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2021-02-05T00:49:03Z
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https://api.github.com/repos/huggingface/datasets/issues/1821
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2021-02-09T17:39:27Z
2021-02-09T17:39:27Z
null
[ "Hi!\r\n\r\nThank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.\r\n\r\nI could take on this issue.\r\n\r\nOn the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pandas.read_csv` by passing additional keyword arguments to `load_dataset`. For example, you may find useful this argument:\r\n- `error_bad_lines` : bool, default True\r\n Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these “bad lines” will be dropped from the DataFrame that is returned.\r\n\r\nYou could try:\r\n```python\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files), error_bad_lines=False)\r\n```\r\n" ]
completed
[]
Provide better exception message when one of many files results in an exception
NONE
https://api.github.com/repos/huggingface/datasets/issues/1821/timeline
I find when I process many files, i.e. ``` train_files = glob.glob('rain*.csv') validation_files = glob.glob(validation*.csv') datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files)) ``` I sometimes encounter an error due to one of the files being misformed (i.e. no data, or a comma in a field that isn't quoted, etc). For example, this is the tail of an exception which I suspect is due to a stray comma. > File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read > File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory > File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows > File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows > File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error > pandas.errors.ParserError: Error tokenizing data. C error: Expected 2 fields in line 559, saw 3 It would be nice if the exception trace contained the name of the file being processed (I have 250 separate files!)
https://api.github.com/repos/huggingface/datasets
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2021-02-04T18:23:50Z
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https://github.com/huggingface/datasets/pull/1820
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2021-02-05T14:00:01Z
2021-02-05T14:00:00Z
null
[]
null
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Add metrics usage examples and tests
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1820/timeline
All metrics finally have usage examples and proper fast + slow tests :) I added examples of usage for every metric, and I use doctest to make sure they all work as expected. For "slow" metrics such as bert_score or bleurt which require to download + run a transformer model, the download + forward pass are only done in the slow test. In the fast test on the other hand, the download + forward pass are monkey patched. Metrics that need to be installed from github are not added to setup.py because it prevents uploading the `datasets` package to pypi. An additional-test-requirements.txt file is used instead. This file also include `comet` in order to not have to resolve its *impossible* dependencies. Also `comet` is not tested on windows because one of its dependencies (fairseq) can't be installed in the CI for some reason.
https://api.github.com/repos/huggingface/datasets
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2021-02-04T16:36:46Z
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https://api.github.com/repos/huggingface/datasets/issues/1819
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https://github.com/huggingface/datasets/pull/1819
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false
2021-02-04T16:52:27Z
2021-02-04T16:52:26Z
null
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null
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Fixed spelling `S3Fileystem` to `S3FileSystem`
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1819/timeline
Fixed documentation spelling errors. Wrong `S3Fileystem` Right `S3FileSystem`
https://api.github.com/repos/huggingface/datasets
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2021-02-04T05:55:23Z
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https://api.github.com/repos/huggingface/datasets/issues/1818
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https://github.com/huggingface/datasets/issues/1818
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false
2022-06-01T15:38:42Z
2022-06-01T15:38:42Z
null
[ "Hi ! Thanks for reporting. This was indeed a bug introduced when we moved the `json` dataset loader inside the `datasets` package (before that, the `json` loader was fetched online, as all the other dataset scripts).\r\n\r\nThis should be fixed on master now. Feel free to install `datasets` from source to try it out.\r\nThe fix will be available in the next release of `datasets` in a few days" ]
completed
[]
Loading local dataset raise requests.exceptions.ConnectTimeout
NONE
https://api.github.com/repos/huggingface/datasets/issues/1818/timeline
Load local dataset: ``` dataset = load_dataset('json', data_files=["../../data/json.json"]) train = dataset["train"] print(train.features) train1 = train.map(lambda x: {"labels": 1}) print(train1[:2]) ``` but it raised requests.exceptions.ConnectTimeout: ``` /Users/littlely/myvirtual/tf2/bin/python3.7 /Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py Traceback (most recent call last): File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 160, in _new_conn (self._dns_host, self.port), self.timeout, **extra_kw File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py", line 84, in create_connection raise err File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/connection.py", line 74, in create_connection sock.connect(sa) socket.timeout: timed out During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 677, in urlopen chunked=chunked, File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 381, in _make_request self._validate_conn(conn) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 978, in _validate_conn conn.connect() File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 309, in connect conn = self._new_conn() File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connection.py", line 167, in _new_conn % (self.host, self.timeout), urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)') During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py", line 449, in send timeout=timeout File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/connectionpool.py", line 727, in urlopen method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/urllib3/util/retry.py", line 439, in increment raise MaxRetryError(_pool, url, error or ResponseError(cause)) urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/Users/littlely/projects/python_projects/pytorch_learning/nlp/dataset/transformers_datasets.py", line 12, in <module> dataset = load_dataset('json', data_files=["../../data/json.json"]) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/load.py", line 263, in prepare_module head_hf_s3(path, filename=name, dataset=dataset, max_retries=download_config.max_retries) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 232, in head_hf_s3 max_retries=max_retries, File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 523, in http_head max_retries=max_retries, File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 458, in _request_with_retry raise err File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 454, in _request_with_retry response = requests.request(verb.upper(), url, **params) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/api.py", line 61, in request return session.request(method=method, url=url, **kwargs) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py", line 530, in request resp = self.send(prep, **send_kwargs) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/sessions.py", line 643, in send r = adapter.send(request, **kwargs) File "/Users/littlely/myvirtual/tf2/lib/python3.7/site-packages/requests/adapters.py", line 504, in send raise ConnectTimeout(e, request=request) requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/json/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')) Process finished with exit code 1 ``` Why it want to connect a remote url when I load local datasets, and how can I fix it?
https://api.github.com/repos/huggingface/datasets
null
800,958,776
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2021-02-04T02:30:23Z
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https://api.github.com/repos/huggingface/datasets/issues/1817
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https://github.com/huggingface/datasets/issues/1817
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2022-10-05T12:42:57Z
2022-10-05T12:42:57Z
null
[ "Hi !\r\nThe error you have is due to the `input_ids` column not having the same number of examples as the other columns.\r\nIndeed you're concatenating the `input_ids` at this line:\r\n\r\nhttps://github.com/LuCeHe/GenericTools/blob/431835d8e13ec24dceb5ee4dc4ae58f0e873b091/KerasTools/lm_preprocessing.py#L134\r\n\r\nHowever the other columns are kept unchanged, and therefore you end up with an `input_ids` column with 599 elements while the others columns like `attention_mask` have 1500.\r\n\r\nTo fix that you can instead concatenate them all using\r\n```python\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n```\r\n\r\nAlso you may need to drop the \"text\" column before applying `group_texts` since strings can't be concatenated with lists. You can drop it at the tokenization step:\r\n```python\r\ndset = dset.map(\r\n tokenize_function,\r\n batched=True,\r\n remove_columns=[\"text\"]\r\n)\r\n```", "You saved my life." ]
completed
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pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500
NONE
https://api.github.com/repos/huggingface/datasets/issues/1817/timeline
I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the script that is failing right at the end https://github.com/LuCeHe/GenericTools/blob/master/KerasTools/lm_preprocessing.py In the last iteration of the last dset.map, it gives the error that I copied in the title. Another issue that I have, if I leave the batch_size set as 1000 in the last .map, I'm afraid it's going to lose most text, so I'm considering setting both writer_batch_size and batch_size to 300 K, but I'm not sure it's the best way to go. Can you help me? Thanks!
https://api.github.com/repos/huggingface/datasets
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2021-02-03T20:08:54Z
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https://github.com/huggingface/datasets/pull/1816
[]
false
2021-02-15T15:15:24Z
2021-02-15T15:04:33Z
null
[ "- update data loader and readme for latest version 1.0.1" ]
null
[]
Doc2dial rc update to latest version
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1816/timeline
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1815
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2021-03-01T12:33:03Z
2021-03-01T10:36:21Z
null
[ "Hi !\r\n\r\nWe already have some datasets that can have many many configurations possible.\r\nTo be able to support that, we allow to subclass BuilderConfig to add as many additional parameters as you may need.\r\nThis way users can load any language they want. For example the [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py) dataset is a dataset for translation and therefore users should be able to provide any language pair. You can check how the subclass of BuilderConfig is defined [here](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py#L49).\r\n\r\nFor testing, only the configurations defined in the `BUILDER_CONFIGS` class attribute are used.\r\nAll the other configs combinations are not tested, but they can be used by users. If a config doesn't already exist in `BUILDER_CONFIGS`, then it is created on the fly.\r\nFor example in [bible_para](https://github.com/huggingface/datasets/blob/master/datasets/bible_para/bible_para.py#L61), only 6 configs are defined in `BUILDER_CONFIGS`.\r\n\r\nSo what I would do in your case is have something like\r\n```python\r\n\r\nclass CCAlignedConfig(datasets.BuilderConfig):\r\n def __init__(self, *args, documents_or_sentences=None, language_code=None, **kwargs):\r\n super().__init__(\r\n *args,\r\n name=f\"{documents_or_sentences}-{language_code}\",\r\n **kwargs,\r\n )\r\n self.documents_or_sentences = documents_or_sentences\r\n self.language_code = language_code\r\n```\r\nAnd of course, feel free to change/rename things if you want to. In particular I think we can improve the name of the parameter `documents_or_sentences`", "Hi @lhoestq,\r\n\r\nThanks a lot! I don't know why I didn't think about that. :P \r\nI'll make these changes and update.", "Hi @lhoestq,\r\n\r\nI have tested and added dummy files. Request you to review.\r\n\r\nAlso, does this mean BUILDER_CONFIGS is only needed while testing?", "Hi @lhoestq,\r\n\r\nAny changes required on this one?\r\n\r\nThanks,\r\nGunjan", "Hi @lhoestq,\r\n\r\nSorry for the delay, I have added the changes from the review. For the ISO format language codes, I just selected the first two characters from the names, hoping those are correct. Let me know if you want me to verify :P\r\n\r\nThanks for taking the time to add such a detailed review. I'll keep all these changes in mind the next time I'm adding a dataset.\r\n\r\nThanks,\r\nGunjan", "Hi @lhoestq,\r\n\r\nI have changed the README, and added a single example per config. Even one example is long enough to make the files heavy. Hope that isn't an issue.\r\n\r\nThanks,\r\nGunjan", "Hi @lhoestq,\r\n\r\nThanks for approving." ]
null
[]
Add CCAligned Multilingual Dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1815/timeline
Hello, I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756. This dataset has two types - Document-Pairs, and Sentence-Pairs. The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted. I'm expecting the usage to be something like - `load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`. It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition. Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments. A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests. Thanks, Gunjan Requesting @lhoestq / @yjernite to review.
https://api.github.com/repos/huggingface/datasets
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2021-02-04T19:47:51Z
2021-02-04T16:21:48Z
null
[ "Hi @lhoestq \r\n\r\nThanks for approving. Request you to close PR #1435 as well." ]
null
[]
Add Freebase QA Dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1814/timeline
Closes PR #1435. Fixed issues with PR #1809. Requesting @lhoestq to review.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1813
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2021-02-05T10:33:48Z
2021-02-05T10:33:47Z
null
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Support future datasets
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1813/timeline
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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https://github.com/huggingface/datasets/pull/1812
[]
false
2021-02-08T11:10:18Z
2021-02-08T10:39:06Z
null
[ "Hi @lhoestq,\r\nI have updated with the changes from the review.", "Thanks for approving :)" ]
null
[]
Add CIFAR-100 Dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1812/timeline
Adding CIFAR-100 Dataset.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1811
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false
2021-02-18T14:16:31Z
2021-02-18T14:16:31Z
null
[ "Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) ", "I can confirm that it comes from TFDS and is not used at the moment.", "Thanks @yjernite @lhoestq \r\n\r\nThe template for new dataset makes it slightly confusing. I suppose the comment suggesting its update can be removed.", "Closing this issue since it was answered." ]
completed
[]
Unable to add Multi-label Datasets
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1811/timeline
I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as `supervised_keys=("img", "fine_label")` raises no issue. But trying `supervised_keys=("img", "fine_label","coarse_label")` leads to this error : ```python Traceback (most recent call last): File "test_script.py", line 2, in <module> d = load_dataset('./datasets/cifar100') File "~/datasets/src/datasets/load.py", line 668, in load_dataset **config_kwargs, File "~/datasets/src/datasets/builder.py", line 896, in __init__ super(GeneratorBasedBuilder, self).__init__(*args, **kwargs) File "~/datasets/src/datasets/builder.py", line 247, in __init__ info.update(self._info()) File "~/.cache/huggingface/modules/datasets_modules/datasets/cifar100/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f/cifar100.py", line 79, in _info citation=_CITATION, File "<string>", line 19, in __init__ File "~/datasets/src/datasets/info.py", line 136, in __post_init__ self.supervised_keys = SupervisedKeysData(*self.supervised_keys) TypeError: __init__() takes from 1 to 3 positional arguments but 4 were given ``` Is there a way I can fix this? Also, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset? Thanks, Gunjan
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1810
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2021-12-08T12:03:59Z
null
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[ "I am not sure, but would `datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value(\"int\")))` work?", "Also, I found the information for loading only subsets of the data [here](https://github.com/huggingface/datasets/blob/master/docs/source/splits.rst).", "Hi @lhoestq,\r\n\r\nRequest you to check this once.\r\n\r\nThanks,\r\nGunjan", "Hi @gchhablani since Array2D doesn't support images of different sizes, I would suggest to store in the dataset the paths to the image file instead of the image data. This has the advantage of not decompressing the data (images are often compressed using jpeg, png etc.). Users can still apply `.map` to load the images if they want to. Though it would en up being Sequences features.\r\n\r\nIn the future we'll add support for ragged tensors for this case and update the relevant dataset with this feature." ]
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Add Hateful Memes Dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1810/timeline
## Add Hateful Memes Dataset - **Name:** Hateful Memes - **Description:** [https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set]( https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set) - **Paper:** [https://arxiv.org/pdf/2005.04790.pdf](https://arxiv.org/pdf/2005.04790.pdf) - **Data:** [This link](https://drivendata-competition-fb-hateful-memes-data.s3.amazonaws.com/XjiOc5ycDBRRNwbhRlgH.zip?AWSAccessKeyId=AKIARVBOBDCY4MWEDJKS&Signature=DaUuGgZWUgDHzEPPbyJ2PhSJ56Q%3D&Expires=1612816874) - **Motivation:** Including multi-modal datasets to 🤗 datasets. I will be adding this dataset. It requires the user to sign an agreement on DrivenData. So, it will be used with a manual download. The issue with this dataset is that the images are of different sizes. The image datasets added so far (CIFAR-10 and MNIST) have a uniform shape throughout. So something like ```python datasets.Array2D(shape=(28, 28), dtype="uint8") ``` won't work for the images. How would I add image features then? I checked `datasets/features.py` but couldn't figure out the appropriate class for this. I'm assuming I would want to avoid re-sizing at all since we want the user to be able to access the original images. Also, in case I want to load only a subset of the data, since the actual data is around 8.8GB, how would that be possible? Thanks, Gunjan
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1809
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false
2021-02-03T17:15:05Z
2021-02-03T16:43:06Z
null
[ "Hi ! It looks like this PR contains changes about other datasets than freebase_qa such as DuoRC.\r\n\r\nCan you remove these changes please ?", "Hi @lhoestq,\r\n\r\nI think this happened because of rebasing. I'm unable to remove the duorc commit from the branch. GEM, Arabic sarcasm datasets are also there. I can't see any merge conflicts, however. Before commiting I always rebase (shouldn't have done that).\r\nCan you explain what is to be done? Should I create a clean PR?", "Hi @gchhablani \r\nI think you can simply create another branch and another PR.\r\n\r\nIf I understand correctly the github diff is messed up because you rebased instead of merge.\r\nRebasing is supposed to be used only before pushing the branch the first time, or github messes up the diff.\r\nIf you want to include changes from master on a branch that is already push you need to use git merge.", "Thanks @lhoestq.\r\n\r\nI understand the issue now. I missed the instructions on the template. Sorry for bothering you unnecessarily, I'm pretty new to contributing on GitHub. I'll make a fresh PR.\r\n", "No problem, I'm not a big fan of this weird behavior tbh.\r\nThanks for making a new PR", "@lhoestq Haha, well, it's not as weird as not reading the [instructions](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#open-a-pull-request-on-the-main-huggingface-repo-and-share-your-work).\r\nAlso, I'm enjoying adding new datasets so it's all cool :)" ]
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Add FreebaseQA dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1809/timeline
Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR. Requesting @lhoestq to review.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1808
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false
2022-06-01T15:38:13Z
2022-06-01T15:38:13Z
null
[ "AFAIK, there is currently no built-in method on the `Dataset` object to do this.\r\nHowever, a workaround is to directly use the Arrow table backing the dataset, **but it implies loading the whole dataset in memory** (correct me if I'm mistaken @lhoestq).\r\n\r\nYou can convert the Arrow table to a pandas dataframe to save the data as csv as follows:\r\n```python\r\narrow_table = dataset.data\r\ndataframe = arrow_table.to_pandas()\r\ndataframe.to_csv(\"/path/to/file.csv\")\r\n```\r\n\r\nSimilarly, you can convert the dataset to a Python dict and save it as JSON:\r\n```python\r\nimport json\r\narrow_table = dataset.data\r\npy_dict = arrow_table.to_pydict()\r\nwith open(\"/path/to/file.json\", \"w+\") as f:\r\n json.dump(py_dict, f)\r\n```", "Indeed this works as long as you have enough memory.\r\nIt would be amazing to have export options like csv, json etc. !\r\n\r\nIt should be doable to implement something that iterates through the dataset batch by batch to write to csv for example.\r\nThere is already an `export` method but currently the only export type that is supported is `tfrecords`.", "Hi! `datasets` now supports `Dataset.to_csv` and `Dataset.to_json` for saving data in a human readable format." ]
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writing Datasets in a human readable format
NONE
https://api.github.com/repos/huggingface/datasets/issues/1808/timeline
Hi I see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable format to a file like json? thanks @lhoestq
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1807
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2021-02-02T22:48:41Z
2021-02-02T18:06:58Z
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[ "Nice !" ]
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Adding an aggregated dataset for the GEM benchmark
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1807/timeline
This dataset gathers modified versions of several other conditional text generation datasets which together make up the shared task for the Generation Evaluation and Metrics workshop (think GLUE for text generation) The changes from the original datasets are detailed in the Dataset Cards on the GEM website, which are linked to in this dataset card. cc @sebastianGehrmann
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1806
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2021-02-01T18:46:28Z
2021-02-01T18:46:21Z
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[ "Thanks!" ]
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Update details to MLSUM dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1806/timeline
Update details to MLSUM dataset
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1805
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false
2021-03-06T14:32:46Z
2021-03-06T14:32:46Z
null
[ "Hi ! Indeed we used to require mapping functions to be picklable with `pickle` or `dill` in order to cache the resulting datasets. And FAISS indexes are not picklable unfortunately.\r\n\r\nBut since #1703 this is no longer required (the caching will simply be disabled). This change will be available in the next release of `datasets`, or you can also install `datasets` from source.", "I totally forgot to answer this issue, I'm so sorry. \r\n\r\nI was able to get it working by installing `datasets` from source. Huge thanks!" ]
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can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1805/timeline
So, I have the following instances in my dataset ``` {'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of this increase in rotation?', 'answer': 'C', 'example_id': 'ARCCH_Mercury_7175875', 'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'}, (...)]} ``` The `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`. I would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index ``` dpr_dataset = load_dataset( "text", data_files=ARC_CORPUS_TEXT, cache_dir=CACHE_DIR, split="train[:100%]", ) dpr_dataset.load_faiss_index("embeddings", f"{ARC_CORPUS_FAISS}") torch.set_grad_enabled(False) ``` Then, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_ ``` def generate_context(example): question_text = example['question'] for option in example['options']: question_with_option = question_text + " " + option['option_text'] tokenize_text = question_tokenizer(question_with_option, return_tensors="pt").to(device) question_embed = ( question_encoder(**tokenize_text) )[0][0].cpu().numpy() _, retrieved_examples = dpr_dataset.get_nearest_examples( "embeddings", question_embed, k=10 ) # option["option_context"] = retrieved_examples["text"] # option["option_context"] = " ".join(option["option_context"]).strip() #result_dict = { # 'example_id': example['example_id'], # 'answer': example['answer'], # 'question': question_text, #options': example['options'] # } return example ``` I intentionally commented on this portion of the code. But when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)` It calls the following error: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-55-75a458ce205c> in <module> ----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0) 301 num_proc=num_proc, 302 ) --> 303 for k, dataset in self.items() 304 } 305 ) ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, 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) 1257 fn_kwargs=fn_kwargs, 1258 new_fingerprint=new_fingerprint, -> 1259 update_data=update_data, 1260 ) 1261 else: ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 155 } 156 # apply actual function --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 159 # re-apply format to the output ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name 157 kwargs[fingerprint_name] = update_fingerprint( --> 158 self._fingerprint, transform, kwargs_for_fingerprint 159 ) 160 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args) 103 for key in sorted(transform_args): 104 hasher.update(key) --> 105 hasher.update(transform_args[key]) 106 return hasher.hexdigest() 107 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update(self, value) 55 def update(self, value): 56 self.m.update(f"=={type(value)}==".encode("utf8")) ---> 57 self.m.update(self.hash(value).encode("utf-8")) 58 59 def hexdigest(self): ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash(cls, value) 51 return cls.dispatch[type(value)](cls, value) 52 else: ---> 53 return cls.hash_default(value) 54 55 def update(self, value): ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash_default(cls, value) 44 @classmethod 45 def hash_default(cls, value): ---> 46 return cls.hash_bytes(dumps(value)) 47 48 @classmethod ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dumps(obj) 387 file = StringIO() 388 with _no_cache_fields(obj): --> 389 dump(obj, file) 390 return file.getvalue() 391 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dump(obj, file) 359 def dump(obj, file): 360 """pickle an object to a file""" --> 361 Pickler(file, recurse=True).dump(obj) 362 return 363 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in dump(self, obj) 452 raise PicklingError(msg) 453 else: --> 454 StockPickler.dump(self, obj) 455 stack.clear() # clear record of 'recursion-sensitive' pickled objects 456 return /usr/lib/python3.7/pickle.py in dump(self, obj) 435 if self.proto >= 4: 436 self.framer.start_framing() --> 437 self.save(obj) 438 self.write(STOP) 439 self.framer.end_framing() /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in save_function(pickler, obj) 554 dill._dill._create_function, 555 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults), --> 556 obj=obj, 557 ) 558 else: /usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj) 636 else: 637 save(func) --> 638 save(args) 639 write(REDUCE) 640 /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 /usr/lib/python3.7/pickle.py in save_tuple(self, obj) 784 write(MARK) 785 for element in obj: --> 786 save(element) 787 788 if id(obj) in memo: /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 880 for k, v in tmp: 881 save(k) --> 882 save(v) 883 write(SETITEMS) 884 elif n: /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 547 548 # Save the reduce() output and finally memoize the object --> 549 self.save_reduce(obj=obj, *rv) 550 551 def persistent_id(self, obj): /usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj) 660 661 if state is not None: --> 662 save(state) 663 write(BUILD) 664 /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 880 for k, v in tmp: 881 save(k) --> 882 save(v) 883 write(SETITEMS) 884 elif n: /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 885 k, v = tmp[0] 886 save(k) --> 887 save(v) 888 write(SETITEM) 889 # else tmp is empty, and we're done /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 547 548 # Save the reduce() output and finally memoize the object --> 549 self.save_reduce(obj=obj, *rv) 550 551 def persistent_id(self, obj): /usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj) 660 661 if state is not None: --> 662 save(state) 663 write(BUILD) 664 /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 880 for k, v in tmp: 881 save(k) --> 882 save(v) 883 write(SETITEMS) 884 elif n: /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 547 548 # Save the reduce() output and finally memoize the object --> 549 self.save_reduce(obj=obj, *rv) 550 551 def persistent_id(self, obj): /usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj) 660 661 if state is not None: --> 662 save(state) 663 write(BUILD) 664 /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 502 f = self.dispatch.get(t) 503 if f is not None: --> 504 f(self, obj) # Call unbound method with explicit self 505 return 506 ~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj) 939 # we only care about session the first pass thru 940 pickler._session = False --> 941 StockPickler.save_dict(pickler, obj) 942 log.info("# D2") 943 return /usr/lib/python3.7/pickle.py in save_dict(self, obj) 854 855 self.memoize(obj) --> 856 self._batch_setitems(obj.items()) 857 858 dispatch[dict] = save_dict /usr/lib/python3.7/pickle.py in _batch_setitems(self, items) 885 k, v = tmp[0] 886 save(k) --> 887 save(v) 888 write(SETITEM) 889 # else tmp is empty, and we're done /usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id) 522 reduce = getattr(obj, "__reduce_ex__", None) 523 if reduce is not None: --> 524 rv = reduce(self.proto) 525 else: 526 reduce = getattr(obj, "__reduce__", None) TypeError: can't pickle SwigPyObject objects ``` Which I have no idea how to solve/deal with it
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2021-02-01T15:57:44Z
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https://api.github.com/repos/huggingface/datasets/issues/1804
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https://github.com/huggingface/datasets/pull/1804
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false
2021-02-05T17:46:28Z
2021-02-05T15:49:25Z
null
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Add SICK dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1804/timeline
Adds the SICK dataset (http://marcobaroni.org/composes/sick.html). Closes #1772. Edit: also closes #1632, which is the original issue requesting the dataset. The newer one is a duplicate.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1803
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2021-08-04T18:11:01Z
2021-08-04T18:10:42Z
null
[ "Hello, @lhoestq / @gaceladri : We have been seeing similar behavior with bigger datasets, where querying time increases. Are you folks aware of any solution that fixes this problem yet? ", "Hi ! I'm pretty sure that it can be fixed by using the Arrow IPC file format instead of the raw streaming format but I haven't tested yet.\r\nI'll take a look at it soon and let you know", "My workaround is to shard the dataset into splits in my ssd disk and feed the data in different training sessions. But it is a bit of a pain when we need to reload the last training session with the rest of the split with the Trainer in transformers.\r\n\r\nI mean, when I split the training and then reloads the model and optimizer, it not gets the correct global_status of the optimizer, so I need to hardcode some things. I'm planning to open an issue in transformers and think about it.\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset(\"bookcorpus\", split=\"train[:25%]\")\r\nwikicorpus = load_dataset(\"wikicorpus\", split=\"train[:25%]\")\r\nopenwebtext = load_dataset(\"openwebtext\", split=\"train[:25%]\")\r\n\r\nbig_dataset = datasets.concatenate_datasets([wikicorpus, openwebtext, book_corpus])\r\nbig_dataset.shuffle(seed=42)\r\nbig_dataset = big_dataset.map(encode, batched=True, num_proc=20, load_from_cache_file=True, writer_batch_size=5000)\r\nbig_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./linear_bert\",\r\n overwrite_output_dir=True,\r\n per_device_train_batch_size=71,\r\n save_steps=500,\r\n save_total_limit=10,\r\n logging_first_step=True,\r\n logging_steps=100,\r\n gradient_accumulation_steps=9,\r\n fp16=True,\r\n dataloader_num_workers=20,\r\n warmup_steps=24000,\r\n learning_rate=0.000545205002870214,\r\n adam_epsilon=1e-6,\r\n adam_beta2=0.98,\r\n weight_decay=0.01,\r\n max_steps=138974, # the total number of steps after concatenating 100% datasets\r\n max_grad_norm=1.0,\r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=big_dataset,\r\n tokenizer=tokenizer))\r\n```\r\n\r\nI do one training pass with the total steps of this shard and I use len(bbig)/batchsize to stop the training (hardcoded in the trainer.py) when I pass over all the examples in this split.\r\n\r\nNow Im working, I will edit the comment with a more elaborated answer when I left the work.", "I just tested and using the Arrow File format doesn't improve the speed... This will need further investigation.\r\n\r\nMy guess is that it has to iterate over the record batches or chunks of a ChunkedArray in order to retrieve elements.\r\n\r\nHowever if we know in advance in which chunk the element is, and at what index it is, then we can access it instantaneously. But this requires dealing with the chunked arrays instead of the pyarrow Table directly which is not practical.", "I have a dataset with about 2.7 million rows (which I'm loading via `load_from_disk`), and I need to fetch around 300k (particular) rows of it, by index. Currently this is taking a really long time (~8 hours). I tried sharding the large dataset but overall it doesn't change how long it takes to fetch the desired rows.\r\n\r\nI actually have enough RAM that I could fit the large dataset in memory. Would having the large dataset in memory speed up querying? To find out, I tried to load (a column of) the large dataset into memory like this:\r\n```\r\ncolumn_data = large_ds['column_name']\r\n```\r\nbut in itself this takes a really long time.\r\n\r\nI'm pretty stuck - do you have any ideas what I should do? ", "Hi ! Feel free to post a message on the [forum](https://discuss.huggingface.co/c/datasets/10). I'd be happy to help you with this.\r\n\r\nIn your post on the forum, feel free to add more details about your setup:\r\nWhat are column names and types of your dataset ?\r\nHow was the dataset constructed ?\r\nIs the dataset shuffled ?\r\nIs the dataset tokenized ?\r\nAre you on a SSD or an HDD ?\r\n\r\nI'm sure we can figure something out.\r\nFor example on my laptop I can access the 6 millions articles from wikipedia in less than a minute.", "Thanks @lhoestq, I've [posted on the forum](https://discuss.huggingface.co/t/fetching-rows-of-a-large-dataset-by-index/4271?u=abisee).", "Fixed by #2122." ]
completed
[]
Querying examples from big datasets is slower than small datasets
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1803/timeline
After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets. For example ```python from datasets import load_dataset b1 = load_dataset("bookcorpus", split="train[:1%]") b50 = load_dataset("bookcorpus", split="train[:50%]") b100 = load_dataset("bookcorpus", split="train[:100%]") %timeit _ = b1[-1] # 12.2 µs ± 70.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) %timeit _ = b50[-1] # 92.5 µs ± 1.24 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) %timeit _ = b100[-1] # 177 µs ± 3.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) ``` It looks like the time to fetch the example increases with the size of the dataset. This is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample. Maybe switching to the Arrow IPC file format could help fixing this issue. Indeed according to the [documentation](https://arrow.apache.org/docs/format/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue: > We define a “file format” supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer. cc @gaceladri since it can help speed up your training when this one is fixed.
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2021-02-01T03:49:19Z
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https://github.com/huggingface/datasets/pull/1802
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2021-02-03T10:09:52Z
2021-02-03T10:06:30Z
null
[ "@lhoestq Can you confirm if this format is fine? I will update cards based on your feedback.", "On HuggingFace side we also have a mapping of hf user => github user (GitHub info used to be required when signing up until not long ago – cc @gary149 @beurkinger) so we can also add a link to HF profile", "All the dataset cards have been updated with GitHub ids :)" ]
null
[]
add github of contributors
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1802/timeline
This PR will add contributors GitHub id at the end of every dataset cards.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1801
[]
false
2021-02-02T13:17:38Z
2021-02-02T13:17:28Z
null
[ "@mounicam we'll keep the original version in the Turk dataset proper, and use the updated file in the GEM aggregated dataset which I'll add later today\r\n\r\n@lhoestq do not merge, I'll close when I've submitted the GEM dataset PR :) ", "Closed by https://github.com/huggingface/datasets/pull/1807" ]
null
[]
[GEM] Updated the source link of the data to update correct tokenized version.
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1801/timeline
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1800
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2021-02-03T05:01:45Z
2021-02-02T22:49:26Z
null
[ "Thanks for approving @lhoestq!\r\nWill apply these changes for the other datasets I've added too." ]
null
[]
Add DuoRC Dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1800/timeline
Hi, DuoRC SelfRC is one type of the [DuoRC Dataset](https://duorc.github.io/). DuoRC SelfRC is a crowdsourced Abstractive/Extractive Question-Answering dataset based on Wikipedia movie plots. It contains examples that may have answers in the movie plot, synthesized answers which are not present in the movie plot, or no answers. I have also added ParaphraseRC - the other type of DuoRC dataset where questions are based on Wikipedia movie plots and answers are based on corresponding IMDb movie plots. Paper : [https://arxiv.org/abs/1804.07927](https://arxiv.org/abs/1804.07927) I want to add this to 🤗 datasets to make it more accessible to the community. I have added all the details that I could find. Please let me know if anything else is needed from my end. Thanks, Gunjan
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1799
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2021-02-09T22:06:13Z
2021-02-09T15:49:58Z
null
[ "@yjernite Pushed all the changes you recommended. Thank you for your help!" ]
null
[]
Update: SWDA - Fixed code to use all metadata features. Added comments and cleaned c…
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1799/timeline
This is a dataset I currently use my research and I realized some features are not being returned. Previous code was not using all available metadata and was kind of messy I fixed code to use all metadata and made some modification to be more efficient and better formatted. Please let me know if I need to make any changes.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1798
[]
false
2021-02-10T20:39:13Z
2021-02-03T10:35:54Z
null
[ "@lhoestq thanks for the comments - I believe these are now addressed. I re-generated the datasets_info.json and dummy data" ]
null
[]
Add Arabic sarcasm dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1798/timeline
This MIT license dataset: https://github.com/iabufarha/ArSarcasm Via https://sites.google.com/view/ar-sarcasm-sentiment-detection/
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1797
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2021-08-04T18:09:37Z
2021-08-04T18:09:37Z
null
[ "Hi ! For future references let me add a link to our discussion here : https://github.com/huggingface/datasets/issues/759#issuecomment-770684693\r\n\r\nLet me know if you manage to fix your proxy issue or if we can do something on our end to help you :)" ]
completed
[]
Connection error
NONE
https://api.github.com/repos/huggingface/datasets/issues/1797/timeline
Hi I am hitting to the error, help me and thanks. `train_data = datasets.load_dataset("xsum", split="train")` `ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/xsum/xsum.py`
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1796
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2024-01-19T13:25:21Z
null
null
[ "When I use the filter on the arrow table directly, it works like butter. But I can't find a way to update the table in `Dataset` object.\r\n\r\n```\r\nds_table = dataset.data.filter(mask=dataset['flag'])\r\n```", "@thomwolf @lhoestq can you guys please take a look and recommend some solution.", "Hi ! Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.\r\nUsing a mask directly on the arrow table doesn't do any read or write operation therefore it's way quicker.\r\n\r\nReplacing the old table by the new one should do the job:\r\n```python\r\ndataset._data = dataset._data.filter(...)\r\n```\r\n\r\nNote: this is a **workaround** and in general users shouldn't have to do that. In particular if you did some `shuffle` or `select` before that then it would not work correctly since the indices mapping (index from `__getitem__` -> index in the table) would not be valid anymore. But if you haven't done any `shuffle`, `select`, `shard`, `train_test_split` etc. then it should work.\r\n\r\nIdeally it would be awesome to update the filter function to allow masking this way !\r\nIf you would like to give it a shot I will be happy to help :) ", "Yes, would be happy to contribute. Thanks", "Hi @lhoestq @ayubSubhaniya,\r\n\r\nIf there's no progress on this one, can I try working on it?\r\n\r\nThanks,\r\nGunjan", "Sure @gchhablani feel free to start working on it, this would be very appreciated :)\r\nThis feature is would be really awesome, especially since arrow allows to mask really quickly and without having to rewrite the dataset on disk", "Hi @lhoestq, any updates on this issue? The `filter` method is still veryyy slow 😕 ", "No update so far, we haven't worked on this yet :/\r\n\r\nThough PyArrow is much more stable than 3 years ago so it would be a good time to dive into this", "Hi @lhoestq, thanks a lot for the update! \r\n\r\nI would like to work on this(if possible). Could you please give me some steps regarding how should I approach this? Also any references would be great! ", "I just played a bit with it to make sure using `table.filter()` is fine, but actually it seems to create a new table **in memory** :/\r\nThis is an issue since it can quickly fill the RAM, and `datasets`'s role is to make sure you can load bigger-than-memory datasets. Therefore I don't think it's a good idea in the end to use `table.filter()`\r\n\r\nAnyway I just ran OP's code an it runs in 20ms now on my side thanks to the I/O optimizations we did.\r\n\r\nAnother way to speed up `filter` is to add support pyarrow expressions though, using e.g. arrow formatting + dataset.filter (runs in 10ms on my side):\r\n\r\n```python\r\nimport pyarrow.dataset as pds\r\nimport pyarrow.compute as pc\r\n\r\nexpr = pc.field(\"flag\") == True\r\n\r\nfiltered = dataset.with_format(\"arrow\").filter(\r\n lambda t: pds.dataset(t).to_table(columns={\"mask\": expr})[0].to_numpy(),\r\n batched=True,\r\n).with_format(None)\r\n```" ]
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[]
Filter on dataset too much slowww
NONE
https://api.github.com/repos/huggingface/datasets/issues/1796/timeline
I have a dataset with 50M rows. For pre-processing, I need to tokenize this and filter rows with the large sequence. My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes. When I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column. Below are the variants I tried. 1. filter() with batch size 1024, single process (takes roughly 3 hr) 2. filter() with batch size 1024, 96 processes (takes 5-6 hrs ¯\\\_(ツ)\_/¯) 3. filter() with loading all data in memory, only a single boolean column (never ends). Can someone please help? Below is a sample code for small dataset. ``` from datasets import load_dataset dataset = load_dataset('glue', 'mrpc', split='train') dataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1}) def _amplify(data): return data dataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag']) ```
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1795
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2022-07-30T09:50:11Z
2021-02-05T09:54:06Z
null
[ "This PR is amazing!!!\r\n\r\nI only looked at `arrow_dataset.py` and `formatting/formatting.py` but those look good to me.\r\n\r\nMy only (tiny) concern is the name of the function: I don't think it's self-evident that `set_format` applies a generic transformation, and some people might not look too far into the doc.\r\n\r\nMaybe we could have an `apply_transform` or `process_columns` method which is called by `set_format` (to keep backward compatibility)?", "What about something like `.set_format` and `.set_transform` ?\r\n- set_format would be the same as right now, i.e. defined by a format type.\r\n- set_transform would define the transformation that is applied on output batches on-the-fly.\r\n\r\nI was also thinking about `._with_format` and `.with_transform`. It could be their equivalent but would create a **new** dataset with the corresponding format or transform ? I know @sgugger was interested in something like that.", "Yup, I think that would make all of these options very clear!", "I like all those options as well (as long as the `_` in `_with_format` is a typo ;-) )", "Yes it's a typo indeed ;)\r\n\r\nAlright I'll do the changes !", "I took all your suggestions into account, thanks :)\r\nLet me know if you have more comments", "Hi @lhoestq , thanks for offering the set_transform() function. It is very handy to process large datasets on the fly. But I ran into a problem when using it (error message shown below). Since we are working with a large collection, there's no way to filter all invalid data points beforehand. Those invalid data points will be problematic with the set_transform and I don't find a good work-around to ignore them. I wonder if you can offer some advice on dealing with invalid data points in this case. Thank you!\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1763, in __getitem__\r\n return self._getitem(\r\n File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1748, in _getitem\r\n formatted_output = format_table(\r\n File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 532, in format_table\r\n return formatter(pa_table, query_type=query_type)\r\n File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 281, in __call__\r\n return self.format_row(pa_table)\r\n File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 391, in format_row\r\n raise TypeError(\r\nTypeError: Custom formatting function must return a dict to be able to pick a row, but got None\r\n\r\n```\r\n", "> Hi @lhoestq , thanks for offering the set_transform() function. It is very handy to process large datasets on the fly. But I ran into a problem when using it (error message shown below). Since we are working with a large collection, there's no way to filter all invalid data points beforehand. Those invalid data points will be problematic with the set_transform and I don't find a good work-around to ignore them. I wonder if you can offer some advice on dealing with invalid data points in this case. Thank you!\r\n> \r\n> ```\r\n> Traceback (most recent call last):\r\n> File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n> data = fetcher.fetch(index)\r\n> File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n> data = [self.dataset[idx] for idx in possibly_batched_index]\r\n> File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n> data = [self.dataset[idx] for idx in possibly_batched_index]\r\n> File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1763, in __getitem__\r\n> return self._getitem(\r\n> File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1748, in _getitem\r\n> formatted_output = format_table(\r\n> File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 532, in format_table\r\n> return formatter(pa_table, query_type=query_type)\r\n> File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 281, in __call__\r\n> return self.format_row(pa_table)\r\n> File \"/export/share/ruimeng/env/anaconda/envs/ir/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 391, in format_row\r\n> raise TypeError(\r\n> TypeError: Custom formatting function must return a dict to be able to pick a row, but got None\r\n> ```\r\n\r\nI found this trick can be helpful: return an empty dict in exception:\r\n```\r\ndef transform_fn(example):\r\n try:\r\n process_your_data(example)\r\n except Exception as e:\r\n print(e)\r\n return {'input_ids': [[]], 'token_type_ids': [[]], 'attention_mask': [[]]}\r\ntrain_dataset = datasets.load_dataset(...)\r\ntrain_dataset = train_dataset.with_transform(parse_fn)\r\n```" ]
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Custom formatting for lazy map + arrow data extraction refactor
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1795/timeline
Hi ! This PR refactors the way data are extracted from pyarrow tables to extend it to the use of custom formatting functions. While the internal storage of the dataset is always the Apache Arrow format, by setting a specific format on a dataset, you can cast the output of `datasets.Dataset.__getitem__` in NumPy/pandas/PyTorch/TensorFlow, on-the-fly. A specific format can be activated with `datasets.Dataset.set_format`. For example: `dataset.set_format(type='torch', columns=['label'])`. ### What's new: You can now also define your own formatting function that is applied on-the-fly. To do so you can pass your formatting function in the `transform` parameter of `datasets.Dataset.set_format`, and keep `type` to `None`. A formatting function is a callable that takes a batch (as a dict, formatted as python) as input and returns a batch. Here is an example to tokenize and pad tokens on-the-fly when accessing the samples: ```python from datasets import load_dataset from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") def encode(batch): return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt") dataset = load_dataset("glue", "mrpc", split="train") dataset.set_format(transform=encode) dataset.format # {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False} dataset[:2] # {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])} ``` Let me know what you think of this API ! We can still change it if we want to. Especially @sgugger since this may be useful when using `datasets` to train models. EDIT: this was changed to `dataset.set_transform(encode)` ------------------- Note: I had to refactor the way data are extracted and formatted from pyarrow tables and I made it more robust and flexible. In particular I modularized it to be able to unit-test it properly. This was very helpful since I detected some bugs in the previous implementation and was able to fix them. Some bugs I found and fixed: - certain slices/ranges were not supported because negative ids were passed to pyarrow - formatting as numpy/torch/tensorflow a column would make it lose its precision information (for example a column as `Value("float32")`) would be returned as a tensor of float64 (default behavior for numpy) - on windows integers formatted as numpy/torch/tensorflow were not always int64 tensors by default but were int32 The unit tests for those are now really extensive :)
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https://github.com/huggingface/datasets/pull/1794
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false
2021-01-29T16:31:39Z
2021-01-29T16:31:38Z
null
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null
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Move silicone directory
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1794/timeline
The dataset was added in #1761 but not in the right directory. I'm moving it to /datasets
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1793
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2021-01-29T16:53:32Z
2021-01-29T16:53:32Z
null
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null
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Minor fix the docstring of load_metric
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1793/timeline
Minor fix: - duplicated attributes - format fix
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https://github.com/huggingface/datasets/pull/1792
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2021-02-12T14:13:28Z
2021-02-12T14:13:28Z
null
[ "I am wondering how to test their difference...", "> ring how to test their difference...\r\n\r\nHmm I don't think pyarrow exposes an API to check if a Table comes from a file that is memory-mapped. In particular since all the buffer/memory logic is in the C++ part of pyarrow.\r\n\r\nOtherwise we can still check the difference of RAM used when loading a big chunk of data.", "> Hmm I don't think pyarrow exposes an API to check if a Table comes from a file that is memory-mapped. In particular since all the buffer/memory logic is in the C++ part of pyarrow.\r\n> \r\n> Otherwise we can still check the difference of RAM used when loading a big chunk of data.\r\n\r\n@lhoestq I think I found a way: `pa.total_allocated_bytes()` :smirk:" ]
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Allow loading dataset in-memory
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1792/timeline
Allow loading datasets either from: - memory-mapped file (current implementation) - from file descriptor, copying data to physical memory Close #708
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https://github.com/huggingface/datasets/pull/1791
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2021-01-29T18:51:10Z
2021-01-29T17:05:07Z
null
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null
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Small fix with corrected logging of train vectors
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1791/timeline
Now you can set `train_size` to the whole dataset size via `train_size = -1` and login writes not `Training the index with the first -1 vectors` but (for example) `Training the index with the first 16123 vectors`. And maybe more than dataset length. Logging will be correct
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https://github.com/huggingface/datasets/issues/1790
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2021-03-25T12:10:51Z
null
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[ "Hi !\r\n\r\nApache Beam is a framework used to define data transformation pipelines. These pipeline can then be run in many runtimes: DataFlow, Spark, Flink, etc. There also exist a local runner called the DirectRunner.\r\nWikipedia is a dataset that requires some parsing, so to allow the processing to be run on this kind of runtime we're using Apache Beam.\r\n\r\nAt Hugging Face we've already processed certain versions of wikipedia (the `20200501.en` one for example) so that users can directly download the processed version instead of using Apache Beam to process it.\r\nHowever for the japanese language we haven't processed it so you'll have to run the processing on your side.\r\nSo you do need Apache Beam to process `20200501.ja`.\r\n\r\nYou can install Apache Beam with\r\n```\r\npip install apache-beam\r\n```\r\n\r\nI think we can probably improve the error message to let users know of this subtlety.\r\nWhat #498 implied is that Apache Beam is not needed when you process a dataset that doesn't use Apache Beam.", "Thanks for your reply! \r\nI understood.\r\n\r\nI tried again with installing apache-beam, add ` beam_runner=\"DirectRunner\"` and an anther `mwparserfromhell` is also required so I installed it.\r\nbut, it also failed. It exited 1 without error message.\r\n\r\n```py\r\nimport datasets\r\n# BTW, 20200501.ja doesn't exist at wikipedia, so I specified date argument\r\nwiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\"./datasets\", beam_runner=\"DirectRunner\")\r\nprint(wiki)\r\n```\r\nand its log is below\r\n```\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nKilled\r\n```\r\n\r\nI also tried on another machine because it may caused by insufficient resources.\r\n```\r\n$ python main.py\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\n\r\nTraceback (most recent call last):\r\n File \"main.py\", line 3, in <module>\r\n wiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\"./datasets\", beam_runner=\"DirectRunner\")\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/load.py\", line 609, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\", line 526, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\", line 1069, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/pipeline.py\", line 561, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py\", line 126, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 182, in run_pipeline\r\n self._latest_run_result = self.run_via_runner_api(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 193, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 358, in run_stages\r\n stage_results = self._run_stage(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 549, in _run_stage\r\n last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 595, in _run_bundle\r\n result, splits = bundle_manager.process_bundle(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 888, in process_bundle\r\n self._send_input_to_worker(process_bundle_id, transform_id, elements)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 765, in _send_input_to_worker\r\n data_out.write(byte_stream)\r\n File \"apache_beam/coders/stream.pyx\", line 42, in apache_beam.coders.stream.OutputStream.write\r\n File \"apache_beam/coders/stream.pyx\", line 47, in apache_beam.coders.stream.OutputStream.write\r\n File \"apache_beam/coders/stream.pyx\", line 109, in apache_beam.coders.stream.OutputStream.extend\r\nAssertionError: OutputStream realloc failed.\r\n```\r\n\r\n", "Hi @miyamonz,\r\n\r\nI tried replicating this issue using the same snippet used by you. I am able to download the dataset without any issues, although I stopped it in the middle because the dataset is huge.\r\n\r\nBased on a similar issue [here](https://github.com/google-research/fixmatch/issues/23), it could be related to your environment setup, although I am just guessing here. Can you share these details?", "thanks for your reply and sorry for my late response.\r\n\r\n## environment\r\nmy local machine environment info\r\n- Ubuntu on WSL2\r\n\r\n`lsb_release -a`\r\n```\r\nNo LSB modules are available.\r\nDistributor ID: Ubuntu\r\nDescription: Ubuntu 20.04.2 LTS\r\nRelease: 20.04\r\nCodename: focal\r\n```\r\n\r\nRTX 2070 super\r\nInside WSL, there is no nvidia-msi command. I don't know why.\r\nBut, `torch.cuda.is_available()` is true and when I start something ML training code GPU usage is growing up, so I think it works.\r\n\r\nFrom PowerShell, there is nvidia-smi.exe and result is below.\r\n```\r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 470.05 Driver Version: 470.05 CUDA Version: 11.3 |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|===============================+======================+======================|\r\n| 0 NVIDIA GeForce ... WDDM | 00000000:09:00.0 On | N/A |\r\n| 0% 30C P8 19W / 175W | 523MiB / 8192MiB | 3% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=============================================================================|\r\n| 0 N/A N/A 1728 C+G Insufficient Permissions N/A |\r\n| 0 N/A N/A 3672 C+G ...ekyb3d8bbwe\\YourPhone.exe N/A |\r\n| 0 N/A N/A 6304 C+G ...2txyewy\\TextInputHost.exe N/A |\r\n| 0 N/A N/A 8648 C+G C:\\Windows\\explorer.exe N/A |\r\n| 0 N/A N/A 9536 C+G ...y\\ShellExperienceHost.exe N/A |\r\n| 0 N/A N/A 10668 C+G ...5n1h2txyewy\\SearchApp.exe N/A |\r\n| 0 N/A N/A 10948 C+G ...artMenuExperienceHost.exe N/A |\r\n| 0 N/A N/A 11988 C+G ...8wekyb3d8bbwe\\Cortana.exe N/A |\r\n| 0 N/A N/A 12464 C+G ...cw5n1h2txyewy\\LockApp.exe N/A |\r\n| 0 N/A N/A 13280 C+G ...upport\\CEF\\Max Helper.exe N/A |\r\n| 0 N/A N/A 15948 C+G ...t\\GoogleIMEJaRenderer.exe N/A |\r\n| 0 N/A N/A 16128 C+G ...ram Files\\Slack\\Slack.exe N/A |\r\n| 0 N/A N/A 19096 C+G ...8bbwe\\WindowsTerminal.exe N/A |\r\n+-----------------------------------------------------------------------------+\r\n```\r\n\r\nI don't know what should I show in such a case. If it's not enough, please tell me some commands.\r\n\r\n---\r\n## what I did\r\nI surveyed more and I found 2 issues.\r\n\r\nAbout the first one, I wrote it as a new issue.\r\nhttps://github.com/huggingface/datasets/issues/2031\r\n\r\nThe error I mentioned in the previous comment above, which occurred on my local machine, is no longer occurring.\r\n\r\nBut, it still failed. In the previous comment, I wrote `AssertionError: OutputStream realloc failed.` happen on another machine. It also happens on my local machine.\r\n\r\nHere's what I've tried.\r\n\r\nthe wikipedia.py downloads these xml.bz2 files based on dumpstatus.json\r\nIn Japanese Wikipedia dataset that I specified, it will download these 6 files.\r\n\r\n\r\n`https://dumps.wikimedia.org/jawiki/20210120/dumpstatus.json`\r\nand filtered json based on wikipedia.py is below.\r\n```json\r\n {\r\n \"jobs\": {\r\n \"articlesmultistreamdump\": {\r\n \"files\": {\r\n \"jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\"\r\n }\r\n }\r\n }\r\n }\r\n }\r\n```\r\n\r\nSo, I tried running with fewer resources by modifying this line.\r\nhttps://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L524\r\nI changed it like this. just change filepaths list.\r\n` | \"Initialize\" >> beam.Create(filepaths[:1])`\r\n\r\nand I added a print line inside for the loop of _extract_content.\r\nlike this `if(i % 100000 == 0): print(i)`\r\n\r\nfirst, without modification, it always stops after all _extract_content is done.\r\n\r\n- `filepaths[:1]` then it succeeded.\r\n- `filepaths[:2]` then it failed.\r\nI don't try all patterns because each pattern takes a long time.\r\n\r\n### my opinion\r\nIt seems it's successful when the entire file size is small.\r\n \r\nso, at least it doesn't file-specific issue.\r\n\r\n\r\nI don't know it's true but I think when beam_writter writes into a file, it consumes memory depends on its entire file.\r\nbut It's correct Apache Beam's behavior? I'm not familiar with this library.\r\n", "I don't know if this is related, but there is this issue on the wikipedia processing that you reported at #2031 (open PR is at #2037 ) .\r\nDoes the fix your proposed at #2037 helps in your case ?\r\n\r\nAnd for information, the DirectRunner of Apache Beam is not optimized for memory intensive tasks, so you must be right when you say that it uses the memory for the entire file.", "the #2037 doesn't solve my problem directly, but I found the point!\r\n\r\nhttps://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/wikipedia/wikipedia.py#L523\r\nthis `beam.transforms.Reshuffle()` cause the memory error.\r\n\r\nit makes sense if I consider the shuffle means. Beam's reshuffle seems need put all data in memory.\r\nPreviously I doubt that this line causes error, but at that time another bug showed in #2037 made error, so I can't found it.\r\n\r\nAnyway, I comment out this line, and run load_dataset, then it works!\r\n\r\n```python\r\nwiki = datasets.load_dataset(\r\n \"./wikipedia.py\",\r\n cache_dir=\"./datasets\",\r\n beam_runner=\"DirectRunner\",\r\n language=\"ja\",\r\n date=\"20210120\",\r\n)[\"train\"]\r\n```\r\n![image](https://user-images.githubusercontent.com/6331508/112283369-6a9f3300-8ccb-11eb-82e5-827bf7fddfb9.png)\r\n\r\nDataset has already shuffle function. https://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/arrow_dataset.py#L2069\r\nSo, though I don't know it's difference correctly, but I think Beam's reshuffle isn't be needed. How do you think?", "The reshuffle is needed when you use parallelism.\r\nThe objective is to redistribute the articles evenly on the workers, since the `_extract_content` step generated many articles per file. By using reshuffle, we can split the processing of the articles of one file into several workers. Without reshuffle, all the articles of one file would be processed on the same worker that read the file, making the whole process take a very long time.", "Maybe the reshuffle step can be added only if the runner is not a DirectRunner ?" ]
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ModuleNotFoundError: No module named 'apache_beam', when specific languages.
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1790/timeline
```py import datasets wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets') ``` then `ModuleNotFoundError: No module named 'apache_beam'` happend. The error doesn't appear when it's '20200501.en'. I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?
https://api.github.com/repos/huggingface/datasets
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2021-01-28T18:13:56Z
2021-01-28T18:13:56Z
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[BUG FIX] typo in the import path for metrics
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1789/timeline
This tiny PR fixes a typo introduced in https://github.com/huggingface/datasets/pull/1726 which prevents loading new metrics
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2021-01-28T18:46:13Z
2021-01-28T18:46:13Z
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Doc2dial rc
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https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1787
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2021-01-28T13:56:29Z
2021-01-28T13:56:29Z
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Update the CommonGen citation information
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https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1786
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2021-04-23T15:17:39Z
2021-04-23T15:17:39Z
null
[ "By default, all 3 splits will be loaded if you run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"lambada\")\r\nprint(dataset[\"train\"])\r\nprint(dataset[\"valid\"])\r\n\r\n```\r\n\r\nIf you wanted to do load this manually, you could do this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndata_files = {\r\n \"train\": \"data/lambada/train.txt\",\r\n \"valid\": \"data/lambada/valid.txt\",\r\n \"test\": \"data/lambada/test.txt\",\r\n}\r\nds = load_dataset(\"text\", data_files=data_files)\r\n```", "Thank you for the quick response! " ]
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How to use split dataset
NONE
https://api.github.com/repos/huggingface/datasets/issues/1786/timeline
![Capture1](https://user-images.githubusercontent.com/78090287/106057436-cb6a1f00-6111-11eb-8c9c-3658065b1fdf.PNG) Hey, I want to split the lambada dataset into corpus, test, train and valid txt files (like penn treebank) but I am not able to achieve this. What I am doing is, executing the lambada.py file in my project but its not giving desired results. Any help will be appreciated!
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1785
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false
2022-11-07T16:33:03Z
2021-01-30T01:07:56Z
null
[ "Hi ! \r\n\r\nWhat do you mean by \"disk_usage(\".\").free` can't compute on the cluster's shared disk\" exactly ?\r\nDoes it return 0 ?", "Yes, that's right. It shows 0 free space even though there is. I suspect it might have to do with permissions on the shared disk.\r\n\r\n```python\r\n>>> disk_usage(\".\")\r\nusage(total=999999, used=999999, free=0)\r\n```", "That's an interesting behavior...\r\nDo you know any other way to get the free space that works in your case ?\r\nAlso if it's a permission issue could you try fix the permissions and let mus know if that helped ?", "I think its an issue on the clusters end (unclear exactly why -- maybe something with docker containers?), will close the issue", "Were you able to figure it out?", "@philippnoah I had fixed it with a small hack where I patched `has_sufficient_disk_space` to always return `True`. you can do that with an import without having to modify the `datasets` package", "@olinguyen Thanks for the suggestion, it works but I had to to edit builder.py in the installed package. Can you please explain how were you able to do this using import?", "I was able to patch the builder code in my notebook before the load data call and it works. \r\n```\r\nimport datasets\r\ndatasets.builder.has_sufficient_disk_space = lambda needed_bytes, directory='.': True\r\n```" ]
completed
[]
Not enough disk space (Needed: Unknown size) when caching on a cluster
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1785/timeline
I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk. The exact error thrown: ```bash >>> load_dataset(dataset, cache_dir="/path/to/cluster/shared/path") OSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size) ``` [`utils.has_sufficient_disk_space`](https://github.com/huggingface/datasets/blob/8a03ab7d123a76ee744304f21ce868c75f411214/src/datasets/utils/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(".").free` can't compute on the cluster's shared disk). This is exactly where the error gets thrown: https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L502 ```python if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root): raise IOError( "Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})".format( utils.size_str(self.info.size_in_bytes or 0), utils.size_str(self.info.download_size or 0), utils.size_str(self.info.dataset_size or 0), utils.size_str(self.info.post_processing_size or 0), ) ) ``` What would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. Would it be possible to pass a flag to skip this check on disk space?
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1784
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false
2021-01-31T08:47:18Z
2021-01-31T08:47:18Z
null
[ "Hi !\r\n\r\nThe `json` dataset script does support this format. For example loading a dataset with this format works on my side:\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n\r\nCan you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?\r\n\r\n", "Hi Quentin!\r\n\r\nI apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it.\r\n\r\nI repeated it with `datasets 1.2.1` and `pyarrow 2.0.0` and it worked.\r\n\r\nClosing this issue. Again, sorry for the bother.\r\n\r\nThanks,\r\nGunjan" ]
completed
[]
JSONDecodeError on JSON with multiple lines
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1784/timeline
Hello :), I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported: ```json {"key1":11, "key2":12, "key3":13} {"key1":21, "key2":22, "key3":23} ``` But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported. When I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either. Please let me know :) Thanks, Gunjan
https://api.github.com/repos/huggingface/datasets
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2021-01-26T20:39:02Z
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https://github.com/huggingface/datasets/issues/1783
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false
2021-02-01T13:58:44Z
2021-02-01T13:58:44Z
null
[ "Hi @ChewKokWah,\r\n\r\nWe're working on it! In the meantime, you can still find the dataset explorer at the following URL: https://huggingface.co/datasets/viewer/", "Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. \r\nHope similar feature can be retain in the future version." ]
completed
[]
Dataset Examples Explorer
NONE
https://api.github.com/repos/huggingface/datasets/issues/1783/timeline
In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version. Hope HuggingFace can re-enable the feature that at least allow viewing of the first 20 examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation.
https://api.github.com/repos/huggingface/datasets
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2021-01-26T11:47:11Z
null
https://api.github.com/repos/huggingface/datasets/issues/1782
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https://github.com/huggingface/datasets/pull/1782
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false
2021-01-26T13:50:50Z
2021-01-26T13:50:49Z
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Update pyarrow import warning
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1782/timeline
Update the minimum version to >=0.17.1 in the pyarrow version check and update the message. I also moved the check at the top of the __init__.py
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1781
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2022-10-05T12:37:06Z
2022-10-05T12:37:06Z
null
[ "Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ?\r\n\r\nThe PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ?\r\n```\r\npip install pyarrow --upgrade\r\n```", "We should bump up the version test of pyarrow maybe no?\r\n\r\nhttps://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60", "Yes indeed.\r\n\r\nAlso it looks like Pyarrow 3.0.0 got released on pypi 10 hours ago. This might be related to the bug, I'll investigate\r\nEDIT: looks like the 3.0.0 release doesn't have unexpected breaking changes for us, so I don't think the issue comes from that", "Maybe colab moved to pyarrow 0.16 by default (instead of 0.14 before)?", "Installing datasets installs pyarrow>=0.17.1 so in theory it doesn't matter which version of pyarrow colab has by default (which is currently pyarrow 0.14.1).\r\n\r\nAlso now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime).\r\n\r\nI guess what happened is that Colab didn't refresh pyarrow for some reason, and the AttributeError was raised *before* the pyarrow version check from `datasets` at https://github.com/huggingface/datasets/blob/master/src/datasets/__init__.py#L60", "Yes colab doesn’t reload preloaded library unless you restart the instance. Maybe we should move the check on top of the init ", "Yes I'll do that :)", "I updated the pyarrow version check in #1782" ]
completed
[]
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import
NONE
https://api.github.com/repos/huggingface/datasets/issues/1781/timeline
I'm using Colab. And suddenly this morning, there is this error. Have a look below! ![screenshot-colab research google com-2021 01 26-08-15-36](https://user-images.githubusercontent.com/45964869/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)
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https://github.com/huggingface/datasets/pull/1780
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2021-01-28T18:48:00Z
2021-01-28T10:19:45Z
null
[ "Hi ! The error you get is the result of some verifications the library is doing when loading a dataset that already has some metadata in the dataset_infos.json. You can ignore the verifications with \r\n```\r\npython datasets-cli test datasets/scifact --save_infos --all_configs --ignore_verifications\r\n```\r\nThis will update the dataset_infos.json :)", "Nice, I ran that command and `dataset_infos` seems to have been updated appropriately; I added this to the PR. But when I try to load the dataset it still seems like it's getting a path to the old URL somehow. I `pip install -e`'d my fork of the repo, so I'm not sure why `load_dataset` is still looking for the old version of the file. Stack trace below.\r\n\r\n```\r\nIn [1]: import datasets\r\n\r\nIn [2]: ds = datasets.load_dataset(\"scifact\", \"claims\")\r\nDownloading: 7.34kB [00:00, 2.58MB/s]\r\nDownloading: 3.38kB [00:00, 1.36MB/s]\r\nDownloading and preparing dataset scifact/claims (download: 2.72 MiB, generated: 258.64 KiB, post-processed: Unknown size, total: 2.97 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/claims/1.0.0/2bb675b2003716a061a4d8ce27fab32ab7f6d010016bab08ffaccea3c14ec6e7...\r\n---------------------------------------------------------------------------\r\nConnectionError Traceback (most recent call last)\r\n<ipython-input-2-9a50b954d89a> in <module>\r\n----> 1 ds = datasets.load_dataset(\"scifact\", \"claims\")\r\n\r\n~/proj/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n 672\r\n 673 # Download and prepare data\r\n--> 674 builder_instance.download_and_prepare(\r\n 675 download_config=download_config,\r\n 676 download_mode=download_mode,\r\n\r\n~/proj/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 560 logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n 561 if not downloaded_from_gcs:\r\n--> 562 self._download_and_prepare(\r\n 563 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 564 )\r\n\r\n~/proj/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 616 split_dict = SplitDict(dataset_name=self.name)\r\n 617 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 618 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 619\r\n 620 # Checksums verification\r\n\r\n~/.cache/huggingface/modules/datasets_modules/datasets/scifact/2bb675b2003716a061a4d8ce27fab32ab7f6d010016bab08ffaccea3c14ec6e7/scifact.py in _split_generators(self, dl_manager)\r\n 92 # dl_manager is a datasets.download.DownloadManager that can be used to\r\n 93 # download and extract URLs\r\n---> 94 dl_dir = dl_manager.download_and_extract(_URL)\r\n 95\r\n 96 if self.config.name == \"corpus\":\r\n\r\n~/proj/datasets/src/datasets/utils/download_manager.py in download_and_extract(self, url_or_urls)\r\n 256 extracted_path(s): `str`, extracted paths of given URL(s).\r\n 257 \"\"\"\r\n--> 258 return self.extract(self.download(url_or_urls))\r\n 259\r\n 260 def get_recorded_sizes_checksums(self):\r\n\r\n~/proj/datasets/src/datasets/utils/download_manager.py in download(self, url_or_urls)\r\n 177\r\n 178 start_time = datetime.now()\r\n--> 179 downloaded_path_or_paths = map_nested(\r\n 180 download_func,\r\n 181 url_or_urls,\r\n\r\n~/proj/datasets/src/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n 223 # Singleton\r\n 224 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 225 return function(data_struct)\r\n 226\r\n 227 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)\r\n\r\n~/proj/datasets/src/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n 348 if is_remote_url(url_or_filename):\r\n 349 # URL, so get it from the cache (downloading if necessary)\r\n--> 350 output_path = get_from_cache(\r\n 351 url_or_filename,\r\n 352 cache_dir=cache_dir,\r\n\r\n~/proj/datasets/src/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries)\r\n 631 elif response is not None and response.status_code == 404:\r\n 632 raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 633 raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n 634\r\n 635 # Try a second time\r\n\r\nConnectionError: Couldn't reach https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz\r\n```", "Hi ! This may be because you need to point `load_dataset` to the path of the dataset script that has the updated url:\r\n```python\r\nload_dataset(\"./datasets/scifact\", \"claims\")\r\n```\r\n\r\nIf you don't use a path to the updated script, then the old one is used by deffault", "Nice, I did\r\n```\r\nload_dataset(\"./datasets/scifact\", \"claims\")\r\n```\r\nand it worked. ", "One more question about the way the code is being preprocessed. The way I've formatted the data, each entry is a claim, which may be associated with multiple evidence documents (similar to FEVER):\r\n```\r\n# My way\r\n{'id': 70,\r\n 'claim': 'Activation of PPM1D suppresses p53 function.',\r\n 'evidence': {'5956380': [{'sentences': [5, 6], 'label': 'SUPPORT'}],\r\n '4414547': [{'sentences': [5], 'label': 'SUPPORT'}]},\r\n 'cited_doc_ids': [5956380, 4414547]}\r\n```\r\n\r\nIn the Hugginface data, each entry is a single claim / evidence document pair. So, the above entry is converted into two separate entries, like so:\r\n```\r\n# huggingface\r\n[{'cited_doc_ids': [5956380, 4414547],\r\n 'claim': 'Activation of PPM1D suppresses p53 function.',\r\n 'evidence_doc_id': '5956380',\r\n 'evidence_label': 'SUPPORT',\r\n 'evidence_sentences': [5, 6],\r\n 'id': 70},\r\n {'cited_doc_ids': [5956380, 4414547],\r\n 'claim': 'Activation of PPM1D suppresses p53 function.',\r\n 'evidence_doc_id': '4414547',\r\n 'evidence_label': 'SUPPORT',\r\n 'evidence_sentences': [5],\r\n 'id': 70}]\r\n```\r\n\r\nWas this done by design? If not, would you mind if I modify the Huggingface code so that it more closely matches the format that people will get if they download the data from the SciFact repo?", "Yes if you think the format is not convenient for training or evaluation we can change it.\r\nAlso I think we're doing something similar for FEVER: one example = one (claim, sentence) pair.\r\n\r\nLet's merge this PR first and then feel free to open a new PR to change the format :) ", "Thanks for merging!\r\n\r\nI don't have super-strong feelings one way or the other in terms of the data, I think it's probably fine. I may revisit later." ]
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Update SciFact URL
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1780/timeline
Hi, I'm following up this [issue](https://github.com/huggingface/datasets/issues/1717). I'm the SciFact dataset creator, and I'm trying to update the SciFact data url in your repo. Thanks again for adding the dataset! Basically, I'd just like to change the `_URL` to `"https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"`. I changed `scifact.py` appropriately and tried running ``` python datasets-cli test datasets/scifact --save_infos --all_configs ``` which I was hoping would update the `dataset_infos.json` for SciFact. But for some reason the code still seems to be looking for the old version of the dataset. Full stack trace below. I've tried to clear all my Huggingface-related caches, and I've `git grep`'d to make sure that the old path to the dataset isn't floating around somewhere. So I'm not sure why this is happening? Can you help me switch the download URL? ``` (datasets) $ python datasets-cli test datasets/scifact --save_infos --all_configs Checking datasets/scifact/scifact.py for additional imports. Found main folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact Found specific version folder for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534 Found script file from datasets/scifact/scifact.py to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.py Found dataset infos file from datasets/scifact/dataset_infos.json to /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/dataset_infos.json Found metadata file for dataset datasets/scifact/scifact.py at /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534/scifact.json Loading Dataset Infos from /Users/dwadden/.cache/huggingface/modules/datasets_modules/datasets/scifact/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534 Testing builder 'corpus' (1/2) Generating dataset scifact (/Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534) Downloading and preparing dataset scifact/corpus (download: 2.72 MiB, generated: 7.63 MiB, post-processed: Unknown size, total: 10.35 MiB) to /Users/dwadden/.cache/huggingface/datasets/scifact/corpus/1.0.0/2b43b4e125ce3369da7d6353961d9d315e6593f24cc7bbe9ede5e5c911d11534... Downloading took 0.0 min Checksum Computation took 0.0 min Traceback (most recent call last): File "/Users/dwadden/proj/datasets/datasets-cli", line 36, in <module> service.run() File "/Users/dwadden/proj/datasets/src/datasets/commands/test.py", line 139, in run builder.download_and_prepare( File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 562, in download_and_prepare self._download_and_prepare( File "/Users/dwadden/proj/datasets/src/datasets/builder.py", line 622, in _download_and_prepare verify_checksums( File "/Users/dwadden/proj/datasets/src/datasets/utils/info_utils.py", line 32, in verify_checksums raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://ai2-s2-scifact.s3-us-west-2.amazonaws.com/release/2020-05-01/data.tar.gz'} ```
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2021-01-26T10:20:20Z
2021-01-26T10:20:19Z
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Ignore definition line number of functions for caching
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1779/timeline
As noticed in #1718 , when a function used for processing with `map` is moved inside its python file, then the change of line number causes the caching mechanism to consider it as a different function. Therefore in this case, it recomputes everything. This is because we were not ignoring the line number definition for such functions (even though we're doing it for lambda functions). For example this code currently prints False: ```python from datasets.fingerprint import Hasher # define once def foo(x): return x h = Hasher.hash(foo) # define a second time elsewhere def foo(x): return x print(h == Hasher.hash(foo)) ``` I changed this by ignoring the line number for all functions.
https://api.github.com/repos/huggingface/datasets
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2021-01-25T15:22:31Z
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2021-01-29T09:35:14Z
2021-01-29T09:34:51Z
null
[ "@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 let me know if this is fine. ", "> Awesome thank you !!\r\n> This looks all good :)\r\n> \r\n> Just before we merge, I was wondering if you knew why the number of examples in the train set went from 1102 to 32747 in your last commit ? I can't see why the changes in the code would cause such a big difference\r\n\r\nOk the change was the way I presented the data. \r\nIn my previous code, I presented a story with a list of questions-answers related to the story per sample. So the total 1102 was the number of stories (not questions) in the train set. \r\n\r\nIn the case of `NarrativeQA`, the code presented each sample data with one single question. So the story gets replicated as many times based on number of questions per story. I felt this was not really memory efficient so I had coded the way I did earlier. \r\n\r\nBut since this would be inconsistent as you pointed out, I modified my code to suit the `NarrativeQA` approach. Hope it's clear now :) ", "Ok I see ! that makes sense", "Thanks for your time and helping me with all this :) Really appreciate the hardwork you guys do. " ]
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Narrative QA Manual
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1778/timeline
Submitting the manual version of Narrative QA script which requires a manual download from the original repository
https://api.github.com/repos/huggingface/datasets
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2021-01-25T10:53:52Z
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2021-01-25T11:12:53Z
2021-01-25T11:12:53Z
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GPT2 MNLI training using run_glue.py
NONE
https://api.github.com/repos/huggingface/datasets/issues/1777/timeline
Edit: I'm closing this because I actually meant to post this in `transformers `not `datasets` Running this on Google Colab, ``` !python run_glue.py \ --model_name_or_path gpt2 \ --task_name mnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_gpu_train_batch_size 10 \ --gradient_accumulation_steps 32\ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir models/gpt2/mnli/ ``` I get the following error, ``` "Asking to pad but the tokenizer does not have a padding token. " ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`. ``` Do I need to modify the trainer to work with GPT2 ?
https://api.github.com/repos/huggingface/datasets
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2021-01-24T09:28:24Z
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2021-05-20T04:15:58Z
2021-05-20T04:15:58Z
null
[ "We are very actively working on this. How does your dataset look like in practice (number/size/type of files)?", "It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py", "Indeed I will submit a PR in a fez days to enable processing on-the-fly :)\r\nThis can be useful in language modeling for tokenization, padding etc.\r\n", "any update on this issue? ...really look forward to use it ", "Hi @acul3,\r\n\r\nPlease look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do.", "@gchhablani thank you so much\r\n\r\nwill try look at it" ]
completed
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[Question & Bug Report] Can we preprocess a dataset on the fly?
NONE
https://api.github.com/repos/huggingface/datasets/issues/1776/timeline
I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it. Can we preprocess a dataset on the fly without generating cache? BTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code: https://github.com/huggingface/datasets/blob/0281f9d881f3a55c89aeaa642f1ba23444b64083/src/datasets/arrow_dataset.py#L1532
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https://github.com/huggingface/datasets/issues/1775
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false
2021-01-24T09:50:39Z
2021-01-24T09:50:39Z
null
[ "It seems that selecting a subset of colums directly from the dataset, i.e., dataset[\"column\"], is slow.", "I was wrong, ```dataset[\"column\"]``` is fast." ]
completed
[]
Efficient ways to iterate the dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1775/timeline
For a large dataset that does not fits the memory, how can I select only a subset of features from each example? If I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this? Thanks
https://api.github.com/repos/huggingface/datasets
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https://api.github.com/repos/huggingface/datasets/issues/1774
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https://github.com/huggingface/datasets/issues/1774
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2024-01-31T15:54:18Z
2024-01-31T15:54:18Z
null
[ "Hi ! Thanks for reporting.\r\nI am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing.\r\nIf you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :)\r\nI'll make a PR in a few days ", "Good if you can take care at your side.\r\nHere is the [colab notebook](https://colab.research.google.com/drive/19c-abm87RTRYgW9G1D8ktfwRW95zDYBZ?usp=sharing)" ]
completed
[]
is it possible to make slice to be more compatible like python list and numpy?
NONE
https://api.github.com/repos/huggingface/datasets/issues/1774/timeline
Hi, see below error: ``` AssertionError: Requested slice [:10000000000000000] incompatible with 20 examples. ```
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1773
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2021-09-06T08:54:46Z
2021-08-04T18:13:01Z
null
[ "Looks like an issue with your csv file. Did you use the right delimiter ?\r\nApparently at line 37 the CSV reader from pandas reads 2 fields instead of 1.", "Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=data_files, sep=\"\\t\")\r\n```\r\n\r\nfor example to use a tab separator.\r\n\r\nYou can see the full list of arguments here: https://github.com/huggingface/datasets/blob/master/src/datasets/packaged_modules/csv/csv.py\r\n\r\n(I've not found the list in the documentation though, we definitely must add them !)", "You can try to convert the file to (CSV UTF-8)" ]
completed
[]
bug in loading datasets
NONE
https://api.github.com/repos/huggingface/datasets/issues/1773/timeline
Hi, I need to load a dataset, I use these commands: ``` from datasets import load_dataset dataset = load_dataset('csv', data_files={'train': 'sick/train.csv', 'test': 'sick/test.csv', 'validation': 'sick/validation.csv'}) print(dataset['validation']) ``` the dataset in sick/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq ``` Using custom data configuration default Downloading and preparing dataset csv/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Traceback (most recent call last): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 485, in incomplete_dir yield tmp_dir File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 604, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 959, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/tqdm-4.49.0-py3.7.egg/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/julia/cache_home_2/modules/datasets_modules/datasets/csv/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2/csv.py", line 129, in _generate_tables for batch_idx, df in enumerate(csv_file_reader): File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1029, in __next__ return self.get_chunk() File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1079, in get_chunk return self.read(nrows=size) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 1052, in read index, columns, col_dict = self._engine.read(nrows) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/pandas-1.2.0-py3.7-linux-x86_64.egg/pandas/io/parsers.py", line 2056, in read data = self._reader.read(nrows) File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "write_sick.py", line 19, in <module> 'validation': 'sick/validation.csv'}) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 534, in download_and_prepare self._save_info() File "/julia/libs/anaconda3/envs/success/lib/python3.7/contextlib.py", line 130, in __exit__ self.gen.throw(type, value, traceback) File "/julia/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets-1.2.0-py3.7.egg/datasets/builder.py", line 491, in incomplete_dir shutil.rmtree(tmp_dir) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 498, in rmtree onerror(os.rmdir, path, sys.exc_info()) File "/julia/libs/anaconda3/envs/success/lib/python3.7/shutil.py", line 496, in rmtree os.rmdir(path) OSError: [Errno 39] Directory not empty: '/julia/cache_home_2/datasets/csv/default-61468fc71a743ec1/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete' ```
https://api.github.com/repos/huggingface/datasets
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false
2021-02-05T15:49:25Z
2021-02-05T15:49:25Z
null
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Adding SICK dataset
NONE
https://api.github.com/repos/huggingface/datasets/issues/1772/timeline
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
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1771
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2021-01-24T23:06:29Z
2021-01-24T23:06:29Z
null
[ "I temporary manually download csv.py as custom dataset loading script", "Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.\r\nSee PR #1726 \r\nWe'll do a new release soon :)", "Thanks." ]
completed
[]
Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py
NONE
https://api.github.com/repos/huggingface/datasets/issues/1771/timeline
Hi, When I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset? ``` Traceback (most recent call last): File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 343, in cached_path max_retries=download_config.max_retries, File "/home/tom/pyenv/pystory/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/csv/csv.py ```
https://api.github.com/repos/huggingface/datasets
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2022-06-01T15:43:15Z
2022-06-01T15:43:15Z
null
[ "Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)\r\nFor now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https://github.com/huggingface/datasets/issues/853#issuecomment-727872188", "Good to hear.\r\nCurrently I did not use map , just fetch src and tgt from the 2 dataset and merge them.\r\nIt will be a release if you can deal with it at the backend.\r\nThanks.", "Hi! You can rename the columns and concatenate the datasets along `axis=1` to get the desired result as follows:\r\n```python\r\nds1 = ds1.rename_column(\"text\", \"src\")\r\nds2 = ds2.rename_column(\"text\", \"tgt\")\r\nds = datasets.concatenate_datasets([\"ds1\", \"ds2\"], axis=1)\r\n```" ]
completed
[]
how can I combine 2 dataset with different/same features?
NONE
https://api.github.com/repos/huggingface/datasets/issues/1770/timeline
to combine 2 dataset by one-one map like ds = zip(ds1, ds2): ds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} or different feature: ds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}
https://api.github.com/repos/huggingface/datasets
null
792,698,148
https://api.github.com/repos/huggingface/datasets/issues/1770/comments
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2021-01-23T10:13:00Z
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https://api.github.com/repos/huggingface/datasets/issues/1769
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https://github.com/huggingface/datasets/issues/1769
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2022-10-05T12:38:51Z
2022-10-05T12:38:51Z
null
[ "More information: `run_mlm.py` will raise same error when `data_args.line_by_line==True`\r\n\r\nhttps://github.com/huggingface/transformers/blob/9152f16023b59d262b51573714b40325c8e49370/examples/language-modeling/run_mlm.py#L300\r\n", "Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?", "> Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?\r\n\r\npython==3.6.10\r\ndatasets==1.2.1\r\ndill==0.3.2\r\npickle.format_version==4.0", "Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.\r\n\r\nHowever objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.\r\nCan you try to update your python version to python>=3.7 ?\r\n" ]
completed
[]
_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2
NONE
https://api.github.com/repos/huggingface/datasets/issues/1769/timeline
It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine. The script I use is https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_wwm.py Script args: ``` --model_name_or_path ../../../model/chinese-roberta-wwm-ext --train_file /nfs/volume-377-2/bert/data/test/train.txt --output_dir test --do_train --per_device_train_batch_size 2 --gradient_accumulation_steps 2 --learning_rate 1e-4 --max_steps 1000 --warmup_steps 10 --save_steps 1000 --save_total_limit 1 --seed 23333 --max_seq_length 512 --preprocessing_num_workers 2 --cache_dir /nfs/volume-377-2/bert/data/test/cache ``` Where the `/nfs/volume-377-2/bert/data/test/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily. Full Traceback: ``` Traceback (most recent call last): File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 398, in <module> main() File "/nfs/volume-377-2/bert/transformers/examples/language-modeling/run_mlm_wwm.py", line 325, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in map transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1318, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get raise self._value File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/pool.py", line 424, in _handle_tasks put(task) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/home/luban/miniconda3/envs/py36/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/home/luban/miniconda3/envs/py36/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ```
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1768
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2021-01-31T12:33:10Z
2021-01-25T09:14:59Z
null
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[]
Mention kwargs in the Dataset Formatting docs
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1768/timeline
Hi, This was discussed in Issue #1762 where the docs didn't mention that keyword arguments to `datasets.Dataset.set_format()` are allowed. To prevent people from having to check the code/method docs, I just added a couple of lines in the docs. Please let me know your thoughts on this. Thanks, Gunjan @lhoestq
https://api.github.com/repos/huggingface/datasets
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2021-01-22T14:54:37Z
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https://api.github.com/repos/huggingface/datasets/issues/1767
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https://github.com/huggingface/datasets/pull/1767
[]
false
2021-01-25T20:38:07Z
2021-01-25T20:37:42Z
null
[ "> Awesome thank you !\r\n> \r\n> The dummy data are quite big but it was expected given that the raw files are flac files.\r\n> Given that the script doesn't even read the flac files I think we can remove them. Or maybe use empty flac files (see [here](https://hydrogenaud.io/index.php?topic=118685.0) for example). What do you think ?\r\n> \r\n> We'll find a better solution to be able to have bigger dummy_data (max 1MB instead of a few KB, maybe using git LFS.\r\n\r\nHmm, I already made the dummy data as small as possible (a single flac filie per split only). I'd like to keep them at least to have complete dummy data and don't think 500KB for all datasets together is a problem (the long-range summarization datasets are similarly heavy). The moment we allow dummy data to be loaded directly for testing, we need the flac files IMO.\r\n\r\nBut I agree that longterm, we need a better solution for the dummy data (maybe stop hosting it on github to not make the repo too heavy)" ]
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Add Librispeech ASR
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1767/timeline
This PR adds the librispeech asr dataset: https://www.tensorflow.org/datasets/catalog/librispeech There are 2 configs: "clean" and "other" whereas there are two "train" datasets for "clean", hence the name "train.100" and "train.360". As suggested by @lhoestq, due to the enormous size of the dataset in `.arrow` format, the speech files are not directly prepared to a float32-array, but instead just the path to the array file is stored.
https://api.github.com/repos/huggingface/datasets
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2021-01-22T14:22:55Z
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https://api.github.com/repos/huggingface/datasets/issues/1766
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https://github.com/huggingface/datasets/issues/1766
[]
false
2021-02-02T10:38:06Z
2021-02-02T10:38:06Z
null
[ "Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace \"default_experiment\" with the experiment id that you provide in the arrow filename. \r\n\r\nAlso when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate.", "Thank you for your response. I fixed the issue by set \"keep_in_memory=True\" when load_metric. \r\nI cannot share the entire source code but below is the wrapper I wrote:\r\n\r\n```python\r\nclass Evaluation:\r\n def __init__(self, metric='sacrebleu'):\r\n # self.metric = load_metric(metric, keep_in_memory=True)\r\n self.metric = load_metric(metric)\r\n\r\n def add(self, predictions, references):\r\n self.metric.add_batch(predictions=predictions, references=references)\r\n\r\n def compute(self):\r\n return self.metric.compute()['score']\r\n```\r\n\r\nThen call the given wrapper as follows:\r\n\r\n```python\r\neval = Evaluation(metric='sacrebleu')\r\nfor query, candidates, labels in tqdm(dataset):\r\n predictions = net.generate(query)\r\n references = [[s] for s in labels]\r\n eval.add(predictions, references)\r\n if n % 100 == 0:\r\n bleu += eval.compute()\r\n eval = Evaluation(metric='sacrebleu')" ]
completed
[]
Issues when run two programs compute the same metrics
NONE
https://api.github.com/repos/huggingface/datasets/issues/1766/timeline
I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read/and/write to the same location (.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow) where it caches the batches: ``` File "train_matching_min.py", line 160, in <module>ch_9_label avg_loss = valid(epoch, args.batch, args.validation, args.with_label) File "train_matching_min.py", line 93, in valid bleu += eval.compute() File "/u/tlhoang/projects/seal/match/models/eval.py", line 23, in compute return self.metric.compute()['score'] File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 387, in compute self._finalize() File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/metric.py", line 355, in _finalize self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths])) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 231, in read_files pa_table = self._read_files(files) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 170, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict) File "/dccstor/know/anaconda3/lib/python3.7/site-packages/datasets/arrow_reader.py", line 299, in _get_dataset_from_filename pa_table = f.read_all() File "pyarrow/ipc.pxi", line 481, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396 ```
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https://github.com/huggingface/datasets/issues/1765
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2022-10-28T02:16:38Z
2021-01-23T03:44:14Z
null
[ "Instead of:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n```\r\nIt should be:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n```\r\n\r\n`batch_sampler` accepts a Sampler object or an Iterable, so you get an error.", "@mariosasko I thought that would fix it, but now I'm getting a different error:\r\n\r\n```\r\n/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n return torch.tensor(x, **format_kwargs)\r\n---------------------------------------------------------------------------\r\nRuntimeError Traceback (most recent call last)\r\n<ipython-input-20-3af1d82bf93a> in <module>()\r\n 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n5 frames\r\n/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch)\r\n 53 storage = elem.storage()._new_shared(numel)\r\n 54 out = elem.new(storage)\r\n---> 55 return torch.stack(batch, 0, out=out)\r\n 56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\r\n 57 and elem_type.__name__ != 'string_':\r\n\r\nRuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1\r\n```\r\n\r\nAny thoughts what this means?I Do I need padding?", "Yes, padding is an answer. \r\n\r\nThis can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. ", "Padding was the fix, thanks!", "dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)\r\nbatch = next(iter(dataloader))\r\n\r\ngetting \r\nValueError: cannot reshape array of size 8192 into shape (1,512,4)\r\n\r\nI had put padding as 2048 for encoded_dataset\r\nkindly help", "data_loader_val = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=True, drop_last=False, num_workers=0)\r\ndataiter = iter(data_loader_val)\r\nimages, _ = next(dataiter)\r\n\r\ngetting -> TypeError: 'list' object is not callable\r\n\r\nCannot iterate through the data. Kindly suggest." ]
completed
[]
Error iterating over Dataset with DataLoader
NONE
https://api.github.com/repos/huggingface/datasets/issues/1765/timeline
I have a Dataset that I've mapped a tokenizer over: ``` encoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids']) encoded_dataset[:1] ``` ``` {'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[ 101, 178, 1198, 1400, 1714, 22233, 21365, 4515, 8618, 1113, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` When I try to iterate as in the docs, I get errors: ``` dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) next(iter(dataloader)) ``` ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-45-05180ba8aa35> in <module>() 1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32) ----> 2 next(iter(dataloader)) 3 frames /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __init__(self, loader) 411 self._timeout = loader.timeout 412 self._collate_fn = loader.collate_fn --> 413 self._sampler_iter = iter(self._index_sampler) 414 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() 415 self._persistent_workers = loader.persistent_workers TypeError: 'int' object is not iterable ```
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2021-01-21T21:00:02Z
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Connection Issues
NONE
https://api.github.com/repos/huggingface/datasets/issues/1764/timeline
Today, I am getting connection issues while loading a dataset and the metric. ``` Traceback (most recent call last): File "src/train.py", line 180, in <module> train_dataset, dev_dataset, test_dataset = create_race_dataset() File "src/train.py", line 130, in create_race_dataset train_dataset = load_dataset("race", "all", split="train") File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path max_retries=download_config.max_retries, File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/race/race.py ``` Or ``` Traceback (most recent call last): File "src/train.py", line 105, in <module> rouge = datasets.load_metric("rouge") File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 500, in load_metric dataset=False, File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path max_retries=download_config.max_retries, File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/metrics/rouge/rouge.py ```
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https://github.com/huggingface/datasets/pull/1763
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false
2021-01-22T10:14:33Z
2021-01-22T10:13:45Z
null
[]
null
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PAWS-X: Fix csv Dictreader splitting data on quotes
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1763/timeline
```python from datasets import load_dataset # load english paws-x dataset datasets = load_dataset('paws-x', 'en') print(len(datasets['train'])) # outputs 49202 but official dataset has 49401 pairs print(datasets['train'].unique('label')) # outputs [1, 0, -1] but labels are binary [0,1] ``` changed `data = csv.DictReader(f, delimiter="\t")` to `data = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)` in the dataloader to make csv module not split by quotes. The results are as expected for all languages after the change.
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https://github.com/huggingface/datasets/issues/1762
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2021-02-02T07:13:22Z
2021-02-02T07:13:22Z
null
[ "Hi ! You can get CUDA tensors with\r\n\r\n```python\r\ndataset.set_format(\"torch\", columns=columns, device=\"cuda\")\r\n```\r\n\r\nIndeed `set_format` passes the `**kwargs` to `torch.tensor`", "Hi @lhoestq,\r\n\r\nThanks a lot. Is this true for all format types?\r\n\r\nAs in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'?", "Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.\r\nWe don't support the kwargs for pandas on the other hand.", "Thanks @lhoestq,\r\nWould it be okay if I added this to the docs and made a PR?", "Sure ! Feel free to open a PR to improve the documentation :) ", "Closing this issue as it has been resolved." ]
completed
[]
Unable to format dataset to CUDA Tensors
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1762/timeline
Hi, I came across this [link](https://huggingface.co/docs/datasets/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors. I tried this, but Dataset doesn't support assignment: ``` columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions'] samples.set_format(type='torch', columns = columns) for column in columns: samples[column].to(torch.device(self.config.device)) ``` There should be an option to do so, or if there is already a way to do this, please let me know. Thanks, Gunjan
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1761
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2021-02-04T14:32:48Z
2021-01-26T13:50:31Z
null
[ "Thanks for the feedback. All your comments have been addressed!", "Thank you for your constructive feedback! I now know how to best format future datasets that our team plans to publish in the near future :)", "Awesome ! Looking forward to it :) ", "Hi @lhoestq ! One last question. Our research team would like to distribute a link to this dataset amongst the spoken dialogue research community but the dataset does not show in the dropdown menu at huggingface.co. Is there anything else we must do in order to find the dataset there ?\r\n\r\nOnce the dataset does show in the dropdown menu, how can I affiliate it with the Telecom Paris organization that I already created at the website ?", "The files are not located in the right place in the repo. Let me move them", "I created a PR at https://github.com/huggingface/datasets/pull/1794", "I just merged the change @eusip, now the dataset page is available at the url:\r\nhttps://huggingface.co/datasets/silicone", "Thank you for moving the folder for me :)" ]
null
[]
Add SILICONE benchmark
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1761/timeline
My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication. This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
https://api.github.com/repos/huggingface/datasets
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2021-01-22T09:40:01Z
2021-01-22T09:40:00Z
null
[ "Conll has `multilingual` but is only tagged as `en`", "good catch, that was a bad copy paste x)" ]
null
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More tags
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1760/timeline
Since the hub v2 is going to be released soon I figured it would be great to add the missing tags at least for some of the datasets of reference listed [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#write-the-loadingprocessing-code)
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2021-01-21T17:22:11Z
2021-01-21T17:21:06Z
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[ "Hi !\r\nFrom what pickle file fo you get this ?\r\nI guess you mean the dataset loaded using `load_dataset` ?", "yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. ", "The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell\r\n\r\nThere also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.\r\n\r\n", "ok great. Thank you, @lhoestq. " ]
completed
[]
wikipedia dataset incomplete
NONE
https://api.github.com/repos/huggingface/datasets/issues/1759/timeline
Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris
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2021-01-22T00:25:50Z
2021-01-22T00:25:50Z
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[ "Hi !\r\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\r\nMaybe this is because the index is not updated yet on the ElasticSearch side ?", "Thanks for the feedback! I added a 30 second \"sleep\" and that seemed to work well!" ]
completed
[]
dataset.search() (elastic) cannot reliably retrieve search results
NONE
https://api.github.com/repos/huggingface/datasets/issues/1758/timeline
I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices. The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer. I am indexing data that looks like the following from the HF SQuAD 2.0 data set: ``` ['57318658e6313a140071d02b', '56f7165e3d8e2e1400e3733a', '570e2f6e0b85d914000d7d21', '5727e58aff5b5019007d97d0', '5a3b5a503ff257001ab8441f', '57262fab271a42140099d725'] ``` To reproduce the issue, try: ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # build base examples, features set of training data shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list() examples = load_dataset("squad_v2").shuffle(seed=1)['train'] features = load_dataset("squad_v2").shuffle(seed=1)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # reorder features by the training process features = features.select(indices=shuffled_idx) # get the example ids to match with the "example" data; get unique entries id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions in the examples data set; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # retrieve the example index example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist] example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list] example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist] len(example_idx_k1) # should be 130319 len(example_idx_k2) # should be 130319 #trial 1 lengths: # k=1: 130314 # k=3: 130319 # trial 2: # just run k=3 first: 130310 # try k=1 after k=3: 130319 ```
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[ "+1", "@dspoka Please check the following link : https://github.com/thunlp/FewRel\r\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\r\n\r\nI am assuming this is the correct link and implementing based on the same.", "Hi @lhoestq,\r\n\r\nThis issue can be closed, I guess.", "Yes :) closing\r\nThanks again for adding FewRel !", "Thanks for adding this @gchhablani ! Sorry didn't see the email notifications sooner!" ]
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FewRel
NONE
https://api.github.com/repos/huggingface/datasets/issues/1757/timeline
## Adding a Dataset - **Name:** FewRel - **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset - **Paper:** @inproceedings{han2018fewrel, title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation}, author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong}, booktitle={EMNLP}, year={2018}} - **Data:** https://github.com/ProKil/FewRel - **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated. 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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Ccaligned multilingual translation dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1756/timeline
## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). - **Paper:** *link to the dataset paper if available* - https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf - **Data:** *link to the Github repository or current dataset location* - http://www.statmt.org/cc-aligned/ - **Motivation:** *what are some good reasons to have this dataset* - The authors says it's an high quality dataset. - it's pretty large and includes many language pairs. It could be interesting training mt5 on this task. 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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[ "You can use `Dataset.flatten_indices()` to make it fast after a select or shuffle.", "Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad`\r\n\r\n```\r\nexamples = examples.flatten_indices()\r\nfeatures = features.flatten_indices()\r\n```\r\n\r\nThat helped quite a bit!" ]
completed
[]
Using select/reordering datasets slows operations down immensely
NONE
https://api.github.com/repos/huggingface/datasets/issues/1755/timeline
I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour. The below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score. The problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes. ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30): all_start_logits, all_end_logits = starting_logits, ending_logits # Build a map example to its corresponding features. example_id_to_index = {k: i for i, k in enumerate(examples["id"])} features_per_example = collections.defaultdict(list) for i, feature in enumerate(features): features_per_example[example_id_to_index[feature["example_id"]]].append(i) # The dictionaries we have to fill. predictions = collections.OrderedDict() # Logging. print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.") # Let's loop over all the examples! for example_index, example in enumerate(tqdm(examples)): # Those are the indices of the features associated to the current example. feature_indices = features_per_example[example_index] min_null_score = None # Only used if squad_v2 is True. valid_answers = [] context = example["context"] # Looping through all the features associated to the current example. for feature_index in feature_indices: # We grab the predictions of the model for this feature. start_logits = all_start_logits[feature_index] end_logits = all_end_logits[feature_index] # This is what will allow us to map some the positions in our logits to span of texts in the original # context. offset_mapping = features[feature_index]["offset_mapping"] # Update minimum null prediction. cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id) feature_null_score = start_logits[cls_index] + end_logits[cls_index] if min_null_score is None or min_null_score < feature_null_score: min_null_score = feature_null_score # Go through all possibilities for the `n_best_size` greater start and end logits. start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist() end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist() for start_index in start_indexes: for end_index in end_indexes: # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond # to part of the input_ids that are not in the context. if ( start_index >= len(offset_mapping) or end_index >= len(offset_mapping) or offset_mapping[start_index] is None or offset_mapping[end_index] is None ): continue # Don't consider answers with a length that is either < 0 or > max_answer_length. if end_index < start_index or end_index - start_index + 1 > max_answer_length: continue start_char = offset_mapping[start_index][0] end_char = offset_mapping[end_index][1] valid_answers.append( { "score": start_logits[start_index] + end_logits[end_index], "text": context[start_char: end_char] } ) if len(valid_answers) > 0: best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0] else: # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid # failure. best_answer = {"text": "", "score": 0.0} # Let's pick our final answer: the best one or the null answer (only for squad_v2) if not squad_v2: predictions[example["id"]] = best_answer["text"] else: answer = best_answer["text"] if best_answer["score"] > min_null_score else "" predictions[example["id"]] = answer return predictions # build base examples, features from training data examples = load_dataset("squad_v2").shuffle(seed=5)['train'] features = load_dataset("squad_v2").shuffle(seed=5)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # sim some shuffled training indices that we want to use to re-order the data to compare how we did shuffle_idx = np.arange(0, 131754) np.random.shuffle(shuffle_idx) # create a new dataset with rows selected following the training shuffle features = features.select(indices=shuffle_idx) # get unique example ids to match with the "example" data id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # search the examples for their index position example_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] # drop the elastic search examples.drop_index(index_name='id') # put examples in the right order examples = examples.select(indices=example_idx) # generate some fake data logits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)} def score_squad(logits, n_best_size, max_answer): # proceed with QA calculation final_predictions = postprocess_qa_predictions(examples=examples, features=features, starting_logits=logits['starting_logits'], ending_logits=logits['ending_logits'], n_best_size=20, max_answer_length=30) metric = load_metric("squad_v2") formatted_predictions = [{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in final_predictions.items()] references = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples] metrics = metric.compute(predictions=formatted_predictions, references=references) return metrics metrics = score_squad(logits, n_best_size=20, max_answer=30) ```
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2021-01-25T09:12:07Z
2021-01-25T09:12:06Z
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Use a config id in the cache directory names for custom configs
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1754/timeline
As noticed by @JetRunner there was some issues when trying to generate a dataset using a custom config that is based on an existing config. For example in the following code the `mnli_custom` would reuse the cache used to create `mnli` instead of generating a new dataset with the new label classes: ```python from datasets import load_dataset mnli = load_dataset("glue", "mnli") mnli_custom = load_dataset("glue", "mnli", label_classes=["contradiction", "entailment", "neutral"]) ``` I fixed that by extending the cache directory definition of a dataset that is being generated. Instead of using the config name in the cache directory name, I switched to using a `config_id`. By default it is equal to the config name. However the name of a config is not sufficent to have a unique identifier for the dataset being generated since it doesn't take into account: - the config kwargs that can be used to overwrite attributes - the custom features used to write the dataset - the data_files for json/text/csv/pandas datasets Therefore the config id is just the config name with an optional suffix based on these. In particular taking into account the config kwargs fixes the issue with the `label_classes` above. I completed the current test cases by adding the case that was missing: overwriting an already existing config.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1753
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2021-01-20T14:39:30Z
2021-01-20T14:39:30Z
null
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fix comet citations
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1753/timeline
I realized COMET citations were not showing in the hugging face metrics page: <img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105164848-8b9da900-5b0d-11eb-9e20-a38f559d2037.png"> This pull request is intended to fix that. Thanks!
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1752
[]
false
2021-01-20T10:27:07Z
2021-01-20T10:25:02Z
null
[ "I think its better to create a new branch with this fix. I forgot I was still using the old branch." ]
null
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COMET metric citation
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1752/timeline
In my last pull request to add COMET metric, the citations where not following the usual "format". Because of that they where not correctly displayed on the website: <img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105158000-686efb80-5b05-11eb-8bb0-9c85fdac2938.png"> This pull request is only intended to fix that.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1751
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2021-01-20T14:56:52Z
2021-01-20T14:56:52Z
null
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Updated README for the Social Bias Frames dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1751/timeline
See the updated card at https://github.com/mcmillanmajora/datasets/tree/add-SBIC-card/datasets/social_bias_frames. I incorporated information from the [SBIC data statement](https://homes.cs.washington.edu/~msap/social-bias-frames/DATASTATEMENT.html), paper, and the corpus README file included with the dataset download.
https://api.github.com/repos/huggingface/datasets
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2021-01-19T11:07:29Z
2021-01-19T09:48:43Z
null
[ "Good catch, thanks!", "Thank you for merging!" ]
null
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Fix typo in README.md of cnn_dailymail
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1750/timeline
When I read the README.md of `CNN/DailyMail Dataset`, there seems to be a typo `CCN`. I am afraid this is a trivial matter, but I would like to make a suggestion for revision.
https://api.github.com/repos/huggingface/datasets
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2021-01-29T18:38:08Z
null
[ "I will push updates tomorrow.", "@lhoestq thank you for your comments! I went ahead and fixed the code 😃. Please let me know if I missed anything." ]
null
[]
Added metadata and correct splits for swda.
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1749/timeline
Switchboard Dialog Act Corpus I made some changes following @bhavitvyamalik recommendation in #1678: * Contains all metadata. * Used official implementation from the [/swda](https://github.com/cgpotts/swda) repo. * Add official train and test splits used in [Stolcke et al. (2000)](https://web.stanford.edu/~jurafsky/ws97) and validation split used in [Probabilistic-RNN-DA-Classifier](https://github.com/NathanDuran/Probabilistic-RNN-DA-Classifier).
https://api.github.com/repos/huggingface/datasets
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2021-09-17T16:53:18Z
2021-01-19T11:26:58Z
null
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null
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add Stuctured Argument Extraction for Korean dataset
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1748/timeline
https://api.github.com/repos/huggingface/datasets
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2022-10-05T12:37:27Z
2022-10-05T12:37:27Z
null
[ "Hi :) \r\nThe slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.\r\nYou can shuffle and then take a subset of your dataset with\r\n```python\r\n# shuffle and take the first 100 examples\r\ndataset = dataset.shuffle(seed=42).select(range(100))\r\n```\r\n\r\nYou can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows", "thank you so much\n\nOn Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi :)\n> The slicing API doesn't shuffle the data.\n> You can shuffle and then take a subset of your dataset with\n>\n> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))\n>\n> You can find more information about shuffling and selecting rows in the\n> documentation:\n> https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/1747#issuecomment-762278134>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ>\n> .\n>\n" ]
completed
[]
datasets slicing with seed
NONE
https://api.github.com/repos/huggingface/datasets/issues/1747/timeline
Hi I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html I could not find a seed option, could you assist me please how I can get a slice for different seeds? thank you. @lhoestq
https://api.github.com/repos/huggingface/datasets
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2021-01-18T11:31:24Z
2021-01-18T11:31:23Z
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Fix release conda worflow
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1746/timeline
The current workflow yaml file is not valid according to https://github.com/huggingface/datasets/actions/runs/487638110
https://api.github.com/repos/huggingface/datasets
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2021-01-18T11:02:43Z
2021-01-18T00:59:34Z
null
[ "From the description given in the dataset script for `wsc.fixed`:\r\n```\r\nThis version fixes issues where the spans are not actually substrings of the text.\r\n```" ]
completed
[]
difference between wsc and wsc.fixed for superglue
NONE
https://api.github.com/repos/huggingface/datasets/issues/1745/timeline
Hi I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq
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https://github.com/huggingface/datasets/pull/1744
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false
2021-01-18T11:26:09Z
2021-01-18T11:26:09Z
null
[ "@lhoestq I ran `make style` but CI code quality still failing and I don't have access to logs", "It's also likely that due to the previous placement of the field initialization, much of the data about topics etc was simply wrong and carried over from previous entries. Model scores seem to improve significantly with this PR." ]
null
[]
Add missing "brief" entries to reuters
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1744/timeline
This brings the number of examples for ModApte to match the stated `Training set (9,603 docs)...Test Set (3,299 docs)`
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1743
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2022-06-01T15:49:34Z
2022-06-01T15:49:34Z
null
[ "Currently it's only possible to define the features for the two columns `references` and `predictions`.\r\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\r\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.\r\n\r\nFor example \r\n```python\r\nfeatures = datasets.Features({\r\n 'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n \"references\": datasets.Sequence({\r\n \"references_ids\": datasets.Value(\"int32\"),\r\n \"offset_mapping\": datasets.Value(\"int32\"),\r\n 'text': datasets.Value('string'),\r\n \"ground\": datasets.Value(\"int32\")\r\n }),\r\n})\r\n```\r\n\r\nAnother option would be to simply have the two features like \r\n```python\r\nfeatures = datasets.Features({\r\n 'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n})\r\n```\r\nand keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).\r\n\r\n\r\nWhat is the metric you would like to implement ?\r\n\r\nI'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users.\r\n\r\nWhat do you think ?", "Hi @lhoestq,\r\n\r\nI am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.\r\n\r\nIncluding them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.", "Resolved via https://github.com/huggingface/datasets/pull/3824." ]
completed
[]
Issue while Creating Custom Metric
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1743/timeline
Hi Team, I am trying to create a custom metric for my training as follows, where f1 is my own metric: ```python def _info(self): # TODO: Specifies the datasets.MetricInfo object return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}), # Homepage of the metric for documentation homepage="http://metric.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_metric"], reference_urls=["http://path.to.reference.url/new_metric"] ) def _compute(self,predictions,references,text,offset_mapping,spans): pred_spans = [] for i,preds in enumerate(predictions): current_preds = [] for j,token_preds in enumerate(preds): if (preds>0.5): current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1])) pred_spans.append(current_spans) return { "Token Wise F1": f1_score(references,predictions,labels=[0,1]), "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)]) } ``` I believe this is not correct. But that's not the issue I am facing right now. I get this error : ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-144-ed7349b50821> in <module>() ----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] ) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch) 802 encoded_batch = {} 803 if set(batch) != set(self): --> 804 print(batch) 805 print(self) 806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self))) ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'} ``` On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references. How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work? Thanks, Gunjan
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https://github.com/huggingface/datasets/pull/1742
[]
false
2023-09-24T09:52:12Z
2021-03-29T12:43:30Z
null
[ "Maybe it would be simpler to just overwrite the order of the label classes of the `glue` dataset ?\r\n```python\r\nmnli = load_dataset(\"glue\", \"mnli\", label_classes=[\"contradiction\", \"entailment\", \"neutral\"])\r\n```", "Sounds good. Will close the issue if that works." ]
null
[]
Add GLUE Compat (compatible with transformers<3.5.0)
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1742/timeline
Link to our discussion on Slack (HF internal) https://huggingface.slack.com/archives/C014N4749J9/p1609668119337400 The next step is to add a compatible option in the new `run_glue.py` I duplicated `glue` and made the following changes: 1. Change the name to `glue_compat`. 2. Change the label assignments for MNLI and AX.
https://api.github.com/repos/huggingface/datasets
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2021-01-16T02:39:28Z
2021-01-16T02:39:18Z
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error when run fine_tuning on text_classification
NONE
https://api.github.com/repos/huggingface/datasets/issues/1741/timeline
dataset:sem_eval_2014_task_1 pretrained_model:bert-base-uncased error description: when i use these resoruce to train fine_tuning a text_classification on sem_eval_2014_task_1,there always be some problem(when i use other dataset ,there exist the error too). And i followed the colab code (url:https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb#scrollTo=TlqNaB8jIrJW). the error is like this : `File "train.py", line 69, in <module> trainer.train() File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/transformers/trainer.py", line 784, in train for step, inputs in enumerate(epoch_iterator): File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] KeyError: 2` this is my code : ```dataset_name = 'sem_eval_2014_task_1' num_labels_size = 3 batch_size = 4 model_checkpoint = 'bert-base-uncased' number_train_epoch = 5 def tokenize(batch): return tokenizer(batch['premise'], batch['hypothesis'], truncation=True, ) def compute_metrics(pred): labels = pred.label_ids preds = pred.predictions.argmax(-1) precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro') acc = accuracy_score(labels, preds) return { 'accuracy': acc, 'f1': f1, 'precision': precision, 'recall': recall } model = BertForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels_size) tokenizer = BertTokenizerFast.from_pretrained(model_checkpoint, use_fast=True) train_dataset = load_dataset(dataset_name, split='train') test_dataset = load_dataset(dataset_name, split='test') train_encoded_dataset = train_dataset.map(tokenize, batched=True) test_encoded_dataset = test_dataset.map(tokenize, batched=True) args = TrainingArguments( output_dir='./results', evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, num_train_epochs=number_train_epoch, weight_decay=0.01, do_predict=True, ) trainer = Trainer( model=model, args=args, compute_metrics=compute_metrics, train_dataset=train_encoded_dataset, eval_dataset=test_encoded_dataset, tokenizer=tokenizer ) trainer.train() trainer.evaluate()
https://api.github.com/repos/huggingface/datasets
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2021-01-20T13:41:26Z
2021-01-20T13:41:26Z
null
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null
[]
add id_liputan6 dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1740/timeline
id_liputan6 is a large-scale Indonesian summarization dataset. The articles were harvested from an online news portal, and obtain 215,827 document-summary pairs: https://arxiv.org/abs/2011.00679
https://api.github.com/repos/huggingface/datasets
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2021-01-29T14:34:06Z
2021-01-29T10:53:03Z
null
[ "The dataset card is fantastic!\r\n\r\nLooks good to me! Did you check that this still passes the slow tests with the existing dummy data?", "Yes, I ran and passed all the tests specified in [this guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata), including the slow ones.", "I just added the `from pathlib import Path` at the top to fix the script", "I ran the tests locally and they all pass, merging", "Thank you for the review!" ]
null
[]
fixes and improvements for the WebNLG loader
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1739/timeline
- fixes test sets loading in v3.0 - adds additional fields for v3.0_ru - adds info to the WebNLG data card
https://api.github.com/repos/huggingface/datasets
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false
2021-01-15T10:08:20Z
2021-01-15T10:08:19Z
null
[ "Nice thanks :) \r\nNote that in `datasets` the tags are simply the version without the `v`. For example `1.2.1`.", "Do you push tags only for versions?", "Yes I've always used tags only for versions" ]
null
[]
Conda support
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1738/timeline
Will push a new version on anaconda cloud every time a tag starting with `v` is pushed (like `v1.2.2`). Will appear here: https://anaconda.org/huggingface/datasets Depends on `conda-forge` for now, so the following is required for installation: ``` conda install -c huggingface -c conda-forge datasets ```
https://api.github.com/repos/huggingface/datasets
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2021-01-14T10:25:24Z
2021-01-14T10:25:24Z
null
[ "Thanks for updating this!" ]
null
[]
update link in TLC to be github links
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1737/timeline
Base on this issue https://github.com/huggingface/datasets/issues/1064, I can now use the official links.
https://api.github.com/repos/huggingface/datasets
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2021-01-14T10:29:38Z
2021-01-14T10:29:38Z
null
[]
null
[]
Adjust BrWaC dataset features name
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1736/timeline
I added this dataset some days ago, and today I used it to train some models and realized that the names of the features aren't so good. Looking at the current features hierarchy, we have "paragraphs" with a list of "sentences" with a list of "sentences?!". But the actual hierarchy is a "text" with a list of "paragraphs" with a list of "sentences". I confused myself trying to use the dataset with these names. So I think it's better to change it.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1735
[]
false
2021-01-14T15:16:01Z
2021-01-14T15:16:00Z
null
[ "Add new \"dataset\"? ;)", "Lol, too used to Transformers ;-)" ]
null
[]
Update add new dataset template
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1735/timeline
This PR fixes a few typos in the "Add new dataset template" and clarifies a bit what to do for the dummy data creation when the `auto_generate` flag can't work.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/pull/1734
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false
2021-01-14T10:42:18Z
2021-01-14T10:42:18Z
null
[]
null
[]
Fix empty token bug for `thainer` and `lst20`
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1734/timeline
add a condition to check if tokens exist before yielding in `thainer` and `lst20`
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1733
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2021-08-04T18:13:55Z
2021-08-04T18:13:55Z
null
[ "Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py" ]
completed
[]
connection issue with glue, what is the data url for glue?
NONE
https://api.github.com/repos/huggingface/datasets/issues/1733/timeline
Hi my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not thanks
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https://github.com/huggingface/datasets/pull/1732
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2021-01-14T10:19:41Z
2021-01-14T10:19:41Z
null
[ "Thank you for the feedback! I updated the code. " ]
null
[]
[GEM Dataset] Added TurkCorpus, an evaluation dataset for sentence simplification.
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1732/timeline
We want to use TurkCorpus for validation and testing of the sentence simplification task.
https://api.github.com/repos/huggingface/datasets
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2021-01-13T11:17:40Z
2021-01-13T11:17:40Z
null
[ "Hi @yangp725,\r\nThe SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.\r\nYou can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471).\r\nLet me know if this helps !", "Thanks @SBrandeis ,\r\nProblem solved by downloading and installing the latest version datasets." ]
completed
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Couldn't reach swda.py
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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py
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2021-01-13T10:19:47Z
2021-01-13T10:19:46Z
null
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null
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Add MNIST dataset
CONTRIBUTOR
https://api.github.com/repos/huggingface/datasets/issues/1730/timeline
This PR adds the MNIST dataset to the library.
https://api.github.com/repos/huggingface/datasets
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https://github.com/huggingface/datasets/issues/1729
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2021-03-31T04:24:07Z
2021-03-31T04:24:07Z
null
[ "Hi @ZurMaD!\r\nThanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 \r\nMNIST is also on the way: #1730 \r\n\r\nIf you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀\r\n" ]
completed
[]
Is there support for Deep learning datasets?
NONE
https://api.github.com/repos/huggingface/datasets/issues/1729/timeline
I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets
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2021-01-18T19:15:32Z
2021-01-18T19:15:32Z
null
[ "Hi @ameet-1997,\r\nI think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets\r\n\r\nFor your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.\r\n\r\nLet me know If this helps!", "That's a great idea! Thank you so much!\r\n\r\nWhen I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?\r\n\r\nCode:\r\n``` python\r\ncombined_dataset = concatenate_datasets([datasets, modified_dataset])\r\n```\r\n\r\nError:\r\n```\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nOutput:\r\n```\r\n(Pdb) datasets\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n num_rows: 493\r\n })\r\n})\r\n(Pdb) modified_dataset\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n num_rows: 493\r\n })\r\n})\r\n```\r\n\r\nThe error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?", "You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`\r\n\r\nDidn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.", "> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.\r\n\r\nMy opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.\r\nFor instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split=\"train\"` is given for example it's always returning a Dataset.", "Thanks @thomwolf. Your solution worked!" ]
completed
[]
Add an entry to an arrow dataset
NONE
https://api.github.com/repos/huggingface/datasets/issues/1728/timeline
Is it possible to add an entry to a dataset object? **Motivation: I want to transform the sentences in the dataset and add them to the original dataset** For example, say we have the following code: ``` python from datasets import load_dataset # Load a dataset and print the first examples in the training set squad_dataset = load_dataset('squad') print(squad_dataset['train'][0]) ``` Is it possible to add an entry to `squad_dataset`? Something like the following? ``` python squad_dataset.append({'text': "This is a new sentence"}) ``` The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset. If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
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2022-06-01T16:06:02Z
null
[ "Upgrading tensorflow to version 2.4.0 solved the issue.", "I still have the same error even with TF 2.4.0.", "And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!", "I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:\r\n```\r\n!pip install git+https://github.com/google-research/bleurt.git\r\nreferences = [\"foo bar baz\", \"one two three\"]\r\nbleurt_metric = load_metric('bleurt')\r\npredictions = [\"foo bar\", \"four five six\"]\r\nbleurt_metric.compute(predictions=predictions, references=references)\r\n```", "@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?\r\n\r\nI tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. \r\nHowever, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.\r\nThis made me suspect that the problem is somehow related to the Jupyter notebook.\r\n\r\nMore environment details:\r\n```\r\nOS: Ubuntu Linux 18.04\r\nconda==4.8.3\r\npython==3.8.5\r\ndatasets==1.3.0\r\ntensorflow==2.4.0\r\nBLEURT==0.0.1\r\nnotebook==6.2.0\r\n```", "This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.\r\n\r\nRunning this before calling bleurt fixes it:\r\n```\r\nimport sys\r\nsys.argv = sys.argv[:1]\r\n```\r\n\r\nNot the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?\r\n\r\nThis is the output of `print(sys.argv)` when running on colab:\r\n```\r\n['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']\r\n```", "I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase.", "Seems to be a known issue in the bleurt codebase: https://github.com/google-research/bleurt/issues/24.", "Hi, the problem should be solved now.", "Hi @tsellam! I can verify that the issue is indeed fixed now. Thanks!" ]
completed
[]
BLEURT score calculation raises UnrecognizedFlagError
NONE
https://api.github.com/repos/huggingface/datasets/issues/1727/timeline
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. My environment: ``` python==3.8.5 datasets==1.2.0 tensorflow==2.3.1 cudatoolkit==11.0.221 ``` Test code for reproducing the error: ``` from datasets import load_metric bleurt = load_metric('bleurt') gen_text = "I am walking on the promenade today" ref_text = "I am walking along the promenade on this sunny day" bleurt.compute(predictions=[test_text], references=[test_text]) ``` Error Output: ``` Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512'). INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128. INFO:tensorflow:Config file found, reading. INFO:tensorflow:Will load checkpoint bert_custom INFO:tensorflow:Performs basic checks... INFO:tensorflow:... name:bert_custom INFO:tensorflow:... vocab_file:vocab.txt INFO:tensorflow:... bert_config_file:bert_config.json INFO:tensorflow:... do_lower_case:True INFO:tensorflow:... max_seq_length:128 INFO:tensorflow:Creating BLEURT scorer. INFO:tensorflow:Loading model... INFO:tensorflow:BLEURT initialized. --------------------------------------------------------------------------- UnrecognizedFlagError Traceback (most recent call last) <ipython-input-12-8b3f4322318a> in <module> 2 gen_text = "I am walking on the promenade today" 3 ref_text = "I am walking along the promenade on this sunny day" ----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text]) ~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs) 396 references = self.data["references"] 397 with temp_seed(self.seed): --> 398 output = self._compute(predictions=predictions, references=references, **kwargs) 399 400 if self.buf_writer is not None: ~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references) 103 104 def _compute(self, predictions, references): --> 105 scores = self.scorer.score(references=references, candidates=predictions) 106 return {"scores": scores} ~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size) 164 """ 165 if not batch_size: --> 166 batch_size = FLAGS.bleurt_batch_size 167 168 candidates, references = list(candidates), list(references) ~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name) 83 # a flag. 84 if not wrapped.is_parsed(): ---> 85 wrapped(_sys.argv) 86 return wrapped.__getattr__(name) 87 ~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only) 643 for name, value in unknown_flags: 644 suggestions = _helpers.get_flag_suggestions(name, list(self)) --> 645 raise _exceptions.UnrecognizedFlagError( 646 name, value, suggestions=suggestions) 647 UnrecognizedFlagError: Unknown command line flag 'f' ``` Possible Fix: Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104 to receive a `batch_size` argument, for example: ``` def _compute(self, predictions, references, batch_size=1): scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size) return {"scores": scores} ```
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https://github.com/huggingface/datasets/pull/1726
[]
false
2022-02-15T10:32:10Z
2021-01-19T16:42:32Z
null
[ "It's maybe a bit annoying to add but could we maybe have as well a version of the local data loading scripts in the package?\r\nThe `text`, `json`, `csv`. Thinking about people like in #1725 who are expecting to be able to work with local data without downloading anything.\r\n\r\nMaybe we can add them to package_data or something?", "Yes I mentioned this in #824 as well. I'm looking into it", "Alright now `csv`, `json`, `text` and `pandas` are \"packaged datasets\", i.e. they're part of the `datasets` package, which makes them available in offline mode without any change in terms of API:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"csv\", data_files=[\"path/to/data.csv\"])\r\n```\r\n\r\nInstead of loading the dataset script from the module cache, it's loaded from inside the `datasets` package.\r\n\r\nI updated the test to still be able to fetch the dummy data files for those datasets from `datasets/{text|csv|pandas|json}/dummy` in the repo.", "Alright now all test pass :)\r\n(I don't thank you windows)", "LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning?", "> LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning?\r\n\r\nYep good idea. I added the date in the warning. For example `(last modified on Mon Nov 30 11:01:56 2020)`" ]
null
[]
Offline loading
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1726/timeline
As discussed in #824 it would be cool to make the library work in offline mode. Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError. This is because `prepare_module` fetches online for the latest version of the module. To make it work in offline mode one suggestion was to reload the latest local version of the module. I implemented that and I also raise a warning saying that the module that is loaded is the latest local version. ```python logger.warning( f"Using the latest cached version of the module from {cached_module_path} since it " f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})." ) ``` I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing. I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet. Close #824, close #761.
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https://github.com/huggingface/datasets/issues/1725
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false
2022-06-01T16:00:59Z
2022-06-01T16:00:59Z
null
[ "You should rephrase your question or give more examples and details on what you want to do.\r\n\r\nit’s not possible to understand it and help you with only this information.", "sorry for that.\r\ni want to know how could i load the train set and the test set from the local ,which api or function should i use .\r\n", "Did you try to follow the instructions in the documentation?\r\nHere: https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files", "thanks a lot \r\ni find that the problem is i dont use vpn...\r\nso i have to keep my net work even if i want to load the local data ?", "We will solve this soon (cf #1724)", "thanks a lot", "Hi! `json` is a packaged dataset now, which means its script comes with the library and doesn't require an internet connection." ]
completed
[]
load the local dataset
NONE
https://api.github.com/repos/huggingface/datasets/issues/1725/timeline
your guidebook's example is like >>>from datasets import load_dataset >>> dataset = load_dataset('json', data_files='my_file.json') but the first arg is path... so how should i do if i want to load the local dataset for model training? i will be grateful if you can help me handle this problem! thanks a lot!
https://api.github.com/repos/huggingface/datasets
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2021-01-12T07:17:34Z
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https://api.github.com/repos/huggingface/datasets/issues/1723
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https://github.com/huggingface/datasets/pull/1723
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false
2021-01-26T17:02:08Z
2021-01-26T17:02:08Z
null
[ "I created the documentation for `FileSystem Integration for cloud storage` with loading and saving datasets to/from a filesystem with an example of using `datasets.filesystem.S3Filesystem`. I added a note on the `Saving a processed dataset on disk and reload` saying that it is also possible to use other filesystems and cloud storages such as S3 with a link to the newly created documentation page from me. \r\nI Attach a screenshot of it here. \r\n![screencapture-localhost-5500-docs-build-html-filesystems-html-2021-01-19-17_16_10](https://user-images.githubusercontent.com/32632186/105062131-8d6a5c80-5a7a-11eb-90b0-f6128b758605.png)\r\n" ]
null
[]
ADD S3 support for downloading and uploading processed datasets
MEMBER
https://api.github.com/repos/huggingface/datasets/issues/1723/timeline
# What does this PR do? This PR adds the functionality to load and save `datasets` from and to s3. You can save `datasets` with either `Dataset.save_to_disk()` or `DatasetDict.save_to_disk`. You can load `datasets` with either `load_from_disk` or `Dataset.load_from_disk()`, `DatasetDict.load_from_disk()`. Loading `csv` or `json` datasets from s3 is not implemented. To save/load datasets to s3 you either need to provide an `aws_profile`, which is set up on your machine, per default it uses the `default` profile or you have to pass an `aws_access_key_id` and `aws_secret_access_key`. The implementation was done with the `fsspec` and `boto3`. ### Example `aws_profile` : <details> ```python dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") ``` </details> ### Example `aws_access_key_id` and `aws_secret_access_key` : <details> ```python dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_access_key_id="fake_access_key", aws_secret_access_key="fake_secret_key" ) load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_access_key_id="fake_access_key", aws_secret_access_key="fake_secret_key" ) ``` </details> If you want to load a dataset from a public s3 bucket you can pass `anon=True` ### Example `anon=True` : <details> ```python dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") load_from_disk("s3://moto-mock-s3-bucketdatasets/sdk",anon=True) ``` </details> ### Full Example ```python import datasets dataset = datasets.load_dataset("imdb") print(f"DatasetDict contains {len(dataset)} datasets") print(f"train Dataset has the size of: {len(dataset['train'])}") dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") remote_dataset = datasets.load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") print(f"DatasetDict contains {len(remote_dataset)} datasets") print(f"train Dataset has the size of: {len(remote_dataset['train'])}") ``` Related to #878 I would also adjust the documentation after the code would be reviewed, as long as I leave the PR in "draft" status. Something that we can consider is renaming the functions and changing the `_disk` maybe to `_filesystem`
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https://github.com/huggingface/datasets/issues/1724
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false
2022-10-05T12:39:07Z
2022-10-05T12:39:07Z
null
[ "Transferred to `datasets` based on the stack trace.", "Hi @lkcao !\r\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.\r\nThen you can change the line 221 of `run_mlm_new.py` into:\r\n```python\r\n datasets = load_dataset('/path/to/text.py', data_files=data_files)\r\n```\r\nWhere `/path/to/text.py` is the path on the server where you saved the `text.py` script.", "We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline", "The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon", "> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\n> You can now use them offline\r\n> \r\n> ```python\r\n> datasets = load_dataset('text', data_files=data_files)\r\n> ```\r\n> \r\n> We'll do a new release soon\r\n\r\nso the new version release now?", "Yes it's been available since datasets 1.3.0 !" ]
completed
[]
could not run models on a offline server successfully
NONE
https://api.github.com/repos/huggingface/datasets/issues/1724/timeline
Hi, I really need your help about this. I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
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https://github.com/huggingface/datasets/pull/1722
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2021-01-12T18:14:53Z
2021-01-12T17:35:57Z
null
[ "The current version of Wiki-Auto dataset contains a filtered version of the aligned dataset. The commit adds unfiltered versions of the data that can be useful the GEM task participants." ]
null
[]
Added unfiltered versions of the Wiki-Auto training data for the GEM simplification task.
CONTRIBUTOR
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