a8ce0d799/src/datasets/data_files.py#L348 The underlying implementation with gcsfs is really slow. Could you go back to the old way if we are simply giving the parquet files and no glob pattern? Thank you. ### Steps to reproduce the bug Load a dataset from a GCS bucket that has many files. ### Expected behavior Used to be fast (3s) in 2.13 ### Environment info datasets==2.14.5

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PR_kwDODunzps5dT5vG

Fix regex `get_data_files` formatting for base paths

closed

CONTRIBUTOR

With this pr https://github.com/huggingface/datasets/pull/6309, it is formatting the entire base path into regex, which results in the undesired formatting error `doesn't match the pattern` because of the line in `glob_pattern_to_regex`: `.replace("//", "/")`: - Input: `hf://datasets/...` - Output: `hf:/datasets/...` This fix will only convert the `split_pattern` to regex and keep the `base_path` unchanged. cc @albertvillanova hopefully this still works with your implementation

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PR_kwDODunzps5dS3Mc

Fix typos

closed

CONTRIBUTOR

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I_kwDODunzps50YpdM

Dataset slice splits can't load training and validation at the same time

closed

NONE

### Describe the bug According to the [documentation](https://huggingface.co/docs/datasets/v2.14.5/loading#slice-splits) is should be possible to run the following command: `train_test_ds = datasets.load_dataset("bookcorpus", split="train+test")` to load the train and test sets from the dataset. However executing the equivalent code: `speech_commands_v1 = load_dataset("superb", "ks", split="train+test")` only yields the following output: > Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 54175 > }) Where loading the dataset without the split argument yields: > DatasetDict({ > train: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 51094 > }) > validation: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 6798 > }) > test: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 3081 > }) > }) Thus, the API seems to be broken in this regard. This is a bit annoying since I want to be able to use the split argument with `split="train[:10%]+test[:10%]"` to have smaller dataset to work with when validating my model is working correctly. ### Steps to reproduce the bug `speech_commands_v1 = load_dataset("superb", "ks", split="train+test")` ### Expected behavior > DatasetDict({ > train: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 51094 > }) > test: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 3081 > }) > }) ### Environment info ``` import datasets print(datasets.__version__) ``` > 2.14.5 ``` import sys print(sys.version) ``` > 3.9.17 (main, Jul 5 2023, 20:41:20) > [GCC 11.2.0]

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I_kwDODunzps50WrVV

Datasets.map is severely broken

open

NONE

### Describe the bug Regardless of how many cores I used, I have 16 or 32 threads, map slows down to a crawl at around 80% done, lingers maybe until 97% extremely slowly and NEVER finishes the job. It just hangs. After watching this for 27 hours I control-C out of it. Until the end one process appears to be doing something, but it never ends. I saw some comments about fast tokenizers using Rust and all and tried different variations. NOTHING works. ### Steps to reproduce the bug Running it without breaking the dataset into parts results in the same behavior. The loop was an attempt to see if this was a RAM issue. for idx in range(100): dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]') dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False, num_proc=1, remove_columns=["text", "meta"]) dataset.save_to_disk(training_args.cache_dir + f"/training_data_{idx}") ### Expected behavior I expect map to run at more or less the same speed it starts with and FINISH its processing. ### Environment info Python 3.8, same with 3.10 makes no difference. Ubuntu 20.04,

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https://github.com/huggingface/datasets/pull/6318

PR_kwDODunzps5dRC9V

Deterministic set hash

closed

MEMBER

Sort the items in a set according to their `datasets.fingerprint.Hasher.hash` hash to get a deterministic hash of sets. This is useful to get deterministic hashes of tokenizers that use a trie based on python sets. reported in https://github.com/huggingface/datasets/issues/3847

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I_kwDODunzps50WKHk

sentiment140 dataset unavailable

closed

NONE

### Describe the bug loading the dataset using load_dataset("sentiment140") returns the following error ConnectionError: Couldn't reach http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip (error 403) ### Steps to reproduce the bug Run the following code (version should not matter). ``` from datasets import load_dataset data = load_dataset("sentiment140") ``` ### Expected behavior The dataset should be loaded just like any other. The main issue is that it is no longer hosted by stanford. It is still available from a [Google Drive Link](https://docs.google.com/file/d/0B04GJPshIjmPRnZManQwWEdTZjg/edit). ### Environment info - `datasets` version: 2.14.5 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.10.8 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1

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https://github.com/huggingface/datasets/pull/6316

PR_kwDODunzps5dQGpg

Fix loading Hub datasets with CSV metadata file

closed

MEMBER

Currently, the reading of the metadata file infers the file extension (.jsonl or .csv) from the passed filename. However, downloaded files from the Hub don't have file extension. For example: - the original file: `hf://datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5916a4-16977085077831/metadata.jsonl` - corresponds to the downloaded path: `/tmp/pytest-of-username/pytest-46/cache/datasets/downloads/9f5374dbb470f711f6b89d66a5eec1f19cc96324b26bcbebe29138bda6cb20e6`, which does not have extension In the case where the metadata file does not have an extension, the reader assumes it is a JSONL file, thus the reported error when trying to read a CSV file as a JSONL one: `ArrowInvalid: JSON parse error: Invalid value. in row 0` This behavior was introduced by: - #4837 This PR extracts the metadata file extension from the original filename (instead of the downloaded one) and passes it as a parameter to the read_metadata function. Fix #6315.

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I_kwDODunzps50Vh3z

Hub datasets with CSV metadata raise ArrowInvalid: JSON parse error: Invalid value. in row 0

closed

MEMBER

When trying to load a Hub dataset that contains a CSV metadata file, it raises an `ArrowInvalid` error: ``` E pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0 pyarrow/error.pxi:100: ArrowInvalid ``` See: https://huggingface.co/datasets/lukarape/public_small_papers/discussions/1

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PR_kwDODunzps5dPo25

Support creating new branch in push_to_hub

closed

NONE

This adds support for creating a new branch when pushing a dataset to the hub. Tested both methods locally and branches are created.

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PR_kwDODunzps5dPGmL

Fix commit message formatting in multi-commit uploads

closed

CONTRIBUTOR

Currently, the commit message keeps on adding: - `Upload dataset (part 00000-of-00002)` - `Upload dataset (part 00000-of-00002) (part 00001-of-00002)` Introduced in https://github.com/huggingface/datasets/pull/6269 This PR fixes this issue to have - `Upload dataset (part 00000-of-00002)` - `Upload dataset (part 00001-of-00002)`

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PR_kwDODunzps5dKWDF

docs: resolving namespace conflict, refactored variable

closed

CONTRIBUTOR

In docs of about_arrow.md, in the below example code  The variable name 'time' was being used in a way that could potentially lead to a namespace conflict with Python's built-in 'time' module. It is not a good convention and can lead to unintended variable shadowing for any user re-using the example code. To ensure code clarity, and prevent potential naming conflicts renamed the variable 'time' to 'elapsed_time' in the example code.

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I_kwDODunzps50MAih

cast_column to Sequence with length=4 occur exception raise in datasets/table.py:2146

closed

NONE

### Describe the bug i load a dataset from local csv file which has 187383612 examples, then use `map` to generate new columns for test. here is my code : ``` import os from datasets import load_dataset from datasets.features import Sequence, Value def add_new_path(example): example["ais_bbox"] = [100,100,200,200] example["ais_image_path"] = os.path.join("images", example["image_path"]) if example["image_path"] else "" return example ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1749/") hf_ds = ais_dataset.map(add_new_path, batched=False, num_proc=32) ds = hf_ds.cast_column("ais_bbox", Sequence(Value("int32"), length=4)) ``` and the `cast_column` raise an exception ``` Casting the dataset: 3%|███▉ ... File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2110, in cast_column return self.cast(features) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2055, in cast dataset = dataset.map( File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3097, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3474, in _map_single batch = apply_function_on_filtered_inputs( File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3353, in apply_function_on_filtered_inputs processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2329, in table_cast return cast_table_to_schema(table, schema) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2288, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2288, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 1831, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 1831, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2145, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type list<item: int64> to Sequence(feature=Value(dtype='int32', id=None), length=4, id=None) ``` i check the source code and make debug info: in datasets/table.py:2092 ``` 2091 if feature.length > -1: 2092 if feature.length * len(array) == len(array.values): 2093 return pa.FixedSizeListArray.from_arrays(_c(array.values, feature.feature), feature.length) 2094 print(len(array)) 2095 print(len(array.values)) ``` my feature.length is 4. but feature.length * len(array) == len(array.values) is false. print(len(array)) is 262 print(len(array.values)) is 4000 then I use "for item in array" to print each item then get 262 * [100,100,200,200] and use "for item in array.values" to print each item and get 4000 int32 which are 1000 * [100,100,200,200] i'm wondering the `chunk` in each `array.chunks`, the "chunk.values" may get all the chunks's value rather than single chunk? but i check the pyarrow's doc seems chunk.values is chunk's value not all. ### Steps to reproduce the bug code provided above. ### Expected behavior feature.length * len(array) == len(array.values) should be true. and there should not has Exception. ### Environment info python3.9 x86_64 datasets: 2.14.4 pyarrow: 13.0.0 or 10.0.0

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PR_kwDODunzps5dBPnY

Add return_file_name in load_dataset

open

NONE

Proposition to fix #5806. Added an optional parameter `return_file_name` in the dataset builder config. When set to `True`, the function will include the file name corresponding to the sample in the returned output. There is a difference between arrow-based and folder-based datasets to return the file name: - for arrow-based: a column is concatenated after the table is cast. - for folder-based: `dataset.info.features` has the entry `file_name` and the original file name is passed to the `sample_metadata` dictionary. The difference in behavior might be a concern, also I do not know whether the `file_name` should return the original file path or the downloaded one for folder-based datasets. I added some tests for the datasets that already had a test file.

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PR_kwDODunzps5c_YcX

Fix get_data_patterns for directories with the word data twice

closed

MEMBER

Before the fix, `get_data_patterns` inferred wrongly the split name for paths with the word "data" twice: - For the URL path: `hf://datasets/piuba-bigdata/articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357/data/train-00001-of-00009.parquet` (note the org name `piuba-bigdata/` ending with `data/`) - The inferred split name was: `articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357/data/train` instead of `train` This PR fixes this issue by passing the `base_path` (`hf://datasets/piuba-bigdata/articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357`) to `_get_data_files_patterns` and prepending it to the regex split pattern (`data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*\\..*`). Fix #6305. Fix https://huggingface.co/datasets/piuba-bigdata/articles_and_comments/discussions/1

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I_kwDODunzps50CfkB

module 'resource' has no attribute 'error'

closed

NONE

### Describe the bug just run import: `from datasets import load_dataset` and then: ``` File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\__init__.py", line 22, in <module> from .arrow_dataset import Dataset File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\arrow_dataset.py", line 66, in <module> from .arrow_reader import ArrowReader File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\download\__init__.py", line 10, in <module> from .streaming_download_manager import StreamingDownloadManager File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\download\streaming_download_manager.py", line 21, in <module> from ..filesystems import COMPRESSION_FILESYSTEMS File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\filesystems\__init__.py", line 8, in <module> import fsspec.asyn File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\fsspec\asyn.py", line 157, in <module> ResourceEror = resource.error AttributeError: module 'resource' has no attribute 'error' Process finished with exit code 1 ``` and the error codes are: ``` try: import resource except ImportError: resource = None ResourceError = OSError else: ResourceEror = resource.error ``` 1. miss spelling : "ResourceEror " should be "ResourceErorr" 2. module 'resource' has no attribute 'error' ### Steps to reproduce the bug only one step: `from datasets import load_dataset` ### Expected behavior slove error: module 'resource' has no attribute 'error' ### Environment info python=3.10 datasets==2.14.5

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PR_kwDODunzps5c9s0j

Fix typo in code example in docs

closed

CONTRIBUTOR

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I_kwDODunzps50AyY8

pyinstaller : OSError: could not get source code

closed

NONE

### Describe the bug I ran a package with pyinstaller and got the following error： ### Steps to reproduce the bug ``` ... File "datasets\__init__.py", line 52, in <module> File "<frozen importlib._bootstrap>", line 1027, in _find_and_load File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 688, in _load_unlocked File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module File "datasets\inspect.py", line 30, in <module> File "<frozen importlib._bootstrap>", line 1027, in _find_and_load File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 688, in _load_unlocked File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module File "datasets\load.py", line 58, in <module> File "<frozen importlib._bootstrap>", line 1027, in _find_and_load File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 688, in _load_unlocked File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module File "datasets\packaged_modules\__init__.py", line 31, in <module> File "inspect.py", line 1147, in getsource File "inspect.py", line 1129, in getsourcelines File "inspect.py", line 958, in findsource OSError: could not get source code ``` ### Expected behavior I have looked up the relevant information, but I can't find a suitable reason ### Environment info ```python python 3.10 datasets 2.14.4 pyinstaller 5.6.2 ```

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I_kwDODunzps5z_cUg

Cannot load dataset with `2.14.5`: `FileNotFound` error

closed

NONE

### Describe the bug I'm trying to load [piuba-bigdata/articles_and_comments] and I'm stumbling with this error on `2.14.5`. However, this works on `2.10.0`. ### Steps to reproduce the bug [Colab link](https://colab.research.google.com/drive/1SAftFMQnFE708ikRnJJHIXZV7R5IBOCE#scrollTo=r2R2ipCCDmsg) ```python Downloading readme: 100% 1.19k/1.19k [00:00<00:00, 30.9kB/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [<ipython-input-2-807c3583d297>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 load_dataset("piuba-bigdata/articles_and_comments", split="train") 2 frames [/usr/local/lib/python3.10/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, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2127 2128 # Create a dataset builder -> 2129 builder_instance = load_dataset_builder( 2130 path=path, 2131 name=name, [/usr/local/lib/python3.10/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, token, use_auth_token, storage_options, **config_kwargs) 1813 download_config = download_config.copy() if download_config else DownloadConfig() 1814 download_config.storage_options.update(storage_options) -> 1815 dataset_module = dataset_module_factory( 1816 path, 1817 revision=revision, [/usr/local/lib/python3.10/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) 1506 raise e1 from None 1507 if isinstance(e1, FileNotFoundError): -> 1508 raise FileNotFoundError( 1509 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1510 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/piuba-bigdata/articles_and_comments/articles_and_comments.py or any data file in the same directory. Couldn't find 'piuba-bigdata/articles_and_comments' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in piuba-bigdata/articles_and_comments. ``` ### Expected behavior It should load normally. ### Environment info ``` - `datasets` version: 2.14.5 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.18.0 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 ```

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PR_kwDODunzps5c7-4q

Update README.md

closed

CONTRIBUTOR

Fixed typos in ReadMe and added punctuation marks Tensorflow --> TensorFlow

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I_kwDODunzps5z1vIk

Parquet uploads off-by-one naming scheme

open

CONTRIBUTOR

### Describe the bug I noticed this numbering scheme not matching up in a different project and wanted to raise it as an issue for discussion, what is the actual proper way to have these stored? <img width="425" alt="image" src="https://github.com/huggingface/datasets/assets/1981179/3ffa2144-7c9a-446f-b521-a5e9db71e7ce"> The `-SSSSS-of-NNNNN` seems to be used widely across the codebase. The section that creates the part in my screenshot is here https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L5287 There are also some edits to this section in the single commit branch. ### Steps to reproduce the bug 1. Upload a dataset that requires at least two parquet files in it 2. Observe the naming scheme ### Expected behavior The couple options here are of course **1. keeping it as is** **2. Starting the index at 1:** train-00001-of-00002-{hash}.parquet train-00002-of-00002-{hash}.parquet **3. My preferred option** (which would solve my specific issue), dropping the total entirely: train-00000-{hash}.parquet train-00001-{hash}.parquet This also solves an issue that will occur with an `append` variable for `push_to_hub` (see https://github.com/huggingface/datasets/issues/6290) where as you add a new parquet file, you need to rename everything in the repo as well. However, I know there are parts of the repo that use 0 as the starting file or may require the total, so raising the question for discussion. ### Environment info - `datasets` version: 2.14.6.dev0 - Platform: macOS-14.0-arm64-arm-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.18.0 - PyArrow version: 12.0.1 - Pandas version: 1.5.3

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I_kwDODunzps5zwgjO

ArrowWriter/ParquetWriter `write` method does not increase `_num_bytes` and hence datasets not sharding at `max_shard_size`

closed

NONE

### Describe the bug An example from [1], does not work when limiting shards with `max_shard_size`. Try the following example with low `max_shard_size`, such as: ```python builder.download_and_prepare(output_dir, storage_options=storage_options, file_format="parquet", max_shard_size="10MB") ``` The reason for this is that, in line [2] `writer._num_bytes > max_shard_size` is never true, because the `write` method of `ArrowWriter` [3] does not increase `self._num_bytes`. Such that respective Arrow/Parquet shards are only written to file based on the `writer_batch_size` or `config.DEFAULT_MAX_BATCH_SIZE`, but not based on `max_shard_size`. [1] https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage [2] https://github.com/huggingface/datasets/blob/3e8d420808718c9a1453a2e7ee3484ca12c9c70d/src/datasets/builder.py#L1677 [3] https://github.com/huggingface/datasets/blob/3e8d420808718c9a1453a2e7ee3484ca12c9c70d/src/datasets/arrow_writer.py#L459 ### Steps to reproduce the bug Get example from: https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage Call `builder.download_and_prepare` with low `max_shard_size` such as `10MB`, e.g.: ```python builder.download_and_prepare(output_dir, storage_options=storage_options, file_format="parquet", max_shard_size="10MB") ``` ### Expected behavior Shards should be written based on `max_shard_size` instead of batch size. ### Environment info ``` >>> import datasets >>> datasets.__version__ '2.14.6.dev0 ```

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PR_kwDODunzps5cpPVh

Unpin `tensorflow` maximum version

closed

CONTRIBUTOR

Removes the temporary pin introduced in #6264

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PR_kwDODunzps5cpIoG

Unpin `jax` maximum version

closed

CONTRIBUTOR

fix #6299 fix #6202

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I_kwDODunzps5znLLW

Support for newer versions of JAX

closed

NONE

### Feature request Hi, I like your idea of adapting the datasets library to be usable with JAX. Thank you for that. However, in your [setup.py](https://github.com/huggingface/datasets/blob/main/setup.py), you enforce old versions of JAX <= 0.3... It is very cumbersome ! What is the rationale for such a limitation ? Can you remove it please ? Thanks, ### Motivation This library is unusable with new versions of JAX ? ### Your contribution Yes.

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PR_kwDODunzps5ckg6j

Doc readme improvements

closed

CONTRIBUTOR

Changes in the doc READMe: * adds two new sections (to be aligned with `transformers` and `hfh`): "Previewing the documentation" and "Writing documentation examples" * replaces the mentions of `transformers` with `datasets` * fixes some dead links

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PR_kwDODunzps5ckXBa

Fix ArrayXD cast

closed

CONTRIBUTOR

Fix #6291

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PR_kwDODunzps5cjUs1

Move `exceptions.py` to `utils/exceptions.py`

open

CONTRIBUTOR

I didn't notice the path while reviewing the PR yesterday :(

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PR_kwDODunzps5cfiW8

Fix parquet columns argument in streaming mode

closed

MEMBER

It was failing when there's a DatasetInfo with non-None info.features from the YAML (therefore containing columns that should be ignored) Fix https://github.com/huggingface/datasets/issues/6293

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I_kwDODunzps5zecL1

IndexError: Invalid key is out of bounds for size 0 despite having a populated dataset

closed

NONE

### Describe the bug I am encountering an `IndexError` when trying to access data from a DataLoader which wraps around a dataset I've loaded using the `datasets` library. The error suggests that the dataset size is `0`, but when I check the length and print the dataset, it's clear that it has `1166` entries. ### Steps to reproduce the bug 1. Load a dataset with `1166` entries. 2. Create a DataLoader using this dataset. 3. Try iterating over the DataLoader. code: ```python def get_train_dataloader(self) -> DataLoader: if self.train_dataset is None: raise ValueError("Trainer: training requires a train_dataset.") train_dataset = self.train_dataset data_collator = self.data_collator print(len(train_dataset)) print(train_dataset) if is_datasets_available() and isinstance(train_dataset, datasets.Dataset): train_dataset = self._remove_unused_columns(train_dataset, description="training") else: data_collator = self._get_collator_with_removed_columns(data_collator, description="training") train_sampler = self._get_train_sampler() dl = DataLoader( train_dataset, batch_size=self._train_batch_size, sampler=train_sampler, collate_fn=data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory, worker_init_fn=seed_worker, ) print(dl) print(len(dl)) for i in dl: print(i) break return dl ``` output : ``` 1166 Dataset({ features: ['input_ids', 'special_tokens_mask'], num_rows: 1166 }) <torch.utils.data.dataloader.DataLoader object ...> 146 ``` Error: ``` Traceback (most recent call last): File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 266, in <module> train() File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 260, in train trainer.train() File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/transformers/trainer.py", line 1506, in train return inner_training_loop( File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/transformers/trainer.py", line 1520, in _inner_training_loop train_dataloader = self.get_train_dataloader() File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 80, in get_train_dataloader for i in dl: File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ data = self._next_data() File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 674, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 1116 is out of bounds for size 0 ``` ### Expected behavior I expect to be able to iterate over the DataLoader without encountering an IndexError since the dataset is populated. ### Environment info - `datasets` library version: [2.14.5] - Platform: [Linux] - Python version: 3.10 - Other libraries involved: HuggingFace Transformers

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I_kwDODunzps5zd-gf

Choose columns to stream parquet data in streaming mode

closed

MEMBER

Currently passing columns= to load_dataset in streaming mode fails ``` Tried to load parquet data with columns '['link']' with mismatching features '{'caption': Value(dtype='string', id=None), 'image': {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='null', id=None)}, 'link': Value(dtype='string', id=None), 'message_id': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None)}' ``` similar to https://github.com/huggingface/datasets/issues/6039 reported at https://huggingface.co/datasets/laion/dalle-3-dataset/discussions/3#65259a09617407d4520f4ad9

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I_kwDODunzps5zdQtm

how to load the image of dtype float32 or float64

open

NONE

_FEATURES = datasets.Features( { "image": datasets.Image(), "text": datasets.Value("string"), }, ) The datasets builder seems only support the unit8 data. How to load the float dtype data?

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I_kwDODunzps5zZv9P

Casting type from Array2D int to Array2D float crashes

closed

NONE

### Describe the bug I am on a school project and the initial type for feature annotations are `Array2D(shape=(None, 4))`. I am trying to cast this type to a `float64` and pyarrow gives me this error : ``` Traceback (most recent call last): File "/home/alan/dev/ClassezDesImagesAvecDesAlgorithmesDeDeeplearning/src/sdd/data/dataset.py", line 141, in <module> dataset = StanfordDogsDataset(size, 5).original(True).demo() File "<attrs generated init __main__.StanfordDogsDataset>", line 4, in __init__ File "/home/alan/dev/ClassezDesImagesAvecDesAlgorithmesDeDeeplearning/src/sdd/data/dataset.py", line 33, in __attrs_post_init__ self.dataset = self.dataset.cast_column( File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, *args, **kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2110, in cast_column return self.cast(features) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2055, in cast dataset = dataset.map( File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3097, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3474, in _map_single batch = apply_function_on_filtered_inputs( File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3353, in apply_function_on_filtered_inputs processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 2328, in table_cast return cast_table_to_schema(table, schema) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 2287, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 2287, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1831, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1831, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 2143, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper return func(array, *args, **kwargs) File "/home/alan/.cache/pypoetry/virtualenvs/sdd-2XWLAjSi-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1967, in array_cast return pa_type.wrap_array(array) File "pyarrow/types.pxi", line 1369, in pyarrow.lib.BaseExtensionType.wrap_array TypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: double>>, got list<item: list<item: int32>> ``` ### Steps to reproduce the bug ```python dataset = datasets.load_dataset("Alanox/stanford-dogs", split="full") dataset = dataset.cast_column("annotations", Array2D((None, 4), "float64")) ``` ### Expected behavior It should simply cast the column feature type to a `float64` without error ### Environment info datasets == 2.14.5

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I_kwDODunzps5zX11v

Incremental dataset (e.g. `.push_to_hub(..., append=True)`)

open

CONTRIBUTOR

### Feature request Have the possibility to do `ds.push_to_hub(..., append=True)`. ### Motivation Requested in this [comment](https://huggingface.co/datasets/laion/dalle-3-dataset/discussions/3#65252597c4edc168202a5eaa) and this [comment](https://huggingface.co/datasets/laion/dalle-3-dataset/discussions/4#6524f675c9607bdffb208d8f). Discussed internally on [slack](https://huggingface.slack.com/archives/C02EMARJ65P/p1696950642610639?thread_ts=1690554266.830949&cid=C02EMARJ65P). ### Your contribution What I suggest to do for parquet datasets is to use `CommitOperationCopy` + `CommitOperationDelete` from `huggingface_hub`: 1. list files 2. copy files from parquet-0001-of-0004 to parquet-0001-of-0005 3. delete files like parquet-0001-of-0004 4. generate + add last parquet file parquet-0005-of-0005 => make a single commit with all commit operations at once I think it should be quite straightforward to implement. Happy to review a PR (maybe conflicting with the ongoing "1 commit push_to_hub" PR https://github.com/huggingface/datasets/pull/6269)

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PR_kwDODunzps5cZiay

testing doc-builder

closed

CONTRIBUTOR

testing https://github.com/huggingface/doc-builder/pull/426

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I_kwDODunzps5zVdcR

Dataset.from_pandas with a DataFrame of PIL.Images

open

MEMBER

Currently type inference doesn't know what to do with a Pandas Series of PIL.Image objects, though it would be nice to get a Dataset with the Image type this way

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I_kwDODunzps5zM4yw

map() not recognizing "text"

closed

NONE

### Describe the bug The [map() documentation](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Dataset.map) reads: ` ds = ds.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)` I have been trying to reproduce it in my code as: `tokenizedDataset = dataset.map(lambda x: tokenizer(x['text']), batched=True)` But it doesn't work as it throws the error: > KeyError: 'text' Can you please guide me on how to fix it? ### Steps to reproduce the bug 1. `from datasets import load_dataset dataset = load_dataset("amazon_reviews_multi")` 2. Then this code: `from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")` 3. The line I quoted above (which I have been trying) ### Expected behavior As mentioned in the documentation, it should run without any error and map the tokenization on the whole dataset. ### Environment info Python 3.10.2

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PR_kwDODunzps5cPKNK

Create DefunctDatasetError

closed

MEMBER

Create `DefunctDatasetError` as a specific error to be raised when a dataset is defunct and no longer accessible. See Hub discussion: https://huggingface.co/datasets/the_pile_books3/discussions/7#6523c13a94f3a1a2092d251b

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I_kwDODunzps5zLKeV

TypeError: expected str, bytes or os.PathLike object, not dict

open

NONE

### Describe the bug my dataset is in form : train- image /n -labels and tried the code: ``` from datasets import load_dataset data_files = { "train": "/content/datasets/PotholeDetectionYOLOv8-1/train/", "validation": "/content/datasets/PotholeDetectionYOLOv8-1/valid/", "test": "/content/datasets/PotholeDetectionYOLOv8-1/test/" } dataset = load_dataset("imagefolder", data_dir=data_files) dataset ``` got error: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-29-2ef1926f73d9>](https://localhost:8080/#) in <cell line: 8>() 6 "test": "/content/datasets/PotholeDetectionYOLOv8-1/test/" 7 } ----> 8 dataset = load_dataset("imagefolder", data_dir=data_files) 9 dataset 6 frames [/usr/lib/python3.10/pathlib.py](https://localhost:8080/#) in _parse_args(cls, args) 576 parts += a._parts 577 else: --> 578 a = os.fspath(a) 579 if isinstance(a, str): 580 # Force-cast str subclasses to str (issue #21127) TypeError: expected str, bytes or os.PathLike object, not dict ``` ### Steps to reproduce the bug as share above ### Expected behavior load images and labels , but my dataset only uploads images - https://huggingface.co/datasets/Andyrasika/potholes-dataset ### Environment info colab pro

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I_kwDODunzps5zAp9g

Add Belebele multiple-choice machine reading comprehension (MRC) dataset

closed

NONE

### Feature request Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems. Please refer to paper for more details, [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884). ## Composition - 900 questions per language variant - 488 distinct passages, there are 1-2 associated questions for each. - For each question, there is 4 multiple-choice answers, exactly 1 of which is correct. - 122 language/language variants (including English). - 900 x 122 = 109,800 total questions. ### Motivation official repo https://github.com/facebookresearch/belebele ### Your contribution -

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PR_kwDODunzps5cBlKq

Fix array cast/embed with null values

closed

CONTRIBUTOR

Fixes issues with casting/embedding PyArrow list arrays with null values. It also bumps the required PyArrow version to 12.0.0 (over 9 months old) to simplify the implementation. Fix #6280, fix #6311, fix #6360 (Also fixes https://github.com/huggingface/datasets/issues/5430 to make Beam compatible with PyArrow>=12.0.0)

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PR_kwDODunzps5cBT5p

Drop data_files duplicates

open

MEMBER

I just added drop_duplicates=True to `.from_patterns`. I used a dict to deduplicate and preserve the order close https://github.com/huggingface/datasets/issues/6259 close https://github.com/huggingface/datasets/issues/6272

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PR_kwDODunzps5cBQPd

Improve documentation of dataset.from_generator

closed

CONTRIBUTOR

Improve documentation to clarify sharding behavior (#6270)

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I_kwDODunzps5y7jru

Couldn't cast array of type fixed_size_list to Sequence(Value(float64))

closed

NONE

### Describe the bug I have a dataset with an embedding column, when I try to map that dataset I get the following exception: ``` Traceback (most recent call last): File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3189, in map for rank, done, content in iflatmap_unordered( File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/Users/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/multiprocess/pool.py", line 774, in get raise self._value TypeError: Couldn't cast array of type fixed_size_list<item: float>[2] to Sequence(feature=Value(dtype='float32', id=None), length=2, id=None) ``` ### Steps to reproduce the bug Here's a simple repro script: ``` from datasets import Features, Value, Sequence, ClassLabel, Dataset dataset_features = Features({ 'text': Value('string'), 'embedding': Sequence(Value('double'), length=2), 'categories': Sequence(ClassLabel(names=sorted([ 'one', 'two', 'three' ]))), }) dataset = Dataset.from_dict( { 'text': ['A'] * 10000, 'embedding': [[0.0, 0.1]] * 10000, 'categories': [[0]] * 10000, }, features=dataset_features ) def test_mapper(r): r['text'] = list(map(lambda t: t + ' b', r['text'])) return r dataset = dataset.map(test_mapper, batched=True, batch_size=10, features=dataset_features, num_proc=2) ``` Removing the embedding column fixes the issue! ### Expected behavior The mapping completes successfully. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-14.0-arm64-arm-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.17.1 - PyArrow version: 13.0.0 - Pandas version: 2.0.3

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I_kwDODunzps5y62BC

Batched IterableDataset

open

NONE

### Feature request Hi, could you add an implementation of a batched `IterableDataset`. It already support an option to do batch iteration via `.iter(batch_size=...)` but this cannot be used in combination with a torch `DataLoader` since it just returns an iterator. ### Motivation The current implementation loads each element of a batch individually which can be very slow in cases of a big batch_size. I did some experiments [here](https://discuss.huggingface.co/t/slow-dataloader-with-big-batch-size/57224) and using a batched iteration would speed up data loading significantly. ### Your contribution N/A

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PR_kwDODunzps5b_iKb

No data files duplicates

closed

MEMBER

I added a new DataFilesSet class to disallow duplicate data files. I also deprecated DataFilesList. EDIT: actually I might just add drop_duplicates=True to `.from_patterns` close https://github.com/huggingface/datasets/issues/6259 close https://github.com/huggingface/datasets/issues/6272 TODO: - [ ] tests - [ ] preserve data files order

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I_kwDODunzps5y3F3C

FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either.

closed

NONE

### Describe the bug I'm encountering a "FileNotFoundError" while attempting to use the "paws-x" dataset to retrain the DistilRoBERTa-base model. The error message is as follows: FileNotFoundError: Couldn't find a module script at /content/paws-x/paws-x.py. Module 'paws-x' doesn't exist on the Hugging Face Hub either. ### Steps to reproduce the bug https://colab.research.google.com/drive/11xUUFxloClpmqLvDy_Xxfmo3oUzjY5nx#scrollTo=kUn74FigzhHm ### Expected behavior The the trained model ### Environment info colab, "paws-x" dataset , DistilRoBERTa-base model

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I_kwDODunzps5yy9iW

I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error

open

NONE

### Describe the bug I'm trying to fine tune the openai/whisper model from huggingface using jupyter notebook and i keep getting this error, i'm following the steps in this blog post https://huggingface.co/blog/fine-tune-whisper I tried google collab and it works but because I'm on the free version the training doesn't complete the error comes in jupyter notebook when i run this line `common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)` here is the error message ``` Map (num_proc=4): 0% 0/2506 [00:52<?, ? examples/s] The above exception was the direct cause of the following exception: NameError Traceback (most recent call last) Cell In[19], line 1 ----> 1 common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4) File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:853, in DatasetDict.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_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( --> 853 { 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 ) File ~\anaconda\Lib\site-packages\datasets\dataset_dict.py:854, in <dictcomp>(.0) 850 if cache_file_names is None: 851 cache_file_names = {k: None for k in self} 852 return DatasetDict( 853 { --> 854 k: dataset.map( 855 function=function, 856 with_indices=with_indices, 857 with_rank=with_rank, 858 input_columns=input_columns, 859 batched=batched, 860 batch_size=batch_size, 861 drop_last_batch=drop_last_batch, 862 remove_columns=remove_columns, 863 keep_in_memory=keep_in_memory, 864 load_from_cache_file=load_from_cache_file, 865 cache_file_name=cache_file_names[k], 866 writer_batch_size=writer_batch_size, 867 features=features, 868 disable_nullable=disable_nullable, 869 fn_kwargs=fn_kwargs, 870 num_proc=num_proc, 871 desc=desc, 872 ) 873 for k, dataset in self.items() 874 } 875 ) File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:592, in transmit_tasks.<locals>.wrapper(*args, **kwargs) 590 self: "Dataset" = kwargs.pop("self") 591 # apply actual function --> 592 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 593 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 594 for dataset in datasets: 595 # Remove task templates if a column mapping of the template is no longer valid File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:557, in transmit_format.<locals>.wrapper(*args, **kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output File ~\anaconda\Lib\site-packages\datasets\arrow_dataset.py:3189, in Dataset.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) 3182 logger.info(f"Spawning {num_proc} processes") 3183 with logging.tqdm( 3184 disable=not logging.is_progress_bar_enabled(), 3185 unit=" examples", 3186 total=pbar_total, 3187 desc=(desc or "Map") + f" (num_proc={num_proc})", 3188 ) as pbar: -> 3189 for rank, done, content in iflatmap_unordered( 3190 pool, Dataset._map_single, kwargs_iterable=kwargs_per_job 3191 ): 3192 if done: 3193 shards_done += 1 File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in iflatmap_unordered(pool, func, kwargs_iterable) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results] File ~\anaconda\Lib\site-packages\datasets\utils\py_utils.py:1394, in <listcomp>(.0) 1391 finally: 1392 if not pool_changed: 1393 # we get the result in case there's an error to raise -> 1394 [async_result.get(timeout=0.05) for async_result in async_results] File ~\anaconda\Lib\site-packages\multiprocess\pool.py:774, in ApplyResult.get(self, timeout) 772 return self._value 773 else: --> 774 raise self._value NameError: name 'feature_extractor' is not defined ``` ### Steps to reproduce the bug 1. follow the steps in this blog post https://huggingface.co/blog/fine-tune-whisper 2. run this line of code `common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=4)` 3. I'm using jupyter notebook from anaconda ### Expected behavior No error message ### Environment info datasets version: 2.8.0 Python version: 3.11 Windows 10

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I_kwDODunzps5yhYu4

Would like to Contribute a dataset

closed

NONE

I have a dataset of 2500 images that can be used for color-blind machine-learning algorithms. Since , there was no dataset available online , I made this dataset myself and would like to contribute this now to community

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I_kwDODunzps5ygLAo

FileNotFoundError for dataset with multiple builder config

closed

NONE

### Describe the bug When there is only one config and only the dataset name is entered when using datasets.load_dataset(), it works fine. But if I create a second builder_config for my dataset and enter the config name when using datasets.load_dataset(), the following error will happen. FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow' The "XXX.incomplete folder" in the cache folder of my dataset will disappear before "generating test split", which does not happen when config name is not entered and the config name is "default" C:\Users\chenx\.cache\huggingface\datasets\my_dataset\0_shot_multiple_choice\1.0.0 The folder that is supposed to remain under the above directory will disappear, and the data generator will not have a place to generate data into. ### Steps to reproduce the bug test = load_dataset('my_dataset', '0_shot_multiple_choice') ### Expected behavior FileNotFoundError: [Errno 2] No such file or directory: 'C:/Users/chenx/.cache/huggingface/datasets/my_dataset/0_shot_multiple_choice/1.0.0/97c3854a012cfd6b045e3be4c864739902af2d818bb9235b047baa94c302e9a2.incomplete/my_dataset-test-00000-00000-of-NNNNN.arrow' ### Environment info datasets 2.14.5 python 3.8.18

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I_kwDODunzps5yfvKU

Broken Link to PubMed Abstracts dataset .

open

NONE

### Describe the bug The link provided for the dataset is broken, data_files = [https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst](url) The ### Steps to reproduce the bug Steps to reproduce: 1) Head over to [https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt#big-data-datasets-to-the-rescue](url) 2) In the Section "What is the Pile?", you can see a code snippet that contains the broken link. ### Expected behavior The link should Redirect to the "PubMed Abstracts dataset" as expected . ### Environment info .

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I_kwDODunzps5yfY__

Duplicate `data_files` when named `<split>/<split>.parquet`

open

MEMBER

e.g. with `u23429/stock_1_minute_ticker` ```ipython In [1]: from datasets import * In [2]: b = load_dataset_builder("u23429/stock_1_minute_ticker") Downloading readme: 100%|██████████████████████████| 627/627 [00:00<00:00, 246kB/s] In [3]: b.config.data_files Out[3]: {NamedSplit('train'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet'], NamedSplit('validation'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet'], NamedSplit('test'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet']} ``` This bug issue is present in the current `datasets` 2.14.5 and also on `main` even after https://github.com/huggingface/datasets/pull/6244 cc @mariosasko

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