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[ "Thanks ! Sorry for the inconvenience ^^' " ]

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[ "@lhoestq I had 2 runs for more than 2 days each, continuously streaming (they were failing before with 3 retries at 1 sec interval).\r\n\r\nThey are running on TPU's (so great internet connection) and only had connection errors a few times each (3 & 4). Each time it worked after only 1 retry.\r\nThe reason for a higher number of retries is for local connections. It would allow for almost 2mn of a wifi/ethernet disconnection. In practice this should not happen very often.\r\n\r\nLet me know if you think it's too much." ]

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[ "Thanks for pushing this dataset :)\r\n\r\nAccording to the CI, the file `cloze_test_val__spring2016 - cloze_test_ALL_val.csv` is missing in the dummy data zip file (the zip files seem empty). Feel free to add this file with 4-5 lines and it should be good\r\n\r\nAnd you can fix the YAML tags with\r\n```yaml\r\npretty_name: Story Cloze Test\r\n```\r\nand filling the other tags task_categories and task_ids\r\n\r\nIf the dataset doesn exist on paperswithcode, you can just leave\r\n```yaml\r\npaperswithcode_id: null\r\n```", "@lhoestq can't fix the last test fails.", "> Thanks @zaidalyafeai, the failing test is due to an issue in the master branch, that has already been fixed.\r\n> \r\n> You can include the fix:\r\n> \r\n> ```\r\n> git checkout add_story_cloze\r\n> git fetch upstream master\r\n> git merge upstream/master\r\n> ```\r\n\r\nThanks @albertvillanova, passed all the tests now. ", "Thanks Albert, I fixed the suggested comments. This dataset has no train splits, it is only used for evaluation." ]

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[ "Hi @lhoestq Any response on this issue?", "Hi ! Sorry for the late response\r\n\r\nI agree `interleave_datasets` would benefit a lot from having more flexibility. If I understand correctly it would be nice to be able to define stopping strategies like `stop=\"first_exhausted\"` (default) or `stop=\"all_exhausted\"`. If you'd like to contribute this feature I'd be happy to give you some pointers :)\r\n\r\nAlso one can already set the max number of iterations per dataset by doing `dataset.take(n)` on the dataset that should only have `n` samples.\r\n\r\nRegarding the `iter_cnt` counter, I think this requires a bit more thoughts, since we might have to be able to backpropagate the the counter if `map` or other transforms have been applied after `interleave_datasets`. ", "@sbmaruf I just notice that (1)`interleave_datasets` only samples indices once and reuse for all epochs, and (2) it's limited by the smallest dataset. Do you figure out an alternative way to achieve the same purpose?" ]

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[ "I think this problem is already fixed:\r\n```python\r\nIn [4]: import fsspec\r\n ...:\r\n ...: url = \"https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes\"\r\n ...:\r\n ...: fsspec.open(url).open()\r\nOut[4]: <File-like object HTTPFileSystem, https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattribu\r\n```", "No I'm still having this issue on my windows, and so does the CI" ]

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[ "@lhoestq, @albertvillanova can we have `**tqdm_kwargs` in `map`? If there are any fields that are important to our tqdm (like iterable or unit), we can pop them before initialising the tqdm object so as to avoid duplicity.", "Hi ! Sounds like a good idea :)\r\n\r\nAlso I think it would be better to have this as an actual parameters instead of kwargs to make it clearer" ]

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[ "Hi @RylanSchaeffer, thanks for reporting.\r\n\r\nI'm sorry, but I was not able to reproduce your problem.\r\n\r\nNormally, the reason for this type of error is that, during your download of the data files, this was not fully complete.\r\n\r\nCould you please try to load the dataset again but forcing its redownload? Please use:\r\n```python\r\ndataset = load_dataset(\"openwebtext\", download_mode=\"FORCE_REDOWNLOAD\")\r\n```\r\n\r\nLet me know if the problem persists.", "I close this issue for the moment. Feel free to re-open it again if the problem persists." ]

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[ "Hi ! I think this issue is more related to the `transformers` project. Could you open an issue on https://github.com/huggingface/transformers ?\r\n\r\nAnyway I think the issue could be that both wikipedia and bookcorpusopen have an additional \"title\" column, contrary to wikitext which only has a \"text\" column. After calling `load_dataset`, can you try doing `dataset = dataset.remove_columns(\"title\")` ?", "Removing the \"title\" column works! Thanks for your advice.\r\n\r\nMaybe I should still create an issue to `transformers' to mark this solution?" ]

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[ "Hi @tidhamecha2, thanks for reporting.\r\n\r\nIndeed, we fixed this issue just one week ago: #3008\r\n\r\nThe fix will be included in our next version release.\r\n\r\nIn the meantime, you can incorporate the fix by installing `datasets` from the master branch:\r\n```\r\npip install -U git+ssh://[email protected]/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```" ]

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[ "I encountered the same bug using different datasets.\r\nany suggestions?", "+1, can reproduce here!", "I get the same error\r\nPlatform: Windows 10\r\nPython: python 3.8.8\r\nPyArrow: 5.0", "I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value(\"int8\"))`, but the actual values can be well outside the max range for 8-bit integers.\r\n\r\nI worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes).", "Addressed in https://huggingface.co./datasets/the_pile_openwebtext2/discussions/4" ]

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[ "Issue was environment inconsistency, updating packages did the trick\r\n\r\n`conda install -c huggingface -c conda-forge datasets`\r\n\r\n> Collecting package metadata (current_repodata.json): done\r\n> Solving environment: |\r\n> The environment is inconsistent, please check the package plan carefully\r\n> The following packages are causing the inconsistency:\r\n> \r\n> - conda-forge/noarch::datasets==1.12.1=pyhd8ed1ab_1\r\n> - conda-forge/win-64::multiprocess==0.70.12.2=py38h294d835_0\r\n> done\r\n> \r\n> Package Plan\r\n> \r\n> environment location: C:\\xxx\\anaconda3\\envs\\UnBias-94-1\r\n> \r\n> added / updated specs:\r\n> - datasets\r\n> \r\n> \r\n> The following NEW packages will be INSTALLED:\r\n> \r\n> dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0\r\n> \r\n> The following packages will be UPDATED:\r\n> \r\n> ca-certificates pkgs/main::ca-certificates-2021.9.30-~ --> conda-forge::ca-certificates-2021.10.8-h5b45459_0\r\n> certifi pkgs/main::certifi-2021.5.30-py38haa9~ --> conda-forge::certifi-2021.10.8-py38haa244fe_0\r\n> \r\n> The following packages will be SUPERSEDED by a higher-priority channel:\r\n> " ]

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[ "I got the same error for another dataset (`multi_woz_v22`):\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']\r\n```", "I'm seeing the same issue as @RylanSchaeffer:\r\nPython 3.7.11, macOs 11.4\r\ndatasets==1.14.0\r\n\r\nfails on:\r\n```python\r\ndataset = datasets.load_dataset(\"multi_woz_v22\")\r\n```" ]

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[ "I'm running a large test.\r\nLet's see if I get any error within a few days.", "This time it stopped after 8h but correctly raised `ConnectionError: Server Disconnected`.\r\n\r\nTraceback:\r\n```\r\nTraceback (most recent call last): \r\n File \"/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py\", line 1027, in <module> \r\n main() \r\n File \"/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py\", line 991, in main \r\n for batch in tqdm( \r\n File \"/home/koush/.pyenv/versions/dev/lib/python3.9/site-packages/tqdm/std.py\", line 1180, in __iter__ \r\n for obj in iterable: \r\n File \"/home/koush/dalle-mini/dev/seq2seq/run_seq2seq_flax.py\", line 376, in data_loader_streaming\r\n for item in dataset:\r\n File \"/home/koush/datasets/src/datasets/iterable_dataset.py\", line 341, in __iter__\r\n for key, example in self._iter():\r\n File \"/home/koush/datasets/src/datasets/iterable_dataset.py\", line 338, in _iter\r\n yield from ex_iterable\r\n File \"/home/koush/datasets/src/datasets/iterable_dataset.py\", line 179, in __iter__\r\n key_examples_list = [(key, example)] + [\r\n File \"/home/koush/datasets/src/datasets/iterable_dataset.py\", line 179, in <listcomp>\r\n key_examples_list = [(key, example)] + [\r\n File \"/home/koush/datasets/src/datasets/iterable_dataset.py\", line 176, in __iter__\r\n for key, example in iterator:\r\n File \"/home/koush/datasets/src/datasets/iterable_dataset.py\", line 225, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/koush/datasets/src/datasets/iterable_dataset.py\", line 99, in __iter__\r\n for key, example in self.generate_examples_fn(**kwargs_with_shuffled_shards):\r\n File \"/home/koush/datasets/src/datasets/iterable_dataset.py\", line 287, in wrapper\r\n for key, table in generate_tables_fn(**kwargs):\r\n File \"/home/koush/datasets/src/datasets/packaged_modules/json/json.py\", line 107, in _generate_tables\r\n batch = f.read(self.config.chunksize)\r\n File \"/home/koush/datasets/src/datasets/utils/streaming_download_manager.py\", line 136, in read_with_retries\r\n raise ConnectionError(\"Server Disconnected\")\r\nConnectionError: Server Disconnected\r\n```\r\n\r\nRight before this error, the warnings were correctly raised:\r\n\r\n```\r\n10/10/2021 06:02:26 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 1sec [1/3]\r\n10/10/2021 06:02:27 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 1sec [2/3] \r\n10/10/2021 06:02:28 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 1sec [3/3\r\n```\r\n\r\nI'm going to see what happens if I change the max retries to 20 and the interval to 5.", "Also maybe we can raise the Server Disconnected error with more info about what kind of error caused it (client error, time out, etc.)", "I have 2 runs:\r\n* [run 1](https://wandb.ai/dalle-mini/dalle-mini/runs/1nj161cl?workspace=user-borisd13) with [this data](https://huggingface.co./datasets/dalle-mini/encoded) that I will remove soon because I now use the 2nd one\r\n* [run 2](https://wandb.ai/dalle-mini/dalle-mini/runs/he9rrc3q?workspace=user-borisd13) with [this data](https://huggingface.co./datasets/dalle-mini/encoded-vqgan_imagenet_f16_16384)\r\n* `load_dataset(dataset_repo, data_files={'train':'data/train/*.jsonl', 'validation':'data/valid/*.jsonl'}, streaming=True)`\r\n\r\nThey have now been running by a bit more than a day for one run and 15h for the other.\r\n\r\nThe error logs are not shown in wandb because the script use `pylogging` (not sure why, I should change it) but basically so far with the new settings I had one timeout in each with successful reconnect afterwards.\r\n\r\nSo I think it's a good idea to have:\r\n* `STREAMING_READ_RETRY_INTERVAL = 5` since before my runs would get 3 errors in a row (with the default 1 second pause)\r\n* `STREAMING_READ_MAX_RETRIES` should also be increased. Since this type of error does not happen a lot, I would still have a large number (at least 10) because a stopped training run may be a big issue if checkpointing/restart is not well implemented which is not always trivial", "I agree ! Feel free to open a PR to increase both values" ]

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[ "Independently of this I think it raises the need to allow multiprocessing during streaming so that we get samples from multiple shards in one batch." ]

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[ "This has been fixed in 1.15, let me know if you still have this issue" ]

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[ "Hi ! Thanks for noticing this inconsistence and suggesting a fix :)\r\n\r\nIf I understand correctly you try to pass the same fingerprint to each processed shard of the dataset. This can be an issue since each shard is actually a different dataset with different data: they shouldn't have the same fingerprint.\r\n\r\nIdeally we want the result after `map` to have this fingerprint. The result after `map` is the concatenation of all the processed shards. In this case what we can do is add the `fingerprint` parameter to `concatenate_datasets` to overwrite the fingerprint here if needed:\r\nhttps://github.com/huggingface/datasets/blob/03b7f123cc17afc517c0aa2f912bbd90cb266185/src/datasets/arrow_dataset.py#L3588-L3590\r\n\r\nthen you can pass the fingerprint to `concatenate_datasets` here:\r\nhttps://github.com/huggingface/datasets/blob/03b7f123cc17afc517c0aa2f912bbd90cb266185/src/datasets/arrow_dataset.py#L2044-L2044", "Hi @lhoestq, thanks for the pointers! Not having a unique fingerprint per shard was indeed was indeed a problem. \r\n\r\nLet me look into this. I'll be back with a fix soon.", "Alright, to clarify about my problem. I using am using `datasets` with large datasets, and want to cache a heavy and non-deterministically fingerprintable function (using `datasets.fingerprint.Hasher`). Using `Dataset.map()` as it is would cause generating a random fingerprint. To circumvent this, I am generating custom deterministic fingerprints, which I pass as an argument to `Dataset.map()`. In that way, a deterministic fingerprint is set, and caching can be used. \r\n\r\nThis approach works well when using `num_proc==1`, but not so well when using `num_proc>1`. In both cases, `dataset._fingerprint` is effectively set to `new_fingerprint` at the end of the `.map()` call. However, caching is not used when `num_proc>1`, a non deterministically fingerprintable function and `new_fingerprint != null. The reason is that caching operates within `Dataset._map_single` and `new_fingerprint` is not passed here. \r\n\r\nThis pull request implements a quick fix (+unit test) by passing `new_fingerprint=f\"{new_fingerprint}-part{rank+1}-{num_proc}\"` to each `_map_single` call. Using a separate name for each call makes sure that each worker uses a different cache file (as you mentioned above).\r\n\r\nHowever, this solution still means that using a different value for `num_proc` will require computing new partial cache files. In the long run, performing the caching within `map()` instead of within `_map_single()` would be a cleaner solution.", "Hi @vlievin,\r\n\r\nIf I understand your example correctly, you are trying to use the `new_fingerprint` param to have a deterministic fingerprint of the transform, which is not hashable due to randomness. Any particular reason why you are not using the `cache_file_name` param instead? I did run your example with the `cache_file_name` specified, and it behaves as expected based on the logs. Internally, `new_fingerprint` is needed to inject the calculated fingerprint into a method by the `fingerprint_transform` decorator, which is then used to compute the cache file name in `Dataset._get_cache_file_path` if the user hasn't specified one. ", "Hi @lhoestq, I have cleaned up the unit test (incl. styling). It should be ready to merge as such. I am using this branch in my project and everything works fine. \r\n\r\nHi @mariosasko, the argument `new_fingerprint` allowed me to deterministically cache my transformation when using `num_proc=1`, so I assumed that was the right way to go. But maybe I have misinterpreted how `new_fingerprint` should be used.\r\n\r\nBut in any case, `map()` should perform consistently with regards to `num_proc`. In my opinion, the behaviour of `Dataset.map()` should perform the same, and this without requiring the user to input `cache_file_name` when `num_proc>1` is set.\r\nBut maybe there is a more elegant way to fix this using `cache_file_name` internally for each `_single_map()` call.\r\n\r\nSo, I think this is a more high level design decision and I will leave it to the maintainers :) ", "Hi @vlievin,\r\n\r\nI appreciate your effort, but `new_fingerprint` behaves as described in the `Dataset.map` docs, and we don't have to follow some artificial consistency in regards to `num_proc`:\r\nhttps://github.com/huggingface/datasets/blob/adc5cec58dd15ee672016086fefdea34b3143e4f/src/datasets/arrow_dataset.py#L1962-L1963\r\n\r\nAdditionally, to compute the cache file name, you are using a private method (`dset._get_cache_file_path(new_fingerprint)`); prefixed with `_`), so this is a sign you may be doing something wrong because you are relying on the internals. I suggest you use cache_file_name instead and follow the suffix template docs, which explain how to compute file paths of the created cache files when `num_proc > 1`.", "Hi @mariosasko, thanks for the pointer regarding the use of the private method in then unit tests. \r\n\r\nYes, `new_fingerprint` behaves as documented. If you don't think this is an issue, feel free to close this pull request. \r\n", "Allowing the users to pass the fingerprint themselves for functions that can't be hashed would be a nice improvements. However I agree that as @mariosasko mentioned this is currently not how we want the API to behave for now - since it has to do with the internals of the library.\r\n\r\nThough we can discuss what could be the right way of doing it in https://github.com/huggingface/datasets/issues/3044 if you don't mind !" ]

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[ "Following the discussion in #3045 if would be nice to have a way to let users have a nice experience with caching even if the function is not hashable.\r\n\r\nCurrently a workaround is to make the function picklable. This can be done by implementing a callable class instead, that can be pickled using by implementing a custom `__getstate__` method for example.\r\n\r\nHowever it sounds pretty complicated for a simple thing. Maybe one idea would be to have something similar to streamlit: they allow users to register the hashing of their own objects.\r\n\r\nSee the documentation about their `hash_funcs` here: https://docs.streamlit.io/library/advanced-features/caching#the-hash_funcs-parameter\r\n\r\nHere is the example they give:\r\n\r\n```python\r\nclass FileReference:\r\n def __init__(self, filename):\r\n self.filename = filename\r\n\r\ndef hash_file_reference(file_reference):\r\n filename = file_reference.filename\r\n return (filename, os.path.getmtime(filename))\r\n\r\[email protected](hash_funcs={FileReference: hash_file_reference})\r\ndef func(file_reference):\r\n ...\r\n```", "My solution was to generate a custom hash, and use the hash as a `new_fingerprint` argument to the `map()` method to enable caching. This works, but is quite hacky.\r\n\r\n@lhoestq, this approach is very neat, this would make the whole caching mechanic more explicit. I don't have so much time to look into this right now, but I might give it a try in the future. ", "Almost a year later and I'm in a similar boat. Using custom fingerprints and when using multiprocessing the cached datasets are saved with a template at the end of the filename (something like \"000001_of_000008\" for every process of num_proc). So if in the next time you run the script you set num_proc to a different number, the cache cannot be used.\r\n\r\nIs there any way to get around this? I am processing a huge dataset so I do the processing on one machine and then transfer the processed data to another in its cache dir but currently that's not possible due to num_proc mismatch. " ]

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[ "@lhoestq @albertvillanova Iwas trying to fix the failing tests in circleCI but is there a test elasticsearch instance somewhere? If not, can I launch a docker container to have one?" ]

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[ "I have an error on windows:\r\n```python\r\naiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]\r\n```\r\nat the `fsspec` call in `xglob`:\r\n```python\r\nfs, *_ = fsspec.get_fs_token_paths(urlpath, storage_options=storage_options)\r\n```\r\n\r\nLooks like the windows CI has an SSL issue... ", "I can reproduce it on my windows machine. On linux it works fine though", "I'm just skipping the windows test for now", "The Windows CI failure seems unrelated to this PR\r\n```python\r\nERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3\r\n```" ]

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[ "Hi,\r\n\r\nthe `save_to_disk` docstring explains that `flatten_indices` has to be called on a dataset before saving it to save only the shard/slice of the dataset.", "That works! Thansk!\r\n\r\nMight be worth doing that automatically actually in case the `save_to_disk` is called on a dataset that has an indices mapping :-)", "I agree with @patrickvonplaten: this issue is reported recurrently, so better if we implement the `.flatten_indices()` automatically?", "That would be great indeed - I don't really see a use case where one would not like to call `.flatten_indices()` before calling `save_to_disk`", "+1 on this !" ]

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[ "It would be nice to protect the master from direct commits, but still having a way to merge our own PRs when no review is required (for example when updating a dataset_infos.json file, or minor bug fixes - things that happen quite often actually).\r\nDo you know if there's a way ?", "you can if you're an admin of the repo", "This is done. Now the master branch is protected:\r\n- [x] Require a pull request before merging: all commits must be made to a non-protected branch and submitted via a pull request\r\n - Required number of approvals before merging: 1 \r\n- [x] Require linear history: prevent merge commits from being pushed\r\n- [x] These requirements are not enforced for administrators\r\n- [x] Additionally, the master branch is also protected against deletion and force pushes\r\n\r\nCC: @lhoestq @julien-c @thomwolf " ]

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[ "Hi ! This is not a bug, this is simply not implemented.\r\n`save_to_disk` is for on-disk serialization and was not made compatible for the Hub.\r\nThat being said, I agree we actually should make it work with the Hub x)", "cc @LysandreJik maybe we can solve this at the same time as adding `push_to_hub`" ]

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[ "We'll do a release tomorrow or on wednesday to make the fix available :)\r\n\r\nThanks for reproting !" ]

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[ "Could you add this function to the documentation please ?\r\n\r\nYou can add it in `main_classes.rst`, and maybe add a `Tip` section in the `map` section in the `process.rst`" ]

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[ "Thanks ! That looks all good :)\r\n\r\nI don't think that batching would help. Indeed we're dealing with python iterators that yield elements one by one, so batched `map` needs to accumulate a batch, apply the function, and then yield examples from the batch.\r\n\r\nThough once we have parallel processing in `map`, we can reconsider it\r\n\r\nAlso feel free to check the CI failure - apparently the import of `Union` is missing", "Thanks for the review and explaining that! \r\nOn top of what you said, I think `remove_columns` is very unlikely to be a bottleneck, so it doesn't matter anyways.", "Thank you for reviewing! @mariosasko \r\n\r\nI wonder how the checking would work. Is there any checking present in `IterableDataset ` now? What if `.remove_columns()` is applied after some arbitrary `.map()`?", "> I wonder how the checking would work. Is there any checking present in IterableDataset now? What if .remove_columns() is applied after some arbitrary .map()?\r\n\r\nThat's the challenge here indeed ^^ In this case it's not trivial to know the names of the columns. Feel free to open an issue so we can discuss this" ]

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[ "I had to run datasets-cli with --ignore_verifications the first time since it was complaining about a missing file, but now it runs without that flag fine. I moved dummy_data.zip to the new folder, but also had to modify the filename of the test file in the zip (should I not have done that?). Finally, I added the pretty name tag.", "Great, thanks for the help." ]

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[ "@lhoestq I would say this does not \"properly\" install `ruamel-yaml`, but the contrary, you overwrite the previous version without desinstalling it first.\r\n\r\nAccording to `pip` docs:\r\n> This can break your system if the existing package is of a different version or was installed with a different package manager!\r\n\r\nNote that our case fulfills both conditions:\r\n- the installing version (`0.17.16`) is different from the existing one (`0.15.87`)\r\n- you are installing using `pip` (`setuptools`), whereas the exisitng version was installed using `distutils`\r\n\r\nThat is why I did not fix the issue this way, made a hotfix pinning `huggingface_hub` (#3025), while looking for a permanent solution for the issue.", "Yea I did this because we need the latest version of `huggingface_hub` for #2986 and because I didn't want to ssh to the windows worker x)\r\nMaybe it can be fixed by installing it with conda - let me try", "Oh yea it may not work since it was first installed with distutils" ]

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[ "Awesome @lhoestq I like the proposal and it works great on my JSON community dataset. Here is the [log](https://gist.github.com/vblagoje/714babc325bcbdd5de579fd8e1648892). ", "From my discussion with @borisdayma it would be more general the files match if their paths contains the split name - not only if the filename contains the split name. For example for a dataset like this:\r\n```\r\ntrain/\r\n└── data.csv\r\ntest/\r\n└── data.csv\r\n```\r\n\r\nBut IMO the default should be \r\n```\r\ndata/\r\n├── train.csv\r\n└── test.csv\r\n```\r\nbecause it allows people to have other directories if they have different subsets of their data (different configurations, not splits)", "I just created a PR for this at https://github.com/huggingface/datasets/pull/3221, let me know what you think :)" ]

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[ "@lhoestq I'm a bit confused by the error message. I haven't touched the YAML code at all - do you have any insight on that?", "I just added the missing `pretty_name` tag in the YAML - sorry about that ;)", "Thanks! Seems like it did the trick since the tests are passing. Let me know if there's anything else I can do in this PR!", "It's all good thank you :)\r\n\r\nmerging !" ]

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[ "I believe the only failed test related to this PR is tests/test_metric_common.py::LocalMetricTest::test_load_metric_competition_math. It gives the following error:\r\n\r\nImportError: To be able to use this dataset, you need to install the following dependencies['math_equivalence'] using 'pip install git+https://github.com/hendrycks/math.git' for instance'\r\n\r\nIt fails along with (these fail with ImportError as well):\r\ntest_load_metric_bertscore\r\ntest_load_metric_bleurt\r\ntest_load_metric_comet\r\ntest_load_metric_coval\r\n\r\nLet me know if there is anything I need to change.", "Hi ! The script looks all good thanks :)\r\n\r\nTo fix the CI you just need to merge `master` into your branch\r\n```\r\ngit fetch upstream/master\r\ngit merge upstream/master\r\n```\r\n\r\nThen you also need to add `math_equivalence` to the list of git packages installed for the tests in `additional-tests-requirements.txt`\r\nhttps://github.com/huggingface/datasets/blob/ba831e4bcd175ae3d52afbf7d12c4f625bf541b0/additional-tests-requirements.txt#L1-L3", "I ran:\r\n\r\ngit fetch upstream\r\ngit merge upstream/master\r\n\r\nAnd I also added math_equivalence to the list of git packages installed for the tests in additional-tests-requirements.txt\r\n\r\ntests/test_metric_common.py fails with the same errors as before. tests/test_dataset_cards.py also fails, but it doesn't look related to this PR (it's an issue datasets/ami/README.md).", "@lhoestq Anything else I can do? I re-merged again and am getting the same test failures as described in the previous comment." ]

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[ "@lhoestq I would like to draw your attention to the proposed API by @lewtun, using `data_dir` to pass the ZIP URL.\r\n\r\nI'm not totally convinced with this... What do you think?\r\n\r\nMaybe we could discuss other approaches...\r\n\r\nOne brainstorming idea: what about using URL chaining with the hop operator in `data_files`?", "`data_dir` is currently exclusively used for manually downloaded data.\r\n\r\nMaybe we can have an API that only uses data_files as you are suggesting, using URL chaining ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https://domain.org/filename.zip\"\r\ndata_files = {\"train\": \"zip://train_filename.csv::\" + url, \"test\": \"zip://test_filename.csv::\" + url}\r\ndataset = load_dataset(\"csv\", data_files=data_files)\r\n```\r\n\r\nURL chaining is used by `fsspec` to get access to files in nested filesystems of any kind. Since `fsspec` is being used by `pandas`, `dask` and also extensively by `datasets` I think it would be nice to use it here too", "URL chaining sounds super nice to me! And it's also a nice way to leverage the same concepts we currently have in the docs around `fsspec` :)" ]

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[ "To keeps things simple maybe we should use `load_dataset_builder` in `get_dataset_infos`.\r\n`load_dataset_builder` instantiates a builder and runs the _infos() method in order to give you the most up-to-date infos, even if the dataset_infos.json is outdated or missing." ]

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[ "Yes I think it should raise an error. Currently it looks like it instantiates a custom configuration with the name given by the user:\r\nhttps://github.com/huggingface/datasets/blob/ba27ce33bf568374cf23a07669fdd875b5718bc2/src/datasets/builder.py#L391-L397" ]

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[ "### Update:\r\nI wrote a bit cleaner code snippet (without transforming to json) that can expose leaking.\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = ['ab', 'c', 'df']\r\n\r\nds = datasets.Dataset.from_dict({'col': items})\r\nprint(list(ds))\r\n# > Prints: [{'col': 'ab'}, {'col': 'c'}, {'col': 'df'}]\r\n\r\nfiltered = ds\r\n\r\n# get all items that are starting with a character with ascii code bigger than 'a'\r\nfiltered = filtered.filter(lambda x: x['col'][0] > 'a', load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'c'}, {'col': 'df'}] as expected\r\n\r\n# get all items that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(x['col']) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'ab'}] -> this is a leaked item from the first filter\r\n# > Should be: [{'col': 'c'}]\r\n```", "Thanks for reporting. I'm looking into it", "I just pushed a fix ! We'll do a new release soon.\r\nIn the meantime feel free to install `datasets` from source to play with it", "Thanks, I'm already using it from your branch!" ]

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[ "Hi @DrMatters, thanks for reporting.\r\n\r\nThis issue was fixed 14 days ago: #2950.\r\n\r\nCurrently, the fix is only in the master branch and will be made available in our next library release.\r\n\r\nIn the meantime, you can incorporate the fix by installing datasets from the master branch:\r\n```shell\r\npip install -U git+ssh://[email protected]/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```shell\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```", "Thanks, sorry for bothering" ]

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[ "Please wait until Tuesday. Arxiv pre-print is pending. 🤗 ", "Hi @lhoestq, I updated the README with the Arxiv publication info and now the tests are not passing.\r\n\r\nIt seems that the error is completely irrelevant to my code:\r\n\r\n```\r\n Attempting uninstall: ruamel.yaml\r\n Found existing installation: ruamel-yaml 0.15.87\r\nERROR: Cannot uninstall 'ruamel-yaml'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.\r\n```", "Hi ! Thanks for adding this one. Amazing work :o \r\n\r\nYea you can ignore the `ruamel-yaml` error, it's unrelated to your PR and fixed on `master`", "Hi @lhoestq, \r\n\r\n- I fetched and merged the upstream master to get the `ruamel-yaml` fix.\r\n- I updated the README.md file including more information for the inputs and labels, while I also turned all tables in HTML format hoping that they will render nicely in the dataset card in the HF website.\r\n- I also simplified the CaseHOLD input, excl. the unused `question` field and the `context` replicas, as suggested.\r\n" ]

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[ "@lhoestq The test passes even without the try/except block!", "Hey, I'm a little late because I was caught up in the course work, but I double-checked this and it looks great. Thanks for fixing!" ]

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