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https://api.github.com/repos/huggingface/datasets/issues/3880
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1,164,406,008
PR_kwDODunzps40MjM3
3,880
Change the framework switches to the new syntax
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3880). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3880). All of your documentation changes will be reflected on...
2022-03-09T20:29:10Z
2022-03-15T14:13:28Z
2022-03-15T14:13:27Z
CONTRIBUTOR
null
0
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This PR updates the syntax of the framework-specific code samples. With this new syntax, you'll be able to: - have paragraphs of text be framework-specific instead of just code samples - have support for Flax code samples if you want. This should be merged after https://github.com/huggingface/doc-builder/pull/63 and https://github.com/huggingface/doc-builder/pull/130
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MDU6SXNzdWU4MzMyOTE0MzI=
2,065
Only user permission of saved cache files, not group
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[ "Hi ! Thanks for reporting.\r\n\r\nCurrently there's no way to specify this.\r\n\r\nWhen loading/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https://github.com/huggingface/datasets/blob/f6b8251eb...
2021-03-17T00:20:22Z
2023-03-31T12:17:06Z
2021-05-10T06:45:29Z
NONE
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Hello, It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?
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5,872
Fix infer module for uppercase extensions
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-05-17T05:56:45Z
2023-05-17T14:26:59Z
2023-05-17T14:19:18Z
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Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`. Before, `None` module was returned.
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4,266
Add HF Speech Bench to Librispeech Dataset Card
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-05-02T16:59:31Z
2022-05-05T08:47:20Z
2022-05-05T08:40:09Z
CONTRIBUTOR
null
0
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Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions? cc @patrickvonplaten: more leaderboard promotion!
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941,497,830
MDExOlB1bGxSZXF1ZXN0Njg3MzY4OTMz
2,626
Use correct logger in metrics.py
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2021-07-11T17:22:30Z
2021-07-12T14:08:54Z
2021-07-12T05:54:29Z
CONTRIBUTOR
null
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Fixes #2624
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1,510
Add Dataset for (qa_srl)Question-Answer Driven Semantic Role Labeling
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[ "Hii please follow me", "merging since the CI is fixed on master" ]
2020-12-12T15:48:11Z
2020-12-17T16:06:22Z
2020-12-17T16:06:22Z
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- Added tags, Readme file - Added code changes
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Getting configs names takes too long
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[ "maybe related to https://github.com/huggingface/datasets/issues/2859\r\n", "It looks like it's currently calling `HfFileSystem.ls()` ~8 times at the root and for each subdirectory:\r\n- \"\"\r\n- \"en.noblocklist\"\r\n- \"en.noclean\"\r\n- \"en\"\r\n- \"multilingual\"\r\n- \"realnewslike\"\r\n\r\nCurrently `ls` ...
2021-12-13T14:27:57Z
2021-12-13T14:53:33Z
null
CONTRIBUTOR
null
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## Steps to reproduce the bug ```python from datasets import get_dataset_config_names get_dataset_config_names("allenai/c4") ``` ## Expected results I would expect to get the answer quickly, at least in less than 10s ## Actual results It takes about 45s on my environment ## Environment info - `datasets` version: 1.16.1 - Platform: Linux-5.11.0-1022-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 4.0.1
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Return Python float instead of numpy.float64 in sklearn metrics
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[ "I opened an issue on the `sklearn` repo to understand why `numpy.float64` is the default: https://github.com/scikit-learn/scikit-learn/discussions/20490", "It could be surprising at first to use `tolist()` on numpy scalars but it works ^^", "did the same for Pearsonr here: https://github.com/huggingface/datase...
2021-07-09T09:48:09Z
2021-07-12T14:12:53Z
2021-07-09T13:03:54Z
MEMBER
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This PR converts the return type of all `sklearn` metrics to be Python `float` instead of `numpy.float64`. The reason behind this is that our Hub evaluation framework relies on converting benchmark-specific metrics to YAML ([example](https://huggingface.co/datasets/autonlp/autonlp-benchmark-raft-neelalex__raft-test-neelalex__raft-predictions-3/blob/main/README.md#L11)) and the `numpy.float64` format produces garbage like: ```python import yaml from datasets import load_metric metric = load_metric("accuracy") score = metric.compute(predictions=[0,1], references=[0,1]) print(yaml.dump(score["accuracy"])) # output below # !!python/object/apply:numpy.core.multiarray.scalar # - !!python/object/apply:numpy.dtype # args: # - f8 # - false # - true # state: !!python/tuple # - 3 # - < # - null # - null # - null # - -1 # - -1 # - 0 # - !!binary | # AAAAAAAA8D8= ```
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Add COCO dataset
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[ "The CI failures other than a missing dummy data file and missing fields in the card are unrelated to this PR. ", "Thanks a lot for this great work and fixing TFDS based script @mariosasko 🤗 will generate the dummy dataset and write the model card tomorrow!", "@mariosasko I added the dataset card, I'm on the d...
2021-12-21T14:07:50Z
2023-09-24T09:33:31Z
2022-10-03T09:36:08Z
CONTRIBUTOR
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This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection. Some notes: * the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here * I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`) * this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427 TODOs: - [x] dataset card - [ ] dummy data cc @merveenoyan Closes #2526
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load_dataset("financial_phrasebank") NonMatchingChecksumError
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[ "Hi! It's probably because this dataset is stored on google drive and it has a per day quota limit. It should work if you retry, I was able to initiate the download.\r\n\r\nSimilar issue [here](https://github.com/huggingface/datasets/issues/2646)", "Hi ! Loading the dataset works on my side as well.\r\nFeel free ...
2021-07-13T21:21:49Z
2022-08-04T08:30:08Z
2022-08-04T08:30:08Z
NONE
null
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## Describe the bug Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("financial_phrasebank", 'sentences_allagree') ``` ## Expected results I expect to see the financial_phrasebank dataset downloaded successfully ## Actual results NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip'] ## Environment info - `datasets` version: 1.9.0 - Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6 - Python version: 3.7.10 - PyArrow version: 4.0.1
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Fix setimes_2 wmt urls
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2021-02-18T09:42:26Z
2021-02-18T09:55:41Z
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Continuation of #1901 Some other urls were missing https
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4,030
Use a constant for the articles regex in SQuAD v2
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-03-26T23:06:30Z
2022-04-12T16:30:45Z
2022-04-12T11:00:24Z
CONTRIBUTOR
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The main reason for doing this is to be able to change the articles list if using another language, for example. It's not the most elegant solution but at least it makes the metric more extensible with no drawbacks. BTW, what could be the best way to make this more generic (i.e., SQuAD in other languages)? Maybe receive a regex as an optional param, with the current value as the default? Similarly for SQuAD v1 (can't they re-use code?).
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Issues with custom dataset in Wav2Vec2
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[ "Closed and moved to transformers." ]
2022-01-27T12:09:05Z
2022-01-27T12:29:48Z
2022-01-27T12:29:48Z
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We are training Vav2Vec using the run_speech_recognition_ctc_bnb.py-script. This is working fine with Common Voice, however using our custom dataset and data loader at [NbAiLab/NPSC]( https://huggingface.co/datasets/NbAiLab/NPSC) it crashes after roughly 1 epoch with the following stack trace: ![image](https://user-images.githubusercontent.com/9079808/151355893-6d5887cc-ca19-4b12-948a-124eb6dac372.png) We are able to work around the issue, for instance by adding this check in line#222 in transformers/models/wav2vec2/modeling_wav2vec2.py: ```python if input_length - (mask_length - 1) < num_masked_span: num_masked_span = input_length - (mask_length - 1) ``` Interestingly, these are the variable values before the adjustment: ``` input_length=10 mask_length=10 num_masked_span=2 ```` After adjusting num_masked_spin to 1, the training script runs. The issue is also fixed by setting “replace=True” in the same function. Do you have any idea what is causing this, and how to fix this error permanently? If you do not think this is an Datasets issue, feel free to move the issue.
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4,367
Remove config names as yaml keys
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[ "I included the change from https://github.com/huggingface/datasets/pull/4302 directly in this PR, this way the datasets will be updated right away in the CI (the CI is only triggered when a dataset card is changed)", "_The documentation is not available anymore as the PR was closed or merged._", "Alright it's ...
2022-05-18T13:59:24Z
2022-05-20T09:35:26Z
2022-05-20T09:27:19Z
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Many datasets have dots in their config names. However it causes issues with the YAML tags of the dataset cards since we can't have dots in YAML keys. I fix this, I removed the tags separations per config name completely, and have a single flat YAML for all configurations. Dataset search doesn't use this info anyway. I removed all the config names used as YAML keys, and I moved them in under a new `config:` key. This is related to https://github.com/huggingface/datasets/pull/2362 (internal https://github.com/huggingface/moon-landing/issues/946). Also removing the dots in the YAML keys would allow us to do as in https://github.com/huggingface/datasets/pull/4302 which removes a hack that replaces all the dots by underscores in the YAML tags. I also added a test in the CI that checks that all the YAML tags to make sure that: - they can be parsed using a YAML parser - they contain only valid YAML tags like languages or task_ids
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Unexpected type after `concatenate_datasets`
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[ "Hi @JulesBelveze, thanks for your question.\r\n\r\nNote that 🤗 `datasets` internally store their data in Apache Arrow format.\r\n\r\nHowever, when accessing dataset columns, by default they are returned as native Python objects (lists in this case).\r\n\r\nIf you would like their columns to be returned in a more...
2021-08-04T07:10:39Z
2021-08-04T16:01:24Z
2021-08-04T16:01:23Z
NONE
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## Describe the bug I am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`. It then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected. ## Steps to reproduce the bug ```python >>> featurized_teacher Dataset({ features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'], num_rows: 502 }) >>> for f in featurized_teacher.features: print(featurized_teacher[f].shape) torch.Size([502]) torch.Size([502, 300]) torch.Size([502, 300]) torch.Size([502, 300]) >>> featurized_student Dataset({ features: ['s_features', 's_labels'], num_rows: 502 }) >>> for f in featurized_student.features: print(featurized_student[f].shape) torch.Size([502, 64]) torch.Size([502]) ``` The shapes seem alright to me. Then the results after concatenation are as follow: ```python >>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1) >>> type(concat_dataset["t_labels"]) <class 'list'> ``` One would expect to obtain the same type as the one before concatenation. Am I doing something wrong here? Any idea on how to fix this unexpected behavior? ## Environment info - `datasets` version: 1.9.0 - Platform: macOS-10.14.6-x86_64-i386-64bit - Python version: 3.9.5 - PyArrow version: 3.0.0
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I_kwDODunzps5CQVEs
3,615
Dataset BnL Historical Newspapers does not work in streaming mode
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[ "@albertvillanova let me know if there is anything I can do to help with this. I had a quick look at the code again and though I could try the following changes:\r\n- use `download` instead of `download_and_extract`\r\nhttps://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bn...
2022-01-22T14:12:59Z
2022-02-04T14:05:21Z
2022-02-04T14:05:21Z
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## Describe the bug When trying to load in streaming mode, it "hangs"... ## Steps to reproduce the bug ```python ds = load_dataset("bnl_newspapers", split="train", streaming=True) ``` ## Expected results The code should be optimized, so that it works fast in streaming mode. CC: @davanstrien
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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 huggingfac...
2022-09-23T07:42:49Z
2023-02-24T16:20:57Z
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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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Weights of model checkpoint not initialized for RobertaModel for Bertscore
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[ "Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.wei...
2021-06-29T18:55:23Z
2021-07-01T07:08:59Z
2021-06-30T07:35:49Z
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When applying bertscore out of the box, ```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']``` Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html ``` from datasets import load_metric metric = load_metric('bertscore') # Example of typical usage for batch in dataset: inputs, references = batch predictions = model(inputs) metric.add_batch(predictions=predictions, references=references) score = metric.compute(lang="en") #score = metric.compute(model_type="roberta-large") # gives the same error ``` I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27 - Python version: 3.9.5 - PyArrow version: 3.0.0
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Fix IndexError by ignoring empty RecordBatch
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2021-08-24T17:06:13Z
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We need to ignore the empty record batches for the interpolation search to work correctly when querying arrow tables Close #2833 cc @SaulLu
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2,118
Remove os.environ.copy in Dataset.map
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[ "I thought deepcopy on `os.environ` is unsafe (see [this](https://stackoverflow.com/questions/13142972/using-copy-deepcopy-on-os-environ-in-python-appears-broken)), but I can't replicate the behavior described in the linked SO thread.\r\n\r\nClosing this one because #2119 has a much cleaner approach." ]
2021-03-26T03:48:17Z
2021-03-26T12:03:23Z
2021-03-26T12:00:05Z
CONTRIBUTOR
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Replace `os.environ.copy` with in-place modification Fixes #2115
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Duplicates in the LAMA dataset
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[ "Hi,\r\n\r\ncurrently the datasets API doesn't have a dedicated function to remove duplicate rows, but since the LAMA dataset is not too big (it fits in RAM), we can leverage pandas to help us remove duplicates:\r\n```python\r\n>>> from datasets import load_dataset, Dataset\r\n>>> dataset = load_dataset('lama', spl...
2021-04-13T18:59:49Z
2021-04-14T21:42:27Z
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I observed duplicates in the LAMA probing dataset, see a minimal code below. ``` >>> import datasets >>> dataset = datasets.load_dataset('lama') No config specified, defaulting to: lama/trex Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc) >>> train_dataset = dataset['train'] >>> train_dataset[0] {'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'} >>> train_dataset[1] {'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'} ``` I checked the original data available at https://dl.fbaipublicfiles.com/LAMA/data.zip. This particular duplicated comes from: ``` {"uuid": "40b2ed1c-0961-482e-844e-32596b6117c8", "obj_uri": "Q150", "obj_label": "French", "sub_uri": "Q441235", "sub_label": "Louis Jules Trochu", "predicate_id": "P103", "evidences": [{"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}, {"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}]} ``` What is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?
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Smooth-BLEU bug fixed
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[ "Thanks @Aktsvigun for your fix.\r\n\r\nHowever, metrics in `datasets` are in deprecation mode:\r\n- #4739\r\n\r\nYou should transfer this PR to the `evaluate` library: https://github.com/huggingface/evaluate\r\n\r\nJust for context, here the link to the PR by @Aktsvigun on tensorflow/nmt:\r\n- https://github.com/t...
2022-06-29T14:51:42Z
2022-09-23T07:42:40Z
2022-09-23T07:42:40Z
NONE
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Hi, the current implementation of smooth-BLEU contains a bug: it smoothes unigrams as well. Consequently, when both the reference and translation consist of totally different tokens, it anyway returns a non-zero value (please see the attached image). This however contradicts the source paper suggesting the smooth-BLEU _(Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic evaluation metrics for machine translation. COLING 2004.)_ : > Add one count to the n-gram hit and total ngram count for n > 1. Therefore, for candidate translations with less than n words, they can still get a positive smoothed BLEU score from shorter n-gram matches; however if nothing matches then they will get zero scores. This pull request aims at fixing this bug. I made a pull request in the target repository `tensorflow/nmt`, which implements this script, yet the last commit there is dating 19.02.2019 and I doubt whether this will be fixed promptly. Yet, this bug is critical, for instance for summarization datasets with short summaries (e.g. AESLC), since smoothing needs to be applied there. Therefore, the easiest solution that I found is to fork the repo and download this script directly from the forked fixed repo. Kind, Akim Tsvigun <img width="516" alt="Снимок экрана 2022-06-29 в 17 49 27" src="https://user-images.githubusercontent.com/36672861/176466935-ac579e6d-6a93-4111-ab41-9b33056e7d47.png">
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Fix logging docstring
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2021-07-14T23:19:58Z
2021-07-18T11:41:06Z
2021-07-15T09:57:31Z
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Remove "no tqdm bars" from the docstring in the logging module to align it with the changes introduced in #2534.
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Update disaster_response_messages download urls (+ add validation split)
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2021-12-13T15:30:12Z
2021-12-14T14:38:30Z
2021-12-14T14:38:29Z
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Fixes #3240, fixes #3416
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HuggingFace does not cache downloaded files aggressively/early enough
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2023-05-25T15:14:36Z
2023-05-25T15:14:36Z
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### Describe the bug I wrote the following script: ``` import datasets dataset = datasets.load.load_dataset("wikipedia", "20220301.en", split="train[:10000]") ``` I ran it and spent 90 minutes downloading a 20GB file. Then I saw: ``` Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20.3G/20.3G [1:30:29<00:00, 3.73MB/s] Traceback (most recent call last): File "/home/jack/Code/Projects/Transformers/Codebase/main.py", line 5, in <module> dataset = datasets.load.load_dataset("wikipedia", "20220301.en", split="train[:10000]") File "/home/jack/.local/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/jack/.local/lib/python3.10/site-packages/datasets/builder.py", line 883, in download_and_prepare self._save_info() File "/home/jack/.local/lib/python3.10/site-packages/datasets/builder.py", line 2037, in _save_info import apache_beam as beam ModuleNotFoundError: No module named 'apache_beam' ``` And the 20GB of data was seemingly instantly gone forever, because when I ran the script again, it had to do the download again. ### Steps to reproduce the bug See above ### Expected behavior See above ### Environment info datasets 2.10.1 Python 3.10
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datasets 2.7 introduces sharding error
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[ "I notice a comment in the code says:\r\n`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users` \r\n \r\n ... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to h...
2022-11-17T15:36:52Z
2022-12-24T01:44:02Z
2022-11-18T12:52:05Z
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### Describe the bug dataset fails to load with runtime error `RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize: - key audio_files has length 46 - key data has length 0 To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.` ### Steps to reproduce the bug With datasets[audio] 2.7 loaded, and logged into hugging face, `data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)` creates the error. Full stack trace: ```--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) [<ipython-input-7-8cb9ca0f79f0>](https://localhost:8080/#) in <module> ----> 1 data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True) 5 frames [/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, num_proc, **config_kwargs) 1745 try_from_hf_gcs=try_from_hf_gcs, 1746 use_auth_token=use_auth_token, -> 1747 num_proc=num_proc, 1748 ) 1749 [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 824 verify_infos=verify_infos, 825 **prepare_split_kwargs, --> 826 **download_and_prepare_kwargs, 827 ) 828 # Sync info [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs) 1554 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs): 1555 super()._download_and_prepare( -> 1556 dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs 1557 ) 1558 [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 911 try: 912 # Prepare split will record examples associated to the split --> 913 self._prepare_split(split_generator, **prepare_split_kwargs) 914 except OSError as e: 915 raise OSError( [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size) 1362 fpath = path_join(self._output_dir, fname) 1363 -> 1364 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs) 1365 if num_input_shards <= 1 and num_proc is not None: 1366 logger.warning( [/usr/local/lib/python3.7/dist-packages/datasets/utils/sharding.py](https://localhost:8080/#) in _number_of_shards_in_gen_kwargs(gen_kwargs) 16 + "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items()) 17 + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, " ---> 18 + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length." 19 ) 20 ) RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize: - key audio_files has length 46 - key data has length 0 To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.``` ### Expected behavior the dataset loads in datasets version 2.6.1 and should load with datasets 2.7 ### Environment info - `datasets` version: 2.7.0 - Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.15 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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Respect row ordering when concatenating datasets along axis=1
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2021-11-15T11:27:14Z
2021-11-17T15:41:11Z
2021-11-17T15:41:11Z
CONTRIBUTOR
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Currently, there is a bug when concatenating datasets along `axis=1` if more than one dataset has the `_indices` attribute defined. In that scenario, all indices mappings except the first one get ignored. A minimal reproducible example: ```python >>> from datasets import Dataset, concatenate_datasets >>> a = Dataset.from_dict({"a": [30, 20, 10]}) >>> b = Dataset.from_dict({"b": [2, 1, 3]}) >>> d = concatenate_datasets([a.sort("a"), b.sort("b")], axis=1) >>> print(d[:3]) # expected: {'a': [10, 20, 30], 'b': [1, 2, 3]} {'a': [10, 20, 30], 'b': [3, 1, 2]} ``` I've noticed the bug while working on #3195.
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2,964
Error when calculating Matthews Correlation Coefficient loaded with `load_metric`
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[ "After some more tests I've realized that this \"issue\" is due to the `numpy.float64` to `float` conversion, but when defining a function named `compute_metrics` as it follows:\r\n\r\n```python\r\ndef compute_metrics(eval_preds):\r\n metric = load_metric(\"matthews_correlation\")\r\n logits, labels = eval_pr...
2021-09-24T15:55:21Z
2021-09-25T08:06:07Z
2021-09-25T08:06:07Z
CONTRIBUTOR
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## Describe the bug After loading the metric named "[Matthews Correlation Coefficient](https://huggingface.co/metrics/matthews_correlation)" from `🤗datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required). ## Steps to reproduce the bug ```python import torch predictions = torch.ones((10,)) references = torch.zeros((10,)) from datasets import load_metric METRIC = load_metric("matthews_correlation") result = METRIC.compute(predictions=predictions, references=references) ``` ## Expected results We should expect a Python `dict` as it follows: ``` { "matthews_correlation": float() } ``` as defined in https://github.com/huggingface/datasets/blob/master/metrics/matthews_correlation/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail. ## Actual results ``` Traceback (most recent call last): File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 59, in main app() File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 214, in __call__ return get_command(self)(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1137, in __call__ return self.main(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1062, in main rv = self.invoke(ctx) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1668, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/click/core.py", line 763, in invoke return __callback(*args, **kwargs) File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/typer/main.py", line 500, in wrapper return callback(**use_params) # type: ignore File "/home/alvaro.bartolome/XXX/xxx/cli.py", line 43, in train metrics = trainer.evaluate() File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2051, in evaluate output = eval_loop( File "/home/alvaro.bartolome/miniconda3/envs/xxx/lib/python3.9/site-packages/transformers/trainer.py", line 2292, in evaluation_loop metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels)) File "/home/alvaro.bartolome/XXX/xxx/metrics.py", line 20, in compute_metrics res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids) File "/home/alvaro.bartolome/miniconda3/envs/lang/lib/python3.9/site-packages/datasets/metric.py", line 402, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/alvaro.bartolome/.cache/huggingface/modules/datasets_modules/metrics/matthews_correlation/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357/matthews_correlation.py", line 88, in _compute "matthews_correlation": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(), AttributeError: 'float' object has no attribute 'item' ``` ## Environment info - `datasets` version: 1.12.1 - Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23 - Python version: 3.9.7 - PyArrow version: 5.0.0
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Fix archive fs test
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
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2023-03-07T13:27:50Z
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Adding an Elastic Search index to a Dataset
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[ "Hi, is this bug deterministic in your poetry env ? I mean, does it always stop at 90% or is it random ?\r\n\r\nAlso, can you try using another version of Elasticsearch ? Maybe there's an issue with the one of you poetry env", "I face similar issue with oscar dataset on remote ealsticsearch instance. It was mainl...
2021-09-09T12:21:39Z
2021-10-20T18:57:11Z
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## Describe the bug When trying to index documents from the squad dataset, the connection to ElasticSearch seems to break: Reusing dataset squad (/Users/andreasmotz/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453) 90%|████████████████████████████████████████████▉ | 9501/10570 [00:01<00:00, 6335.61docs/s] No error is thrown, but the indexing breaks ~90%. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset from elasticsearch import Elasticsearch es = Elasticsearch() squad = load_dataset('squad', split='validation') index_name = "corpus" es_config = { "settings": { "number_of_shards": 1, "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}}, }, "mappings": { "properties": { "idx" : {"type" : "keyword"}, "title" : {"type" : "keyword"}, "text": { "type": "text", "analyzer": "standard", "similarity": "BM25" }, } }, } class IndexBuilder: """ Elastic search indexing of a corpus """ def __init__( self, *args, #corpus : None, dataset : squad, index_name = str, query = str, config = dict, **kwargs, ): #instantiate HuggingFace dataset self.dataset = dataset #instantiate ElasticSearch config self.config = config self.es = Elasticsearch() self.index_name = index_name self.query = query def elastic_index(self): print(self.es.info) self.es.indices.delete(index=self.index_name, ignore=[400, 404]) search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config) return search_index def exact_match_method(self, index): scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1) return scores, retrieved_examples if __name__ == "__main__": print(type(squad)) Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config) search_index = Index.elastic_index() scores, examples = Index.exact_match_method(search_index) print(scores, examples) for name in squad.column_names: print(type(squad[name])) ``` ## Environment info We run the code in Poetry. This might be the issue, since the script runs successfully in our local environment. Poetry: - Python version: 3.8 - PyArrow: 4.0.1 - Elasticsearch: 7.13.4 - datasets: 1.10.2 Local: - Python version: 3.8 - PyArrow: 3.0.0 - Elasticsearch: 7.7.1 - datasets: 1.7.0
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ethos first commit
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[ "> Nice thanks !\r\n> \r\n> I left a few comments\r\n> \r\n> Also it looks like this PR includes changes about other files than the ones for ethos\r\n> \r\n> Can you create another branch and another PR please ?\r\n\r\n@lhoestq Should I close this PR? The new one is the: #1453", "You can create another PR and clo...
2020-12-08T15:59:47Z
2020-12-10T14:45:57Z
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CONTRIBUTOR
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Ethos passed all the tests except from this one: RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your-dataset-name> with this error: E OSError: Cannot find data file. E Original error: E [Errno 2] No such file or directory:
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can't set verbosity for `metric.py`
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[ "Thanks @thomas-happify for reporting and thanks @mariosasko for the fix." ]
2021-07-10T20:23:45Z
2021-07-12T05:54:29Z
2021-07-12T05:54:29Z
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## Describe the bug ``` [2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock [2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow. [2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns. [2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow ``` As you can see, `datasets` logging come from different places. `filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/` So when setting `datasets.utils.logging.set_verbosity_error()`, it still logs the last message which is annoying during evaluation. I had to do ``` logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR) ``` to fully mute these messages ## Expected results it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: tried both 1.8.0 & 1.9.0 - Platform: Ubuntu 18.04.5 LTS - Python version: 3.8.10 - PyArrow version: 3.0.0
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Remove redundant local path information in audio/image datasets
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[ "Cool, I'm in favor of this PR. Our official examples in speech already make use of `\"audio\"` so no need to change anything there. It would be great if we could prominently feature how one can get the audio path without decoding in the docs.", "@patrickvonplaten Yes, I agree.\r\n\r\ncc @stevhliu we should add a...
2021-12-16T16:35:15Z
2023-09-24T10:09:30Z
2023-09-24T10:09:27Z
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Remove the redundant path information in the audio/image dataset as discussed in https://github.com/huggingface/datasets/pull/3430#issuecomment-994734828 TODOs: * [ ] merge https://github.com/huggingface/datasets/pull/3430 * [ ] merge https://github.com/huggingface/datasets/pull/3364 * [ ] re-generate the info files of the updated audio datasets cc: @patrickvonplaten @anton-l @nateraw (I expect this to break the audio/vision examples in Transformers; after this change you'll be able to access underlying paths as follows `dset = dset.cast_column("audio", Audio(..., decode=False)); path = dset[0]["audio"]`)
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`FaissIndex` to support multiple GPU and `custom_index`
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[ "Hi @rentruewang, thansk for reporting and for your PR!!! We should definitely support this. ", "@albertvillanova Great! :)" ]
2022-02-14T06:21:43Z
2022-03-07T16:28:56Z
2022-03-07T16:28:56Z
CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** Currently, because `device` is of the type `int | None`, to leverage `faiss-gpu`'s multi-gpu support, you need to create a `custom_index`. However, if using a `custom_index` created by e.g. `faiss.index_cpu_to_all_gpus`, then `FaissIndex.save` does not work properly because it checks the device id (which is an int, so no multiple GPUs). **Describe the solution you'd like** I would like `FaissIndex` to support multiple GPUs, by passing in a list to `add_faiss_index`. **Describe alternatives you've considered** Alternatively, I would like it to at least provide a warning cause it wasn't the behavior that I expected. **Additional context** Relavent source code here: https://github.com/huggingface/datasets/blob/6ed6ac9448311930557810383d2cfd4fe6aae269/src/datasets/search.py#L340-L349 Device management needs changing to support multiple GPUs, probably by `isinstance` calls. I can provide a PR if you like :) Thanks for reading!
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Add JNLPBA
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2020-12-09T06:48:51Z
2020-12-10T14:24:36Z
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`Dataset.to_tf_dataset` fails when strings cannot be encoded as `np.bytes_`
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[ "May eventually be solved in #5883 ", "#self-assign" ]
2023-05-22T12:03:06Z
2023-06-09T16:04:56Z
2023-06-09T16:04:55Z
CONTRIBUTOR
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### Describe the bug When loading any dataset that contains a column with strings that are not ASCII-compatible, looping over those records raises the following exception e.g. for `é` character `UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128)`. ### Steps to reproduce the bug Running the following script will eventually fail, when reaching to the batch that contains non-ASCII compatible strings. ```python from datasets import load_dataset ds = load_dataset("imdb", split="train") tfds = ds.to_tf_dataset(batch_size=16) for batch in tfds: print(batch) >>> UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128) ``` ### Expected behavior The following script to run properly, making sure that the strings are either `numpy.unicode_` or `numpy.string` instead of `numpy.bytes_` since some characters are not ASCII compatible and that would lead to an issue when applying the `map`. ```python from datasets import load_dataset ds = load_dataset("imdb", split="train") tfds = ds.to_tf_dataset(batch_size=16) for batch in tfds: print(batch) ``` ### Environment info - `datasets` version: 2.12.1.dev0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Mapping gets stuck at 99%
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[ "The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass).\r\n\r\nAlso, these arrays are big, so i...
2023-07-26T14:00:40Z
2023-07-28T09:21:07Z
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### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2
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5,324
Fix docstrings and types in documentation that appears on the website
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[ "I agree we have a mess with docstrings...", "Ok, I believe we've cleaned up most of the old syntax we were using for the user-facing docs! There are still a couple of `:obj:`'s and `:class:` floating around in the docstrings we don't expose that I'll track down :)" ]
2022-12-01T15:34:53Z
2022-12-13T19:03:55Z
null
CONTRIBUTOR
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While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the documentation on the website. Would be nice someday, maybe before releasing datasets 3.0.0, to unify it......
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PR_kwDODunzps46HyPK
4,541
Fix timestamp conversion from Pandas to Python datetime in streaming mode
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[ "_The documentation is not available anymore as the PR was closed or merged._", "CI failures are unrelated to this PR, merging" ]
2022-06-22T13:40:01Z
2022-06-22T16:39:27Z
2022-06-22T16:29:09Z
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Arrow accepts both pd.Timestamp and datetime.datetime objects to create timestamp arrays. However a timestamp array is always converted to datetime.datetime objects. This created an inconsistency between streaming in non-streaming. e.g. the `ett` dataset outputs datetime.datetime objects in non-streaming but pd.timestamp in streaming. I fixed this by always converting pd.Timestamp to datetime.datetime during the example encoding step. I fixed the same issue for pd.Timedelta as well. Finally I added an extra step of conversion for Series and DataFrame to take this into account in case such data are passed as Series or DataFrame. Fix https://github.com/huggingface/datasets/issues/4533 Related to https://github.com/huggingface/datasets-server/issues/397
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5,993
ValueError: Table schema does not match schema used to create file
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[ "We'll do a new release of `datasets` soon to make the fix available :)\r\n\r\nIn the meantime you can use `datasets` from source (main)", "Thank you very much @lhoestq ! 🚀 " ]
2023-06-27T10:54:07Z
2023-06-27T15:36:42Z
2023-06-27T15:32:44Z
NONE
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### Describe the bug Saving a dataset as parquet fails with a `ValueError: Table schema does not match schema used to create file` if the dataset was obtained out of a `.select_columns()` call with columns selected out of order. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_parquet("demo.parquet") ``` ```shell >>> ValueError: Table schema does not match schema used to create file: table: x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 vs. file: x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ``` --- I think this is because after the `.select_columns()` call with out of order columns, the output dataset features' schema ends up being out of sync with the schema of the arrow table backing it. ```python ds.features.arrow_schema >>> x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ds.data.schema >>> x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 ``` So when we call `.to_parquet()`, the call behind the scenes to `datasets.io.parquet.ParquetDatasetWriter(...).write()` which initialises the backend `pyarrow.parquet.ParquetWriter` with `schema = self.dataset.features.arrow_schema` triggers `pyarrow` on write when [it checks](https://github.com/apache/arrow/blob/11b140a734a516e436adaddaeb35d23f30dcce44/python/pyarrow/parquet/core.py#L1086-L1090) that the `ParquetWriter` schema matches the schema of the table being written 🙌 https://github.com/huggingface/datasets/blob/6ed837325cb539a5deb99129e5ad181d0269e050/src/datasets/io/parquet.py#L139-L141 ### Expected behavior The dataset gets successfully saved as parquet. *In the same way as it does if saving it as csv: ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_csv("demo.csv") ``` ### Environment info `python==3.11` `datasets==2.13.1`
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REUSE_DATASET_IF_EXISTS don't work
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[ "Hi! Unfortunately, the current behavior is to delete the downloaded data when this error happens. So, I've opened a PR that removes the problematic import to avoid losing data due to `apache_beam` not being installed (we host the preprocessed version of `natual_questions` on the HF GCS, so requiring `apache_beam` ...
2023-09-26T03:02:16Z
2023-09-28T18:23:36Z
2023-09-28T18:23:36Z
NONE
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### Describe the bug I use the following code to download natural_question dataset. Even though I have completely download it, the next time I run this code, the new download procedure will start and cover the original /data/lxy/NQ config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ') data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS) --- Since I don't have apache_beam installed, it throw a exception. After I pip install apache_beam ,the download restart.. ![image](https://github.com/huggingface/datasets/assets/88258534/f28ce7fe-29ea-4348-b87f-e69182a8bd41) ### Steps to reproduce the bug run this two line code config=datasets.DownloadConfig(resume_download=True,max_retries=100,cache_dir=r'/data/lxy/NQ',download_desc='NQ') data=datasets.load_dataset('natural_questions',cache_dir=r'/data/lxy/NQ',download_config=config,download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS) ### Expected behavior Download behavior can be correctly follow DownloadMode ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-3.10.0-1160.88.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.3
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Bring back imgs so that forsk dont get broken
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3866). All of your documentation changes will be reflected on that endpoint.", "I think we just need to keep `datasets_logo_name.jpg` and `course_banner.png` because they appear in the README.md of the forks of `datasets`. The ...
2022-03-08T16:01:31Z
2022-03-08T17:37:02Z
2022-03-08T17:37:01Z
CONTRIBUTOR
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Adds APPS dataset to the hub [WIP]
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[ "Thanks for your contribution, @arampacha. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if...
2021-07-17T13:13:17Z
2022-10-03T09:38:10Z
2022-10-03T09:38:10Z
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A loading script for [APPS dataset](https://github.com/hendrycks/apps)
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Fix checking patterns to infer packaged builder
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2023-09-06T10:34:00Z
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Don't ignore results of pattern resolving if `self.data_files` is not None. Otherwise lines 854 and 1037 make no sense.
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Fix importlib metdata import in py38
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In Python 3.8 there's no need to install `importlib_metadata` since it already exists as `importlib.metadata` in the standard lib.
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Fix: wmt datasets - fix CWMT zh subsets
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4871). All of your documentation changes will be reflected on that endpoint." ]
2022-08-22T16:42:09Z
2022-08-23T10:00:20Z
2022-08-23T10:00:19Z
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Fix https://github.com/huggingface/datasets/issues/4575 TODO: run `datasets-cli test`: - [x] wmt17 - [x] wmt18 - [x] wmt19
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add iapp_wiki_qa_squad
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2021-02-13T13:34:27Z
2021-02-16T14:21:58Z
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`iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles. It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset) to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in 5761/742/739 questions from 1529/191/192 articles.
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Release: 2.14.1
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6082). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma...
2023-07-27T17:05:54Z
2023-07-31T06:32:16Z
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When using `dataset.map()` if passed `Features` types do not match what is returned from the mapped function, execution does not except in an obvious way
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[ "Hi ! Thanks for reporting :) `datasets` usually returns a `pa.lib.ArrowInvalid` error if the feature types don't match.\r\n\r\nIt would be awesome if we had a way to reproduce the `OverflowError` in this case, to better understand what happened and be able to provide the best error message" ]
2022-05-14T17:55:15Z
2022-05-16T15:09:17Z
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## Describe the bug Recently I was trying to using `.map()` to preprocess a dataset. I defined the expected Features and passed them into `.map()` like `dataset.map(preprocess_data, features=features)`. My expected `Features` keys matched what came out of `preprocess_data`, but the types i had defined for them did not match the types that came back. Because of this, i ended up in tracebacks deep inside arrow_dataset.py and arrow_writer.py with exceptions that [did not make clear what the problem was](https://github.com/huggingface/datasets/issues/4349). In short i ended up with overflows and the OS killing processes when Arrow was attempting to write. It wasn't until I dug into `def write_batch` and the loop that loops over cols that I figured out what was going on. It seems like `.map()` could set a boolean that it's checked that for at least 1 instance from the dataset, the returned data's types match the types provided by the `features` param and error out with a clear exception if they don't. This would make the cause of the issue much more understandable and save people time. This could be construed as a feature but it feels more like a bug to me. ## Steps to reproduce the bug I don't have explicit code to repro the bug, but ill show an example Code prior to the fix: ```python def preprocess(examples): # returns an encoded data dict with keys that match the features, but the types do not match ... def get_encoded_data(data): dataset = Dataset.from_pandas(data) unique_labels = data['audit_type'].unique().tolist() features = Features({ 'image': Array3D(dtype="uint8", shape=(3, 224, 224))), 'input_ids': Sequence(feature=Value(dtype='int64'))), 'attention_mask': Sequence(Value(dtype='int64'))), 'token_type_ids': Sequence(Value(dtype='int64'))), 'bbox': Array2D(dtype="int64", shape=(512, 4))), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) encoded_dataset = dataset.map(preprocess_data, features=features, remove_columns=dataset.column_names) ``` The Features set that fixed it: ```python features = Features({ 'image': Sequence(Array3D(dtype="uint8", shape=(3, 224, 224))), 'input_ids': Sequence(Sequence(feature=Value(dtype='int64'))), 'attention_mask': Sequence(Sequence(Value(dtype='int64'))), 'token_type_ids': Sequence(Sequence(Value(dtype='int64'))), 'bbox': Sequence(Array2D(dtype="int64", shape=(512, 4))), 'label': ClassLabel(num_classes=len(unique_labels), names=unique_labels), }) ``` The difference between my original code (which was based on documentation) and the working code is the addition of the `Sequence(...)` to 4/5 features as I am working with paginated data and the doc examples are not. ## Expected results Dataset.map() attempts to validate the data types for each Feature on the first iteration and errors out if they are not validated. ## Actual results Specify the actual results or traceback. Based on the value of `writer_batch_size`, execution errors out when Arrow attempts to write because the types do not match, though its error messages dont make this obvious Example errors: ``` OverflowError: There was an overflow with type <class 'list'>. Try to reduce writer_batch_size to have batches smaller than 2GB. (offset overflow while concatenating arrays) ``` ``` zsh: killed python doc_classification.py UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> datasets version: 2.1.0 Platform: macOS-12.2.1-arm64-arm-64bit Python version: 3.9.12 PyArrow version: 6.0.1 Pandas version: 1.4.2
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Proposed update to the documentation for WER
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2021-11-15T23:28:31Z
2021-11-16T11:19:37Z
2021-11-16T11:19:37Z
CONTRIBUTOR
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I wanted to submit a minor update to the description of WER for your consideration. Because of the possibility of insertions, the numerator in the WER formula can be larger than N, so the value of WER can be greater than 1.0: ``` >>> from datasets import load_metric >>> metric = load_metric("wer") >>> metric.compute(predictions=["hello how are you"], references=["hello"]) 3.0 ``` and similarly from the underlying jiwer module's `wer` function: ``` >>> from jiwer import wer >>> wer("hello", "hello how are you") 3.0 ```
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HTTP error 504 Server Error: Gateway Time-out
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[ "is pushing directly with git (and git-lfs) an option for you?", "I have installed git-lfs and doing this push with that\r\n", "yes but is there any way you could try pushing with `git` command line directly instead of `push_to_hub`?", "Okay. I didnt saved the dataset to my local machine. So, I processed the ...
2022-03-09T12:03:37Z
2022-03-15T16:19:50Z
2022-03-15T16:19:50Z
NONE
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I am trying to push a large dataset(450000+) records with the help of `push_to_hub()` While pushing, it gives some error like this. ``` Traceback (most recent call last): File "data_split_speech.py", line 159, in <module> data_new_2.push_to_hub("user-name/dataset-name",private=True) File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub api.upload_file( File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file raise err File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file r.raise_for_status() File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet ``` Can anyone help me to resolve this issue.
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Upload README via `push_to_hub`
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[ "You can use `huggingface_hub`'s [Card API](https://huggingface.co/docs/huggingface_hub/package_reference/cards) to programmatically push a dataset card to the Hub." ]
2023-08-04T21:00:27Z
2023-08-21T18:18:54Z
2023-08-21T18:18:54Z
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### Feature request `push_to_hub` now allows users to upload datasets programmatically. However, based on the latest doc, we still need to open the dataset page to add readme file manually. However, I do discover snippets to intialize a README for every `push_to_hub`: ``` dataset_card = ( DatasetCard( "---\n" + str(dataset_card_data) + "\n---\n" + f'# Dataset Card for "{repo_id.split("/")[-1]}"\n\n[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)' ) if dataset_card is None else dataset_card ) HfApi(endpoint=config.HF_ENDPOINT).upload_file( path_or_fileobj=str(dataset_card).encode(), path_in_repo="README.md", repo_id=repo_id, token=token, repo_type="dataset", revision=branch, ) ``` So, if we can enable `push_to_hub` to upload a readme file by ourselves instead of using the auto generated ones, it can save ton of time, and will definitely alleviate the current "lack-of-dataset-card" situation. ### Motivation as elabrated above. ### Your contribution I might be able to make a pr.
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[DOC] Mistaken docs on set_format
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[ "Thanks for reporting!" ]
2023-02-27T08:03:09Z
2023-02-28T19:19:17Z
2023-02-28T19:19:17Z
CONTRIBUTOR
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### Describe the bug https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format <img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png"> While actually running it will result in: <img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png"> ### Steps to reproduce the bug _ ### Expected behavior _ ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5
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3,691
Upgrade black to version ~=22.0
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2022-02-08T18:45:19Z
2022-02-08T19:56:40Z
2022-02-08T19:56:39Z
MEMBER
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Upgrades the `datasets` library quality tool `black` to use the first stable release of `black`, version 22.0.
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PR_kwDODunzps4z1T48
3,803
Remove deprecated methods/params (preparation for v2.0)
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2022-03-02T14:29:12Z
2022-03-02T14:53:21Z
2022-03-02T14:53:21Z
CONTRIBUTOR
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This PR removes the following deprecated methos/params: * `Dataset.cast_`/`DatasetDict.cast_` * `Dataset.dictionary_encode_column_`/`DatasetDict.dictionary_encode_column_` * `Dataset.remove_columns_`/`DatasetDict.remove_columns_` * `Dataset.rename_columns_`/`DatasetDict.rename_columns_` * `prepare_module` * param `script_version` in `load_dataset`/`load_metric` * param `version` in `hf_github_url`
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Duplicate `data_files` when named `<split>/<split>.parquet`
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[ "Also reported in https://github.com/huggingface/datasets/issues/6259", "I think it's best to drop duplicates with a `set` (as a temporary fix) and improve the patterns when/if https://github.com/fsspec/filesystem_spec/pull/1382 gets merged. @lhoestq Do you have some other ideas?", "Alternatively we could just...
2023-10-01T15:43:56Z
2023-10-05T10:32:27Z
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MEMBER
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e.g. with `u23429/stock_1_minute_ticker` ```ipython In [1]: from datasets import * In [2]: b = load_dataset_builder("u23429/stock_1_minute_ticker") Downloading readme: 100%|██████████████████████████| 627/627 [00:00<00:00, 246kB/s] In [3]: b.config.data_files Out[3]: {NamedSplit('train'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/train/train.parquet'], NamedSplit('validation'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/validation/validation.parquet'], NamedSplit('test'): ['hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet', 'hf://datasets/u23429/stock_1_minute_ticker@65c973cf4ec061f01a363b40da4c1bb128ba4166/test/test.parquet']} ``` This bug issue is present in the current `datasets` 2.14.5 and also on `main` even after https://github.com/huggingface/datasets/pull/6244 cc @mariosasko
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2,936
Check that array is not Float as nan != nan
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2021-09-17T16:16:41Z
2021-09-21T09:39:05Z
2021-09-21T09:39:04Z
CONTRIBUTOR
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The Exception wants to check for issues with StructArrays/ListArrays but catches FloatArrays with value nan as nan != nan. Pass on FloatArrays as we should not raise an Exception for them.
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I_kwDODunzps5GDIhY
3,978
I can't view HFcallback dataset for ASR Space
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[ "the dataset viewer is working on this dataset. I imagine the issue is that we would expect to be able to listen to the audio files in the `Please Record Your Voice file` column, right?\r\n\r\nmaybe @lhoestq or @albertvillanova could help\r\n\r\n<img width=\"1019\" alt=\"Capture d’écran 2022-03-24 à 17 36 20\" sr...
2022-03-21T11:07:49Z
2023-09-25T12:19:53Z
null
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## Dataset viewer issue for '*Urdu-ASR-flags*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)* *I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.* Am I the one who added this dataset ? Yes
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made suggested changes in fake-news-english
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2020-12-17T20:06:29Z
2020-12-18T09:43:58Z
2020-12-18T09:43:57Z
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4,080
NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset
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[ "Hi @richarddwang,\r\n\r\n\r\nIndeed, we have recently updated the loading script of that dataset (and fixed that bug as well):\r\n- #4002\r\n\r\nThat fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by:\r\n- installing `datasets` from our GitHub repo:\r\n`...
2022-04-01T11:34:28Z
2022-04-01T13:59:10Z
2022-04-01T13:59:10Z
CONTRIBUTOR
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## Steps to reproduce the bug ```python datasets.load_dataset("conll2012_ontonotesv5", "english_v12") ``` ## Actual results ``` Downloading builder script: 32.2kB [00:00, 9.72MB/s] Downloading metadata: 20.0kB [00:00, 10.4MB/s] Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size , total: 379.12 MiB) to ... Traceback (most recent call last): [315/390] File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module> train() File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train trainer.fit(model, datamodule=dm) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit self._call_and_handle_interrupt( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte rrupt return trainer_fn(*args, **kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run self._data_connector.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre pare_data self.trainer.datamodule.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn fn(*args, **kwargs) File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data raw_dsets = datasets.load_dataset(**load_dataset_kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset builder_instance.download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare verify_checksums( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0
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Add WIT Dataset
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[ "@hassiahk is working on it #2810 ", "WikiMedia is now hosting the pixel values directly which should make it a lot easier!\r\nThe files can be found here:\r\nhttps://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/\r\nhttps://analyti...
2021-09-13T19:38:49Z
2022-06-01T17:28:40Z
2022-06-01T17:28:40Z
CONTRIBUTOR
null
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## Adding a Dataset - **Name:** *WIT* - **Description:** *Wikipedia-based Image Text Dataset* - **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning ](https://arxiv.org/abs/2103.01913)* - **Data:** *https://github.com/google-research-datasets/wit* - **Motivation:** (excerpt from their Github README.md) > - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples. > - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages. > - A collection of diverse set of concepts and real world entities. > - Brings forth challenging real-world test sets. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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ArrowInvalid issue for squad v2 dataset
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[ "Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a b...
2021-03-17T13:51:49Z
2021-08-04T17:57:16Z
2021-08-04T17:57:16Z
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Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb). In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error: `ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178` My code is as follows: ``` def generate_candidate_questions(examples): val_questions = examples["question"] candididate_questions = random.sample(datasets["train"]["question"], len(val_questions)) candididate_questions = [x[:max_length] for x in candididate_questions] return candididate_questions def prepare_validation_features(examples, use_mixing=False): pad_on_right = tokenizer.padding_side == "right" tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) if use_mixing: candidate_questions = generate_candidate_questions(examples) tokenized_candidates = tokenizer( candidate_questions if pad_on_right else examples["context"], examples["context"] if pad_on_right else candidate_questions, truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") tokenized_examples["example_id"] = [] if use_mixing: tokenized_examples["candidate_input_ids"] = tokenized_candidates["input_ids"] tokenized_examples["candidate_attention_mask"] = tokenized_candidates["attention_mask"] tokenized_examples["candidate_token_type_ids"] = tokenized_candidates["token_type_ids"] for i in range(len(tokenized_examples["input_ids"])): sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) tokenized_examples["offset_mapping"][i] = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples validation_features = datasets["validation"].map( lambda xs: prepare_validation_features(xs, True), batched=True, remove_columns=datasets["validation"].column_names ) ``` I guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help!
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add `desc` in `map` for `DatasetDict` object
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[ "The CI error is unrelated to the PR, merging", "@lhoestq, can we release this feature if you guys are planning for any patch release for Datasets. It'll slow down [#11927](https://github.com/huggingface/transformers/pull/11927) otherwise :/ ", "Sure definitely, having a discrepancy between Dataset.map and Data...
2021-05-28T19:28:44Z
2021-05-31T14:51:23Z
2021-05-31T13:08:04Z
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`desc` in `map` currently only works with `Dataset` objects. This PR adds support for `DatasetDict` objects as well
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Enumerate all ner_tags values in WNUT 17 dataset
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2021-07-26T09:30:55Z
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This PR does: - Enumerate all ner_tags in dataset card Data Fields section - Add all metadata tags to dataset card Close #2709.
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News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
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[ "Hi @anassalamah,\r\n\r\nCould you please try with this:\r\n```python\r\ntrain_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[98%:]')\r\n```", "Hello @albertvillanova, \r\n\r\nThanks for...
2021-04-08T07:53:43Z
2021-05-24T10:03:55Z
2021-05-24T10:03:55Z
NONE
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I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi. ``` train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]') val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]') # filtering out examples that are not ar-en translations but ar-hi val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True) ``` * I'm fairly new to using datasets so I might be doing something wrong
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py3.7: TypeError: can't pickle _LazyModule objects
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[ "\r\nThis wasn't a `datasets` problem, but `transformers`' and it was solved here https://github.com/huggingface/transformers/pull/11168\r\n" ]
2021-04-08T21:02:48Z
2021-04-09T16:56:50Z
2021-04-09T01:52:57Z
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While this works fine with py3.8, under py3.7, with a totally new conda env and transformers install: ``` git clone https://github.com/huggingface/transformers cd transformers pip install -e .[testing] export BS=1; rm -rf /tmp/test-clm; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python \ examples/language-modeling/run_clm.py --model_name_or_path distilgpt2 --dataset_name wikitext \ --dataset_config_name wikitext-2-raw-v1 --do_train --max_train_samples 1 \ --per_device_train_batch_size $BS --output_dir /tmp/test-clm --block_size 128 --logging_steps 1 \ --fp16 ``` ``` Traceback (most recent call last): File "examples/language-modeling/run_clm.py", line 453, in <module> main() File "examples/language-modeling/run_clm.py", line 336, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1259, in map update_data=update_data, File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 157, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 158, in wrapper self._fingerprint, transform, kwargs_for_fingerprint File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 105, in update_fingerprint hasher.update(transform_args[key]) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 57, in update self.m.update(self.hash(value).encode("utf-8")) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 53, in hash return cls.hash_default(value) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 46, in hash_default return cls.hash_bytes(dumps(value)) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 389, in dumps dump(obj, file) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 361, in dump Pickler(file, recurse=True).dump(obj) File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 556, in save_function obj=obj, File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 524, in save rv = reduce(self.proto) TypeError: can't pickle _LazyModule objects ``` ``` $ python --version Python 3.7.4 $ python -m torch.utils.collect_env Collecting environment information... PyTorch version: 1.8.0.dev20210110+cu110 Is debug build: False CUDA used to build PyTorch: 11.0 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.2 LTS (x86_64) GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.16.3 ``` Thanks.
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asserts replaced with exception for image classification task, csv, json
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2021-11-12T22:34:59Z
2021-11-15T11:08:37Z
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Fixes for csv, json in io module and image_classification task with tests referenced in https://github.com/huggingface/datasets/issues/3171
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fsspec dependency is set too low
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[ "Thanks for reporting! A PR with a fix has just been merged." ]
2023-07-27T20:08:22Z
2023-07-28T10:07:56Z
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### Describe the bug fsspec.callbacks.TqdmCallback (used in https://github.com/huggingface/datasets/blob/73bed12ecda17d1573fd3bf73ed5db24d3622f86/src/datasets/utils/file_utils.py#L338) was first released in fsspec [2022.3.0](https://github.com/fsspec/filesystem_spec/releases/tag/2022.3.0, commit where it was added: https://github.com/fsspec/filesystem_spec/commit/9577c8a482eb0a69092913b81580942a68d66a76#diff-906155c7e926a9ff58b9f23369bb513b09b445f5b0f41fa2a84015d0b471c68cR180), however the dependency is set to 2021.11.1 https://github.com/huggingface/datasets/blob/main/setup.py#L129 ### Steps to reproduce the bug 1. Install fsspec==2021.11.1 2. Install latest datasets==2.14.1 3. Import datasets, import fails due to lack of `fsspec.callbacks.TqdmCallback` ### Expected behavior No import issue ### Environment info N/A
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[tiny] fix typo in stream docs
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2021-11-10T10:40:02Z
2021-11-10T11:10:39Z
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Allow stateful function in dataset.map
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[ "@lhoestq Added a test. If you can come up with a better stateful callable, I'm all ears 😄. ", "Sorry I said earlier that it was good to have it inside the loop, my mistake !", "@lhoestq Okay, did some refactoring and now the \"cache\" part comes before the for loop. Thanks for the guidance.\r\n\r\nThink this...
2021-02-28T01:29:05Z
2021-03-23T15:26:49Z
2021-03-23T15:26:49Z
CONTRIBUTOR
null
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Removes the "test type" section in Dataset.map which would modify the state of the stateful function. Now, the return type of the map function is inferred after processing the first example. Fixes #1940 @lhoestq Not very happy with the usage of `nonlocal`. Would like to hear your opinion on this.
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[experiment] missing default_experiment-1-0.arrow
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[ "Hi !\r\n\r\nThe cache at `~/.cache/huggingface/metrics` stores the users data for metrics computations (hence the arrow files).\r\n\r\nHowever python modules (i.e. dataset scripts, metric scripts) are stored in `~/.cache/huggingface/modules/datasets_modules`.\r\n\r\nIn particular the metrics are cached in `~/.cach...
2021-02-25T03:02:15Z
2022-10-05T13:08:45Z
2022-10-05T13:08:45Z
CONTRIBUTOR
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the original report was pretty bad and incomplete - my apologies! Please see the complete version here: https://github.com/huggingface/datasets/issues/1942#issuecomment-786336481 ------------ As mentioned here https://github.com/huggingface/datasets/issues/1939 metrics don't get cached, looking at my local `~/.cache/huggingface/metrics` - there are many `*.arrow.lock` files but zero metrics files. w/o the network I get: ``` FileNotFoundError: [Errno 2] No such file or directory: '~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow ``` there is just `~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock` I did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind. this is with master. Thank you.
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Ignore duplicate keys if `ignore_verifications=True`
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3868). All of your documentation changes will be reflected on that endpoint.", "Cool thanks ! Could you add a test please ?" ]
2022-03-08T17:14:56Z
2022-03-09T13:50:45Z
2022-03-09T13:50:44Z
CONTRIBUTOR
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Currently, it's impossible to generate a dataset if some keys from `_generate_examples` are duplicated. This PR allows skipping the check for duplicate keys if `ignore_verifications` is set to `True`.
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Deprecate task api
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[ "_The documentation is not available anymore as the PR was closed or merged._", "If it's easy to keep supporting it we can keep it no ? There are many datasets on the hub that implement the tasks templates in dataset scripts and it's maybe easier to keep task templates than opening PRs to those datasets.", "do ...
2023-05-15T16:48:24Z
2023-07-10T12:33:59Z
2023-07-10T12:24:01Z
CONTRIBUTOR
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The task API is not well adopted in the ecosystem, so this PR deprecates it. The `train_eval_index` is a newer, more flexible solution that should be used instead (I think?). These are the projects that still use the task API : * the image classification example in Transformers: [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/pytorch/image-classification/run_image_classification_no_trainer.py#L262) and [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/tensorflow/image-classification/run_image_classification.py#L277) * autotrain: [here](https://github.com/huggingface/autotrain-backend/blob/455e274004b56f9377d64db4ab03671508fcc4cd/zeus/zeus/run/utils.py#L666) * api-inference-community: [here](https://github.com/huggingface/api-inference-community/blob/fb8fb29d577a5bf01c82944db745489a6d6ed3d4/manage.py#L64) (but the rest of the code does not call the `resolve_dataset` function) So we need to update these files after the merge. cc @lewtun
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Transcript string 'null' converted to [None] by load_dataset()
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[ "Hi @mbarnig, thanks for reporting.\r\n\r\nPlease note that is an expected behavior by `pandas` (we use the `pandas` library to parse CSV files): https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html\r\n```\r\nBy default the following values are interpreted as NaN: \r\n‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1...
2022-06-09T14:26:00Z
2023-07-04T02:18:39Z
2022-06-09T16:29:02Z
NONE
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## Issue I am training a luxembourgish speech-recognition model in Colab with a custom dataset, including a dictionary of luxembourgish words, for example the speaken numbers 0 to 9. When preparing the dataset with the script `ds_train1 = mydataset.map(prepare_dataset)` the following error was issued: ``` ValueError Traceback (most recent call last) <ipython-input-69-1e8f2b37f5bc> in <module>() ----> 1 ds_train = mydataset_train.map(prepare_dataset) 11 frames /usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs) 2450 if not _is_valid_text_input(text): 2451 raise ValueError( -> 2452 "text input must of type str (single example), List[str] (batch or single pretokenized example) " 2453 "or List[List[str]] (batch of pretokenized examples)." 2454 ) ValueError: text input must of type str (single example), List[str] (batch or single pretokenized example) or List[List[str]] (batch of pretokenized examples). ``` Debugging this problem was not easy, all transcriptions in the dataset are correct strings. Finally I discovered that the transcription string 'null' is interpreted as [None] by the `load_dataset()` script. By deleting this row in the dataset the training worked fine. ## Expected result: transcription 'null' interpreted as 'str' instead of 'None'. ## Reproduction Here is the code to reproduce the error with a one-row-dataset. ``` with open("null-test.csv") as f: reader = csv.reader(f) for row in reader: print(row) ``` ['wav_filename', 'wav_filesize', 'transcript'] ['wavs/female/NULL1.wav', '17530', 'null'] ``` dataset = load_dataset('csv', data_files={'train': 'null-test.csv'}) ``` Using custom data configuration default-81ac0c0e27af3514 Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-81ac0c0e27af3514/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 1/1 [00:00<00:00, 29.55it/s] Extracting data files: 100% 1/1 [00:00<00:00, 23.66it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-81ac0c0e27af3514/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data. 100% 1/1 [00:00<00:00, 25.84it/s] ``` print(dataset['train']['transcript']) ``` [None] ## Environment info ``` !pip install datasets==2.2.2 !pip install transformers==4.19.2 ```
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Fix language tags in dataset cards
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[ "_The documentation is not available anymore as the PR was closed or merged._", "The non-passing tests are caused by other missing information in the dataset cards." ]
2022-08-11T13:47:14Z
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Fix language tags in all dataset cards, so that they are validated (aligned with our `languages.json` resource).
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MDExOlB1bGxSZXF1ZXN0NjE1MTM4NDQz
2,220
Fix infinite loop in WindowsFileLock
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[ "How is it possible to get an infinite loop ? Can you add more details ?", "Yes, in Windows, if the filename is too long, a `FileNotFoundError` is raised. The exception should be raised in this case. Otherwise, we get into an infinite loop.\r\n\r\nIf other process has the file locked, then `PermissionError` is ra...
2021-04-14T10:49:58Z
2021-04-14T14:59:50Z
2021-04-14T14:59:34Z
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Raise exception to avoid infinite loop.
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I_kwDODunzps5j_v-h
5,777
datasets.load_dataset("code_search_net", "python") : NotADirectoryError: [Errno 20] Not a directory
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[ "Note:\r\nI listed the datasets and grepped around to find what appears to be an alternative source for this:\r\n\r\nraw_datasets = load_dataset(\"espejelomar/code_search_net_python_10000_examples\", \"python\")", "Thanks for reporting, @jason-brian-anderson.\r\n\r\nYes, this is a known issue: the [CodeSearchNet]...
2023-04-21T02:08:07Z
2023-06-05T05:49:52Z
2023-05-11T11:51:56Z
NONE
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### Describe the bug While checking out the [tokenizer tutorial](https://huggingface.co/course/chapter6/2?fw=pt), i noticed getting an error while initially downloading the python dataset used in the examples. The [collab with the error is here](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter6/section2.ipynb#scrollTo=hGb69Yo3eV8S) ``` from datasets import load_dataset import os os.environ["HF_DATASETS_CACHE"] = "/workspace" # This can take a few minutes to load, so grab a coffee or tea while you wait! raw_datasets = load_dataset("code_search_net", "python") ``` yeilds: ``` ile /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:524, in xlistdir(path, use_auth_token) 522 main_hop, *rest_hops = _as_str(path).split("::") 523 if is_local_path(main_hop): --> 524 return os.listdir(path) 525 else: 526 # globbing inside a zip in a private repo requires authentication 527 if not rest_hops and (main_hop.startswith("http://") or main_hop.startswith("https://")): NotADirectoryError: [Errno 20] Not a directory: '/workspace/downloads/25ceeb4c25ab737d688bd56ea92bfbb1f199fe572470456cf2d675479f342ac7/python/final/jsonl/train' ``` I was able to reproduce this erro both in the collab and on my own pytorch/pytorch container pulled from the dockerhub official pytorch image, so i think it may be a server side thing. ### Steps to reproduce the bug Steps to reproduce the issue: 1. run `raw_datasets = load_dataset("code_search_net", "python")` ### Expected behavior expect the code to not exception during dataset pull. ### Environment info i tried both the default HF_DATASETS_CACHE on Collab, and on my local container. i then pointed to the HF_DATASETS_CACHE to a large capacity local storage and the problem was consisten across all 3 scenarios.
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MDExOlB1bGxSZXF1ZXN0NTcxMjc2ODAx
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Fix writing GPU Faiss index
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As reported in by @corticalstack there is currently an error when we try to save a faiss index on GPU. I fixed that by checking the index `getDevice()` method before calling `index_gpu_to_cpu` Close #1859
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Use logging instead of printing to console
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[ "Actually upon closer inspection, it is documented in the code that this behavior is intentional, so I'll close this." ]
2022-11-05T23:48:02Z
2022-11-06T00:06:00Z
2022-11-06T00:05:59Z
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### Describe the bug Some logs ([here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L778), [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L786), and [here](https://github.com/huggingface/datasets/blob/4a6e1fe2735505efc7e3a3dbd3e1835da0702575/src/datasets/builder.py#L830)) generated by the `DatasetBuilder` are printed to the console instead of passed to `datasets` logger. ### Steps to reproduce the bug ```python >> import datasets >> datasets.load_dataset("some-dataset") Downloading and preparing dataset csv/data to <path>... Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 7729.06it/s] Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 527.23it/s] Dataset csv downloaded and prepared to <path>. Subsequent calls will reuse this data. ``` ### Expected behavior The logs should not be printed to the console directly but passed to the logger so that the user can redirect them wherever he wants. ### Environment info - `datasets` version: 2.6.1 - Platform: macOS-13.0-x86_64-i386-64bit - Python version: 3.9.15 - PyArrow version: 10.0.0 - Pandas version: 1.5.1
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3,165
Deprecate prepare_module
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2021-10-26T15:27:15Z
2021-11-05T09:27:36Z
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In version 1.13, `prepare_module` was deprecated. Add deprecation warning and remove its usage from all the library.
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connection issue with glue, what is the data url for glue?
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[ "Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py" ]
2021-01-13T08:37:40Z
2021-08-04T18:13:55Z
2021-08-04T18:13:55Z
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Hi my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not thanks
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Document to compress data files before uploading
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[ "Great idea!\r\n\r\nShould we also take this opportunity to include some audio/image file formats? Currently, it still reads very text heavy. Something like:\r\n\r\n> We support many text, audio, and image data extensions such as `.zip`, `.rar`, `.mp3`, and `.jpg` among many others. For data extensions like `.csv`,...
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In our docs to [Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset), we tell users to upload directly their data files, like CSV, JSON, JSON-Lines, text,... However, these extensions are not tracked by Git LFS by default, as they are not in the `.giattributes` file. Therefore, if they are too large, Git will fail to commit/upload them. I think for those file extensions (.csv, .json, .jsonl, .txt), we should better recommend to **compress** their data files (using ZIP for example) before uploading them to the Hub. - Compressed files are tracked by Git LFS in our default `.gitattributes` file What do you think? CC: @stevhliu See related issue: - https://huggingface.co/datasets/tcor0005/langchain-docs-400-chunksize/discussions/1
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I_kwDODunzps48hG4F
3,018
Support multiple zipped CSV data files
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[ "@lhoestq I would like to draw your attention to the proposed API by @lewtun, using `data_dir` to pass the ZIP URL.\r\n\r\nI'm not totally convinced with this... What do you think?\r\n\r\nMaybe we could discuss other approaches...\r\n\r\nOne brainstorming idea: what about using URL chaining with the hop operator in...
2021-10-04T15:16:59Z
2021-10-05T14:32:57Z
null
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As requested by @lewtun, support loading multiple zipped CSV data files. ```python from datasets import load_dataset url = "https://domain.org/filename.zip" data_files = {"train": "train_filename.csv", "test": "test_filename.csv"} dataset = load_dataset("csv", data_dir=url, data_files=data_files) ```
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Format code with `ruff`
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-02-09T17:50:21Z
2023-02-14T16:28:27Z
2023-02-14T16:18:38Z
CONTRIBUTOR
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Use `ruff` for formatting instead of `isort` and `black` to be consistent with [`transformers`](https://github.com/huggingface/transformers/pull/21480) and [`hfh`](https://github.com/huggingface/huggingface_hub/pull/1323). TODO: - [x] ~Merge the community contributors' PR to avoid having to run `make style` on their PR branches~ (we have some new PRs, but fixing those shouldn't be too big of a problem)
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1,173,148,884
I_kwDODunzps5F7NTU
3,960
Load local dataset error
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[ "Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train/**'], 'validation': ['/ssd/datasets/imagenet/pytorch/val/**']}\r\n>>> ds = load_dataset('image...
2022-03-18T03:32:49Z
2023-08-02T17:12:20Z
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When i used the datasets==1.11.0, it's all right. Util update the latest version, it get the error like this: ``` >>> from datasets import load_dataset >>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train'], 'validation': ['/ssd/datasets/imagenet/pytorch/val']} >>> ds = load_dataset('nateraw/image-folder', data_files=data_files, cache_dir='./', task='image-classification') [] https://huggingface.co/datasets/nateraw/image-folder/resolve/main/ /dat/txacs/git/txacs/examples/image-classification/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/load.py", line 1671, in load_dataset **config_kwargs, File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/load.py", line 1521, in load_dataset_builder **config_kwargs, File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/builder.py", line 1031, in __init__ super().__init__(*args, **kwargs) File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/builder.py", line 255, in __init__ sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 584, in from_local_or_remote if not isinstance(patterns_for_key, DataFilesList) File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 546, in from_local_or_remote data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 196, in resolve_patterns_locally_or_by_urls for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions): File "/gf3/home/txacs/gv3/anaconda3/envs/txacs/lib/python3.6/site-packages/datasets/data_files.py", line 146, in _resolve_single_pattern_locally raise FileNotFoundError(error_msg) FileNotFoundError: Unable to find '/ssd/datasets/imagenet/pytorch/train' at /dat/txacs/git/txacs/examples/image-classification/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main ``` I need some help to solve the problem, thanks!
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5,297
Fix xjoin for Windows pathnames
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-11-25T13:30:17Z
2022-11-29T08:07:39Z
2022-11-29T08:05:12Z
MEMBER
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This PR fixes a bug in `xjoin` function with Windows pathnames. Fix #5296.
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The data preview of XGLUE
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[ "![image](https://user-images.githubusercontent.com/49108847/165700611-915b4343-766f-4b81-bdaa-b31950250f06.png)\r\n", "Thanks for reporting @czq1999.\r\n\r\nNote that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet.\r\n\r\nThat is the case for XGLUE dataset (...
2022-04-28T07:30:50Z
2022-04-29T08:23:28Z
2022-04-28T16:08:03Z
NONE
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It seems that something wrong with the data previvew of XGLUE
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Dataset loader for the MSLR2022 shared task
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[ "I think the underlying issue is in https://github.com/huggingface/datasets/blob/c0ed6fdc29675b3565b01b77fde5ab5d9d8b60ec/src/datasets/commands/dummy_data.py#L124 - where `CSV`s are considered to be in the same class of file as text, jsonl, and tsv.\r\n\r\nI think this is an error because CSVs can have newlines wit...
2022-05-12T21:23:41Z
2022-07-18T17:19:27Z
2022-07-18T16:58:34Z
CONTRIBUTOR
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This PR adds a dataset loader for the [MSLR2022 Shared Task](https://github.com/allenai/mslr-shared-task). Both the MS^2 and Cochrane datasets can be loaded with this dataloader: ```python from datasets import load_dataset ms2 = load_dataset("mslr2022", "ms2") cochrane = load_dataset("mslr2022", "cochrane") ``` Usage looks like: ```python >>> ms2 = load_dataset("mslr2022", "ms2", split="validation") >>> ms2.keys() dict_keys(['review_id', 'pmid', 'title', 'abstract', 'target', 'background', 'reviews_info']) >>> ms2[0].target 'Conclusions SC therapy is effective for PAH in pre clinical studies .\nThese results may help to st and ardise pre clinical animal studies and provide a theoretical basis for clinical trial design in the future .' ``` I have tested this works with the following command: ```bash datasets-cli test datasets/mslr2022 --save_infos --all_configs ``` However I have having a little trouble generating the dummy data ```bash datasets-cli dummy_data datasets/mslr2022 --auto_generate ``` errors out with the following stack trace: ``` Couldn't generate dummy file 'datasets/mslr2022/dummy/ms2/1.0.0/dummy_data/mslr_data.tar.gz/mslr_data/ms2/convert_to_cochrane.py'. Ignore that if this file is not useful for dummy data. Traceback (most recent call last): File "/Users/johngiorgi/.pyenv/versions/datasets/bin/datasets-cli", line 11, in <module> load_entry_point('datasets', 'console_scripts', 'datasets-cli')() File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/dummy_data.py", line 319, in run keep_uncompressed=self._keep_uncompressed, File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/commands/dummy_data.py", line 361, in _autogenerate_dummy_data dataset_builder._prepare_split(split_generator, check_duplicate_keys=False) File "/Users/johngiorgi/Documents/dev/datasets/src/datasets/builder.py", line 1146, in _prepare_split desc=f"Generating {split_info.name} split", File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__ for obj in iterable: File "/Users/johngiorgi/.cache/huggingface/modules/datasets_modules/datasets/mslr2022/b4becd2f52cf18255d4934d7154c2a1127fb393371b87b3c1fc2c8b35a777cea/mslr2022.py", line 149, in _generate_examples reviews_info_df = pd.read_csv(reviews_info_filepath, index_col=0) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper return func(*args, **kwargs) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 586, in read_csv return _read(filepath_or_buffer, kwds) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 488, in _read return parser.read(nrows) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 1047, in read index, columns, col_dict = self._engine.read(nrows) File "/Users/johngiorgi/.pyenv/versions/3.7.13/envs/datasets/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 224, in read chunks = self._reader.read_low_memory(nrows) File "pandas/_libs/parsers.pyx", line 801, in pandas._libs.parsers.TextReader.read_low_memory File "pandas/_libs/parsers.pyx", line 857, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 843, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 1925, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: EOF inside string starting at row 2 ``` I think this may have to do with unusual line terminators in the original data. When I open it in VSCode, it complains: ``` The file 'dev-inputs.csv' contains one or more unusual line terminator characters, like Line Separator (LS) or Paragraph Separator (PS). It is recommended to remove them from the file. This can be configured via `editor.unusualLineTerminators`. ``` Tagging the organizers of the shared task in case they want to sanity check this or add any info to the model card :) @lucylw @jayded
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Support cloud storage for loading datasets
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2023-04-19T12:43:53Z
2023-05-07T17:47:41Z
2023-05-07T17:47:41Z
CONTRIBUTOR
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### Feature request It seems that the the current implementation supports cloud storage only for `load_from_disk`. It would be nice if a similar functionality existed in `load_dataset`. ### Motivation Motivation is pretty clear -- let users work with datasets located in the cloud. ### Your contribution I can help implementing this.
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NonMatchingChecksumError when attempting to download GLUE
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[ "Hi :)\r\n\r\nI think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with:\r\n\r\n```py\r\npip install -U datasets\r\n```\r\n\r\nThen you can download:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nds = load_data...
2022-04-27T14:14:21Z
2022-04-28T07:45:27Z
2022-04-28T07:45:27Z
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## Describe the bug I am trying to download the GLUE dataset from the NLP module but get an error (see below). ## Steps to reproduce the bug ```python import nlp nlp.__version__ # '0.2.0' nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ``` ## Expected results I expect the dataset to download without an error. ## Actual results ``` INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports. INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4 INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0) INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805 Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0... Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s] INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64 --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-7-669a8343dcc1> in <module> ----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload") ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 418 verify_infos = not save_infos and not ignore_verifications 419 self._download_and_prepare( --> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 421 ) 422 # Sync info ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 458 # Checksums verification 459 if verify_infos: --> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums()) 461 for split_generator in split_generators: 462 if str(split_generator.split_info.name).lower() == "all": ~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums) 34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] 35 if len(bad_urls) > 0: ---> 36 raise NonMatchingChecksumError(str(bad_urls)) 37 logger.info("All the checksums matched successfully.") 38 NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa - Python version: 3.6.13 - PyArrow version: 6.0.1 - Pandas version: 1.1.5
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Added more information in the README about contributors of the Arabic Speech Corpus
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2022-07-18T09:48:03Z
2022-07-28T10:33:05Z
2022-07-28T10:33:05Z
CONTRIBUTOR
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Added more information in the README about contributors and encouraged reading the thesis for more infos
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pyarrow.lib.ArrowInvalid: Unable to merge: Field <field> has incompatible types
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[ "Hi! The link pointing to the code that generated the dataset is broken. Can you please fix it to make debugging easier?", "> Hi! The link pointing to the code that generated the dataset is broken. Can you please fix it to make debugging easier?\r\n\r\nSorry about that, it's fixed now.\r\n", "@cyanic-selkie cou...
2023-03-31T18:19:40Z
2023-09-07T11:42:49Z
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### Describe the bug When loading the dataset [wikianc-en](https://huggingface.co/datasets/cyanic-selkie/wikianc-en) which I created using [this](https://github.com/cyanic-selkie/wikianc) code, I get the following error: ``` Traceback (most recent call last): File "/home/sven/code/rector/answer-detection/train.py", line 106, in <module> (dataset, weights) = get_dataset(args.dataset, tokenizer, labels, args.padding) File "/home/sven/code/rector/answer-detection/dataset.py", line 106, in get_dataset dataset = load_dataset("cyanic-selkie/wikianc-en") File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/load.py", line 1794, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/builder.py", line 1106, in as_dataset datasets = map_nested( File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 443, in map_nested mapped = [ File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 444, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested return function(data_struct) File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/builder.py", line 1136, in _build_single_dataset ds = self._as_dataset( File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/builder.py", line 1207, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 239, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 260, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 203, in _read_files pa_table = concat_tables(pa_tables) if len(pa_tables) != 1 else pa_tables[0] File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1808, in concat_tables return ConcatenationTable.from_tables(tables, axis=axis) File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1514, in from_tables return cls.from_blocks(blocks) File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1427, in from_blocks table = cls._concat_blocks(blocks, axis=0) File "/home/sven/.cache/pypoetry/virtualenvs/rector-Z2mdKRnn-py3.10/lib/python3.10/site-packages/datasets/table.py", line 1373, in _concat_blocks return pa.concat_tables(pa_tables, promote=True) File "pyarrow/table.pxi", line 5224, in pyarrow.lib.concat_tables File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Unable to merge: Field paragraph_anchors has incompatible types: list<: struct<start: uint32 not null, end: uint32 not null, qid: uint32, pageid: uint32, title: string not null> not null> vs list<item: struct<start: uint32, end: uint32, qid: uint32, pageid: uint32, title: string>> ``` This only happens when I load the `train` split, indicating that the size of the dataset is the deciding factor. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("cyanic-selkie/wikianc-en", split="train") ``` ### Expected behavior The dataset should load normally without any errors. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-6.2.8-arch1-1-x86_64-with-glibc2.37 - Python version: 3.10.10 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Fix missing tags in dataset cards
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-09-15T16:51:03Z
2022-09-22T12:37:55Z
2022-09-15T17:12:09Z
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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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[Timit_asr] Make sure not only the first sample is used
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[ "cc @lhoestq @vrindaprabhu", "Failing `run (push)` is unrelated -> merging", "Thanks for fixing this, it was affecting my runs for https://github.com/huggingface/transformers/pull/10581/", "I am seeing this very late! Sorry for the blunder everyone! :(" ]
2021-03-05T08:42:51Z
2021-06-30T06:25:53Z
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When playing around with timit I noticed that only the first sample is used for all indices. I corrected this typo so that the dataset is correctly loaded.
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add utf-8 while reading README
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[ "Can you please add encoding to this line as well to fix the issue (and maybe replace `path.open(...)` with `open(path, ...)`)?\r\nhttps://github.com/huggingface/datasets/blob/7bee4be44706a59b084b9b69c4cd00f73ee72f76/src/datasets/utils/metadata.py#L58", "Sure, in fact even I was thinking of adding this in order t...
2021-05-27T18:12:28Z
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It was causing tests to fail in Windows (see #2416). In Windows, the default encoding is CP1252 which is unable to decode the character byte 0x9d
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autoeval config
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add train eval index for autoeval
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Make split slicing consisten with list slicing
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5891). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma...
2023-05-23T16:04:33Z
2023-05-23T16:11:12Z
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Fix #1774, fix #5875 TODO: a test
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Fix column list comparison in transmit format
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As noticed in #1718 the cache might not reload the cache files when new columns were added. This is because of an issue in `transmit_format` where the column list comparison fails because the order was not deterministic. This causes the `transmit_format` to apply an unnecessary `set_format` transform with shuffled column names. I fixed that by sorting the columns for the comparison and added a test. To properly test that I added a third column `col_3` to the dummy_dataset used for tests.
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1,315,222,166
PR_kwDODunzps479hq4
4,738
Use CI unit/integration tests
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think this PR can be merged. Willing to see it in action.\r\n\r\nCC: @lhoestq " ]
2022-07-22T16:48:00Z
2022-07-26T20:19:22Z
2022-07-26T20:07:05Z
MEMBER
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This PR: - Implements separate unit/integration tests - A fail in integration tests does not cancel the rest of the jobs - We should implement more robust integration tests: work in progress in a subsequent PR - For the moment, test involving network requests are marked as integration: to be evolved
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