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https://github.com/huggingface/datasets/issues/6522

I_kwDODunzps56VBvw

Loading HF Hub Dataset (private org repo) fails to load all features

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### Describe the bug When pushing a `Dataset` with multiple `Features` (`input`, `output`, `tags`) to Huggingface Hub (private org repo), and later downloading the `Dataset`, only `input` and `output` load - I believe the expected behavior is for all `Features` to be loaded by default? ### Steps to reproduce the bug Pushing the data. `data_concat` is a `list` of `dict`s. ```python for datum in data_concat: datum_tags = {d["key"]: d["value"] for d in datum["tags"]} split_fraction = # some logic that generates a train/test split number if split_faction < test_fraction: data_test.append(datum) else: data_train.append(datum) dataset = DatasetDict( { "train": Dataset.from_list(data_train), "test": Dataset.from_list(data_test), "full": Dataset.from_list(data_concat), }, ) dataset_shuffled = dataset.shuffle(seed=shuffle_seed) dataset_shuffled.push_to_hub( repo_id=hf_repo_id, private=True, config_name=m, revision=revision, token=hf_token, ) ``` Loading it later: ```python dataset = datasets.load_dataset( path=hf_repo_id, name=name, token=hf_token, ) ``` Produces: ``` DatasetDict({ train: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) test: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) full: Dataset({ features: ['input', 'output'], num_rows: <obfuscated> }) }) ``` ### Expected behavior The expected result is below: ``` DatasetDict({ train: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) test: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) full: Dataset({ features: ['input', 'output', 'tags'], num_rows: <obfuscated> }) }) ``` My workaround is as follows: ```python dsinfo = datasets.get_dataset_config_info( path=data_files, config_name=data_config, token=hf_token, ) allfeatures = dsinfo.features.copy() if "tags" not in allfeatures: allfeatures["tags"] = [{"key": Value(dtype="string", id=None), "value": Value(dtype="string", id=None)}] dataset = datasets.load_dataset( path=data_files, name=data_config, features=allfeatures, token=hf_token, ) ``` Interestingly enough (and perhaps a related bug?), if I don't add the `tags` to `allfeatures` above (i.e. only loading `input` and `output`), it throws an error when executing `load_dataset`: ``` ValueError: Couldn't cast tags: list<element: struct<key: string, value: string>> child 0, element: struct<key: string, value: string> child 0, key: string child 1, value: string input: <obfuscated> output: <obfuscated> -- schema metadata -- huggingface: '{"info": {"features": {"tags": [{"key": {"dtype": "string",' + 532 to {'input': <obfuscated>, 'output': <obfuscated> because column names don't match ``` Traceback for this: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/load.py", line 2152, in load_dataset builder_instance.download_and_prepare( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 948, in download_and_prepare self._download_and_prepare( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1043, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1805, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1950, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info - `datasets` version: 2.15.0 - Platform: macOS-14.0-arm64-arm-64bit - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

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https://github.com/huggingface/datasets/issues/6521

I_kwDODunzps56Uomi

The order of the splits is not preserved

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We had a regression and the order of the splits is not preserved. They are alphabetically sorted, instead of preserving original "train", "validation", "test" order. Check: In branch "main" ```python In [9]: dataset = load_dataset("adversarial_qa", '"adversarialQA") In [10]: dataset Out[10]: DatasetDict({ test: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) train: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 30000 }) validation: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) }) ``` Before (2.15.0) it was: ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 30000 }) validation: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) test: Dataset({ features: ['id', 'title', 'context', 'question', 'answers', 'metadata'], num_rows: 3000 }) }) ``` See: https://huggingface.co/datasets/adversarial_qa/discussions/3

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https://github.com/huggingface/datasets/pull/6520

PR_kwDODunzps5ijUiw

Support commit_description parameter in push_to_hub

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Support `commit_description` parameter in `push_to_hub`. CC: @Wauplin

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https://github.com/huggingface/datasets/pull/6519

PR_kwDODunzps5ie4MA

Support push_to_hub canonical datasets

closed

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Support `push_to_hub` canonical datasets. This is necessary in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet Note that before this PR, the `repo_id` "dataset_name" was transformed to "user/dataset_name". This behavior was introduced by: - #6269

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https://github.com/huggingface/datasets/pull/6518

PR_kwDODunzps5icu-W

fix get_metadata_patterns function args error

closed

CONTRIBUTOR

Bug get_metadata_patterns arg error https://github.com/huggingface/datasets/issues/6517

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I_kwDODunzps56Ml90

Bug get_metadata_patterns arg error

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CONTRIBUTOR

https://github.com/huggingface/datasets/blob/3f149204a2a5948287adcade5e90707aa5207a92/src/datasets/load.py#L1240C1-L1240C69 metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config)

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https://github.com/huggingface/datasets/pull/6516

PR_kwDODunzps5icYX0

Support huggingface-hub pre-releases

closed

MEMBER

Support `huggingface-hub` pre-releases. This way we will have our CI green when testing `huggingface-hub` release candidates. See: https://github.com/huggingface/datasets/tree/ci-test-huggingface-hub-v0.20.0.rc1 Close #6513.

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https://github.com/huggingface/datasets/issues/6515

I_kwDODunzps56LE9b

Why call http_head() when fsspec_head() succeeds

open

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/a91582de288d98e94bcb5ab634ca1cfeeff544c5/src/datasets/utils/file_utils.py#L510C1-L523C14

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https://github.com/huggingface/datasets/pull/6514

PR_kwDODunzps5ia6Os

Cache backward compatibility with 2.15.0

open

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...for datasets without scripts It takes into account the changes in cache from - https://github.com/huggingface/datasets/pull/6493: switch to `config/version/commit_sha` schema - https://github.com/huggingface/datasets/pull/6454: fix `DataFilesDict` keys ordering when hashing requires https://github.com/huggingface/datasets/pull/6493 to be merged

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I_kwDODunzps56H0Mf

Support huggingface-hub 0.20.0

closed

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CI to test the support of `huggingface-hub` 0.20.0: https://github.com/huggingface/datasets/compare/main...ci-test-huggingface-hub-v0.20.0.rc1 We need to merge: - #6510 - #6512 - #6516

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https://github.com/huggingface/datasets/pull/6512

PR_kwDODunzps5iYI5z

Remove deprecated HfFolder

closed

MEMBER

...and use `huggingface_hub.get_token()` instead

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PR_kwDODunzps5iXAXR

Implement get dataset default config name

closed

MEMBER

Implement `get_dataset_default_config_name`. Now that we support setting a configuration as default in `push_to_hub` (see #6500), we need a programmatically way to know in advance which is the default configuration. This will be used in the Space to convert script-datasets to Parquet: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet Follow-up of: - #6500 CC: @severo

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https://github.com/huggingface/datasets/pull/6510

PR_kwDODunzps5iRyiV

Replace `list_files_info` with `list_repo_tree` in `push_to_hub`

closed

CONTRIBUTOR

Starting from `huggingface_hub` 0.20.0, `list_files_info` will be deprecated in favor of `list_repo_tree` (see https://github.com/huggingface/huggingface_hub/pull/1910)

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https://github.com/huggingface/datasets/pull/6509

PR_kwDODunzps5iREyE

Better cast error when generating dataset

closed

MEMBER

I want to improve the error message for datasets like https://huggingface.co/datasets/m-a-p/COIG-CQIA Cc @albertvillanova @severo is this new error ok ? Or should I use a dedicated error class ? New: ```python Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1920, in _prepare_split_single writer.write_table(table) File "/Users/quentinlhoest/hf/datasets/src/datasets/arrow_writer.py", line 574, in write_table pa_table = table_cast(pa_table, self._schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2322, in table_cast return cast_table_to_schema(table, schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2276, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast instruction: string other: string index: string domain: list<item: string> child 0, item: string output: string task_type: struct<major: list<item: string>, minor: list<item: string>> child 0, major: list<item: string> child 0, item: string child 1, minor: list<item: string> child 0, item: string task_name_in_eng: string input: string to {'answer_from': Value(dtype='string', id=None), 'instruction': Value(dtype='string', id=None), 'human_verified': Value(dtype='bool', id=None), 'domain': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'output': Value(dtype='string', id=None), 'task_type': {'major': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'minor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'copyright': Value(dtype='string', id=None), 'input': Value(dtype='string', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/playground/ttest.py", line 74, in <module> load_dataset("m-a-p/COIG-CQIA") File "/Users/quentinlhoest/hf/datasets/src/datasets/load.py", line 2529, in load_dataset builder_instance.download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 936, in download_and_prepare self._download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1031, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1791, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1922, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 3 new columns (other, index, task_name_in_eng) and 3 missing columns (answer_from, copyright, human_verified). This happened while the json dataset builder was generating data using hf://datasets/m-a-p/COIG-CQIA/coig_pc/coig_pc_core_sample.json (at revision b7b7ecf290f6515036c7c04bd8537228ac2eb474) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) ``` Previously: ```python Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1931, in _prepare_split_single writer.write_table(table) File "/Users/quentinlhoest/hf/datasets/src/datasets/arrow_writer.py", line 574, in write_table pa_table = table_cast(pa_table, self._schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2295, in table_cast return cast_table_to_schema(table, schema) File "/Users/quentinlhoest/hf/datasets/src/datasets/table.py", line 2253, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") ValueError: Couldn't cast task_type: struct<major: list<item: string>, minor: list<item: string>> child 0, major: list<item: string> child 0, item: string child 1, minor: list<item: string> child 0, item: string other: string instruction: string task_name_in_eng: string domain: list<item: string> child 0, item: string index: string output: string input: string to {'human_verified': Value(dtype='bool', id=None), 'task_type': {'major': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'minor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'answer_from': Value(dtype='string', id=None), 'copyright': Value(dtype='string', id=None), 'instruction': Value(dtype='string', id=None), 'domain': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'output': Value(dtype='string', id=None), 'input': Value(dtype='string', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/quentinlhoest/hf/datasets/playground/ttest.py", line 74, in <module> load_dataset("m-a-p/COIG-CQIA") File "/Users/quentinlhoest/hf/datasets/src/datasets/load.py", line 2529, in load_dataset builder_instance.download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 949, in download_and_prepare self._download_and_prepare( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1044, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1804, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/Users/quentinlhoest/hf/datasets/src/datasets/builder.py", line 1949, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ```

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https://github.com/huggingface/datasets/pull/6508

PR_kwDODunzps5iNvAu

Read GeoParquet files using parquet reader

open

NONE

Let GeoParquet files with the file extension `*.geoparquet` or `*.gpq` be readable by the default parquet reader. Those two file extensions are the ones most commonly used for GeoParquet files, and is included in the `gpq` validator tool at https://github.com/planetlabs/gpq/blob/e5576b4ee7306b4d2259d56c879465a9364dab90/cmd/gpq/command/convert.go#L73-L75 Addresses https://github.com/huggingface/datasets/issues/6438

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https://github.com/huggingface/datasets/issues/6507

I_kwDODunzps555o6g

where is glue_metric.py> @Frankie123421 what was the resolution to this?

closed

NONE

> @Frankie123421 what was the resolution to this? use glue_metric.py instead of glue.py in load_metric _Originally posted by @Frankie123421 in https://github.com/huggingface/datasets/issues/2117#issuecomment-905093763_

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I_kwDODunzps5549e-

Incorrect test set labels for RTE and CoLA datasets via load_dataset

closed

NONE

### Describe the bug The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1. Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax). Is this intended behavior to safeguard the test set for evaluation purposes? ### Steps to reproduce the bug !pip install datasets from datasets import load_dataset rte_data = load_dataset('glue', 'rte') cola_data = load_dataset('glue', 'cola') print(rte_data['test'][0:30]['label']) print(cola_data['test'][0:30]['label']) Output: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] The non-label test data seems to be fine: e.g. rte_data['test'][1] is: {'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.", 'sentence2': 'Authorities in Brazil hold 200 people as hostage.', 'label': -1, 'idx': 1} Training and validation data are also fine: e.g. rte_data['train][0] is: {'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.', 'sentence2': 'Weapons of Mass Destruction Found in Iraq.', 'label': 1, 'idx': 0} ### Expected behavior Expected the labels to be binary 0/1 values; Got all -1s instead ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0

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I_kwDODunzps553_iI

Got stuck when I trying to load a dataset

open

NONE

### Describe the bug Hello, everyone. I met a problem when I am trying to load a data file using load_dataset method on a Debian 10 system. The data file is not very large, only 1.63MB with 600 records. Here is my code: from datasets import load_dataset dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json') I waited it for 20 minutes. It still no response. I cannot using Ctrl+C to cancel the command. I have to use Ctrl+Z to kill it. I also try it with a txt file, it still no response in a long time. I can load the same file successfully using my laptop (windows 10, python 3.8.5, datasets==2.14.5). I can also make it on another computer (Ubuntu 20.04.5 LTS, python 3.10.13, datasets 2.14.7). It only takes me 1-2 miniutes. Could you give me some suggestions? Thank you. ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset('json', data_files='mypath/oaast_rm_zh.json') ### Expected behavior I hope it can load the file successfully. ### Environment info OS: Debian GNU/Linux 10 Python: Python 3.10.13 Pip list: Package Version ------------------------- ------------ accelerate 0.25.0 addict 2.4.0 aiofiles 23.2.1 aiohttp 3.9.1 aiosignal 1.3.1 aliyun-python-sdk-core 2.14.0 aliyun-python-sdk-kms 2.16.2 altair 5.2.0 annotated-types 0.6.0 anyio 3.7.1 async-timeout 4.0.3 attrs 23.1.0 certifi 2023.11.17 cffi 1.16.0 charset-normalizer 3.3.2 click 8.1.7 contourpy 1.2.0 crcmod 1.7 cryptography 41.0.7 cycler 0.12.1 datasets 2.14.7 dill 0.3.7 docstring-parser 0.15 einops 0.7.0 exceptiongroup 1.2.0 fastapi 0.105.0 ffmpy 0.3.1 filelock 3.13.1 fonttools 4.46.0 frozenlist 1.4.1 fsspec 2023.10.0 gast 0.5.4 gradio 3.50.2 gradio_client 0.6.1 h11 0.14.0 httpcore 1.0.2 httpx 0.25.2 huggingface-hub 0.19.4 idna 3.6 importlib-metadata 7.0.0 importlib-resources 6.1.1 jieba 0.42.1 Jinja2 3.1.2 jmespath 0.10.0 joblib 1.3.2 jsonschema 4.20.0 jsonschema-specifications 2023.11.2 kiwisolver 1.4.5 markdown-it-py 3.0.0 MarkupSafe 2.1.3 matplotlib 3.8.2 mdurl 0.1.2 modelscope 1.10.0 mpmath 1.3.0 multidict 6.0.4 multiprocess 0.70.15 networkx 3.2.1 nltk 3.8.1 numpy 1.26.2 nvidia-cublas-cu12 12.1.3.1 nvidia-cuda-cupti-cu12 12.1.105 nvidia-cuda-nvrtc-cu12 12.1.105 nvidia-cuda-runtime-cu12 12.1.105 nvidia-cudnn-cu12 8.9.2.26 nvidia-cufft-cu12 11.0.2.54 nvidia-curand-cu12 10.3.2.106 nvidia-cusolver-cu12 11.4.5.107 nvidia-cusparse-cu12 12.1.0.106 nvidia-nccl-cu12 2.18.1 nvidia-nvjitlink-cu12 12.3.101 nvidia-nvtx-cu12 12.1.105 orjson 3.9.10 oss2 2.18.3 packaging 23.2 pandas 2.1.4 peft 0.7.1 Pillow 10.1.0 pip 23.3.1 platformdirs 4.1.0 protobuf 4.25.1 psutil 5.9.6 pyarrow 14.0.1 pyarrow-hotfix 0.6 pycparser 2.21 pycryptodome 3.19.0 pydantic 2.5.2 pydantic_core 2.14.5 pydub 0.25.1 Pygments 2.17.2 pyparsing 3.1.1 python-dateutil 2.8.2 python-multipart 0.0.6 pytz 2023.3.post1 PyYAML 6.0.1 referencing 0.32.0 regex 2023.10.3 requests 2.31.0 rich 13.7.0 rouge-chinese 1.0.3 rpds-py 0.13.2 safetensors 0.4.1 scipy 1.11.4 semantic-version 2.10.0 sentencepiece 0.1.99 setuptools 68.2.2 shtab 1.6.5 simplejson 3.19.2 six 1.16.0 sniffio 1.3.0 sortedcontainers 2.4.0 sse-starlette 1.8.2 starlette 0.27.0 sympy 1.12 tiktoken 0.5.2 tokenizers 0.15.0 tomli 2.0.1 toolz 0.12.0 torch 2.1.2 tqdm 4.66.1 transformers 4.36.1 triton 2.1.0 trl 0.7.4 typing_extensions 4.9.0 tyro 0.6.0 tzdata 2023.3 urllib3 2.1.0 uvicorn 0.24.0.post1 websockets 11.0.3 wheel 0.41.2 xxhash 3.4.1 yapf 0.40.2 yarl 1.9.4 zipp 3.17.0

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I_kwDODunzps553Tji

Error Pushing to Hub

closed

NONE

### Describe the bug Error when trying to push a dataset in a special format to hub ### Steps to reproduce the bug ``` import datasets from datasets import Dataset dataset_dict = { "filename": ["apple", "banana"], "token": [[[1,2],[3,4]],[[1,2],[3,4]]], "label": [0, 1], } dataset = Dataset.from_dict(dataset_dict) dataset = dataset.cast_column("token", datasets.features.features.Array2D(shape=(2, 2),dtype="int16")) dataset.push_to_hub("SequenceModel/imagenet_val_256") ``` Error: ``` ... ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/tuple' in "<unicode string>", line 8, column 16: shape: !!python/tuple ^ ``` ### Expected behavior Dataset being pushed to hub ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.19.0-1022-gcp-x86_64-with-glibc2.35 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5iHgZf

Fix streaming xnli

closed

MEMBER

This code was failing ```python In [1]: from datasets import load_dataset In [2]: ...: ds = load_dataset("xnli", "all_languages", split="test", streaming=True) ...: ...: sample_data = next(iter(ds))["premise"] # pick up one data ...: input_text = list(sample_data.values()) ``` ``` File ~/hf/datasets/src/datasets/features/translation.py:104, in TranslationVariableLanguages.encode_example(self, translation_dict) 102 return translation_dict 103 elif self.languages and set(translation_dict) - lang_set: --> 104 raise ValueError( 105 f'Some languages in example ({", ".join(sorted(set(translation_dict) - lang_set))}) are not in valid set ({", ".join(lang_set)}).' 106 ) 108 # Convert dictionary into tuples, splitting out cases where there are 109 # multiple translations for a single language. 110 translation_tuples = [] ValueError: Some languages in example (language, translation) are not in valid set (ur, fr, hi, sw, vi, el, de, th, en, tr, zh, ar, bg, ru, es). ``` because in streaming mode we expect features encode methods to be no-ops if the example is already encoded. I fixed `TranslationVariableLanguages` to account for that

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PR_kwDODunzps5iHPt-

Pickle support for `torch.Generator` objects

closed

CONTRIBUTOR

Fix for https://discuss.huggingface.co/t/caching-a-dataset-processed-with-randomness/65616

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I_kwDODunzps55y3ZY

OverflowError: value too large to convert to int32_t

open

NONE

### Describe the bug  ### Steps to reproduce the bug just loading datasets ### Expected behavior how can I fix it ### Environment info pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl done

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PR_kwDODunzps5iFc6e

Enable setting config as default when push_to_hub

closed

MEMBER

Fix #6497.

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PR_kwDODunzps5iFIUF

docs: add reference Git over SSH

closed

CONTRIBUTOR

see https://discuss.huggingface.co/t/update-datasets-getting-started-to-new-git-security/65893

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PR_kwDODunzps5iBcFj

Fallback on dataset script if user wants to load default config

closed

MEMBER

Right now this code is failing on `main`: ```python load_dataset("openbookqa") ``` This is because it tries to load the dataset from the Parquet export but the dataset has multiple configurations and the Parquet export doesn't know which one is the default one. I fixed this by simply falling back on using the dataset script (which tells the user to pass `trust_remote_code=True`): ```python load_dataset("openbookqa", trust_remote_code=True) ``` Note that if the user happened to specify a config name I don't fall back on the script since we can use the Parquet export in this case (no need to know which config is the default) ```python load_dataset("openbookqa", "main") ```

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I_kwDODunzps55tlwi

Support setting a default config name in push_to_hub

closed

MEMBER

In order to convert script-datasets to no-script datasets, we need to support setting a default config name for those scripts that set one.

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I_kwDODunzps55sC6K

Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.

open

NONE

**Describe the bug** Getting a `412 Client Error: Precondition Failed` when trying to write a dataset to the HF hub. ``` huggingface_hub.utils._errors.HfHubHTTPError: 412 Client Error: Precondition Failed for url: https://huggingface.co/api/datasets/GLorr/test-dask/commit/main (Request ID: Root=1-657ae26f-3bd92bf861bb254b2cc0826c;50a09ab7-9347-406a-ba49-69f98abee9cc) A commit has happened since. Please refresh and try again. ``` **Steps to reproduce the bug** This is a minimal reproducer: ``` import dask.dataframe as dd import pandas as pd import random import os import huggingface_hub import datasets huggingface_hub.login(token=os.getenv("HF_TOKEN")) data = {"number": [random.randint(0,10) for _ in range(1000)]} df = pd.DataFrame.from_dict(data) dataframe = dd.from_pandas(df, npartitions=1) dataframe = dataframe.repartition(npartitions=3) schema = datasets.Features({"number": datasets.Value("int64")}).arrow_schema repo_id = "GLorr/test-dask" repo_path = f"hf://datasets/{repo_id}" huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True) dd.to_parquet(dataframe, path=f"{repo_path}/data", schema=schema) ``` **Expected behavior** Would expect to write to the hub without any problem. **Environment info** ``` datasets==2.15.0 huggingface-hub==0.19.4 ```

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I_kwDODunzps55kx7n

Image Data loaded Twice

open

NONE

### Describe the bug  When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images ### Steps to reproduce the bug from datasets import Dataset, load_dataset dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False) # print(dataset["train"][0]["image"] == dataset["train"][1]["image"]) print(dataset) print(dataset["train"]["image"]) print(len(dataset["train"]["image"])) ### Expected behavior DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 8 }) }) [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>] 8 ### Environment info - `datasets` version: 2.14.5 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.9.17 - Huggingface_hub version: 0.19.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3

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I_kwDODunzps55k3tx

Newline characters don't behave as expected when calling dataset.info

open

NONE

### System Info - `transformers` version: 4.32.1 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.11.5 - Huggingface_hub version: 0.15.1 - Safetensors version: 0.3.2 - Accelerate version: not installed - Accelerate config: not found - PyTorch version (GPU?): 2.1.1+cpu (False) - Tensorflow version (GPU?): 2.15.0 (False) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: no - Using distributed or parallel set-up in script?: no ### Who can help? @marios ### Information - [X] The official example scripts - [ ] My own modified scripts ### Tasks - [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction [Source](https://huggingface.co/docs/datasets/v2.2.1/en/access) ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo(description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='glue', dataset_name=None, config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943843, num_examples=3668, shard_lengths=None, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105879, num_examples=408, shard_lengths=None, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442410, num_examples=1725, shard_lengths=None, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': None}}, download_size=1494541, post_processing_size=None, dataset_size=1492132, size_in_bytes=2986673) ### Expected behavior ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset.info ``` DatasetInfo( description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, builder_name='glue', config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943851, num_examples=3668, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105887, num_examples=408, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442418, num_examples=1725, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': '971d7767d81b997fd9060ade0ec23c4fc31cbb226a55d1bd4a1bac474eb81dc7'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': '60a9b09084528f0673eedee2b69cb941920f0b8cd0eeccefc464a98768457f89'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': 'a04e271090879aaba6423d65b94950c089298587d9c084bf9cd7439bd785f784'}}, download_size=1494541, post_processing_size=None, dataset_size=1492156, size_in_bytes=2986697 )

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PR_kwDODunzps5h0XJK

Lazy data files resolution and offline cache reload

open

MEMBER

Includes both https://github.com/huggingface/datasets/pull/6458 and https://github.com/huggingface/datasets/pull/6459 This PR should be merged instead of the two individually, since they are conflicting ## Offline cache reload it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset since lhoestq/tmp couldn't be found on the Hugging Face Hub Found the latest cached dataset configuration 'default' at /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/default/0.0.0/da0e902a945afeb9 (last modified on Wed Dec 13 14:55:52 2023). DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` - Updated `CachedDatasetModuleFactory` to look for datasets in the cache at `<namespace>___<dataset_name>/<config_id>` - Since the metadata configs parameters are not available in offline mode, we don't know which folder to load (config_id and hash change), so I simply load the latest one - I instantiate a BuilderConfig even if there is no metadata config with the right config_name - Its config_id is equal to the config_name to be able to retrieve it in the cache (no more suffix for configs from metadata configs) - We can reload this config if offline mode by specifying the right config_name (same as online !) - Consequences of this change: - Only when there are user's parameters it creates a custom builder config with config_id = config_name + user parameters hash - the hash used to name the cache folder takes into account the metadata config and the dataset info, so that the right cache can be reloaded when there is internet connection without redownloading the data or resolving the data files. For local directories I hash the builder configs and dataset info, and for datasets on the hub I use the commit sha as hash. - cache directories now look like `config/version/commit_sha` for hub datasets which is clean :) Fix https://github.com/huggingface/datasets/issues/3547 ## Lazy data files resolution this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict

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PR_kwDODunzps5hzjhQ

Make push_to_hub return CommitInfo

closed

MEMBER

Make `push_to_hub` return `CommitInfo`. This is useful, for example, if we pass `create_pr=True` and we want to know the created PR ID. CC: @severo for the use case in https://huggingface.co/datasets/jmhessel/newyorker_caption_contest/discussions/4

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PR_kwDODunzps5hyiTY

Fix metrics dead link

closed

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I_kwDODunzps55bUec

`load_dataset(...,save_infos=True)` not working without loading script

open

NONE

### Describe the bug It seems that saving a dataset infos back into the card file is not working for datasets without a loading script. After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory. Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`). ### Steps to reproduce the bug 1. Have a local dataset without any loading script 2. Make sure there are no dataset infos in the README.md 3. Load with `save_infos=True` 4. No change in the dataset README.md 5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`) ### Expected behavior The dataset README.md should be updated and no file should be created in the python environment. ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.6.0

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I_kwDODunzps55Zj5h

load_dataset imageflder for aws s3 path

open

NONE

### Feature request I would like to load a dataset from S3 using the imagefolder option something like `dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True) ` ### Motivation no need of data_files ### Your contribution no experience with this

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I_kwDODunzps55WV36

429 Client Error

open

NONE

Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it? Thanks Dataset: https://huggingface.co/datasets/cerebras/SlimPajama-627B Error: `requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`

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PR_kwDODunzps5hqyfV

Update builder hash with info

closed

MEMBER

Currently if you change the `dataset_info` of a dataset (e.g. in the YAML part of the README.md), the cache ignores this change. This is problematic because you want to regenerate a dataset if you change the features or the split sizes for example (e.g. after push_to_hub) Ideally we should take the resolved files into account as well but this will be for another PR

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PR_kwDODunzps5hqCSc

Fix docs phrasing about supported formats when sharing a dataset

closed

MEMBER

Fix docs phrasing.

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I_kwDODunzps55Tcz8

FileNotFoundError: [Errno 2] No such file or directory: 'nul'

closed

NONE

### Describe the bug it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets" i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul'   ### Steps to reproduce the bug 1.import datasets ### Expected behavior i just run a single line code and stuct in this bug ### Environment info OS: Windows10 Datasets==2.15.0 python=3.10

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I_kwDODunzps55LE8l

Iterable Dataset: rename column clashes with remove column

closed

CONTRIBUTOR

### Describe the bug Suppose I have a two iterable datasets, one with the features: * `{"audio", "text", "column_a"}` And the other with the features: * `{"audio", "sentence", "column_b"}` I want to combine both datasets using `interleave_datasets`, which requires me to unify the column names. I would typically do this by: 1. Renaming the common columns to the same name (e.g. `"text"` -> `"sentence"`) 2. Removing the unwanted columns (e.g. `"column_a"`, `"column_b"`) However, the process of renaming and removing columns in an iterable dataset doesn't work, since we need to preserve the original text column, meaning we can't combine the datasets. ### Steps to reproduce the bug ```python from datasets import load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features dataset_features = dataset.features.keys() print("Original features: ", dataset_features) # rename "text" -> "sentence" dataset = dataset.rename_column("text", "sentence") # remove unwanted columns COLUMNS_TO_KEEP = {"audio", "sentence"} dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) # stream first sample, should return "audio" and "sentence" columns print(next(iter(dataset))) ``` Traceback: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[5], line 17 14 COLUMNS_TO_KEEP = {"audio", "sentence"} 15 dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) ---> 17 print(next(iter(dataset))) File ~/datasets/src/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self) 1350 yield formatter.format_row(pa_table) 1351 return -> 1353 for key, example in ex_iterable: 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) File ~/datasets/src/datasets/iterable_dataset.py:652, in MappedExamplesIterable.__iter__(self) 650 yield from ArrowExamplesIterable(self._iter_arrow, {}) 651 else: --> 652 yield from self._iter() File ~/datasets/src/datasets/iterable_dataset.py:729, in MappedExamplesIterable._iter(self) 727 if self.remove_columns: 728 for c in self.remove_columns: --> 729 del transformed_example[c] 730 yield key, transformed_example 731 current_idx += 1 KeyError: 'text' ``` => we see that `datasets` is looking for the column "text", even though we've renamed this to "sentence" and then removed the un-wanted "text" column from our dataset. ### Expected behavior Should be able to rename and remove columns from iterable dataset. ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: macOS-13.5.1-arm64-arm-64bit - Python version: 3.11.6 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2

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I_kwDODunzps55MjQu

[Feature Request] Dataset versioning

open

NONE

**Is your feature request related to a problem? Please describe.** I am working on a project, where I would like to test different preprocessing methods for my ML-data. Thus, I would like to work a lot with revisions and compare them. Currently, I was not able to make it work with the revision keyword because it was not redownloading the data, it was reading in some cached data, until I put `download_mode="force_redownload"`, even though the reversion was different. Of course, I may have done something wrong or missed a setting somewhere! **Describe the solution you'd like** The solution would allow me to easily work with revisions: - create a new dataset (by combining things, different preprocessing, ..) and give it a new revision (v.1.2.3), maybe like this: `dataset_audio.push_to_hub('kenfus/xy', revision='v1.0.2')` - then, get the current revision as follows: ``` dataset = load_dataset( 'kenfus/xy', revision='v1.0.2', ) ``` this downloads the new version and does not load in a different revision and all future map, filter, .. operations are done on this dataset and not loaded from cache produced from a different revision. - if I rerun the run, the caching should be smart enough in every step to not reuse a mapping operation on a different revision. **Describe alternatives you've considered** I created my own caching, putting `download_mode="force_redownload"` and `load_from_cache_file=False,` everywhere. **Additional context** Thanks a lot for your great work! Creating NLP datasets and training a model with them is really easy and straightforward with huggingface. This is the data loading in my script: ``` ## CREATE PATHS prepared_dataset_path = os.path.join( DATA_FOLDER, str(DATA_VERSION), "prepared_dataset" ) os.makedirs(os.path.join(DATA_FOLDER, str(DATA_VERSION)), exist_ok=True) ## LOAD DATASET if os.path.exists(prepared_dataset_path): print("Loading prepared dataset from disk...") dataset_prepared = load_from_disk(prepared_dataset_path) else: print("Loading dataset from HuggingFace Datasets...") dataset = load_dataset( PATH_TO_DATASET, revision=DATA_VERSION, download_mode="force_redownload" ) print("Preparing dataset...") dataset_prepared = dataset.map( prepare_dataset, remove_columns=["audio", "transcription"], num_proc=os.cpu_count(), load_from_cache_file=False, ) dataset_prepared.save_to_disk(prepared_dataset_path) del dataset if CHECK_DATASET: ## CHECK DATASET dataset_prepared = dataset_prepared.map( check_dimensions, num_proc=os.cpu_count(), load_from_cache_file=False ) dataset_filtered = dataset_prepared.filter( lambda example: not example["incorrect_dimension"], load_from_cache_file=False, ) for example in dataset_prepared.filter( lambda example: example["incorrect_dimension"], load_from_cache_file=False ): print(example["path"]) print( f"Number of examples with incorrect dimension: {len(dataset_prepared) - len(dataset_filtered)}" ) print("Number of examples train: ", len(dataset_filtered["train"])) print("Number of examples test: ", len(dataset_filtered["test"])) ```

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PR_kwDODunzps5hhl23

Fix max lock length on unix

closed

MEMBER

reported in https://github.com/huggingface/datasets/pull/6482

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I_kwDODunzps55J8cT

using torchrun, save_to_disk suddenly shows SIGTERM

open

NONE

### Describe the bug When I run my code using the "torchrun" command, when the code reaches the "save_to_disk" part, suddenly I get the following warning and error messages: Because the dataset is too large, the "save_to_disk" function splits it into 70 parts for saving. However, an error occurs suddenly when it reaches the 14th shard. WARNING: torch.distributed.elastic.multiprocessing.api: Sending process 2224968 closing signal SIGTERM ERROR: torch.distributed.elastic.multiprocessing.api: failed (exitcode: -7). traceback: Signal 7 (SIGBUS) received by PID 2224967. ### Steps to reproduce the bug ds_shard = ds_shard.map(map_fn, *args, **kwargs) ds_shard.save_to_disk(ds_shard_filepaths[rank]) Saving the dataset (14/70 shards): 20%|██ | 875350/4376702 [00:19<01:53, 30863.15 examples/s] WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 2224968 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -7) local_rank: 0 (pid: 2224967) of binary: /home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/python Traceback (most recent call last): File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/torchrun", line 8, in <module> sys.exit(main()) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(*args, **kwargs) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main run(args) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run elastic_launch( File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ========================================================== run.py FAILED ---------------------------------------------------------- Failures: <NO_OTHER_FAILURES> ---------------------------------------------------------- Root Cause (first observed failure): [0]: time : 2023-12-08_20:09:04 rank : 0 (local_rank: 0) exitcode : -7 (pid: 2224967) error_file: <N/A> traceback : Signal 7 (SIGBUS) received by PID 2224967 ### Expected behavior I hope it can save successfully without any issues, but it seems there is a problem. ### Environment info `datasets` version: 2.14.6 - Platform: Linux-4.19.90-24.4.v2101.ky10.aarch64-aarch64-with-glibc2.28 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 14.0.0 - Pandas version: 2.1.2

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PR_kwDODunzps5hcS7P

Add IterableDataset `__repr__`

closed

MEMBER

Example for glue sst2: Dataset ``` DatasetDict({ test: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 1821 }) train: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 67349 }) validation: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 872 }) }) ``` IterableDataset (new) ``` IterableDatasetDict({ test: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) train: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) validation: IterableDataset({ features: ['sentence', 'label', 'idx'], n_shards: 1 }) }) ``` IterableDataset (before) ``` {'test': <datasets.iterable_dataset.IterableDataset object at 0x130d421f0>, 'train': <datasets.iterable_dataset.IterableDataset object at 0x136f3aaf0>, 'validation': <datasets.iterable_dataset.IterableDataset object at 0x136f4b100>} {'sentence': 'hide new secretions from the parental units ', 'label': 0, 'idx': 0} ```

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PR_kwDODunzps5hVLom

More robust preupload retry mechanism

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I_kwDODunzps544eqs

How to load data from lakefs

open

CONTRIBUTOR

My dataset is stored on the company's lakefs server. How can I write code to load the dataset? It would be great if I could provide code examples or provide some references

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PR_kwDODunzps5hRq_N

Fix PermissionError on Windows CI

closed

MEMBER

Fix #6476.

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I_kwDODunzps544Ruk

CI on windows is broken: PermissionError

closed

MEMBER

See: https://github.com/huggingface/datasets/actions/runs/7104781624/job/19340572394 ``` FAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\RUNNER~1\\AppData\\Local\\Temp\\tmpfcnps56i\\hf-internal-testing___dataset_with_script\\default\\0.0.0\\c240e2be3370bdbd\\dataset_with_script-train.arrow' ```

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I_kwDODunzps5410Sm

laion2B-en failed to load on Windows with PrefetchVirtualMemory failed

open

NONE

### Describe the bug I have downloaded laion2B-en, and I'm receiving the following error trying to load it: ``` Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s] Traceback (most recent call last): File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <module> count = compute_frequencies() ^^^^^^^^^^^^^^^^^^^^^ File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 17, in compute_frequencies laion2b_dataset = load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\load.py", line 2165, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1187, in as_dataset datasets = map_nested( ^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\utils\py_utils.py", line 456, in map_nested return function(data_struct) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1217, in _build_single_dataset ds = self._as_dataset( ^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1291, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 66, in _memory_mapped_arrow_table_from_file pa_table = opened_stream.read_all() ^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow\ipc.pxi", line 757, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status OSError: [WinError 8] PrefetchVirtualMemory failed. Detail: [Windows error 8] Not enough memory resources are available to process this command. ``` This error is probably a red herring: https://stackoverflow.com/questions/50263929/numpy-memmap-returns-not-enough-memory-while-there-are-plenty-available In other words, the issue is related to asking for a memory mapping of length N > M the length of the file on Windows. This gracefully succeeds on Linux. I have 1024 arrow files in my cache instead of 128 like in the repository for it. Probably related. I don't know why `datasets` reorganized/rewrote the dataset in my cache to be 1024 slices instead of the original 128. ### Steps to reproduce the bug ``` # as a huggingface developer, you may already have laion2B-en somewhere _CACHE_DIR = "." from datasets import load_dataset load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ``` ### Expected behavior This should correctly load as a memory mapped Arrow dataset. ### Environment info - `datasets` version: 2.15.0 - Platform: Windows-10-10.0.20348-SP0 (this is windows 2022) - Python version: 3.11.4 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5hONZc

Deprecate Beam API and download from HF GCS bucket

open

CONTRIBUTOR

Deprecate the Beam API and download from the HF GCS bucked. TODO: - [ ] Deprecate the Beam-based [`wikipedia`](https://huggingface.co/datasets/wikipedia) in favor of [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia) ([Hub PR](https://huggingface.co/datasets/wikipedia/discussions/19)) - [ ] Make [`natural_questions`](https://huggingface.co/datasets/natural_questions) a no-code dataset - [ ] Make [`wiki40b`](https://huggingface.co/datasets/wiki40b) a no-code dataset - [ ] Make [`wiki_dpr`](https://huggingface.co/datasets/wiki_dpr) an Arrow-based dataset

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PR_kwDODunzps5hMbvz

Fix CI quality

closed

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Fix #6472.

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I_kwDODunzps54ydX_

CI quality is broken

closed

MEMBER

See: https://github.com/huggingface/datasets/actions/runs/7100835633/job/19327734359 ``` Would reformat: src/datasets/features/image.py 1 file would be reformatted, 253 files left unchanged ```

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PR_kwDODunzps5hLEni

Remove delete doc CI

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I_kwDODunzps54rtdf

If an image in a dataset is corrupted, we get unescapable error

open

NONE

### Describe the bug Example discussed in detail here: https://huggingface.co/datasets/sasha/birdsnap/discussions/1 ### Steps to reproduce the bug ``` from datasets import load_dataset, VerificationMode dataset = load_dataset( 'sasha/birdsnap', split="train", verification_mode=VerificationMode.ALL_CHECKS, streaming=True # I recommend using streaming=True when reproducing, as this dataset is large ) for idx, row in enumerate(dataset): # Iterating to 9287 took 7 minutes for me # If you already have the data locally cached and set streaming=False, you see the same error just by with dataset[9287] pass # error at 9287 OSError: image file is truncated (45 bytes not processed) # note that we can't avoid the error using a try/except + continue inside the loop ``` ### Expected behavior Able to escape errors in casting to Image() without killing the whole loop ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.31 - Python version: 3.11.5 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.3 - `fsspec` version: 2023.10.0

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PR_kwDODunzps5hC6xf

Don't expand_info in HF glob

closed

MEMBER

Finally fix https://github.com/huggingface/datasets/issues/5537

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PR_kwDODunzps5hCpbN

Use auth to get parquet export

closed

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added `token` to the `_datasets_server` functions

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I_kwDODunzps54lzBZ

New version release request

closed

CONTRIBUTOR

### Feature request Hi! I am using `datasets` in library `xtuner` and am highly interested in the features introduced since v2.15.0. To avoid installation from source in our pypi wheels, we are eagerly waiting for the new release. So, Does your team have a new release plan for v2.15.1 and could you please share it with us? Thanks very much! ### Motivation . ### Your contribution .

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I_kwDODunzps54jnHY

Can't align optional features of struct

open

CONTRIBUTOR

### Describe the bug Hello! I'm currently experiencing an issue where I can't concatenate datasets if an inner field of a Feature is Optional. I have a column named `speaker`, and this holds some information about a speaker. ```python @dataclass class Speaker: name: str email: Optional[str] ``` If I have two datasets, one happens to have `email` always None, then I get `The features can't be aligned because the key email of features` ### Steps to reproduce the bug You can run the following script: ```python ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]}) concatenate_datasets([ds, ds2]) >>>The features can't be aligned because the key speaker of features {'speaker': {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)}} has unexpected type - {'email': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None)} (expected either {'email': Value(dtype='null', id=None), 'name': Value(dtype='string', id=None)} or Value("null"). ``` ### Expected behavior I think this should work; if two top-level columns were in the same situation it would properly cast to `string`. ```python ds = Dataset.from_dict({'email': [None, None]}) ds2 = Dataset.from_dict({'email': ['abc@aol.com', 'one@yahoo.com']}) concatenate_datasets([ds, ds2]) >>> # Works! ``` ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35 - Python version: 3.9.13 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.4.4 - `fsspec` version: 2023.6.0 I would be happy to fix this issue.

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I_kwDODunzps54iIN0

`load_dataset` uses out-of-date cache instead of re-downloading a changed dataset

open

NONE

### Describe the bug When a dataset is updated on the hub, using `load_dataset` will load the locally cached dataset instead of re-downloading the updated dataset ### Steps to reproduce the bug Here is a minimal example script to 1. create an initial dataset and upload 2. download it so it is stored in cache 3. change the dataset and re-upload 4. redownload ```python import time from datasets import Dataset, DatasetDict, DownloadMode, load_dataset username = "YOUR_USERNAME_HERE" initial = Dataset.from_dict({"foo": [1, 2, 3]}) print(f"Intial {initial['foo']}") initial_ds = DatasetDict({"train": initial}) initial_ds.push_to_hub("test") time.sleep(1) download = load_dataset(f"{username}/test", split="train") changed = download.map(lambda x: {"foo": x["foo"] + 1}) print(f"Changed {changed['foo']}") changed.push_to_hub("test") time.sleep(1) download_again = load_dataset(f"{username}/test", split="train") print(f"Download Changed {download_again['foo']}") # >>> gives the out-dated [1,2,3] when it should be changed [2,3,4] ``` The redownloaded dataset should be the changed dataset but it is actually the cached, initial dataset. Force-redownloading gives the correct dataset ```python download_again_force = load_dataset(f"{username}/test", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD) print(f"Force Download Changed {download_again_force['foo']}") # >>> [2,3,4] ``` ### Expected behavior I assumed there should be some sort of hashing that should check for changes in the dataset and re-download if the hashes don't match ### Environment info - `datasets` version: 2.15.0 │ - Platform: Linux-5.15.0-1028-nvidia-x86_64-with-glibc2.17 │ - Python version: 3.8.17 │ - `huggingface_hub` version: 0.19.4 │ - PyArrow version: 13.0.0 │ - Pandas version: 2.0.3 │ - `fsspec` version: 2023.6.0

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PR_kwDODunzps5g5djo

Add concurrent loading of shards to datasets.load_from_disk

open

NONE

In some file systems (like luster), memory mapping arrow files takes time. This can be accelerated by performing the mmap in parallel on processes or threads. - Threads seem to be faster than processes when gathering the list of tables from the workers (see https://github.com/huggingface/datasets/issues/2252). - I'm not sure if using threads would respect the `IN_MEMORY_MAX_SIZE` config. - I'm not sure if we need to expose num_proc from `BaseReader.read` to `DatasetBuilder.as_dataset`. Since ` DatasetBuilder.as_dataset` is used in many places beside `load_dataset`. ### Tests on luster file system (on a shared partial node): Loading 1231 shards of ~2GBs. The files were pre-loaded in another process before the script runs (couldn't get a fresh node). ```python import logging from time import perf_counter import datasets logger = datasets.logging.get_logger(__name__) datasets.logging.set_verbosity_info() logging.basicConfig(level=logging.DEBUG, format="%(message)s") class catchtime: # context to measure loading time: https://stackoverflow.com/questions/33987060/python-context-manager-that-measures-time def __init__(self, debug_print="Time", logger=logger): self.debug_print = debug_print self.logger = logger def __enter__(self): self.start = perf_counter() return self def __exit__(self, type, value, traceback): self.time = perf_counter() - self.start readout = f"{self.debug_print}: {self.time:.3f} seconds" self.logger.info(readout) dataset_path="" # warmup with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=16) # num_proc=16 with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=16) # num_proc=32 with catchtime("Loading in parallel", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=32) # num_proc=1 with catchtime("Loading in conseq", logger=logger): ds = datasets.load_from_disk(dataset_path,num_proc=1) ``` #### Run 1 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:28<00:00, 13.96shards/s] Loading in parallel: 88.690 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:48<00:00, 11.31shards/s] Loading in parallel: 109.339 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:06<00:00, 18.56shards/s] Loading in parallel: 66.931 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:09<00:00, 3.98shards/s] Loading in conseq: 309.792 seconds ``` #### Run 2 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:38<00:00, 12.53shards/s] Loading in parallel: 98.831 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:01<00:00, 10.16shards/s] Loading in parallel: 121.669 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:07<00:00, 18.18shards/s] Loading in parallel: 68.192 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:19<00:00, 3.86shards/s] Loading in conseq: 319.759 seconds ``` #### Run 3 ``` open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:36<00:00, 12.74shards/s] Loading in parallel: 96.936 seconds open file: .../dataset_dict.json Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:00<00:00, 10.24shards/s] Loading in parallel: 120.761 seconds open file: .../dataset_dict.json Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:08<00:00, 18.04shards/s] Loading in parallel: 68.666 seconds open file: .../dataset_dict.json Loading the dataset from disk: 100%|██████████| 1231/1231 [05:35<00:00, 3.67shards/s] Loading in conseq: 335.777 seconds ``` fix #2252

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PR_kwDODunzps5g46_4

Disable benchmarks in PRs

closed

MEMBER

In order to keep PR pages less spammy / more readable. Having the benchmarks on commits on `main` is enough imo

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PR_kwDODunzps5gz68T

Missing DatasetNotFoundError

closed

MEMBER

continuation of https://github.com/huggingface/datasets/pull/6431 this should fix the CI in https://github.com/huggingface/datasets/pull/6458 too

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PR_kwDODunzps5gykvO

Fix shard retry mechanism in `push_to_hub`

closed

CONTRIBUTOR

When it fails, `preupload_lfs_files` throws a [`RuntimeError`](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/_commit_api.py#L402) error and chains the original HTTP error. This PR modifies the retry mechanism's error handling to account for that. Fix https://github.com/huggingface/datasets/issues/6392

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I_kwDODunzps54P5kr

jsonlines files don't load with `load_dataset`

closed

NONE

### Describe the bug While [the docs](https://huggingface.co/docs/datasets/upload_dataset#upload-dataset) seem to state that `.jsonl` is a supported extension for `datasets`, loading the dataset results in a `JSONDecodeError`. ### Steps to reproduce the bug Code: ``` from datasets import load_dataset dset = load_dataset('slotreck/pickle') ``` Traceback: ``` Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 925/925 [00:00<00:00, 3.11MB/s] Downloading and preparing dataset json/slotreck--pickle to /mnt/home/lotrecks/.cache/huggingface/datasets/slotreck___json/slotreck--pickle-0c311f36ed032b04/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96... Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 589k/589k [00:00<00:00, 18.9MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 104k/104k [00:00<00:00, 4.61MB/s] Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 170k/170k [00:00<00:00, 7.71MB/s] Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3.77it/s] Extracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 523.92it/s] Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/mnt/home/lotrecks/.cache/huggingface/datasets/downloads/6ec07bb2f279c9377036af6948532513fa8f48244c672d2644a2d7018ee5c9cb' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 144, in _generate_tables dataset = json.load(f) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 296, in load parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 348, in loads return _default_decoder.decode(s) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 3086) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 147, in _generate_tables raise e File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 259, in pyarrow._json.read_json File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/load.py", line 1815, in load_dataset storage_options=storage_options, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 913, in download_and_prepare **download_and_prepare_kwargs, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1768, in _prepare_split gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior For the dataset to be loaded without error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core - Python version: 3.7.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 8.0.0 - Pandas version: 1.3.5

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PR_kwDODunzps5gsWlz

Retrieve cached datasets that were pushed to hub when offline

open

MEMBER

I drafted the logic to retrieve a no-script dataset in the cache. For example it can reload datasets that were pushed to hub if they exist in the cache. example: ```python >>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp") >>> load_dataset("lhoestq/tmp") DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` and later, without connection: ```python >>> load_dataset("lhoestq/tmp") Using the latest cached version of the dataset from /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/*/*/0b3caccda1725efb(last modified on Wed Nov 29 16:50:27 2023) since it couldn't be found locally at lhoestq/tmp. DatasetDict({ train: Dataset({ features: ['a'], num_rows: 2 }) }) ``` fix https://github.com/huggingface/datasets/issues/3547 ## Implementation details (EDITED) I continued in https://github.com/huggingface/datasets/pull/6493, see the changes there TODO: - [x] tests - [ ] compatible with https://github.com/huggingface/datasets/pull/6458

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PR_kwDODunzps5gqy4M

Lazy data files resolution

open

MEMBER

Related to discussion at https://github.com/huggingface/datasets/pull/6255 this makes this code run in 2sec instead of >10sec ```python from datasets import load_dataset ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False) ``` For some datasets with many configs and files it can be up to 100x faster. This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts. The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict

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I_kwDODunzps54JGMD

`TypeError`: huggingface_hub.hf_file_system.HfFileSystem.find() got multiple values for keyword argument 'maxdepth'

closed

NONE

### Describe the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Steps to reproduce the bug Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Expected behavior Please see https://github.com/huggingface/huggingface_hub/issues/1872 ### Environment info Please see https://github.com/huggingface/huggingface_hub/issues/1872

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PR_kwDODunzps5gmDJY

Don't require trust_remote_code in inspect_dataset

closed

MEMBER

don't require `trust_remote_code` in (deprecated) `inspect_dataset` (it defeats its purpose) (not super important but we might as well keep it until the next major release) this is needed to fix the tests in https://github.com/huggingface/datasets/pull/6448

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PR_kwDODunzps5gej3H

Refactor `dill` logic

closed

CONTRIBUTOR

Refactor the `dill` logic to make it easier to maintain (and fix some issues along the way) It makes the following improvements to the serialization API: * consistent order of a `dict`'s keys * support for hashing `torch.compile`-ed modules and functions * deprecates `datasets.fingerprint.hashregister` as the `hashregister`-ed reducers are never invoked anyways (does not support nested data as `pickle`/`dill` do) ~~TODO: optimize hashing of `pa.Table` and `datasets.table.Table`~~ The `pa_array.to_string` approach is faster for large arrays because it outputs the first 10 and last 10 elements (by default). The problem is that this can produce identical hashes for non-identical arrays if their differing elements get ellipsed... Fix https://github.com/huggingface/datasets/issues/6440, fix https://github.com/huggingface/datasets/issues/5839

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PR_kwDODunzps5ga0rv

Update hub-docs reference

closed

CONTRIBUTOR

Follow up to huggingface/huggingface.js#296

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PR_kwDODunzps5gZ5oe

Praveen_repo_pull_req

closed

NONE