Datasets:

lewtun

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like 7

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[ "Thanks ! To fix the CI you just have to rename the dummy data file in the dummy_data.zip files", "thanks for the tip with the dummy data - all fixed now!" ]

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[ "Hi,\r\n\r\nThis is a known issue. More info on this issue can be found in #2061. If you are looking for an open-source contribution, there are step-by-step instructions in the linked issue that you can follow to fix it.", "Closed by #2466." ]

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[ "Hi ! That would be nice indeed to at least have a warning, since we don't handle the max path length limit.\r\nAlso if we could have an error instead of an infinite loop I'm sure windows users will appreciate that", "Unfortunately, I know this problem very well... 😅 \r\n\r\nI remember having proposed to throw an error instead of hanging in an infinite loop #2220: 60c7d1b6b71469599a27147a08100f594e7a3f84, 8c8ab60018b00463edf1eca500e434ff061546fc \r\nbut @lhoestq told me:\r\n> Note that the filelock module comes from this project that hasn't changed in years - while still being used by ten of thousands of projects:\r\nhttps://github.com/benediktschmitt/py-filelock\r\n> \r\n> Unless we have proper tests for this, I wouldn't recommend to change it\r\n\r\nI opened an Issue requesting a warning/error at startup for that case: #2224", "@albertvillanova Thanks for additional info on this issue.\r\n\r\nYes, I think the best option is to throw an error instead of suppressing it in a loop. I've considered 2 more options, but I don't really like them:\r\n1. create a temporary file with a filename longer than 255 characters on import; if this fails, long paths are not enabled and raise a warning. I'm not sure about this approach because I don't like the idea of creating a temporary file on import for this purpose.\r\n2. check if long paths are enabled with [this code](https://stackoverflow.com/a/46546731/14095927). As mentioned in the comment, this code relies on an undocumented function and Win10-specific." ]

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[ "Fixing the tags of all the datasets is out of scope for this PR so I'm merging even though the CI fails because of the missing tags" ]

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[ "Hi ! In your dataset script you must be yielding examples like\r\n```python\r\nfor line in file:\r\n ...\r\n yield key, {...}\r\n```\r\n\r\nSince `datasets` 1.7.0 we enforce the keys to be unique.\r\nHowever it looks like your examples generator creates duplicate keys: at least two examples have key 0.\r\n\r\nYou can fix that by making sure that your keys are unique.\r\n\r\nFor example if you use a counter to define the key of each example, make sure that your counter is not reset to 0 in during examples generation (between two open files for examples).\r\n\r\nLet me know if you have other questions :)", "Yup, I indeed was generating duplicate keys. Fixed it and now it's working." ]

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[ "The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too", "Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https://github.com/huggingface/datasets-tagging\r\nOtherwise I can probably fix it next week", "I've opened a PR on `datasets-tagging` to fix the issue 🚀 ", "thanks ! this is fixed now" ]

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[ "We also have other cases where people are lost between Dataset and DatasetDict, maybe let's gather and solve them all here?\r\n\r\nFor instance, I remember that some people thought they would request a single element of a split but are calling this on a DatasetDict. Maybe here also a better error message when the split requested in not in the dict? pointing to the list of split and the fact that this is a datasetdict containing several datasets?", "Good idea, let me add a better error message for this case too", "As a digression from the topic of this PR, IMHO I think that the difference between Dataset and DatasetDict is an additional abstraction complexity that confuses \"typical\" end users. I think a user expects a \"Dataset\" (whatever it contains multiple or a single split) and maybe it could be interesting to try to simplify the user-facing API as much as possible to hide this complexity from the end user.\r\n\r\nI don't know your opinion about this, but it might be worth discussing...\r\n\r\nFor example, I really like the line of the solution of using the function `load_from_disk`, which hides the previous mentioned complexity and handles under the hood whether Dataset/DatasetDict instances should be created...", "I totally agree, I just haven't found a solution that doesn't imply major breaking changes x)", "Yes I would also like to find a better solution. Do we have any solution actually? (even implying breaking changes)\r\n\r\nHere is a proposal for discussion and refined (and potential abandon if it's not good enough):\r\n- let's consider that a DatasetDict is also a Dataset with the various split concatenated one after the other\r\n- let's disallow the use of integers in split names (probably not a very big breaking change)\r\n- when you index with integers you access the examples progressively in split after the other is finished (in a deterministic order)\r\n- when you index with strings/split name you have the same behavior as now (full backward compat)\r\n- let's then also have all the methods of a Dataset on the DatasetDict", "The end goal would be to merge both `Dataset` and `DatasetDict` object in a single object that would be (pretty much totally) backward compatible with both.", "I like the direction :) I think it can make sense to concatenate them.\r\n\r\nThere are a few things that I we could discuss if we want to merge Dataset and DatasetDict:\r\n1. what happens if you index by a string ? Does it return the column or the split ? We could disallow conflicts between column names and split names to avoid ambiguities. It can be surprising to be able to get a column or a split using the same indexing feature\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(...)\r\ndataset[\"train\"]\r\ndataset[\"input_ids\"]\r\n```\r\n2. what happens when you iterate over the object ? I guess it should iterate over the examples as a Dataset object, but a DatasetDict used to iterate over the splits as they are the dictionary keys. This is a breaking change that we can discuss.\r\n\r\nMoreover regarding your points:\r\n- integers are not allowed as split names already\r\n- it's definitely doable to have all the methods. Maybe some of them like `train_test_split` that is currently only available for Dataset can be tweaked to work for a split dataset", "Instead of suggesting the use of `Dataset.load_from_disk` and `DatasetDict.load_from_disk`, the error message now suggests to use `datasets.load_from_disk` directly", "Merging the error message improvement, feel free to continue the discussion here or in a github issue" ]

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[ "hi @lhoestq @SBrandeis i've now added the missing YAML tags, so this PR should be good to go :)", "urgh, the windows tests are failing because of encoding issues 😢 \r\n\r\n```\r\ndataset_name = 'squad_kor_v1'\r\n\r\n @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\n def test_changed_dataset_card(dataset_name):\r\n card_path = repo_path / \"datasets\" / dataset_name / \"README.md\"\r\n assert card_path.exists()\r\n error_messages = []\r\n try:\r\n ReadMe.from_readme(card_path)\r\n except Exception as readme_error:\r\n error_messages.append(f\"The following issues have been found in the dataset cards:\\nREADME:\\n{readme_error}\")\r\n try:\r\n DatasetMetadata.from_readme(card_path)\r\n except Exception as metadata_error:\r\n error_messages.append(\r\n f\"The following issues have been found in the dataset cards:\\nYAML tags:\\n{metadata_error}\"\r\n )\r\n \r\n if error_messages:\r\n> raise ValueError(\"\\n\".join(error_messages))\r\nE ValueError: The following issues have been found in the dataset cards:\r\nE README:\r\nE 'charmap' codec can't decode byte 0x90 in position 2283: character maps to <undefined>\r\nE The following issues have been found in the dataset cards:\r\nE YAML tags:\r\nE 'charmap' codec can't decode byte 0x90 in position 2283: character maps to <undefined>\r\n```", "Seems like the encoding issues on windows is also being tackled in #2418 - will see if this solves the problem in the current PR" ]

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[ "this is also the case for the following datasets and configurations:\r\n\r\n* `mlqa` with config `mlqa-translate-train.ar`\r\n\r\n" ]

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[ "Thanks for reporting !\r\n#2433 fixed the issue, thanks @mariosasko :)\r\n\r\nWe'll do a patch release soon of the library.\r\nIn the meantime, you can use the fixed version of adversarial_qa by adding `script_version=\"master\"` in `load_dataset`" ]

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[ "Maybe we should only keep one citation ?\r\ncc @thomwolf @yjernite ", "For info:\r\n- The one automatically generated by Zenodo is version-specific, and a new one will be generated after each release.\r\n- Zenodo has also generated a project-specific DOI (they call it *Concept DOI* as opposed to *Version DOI*), but currently this only redirects to the DOI page of the latest version.\r\n- All the information automatically generated by Zenodo can be corrected/customized if necessary.\r\n - If we decide to correct/update metadata, take into account that there are the following fields (among others): Authors, Contributors, Title, Description, Keywords, Additional Notes, License,...\r\n\r\nAccording to Zenodo: https://help.zenodo.org/#versioning\r\n> **Which DOI should I use in citations?**\r\n> \r\n> You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility. By default, Zenodo uses the specific version to generate citations.\r\n> \r\n> You can use the Concept DOI representing all versions in citations when it is desirable to cite an evolving research artifact, without being specific about the version.", "Thanks for the details ! As zenodo says we should probably just show the versioned DOI. And we can remove the old citation.", "I have removed the old citation.\r\n\r\nWhat about the new one? Should we customize it? I have fixed some author names (replaced nickname with first and family names). Note that the list of authors is created automatically by Zenodo from this list: https://github.com/huggingface/datasets/graphs/contributors\r\nI do not know if this default automatic list of authors is what we want to show in the citation..." ]

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[ "> I like having \"extractive\" in the name to make things explicit. However this creates an inconsistency with transformers.\r\n> \r\n> See\r\n> https://huggingface.co/transformers/task_summary.html#extractive-question-answering\r\n> \r\n> But this is minor IMO and I'm ok with this renaming\r\n\r\nyes i chose this convention because it allows us to match the `QuestionAnsweringXxx` naming and i think it's better to have `task_name-subtask_name` should auto-complete ever become part of the Hub :)" ]

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[ "Build fails but this change should not be the reason...", "rebased on master", "rebased on master" ]

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[ "Build fails but this change should not be the reason...", "rebased on master" ]

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[ "It should probably work out of the box to save structured data. If you want to show an example we can help you.", "An example of a toy dataset is like:\r\n```json\r\n[\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"aaaaa\",\r\n \"reader\": [\r\n \"tom\",\r\n \"lucy\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"tom\",\r\n \"friends\": [\r\n \"mike\",\r\n \"bbb\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"xxxxx\",\r\n \"reader\": [\r\n \"tom\",\r\n \"qqqq\"\r\n ]\r\n }\r\n ]\r\n }\r\n]\r\n```\r\nWe can use the friendship relation to build a directional graph, and a user node can be represented using the articles written by himself. And the relationship between articles can be built when the article has read by the same user.\r\nThis dataset can be used to model the heterogeneous relationship between users and articles, and this graph can be used to build recommendation systems to recommend articles to the user, or potential friends to the user.", "Hi,\r\n\r\nyou can do the following to load this data into a `Dataset`:\r\n```python\r\nfrom datasets import Dataset\r\nexamples = [\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"aaaaa\",\r\n \"reader\": [\r\n \"tom\",\r\n \"lucy\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"tom\",\r\n \"friends\": [\r\n \"mike\",\r\n \"bbb\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"xxxxx\",\r\n \"reader\": [\r\n \"tom\",\r\n \"qqqq\"\r\n ]\r\n }\r\n ]\r\n }\r\n]\r\n\r\nkeys = examples[0].keys()\r\nvalues = [ex.values() for ex in examples]\r\ndataset = Dataset.from_dict({k: list(v) for k, v in zip(keys, zip(*values))})\r\n```\r\n\r\nLet us know if this works for you.", "Thank you so much, and that works! I also have a question that if the dataset is very large, that cannot be loaded into the memory. How to create the Dataset?", "If your dataset doesn't fit in memory, store it in a local file and load it from there. Check out [this chapter](https://huggingface.co/docs/datasets/master/loading_datasets.html#from-local-files) in the docs for more info.", "Nice! Thanks for your help." ]

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[ "IMO this PR is ready for review. I do not know why tests fail...", "The CI fail is unrelated to this PR, and it has been fixed on master, merging :)", "> I just have one comment: we use rouge, not rogue :p\r\n\r\nOops!", "rebased on master" ]

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[ "Hi,\r\n\r\n`load_dataset` returns an instance of `DatasetDict` if `split` is not specified, so instead of `Dataset.load_from_disk`, use `DatasetDict.load_from_disk` to load the dataset from disk.", "Thanks it worked!", "Though I see a stream of issues open by people lost between datasets and datasets dicts so maybe there is here something that could be better in terms of UX. Could be better error handling or something else smarter to even avoid said errors but maybe we should think about this. Reopening to use this issue as a discussion place but feel free to open a new open if you prefer @lhoestq @albertvillanova ", "We should probably improve the error message indeed.\r\n\r\nAlso note that there exists a function `load_from_disk` that can load a Dataset or a DatasetDict. Under the hood it calls either `Dataset.load_from_disk` or `DatasetDict.load_from_disk`:\r\n\r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset_dict = load_from_disk(\"path/to/dataset/dict\")\r\nsingle_dataset = load_from_disk(\"path/to/single/dataset\")\r\n```", "I just opened #2437 to improve the error message", "Superseded by #2462 " ]

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[ "The CI error is unrelated to the PR, merging", "@lhoestq, can we release this feature if you guys are planning for any patch release for Datasets. It'll slow down [#11927](https://github.com/huggingface/transformers/pull/11927) otherwise :/ ", "Sure definitely, having a discrepancy between Dataset.map and DatasetDict.map is an issue that we should fix and include in a patch release. Will do it in the coming days" ]

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[ "Thanks! Can you also add it as metadata in the YAML block at the top of the file?\r\n\r\nShould be in the form:\r\n\r\n```\r\nlicenses:\r\n- cc-by-sa-4.0\r\n```", "seems like we need to add all the other tags ? \r\n``` \r\nif error_messages:\r\n> raise ValueError(\"\\n\".join(error_messages))\r\nE ValueError: The following issues have been found in the dataset cards:\r\nE YAML tags:\r\nE __init__() missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'languages', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n```", "I'll let @lhoestq or @yjernite chime in (and maybe complete/merge). Thanks!", "Looks like CircleCI has an incident. Let's wait for it to be working again and make sure the CI is green", "The remaining error is unrelated to this PR, merging" ]

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[ "Can you please add encoding to this line as well to fix the issue (and maybe replace `path.open(...)` with `open(path, ...)`)?\r\nhttps://github.com/huggingface/datasets/blob/7bee4be44706a59b084b9b69c4cd00f73ee72f76/src/datasets/utils/metadata.py#L58", "Sure, in fact even I was thinking of adding this in order to maintain the consistency!" ]

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[ "This is super cool, I love that ❤️ " ]

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[ "I'm not sure why I got error like below when I auto-generate dummy data \"mrc\" \r\n```\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```", "> I'm not sure why I got error like below when I auto-generate dummy data \"mrc\"\r\n> \r\n> ```\r\n> datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n> Found duplicate Key: 0\r\n> Keys should be unique and deterministic in nature\r\n> ```\r\n\r\nPlease check out the suggestion below. I think it might be a cause.", "> > I'm not sure why I got error like below when I auto-generate dummy data \"mrc\"\r\n> > ```\r\n> > datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n> > Found duplicate Key: 0\r\n> > Keys should be unique and deterministic in nature\r\n> > ```\r\n> \r\n> Please check out the suggestion below. I think it might be a cause.\r\n\r\nThe problem was `id_` in mrc when yield was not unique. (I used index in `enumerate(paragraphs)` by mistake)\r\nI fixed it and update all the things", "To fix the CI you can just merge master into your branch and it should be all green hopefully :)", "@lhoestq\r\nThanks for reviewing!\r\n\r\nIt's harder than I thought to add dataset card. 😅 \r\nI checked and updated your suggestion (script, readme details, dummy data). \r\n\r\ndummy data is little bit larger than expected because `ner` dataset is about 80 lines and `dp` dataset is about 25 lines to avoid 0 examples.\r\n\r\nI'm not sure why some CI keep fails, can u check for this?", "Thanks ! That makes sense for ner and dp\r\n\r\nFor mrc on the other hand there are still too many examples, maybe you can generate the dummy data for 5 examples for all tasks except ner and dp ?", "> Thanks ! That makes sense for ner and dp\r\n> \r\n> For mrc on the other hand there are still too many examples, maybe you can generate the dummy data for 5 examples for all tasks except ner and dp ?\r\n\r\nYes, I generate default lines in dataset-cli for other dataset except \"dp\" and \"ner\"\r\nI fixed mrc dataset, hope it's fine now :)\r\n\r\nthe reason CI failed was I forgot to merge master into my branch 😅 " ]

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[ "It actually seems to happen all the time in above configuration:\r\n* the function `filter_by_duration` correctly loads cached processed dataset\r\n* the function `prepare_dataset` is always reexecuted\r\n\r\nI end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug or limitation here.", "Hi ! The hash used for caching `map` results is the fingerprint of the resulting dataset. It is computed using three things:\r\n- the old fingerprint of the dataset\r\n- the hash of the function\r\n- the hash of the other parameters passed to `map`\r\n\r\nYou can compute the hash of your function (or any python object) with\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nmy_func = lambda x: x + 1\r\nprint(Hasher.hash(my_func))\r\n```\r\n\r\nIf `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.", "> If `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.\r\n\r\nYes I think that was the issue.\r\n\r\nFor the hash of the function:\r\n* does it consider just the name or the actual code of the function\r\n* does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)", "> does it consider just the name or the actual code of the function\r\n\r\nIt looks at the name and the actual code and all variables such as recursively. It uses `dill` to do so, which is based on `pickle`.\r\nBasically the hash is computed using the pickle bytes of your function (computed using `dill` to support most python objects).\r\n\r\n> does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)\r\n\r\nYes it does thanks to recursive pickling.", "Thanks for these explanations. I'm closing the issue." ]

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[ "Merging since the CI error is unrelated to this PR and has been fixed on master", "Thank you for taking a look at the CI error - I was a bit confused with that. Thanks!" ]

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[ "Hi ! Can you try using a more up-to-date version ? We added the task_templates in `datasets` 1.7.0.\r\n\r\nIdeally when you're working on new datasets, you should install and use the local version of your fork of `datasets`. Here I think you tried to run the 1.7.0 tests with the 1.6.2 code" ]

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[ "> I can provide a PR l8er...\r\n\r\nSee #2425 " ]

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[ "LGTM, but I'm not sure what's going on with the Unix tests @lhoestq ", "The CI error is unrelated to this PR, it's been fixed now on master.", "Thanks @PaulLerner !", "> #- [ ] - Hey@fr[fr_fr**fr~~fr `fr```\nFR\n````~~**_]()", "Oh that was unexpected. I didn't know pokemons were into NLP" ]

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[ "I thought the renaming was suggested only for the env var, and not for the config variable... As you think is better! ;)", "I think it's better if they match, so that users understand directly that they're directly connected", "Well, if you're not concerned about back-compat here, perhaps it could be renamed and shortened too ;)\r\n\r\nI'd suggest one of:\r\n\r\n* `HF_DATASETS_IN_MEMORY_MAX_SIZE`\r\n* `HF_DATASETS_MAX_IN_MEMORY_SIZE`\r\n\r\nthe itention is to:\r\n1. make it consistent with all the other `datasets` env vars which all start with `HF_DATASETS_`, e.g.:\r\n```\r\nHF_DATASETS_CACHE\r\nHF_DATASETS_OFFLINE \r\n```\r\n2. allow to recode in the future to support 1M, 4K, 1T and not just bytes - bytes is not a great choice for this type of variable since it will be at least X Mbytes for most reasonable uses.\r\n\r\nAnd I agree with @albertvillanova that the config variable name shouldn't have the HF prefix - it's preaching to the choir - the user already knows it's a local variable. \r\n\r\nThe only reason we prefix env vars, is because they are used outside of the software.\r\n\r\nBut I do see a good point of you trying to make things consistent too. How about this:\r\n\r\n`config.IN_MEMORY_MAX_SIZE` (or whatever the final env var will be minus `HF_DATASETS_` prefix).\r\n\r\nThis is of course just my opinion.\r\n\r\n", "Thanks for the comment :)\r\nI like both propositions, and I agree this would be better in order to allow support for 1M, 1T etc. \r\nRegarding the prefix of the variable in config.py I don't have a strong opinion. I just added it for consistency with the other variables that default to the env variables like HF_DATASETS_CACHE. However I agree this would be nice to have shorter names so I'm not against removing the prefix either. Since the feature is relatively new, I think we can still allow ourself to rename it", "Awesome, \r\n\r\nLet's use then:\r\n\r\n- `HF_DATASETS_IN_MEMORY_MAX_SIZE` for the env var\r\n- `config.IN_MEMORY_MAX_SIZE` for config.\r\n\r\nand for now bytes will be documented as the only option and down the road add support for K/M/G.\r\n\r\n@albertvillanova, does that sound good to you?", "Great!!! 🤗 ", "Did I miss a PR with this change?\r\n\r\nI want to make sure to add it to transformers tests to avoid the overheard of rebuilding the datasets.\r\n\r\nThank you!", "@stas00 I'm taking on this now that I have finally finished the collaborative training experiment. Sorry for the delay.", "Yes, of course! Thank you for taking care of it, @albertvillanova ", "Actually, why is this feature on by default? \r\n\r\nUsers are very unlikely to understand what is going on or to know where to look. Should it at the very least emit a warning that this was done w/o asking the user to do so and how to turn it off?\r\n\r\nIMHO, this feature should be enabled explicitly by those who want it and not be On by default. This is an optimization that benefits only select users and is a burden on the rest.\r\n\r\nIn my line of dev/debug work (multiple short runs that have to be very fast) now I have to remember to disable this feature explicitly on every machine I work :(\r\n", "Having the dataset in memory is nice to get the speed but I agree that the lack of caching for dataset in memory is an issue. By default we always had caching on.\r\nHere the issue is that in-memory datasets are still not able to use the cache - we should fix this asap IMO.\r\n\r\nHere is the PR that fixes this: https://github.com/huggingface/datasets/pull/2329", "But why do they have to be datasets in memory in the first place? Why not just have the default that all datasets are normal and are cached which seems to be working solidly. And only enable in memory datasets explicitly if the user chooses to and then it doesn't matter if it's cached on not for the majority of the users who will not make this choice.\r\n\r\nI mean the definition of in-memory-datasets is very arbitrary - why 250MB and not 5GB? It's very likely that the user will want to set this threshold based on their RAM availability. So while doing that they can enable the in-memory-datasets. Unless I'm missing something here.\r\n\r\nThe intention here is that things work well in general out of the box, and further performance optimizations are available to those who know what they are doing.\r\n", "This is just for speed improvements, especially for data exploration/experiments in notebooks. Ideally it shouldn't have changed anything regarding caching behavior in the first place (i.e. have the caching enabled by default).\r\n\r\nThe 250MB limit has also been chosen to not create unexpected high memory usage on small laptops.", "Won't it be more straight-forward to create a performance optimization doc and share all these optimizations there? That way the user will be in the knowing and will be able to get faster speeds if their RAM is large. \r\n\r\nIt is hard for me to tell the average size of a dataset an average user will have, but my gut feeling is that many NLP datasets are larger than 250MB. Please correct me if I'm wrong.\r\n\r\nBut at the same time what you're saying is that once https://github.com/huggingface/datasets/pull/2329 is completed and merged, the in-memory-datasets will be cached too. So if I wait long enough the whole issue will go away altogether, correct?" ]

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[ "Hi @cindyxinyiwang,\r\nDid you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:\r\nhttps://github.com/huggingface/datasets/blob/e08362256fb157c0b3038437fc0d7a0bbb50de5c/src/datasets/arrow_dataset.py#L1556-L1558", "Hi ! `cache_file_name` is an argument of the `Dataset.map` method. Can you check that your `dataset` is indeed a `Dataset` object ?\r\n\r\nIf you loaded several splits, then it would actually be a `DatasetDict` (one dataset per split, in a dictionary).\r\nIn this case, since there are several datasets in the dict, the `DatasetDict.map` method requires a `cache_file_names` argument (with an 's'), so that you can provide one file name per split.", "I think you are right. I used cache_file_names={data1: name1, data2: name2} and it works. Thank you!" ]

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[ "Thanks!" ]

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[ "messed up my branch, repushing", "live mapping can be found at https://huggingface.co/api/pwc/datasets-mapping and will be kept up to date going forward" ]

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[ "I faced the similar problem. Downgrading datasets to 1.5.0 fixed it.", "Thanks for reporting, I'm looking into it", "I just opened #2438 to fix this :)", "Hi ! This has been fixed in the 1.8.0 release of `datasets`" ]

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[ "Hi,\r\n\r\ndid you fill out the env info section manually or by copy-pasting the output of the `datasets-cli env` command?\r\n\r\nThis code should work without issues on 1.6.2 version (I'm working on master (1.6.2.dev0 version) and can't reproduce this error).", "@mariosasko you are right I was still on `1.5.0`. " ]

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[ "Thank you for clarifying the precedence, @albertvillanova \r\n\r\nIsn't it typically the case where env vars have the highest precedence? \r\n\r\nIn my understanding the point of env vars is to be able to override software w/o needing to touch the code. \r\n\r\nPlease correct me if this is not so in the general case.", "Hi @stas00, \r\n\r\nWell, I'm not an expert on this topic, but the precedence hierarchy I have normally found is from higher to lower:\r\n- command line parameters\r\n- env vars\r\n- config files\r\nSo yes, normally env vars have precedence over configuration files.\r\n\r\nAnyway, for Datasets, there are no configuration files. The _in-memory_ config is set from default values or env vars (which have precedence over default values). But this is done at import.\r\n\r\nHowever, once the library is imported, the user can modify the in-memory config, and this will have precedence over the rest of mechanisms (which take place only at import).", "In my limited experience env vars are typically above cmd line args, so that one can override scripts with cmd lines using env vars, but usually one then uses env vars inside cmd line args, so it's loud and clear.\r\n\r\nFor example specifying a specific gpu number on a command line will depend on `CUDA_VISIBLE_DEVICES` so gpu0 will be different if someone sets `CUDA_VISIBLE_DEVICES=2,3` vs `CUDA_VISIBLE_DEVICES=0,1`.\r\n\r\n> However, once the library is imported, the user can modify the in-memory config, and this will have precedence over the rest of mechanisms (which take place only at import).\r\n\r\nAnd this is exactly the problem we are trying to solve here. For a good reason HF examples don't want to use `keep_in_memory=False`, and they may choose to now set `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` and which means we again can't override it via env var.\r\n\r\n", "oops, sorry, didn't think earlier - do we need to prefix this with `HF_DATASETS` or `HF_` like all the other env vars? or is it long enough already to be unique - it's just not telling the user in the config file what projet this variable is for...", "You're right, I just opened https://github.com/huggingface/datasets/pull/2409" ]

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[ "@lhoestq there are many things missing in the README.md file, but this correction is right despite not passing the validation tests...", "Yes indeed. We run the validation in all modified readme because we think that it is the time when contributors are the most likely to fix a dataset card - or it will never be done" ]

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[ "Hi ! Thanks for reporting\r\ncc @pjox is this an issue from the data ?\r\n\r\nAnyway we should at least mention that OSCAR could contain such contents in the dataset card, you're totally right @jerryIsHere ", "Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https://arxiv.org/pdf/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https://github.com/oscar-corpus/oscar-website/issues) as well so that we can track it?" ]

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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 :)" ]

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[ "If I'm not mistaken, one way to fix this would be to drop the task templates when copying the info by inserting `dataset.info.task_templates = None` before the `Dataset.cast` call in `Dataset.prepare_for_task`. Moreover, we should do this change independently of the KeyError being raised because currently the following is possible:\r\n```python\r\ndset = load_dataset(\"some_dataset\") # let's say 'some_dataset' supports text classification and question answering\r\ndset_tc = dset.prepare_for_task(\"text-classification\")\r\ndset_tc.preprare_for_task(\"question-answering\") # this should raise an error because the schema is no longer valid for this task; currently this fails on 'rename_columns'\r\n```\r\nI see 2 options:\r\n1. to drop the task templates after the first `Dataset.prepare_for_task` call\r\n2. to save only the tasks compatible with the new schema after Dataset.prepare_for_task` (but then we have to update the column names of the compatible tasks to make sure the column mapping is still valid) ", "> If I'm not mistaken, one way to fix this would be to drop the task templates when copying the info by inserting `dataset.info.task_templates = None` before the `Dataset.cast` call in `Dataset.prepare_for_task`. Moreover, we should do this change independently of the KeyError being raised because currently the following is possible:\r\n> \r\n> ```python\r\n> dset = load_dataset(\"some_dataset\") # let's say 'some_dataset' supports text classification and question answering\r\n> dset_tc = dset.prepare_for_task(\"text-classification\")\r\n> dset_tc.preprare_for_task(\"question-answering\") # this should raise an error because the schema is no longer valid for this task; currently this fails on 'rename_columns'\r\n> ```\r\n> \r\n> I see 2 options:\r\n> \r\n> 1. to drop the task templates after the first `Dataset.prepare_for_task` call\r\n> 2. to save only the tasks compatible with the new schema after Dataset.prepare_for_task` (but then we have to update the column names of the compatible tasks to make sure the column mapping is still valid)\r\n\r\nthanks for the great idea @mariosasko and for spotting the problem with sequential task preparation! i am in favour of your option (1) since it is simple and saves us from having to keep track of the column mappings across multiple steps. \r\n\r\ni've implemented the change and refactored the tests to account for the new approach (including a new test that the templates are flushed after we call `prepare_for_task`). perhaps the slightly inelegant aspect here is that if we want to allow the user to set `labels` in the `TextClassification` template, then we have two places (`DatasetInfo.__post_init__` and `TextClassification.__post_init__`) where we need to update `label_schema`. \r\n\r\non the other hand, dropping `labels` from the `TextClassification` signature would have the nice effect that users only have to think about column names when defining their tasks.\r\n\r\nin any case, i think it would be a good idea to merge #2376 soon as the current PR is touching a lot of the same places in the codebase 😄 \r\n", "cc @SBrandeis who might also be interested in this feature :)", "Tests are failing only because the `emotion` dataset card doesn't pass our dataset card validator (tags are missing), you can ignore this since it's unrelated to this PR.", "@lhoestq @SBrandeis i've fixed the tests and think this is now in a good state for another review :)", "Maybe @SBrandeis you can also take a look to make sure you're fine with it ?" ]

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[ "That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) ", "I can open a PR but there is 2 details to fix:\r\n- the name for the corresponding key (e.g. `original_answer`)\r\n- how to implement it: I’m not sure what happens when you map `lambda x: {'input': ...}` as it keeps the other keys (e.g. `output`) intact but here since we want to set a nested value (e.g. `x['output']['original_answer']`) I implemented it with a regular function (not lambda), see below\r\n\r\n```py\r\ndef add_original_answer(x, trivia_qa, triviaqa_map):\r\n i = triviaqa_map[x['id']]\r\n x['output']['original_answer'] = trivia_qa['validation'][i]['answer']['value']\r\n return x\r\n```" ]

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[ "LGTM now, thank you =)" ]

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[ "Update: found a few datasets that slipped through the net. Adding them shortly!", "You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?", "> You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?\r\n\r\nhi @yjernite, these code insertions are auto-generated so could certainly be improved :) \r\n\r\njust so i understand, your idea is that instead of doing something like\r\n\r\n```python\r\nclass AGNews(datasets.GeneratorBasedBuilder):\r\n \"\"\"AG News topic classification dataset.\"\"\"\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"text\": datasets.Value(\"string\"),\r\n \"label\": datasets.features.ClassLabel(\r\n names=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]\r\n ),\r\n }\r\n ),\r\n homepage=\"http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html\",\r\n citation=_CITATION,\r\n task_templates=[\r\n TextClassification(\r\n labels=(\"Business\", \"Sci/Tech\", \"Sports\", \"World\"),\r\n text_column=\"text\",\r\n label_column=\"label\",\r\n )\r\n ],\r\n )\r\n```\r\n\r\nwe could do the following:\r\n\r\n```python\r\nclass AGNews(datasets.GeneratorBasedBuilder):\r\n \"\"\"AG News topic classification dataset.\"\"\"\r\n\r\n def _info(self):\r\n info = datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"text\": datasets.Value(\"string\"),\r\n \"label\": datasets.features.ClassLabel(\r\n names=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]\r\n ),\r\n }\r\n ),\r\n homepage=\"http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html\",\r\n citation=_CITATION,\r\n )\r\n\r\n info.task_templates = [\r\n TextClassification(\r\n labels=info.features.names,\r\n text_column=\"text\",\r\n label_column=\"label\",\r\n )\r\n ]\r\n return info\r\n```\r\n\r\n", "Or we could simply not specify the labels and update the template in the DatasetInfo postinit to give it the labels ?", "> Or we could simply not specify the labels and update the template in the DatasetInfo postinit to give it the labels ?\r\n\r\nOh yes, that would be great! It does mean enforcing that people use the right feature type (sometimes people still use a `string` feature still because they don't want to enumerate the classes, but I guess you've been catching most of those in reviews @lhoestq )\r\n\r\nThere might be reasons where there should be a legitimate difference, but I can't really think of nay right now, and we can always duplicate the feature", "Let's ignore the CI fails since they are unrelated to your changes. They're about dataset cards issues" ]

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[ "Looks like there are multiple issues regarding this (#2386, #2322) and it's a WIP #2329. Currently these datasets are being loaded in-memory which is causing this issue. Quoting @mariosasko here for a quick fix:\r\n\r\n> set `keep_in_memory` to `False` when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them)\r\n\r\n", "Hi ! Since `datasets` 1.6.0 we no longer keep small datasets (<250MB) on disk and load them in RAM instead by default. This makes data processing and iterating on data faster. However datasets in RAM currently have no way to reload previous results from the cache (since nothing is written on disk). We are working on making the caching work for datasets in RAM.\r\n\r\nUntil then, I'd recommend passing `keep_in_memory=False` to the calls to `load_dataset` like here:\r\n\r\nhttps://github.com/huggingface/transformers/blob/223943872e8c9c3fc11db3c6e93da07f5177423f/examples/pytorch/language-modeling/run_clm.py#L233\r\n\r\nThis way you say explicitly that you want your dataset to stay on the disk, and it will be able to recover previously computed results from the cache.", "gotcha! thanks Quentin", "OK, It doesn't look like we can use the proposed workaround - see https://github.com/huggingface/transformers/issues/11801\r\n\r\nCould you please add an env var for us to be able to turn off this unwanted in our situation behavior? It is really problematic for dev work, when one needs to restart the training very often and needs a quick startup time. Manual editing of standard scripts is not a practical option when one uses examples.\r\n\r\nThis could also be a problem for tests, which will be slower because of lack of cache, albeit usually we use tiny datasets there. I think we want caching for tests.\r\n\r\nThank you.", "Hi @stas00, \r\n\r\nYou are right: an env variable is needed to turn off this behavior. I am adding it.\r\n\r\nFor the moment there is a config parameter to turn off this behavior: `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None`\r\n\r\nYou can find this info in the docs:\r\n- in the docstring of the parameter `keep_in_memory` of the function [`load_datasets`](https://huggingface.co/docs/datasets/package_reference/loading_methods.html#datasets.load_dataset):\r\n- in a Note in the docs about [Loading a Dataset](https://huggingface.co/docs/datasets/loading_datasets.html#from-the-huggingface-hub)\r\n\r\n> The default in 🤗Datasets is to memory-map the dataset on drive if its size is larger than datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES (default 250 MiB); otherwise, the dataset is copied in-memory. This behavior can be disabled by setting datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None, and in this case the dataset is not loaded in memory.", "Yes, but this still requires one to edit the standard example scripts, so if I'm doing that already I just as well can add `keep_in_memory=False`.\r\n\r\nMay be the low hanging fruit is to add `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` env var to match the config, and if the user sets it to 0, then it'll be the same as `keep_in_memory=False` or `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0`?", "@stas00, however, for the moment, setting the value to `0` is equivalent to the opposite, i.e. `keep_in_memory=True`. This means the max size until which I load in memory is 0 bytes.\r\n\r\nTell me if this is logical/convenient, or I should change it.", "In my PR, to turn off current default bahavior, you should set env variable to one of: `{\"\", \"OFF\", \"NO\", \"FALSE\"}`.\r\n\r\nFor example:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=\r\n```", "IMHO, this behaviour is not very intuitive, as 0 is a normal quantity of bytes. So `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` to me reads as don't cache ever.\r\n\r\nAlso \"SIZE_IN_BYTES\" that can take one of `{\"\", \"OFF\", \"NO\", \"FALSE\"}` is also quite odd.\r\n\r\nI think supporting a very simple `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` that can accept any numerical value to match the name of the variable, requires minimal logic and is very straightforward. \r\n\r\nSo if you could adjust this logic - then `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` is all that's needed to not do in-memory datasets.\r\n\r\nDoes it make sense?", "I understand your point @stas00, as I am not very convinced with current implementation.\r\n\r\nMy concern is: which numerical value should then pass a user who wants `keep_in_memory=True` by default, independently of dataset size? Currently it is `0` for this case.", "That's a good question, and again the normal bytes can be used for that:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=1e12 # (~2**40)\r\n```\r\nSince it's unlikely that anybody will have more than 1TB RAM.\r\n\r\nIt's also silly that it uses BYTES and not MBYTES - that level of refinement doesn't seem to be of a practical use in this context.\r\n\r\nNot sure when it was added and if there are back-compat issues here, but perhaps it could be renamed `MAX_IN_MEMORY_DATASET_SIZE` and support 1M, 1G, 1T, etc. \r\n\r\nBut scientific notation is quite intuitive too, as each 000 zeros is the next M, G, T multiplier. Minus the discrepancy of 1024 vs 1000, which adds up. And it is easy to write down `1e12`, as compared to `1099511627776` (2**40). (`1.1e12` is more exact).\r\n", "Great! Thanks, @stas00.\r\n\r\nI am implementing your suggestion to turn off default value when set to `0`.\r\n\r\nFor the other suggestion (allowing different metric prefixes), I will discuss with @lhoestq to agree on its implementation.", "Awesome! Thank you, @albertvillanova!!!\r\n\r\n" ]

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[ "Thanks @bhavitvyamalik for referencing the workaround. Setting `keep_in_memory=False` is working." ]

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[ "Thanks for the suggestions! I've included them and made a few minor tweaks along the way", "Please merge master into this branch to fix the CI, I just fixed metadata validation tests." ]

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[ "Hi ! This is because we are actually using the arrow streaming format. We plan to switch to the arrow IPC format.\r\nMore info at #1933 " ]

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[ "Looks good thanks, what do you think @lewtun ?", "thanks for including the lazy `ClassLabel` class @mariosasko ! from my side this LGTM!" ]

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[ "Once this is merged, let's update `transformers` examples to use this new code. As currently all those tqdm bars are who knows what they are....\r\n\r\nhttps://github.com/huggingface/transformers/issues/11797", "Sure @stas00! Once this is merged let's discuss what all changes can be done on `transformers` side", "@bhavitvyamalik, as it has been merged would you like to tackle https://github.com/huggingface/transformers/issues/11797?\r\n", "Definitely @stas00. From what I could gather, you guys want more meaningful `.map` calls for all examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch)?", "That's exactly right, @bhavitvyamalik \r\n\r\nPerhaps the best approach is to do one example, see that other maintainers agree on it. and then replicate to other." ]

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[ "Version below works, checked again in the docs, and data_files should be a path.\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n data_files='/data/dir/corpus.txt', \r\n cache_dir='.')\r\n```" ]

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[ "Thanks for your helpful comments and suggestions! :)\r\nI integrated the additional fields, and extended some of the README/dataset card.\r\nAnd I actually realized that we had the cc-by-4.0 for the dataset, so this was also changed.", "I added the answers to the test set actually :)", "Oh great ! Let me revert my change then" ]

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[ "@lhoestq Thank you for the review. I've made the suggested changes. There still might be some problems with dummy data though due to some csv loading issues (which I haven't found the cause to).", "I took a look at the dummy data and some csv lines were cropped. I fixed them :)" ]

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