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https://api.github.com/repos/huggingface/datasets/issues/5798
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1,685,904,526
I_kwDODunzps5kfNyO
5,798
Support parallelized downloading and processing in load_dataset with Spark
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[ "Hi ! We're using process pools for parallelism right now. I was wondering if there's a package that implements the same API as a process pool but runs with Spark under the hood ? That or something similar would be cool because users could use whatever distributed framework they want this way.\r\n\r\nFeel free to p...
2023-04-27T00:16:11Z
2023-05-25T14:11:41Z
null
CONTRIBUTOR
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### Feature request When calling `load_dataset` for datasets that have multiple files, support using Spark to distribute the downloading and processing job to worker nodes when `cache_dir` is a cloud file system shared among nodes. ```python load_dataset(..., use_spark=True) ``` ### Motivation Further speed up `dl_manager.download` and `_prepare_split` by distributing the workloads to worker nodes. ### Your contribution I can submit a PR to support this.
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4,934
Dataset Viewer issue for indonesian-nlp/librivox-indonesia
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[ "The error is not related to the dataset viewer. I'm having a look...", "Thanks @albertvillanova for checking the issue. Actually, I can use the dataset like following:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> ds=load_dataset(\"indonesian-nlp/librivox-indonesia\")\r\nNo config specified, defaulting ...
2022-09-06T10:03:23Z
2022-09-06T12:46:40Z
2022-09-06T12:46:40Z
CONTRIBUTOR
null
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### Link https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia ### Description I created a new speech dataset https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia, but the dataset preview doesn't work with following error message: ``` Server error Status code: 400 Exception: TypeError Message: unsupported operand type(s) for +: 'NoneType' and 'str' ``` Please help, I am not sure what the problem here is. Thanks a lot. ### Owner Yes
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5,455
Single TQDM bar in multi-proc map
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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...
2023-01-23T12:49:40Z
2023-02-13T20:23:34Z
2023-02-13T20:16:38Z
CONTRIBUTOR
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Use the "shard generator approach with periodic progress updates" (used in `save_to_disk` and multi-proc `load_dataset`) in `Dataset.map` to enable having a single TQDM progress bar in the multi-proc mode. Closes https://github.com/huggingface/datasets/issues/771, closes https://github.com/huggingface/datasets/issues/3177 TODO: - [x] cleaner refactor of the `_map_single` decorators now that they also have to wrap generator functions (decorate `map` instead of `map_single` with the `transmit_` decorators and predict the shards' fingerprint in `map`)
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1,871
Add newspop dataset
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[ "Thanks for the changes :)\r\nmerging" ]
2021-02-13T07:31:23Z
2021-03-08T10:12:45Z
2021-03-08T10:12:45Z
CONTRIBUTOR
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URL query parameters are set as path in the compression hop for fsspec
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[ "I think the test passes because it simply ignore what's after `gzip://`.\r\n\r\nThe returned urlpath is expected to look like `gzip://filename::url`, and the filename is currently considered to be what's after the final `/`, hence the result.\r\n\r\nWe can decide to change this and simply have `gzip://::url`, this...
2021-12-27T16:29:00Z
2022-01-05T15:15:25Z
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## Describe the bug There is an ssue with `StreamingDownloadManager._extract`. I don't know how the test `test_streaming_gg_drive_gzipped` passes: For ```python TEST_GG_DRIVE_GZIPPED_URL = "https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz" urlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL) ``` gives `urlpath`: ```python 'gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz' ``` The gzip path makes no sense: `gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz` ## Steps to reproduce the bug ```python from datasets.utils.streaming_download_manager import StreamingDownloadManager dl_manager = StreamingDownloadManager() urlpath = dl_manager.extract("https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz") print(urlpath) ``` ## Expected results The query parameters should not be set as part of the path.
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Remove unncessary 'pylint disable' message in ReadMe
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2022-03-17T11:16:55Z
2022-04-12T14:28:35Z
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Replace dbpedia_14 data url
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-03-25T13:47:21Z
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I replaced the Google Drive URL of the dataset by the FastAI one, since we've had some issues with Google Drive.
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Add Tanzil Dataset
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2020-12-10T11:17:56Z
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Inject ASR template for lj_speech dataset
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2021-07-13T09:05:09Z
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Related to: #2565, #2633. cc: @lewtun
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2022-04-20T16:08:01Z
2022-04-22T13:58:25Z
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This PR adds a `max_shard_size` param to `push_to_hub` and deprecates `shard_size` in favor of this new param to have a more descriptive name (a shard has at most the `shard_size` bytes in `push_to_hub`) for the param and to align the API with [Transformers](https://github.com/huggingface/transformers/blob/ff06b177917384137af2d9585697d2d76c40cdfc/src/transformers/modeling_utils.py#L1350).
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Fix `to_numpy` when None values in the sequence
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[ "I just added the same test with dynamic shape", "_The documentation is not available anymore as the PR was closed or merged._", "Awesome ! I'm merging now if you don't mind :)\r\nWe should probably give you permissions to merge your own PRs when you have an approval", "<details>\n<summary>Show benchmarks</su...
2023-06-08T08:38:56Z
2023-06-09T13:49:41Z
2023-06-09T13:23:48Z
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Closes #5927 I've realized that the error was overlooked during testing due to the presence of only one None value in the sequence. Unfortunately, it was the only case where the function works as expected. When the sequence contained more than one None value, the function failed. Consequently, I've updated the tests to include sequences with multiple None values.
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Add return_file_name in load_dataset
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2023-10-17T13:36:57Z
2023-10-17T13:36:57Z
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Proposition to solve #5806. Added an optional parameter `return_file_name` in the dataset builder config. When set to `True`, the function will include the file name corresponding to the sample in the returned output. There is a difference between arrow-based and folder-based datasets - for arrow-based: a column is concatenated after the table is cast. - for folder-based: `dataset.info.features` has the entry `file_name` and the original file name is passed to the `sample_metadata` dictionary. The difference in behavior might be a concern, also I do not know whether the `file_name` should return the original file path or the downloaded one for folder-based datasets. I added some tests for the datasets that already had a test file.
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2022-08-26T17:16:30Z
2022-08-26T17:49:33Z
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Fix CI so that it reports defaults (failed and error) besides the custom (xfailed and xpassed) in the test summary. This PR fixes a regression introduced by: - #4845 This introduced the reporting of xfailed and xpassed, but wrongly removed the reporting of the defaults failed and error.
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Skip None encoding (line deleted by accident in #3195)
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2021-12-09T21:17:33Z
2021-12-10T11:00:03Z
2021-12-10T11:00:02Z
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Return the line deleted by accident in #3195 while [resolving merge conflicts](https://github.com/huggingface/datasets/pull/3195/commits/8b0ed15be08559056b817836a07d47acda0c4510). Fix #3181 (finally :))
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Load a local cached dataset that has been modified
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[ "Hi! `datasets` caches every modification/loading, so you can either rerun the pipeline up to the `map` call or use `Dataset.from_file(modified_dataset)` to load the dataset directly from the cache file.", "Awesome, hvala Mario! This works. " ]
2022-06-02T01:51:49Z
2022-06-02T23:59:26Z
2022-06-02T23:59:18Z
NONE
null
null
null
## Describe the bug I have loaded a dataset as follows: ``` d = load_dataset("emotion", split="validation") ``` Afterwards I make some modifications to the dataset via a `map` call: ``` d.map(some_update_func, cache_file_name=modified_dataset) ``` This generates a cached version of the dataset on my local system in the same directory as the original download of the data (/path/to/cache). Running an `ls` returns: ``` modified_dataset dataset_info.json emotion-test.arrow emotion-train.arrow emotion-validation.arrow ``` as expected. However, when I try to load up the modified cached dataset via a call to ``` modified = load_dataset("emotion", split="validation", data_files="/path/to/cache/modified_dataset") ``` it simply redownloads a new version of the dataset and dumps to a new cache rather than loading up the original modified dataset: ``` Using custom data configuration validation-cdbf51685638421b Downloading and preparing dataset emotion/validation to ... ``` How am I supposed to load the original modified local cache copy of the dataset? ## Environment info - `datasets` version: 2.2.2 - Platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.2
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PR_kwDODunzps5Icjwe
5,459
Disable aiohttp requoting of redirection URL
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Comment by @lhoestq:\r\n> Do you think we need this in `datasets` if it's fixed on the moon landing side ? In the aiohttp doc they consider those symbols as \"non-safe\" ", "The lib `requests` does not perform that requote on redir...
2023-01-24T17:18:59Z
2023-02-01T08:45:33Z
2023-01-31T08:37:54Z
MEMBER
null
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The library `aiohttp` performs a requoting of redirection URLs that unquotes the single quotation mark character: `%27` => `'` This is a problem for our Hugging Face Hub, which requires exact URL from location header. Specifically, in the query component of the URL (`https://netloc/path?query`), the value for `response-content-disposition` contains `%27`: ``` response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B ``` and after the requoting, the `%27` characters get unquoted to `'`: ``` response-content-disposition=attachment%3B+filename*%3DUTF-8''sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B ``` This PR disables the `aiohttp` requoting of redirection URLs.
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PR_kwDODunzps5DjLgG
5,285
Save file name in embed_storage
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I updated the tests, met le know if it sounds good to you now :)" ]
2022-11-23T10:55:54Z
2022-11-24T14:11:41Z
2022-11-24T14:08:37Z
MEMBER
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Having the file name is useful in case we need to check the extension of the file (e.g. mp3), or in general in case it includes some metadata information (track id, image id etc.) Related to https://github.com/huggingface/datasets/issues/5276
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4,615
Fix `embed_storage` on features inside lists/sequences
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-07-01T11:52:08Z
2022-07-08T12:13:10Z
2022-07-08T12:01:36Z
CONTRIBUTOR
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Add a dedicated function for embed_storage to always preserve the embedded/casted arrays (and to have more control over `embed_storage` in general). Fix #4591 ~~(Waiting for #4608 to be merged to mark this PR as ready for review - required for fixing `xgetsize` in private repos)~~ Done!
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3,964
Add default Audio Loader
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2022-03-18T12:58:55Z
2022-08-22T14:20:46Z
2022-08-22T14:20:46Z
CONTRIBUTOR
null
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**Is your feature request related to a problem? Please describe.** Writing a custom loading dataset script might be a bit challenging for users. **Describe the solution you'd like** Add default Audio loader (analogous to ImageFolder) for small datasets with standard directory structure. **Describe alternatives you've considered** Create a custom loading script? that's what users doing now.
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#2837 Use cache folder for lockfile
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[ "The CI fail about the meteor metric is unrelated to this PR " ]
2021-09-09T19:55:56Z
2021-10-05T17:58:22Z
2021-10-05T17:58:22Z
CONTRIBUTOR
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Fixes #2837 Use a cache folder directory to store the FileLock. The issue was that the lock file was in a readonly folder.
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Shuffle a sharded iterable dataset without seed can lead to duplicate data
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2023-04-03T09:40:03Z
2023-04-04T14:58:18Z
2023-04-04T14:58:18Z
MEMBER
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As reported in https://github.com/huggingface/datasets/issues/5360 If `seed=None` in `.shuffle()`, shuffled datasets don't use the same shuffling seed across nodes. Because of that, the lists of shards is not shuffled the same way across nodes, and therefore some shards may be assigned to multiple nodes instead of exactly one. This can happen only when you have a number of shards that is a factor of the number of nodes. The current workaround is to always set a `seed` in `.shuffle()`
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1,116,021,420
I_kwDODunzps5ChSKs
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same value of precision, recall, f1 score at each epoch for classification task.
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[ "Hi @Dhanachandra, \r\n\r\nWe have tests for all our metrics and they work as expected: under the hood, we use scikit-learn implementations.\r\n\r\nMaybe the cause is somewhere else. For example:\r\n- Is it a binary or a multiclass or a multilabel classification? Default computation of these metrics is for binary c...
2022-01-27T10:14:16Z
2022-02-24T09:02:18Z
2022-02-24T09:02:17Z
NONE
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**1st Epoch:** 1/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.59it/s] 01/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:30:49 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7612903225806451} RECALL: {'recall': 0.7612903225806451} F1: {'f1': 0.7612903225806451} {'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0} **4th Epoch:** 1/27/2022 09:56:55 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.92it/s] 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7698924731182796} RECALL: {'recall': 0.7698924731182796} F1: {'f1': 0.7698924731182796} ## Environment info !git clone https://github.com/huggingface/transformers %cd transformers !pip install . !pip install -r /content/transformers/examples/pytorch/token-classification/requirements.txt !pip install datasets
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Fix typo in doc build yml
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3819). All of your documentation changes will be reflected on that endpoint." ]
2022-03-03T20:08:44Z
2022-03-04T13:07:41Z
2022-03-04T13:07:41Z
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cc: @lhoestq
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Add CMU Hinglish DoG Dataset for MT
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[ "Hi @lhoestq, thanks a lot for the help. I have moved the part as suggested. \r\nAlthough still while running the dummy data script, I face this issue\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/ishan/anaconda3/bin/datasets-cli\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/home/...
2021-10-22T16:17:25Z
2021-11-15T11:36:42Z
2021-11-15T10:27:45Z
CONTRIBUTOR
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Address part of #2841 Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model. Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two. The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py) The dummy data part is not working properly, it shows ``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ``` when I run without ``--auto_generate``. Please let me know how I can fix that. Thanks
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fix: 🐛 fix two typos
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Add remove_columns=True
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Any reason why we can't just do `[inputs.copy()]` in this line for in-place operations to not have effects anymore:\r\nhttps://github.com/huggingface/datasets/blob/bf432011ff9155a5bc16c03956bc63e514baf80d/src/datasets/arrow_dataset.p...
2022-04-21T17:28:13Z
2023-09-24T10:02:32Z
2022-04-22T14:45:30Z
CONTRIBUTOR
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This should fix all the issue we have with in place operations in mapping functions. This is crucial as where we do some weird things like: ``` def apply(batch): batch_size = len(batch["id"]) batch["text"] = ["potato" for _ range(batch_size)] return {} # Columns are: {"id": int} dset.map(apply, batched=True, remove_columns="text") # crashes because `text` is not in the original columns dset.map(apply, batched=True) # mapped datasets has `text` column ``` In this PR we suggest to have `remove_columns=True` so that we ignore the input completely, and just use the output to generate mapped dataset. This means that inplace operations won't have any effects anymore.
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Asserts replaced by exceptions (huggingface#3171)
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[ "Your first PR went smoothly, well done!\r\nYou are welcome to continue contributing to this project.\r\nGràcies, @joseporiolayats! 😉 " ]
2021-10-28T11:55:45Z
2021-11-06T06:35:32Z
2021-10-29T13:08:43Z
CONTRIBUTOR
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I've replaced two asserts with their proper exceptions following the guidelines described in issue #3171 by following the contributing guidelines. PS: This is one of my first PRs, hoping I don't break anything!
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[Quick poll] Give your opinion on the future of the Hugging Face Open Source ecosystem!
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2021-12-04T09:18:33Z
2022-01-11T12:29:53Z
2022-01-11T12:29:53Z
MEMBER
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Thanks to all of you, `datasets` will pass 11.5k stars :star2: this week! If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts: [**hf.co/oss-survey**](https://hf.co/oss-survey) (please reply in the above feedback form rather than to this thread) Thank you all on behalf of the HuggingFace team! 🤗
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Check if dataset features match before push in `DatasetDict.push_to_hub`
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-05-19T12:32:30Z
2022-05-20T15:23:36Z
2022-05-20T15:15:30Z
CONTRIBUTOR
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Fix #4211
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PR_kwDODunzps4xRXVm
3,602
Update url for conll2003
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[ "Hi. lhoestq \r\n\r\n![image](https://user-images.githubusercontent.com/21982975/150345097-154f2b1a-bb12-47af-bddf-40eec0a0dadb.png)\r\nWhat is the solution for it?\r\nyou can see it is still doesn't work here.\r\nhttps://colab.research.google.com/drive/1l52FGWuSaOaGYchit4CbmtUSuzNDx_Ok?usp=sharing\r\nThank you.\r\...
2022-01-19T15:35:04Z
2022-01-20T16:23:03Z
2022-01-19T15:43:53Z
MEMBER
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Following https://github.com/huggingface/datasets/issues/3582 I'm changing the download URL of the conll2003 data files, since the previous host doesn't have the authorization to redistribute the data
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2,160
data_args.preprocessing_num_workers almost freezes
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[ "Hi.\r\nI cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks\r\n\r\n```\r\n#3: 11%|███████████████▊ ...
2021-04-02T07:56:13Z
2021-04-02T10:14:32Z
2021-04-02T10:14:31Z
NONE
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Hi @lhoestq I am running this code from huggingface transformers https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py to speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up. thanks
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1,099,214,403
PR_kwDODunzps4wzSOL
3,564
Add the KMWP & DKTC dataset.
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[ "I reflect your review. cc. @lhoestq ", "Ah sorry, I missed KMWP comment, wait.", "I request 2 new pull requests. #3569 #3570" ]
2022-01-11T14:14:08Z
2022-01-12T15:33:49Z
2022-01-12T15:33:28Z
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Add the DKTC dataset. - https://github.com/tunib-ai/DKTC
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MDExOlB1bGxSZXF1ZXN0NTcxMjAwNjA1
1,861
Fix Limit url
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2021-02-10T15:44:56Z
2021-02-10T16:15:00Z
2021-02-10T16:14:59Z
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The test.json file of the Literal-Motion-in-Text (LiMiT) dataset was removed recently on the master branch of the repo at https://github.com/ilmgut/limit_dataset This PR uses the previous commit sha to download the file instead, as suggested by @Paethon Close #1836
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c4 dataset streaming fails with `FileNotFoundError`
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[ "Also encountering this issue for every dataset I try to stream! Installed datasets from main:\r\n```\r\n- `datasets` version: 2.10.1.dev0\r\n- Platform: macOS-13.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 10.0.1\r\n- Pandas version: 1.5.2\r\n```\r\n\r\nRepro:\r\n```python\r\nfrom datasets ...
2023-02-24T07:57:32Z
2023-03-06T13:14:11Z
2023-02-27T04:03:38Z
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### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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I_kwDODunzps5C_9O1
3,679
Download datasets from a private hub
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[ "For reference:\r\nhttps://github.com/huggingface/transformers/issues/15514\r\nhttps://github.com/huggingface/huggingface_hub/issues/650", "Hi ! For information one can set the environment variable `HF_ENDPOINT` (default is `https://huggingface.co`) if they want to use a private hub.\r\n\r\nWe may need to coordin...
2022-02-04T10:49:06Z
2022-02-22T11:08:07Z
2022-02-22T11:08:07Z
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In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature. The obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted. The same issue exists with the transformers library and the CLI. I'm going to create issues there as well, and I'll reference them below.
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5,661
CI is broken: Unnecessary `dict` comprehension
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2023-03-23T09:13:01Z
2023-03-23T09:37:51Z
2023-03-23T09:37:51Z
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CI check_code_quality is broken: ``` src/datasets/arrow_dataset.py:3267:35: C416 [*] Unnecessary `dict` comprehension (rewrite using `dict()`) Found 1 error. ```
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Can't pass `sep=None` to load_dataset("csv", ...) to infer the separator via pandas.read_csv
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2021-07-16T10:05:44Z
2021-07-16T12:46:06Z
2021-07-16T12:46:06Z
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When doing `load_dataset("csv", sep=None)`, the `sep` passed to `pd.read_csv` is still the default `sep=","` instead, which makes it impossible to make the csv loader infer the separator. Related to https://github.com/huggingface/datasets/pull/2656 cc @SBrandeis
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3,932
Create SARI metric card
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-03-15T20:37:23Z
2022-03-18T17:37:01Z
2022-03-18T17:32:55Z
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SARI metric card! (do we have an expert in text simplification to validate?.. :sweat_smile: )
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Adding the NorNE dataset for NER
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[ "Quick question, @lhoestq. In this specific dataset, two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. However, I have not found an easy...
2021-01-11T21:34:13Z
2021-03-31T14:23:49Z
2021-03-31T14:13:17Z
CONTRIBUTOR
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NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
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Misalignment between card tag validation and docs
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[ "(Note that the doc is aligned with the hub validation rules, and the \"ground truth\" is the hub validation rules given that they apply to all datasets, not just the canonical ones)", "Instead of our own implementation, we now use `huggingface_hub`'s `DatasetCardData`, which has the correct type hint, so I think...
2022-08-11T14:44:45Z
2023-07-21T15:38:02Z
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## Describe the bug As pointed out in other issue: https://github.com/huggingface/datasets/pull/4827#discussion_r943536284 the validation of the dataset card tags is not aligned with its documentation: e.g. - implementation: `license: List[str]` - docs: `license: Union[str, List[str]]` They should be aligned. CC: @julien-c
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IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
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[ "Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nimport torch\r\n\r\ndataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='t...
2023-02-16T16:08:51Z
2023-02-22T10:30:30Z
2023-02-21T13:03:57Z
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### Describe the bug When dataset contains a 0-dim tensor, formatting.py raises a following error and fails. ```bash Traceback (most recent call last): File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row return _unnest(formatted_batch) File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest return {key: array[0] for key, array in py_dict.items()} File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp> return {key: array[0] for key, array in py_dict.items()} IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number ``` ### Steps to reproduce the bug Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g. ```python from datasets import load_dataset import torch dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train') def t(batch): return {"test": torch.tensor(1)} dataset.set_transform(t) d_0 = dataset[0] ``` ### Expected behavior Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor. ### Environment info `datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February)
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Replace assertTrue(isinstance with assertIsInstance in tests
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2021-04-03T21:07:02Z
2021-04-06T14:41:09Z
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Replaces all the occurrences of the `assertTrue(isinstance(` pattern with `assertIsInstance`.
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4,414
Rename DatasetBuilder config_name
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-05-27T09:28:02Z
2022-05-31T15:07:21Z
2022-05-31T14:58:51Z
MEMBER
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This PR renames the DatasetBuilder keyword argument `name` to `config_name` so that: - it avoids confusion with the attribute `DatasetBuilder.name`, which is different - it aligns with the Dataset property name `config_name`, defined in `DatasetInfoMixin.config_name` Other simpler possibility could be to rename it to just `config` instead. Please note I have only renamed this argument of DatasetBuilder because I think this refactoring has a low impact on users: we can assume this is not a public facing parameter, but private or related to the inners of our library. It would have a major impact to rename it also in: - load_dataset - load_dataset_builder: although this could also be assumed as inners... - in our CLI commands Besides the naming of `name`, I also find really confusing the naming of `path` in `load_dataset`. IMHO, they should have a more simpler and precise meaning (currently, they are too vague). I would propose (maybe for next major release): ``` load_dataset(dataset, config,... ``` instead of ``` load_dataset(path, name,... ```
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Add Korean NER dataset
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2020-12-06T20:19:06Z
2021-12-29T00:50:59Z
2020-12-08T10:25:33Z
CONTRIBUTOR
null
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Supersedes #1177 > This PR adds the [Korean named entity recognition dataset](https://github.com/kmounlp/NER). This dataset has been used in many downstream tasks, such as training [KoBERT](https://github.com/SKTBrain/KoBERT) for NER, as seen in this [KoBERT-CRF implementation](https://github.com/eagle705/pytorch-bert-crf-ner).
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IndexError: Invalid key is out of bounds for size 0 despite having a populated dataset
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[ "It looks to be the same issue as the one reported in https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0.\r\n\r\nCan you check the length of `train_dataset` before the `train_sampler = self._get_train_sampler()` (and after `_remove_unused_columns`) line?" ]
2023-10-11T09:59:38Z
2023-10-17T11:24:06Z
2023-10-17T11:24:06Z
NONE
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### Describe the bug I am encountering an `IndexError` when trying to access data from a DataLoader which wraps around a dataset I've loaded using the `datasets` library. The error suggests that the dataset size is `0`, but when I check the length and print the dataset, it's clear that it has `1166` entries. ### Steps to reproduce the bug 1. Load a dataset with `1166` entries. 2. Create a DataLoader using this dataset. 3. Try iterating over the DataLoader. code: ```python def get_train_dataloader(self) -> DataLoader: if self.train_dataset is None: raise ValueError("Trainer: training requires a train_dataset.") train_dataset = self.train_dataset data_collator = self.data_collator print(len(train_dataset)) print(train_dataset) if is_datasets_available() and isinstance(train_dataset, datasets.Dataset): train_dataset = self._remove_unused_columns(train_dataset, description="training") else: data_collator = self._get_collator_with_removed_columns(data_collator, description="training") train_sampler = self._get_train_sampler() dl = DataLoader( train_dataset, batch_size=self._train_batch_size, sampler=train_sampler, collate_fn=data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory, worker_init_fn=seed_worker, ) print(dl) print(len(dl)) for i in dl: print(i) break return dl ``` output : ``` 1166 Dataset({ features: ['input_ids', 'special_tokens_mask'], num_rows: 1166 }) <torch.utils.data.dataloader.DataLoader object ...> 146 ``` Error: ``` Traceback (most recent call last): File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 266, in <module> train() File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 260, in train trainer.train() File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/transformers/trainer.py", line 1506, in train return inner_training_loop( File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/transformers/trainer.py", line 1520, in _inner_training_loop train_dataloader = self.get_train_dataloader() File "/home/dl/zym/llamaJP/TestUseContinuePretrainLlama.py", line 80, in get_train_dataloader for i in dl: File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ data = self._next_data() File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 674, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "/root/miniconda3/envs/LLM/lib/python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 1116 is out of bounds for size 0 ``` ### Expected behavior I expect to be able to iterate over the DataLoader without encountering an IndexError since the dataset is populated. ### Environment info - `datasets` library version: [2.14.5] - Platform: [Linux] - Python version: 3.10 - Other libraries involved: HuggingFace Transformers
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2,189
save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object.
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[ "Hi ! We refactored save_to_disk in #2025 so this doesn't happen.\r\nFeel free to try it on master for now\r\nWe'll do a new release soon" ]
2021-04-08T04:42:53Z
2022-06-01T16:32:15Z
2022-06-01T16:32:15Z
NONE
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As you can see, it saves the entire dataset. @lhoestq You can check by going through the following example, ``` from datasets import load_from_disk,concatenate_datasets loaded_data=load_from_disk('/home/gsir059/HNSW-ori/my_knowledge_dataset') n=20 kb_list=[loaded_data.shard(n, i, contiguous=True) for i in range(n)] final_dataset=concatenate_datasets([kb_list[1],kb_list[2]]) final_dataset.save_to_disk('/home/gsir059/haha/k.arrow') ```
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Can you please add the Stanford dog dataset?
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[ "would you like to give it a try, @dgrnd4? (maybe with the help of the dataset author?)", "@julien-c i am sorry but I have no idea about how it works: can I add the dataset by myself, following \"instructions to add a new dataset\"?\r\nCan I add a dataset even if it's not mine? (it's public in the link that I wro...
2022-06-15T15:39:35Z
2023-10-09T06:53:08Z
null
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## Adding a Dataset - **Name:** *Stanford dog dataset* - **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/* - **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/* - **Data:** *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/ImageNetDogs/)* - **Motivation:** *The dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is useful for fine-grain purpose * 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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[DO-NOT-MERGE] Debug Windows issue at #3
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2023-05-02T07:19:34Z
2023-05-02T07:21:30Z
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TBD
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Fix style with ruff 0.0.278
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6026). 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...
2023-07-13T12:34:24Z
2023-07-13T12:46:26Z
2023-07-13T12:37:01Z
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5,663
CI is broken: ModuleNotFoundError: jax requires jaxlib to be installed
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2023-03-23T09:39:43Z
2023-03-23T10:09:55Z
2023-03-23T10:09:55Z
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CI test_py310 is broken: see https://github.com/huggingface/datasets/actions/runs/4498945505/jobs/7916194236?pr=5662 ``` FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_in_memory - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_on_disk - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_audio - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_device - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_image - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_jnp_array_kwargs - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. FAILED tests/features/test_features.py::CastToPythonObjectsTest::test_cast_to_python_objects_jax - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions. ===== 8 failed, 2147 passed, 10 skipped, 37 warnings in 228.69s (0:03:48) ====== ```
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I_kwDODunzps5D4SGh
3,724
Bug while streaming CSV dataset with pandas 1.4
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2022-02-15T15:16:19Z
2022-02-15T16:55:44Z
2022-02-15T16:55:44Z
MEMBER
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## Describe the bug If we upgrade to pandas `1.4`, the patching of the pandas module is no longer working ``` AttributeError: '_PatchedModuleObj' object has no attribute '__version__' ``` ## Steps to reproduce the bug ``` pip install pandas==1.4 ``` ```python from datasets import load_dataset ds = load_dataset("lvwerra/red-wine", split="train", streaming=True) item = next(iter(ds)) item ```
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I_kwDODunzps5VUbTS
5,183
Loading an external dataset in a format similar to conll2003
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2022-11-01T13:18:29Z
2022-11-02T11:57:50Z
2022-11-02T11:57:50Z
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I'm trying to load a custom dataset in a Dataset object, it's similar to conll2003 but with 2 columns only (word entity), I used the following script: features = datasets.Features( {"tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=["B-PER", .... etc.]))} ) from datasets import Dataset INPUT_COLUMNS = "tokens ner_tags".split(" ") def read_conll(file): #all_labels = [] example = {col: [] for col in INPUT_COLUMNS} idx = 0 with open(file) as f: for line in f: if line: if line.startswith("-DOCSTART-") and example["tokens"] != []: print(idx, example) yield idx, example idx += 1 example = {col: [] for col in INPUT_COLUMNS} elif line == "\n" or (line.startswith("-DOCSTART-") and example["tokens"] == []): continue else: row_cols = line.split(" ") for i, col in enumerate(example): example[col] = row_cols[i].rstrip() dset = Dataset.from_generator(read_conll, gen_kwargs={"file": "/content/new_train.txt"}, features = features) The following error happened: [/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <genexpr>(.0) 285 for key in unique_values(itertools.chain(*dicts)): # set merge all keys 286 # Will raise KeyError if the dict don't have the same keys --> 287 yield key, tuple(d[key] for d in dicts) 288 TypeError: tuple indices must be integers or slices, not str What does this mean and what should I modify?
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PR_kwDODunzps5Mjxoo
5,656
Fix `fsspec.open` when using an HTTP proxy
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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...
2023-03-21T15:23:29Z
2023-03-23T14:14:50Z
2023-03-23T13:15:46Z
CONTRIBUTOR
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Most HTTP(S) downloads from this library support proxy automatically by reading the `HTTP_PROXY` environment variable (et al.) because `requests` is widely used. However, in some parts of the code, `fsspec` is used, which in turn uses `aiohttp` for HTTP(S) requests (as opposed to `requests`), which in turn doesn't support reading proxy env variables by default. This PR enables reading them automatically. Read [aiohttp docs on using proxies](https://docs.aiohttp.org/en/stable/client_advanced.html?highlight=trust_env#proxy-support). For context, [the Python library requests](https://requests.readthedocs.io/en/latest/user/advanced/?highlight=http_proxy#proxies) and [the official Python library via `urllib.urlopen` support this automatically by default](https://docs.python.org/3/library/urllib.request.html#urllib.request.urlopen). Many (most common ones?) programs also do the same, including cURL, APT, Wget, and many others.
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I_kwDODunzps5S7V05
5,045
Automatically revert to last successful commit to hub when a push_to_hub is interrupted
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[ "Could you share the error you got please ? Maybe the full stack trace if you have it ?\r\n\r\nMaybe `push_to_hub` be implemented as a single commit @Wauplin ? This way if it fails, the repo is still at the previous (valid) state instead of ending-up in an invalid/incimplete state.", "> Maybe push_to_hub be imple...
2022-09-29T18:08:12Z
2023-10-16T13:30:49Z
2023-10-16T13:30:49Z
NONE
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**Is your feature request related to a problem? Please describe.** I pushed a modification of a large dataset (remove a column) to the hub. The push was interrupted after some files were committed to the repo. This left the dataset to raise an error on load_dataset() (ValueError couldn’t cast … because column names don’t match). Only by specifying the previous (complete) commit as revision=commit_hash in load_data(), I was able to repair this and after a successful, complete push, the dataset loads without error again. **Describe the solution you'd like** Would it make sense to detect an incomplete push_to_hub() and automatically revert to the previous commit/revision? **Describe alternatives you've considered** Leave everything as is, the revision parameter in load_dataset() allows to manually fix this problem. **Additional context** Provide useful defaults
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1,860
Add loading from the Datasets Hub + add relative paths in download manager
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[ "I just added the steps to share a dataset on the datasets hub. It's highly inspired by the steps to share a model in the `transformers` doc.\r\n\r\nMoreover once the new huggingface_hub is released we can update the version in the setup.py. We also need to update the command to create a dataset repo in the documen...
2021-02-10T13:24:11Z
2021-02-12T19:13:30Z
2021-02-12T19:13:29Z
MEMBER
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With the new Datasets Hub on huggingface.co it's now possible to have a dataset repo with your own script and data. For example: https://huggingface.co/datasets/lhoestq/custom_squad/tree/main contains one script and two json files. You can load it using ```python from datasets import load_dataset d = load_dataset("lhoestq/custom_squad") ``` To be able to use the data files that live right next to the dataset script on the repo in the hub, I added relative paths support for the DownloadManager. For example in the repo mentioned above, there are two json files that can be downloaded via ```python _URLS = { "train": "train-v1.1.json", "dev": "dev-v1.1.json", } downloaded_files = dl_manager.download_and_extract(_URLS) ``` To make it work, I set the `base_path` of the DownloadManager to be the parent path of the dataset script (which comes from either a local path or a remote url). I also had to add the auth header of the requests to huggingface.co for private datasets repos. The token is fetched from [huggingface_hub](https://github.com/huggingface/huggingface_hub).
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759,844,137
MDExOlB1bGxSZXF1ZXN0NTM0Nzc2ODE5
1,346
Add MultiBooked dataset
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[ "There' still an issue with the dummy data, let me take a look" ]
2020-12-08T22:49:36Z
2020-12-15T17:02:09Z
2020-12-15T17:02:09Z
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Add dataset.
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5,784
Raise subprocesses traceback when interrupting
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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...
2023-04-24T10:34:03Z
2023-04-26T16:04:42Z
2023-04-26T15:54:44Z
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When a subprocess hangs in `filter` or `map`, one should be able to get the subprocess' traceback when interrupting the main process. Right now it shows nothing. To do so I `.get()` the subprocesses async results even the main process is stopped with e.g. `KeyboardInterrupt`. I added a timeout in case the subprocess is hanging or crashed.
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MDExOlB1bGxSZXF1ZXN0NTQ2MjgwODIy
1,654
lener_br dataset: add instances and data splits info
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2020-12-29T00:35:12Z
2020-12-30T16:49:32Z
2020-12-30T16:49:32Z
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1,248,671,778
I_kwDODunzps5KbTgi
4,407
Dataset Viewer issue for conll2012_ontonotesv5
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[ "Thanks for reporting, @jiangwy99.\r\n\r\nI guess this could be addressed only once we fix our issue with irresponsive backend endpoint.\r\n\r\nCC: @severo ", "I've just sent the forcing of the refresh of the preview to the new endpoint.", "Fixed, thanks for the patience. The issue was the amount of RAM allowed...
2022-05-25T20:18:33Z
2022-06-07T18:39:16Z
2022-06-07T18:39:16Z
NONE
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### Link https://huggingface.co/datasets/conll2012_ontonotesv5 ### Description Dataset viewer outage. ### Owner No
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2,355
normalized TOCs and titles in data cards
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[ "Oh right! I'd be in favor of still having the same TOC across the board, we can either leave it as is or add a `[More Info Needed]` `Contributions` Section wherever it's currently missing, wdyt?", "(I thought those were programmatically updated based on git history :D )", "Merging for now to avoid conflict sin...
2021-05-12T20:59:59Z
2021-05-14T13:23:12Z
2021-05-14T13:23:12Z
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I started fixing some of the READMEs that were failing the tests introduced by @gchhablani but then realized that there were some consistent differences between earlier and newer versions of some of the titles (e.g. Data Splits vs Data Splits Sample Size, Supported Tasks vs Supported Tasks and Leaderboards). We also had different versions of the Table of Content This PR normalizes all of them to the newer version
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Fix link in docs
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2023-04-13T20:45:19Z
2023-04-14T13:15:38Z
2023-04-14T13:08:42Z
CONTRIBUTOR
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Fixes a broken link in the use_with_pytorch docs
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Persian Abstractive/Extractive Text Summarization
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2020-12-24T17:47:12Z
2021-01-04T15:11:04Z
2021-01-04T15:11:04Z
CONTRIBUTOR
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null
null
Assembling datasets tailored to different tasks and languages is a precious target. This would be great to have this dataset included. ## Adding a Dataset - **Name:** *pn-summary* - **Description:** *A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.* - **Paper:** *https://arxiv.org/abs/2012.11204* - **Data:** *https://github.com/hooshvare/pn-summary/#download* - **Motivation:** *It is the first Persian abstractive/extractive Text summarization dataset (like cnn_dailymail for English)!* 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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1,431,029,547
I_kwDODunzps5VS8cr
5,182
Add notebook / other resource links to the task-specific data loading guides
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[ "Yea this would be great! We would need an object detection tutorial notebook too if it doesn't already exist there. ", "There is one: https://huggingface.co/docs/datasets/object_detection.\r\n\r\nI will start the work. " ]
2022-11-01T07:57:26Z
2022-11-03T01:49:57Z
2022-11-03T01:49:57Z
MEMBER
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Does it make sense to include links to notebooks / scripts that show how to use a dataset for training / fine-tuning a model? For example, here in [https://huggingface.co/docs/datasets/image_classification] we could include a mention of https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb. Applies to https://huggingface.co/docs/datasets/object_detection as well. Cc: @osanseviero @nateraw
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4,328
Fix and clean Apache Beam functionality
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-05-12T11:41:07Z
2022-05-24T13:43:11Z
2022-05-24T13:34:32Z
MEMBER
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5,732
Enwik8 should support the standard split
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[ "#self-assign", "The Enwik8 pipeline is not present in this codebase, and is hosted elsewhere. I have opened a PR [there](https://huggingface.co/datasets/enwik8/discussions/4) instead. " ]
2023-04-11T08:38:53Z
2023-04-11T09:28:17Z
2023-04-11T09:28:16Z
NONE
null
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null
### Feature request The HuggingFace Datasets library currently supports two BuilderConfigs for Enwik8. One config yields individual lines as examples, while the other config yields the entire dataset as a single example. Both support only a monolithic split: it is all grouped as "train". The HuggingFace Datasets library should include a BuilderConfig for Enwik8 with train, validation, and test sets derived from the first 90 million bytes, next 5 million bytes, and last 5 million bytes, respectively. This Enwik8 split is standard practice in LM papers, as elaborated and motivated below. ### Motivation Enwik8 is commonly split into 90M, 5M, 5M consecutive bytes. This is done in the Transformer-XL [codebase](https://github.com/kimiyoung/transformer-xl/blob/44781ed21dbaec88b280f74d9ae2877f52b492a5/getdata.sh#L34), and is additionally mentioned in the Sparse Transformers [paper](https://arxiv.org/abs/1904.10509) and the Compressive Transformers [paper](https://arxiv.org/abs/1911.05507). This split is pretty much universal among language modeling papers. One may obtain the splits by manual wrangling, using the data yielded by the ```enwik8-raw``` BuilderConfig. However, this undermines the seamless functionality of the library: one must slice the single raw example, extract it into three tensors, and wrap each in a separate dataset. This becomes even more of a nuisance if using the current Enwik8 HuggingFace dataset as a TfdsDataSource with [SeqIO](https://github.com/google/seqio), where a pipeline of preprocessors is typically included in a SeqIO Task definition, to be applied immediately after loading the data with TFDS. ### Your contribution Supporting this functionality in HuggingFace Datasets will only require an additional BuilderConfig for Enwik8 and a few additional lines of code. I will submit a PR.
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Image decoding often fails when transforming Image datasets
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[ "A quick hack I have found is that we can call the image first before running the transforms and it makes sure the image is decoded before being passed on.\r\n\r\nFor this I just needed to add `example['img'] = example['img']` to the top of my `generate_flipped_data` function, defined above, so that image decode in...
2022-04-07T19:17:25Z
2022-04-13T14:01:16Z
2022-04-13T14:01:16Z
NONE
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## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|█▉ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0
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MDExOlB1bGxSZXF1ZXN0NTYyODI1NTI3
1,787
Update the CommonGen citation information
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2021-01-27T22:12:47Z
2021-01-28T13:56:29Z
2021-01-28T13:56:29Z
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5,956
Fix ArrowExamplesIterable.shard_data_sources
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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...
2023-06-14T13:50:38Z
2023-06-14T14:43:12Z
2023-06-14T14:33:45Z
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ArrowExamplesIterable.shard_data_sources was outdated I also fixed a warning message by not using format_type= in with_format()
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5,527
Fix benchmarks CI - pin protobuf
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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...
2023-02-12T11:51:25Z
2023-02-13T10:29:03Z
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fix https://github.com/huggingface/datasets/actions/runs/4156059127/jobs/7189576331
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The current implementation has a potential bug in the sort method
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[ "Thanks for reporting, @wangyuxinwhy. " ]
2023-06-30T03:16:57Z
2023-06-30T14:21:03Z
2023-06-30T14:11:25Z
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### Describe the bug In the sort method,here's a piece of code ```python # column_names: Union[str, Sequence_[str]] # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): column_names = [column_names] ``` I get an error when I pass in a tuple based on the column_names type annotation, it will raise an errror.As in the example below, while the type annotation implies that a tuple can be passed. ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` Of course, after I modified the tuple into a list, everything worked fine Change the code to the following so there will be no problem ```python # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): if isinstance(column_names, str): column_names = [column_names] else: column_names = list(column_names) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` ### Expected behavior Passing tuple into column_names should be equivalent to passing list ### Environment info - `datasets` version: 2.13.0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2
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5,605
Update README logo
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Are you sure it's safe to remove? https://github.com/huggingface/datasets/pull/3866", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benc...
2023-03-03T15:46:31Z
2023-03-03T21:57:18Z
2023-03-03T21:50:17Z
CONTRIBUTOR
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Is dataset timit_asr broken?
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[ "Hi,\r\n\r\nthanks for the report, but this is a duplicate of #2052. ", "@mariosasko \r\nThank you for your quick response! Following #2052, I've fixed the problem." ]
2021-03-28T08:30:18Z
2021-03-28T12:29:25Z
2021-03-28T12:29:25Z
NONE
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Using `timit_asr` dataset, I saw all records are the same. ``` python from datasets import load_dataset, load_metric timit = load_dataset("timit_asr") from datasets import ClassLabel import random import pandas as pd from IPython.display import display, HTML def show_random_elements(dataset, num_examples=10): assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset." picks = [] for _ in range(num_examples): pick = random.randint(0, len(dataset)-1) while pick in picks: pick = random.randint(0, len(dataset)-1) picks.append(pick) df = pd.DataFrame(dataset[picks]) display(HTML(df.to_html())) show_random_elements(timit['train'].remove_columns(["file", "phonetic_detail", "word_detail", "dialect_region", "id", "sentence_type", "speaker_id"]), num_examples=20) ``` `output` <img width="312" alt="Screen Shot 2021-03-28 at 17 29 04" src="https://user-images.githubusercontent.com/42398050/112746646-21acee80-8feb-11eb-84f3-dbb5d4269724.png"> I double-checked it [here](https://huggingface.co/datasets/viewer/), and met the same problem. <img width="1374" alt="Screen Shot 2021-03-28 at 17 32 07" src="https://user-images.githubusercontent.com/42398050/112746698-9bdd7300-8feb-11eb-97ed-5babead385f4.png">
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Sync Features dictionaries
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-03-23T19:23:51Z
2022-04-13T15:52:27Z
2022-04-13T15:46:19Z
CONTRIBUTOR
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This PR adds a wrapper to the `Features` class to keep the secondary dict, `_column_requires_decoding`, aligned with the main dict (as discussed in https://github.com/huggingface/datasets/pull/3723#discussion_r806912731). A more elegant approach would be to subclass `UserDict` and override `__setitem__` and `__delitem__`, but this PR doesn't implement it for the following reasons: * it requires replacing all occurrences of `isinstance(obj, dict)` with `isinstance(obj, Mapping)`, which is five times slower than `isinstance(obj, dict)` on my machine, in `features.py` * is a breaking change, i.e., `isinstance(Features(...), dict)` would return `False` after it * IMO, it makes sense to be consistent in the user-facing API and subclass either `dict` or `UserDict`. The problem with the latter is that it can't be used for `DatasetDict` because `DatasetDict` exposes the `data` property, which is also used by `UserDict`, so this would result in a collision.
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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...
2023-03-07T13:22:41Z
2023-03-07T13:47:01Z
2023-03-07T13:39:02Z
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close https://github.com/huggingface/datasets/issues/5618
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Exceeded maximum rows when reading large files
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[ "Hi @zijwang, thanks for reporting this issue.\r\n\r\nYou did not mention which `datasets` version you are using, but looking at the code in the stack trace, it seems you are using an old version.\r\n\r\nCould you please update `datasets` (`pip install -U datasets`) and check if the problem persists?" ]
2021-09-29T04:49:22Z
2021-10-12T06:05:42Z
2021-10-12T06:05:42Z
NONE
null
null
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## Describe the bug A clear and concise description of what the bug is. When using `load_dataset` with json files, if the files are too large, there will be "Exceeded maximum rows" error. ## Steps to reproduce the bug ```python dataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file ``` ## Expected results No error ## Actual results ``` ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 134 with open(file, encoding="utf-8") as f: --> 135 dataset = json.load(f) 136 except json.JSONDecodeError: ~/anaconda3/envs/python/lib/python3.9/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 292 """ --> 293 return loads(fp.read(), 294 cls=cls, object_hook=object_hook, ~/anaconda3/envs/python/lib/python3.9/json/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 345 parse_constant is None and object_pairs_hook is None and not kw): --> 346 return _default_decoder.decode(s) 347 if cls is None: ~/anaconda3/envs/python/lib/python3.9/json/decoder.py in decode(self, s, _w) 339 if end != len(s): --> 340 raise JSONDecodeError("Extra data", s, end) 341 return obj JSONDecodeError: Extra data: line 2 column 1 (char 20321) During handling of the above exception, another exception occurred: ArrowInvalid Traceback (most recent call last) <ipython-input-20-ab3718a6482f> in <module> ----> 1 dataset = load_dataset('json', data_files=data_files) ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs) 841 842 # Download and prepare data --> 843 builder_instance.download_and_prepare( 844 download_config=download_config, 845 download_mode=download_mode, ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 606 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 607 if not downloaded_from_gcs: --> 608 self._download_and_prepare( 609 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 610 ) ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 684 try: 685 # Prepare split will record examples associated to the split --> 686 self._prepare_split(split_generator, **prepare_split_kwargs) 687 except OSError as e: 688 raise OSError( ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1153 generator = self._generate_tables(**split_generator.gen_kwargs) 1154 with ArrowWriter(features=self.info.features, path=fpath) as writer: -> 1155 for key, table in utils.tqdm( 1156 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET) 1157 ): ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 135 dataset = json.load(f) 136 except json.JSONDecodeError: --> 137 raise e 138 raise ValueError( 139 f"Not able to read records in the JSON file at {file}. " ~/anaconda3/envs/python/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 114 while True: 115 try: --> 116 pa_table = paj.read_json( 117 BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) 118 ) ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json() ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/anaconda3/envs/python/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Exceeded maximum rows ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Linux - Python version: 3.9 - PyArrow version: 4.0.1
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Fix missing tags in dataset cards
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2022-08-25T16:41:43Z
2022-09-22T14:37:16Z
2022-08-26T04:41:48Z
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Fix missing tags in dataset cards: - anli - coarse_discourse - commonsense_qa - cos_e - ilist - lc_quad - web_questions - xsum This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. Related to: - #4833 - #4891
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Metric kwargs are not passed to underlying external metric f1_score
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[ "Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst, note that `\"min\"` is not an allowed value for `average`. According to scikit-learn [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), `average` can only take the values: `{\"micro\", \"macro\", \"samples\", \"weigh...
2021-07-18T08:32:31Z
2021-07-18T18:36:05Z
2021-07-18T11:19:04Z
CONTRIBUTOR
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## Describe the bug When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so. ## Steps to reproduce the bug ```python import datasets f1 = datasets.load_metric("f1", keep_in_memory=True, average="min") f1.add_batch(predictions=[0,2,3], references=[1, 2, 3]) f1.compute() ``` ## Expected results No error, because `average="min"` should be passed correctly to f1_score in sklearn. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute "f1": f1_score( File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score return fbeta_score(y_true, y_pred, beta=1, labels=labels, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score _, _, f, _ = precision_recall_fscore_support(y_true, y_pred, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support labels = _check_set_wise_labels(y_true, y_pred, average, labels, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels raise ValueError("Target is %s but average='binary'. Please " ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted']. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.2 - PyArrow version: 4.0.1
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PR_kwDODunzps5bhgs7
6,268
Add repo_id to DatasetInfo
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6268). All of your documentation changes will be reflected on that endpoint.", "In https://github.com/huggingface/datasets/issues/4129 we want to track the origin of a dataset, e.g. if it comes from multiple datasets.\r\n\r\nI ...
2023-09-29T10:24:55Z
2023-10-01T15:29:45Z
null
MEMBER
null
1
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```python from datasets import load_dataset ds = load_dataset("lhoestq/demo1", split="train") ds = ds.map(lambda x: {}, num_proc=2).filter(lambda x: True).remove_columns(["id"]) print(ds.repo_id) # lhoestq/demo1 ``` - repo_id is None when the dataset doesn't come from the Hub, e.g. from Dataset.from_dict - repo_id is set to None when concatenating datasets with different repo ids related to https://github.com/huggingface/datasets/issues/4129 TODO: - [ ] discuss if it's ok for now - [ ] tests
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989,453,069
MDExOlB1bGxSZXF1ZXN0NzI4MTkzMjkz
2,872
adding swedish_medical_ner
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2021-09-06T22:00:52Z
2021-09-07T04:36:32Z
2021-09-07T04:36:32Z
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Adding the Swedish Medical NER dataset, listed in "Biomedical Datasets - BigScience Workshop 2021"
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1,065,345,853
PR_kwDODunzps4vGBig
3,332
Fix error message and add extension fallback
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2021-11-28T14:25:29Z
2021-11-29T13:34:15Z
2021-11-29T13:34:14Z
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Fix the error message raised if `infered_module_name` is `None` in `CommunityDatasetModuleFactoryWithoutScript.get_module` and make `infer_module_for_data_files` more robust. In the linked issue, `infer_module_for_data_files` returns `None` because `json` is the second most common extension due to the suffix ordering. Now, we go from the most common to the least common extension and try to map it or return `None`. Fix #3331
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1,150,646,899
PR_kwDODunzps4zedMa
3,790
Add doc builder scripts
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[ "I think we're only missing the hosted runner to be configured for this repository and we should be good", "Regarding the self-hosted runner, I actually encourage using the approach defined here: https://github.com/huggingface/transformers/pull/15710, which doesn't leverage a self-hosted runner. This prevents que...
2022-02-25T16:38:47Z
2022-03-01T15:55:42Z
2022-03-01T15:55:41Z
MEMBER
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I added the three scripts: - build_dev_documentation.yml - build_documentation.yml - delete_dev_documentation.yml I got them from `transformers` and did a few changes: - I removed the `transformers`-specific dependencies - I changed all the paths to be "datasets" instead of "transformers" - I passed the `--library_name datasets` arg to the `doc-builder build` command (according to https://github.com/huggingface/doc-builder/pull/94/files#diff-bcc33cf7c223511e498776684a9a433810b527a0a38f483b1487e8a42b6575d3R26) cc @LysandreJik @mishig25
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MDU6SXNzdWU3NzcyNjM2NTE=
1,673
Unable to Download Hindi Wikipedia Dataset
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[ "Currently this dataset is only available when the library is installed from source since it was added after the last release.\r\n\r\nWe pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.\r\n\r\nWe'll see if we can provide access ...
2021-01-01T10:52:53Z
2021-01-05T10:22:12Z
2021-01-05T10:22:12Z
NONE
null
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I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue. ![Code](https://user-images.githubusercontent.com/30871963/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png) ![Error](https://user-images.githubusercontent.com/30871963/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)
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5,652
Copy features
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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...
2023-03-20T17:17:23Z
2023-03-23T13:19:19Z
2023-03-23T13:12:08Z
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Some users (even internally at HF) are doing ```python dset_features = dset.features dset_features.pop(col_to_remove) dset = dset.map(..., features=dset_features) ``` Right now this causes issues because it modifies the features dict in place before the map. In this PR I modified `dset.features` to return a copy of the features, so that users can modify it if they want.
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784,023,338
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1,724
could not run models on a offline server successfully
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[ "Transferred to `datasets` based on the stack trace.", "Hi @lkcao !\r\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/...
2021-01-12T06:08:06Z
2022-10-05T12:39:07Z
2022-10-05T12:39:07Z
NONE
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Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
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Adapt image datasets
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[ "This PR can be merged after #3163 is merged (this PR is pretty big because I was working on the forked branch).\r\n\r\n@lhoestq @albertvillanova Could you please take a look at the changes in `src/datasets/utils/streaming_download_manager.py`? These changes were required to support streaming of the `cats_vs_dogs` ...
2021-12-01T19:52:01Z
2021-12-09T18:37:42Z
2021-12-09T18:37:41Z
CONTRIBUTOR
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This PR: * adapts the ImageClassification template to use the new Image feature * adapts the following datasets to use the new Image feature: * beans (+ fixes streaming) * cast_vs_dogs (+ fixes streaming) * cifar10 * cifar100 * fashion_mnist * mnist * head_qa cc @nateraw
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XSum dataset download link broken
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[ "Never mind, I ran it again and it worked this time. Strange." ]
2021-02-27T21:47:56Z
2021-02-27T21:50:16Z
2021-02-27T21:50:16Z
NONE
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I did ``` from datasets import load_dataset dataset = load_dataset("xsum") ``` This returns `ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz`
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Use prefix to allow exceed Windows MAX_PATH
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[ "Does this mean the `FileNotFoundError` that avoids infinite loop can be removed?", "Yes, I think so...", "Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?", " > Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?\r\n\r\nWhat about converting relative p...
2021-07-09T16:39:53Z
2021-07-16T15:28:12Z
2021-07-16T15:28:11Z
MEMBER
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By using this prefix, you can exceed the Windows MAX_PATH limit. See: https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces Related to #2524, #2220.
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HfHubHTTPError - exceeded our hourly quotas for action: commit
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[ "how is your dataset structured? (file types, how many commits and files are you trying to push, etc)", "I succeeded in uploading it after several attempts with an hour gap between each attempt (inconvenient but worked). The final dataset is [here](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2), code ...
2023-09-25T06:11:43Z
2023-10-16T13:30:49Z
2023-10-16T13:30:48Z
NONE
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### Describe the bug I try to upload a very large dataset of images, and get the following error: ``` File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/hf_api.py:2712, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future) 2710 try: 2711 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params) -> 2712 hf_raise_for_status(commit_resp, endpoint_name="commit") 2713 except RepositoryNotFoundError as e: 2714 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE) File /fsx-multigen/yuvalkirstain/miniconda/envs/pickapic/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name) 297 raise BadRequestError(message, response=response) from e 299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information 300 # as well (request id and/or server error message) --> 301 raise HfHubHTTPError(str(e), response=response) from e HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/yuvalkirstain/pickapic_v2/commit/main (Request ID: Root=1-65112399-12d63f7d7f28bfa40a36a0fd) You have exceeded our hourly quotas for action: commit. We invite you to retry later. ``` this makes it much less convenient to host large datasets on HF hub. ### Steps to reproduce the bug Upload a very large dataset of images ### Expected behavior the upload to work well ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-1033-aws-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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consumer-finance-complaints dataset not loading
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[ "Thanks for reporting, @adiprasad.\r\n\r\nWe are having a look at it.", "I have opened an issue in that dataset Community tab on the Hub: https://huggingface.co/datasets/consumer-finance-complaints/discussions/1\r\n\r\nPlease note that in the meantime, you can load the dataset by passing `ignore_verifications=Tru...
2022-11-17T20:10:26Z
2022-11-18T10:16:53Z
null
NONE
null
null
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### Describe the bug Error during dataset loading ### Steps to reproduce the bug ``` >>> import datasets >>> cf_raw = datasets.load_dataset("consumer-finance-complaints") Downloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8.42k/8.42k [00:00<00:00, 3.33MB/s] Downloading metadata: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.60k/5.60k [00:00<00:00, 2.90MB/s] Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16.6k/16.6k [00:00<00:00, 510kB/s] Downloading and preparing dataset consumer-finance-complaints/default to /root/.cache/huggingface/datasets/consumer-finance-complaints/default/0.0.0/30e483d37fb4b25bb98cad1bfd2dc48f6ed6d1f3371eb4568c625a61d1a79b69... Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 511M/511M [00:04<00:00, 103MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/load.py", line 1741, in load_dataset builder_instance.download_and_prepare( File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare super()._download_and_prepare( File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 931, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=1605177353, num_examples=2455765, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=2043641693, num_examples=3079747, shard_lengths=[721000, 656000, 788000, 846000, 68747], dataset_name='consumer-finance-complaints')}] ``` ### Expected behavior dataset should load ### Environment info >>> datasets.__version__ '2.7.0' Python 3.8.10 "Ubuntu 20.04.4 LTS"
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Adding PersiNLU reading-comprehension
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[ "@lhoestq I think I have addressed all your comments. ", "Thanks! @lhoestq Let me know if you want me to address anything to get this merged. ", "It's all good thanks ;)\r\nmerging" ]
2021-03-11T04:41:13Z
2021-03-15T09:39:57Z
2021-03-15T09:39:57Z
CONTRIBUTOR
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Support for legal NLP datasets (EURLEX and ECtHR cases)
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2021-03-25T16:24:17Z
2021-03-25T18:39:31Z
2021-03-25T18:34:31Z
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Add support for two legal NLP datasets: - EURLEX (https://www.aclweb.org/anthology/P19-1636/) - ECtHR cases (https://arxiv.org/abs/2103.13084)
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Support `topdown` parameter in `xwalk`
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I like the `kwargs` approach, thanks!" ]
2022-11-28T14:42:41Z
2022-12-09T12:58:55Z
2022-12-09T12:55:59Z
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Add support for the `topdown` parameter in `xwalk` when `fsspec>=2022.11.0` is installed.
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Fix empty splitinfo json
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[ "<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 | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-09-04T13:13:53Z
2023-09-04T14:58:34Z
2023-09-04T14:47:17Z
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If a split is empty, then the JSON split info should mention num_bytes = 0 and num_examples = 0. Until now they were omited because the JSON dumps ignore the fields that are equal to the default values. This is needed in datasets-server since we parse this information to the viewer
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Unable to upload "community provided" dataset - 400 Client Error
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[ "Hi ! We're in the process of switching the community datasets to git repos, exactly like what we're doing for models.\r\nYou can find an example here:\r\nhttps://huggingface.co/datasets/lhoestq/custom_squad/tree/main\r\n\r\nWe'll update the CLI in the coming days and do a new release :)\r\n\r\nAlso cc @julien-c ma...
2021-02-10T10:39:01Z
2021-08-03T05:06:13Z
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Hi, i'm trying to a upload a dataset as described [here](https://huggingface.co/docs/datasets/v1.2.0/share_dataset.html#sharing-a-community-provided-dataset). This is what happens: ``` $ datasets-cli login $ datasets-cli upload_dataset my_dataset About to upload file /path/to/my_dataset/dataset_infos.json to S3 under filename my_dataset/dataset_infos.json and namespace username About to upload file /path/to/my_dataset/my_dataset.py to S3 under filename my_dataset/my_dataset.py and namespace username Proceed? [Y/n] Y Uploading... This might take a while if files are large 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/presign huggingface.co migrated to a new model hosting system. You need to upgrade to transformers v3.5+ to upload new models. More info at https://discuss.hugginface.co or https://twitter.com/julien_c. Thank you! ``` I'm using the latest releases of datasets and transformers.
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Add Google BLEU (aka GLEU) metric
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2021-10-19T14:48:38Z
2021-10-25T14:07:04Z
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This PR adds the NLTK implementation of Google BLEU metric. This is also a part of an effort to resolve an unfortunate naming collision between GLEU for machine translation and GLEU for grammatical error correction. I used [this page](https://huggingface.co/docs/datasets/add_metric.html) for reference. Please, point me to the right direction if I missed anything.
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More control over TQDM when using map/filter with multiple processes
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[ "Hi,\r\n\r\nIt's hard to provide an API that would cover all use-cases with tqdm in this project.\r\n\r\nHowever, you can make it work by defining a custom decorator (a bit hacky tho) as follows:\r\n```python\r\nimport datasets\r\n\r\ndef progress_only_on_rank_0(func):\r\n def wrapper(*args, **kwargs):\r\n ...
2021-10-29T11:56:16Z
2023-02-13T20:16:40Z
2023-02-13T20:16:40Z
CONTRIBUTOR
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It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets. ```python dataset.map(lambda examples: tokenize(examples["text"]), batched=True, num_proc=6) ``` The above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down. ``` #0: 0%| | 0/13 [00:00<?, ?ba/s] #1: 0%| | 0/13 [00:00<?, ?ba/s] #2: 0%| | 0/13 [00:00<?, ?ba/s] #3: 0%| | 0/13 [00:00<?, ?ba/s] #4: 0%| | 0/13 [00:00<?, ?ba/s] #5: 0%| | 0/13 [00:00<?, ?ba/s] #0: 8%| | 1/13 [00:00<?, ?ba/s] #1: 8%| | 1/13 [00:00<?, ?ba/s] ... ``` Instead, it would be welcome if we had the option to only show the progress of rank 0.
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Fix exact match
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-04-13T20:28:06Z
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Clarify docs and add clarifying example to the exact_match metric
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Doc2dial update data_infos and data_loaders
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2021-03-12T14:39:29Z
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Pip-compile: Could not find a version that matches dill<0.3.6,>=0.3.6
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[ "Thanks for reporting, @Rijgersberg.\r\n\r\nWe were waiting for the release of `dill` 0.3.6, that happened 2 days ago (24 Oct 2022): https://github.com/uqfoundation/dill/releases/tag/dill-0.3.6\r\n- See comment: https://github.com/huggingface/datasets/pull/4397#discussion_r880629543\r\n\r\nAlso `multiprocess` 0.70....
2022-10-25T13:23:50Z
2022-11-14T08:25:37Z
2022-10-28T05:38:15Z
NONE
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### Describe the bug When using `pip-compile` (part of `pip-tools`) to generate a pinned requirements file that includes `datasets`, a version conflict of `dill` appears. It is caused by a transitive dependency conflict between `datasets` and `multiprocess`. ### Steps to reproduce the bug ```bash $ echo "datasets" > requirements.in $ pip install pip-tools $ pip-compile requirements.in Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets==2.6.1->-r requirements.in (line 1)) Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6 Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1 There are incompatible versions in the resolved dependencies: dill<0.3.6 (from datasets==2.6.1->-r requirements.in (line 1)) dill>=0.3.6 (from multiprocess==0.70.14->datasets==2.6.1->-r requirements.in (line 1)) ``` ### Expected behavior A correctly generated file `requirements.txt` with pinned dependencies ### Environment info Tested with versions `2.6.1, 2.6.0, 2.5.2` on Python 3.8 and 3.10 on Ubuntu 20.04LTS and Python 3.10 on MacOS 12.6 (M1).
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[WIP] Add ArrayXD support for fixed size list.
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[ "Awesome thanks ! To fix the CI you just need to merge master into your branch.\r\nThe error is unrelated to your PR" ]
2021-04-16T13:04:08Z
2022-07-06T15:19:48Z
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Add support for fixed size list for ArrayXD when shape is known . See https://github.com/huggingface/datasets/issues/2146 Since offset are not stored anymore, the file size is now roughly equal to the actual data size.
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