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https://api.github.com/repos/huggingface/datasets/issues/1490
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1,490
ADD: opus_rf dataset for translation
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[ "merging since the CI is fixed on master" ]
2020-12-11T21:16:43Z
2020-12-13T19:12:24Z
2020-12-13T19:12:24Z
CONTRIBUTOR
null
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Passed all local tests. Hopefully passes all Circle CI tests too. Tried to keep the commit history clean.
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4,539
Replace deprecated logging.warn with logging.warning
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2022-06-22T08:32:29Z
2022-06-22T13:43:23Z
2022-06-22T12:51:51Z
CONTRIBUTOR
null
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Replace `logging.warn` (deprecated in [Python 2.7, 2011](https://github.com/python/cpython/commit/04d5bc00a219860c69ea17eaa633d3ab9917409f)) with `logging.warning` (added in [Python 2.3, 2003](https://github.com/python/cpython/commit/6fa635df7aa88ae9fd8b41ae42743341316c90f7)). * https://docs.python.org/3/library/logging.html#logging.Logger.warning * https://github.com/python/cpython/issues/57444
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I_kwDODunzps5XHQV1
5,284
Features of IterableDataset set to None by remove column
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[ "Related to https://github.com/huggingface/datasets/issues/5245", "#self-assign", "Thanks @lhoestq and @alvarobartt!\r\n\r\nThis would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working t...
2022-11-23T10:54:59Z
2023-02-02T09:05:51Z
2022-11-28T12:53:24Z
CONTRIBUTOR
null
null
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### Describe the bug The `remove_column` method of the IterableDataset sets the dataset features to None. ### Steps to reproduce the bug ```python from datasets import Audio, load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features print("Original features: ", dataset.features.keys()) # define features to remove: we KEEP audio and text COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id'] dataset = dataset.remove_columns(COLUMNS_TO_REMOVE) # check processed features, uh-oh! print("Processed features: ", dataset.features) # streaming the first audio sample still works print("First sample:", next(iter(ds))) ``` **Print Output:** ``` Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id']) Processed features: None First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656, -0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"} ``` ### Expected behavior The features should be those **not** removed by the `remove_column` method, i.e. audio and text. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.15 - PyArrow version: 9.0.0 - Pandas version: 1.3.5 (Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml) cc @polinaeterna @lhoestq
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3,843
Fix Google Drive URL to avoid Virus scan warning in streaming mode
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3843). All of your documentation changes will be reflected on that endpoint.", "Cool ! Looks like it breaks `test_streaming_gg_drive_gzipped` for some reason..." ]
2022-03-07T13:09:19Z
2022-03-15T12:30:25Z
2022-03-15T12:30:23Z
CONTRIBUTOR
null
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The streaming version of https://github.com/huggingface/datasets/pull/3787. Fix #3835 CC: @albertvillanova
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3,237
wikitext description wrong
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[ "Hi @hongyuanmei, thanks for reporting.\r\n\r\nI'm fixing it.", "Duplicate of:\r\n- #795" ]
2021-11-09T04:06:52Z
2022-02-14T15:45:11Z
2021-11-09T13:49:28Z
NONE
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## Describe the bug Descriptions of the wikitext datasests are wrong. ## Steps to reproduce the bug Please see: https://github.com/huggingface/datasets/blob/f6dcafce996f39b6a4bbe3a9833287346f4a4b68/datasets/wikitext/wikitext.py#L50 ## Expected results The descriptions for raw-v1 and v1 should be switched.
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5,735
Implement sharding on merged iterable datasets
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi ! What if one of the sub-iterables only has one shard ? In that case I don't think we'd end up with a correctly interleaved dataset, since only rank 0 would yield examples from this sub-iterable", "Hi ! \r\nI just tested this ou...
2023-04-11T10:02:25Z
2023-04-27T16:39:04Z
2023-04-27T16:32:09Z
CONTRIBUTOR
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This PR allows sharding of merged iterable datasets. Merged iterable datasets with for instance the `interleave_datasets` command are comprised of multiple sub-iterable, one for each dataset that has been merged. With this PR, sharding a merged iterable will result in multiple merged datasets each comprised of sharded sub-iterable, ensuring that there is no duplication of data. As a result it is now possible to set any amount of workers in the dataloader as long as it is lower or equal to the lowest amount of shards amongst the datasets. Before it had to be set to 0. I previously talked about this issue on the forum [here](https://discuss.huggingface.co/t/interleaving-iterable-dataset-with-num-workers-0/35801)
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Loading of datasets changed in #3110 returns no examples
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[ "Hi @eladsegal, thanks for reporting.\r\n\r\nI am sorry, but I can't reproduce the bug:\r\n```\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"qasper\")\r\nDownloading: 5.11kB [00:00, ?B/s]\r\nDownloading and preparing dataset qasper/qasper (download: 9.88 MiB, generated: 35.11 MiB, ...
2021-11-08T23:29:46Z
2021-11-09T16:46:05Z
2021-11-09T16:45:47Z
CONTRIBUTOR
null
null
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## Describe the bug Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples: ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 0 }) validation: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 0 }) }) ``` ## Steps to reproduce the bug Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110: ```python from datasets import load_dataset load_dataset("qasper") # The problem only started with the commit of #3110 load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780") ``` ## Expected results ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 888 }) validation: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 281 }) }) ``` Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110: ```python from datasets import load_dataset load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d") ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.2.dev0 (master) - Python version: 3.8.10 - PyArrow version: 3.0.0
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[Extract] Place the lock file next to the destination directory
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-12-01T13:55:49Z
2022-12-01T15:36:44Z
2022-12-01T15:33:58Z
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Previously it was placed next to the archive to extract, but the archive can be in a read-only directory as noticed in https://github.com/huggingface/datasets/issues/5295 Therefore I moved the lock location to be next to the destination directory, which is required to have write permissions
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Minor fix in `iter_files` for hidden files
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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...
2023-07-28T09:50:12Z
2023-07-28T10:59:28Z
2023-07-28T10:50:10Z
CONTRIBUTOR
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Fix #6090
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Datasets' cache not re-used
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[ "<s>I think this is because the tokenizer is stateful and because the order in which the splits are processed is not deterministic. Because of that, the hash of the tokenizer may change for certain splits, which causes issues with caching.\r\n\r\nTo fix this we can try making the order of the splits deterministic f...
2022-03-07T19:55:15Z
2023-09-12T08:42:38Z
null
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## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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How to assign new values to Dataset?
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[ "Hi! One option is use `map` with a function that overwrites the labels (`dset = dset.map(lamba _: {\"label\": 0}, features=dset.features`)). Or you can use the `remove_column` + `add_column` combination (`dset = dset.remove_columns(\"label\").add_column(\"label\", [0]*len(data)).cast(dset.features)`, but note that...
2022-07-15T04:17:57Z
2023-03-20T15:50:41Z
2022-10-10T11:53:38Z
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![image](https://user-images.githubusercontent.com/37113676/179149159-bbbda0c8-a661-403c-87ed-dc2b4219cd68.png) Hi, if I want to change some values of the dataset, or add new columns to it, how can I do it? For example, I want to change all the labels of the SST2 dataset to `0`: ```python from datasets import load_dataset data = load_dataset('glue','sst2') data['train']['label'] = [0]*len(data) ``` I will get the error: ``` TypeError: 'Dataset' object does not support item assignment ```
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Make datasets PEP-561 compliant
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[ "This is super cool, I love that ❤️ " ]
2021-05-27T16:16:17Z
2021-05-28T13:10:10Z
2021-05-28T13:09:16Z
CONTRIBUTOR
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Allows to type-check datasets with `mypy` when imported as a third-party library PEP-561: https://www.python.org/dev/peps/pep-0561 MyPy doc on the subject: https://mypy.readthedocs.io/en/stable/installed_packages.html
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Setting to torch format not working with torchvision and MNIST
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[ "Adding to the previous information, I think `torch.utils.data.DataLoader` is doing some conversion. \r\nWhat I tried:\r\n```python\r\ntrain_dataset = load_dataset('mnist')\r\n```\r\nI don't use any `map` or `set_format` or any `transform`. I use this directly, and try to load batches using the `DataLoader` with ba...
2021-03-08T07:38:11Z
2021-03-09T17:58:13Z
2021-03-09T17:58:13Z
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Hi I am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object. A snippet of what I am trying to do: ```python def prepare_features(examples): images = [] labels = [] for example_idx, example in enumerate(examples["image"]): if transform is not None: images.append(transform( np.array(examples["image"][example_idx], dtype=np.uint8) )) else: images.append(torch.tensor(np.array(examples["image"][example_idx], dtype=np.uint8))) labels.append(torch.tensor(examples["label"][example_idx])) output = {"label":labels, "image":images} return output raw_dataset = load_dataset('mnist') train_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000) train_dataset.set_format("torch",columns=["image","label"]) ``` After this, I check the type of the following: ```python print(type(train_dataset["train"]["label"])) print(type(train_dataset["train"]["image"][0])) ``` This leads to the following output: ```python <class 'torch.Tensor'> <class 'list'> ``` I use `torch.utils.DataLoader` for batches, the type of `batch["train"]["image"]` is also `<class 'list'>`. I don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue? Thanks, Gunjan EDIT: I just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28). EDIT 2: Inside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.
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Fix duplicated tag in wikicorpus dataset card
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[ "CI is failing just because of empty sections - merging" ]
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5,504
don't zero copy timestamps
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2023-02-03T23:39:04Z
2023-02-08T17:28:50Z
2023-02-08T14:33:17Z
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Fixes https://github.com/huggingface/datasets/issues/5495 I'm not sure whether we prefer a test here or if timestamps are known to be unsupported (like booleans). The current test at least covers the bug
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big_patent cased version
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2022-03-08T14:08:55Z
2023-04-21T14:32:03Z
2023-04-21T14:32:03Z
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Hi! I am interested in working with the big_patent dataset. In Tensorflow, there are a number of versions of the dataset: - 1.0.0 : lower cased tokenized words - 2.0.0 : Update to use cased raw strings - 2.1.2 (default): Fix update to cased raw strings. The version in the huggingface `datasets` library is the 1.0.0. I would be very interested in using the 2.1.2 cased version (used more, recently, for example in the Pegasus paper), but it does not seem to be supported (I tried using the `revision` parameter in `load_datasets`). Is there a way to already load it, or would it be possible to add that version?
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Properly install ruamel-yaml for windows CI
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[ "@lhoestq I would say this does not \"properly\" install `ruamel-yaml`, but the contrary, you overwrite the previous version without desinstalling it first.\r\n\r\nAccording to `pip` docs:\r\n> This can break your system if the existing package is of a different version or was installed with a different package ma...
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Fix CI by temporarily pinning fsspec < 2023.3.0
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2023-03-07T08:44:55Z
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As a hotfix for our CI, temporarily pin `fsspec`: Fix #5616. Until root cause is fixed, see: - #5614
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Add section in tutorial for IterableDataset
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Introduces an `IterableDataset` and how to access it in the tutorial section. It also adds a brief next step section at the end to provide a path for users who want more explanation and a path for users who want something more practical and learn how to preprocess these dataset types. It'll complement the awesome new doc introduced in: - #5410
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AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
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[ "Hi @arymbe, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your problem.\r\n\r\nCould you please write the complete stack trace? That way we will be able to see which package originates the exception.", "Hello, thank you for your fast replied. this is the complete error that I got\r\n\r\n-...
2022-04-07T05:52:36Z
2022-07-28T16:44:04Z
2022-04-19T15:36:35Z
NONE
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## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance.
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XNLI cache reload is very slow
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[ "Hi,\r\nCould you tell us how you are running this code?\r\nI tested on my machine (M1 Mac). And it is running fine both on and off internet.\r\n\r\n<img width=\"1033\" alt=\"Screen Shot 2022-07-03 at 1 32 25 AM\" src=\"https://user-images.githubusercontent.com/8711912/177026364-4ad7cedb-e524-4513-97f7-7961bbb34c90...
2022-06-25T16:43:56Z
2022-07-04T14:29:40Z
2022-07-04T14:29:40Z
CONTRIBUTOR
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### Reproduce Using `2.3.3.dev0` `from datasets import load_dataset` `load_dataset("xnli", "en")` Turn off Internet `load_dataset("xnli", "en")` I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet. ``` --------------------------------------------------------------------------- gaierror Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self) 174 conn = connection.create_connection( --> 175 (self._dns_host, self.port), self.timeout, **extra_kw 176 ) /opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 71 ---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM): 73 af, socktype, proto, canonname, sa = res /opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags) 751 addrlist = [] --> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags): 753 af, socktype, proto, canonname, sa = res gaierror: [Errno -3] Temporary failure in name resolution During handling of the above exception, another exception occurred: KeyboardInterrupt Traceback (most recent call last) /tmp/ipykernel_33/3594208039.py in <module> ----> 1 load_dataset("xnli", "en") /opt/conda/lib/python3.7/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, revision, use_auth_token, task, streaming, **config_kwargs) 1673 revision=revision, 1674 use_auth_token=use_auth_token, -> 1675 **config_kwargs, 1676 ) 1677 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1494 download_mode=download_mode, 1495 data_dir=data_dir, -> 1496 data_files=data_files, 1497 ) 1498 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1182 download_config=download_config, 1183 download_mode=download_mode, -> 1184 dynamic_modules_path=dynamic_modules_path, 1185 ).get_module() 1186 elif path.count("/") == 1: # community dataset on the Hub /opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path) 506 self.dynamic_modules_path = dynamic_modules_path 507 assert self.name.count("/") == 0 --> 508 increase_load_count(name, resource_type="dataset") 509 510 def download_loading_script(self, revision: Optional[str]) -> str: /opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type) 166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS: 167 try: --> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset")) 169 except Exception: 170 pass /opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries) 93 return http_head( 94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset), ---> 95 max_retries=max_retries, 96 ) 97 /opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries) 445 allow_redirects=allow_redirects, 446 timeout=timeout, --> 447 max_retries=max_retries, 448 ) 449 return response /opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params) 366 tries += 1 367 try: --> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) 369 success = True 370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err: /opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 /opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 527 } 528 send_kwargs.update(settings) --> 529 resp = self.send(prep, **send_kwargs) 530 531 return resp /opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs) 643 644 # Send the request --> 645 r = adapter.send(request, **kwargs) 646 647 # Total elapsed time of the request (approximately) /opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 decode_content=False, 449 retries=self.max_retries, --> 450 timeout=timeout 451 ) 452 /opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 708 body=body, 709 headers=headers, --> 710 chunked=chunked, 711 ) 712 /opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 384 # Trigger any extra validation we need to do. 385 try: --> 386 self._validate_conn(conn) 387 except (SocketTimeout, BaseSSLError) as e: 388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout. /opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 1038 # Force connect early to allow us to validate the connection. 1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` -> 1040 conn.connect() 1041 1042 if not conn.is_verified: /opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self) 356 def connect(self): 357 # Add certificate verification --> 358 self.sock = conn = self._new_conn() 359 hostname = self.host 360 tls_in_tls = False /opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self) 173 try: 174 conn = connection.create_connection( --> 175 (self._dns_host, self.port), self.timeout, **extra_kw 176 ) 177 KeyboardInterrupt: ```
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PR_kwDODunzps4-S0we
4,927
fix BLEU metric card
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2022-09-02T17:00:56Z
2022-09-09T16:28:15Z
2022-09-09T16:28:15Z
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I've fixed some typos in BLEU metric card.
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Fix typos in documentation
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-08-21T15:10:03Z
2022-08-22T09:25:39Z
2022-08-22T09:09:58Z
CONTRIBUTOR
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[ "Hi @theo-m !\r\n\r\nA few lines above this line, you'll find that the `_split_generators` method returns a list of `SplitGenerator`s objects:\r\n\r\n```python\r\ndatasets.SplitGenerator(\r\n name=datasets.Split.VALIDATION,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n ...
2021-03-09T08:42:11Z
2021-03-12T15:01:34Z
2021-03-12T15:01:34Z
CONTRIBUTOR
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https://github.com/huggingface/datasets/blob/2ac9a0d24a091989f869af55f9f6411b37ff5188/templates/new_dataset_script.py#L156-L158 Looking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from. Happy to push a PR with a clearer statement when I understand the meaning.
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Fix METEOR missing NLTK's omw-1.4
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[ "I also modified the doctest call to raise the exception that doctest may catch, instead of `doctest.UnexpectedException`.\r\nThis will make debugging easier if it happens again" ]
2021-12-21T14:19:11Z
2021-12-21T14:52:28Z
2021-12-21T14:49:28Z
MEMBER
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NLTK 3.6.6 now requires `omw-1.4` to be downloaded for METEOR to work. This should fix the CI on master
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adding ted_talks_iwslt
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[ "Closing this with reference to the new approach #1676 " ]
2020-12-19T07:36:41Z
2021-01-02T15:44:12Z
2021-01-02T15:44:11Z
CONTRIBUTOR
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UPDATE2: (2nd Jan) Wrote a long writeup on the slack channel. I don't think this approach is correct. Basically this created language pairs (109*108) Running the `pytest `went for more than 40+ hours and it was still running! So working on a different approach, such that the number of configs = number of languages. Will make a new pull request with that. UPDATE: This requires manual download dataset This is a draft version
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bug in mlqa dataset
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[ "If you print those questions, you get readable texts:\r\n```python\r\n>>> questions = [\r\n... \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u064...
2021-03-29T09:03:09Z
2021-03-30T17:40:57Z
2021-03-30T17:40:57Z
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Hi Looking into MLQA dataset for langauge "ar": ``` "question": [ "\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?", "\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?" ] ``` the questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq
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[FR] Transform Chaining, Lazy Mapping
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[ "You can use `with_transform` to get a new dataset object.\r\n\r\nSupport for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex. ", "> You can use `with_transform` to get a new datas...
2023-07-09T21:40:21Z
2023-07-14T13:12:40Z
null
CONTRIBUTOR
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### Feature request Currently using a `map` call processes and duplicates the whole dataset, which takes both time and disk space. The solution is to allow lazy mapping, which is essentially a saved chain of transforms that are applied on the fly whenever a slice of the dataset is requested. The API should look like `map`, as `set_transform` changes the current dataset while `map` returns another dataset. ### Motivation Lazy processing allows lower disk usage and faster experimentation. ### Your contribution _
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`ArrowInvalid: Added column's length must match table's length.` after using `select`
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[ "Hi,\r\n\r\nthe `select` method creates an indices mapping and doesn't modify the underlying PyArrow table by default for better performance. To modify the underlying table after the `select` call, call `flatten_indices` on the dataset object as follows:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds =...
2021-08-07T13:17:29Z
2021-08-09T11:26:43Z
2021-08-09T11:26:43Z
MEMBER
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## Describe the bug I would like to add a column to a downsampled dataset. However I get an error message saying the length don't match with the length of the unsampled dataset indicated. I suspect that the dataset size is not updated when calling `select`. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128)) ds = ds.add_column('ones', [1]*128) ``` ## Expected results I would expect a new column named `ones` filled with `1`. When I check the length of `ds` it says `128`. Interestingly, it works when calling `ds = ds.map(lambda x: x)` before adding the column. ## Actual results Specify the actual results or traceback. ```python --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) /var/folders/l4/2905jygx4tx5jv8_kn03vxsw0000gn/T/ipykernel_6301/868709636.py in <module> 1 from datasets import load_dataset 2 ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128)) ----> 3 ds = ds.add_column('ones', [0]*128) ~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 183 } 184 # apply actual function --> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 187 # re-apply format to the output ~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 395 # Call actual function 396 --> 397 out = func(self, *args, **kwargs) 398 399 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint) 2965 column_table = InMemoryTable.from_pydict({name: column}) 2966 # Concatenate tables horizontally -> 2967 table = ConcatenationTable.from_tables([self._data, column_table], axis=1) 2968 # Update features 2969 info = self.info.copy() ~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in from_tables(cls, tables, axis) 715 table_blocks = to_blocks(table) 716 blocks = _extend_blocks(blocks, table_blocks, axis=axis) --> 717 return cls.from_blocks(blocks) 718 719 @property ~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in from_blocks(cls, blocks) 663 return cls(table, blocks) 664 else: --> 665 table = cls._concat_blocks_horizontally_and_vertically(blocks) 666 return cls(table, blocks) 667 ~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in _concat_blocks_horizontally_and_vertically(cls, blocks) 623 if not tables: 624 continue --> 625 pa_table_horizontally_concatenated = cls._concat_blocks(tables, axis=1) 626 pa_tables_to_concat_vertically.append(pa_table_horizontally_concatenated) 627 return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0) ~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in _concat_blocks(blocks, axis) 612 else: 613 for name, col in zip(table.column_names, table.columns): --> 614 pa_table = pa_table.append_column(name, col) 615 return pa_table 616 else: ~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.append_column() ~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.add_column() ~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Added column's length must match table's length. Expected length 31962 but got length 128 ``` ## Environment info - `datasets` version: 1.11.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.5 - PyArrow version: 5.0.0
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Add LIAR dataset
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[ "@lhoestq done! The failing testes don't seem to be related, it seems to be a connection issue, if I understand it correctly.", "merging since the CI is fixed on master" ]
2020-12-09T14:16:55Z
2020-12-14T18:06:43Z
2020-12-14T16:23:59Z
CONTRIBUTOR
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Add LIAR dataset from [“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection](https://www.aclweb.org/anthology/P17-2067/).
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Can't import datasets AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)
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[ "Thanks for reporting, @OPterminator.\r\n\r\nHowever, we are not able to reproduce this issue.\r\n\r\nThere might be 2 reasons why you get this exception:\r\n- Either the name of your local Python file: if it is called `datasets.py` this could generate a circular import when trying to import the Hugging Face `data...
2022-08-28T02:23:24Z
2023-05-13T13:53:30Z
2022-10-03T12:22:50Z
NONE
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## Describe the bug A clear and concise description of what the bug is. Not able to import datasets ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import os os.environ["WANDB_API_KEY"] = "0" ## to silence warning import numpy as np import random import sklearn import matplotlib.pyplot as plt import pandas as pd import sys import tensorflow as tf import plotly.express as px import transformers import tokenizers import nlp as nlp import utils import datasets ``` ## Expected results A clear and concise description of the expected results. import should work normal ## Actual results Specify the actual results or traceback. --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-21-b3b5b0b62103> in <module> 13 import nlp as nlp 14 import utils ---> 15 import datasets ~\anaconda3\lib\site-packages\datasets\__init__.py in <module> 44 from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled 45 from .info import DatasetInfo, MetricInfo ---> 46 from .inspect import ( 47 get_dataset_config_info, 48 get_dataset_config_names, ~\anaconda3\lib\site-packages\datasets\inspect.py in <module> 28 from .download.streaming_download_manager import StreamingDownloadManager 29 from .info import DatasetInfo ---> 30 from .load import dataset_module_factory, import_main_class, load_dataset_builder, metric_module_factory 31 from .utils.file_utils import relative_to_absolute_path 32 from .utils.logging import get_logger ~\anaconda3\lib\site-packages\datasets\load.py in <module> 53 from .iterable_dataset import IterableDataset 54 from .metric import Metric ---> 55 from .packaged_modules import ( 56 _EXTENSION_TO_MODULE, 57 _MODULE_SUPPORTS_METADATA, ~\anaconda3\lib\site-packages\datasets\packaged_modules\__init__.py in <module> 4 from typing import List 5 ----> 6 from .csv import csv 7 from .imagefolder import imagefolder 8 from .json import json ~\anaconda3\lib\site-packages\datasets\packaged_modules\csv\csv.py in <module> 13 14 ---> 15 logger = datasets.utils.logging.get_logger(__name__) 16 17 _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS = ["names", "prefix"] AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.4.0 - Platform: Windows-10-10.0.22000-SP0 - Python version: 3.8.8 - PyArrow version: 9.0.0 - Pandas version: 1.2.4
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Adding SICK dataset
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2021-01-24T02:15:31Z
2021-02-05T15:49:25Z
2021-02-05T15:49:25Z
NONE
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Hi It would be great to include SICK dataset. ## Adding a Dataset - **Name:** SICK - **Description:** a well known entailment dataset - **Paper:** http://marcobaroni.org/composes/sick.html - **Data:** http://marcobaroni.org/composes/sick.html - **Motivation:** this is an important NLI benchmark Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). thanks
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1,158,143,480
I_kwDODunzps5FB934
3,809
Checksums didn't match for datasets on Google Drive
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[ "Hi @muelletm, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nUntil our next `datasets` library release, you can get this fix by installing our library from the GitHub ...
2022-03-03T09:01:10Z
2022-03-03T09:24:58Z
2022-03-03T09:24:05Z
NONE
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## Describe the bug Datasets hosted on Google Drive do not seem to work right now. Loading them fails with a checksum error. ## Steps to reproduce the bug ```python from datasets import load_dataset for dataset in ["head_qa", "yelp_review_full"]: try: load_dataset(dataset) except Exception as exception: print("Error", dataset, exception) ``` Here is a [colab](https://colab.research.google.com/drive/1wOtHBmL8I65NmUYakzPV5zhVCtHhi7uQ#scrollTo=cDzdCLlk-Bo4). ## Expected results The datasets should be loaded. ## Actual results ``` Downloading and preparing dataset head_qa/es (download: 75.69 MiB, generated: 2.86 MiB, post-processed: Unknown size, total: 78.55 MiB) to /root/.cache/huggingface/datasets/head_qa/es/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9... Error head_qa Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t'] Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43... Error yelp_review_full Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0'] ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1
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Update `ruff` version in pre-commit config
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6049). All of your documentation changes will be reflected on that endpoint." ]
2023-07-18T17:13:50Z
2023-08-29T13:16:28Z
null
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so that it corresponds to the one that is being run in CI
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Push to hub can push splits that do not respect the regex
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2022-09-07T13:45:17Z
2022-09-13T10:16:35Z
2022-09-13T10:16:35Z
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## Describe the bug The `push_to_hub` method can push splits that do not respect the regex check that is used for downloads. Therefore, splits may be pushed but never re-used, which can be painful if the split was done after runtime preprocessing. ## Steps to reproduce the bug ```python >>> from datasets import Dataset, DatasetDict, load_dataset >>> d = Dataset.from_dict({'x': [1,2,3], 'y': [1,2,3]}) >>> di = DatasetDict() >>> di['identifier-with-column'] = d >>> di.push_to_hub('open-source-metrics/test') Pushing split identifier-with-column to the Hub. Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:04<00:00, 4.40s/it] ``` Loading it afterwards: ```python >>> load_dataset('open-source-metrics/test') Downloading: 100%|██████████| 610/610 [00:00<00:00, 432kB/s] Using custom data configuration open-source-metrics--test-28b63ec7cde80488 Downloading and preparing dataset None/None (download: 950 bytes, generated: 48 bytes, post-processed: Unknown size, total: 998 bytes) to /home/lysandre/.cache/huggingface/datasets/open-source-metrics___parquet/open-source-metrics--test-28b63ec7cde80488/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec... Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 100%|██████████| 950/950 [00:00<00:00, 1.01MB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.48s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 2291.97it/s] Traceback (most recent call last): File "/home/lysandre/.pyenv/versions/3.10.6/lib/python3.10/code.py", line 90, in runcode exec(code, self.locals) File "<input>", line 1, in <module> File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/load.py", line 1746, in load_dataset builder_instance.download_and_prepare( File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/builder.py", line 771, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 48, in _split_generators splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files})) File "<string>", line 5, in __init__ File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 599, in __post_init__ NamedSplit(self.name) # check that it's a valid split name File "/home/lysandre/Workspaces/python/Metrics/GitHub-Metrics/.env/lib/python3.10/site-packages/datasets/splits.py", line 346, in __init__ raise ValueError(f"Split name should match '{_split_re}' but got '{split_name}'.") ValueError: Split name should match '^\w+(\.\w+)*$' but got 'identifier-with-column'. ``` ## Expected results I would expect `push_to_hub` to stop me in my tracks if trying to upload a split that will not be working afterwards. ## Actual results See above ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.64-1-lts-x86_64-with-glibc2.36 - Python version: 3.10.6 - PyArrow version: 9.0.0 - Pandas version: 1.4.4
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Question: Shouldn't .info be a part of DatasetDict?
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[ "We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work.", "Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). " ]
2021-01-05T13:08:41Z
2021-01-07T10:18:06Z
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CONTRIBUTOR
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Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. For instance: ``` >>> ds = datasets.load_dataset("conll2002", "es") >>> ds.info Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'DatasetDict' object has no attribute 'info' ``` I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset. It is entirely possible that I am missing another perspective
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1,837
Add VCTK
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[ "@patrickvonplaten I'd like to take this, if nobody has already done it. I have added datasets before through the datasets sprint, but I feel rusty on the details, so I'll look at the guide as well as similar audio PRs (#1878 in particular comes to mind). If there is any detail I should be aware of please, let me k...
2021-02-08T13:15:28Z
2021-12-28T15:05:08Z
2021-12-28T15:05:08Z
MEMBER
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## Adding a Dataset - **Name:** *VCTK* - **Description:** *This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.* - **Paper:** Homepage: https://datashare.ed.ac.uk/handle/10283/3443 - **Data:** https://datashare.ed.ac.uk/handle/10283/3443 - **Motivation:** Important speech dataset - **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/vctk If interested in tackling this issue, feel free to tag @patrickvonplaten 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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concatenate_datasets for iterable datasets
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[ "It is probably worth noting here that the [documentation](https://huggingface.co/docs/datasets/process#concatenate) is misleading (indicating that it does work for IterableDatasets):\r\n\r\n> You can also mix several datasets together by taking alternating examples from each one to create a new dataset. This is kn...
2021-06-29T08:59:41Z
2022-06-28T21:15:04Z
2022-06-28T21:15:04Z
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Currently `concatenate_datasets` only works for map-style `Dataset`. It would be nice to have it work for `IterableDataset` objects as well. It would simply chain the iterables of the iterable datasets.
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Datasets library not suitable for huge text datasets.
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[ "Hi ! Looks related to #861 \r\n\r\nYou are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which...
2021-02-05T11:06:50Z
2021-03-30T14:04:01Z
2021-03-16T09:44:00Z
NONE
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Hi, I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. Moreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts). Any suggestions??
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Narrative QA Manual
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[ "@lhoestq sorry I opened a new pull request because of some issues with the previous code base. This pull request is originally from #1364", "Excellent comments. Thanks for those valuable suggestions. I changed everything as you have pointed out :) ", "I've copied the same template as NarrativeQA now. Please le...
2021-01-25T15:22:31Z
2021-01-29T09:35:14Z
2021-01-29T09:34:51Z
CONTRIBUTOR
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Submitting the manual version of Narrative QA script which requires a manual download from the original repository
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Add `accelerate` as metric's test dependency to fix CI error
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-05-12T12:01:01Z
2023-05-12T13:48:47Z
2023-05-12T13:39:06Z
CONTRIBUTOR
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The `frugalscore` metric uses Transformers' Trainer, which requires `accelerate` (as of recently). Fixes the following [CI error](https://github.com/huggingface/datasets/actions/runs/4950900048/jobs/8855148703?pr=5845).
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NLU evaluation data
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2021-04-19T16:47:20Z
2021-04-23T15:32:05Z
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New intent classification dataset from https://github.com/xliuhw/NLU-Evaluation-Data
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Checksum Error when loading multi-news dataset
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[ "Thanks for reporting @byw2.\r\nWe are fixing it.\r\nIn the meantime, you can load the dataset by passing `ignore_verifications=True`:\r\n ```python\r\ndataset = load_dataset(\"multi_news\", ignore_verifications=True)" ]
2022-02-16T05:11:08Z
2022-02-16T20:05:06Z
2022-02-16T08:48:46Z
NONE
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## Describe the bug When using the load_dataset function from datasets module to load the Multi-News dataset, does not load the dataset but throws Checksum Error instead. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("multi_news") ``` ## Expected results Should download and load Multi-News dataset. ## Actual results Throws the following error and cannot load data successfully: ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C'] ``` Could this issue please be looked at? Thanks!
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PyTorch BatchSampler still loads from Dataset one-by-one
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[ "This change seems to come from a few months ago in the PyTorch side. That's good news and it means we may not need to pass a batch_sampler as soon as we add `Dataset.__getitems__` to get the optimal speed :)\r\n\r\nThanks for reporting ! Would you like to open a PR to add `__getitems__` and remove this outdated do...
2023-02-06T01:14:55Z
2023-02-19T18:27:30Z
2023-02-19T18:27:30Z
NONE
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### Describe the bug In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue. I'm not sure if this is a mistake in the docs or the code, but it seems that the only way for a Dataset to be passed a list of indexes by PyTorch (instead of one index at a time) is to define a `__getitems__` method (note the plural) on the Dataset object, and since the HF Dataset doesn't have this, PyTorch executes [this line of code](https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/fetch.py#L58), reverting to fetching one-by-one. ### Steps to reproduce the bug You can put a breakpoint in `Dataset.__getitem__()` or just print the args from there and see that it's called multiple times for a single `next(iter(dataloader))`, even when using the code from the docs: ```py from torch.utils.data.sampler import BatchSampler, RandomSampler batch_sampler = BatchSampler(RandomSampler(ds), batch_size=32, drop_last=False) dataloader = DataLoader(ds, batch_sampler=batch_sampler) ``` ### Expected behavior The expected behaviour would be for it to fetch batches from the dataset, rather than one-by-one. To demonstrate that there is room for improvement: once I have a HF dataset `ds`, if I just add this line: ```py ds.__getitems__ = ds.__getitem__ ``` ...then the time taken to loop over the dataset improves considerably (for wikitext-103, from one minute to 13 seconds with batch size 32). Probably not a big deal in the grand scheme of things, but seems like an easy win. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.3
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3,409
Pass new_fingerprint in multiprocessing
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[ "@lhoestq Hi~, does this support that `datasets.map(func, batched=True, batch_size, num_proc>1, new_fingerprint=\"func_v1\")` even if `func` can't pickle. I also notice that you said \"Unfortunately you need picklable mapping functions to make multiprocessing work :confused: Also feel free to open an issue or send ...
2021-12-09T15:12:00Z
2022-08-19T10:41:04Z
2021-12-09T17:38:43Z
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Following https://github.com/huggingface/datasets/pull/3045 Currently one can pass `new_fingerprint` to `.map()` to use a custom fingerprint instead of the one computed by hashing the map transform. However it's ignored if `num_proc>1`. In this PR I fixed that by passing `new_fingerprint` to `._map_single()` when `num_proc>1`. More specifically, `new_fingerprint` with a suffix based on the process `rank` is passed, so that each process has a different `new_fingerprint` cc @TevenLeScao @vlievin
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added conv_ai_3 dataset
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[ "@lhoestq Thank you for suggesting changes. I fixed all the changes you suggested. Can you please review it again? ", "@lhoestq Thank you for reviewing and suggesting changes. I made the requested changes. Can you please review it again?", "Thanks @lhoestq for reviewing it again. I made the required changes. Ca...
2020-12-11T19:26:26Z
2020-12-28T09:38:40Z
2020-12-28T09:38:39Z
CONTRIBUTOR
null
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Dataset : https://github.com/aliannejadi/ClariQ/
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Add id_nergrit_corpus
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[ "merging since the CI is fixed on master" ]
2020-12-10T13:20:34Z
2020-12-17T10:45:15Z
2020-12-17T10:45:15Z
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Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis. Recently my PR for id_nergrit_ner has been accepted and merged to the main branch. The id_nergrit_ner has only one dataset (NER), and this new PR renamed the dataset from id_nergrit_ner to id_nergrit_corpus and added 2 other remaining datasets (Statement Extraction, and Sentiment Analysis.)
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3,751
Fix typo in train split name
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2022-02-18T08:18:04Z
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In the README guide (and consequently in many datasets) there was a typo in the train split name: ``` | Tain | Valid | Test | ``` This PR: - fixes the typo in the train split name - fixes the column alignment of the split tables in the README guide and in all datasets.
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Add KLUE-MRC metrics
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[ "The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)." ]
2023-07-03T12:11:10Z
2023-07-09T11:57:20Z
2023-07-09T11:57:20Z
NONE
null
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## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension) Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue). KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC. Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC. - [x] All tests passed - [x] Added a metric card (referred the metric card of SQuAD 2.0) - [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed ### References - [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf) - [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue) - #2416
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info messages logged as warnings
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[ "Looks like a duplicate of https://github.com/huggingface/datasets/issues/1948. \r\n\r\nI also think these should be logged as INFO messages, but let's see what @lhoestq thinks.", "It can be considered unexpected to see a `map` function return instantaneously. The warning is here to explain this case by mentionin...
2023-01-20T01:19:18Z
2023-07-12T17:19:31Z
2023-07-12T17:19:31Z
NONE
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### Describe the bug Code in `datasets` is using `logger.warning` when it should be using `logger.info`. Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category. Definitions from the Python docs for reference: * INFO: Confirmation that things are working as expected. * WARNING: An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected. In theory, a user should be able to resolve things such that there are no warnings. ### Steps to reproduce the bug Load any dataset that's already cached. ### Expected behavior No output when log level is at the default WARNING level. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 9.0.0 - Pandas version: 1.5.2
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Tasks alignment with models
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Yay! This is exciting! Note that we would probably be able to generate this JSON directly from `huggingface/hub-docs`' `Types.ts` file (cc @osanseviero)", "The following issue should make this much easier :smile: https://github.com...
2022-03-30T16:45:56Z
2022-04-13T13:12:52Z
2022-04-08T12:20:00Z
MEMBER
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I updated our `tasks.json` file with the new task taxonomy that is aligned with models. The rule that defines a task is the following: **Two tasks are different if and only if the steps of their pipelines** are different, i.e. if they can’t reasonably be implemented using the same coherent code (level of granularity/complexity of the code to be defined - ideally I’d like to say “HF user’s level”) - this is the same definition in `transformers` I will update the tags of all the datasets in this repository [in another PR](https://github.com/huggingface/datasets/pull/4067) for readability. Main changes: - conditional-text-generation is split between summarization, translation, text-generation and text2text-generation - speech-processing is split into automatic-speech-recognition, audio-classification, etc. - structure-prediction is renamed token-classification - abstractive-qa now belongs to text2text-generation Here is just a simplified YAML dump of `tasks.json`: ```yaml audio-classification: - keyword-spotting - speaker-identification - speaker-intent-classification - emotion-recognition - speaker-language-identification audio-to-audio: [] automatic-speech-recognition: [] conversational: - dialogue-generation feature-extraction: [] fill-mask: - slot-filling - masked-language-modeling image-classification: - multi-label-image-classification - multi-class-image-classification image-segmentation: - instance-segmentation - semantic-segmentation - panoptic-segmentation image-to-text: - image-captioning multiple-choice: - multiple-choice-qa - multiple-choice-coreference-resolution object-detection: - face-detection - vehicle-detection question-answering: - extractive-qa - open-domain-qa - closed-domain-qa sentence-similarity: [] tabular-classification: [] tabular-to-text: - rdf-to-text summarization: - news-articles-summarization - news-articles-headline-generation table-to-text: [] table-question-answering: [] text-classification: - acceptability-classification - entity-linking-classification - fact-checking - intent-classification - multi-class-classification - multi-label-classification - natural-language-inference - semantic-similarity-classification - sentiment-classification - topic-classification - semantic-similarity-scoring - sentiment-scoring - sentiment-analysis - hate-speech-detection - text-scoring text-generation: - dialogue-modeling - language-modeling text-retrieval: - document-retrieval - utterance-retrieval - entity-linking-retrieval - fact-checking-retrieval text-to-image: [] text-to-tabular: - relation-extraction - semantic-role-labeling text-to-speech: [] text2text-generation: - text-simplification - explanation-generation - abstractive-qa - open-domain-abstractive-qa - closed-domain-qa - open-book-qa - closed-book-qa time-series-forecasting: - univariate-time-series-forecasting - multivariate-time-series-forecasting token-classification: - named-entity-recognition - part-of-speech-tagging - parsing - lemmatization - word-sense-disambiguation - coreference-resolution translation: [] visual-question-answering: [] voice-activity-detection: [] zero-shot-classification: [] zero-shot-image-classification: [] reinforcement-learning: [] other: [] ``` Feel free to comment and give suggestions, especially if you think we can also align this list with other projects cc @julien-c @osanseviero @severo @lewtun @yjernite @albertvillanova @mariosasko @polinaeterna
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Replace legacy torch.Tensor constructor with torch.tensor
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2021-03-28T16:57:30Z
2021-03-29T09:27:14Z
2021-03-29T09:27:13Z
CONTRIBUTOR
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The title says it all (motivated by [this issue](https://github.com/pytorch/pytorch/issues/53146) in the pytorch repo).
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Windows test suite fails
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2021-10-05T08:46:46Z
2021-10-05T09:58:27Z
2021-10-05T09:58:27Z
MEMBER
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## Describe the bug There is an error during installation of tests dependencies for Windows: https://app.circleci.com/pipelines/github/huggingface/datasets/7981/workflows/9b6a0114-2b8e-4069-94e5-e844dbbdba4e/jobs/49206 ``` ERROR: Cannot uninstall 'ruamel-yaml'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall. ```
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Support streaming for pubmed
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[ "@albertvillanova just FYI, since you were so helpful with the previous pubmed issue :) ", "IIRC streaming from FTP is not fully tested yet, so I'm fine with switching to HTTPS for now, as long as the download speed/availability is great", "@albertvillanova Thanks for pointing me to the `ET` module replacement....
2022-02-17T00:18:22Z
2022-02-18T14:42:13Z
2022-02-18T14:42:13Z
CONTRIBUTOR
null
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This PR makes some minor changes to the `pubmed` dataset to allow for `streaming=True`. Fixes #3739. Basically, I followed the C4 dataset which works in streaming mode as an example, and made the following changes: * Change URL prefix from `ftp://` to `https://` * Explicilty `open` the filename and pass the XML contents to `etree.fromstring(xml_str)` The Github diff tool makes it look like the changes are larger than they are, sorry about that. I tested locally and the `pubmed` dataset now works in both normal and streaming modes. There is some overhead at the start of each shard in streaming mode as building the XML tree online is quite slow (each pubmed .xml.gz file is ~20MB), but the overhead gets amortized over all the samples in the shard. On my laptop with a single CPU worker I am able to stream at about ~600 samples/s.
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IterableDataset columns and feature types
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[ "#self-assign", "@alvarobartt I've assigned you the issue since I'm not actively working on it.", "Cool thanks @mariosasko I'll try to fix it in the upcoming days, thanks!", "@lhoestq so in order to address what’s not completed in this issue, do you think it makes sense to add a param `features` to `IterableD...
2022-03-10T16:19:12Z
2022-11-29T11:39:24Z
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Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova
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Add ProgressBar for `to_parquet`
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[ "Thanks for your proposal, @zanussbaum. Yes, I agree that would definitely be a nice feature to have!", "@albertvillanova I’m happy to make a quick PR for the feature! let me know ", "That would be awesome ! You can comment `#self-assign` to assign you to this issue and open a PR :) Will be happy to review", ...
2023-01-12T05:06:20Z
2023-01-24T18:18:24Z
2023-01-24T18:18:24Z
CONTRIBUTOR
null
null
null
### Feature request Add a progress bar for `Dataset.to_parquet`, similar to how `to_json` works. ### Motivation It's a bit frustrating to not know how long a dataset will take to write to file and if it's stuck or not without a progress bar ### Your contribution Sure I can help if needed
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Better list array values handling in cast/embed storage
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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...
2023-09-04T16:21:23Z
2023-10-05T15:25:05Z
2023-10-05T15:24:34Z
CONTRIBUTOR
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Use [`array.flatten`](https://arrow.apache.org/docs/python/generated/pyarrow.ListArray.html#pyarrow.ListArray.flatten) that takes `.offset` into account instead of `array.values` in array cast/embed.
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load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub
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[ "Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?", "The error is now more informative:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the sa...
2023-02-23T16:44:32Z
2023-07-24T15:18:50Z
2023-07-24T15:18:50Z
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### Describe the bug When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting. ### Steps to reproduce the bug ``` from datasets import load_dataset imagenet = load_dataset("imagenet-1k", split="train", streaming=True) FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub ``` tested on a colab notebook. ### Expected behavior I would expect a specific error indicating that I have to login then accept the dataset licence. I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable) ### Environment info google colab cpu-only instance
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Add support for streaming Zarr stores for hosted datasets
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[ "Hi @jacobbieker, thanks for your request and study of possible alternatives.\r\n\r\nWe are very interested in finding a way to make `datasets` useful to you.\r\n\r\nLooking at the Zarr docs, I saw that among its storage alternatives, there is the ZIP file format: https://zarr.readthedocs.io/en/stable/api/storage.h...
2022-04-05T13:38:32Z
2022-04-25T08:04:12Z
2022-04-21T08:12:58Z
NONE
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**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
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Add Wikisource dataset
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[ "As we are removing the dataset scripts from GitHub and moving them to the Hugging Face Hub, I am going to transfer this script to the repo: https://huggingface.co/datasets/wikimedia/wikisource" ]
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Add loading script for Wikisource dataset. Fix #3399. CC: @geohci, @yjernite
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Update BibTeX entry
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Added RONEC dataset.
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[ "Thanks for the PR @iliemihai . \r\n\r\nFew comments - \r\n\r\nCan you run - \r\n`python datasets-cli dummy_data ./datasets/ronec --auto_generate` to generate dummy data.\r\n\r\nAlso, before committing files run : \r\n`make style`\r\n`flake8 datasets`\r\nthen you can add and commit files.", "> Thanks for the PR @...
2020-12-11T22:14:50Z
2020-12-21T14:48:56Z
2020-12-21T14:48:56Z
CONTRIBUTOR
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how to convert a dict generator into a huggingface dataset.
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[ "@albertvillanova @lhoestq , could you please help me on this issue. ", "Hi ! As mentioned on the [forum](https://discuss.huggingface.co/t/how-to-wrap-a-generator-with-hf-dataset/18464), the simplest for now would be to define a [dataset script](https://huggingface.co/docs/datasets/dataset_script) which can conta...
2022-05-29T16:28:27Z
2022-09-16T14:44:19Z
2022-09-16T14:44:19Z
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### Link _No response_ ### Description Hey there, I have used seqio to get a well distributed mixture of samples from multiple dataset. However the resultant output from seqio is a python generator dict, which I cannot produce back into huggingface dataset. The generator contains all the samples needed for training the model but I cannot convert it into a huggingface dataset. The code looks like this: ``` for ex in seqio_data: print(ex[“text”]) ``` I need to convert the seqio_data (generator) into huggingface dataset. the complete seqio code goes here: ``` import functools import seqio import tensorflow as tf import t5.data from datasets import load_dataset from t5.data import postprocessors from t5.data import preprocessors from t5.evaluation import metrics from seqio import FunctionDataSource, utils TaskRegistry = seqio.TaskRegistry def gen_dataset(split, shuffle=False, seed=None, column="text", dataset_params=None): dataset = load_dataset(**dataset_params) if shuffle: if seed: dataset = dataset.shuffle(seed=seed) else: dataset = dataset.shuffle() while True: for item in dataset[str(split)]: yield item[column] def dataset_fn(split, shuffle_files, seed=None, dataset_params=None): return tf.data.Dataset.from_generator( functools.partial(gen_dataset, split, shuffle_files, seed, dataset_params=dataset_params), output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_name) ) @utils.map_over_dataset def target_to_key(x, key_map, target_key): """Assign the value from the dataset to target_key in key_map""" return {**key_map, target_key: x} dataset_name = 'oscar-corpus/OSCAR-2109' subset= 'mr' dataset_params = {"path": dataset_name, "language":subset, "use_auth_token":True} dataset_shapes = None TaskRegistry.add( "oscar_marathi_corpus", source=seqio.FunctionDataSource( dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params), splits=("train", "validation"), caching_permitted=False, num_input_examples=dataset_shapes, ), preprocessors=[ functools.partial( target_to_key, key_map={ "targets": None, }, target_key="targets")], output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)}, metric_fns=[] ) dataset = seqio.get_mixture_or_task("oscar_marathi_corpus").get_dataset( sequence_length=None, split="train", shuffle=True, num_epochs=1, shard_info=seqio.ShardInfo(index=0, num_shards=10), use_cached=False, seed=42 ) for _, ex in zip(range(5), dataset): print(ex['targets'].numpy().decode()) ``` ### Owner _No response_
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add_faisis_index gets very slow when doing it interatively
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[ "I think faiss automatically sets the number of threads to use to build the index.\r\nCan you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?", "Hi,\r\n I am running the add_faiss_in...
2021-03-12T20:27:18Z
2021-03-24T22:29:11Z
2021-03-24T22:29:11Z
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As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster? @lhoestq ``` def training_step(self, batch, batch_idx) -> Dict: if (not batch_idx==0) and (batch_idx%5==0): print("******************************************************") ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff list_of_gpus = ['cuda:2','cuda:3'] c_dir='/custom/cache/dir' kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir) print(kb_dataset) n=len(list_of_gpus) #nunber of dedicated GPUs kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)] #kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir') print(self.trainer.global_rank) dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank]) output = [None for _ in list_of_gpus] #self.trainer.accelerator_connector.accelerator.barrier("embedding_process") dist.all_gather_object(output, dataset_shards) #This creation and re-initlaization of the new index if (self.trainer.global_rank==0): #saving will be done in the main process combined_dataset = concatenate_datasets(output) passages_path =self.config.passages_path logger.info("saving the dataset with ") #combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage') combined_dataset.save_to_disk(passages_path) logger.info("Add faiss index to the dataset that consist of embeddings") embedding_dataset=combined_dataset index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT) embedding_dataset.add_faiss_index("embeddings", custom_index=index) embedding_dataset.get_index("embeddings").save(self.config.index_path)
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It's not possible download `20200501.pt` dataset
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[ "Hi @jvanz, thanks for reporting.\r\n\r\nPlease note that Wikimedia website does not longer host Wikipedia dumps for so old dates.\r\n\r\nFor a list of accessible dump dates of `pt` Wikipedia, please see: https://dumps.wikimedia.org/ptwiki/\r\n\r\nYou can load for example `20220220` `pt` Wikipedia:\r\n```python\r\n...
2022-02-20T18:34:58Z
2022-02-21T12:06:12Z
2022-02-21T09:25:06Z
NONE
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## Describe the bug The dataset `20200501.pt` is broken. The available datasets: https://dumps.wikimedia.org/ptwiki/ ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("wikipedia", "20200501.pt", beam_runner='DirectRunner') ``` ## Expected results I expect to download the dataset locally. ## Actual results ``` >>> from datasets import load_dataset >>> dataset = load_dataset("wikipedia", "20200501.pt", beam_runner='DirectRunner') Downloading and preparing dataset wikipedia/20200501.pt to /home/jvanz/.cache/huggingface/datasets/wikipedia/20200501.pt/1.0.0/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475... /home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/apache_beam/__init__.py:79: UserWarning: This version of Apache Beam has not been sufficiently tested on Python 3.9. You may encounter bugs or missing features. warnings.warn( 0%| | 0/1 [00:00<?, ?it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 1245, in _download_and_prepare super()._download_and_prepare( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/jvanz/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475/wikipedia.py", line 420, in _split_generators downloaded_files = dl_manager.download_and_extract({"info": info_url}) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 307, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 195, in download downloaded_path_or_paths = map_nested( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 260, in map_nested mapped = [ File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 261, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 196, in _single_map_nested return function(data_struct) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 216, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 612, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/ptwiki/20200501/dumpstatus.json ``` ## Environment info ``` - `datasets` version: 1.18.3 - Platform: Linux-5.3.18-150300.59.49-default-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 6.0.1 ```
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`map()` fails midway due to format incompatibility
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[ "Cc: @lhoestq ", "You can end up with a list instead of a tensor if all the tensors inside the list can't be stacked together - can you make sure all your inputs are tensors with the same shape ?", "Is there an easy way to ensure it?", "You can make sure your `tokenize` function always return tensors of the s...
2022-11-01T03:57:59Z
2022-11-08T11:35:26Z
2022-11-08T11:35:26Z
MEMBER
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### Describe the bug I am using the `emotion` dataset from Hub for sequence classification. After training the model, I am using it to generate predictions for all the entries present in the `validation` split of the dataset. ```py def get_test_accuracy(model): def fn(batch): inputs = {k:v.to(device) for k,v in batch.items() if k in tokenizer.model_input_names} with torch.no_grad(): output = model(**inputs) pred_label = torch.argmax(output.logits, axis=-1) return {"predicted_label": pred_label.cpu().numpy()} return fn ``` This is how the `get_test_accuracy()` is being used: ```py emotions = load_dataset("emotion") def tokenize(batch): return tokenizer(batch["text"], padding=True, truncation=True) emotions_encoded = emotions.map(tokenize, batched=True) emotions_encoded.set_format("torch", columns=["input_ids", "attention_mask", "label"]) new_dataset = emotions_encoded["validation"].map( accuracy_fn, batched=True, batch_size=128 ) ``` Complete code is available in the Colab Notebook provided below. The `map()` process fails midway giving: ```shell AttributeError Traceback (most recent call last) <ipython-input-8-ad24ac288eb4> in <module> 2 3 new_dataset = emotions_encoded["validation"].map( ----> 4 accuracy_fn, batched=True, batch_size=128 5 ) 7 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2588 new_fingerprint=new_fingerprint, 2589 disable_tqdm=disable_tqdm, -> 2590 desc=desc, 2591 ) 2592 else: /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 582 self: "Dataset" = kwargs.pop("self") 583 # apply actual function --> 584 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 585 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 586 for dataset in datasets: /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output /usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 478 # Call actual function 479 --> 480 out = func(self, *args, **kwargs) 481 482 # Update fingerprint of in-place transforms + update in-place history of transforms /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2970 indices, 2971 check_same_num_examples=len(input_dataset.list_indexes()) > 0, -> 2972 offset=offset, 2973 ) 2974 except NumExamplesMismatchError: /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 2850 if with_rank: 2851 additional_args += (rank,) -> 2852 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) 2853 if update_data is None: 2854 # Check if the function returns updated examples <ipython-input-6-4e0d280426f6> in fn(batch) 1 def get_test_accuracy(model): 2 def fn(batch): ----> 3 inputs = {k:v.to(device) for k,v in batch.items() 4 if k in tokenizer.model_input_names} 5 with torch.no_grad(): <ipython-input-6-4e0d280426f6> in <dictcomp>(.0) 2 def fn(batch): 3 inputs = {k:v.to(device) for k,v in batch.items() ----> 4 if k in tokenizer.model_input_names} 5 with torch.no_grad(): 6 output = model(**inputs) AttributeError: 'list' object has no attribute 'to' ``` As you'd notice in the notebook, the process fails _midway_ and not at the beginning. Is this expected? ### Steps to reproduce the bug Colab Notebook: https://colab.research.google.com/gist/sayakpaul/d1570d537faf39040d02d77b1ed7de07/scratchpad.ipynb ### Expected behavior The mapping process should complete as is. If you switch the `split` to `test` it works as expected. ### Environment info Colab
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1,962
Fix unused arguments
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[ "@lhoestq Re-added the arg. The ConnectionError in CI seems unrelated to this PR (the same test fails on master as well).", "Thanks !\r\nI'm re-running the CI, maybe this was an issue with circleCI", "Looks all good now, merged :)" ]
2021-02-28T02:47:07Z
2021-03-11T02:18:17Z
2021-03-03T16:37:50Z
CONTRIBUTOR
null
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Noticed some args in the codebase are not used, so managed to find all such occurrences with Pylance and fix them.
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I_kwDODunzps4_osbt
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Failed to convert `string` with pyarrow for QED since 1.15.0
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[ "Scratch that, probably the old and incompatible usage of dataset builder from promptsource.", "Actually, re-opening this issue cause the error persists\r\n\r\n```python\r\n>>> load_dataset(\"qed\")\r\nDownloading and preparing dataset qed/qed (download: 13.43 MiB, generated: 9.70 MiB, post-processed: Unknown siz...
2021-11-30T20:11:42Z
2021-12-14T14:39:05Z
2021-12-14T14:39:05Z
CONTRIBUTOR
null
null
null
## Describe the bug Loading QED was fine until 1.15.0. related: bigscience-workshop/promptsource#659, bigscience-workshop/promptsource#670 Not sure where the root cause is, but here are some candidates: - #3158 - #3120 - #3196 - #2891 ## Steps to reproduce the bug ```python load_dataset("qed") ``` ## Expected results Loading completed. ## Actual results ```shell ArrowInvalid: Could not convert in with type str: tried to convert to boolean Traceback: File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/streamlit/script_runner.py", line 354, in _run_script exec(code, module.__dict__) File "/Users/s0s0cr3/Documents/GitHub/promptsource/promptsource/app.py", line 260, in <module> dataset = get_dataset(dataset_key, str(conf_option.name) if conf_option else None) File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/streamlit/caching.py", line 543, in wrapped_func return get_or_create_cached_value() File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/streamlit/caching.py", line 527, in get_or_create_cached_value return_value = func(*args, **kwargs) File "/Users/s0s0cr3/Documents/GitHub/promptsource/promptsource/utils.py", line 49, in get_dataset builder_instance.download_and_prepare() File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/builder.py", line 1106, in _prepare_split num_examples, num_bytes = writer.finalize() File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/arrow_writer.py", line 456, in finalize self.write_examples_on_file() File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/arrow_writer.py", line 325, in write_examples_on_file pa_array = pa.array(typed_sequence) File "pyarrow/array.pxi", line 222, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/arrow_writer.py", line 121, in __arrow_array__ out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type) File "pyarrow/array.pxi", line 305, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.0, 1.16.1 - Platform: macOS 1.15.7 or above - Python version: 3.7.12 and 3.9 - PyArrow version: 3.0.0, 5.0.0, 6.0.1
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Skip dataset verifications by default
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[ "_The documentation is not available anymore as the PR was closed or merged._", "100% agree that the checksum verification is overkill and not super useful. But I think this PR would also disable the check on num_examples no ?\r\n \r\nAs a user I would like to know if the dataset I'm loading changed significantly...
2022-11-25T18:39:09Z
2023-02-13T16:50:42Z
2023-02-13T16:43:47Z
CONTRIBUTOR
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Skip the dataset verifications (split and checksum verifications, duplicate keys check) by default unless a dataset is being tested (`datasets-cli test/run_beam`). The main goal is to avoid running the checksum check in the default case due to how expensive it can be for large datasets. PS: Maybe we should deprecate `ignore_verifications`, which is `True` now by default, and give it a different name?
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Batched in filter throws error
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2021-12-29T12:01:04Z
2022-01-04T10:24:27Z
2022-01-04T10:24:27Z
CONTRIBUTOR
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I hope this is really a bug, I could not find it among the open issues ## Describe the bug using `batched=False` in DataSet.filter throws error ```python TypeError: filter() got an unexpected keyword argument 'batched' ``` but in the docs it is lister as an argument. ## Steps to reproduce the bug ```python task = "mnli" max_length = 128 tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/") dataset = load_dataset("glue", task) task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mnli-mm": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } ##### tokenization_parameters sentence1_key, sentence2_key = task_to_keys[task] def preprocess_function(examples, max_length): if sentence2_key is None: return tokenizer( examples[sentence1_key], truncation=True, max_length=max_length ) return tokenizer( examples[sentence1_key], examples[sentence2_key], truncation=False, padding="max_length", max_length=max_length, ) encoded_dataset = dataset.map( lambda x: preprocess_function(x, max_length=max_length), batched=False ) encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1, 1.17.0 - Platform: ubuntu - Python version: 3.8.12
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Mapping in the distributed setting
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2021-05-01T21:23:05Z
2021-05-03T13:54:53Z
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The barrier trick for distributed mapping as discussed on Thursday with @lhoestq
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Fix ValueError message formatting in int2str
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2022-02-17T10:50:08Z
2022-02-17T15:32:02Z
2022-02-17T15:32:02Z
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Hi! I bumped into this particular `ValueError` during my work (because an instance of `np.int64` was passed instead of regular Python `int`), and so I had to `print(type(values))` myself. Apparently, it's just the missing `f` to make message an f-string. It ain't much for a contribution, but it's honest work. Hope it spares someone else a few seconds in the future 😃
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Support future datasets
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2021-02-03T15:26:49Z
2021-02-05T10:33:48Z
2021-02-05T10:33:47Z
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If a dataset is available at the version of the local installation of `datasets` (e.g. 1.2.0), then loading this dataset means loading the script at this version. However when trying to load a dataset that is only available on master, currently users have to specify `script_version="master"` in `load_dataset` to make it work. However we could automatically get the dataset from master instead in this case. I added this feature in this PR. I also added a warning if a dataset is not available at the version of the local installation of `datasets` but is loaded from master: ```python >>> load_dataset("silicone", "dyda_da") Couldn't find file locally at silicone/silicone.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/silicone/silicone.py. The file was picked from the master branch on github instead at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/silicone/silicone.py. Downloading and preparing dataset silicone/dyda_da (download: 8.46 MiB, generated: 9.39 MiB, post-processed: Unknown size, total: 17.86 MiB) to /Users/quentinlhoest/.cache/huggingface/datasets/silicone/dyda_da/1.0.0/d41d8c0b73c6df035b1369c45774418f0051163ea689b5502b8bda783adf6342... ... ```
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timit_asr doesn't load the test split.
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[ "The [timit_asr.py](https://huggingface.co/datasets/timit_asr/blob/main/timit_asr.py) script iterates over the WAV files per split directory using this:\r\n```python\r\nwav_paths = sorted(Path(data_dir).glob(f\"**/{split}/**/*.wav\"))\r\nwav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f\"**/{spli...
2022-11-26T10:18:22Z
2023-02-10T16:33:21Z
2023-02-10T16:33:21Z
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### Describe the bug When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split. I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all. ```python DatasetDict({ train: Dataset({ features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], num_rows: 4620 }) test: Dataset({ features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], num_rows: 0 }) }) ``` The directory structure of both splits are same. (DIALECT_REGION / SPEAKER_CODE / DATA_FILES) ### Steps to reproduce the bug 1. just use ```timit = load_dataset('timit_asr', data_dir=data_dir)``` ### Expected behavior ```python DatasetDict({ train: Dataset({ features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], num_rows: 4620 }) test: Dataset({ features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'], num_rows: 1680 }) }) ``` ### Environment info - ubuntu 20.04 - python 3.9.13 - datasets 2.7.1
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Remove tasks.json
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
2022-12-08T11:04:35Z
2022-12-09T12:26:21Z
2022-12-09T12:23:20Z
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After discussions in https://github.com/huggingface/datasets/pull/5335 we should remove this file that is not used anymore. We should update https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts instead.
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3,921
Fix NonMatchingChecksumError in CRD3 dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3921). All of your documentation changes will be reflected on that endpoint.", "Unrelated test failure. This PR can be merged." ]
2022-03-15T14:27:14Z
2022-03-15T15:54:27Z
2022-03-15T15:54:26Z
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Fix #3051
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Add ElkarHizketak v1.0 dataset
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[ "I also filled some missing sections in the dataset card" ]
2022-02-23T14:44:17Z
2022-03-04T19:04:29Z
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When training with Multi-Node Multi-GPU the worker 2 has TypeError: 'NoneType' object
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[ "Hi ! Thanks for reporting\r\nI was able to reproduce this issue. This was caused by missing split infos if a worker reloads the cache of the other worker.\r\n\r\nI just opened https://github.com/huggingface/datasets/pull/2137 to fix this issue", "The PR got merged :)\r\nFeel free to try it out on the `master` br...
2021-03-29T08:45:58Z
2021-04-10T11:08:55Z
2021-04-10T11:08:55Z
NONE
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version: 1.5.0 met a very strange error, I am training large scale language model, and need train on 2 machines(workers). And sometimes I will get this error `TypeError: 'NoneType' object is not iterable` This is traceback ``` 71 |   | Traceback (most recent call last): -- | -- | -- 72 |   | File "run_gpt.py", line 316, in <module> 73 |   | main() 74 |   | File "run_gpt.py", line 222, in main 75 |   | delimiter="\t", column_names=["input_ids", "attention_mask", "chinese_ref"]) 76 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/load.py", line 747, in load_dataset 77 |   | use_auth_token=use_auth_token, 78 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 513, in download_and_prepare 79 |   | self.download_post_processing_resources(dl_manager) 80 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 673, in download_post_processing_resources 81 |   | for split in self.info.splits: 82 |   | TypeError: 'NoneType' object is not iterable 83 |   | WARNING:datasets.builder:Reusing dataset csv (/usr/local/app/.cache/huggingface/datasets/csv/default-1c257ebd48e225e7/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2) 84 |   | Traceback (most recent call last): 85 |   | File "/data/miniconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main 86 |   | "__main__", mod_spec) 87 |   | File "/data/miniconda3/lib/python3.7/runpy.py", line 85, in _run_code 88 |   | exec(code, run_globals) 89 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in <module> 90 |   | main() 91 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main 92 |   | sigkill_handler(signal.SIGTERM, None) # not coming back 93 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler 94 |   | raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd) ``` On worker 1 it loads the dataset well, however on worker 2 will get this error. And I will meet this error from time to time, sometimes it just goes well.
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Fix misalignment in SQuAD
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2021-07-05T06:42:20Z
2021-07-12T14:11:10Z
2021-07-07T13:18:51Z
MEMBER
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Fix misalignment between: - the answer text and - the answer_start within the context by keeping original leading blank spaces in the context. Fix #2585.
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Fix overflow issue in interpolation search
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[ "~~Seems like the CI failure is unrelated to this PR~~ (fixed with the merge). \r\n\r\n@lhoestq Can you please verify that everything is OK in terms of speed? Another solution is to change the offsets array dtype to np.int64 (but this doesn't scale in theory compared to Python integer which is unbound). I'm not sur...
2021-05-08T20:51:36Z
2021-05-10T13:29:07Z
2021-05-10T13:26:12Z
CONTRIBUTOR
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Fixes #2335 More info about this error can be found [here](https://stackoverflow.com/questions/53239890/why-do-i-keep-getting-this-error-runtimewarning-overflow-encountered-in-int-sc/53240100).
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Use `iter_files` instead of `str(Path(...)` in image dataset
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[ "`iter_archive` is about to support ZIP archives. I think we should use this no ?\r\n\r\nsee #3347 https://github.com/huggingface/datasets/pull/3379", "I was interested in the support for isfile/dir in remote.\r\n\r\nAnyway, `iter_files` will be available for community users.", "I'm not a big fan of having two ...
2021-12-23T16:26:55Z
2021-12-28T15:15:02Z
2021-12-28T15:15:02Z
CONTRIBUTOR
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Use `iter_files` in the `beans` and the `cats_vs_dogs` dataset scripts as suggested by @albertvillanova. Additional changes: * Fix `iter_files` in `MockDownloadManager` (see this [CI error](https://app.circleci.com/pipelines/github/huggingface/datasets/9247/workflows/2657ff8a-b531-4fd9-a9fc-6541a72e8d83/jobs/57028)) * Add support for `os.path.isdir` and `os.path.isfile` in streaming (`os.path.isfile` is needed in `StreamingDownloadManager`'s `iter_files` to make `cats_vs_dogs` streamable) TODO: - [ ] add tests for `xisdir` and `xisfile`
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Add TimeDial
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[ "Hi @lhoestq,\r\nI've pushed the updated README and tags. Let me know if anything is missing/needs some improvement!\r\n\r\n~PS. I don't know why it's not triggering the build~" ]
2021-06-10T18:33:07Z
2021-07-30T12:57:54Z
2021-07-30T12:57:54Z
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Dataset: https://github.com/google-research-datasets/TimeDial To-Do: Update README.md and add YAML tags
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Unexpected behavior doing Split + Filter
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[ "Hi ! This is an issue with `datasets` 1.12. Sorry for the inconvenience. Can you update to `>=1.13` ?\r\nsee https://github.com/huggingface/datasets/issues/3190\r\n\r\nMaybe we should also backport the bug fix to `1.12` (in a new version `1.12.2`)" ]
2021-12-17T17:00:39Z
2023-07-25T15:38:47Z
2023-07-25T15:38:47Z
NONE
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## Describe the bug I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter') ## Steps to reproduce the bug ``` from datasets import Dataset import pandas as pd dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']} df = pd.DataFrame.from_dict(dic) dataset = Dataset.from_pandas(df) split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42) train_dataset = split_dataset["train"] eval_dataset = split_dataset["test"] eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0) print( eval_dataset['x']) print(eval_dataset_2['x']) ``` One observes that elements in eval_dataset2 are actually coming from the training dataset... ## Expected results The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Windows 10 - Python version: 3.7 - PyArrow version: 5.0.0
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Add perplexity to metrics
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[ "Awesome thank you ! The implementation of the parent `Metric` class was assuming that all metrics were supposed to have references/predictions pairs - I just changed that so you don't have to override `compute()`. I took the liberty of doing the changes directly inside this PR to make sure it works as expected wit...
2022-02-18T15:52:23Z
2022-02-25T17:13:34Z
2022-02-25T17:13:34Z
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Adding perplexity metric This code differs from the code in [this](https://huggingface.co/docs/transformers/perplexity) HF blog post because the blogpost code fails in at least the following circumstances: - returns nans whenever the stride = 1 - hits a runtime error when the stride is significantly larger than the max model length (e.g. if max_model_length = 512 and stride = 1024) Note that: - As it is, it only works for causal models. Pseudoperplexity can be added later as another metric to work with masked language models. - It takes in a list of strings so that it can be dataset independent. This does mean that it doesn't currently batch inputs, and is therefore relatively slow. - It overwrites the metrics compute() function for a specific perplexity compute() function. This is because the current general metrics compute() function requires model-generated predictions, which doesn't make sense in the context of perplexity
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For data-only datasets, streaming and non-streaming don't behave the same
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[ "Note that we might change the heuristic and create a different config per file, at least in that case.", "Hi @severo, thanks for reporting.\r\n\r\nYes, this happens because when non-streaming, a cast of all data is done in order to \"concatenate\" it all into a single dataset (thus the error), while this casting...
2022-02-16T15:20:57Z
2022-02-21T14:24:55Z
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See https://huggingface.co/datasets/huggingface/transformers-metadata: it only contains two JSON files. In streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys: ```python import datasets as ds iterable_dataset = ds.load_dataset("huggingface/transformers-metadata", split="train", streaming=True); rows = list(iterable_dataset.take(100)) rows[0] # {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'} rows[99] # {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'} ``` In normal mode, an exception is thrown: ```python import datasets as ds dataset = ds.load_dataset("huggingface/transformers-metadata", split="train"); ``` ``` ValueError: Couldn't cast model_class: string pipeline_tag: string auto_class: string to {'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)} because column names don't match ```
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None batched `with_transform`, `set_transform`
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[ "Hi ! Thanks for the suggestion :)\r\nIt makes sense to me, and it can surely be implemented by wrapping the user's function to make it a batched function. However I'm not a big fan of the inconsistency it would create with `map`: `with_transform` is batched by default while `map` isn't.\r\n\r\nIs there something y...
2021-12-06T05:20:54Z
2022-01-17T15:25:01Z
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**Is your feature request related to a problem? Please describe.** A `torch.utils.data.Dataset.__getitem__` operates on a single example. But 🤗 `Datasets.with_transform` doesn't seem to allow non-batched transform. **Describe the solution you'd like** Have a `batched=True` argument in `Datasets.with_transform` **Describe alternatives you've considered** * Convert a non-batched transform function to batched one myself. * Wrap a 🤗 Dataset with torch Dataset, and add a `__getitem__`. 🙄 * Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change.
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[ "Thank you for this welcome change guys!" ]
2021-06-08T17:14:23Z
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Close #2458
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Use scikit-learn package rather than sklearn in setup.py
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The sklearn package is an historical thing and should probably not be used by anyone, see https://github.com/scikit-learn/scikit-learn/issues/8215#issuecomment-344679114 for some caveats. Note: this affects only TESTS_REQUIRE so I guess only developers not end users.
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Fix Hub repos update when there's a new release
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It was not listing the full list of datasets correctly cc @SBrandeis this is why it failed for 1.18.0 We should be good now !
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cast_column function not working with map function in streaming mode for Audio features
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[ "Hi! This is probably due to the fact that `IterableDataset.map` sets `features` to `None` before mapping examples. We can fix the issue by passing the old `features` dict to the map generator and performing encoding/decoding there (before calling the map transform function)." ]
2021-12-30T14:52:01Z
2022-01-18T19:54:07Z
2022-01-18T19:54:07Z
NONE
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## Describe the bug I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only. I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset, Audio from transformers import Wav2Vec2Processor def encode(batch, processor): print("Audio: ",batch['audio']) batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values return batch def print_ds(ds): iterator = iter(ds) for d in iterator: print("Data: ",d) break processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path) dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'}, data_dir="data", streaming=True, split="train") print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000)) print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.map(lambda x: encode(x,processor)) print("Features: ",dataset.features) print_ds(dataset) ``` ## Expected results map function not printing Audio type features be used with processor function and getting error in processor call due to this. ## Actual results # after load_dataset call Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'} # after cast_column call Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ..., 1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}} # after map call Features: None Audio: data/0116_003.wav Traceback (most recent call last): File "demo2.py", line 36, in <module> print_ds(dataset) File "demo2.py", line 11, in print_ds for d in iterator: File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__ yield key, self.function(example) File "demo2.py", line 32, in <lambda> dataset = dataset.map(lambda x: batch_encode(x,processor)) File "demo2.py", line 6, in batch_encode batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values TypeError: string indices must be integers ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-4.14.243 with-debian-bullseye-sid - Python version: 3.7.9 - PyArrow version: 6.0.1
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Fix deprecation of use_auth_token in file_utils
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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...
2023-07-31T16:32:01Z
2023-08-03T10:13:32Z
2023-08-03T10:04:18Z
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Fix issues with the deprecation of `use_auth_token` introduced by: - #5996 in functions: - `get_authentication_headers_for_url` - `request_etag` - `get_from_cache` Currently, `TypeError` is raised: https://github.com/huggingface/datasets-server/actions/runs/5711650666/job/15484685570?pr=1588 ``` FAILED tests/job_runners/config/test_parquet_and_info.py::test__is_too_big_external_files[None-None-False] - TypeError: get_authentication_headers_for_url() got an unexpected keyword argument 'use_auth_token' FAILED tests/job_runners/config/test_parquet_and_info.py::test_fill_builder_info[None-False] - libcommon.exceptions.FileSystemError: Could not read the parquet files: get_authentication_headers_for_url() got an unexpected keyword argument 'use_auth_token' ``` Related to: - #6094
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Fix inconsistent caching behaviour in Dataset.map() with multiprocessing #3044
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[ "Hi ! Thanks for noticing this inconsistence and suggesting a fix :)\r\n\r\nIf I understand correctly you try to pass the same fingerprint to each processed shard of the dataset. This can be an issue since each shard is actually a different dataset with different data: they shouldn't have the same fingerprint.\r\n\...
2021-10-08T10:59:21Z
2021-10-21T16:58:32Z
2021-10-21T14:22:44Z
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Fix #3044 1. A rough unit test that fails without the fix. It probably doesn't comply with your code standards, but that just to draft the idea. 2. A one liner fix
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`load_dataset` does not work with certain filename.
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[ "Never mind. It is because of the caching of datasets..." ]
2022-05-10T13:14:04Z
2022-05-10T18:58:36Z
2022-05-10T18:58:09Z
NONE
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## Describe the bug This is a weird bug that took me some time to find out. I have a JSON dataset that I want to load with `load_dataset` like this: ``` data_files = dict(train="train.json.zip", val="val.json.zip") dataset = load_dataset("json", data_files=data_files, field="data") ``` ## Expected results No error. ## Actual results The val file is loaded as expected, but the train file throws JSON decoding error: ``` ╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮ │ <ipython-input-74-97947e92c100>:5 in <module> │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/load.py:1687 in │ │ load_dataset │ │ │ │ 1684 │ try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES │ │ 1685 │ │ │ 1686 │ # Download and prepare data │ │ ❱ 1687 │ builder_instance.download_and_prepare( │ │ 1688 │ │ download_config=download_config, │ │ 1689 │ │ download_mode=download_mode, │ │ 1690 │ │ ignore_verifications=ignore_verifications, │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:605 in │ │ download_and_prepare │ │ │ │ 602 │ │ │ │ │ │ except ConnectionError: │ │ 603 │ │ │ │ │ │ │ logger.warning("HF google storage unreachable. Downloa │ │ 604 │ │ │ │ │ if not downloaded_from_gcs: │ │ ❱ 605 │ │ │ │ │ │ self._download_and_prepare( │ │ 606 │ │ │ │ │ │ │ dl_manager=dl_manager, verify_infos=verify_infos, **do │ │ 607 │ │ │ │ │ │ ) │ │ 608 │ │ │ │ │ # Sync info │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:694 in │ │ _download_and_prepare │ │ │ │ 691 │ │ │ │ │ 692 │ │ │ try: │ │ 693 │ │ │ │ # Prepare split will record examples associated to the split │ │ ❱ 694 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │ │ 695 │ │ │ except OSError as e: │ │ 696 │ │ │ │ raise OSError( │ │ 697 │ │ │ │ │ "Cannot find data file. " │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:1151 in │ │ _prepare_split │ │ │ │ 1148 │ │ │ │ 1149 │ │ generator = self._generate_tables(**split_generator.gen_kwargs) │ │ 1150 │ │ with ArrowWriter(features=self.info.features, path=fpath) as writer: │ │ ❱ 1151 │ │ │ for key, table in logging.tqdm( │ │ 1152 │ │ │ │ generator, unit=" tables", leave=False, disable=True # not loggin │ │ 1153 │ │ │ ): │ │ 1154 │ │ │ │ writer.write_table(table) │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/notebook.py:257 in │ │ __iter__ │ │ │ │ 254 │ │ │ 255 │ def __iter__(self): │ │ 256 │ │ try: │ │ ❱ 257 │ │ │ for obj in super(tqdm_notebook, self).__iter__(): │ │ 258 │ │ │ │ # return super(tqdm...) will not catch exception │ │ 259 │ │ │ │ yield obj │ │ 260 │ │ # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/std.py:1183 in │ │ __iter__ │ │ │ │ 1180 │ │ # If the bar is disabled, then just walk the iterable │ │ 1181 │ │ # (note: keep this check outside the loop for performance) │ │ 1182 │ │ if self.disable: │ │ ❱ 1183 │ │ │ for obj in iterable: │ │ 1184 │ │ │ │ yield obj │ │ 1185 │ │ │ return │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/packaged_modules/j │ │ son/json.py:90 in _generate_tables │ │ │ │ 87 │ │ │ # If the file is one json object and if we need to look at the list of │ │ 88 │ │ │ if self.config.field is not None: │ │ 89 │ │ │ │ with open(file, encoding="utf-8") as f: │ │ ❱ 90 │ │ │ │ │ dataset = json.load(f) │ │ 91 │ │ │ │ │ │ 92 │ │ │ │ # We keep only the field we are interested in │ │ 93 │ │ │ │ dataset = dataset[self.config.field] │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:293 in load │ │ │ │ 290 │ To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` │ │ 291 │ kwarg; otherwise ``JSONDecoder`` is used. │ │ 292 │ """ │ │ ❱ 293 │ return loads(fp.read(), │ │ 294 │ │ cls=cls, object_hook=object_hook, │ │ 295 │ │ parse_float=parse_float, parse_int=parse_int, │ │ 296 │ │ parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:357 in loads │ │ │ │ 354 │ if (cls is None and object_hook is None and │ │ 355 │ │ │ parse_int is None and parse_float is None and │ │ 356 │ │ │ parse_constant is None and object_pairs_hook is None and not kw): │ │ ❱ 357 │ │ return _default_decoder.decode(s) │ │ 358 │ if cls is None: │ │ 359 │ │ cls = JSONDecoder │ │ 360 │ if object_hook is not None: │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:337 in decode │ │ │ │ 334 │ │ containing a JSON document). │ │ 335 │ │ │ │ 336 │ │ """ │ │ ❱ 337 │ │ obj, end = self.raw_decode(s, idx=_w(s, 0).end()) │ │ 338 │ │ end = _w(s, end).end() │ │ 339 │ │ if end != len(s): │ │ 340 │ │ │ raise JSONDecodeError("Extra data", s, end) │ │ │ │ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:353 in raw_decode │ │ │ │ 350 │ │ │ │ 351 │ │ """ │ │ 352 │ │ try: │ │ ❱ 353 │ │ │ obj, end = self.scan_once(s, idx) │ │ 354 │ │ except StopIteration as err: │ │ 355 │ │ │ raise JSONDecodeError("Expecting value", s, err.value) from None │ │ 356 │ │ return obj, end │ ╰───────────────────────────────────────────────────────────────────────────────────────────╯ JSONDecodeError: Unterminated string starting at: line 85 column 20 (char 60051) ``` However, when I rename the `train.json.zip` to other names (like `training.json.zip`, or even to `train.json`), everything works fine; when I unzip the file to `train.json`, it works as well. ## Environment info ``` - `datasets` version: 2.1.0 - Platform: Linux-4.4.0-131-generic-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 7.0.0 - Pandas version: 1.4.2 ```
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Dataset scan time is much slower than using native arrow
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[ "Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:\r\n```diff\r\n- for _ in range(0, len(table), bsz):\r\n- _ = {k:table[k][_ : _ + bsz] for k in cols}\r\n+ for _ in range(0, len(table)...
2023-01-27T01:32:25Z
2023-01-30T16:17:11Z
2023-01-30T16:17:11Z
CONTRIBUTOR
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### Describe the bug I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version. I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that explains this phenomenon? ### Steps to reproduce the bug https://colab.research.google.com/drive/11EtHDaGAf1DKCpvYnAPJUW-LFfAcDzHY?usp=sharing ### Expected behavior I expect scan times to be on par with using pyarrow directly. ### Environment info standard colab environment
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Can't load a dataset
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[ "I believe is due to the fact that doesn't work with .tgz files.", "`JourneyDB/JourneyDB` is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.\r\n\r\n> I believe is due to the fact that d...
2023-09-26T15:46:25Z
2023-10-05T10:23:23Z
2023-10-05T10:23:22Z
NONE
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### Describe the bug Can't seem to load the JourneyDB dataset. It throws the following error: ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[15], line 2 1 # If the dataset is gated/private, make sure you have run huggingface-cli login ----> 2 dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1664, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1661 ignore_verifications = ignore_verifications or save_infos 1663 # Create a dataset builder -> 1664 builder_instance = load_dataset_builder( 1665 path=path, 1666 name=name, 1667 data_dir=data_dir, 1668 data_files=data_files, 1669 cache_dir=cache_dir, 1670 features=features, 1671 download_config=download_config, 1672 download_mode=download_mode, 1673 revision=revision, 1674 use_auth_token=use_auth_token, 1675 **config_kwargs, 1676 ) 1678 # Return iterable dataset in case of streaming 1679 if streaming: File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1490, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs) 1488 download_config = download_config.copy() if download_config else DownloadConfig() 1489 download_config.use_auth_token = use_auth_token -> 1490 dataset_module = dataset_module_factory( 1491 path, 1492 revision=revision, 1493 download_config=download_config, 1494 download_mode=download_mode, 1495 data_dir=data_dir, 1496 data_files=data_files, 1497 ) 1499 # Get dataset builder class from the processing script 1500 builder_cls = import_main_class(dataset_module.module_path) File /opt/conda/lib/python3.10/site-packages/datasets/load.py:1238, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1236 raise ConnectionError(f"Couln't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1237 if isinstance(e1, FileNotFoundError): -> 1238 raise FileNotFoundError( 1239 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1240 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" 1241 ) from None 1242 raise e1 from None 1243 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/JourneyDB/JourneyDB/JourneyDB.py or any data file in the same directory. Couldn't find 'JourneyDB/JourneyDB' on the Hugging Face Hub either: FileNotFoundError: Unable to find data in dataset repository JourneyDB/JourneyDB with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] ``` ### Steps to reproduce the bug 1) ``` from huggingface_hub import notebook_login notebook_login() ``` 2) ``` !pip install -q datasets from datasets import load_dataset ``` 3) `dataset = load_dataset("JourneyDB/JourneyDB", data_files="data", use_auth_token=True)` ### Expected behavior Load the dataset ### Environment info Notebook
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Fix `str(Path(...))` conversion in streaming on Linux
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2021-12-22T15:06:03Z
2021-12-22T16:52:53Z
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Fix `str(Path(...))` conversion in streaming on Linux. This should fix the streaming of the `beans` and `cats_vs_dogs` datasets.
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Fix reddit_tifu data URL
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2022-02-22T12:21:15Z
2022-02-22T12:38:45Z
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Fix #3773.
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Update builder docstring for deprecated/added arguments
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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mishig25 is investigating why deprecated/added do not affect the enclosed text format when used in args docstring: no special formatting appears: \r\n- https://moon-ci-docs.huggingface.co/docs/datasets/pr_4429/en/package_reference/b...
2022-05-31T17:37:25Z
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This PR updates the builder docstring with deprecated/added directives for arguments name/config_name. Follow up of: - #4414 - huggingface/doc-builder#233 First merge: - #4432
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Support DataLoader with num_workers > 0 in streaming mode
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[ "_The documentation is not available anymore as the PR was closed or merged._", "Alright this is finally ready for review ! It's quite long I'm sorry, but it's not easy to disentangle everything ^^'\r\n\r\nThe main additions are in\r\n- src/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py\r\n- src/d...
2022-05-19T15:00:31Z
2022-07-04T16:05:14Z
2022-06-10T20:47:27Z
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### Issue It's currently not possible to properly stream a dataset using multiple `torch.utils.data.DataLoader` workers: - the `TorchIterableDataset` can't be pickled and passed to the subprocesses: https://github.com/huggingface/datasets/issues/3950 - streaming extension is failing: https://github.com/huggingface/datasets/issues/3951 - `fsspec` doesn't work out of the box in subprocesses ### Solution in this PR I fixed these to enable passing an `IterableDataset` to a `torch.utils.data.DataLoader` with `num_workers > 0`. I also had to shard the `IterableDataset` to give each worker a shard, otherwise data would be duplicated. This is implemented in `TorchIterableDataset.__iter__` and uses the new `IterableDataset._iter_shard(shard_idx)` method I also had to do a few changes the patching that enable streaming in dataset scripts: - the patches are now always applied - not just for streaming mode. They're applied when a builder is instantiated - I improved it to also check for renamed modules or attributes (ex: pandas vs pd) - I grouped all the patches of pathlib.Path into a class `xPath`, so that `Path` outside of dataset scripts stay unchanged - otherwise I didn't change the content of the extended Path methods for streaming - I fixed a bug with the `pd.read_csv` patch, opening the file in "rb" mode was missing and causing some datasets to not work in streaming mode, and compression inference was missing ### A few details regarding `fsspec` in multiprocessing From https://github.com/fsspec/filesystem_spec/pull/963#issuecomment-1131709948 : > Non-async instances might be safe in the forked child, if they hold no open files/sockets etc.; I'm not sure any implementations pass this test! > If any async instance has been created, the newly forked processes must: > 1. discard references to locks, threads and event loops and make new ones > 2. not use any async fsspec instances from the parent process > 3. clear all class instance caches Therefore in a DataLoader's worker, I clear the reference to the loop and thread (1). We should be fine for 2 and 3 already since we don't use fsspec class instances from the parent process. Fix https://github.com/huggingface/datasets/issues/3950 Fix https://github.com/huggingface/datasets/issues/3951 TODO: - [x] fix tests
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Pass use_auth_token to request_etags
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2021-07-28T16:13:29Z
2021-07-28T16:38:02Z
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Fix #2724.
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