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PR_kwDODunzps5APjkH

Fix header level in Audio docs

closed

MEMBER

Fixes header level so `Dataset features` is the doc title instead of `The Audio type`: 

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PR_kwDODunzps5AOs9L

Fix passed download_config in HubDatasetModuleFactoryWithoutScript

closed

MEMBER

Fix passed `download_config` in `HubDatasetModuleFactoryWithoutScript`.

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PR_kwDODunzps5AOJp7

fix: update exception throw from OSError to EnvironmentError in `push…

closed

CONTRIBUTOR

Status: Ready for review Description of Changes: Fixes #5075 Changes proposed in this pull request: - Throw EnvironmentError instead of OSError in `push_to_hub` when the Hub token is not present.

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I_kwDODunzps5TUbwd

Throw EnvironmentError when token is not present

closed

CONTRIBUTOR

Throw EnvironmentError instead of OSError ([link](https://github.com/huggingface/datasets/blob/6ad430ba0cdeeb601170f732d4bd977f5c04594d/src/datasets/arrow_dataset.py#L4306) to the line) in `push_to_hub` when the Hub token is not present.

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I_kwDODunzps5TUYDw

Replace AssertionErrors with more meaningful errors

closed

CONTRIBUTOR

Replace the AssertionErrors with more meaningful errors such as ValueError, TypeError, etc. The files with AssertionErrors that need to be replaced: ``` src/datasets/arrow_reader.py src/datasets/builder.py src/datasets/utils/version.py ```

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PR_kwDODunzps5AN3Gn

Restore saved format state in `load_from_disk`

closed

CONTRIBUTOR

Hello! @mariosasko This pull request relates to issue #5050 and intends to add the format to datasets loaded from disk. All I did was add a set_format in the Dataset.load_from_disk, as DatasetDict.load_from_disk relies on the first. I don't know if I should add a test and where, so let me know if I should and I can work on that as well!

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PR_kwDODunzps5ANoo5

Image & Audio formatting for numpy/torch/tf/jax

closed

MEMBER

Added support for image and audio formatting for numpy, torch, tf and jax. For images, the dtype used is the one of the image (the one returned by PIL.Image), e.g. uint8 I also added support for string, binary and None types. In particular for torch and jax, strings are kept unchanged (previously it was returning an error because you can't create a tensor of strings)

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PR_kwDODunzps5AMG3g

Support DEFAULT_CONFIG_NAME when no BUILDER_CONFIGS

closed

MEMBER

This PR supports defining a default config name, even if no predefined allowed config names are set. Fix #5070. CC: @stas00

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I_kwDODunzps5TQPPP

Support default config name when no builder configs

closed

MEMBER

**Is your feature request related to a problem? Please describe.** As discussed with @stas00, we could support defining a default config name, even if no predefined allowed config names are set. That is, support `DEFAULT_CONFIG_NAME`, even when `BUILDER_CONFIGS` is not defined. **Additional context** In order to support creating configs on the fly **by name** (not using kwargs), the list of allowed builder configs `BUILDER_CONFIGS` must not be set. However, if so, then `DEFAULT_CONFIG_NAME` is not supported.

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PR_kwDODunzps5AI86d

Fix CONTRIBUTING once dataset scripts transferred to Hub

closed

MEMBER

This PR updates the `CONTRIBUTING.md` guide, once the all dataset scripts have been removed from the GitHub repo and transferred to the HF Hub: - #4974 See diff here: https://github.com/huggingface/datasets/commit/e3291ecff9e54f09fcee3f313f051a03fdc3d94b Additionally, this PR fixes the line separator that by some previous mistake was CRLF instead of LF.

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PR_kwDODunzps5AIDWj

Support streaming gzip.open

closed

MEMBER

This PR implements support for streaming out-of-the-box dataset scripts containing `gzip.open`. This has been a recurring issue. See, e.g.: - #5060 - #3191

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PR_kwDODunzps5AHxlQ

Ci py3.10

closed

MEMBER

Added a CI job for python 3.10 Some dependencies don't work on 3.10 like apache beam, so I remove them from the extras in this case. I also removed some s3 fixtures that we don't use anymore (and that don't work on 3.10 anyway)

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PR_kwDODunzps5AHsP0

Align signature of create/delete_repo with latest hfh

closed

MEMBER

This PR aligns the signature of `create_repo`/`delete_repo` with the current one in hfh, by removing deprecated `name` and `organization`, and using `repo_id` instead. Related to: - #5063 CC: @lhoestq

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PR_kwDODunzps5AHasG

Align signature of list_repo_files with latest hfh

closed

MEMBER

This PR aligns the signature of `list_repo_files` with the current one in `hfh`, by renaming deprecated `token` to `use_auth_token`. This is already the case for `dataset_info`. CC: @lhoestq

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PR_kwDODunzps5AG6SA

Fix CI hfh token warning

closed

MEMBER

In our CI, we get warnings from `hfh` about using deprecated `token`: https://github.com/huggingface/datasets/actions/runs/3174626525/jobs/5171672431 ``` tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_private tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files_with_max_shard_size tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_overwrite_files C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\huggingface_hub\utils\_deprecation.py:97: FutureWarning: Deprecated argument(s) used in 'dataset_info': token. Will not be supported from version '0.12'. warnings.warn(message, FutureWarning) ``` This PR fixes the tests in `TestPushToHub` so that we fix these warnings. Continuation of: - #5031 CC: @lhoestq

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I_kwDODunzps5TLUki

`_pickle.PicklingError: logger cannot be pickled` in multiprocessing `map`

open

NONE

## Describe the bug When I `map` with multiple processes, this error occurs. The `.name` of the `logger` that fails to pickle in the final line is `datasets.fingerprint`. ``` File "~/project/dataset.py", line 204, in <dictcomp> split: dataset.map( File ".../site-packages/datasets/arrow_dataset.py", line 2489, in map transformed_shards[index] = async_result.get() File ".../site-packages/multiprocess/pool.py", line 771, in get raise self._value File ".../site-packages/multiprocess/pool.py", line 537, in _handle_tasks put(task) File ".../site-packages/multiprocess/connection.py", line 214, in send self._send_bytes(_ForkingPickler.dumps(obj)) File ".../site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File ".../site-packages/dill/_dill.py", line 620, in dump StockPickler.dump(self, obj) File ".../pickle.py", line 487, in dump self.save(obj) File ".../pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File ".../pickle.py", line 902, in save_tuple save(element) File ".../pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File ".../site-packages/dill/_dill.py", line 1963, in save_function _save_with_postproc(pickler, (_create_function, ( File ".../site-packages/dill/_dill.py", line 1140, in _save_with_postproc pickler.save_reduce(*reduction, obj=obj) File ".../pickle.py", line 717, in save_reduce save(state) File ".../pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File ".../pickle.py", line 887, in save_tuple save(element) File ".../pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File ".../site-packages/dill/_dill.py", line 1251, in save_module_dict StockPickler.save_dict(pickler, obj) File ".../pickle.py", line 972, in save_dict self._batch_setitems(obj.items()) File ".../pickle.py", line 998, in _batch_setitems save(v) File ".../pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File ".../site-packages/dill/_dill.py", line 1963, in save_function _save_with_postproc(pickler, (_create_function, ( File ".../site-packages/dill/_dill.py", line 1140, in _save_with_postproc pickler.save_reduce(*reduction, obj=obj) File ".../pickle.py", line 717, in save_reduce save(state) File ".../pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File ".../pickle.py", line 887, in save_tuple save(element) File ".../pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File ".../site-packages/dill/_dill.py", line 1251, in save_module_dict StockPickler.save_dict(pickler, obj) File ".../pickle.py", line 972, in save_dict self._batch_setitems(obj.items()) File ".../pickle.py", line 998, in _batch_setitems save(v) File ".../pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File ".../site-packages/dill/_dill.py", line 1963, in save_function _save_with_postproc(pickler, (_create_function, ( File ".../site-packages/dill/_dill.py", line 1154, in _save_with_postproc pickler._batch_setitems(iter(source.items())) File ".../pickle.py", line 998, in _batch_setitems save(v) File ".../pickle.py", line 578, in save rv = reduce(self.proto) File ".../logging/__init__.py", line 1774, in __reduce__ raise pickle.PicklingError('logger cannot be pickled') _pickle.PicklingError: logger cannot be pickled ``` ## Steps to reproduce the bug Sorry I failed to have a minimal reproducible example, but the offending line on my end is ```python dataset.map( lambda examples: self.tokenize(examples), # this doesn't matter, lambda e: [1] * len(...) also breaks. In fact I'm pretty sure it breaks before executing this lambda batched=True, num_proc=4, ) ``` This does work when `num_proc=1`, so it's likely a multiprocessing thing. ## Expected results `map` succeeds ## Actual results The error trace above. ## Environment info - `datasets` version: 1.16.1 and 2.5.1 both failed - Platform: Ubuntu 20.04.4 LTS - Python version: 3.10.4 - PyArrow version: 9.0.0

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I_kwDODunzps5TK9qc

Unable to Use Custom Dataset Locally

closed

CONTRIBUTOR

## Describe the bug I have uploaded a [dataset](https://huggingface.co/datasets/zpn/pubchem_selfies) and followed the instructions from the [dataset_loader](https://huggingface.co/docs/datasets/dataset_script#download-data-files-and-organize-splits) tutorial. In that tutorial, it says ``` If the data files live in the same folder or repository of the dataset script, you can just pass the relative paths to the files instead of URLs. ``` Accordingly, I put the [relative path](https://huggingface.co/datasets/zpn/pubchem_selfies/blob/main/pubchem_selfies.py#L76) to the data to be used. I was able to test the dataset and generate the metadata locally with `datasets-cli test path/to/<your-dataset-loading-script> --save_infos --all_configs` However, if I try to load the data using `load_dataset`, I get the following error ``` with gzip.open(filepath, mode="rt") as f: File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 58, in open binary_file = GzipFile(filename, gz_mode, compresslevel) File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 173, in __init__ fileobj = self.myfileobj = builtins.open(filename, mode or 'rb') FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/zpn/pubchem_selfies/resolve/main/data/Compound_021000001_021500000/Compound_021000001_021500000_SELFIES.jsonl.gz' ``` ## Steps to reproduce the bug ```python >>> from datasets import load_dataset >>> dataset = load_dataset("zpn/pubchem_selfies", streaming=True) >>> t = dataset["train"] >>> for item in t: ...... print(item) ...... break Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 723, in __iter__ for key, example in self._iter(): File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 713, in _iter yield from ex_iterable File "/Users/zachnussbaum/env/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__ yield from self.generate_examples_fn(**self.kwargs) File "/Users/zachnussbaum/.cache/huggingface/modules/datasets_modules/datasets/zpn--pubchem_selfies/d2571f35996765aea70fd3f3f8e3882d59c401fb738615c79282e2eb1d9f7a25/pubchem_selfies.py", line 475, in _generate_examples with gzip.open(filepath, mode="rt") as f: File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 58, in open binary_file = GzipFile(filename, gz_mode, compresslevel) File "/usr/local/Cellar/python@3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/gzip.py", line 173, in __init__ fileobj = self.myfileobj = builtins.open(filename, mode or 'rb') FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/zpn/pubchem_selfies/resolve/main/data/Compound_021000001_021500000/Compound_021000001_021500000_SELFIES.jsonl.gz' ```` ``` ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.5.1 - Platform: macOS-12.5.1-x86_64-i386-64bit - Python version: 3.9.7 - PyArrow version: 9.0.0 - Pandas version: 1.5.0

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PR_kwDODunzps5AEoX7

Fix typo

closed

MEMBER

Fixes a small typo :)

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PR_kwDODunzps5AEVWn

Mark CI tests as xfail when 502 error

closed

MEMBER

To make CI more robust, we could mark as xfail when the Hub raises a 502 error (besides 500 error): - FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_skip_identical_files - https://github.com/huggingface/datasets/actions/runs/3174626525/jobs/5171672431 ``` > raise HTTPError(http_error_msg, response=self) E requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16648055339047.git/info/lfs/objects/batch ``` - FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_overwrite_files - https://github.com/huggingface/datasets/actions/runs/3145587033/jobs/5113074889 ``` > raise HTTPError(http_error_msg, response=self) E requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16643866807322.git/info/lfs/objects/verify ``` Currently, we mark as xfail when 500 error: - #4845

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PR_kwDODunzps5AD4c6

Support `converters` in `CsvBuilder`

closed

CONTRIBUTOR

Add the `converters` param to `CsvBuilder`, to help in situations like [this one](https://discuss.huggingface.co/t/typeerror-in-load-dataset-related-to-a-sequence-of-strings/23545).

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PR_kwDODunzps5ADfxN

Fix broken URL's (GEM)

closed

CONTRIBUTOR

This PR fixes the broken URL's in GEM. cc. @lhoestq, @albertvillanova

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PR_kwDODunzps5ACyVU

Fix backward compatibility for dataset_infos.json

closed

MEMBER

While working on https://github.com/huggingface/datasets/pull/5018 I noticed a small bug introduced in #4926 regarding backward compatibility for dataset_infos.json Indeed, when a dataset repo had both dataset_infos.json and README.md, the JSON file was ignored. This is unexpected: in practice it should be ignored only if the README.md has a dataset_info field, which has precedence over the data in the JSON file.

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PR_kwDODunzps5ABnd3

Fix license/citation information of squadshifts dataset card

closed

MEMBER

This PR fixes the license/citation information of squadshifts dataset card, once the dataset owners have responded to our request for information: - https://github.com/modestyachts/squadshifts-website/issues/1 Additionally, we have updated the mention in their website to our `datasets` library (they were referring old name `nlp`): - https://github.com/modestyachts/squadshifts-website/pull/2#event-7500953009

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I_kwDODunzps5TEshq

Intermittent JSON parse error when streaming the Pile

open

NONE

## Describe the bug I have an intermittent error when streaming the Pile, where I get a JSON parse error which causes my program to crash. This is intermittent - when I rerun the program with the same random seed it does not crash in the same way. The exact point this happens also varied - it happened to me 11B tokens and 4 days into a training run, and now just happened 2 minutes into one, but I can't reliably reproduce it. I'm using a remote machine with 8 A6000 GPUs via runpod.io ## Expected results I have a DataLoader which can iterate through the whole Pile ## Actual results Stack trace: ``` Failed to read file 'zstd://12.jsonl::https://the-eye.eu/public/AI/pile/train/12.jsonl.zst' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0 ``` I'm currently using HuggingFace accelerate, which also gave me the following stack trace, but I've also experienced this problem intermittently when using DataParallel, so I don't think it's to do with parallelisation ``` Traceback (most recent call last): File "ddp_script.py", line 1258, in <module> main() File "ddp_script.py", line 1143, in main for c, batch in tqdm.tqdm(enumerate(data_iter)): File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 503, in __iter__ next_batch, next_batch_info, next_skip = self._fetch_batches(main_iterator) File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 454, in _fetch_batches broadcast_object_list(batch_info) File "/opt/conda/lib/python3.7/site-packages/accelerate/utils/operations.py", line 333, in broadcast_object_list torch.distributed.broadcast_object_list(object_list, src=from_process) File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1900, in broadcast_object_list object_list[i] = _tensor_to_object(obj_view, obj_size) File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1571, in _tensor_to_object return _unpickler(io.BytesIO(buf)).load() _pickle.UnpicklingError: invalid load key, '@'. ``` ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset( cfg["dataset_name"], streaming=True, split="train") dataset = dataset.remove_columns("meta") dataset = dataset.map(tokenize_and_concatenate, batched=True) dataset = dataset.with_format(type="torch") train_data_loader = DataLoader( dataset, batch_size=cfg["batch_size"], num_workers=3) for batch in train_data_loader: continue ``` `tokenize_and_concatenate` is a custom tokenization function I defined on the GPT-NeoX tokenizer to tokenize the text, separated by endoftext tokens, and reshape to have length batch_size, I don't think this is related to tokenization: ``` import numpy as np import einops import torch def tokenize_and_concatenate(examples): texts = examples["text"] full_text = tokenizer.eos_token.join(texts) div = 20 length = len(full_text) // div text_list = [full_text[i * length: (i + 1) * length] for i in range(div)] tokens = tokenizer(text_list, return_tensors="np", padding=True)[ "input_ids" ].flatten() tokens = tokens[tokens != tokenizer.pad_token_id] n = len(tokens) curr_batch_size = n // (seq_len - 1) tokens = tokens[: (seq_len - 1) * curr_batch_size] tokens = einops.rearrange( tokens, "(batch_size seq) -> batch_size seq", batch_size=curr_batch_size, seq=seq_len - 1, ) prefix = np.ones((curr_batch_size, 1), dtype=np.int64) * \ tokenizer.bos_token_id return { "text": np.concatenate([prefix, tokens], axis=1) } ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-105-generic-x86_64-with-debian-buster-sid - Python version: 3.7.13 - PyArrow version: 9.0.0 - Pandas version: 1.3.5 ZStandard data: Version: 0.18.0 Summary: Zstandard bindings for Python Home-page: https://github.com/indygreg/python-zstandard Author: Gregory Szorc Author-email: gregory.szorc@gmail.com License: BSD Location: /opt/conda/lib/python3.7/site-packages Requires: Required-by:

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PR_kwDODunzps4_-PZw

added from_generator method to IterableDataset class.

closed

CONTRIBUTOR

Hello, This resolves issues #4988. I added a method `from_generator` to class `IterableDataset`. I modified the `read` method of input stream generator to also return Iterable_dataset.

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PR_kwDODunzps4_8drw

Revert task removal in folder-based builders

closed

CONTRIBUTOR

Reverts the removal of `task_templates` in the folder-based builders. I also added the `AudioClassifaction` task for consistency. This is needed to fix https://github.com/huggingface/transformers/issues/19177. I think we should soon deprecate and remove the current task API (and investigate if it's possible to integrate the `train eval index` API), but we need to update the Transformers examples before that so we don't break them. cc @NielsRogge

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I_kwDODunzps5S_g-6

Restore saved format state in `load_from_disk`

closed

CONTRIBUTOR

Even though we save the `format` state in `save_to_disk`, we don't restore it in `load_from_disk`. We should fix that. Reported here: https://discuss.huggingface.co/t/save-to-disk-loses-formatting-information/23815

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PR_kwDODunzps4_7zOY

Add `kwargs` to `Dataset.from_generator`

closed

CONTRIBUTOR

Add the `kwargs` param to `from_generator` to align it with the rest of the `from_` methods (this param allows passing custom `writer_batch_size` for instance).

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PR_kwDODunzps4_7KI2

Fix bug with labels of eurlex config of lex_glue dataset

closed

CONTRIBUTOR

Fix for a critical bug in the EURLEX dataset label list to make LexGLUE EURLEX results replicable. In LexGLUE (Chalkidis et al., 2022), the following is mentioned w.r.t. EUR-LEX: _"It supports four different label granularities, comprising 21, 127, 567, 7390 EuroVoc concepts, respectively. We use the 100 most frequent concepts from level 2 [...]”._ The current label list has all 127 labels, which leads to different (lower) results, as communicated by users. Thanks!

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PR_kwDODunzps4_64bS

Fix cats_vs_dogs

closed

MEMBER

Reported in https://github.com/huggingface/datasets/pull/3878 I updated the number of examples

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I_kwDODunzps5S7qjn

Audiofolder creates empty Dataset if files same level as metadata

closed

NONE

## Describe the bug When audio files are at the same level as the metadata (`metadata.csv` or `metadata.jsonl` ), the `load_dataset` returns a `DatasetDict` with no rows but the correct columns. https://github.com/huggingface/datasets/blob/1ea4d091b7a4b83a85b2eeb8df65115d39af3766/docs/source/audio_dataset.mdx?plain=1#L88 ## Steps to reproduce the bug `metadata.csv`: ```csv file_name,duration,transcription ./2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav,10.768,hello ``` ```python >>> audio_dataset = load_dataset("audiofolder", data_dir="/audio-data/") >>> audio_dataset DatasetDict({ train: Dataset({ features: ['audio', 'duration', 'transcription'], num_rows: 0 }) validation: Dataset({ features: ['audio', 'duration', 'transcription'], num_rows: 0 }) }) ``` I've tried, with no success,: - setting `split` to something else so I don't get a `DatasetDict`, - removing the `./`, - using `.jsonl`. ## Expected results ``` Dataset({ features: ['audio', 'duration', 'transcription'], num_rows: 1 }) ``` ## Actual results ``` DatasetDict({ train: Dataset({ features: ['audio', 'duration', 'transcription'], num_rows: 0 }) validation: Dataset({ features: ['audio', 'duration', 'transcription'], num_rows: 0 }) }) ``` ## Environment info - `datasets` version: 2.5.1 - Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.5.0

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I_kwDODunzps5S7V05

Automatically revert to last successful commit to hub when a push_to_hub is interrupted

open

NONE

**Is your feature request related to a problem? Please describe.** I pushed a modification of a large dataset (remove a column) to the hub. The push was interrupted after some files were committed to the repo. This left the dataset to raise an error on load_dataset() (ValueError couldn’t cast … because column names don’t match). Only by specifying the previous (complete) commit as revision=commit_hash in load_data(), I was able to repair this and after a successful, complete push, the dataset loads without error again. **Describe the solution you'd like** Would it make sense to detect an incomplete push_to_hub() and automatically revert to the previous commit/revision? **Describe alternatives you've considered** Leave everything as is, the revision parameter in load_dataset() allows to manually fix this problem. **Additional context** Provide useful defaults

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I_kwDODunzps5S7K6c

integrate `load_from_disk` into `load_dataset`

open

MEMBER

**Is your feature request related to a problem? Please describe.** Is it possible to make `load_dataset` more universal similar to `from_pretrained` in `transformers` so that it can handle the hub, and the local path datasets of all supported types? Currently one has to choose a different loader depending on how the dataset has been created. e.g. this won't work: ``` $ git clone https://huggingface.co/datasets/severo/test-parquet $ python -c 'from datasets import load_dataset; ds=load_dataset("test-parquet"); \ ds.save_to_disk("my_dataset"); load_dataset("my_dataset")' [...] Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/load.py", line 1746, in load_dataset builder_instance.download_and_prepare( File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 1277, in _prepare_split writer.write_table(table) File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_writer.py", line 524, in write_table pa_table = table_cast(pa_table, self._schema) File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/table.py", line 2005, in table_cast return cast_table_to_schema(table, schema) File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/table.py", line 1968, 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 _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string ``` both times the dataset is being loaded from disk. Why does it fail the second time? Why can't `save_to_disk` generate a dataset that can be immediately loaded by `load_dataset`? e.g. the simplest hack would be to have `save_to_disk` add some flag to the saved dataset, that tells `load_dataset` to internally call `load_from_disk`. like having `save_to_disk` create a `load_me_with_load_from_disk.txt` file ;) and `load_dataset` will support that feature from saved datasets from new `datasets` versions. The old ones will still need to use `load_from_disk` explicitly. Unless the flag is not needed and one can immediately tell by looking at the saved dataset that it was saved via `save_to_disk` and thus use `load_from_disk` internally. The use-case is defining a simple API where the user only ever needs to pass a `dataset_name_or_path` and it will always just work. Currently one needs to manually add additional switches telling the system whether to use one loading method or the other which works but it's not smooth. Thank you!

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PR_kwDODunzps4_3uzy

Fix `flatten_indices` with empty indices mapping

closed

CONTRIBUTOR

Fix #5038

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PR_kwDODunzps4_2eqa

Update swiss judgment prediction

closed

CONTRIBUTOR

I forgot to add the new citation.

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PR_kwDODunzps4_2WES

Support streaming hendrycks_test dataset.

closed

MEMBER

This PR: - supports streaming - fixes the description section of the dataset card

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PR_kwDODunzps4_11O2

Fix NonMatchingChecksumError in hendrycks_test dataset

closed

MEMBER

Update metadata JSON. Fix #5039.

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I_kwDODunzps5S3xuj

Hendrycks Checksum

closed

NONE

Hi, The checksum for [hendrycks_test](https://huggingface.co/datasets/hendrycks_test) does not compare correctly, I guess it has been updated on the remote. ``` datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://people.eecs.berkeley.edu/~hendrycks/data.tar'] ```

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I_kwDODunzps5S1BaS

`Dataset.unique` showing wrong output after filtering

closed

CONTRIBUTOR

## Describe the bug After filtering a dataset, and if no samples remain, `Dataset.unique` will return the unique values of the unfiltered dataset. ## Steps to reproduce the bug ```python from datasets import Dataset dataset = Dataset.from_dict({'id': [0]}) dataset = dataset.filter(lambda _: False) print(dataset.unique('id')) ``` ## Expected results The above code should return an empty list since the dataset is empty. ## Actual results ```bash [0] ``` ## Environment info - `datasets` version: 2.5.1 - Platform: Linux-5.18.19-100.fc35.x86_64-x86_64-with-glibc2.34 - Python version: 3.9.14 - PyArrow version: 7.0.0 - Pandas version: 1.3.5

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PR_kwDODunzps4_xcp0

Improve CI performance speed of PackagedDatasetTest

closed

MEMBER

This PR improves PackagedDatasetTest CI performance speed. For Ubuntu (latest): - Duration (without parallelism) before: 334.78s (5.58m) - Duration (without parallelism) afterwards: 0.48s The approach is passing a dummy `data_files` argument to load the builder, so that it avoids the slow inferring of it over the entire root directory of the repo. ## Total duration of PackagedDatasetTest | | Before | Afterwards | Improvement |---|---:|---:|---:| | Linux | 334.78s | 0.48s | x700 | Windows | 513.02s | 1.09s | x500 ## Durations by each individual sub-test More accurate durations, running them on GitHub, for Linux (latest). Before this PR, the total test time (without parallelism) for `tests/test_dataset_common.py::PackagedDatasetTest` is 334.78s (5.58m) ``` 39.07s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_imagefolder 38.94s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_audiofolder 34.18s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_parquet 34.12s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_csv 34.00s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_pandas 34.00s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_text 33.86s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_json 10.39s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_audiofolder 6.50s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_audiofolder 6.46s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_imagefolder 6.40s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_imagefolder 5.77s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_csv 5.77s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_text 5.74s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_parquet 5.69s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_json 5.68s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_pandas 5.67s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_parquet 5.67s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_pandas 5.66s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_json 5.66s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_csv 5.55s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_text (42 durations < 0.005s hidden.) ``` With this PR: 0.48s ``` 0.09s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_audiofolder 0.08s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_csv 0.08s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_imagefolder 0.06s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_json 0.05s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_audiofolder 0.05s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_parquet 0.04s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_pandas 0.03s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_text (55 durations < 0.005s hidden.) ```

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PR_kwDODunzps4_w8Bs

Add oversampling strategy iterable datasets interleave

closed

CONTRIBUTOR

Hello everyone, Following the issue #4893 and the PR #4831, I propose here an oversampling strategy for a `IterableDataset` list. The `all_exhausted` strategy stops building the new dataset as soon as all samples in each dataset have been added at least once. It follows roughly the same logic behind #4831, namely: - if ``probabilities`` is `None` and the strategy is `all_exhausted`, it simply performs a round robin interleaving that stops when the longest dataset is out of samples. Here the new dataset length will be $maxLengthDataset*nbDataset$. - if ``probabilities`` is not `None` and the strategy is `all_exhausted`, it keeps trace of the datasets which were out of samples but continues to add them to the new dataset, and stops as soons as every dataset runs out of samples at least once. In order to be consistent and also to align with the `Dataset` behavior, please note that the behavior of the default strategy (`first_exhausted`) has been changed. Namely, it really stops when a dataset is out of samples whereas it used to stop when receiving the `StopIteration` error. To give an example of the last note, with the following snippet: ``` >>> from tests.test_iterable_dataset import * >>> d1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [0, 1, 2]])), {})) >>> d2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [10, 11, 12, 13]])), {})) >>> d3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [20, 21, 22, 23, 24]])), {})) >>> dataset = interleave_datasets([d1, d2, d3]) >>> [x["a"] for x in dataset] ``` The result here will then be `[10, 0, 11, 1, 2]` instead of `[10, 0, 11, 1, 2, 20, 12, 13]`. I modified the behavior because I found it to be consistent with the under/oversampling approach and because it unified the undersampling and oversampling code, but I stay open to any suggestions.

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PR_kwDODunzps4_wVie

Fix typos in load docstrings and comments

closed

MEMBER

Minor fix of typos in load docstrings and comments

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PR_kwDODunzps4_wJCu

Update README.md of yahoo_answers_topics dataset

closed

CONTRIBUTOR

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PR_kwDODunzps4_wGSE

Remove redundant code from some dataset module factories

closed

MEMBER

This PR removes some redundant code introduced by mistake after a refactoring in: - #4576

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I_kwDODunzps5Sv1VX

new dataset type: single-label and multi-label video classification

open

NONE

**Is your feature request related to a problem? Please describe.** In my research, I am dealing with multi-modal (audio+text+frame sequence) video classification. It would be great if the datasets library supported generating multi-modal batches from a video dataset. **Describe the solution you'd like** Assume I have video files having single/multiple labels. I want to train a single/multi-label video classification model. I want datasets to support generating multi-modal batches (audio+frame sequence) from video files. Audio waveform and frame sequence can be extracted from each video clip then I can use any audio, image and video model from transformers library to extract features which will be fed into my model. **Describe alternatives you've considered** Currently, I am using https://github.com/facebookresearch/pytorchvideo dataloaders. There seems to be not much alternative. **Additional context** I am wiling to open a PR but don't know where to start.

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PR_kwDODunzps4_t82_

Support hfh 0.10 implicit auth

closed

MEMBER

In huggingface-hub 0.10 the `token` parameter is deprecated for dataset_info and list_repo_files in favor of use_auth_token. Moreover if use_auth_token=None then the user's token is used implicitly. I took those two changes into account Close https://github.com/huggingface/datasets/issues/4990 TODO: - [x] fix tests We should wait hfh 0.10 to be relased first to make sure it works correctly before merging

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PR_kwDODunzps4_tfBO

Fast dataset iter

closed

CONTRIBUTOR

Use `pa.Table.to_reader` to make iteration over examples/batches faster in `Dataset.{__iter__, map}` TODO: * [x] benchmarking (the only benchmark for now - iterating over (single) examples of `bookcorpus` (75 mil examples) in Colab is approx. 2.3x faster) * [x] check if iterating over bigger chunks + slicing to fetch individual examples in `_iter` yields better performance

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PR_kwDODunzps4_r8-j

Fix import in `ClassLabel` docstring example

closed

CONTRIBUTOR

This PR addresses a super-simple fix: adding a missing `import` to the `ClassLabel` docstring example, as it was formatted as `from datasets Features`, so it's been fixed to `from datasets import Features`.

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I_kwDODunzps5SoNcV

passing parameters to the method passed to Dataset.from_generator()

closed

NONE

Big thanks for providing dataset creation via a generator. I want to ask whether there is any way that parameters can be passed to the method Dataset.from_generator() method, like as follows. ``` from datasets import Dataset def gen(param1): for idx in len(custom_dataset): yield custom_dataset[idx] + param1 ds = Dataset.from_generator(gen(param1)) ```

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PR_kwDODunzps4_nFUE

Fix typo in error message

closed

CONTRIBUTOR

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PR_kwDODunzps4_mz1w

patch CI_HUB_TOKEN_PATH with Path instead of str

closed

CONTRIBUTOR

Should fix the tests for `huggingface_hub==0.10.0rc0` prerelease (see [failed CI](https://github.com/huggingface/datasets/actions/runs/3127805250/jobs/5074879144)). Related to [this thread](https://huggingface.slack.com/archives/C02V5EA0A95/p1664195165294559) (internal link). Note: this should be a backward compatible fix (e.g. works also with previous versions of `huggingface_hub`) I am not sure where to put the changes so feel free to cherry-pick the commit and close this one without merging. cc @lhoestq

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I_kwDODunzps5SnNpn

Custom Json Dataset Throwing Error when batch is False

closed

NONE

## Describe the bug A clear and concise description of what the bug is. I tried to create my custom dataset using below code ``` from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D from torchvision import transforms from transformers import AutoProcessor # we'll use the Auto API here - it will load LayoutLMv3Processor behind the scenes, # based on the checkpoint we provide from the hub from datasets import load_dataset def prepare_examples(examples): #Some preporcessing for each image and text as all my data saved in cloud #For this reason I couldn't set the batch to True. encoding = processor(img_as_tensor, words, boxes=boxes, word_labels=labels, truncation=True, padding="max_length") # encoding['pixel_values']=np.array(encoding['pixel_values']) return encoding dataset = load_dataset("json", data_files='issues.jsonl') processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) features = dataset["train"].features column_names = dataset["train"].column_names # we need to define custom features for `set_format` (used later on) to work properly features = Features({ 'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'labels': Sequence(feature=Value(dtype='int64')), }) train_dataset = dataset["train"].map( prepare_examples, batched=False, remove_columns=column_names, features=features ) ``` It throws below error. ``` /opt/conda/lib/python3.7/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 172 storage = to_pyarrow_listarray(data, pa_type) --> 173 return pa.ExtensionArray.from_storage(pa_type, storage) 174 /opt/conda/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.ExtensionArray.from_storage() TypeError: Incompatible storage type list<item: list<item: list<item: list<item: float>>>> for extension type extension<arrow.py_extension_type<Array3DExtensionType>> ``` ## Steps to reproduce the bug ```python # Sample code to reproduce the bug ``` rom datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D from torchvision import transforms from transformers import AutoProcessor # we'll use the Auto API here - it will load LayoutLMv3Processor behind the scenes, # based on the checkpoint we provide from the hub from datasets import load_dataset def prepare_examples(examples): #Some preporcessing for each image and text as all my data saved in cloud encoding = processor(img_as_tensor, words, boxes=boxes, word_labels=labels, truncation=True, padding="max_length") # encoding['pixel_values']=np.array(encoding['pixel_values']) return encoding dataset = load_dataset("json", data_files='issues.jsonl') processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) features = dataset["train"].features column_names = dataset["train"].column_names # we need to define custom features for `set_format` (used later on) to work properly features = Features({ 'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'labels': Sequence(feature=Value(dtype='int64')), }) train_dataset = dataset["train"].map( prepare_examples, batched=False, remove_columns=column_names, features=features ) ## Expected results A clear and concise description of the expected results. Expected would be similar to all the otherdatasets with no error. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Unix - Python version: 3.9 - PyArrow version: 9.0.0

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PR_kwDODunzps4_mZ3J

Fix string features of xcsr dataset

closed

MEMBER

This PR fixes string features of `xcsr` dataset to avoid character splitting. Fix #5023. CC: @yangxqiao, @yuchenlin

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I_kwDODunzps5Smt4Y

Text strings are split into lists of characters in xcsr dataset

closed

MEMBER

## Describe the bug Text strings are split into lists of characters. Example for "X-CSQA-en": ``` {'id': 'd3845adc08414fda', 'lang': 'en', 'question': {'stem': ['T', 'h', 'e', ' ', 'd', 'e', 'n', 't', 'a', 'l', ' ', 'o', 'f', 'f', 'i', 'c', 'e', ' ', 'h', 'a', 'n', 'd', 'l', 'e', 'd', ' ', 'a', ' ', 'l', 'o', 't', ' ', 'o', 'f', ' ', 'p', 'a', 't', 'i', 'e', 'n', 't', 's', ' ', 'w', 'h', 'o', ' ', 'e', 'x', 'p', 'e', 'r', 'i', 'e', 'n', 'c', 'e', 'd', ' ', 't', 'r', 'a', 'u', 'm', 'a', 't', 'i', 'c', ' ', 'm', 'o', 'u', 't', 'h', ' ', 'i', 'n', 'j', 'u', 'r', 'y', ',', ' ', 'w', 'h', 'e', 'r', 'e', ' ', 'w', 'e', 'r', 'e', ' ', 't', 'h', 'e', 's', 'e', ' ', 'p', 'a', 't', 'i', 'e', 'n', 't', 's', ' ', 'c', 'o', 'm', 'i', 'n', 'g', ' ', 'f', 'r', 'o', 'm', '?'], 'choices': [{'label': ['A'], 'text': ['t', 'o', 'w', 'n']}, {'label': ['B'], 'text': ['m', 'i', 'c', 'h', 'i', 'g', 'a', 'n']}, {'label': ['C'], 'text': ['h', 'o', 's', 'p', 'i', 't', 'a', 'l']}, {'label': ['D'], 'text': ['s', 'c', 'h', 'o', 'o', 'l', 's']}, {'label': ['E'], 'text': ['o', 'f', 'f', 'i', 'c', 'e', ' ', 'b', 'u', 'i', 'l', 'd', 'i', 'n', 'g']}]}, 'answerKey': 'C'} ## Steps to reproduce the bug ```python ds = load_dataset("datasets/xcsr", "X-CSQA-en", split="validation", streaming=True) item = next(iter(ds)) item ``` ## Expected results ``` {'id': 'd3845adc08414fda', 'lang': 'en', 'question': {'stem': 'The dental office handled a lot of patients who experienced traumatic mouth injury, where were these patients coming from?', 'choices': {'label': ['A', 'B', 'C', 'D', 'E'], 'text': ['town', 'michigan', 'hospital', 'schools', 'office building']}}, 'answerKey': 'C'} ```

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PR_kwDODunzps4_kxYe

Fix languages of X-CSQA configs in xcsr dataset

closed

MEMBER

Fix #5017. CC: @yangxqiao, @yuchenlin

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I_kwDODunzps5SkshS

Split is inferred from filename and overrides metadata.jsonl

closed

NONE

## Describe the bug Including the strings "test" or "train" anywhere in a filename causes `datasets` to infer the split and silently ignore all other files. This behavior is documented for directory names but not filenames: https://huggingface.co/docs/datasets/image_dataset#imagefolder ## Steps to reproduce the bug `metadata.jsonl` ```json {"file_name": "photo of a cat.jpg", "text": "a photo of a cat"} {"file_name": "photo of a dog.jpg", "text": "a photo of a dog"} {"file_name": "photo of a train.jpg", "text": "a photo of a train"} {"file_name": "photo of test tubes.jpg", "text": "a photo of test tubes"} ``` `bug.py` ```python from datasets import load_dataset dataset = load_dataset("dataset") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['image', 'text'], # num_rows: 1 # }) # test: Dataset({ # features: ['image', 'text'], # num_rows: 1 # }) # }) for split in dataset: for n in dataset[split]: print(n['text']) # a photo of a train # a photo of test tubes ``` ## Expected results One single dataset with all four images / a warning for unused files / documentation of this behavior ## Actual results Only the images with "test" or "train" in the name are loaded ## Environment info - `datasets` version: 2.5.1 - Platform: macOS-12.5.1-x86_64-i386-64bit - Python version: 3.10.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.0

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PR_kwDODunzps4_istJ

Fix URLs of sbu_captions dataset

closed

CONTRIBUTOR

Forbidden You don't have permission to access /~vicente/sbucaptions/sbu-captions-all.tar.gz on this server. Additionally, a 403 Forbidden error was encountered while trying to use an ErrorDocument to handle the request. Apache/2.4.6 (Red Hat Enterprise Linux) OpenSSL/1.0.2k-fips PHP/5.4.16 mod_fcgid/2.3.9 mod_wsgi/3.4 Python/2.7.5 mod_perl/2.0.11 Perl/v5.16.3 Server at [www.cs.virginia.edu](mailto:csroot@virginia.edu) Port 443

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PR_kwDODunzps4_iq9b

Update swiss judgment prediction

closed

CONTRIBUTOR

Hi, I updated the dataset to include additional data made available recently. When I test it locally, it seems to work. However, I get the following error with the dummy data creation: `Dummy data generation done but dummy data test failed since splits ['train', 'validation', 'test'] have 0 examples for config 'fr'`. Do you know why this could be the case? Cheers, Joel

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PR_kwDODunzps4_hA0V

Create all YAML dataset_info

closed

MEMBER

Following https://github.com/huggingface/datasets/pull/4926 Creates all the `dataset_info` YAML fields in the dataset cards The JSON are also updated using the simplified backward compatible format added in https://github.com/huggingface/datasets/pull/4926 Needs https://github.com/huggingface/datasets/pull/4926 to be merged first

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I_kwDODunzps5SfoG_

xcsr: X-CSQA simply uses english for all alleged non-english data

closed

NONE

## Describe the bug All the alleged non-english subcollections for the X-CSQA task in the [xcsr benchmark dataset ](https://huggingface.co/datasets/xcsr) seem to be copies of the english subcollection, rather than translations. This is in contrast to the data description: > we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR ## Steps to reproduce the bug ```python # let's say you want to load the french X-CSQA subcollection french = datasets.load_dataset("xcsr", "X-CSQA-fr") # for good measure, let's load english too english = datasets.load_dataset("xcsr", "X-CSQA-en") # let's inspect "".join(english['test'][0]['question']['stem']) # output: 'The people wanted to stop the parade, so what did they set up to thwart it?' "".join(french['test'][0]['question']['stem']) # output: 'The people wanted to stop the parade, so what did they set up to thwart it?' # what? Why are they both in english? # I've checked this for validation and train splits too, across many datapoints. It's all the same english dataset # maybe i need to look better? french['test'].unique('lang') # output: ['en'] # no, it's all english ``` ## Expected results Accessing a subcollection in language X should return a subcollection containg samples in language X ## Actual results Accessing a subcollection in language X returns a subcollection containing samples in English. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.5.1 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps4_gKny

Fix tar extraction vuln

closed

MEMBER

Fix for CVE-2007-4559 Description: Directory traversal vulnerability in the (1) extract and (2) extractall functions in the tarfile module in Python allows user-assisted remote attackers to overwrite arbitrary files via a .. (dot dot) sequence in filenames in a TAR archive, a related issue to CVE-2001-1267. I fixed it by using the solution proposed in https://stackoverflow.com/questions/10060069/safely-extract-zip-or-tar-using-python It blocks extraction of files with an absolute path or double dots and symlinks.

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I_kwDODunzps5SdlB2

Transfer dataset scripts to Hub

closed

MEMBER

Before merging: - #4974 TODO: - [x] Create label: ["dataset contribution"](https://github.com/huggingface/datasets/pulls?q=label%3A%22dataset+contribution%22) - [x] Create project: [Datasets: Transfer datasets to Hub](https://github.com/orgs/huggingface/projects/22/) - [x] PRs: - [x] Add dataset: we should recommend transfer all additions of datasets to the Hub, under the appropriate namespace; no more additions of datasets on GitHub - [x] Update dataset: in general, we should merge bug fixes; enhancements should be considered on a case-by-case basis, depending on whether there is a more suitable namespace on the Hub - [ ] Issues Finally: - [x] #4974 Let me know what you think! :hugs:

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I_kwDODunzps5SdVqv

I need to read the custom dataset in conll format

open

NONE

I need to read the custom dataset in conll format

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I_kwDODunzps5SdUCj

would huggingface like publish cpp binding for datasets package ?

closed

NONE

HI: I use cpp env libtorch, I like use hugggingface ,but huggingface not cpp binding, would you like publish cpp binding for it. thanks

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I_kwDODunzps5SbKIY

Force JSON format regardless of file naming on S3

open

NONE

I have a file on S3 created by Data Version Control, it looks like `s3://dvc/ac/badff5b134382a0f25248f1b45d7b2` but contains a json file. If I run ```python dataset = load_dataset( "json", data_files='s3://dvc/ac/badff5b134382a0f25248f1b45d7b2' ) ``` It gives me ``` InvalidSchema: No connection adapters were found for 's3://dvc/ac/badff5b134382a0f25248f1b45d7b2' ``` However, I cannot go ahead and change the names of the s3 file. Is there a way to "force" load a S3 url with certain decoder (JSON, CSV, etc.) regardless of s3 URL naming?

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I_kwDODunzps5SaPKz

Audio: `encode_example` fails with IndexError

closed

CONTRIBUTOR

## Describe the bug Loading the dataset [earnings-22](https://huggingface.co/datasets/sanchit-gandhi/earnings22_split) from the Hub yields an Index Error. I created this dataset locally and then pushed to hub at the specified URL. Thus, I expect the dataset should work out-of-the-box! Indeed, the dataset viewer functions correctly, and there were no issues when I had the dataset locally. Don't think it's a sound file bug as the version matches what worked previously. Update: the bug appeared for me on a GPU, mysteriously on a TPU I can't repro and it downloads correctly... ## Steps to reproduce the bug ```python from datasets import load_dataset earnings22 = load_dataset("sanchit-gandhi/earnings22_split") ``` ## Expected results ``` >>> earnings22 DatasetDict({ validation: Dataset({ features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'], num_rows: 2650 }) train: Dataset({ features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'], num_rows: 52006 }) test: Dataset({ features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'], num_rows: 2735 }) }) ``` ## Actual results ``` Traceback (most recent call last): File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2764, in _map_single writer.write(example) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 451, in write self.write_examples_on_file() File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 409, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 508, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 231, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 197, in __arrow_array__ out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1683, in wrapper return func(array, *args, **kwargs) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1795, in cast_array_to_feature return feature.cast_storage(array) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in cast_storage storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()]) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in <listcomp> storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()]) File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 92, in encode_example sf.write(buffer, value["array"], value["sampling_rate"], format="wav") File "/opt/conda/envs/hf/lib/python3.8/site-packages/soundfile.py", line 313, in write channels = data.shape[1] IndexError: tuple index out of range ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3 Plus: - SoundFile version: 0.10.3.post1 cc @lhoestq @polinaeterna

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PR_kwDODunzps4_bB3q

Add deprecation warning to multilingual_librispeech dataset card

closed

MEMBER

Besides the current deprecation warning in the script of `multilingual_librispeech`, this PR adds a deprecation warning to its dataset card as well. The format of the deprecation warning is aligned with the one in the library documentation when docstrings contain the `<Deprecated/>` tag. Related to: - #4060

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I_kwDODunzps5SU1lT

Error loading StonyBrookNLP/tellmewhy dataset from hub even though local copy loads correctly

closed

NONE

## Describe the bug I have added a new dataset with the identifier `StonyBrookNLP/tellmewhy` to the hub. When I load the individual files using my local copy using `dataset = datasets.load_dataset("json", data_files="data/train.jsonl")`, it loads the dataset correctly. However, when I try to load it from the hub, I get an error (pasted below). Additionally, `dataset = datasets.load_dataset("json", data_dir="data/")` throws the same error. ## Steps to reproduce the bug ```python dataset = datasets.load_dataset('StonyBrookNLP/tellmewhy') ``` ## Expected results Successfully load the `StonyBrookNLP/tellmewhy` dataset. ## Actual results ``` Using custom data configuration StonyBrookNLP--tellmewhy-82712924092694ff Downloading and preparing dataset json/StonyBrookNLP--tellmewhy to /home/yklal95/.cache/huggingface/datasets/StonyBrookNLP___json/StonyBrookNLP--tellmewhy-82712924092694ff/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253... Downloading data files: 100%|██████████████████████████████| 3/3 [00:00<00:00, 957.46it/s] Extracting data files: 100%|███████████████████████████████| 3/3 [00:00<00:00, 299.14it/s] Traceback (most recent call last): File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 17, in <module> main(args) File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 11, in main dataset = datasets.load_dataset(args.dataset_name) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/load.py", line 1746, in load_dataset builder_instance.download_and_prepare( File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 1277, in _prepare_split writer.write_table(table) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/arrow_writer.py", line 524, in write_table pa_table = table_cast(pa_table, self._schema) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 2005, in table_cast return cast_table_to_schema(table, schema) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1822, in cast_array_to_feature casted_values = _c(array.values, feature.feature) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper return func(array, *args, **kwargs) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1853, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper return func(array, *args, **kwargs) File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1761, in array_cast raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") TypeError: Couldn't cast array of type int64 to null ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.4.0 - Platform: Linux-4.15.0-121-generic-x86_64-with-glibc2.27 - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.5.0

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PR_kwDODunzps4_XAc5

Re-apply input columns change

closed

CONTRIBUTOR

Fixes the `filter` + `input_columns` combination, which is used in the `transformers` examples for instance. Revert #5006 (which in turn reverts #4971) Fix https://github.com/huggingface/datasets/issues/4858

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PR_kwDODunzps4_WvFQ

Add some note about running the transformers ci before a release

closed

MEMBER

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PR_kwDODunzps4_Wm8z

Revert input_columns change

closed

MEMBER

Revert https://github.com/huggingface/datasets/pull/4971 Fix https://github.com/huggingface/datasets/issues/5005

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I_kwDODunzps5ST6uA

Release 2.5.0 breaks transformers CI

closed

MEMBER

## Describe the bug As reported by @lhoestq: > see https://app.circleci.com/pipelines/github/huggingface/transformers/47634/workflows/b491886b-e66e-4edb-af96-8b459e72aa25/jobs/564563 this is used here: [https://github.com/huggingface/transformers/blob/3b19c0317b6909e2d7f11b5053895ac55[…]torch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py](https://github.com/huggingface/transformers/blob/3b19c0317b6909e2d7f11b5053895ac55250e7da/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py#L482-L488)

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PR_kwDODunzps4_WQck

Remove license tag file and validation

closed

MEMBER

As requested, we are removing the validation of the licenses from `datasets` because this is done on the Hub. Fix #4994. Related to: - #4926, which is removing all the validation from `datasets`

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