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[ "@lhoestq sorry I opened a new pull request because of some issues with the previous code base. This pull request is originally from #1364", "Excellent comments. Thanks for those valuable suggestions. I changed everything as you have pointed out :) ", "I've copied the same template as NarrativeQA now. Please 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. " ]

"2021-01-25T15:22:31Z"

"2021-01-29T09:35:14Z"

"2021-01-29T09:34:51Z"

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"2021-01-25T10:53:52Z"

"2021-01-25T11:12:53Z"

"2021-01-25T11:12:53Z"

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[ "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" ]

"2021-01-24T09:28:24Z"

"2021-05-20T04:15:58Z"

"2021-05-20T04:15:58Z"

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[ "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." ]

"2021-01-24T07:54:31Z"

"2021-01-24T09:50:39Z"

"2021-01-24T09:50:39Z"

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[ "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)" ]

"2021-01-24T06:15:52Z"

"2022-06-01T15:54:50Z"

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[ "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)" ]

"2021-01-24T02:53:45Z"

"2021-09-06T08:54:46Z"

"2021-08-04T18:13:01Z"

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"2021-01-24T02:15:31Z"

"2021-02-05T15:49:25Z"

"2021-02-05T15:49:25Z"

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[ "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." ]

"2021-01-24T01:53:52Z"

"2021-01-24T23:06:29Z"

"2021-01-24T23:06:29Z"

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[ "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```" ]

"2021-01-24T01:26:06Z"

"2022-06-01T15:43:15Z"

"2022-06-01T15:43:15Z"

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[ "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" ]

"2021-01-23T10:13:00Z"

"2022-10-05T12:38:51Z"

"2022-10-05T12:38:51Z"

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"2021-01-22T16:43:20Z"

"2021-01-31T12:33:10Z"

"2021-01-25T09:14:59Z"

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[ "> 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)" ]

"2021-01-22T14:54:37Z"

"2021-01-25T20:38:07Z"

"2021-01-25T20:37:42Z"

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[ "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')" ]

"2021-01-22T14:22:55Z"

"2021-02-02T10:38:06Z"

"2021-02-02T10:38:06Z"

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[ "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." ]

"2021-01-21T22:56:45Z"

"2022-10-28T02:16:38Z"

"2021-01-23T03:44:14Z"

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[ "Academic WIFI was blocking." ]

"2021-01-21T20:56:09Z"

"2021-01-21T21:00:19Z"

"2021-01-21T21:00:02Z"

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"2021-01-21T18:21:01Z"

"2021-01-22T10:14:33Z"

"2021-01-22T10:13:45Z"

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[ "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." ]

"2021-01-21T15:31:23Z"

"2021-02-02T07:13:22Z"

"2021-02-02T07:13:22Z"

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[ "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 :)" ]

"2021-01-21T14:29:12Z"

"2021-02-04T14:32:48Z"

"2021-01-26T13:50:31Z"

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[ "Conll has `multilingual` but is only tagged as `en`", "good catch, that was a bad copy paste x)" ]

"2021-01-21T13:50:10Z"

"2021-01-22T09:40:01Z"

"2021-01-22T09:40:00Z"

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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. " ]

"2021-01-21T11:47:15Z"

"2021-01-21T17:22:11Z"

"2021-01-21T17:21:06Z"

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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!" ]

"2021-01-21T02:26:37Z"

"2021-01-22T00:25:50Z"

"2021-01-22T00:25:50Z"

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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!" ]

"2021-01-20T23:56:03Z"

"2021-03-09T02:52:05Z"

"2021-03-08T14:34:52Z"

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"2021-01-20T22:18:44Z"

"2021-03-01T10:36:21Z"

"2021-03-01T10:36:21Z"

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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!" ]

"2021-01-20T21:12:12Z"

"2021-01-20T22:03:39Z"

"2021-01-20T22:03:39Z"

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"2021-01-20T11:11:00Z"

"2021-01-25T09:12:07Z"

"2021-01-25T09:12:06Z"

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"2021-01-20T10:52:38Z"

"2021-01-20T14:39:30Z"

"2021-01-20T14:39:30Z"

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[ "I think its better to create a new branch with this fix. I forgot I was still using the old branch." ]

"2021-01-20T09:54:43Z"

"2021-01-20T10:27:07Z"

"2021-01-20T10:25:02Z"

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"2021-01-19T17:53:00Z"

"2021-01-20T14:56:52Z"

"2021-01-20T14:56:52Z"

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[ "Good catch, thanks!", "Thank you for merging!" ]

"2021-01-19T03:06:05Z"

"2021-01-19T11:07:29Z"

"2021-01-19T09:48:43Z"

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[ "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." ]

"2021-01-18T18:36:32Z"

"2021-01-29T19:35:52Z"

"2021-01-29T18:38:08Z"

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"2021-01-18T17:14:19Z"

"2021-09-17T16:53:18Z"

"2021-01-19T11:26:58Z"

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[ "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 <[email protected]>\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" ]

"2021-01-18T14:08:55Z"

"2022-10-05T12:37:27Z"

"2022-10-05T12:37:27Z"

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"2021-01-18T11:29:10Z"

"2021-01-18T11:31:24Z"

"2021-01-18T11:31:23Z"

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[ "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```" ]

"2021-01-18T00:50:19Z"

"2021-01-18T11:02:43Z"

"2021-01-18T00:59:34Z"

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[ "@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." ]

"2021-01-17T07:58:49Z"

"2021-01-18T11:26:09Z"

"2021-01-18T11:26:09Z"

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[ "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." ]

"2021-01-17T07:01:14Z"

"2022-06-01T15:49:34Z"

"2022-06-01T15:49:34Z"

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[ "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." ]

"2021-01-17T05:54:25Z"

"2021-03-29T12:43:30Z"

"2021-03-29T12:43:30Z"

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[]

[]

[ "none" ]

"2021-01-16T02:23:19Z"

"2021-01-16T02:39:28Z"

"2021-01-16T02:39:18Z"

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[]

"2021-01-15T22:58:34Z"

"2021-01-20T13:41:26Z"

"2021-01-20T13:41:26Z"

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[ "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!" ]

"2021-01-15T21:45:23Z"

"2021-01-29T14:34:06Z"

"2021-01-29T10:53:03Z"

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[ "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" ]

"2021-01-14T15:11:25Z"

"2021-01-15T10:08:20Z"

"2021-01-15T10:08:19Z"

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[ "Thanks for updating this!" ]

"2021-01-14T02:49:21Z"

"2021-01-14T10:25:24Z"

"2021-01-14T10:25:24Z"

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"2021-01-13T20:39:04Z"

"2021-01-14T10:29:38Z"

"2021-01-14T10:29:38Z"

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[ "Add new \"dataset\"? ;)", "Lol, too used to Transformers ;-)" ]

"2021-01-13T15:08:09Z"

"2021-01-14T15:16:01Z"

"2021-01-14T15:16:00Z"

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"2021-01-13T09:55:09Z"

"2021-01-14T10:42:18Z"

"2021-01-14T10:42:18Z"

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[ "Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py" ]

"2021-01-13T08:37:40Z"

"2021-08-04T18:13:55Z"

"2021-08-04T18:13:55Z"

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[ "Thank you for the feedback! I updated the code. " ]

"2021-01-13T07:50:19Z"

"2021-01-14T10:19:41Z"

"2021-01-14T10:19:41Z"

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[ "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." ]

"2021-01-13T02:57:40Z"

"2021-01-13T11:17:40Z"

"2021-01-13T11:17:40Z"

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"2021-01-12T21:48:02Z"

"2021-01-13T10:19:47Z"

"2021-01-13T10:19:46Z"

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[ "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" ]

"2021-01-12T20:22:41Z"

"2021-03-31T04:24:07Z"

"2021-03-31T04:24:07Z"

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[ "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!" ]

"2021-01-12T18:01:47Z"

"2021-01-18T19:15:32Z"

"2021-01-18T19:15:32Z"

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[ "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!" ]

"2021-01-12T17:27:02Z"

"2022-06-01T16:06:02Z"

"2022-06-01T16:06:02Z"

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[ "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)`" ]

"2021-01-12T15:21:57Z"

"2022-02-15T10:32:10Z"

"2021-01-19T16:42:32Z"

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[ "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." ]

"2021-01-12T12:12:55Z"

"2022-06-01T16:00:59Z"

"2022-06-01T16:00:59Z"

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[ "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\r\n" ]

"2021-01-12T07:17:34Z"

"2021-01-26T17:02:08Z"

"2021-01-26T17:02:08Z"

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[ "Transferred to `datasets` based on the stack trace.", "Hi @lkcao !\r\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/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 !" ]

"2021-01-12T06:08:06Z"

"2022-10-05T12:39:07Z"

"2022-10-05T12:39:07Z"

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[ "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." ]

"2021-01-12T05:26:04Z"

"2021-01-12T18:14:53Z"

"2021-01-12T17:35:57Z"

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[ "> Nice !\r\n> \r\n> Could you try to reduce the size of the dummy_data.zip files ? they're quite big (300KB)\r\n\r\nYes, I think it might make sense to enhance the tool a tiny bit to prevent this automatically", "That's the lightest I can make it...it's long-range summarization so a single sample has ~11000 tokens. ", "Ok thanks :)", "Awesome good to merge for me :-) " ]

"2021-01-12T01:15:40Z"

"2021-01-12T11:49:15Z"

"2021-01-12T11:41:47Z"

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[ "Quick question, @lhoestq. In this specific dataset, two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. However, I have not found an easy way to implement that. Using splits or configs does not seem appropriate.\r\n", "About the `GPE_LOC` and `GPE_ORG`. The original NorNE paper in which they published the dataset, does an evaluation on three different NER tag sets, one considering `GPE_LOC` and `GPE_ORG` as they are, another changing them to be just `GPE`, and another one by changing it to become `LOC` and `ORG`. The called these sets, `norne-full`, `norne-7`, and `norne-9`. What I would like is to provide a way for the user of this dataset to get `norne-7` and `norne-9` without having to duplicate the code.", "Ok I see !\r\nI guess you can have three configurations `norne-full`, `norne-7` and `norne-9`.\r\nEach config can have different feature types. You can simply check for the `self.config.name` in the `_info(self)` method and pick the right ClassLabel names accordingly. And then in `_generate_examples` as well you can check for `self.config.name` to know how to process the labels to yield either GPE_LOC/GPE_ORG, GPE or LOC/ORG", "But I'm already using the configurations for the different language\nvarieties. So you propose having something like `bokmaal`, `bokmaal-7`,\netc? Would there be a different way? If not, I'd be fine the corpus as it\nis until we come up with a solution. Thanks in any case.\n\n--\nSent using a cell-phone, so sorry for the typos and wrong auto-corrections.\n\nOn Tue, Jan 19, 2021, 4:56 PM Quentin Lhoest <[email protected]>\nwrote:\n\n> Ok I see !\n> I guess you can have three configurations norne-full, norne-7 and norne-9.\n> Each config can have different feature types. You can simply check for the\n> self.config.name in the _info(self) method and pick the right ClassLabel\n> names accordingly. And then in _generate_examples as well you can check\n> for self.config.name to know how to process the labels to yield either\n> GPE_LOC/GPE_ORG, GPE or LOC/ORG\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/pull/1720#issuecomment-762936612>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AABKLYOWNDBD76WZPJHFCWLS2WTTHANCNFSM4V6GSUQA>\n> .\n>\n", "The first option about having configurations like `bokmaal-7`, `bokmaal-9` etc. would definitely work.\r\n\r\nA second option would be to add a parameter `ner_tags_set` to `NorneConfig` and then one could load them with\r\n```python\r\nbokmaal_full = load_dataset(\"norne\", \"bokmaal\", ner_tags_set=\"norne-full\")\r\n```\r\nfor example.\r\n\r\nWhat do you think ?", "Hi @versae have you had a chance to consider one of the two options for the config ?\r\nI think both are ok but I have a small preference for the first one since it's simpler to implement.\r\n\r\nFeel free to ping me if you have questions or if I can help :) ", "Hi @lhoestq. Agree, option 1 seems easier to implement. Just haven't had bandwidth to get to it yet. Hopefully starting next week I'll be able to update the PR.", "Hi @versae ! Did you manage to add the configurations ? Let me know if we can help you on this", "Hi @lhoestq, I do actually have to code ready, just need to generate the dummy data for it. ", "One thing I don't know how to do is to make `_info(self)` return the different NER tags in its `DatasetInfo` object depending on the specific config.", "OK, I think it's ready now.", "Closing this one and opening a new one with a cleaner commit log.", "All set now in #2154." ]

"2021-01-11T21:34:13Z"

"2021-03-31T14:23:49Z"

"2021-03-31T14:13:17Z"

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"2021-01-11T17:23:56Z"

"2021-01-11T18:45:03Z"

"2021-01-11T18:45:02Z"

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[ "Thanks for reporting !\r\nI was able to reproduce thanks to your code and find the origin of the bug.\r\nThe cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.\r\nI'm opening a PR to fix this.\r\n\r\nAlso we plan to do a new release in the coming days so you can expect the fix to be available soon.\r\nNote that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.", "Thanks for the fast reply, waiting for the fix :)\r\n\r\nI tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:\r\n```\r\ntokenized_datasets = tokenized_datasets.map(\r\n group_texts,\r\n batched=True,\r\n num_proc=60,\r\n load_from_cache_file=True,\r\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\r\n)\r\n```\r\n\r\nand got an error:\r\n```\r\nmultiprocess.pool.RemoteTraceback:\r\n\"\"\"\r\nTraceback (most recent call last):\r\n File \"/venv/lib/python3.6/site-packages/multiprocess/pool.py\", line 119, in worker\r\n result = (True, func(*args, **kwds))\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 157, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/venv/lib/python3.6/site-packages/datasets/fingerprint.py\", line 163, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1491, in _map_single\r\n tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n File \"/usr/lib/python3.6/tempfile.py\", line 690, in NamedTemporaryFile\r\n (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\r\n File \"/usr/lib/python3.6/tempfile.py\", line 401, in _mkstemp_inner\r\n fd = _os.open(file, flags, 0o600)\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"test.py\", line 48, in <module>\r\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\r\n File \"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in map\r\n for k, dataset in self.items()\r\n File \"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in <dictcomp>\r\n for k, dataset in self.items()\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1317, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1317, in <listcomp>\r\n transformed_shards = [r.get() for r in results]\r\n File \"/venv/lib/python3.6/site-packages/multiprocess/pool.py\", line 644, in get\r\n raise self._value\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\r\n```\r\n", "The documentation says\r\n```\r\ncache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the\r\n results of the computation instead of the automatically generated cache file name.\r\n You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.\r\n```\r\nWhat is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like\r\n```python\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}\r\n```", "Managed to get `cache_file_names` working and caching works well with it\r\nHad to make a small modification for it to work:\r\n```\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}\r\n```", "Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.\r\nI can confirm that this is how it works in `datasets==1.1.3`", "Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path", "I fixed the docstring. Hopefully this is less confusing now: https://github.com/huggingface/datasets/commit/42ccc0012ba8864e6db1392430100f350236183a", "I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n# CHANGE\r\nprint('hello')\r\n# CHANGE\r\n\r\ntokenized_datasets = datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n...\r\n```\r\nI am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird\r\n\r\nEdit: accidently clicked the close issue button ", "This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.\r\n\r\nNot sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.\r\nMoreover we're already ignoring the line definition for lambda functions.", "I opened a PR to change this, let me know what you think.", "Sounds great, thank you for your quick responses and help! Looking forward for the next release.", "I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!\r\n\r\n\r\n```python\r\n # Datasets\r\n train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))\r\n if args.dev_split >= len(train):\r\n raise ValueError(\"Not enough dev files\")\r\n dev = []\r\n state = random.Random(1001)\r\n for _ in range(args.dev_split):\r\n dev.append(train.pop(state.randint(0, len(train) - 1)))\r\n\r\n max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\r\n\r\n def tokenize_function(examples):\r\n return tokenizer(examples['text'], return_special_tokens_mask=True)\r\n\r\n def group_texts(examples):\r\n # Concatenate all texts from our dataset and generate chunks of max_seq_length\r\n concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n # Truncate (not implementing padding)\r\n total_length = (total_length // max_seq_length) * max_seq_length\r\n # Split by chunks of max_seq_length\r\n result = {\r\n k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n for k, t in concatenated_examples.items()\r\n }\r\n return result\r\n\r\n datasets = load_dataset(\r\n 'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, \r\n cache_dir=args.data_cache_dir)\r\n datasets = datasets.map(tokenize_function, \r\n batched=True, remove_columns=['text'], \r\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},\r\n load_from_cache_file=not args.overwrite_cache)\r\n datasets = datasets.map(group_texts, \r\n batched=True,\r\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},\r\n load_from_cache_file=not args.overwrite_cache)\r\n```\r\n\r\nAnd this is the log\r\n\r\n```\r\n04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1\r\n04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]\r\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped\r\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped\r\n```\r\n", "Hi ! What tokenizer are you using ?", "It's the ByteLevelBPETokenizer", "This error happened to me too, when I tried to supply my own fingerprint to `map()` via the `new_fingerprint` arg.\r\n\r\nEdit: realized it was because my path was weird and had colons and brackets and slashes in it, since one of the variable values I included in the fingerprint was a dataset split like \"train[:10%]\". I fixed it with [this solution](https://stackoverflow.com/a/13593932/2287177) from StackOverflow to just remove those invalid characters from the fingerprint.", "Good catch @jxmorris12, maybe we should do additional checks on the valid characters for fingerprints ! Would you like to contribute this ?\r\n\r\nI think this can be added here, when we set the fingerprint(s) that are passed `map`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/25bb7c9cbf519fbbf9abf3898083b529e7762705/src/datasets/fingerprint.py#L449-L454\r\n\r\nmaybe something like\r\n```python\r\nif kwargs.get(fingerprint_name) is None:\r\n ...\r\nelse:\r\n # In this case, it's the user who specified the fingerprint manually:\r\n # we need to make sure it's a valid hash\r\n validate_fingerprint(kwargs[fingerprint_name])\r\n```\r\n\r\nOtherwise I can open a PR later", "I opened a PR here to add the fingerprint validation: https://github.com/huggingface/datasets/pull/4587\r\n\r\nEDIT: merged :)", "thank you!" ]

"2021-01-11T15:37:31Z"

"2022-06-29T14:54:42Z"

"2021-01-26T02:47:59Z"

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[ "Hi Dave,\r\nYou are more than welcome to open a PR to make these changes! 🤗\r\nYou will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n\r\nPinging also @lhoestq for the Google cloud matter.", "> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nSure ! Also feel free to ping us for reviews or if we can help :)\r\n\r\n> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n\r\nWhat makes you think that ?\r\nAfaik there's no scifact on our google storage\r\n", "\r\n\r\n> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n> \r\n> Sure ! Also feel free to ping us for reviews or if we can help :)\r\n> \r\nOK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.\r\n\r\n> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n> \r\n> What makes you think that ?\r\n> Afaik there's no scifact on our google storage\r\n\r\nYou're right, I had the data cached on my machine somewhere. \r\n\r\n", "I opened a PR about this: https://github.com/huggingface/datasets/pull/1780. Closing this issue, will continue there." ]

"2021-01-11T05:26:40Z"

"2021-01-26T02:52:17Z"

"2021-01-26T02:52:17Z"

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"2021-01-10T13:30:01Z"

"2021-01-18T14:21:42Z"

"2021-01-18T14:21:42Z"

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"2021-01-10T06:29:04Z"

"2021-09-17T16:54:13Z"

"2021-01-12T17:14:33Z"

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[ "Oh that's a really cool one, we'll review/merge it soon!\r\n\r\nIn the meantime, do you have any specific positive/negative feedback on the process of adding a datasets Max?\r\nDid you follow the instruction in the [detailed step-by-step](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)?", "Thanks Thom, been a while, hope all is well!\r\n\r\nYes, I followed the step by step instructions and found them pretty straightforward. The only things I wasn't sure of were what should go into the YAML tags field for the dataset card, and whether there was a list of options somewhere (maybe akin to the metrics?) of the possible supported tasks. I found the rest very intuitive and the automated metadata and dummy data generation very handy. Thanks!", "Good point! pinging @yjernite here so he can improve this part!", "@maxbartolo cool addition!\r\n\r\nFor the YAML tag, you should use the tagging app we provide to choose from a drop-down menu:\r\nhttps://github.com/huggingface/datasets-tagging\r\n\r\nThe process is described toward the end of the [step-by-step guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card), do you have any suggestions for making it easier to find?\r\n\r\nOtherwise, the dataset card is really cool, thanks for making it so complete!\r\n", "@yjernite\r\n\r\nThanks, YAML tags added. I think my main issue was with the flow of the [step-by-step guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). For example, the [card creator](https://huggingface.co/datasets/card-creator/) is introduced in Step 4, right after creating an empty directory for your dataset. The first field it requires are the YAML tags, which (at least for me) was the last step of the process.\r\n\r\nI'd suggest having the guide structured in the same order as the creation process. For me it was something like:\r\n- Step 1: Preparing your env\r\n- Step 2: Write the loading/processing code\r\n- Step 3: Automatically generate dummy data and `dataset_infos.json`\r\n- Step 4: Tag the dataset\r\n- Step 5: Write the dataset card using the [card creator](https://huggingface.co/datasets/card-creator/)\r\n- Step 6: Open a Pull Request on the main HuggingFace repo and share your work!!\r\n\r\nThanks again!" ]

"2021-01-08T21:46:09Z"

"2021-01-13T16:05:24Z"

"2021-01-13T16:05:24Z"

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[ "Yes indeed the idea is to have the next release on conda cc @LysandreJik ", "Great! Did you guys have a timeframe in mind for the next release?\r\n\r\nThank you for all the great work in developing this library.", "I think we can have `datasets` on conda by next week. Will see what I can do!", "Thank you. Looking forward to it.", "`datasets` has been added to the huggingface channel thanks to @LysandreJik :)\r\nIt depends on conda-forge though\r\n\r\n```\r\nconda install -c huggingface -c conda-forge datasets\r\n```" ]

"2021-01-08T19:12:15Z"

"2021-09-17T12:47:40Z"

"2021-09-17T12:47:40Z"

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[ "When should we expect to see our dataset appear in the search dropdown at huggingface.co?", "Hi @eusip,\r\n\r\n> When should we expect to see our dataset appear in the search dropdown at huggingface.co?\r\n\r\nwhen this PR is merged.", "Thanks!", "I've implemented all the changes requested by @lhoestq but I made the mistake of trying to change the remote branch name. \r\n\r\nHopefully the changes are seen on your end as both branches `silicone` and `main` should be up-to-date.", "It looks like the PR includes changes about many other files than the ones for Silicone (+30,000 line changes)\r\n\r\nMaybe you can try to create another branch and another PR ?", "> It looks like the PR includes changes about many other files than the ones for Silicone (+30,000 line changes)\r\n> \r\n> Maybe you can try to create another branch and another PR ?\r\n\r\nSure. I will make a new pull request." ]

"2021-01-08T18:24:18Z"

"2021-01-21T14:12:37Z"

"2021-01-21T10:31:11Z"

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"2021-01-08T13:45:56Z"

"2021-01-11T09:23:20Z"

"2021-01-11T09:23:19Z"

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[ "I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.\r\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.\r\nValid files are paths to local files or URLs to remote files.", "Fixed once processed data is used instead:\r\n- #2575" ]

"2021-01-08T07:31:30Z"

"2022-08-04T11:56:10Z"

"2022-08-04T11:55:04Z"

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"2021-01-08T06:14:03Z"

"2021-01-08T09:00:08Z"

"2021-01-08T09:00:08Z"

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