Datasets:

Francesco-A

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[ "Sounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?", "Well I just sharded the original commonVoice dataset and pushed a small chunk of it in a private rep\n\nWhat did go wrong?\n\nHolen Sie sich Outlook für iOS<https://aka.ms/o0ukef>\n________________________________\nVon: Quentin Lhoest ***@***.***>\nGesendet: Tuesday, November 22, 2022 3:03:40 PM\nAn: huggingface/datasets ***@***.***>\nCc: capsabogdan ***@***.***>; Author ***@***.***>\nBetreff: Re: [huggingface/datasets] Bug in downloading common_voice data and snall chunk of it to one's own hub (Issue #5276)\n\n\nSounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5276#issuecomment-1323727434>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ALSIFOAPAL2V4TBJTSPMAULWJTHDZANCNFSM6AAAAAASHQJ63U>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n", "It should be all good then !\r\nCould you share a link to your repository for me to investigate what went wrong ?", "https://huggingface.co/datasets/DTU54DL/common-voice-test16k\n\nAm Di., 22. Nov. 2022 um 16:43 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> It should be all good then !\n> Could you share a link to your repository for me to investigate what went\n> wrong ?\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1323876682>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOEUJRZWXAM7DYA5VJDWJTS3NANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n", "I see ! This is a bug with MP3 files.\r\n\r\nWhen we store audio data in parquet, we store the bytes and the file name. From the file name extension we know if it's a WAV, an MP3 or else. But here it looks like the paths are all None.\r\n\r\nIt looks like it comes from here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/features/audio.py#L212\r\n\r\nCc @polinaeterna maybe we should simply put the file name instead of None values ?", "@lhoestq I remember we wanted to avoid storing redundant data but maybe it's not that crucial indeed to store one more string value. \r\nOr we can store paths only for mp3s, considering that for other formats we don't have such a problem with reading from bytes without format specified. ", "It doesn't cost much to always store the file name IMO", "thanks for the help!\n\ncan I do anything on my side? we are doing a DL project and we need the\ndata really quick.\n\nthanks\nbogdan\n\n> Message ID: ***@***.***>\n>\n", "I opened a pull requests here: https://github.com/huggingface/datasets/pull/5285, we'll do a new release soon with this fix.\r\n\r\nOtherwise if you're really in a hurry you can install `datasets` from this PR", "[image: image.png]\n\n> Message ID: ***@***.***>\n>\n", "any idea on what's going wrong here?\n\nthanks\n\nAm So., 27. Nov. 2022 um 13:53 Uhr schrieb Bogdan Capsa <\n***@***.***>:\n\n> [image: image.png]\n>\n>> Message ID: ***@***.***>\n>>\n>\n", "hi @capsabogdan! \r\ncould you please share more specifically what problem do you have now?", "I have attached this screenshot above . can u pls help? So can not pip from pull request\r\n\r\n\r\n", "The pull request has been merged on `main`.\r\nYou can install `datasets` from `main` using\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```", "I've tried to load this dataset DTU54DL/common-voice-test16k, but am\ngetting the same error.\n\nSo the bug fix will fix only if I upload a new dataset, or also loading\npreviously uploaded datasets?\n\nthanks\n\nAm Mo., 28. Nov. 2022 um 19:51 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> The pull request has been merged on main.\n> You can install datasets from main using\n>\n> pip install git+https://github.com/huggingface/datasets.git\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1329587334>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOCNYYIGHM2EX3ZIO6DWKT5MXANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "> So the bug fix will fix only if I upload a new dataset, or also loading\r\npreviously uploaded datasets?\r\n\r\nYou have to reupload the dataset, sorry for the inconvenience", "thank you so much for the help! works like a charm!\n\nAm Di., 29. Nov. 2022 um 12:15 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> So the bug fix will fix only if I upload a new dataset, or also loading\n> previously uploaded datasets?\n>\n> You have to reupload the dataset, sorry for the inconvenience\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1330468393>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOBKEFZO57BAKY4IGW3WKXQUZANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n" ]

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[ "The CI fails are unrelated to your PR and fixed on master, I think we can merge now !" ]

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[ "Hi @tsvm Great work! \r\nSince you have raised a clean PR could you close the earlier one - #1445 ? \r\n", "> Hi @tsvm Great work!\r\n> Since you have raised a clean PR could you close the earlier one - #1445 ?\r\n\r\nDone." ]

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[ "I fixed those two things, the two remaining failing checks seem to be due to some dependency missing in the tests." ]

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[ "thanks!" ]

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[ "Initially I removed the ` - ` since there was only one `pretty_name` per config but turns out it was breaking here in `from_yaml_string`https://github.com/huggingface/datasets/blob/74751e3f98c74d22c48c6beb1fab0c13b5dfd075/src/datasets/utils/metadata.py#L197 in `/utils/metadata.py`", "@lhoestq I guess this will also need some validation?", "Looks like the parser doesn't allow things like\r\n```\r\npretty_name:\r\n config_name1: My awesome config number 1\r\n config_name2: My amazing config number 2\r\n```\r\ntherefore you had to use `-` and consider them as a list.\r\n\r\nI would be nice to add support for this case in the validator.\r\n\r\nThere's one thing though: the DatasetMetadata object currently corresponds to the yaml tags that are flattened: the config names are just ignored, and the lists are concatenated.\r\n\r\nTherefore I think we would potentially need to instantiate several `DatasetMetadata` objects: one per config. Otherwise we would end up with a list of several pretty_name while we actually need at most 1 per config.\r\n\r\nWhat do you think @gchhablani ?", "I was thinking of returning `metada_dict` (on line 193) whenever `load_dataset_card` is called (we can pass an extra parameter to `from_readme` or `from_yaml_string` for that to happen).\r\n\r\nOne just needs config_name as key for the dictionary inside `pretty_name` dict and for single config, there would be only one value to print. We can do this for other fields as well like `size_categories`, `languages` etc. This will obviate the need to flatten the YAML tags so that don't have to instantiate several DatasetMetadata objects. What do you guys think @lhoestq @gchhablani? \r\n\r\nUpdate: I was thinking of returning the whole dictionary before flattening so that user can access whatever they want with specific configs. Let's say [this](https://pastebin.com/eJ84314f) is my `metadata_dict` before flattening (the loaded YAML string), so instead of validating it and then returning the items individually we can return it just after loading the YAML string.", "Hi @lhoestq @bhavitvyamalik \r\n\r\n@bhavitvyamalik, I'm not sure I understand your approach, can you please elaborate? The `metadata_dict` is flattened before instantiating the object, do you want to remove that? Still confused.\r\n\r\nFew things come to my mind after going through this PR. They might not be entirely relevant to the current task, but I'm just trying to think about possible cases and discuss them here.\r\n\r\n1. Instead of instantiating a new `DatasetMetadata` for each config with flattened tags, why can't we make it more flexible and validate only non-dict items? However, in that case, the types wouldn't be as strict for the class attributes. It would also not work for cases that are like `Dict[str,List[Dict[str,str]]`, but I guess that won't be needed anyway in the foreseeable future?\r\n\r\n Ideally, it would be something like - Check the metadata tag type (root), do a DFS, and find the non-dict objects (leaves), and validate them. Is this an overkill to handle the problem?\r\n2. For single-config datasets, there can be slightly different validation for `pretty_names`, than for multi-config. The user shouldn't need to provide a config name for single-config datasets, wdyt @bhavitvyamalik @lhoestq? Either way, for multi-config, the validation can use the dictionary keys in the path to that leaf node to verify `pretty_names: ... (config)` as well. This will check whether the config name is same as the key (might be unnecessary but prevents typos, so less work for the reviewer(s)). For future, however, it might be beneficial to have something like this.\r\n3. Should we have a default config name for single-config datasets? People use any string they feel like. I've seen `plain_text`, `default` and the dataset name. I've used `image` for a few datasets myself AFAIR. For smarter validation (again, a future case ;-;), it'd be easier for us to have a simple rule for naming configs in single-config datasets. Wdyt @lhoestq?", "Btw, `pretty_names` can also be used to handle this during validation :P \r\n\r\n```\r\n-# Dataset Card for [Dataset Name]\r\n+# Dataset Card for Allegro Reviews\r\n```\r\n\r\nThis is where `DatasetMetadata` and `ReadMe` should be combined. But there are very few overlaps, I guess.\r\n\r\n\n@bhavitvyamalik @lhoestq What about adding a pretty name across all configs, and then config-specific names?\n\nLike\n\n```yaml\npretty_names:\n all_configs: X (dataset_name)\n config_1: X1 (config_1_name)\n config_2: X2 (config_2_name)\n```\nThen, using the `metadata_dict`, the ReadMe header can be validated against `X`.\n\nSorry if I'm throwing too many ideas at once.", "@bhavitvyamalik\r\n\r\nNow, I think I better understand what you're saying. So you want to skip validation for the unflattened metadata and just return it? And let the validation run for the flattened version?", "Exactly! Validation is important but once the YAML tags are validated I feel we shouldn't do that again while calling `load_dataset_card`. +1 for default config name for single-config datasets.", "@bhavitvyamalik\r\nActually, I made the `ReadMe` validation similar to `DatasetMetadata` validation and the class was validating the metadata during the creation. \r\n\r\nMaybe we need to have a separate validation method instead of having it in `__post_init__`? Wdyt @lhoestq? \r\n\r\nI'm sensing too many things to look into. It'd be great to discuss these sometime. \r\n\r\nBut if this PR is urgent then @bhavitvyamalik's logic seems good to me. It doesn't need major modifications in validation.", "> Maybe we need to have a separate validation method instead of having it in __post_init__? Wdyt @lhoestq?\r\n\r\nWe can definitely have a `is_valid()` method instead of doing it in the post init.\r\n\r\n> What about adding a pretty name across all configs, and then config-specific names?\r\n\r\nLet's keep things simple to starts with. If we can allow both single-config and multi-config cases it would already be great :)\r\n\r\nfor single-config:\r\n```yaml\r\npretty_name: Allegro Reviews\r\n```\r\n\r\nfor multi-config:\r\n```yaml\r\npretty_name:\r\n mrpc: Microsoft Research Paraphrase Corpus (MRPC)\r\n sst2: Stanford Sentiment Treebank\r\n ...\r\n```\r\n\r\nTo support the multi-config case I see two options:\r\n1. Don't allow DatasetMetadata to have dictionaries but instead have separate DatasetMetadata objects per config\r\n2. allow DatasetMetadata to have dictionaries. It implies to remove the flattening step. Then we could get metadata for a specific config this way for example:\r\n```python\r\nfrom datasets import load_dataset_card\r\n\r\nglue_dataset_card = load_dataset_card(\"glue\")\r\nprint(glue_dataset_card.metadata)\r\n# DatasetMetatada object with dictionaries since there are many configs\r\nprint(glue_dataset_card.metadata.get_metadata_for_config(\"mrpc\"))\r\n# DatasetMetatada object with no dictionaries since there are only the mrpc tags\r\n```\r\n\r\nLet me know what you think or if you have other ideas.", "I think Option 2 is better.\n\nJust to clarify, will `get_metadata_for_config` also return common details, like language, say?", "> Just to clarify, will get_metadata_for_config also return common details, like language, say?\r\n\r\nYes that would be more convenient IMO. For example a dataset card like this\r\n```yaml\r\nlanguages:\r\n- en\r\npretty_name:\r\n config1: Pretty Name for Config 1\r\n config3: Pretty Name for Config 2\r\n```\r\n\r\nthen `metadat.get_metadata_for_config(\"config1\")` would return something like\r\n```python\r\nDatasetMetadata(languages=[\"en\"], pretty_name=\"Pretty Name for Config 1\")\r\n```", "@lhoestq, should we do this post-processing in `load_dataset_card` by returning unflattened dictionary from `DatasetMetadata` or send this from `DatasetMetadata`? Since there isn't much to do I feel once we have the unflattened dictionary", "Not sure I understand the difference @bhavitvyamalik , could you elaborate please ?", "I was talking about this unflattened dictionary:\r\n\r\n> I was thinking of returning the whole dictionary before flattening so that user can access whatever they want with specific configs. Let's say [this](https://pastebin.com/eJ84314f) is my metadata_dict before flattening (the loaded YAML string), so instead of validating it and then returning the items individually we can return it just after loading the YAML string.\r\n\r\nPost-processing meant extracting config-specific fields from this dictionary and then return this `languages=[\"en\"], pretty_name=\"Pretty Name for Config 1\"`", "I still don't understand what you mean by \"returning unflattened dictionary from DatasetMetadata or send this from DatasetMetadata\", sorry. Can you give an example or rephrase this ?\r\n\r\nIMO load_dataset_card can return a dataset card object with a metadata field. If the metadata isn't flat (i.e. it has several configs), you can get the flat metadata of 1 specific config with `get_metadata_for_config`. But of course if you have better ideas or suggestions, we can discuss this", "@lhoestq, I think he is saying whatever `get_metadata_for_config` is doing can be done in `load_dataset_card` by taking the unflattened `metadata_dict` as input.\r\n\r\n@bhavitvyamalik, I think it'd be better to have this \"post-processing\" in `DatasetMetadata` instead of `load_dataset_card`, as @lhoestq has shown. I'll quickly get on that.\r\n\r\n---\r\nThree things that are to be changed in `DatasetMetadata`:\r\n1. Allow Non-flat elements and their validation.\r\n2. Create a method to get metadata by config name.\r\n3. Create a `validate()` method.\r\n\r\nOnce that is done, this PR can be updated and reviewed, wdys?", "Thanks @gchhablani for the help ! Now that https://github.com/huggingface/datasets/pull/2436 is merged you can remove the `-` in the pretty_name @bhavitvyamalik :)", "Thanks @bhavitvyamalik.\r\n\r\nI think this PR was superseded by these others also made by you:\r\n- #3498\r\n- #3536\r\n\r\nI'm closing this." ]

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[ "I have to manually copy a dataset_infos.json file from other dataset and modify it since the `datasets-cli` isn't able to handle manually downloaded datasets yet (as far as I know).", "you can generate the dataset_infos.json file even for dataset with manual data\r\nTo do so just specify `--data_dir <path/to/the/folder/containing/the/manual/data>`", "Also, dummy_data seems having difficulty to handle manually downloaded datasets. `python datasets-cli dummy_data datasets/ms_terms --data_dir ...` reported `error: unrecognized arguments: --data_dir` error. Without `--data_dir`, it reported this error:\r\n```\r\nDataset ms_terms with config BuilderConfig(name='ms_terms-full', version=1.0.0, data_dir=None, data_files=None, description='...\\n') seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has to be created with less guidance. Make sure you create the file None.\r\nTraceback (most recent call last):\r\n File \"datasets-cli\", line 36, in <module>\r\n service.run()\r\n File \"/Users/lzhao/Downloads/huggingface/datasets/src/datasets/commands/dummy_data.py\", line 326, in run\r\n dataset_builder=dataset_builder, mock_dl_manager=mock_dl_manager\r\n File \"/Users/lzhao/Downloads/huggingface/datasets/src/datasets/commands/dummy_data.py\", line 406, in _print_dummy_data_instructions\r\n for split in generator_splits:\r\nUnboundLocalError: local variable 'generator_splits' referenced before assignment\r\n```", "Oh yes `--data_dir` seems to only be supported for the `datasets_cli test` command. Sorry about that.\r\n\r\nCan you try to build the dummy_data.zip file manually ?\r\n\r\nIt has to be inside `./datasets/ms_terms/dummy/ms_terms-full/1.0.0`.\r\nInside this folder, please create a folder `dummy_data` that contains a dummy file `MicrosoftTermCollection.tbx` (with just a few examples in it). Then you can zip the `dummy_data` folder to `dummy_data.zip`\r\n\r\nThen you can check if it worked using the command\r\n```\r\npytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ms_terms\r\n```\r\n\r\nFeel free to use some debugging print statements in your script if it doesn't work first try to see what `dl_manager.manual_dir` ends up being and also `path_to_manual_file`.\r\n\r\nFeel free to ping me if you have other questions", "`pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ms_terms` gave `1 passed, 4 warnings in 8.13s`. Existing datasets, like `wikihow`, and `newsroom`, also report 4 warnings. So, I guess that is not related to this dataset.", "Could you run `make style` before we merge @leoxzhao ?", "the other errors are fixed on master so it's fine", "> Could you run `make style` before we merge @leoxzhao ?\r\n\r\nSure thing. Done. Thanks Quentin. I have other datasets in mind. All of which requires manual download. This process is very helpful", "Thank you :) " ]

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[ "🤗 This PR closes https://github.com/huggingface/datasets/issues/4398", "_The documentation is not available anymore as the PR was closed or merged._", "Hi! Thanks for reporting and providing a fix. I made a small change to make the fix easier to understand.", "Hi, @mariosasko thanks! It makes sense, sorry I'm not that familiar with `datasets` code 😩 ", "Sure @albertvillanova I'll do that later today and ping you once done, thanks! :hugs:", "Hi again @albertvillanova! Let me know if those tests are fine 🤗 ", "Hi @alvarobartt,\r\n\r\nI think your tests are failing. I don't know why previously, after your last commit, the CI tests were not triggered. \r\n\r\nIn order to force the re-running of the CI tests, I had to edit your file using the GitHub UI.\r\n\r\nFirst I tried to do it using my terminal, but I don't have push right to your PR branch: I would ask you next time you open a PR, please mark the checkbox \"Allow edits from maintainers\": https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests", "Hi @albertvillanova, let me check those again! And regarding that checkbox I thought it was already checked so my bad there 😩 ", "@albertvillanova again it seems that the tests were not automatically triggered, but I tested those locally and now they work, as previously those were failing as I created an assertion as `self.assertEqual` over an empty list that was compared as `None` while the value was `[]` so I updated it to be `self.assertListEqual` and changed the comparison value to `[]`.", "@lhoestq any idea why the CI is not triggered?", "@alvarobartt I have tested locally and the tests continue failing.\r\n\r\nI think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n", "You're right @albertvillanova I was indeed running the tests with `datasets==2.2.0` rather than with the branch version, I'll check it again! Sorry for the inconvenience...", "> @alvarobartt I have tested locally and the tests continue failing.\r\n> \r\n> I think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n\r\nIn order to have some regressions tests for the fixed scenario, I've manually updated the value of `_format_columns` in the `ArrowDataset` so as to check whether it's updated or not right after calling `remove_columns`, and it does behave as expected, so with the latest version of this branch the reported issue doesn't occur anymore.", "Hi again @albertvillanova sorry I was on leave! I'll do that ASAP :hugs:", "@albertvillanova, does it make sense to add regression tests for `DatasetDict`? As `DatasetDict` doesn't have the attribute `_format_columns`, when we call `remove_columns` over a `DatasetDict` it removes the columns and updates the attributes of each split which is an `ArrowDataset`.\r\n\r\nSo on, we can either:\r\n- Update first the `_format_columns` attribute of each split and then remove the columns over the `DatasetDict`\r\n- Loop over the splits of `DatasetDict` and remove the columns right after updating `_format_columns` of each `ArrowDataset`.\r\n\r\nI assume that the best regression test is the one implemented (mentioned first above), let me know if there's a better way to do that 👍🏻 ", "I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?", "> I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?\r\n\r\nHi @lhoestq I can check now!", "It worked indeed @lhoestq, thanks for the proposal and the review! 🤗 ", "Oops, I forgot about `@transmit_format`'s existence. From what I see, we should also use this decorator in `flatten`, `rename_column` and `rename_columns`. \r\n\r\n@alvarobartt Let me know if you'd like to work on this (in a subsequent PR).", "Sure @mariosasko I can prepare another PR to add those too, thanks! " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thank you for your review @lhoestq, I've changed the types of `qid` and `ra` and now they are integers as `aid`.\r\n\r\nReady for another review!" ]

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[ "NB: even if the error is raised, the dataset is successfully cached. So restarting the script after every `map()` allows to ultimately run the whole preprocessing. But this prevents to realistically run the code over multiple nodes.", "Hi,\r\n\r\nIt's not easy to debug the problem without the script. I may be wrong since I'm not very familiar with PyTorch Lightning, but shouldn't you preprocess the data in the `prepare_data` function of `LightningDataModule` and not in the `setup` function.\r\nAs you can't modify the module state in `prepare_data` (according to the docs), use the `cache_file_name` argument in `Dataset.map` there, and reload the processed data in `setup` with `Dataset.from_file(cache_file_name)`. If `num_proc>1`, check the docs on the `suffix_template` argument of `Dataset.map` to get an idea what the final `cache_file_names` are going to be.\r\n\r\nLet me know if this helps.", "Hi @mariosasko, thank you for the hint, that helped me to move forward with that issue. \r\n\r\nI did a major refactoring of my project to disentangle my `LightningDataModule` and `Dataset`. Just FYI, it looks like:\r\n\r\n```python\r\nclass Builder():\r\n def __call__() -> DatasetDict:\r\n # load and preprocess the data\r\n return dataset\r\n\r\nclass DataModule(LightningDataModule):\r\n def prepare_data():\r\n self.builder()\r\n def setup():\r\n self.dataset = self.builder()\r\n```\r\n\r\nUnfortunately, the entanglement between `LightningDataModule` and `Dataset` was not the issue.\r\n\r\nThe culprit was `hydra` and a slight adjustment of the structure of my project solved this issue. The problematic project structure was:\r\n\r\n```\r\nsrc/\r\n | - cli.py\r\n | - training/\r\n | -experiment.py\r\n\r\n# code in experiment.py\r\ndef run_experiment(config):\r\n # preprocess data and run\r\n \r\n# code in cli.py\r\n@hydra.main(...)\r\ndef run(config):\r\n return run_experiment(config)\r\n```\r\n\r\nMoving `run()` from `clip.py` to `training.experiment.py` solved the issue with `SystemError 15`. No idea why. \r\n\r\nEven if the traceback was referring to `Dataset.__del__`, the problem does not seem to be primarily related to `datasets`, so I will close this issue. Thank you for your help!", "Please allow me to revive this discussion, as I have an extremely similar issue. Instead of an error, my datasets functions simply aren't caching properly. My setup is almost the same as yours, with hydra to configure my experiment parameters.\r\n\r\n@vlievin Could you confirm if your code correctly loads the cache? If so, do you have any public code that I can reference for comparison?\r\n\r\nI will post a full example with hydra that illustrates this problem in a little bit, probably on another thread.", "Hello @mariomeissner, very sorry for the late reply, I hope you have found a solution to your problem!\r\n\r\nI don't have public code at the moment. I have not experienced any other issue with hydra, even if I don't understand why changing the location of the definition of `run()` fixed the problem. \r\n\r\nOverall, I don't have issue with caching anymore, even when \r\n1. using custom fingerprints using the argument `new_fingerprint \r\n2. when using `num_proc>1`", "I solved my issue by turning the map callable into a class static method, like they do in `lightning-transformers`. Very strange...", "I have this issue with datasets v2.5.2 with Python 3.8.10 on Ubuntu 20.04.4 LTS. It does not occur when num_proc=1. When num_proc>1, it intermittently occurs and will cause process to hang. As previously mentioned, it occurs even when datasets have been previously cached. I have tried wrapping logic in a static class as suggested with @mariomeissner with no improvement.", "@philipchung hello ,i have the same issue like yours,did you solve it?", "No. I was not able to get num_proc>1 to work." ]

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[ "@lucidrains - Thanks a lot for making the PR - PG19 is a super important dataset! Thanks for making it. Many people are asking for PG-19, so it would be great to have that in the library as soon as possible @thomwolf .", "@mariamabarham yup! around 11GB!", "I'm looking forward to our first deep learning written novel already lol. It's definitely happening", "Good to merge IMO.", "Oh I just noticed but as we changed the urls to download the files, we have to update `dataset_infos.json`.\r\nCould you re-rurn `nlp-cli test ./datasets/pg19 --save_infos` ?", "@lhoestq on it!", "should be good!", "@lhoestq - I think it's good to merge no?", "`dataset_infos.json` is still not up to date with the new urls (we can see that there are urls like `gs://deepmind-gutenberg/train/*` instead of `https://storage.googleapis.com/deepmind-gutenberg/train/*` in the json file)\r\n\r\nCan you check that you re-ran the command to update the json file, and that you pushed the changes @lucidrains ?", "@lhoestq ohhh, I made the change in this commit https://github.com/lucidrains/nlp/commit/f3e23d823ad9942031be80b7c4e4212c592cd90c , that's interesting that the pull request didn't pick it up. maybe it's because I did it on another machine, let me check and get back to you!", "@lhoestq wrong branch 😅 thanks for catching! ", "Awesome thanks 🎉" ]

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[ "Hi @AmitMY, sorry for leaving you hanging for a minute :) \r\n\r\nWe've developed a new pipeline for adding datasets with a few extra steps, including adding a dataset card. You can find the full process [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)\r\n\r\nWould you be up for adding the tags and description in the README.md so we can merge this cool dataset?", "@lhoestq should be ready for another review :) ", "Awesome thank you !\r\n\r\nIt looks like the PR now includes changes from other PR that were previously merged. \r\nFeel free to create another branch and another PR so that we can have a clean diff.\r\n", "Closing for #958 " ]

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[ "We no longer host datasets in this repo. You should use the HF Hub instead." ]

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[ "Sorry, I'm late to check! I'll send it to you soon!", "Thanks for your contribution, @sooftware. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there, under this organization namespace: https://huggingface.co/tunib\r\n\r\nPlease, feel free to tell us if you need some help.", "Close this PR. Thanks!" ]

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[ "tagging @nreimers ", "The dataset is sadly not really compatible to be loaded with `load_dataset`. So far it is better to git clone it and to use the files directly.\r\n\r\nA data loading script would be needed to be added to this dataset. But this was too much overhead / not really intuitive how to create it.", "Since the dataset is a bunch of TSVs we should not need a dataset script I think.\r\n\r\nBy default it tries to load all the TSVs at once, which fails here because they don't all have the same columns (pd.read_csv uses the first line as header by default). But those files have no header ! So, to properly load any TSV file in this repo, one has to pass `names=[...]` for pd.read_csv to know which column names to use.\r\n\r\nTo fix this situation, we can either do\r\n1. replace the TSVs by TSV with column names\r\n2. OR specify the pd.read_csv kwargs as YAML in the dataset card - and `datasets` would use that by default\r\n\r\nWDTY ?", "There are more issues in the dataset.\r\nTo load OpenSubtitles I have to provide this (see `skiprows`):\r\n\r\n```python\r\ndf_os = pd.read_csv(\r\n \"./parallel-sentences/OpenSubtitles/OpenSubtitles-en-de-train.tsv.gz\", \r\n sep=\"\\t\", \r\n quoting=csv.QUOTE_NONE,\r\n header=None,\r\n names=[\"en\", \"de\"],\r\n skiprows=[540344, 9151700, 10040173, 10040199, 11314673, 11338258, 11869223, 12159297, 12251078, 12303334],\r\n)\r\n```", "What's wrong with those lines exactly ?\r\nMaybe passing `error_bad_lines=False` (and maybe `warn_bad_lines=True`) can be helpful", "> What's wrong with those lines exactly ? \r\n\r\nStuff like this: `ParserError: Error tokenizing data. C error: Expected 2 fields in line 540345, saw 3`\r\n\r\n", "> Maybe passing error_bad_lines=False (and maybe warn_bad_lines=True) can be helpful\r\n\r\nYes. That would hide the issue but not solve it.", "@nreimers WDYT about the two options mentioned above ?" ]

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[ "I think it's because the file you download uncompresses into a file and not a folder so `--autogenerate` couldn't create dummy data for you. See in your dummy_data.zip if there is a file there. If not, manually create your dummy data and compress them to dummy_data.zip.", "@cstorm125 Thank you for the comment! \r\nAs you point out, it seems my code has something wrong about downloading and uncompressing the file.\r\nHowever, my manually created dummy data seems to contain a file of the required format.\r\n\r\nOn Colaboratory,\r\n`!unzip /content/datasets/datasets/covid_tweets_japanese/dummy/1.1.0/dummy_data.zip`\r\nreturns:\r\n\r\n```\r\nArchive: /content/datasets/datasets/covid_tweets_japanese/dummy/1.1.0/dummy_data.zip\r\n creating: content/datasets/datasets/covid_tweets_japanese/dummy/1.1.0/dummy_data/\r\n extracting: content/datasets/datasets/covid_tweets_japanese/dummy/1.1.0/dummy_data/data.csv.bz2 \r\n```\r\n\r\nThe original data is `data.csv.bz2`, and I had a very hard time dealing with uncompressing bzip2.\r\nI think I could handle it, but there may be problems remain." ]

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[ "I'm creating a new branch `stream-tar-audio` just for the audio datasets since they need https://github.com/huggingface/datasets/pull/3129 to be merged first", "The CI fails are only related to missing sections or tags in the dataset cards - which is unrelated to this PR" ]

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[ "Hi @yjernite can I help in adding this dataset? \r\n\r\nI am excited about this because this will be my first contribution to the datasets library as well as to hugginface.", "Resolved via https://github.com/huggingface/datasets/pull/979" ]

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[ "Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in the code of `code_search_net`, how can I debug this actually?", "Thanks for reporting. There was indeed an issue with the keys. The key was the addition of the file id and row id, which resulted in collisions. I just opened a PR to fix this at https://github.com/huggingface/datasets/pull/2555\r\n\r\nTo help users debug this kind of errors we could try to show a message like this\r\n```python\r\nDuplicateKeysError: both 42th and 1337th examples have the same keys `48`.\r\nPlease fix the dataset script at <path/to/the/dataset/script>\r\n```\r\n\r\nThis way users who what to look for if they want to debug this issue. I opened an issue to track this: https://github.com/huggingface/datasets/issues/2556", "and are we sure there are not a lot of datasets which are now broken with this change?", "Thanks to the dummy data, we know for sure that most of them work as expected.\r\n`code_search_net` wasn't caught because the dummy data only have one dummy data file while the dataset script can actually load several of them using `os.listdir`. Let me take a look at all the other datasets that use `os.listdir` to see if the keys are alright", "I found one issue on `fever` (PR here: https://github.com/huggingface/datasets/pull/2557)\r\nAll the other ones seem fine :)", "Hi! Got same error when loading other dataset:\r\n```python3\r\nload_dataset('wikicorpus', 'raw_en')\r\n```\r\n\r\ntb:\r\n```pytb\r\n---------------------------------------------------------------------------\r\nDuplicatedKeysError Traceback (most recent call last)\r\n/opt/conda/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator)\r\n 1109 example = self.info.features.encode_example(record)\r\n-> 1110 writer.write(example, key)\r\n 1111 finally:\r\n\r\n/opt/conda/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n 341 if self._check_duplicates:\r\n--> 342 self.check_duplicate_keys()\r\n 343 # Re-intializing to empty list for next batch\r\n\r\n/opt/conda/lib/python3.8/site-packages/datasets/arrow_writer.py in check_duplicate_keys(self)\r\n 352 if hash in tmp_record:\r\n--> 353 raise DuplicatedKeysError(key)\r\n 354 else:\r\n\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 519\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\nVersion: datasets==1.11.0", "Fixed by #2555.", "The wikicorpus issue has been fixed by https://github.com/huggingface/datasets/pull/2844\r\n\r\nWe'll do a new release of `datasets` soon :)" ]

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[ "Hi ! Files that starts with `cache-*` are cached computation files, i.e. they are the cached results of map/filter/cast/etc. operations. For example if you used `map` on your dataset to transform it, then the resulting dataset is going to be stored and cached in a `cache-*` file. These files are used to avoid having to load the dataset in RAM, even after many transforms", "Thanks @lhoestq! Hmm.. that's strange because I specifically turned off auto caching, and saved mapped result, using `save_to_disk`, to another location. At this location, the following file is created:`355G\tcache-ed205e500a7dc44c.arrow`\r\n\r\nTo my observation, both `load_dataset` and `map` creates `cache-*` files, and I wonder what the `cache-*` file from `load_dataset` is for (as I believe the same information is stored in `json-train.arrow`.", "This is a wrong report -- `cache-*` files are created only my `map`, not by `load_dataset`. " ]

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[ "Yes it allows to monitor the speed of each process. Currently each process takes care of one shard of the dataset.\r\n\r\nAt one point we can consider using streaming batches to a pool of processes instead of sharding the dataset in `num_proc` parts. At that point it will be easy to use only one progress bar", "Hi @lhoestq, I am facing a similar issue, it is annoying when lots of progress bars are printed. Is there a way to turn off this behavior? ", "You can disable the progress bars with\r\n```python\r\nimport datasets\r\n\r\ndatasets.disable_progress_bar()\r\n```" ]

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[ "Thanks for reporting !\r\n\r\nThis issue must have appeared when we refactored type inference in `nlp`\r\nBy default the library tries to keep the same feature types when applying `map` but apparently it has troubles with nested structures. I'll try to fix that next week" ]

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[ "After looking into this, discovered that this is already supported if the `dataset_infos.json` file is configured correctly! Here is a working example: https://huggingface.co/datasets/abidlabs/test-audio-13\r\n\r\nThis should be probably be documented, though. " ]

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[ "Hi @anassalamah,\r\n\r\nCould you please try with this:\r\n```python\r\ntrain_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[98%:]')\r\n```", "Hello @albertvillanova, \r\n\r\nThanks for the suggestion. I didn't know you could do that. however, it didn't resolve the issue\r\n\r\n\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Very nice !" ]

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[ "Same problem, \r\ntransformers 4.28.1\r\ndatasets 2.12.0\r\n\r\nleak around 100Mb per 10 seconds when use dataloader_num_werker > 0 in training argumennts for transformer train, possile bug in transformers repo, but still not found solution :(\r\n", "found an article described a problem, may be helpful for somebody:\r\nhttps://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/\r\nI confirm, it`s not memory leak, after some time memory growing has stopped" ]

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[ "Hi! We've recently added a check to the `ClassLabel` type to ensure the values are in the valid label range `-1, 0, ..., num_classes-1` (-1 is used for missing values). The error in your case happens only if the `labels` column is of type `Sequence(ClassLabel(...))` before the `map` call and can be avoided by calling `dataset = dataset.cast_column(\"labels\", Sequence(Value(\"int\")))` beforehand. The token-classification examples in Transformers introduce a new `labels` column, so their type is also `Sequence(Value(\"int\"))`, which doesn't lead to an error as this type unbounded. ", "I'm fine with re-adding support for all negative values for unknown/missing labels @mariosasko, wdyt ?" ]

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[ "Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_names = [...]\r\n# OR get them from the dataset\r\nlabel_names = sorted(set(label for labels in raw_datasets[\"train\"][\"isDif\"] for label in labels))\r\n\r\n# Cast to ClassLabel\r\nraw_datasets = raw_datasets.cast_column(\"isDif\", Sequence(ClassLabel(names=label_names)))\r\n```\r\n", "thank you \r\nit works 💯 " ]

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[ "For conflicts, I think the mention hint \"This should be modified because it mentions ...\" is missing.", "Looks great!" ]

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[ "Errors are in the CI are not related to this PR (RemoteDatasetError)\r\nthe CI is fixed on master so it's fine ", "@lhoestq Added the dataset card. Please let me know if more information needs to be added." ]

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[ "We plan to improve this eventually (see https://github.com/huggingface/datasets/issues/5454 and https://github.com/huggingface/datasets/issues/5380).\r\n\r\n> Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.\r\nIf not, I could do it using a plain Pytorch dataset. Then I would need to convert it to a datasets' dataset to get all the features of datasets. Is it something possible ?\r\n\r\nYes, by creating a mapped dataset that stores audio URLs. Indexing a dataset in such format only downloads and decodes the bytes of the accessed samples (without storing them on disk).\r\n\r\nYou can do the following to create this dataset:\r\n```python\r\n\r\ndef gen():\r\n # Generator that yields (audio URL, text) pairs as dict\r\n ...\r\n yield {\"audio\": \"audio_url\", \"text\": \"some text\"}\r\n\r\nfeatures = Features({\"audio\": datasets.Audio(), \"text\": datasets.Value(\"string\")})\r\nds = Dataset.from_generator(gen, features=features)\r\nds[2:5] # downloads and decodes the samples each time they are accessed\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4879). All of your documentation changes will be reflected on that endpoint." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I'm merging this PR because it is quite a trivial fix and this is required by:\r\n- #5166" ]

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[ "Hi ! The indices mapping is written in the same cachedirectory as your dataset.\r\n\r\nCan you run this to show your current cache directory ?\r\n```python\r\nprint(train_dataset.cache_files)\r\n```", "```\r\n[{'filename': '.../train/dataset.arrow'}, {'filename': '.../train/dataset.arrow'}]\r\n```\r\n\r\nThese are the actual paths where `.hf` files are stored. ", "I'm not aware of any `.hf` file ? What are you referring to ?\r\n\r\nAlso the error says \"Protocol unknown: parent\". Is there a chance you may have ended up with a path that contains this string `parent://` ?", "I figured out why the issue was occuring but don't know the long-term fix.\r\nThe dataset I was trying to shuffle was loaded from a saved file which had `::` delimiter in filename. When I try with the exact same file without `::` in filename, it works as expected.\r\nQuick fix is to not use colons in filename. But if this is expected behaviour, this should be clearly stated in the documentation.\r\nThanks for help @lhoestq " ]

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[ "Datasets are not tracked in this repository anymore. You should move this PR to the [discussions page of this dataset](https://huggingface.co/datasets/sem_eval_2018_task_1/discussions)" ]

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[ "Hi ! Can you give some details about why you need these changes ?", "Hey, sorry, I should have explained! I've been getting a lot of `VisibleDeprecationWarning` from Numpy, due to an issue in the formatter, see #3084 . This is a temporary workaround (since I'm using these methods in the upcoming course) until I can fix that issue, because I couldn't see an obvious fix for the Numpy formatter. If you can see a quick way to fix that, though, that might be even better!" ]

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[ "Update: `\"python3 -c \"from from datasets import Dataset, DatasetDict\"` works, but not if I import without the `python3 -c`", "Hi @arunasank, thanks for reporting.\r\n\r\nIt seems that this can be caused because you are using an old version of `fsspec`: the reason why it works if you run `python3` seems to be that `python3` runs in a Python virtual env (with an updated version of `fsspec`); whereas the error arises when you run the import from other Python virtual env (with an old version of `fsspec`).\r\n\r\nIn order to fix this, you should update `fsspec` from within the \"problematic\" Python virtual env:\r\n```\r\npip install -U \"fsspec[http]>=2021.05.0\"", "I'm closing this issue, @arunasank.\r\n\r\nFeel free to re-open it if the problem persists. ", "from lightgbm import LGBMModel,LGBMClassifier, plot_importance\r\nafter importing lib getting (partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) error, can help me", "@deepakmahtha I think you are not using `datasets`: this is the GitHub repository of Hugging Face Datasets.\r\n\r\nIf you are using `lightgbm`, you should report the issue to their repository instead.\r\n\r\nAnyway, we have proposed a possible fix just in a comment above: to update fsspec.\r\nhttps://github.com/huggingface/datasets/issues/3902#issuecomment-1066517824" ]

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[ "Thanks for reporting, @thesofakillers. Good catch. We are fixing this. " ]

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[ "linked to https://github.com/huggingface/datasets/issues/4580#issuecomment-1168373066?", "Note that I refreshed twice this dataset, and I still have (another) error on one of the splits\r\n\r\n```\r\nStatus code: 400\r\nException: ClientResponseError\r\nMessage: 403, message='Forbidden', url=URL('https://doc-14-4c-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/pgotjmcuh77q0lk7p44rparfrhv459kp/1656403650000/11771870722949762109/*/16OgJ_OrfzUF_i3ftLjFn9kpcyoi7UJeO?e=download')\r\n```\r\n\r\nLike the three splits are processed in parallel by the workers, I imagine that the Google hosting is rate-limiting us.\r\n\r\ncc @albertvillanova \r\n\r\n", "Exactly, Google Drive bans our loading scripts.\r\n\r\nWhen possible, we should host somewhere else." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006961 / 0.011353 (-0.004392) | 0.004390 / 0.011008 (-0.006618) | 0.103249 / 0.038508 (0.064741) | 0.048084 / 0.023109 (0.024975) | 0.351213 / 0.275898 (0.075315) | 0.416918 / 0.323480 (0.093439) | 0.005539 / 0.007986 (-0.002446) | 0.003555 / 0.004328 (-0.000774) | 0.079306 / 0.004250 (0.075055) | 0.066937 / 0.037052 (0.029884) | 0.382601 / 0.258489 (0.124112) | 0.406125 / 0.293841 (0.112284) | 0.032269 / 0.128546 (-0.096277) | 0.009133 / 0.075646 (-0.066514) | 0.354449 / 0.419271 (-0.064822) | 0.068978 / 0.043533 (0.025445) | 0.352314 / 0.255139 (0.097175) | 0.390398 / 0.283200 (0.107199) | 0.025640 / 0.141683 (-0.116043) | 1.553865 / 1.452155 (0.101710) | 1.601292 / 1.492716 (0.108576) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208310 / 0.018006 (0.190303) | 0.440076 / 0.000490 (0.439586) | 0.000363 / 0.000200 (0.000163) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029173 / 0.037411 (-0.008238) | 0.111323 / 0.014526 (0.096797) | 0.123001 / 0.176557 (-0.053556) | 0.180180 / 0.737135 (-0.556955) | 0.125804 / 0.296338 (-0.170534) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419919 / 0.215209 (0.204710) | 4.194515 / 2.077655 (2.116860) | 1.881234 / 1.504120 (0.377114) | 1.672914 / 1.541195 (0.131720) | 1.723102 / 1.468490 (0.254612) | 0.543584 / 4.584777 (-4.041193) | 3.822477 / 3.745712 (0.076765) | 1.837946 / 5.269862 (-3.431915) | 1.094975 / 4.565676 (-3.470701) | 0.066788 / 0.424275 (-0.357487) | 0.011689 / 0.007607 (0.004082) | 0.520983 / 0.226044 (0.294938) | 5.209245 / 2.268929 (2.940316) | 2.392916 / 55.444624 (-53.051708) | 2.060042 / 6.876477 (-4.816434) | 2.162291 / 2.142072 (0.020219) | 0.668472 / 4.805227 (-4.136755) | 0.144373 / 6.500664 (-6.356291) | 0.066152 / 0.075469 (-0.009318) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251256 / 1.841788 (-0.590532) | 15.161338 / 8.074308 (7.087030) | 14.416133 / 10.191392 (4.224741) | 0.166145 / 0.680424 (-0.514279) | 0.018168 / 0.534201 (-0.516033) | 0.433364 / 0.579283 (-0.145919) | 0.417484 / 0.434364 (-0.016880) | 0.502543 / 0.540337 (-0.037794) | 0.602904 / 1.386936 (-0.784032) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006946 / 0.011353 (-0.004407) | 0.004248 / 0.011008 (-0.006761) | 0.079707 / 0.038508 (0.041199) | 0.046226 / 0.023109 (0.023117) | 0.375864 / 0.275898 (0.099966) | 0.430740 / 0.323480 (0.107260) | 0.006222 / 0.007986 (-0.001764) | 0.003474 / 0.004328 (-0.000854) | 0.079622 / 0.004250 (0.075372) | 0.066666 / 0.037052 (0.029613) | 0.379487 / 0.258489 (0.120998) | 0.423002 / 0.293841 (0.129161) | 0.032836 / 0.128546 (-0.095710) | 0.008976 / 0.075646 (-0.066670) | 0.086578 / 0.419271 (-0.332693) | 0.055651 / 0.043533 (0.012118) | 0.360787 / 0.255139 (0.105648) | 0.384265 / 0.283200 (0.101065) | 0.025350 / 0.141683 (-0.116333) | 1.547880 / 1.452155 (0.095725) | 1.605850 / 1.492716 (0.113134) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184227 / 0.018006 (0.166220) | 0.442071 / 0.000490 (0.441582) | 0.002887 / 0.000200 (0.002687) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031923 / 0.037411 (-0.005488) | 0.119093 / 0.014526 (0.104568) | 0.128704 / 0.176557 (-0.047853) | 0.187065 / 0.737135 (-0.550070) | 0.134135 / 0.296338 (-0.162204) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455731 / 0.215209 (0.240522) | 4.562911 / 2.077655 (2.485256) | 2.247431 / 1.504120 (0.743311) | 2.053346 / 1.541195 (0.512151) | 2.049611 / 1.468490 (0.581121) | 0.546069 / 4.584777 (-4.038708) | 3.821852 / 3.745712 (0.076140) | 3.358497 / 5.269862 (-1.911364) | 1.667697 / 4.565676 (-2.897979) | 0.067968 / 0.424275 (-0.356307) | 0.012344 / 0.007607 (0.004737) | 0.550864 / 0.226044 (0.324820) | 5.496867 / 2.268929 (3.227939) | 2.680031 / 55.444624 (-52.764594) | 2.328673 / 6.876477 (-4.547804) | 2.436754 / 2.142072 (0.294682) | 0.681195 / 4.805227 (-4.124033) | 0.148761 / 6.500664 (-6.351904) | 0.067716 / 0.075469 (-0.007753) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353798 / 1.841788 (-0.487990) | 15.992965 / 8.074308 (7.918657) | 14.051539 / 10.191392 (3.860147) | 0.181087 / 0.680424 (-0.499337) | 0.018653 / 0.534201 (-0.515548) | 0.433499 / 0.579283 (-0.145784) | 0.428845 / 0.434364 (-0.005519) | 0.501100 / 0.540337 (-0.039238) | 0.603666 / 1.386936 (-0.783270) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10cfa871a2f387fe9c6360e1873ea74c6d69ff67 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010983 / 0.011353 (-0.000370) | 0.005630 / 0.011008 (-0.005378) | 0.109967 / 0.038508 (0.071458) | 0.101580 / 0.023109 (0.078471) | 0.490205 / 0.275898 (0.214307) | 0.534653 / 0.323480 (0.211173) | 0.008365 / 0.007986 (0.000379) | 0.004317 / 0.004328 (-0.000012) | 0.082429 / 0.004250 (0.078179) | 0.080556 / 0.037052 (0.043504) | 0.494627 / 0.258489 (0.236138) | 0.544189 / 0.293841 (0.250348) | 0.049419 / 0.128546 (-0.079127) | 0.014033 / 0.075646 (-0.061613) | 0.370406 / 0.419271 (-0.048866) | 0.083468 / 0.043533 (0.039935) | 0.463829 / 0.255139 (0.208690) | 0.507516 / 0.283200 (0.224316) | 0.053266 / 0.141683 (-0.088417) | 1.778680 / 1.452155 (0.326525) | 1.916616 / 1.492716 (0.423900) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267646 / 0.018006 (0.249640) | 0.617824 / 0.000490 (0.617334) | 0.007720 / 0.000200 (0.007520) | 0.000139 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034464 / 0.037411 (-0.002948) | 0.113626 / 0.014526 (0.099100) | 0.118911 / 0.176557 (-0.057646) | 0.194701 / 0.737135 (-0.542434) | 0.123431 / 0.296338 (-0.172907) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.606073 / 0.215209 (0.390863) | 6.086393 / 2.077655 (4.008738) | 2.568712 / 1.504120 (1.064593) | 2.260801 / 1.541195 (0.719606) | 2.411798 / 1.468490 (0.943307) | 0.876433 / 4.584777 (-3.708344) | 5.521280 / 3.745712 (1.775568) | 5.969722 / 5.269862 (0.699861) | 3.671028 / 4.565676 (-0.894649) | 0.097082 / 0.424275 (-0.327193) | 0.011354 / 0.007607 (0.003747) | 0.713842 / 0.226044 (0.487798) | 7.291172 / 2.268929 (5.022244) | 3.315272 / 55.444624 (-52.129352) | 2.777487 / 6.876477 (-4.098990) | 3.025449 / 2.142072 (0.883377) | 1.014115 / 4.805227 (-3.791112) | 0.217928 / 6.500664 (-6.282736) | 0.083097 / 0.075469 (0.007627) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.640060 / 1.841788 (-0.201728) | 25.342172 / 8.074308 (17.267864) | 22.776510 / 10.191392 (12.585118) | 0.227300 / 0.680424 (-0.453124) | 0.032233 / 0.534201 (-0.501968) | 0.507547 / 0.579283 (-0.071736) | 0.647044 / 0.434364 (0.212680) | 0.607019 / 0.540337 (0.066682) | 0.823548 / 1.386936 (-0.563388) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009576 / 0.011353 (-0.001777) | 0.009322 / 0.011008 (-0.001687) | 0.087184 / 0.038508 (0.048676) | 0.100795 / 0.023109 (0.077685) | 0.492138 / 0.275898 (0.216240) | 0.528386 / 0.323480 (0.204906) | 0.006689 / 0.007986 (-0.001296) | 0.004735 / 0.004328 (0.000406) | 0.085519 / 0.004250 (0.081269) | 0.072648 / 0.037052 (0.035595) | 0.496068 / 0.258489 (0.237579) | 0.549634 / 0.293841 (0.255793) | 0.049709 / 0.128546 (-0.078837) | 0.015077 / 0.075646 (-0.060569) | 0.099445 / 0.419271 (-0.319826) | 0.068080 / 0.043533 (0.024547) | 0.500426 / 0.255139 (0.245287) | 0.531437 / 0.283200 (0.248238) | 0.053176 / 0.141683 (-0.088507) | 1.827942 / 1.452155 (0.375787) | 1.914286 / 1.492716 (0.421570) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247658 / 0.018006 (0.229652) | 0.590805 / 0.000490 (0.590315) | 0.005319 / 0.000200 (0.005119) | 0.000165 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036993 / 0.037411 (-0.000418) | 0.112944 / 0.014526 (0.098419) | 0.118964 / 0.176557 (-0.057593) | 0.194867 / 0.737135 (-0.542269) | 0.120816 / 0.296338 (-0.175523) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.638062 / 0.215209 (0.422853) | 6.246785 / 2.077655 (4.169130) | 2.957779 / 1.504120 (1.453659) | 2.739118 / 1.541195 (1.197924) | 2.795362 / 1.468490 (1.326872) | 0.890532 / 4.584777 (-3.694245) | 5.508198 / 3.745712 (1.762486) | 5.222315 / 5.269862 (-0.047547) | 3.152731 / 4.565676 (-1.412946) | 0.098344 / 0.424275 (-0.325931) | 0.008800 / 0.007607 (0.001193) | 0.757889 / 0.226044 (0.531845) | 7.545715 / 2.268929 (5.276787) | 3.694536 / 55.444624 (-51.750088) | 3.112872 / 6.876477 (-3.763605) | 3.182358 / 2.142072 (1.040285) | 1.028171 / 4.805227 (-3.777056) | 0.215223 / 6.500664 (-6.285441) | 0.085856 / 0.075469 (0.010387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.853138 / 1.841788 (0.011350) | 25.939672 / 8.074308 (17.865364) | 23.118029 / 10.191392 (12.926637) | 0.250599 / 0.680424 (-0.429825) | 0.029942 / 0.534201 (-0.504259) | 0.508748 / 0.579283 (-0.070535) | 0.593966 / 0.434364 (0.159602) | 0.605499 / 0.540337 (0.065162) | 0.863827 / 1.386936 (-0.523109) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d15950d99677e9473cdcd31cfd83aa17e313e28 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks for reporting !\r\nWe'll add support for other keys" ]

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[ "There seems to be problems with datasets metadata, of which I dont have access to. I think one of the datasets is from reddit. Can anyone help?", "Hello @LashaO , I think the errors were caused by `_DATA_FILES` in `definite_pronoun_resolution.py`. Here are details of the test error.\r\n```\r\nself = BuilderConfig(name='plain_text', version=1.0.0, data_dir=None, data_files={'train': 'train.c.txt', 'test': 'test.c.txt'}, description='Plain text import of the Definite Pronoun Resolution Dataset.')\r\n\r\n def __post_init__(self):\r\n # The config name is used to name the cache directory.\r\n invalid_windows_characters = r\"<>:/\\|?*\"\r\n for invalid_char in invalid_windows_characters:\r\n if invalid_char in self.name:\r\n raise InvalidConfigName(\r\n f\"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. \"\r\n f\"They could create issues when creating a directory for this config on Windows filesystem.\"\r\n )\r\n if self.data_files is not None and not isinstance(self.data_files, DataFilesDict):\r\n> raise ValueError(f\"Expected a DataFilesDict in data_files but got {self.data_files}\")\r\nE ValueError: Expected a DataFilesDict in data_files but got {'train': 'train.c.txt', 'test': 'test.c.txt'}\r\n```", "Hi ! Thanks for the fixes :)\r\n\r\nInstead of uploading the `definite_pronoun_resolution` data files in this PR, maybe we can just update the URL ?\r\nThe old url was http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt, but now it's https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt (https instead of http)", "Actually the bad certificate creates an issue with the download\r\n```python\r\nimport datasets \r\ndatasets.DownloadManager().download(\"https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt\")\r\n# raises: ConnectionError: Couldn't reach https://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt\r\n```\r\n\r\nLet me see if I can fix that", "I uploaded them to these URLs, feel free to use them instead of having the text files here in the PR :)\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/definite_pronoun_resolution/train.c.txt\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/definite_pronoun_resolution/test.c.txt", "Thank you for the tips! Having a busy week so anyone willing to commit the suggestions is welcome. Else, I will try to get back to this in a while.", "@LashaO Thanks for working on this. Yes, I'll take over as we already have a request to fix the URL of the Jeopardy! dataset in a separate issue.", "~~Still have to fix the error in the dummy data test of the WikiAuto dataset (so please don't merge).~~ Done! Ready for merging.", "Thank you, Mario!", "The CI failure is only related to missing tags in the dataset cards, merging :)" ]

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[ "Hi ! Good catch, this is wrong indeed and thanks for opening a PR :)" ]

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[ "Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. ", "sorry I misunderstood length of dataset with dataloader, closed. thanks " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks for reporting @samsontmr.\r\n\r\nIt seems a backward compatibility issue...", "Thanks @samsontmr this should be fixed on master now\r\n\r\nFeel free to reopen if you're still having issues" ]

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[ "Note that this same issue occurs even if i preprocess with the more default way of tokenizing that uses LayoutLMv2Processor's internal OCR:\r\n\r\n```python\r\n feature_extractor = LayoutLMv2FeatureExtractor()\r\n tokenizer = LayoutLMv2Tokenizer.from_pretrained(\"microsoft/layoutlmv2-base-uncased\")\r\n processor = LayoutLMv2Processor(feature_extractor, tokenizer)\r\n encoded_inputs = processor(images, padding=\"max_length\", truncation=True)\r\n encoded_inputs[\"image\"] = np.array(encoded_inputs[\"image\"])\r\n encoded_inputs[\"label\"] = examples['label_id']\r\n```", "Wanted to make sure anyone that finds this also finds my other report: https://github.com/huggingface/datasets/issues/4352", "Did you close it because you found that it was due to the incorrect Feature types ?", "Yeah-- my analysis of the issue was wrong in this one so I just closed it while linking to the new issue", "I met with the same problem when doing some experiments about layoutlm. I tried to set the writer_batch_size to 1, and the error still exists. Is there any solutions to this problem?", "The problem lies in how your Features are defined. It's erroring out when it actually goes to write them to disk" ]

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[ "Thanks for reporting, @agombert.\r\n\r\nIn this case, I think the root issue is authentication: before pushing to Hub, you should authenticate. See our docs: https://huggingface.co/docs/datasets/upload_dataset#upload-with-python\r\n> 2. To upload a dataset on the Hub in Python, you need to log in to your Hugging Face account:\r\n ```\r\n huggingface-cli login\r\n ```", "Hey @albertvillanova well I actually did :D \r\n\r\n<img width=\"1079\" alt=\"Capture d’écran 2023-05-09 à 18 02 58\" src=\"https://github.com/huggingface/datasets/assets/17645711/e091aa20-06b1-4dd3-bfdb-35e832c66f8d\">\r\n", "That is weird that you get a Forbidden error if you are properly authenticated...\r\n\r\nToday we had a big outage issue affecting the Hugging Face Hub. Could you please retry to push_to_hub your dataset? Maybe that was the cause...", "Yes I've just tried again and same error 403 :/", "Login successful but also got this error \"Forbidden: pass `create_pr=1` as a query parameter to create a Pull Request\"", "Make sure your API token has a `write` role. I had the same issue as you with the `read` token. Creating a `write` token and using that solved the issue.", "> Make sure your API token has a `write` role. I had the same issue as you with the `read` token. Creating a `write` token and using that solved the issue.\r\n\r\nI generate a token with write role. It works! thank you so much.", "@dmitrijsk amazing thanks so much ! \r\nThe error should be clearer when the token is read-only – I wasted a lot of time there.." ]

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[ "Is there a dataset which uses these four datatypes for tests purposes?\r\n", "@severo Not yet. I'll let you know if that changes." ]

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[ "Thanks for the suggestion! I think the current checked-in `CHECKPOINT_URLS` is already not working. I believe anyone who tried using the new ckpts (`BLEURT-20-X`) can't unless this fix is in. The zip file from bleurt side unzips to directory name matching the filename (capitalized for new ones). For example without current changes I'd get the following error\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError Traceback (most recent call last)\r\n<ipython-input-5-f6832fe20f84> in <module>()\r\n 1 predictions = [\"hello there\", \"general kenobi\"]\r\n 2 references = [\"hello there\", \"general kenobi\"]\r\n----> 3 bleurt = datasets.load_metric(\"bleurt\", \"bleurt-20\")\r\n 4 results = bleurt.compute(predictions=predictions, references=references)\r\n\r\n4 frames\r\n/usr/local/lib/python3.7/dist-packages/bleurt/checkpoint.py in read_bleurt_config(path)\r\n 84 \"\"\"Reads and checks config file from a BLEURT checkpoint.\"\"\"\r\n 85 assert tf.io.gfile.exists(path), \\\r\n---> 86 \"Could not find BLEURT checkpoint {}\".format(path)\r\n 87 config_path = os.path.join(path, CONFIG_FILE)\r\n 88 assert tf.io.gfile.exists(config_path), \\\r\n\r\nAssertionError: Could not find BLEURT checkpoint /root/.cache/huggingface/metrics/bleurt/bleurt-20/downloads/extracted/e34c60f1a05394ecda54e253a10413ca7b5d59f9a23f3cc73258c6b78ffa2f50/bleurt-20\r\n```\r\ninspecting specified path I see that directory name is `BLEURT-20` instead of `bleurt-20`. \r\nOther solution similar to your suggestion is meddle with `dl_manager.download_and_extract` to unzip to paths with lowering all the paths but I imagine this will affect other parts of the library. ", "Indeed, good catch ! Your solution that fixes `CHECKPOINT_URLS ` is simple and works well, thanks :)\r\n\r\nFurthermore to avoid breaking changes though we could also keep the support for the lowercase one:\r\n```python\r\n if self.config_name.lower() in CHECKPOINT_URLS:\r\n checkpoint_name = self.config_name.lower()\r\n elif self.config_name.upper() in CHECKPOINT_URLS:\r\n checkpoint_name = self.config_name.upper()\r\n else:\r\n raise KeyError(\r\n f\"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}\"\r\n )\r\n```\r\nand then we can use `checkpoint_name` instead of `self.config_name` to download and instantiate the model:\r\n```python\r\n model_path = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])\r\n self.scorer = score.BleurtScorer(os.path.join(model_path, checkpoint_name))\r\n```\r\n\r\nPlease let me know if that sounds reasonable to you !", "Thanks for the suggestion! I believe your suggestion should work to make keys case insensitive. Changes are committed to the PR now. " ]

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[ "The code quality check is going to be fixed by #2265 ", "The memory issue didn't come from `self.__dict__.copy()` but from the fact that this dict contains `_batches` which has all the batches of the table in it.\r\nTherefore for a MemoryMappedTable all the data in `_batches` were copied in memory when pickling and this is the issue.", "I'm still investigating why we didn't catch this issue in the tests.\r\nThis test should have caught it but didn't:\r\n\r\nhttps://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/tests/test_table.py#L350-L353", "I'll focus on the patch release and fix the test in another PR after the release", "Yes, I think it is better that way..." ]

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[ "> Nice thank you ! Actually as it is a translation dataset we should probably have one configuration = one language pair no ?\r\n> \r\n> Could you use the same trick for this dataset ?\r\n\r\nI was looking for this input, infact I had written a long post on the Slack channel,...(_but unfortunately due to the holidays didn;t get a respones_). Initially I had tried with language pairs and then with specific language configs. \r\n\r\nI'll have a look at the `opus-gnomes` dataset\r\n", "Oh sorry I must have missed your message then :/\r\nI was off a few days during the holidays\r\n\r\nHopefully this trick can enable the use of any language pair (+ year ?) combination and also simplify a lot the dummy data creation since it will only require a few configs.", "Updated it as per the comments. But couldn't figure out why the dummy tests are failing >> \r\n```\r\n$RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_ted_talks_iwslt\r\n.....\r\n....\r\ntests/test_dataset_common.py:198: in check_load_dataset\r\n self.parent.assertTrue(len(dataset[split]) > 0)\r\nE AssertionError: False is not true\r\n```" ]

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[ "Hello, thanks. I had a talk with the dataset authors, found out that the data now is obsolete and they'll get a stable version soon. So temporality closing the PR.\r\n Although I have a question, What should _id_ be in the return statement? Should that be something like a start index (or) the type of split will do? Thanks. ", "@reshinthadithyan we should hold off on this for a couple of weeks till NeurIPS concludes. The [NLC2CMD](http://nlc2cmd.us-east.mybluemix.net/) data will be out then; which includes a cleaner version of this NL2Bash data. The older data is sort of obsolete now. ", "Ah nvm you already commented 😆 " ]

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