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"2023-01-20T10:05:19"

"2023-01-20T10:43:22"

"2023-01-20T10:13:48"

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"2023-01-20T10:03:10"

"2023-01-20T10:28:44"

"2023-01-20T10:28:44"

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"2023-01-20T01:09:14"

"2023-01-20T15:44:45"

"2023-01-20T15:37:30"

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[ "Hi! \r\n\r\nWe use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).\r\n\r\n[`gdrivefs`](https://github.com/fsspec/gdrivefs) makes it possible to use Google Drive as a storage service in Datasets, but this is not the case for OneDrive, since its[ Python SDK](https://github.com/OneDrive/onedrive-sdk-python) is not integrated with `fsspec`. Can you please request the integration with `fsspec` in their repo to address this limitation?", "I'm closing this issue as implementing a fsspec-compliant OneDrive filesystem is not our responsibility." ]

"2023-01-19T23:12:08"

"2023-02-24T16:17:51"

"2023-02-24T16:17:51"

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"2023-01-18T17:04:27"

"2023-01-18T17:57:29"

"2023-01-18T17:50:04"

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"2023-01-18T06:53:15"

"2023-01-18T13:49:51"

"2023-01-18T13:42:49"

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[ "Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\r\n\r\n", "> Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\n> \n> \n\nI have only 1 folder that I use in the load_dataset function with the name \"IMGDATA\" and all my 9000 images are located in this folder.\n`\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"IMGDATA\")\n`\nAt the same time, using another data set with images consisting of 3 RGB channels, everything works", "Okay, I figured out what was wrong. When uploading my dataset via Google Drive, the images broke and Pillow couldn't open them. As a result, I solved the problem by downloading the ZIP archive" ]

"2023-01-17T18:22:27"

"2023-01-18T20:20:15"

"2023-01-18T20:20:15"

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"2023-01-17T15:59:50"

"2023-01-18T09:05:49"

"2023-01-18T06:29:06"

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[ "Just for your information, Tensorflow confirmed this issue [here.](https://github.com/tensorflow/tensorflow/issues/59279)", "Thanks for reporting, @HaoyuYang59.\r\n\r\nPlease note that these are different \"dataset\" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Dataset`.\r\n\r\nOur `datasets.Dataset.shuffle` method does not have a `reshuffle_each_iteration` argument. Therefore, I would say the statement in our docs is True because they refer to `datasets.Dataset.shuffle`, `datasets.Dataset.skip` and `datasets.Dataset.take`.\r\n\r\nI think this issue is restricted to TensorFlow dataset, and this would be addressed by them in the issue you opened in their repo: https://github.com/tensorflow/tensorflow/issues/59279", "Also note that you are referring to an outdated documentation page: datasets 1.10.2 version\r\n\r\nCurrent datasets version is 2.8.0 and the corresponding documentation page is: https://huggingface.co/docs/datasets/stream#split-dataset", "Hi @albertvillanova thanks for your reply and your explaination here. \r\n\r\nSorry for the confusion as I'm not actually a user of your repo and I just happen to find the thread by Google (and didn't read carefully).\r\n\r\nGreat to know that and you made everything very clear now.\r\n\r\nThanks for your time and sorry for the consusion.\r\n\r\nWishing you a wonderful time. \r\n\r\nRegards" ]

"2023-01-17T10:04:16"

"2023-01-19T09:56:03"

"2023-01-19T09:56:03"

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[ "Hi! Can you describe what the actual error is?", "working on the setfit example script\r\n\r\n from setfit import SetFitModel, SetFitTrainer, sample_dataset\r\n\r\nImportError: cannot import name 'sample_dataset' from 'setfit' (C:\\Python\\Python38\\lib\\site-packages\\setfit\\__init__.py)\r\n\r\n apart from that, I also had to hack these loads to import thses modules:\r\n from datasets.load import load_dataset \r\n from datasets.arrow_dataset import Dataset\r\n from datasets.dataset_dict import DatasetDict", "Hi! This issue is related to the [SetFit](https://github.com/huggingface/setfit) project, so can you please open it there?" ]

"2023-01-17T09:57:54"

"2023-01-19T13:52:12"

"2023-01-19T07:55:11"

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[ "Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).\r\n\r\n[^1]: More or less, see https://github.com/yargs/yargs/issues/873.", "Opened https://github.com/huggingface/datasets/pull/5436 unpinning again the container image.", "Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!\r\n\r\nWe are reviewing your PR to unpin the container image." ]

"2023-01-17T09:06:08"

"2023-01-18T06:29:08"

"2023-01-18T06:29:08"

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"2023-01-17T07:15:31"

"2023-01-17T08:58:22"

"2023-01-17T08:51:17"

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"2023-01-17T06:49:57"

"2023-01-18T06:33:24"

"2023-01-17T08:51:18"

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"2023-01-16T16:20:09"

"2023-01-16T16:51:42"

"2023-01-16T16:49:03"

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[ "Thanks for reporting, @ilos-vigil.\r\n\r\nWe have transferred this issue to the corresponding dataset on the Hugging Face Hub: https://huggingface.co/datasets/id_clickbait/discussions/1 " ]

"2023-01-16T16:05:36"

"2023-01-18T09:51:28"

"2023-01-18T09:25:19"

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"2023-01-16T16:02:07"

"2023-01-17T07:17:12"

"2023-01-16T16:49:04"

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[ "Hi! \r\n\r\n`Dataset.sort` calls `df.sort_values` internally, and `df.sort_values` brings all the \"sort\" columns in memory, so sorting on multiple keys could be very expensive. This makes me think that maybe we can replace `df.sort_values` with `pyarrow.compute.sort_indices` - the latter can also sort on multiple keys and currently loads the data into memory; however, there is a plan to eventually implement \"memory-map\" friendly kernels for the Arrow compute ops (using the Acero execution engine). \r\n\r\nSo to address this issue, you should replace `df.sort_values` with `pyarrow.compute.sort_indices` in `Dataset.sort` and adjust the signature of this function (deprecate the `kind` parameter, etc.).\r\n\r\nPS: Feel free to ping us if you need some additional help/pointers", "@mariosasko If I understand the code right, using `pyarrow.compute.sort_indices` would also require changes to the `select` method if it is meant to sort multiple keys. That's because `select` only accepts 1D input for `indices`, not an iterable or similar which would be required for multiple keys unless you want some looping over selects. Doesn't seem that straight-forward but I might be missing something here... ", "@MichlF No, it doesn't require modifying select because sorting on multiple keys also returns a 1D array.\r\n\r\nIt's easier to understand with an example:\r\n```python\r\n>>> import pyarrow as pa\r\n>>> import pyarrow.compute as pc\r\n>>> table = pa.table({\r\n... \"name\": [\"John\", \"Eve\", \"Peter\", \"John\"],\r\n... \"surname\": [\"Johnson\", \"Smith\", \"Smith\", \"Doe\"],\r\n... \"age\": [20, 40, 30, 50],\r\n... })\r\n>>> indices = pc.sort_indices(table, sort_keys=[(\"name\", \"ascending\"), (\"surname\", \"ascending\")])\r\n>>> print(indices)\r\n[\r\n 1,\r\n 3,\r\n 0,\r\n 2\r\n]\r\n```\r\n\r\n", "Thanks for clarifying.\r\nI can prepare a PR to address this issue. This would be my first PR here so I have a few maybe silly questions but:\r\n- What is the preferred input type of `sort_keys` for the sort method? A sequence with name, order tuples like pyarrow's `sort_indices` requires?\r\n- What about backwards compatability: is it supposed to also accept the old way of calling sort() or should both `column` and `kind` be deprecated?\r\n- If `sort_keys` is provided in the same format as for pyarrow's `sort_indices` - i.e. along with order for each column -, `reverse` doesn't make much sense either and should be deprecated as well I assume.", "I think we can have the following signature:\r\n```python\r\ndef sort(\r\n self,\r\n column_names: Union[str, Sequence[str]],\r\n reverse: Union[bool, Sequence[bool]] = False,\r\n kind=\"deprecated\",\r\n null_placement: str = \"last\",\r\n keep_in_memory: bool = False,\r\n load_from_cache_file: bool = True,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n ) -> \"Dataset\":\r\n``` \r\n\r\nSo we should:\r\n* rename`column` to `column_names`. `column` is a positional argument, so it's OK to rename it (not marked as positional-only with \"/\", but still should be fine)\r\n* deprecate `kind`\r\n* keep `reverse` instead of introducing `sort_keys`, but we should allow passing a list of booleans that defines the sort order of each column from `column_names` to it (`reverse = False` would be equal to `[False] * len(column_names)` and `reverse = True` to `[True] * len(column_names)`)", "I am pretty much done with the PR. Just one clarification: `Sequence` in `arrow_dataset.py` is a custom dataclass from `features.py` instead of the `type.hinting` class `Sequence` from Python. Do you suggest using that custom `Sequence` class somehow ? Otherwise signature currently reads instead:\r\n```Python\r\n def sort(\r\n self,\r\n column_names: Union[str, List[str]],\r\n reverse: Union[bool, List[bool]] = False,\r\n kind = \"deprecated\",\r\n null_placement: str = \"last\",\r\n keep_in_memory: bool = False,\r\n load_from_cache_file: bool = True,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n )\r\n```\r\n\r\nAlso, to maintain backwards compatibility, I added conditionals for `null_placement`, because pyarrow's `null_placement` only accepts `at_start` and `at_end`, and not `last` and `first`.\r\nIf that is all good, I think I can open the PR.", "I meant `typing.Sequence` (`datasets.Sequence` is a feature type). \r\n\r\nRegarding `null_placement`, I think we can support both `at_start` and `at_end`, and `last` and `first` (for backward compatibility; convert internally to `at_end` and `at_start` respectively).", "> I meant typing.Sequence (datasets.Sequence is a feature type).\r\n\r\nSorry, I actually meant `typing.Sequence` and not `type.hinting`. However, the issue is still that `dataset.Sequence` is imported in `arrow_dataset.py` so I cannot import and use `typing.Sequence` for the `sort`'s signature without overwriting the `dataset.Sequence` import. The latter is used in the `align_labels_with_mapping` method so it's a necessary import for `arrow_dataset.py`. \r\nTo import `typing.Sequence` as something else than `Sequence` to avoid overwriting may only be confusing and doesn't seem good practice!? The other solution is to keep `List` type hinting as in the signature I posted in my previous post but this excludes other Sequence types and may cause problems further down the line.\r\nPlease advise,\r\nThanks for all the clarifications!", "You can avoid the name collision by renaming `typing.Sequence` to `Sequence_` when importing:\r\n```python\r\nfrom typing import Sequence as Sequence_\r\n```", "Resolved via #5502 " ]

"2023-01-16T09:22:26"

"2023-02-24T16:15:11"

"2023-02-24T16:15:11"

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[ "Hi! You can get a `DatasetDict` if you pass a dictionary with read instructions as follows:\r\n```python\r\ninstructions = [\r\n ReadInstruction(split_name=\"train\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"dev\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"test\", from_=0, to=5, unit='%', rounding='closest')\r\n]\r\n\r\ndataset = load_dataset('csv', data_dir=\"data/\", data_files={\"train\":\"train.tsv\", \"dev\":\"dev.tsv\", \"test\":\"test.tsv\"}, delimiter=\"\\t\", split={inst.split_name: inst for inst in instructions})\r\n```\r\n" ]

"2023-01-16T06:54:28"

"2023-02-24T16:19:00"

"2023-02-24T16:19:00"

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[ "Closing as case-insensitivity should be only for URL redirection on the Hub. In the APIs, we will only support the canonical name (https://github.com/huggingface/moon-landing/pull/2399#issuecomment-1382085611)" ]

"2023-01-13T13:07:07"

"2023-01-13T20:12:32"

"2023-01-13T20:12:32"

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"2023-01-13T12:58:43"

"2023-01-13T13:36:00"

"2023-01-13T13:29:01"

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[ "Thanks for your proposal, @zanussbaum. Yes, I agree that would definitely be a nice feature to have!", "@albertvillanova I’m happy to make a quick PR for the feature! let me know ", "That would be awesome ! You can comment `#self-assign` to assign you to this issue and open a PR :) Will be happy to review", "Closing as this has been merged @lhoestq " ]

"2023-01-12T05:06:20"

"2023-01-24T18:18:24"

"2023-01-24T18:18:24"

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"2023-01-10T08:43:19"

"2023-01-18T17:12:17"

"2023-01-18T14:09:02"

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"2023-01-10T07:36:11"

"2023-01-18T14:09:04"

"2023-01-18T14:09:03"

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[ "Thanks for reporting, @Nithin-Holla.\r\n\r\nThis is a known issue for multiple datasets and we are investigating it:\r\n- See e.g.: https://huggingface.co/datasets/ami/discussions/3", "Main issue:\r\n- #5415", "@albertvillanova Thanks! As a workaround for now, can I use the dataset in streaming mode?", "Yes, @Nithin-Holla, in the meantime you can use this dataset in streaming mode." ]

"2023-01-09T14:45:31"

"2023-01-18T14:09:04"

"2023-01-18T14:09:04"

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[ "Hi ! Thanks for reporting :)\r\n\r\nI managed to reproduce the hub using\r\n```python\r\n\r\nfrom datasets import concatenate_datasets, Dataset, load_from_disk\r\n\r\nDataset.from_dict({\"a\": range(9)}).save_to_disk(\"tmp/ds1\")\r\nds1 = load_from_disk(\"tmp/ds1\")\r\nds1 = concatenate_datasets([ds1, ds1])\r\n\r\nDataset.from_dict({\"b\": range(6)}).save_to_disk(\"tmp/ds2\")\r\nds2 = load_from_disk(\"tmp/ds2\")\r\nds2 = concatenate_datasets([ds2, ds2, ds2])\r\n\r\nconcatenate_datasets([ds1, ds2], axis=1)\r\n```\r\nand I get\r\n```python\r\nTraceback (most recent call last): \r\n File \"test.py\", line 98, in <module>\r\n dds = concatenate_datasets([ds1, ds2], axis=1)\r\n File \"/Users/.../datasets/combine.py\", line 182, in concatenate_datasets\r\n return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)\r\n File \"/Users/.../datasets/arrow_dataset.py\", line 5499, in _concatenate_map_style_datasets\r\n table = concat_tables([dset._data for dset in dsets], axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1778, in concat_tables\r\n return ConcatenationTable.from_tables(tables, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1483, in from_tables\r\n blocks = _extend_blocks(blocks, table_blocks, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1477, in _extend_blocks\r\n result[i].extend(row_blocks)\r\nIndexError: list index out of range\r\n```\r\n\r\nIt appears to happen when the two datasets have a number of shards that is not the same" ]

"2023-01-08T17:01:52"

"2023-01-26T09:27:21"

"2023-01-26T09:27:21"

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[ "Hi ! It fails because the dataset is already being prepared by your first run. I'd encourage you to prepare your dataset before using it for multiple trainings.\r\n\r\nYou can also specify another cache directory by passing `cache_dir=` to `load_dataset()`.", "Thank you! What do you mean by prepare it beforehand? I am unclear how to conduct dataset preparation outside of using the `load_dataset` function.", "You can have a separate script that does load_dataset + map + save_to_disk to save your prepared dataset somewhere. Then in your training script you can reload the dataset with load_from_disk", "Thank you! I believe I was running additional map steps after loading, resulting in the cache conflict. " ]

"2023-01-08T00:44:32"

"2023-01-19T20:28:43"

"2023-01-19T20:28:43"

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"2023-01-06T23:19:17"

"2023-01-18T11:18:59"

"2023-01-18T11:12:04"

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"2023-01-05T18:12:17"

"2023-02-01T18:11:45"

"2023-02-01T16:36:01"

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"2023-01-05T09:10:58"

"2023-01-06T11:06:16"

"2023-01-06T10:59:13"

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[ "Hi ! On macos I tried with\r\n- py 3.9.11\r\n- datasets 2.8.0\r\n- transformers 4.25.1\r\n- dill 0.3.4\r\n\r\nand I was able to hash `prepare_dataset` correctly:\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\nHasher.hash(prepare_dataset)\r\n```\r\n\r\nWhat version of transformers do you have ? Can you try to call `Hasher.hash` on the the tokenizer and the feature extractor to see which one can't be hashed ?", "Thanks for your prompt reply.\r\n\r\nI update datasets version to 2.8.0 and the warning is gong." ]

"2023-01-05T01:59:59"

"2023-01-06T13:22:19"

"2023-01-06T13:22:18"

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[ "Thanks for reporting @msummerfield. We are fixing it." ]

"2023-01-05T00:43:17"

"2023-01-06T10:59:14"

"2023-01-06T10:59:14"

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"2023-01-03T14:26:32"

"2023-01-03T15:06:18"

"2023-01-03T14:59:01"

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"2023-01-03T07:42:37"

"2023-01-06T10:42:44"

"2023-01-06T10:35:44"

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"2023-01-01T13:00:11"

"2023-01-02T07:21:52"

"2023-01-02T07:21:52"

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"2022-12-30T10:37:31"

"2022-12-30T10:43:41"

"2022-12-30T10:43:41"

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"2022-12-30T10:22:09"

"2022-12-30T10:53:11"

"2022-12-30T10:43:40"

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"2022-12-29T19:45:17"

"2023-02-13T11:11:22"

"2023-02-13T11:11:22"

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"2022-12-29T19:34:19"

"2022-12-30T06:36:57"

"2022-12-29T21:00:26"

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[ "I still getting the same error :\r\n\r\n`python -m spacy download fr_core_news_lg\r\n`.\r\n`import spacy`", "@MFatnassi, this issue and the corresponding fix only affect our Continuous Integration testing environment.\r\n\r\nNote that `datasets` does not depend on `spacy`." ]

"2022-12-29T18:58:44"

"2022-12-30T10:40:51"

"2022-12-29T21:00:27"

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"2022-12-28T15:33:17"

"2023-01-18T12:42:33"

"2023-01-18T12:35:32"

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"2022-12-28T11:44:53"

"2023-01-03T15:36:14"

"2023-01-03T15:27:31"

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[ "Hmmm, git bisect tells me that the behavior is the same since https://github.com/huggingface/datasets/commit/67e65c90e9490810b89ee140da11fdd13c356c9c (3 Oct), i.e. https://github.com/huggingface/datasets/pull/4926", "Maybe related to the discussions in https://github.com/huggingface/datasets/pull/5196", "Maybe the current version of moonlanding in Hub CI is the issue.\r\n\r\nI relaunched tests that were working two days ago: now they are failing. https://github.com/huggingface/datasets-server/commit/746414449cae4b311733f8a76e5b3b4ca73b38a9 for example\r\n\r\ncc @huggingface/moon-landing ", "Hi! I don't think this has anything to do with `datasets`. Hub CI seems to be the culprit - the identical failure can be found in [this](https://github.com/huggingface/datasets/pull/5389) PR (with unrelated changes) opened today.", "OK! Thanks for looking at it. Closing then." ]

"2022-12-23T13:36:37"

"2022-12-23T20:29:02"

"2022-12-23T20:29:02"

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"2022-12-23T13:26:31"

"2023-01-25T19:00:43"

"2023-01-24T16:33:38"

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[ "Hi! I can't reproduce this error locally (Mac) or in Colab. What version of `datasets` are you using?", "Hi [mariosasko](https://github.com/mariosasko), the datasets version is '2.8.0'.", "@valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n\r\nPlease, restart your Colab runtime and execute the install commands before importing `datasets`:\r\n```python\r\n!pip install datasets\r\n!pip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\r\n \"json\",\r\n data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n split=\"train\",\r\n streaming=True,\r\n)\r\nnext(iter(ds))\r\n```", "> @valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n> \r\n> Please, restart your Colab runtime and execute the install commands before importing `datasets`:\r\n> \r\n> ```python\r\n> !pip install datasets\r\n> !pip install zstandard\r\n> \r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\r\n> \"json\",\r\n> data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n> split=\"train\",\r\n> streaming=True,\r\n> )\r\n> next(iter(ds))\r\n> ```\r\n\r\nI guess that was the problem, importing datasets before the installation of zstandard. Thank you for the feedback. " ]

"2022-12-23T08:16:43"

"2022-12-29T08:36:33"

"2022-12-27T17:59:09"

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[ "Hi! Our documentation builder does not support links to sections, hence the bug. This is the link it should point to https://huggingface.co/docs/datasets/v2.8.0/en/cache#improve-performance." ]

"2022-12-23T01:12:57"

"2023-01-24T16:33:40"

"2023-01-24T16:33:40"

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[ "Hi! \r\n\r\nThis behavior stems from the fact that we don't always embed image bytes in the underlying arrow table, which can lead to bad size estimation (we use the first 1000 table rows to [estimate](https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L4627) the external file size). We plan to address this in the next major release by always embedding external bytes. In the meantime, you can either shuffle the dataset with `.shuffle().flatten_indices()` to make the estimation more precise or embed the bytes in the table like so:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\nformat = ds.format\r\nds = ds.with_format(\"arrow\")\r\nds = ds.map(embed_table_storage, batched=True)\r\nds = ds.with_format(**format)\r\n...\r\nds.push_to_hub(...)\r\n```", "Embedding the bytes worked like charm. Thanks @mariosasko!" ]

"2022-12-22T21:50:58"

"2022-12-26T23:45:51"

"2022-12-26T23:45:51"

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[ "Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ", "> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywhere else where fs is an arg.", "Closed by #5393." ]

"2022-12-22T21:00:45"

"2023-01-23T10:50:05"

"2023-01-23T10:50:04"

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"2022-12-22T16:15:30"

"2023-01-13T16:10:15"

"2023-01-13T16:00:52"

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"2022-12-20T15:52:44"

"2023-01-26T09:51:13"

"2023-01-26T09:42:45"

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[ "Hi! This error can happen if there is a local file/folder with the same name as the requested dataset. And to avoid it, rename the local file/folder.\r\n\r\nSoon, it will be possible to explicitly request a Hub dataset as follows:https://github.com/huggingface/datasets/issues/5228#issuecomment-1313494020" ]

"2022-12-20T12:40:14"

"2023-02-15T16:24:57"

"2023-02-15T16:24:57"

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"2022-12-20T05:32:11"

"2023-01-13T12:30:31"

"2023-01-13T12:23:34"

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[ "Thanks for reporting @shaoyuta. We are investigating it.\r\n\r\nWe are transferring the issue to \"the_pile\" Community tab on the Hub: https://huggingface.co/datasets/the_pile/discussions/4" ]

"2022-12-20T02:19:13"

"2022-12-20T07:52:54"

"2022-12-20T07:52:54"

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"2022-12-19T19:40:27"

"2023-01-25T16:28:44"

"2023-01-25T16:21:40"

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"2022-12-19T10:56:56"

"2022-12-19T11:01:55"

"2022-12-19T10:57:16"

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"2022-12-19T10:48:26"

"2022-12-19T10:55:43"

"2022-12-19T10:53:15"

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"2022-12-17T17:23:52"

"2022-12-18T21:43:31"

"2022-12-18T21:40:21"

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[ "Thanks for reporting, @shaoyuta.\r\n\r\nWe have checked and yes, apparently there is an issue with the server hosting the data of the \"enron_emails\" subset of \"the_pile\" dataset: http://eaidata.bmk.sh/data/enron_emails.jsonl.zst\r\nIt seems to be down: The connection has timed out.\r\n\r\nPlease note that at the Hugging Face Hub, we are not hosting their data for this dataset, but only a script that downloads the data from their servers. We are updating the data URL to one in another server.\r\n\r\nIn the meantime, please note that you can train your model in the entire \"the_pile\" dataset, by passing the \"all\" config (instead of the \"enron_emails\" one).", "We have transferred this issue to the corresponding dataset Community tab: https://huggingface.co/datasets/the_pile/discussions/2\r\n\r\nPlease, follow the updates there." ]

"2022-12-15T01:23:03"

"2022-12-15T07:45:54"

"2022-12-15T07:45:53"

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[ "If you use huggingface trainer, you will find the trainer has wrapped a `IterableDatasetShard` to avoid duplication.\r\nSee:\r\nhttps://github.com/huggingface/transformers/blob/dfd818420dcbad68e05a502495cf666d338b2bfb/src/transformers/trainer.py#L835\r\n", "If you want to support it by datasets natively, maybe we also need to change the code in `transformers` ?", "Opened https://github.com/huggingface/transformers/issues/20770 to discuss this :)", "Maybe something like this then ?\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\nFor map-style datasets the implementation is trivial (it can simply use `.shard()`).\r\n\r\nFor iterable datasets we would need to implement a new ExamplesIterable that would only iterate on a subset of the (possibly shuffled and re-shuffled after each epoch) list of shards, based on the rank and world size.", "My plan is to skip examples by default to not end up with duplicates.\r\n\r\nAnd if a dataset has a number of shards that is a factor of the world size, then I'd make it more optimized by distributing the shards evenly across nodes instead.", "Opened a PR here: https://github.com/huggingface/datasets/pull/5369\r\n\r\nfeel free to play with it and share your feedbacks :)" ]

"2022-12-14T16:06:19"

"2023-01-16T13:33:33"

"2023-01-16T13:33:33"

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"2022-12-13T23:04:06"

"2022-12-22T16:35:49"

"2022-12-22T16:32:49"

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"2022-12-13T22:35:51"

"2023-01-05T16:46:31"

"2023-01-05T15:59:51"

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"2022-12-13T19:39:24"

"2023-01-04T12:45:40"

"2022-12-15T19:15:54"

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"2022-12-13T18:54:09"

"2022-12-14T17:25:58"

"2022-12-14T17:22:16"

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"2022-12-13T00:29:47"

"2022-12-13T18:17:56"

"2022-12-13T18:14:42"

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[]

[ "Just seen https://github.com/huggingface/datasets/issues/5281" ]

"2022-12-12T13:22:13"

"2022-12-12T13:37:14"

"2022-12-12T13:37:14"

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