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Fix broken URL's (GEM)
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"2022-10-03T13:13:22"
"2022-10-04T13:49:00"
"2022-10-04T13:48:59"
CONTRIBUTOR
null
This PR fixes the broken URL's in GEM. cc. @lhoestq, @albertvillanova
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Fix backward compatibility for dataset_infos.json
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"2022-10-03T10:30:14"
"2022-10-03T13:43:55"
"2022-10-03T13:41:32"
MEMBER
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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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Fix license/citation information of squadshifts dataset card
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"2022-10-03T05:19:13"
"2022-10-03T09:26:49"
"2022-10-03T09:24:30"
MEMBER
null
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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added from_generator method to IterableDataset class.
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"2022-09-30T22:14:05"
"2022-10-05T12:51:48"
"2022-10-05T12:10:48"
CONTRIBUTOR
null
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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Revert task removal in folder-based builders
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"2022-09-30T14:50:03"
"2022-10-03T12:23:35"
"2022-10-03T12:21:31"
CONTRIBUTOR
null
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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Restore saved format state in `load_from_disk`
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"2022-09-30T12:40:07"
"2022-10-11T16:49:24"
"2022-10-11T16:49:24"
CONTRIBUTOR
null
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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Add `kwargs` to `Dataset.from_generator`
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"2022-09-30T12:24:27"
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CONTRIBUTOR
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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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Fix bug with labels of eurlex config of lex_glue dataset
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"2022-09-30T09:47:12"
"2022-09-30T16:30:25"
"2022-09-30T16:21:41"
CONTRIBUTOR
null
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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Fix cats_vs_dogs
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MEMBER
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Reported in https://github.com/huggingface/datasets/pull/3878 I updated the number of examples
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Audiofolder creates empty Dataset if files same level as metadata
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[ "Hi! Unfortunately, I can't reproduce this behavior. Instead, I get `ValueError: audio at 2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav doesn't have metadata in /audio-data/metadata.csv`, which can be fixed by removing the `./` from the file name.\r\n\r\n(Link to a Colab that tries to reproduce this behavior: https://colab.research.google.com/drive/1IhQzULYi0Van1xLrN_SddBX1JF7mLZZK?usp=sharing)", "I think we can make the file name matching part more robust by replacing `file_name` with `os.path.normpath(file_name)`, to ignore \"./\" among other things, in these two places:\r\n* https://github.com/huggingface/datasets/blob/85cd129bde605cd9acacdff0d065fc02e39e09b1/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L319\r\n* https://github.com/huggingface/datasets/blob/85cd129bde605cd9acacdff0d065fc02e39e09b1/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L388", "@mariosasko Some tests failed (see my PR). Any thoughts on that?", "Yes, I mentioned the solution in my review.", "I realized what I was doing wrong.\r\n\r\nThe documentation puts the files in a subfolder.\r\nOnce I have done that, it worked.\r\n\r\nBut l agree that this should be handled better if possible." ]
"2022-09-29T19:17:23"
"2022-10-28T13:05:07"
"2022-10-28T13:05:07"
NONE
null
## 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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Fix `flatten_indices` with empty indices mapping
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"2022-09-29T16:17:28"
"2022-09-30T15:46:39"
"2022-09-30T15:44:25"
CONTRIBUTOR
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Fix #5038
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Update swiss judgment prediction
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"2022-09-29T12:10:02"
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CONTRIBUTOR
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I forgot to add the new citation.
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Support streaming hendrycks_test dataset.
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"2022-09-29T11:37:58"
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MEMBER
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This PR: - supports streaming - fixes the description section of the dataset card
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Fix NonMatchingChecksumError in hendrycks_test dataset
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"2022-09-29T09:37:43"
"2022-09-29T10:06:22"
"2022-09-29T10:04:19"
MEMBER
null
Update metadata JSON. Fix #5039.
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I_kwDODunzps5S3xuj
5,039
Hendrycks Checksum
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[ "Thanks for reporting, @DanielHesslow. We are fixing it. ", "@albertvillanova thanks for taking care of this so quickly!", "The dataset metadata is fixed. You can download it normally." ]
"2022-09-29T06:56:20"
"2022-09-29T10:23:30"
"2022-09-29T10:04:20"
NONE
null
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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`Dataset.unique` showing wrong output after filtering
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[ "Hi! It seems like `flatten_indices` (called in `unique`) doesn't know how to handle empty indices mappings. I'm working on the fix.", "Thanks, that was fast!" ]
"2022-09-28T16:20:35"
"2022-09-30T15:44:25"
"2022-09-30T15:44:25"
CONTRIBUTOR
null
## 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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Improve CI performance speed of PackagedDatasetTest
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"2022-09-28T12:08:16"
"2022-09-30T16:05:42"
"2022-09-30T16:03:24"
MEMBER
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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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5,036
Add oversampling strategy iterable datasets interleave
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"2022-09-28T10:10:23"
"2022-09-30T12:30:48"
"2022-09-30T12:28:23"
CONTRIBUTOR
null
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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Fix typos in load docstrings and comments
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"2022-09-28T08:05:07"
"2022-09-28T17:28:40"
"2022-09-28T17:26:15"
MEMBER
null
Minor fix of typos in load docstrings and comments
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Update README.md of yahoo_answers_topics dataset
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"2022-09-28T07:17:33"
"2022-10-06T15:56:05"
"2022-10-04T13:49:25"
CONTRIBUTOR
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5,033
Remove redundant code from some dataset module factories
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"2022-09-28T07:06:26"
"2022-09-28T16:57:51"
"2022-09-28T16:55:12"
MEMBER
null
This PR removes some redundant code introduced by mistake after a refactoring in: - #4576
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5,031
Support hfh 0.10 implicit auth
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"2022-09-27T18:37:49"
"2022-09-30T09:18:24"
"2022-09-30T09:15:59"
MEMBER
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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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Fast dataset iter
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"2022-09-27T16:44:51"
"2022-09-29T15:50:44"
"2022-09-29T15:48:17"
CONTRIBUTOR
null
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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5,029
Fix import in `ClassLabel` docstring example
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"2022-09-27T11:35:29"
"2022-09-27T14:03:24"
"2022-09-27T12:27:50"
CONTRIBUTOR
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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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passing parameters to the method passed to Dataset.from_generator()
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[ "Hi! Yes, you can either use the `gen_kwargs` param in `Dataset.from_generator` (`ds = Dataset.from_generator(gen, gen_kwargs={\"param1\": val})`) or wrap the generator function with `functools.partial`\r\n(`ds = Dataset.from_generator(functools.partial(gen, param1=\"val\"))`) to pass custom parameters to it.\r\n" ]
"2022-09-26T15:20:06"
"2022-10-03T13:00:00"
"2022-10-03T13:00:00"
NONE
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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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5,027
Fix typo in error message
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"2022-09-26T14:10:09"
"2022-09-27T12:28:03"
"2022-09-27T12:26:02"
CONTRIBUTOR
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patch CI_HUB_TOKEN_PATH with Path instead of str
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"2022-09-26T13:19:01"
"2022-09-26T14:30:55"
"2022-09-26T14:28:45"
CONTRIBUTOR
null
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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Custom Json Dataset Throwing Error when batch is False
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[ "Hi! Our processors are meant to be used in `batched` mode, so if `batched` is `False`, you need to drop the batch dimension (the error message warns you that the array has an extra dimension meaning it's 4D instead of 3D) to avoid the error:\r\n```python\r\ndef prepare_examples(examples):\r\n #Some preporcessing for each image and text as all my data saved in cloud\r\n #For this reason I couldn't set the batch to True. \r\n encoding = processor(img_as_tensor, words, boxes=boxes, word_labels=labels,\r\n truncation=True, padding=\"max_length\", return_tensors=\"np\")\r\n # drop extra dim\r\n for k in encoding.items():\r\n encoding[k]=encoding[k][0]\r\n return encoding\r\n```", "> Hi! Our processors are meant to be used in `batched` mode, so if `batched` is `False`, you need to drop the batch dimension (the error message warns you that the array has an extra dimension meaning it's 4D instead of 3D) to avoid the error:\r\n> \r\n> ```python\r\n> def prepare_examples(examples):\r\n> #Some preporcessing for each image and text as all my data saved in cloud\r\n> #For this reason I couldn't set the batch to True. \r\n> encoding = processor(img_as_tensor, words, boxes=boxes, word_labels=labels,\r\n> truncation=True, padding=\"max_length\", return_tensors=\"np\")\r\n> # drop extra dim\r\n> for k in encoding.items():\r\n> encoding[k]=encoding[k][0]\r\n> return encoding\r\n> ```\r\n\r\nThank you it did work\r\n\r\n```\r\nfor k,v in encoding.items():\r\n encoding[k]=encoding[k][0]\r\n```" ]
"2022-09-26T12:38:39"
"2022-09-27T19:50:00"
"2022-09-27T19:50:00"
NONE
null
## 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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Fix string features of xcsr dataset
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"2022-09-26T11:55:36"
"2022-09-28T07:56:18"
"2022-09-28T07:54:19"
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This PR fixes string features of `xcsr` dataset to avoid character splitting. Fix #5023. CC: @yangxqiao, @yuchenlin
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5,023
Text strings are split into lists of characters in xcsr dataset
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"2022-09-26T11:11:50"
"2022-09-28T07:54:20"
"2022-09-28T07:54:20"
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## 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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Fix languages of X-CSQA configs in xcsr dataset
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"2022-09-26T05:13:39"
"2022-09-26T12:27:20"
"2022-09-26T10:57:30"
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Fix #5017. CC: @yangxqiao, @yuchenlin
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Split is inferred from filename and overrides metadata.jsonl
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[ "Hi! What's the structure of your image folder? `datasets` by default tries to infer to what split each file belongs based on directory/file names. If it's OK to load all the images inside the `dataset` folder in the `train` split, you can do the following:\r\n```python\r\ndataset = load_dataset(\"imagefolder\", data_files=\"dataset/**\")\r\n```", "Thanks! Specifying `data_files` worked for that case.\r\n\r\nI'm new to the library, so let me try rephrasing the issue. If there's no actual bug here, sorry for the trouble.\r\n\r\nI've uploaded an example [here](https://files.catbox.moe/nfj2pd.zip) with the following files: \r\n\r\n```\r\n.\r\nβ”œβ”€β”€ bug.py\r\n└── imagefolder\r\n β”œβ”€β”€ test\r\n β”‚ β”œβ”€β”€ metadata.jsonl\r\n β”‚ β”œβ”€β”€ dog.jpg\r\n β”‚ └── personal trainer.jpg\r\n └── train\r\n β”œβ”€β”€ metadata.jsonl\r\n β”œβ”€β”€ cat.jpg\r\n └── testing center.jpg\r\n```\r\n\r\n`bug.py`\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"imagefolder\")\r\n\r\nprint(dataset)\r\n# DatasetDict({\r\n# test: Dataset({\r\n# features: ['image', 'text'],\r\n# num_rows: 1\r\n# })\r\n# })\r\n\r\nfor split in dataset:\r\n print(\"Split:\", split)\r\n for n in dataset[split]:\r\n print(n['text'])\r\n\r\n\r\n# Split: test\r\n# testing center\r\n```\r\n\r\nAs far as I can tell, this conforms with the example given here: https://huggingface.co/docs/datasets/image_dataset#imagefolder. It appears to me that, even though `metadata.jsonl` is present, the inferred labels from the path are taking precedent. Does this sound like a bug/undocumented behavior?", "This looks like a duplicate of https://github.com/huggingface/datasets/issues/4895 (the problem is explained in this comment: https://github.com/huggingface/datasets/issues/4895#issuecomment-1248269550).\r\n\r\nIn the meantime, you can do the following to fetch all the splits:\r\n```python\r\ndataset = load_dataset(\"imagefolder\", data_files={\"train\": \"imagefolder/train/**\", \"test\": \"imagefolder/test/**\"})\r\n```\r\n" ]
"2022-09-26T03:22:14"
"2022-09-29T08:07:50"
"2022-09-29T08:07:50"
NONE
null
## 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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Fix URLs of sbu_captions dataset
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"2022-09-24T14:00:33"
"2022-09-28T07:20:20"
"2022-09-28T07:18:23"
CONTRIBUTOR
null
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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Update swiss judgment prediction
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"2022-09-24T13:28:57"
"2022-09-28T07:13:39"
"2022-09-28T05:48:50"
CONTRIBUTOR
null
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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Create all YAML dataset_info
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"2022-09-23T18:08:15"
"2022-10-03T17:08:05"
"2022-10-03T17:08:05"
MEMBER
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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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xcsr: X-CSQA simply uses english for all alleged non-english data
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[ "Thanks for reporting, @thesofakillers. Good catch. We are fixing this. " ]
"2022-09-23T16:11:54"
"2022-09-26T10:57:31"
"2022-09-26T10:57:31"
NONE
null
## 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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Fix tar extraction vuln
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"2022-09-23T14:22:21"
"2022-09-29T12:42:26"
"2022-09-29T12:40:28"
MEMBER
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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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Transfer dataset scripts to Hub
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[ "Sounds good ! Can I help with anything ?" ]
"2022-09-23T08:48:10"
"2022-10-05T07:15:57"
"2022-10-05T07:15:57"
MEMBER
null
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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5,013
would huggingface like publish cpp binding for datasets package ?
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[ "Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?", "> Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?\r\n\r\nfor example ,the huggingface load_model() and load_dataset() can execute in cpp env", "If it's a viable option for you, you can check [tch-rs](https://github.com/LaurentMazare/tch-rs) to load models in Rust. Regarding datasets, you can first download them in python and then use Arrow C++ or Rust to load them", "If you are more adventurous, another option is to embed python calls inside c++ e.g. with `pybind11`.", "> pybind11\r\n\r\nI think it is not the best solution" ]
"2022-09-23T07:42:49"
"2023-02-24T16:20:57"
"2023-02-24T16:20:57"
NONE
null
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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5,011
Audio: `encode_example` fails with IndexError
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[ "Sorry bug on my part πŸ˜… Closing " ]
"2022-09-22T15:07:27"
"2022-09-23T09:05:18"
"2022-09-23T09:05:18"
CONTRIBUTOR
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## 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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5,010
Add deprecation warning to multilingual_librispeech dataset card
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"2022-09-22T11:41:59"
"2022-09-23T12:04:37"
"2022-09-23T12:02:45"
MEMBER
null
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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5,009
Error loading StonyBrookNLP/tellmewhy dataset from hub even though local copy loads correctly
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[ "I think this is because some columns are mostly empty lists. In particular the train and validation splits only have empty lists for `val_ann`. Therefore the type inference doesn't know which type is inside (or it would have to scan the other splits first before knowing).\r\n\r\nYou can fix that by specifying the features types explicitly.\r\nThen you can save the feature types inside the dataset repository, so that you won't need to specify the features in subsequent calls:\r\n```python\r\nfrom datasets import load_dataset, Features, Sequence, Value\r\nfrom datasets.info import DatasetInfosDict\r\n\r\nfeatures = Features({\r\n 'narrative': Value('string'),\r\n 'question': Value('string'),\r\n 'original_sentence_for_question': Value('string'),\r\n 'narrative_lexical_overlap': Value('float64'),\r\n 'is_ques_answerable': Value('string'),\r\n 'answer': Value('string'),\r\n 'is_ques_answerable_annotator': Value('string'),\r\n 'original_narrative_form': Sequence(Value('string')),\r\n 'question_meta': Value('string'),\r\n 'helpful_sentences': Sequence(Value('int64')),\r\n 'human_eval': Value('bool'),\r\n 'val_ann': Sequence(Value('int64')),\r\n 'gram_ann': Sequence(Value('int64'))\r\n})\r\nds = load_dataset('StonyBrookNLP/tellmewhy', features=features)\r\nDatasetInfosDict({\"default\": ds[\"train\"].info}).write_to_directory(\"path/to/local/tellmewhy\")\r\n```\r\nand then after pushing the change to the dataset repository on the Hub, `load_dataset(\"StonyBrookNLP/tellmewhy\")` will work directly`", "(Note that specifying explicit types will be made easier with https://github.com/huggingface/datasets/pull/4926)", "`gram_ann` and `val_ann` are annotations that only exist for part of the test set. I wanted to keep all the columns consistent across all files, so I added them to train and validation as well. I'll check if removing them from those files is still compliant with this repo. Otherwise, I will do as you suggested. Thanks @lhoestq !", "@lhoestq I followed the exact steps you described but it seems like I'm getting the same error unfortunately. Any other ideas? Thanks in advance", "Hi ! If you move `dataset_infos.json` from `data/` to the root of your dataset repository if should work :)", "I tried that and pushed to the [hub](https://huggingface.co/datasets/StonyBrookNLP/tellmewhy/tree/main). Now, there is a new error.\r\n```\r\n File \"/home/yklal95/tellmewhy/src/prepare_data.py\", line 67, in main\r\n dataset = load_dataset('StonyBrookNLP/tellmewhy')\r\n File \"/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/load.py\", line 1746, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py\", line 704, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py\", line 775, in _download_and_prepare\r\n verify_checksums(\r\n File \"/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/utils/info_utils.py\", line 33, in verify_checksums\r\n raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'/home/yklal95/tellmewhy/data/test.json', '/home/yklal95/tellmewhy/data/validation.json', '/home/yklal95/tellmewhy/data/train.json'}\r\n```\r\nNo changes were made to any of the other files and they are still on the hub. Let me know if you have any ideas @lhoestq Thanks!", "Oh I see - the code I gave you returns local paths instead of URLs to store metadata about files to download.\r\nI opened a PR in your repo here to remove this: https://huggingface.co/datasets/StonyBrookNLP/tellmewhy/discussions/1\r\nsorry for the inconvenience !", "It works now! Thanks a lot @lhoestq " ]
"2022-09-21T16:23:06"
"2022-09-29T13:07:29"
"2022-09-29T13:07:29"
NONE
null
## 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%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:00<00:00, 957.46it/s] Extracting data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 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
5,008
Re-apply input columns change
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"2022-09-21T15:09:01"
"2022-09-22T13:57:36"
"2022-09-22T13:55:23"
CONTRIBUTOR
null
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
5,007
Add some note about running the transformers ci before a release
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"2022-09-21T14:14:25"
"2022-09-22T10:16:14"
"2022-09-22T10:14:06"
MEMBER
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5,006
Revert input_columns change
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"2022-09-21T13:49:20"
"2022-09-21T14:14:33"
"2022-09-21T14:11:57"
MEMBER
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Revert https://github.com/huggingface/datasets/pull/4971 Fix https://github.com/huggingface/datasets/issues/5005
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Release 2.5.0 breaks transformers CI
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[ "Shall we revert https://github.com/huggingface/datasets/pull/4971 @mariosasko ?\r\n\r\nAnd for consistency we can update IterableDataset.map later" ]
"2022-09-21T13:39:19"
"2022-09-21T14:11:57"
"2022-09-21T14:11:57"
MEMBER
null
## 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
5,004
Remove license tag file and validation
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"2022-09-21T12:35:14"
"2022-09-22T11:47:41"
"2022-09-22T11:45:46"
MEMBER
null
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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5,003
Fix missing use_auth_token in streaming docstrings
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"2022-09-21T09:27:03"
"2022-09-21T16:24:01"
"2022-09-21T16:20:59"
MEMBER
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This PRs fixes docstrings: - adds the missing `use_auth_token` param - updates syntax of param types - adds params to docstrings without them - fixes return/yield types - fixes syntax
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5,002
Dataset Viewer issue for loubnabnl/humaneval-x
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null
[ "It's a bug! Thanks for reporting, I'm looking at it", "Fixed." ]
"2022-09-21T09:06:17"
"2022-09-21T11:49:49"
"2022-09-21T11:49:49"
NONE
null
### Link https://huggingface.co/datasets/loubnabnl/humaneval-x/viewer/ ### Description The dataset has subsets but the viewer gets stuck in the default subset even when I select another one (the data loading of the subsets works fine) ### Owner Yes
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5,000
Dataset Viewer issue for asapp/slue
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[ "<img width=\"519\" alt=\"Capture d’écran 2022-09-20 aΜ€ 22 33 47\" src=\"https://user-images.githubusercontent.com/1676121/191358952-1220cb7d-745a-4203-a66b-3c707b25038f.png\">\r\n\r\n```\r\nNot found.\r\n\r\nError code: SplitsResponseNotFound\r\n```\r\n\r\nhttps://datasets-server.huggingface.co/splits?dataset=asapp/slue\r\n\r\n```json\r\n{\"error\":\"Not found.\"}\r\n```", "I just launched a refresh. It's weird, I don't see any entry for this dataset in the cache, it's a bug on our side. In order to try to understand what happened, did you change the visibility status from private to public, by any chance?", "The dataset is being refreshed, please retry later.\r\n\r\n<img width=\"802\" alt=\"Capture d’écran 2022-09-20 aΜ€ 22 39 46\" src=\"https://user-images.githubusercontent.com/1676121/191360072-7cc86486-4e84-4b47-8f9a-4a69fe84a5ac.png\">\r\n", "OK. We now have an issue because the dataset cannot be streamed, and the dataset viewer relies on it.\r\n\r\nMaybe @huggingface/datasets can help:\r\n\r\n```\r\nError code: StreamingRowsError\r\nException: NotImplementedError\r\nMessage: Extraction protocol for TAR archives like 'https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxpopuli_v0.2_blind.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.\r\nTraceback: Traceback (most recent call last):\r\n File \"/src/services/worker/src/worker/responses/first_rows.py\", line 337, in get_first_rows_response\r\n rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)\r\n File \"/src/services/worker/src/worker/utils.py\", line 123, in decorator\r\n return func(*args, **kwargs)\r\n File \"/src/services/worker/src/worker/responses/first_rows.py\", line 65, in get_rows\r\n ds = load_dataset(\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1739, in load_dataset\r\n return builder_instance.as_streaming_dataset(split=split)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py\", line 1025, in as_streaming_dataset\r\n splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n File \"/tmp/modules-cache/datasets_modules/datasets/asapp--slue/adaa0c78233e1a1df9c2f054e690ec5fc3eaf453bd76b80fe5cbe5728e55d9b1/slue.py\", line 189, in _split_generators\r\n dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name])\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 944, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 907, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 385, in map_nested\r\n return function(data_struct)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 912, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 390, in _get_extraction_protocol\r\n raise NotImplementedError(\r\n NotImplementedError: Extraction protocol for TAR archives like 'https://public-dataset-model-store.awsdev.asapp.com/users/sshon/public/slue/slue-voxpopuli_v0.2_blind.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.\r\n```", "Thanks @severo, \r\n\r\nDo I have to modify the python script to support streaming so that it can be previewed?\r\nIs there a document somewhere that I can follow?\r\n", "Hi @fwu-asapp thanks for reporting, and thanks @severo for the investigation.\r\n\r\nAs explained by @severo, the preview requires that your dataset loading script supports streaming.\r\n\r\nThere are several options here:\r\n- the easiest would be to replace the source files, archived using ZIP instead TAR: the TAR format does not allow random access while streaming, but only sequential access; the ZIP files support streaming out of the box.\r\n- alternatively, to stream TAR archives you can use `dl_manager.iter_archive`: the only prerequisite is that your \"index\" files (.tsv) should have been archived before their corresponding audio files, so while iterating the content of the TAR archive, the metadata files appear first. I think this is the case for voxpopuli tar but not for voxceleb.\r\n- if your .tsv files were not archived before their corresponding audio files (I think this is the case for voxceleb), then you should extract the .tsv files and host them separately (you can host them on the same Hugging Face Hub).\r\n - you can take as example, e.g.: https://huggingface.co/datasets/vivos/blob/main/vivos.py\r\n\r\nAs an advanced approach, you can handle both streaming and non-streaming cases separately.\r\n- as for example: https://huggingface.co/datasets/librispeech_asr/blob/main/librispeech_asr.py or https://huggingface.co/datasets/google/fleurs/blob/main/fleurs.py\r\n\r\nSee related discussion:\r\n- https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492", "Thanks @albertvillanova for your clarification. I'll talk to my collaborators to see if we can replace those files. Let me just close this issue for now.", "FYI, after replacing the source files with the ZIP ones, the dataset viewer works well. Thanks again to @severo and @albertvillanova for your help!", "Great! And thank you for sharing that interesting dataset!" ]
"2022-09-20T16:45:45"
"2022-09-27T07:04:03"
"2022-09-21T07:24:07"
NONE
null
### Link https://huggingface.co/datasets/asapp/slue/viewer/ ### Description Hi, I wonder how to get the dataset viewer of our slue dataset to work. Best, Felix ### Owner Yes
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PR_kwDODunzps4_SQxL
4,999
Add EmptyDatasetError
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"2022-09-20T15:28:05"
"2022-09-21T12:23:43"
"2022-09-21T12:21:24"
MEMBER
null
examples: from the hub: ```python Traceback (most recent call last): File "playground/ttest.py", line 3, in <module> print(load_dataset("lhoestq/empty")) File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1686, in load_dataset **config_kwargs, File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1458, in load_dataset_builder data_files=data_files, File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1171, in dataset_module_factory raise e1 from None File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1162, in dataset_module_factory download_mode=download_mode, File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 760, in get_module else get_data_patterns_in_dataset_repository(hfh_dataset_info, self.data_dir) File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/data_files.py", line 678, in get_data_patterns_in_dataset_repository ) from None datasets.data_files.EmptyDatasetError: The dataset repository at 'lhoestq/empty' doesn't contain any data file. ``` from local directory: ```python Traceback (most recent call last): File "playground/ttest.py", line 3, in <module> print(load_dataset("playground/empty")) File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1686, in load_dataset **config_kwargs, File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1458, in load_dataset_builder data_files=data_files, File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1107, in dataset_module_factory path, data_dir=data_dir, data_files=data_files, download_mode=download_mode File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 625, in get_module else get_data_patterns_locally(base_path) File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/data_files.py", line 460, in get_data_patterns_locally raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data file") from None datasets.data_files.EmptyDatasetError: The directory at playground/empty doesn't contain any data file ``` Close https://github.com/huggingface/datasets/issues/4995
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PR_kwDODunzps4_Ryp3
4,998
Don't add a tag on the Hub on release
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"2022-09-20T13:54:57"
"2022-09-20T14:11:46"
"2022-09-20T14:08:54"
MEMBER
null
Datasets with no namespace on the Hub have tags to redirect to the version of datasets where they come from. I’m about to remove them all because I think it looks bad/unexpected in the UI and it’s not actually useful Therefore I'm also disabling tagging. Note that the CI job will be completely removed in https://github.com/huggingface/datasets/pull/4974 anyway
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4,997
Add support for parsing JSON files in array form
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"2022-09-20T13:31:26"
"2022-09-20T15:42:40"
"2022-09-20T15:40:06"
CONTRIBUTOR
null
Support parsing JSON files in the array form (top-level object is an array). For simplicity, `json.load` is used for decoding. This means the entire file is loaded into memory. If requested, we can optimize this by introducing a param similar to `lines` in [`pandas.read_json`](https://pandas.pydata.org/docs/reference/api/pandas.read_json.html), which, if set to `True`, would allow us to read in chunks. Fixes https://github.com/huggingface/datasets/issues/4963
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Dataset Viewer issue for Jean-Baptiste/wikiner_fr
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[ "The script uses `Dataset.load_from_disk`, which as you can expect, doesn't work in streaming mode.\r\n\r\nIt would probably be more practical to load the dataset locally using `Dataset.load_from_disk` first and then `push_to_hub` to upload it in Parquet on the Hub", "I've transferred this issue to the Hub repo: https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr/discussions/3\r\n\r\nI'm closing this." ]
"2022-09-20T12:32:07"
"2022-09-27T12:35:44"
"2022-09-27T12:35:44"
CONTRIBUTOR
null
### Link https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr ### Description ``` Error code: StreamingRowsError Exception: FileNotFoundError Message: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json' Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token) File "/src/services/worker/src/worker/utils.py", line 123, in decorator return func(*args, **kwargs) File "/src/services/worker/src/worker/responses/first_rows.py", line 77, in get_rows rows_plus_one = list(itertools.islice(ds, rows_max_number + 1)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__ for key, example in self._iter(): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter yield from ex_iterable File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__ yield from self.generate_examples_fn(**self.kwargs) File "/tmp/modules-cache/datasets_modules/datasets/Jean-Baptiste--wikiner_fr/683a580ba6ec769d508f7dfc603a651667b0ed3817b1ae5bfd45f97cc024923f/wikiner_fr.py", line 165, in _generate_examples dataset = Dataset.load_from_disk(filepath) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1210, in load_from_disk with open(Path(dataset_path, config.DATASET_STATE_JSON_FILENAME).as_posix(), encoding="utf-8") as state_file: FileNotFoundError: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json' ``` Is it an error with the dataset script, or the data itself, @huggingface/datasets? https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr/tree/main ### Owner No
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Get a specific Exception when the dataset has no data
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"2022-09-20T09:31:59"
"2022-09-21T12:21:25"
"2022-09-21T12:21:25"
CONTRIBUTOR
null
In the dataset viewer on the Hub (https://huggingface.co/datasets/glue/viewer), we would like (https://github.com/huggingface/moon-landing/issues/3882) to show a specific message when the repository lacks any data files. In that case, instead of showing a complex traceback, we want to show a call to action to help the user upload data. To do that, it would be very helpful to know for sure that the repository is missing any (supported) data files. It could be done by raising a custom exception, for example, `NoDataError`.
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delete the hardcoded license list in `datasets`
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"2022-09-20T09:14:41"
"2022-09-22T11:45:47"
"2022-09-22T11:45:47"
MEMBER
null
> Feel free to delete the license list in `datasets` [...] > > Also FYI in #4926 I also removed all the validation steps anyway (language, license, types etc.) _Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238401662_ > [...], in my opinion we can just delete this file from `datasets`, the validation is happening hub-side anyways now? _Originally posted by @julien-c in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238390659_
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fix: avoid casting tuples after Dataset.map
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"2022-09-20T08:45:16"
"2022-09-20T16:11:27"
"2022-09-20T13:08:29"
CONTRIBUTOR
null
This PR updates features.py to avoid casting tuples to lists when reading the results of Dataset.map as suggested by @lhoestq [here](https://github.com/huggingface/datasets/issues/4676#issuecomment-1187371367) in https://github.com/huggingface/datasets/issues/4676.
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Support streaming iwslt2017 dataset
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"2022-09-20T08:35:41"
"2022-09-20T09:27:55"
"2022-09-20T09:15:24"
MEMBER
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Support streaming iwslt2017 dataset. Once this PR is merged: - [x] Remove old ".tgz" data files from the Hub.
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Fix missing tags in dataset cards
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"2022-09-20T06:42:07"
"2022-09-22T12:25:32"
"2022-09-20T07:37:30"
MEMBER
null
Fix missing tags in dataset cards: - aeslc - empathetic_dialogues - event2Mind - gap - iwslt2017 - newsgroup - qa4mre - scicite This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. Related to: - #4833 - #4891 - #4896 - #4908 - #4921 - #4931 - #4979
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"no-token" is passed to `huggingface_hub` when token is `None`
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null
[ "Hi @Wauplin, thanks for raising this potential issue.\r\n\r\nThe choice of passing `\"no-token\"` instead of `None` was made in this PR:\r\n- #4536 \r\n\r\nAccording to the PR description, the reason why it is passed is to avoid that `HfApi.dataset_info` uses the local token when no token should be used.", "Hi @albertvillanova , thanks for finding the original issue :+1: \r\n\r\nAs of next release of `huggingface_hub`, the `token` argument will be deprecated in favor of the `use_auth_token` argument in `dataset_info` method. This change as been done by @SBrandeis in https://github.com/huggingface/huggingface_hub/pull/928. `use_auth_token` is a bit different and allow the case \"don't sent the cached token by default\".\r\n\r\nIf you want to strictly avoid sending the cached token from `datasets`, you can use:\r\n```py\r\n# token=token if token else \"no-token\", <- will fail because token is not valid\r\n\r\nuse_auth_token=token if token else False, # using the new `use_auth_token` parameter\r\n```\r\n\r\nAnd as a note, I am currently updating the \"don't send the cached token by default\"-rule to \"don't send the cached token on public repos by default but use it in private ones\" in https://github.com/huggingface/huggingface_hub/pull/1064. This will not change the fact that `use_auth_token=False` doesn't send the token at all.\r\n", "What is current strategy in term of updating `huggingface_hub` version in `datasets` ? I don't want to break stuff in the next release so let's find a proper solution :) ", "As soon as `token` is deprecated and hfh has a new release, we'll update `datasets` to use the new argument instead. Does it sound good to you ?", "Perfect :ok_hand: ", "Hi @Wauplin, thanks for the warning about the deprecation of `token` in favor of `use_auth_token`.\r\n\r\nIndeed, in datasets we use internally `use_auth_token`, which in this case was transformed to `token` to call `HfApi.dataset_info`:\r\nhttps://github.com/huggingface/datasets/blob/1a9385d7cc8a3241b44015145ef56a230fdadc51/src/datasets/load.py#L747\r\n\r\nTherefore, for the new hfh release, the fix will be trivial: we will pass directly `use_auth_token`.\r\n\r\nAs discussed during our meeting yesterday, due to the fact that at datasets we support multiple hfh versions, I think we should handle passing `token` or `use_auth_token` depending on the hfh version." ]
"2022-09-19T15:14:40"
"2022-09-30T09:16:00"
"2022-09-30T09:16:00"
CONTRIBUTOR
null
## Describe the bug In the 2 lines listed below, a token is passed to `huggingface_hub` to get information from a dataset. If no token is provided, a "no-token" string is passed. What is the purpose of it ? If no real, I would prefer if the `None` value could be sent directly to be handle by `huggingface_hub`. I feel that here it is working because we assume the token will never be validated. https://github.com/huggingface/datasets/blob/5b23f58535f14cc4dd7649485bce1ccc836e7bca/src/datasets/load.py#L753 https://github.com/huggingface/datasets/blob/5b23f58535f14cc4dd7649485bce1ccc836e7bca/src/datasets/load.py#L1121 ## Expected results Pass `token=None` to `huggingface_hub`. ## Actual results `token="no-token"` is passed. ## Environment info `huggingface_hub v0.10.0dev`
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Running add_column() seems to corrupt existing sequence-type column info
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[ "Nevermind, I was incorrect." ]
"2022-09-17T17:42:05"
"2022-09-19T12:54:54"
"2022-09-19T12:54:54"
NONE
null
I have a dataset that contains a column ("foo") that is a sequence type of length 4. So when I run .to_pandas() on it, the resulting dataframe correctly contains 4 columns - foo_0, foo_1, foo_2, foo_3. So the 1st row of the dataframe might look like: ds = load_dataset(...) df = ds.to_pandas() df: foo_0 | foo_1 | foo_2 | foo_3 0.0 | 1.0 | 2.0 | 3.0 If I run .add_column("new_col", data) on the dataset, and then .to_pandas() on the resulting new dataset, the resulting dataframe contains only 2 columns - foo, new_col. The values in column foo are lists of length 4, the 4 elements that should have been split into separate columns. Dataframe 1st row would be: ds = load_dataset(...) new_ds = ds.add_column("new_col", data) df = new_ds.to_pandas() df: foo | new_col [0.0, 1.0, 2.0, 3.0] | new_val I've explored the 2 datasets in a debugger and haven't noticed any changes to any attributes related to the foo column, but I can't determine why the dataframes are so different.
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Add `IterableDataset.from_generator` to the API
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null
[ "#take", "Thanks @hamid-vakilzadeh ! Let us know if you have some questions or if we can help", "Thank you! I certainly will reach out if I need any help." ]
"2022-09-16T15:19:41"
"2022-10-05T12:10:49"
"2022-10-05T12:10:49"
CONTRIBUTOR
null
We've just added `Dataset.from_generator` to the API. It would also be cool to add `IterableDataset.from_generator` to support creating an iterable dataset from a generator. cc @lhoestq
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Embed image/audio data in dl_and_prepare parquet
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"2022-09-16T14:09:27"
"2022-09-16T16:24:47"
"2022-09-16T16:22:35"
MEMBER
null
Embed the bytes of the image or audio files in the Parquet files directly, instead of having a "path" that points to a local file. Indeed Parquet files are often used to share data or to be used by workers that may not have access to the local files.
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[doc] Fix broken snippet that had too many quotes
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"2022-09-16T12:41:07"
"2022-09-16T22:12:21"
"2022-09-16T17:32:14"
CONTRIBUTOR
null
Hello! ### Pull request overview * Fix broken snippet in https://huggingface.co/docs/datasets/main/en/process that has too many quotes ### Details The snippet in question can be found here: https://huggingface.co/docs/datasets/main/en/process#map This screenshot shows the issue, there is a quote too many, causing the snippet to be colored incorrectly: ![image](https://user-images.githubusercontent.com/37621491/190640627-f7587362-0e44-4464-a5d1-a0b98df6986f.png) The change speaks for itself. Thank you for the detailed documentation, by the way. - Tom Aarsen
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Prefer split patterns from directories over split patterns from filenames
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"2022-09-16T11:20:40"
"2022-11-02T11:54:28"
"2022-09-29T08:07:49"
CONTRIBUTOR
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related to https://github.com/huggingface/datasets/issues/4895
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docs: ✏️ add links to the Datasets API
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"2022-09-16T09:34:12"
"2022-09-16T13:10:14"
"2022-09-16T13:07:33"
CONTRIBUTOR
null
I added some links to the Datasets API in the docs. See https://github.com/huggingface/datasets-server/pull/566 for a companion PR in the datasets-server. The idea is to improve the discovery of the API through the docs. I'm a bit shy about pasting a lot of links to the API in the docs, so it's minimal for now. I'm interested in ideas to integrate the API better in these docs without being too much. cc @lhoestq @julien-c @albertvillanova @stevhliu.
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How to convert torch.utils.data.Dataset to huggingface dataset?
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[ "Hi! I think you can use the newly-added `from_generator` method for that:\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndef gen():\r\n for idx in len(torch_dataset):\r\n yield torch_dataset[idx] # this has to be a dictionary\r\n ## or if it's an IterableDataset\r\n # for ex in torch_dataset:\r\n # yield ex\r\n\r\ndset = Dataset.from_generator(gen)\r\n```", "Maybe `Dataset.from_list` can work as well no ?\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndset = Dataset.from_list(torch_dataset)\r\n```", "> ```python\r\n> from datasets import Dataset\r\n> \r\n> def gen():\r\n> for idx in len(torch_dataset):\r\n> yield torch_dataset[idx] # this has to be a dictionary\r\n> ## or if it's an IterableDataset\r\n> # for ex in torch_dataset:\r\n> # yield ex\r\n> \r\n> dset = Dataset.from_generator(gen)\r\n> ```\r\n\r\nI try to use `Dataset.from_generator()` method, and it returns an error:\r\n```bash\r\nAttributeError: type object 'Dataset' has no attribute 'from_generator'\r\n```\r\nAnd I think it maybe the version of my datasets package is out-of-date, so I update it\r\n```bash\r\npip install --upgrade datasets\r\n```\r\nBut after that, the code still return the above Error. ", "> ```python\r\n> dset = Dataset.from_list(torch_dataset)\r\n> ```\r\n\r\nIt seems that Dataset also has no `from_list` method πŸ˜‚\r\n```bash\r\nAttributeError: type object 'Dataset' has no attribute 'from_list'\r\n```", "> I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:\r\n> \r\n> ```python\r\n> from datasets import Dataset\r\n> data = [[1, 2],[3, 4]]\r\n> ds = Dataset.from_dict({\"data\": data})\r\n> ds = ds.with_format(\"torch\")\r\n> ds[0]\r\n> ds[:2]\r\n> ```\r\n> \r\n> So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert? Thanks.\r\n\r\nMy dummy code is like:\r\n```python\r\nimport os\r\nimport json\r\nfrom torch.utils import data\r\nimport datasets\r\n\r\ndef gen(torch_dataset):\r\n for idx in len(torch_dataset):\r\n yield torch_dataset[idx] # this has to be a dictionary\r\n\r\nclass MyDataset(data.Dataset):\r\n def __init__(self, path):\r\n self.dict = []\r\n for line in open(path, 'r', encoding='utf-8'):\r\n j_dict = json.loads(line)\r\n self.dict.append(j_dict['context'])\r\n \r\n def __getitem__(self, idx):\r\n return self.dict[idx]\r\n\r\n def __len__(self):\r\n return len(self.dict)\r\n\r\nroot_path = os.path.dirname(os.path.abspath(__file__))\r\npath = os.path.join(root_path, 'dataset', 'train.json')\r\ntorch_dataset = MyDataset(path)\r\n\r\ndit = []\r\nfor line in open(path, 'r', encoding='utf-8'):\r\n j_dict = json.loads(line)\r\n dit.append(j_dict['context'])\r\ndset1 = datasets.Dataset.from_list(dit)\r\nprint(dset1)\r\ndset2 = datasets.Dataset.from_generator(gen)\r\nprint(dset2)\r\n```", "We're releasing `from_generator` and `from_list` today :)\r\nIn the meantime you can play with them by installing `datasets` from source", "> We're releasing `from_generator` and `from_list` today :) In the meantime you can play with them by installing `datasets` from source\r\n\r\nThanks a lot for your work!" ]
"2022-09-16T09:15:10"
"2022-09-20T11:23:43"
"2022-09-20T11:23:43"
NONE
null
I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below: ```python from datasets import Dataset data = [[1, 2],[3, 4]] ds = Dataset.from_dict({"data": data}) ds = ds.with_format("torch") ds[0] ds[:2] ``` So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert? Thanks.
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I_kwDODunzps5R_g_V
4,982
Create dataset_infos.json with VALIDATION and TEST splits
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[ "@mariosasko could you help me with this issue? we've started the discussion from [here](https://github.com/huggingface/datasets/issues/4895#issuecomment-1248227130)", "Hi again! Can you please pass the directory name containing the dataset script instead of the script name to `datasets-cli test`?", "Yes, it worked! thanks a lot" ]
"2022-09-16T08:21:19"
"2022-09-28T07:59:39"
"2022-09-28T07:59:39"
NONE
null
The problem is described in that [issue](https://github.com/huggingface/datasets/issues/4895#issuecomment-1247975569). > When I try to create data_infos.json using datasets-cli test Peter.py --save_infos --all_configs I get an error: > ValueError: Unknown split "test". Should be one of ['train']. > > The data_infos.json is created perfectly fine when I use only one split - datasets.Split.TRAIN > > You can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch) I tried to clear the cache folder, than I got an another error. I run: ``` git clone https://huggingface.co/datasets/sberbank-ai/Peter cd Peter git checkout add_splits # switch to a add_splits branch rm dataset_infos.json # remove local dataset_infos.json rm -r ~/.cache/huggingface # remove cached dataset_infos.json datasets-cli test Peter.py --save_infos --all_configs # trying to create new dataset_infos.json ``` The error message: ``` Using custom data configuration default Testing builder 'default' (1/1) Downloading and preparing dataset peter/default to /Users/kalinin/.cache/huggingface/datasets/peter/default/0.0.0/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d... Downloading data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 4/4 [00:00<00:00, 5160.63it/s] Extracting data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last): File "/usr/local/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/usr/local/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/usr/local/lib/python3.9/site-packages/datasets/commands/test.py", line 137, in run builder.download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 1227, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 771, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/kalinin/.cache/huggingface/modules/datasets_modules/datasets/Peter/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d/Peter.py", line 23, in _split_generators data_files = dl_manager.download_and_extract(_URLS) File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 431, in download_and_extract return self.extract(self.download(url_or_urls)) File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 403, in extract extracted_paths = map_nested( File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 393, in map_nested mapped = [ File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 394, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 330, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 213, in cached_path output_path = ExtractManager(cache_dir=download_config.cache_dir).extract( File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 46, in extract self.extractor.extract(input_path, output_path, extractor_format) File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 263, in extract with FileLock(lock_path): File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 399, in __init__ max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax FileNotFoundError: [Errno 2] No such file or directory: '' Exception ignored in: <function BaseFileLock.__del__ at 0x11caeec10> Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 328, in __del__ self.release(force=True) File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 303, in release with self._thread_lock: AttributeError: 'UnixFileLock' object has no attribute '_thread_lock' Extracting data files: 0%| | 0/4 [00:00<?, ?it/s] ``` Can you help me please? ## Environment info - `datasets` version: 2.4.0 - Platform: macOS-12.5.1-x86_64-i386-64bit - Python version: 3.9.5 - PyArrow version: 9.0.0 - Pandas version: 1.2.4
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Make `pyarrow` optional
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[ "The whole datasets library is pretty much a wrapper to pyarrow (just take a look at some of the source for a Dataset) https://github.com/huggingface/datasets/blob/51aef08ad7053c0bfe8f9a961207b26df15850d3/src/datasets/arrow_dataset.py#L639 \r\n\r\nI think removing the pyarrow dependency would involve a complete rewrite / a different library with minimal functionality (datasets-lite ?)", "Thanks for the proposal, @KOLANICH. And also thanks for your answer, @dconathan.\r\n\r\nIndeed, we are using `pyarrow` as the backend for our datasets, in order to cache them and also allow memory-mapping (using datasets larger than your RAM memory).\r\n\r\nOne way to avoid using `pyarrow` could be loading the datasets in streaming mode, by passing `streaming=True` to `load_dataset`. This way you basically get a generator for the dataset; nothing is downloaded, nor cached. ", "Thanks for the info. Could `datasets` then be made optional for `transformers` instead? I used `transformers` only to deal with pretrained models to deploy them (convert to ONNX, and then I use TVM), so I don't really need `pyarrow` and `datasets` by now.\r\n" ]
"2022-09-15T17:38:03"
"2022-09-16T17:23:47"
"2022-09-16T17:23:47"
NONE
null
**Is your feature request related to a problem? Please describe.** Is `pyarrow` really needed for every dataset? **Describe the solution you'd like** It is made optional. **Describe alternatives you've considered** Likely, no.
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Fix missing tags in dataset cards
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MEMBER
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Fix missing tags in dataset cards: - amazon_us_reviews - art - discofuse - indic_glue - ubuntu_dialogs_corpus This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. Related to: - #4833 - #4891 - #4896 - #4908 - #4921 - #4931
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Update IndicGLUE download links
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"2022-09-15T10:05:57"
"2022-09-15T22:00:20"
"2022-09-15T21:57:34"
CONTRIBUTOR
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Add `fn_kwargs` param to `IterableDataset.map`
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"2022-09-13T16:19:05"
"2022-09-13T16:47:47"
"2022-09-13T16:45:34"
CONTRIBUTOR
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Add the `fn_kwargs` parameter to `IterableDataset.map`. ("Resolves" https://discuss.huggingface.co/t/how-to-use-large-image-text-datasets-in-hugging-face-hub-without-downloading-for-free/22780/3)
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[GH->HF] Part 2: Remove all dataset scripts from github
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"2022-09-13T16:01:12"
"2022-10-03T17:09:39"
"2022-10-03T17:07:32"
MEMBER
null
Now that all the datasets live on the Hub we can remove the /datasets directory that contains all the dataset scripts of this repository - [x] Needs https://github.com/huggingface/datasets/pull/4973 to be merged first - [x] and PR to be enabled on the Hub for non-namespaced datasets
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[GH->HF] Load datasets from the Hub
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"2022-09-13T15:01:41"
"2022-09-15T15:26:51"
"2022-09-15T15:24:26"
MEMBER
null
Currently datasets with no namespace (e.g. squad, glue) are loaded from github. In this PR I changed this logic to use the Hugging Face Hub instead. This is the first step in removing all the dataset scripts in this repository related to discussions in https://github.com/huggingface/datasets/pull/4059 (I should have continued from this PR actually)
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Fix map batched with torch output
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"2022-09-13T13:16:34"
"2022-09-20T09:42:02"
"2022-09-20T09:39:33"
MEMBER
null
Reported in https://discuss.huggingface.co/t/typeerror-when-applying-map-after-set-format-type-torch/23067/2 Currently it fails if one uses batched `map` and the map function returns a torch tensor. I fixed it for torch, tf, jax and pandas series.
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Preserve non-`input_colums` in `Dataset.map` if `input_columns` are specified
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"2022-09-12T18:08:24"
"2022-09-13T13:51:08"
"2022-09-13T13:48:45"
CONTRIBUTOR
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Currently, if the `input_columns` list in `Dataset.map` is specified, the columns not in that list are dropped after the `map` transform. This makes the behavior inconsistent with `IterableDataset.map`. (It seems this issue was introduced by mistake in https://github.com/huggingface/datasets/pull/2246) Fix https://github.com/huggingface/datasets/issues/4858
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Support streaming nli_tr dataset
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"2022-09-12T07:48:45"
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MEMBER
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Support streaming nli_tr dataset. This PR removes legacy `codecs.open` and replaces it with `open` that supports passing encoding. Fix #3186.
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Fix data URL and metadata of vivos dataset
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"2022-09-12T06:12:34"
"2022-09-12T07:16:15"
"2022-09-12T07:14:19"
MEMBER
null
After contacting the authors of the VIVOS dataset to report that their data server is down, we have received a reply from Hieu-Thi Luong that their data is now hosted on Zenodo: https://doi.org/10.5281/zenodo.7068130 This PR updates their data URL and some metadata (homepage, citation and license). Fix #4936.
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Support streaming compguesswhat dataset
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"2022-09-12T05:42:24"
"2022-09-12T08:00:06"
"2022-09-12T07:58:06"
MEMBER
null
Support streaming `compguesswhat` dataset. Fix #3191.
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PR_kwDODunzps4-vbS-
4,967
Strip "/" in local dataset path to avoid empty dataset name error
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"2022-09-11T23:09:16"
"2022-09-29T10:46:21"
"2022-09-12T15:30:38"
CONTRIBUTOR
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4,963
Dataset without script does not support regular JSON data file
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[ "Hi @julien-c,\r\n\r\nOut of the box, we only support JSON lines (NDJSON) data files, but your data file is a regular JSON file. The reason is we use `pyarrow.json.read_json` and this only supports line-delimited JSON. " ]
"2022-09-09T18:45:33"
"2022-09-20T15:40:07"
"2022-09-20T15:40:07"
MEMBER
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### Link https://huggingface.co/datasets/julien-c/label-studio-my-dogs ### Description <img width="1115" alt="image" src="https://user-images.githubusercontent.com/326577/189422048-7e9c390f-bea7-4521-a232-43f049ccbd1f.png"> ### Owner Yes
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4,962
Update setup.py
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"2022-09-09T17:57:56"
"2022-09-12T14:33:04"
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exclude broken version of fsspec. See the [related issue](https://github.com/huggingface/datasets/issues/4961)
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4,961
fsspec 2022.8.2 breaks xopen in streaming mode
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[ "loading `fsspec==2022.7.1` fixes this issue, setup.py would need to be changed to prevent users from using the latest version of fsspec.", "Opened [PR](https://github.com/huggingface/datasets/pull/4962) to address this.", "Hi @DCNemesis, thanks for reporting.\r\n\r\nThat was a temporary issue in `fsspec` releases 2022.8.0 and 2022.8.1. But they fixed it in their patch release 2022.8.2 (and yanked both previous versions). See:\r\n- https://github.com/huggingface/transformers/pull/18846\r\n\r\nAre you sure you have version 2022.8.2 installed?\r\n```shell\r\npip install -U fsspec\r\n```\r\n", "@albertvillanova I was using a temporary Google Colab instance, but checking it again today it seems it was loading 2022.8.1 rather than 2022.8.2. It's surprising that colab is using the version that was replaced the same day it was released. Testing with 2022.8.2 did work. It appears Colab [will be fixing it](https://github.com/googlecolab/colabtools/issues/3055) on their end too. ", "Thanks for the additional information.\r\n\r\nOnce we know 2022.8.2 works, I'm closing this issue. Feel free to reopen it if necessary.", "Colab just upgraded their default `fsspec` version to 2022.8.2:\r\n- https://github.com/googlecolab/colabtools/issues/3055#issuecomment-1244019010" ]
"2022-09-09T17:26:55"
"2022-09-12T17:45:50"
"2022-09-12T14:32:05"
NONE
null
## Describe the bug When fsspec 2022.8.2 is installed in your environment, xopen will prematurely close files, making streaming mode inoperable. ## Steps to reproduce the bug ```python import datasets data = datasets.load_dataset('MLCommons/ml_spoken_words', 'id_wav', split='train', streaming=True) ``` ## Expected results Dataset should load as iterator. ## Actual results ``` [/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1737 # Return iterable dataset in case of streaming 1738 if streaming: -> 1739 return builder_instance.as_streaming_dataset(split=split) 1740 1741 # Some datasets are already processed on the HF google storage [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path) 1023 ) 1024 self._check_manual_download(dl_manager) -> 1025 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)} 1026 # By default, return all splits 1027 if split is None: [~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _split_generators(self, dl_manager) 182 name=datasets.Split.TRAIN, 183 gen_kwargs={ --> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages], 185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in 186 self.config.languages] if not dl_manager.is_streaming else None, [~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in <listcomp>(.0) 182 name=datasets.Split.TRAIN, 183 gen_kwargs={ --> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages], 185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in 186 self.config.languages] if not dl_manager.is_streaming else None, [~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives(dl_manager, lang, format, split) 267 # for streaming case 268 def _download_audio_archives(dl_manager, lang, format, split): --> 269 archives_paths = _download_audio_archives_paths(dl_manager, lang, format, split) 270 return [dl_manager.iter_archive(archive_path) for archive_path in archives_paths] [~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives_paths(dl_manager, lang, format, split) 251 n_files_path = dl_manager.download(n_files_url) 252 --> 253 with open(n_files_path, "r", encoding="utf-8") as file: 254 n_files = int(file.read().strip()) # the file contains a number of archives 255 ValueError: I/O operation on closed file. ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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4,959
Fix data URLs of compguesswhat dataset
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"2022-09-09T14:36:10"
"2022-09-09T16:01:34"
"2022-09-09T15:59:04"
MEMBER
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After we informed the `compguesswhat` dataset authors about an error with their data URLs, they have updated them: - https://github.com/CompGuessWhat/compguesswhat.github.io/issues/1 This PR updates their data URLs in our loading script. Related to: - #3191
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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.4.0/datasets/jsonl/jsonl.py
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[ "I have solved this problem... The extension of the file should be `.json` not `.jsonl`" ]
"2022-09-09T11:29:55"
"2022-09-09T11:38:44"
"2022-09-09T11:38:44"
NONE
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Hi, When I use load_dataset from local jsonl files, below error happens, and I type the link into the browser prompting me `404: Not Found`. I download the other `.py` files using the same method and it works. It seems that the server is missing the appropriate file, or it is a problem with the code version. ``` ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x2b08342004c0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))"))) ```
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4,957
Add `Dataset.from_generator`
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"2022-09-08T15:08:25"
"2022-09-16T14:46:35"
"2022-09-16T14:44:18"
CONTRIBUTOR
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Add `Dataset.from_generator` to the API to allow creating datasets from data larger than RAM. The implementation relies on a packaged module not exposed in `load_dataset` to tie this method with `datasets`' caching mechanism. Closes https://github.com/huggingface/datasets/issues/4417
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PR_kwDODunzps4-m5NU
4,956
Fix TF tests for 2.10
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"2022-09-08T14:39:10"
"2022-09-08T15:16:51"
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MEMBER
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Fixes #4953
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PR_kwDODunzps4-mhl5
4,954
Pin TensorFlow temporarily
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"2022-09-08T14:12:33"
"2022-09-08T14:10:03"
MEMBER
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Temporarily fix TensorFlow until a permanent solution is found. Related to: - #4953
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I_kwDODunzps5RcPIi
4,953
CI test of TensorFlow is failing
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"2022-09-08T13:39:29"
"2022-09-08T15:14:45"
"2022-09-08T15:14:45"
MEMBER
null
## Describe the bug The following CI test fails: https://github.com/huggingface/datasets/runs/8246722693?check_suite_focus=true ``` FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - AssertionError: ``` Details: ``` _________________________ TempSeedTest.test_tensorflow _________________________ [gw0] linux -- Python 3.7.13 /opt/hostedtoolcache/Python/3.7.13/x64/bin/python self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow> @require_tf def test_tensorflow(self): import tensorflow as tf from tensorflow.keras import layers def gen_random_output(): model = layers.Dense(2) x = tf.random.uniform((1, 3)) return model(x).numpy() with temp_seed(42, set_tensorflow=True): out1 = gen_random_output() with temp_seed(42, set_tensorflow=True): out2 = gen_random_output() out3 = gen_random_output() > np.testing.assert_equal(out1, out2) E AssertionError: E Arrays are not equal E E Mismatched elements: 2 / 2 (100%) E Max absolute difference: 0.84619296 E Max relative difference: 16.083529 E x: array([[-0.793581, 0.333286]], dtype=float32) E y: array([[0.052612, 0.539708]], dtype=float32) tests/test_py_utils.py:149: AssertionError ```
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PR_kwDODunzps4-meM0
4,952
Add test-datasets CI job
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"2022-09-08T13:38:30"
"2022-09-16T13:28:02"
"2022-09-16T13:25:48"
MEMBER
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To avoid having too many conflicts in the datasets and metrics dependencies I split the CI into test and test-catalog test does the test of the core of the `datasets` lib, while test-catalog tests the datasets scripts and metrics scripts This also makes `pip install -e .[dev]` much smaller for developers WDYT @albertvillanova ?
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PR_kwDODunzps4-lDqd
4,951
Fix license information in qasc dataset card
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"2022-09-08T10:04:39"
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MEMBER
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This PR adds the license information to `qasc` dataset, once reported via GitHub by Tushar Khot, the dataset is licensed under CC BY 4.0: - https://github.com/allenai/qasc/issues/5
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Update Enwik8 broken link and information
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"2022-09-08T03:15:00"
"2022-09-24T22:14:35"
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CONTRIBUTOR
null
The current enwik8 dataset link give a 502 bad gateway error which can be view on https://huggingface.co/datasets/enwik8 (click the dropdown to see the dataset preview, it will show the error). This corrects the links, and json metadata as well as adds a little bit more information about enwik8.
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Update enwik8 fixing the broken link
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"2022-09-07T22:17:14"
"2022-09-08T03:14:04"
"2022-09-08T03:14:04"
CONTRIBUTOR
null
The current enwik8 dataset link give a 502 bad gateway error which can be view on https://huggingface.co/datasets/enwik8 (click the dropdown to see the dataset preview, it will show the error). This corrects the links, and json metadata as well as adds a little bit more information about enwik8.
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Fix minor typo in error message for missing imports
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Try to fix the Windows CI after TF update 2.10
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"2022-09-07T17:14:49"
"2022-09-08T09:13:10"
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Introduce regex check when pushing as well
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"2022-09-07T13:45:58"
"2022-09-13T10:19:01"
"2022-09-13T10:16:34"
MEMBER
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Closes https://github.com/huggingface/datasets/issues/4945 by adding a regex check when pushing to hub. Let me know if this is helpful and if it's the fix you would have in mind for the issue and I'm happy to contribute tests.
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Push to hub can push splits that do not respect the regex
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"2022-09-07T13:45:17"
"2022-09-13T10:16:35"
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MEMBER
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## Describe the bug The `push_to_hub` method can push splits that do not respect the regex check that is used for downloads. Therefore, splits may be pushed but never re-used, which can be painful if the split was done after runtime preprocessing. ## Steps to reproduce the bug ```python >>> from datasets import Dataset, DatasetDict, load_dataset >>> d = Dataset.from_dict({'x': [1,2,3], 'y': [1,2,3]}) >>> di = DatasetDict() >>> di['identifier-with-column'] = d >>> di.push_to_hub('open-source-metrics/test') Pushing split identifier-with-column to the Hub. Pushing dataset shards to the dataset hub: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1/1 [00:04<00:00, 4.40s/it] ``` Loading it afterwards: ```python >>> load_dataset('open-source-metrics/test') Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 610/610 [00:00<00:00, 432kB/s] Using custom data configuration open-source-metrics--test-28b63ec7cde80488 Downloading and preparing dataset None/None (download: 950 bytes, generated: 48 bytes, post-processed: Unknown size, total: 998 bytes) to /home/lysandre/.cache/huggingface/datasets/open-source-metrics___parquet/open-source-metrics--test-28b63ec7cde80488/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec... Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 950/950 [00:00<00:00, 1.01MB/s] Downloading data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1/1 [00:01<00:00, 1.48s/it] Extracting data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1/1 [00:00<00:00, 2291.97it/s] Traceback (most recent call last): File "/home/lysandre/.pyenv/versions/3.10.6/lib/python3.10/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/load.py", line 1746, in load_dataset builder_instance.download_and_prepare( File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 771, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 48, in _split_generators splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files})) File "<string>", line 5, in __init__ File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 599, in __post_init__ NamedSplit(self.name) # check that it's a valid split name File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 346, in __init__ raise ValueError(f"Split name should match '{_split_re}' but got '{split_name}'.") ValueError: Split name should match '^\w+(\.\w+)*$' but got 'identifier-with-column'. ``` ## Expected results I would expect `push_to_hub` to stop me in my tracks if trying to upload a split that will not be working afterwards. ## Actual results See above ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.64-1-lts-x86_64-with-glibc2.36 - Python version: 3.10.6 - PyArrow version: 9.0.0 - Pandas version: 1.4.4
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4,944
larger dataset, larger GPU memory in the training phase? Is that correct?
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[ "does the trainer save it in GPU? sooo curious... how to fix it", "It's my bad. didn't limit the input length" ]
"2022-09-07T08:46:30"
"2022-09-07T12:34:58"
"2022-09-07T12:34:58"
NONE
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from datasets import set_caching_enabled set_caching_enabled(False) for ds_name in ["squad","newsqa","nqopen","narrativeqa"]: train_ds = load_from_disk("../../../dall/downstream/processedproqa/{}-train.hf".format(ds_name)) break train_ds = concatenate_datasets([train_ds,train_ds,train_ds,train_ds]) #operation 1 trainer = QuestionAnsweringTrainer( #huggingface trainer model=model, args=training_args, train_dataset=train_ds, eval_dataset= None, eval_examples=None, answer_column_name=answer_column, dataset_name="squad", tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics if training_args.predict_with_generate else None, ) with operation 1, the GPU memory increases from 16G to 23G
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Add splits to MBPP dataset
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"2022-09-07T01:18:31"
"2022-09-13T12:29:19"
"2022-09-13T12:27:21"
CONTRIBUTOR
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This PR addresses https://github.com/huggingface/datasets/issues/4795
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Trec Dataset has incorrect labels
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[ "Thanks for reporting, @wmpauli. \r\n\r\nIndeed we recently fixed this issue:\r\n- #4801 \r\n\r\nThe fix will be accessible after our next library release. In the meantime, you can have it by passing `revision=\"main\"` to `load_dataset`." ]
"2022-09-06T22:13:40"
"2022-09-08T11:12:03"
"2022-09-08T11:12:03"
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## Describe the bug Both coarse and fine labels seem to be out of line. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = "trec" raw_datasets = load_dataset(dataset) df = pd.DataFrame(raw_datasets["test"]) df.head() ``` ## Expected results text (string) | coarse_label (class label) | fine_label (class label) -- | -- | -- How far is it from Denver to Aspen ? | 5 (NUM) | 40 (NUM:dist) What county is Modesto , California in ? | 4 (LOC) | 32 (LOC:city) Who was Galileo ? | 3 (HUM) | 31 (HUM:desc) What is an atom ? | 2 (DESC) | 24 (DESC:def) When did Hawaii become a state ? | 5 (NUM) | 39 (NUM:date) ## Actual results index | label-coarse |label-fine | text -- |-- | -- | -- 0 | 4 | 40 | How far is it from Denver to Aspen ? 1 | 5 | 21 | What county is Modesto , California in ? 2 | 3 | 12 | Who was Galileo ? 3 | 0 | 7 | What is an atom ? 4 | 4 | 8 | When did Hawaii become a state ? ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-1086-azure-x86_64-with-glibc2.27 - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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