Datasets:

marasaki

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[ "I don't think you need to generate new dummy data, since they're in the same format as the original data.\r\n\r\nThe CI is failing because of this error:\r\n```python\r\n> turn[\"names\"] = turn[\"NAMES\"]\r\nE TypeError: tuple indices must be integers or slices, not str\r\n```\r\n\r\nDo you know what could cause this ? If I understand correctly, `turn` is supposed to be a list of dictionaries right ?", "> ``` \r\n> \r\n> Do you know what could cause this ? If I understand correctly, turn is supposed to be a list of dictionaries right ?\r\n> ```\r\n\r\nThis is strange. Let me look into this. As per https://github.com/RevanthRameshkumar/CRD3/blob/master/data/aligned%20data/c%3D2/C1E001_2_0.json TURNS is a list of dictionaries. So when we iterate over `row[\"TURNS]\"` each `turn` is essentially a dictionary. Not sure why it's being considered a tuple here." ]

"2022-04-27T12:31:36Z"

"2022-04-29T12:41:41Z"

"2022-04-29T12:41:41Z"

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"2021-06-13T05:10:59Z"

"2021-06-22T09:49:04Z"

"2021-06-16T10:41:46Z"

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[ "This is because you have to specify which split corresponds to what file:\r\n```python\r\ndata_files = {\"train\": \"train/split.parquet\", \"validation\": \"validation/split.parquet\"}\r\nbrand_new_dataset_2 = load_dataset(\"ds\", data_files=data_files)\r\n```\r\n\r\nOtherwise it tries to concatenate the two splits, and it fails because they don't have the same features.\r\n\r\nIt works with save_to_disk/load_from_disk because it also stores json files that contain the information about which files goes into which split", "Wonderful, thanks for the help!", "I may be mistaken but I think the following doesn't work either:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\"./ds/train/split.parquet\")\r\nds['validation'].to_parquet(\"./ds/validation/split.parquet\")\r\n\r\ndata_files = {\"train\": \"train/split.parquet\", \"validation\": \"validation/split.parquet\"}\r\nbrand_new_dataset_2 = load_dataset(\"ds\", data_files=data_files)\r\n```", "It works on my side as soon as the directories named `ds/train` and `ds/validation` exist (otherwise it returns a FileNotFoundError). What error are you getting ?", "Also we may introduce a default mapping for the data files:\r\n```python\r\n{\r\n \"train\": [\"*train*\"],\r\n \"test\": [\"*test*\"],\r\n \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\nthis way if you name your files according to the splits you won't have to specify the data_files parameter. What do you think ?\r\n\r\nI moved this discussion to #3027 ", "I'm getting the following error:\r\n\r\n```\r\nDownloading and preparing dataset custom_squad/plain_text to /home/lysandre/.cache/huggingface/datasets/lhoestq___custom_squad)/plain_text/1.0.0/397916d1ae99584877e0fb4f5b8b6f01e66fcbbeff4d178afb30c933a8d0d93a...\r\n100%|██████████| 2/2 [00:00<00:00, 7760.04it/s]\r\n100%|██████████| 2/2 [00:00<00:00, 2020.38it/s]\r\n 0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):\r\n File \"<input>\", line 1, in <module>\r\n File \"/opt/pycharm-professional/plugins/python/helpers/pydev/_pydev_bundle/pydev_umd.py\", line 198, in runfile\r\n pydev_imports.execfile(filename, global_vars, local_vars) # execute the script\r\n File \"/opt/pycharm-professional/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py\", line 18, in execfile\r\n exec(compile(contents+\"\\n\", file, 'exec'), glob, loc)\r\n File \"/home/lysandre/.config/JetBrains/PyCharm2021.2/scratches/datasets/upload_dataset.py\", line 12, in <module>\r\n ds = load_dataset(\"lhoestq/custom_squad\")\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/load.py\", line 1207, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py\", line 823, in as_dataset\r\n datasets = utils.map_nested(\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py\", line 207, in map_nested\r\n mapped = [\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py\", line 208, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True))\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/utils/py_utils.py\", line 143, in _single_map_nested\r\n return function(data_struct)\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py\", line 854, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/builder.py\", line 924, in _as_dataset\r\n dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py\", line 217, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py\", line 238, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py\", line 173, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py\", line 308, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/arrow_reader.py\", line 327, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py\", line 458, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/home/lysandre/Workspaces/Python/datasets/src/datasets/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n pa_table = opened_stream.read_all()\r\n File \"pyarrow/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n File \"pyarrow/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n\r\nTried on current master, after updating latest dependencies and obtained the same result", "The proposal in #3027 sounds good to me!", "I just tried again on colab by installing `datasets` from source with pyarrow 3.0.0 and didn't get any error.\r\n\r\nYou error seems to happen when doing\r\n```python\r\nds = load_dataset(\"lhoestq/custom_squad\")\r\n```\r\n\r\nMore specifically it fails when trying to read the arrow file that just got generated. I haven't issues like this before. Can you make sure you have a recent version of `pyarrow` ? Maybe it was an old version that wrote the arrow file and some header was missing.", "Thank you for your pointer! This seems to have been linked to Python 3.9.7: it works flawlessly with Python 3.8.6. This can be closed, thanks a lot for your help." ]

"2021-09-25T22:18:39Z"

"2021-10-07T22:47:42Z"

"2021-10-07T22:47:26Z"

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"2021-03-13T18:03:32Z"

"2022-04-01T15:27:10Z"

"2022-04-01T15:27:10Z"

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[ "Thanks for approving it!" ]

"2021-05-09T14:17:39Z"

"2021-05-11T09:16:59Z"

"2021-05-11T09:16:59Z"

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

"2022-09-08T03:15:00Z"

"2022-09-24T22:14:35Z"

"2022-09-08T14:51:00Z"

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"2023-04-11T09:04:17Z"

"2023-04-11T11:04:52Z"

"2023-04-11T11:04:52Z"

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"2022-01-05T18:29:09Z"

"2022-01-12T14:14:35Z"

"2022-01-12T14:14:35Z"

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{ "closed_at": "2021-07-09T05:50:07Z", "closed_issues": 12, "created_at": "2021-05-31T16:13:06Z", "creator": { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }, "description": "Next minor release", "due_on": "2021-07-08T07:00:00Z", "html_url": "https://github.com/huggingface/datasets/milestone/5", "id": 6808903, "labels_url": "https://api.github.com/repos/huggingface/datasets/milestones/5/labels", "node_id": "MDk6TWlsZXN0b25lNjgwODkwMw==", "number": 5, "open_issues": 0, "state": "closed", "title": "1.9", "updated_at": "2021-07-12T14:12:00Z", "url": "https://api.github.com/repos/huggingface/datasets/milestones/5" }

[ "I have received a reply from Zenodo support:\r\n> We are currently investigating and fixing this issue related to GitHub releases. As soon as we have solved it we will reach back to you.", "Other repo maintainers had the same problem with Zenodo. \r\n\r\nThere is an open issue on their GitHub repo: zenodo/zenodo#2181", "I have received the following request from Zenodo support:\r\n> Could you send us the link to the repository as well as the release tag?\r\n\r\nMy reply:\r\n> Sure, here it is:\r\n> - Link to the repository: https://github.com/huggingface/datasets\r\n> - Link to the repository at the release tag: https://github.com/huggingface/datasets/releases/tag/1.8.0\r\n> - Release tag: 1.8.0", "Zenodo issue has been fixed. The 1.8.0 release DOI can be found here: https://zenodo.org/record/4946100#.YMd6vKj7RPY" ]

"2021-06-10T15:15:46Z"

"2021-06-14T16:49:42Z"

"2021-06-14T16:49:42Z"

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[ "Having talked to @lhoestq, I see that this feature is no longer supported. \r\n\r\nI really don't think this was a good idea. It is a major breaking change and one for which we don't even have a working solution at the moment, which is bad for PyTorch as we don't want to force people to have `datasets` decode audio files automatically, but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files - e.g. `common_voice` doesn't work anymore in a TF training script. Note this worked perfectly fine before making the change (think it was done [here](https://github.com/huggingface/datasets/pull/3290) no?)\r\n\r\nIMO, it's really important to think about a solution here and I strongly favor to make a difference here between loading a dataset in streaming mode and in non-streaming mode, so that in non-streaming mode the actual downloaded file is displayed. It's really crucial for people to be able to analyse the original files IMO when the dataset is not downloaded in streaming mode. \r\n\r\nThere are the following reasons why it is paramount to have access to the **original** audio file in my opinion (in non-streaming mode):\r\n- There are a wide variety of different libraries to load audio data with varying support on different platforms. For me it was quite clear that there is simply to single good library to load audio files for all platforms - so we have to leave the option to the user to decide which loading to use.\r\n- We had support for audio datasets a long time before streaming audio was possible. There were quite some versions where we advertised **everywhere** to load the audio from the path name (and there are many places where we still do even though it's not possible anymore). To give some examples:\r\n - Official example of TF Wav2Vec2: https://github.com/huggingface/transformers/blob/f427e750490b486944cc9be3c99834ad5cf78b57/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1423 Wav2Vec2 is as important for speech as BERT is for NLP - so it's **very** important. The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment. Same goes for Flax.\r\n - The most downloaded non-nlp checkpoint: https://huggingface.co/facebook/wav2vec2-base-960h#usage has a usage example which doesn't work anymore with the current datasets implementation. I'll update this now, but we have >1000 wav2vec2 checkpoints on the Hub and we can't update all the model cards.\r\n => This is a big breaking change with no current solution. For `transformers` breaking changes are one of the biggest complaints.\r\n- Similar to this we also shouldn't assume that there is only one resampling method for Audio. I think it's good to have one offered automatically by `datasets`, but we have to leave the user the freedom to choose her/his own resampling as well. Resampling can take very different filtering windows and other parameters which are currently somewhat hardcoded in `datasets`, which users might very well want to change.\r\n\r\n\r\n=> IMO, it's a **very** big priority to again have the correct absolute path in non-streaming mode. The other solution of providing a path-like object derived from the bytes stocked in the `.array` file is not nearly as user-friendly, but better than nothing. ", "Agree that we need to have access to the original sound files. Few days ago I was looking for these original files because I suspected there is bug in the audio resampling (confirmed in https://github.com/huggingface/datasets/issues/3662) and I want to do my own resampling to workaround the bug, which is now not possible anymore due to the unavailability of the original files.", "@patrickvonplaten \r\n> The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n\r\nJust to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\n> The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n\r\nI'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 (https://github.com/huggingface/datasets/pull/3667#issuecomment-1030090627).\r\n\r\nYour concern is reasonable, but there are situations where we can only serve bytes (see https://github.com/huggingface/datasets/pull/3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n", "Related to this discussion: in https://github.com/huggingface/datasets/pull/3664#issuecomment-1031866858 I propose how we could change `iter_archive` to work for streaming and also return local paths (as it used too !). I'd love your opinions on this", "> @patrickvonplaten\r\n> \r\n> > The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n> \r\n> Just to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\nYes! \r\n\r\n> \r\n> > The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n> \r\n> I'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 ([#3667 (comment)](https://github.com/huggingface/datasets/pull/3667#issuecomment-1030090627)). \r\n> Your concern is reasonable, but there are situations where we can only serve bytes (see #3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n\r\nYes this might be, but I highly doubt that `soundfile` is the go-to library for audio then. @anton-l and I have tried out a bunch of different audio loading libraries (`soundfile`, `librosa`, `torchaudio`, pure `ffmpeg`, `audioread`, ...). One thing that was pretty clear to me is that there is just no \"de-facto standard\" library and they all have pros and cons. None of the libraries really supports \"batch\"-ed audio loading. Some depend on PyTorch. `torchaudio` is 100x faster (really!) than `librosa's` fallback on MP3. `torchaudio` often has problems with multi-proessing, ... Also we should keep in mind that resampling is similarly not as simple as reading a text file. It's a pretty complex signal processing transform and people very well might want to use special filters, etc...at the moment we just hard-code `torchaudio's` or `librosa's` default filter when doing resampling.\r\n\r\n=> All this to say that we **should definitely** care about whether we rely on local paths or bytes IMO. We don't want to loose all users that are forced to use `datasets` decoding or resampling or have to built a very much not intuitive way of loading bytes into a numpy array. It's much more intuitive to be able to inspect a local file. I feel pretty strongly about this and am happy to also jump on a call. Keeping libraries flexible and lean as well as exposing internals is very important IMO (this philosophy has worked quite well so far with Transformers).\r\n\r\n", "Thanks a lot for the very detailed explanation. Now everything makes much more sense.", "From https://github.com/huggingface/datasets/pull/3736 the Common Voice dataset now gives access to the local audio files as before", "I understand the argument that it is bad to have a breaking change. How to deal with the introduction of breaking changes is a topic of its own and not sure how you want to deal with that (or is the policy this is never allowed, and there must be a `load_dataset_v2` or so if you really want to introduce a breaking change?).\r\n\r\nRegardless of whether it is a breaking change, however, I don't see the other arguments.\r\n\r\n> but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files\r\n\r\nI don't exactly understand this. Why not?\r\n\r\nWhy does the HF dataset on-the-fly decoding mechanism not work? Why is it anyway specific to PyTorch or TensorFlow? Isn't this independent?\r\n\r\nBut even if you just provide the raw bytes to TF, on TF you could just use sth like `tfio.audio.decode_mp3` or `tf.audio.decode_ogg` or `tfio.audio.decode_flac`?\r\n\r\n> There are the following reasons why it is paramount to have access to the original audio file in my opinion ...\r\n\r\nI don't really understand the arguments (despite that it maybe breaks existing code). You anyway have the original audio files but it is just embedded in the dataset? I don't really know about any library which cannot also load the audio from memory (i.e. from the dataset).\r\n\r\nBtw, on librosa being slow for decoding audio files, I saw that as well, so we have this comment RETURNN:\r\n\r\n> Don't use librosa.load which internally uses audioread which would use Gstreamer as a backend which has multiple issues:\r\n> https://github.com/beetbox/audioread/issues/62\r\n> https://github.com/beetbox/audioread/issues/63\r\n> Instead, use PySoundFile (soundfile), which is also faster. See here for discussions:\r\n> https://github.com/beetbox/audioread/issues/64\r\n> https://github.com/librosa/librosa/issues/681\r\n\r\nResampling is also a separate aspect, which is also less straightforward and with different compromises between speed and quality. So there the different tradeoffs and different implementations can make a difference.\r\n\r\nHowever, I don't see how this is related to the question whether there should be the raw bytes inside the dataset or as separate local files.\r\n", "Thanks for your comments here @albertz - cool to get your input! \r\n\r\nAnswering a bit here between the lines:\r\n\r\n> I understand the argument that it is bad to have a breaking change. How to deal with the introduction of breaking changes is a topic of its own and not sure how you want to deal with that (or is the policy this is never allowed, and there must be a `load_dataset_v2` or so if you really want to introduce a breaking change?).\r\n> \r\n> Regardless of whether it is a breaking change, however, I don't see the other arguments.\r\n> \r\n> > but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files\r\n> \r\n> I don't exactly understand this. Why not?\r\n\r\n> Why does the HF dataset on-the-fly decoding mechanism not work? Why is it anyway specific to PyTorch or TensorFlow? Isn't this independent?\r\n\r\nThe problem with decoding on the fly is that we currently rely on `torchaudio` for this now which relies on `torch` which is not necessarily something people would like to install when using `tensorflow` or `flax`. Therefore we cannot just rely on people using the decoding on the fly method. We just didn't find a library that is ML framework independent and fast enough for all formats. `torchaudio` is currently in our opinion by far the best here.\r\n\r\nSo for TF and Flax it's important that users can load audio files or bytes they way the want to - this might become less important if we find (or make) a good library with few dependencies that is fast for all kinds of platforms / use cases.\r\n\r\n\r\nNow the question is whether it's better to store audio data as a path to a file or as raw bytes I guess.\\\r\nMy main arguments for storing the audio data as a path to a file is pretty much all about users experience - I don't really expect our users to understand the inner workings of datasets:\r\n\r\n- 1. It's not straightforward to know which function to use to decode it - not all `load_audio(...)` or `read_audio(...)` work on raw bytes. E.g. Looking at https://pytorch.org/audio/stable/torchaudio.html?highlight=load#torchaudio.load one would not see directly how to load raw bytes . There are also some functions of other libraries which only work on files which would require the user to save the bytes as a file first before being able to load it.\r\n- 2. It's difficult to see which format the bytes are coming from (mp3, ogg, ...) - guess this could be remedied by adding the format to each sample though\r\n- 3. It is a bit scary IMO to see raw bytes for users. Overall, I think it's better to leave the data in it's raw form as this way it's much easier for people to play around with the audio files, less need to read docs because people don't worry about what happened to the audio files (are the bytes already resampled?)\r\n\r\nBut the argument that the audio should be loadable directly from memory is good - haven't thought about this too much. \r\nI guess it's still very much possible for the user to do this:\r\n\r\n```python\r\ndef save_as_bytes:\r\n batch[\"bytes\"] = read_in_bytes_from_file(batch[\"file\"])\\\r\n os.remove(batch[\"file\"])\r\n\r\nds = ds.map(save_as_bytes)\r\n\r\nds.save_to_disk(...)\r\n```\r\n\r\nGuess the question is more a bit about what should be the default case?", "> The problem with decoding on the fly is that we currently rely on `torchaudio` for this now which relies on `torch` which is not necessarily something people would like to install when using `tensorflow` or `flax`. Therefore we cannot just rely on people using the decoding on the fly method. We just didn't find a library that is ML framework independent and fast enough for all formats. `torchaudio` is currently in our opinion by far the best here.\r\n\r\nBut how is this relevant for this issue here? I thought this issue here is about having the (correct) path in the dataset or having raw bytes in the dataset.\r\n\r\nHow did TF users use it at all then? Or they just do not use on-the-fly decoding? I did not even notice this problem (maybe because I had `torchaudio` installed). But what do they use instead?\r\n\r\nBut as I outlined before, they could just use `tfio.audio.decode_flac` and co, where it would be more natural if you already provide the raw bytes.\r\n\r\n> Looking at https://pytorch.org/audio/stable/torchaudio.html?highlight=load#torchaudio.load one would not see directly how to load raw bytes\r\n\r\nI was not really familiar with `torchaudio`. It seems that they really don't provide an easy/direct API to operate on raw bytes. Which is very strange and unfortunate because as far as I can see, all the underlying backend libraries (e.g. soundfile) easily allow that. So I would say that this is the fault of `torchaudio` then. But despite, if you anyway use `torchaudio` with `soundfile` backend, why not just use `soundfile` directly. It's very simple to use and crossplatform.\r\n\r\nBut ok, now we are just discussing how to handle the on-the-fly decoding. I still think this is a separate issue and having raw bytes in the dataset instead of local files should just be fine as well.\r\n\r\n\r\n> It is a bit scary IMO to see raw bytes for users.\r\n\r\nI think nobody who writes code is scared by seeing the raw bytes content of a binary file. :)\r\n\r\n\r\n> I guess it's still very much possible for the user to do this:\r\n> \r\n> ```python\r\n> def save_as_bytes:\r\n> batch[\"bytes\"] = read_in_bytes_from_file(batch[\"file\"])\\\r\n> os.remove(batch[\"file\"])\r\n> \r\n> ds = ds.map(save_as_bytes)\r\n> \r\n> ds.save_to_disk(...)\r\n> ```\r\n\r\nIn https://github.com/huggingface/datasets/pull/4184#issuecomment-1105191639, you said/proposed that this `map` is not needed anymore and `save_to_disk` could do it automatically (maybe via some option)?\r\n\r\n> Guess the question is more a bit about what should be the default case?\r\n\r\nYea this is up to you. I'm happy as long as we can get it the way we want easily and this is a well supported use case. :)\r\n", "> In https://github.com/huggingface/datasets/pull/4184#issuecomment-1105191639, you said/proposed that this map is not needed anymore and save_to_disk could do it automatically (maybe via some option)?\r\n\r\nYes! Should be super easy now see discussion here: https://github.com/rwth-i6/i6_core/issues/257#issuecomment-1105494468\r\n\r\nThanks for the super useful input :-)", "Despite the comments that this has been fixed, I am finding the exact same problem is occurring again (with datasets version 2.3.2)", "> Despite the comments that this has been fixed, I am finding the exact same problem is occurring again (with datasets version 2.3.2)\r\n\r\nIt appears downgrading to torchaudio 0.11.0 fixed this problem.", "@DCNemesis, sorry which problem exactly is occuring again? Also cc @lhoestq @polinaeterna here", "@patrickvonplaten @lhoestq @polinaeterna I was unable to load audio from Common Voice using 🤗 with the current version of torchaudio, but downgrading to torchaudio 0.11.0 fixed it. This is probably more of a torch problem than a Hugging Face problem.", "@DCNemesis that's interesting, could you please share the error message if you still can access it? ", "@polinaeterna I believe it is the same exact error as above. It occurs on other .mp3 sources as well, but the problem is with torchaudio > 0.11.0. I've created a short colab notebook that reproduces the error, and the fix here: https://colab.research.google.com/drive/18wsuwdHwBPN3JkcnhEtk8MUYqF9swuWZ?usp=sharing", "Hi @DCNemesis,\r\n\r\nYour issue was slightly different from the original one in this issue page. Yours seems related to a change in the backend used by `torchaudio` (`ffmpeg` instead of `sox`). Refer to the issue page here:\r\n- #4776\r\n\r\nNormally, it should be circumvented with the patch made by @polinaeterna in:\r\n- #4923", "I think the original issue reported here was already fixed by:\r\n- #3736\r\n\r\nOtherwise, feel free to reopen." ]

"2022-02-01T18:40:10Z"

"2022-09-21T15:03:09Z"

"2022-09-21T14:56:22Z"

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[ "My bad, didn't see this on the open dataset list. Closing this since it overlaps with PR#1256" ]

"2020-12-06T19:46:18Z"

"2020-12-08T07:52:11Z"

"2020-12-08T07:52:11Z"

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

"2022-12-15T22:04:12Z"

"2022-12-15T22:27:53Z"

"2022-12-15T22:24:50Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mariosasko Would you or another HF datasets maintainer be able to review this, please?", "Amazing ! Great job @maddiedawson \r\n\r\nDo you know if it's possible to also support writing to Parquet using the HF ParquetWriter if `file_format=\"parquet\"` ?\r\n\r\nParquet is often used when people want to stream the data to train models - which is suitable for big datasets. On the other hand Arrow is generally used for local memory mapping with random access.\r\n\r\n> Please note there was a previous PR adding this functionality\r\n\r\nAm I right to say that it uses the spark workers to prepare the Arrow files ? If so this should make the data preparation fast and won't fill up the executor's memory as in the previously proposed PR", "Thanks for taking a look! Unlike the previous PR's approach, this implementation takes advantage of Spark mapping to distribute file writing over multiple tasks. (Also it doesn't load the entire dataset into memory :) )\r\n\r\nSupporting Parquet here sgtm; I'll modify the PR.\r\n\r\nI also updated the PR description with a common Spark-HF use case that we want to improve.", "Hey @albertvillanova @lhoestq , would one of you be able to re-review please? Thank you!", "@lhoestq this is ready for another pass! Thanks so much 🙏 ", "Friendly ping @lhoestq , also cc @polinaeterna who may be able to help take a look?", "Merging `main` into this branch should fix the CI", "Just rebased @lhoestq ", "Thanks @lhoestq ! Is there a way for me to trigger the github workflow myself to triage the test failure? I'm not able to repro the test failures locally.", "There were two test issues in the workflow that I wasn't able to reproduce locally:\r\n\r\n- Python 3.7: createDataFrame fails due to a pickling error. I modified the tests to instead write and read from json files\r\n- Python 3.10: A worker crashes for unknown reasons. I modified the spark setup to explicitly specify local mode in case it was trying to do something else; let's see if that fixes the issue", "Also one more question @lhoestq when is the next datasets release? We're hoping this can make it in", "I just re-ran the CI.\r\nI think we can do a release right after this PR is merged ;)", "Thanks all! @lhoestq could we re-run CI again please? I think we have to disable this feature on python 3.7 due to the pickling error. The other failure was due to https://issues.apache.org/jira/browse/SPARK-30952 so I rewrote the df processing", "Thanks @lhoestq , this is ready for another CI run. I pinned the pyspark version to see if that fixes the pickling issue", "The remaining CI issues have been addressed! They were\r\n\r\n- dill=0.3.1.1 is incompatible with cloudpickle, used by Spark. The min-dependency tests use this dill version, and those were failing. I added a skip-test annotation to skip Spark tests when using this dill version. This shouldn't be a production issue since if users are using that version of dill, they won't really be able to do anything with Spark anyway.\r\n- One of the Spark APIs used in this feature (mapInArrow) is incompatible with Windows. I filed a Spark ticket for the team to investigate. For the tests, I added another annotation to skip Spark tests on Windows. In the next PR (adding streaming mode), we should be able to support Windows since that won't use mapInArrow.\r\n\r\nI ran the CI on my forked branch: https://github.com/maddiedawson/datasets/pull/2 Everything passes except one instance of tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore; it looks like a flake.\r\n\r\n@lhoestq granted that the CI passes here, is this ok to merge and release? We'd like to put out a blog post tomorrow to broadcast this to Spark users!", "Thanks @lhoestq ! Could you help take a look at the error please? Seems unrelated...\r\n\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_multiprocessing_on_disk - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\\\Users\\\\RUNNER~1\\\\AppData\\\\Local\\\\Temp\\\\tmptfnrdj4x\\\\cache-5c5687cf5629c97a_00000_of_00002.arrow'\r\n===== 1 failed, 2152 passed, 23 skipped, 20 warnings in 461.68s (0:07:41) =====", "The blog is live btw! https://www.databricks.com/blog/contributing-spark-loader-for-hugging-face-datasets Hopefully there can be a release today?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012686 / 0.011353 (0.001333) | 0.006051 / 0.011008 (-0.004957) | 0.123057 / 0.038508 (0.084549) | 0.033238 / 0.023109 (0.010128) | 0.388207 / 0.275898 (0.112309) | 0.393972 / 0.323480 (0.070492) | 0.006645 / 0.007986 (-0.001340) | 0.006715 / 0.004328 (0.002386) | 0.098348 / 0.004250 (0.094097) | 0.041410 / 0.037052 (0.004358) | 0.380123 / 0.258489 (0.121634) | 0.427982 / 0.293841 (0.134141) | 0.052194 / 0.128546 (-0.076352) | 0.018775 / 0.075646 (-0.056871) | 0.399063 / 0.419271 (-0.020209) | 0.061019 / 0.043533 (0.017487) | 0.370943 / 0.255139 (0.115804) | 0.398326 / 0.283200 (0.115127) | 0.136893 / 0.141683 (-0.004790) | 1.777431 / 1.452155 (0.325276) | 1.844354 / 1.492716 (0.351638) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267296 / 0.018006 (0.249289) | 0.565133 / 0.000490 (0.564643) | 0.005811 / 0.000200 (0.005611) | 0.000122 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027009 / 0.037411 (-0.010402) | 0.125907 / 0.014526 (0.111381) | 0.122111 / 0.176557 (-0.054445) | 0.189023 / 0.737135 (-0.548112) | 0.140510 / 0.296338 (-0.155829) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.589269 / 0.215209 (0.374060) | 6.038038 / 2.077655 (3.960384) | 2.394681 / 1.504120 (0.890561) | 2.099268 / 1.541195 (0.558073) | 2.105146 / 1.468490 (0.636656) | 1.216304 / 4.584777 (-3.368473) | 5.823110 / 3.745712 (2.077397) | 4.999323 / 5.269862 (-0.270539) | 2.781554 / 4.565676 (-1.784122) | 0.148370 / 0.424275 (-0.275905) | 0.015163 / 0.007607 (0.007556) | 0.775153 / 0.226044 (0.549109) | 7.425314 / 2.268929 (5.156385) | 3.320254 / 55.444624 (-52.124370) | 2.718595 / 6.876477 (-4.157881) | 2.696215 / 2.142072 (0.554142) | 1.452249 / 4.805227 (-3.352978) | 0.281355 / 6.500664 (-6.219309) | 0.088146 / 0.075469 (0.012677) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.495718 / 1.841788 (-0.346070) | 17.498714 / 8.074308 (9.424405) | 20.109705 / 10.191392 (9.918313) | 0.233053 / 0.680424 (-0.447371) | 0.028336 / 0.534201 (-0.505865) | 0.538146 / 0.579283 (-0.041137) | 0.642106 / 0.434364 (0.207742) | 0.597214 / 0.540337 (0.056876) | 0.732219 / 1.386936 (-0.654717) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008153 / 0.011353 (-0.003200) | 0.005605 / 0.011008 (-0.005403) | 0.096159 / 0.038508 (0.057651) | 0.034102 / 0.023109 (0.010992) | 0.428091 / 0.275898 (0.152193) | 0.476535 / 0.323480 (0.153056) | 0.006278 / 0.007986 (-0.001708) | 0.006752 / 0.004328 (0.002424) | 0.100553 / 0.004250 (0.096302) | 0.045546 / 0.037052 (0.008494) | 0.463236 / 0.258489 (0.204747) | 0.502512 / 0.293841 (0.208671) | 0.051014 / 0.128546 (-0.077533) | 0.018499 / 0.075646 (-0.057148) | 0.127587 / 0.419271 (-0.291685) | 0.059254 / 0.043533 (0.015722) | 0.432248 / 0.255139 (0.177109) | 0.462002 / 0.283200 (0.178802) | 0.124918 / 0.141683 (-0.016765) | 1.689740 / 1.452155 (0.237585) | 1.871546 / 1.492716 (0.378830) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274844 / 0.018006 (0.256838) | 0.570522 / 0.000490 (0.570032) | 0.004008 / 0.000200 (0.003808) | 0.000146 / 0.000054 (0.000091) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025323 / 0.037411 (-0.012088) | 0.116323 / 0.014526 (0.101797) | 0.129434 / 0.176557 (-0.047122) | 0.187069 / 0.737135 (-0.550067) | 0.134459 / 0.296338 (-0.161880) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.633551 / 0.215209 (0.418341) | 6.290078 / 2.077655 (4.212423) | 2.692071 / 1.504120 (1.187951) | 2.354344 / 1.541195 (0.813149) | 2.409260 / 1.468490 (0.940770) | 1.270515 / 4.584777 (-3.314261) | 5.552982 / 3.745712 (1.807270) | 3.041417 / 5.269862 (-2.228444) | 1.920634 / 4.565676 (-2.645043) | 0.142500 / 0.424275 (-0.281775) | 0.014378 / 0.007607 (0.006770) | 0.786444 / 0.226044 (0.560399) | 7.711558 / 2.268929 (5.442630) | 3.439688 / 55.444624 (-52.004936) | 2.742314 / 6.876477 (-4.134163) | 2.800531 / 2.142072 (0.658458) | 1.405843 / 4.805227 (-3.399385) | 0.245322 / 6.500664 (-6.255342) | 0.076662 / 0.075469 (0.001193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.592961 / 1.841788 (-0.248827) | 18.165647 / 8.074308 (10.091339) | 20.011433 / 10.191392 (9.820041) | 0.240558 / 0.680424 (-0.439866) | 0.026045 / 0.534201 (-0.508156) | 0.529610 / 0.579283 (-0.049674) | 0.652494 / 0.434364 (0.218130) | 0.612284 / 0.540337 (0.071947) | 0.733180 / 1.386936 (-0.653756) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea251c726c73bd076a1bef7e39e2ac4e97c8d166 \"CML watermark\")\n", "python 3.9.2\r\nGot an error _pickle.PicklingError use Dataset.from_spark.\r\n\r\nDid the dataset import load data from spark dataframe using multi-node Spark cluster\r\ndf = spark.read.parquet(args.input_data).repartition(50)\r\nds = Dataset.from_spark(df, keep_in_memory=True,\r\n cache_dir=\"/pnc-data/data/nuplan/t5_spark/cache_data\")\r\nds.save_to_disk(args.output_data)\r\n\r\nError : \r\n_pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforma\r\ntion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.\r\n23/06/16 21:17:20 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)\r\n", "Hi @yanzia12138 ! Could you open a new issue please and share the full stack trace ? This will help to know what happened exactly" ]

"2023-04-03T23:51:29Z"

"2023-06-16T16:39:32Z"

"2023-04-26T15:43:39Z"

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[]

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

"2022-05-09T21:30:26Z"

"2022-05-25T16:23:15Z"

"2022-05-25T09:20:13Z"

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[]

[ "@lhoestq I am not sure how to test `dictionary_encode_column` (in-place version was not tested before)", "I can take a look at dictionary_encode_column tomorrow.\r\nAlthough it's likely that it doesn't work then. It was added at the beginning of the lib and never tested nor used afaik.", "Now let's update the documentation to use the new methods x)" ]

"2021-02-15T18:44:26Z"

"2021-02-24T14:54:49Z"

"2021-02-24T14:53:26Z"

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[ "none" ]

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

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

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

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[ "Hi ! Could you clarify what it fixes exactly and give more details please ? Especially why this is related to the windows hanging error ?", "This has already been merged, but I'll clarify the idea of this PR. Before this merge, FileLock was the only component affected by the max path limit on Windows (that came to my notice) because of its infinite loop that would suppress errors. So instead of suppressing the `FileNotFoundError` that is thrown by `os.open` if the file name is longer than the max allowed path length, this PR reraises it to notify the user." ]

"2021-06-20T14:25:11Z"

"2021-06-28T09:56:22Z"

"2021-06-28T08:47:39Z"

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[ "Issue unresolved, see [4000](https://github.com/huggingface/datasets/issues/4009#issue-1179658611)" ]

"2022-03-24T15:13:38Z"

"2022-03-24T15:46:38Z"

"2022-03-24T15:17:29Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Cool thanks ! Maybe let's include this change after the refactoring from FolderBasedBuilder in #3963 to avoid dealing with too many unpleasant conflicts ?", "@lhoestq I resolved the conflicts (AudioFolder also supports CSV metadata now). Let me know what you think.\r\n", "@lhoestq Thanks for the suggestion! Indeed it makes more sense to use CSV as the default format in the folder-based builders." ]

"2022-08-12T11:19:18Z"

"2022-08-31T12:01:27Z"

"2022-08-31T11:59:07Z"

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[ "Hi ! The CI was failing because `mauve-text` wasn't installed. I added it to the CI setup :)\r\n\r\nI also did some minor changes to the script itself, especially to remove `**kwargs` and explicitly mentioned all the supported arguments (this way if someone does a typo with some parameters they get an error)" ]

"2022-01-13T03:52:48Z"

"2022-01-20T15:00:08Z"

"2022-01-20T15:00:08Z"

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"2022-07-06T19:49:18Z"

"2022-07-06T19:49:18Z"

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"2020-12-12T22:47:18Z"

"2020-12-17T16:37:16Z"

"2020-12-17T16:37:16Z"

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[ "@kashif Some commits on the PR branch are not authored by you, so could you please open a new PR and not use rebase this time :)? You can copy and paste the dataset dir to the new branch. \r\n\r\n", "making a new PR" ]

"2022-01-12T10:21:32Z"

"2022-02-15T13:31:48Z"

"2022-02-15T13:31:48Z"

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[ "I've aligned the release script with Transformers in #6004, so I think this issue can be closed." ]

"2021-06-11T09:38:02Z"

"2023-07-20T13:22:23Z"

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[ "@vblagoje nice work, please add the README.md file and it would be ready", "@lhoestq @tanmoyio @yjernite please have a look at the dataset card. Don't forget that the dataset is still hosted on my private gs bucket and should eventually be moved to the HF bucket", "I will move the files soon and ping you when it's done and with the new URLs :) ", "Hi !\r\n\r\nI just moved the file to a HF bucket. It's available at https://storage.googleapis.com/huggingface-nlp/datasets/cc_news/cc_news.tar.gz\r\n\r\nSorry for the delay ^^'", "@lhoestq no worries, updated PR with the new URL and rebased to master\r\n" ]

"2020-12-08T16:18:15Z"

"2021-02-01T16:55:49Z"

"2021-02-01T16:55:49Z"

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[ "Just a small revision from simon's review: 20KB for the dummy_data.zip is fine, you can keep them this way.", "Also the CI is failing because of an error `tests/test_file_utils.py::TempSeedTest::test_tensorflow` that is not related to your dataset and is fixed on master. You can ignore it", "merging since the Ci is fixed on master" ]

"2020-12-14T06:32:48Z"

"2020-12-21T13:14:46Z"

"2020-12-21T13:14:46Z"

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"2020-12-29T15:41:43Z"

"2020-12-30T17:18:49Z"

"2020-12-30T17:18:49Z"

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[ "Merging then 😄 " ]

"2021-05-04T12:57:53Z"

"2021-05-04T13:43:13Z"

"2021-05-04T13:43:12Z"

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[ "Hi Hubert! Could you please probably create a publicly available `gs://` dataset link? I think this would be easier for others to directly start to debug.", "What seems to work is to change the line to:\r\n```\r\nfs.download(src_dataset_path, dataset_path.parent.as_posix(), recursive=True)\r\n```" ]

"2022-10-14T11:54:53Z"

"2022-11-19T07:13:10Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Related to: https://github.com/huggingface/autonlp-backend/issues/414 and https://github.com/huggingface/autonlp-backend/issues/424", "The tests are failing due to the changed metadata:\r\n\r\n```\r\ngot an unexpected keyword argument 'train-eval-index'\r\n```\r\n\r\nI think you can fix this by updating the `DatasetMetadata` class and implementing an appropriate `validate_train_eval_index()` function\r\n\r\n@lhoestq we are working with an arbitrary set of tags for `autoeval config`. See https://github.com/huggingface/autonlp-backend/issues/414\r\nI need to add a validator function though for the tests to pass. Our set is not well-defined as in the rest https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources. What's a workaround for this?", "On the question of validating the `train-eval-index` metadata, I think the simplest approach would be to validate that the required fields exist and not worry about their values (which are open-ended).\r\n\r\nFor me, the required fields include:\r\n\r\n* `config`\r\n* `task`\r\n* `task_id`\r\n* `splits` (train / validation / eval)\r\n* `col_mapping`\r\n* `metrics` (checking that each one has `type`, `name`) \r\n\r\nHere I'm using the spec defined in https://github.com/huggingface/autonlp-backend/issues/414 as a guide.\r\n\r\nWDYT @lhoestq ?", "Makes sense ! Currently the metadata type validator doesn't support subfields - let me open a PR to add it", "I ended up improving the metadata validation in this PR x)\r\n\r\nIn particular:\r\n- I added support YAML keys with dashes instead of underscores for `train-eval-index`\r\n- I added `train-eval-index` validation with `validate_train_eval_index`. It does nothing fancy, it just checks that it is a list if it exists in the YAML, but feel free to improve it if you want\r\n\r\nLet me know if it sounds good to you ! I think we can improve `validate_train_eval_index` in another PR", "Come on windows... I didn't do anything advanced...\r\n\r\nAnyway, will try to fix this when I get back home x)", "> Come on windows... I didn't do anything advanced...\r\n> \r\n> Anyway, will try to fix this when I get back home x)\r\n\r\nHehe, thanks!", "Thanks, @lhoestq this is great! ", "Did I just fix it for windows and now it fails on linux ? xD", "> Did I just fix it for windows and now it fails on linux ? xD\r\n\r\nLooks like the Heisenberg uncertainty principle is at play here - you cannot simultaneously have unit tests passing in both Linux and Windows 😅 ", "The worst is that the tests pass locally both on my windows and my linux x)", "Ok fixed it, the issue came from python 3.6 that doesn't return the right `__origin__` for Dict and List types", "> Alright thanks for adding the first Autoeval config ! :D\r\n\r\nWoohoo! Thank you so much 🤗 ", "This is cool!" ]

"2022-04-27T05:32:10Z"

"2022-05-06T13:20:31Z"

"2022-05-05T18:20:58Z"

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"2021-03-30T14:55:43Z"

"2021-03-31T13:11:05Z"

"2021-03-31T13:11:05Z"

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[ "Hi ! That looks like a bug, can you provide some code so that we can reproduce ?\r\nIt's not supposed to update the original dataset", "Hi, I experimented with RAG. \r\n\r\nActually, you can run the [use_own_knowldge_dataset.py](https://github.com/shamanez/transformers/blob/rag-end-to-end-retrieval/examples/research_projects/rag/use_own_knowledge_dataset.py#L80). In the 80 you can save the dataset object to the disk with save_to_disk. Then in order to compute the embeddings in this use **load_from_disk**. \r\n\r\nThen finally save it. You can see the original dataset object (CSV after splitting also will be changed)\r\n\r\nOne more thing- when I save the dataset object with **save_to_disk** it name the arrow file with cache.... rather than using dataset. arrow. Can you add a variable that we can feed a name to save_to_disk function?", "@lhoestq I also found that cache in tmp directory gets updated after transformations. This is really problematic when using datasets interactively. Let's say we use the shards function to a dataset loaded with csv, atm when we do transformations to shards and combine them it updates the original csv cache. ", "I plan to update the save_to_disk method in #2025 so I can make sure the new save_to_disk doesn't corrupt your cache files.\r\nBut from your last message it looks like save_to_disk isn't the root cause right ?", "ok, one more thing. When we use save_to_disk there are two files other than .arrow. dataset_info.json and state.json. Sometimes most of the fields in the dataset_infor.json are null, especially when saving dataset objects. Anyways I think load_from_disk uses the arrow files mentioned in state.json right? ", "> Anyways I think load_from_disk uses the arrow files mentioned in state.json right?\r\n\r\nYes exactly", "Perfect. For now, I am loading the dataset from CSV in my interactive process and will wait until you make the PR!" ]

"2021-03-05T05:25:50Z"

"2021-03-22T04:05:50Z"

"2021-03-22T04:05:50Z"

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"2021-05-28T15:03:28Z"

"2021-05-28T15:26:57Z"

"2021-05-28T15:26:56Z"

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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> " ]

"2021-10-10T10:31:36Z"

"2021-10-11T10:57:09Z"

"2021-10-11T10:56:36Z"

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[ "Hi! \r\n\r\nYou can use [`.set_format(\"np\")`](https://huggingface.co/docs/datasets/process#format) to get NumPy arrays (or Pytorch tensors with `.set_format(\"torch\")`) in `__getitem__`.\r\n\r\nAlso, have you been able to reproduce the linked PyTorch issue with a HF dataset?\r\n " ]

"2023-04-06T13:57:48Z"

"2023-04-20T17:16:26Z"

"2023-04-20T17:16:26Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The Hub doesn't allow these characters in the YAML tags, and git push fails if you want to push a dataset card containing these characters.", "Ok, let me rename the bad config names :) I think I can also keep backward compatibility with a warning", "Almost done with it btw, will submit a PR that shows all the configuration name changes (from a bit more than 20 datasets)", "Please, let me know when the renaming of configs is done. If not enough bandwidth, I can take care of it...", "Will focus on this this afternoon ;)", "I realized when renaming all the configurations with dots in https://github.com/huggingface/datasets/pull/4365 that it's not ideal for certain cases. For example:\r\n- many configurations have a version like \"1.0.0\" in their names\r\n- to avoid breaking changes we need to replace dots with underscores in the user input and show a warning, which hurts the experience\r\n- our second most downloaded dataset at the moment is affected: `newsgroup`\r\n- if we disallow dots, then we'll never be able to make the [allenai/c4](https://huggingface.co/datasets/allenai/c4) work with its different configurations since they contain dots, and we can't rename them because they are the official download links\r\n\r\nI was thinking of other alternatives:\r\n1. just stop separating tags per config name completely, and have a single flat YAML for all configurations. Dataset search doesn't use this info anyway\r\n2. use another YAML structure to avoid having config names as keys, such as\r\n```yaml\r\nlanguages:\r\n- config: 20220301_en\r\n values:\r\n - en\r\n```\r\n\r\nI'm down for 1, to keep things simple", "@lhoestq I agree:\r\n- better not changing config names (so that we do not introduce any braking change)\r\n- therefore, we should not use them as keys\r\n\r\nIn relation with the proposed solutions, I have no strong opinion:\r\n- option 1 is simpler and aligns better with current usage on the Hub (configs are ignored)\r\n- however:\r\n - we will lose all the information per config we already have (for those datasets containing config keys; contributors made an effort to put that information per config)\r\n - and this information might be useful on the Hub in the future, in case we would like to enrich the search feature with more granularity; this is only applicable if this feature could eventually make sense\r\n\r\nSo, no strong opinion...", "Closing in favor of https://github.com/huggingface/datasets/pull/4367" ]

"2022-05-10T05:52:46Z"

"2022-05-20T09:48:30Z"

"2022-05-20T09:40:20Z"

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[ "known issue due to https://github.com/huggingface/datasets/pull/2362 (and [internal](https://github.com/huggingface/moon-landing/issues/946)) , will be solved soon", "hmm actually i have no recollection of why I said that", "Because it has dots in some YAML keys, it can't be parsed and indexed by the back-end" ]

"2022-01-05T09:36:30Z"

"2022-05-12T14:45:57Z"

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"2021-09-20T10:18:59Z"

"2021-09-20T16:25:09Z"

"2021-09-20T13:43:02Z"

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"2021-02-20T07:32:17Z"

"2021-02-22T13:35:06Z"

"2021-02-22T13:35:06Z"

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

"2022-06-10T08:13:25Z"

"2022-06-10T16:42:08Z"

"2022-06-10T16:32:46Z"

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[ "Seems ok for me, what do you think @lhoestq ?" ]

"2020-12-29T14:15:37Z"

"2021-01-18T09:27:23Z"

"2021-01-18T09:27:23Z"

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"2023-05-11T20:52:13Z"

"2023-05-23T10:32:59Z"

"2023-05-23T10:32:59Z"

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

"2022-04-29T08:21:43Z"

"2022-05-06T08:38:16Z"

"2022-04-29T16:26:09Z"

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"2021-08-08T04:01:30Z"

"2021-08-08T08:48:01Z"

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[ "Hi @klyuhang9,\r\n\r\nI'm sorry but I can't reproduce your problem:\r\n```python\r\nIn [4]: ds = load_dataset(\"glue\", \"sst2\", download_mode=\"force_redownload\")\r\nDownloading builder script: 28.8kB [00:00, 9.15MB/s] \r\nDownloading metadata: 28.7kB [00:00, 10.7MB/s] \r\nDownloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.78 MiB, post-processed: Unknown size, total: 11.88 MiB) to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.44M/7.44M [00:01<00:00, 4.12MB/s]\r\nDataset glue downloaded and prepared to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data. \r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1047.96it/s]\r\n\r\nIn [5]: ds\r\nOut[5]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 67349\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 872\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1821\r\n })\r\n})\r\n```\r\n\r\nPlease, note that sometimes GitHub has some temporary connectivity issues. Feel free to retry and re-open this issue if the problem persists.", "Maybe it's because we are in China.", "Are you able to access the URL in your web browser?", "> Are you able to access the URL in your web browser?\r\n\r\nYes, with or without a VPN, we (people in China) can access the URL. And we can even use wget to download these files. We can download the pretrained language model automatically with the code.\r\nHowever, we CANNOT access glue.py & metric.py automatically. Every time, it will raise ConnectionError, and we have to download datasets manually (SQuAD is extremely hard to preprocess) and replace metric.py with scipy.metrics. If this problem is solved, many Chinese will save a lot of time.", "> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py\r\n> \r\n> I don't know why; it is ok when I use\r\n\r\nIf you would query the question `ConnectionError: Couldn't reach` in www.baidu.com (Chinese Google, Google is banned and some people cannot access it), you will find that there are so many questions about accessing `https://raw.githubusercontent.com`. There are some solutions like adding `185.199.108.133 raw.githubusercontent.com` to `C:/windows/systen32/drives/etc/hosts`, but it is time-consuming, hard for green-hand, and invalid sometimes." ]

"2022-03-29T07:00:31Z"

"2022-05-08T07:27:32Z"

"2022-03-29T08:29:25Z"

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[ "Hi @nbroad1881, thanks for reporting.\r\n\r\nLet me have a look to try to improve its performance. ", "Thanks @nbroad1881 for reporting! I don't recall it taking so long. I will also have a look at this. \r\n@albertvillanova please let me know if I am doing something unnecessary or time consuming.", "Hi @nbroad1881 and @vrindaprabhu,\r\n\r\nAs a workaround for the performance of the parsing of the raw data files (this could be addressed in a subsequent PR), I have found that there are also processed data files, that do not require parsing. I have added these as new configurations `processed.en` and `processed.zh`:\r\n```python\r\nds = load_dataset(\"medical_dialog\", \"processed.zh\")\r\n```" ]

"2022-04-07T14:00:51Z"

"2022-04-08T16:20:51Z"

"2022-04-08T16:20:51Z"

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[]

[ "Hi ! I wasn't able to reproduce your error.\r\n\r\nCan you try to clear your cache at `~/.cache/huggingface/modules` and try again ?" ]

"2021-12-13T02:49:51Z"

"2022-01-07T14:06:47Z"

"2022-01-07T14:06:47Z"

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[ "Hi ! We just released a new version of `datasets` that should fix this.\r\n\r\nI tested resampling and using save/load_from_disk afterwards and it seems to be fixed now", "Hi @lhoestq, \r\n\r\nJust tested the latest datasets version, and confirming that this is fixed for me. \r\n\r\nThanks!", "Also, just an FYI, data that I had saved (with save_to_disk) previously from common voice using datasets==1.17.0 now give the error below when loading (with load_from disk) using datasets==1.18.0. \r\n\r\nHowever, when starting fresh using load_dataset, then doing the resampling, the save/load_from disk worked fine. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1747 return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1748 elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()):\r\n-> 1749 return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1750 else:\r\n 1751 raise FileNotFoundError(\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/dataset_dict.py in load_from_disk(dataset_dict_path, fs, keep_in_memory)\r\n 769 else Path(dest_dataset_dict_path, k).as_posix()\r\n 770 )\r\n--> 771 dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n 772 return dataset_dict\r\n 773 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1118 info=dataset_info,\r\n 1119 split=split,\r\n-> 1120 fingerprint=state[\"_fingerprint\"],\r\n 1121 )\r\n 1122 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n 655 if self.info.features.type != inferred_features.type:\r\n 656 raise ValueError(\r\n--> 657 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n 658 )\r\n 659 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=48000, mono=True, id=None), 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<bytes: binary, path: string>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64>\r\n\r\nbut expected something like\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<path: string, bytes: binary>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64> \r\n```" ]

"2022-01-20T06:37:10Z"

"2022-01-23T01:41:01Z"

"2022-01-23T01:24:14Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Sorry for the issue @albertvillanova! I think it's now fixed! :heart: " ]

"2022-08-24T13:11:49Z"

"2022-08-26T10:31:32Z"

"2022-08-26T10:29:20Z"

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[ "Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?", "Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:\r\n- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).\r\n- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check/region_1`)" ]

"2021-10-22T08:02:50Z"

"2021-10-28T05:44:49Z"

"2021-10-28T05:44:49Z"

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[ "Good start! Here are some proposed next steps:\r\n- We want the Class structure to reflect the template - so the parser know what section titles to expect and when something has gone wrong\r\n- As a result, we don't need to parse the table of contents, since it will always be the same\r\n- For each section/subsection it would be cool to have a variable saying whether it's filled out or not (when it's either empty or has `[More Information Needed]`)\r\n- `attributes` should probably be `text`", "@yjernite @lhoestq \r\n\r\nI have added basic validation checking in the class. It works based on a YAML string. The YAML string determines the expected structure and which text is to be checked. The `text` can be true or false showing whether the text has to be checked or not for emptiness. Similarly, each subsection is parsed recursively. I have used print statement currently so that all issues are shown.\r\n\r\nPlease let me know your thoughts.\r\n\r\nI haven't added a variable that keeps a track of whether the text is empty or not but it can be done easliy if required.", "This looks like a good start !\r\nMaybe we can use a field named `allow_empty` instead of `text` ?\r\nAlso +1 for keeping track of empty texts\r\n\r\nDo you think you can have a way to collect all the validation fails of a readme and then raise an error showing all the failures instead of using print ?\r\n\r\nThen we can create a `tests/test_dataset_cards.py` test file to make sure all the readmes of the repo are valid !", "Hi @lhoestq \r\n\r\nI have added changes accordingly. I prepared a list which stores all the errors and raises them at the end. I'm not sure if there is a better way.", "Hi @lhoestq @yjernite \r\n\r\nPlease find the output for the existing READMEs here: http://p.ip.fi/2vYU\r\n\r\nThanks,\r\nGunjan", "Hi @lhoestq\r\n\r\nI have added some basic tests, also have restructured `ReadMe` class slightly.\r\n\r\nThere is one print statement currently, I'm not sure how to remove it. Basically, I want to warn but not stop further validation. I can't append to a list because the `error_list` and `warning_list` are both only present in `validate` method, and this print is present in the `parse` method. This is done when someone has repeated a section multiple times. For e.g.:\r\n\r\n```markdown\r\n---\r\n---\r\n\r\n# Dataset Card for FashionMNIST\r\n## Dataset Description\r\n## Dataset Description\r\n```\r\n\r\nIn this case, I check for validation only in the latest entry.\r\n\r\nI can also raise an error (ideal case scenario), but still, it is in the `parse`. Should I add `error_lines` and `warning_lines` as instance variables? That would probably solve the issue.\r\n\r\nIn tests, I'm using a dummy YAML string for structure, we can also make it into a file but I feel that is not a hard requirement. Let me know your thoughts.\r\n\r\nI will add tests for `from_readme` as well.\r\n\r\nHowever, I would love to be able to check the exact message in the test when an error is raised. I checked a couple of methods but couldn't get it working. Let me know if you're aware of a way to do that.", "Hi @lhoestq \r\n\r\nThanks for merging. :)\r\nThanks a lot to you and @yjernite for guiding me and helping me out.\r\n\r\nYes, I'll also use the next PR for combining the readme and tags validation. ^_^" ]

"2021-03-26T17:02:17Z"

"2021-05-10T13:17:18Z"

"2021-05-10T09:41:41Z"

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[ "The `task_templates` API is deprecated in favor of the `train-eval-index` YAML field, so I'm closing this issue." ]

"2021-06-18T15:33:57Z"

"2023-07-20T13:20:32Z"

"2023-07-20T13:20:32Z"

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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." ]

"2021-10-12T16:36:53Z"

"2021-10-13T13:48:13Z"

"2021-10-13T13:48:13Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4877). All of your documentation changes will be reflected on that endpoint." ]

"2022-08-23T16:52:33Z"

"2022-08-23T18:05:01Z"

"2022-08-23T18:05:00Z"

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[ "Fixed, thanks!\r\n![Uploading Capture d’écran 2022-06-21 à 18.42.40.png…]()\r\n\r\n" ]

"2022-06-20T08:47:42Z"

"2022-06-21T16:42:46Z"

"2022-06-21T16:42:45Z"

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[ "Hi @lhoestq , I am not sure about the error message:\r\n```\r\n#!/bin/bash -eo pipefail\r\n./scripts/datasets_metadata_validator.py\r\nWARNING:root:❌ Failed to validate 'datasets/openslr/README.md':\r\n__init__() got an unexpected keyword argument 'SLR32'\r\nINFO:root:❌ Failed on 1 files.\r\n\r\nExited with code exit status 1\r\nCircleCI received exit code 1 \r\n```\r\nCould you have a look please? Thanks.", "Hi ! The error is unrelated to your PR and has been fixed on master\r\nNext time feel free to merge master into your branch to fix the CI error ;)" ]

"2021-05-04T09:08:03Z"

"2021-05-07T09:50:55Z"

"2021-05-07T09:50:55Z"

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"2021-03-12T14:49:52Z"

"2021-03-12T15:04:23Z"

"2021-03-12T15:04:22Z"

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"2021-08-27T20:54:05Z"

"2021-09-13T20:39:50Z"

"2021-09-07T09:34:23Z"

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"2021-11-11T15:57:16Z"

"2021-11-12T14:06:44Z"

"2021-11-12T14:06:43Z"

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"2021-08-22T14:48:37Z"

"2021-08-26T13:23:32Z"

"2021-08-26T11:55:16Z"

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[ "Same bug exists with `num_proc=1` on colab. `3.7.14 (default, Sep 8 2022, 00:06:44) [GCC 7.5.0]` ", "Thanks for reporting, @loubnabnl and for the additional information, @PartiallyTyped.\r\n\r\nHowever, I'm not able to reproduce this issue, neither locally nor on Colab:\r\n```\r\nDataset({\r\n features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],\r\n num_rows: 10\r\n})\r\nDataset({\r\n features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],\r\n num_rows: 10\r\n})\r\n```\r\nCC: @huggingface/datasets can anybody reproduce this?", "This is the minimum reproducible example. I ran this on the premium instances of colab.\r\n\r\n```\r\n# !pip install datasets\r\nimport datasets\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"copenlu/answerable_tydiqa\").filter(\"english\".__eq__, input_columns=\"language\")\r\nassert all(map(\"english\".__eq__, ds[\"train\"][\"language\"]))\r\n```\r\n\r\nIn my case, the number of samples is correct, however, the samples selected when indexing are wrong.\r\n\r\n```python\r\nDatasetDict({\r\n validation: Dataset({\r\n features: ['question_text', 'document_title', 'language', 'annotations', 'document_plaintext', 'document_url'],\r\n num_rows: 990\r\n })\r\n train: Dataset({\r\n features: ['question_text', 'document_title', 'language', 'annotations', 'document_plaintext', 'document_url'],\r\n num_rows: 7389\r\n })\r\n})\r\n```\r\n\r\nThe number of rows is indeed correct, and i have checked it with a version that works.", "I can reproduce the issue on my mac too \r\n```\r\n- `datasets` version: 2.6.0\r\n- Platform: macOS-12.2.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 9.0.0\r\n- Pandas version: 1.4.3\r\n```\r\nBut not on Colab with python 3.7, maybe related to python version? (didn't manage to install python 3.9)\r\n```\r\n- `datasets` version: 2.6.0\r\n- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.14\r\n- PyArrow version: 9.0.0\r\n- Pandas version: 1.3.5\r\n```", "I have the same issue, here's a simple notebook to reproduce: https://colab.research.google.com/drive/1Lvo9fg5DSpGUUgXW5JAutZ0bFsR-WV--?usp=sharing\r\n\r\n\r\n\r\n", "I think there are 2 different issues here:\r\n- the one reported by @loubnabnl is related to multiprocessing in map and then filter; we should reproduce it first: I have tried with Python version 3.9.7 and I can't reproduce it either; maybe it is related to the version of PyArrow? To be checked.\r\n- the issue reported by @PartiallyTyped is related just to \"filter\" (without multiprocessing) and I can reproduce it.", "Could you create another issue for the @PartiallyTyped one please ?\r\n\r\nRegarding the OP issue, I also tried on colab or locally on py3.7 or py3.10 but didn't reproduce", "I have created another issue for the one reported by @PartiallyTyped: \r\n- #5112 ", "I managed to reproduce your issue @loubnabnl on colab by upgrading pyarrow to 9.0.0 instead of 6.0.1", "I managed to have a _super_ minimal reproducible example:\r\n```python\r\n\r\nfrom datasets import Dataset, concatenate_datasets\r\n\r\nds = concatenate_datasets([Dataset.from_dict({\"a\": [i]}) for i in range(10)])\r\nds2 = ds.map(lambda _: {}, batched=True)\r\nassert list(ds2) == list(ds)\r\n```\r\n(filter uses a batched `map` under the hood)", "> the one reported by @loubnabnl is related to multiprocessing in map and then filter; we should reproduce it first: I have tried with Python version 3.9.7 and I can't reproduce it either; maybe it is related to the version of PyArrow? To be checked.\r\n\r\nSo finally it was related to PyArrow version! :+1: ", "Doing a patch release asap :)", "Did the patch release yesterday, lmk if you still have issues", "It works now, thanks!\r\n" ]

"2022-10-13T17:00:55Z"

"2022-10-17T08:26:59Z"

"2022-10-14T14:59:59Z"

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[ "As mentioned in https://github.com/huggingface/datasets/issues/5105 I think we just need to keep the existing files instead of deleting them.\r\nThe `dataset_info.json` file contains the split names anyway, so we know which files belong to the dataset, and which ones don't." ]

"2022-10-17T01:30:37Z"

"2022-11-05T12:23:53Z"

"2022-11-05T12:23:53Z"

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[ "hi @jplu ! Did I understand you correctly that you create the dataset, push it to the Hub with `.push_to_hub` and you see a `dataset_infos.json` file there, then you edit this file, load the dataset with `load_dataset` and you don't see any changes in `.info` attribute of a dataset object? \r\n\r\nThis is actually weird that when you push your dataset to the Hub, a `dataset_infos.json` file is created, because this file is deprecated and it should create `README.md` with the `dataset_info` field instead. Some keys are also deprecated, like \"supervised_keys\" and \"task_templates\".\r\n\r\nCan you please provide a toy reproducible example of how you create and push the dataset? And also why do you want to change this file, especially the number of bytes and examples?", "Hi @polinaeterna Yes I have created the dataset with `Dataset.from_dict` applied some updates afterward and when I pushed to the hub I had a `dataset_infos.json` file and there was a `README.md` file as well.\r\n\r\nI didn't know that the JSON file was deprecated. So I have built my dataset with `ImageBuilder` instead and now it works like a charm without having to touch anything.\r\n\r\nI haven't succeed to reproduce the creation of the JSON file with a toy example, hence, I certainly did some mistakes when I have manipulated my dataset manually at first. My bad." ]

"2023-04-05T11:15:17Z"

"2023-04-06T08:52:20Z"

"2023-04-06T08:52:19Z"

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[ "Hi\r\nnot sure how change the lable after creation, but this is an issue not dataset request. thanks ", "Hi ! Currently you have to use `map` . You can see an example of how to do it in this comment: https://github.com/huggingface/datasets/issues/853#issuecomment-727872188\r\n\r\nIn the future we'll add support for a more native way of adding a new column ;)" ]

"2021-02-26T18:17:27Z"

"2021-04-29T14:50:43Z"

"2021-04-29T14:50:43Z"

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"2022-02-03T18:09:02Z"

"2022-02-21T09:14:56Z"

"2022-02-04T13:21:39Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Just took your comment into account @mariosasko , let me know if it's good for you now :)" ]

"2022-07-28T17:28:11Z"

"2022-09-09T17:17:39Z"

"2022-09-09T17:15:30Z"

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[ "Hi! \r\n\r\nThis issue is due to our audio/image datasets not being self-contained. This allows us to save disk space (files are written only once) but also leads to the issues like this one. We plan to make all our datasets self-contained in Datasets 3.0.\r\n\r\nIn the meantime, you can run the following map to ensure your dataset is self-contained:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset ...\r\ndset = dset.with_format(\"arrow\")\r\ndset.map(embed_table_storage, batched=True)\r\ndset = dset.with_format(\"python\")\r\n```\r\n", "Understood. Thank you, Mario.\r\n\r\nPerhaps the solution could be very simple - move the extracted files into the directory of the cached dataset? Which would make it self-contained already and won't require waiting for a new major release. Unless I'm missing some back-compat nuance.\r\n\r\nBut regardless if X relies on Y - it could check if Y is still there when loading X. so not checking full consistency but just the top-level directory it relies on." ]

"2023-01-24T02:09:32Z"

"2023-01-24T18:14:10Z"

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"2022-02-15T15:21:00Z"

"2022-02-15T15:52:38Z"

"2022-02-15T15:52:37Z"

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[]

[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/QQP_triplets)." ]

"2022-07-07T02:43:42Z"

"2022-07-14T02:13:50Z"

"2022-07-14T02:13:50Z"

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[]

[ "sorry my mistake, datasets were overwritten closing now, thanks a lot" ]

"2021-02-25T13:09:45Z"

"2021-02-25T13:20:35Z"

"2021-02-25T13:20:26Z"

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[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5804). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006448 / 0.011353 (-0.004905) | 0.004440 / 0.011008 (-0.006568) | 0.097837 / 0.038508 (0.059328) | 0.027754 / 0.023109 (0.004645) | 0.306462 / 0.275898 (0.030564) | 0.332454 / 0.323480 (0.008975) | 0.004984 / 0.007986 (-0.003001) | 0.004703 / 0.004328 (0.000375) | 0.075213 / 0.004250 (0.070962) | 0.036524 / 0.037052 (-0.000529) | 0.310149 / 0.258489 (0.051659) | 0.346392 / 0.293841 (0.052552) | 0.031012 / 0.128546 (-0.097534) | 0.011598 / 0.075646 (-0.064049) | 0.323066 / 0.419271 (-0.096206) | 0.042945 / 0.043533 (-0.000588) | 0.302286 / 0.255139 (0.047147) | 0.327813 / 0.283200 (0.044614) | 0.092540 / 0.141683 (-0.049143) | 1.532893 / 1.452155 (0.080739) | 1.556676 / 1.492716 (0.063960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195126 / 0.018006 (0.177120) | 0.399623 / 0.000490 (0.399133) | 0.003176 / 0.000200 (0.002976) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023612 / 0.037411 (-0.013799) | 0.097794 / 0.014526 (0.083268) | 0.104665 / 0.176557 (-0.071891) | 0.167145 / 0.737135 (-0.569990) | 0.108769 / 0.296338 (-0.187570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437818 / 0.215209 (0.222608) | 4.354896 / 2.077655 (2.277242) | 2.092832 / 1.504120 (0.588712) | 1.957630 / 1.541195 (0.416435) | 2.033135 / 1.468490 (0.564645) | 0.702316 / 4.584777 (-3.882461) | 3.448035 / 3.745712 (-0.297678) | 1.906762 / 5.269862 (-3.363100) | 1.253274 / 4.565676 (-3.312402) | 0.082486 / 0.424275 (-0.341789) | 0.012442 / 0.007607 (0.004835) | 0.532096 / 0.226044 (0.306052) | 5.366580 / 2.268929 (3.097652) | 2.441904 / 55.444624 (-53.002720) | 2.112116 / 6.876477 (-4.764361) | 2.185471 / 2.142072 (0.043398) | 0.797905 / 4.805227 (-4.007322) | 0.149811 / 6.500664 (-6.350853) | 0.066507 / 0.075469 (-0.008962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206300 / 1.841788 (-0.635487) | 13.620851 / 8.074308 (5.546543) | 14.190666 / 10.191392 (3.999274) | 0.142343 / 0.680424 (-0.538081) | 0.016867 / 0.534201 (-0.517334) | 0.381557 / 0.579283 (-0.197726) | 0.373935 / 0.434364 (-0.060429) | 0.437856 / 0.540337 (-0.102481) | 0.525235 / 1.386936 (-0.861701) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006598 / 0.011353 (-0.004755) | 0.004487 / 0.011008 (-0.006522) | 0.077582 / 0.038508 (0.039073) | 0.028008 / 0.023109 (0.004899) | 0.341602 / 0.275898 (0.065704) | 0.377105 / 0.323480 (0.053625) | 0.004999 / 0.007986 (-0.002986) | 0.004791 / 0.004328 (0.000462) | 0.076418 / 0.004250 (0.072167) | 0.038347 / 0.037052 (0.001295) | 0.343196 / 0.258489 (0.084707) | 0.382459 / 0.293841 (0.088618) | 0.030597 / 0.128546 (-0.097950) | 0.011579 / 0.075646 (-0.064067) | 0.085876 / 0.419271 (-0.333396) | 0.043241 / 0.043533 (-0.000292) | 0.343754 / 0.255139 (0.088615) | 0.380689 / 0.283200 (0.097489) | 0.096015 / 0.141683 (-0.045668) | 1.464419 / 1.452155 (0.012264) | 1.574010 / 1.492716 (0.081294) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.156433 / 0.018006 (0.138427) | 0.403179 / 0.000490 (0.402690) | 0.002415 / 0.000200 (0.002215) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024946 / 0.037411 (-0.012465) | 0.100568 / 0.014526 (0.086042) | 0.106440 / 0.176557 (-0.070117) | 0.158457 / 0.737135 (-0.578678) | 0.110774 / 0.296338 (-0.185564) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434734 / 0.215209 (0.219525) | 4.343874 / 2.077655 (2.266220) | 2.059759 / 1.504120 (0.555639) | 1.855124 / 1.541195 (0.313930) | 1.908567 / 1.468490 (0.440077) | 0.695283 / 4.584777 (-3.889494) | 3.347724 / 3.745712 (-0.397988) | 2.979498 / 5.269862 (-2.290364) | 1.532040 / 4.565676 (-3.033636) | 0.083021 / 0.424275 (-0.341254) | 0.012522 / 0.007607 (0.004915) | 0.540934 / 0.226044 (0.314890) | 5.385690 / 2.268929 (3.116762) | 2.507409 / 55.444624 (-52.937216) | 2.160537 / 6.876477 (-4.715939) | 2.269195 / 2.142072 (0.127123) | 0.804718 / 4.805227 (-4.000509) | 0.152432 / 6.500664 (-6.348232) | 0.068783 / 0.075469 (-0.006686) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294698 / 1.841788 (-0.547090) | 14.152792 / 8.074308 (6.078484) | 14.233132 / 10.191392 (4.041740) | 0.143655 / 0.680424 (-0.536768) | 0.016844 / 0.534201 (-0.517357) | 0.380246 / 0.579283 (-0.199037) | 0.381730 / 0.434364 (-0.052633) | 0.456838 / 0.540337 (-0.083499) | 0.543677 / 1.386936 (-0.843259) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b28d5610887f2e107765f5f1557679184db08214 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008586 / 0.011353 (-0.002767) | 0.005886 / 0.011008 (-0.005122) | 0.114522 / 0.038508 (0.076014) | 0.040966 / 0.023109 (0.017857) | 0.366655 / 0.275898 (0.090757) | 0.408765 / 0.323480 (0.085285) | 0.006822 / 0.007986 (-0.001164) | 0.004508 / 0.004328 (0.000180) | 0.084715 / 0.004250 (0.080465) | 0.054007 / 0.037052 (0.016954) | 0.380500 / 0.258489 (0.122011) | 0.410377 / 0.293841 (0.116536) | 0.041040 / 0.128546 (-0.087507) | 0.013940 / 0.075646 (-0.061707) | 0.398456 / 0.419271 (-0.020816) | 0.059315 / 0.043533 (0.015782) | 0.353640 / 0.255139 (0.098501) | 0.388682 / 0.283200 (0.105482) | 0.121744 / 0.141683 (-0.019939) | 1.729306 / 1.452155 (0.277151) | 1.824768 / 1.492716 (0.332052) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228806 / 0.018006 (0.210800) | 0.492790 / 0.000490 (0.492300) | 0.010815 / 0.000200 (0.010615) | 0.000372 / 0.000054 (0.000318) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031750 / 0.037411 (-0.005662) | 0.127160 / 0.014526 (0.112635) | 0.136717 / 0.176557 (-0.039839) | 0.205590 / 0.737135 (-0.531545) | 0.142596 / 0.296338 (-0.153742) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486419 / 0.215209 (0.271210) | 4.858572 / 2.077655 (2.780918) | 2.173867 / 1.504120 (0.669747) | 1.934619 / 1.541195 (0.393424) | 2.104185 / 1.468490 (0.635695) | 0.837913 / 4.584777 (-3.746864) | 4.552192 / 3.745712 (0.806480) | 2.565040 / 5.269862 (-2.704822) | 1.808499 / 4.565676 (-2.757178) | 0.103283 / 0.424275 (-0.320993) | 0.015040 / 0.007607 (0.007433) | 0.602325 / 0.226044 (0.376281) | 6.038655 / 2.268929 (3.769727) | 2.759789 / 55.444624 (-52.684835) | 2.330990 / 6.876477 (-4.545487) | 2.404111 / 2.142072 (0.262038) | 1.011637 / 4.805227 (-3.793590) | 0.202142 / 6.500664 (-6.298522) | 0.079496 / 0.075469 (0.004026) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.429543 / 1.841788 (-0.412245) | 18.052409 / 8.074308 (9.978101) | 16.989154 / 10.191392 (6.797762) | 0.208981 / 0.680424 (-0.471443) | 0.020490 / 0.534201 (-0.513711) | 0.502746 / 0.579283 (-0.076537) | 0.491769 / 0.434364 (0.057405) | 0.581970 / 0.540337 (0.041632) | 0.695816 / 1.386936 (-0.691120) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008449 / 0.011353 (-0.002904) | 0.006633 / 0.011008 (-0.004375) | 0.088638 / 0.038508 (0.050130) | 0.040013 / 0.023109 (0.016904) | 0.413108 / 0.275898 (0.137210) | 0.446310 / 0.323480 (0.122830) | 0.006515 / 0.007986 (-0.001471) | 0.006223 / 0.004328 (0.001894) | 0.089823 / 0.004250 (0.085573) | 0.052029 / 0.037052 (0.014977) | 0.407263 / 0.258489 (0.148774) | 0.449416 / 0.293841 (0.155576) | 0.041810 / 0.128546 (-0.086736) | 0.014604 / 0.075646 (-0.061042) | 0.103728 / 0.419271 (-0.315543) | 0.058212 / 0.043533 (0.014679) | 0.408936 / 0.255139 (0.153797) | 0.436727 / 0.283200 (0.153528) | 0.124344 / 0.141683 (-0.017339) | 1.752112 / 1.452155 (0.299957) | 1.859104 / 1.492716 (0.366387) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231172 / 0.018006 (0.213166) | 0.502974 / 0.000490 (0.502485) | 0.005586 / 0.000200 (0.005386) | 0.000137 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034097 / 0.037411 (-0.003314) | 0.133780 / 0.014526 (0.119254) | 0.142321 / 0.176557 (-0.034236) | 0.199807 / 0.737135 (-0.537329) | 0.150073 / 0.296338 (-0.146266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515658 / 0.215209 (0.300449) | 5.129783 / 2.077655 (3.052129) | 2.534767 / 1.504120 (1.030648) | 2.352468 / 1.541195 (0.811274) | 2.430708 / 1.468490 (0.962218) | 0.850087 / 4.584777 (-3.734690) | 4.529622 / 3.745712 (0.783910) | 2.451986 / 5.269862 (-2.817876) | 1.569568 / 4.565676 (-2.996109) | 0.102907 / 0.424275 (-0.321368) | 0.014420 / 0.007607 (0.006813) | 0.635124 / 0.226044 (0.409080) | 6.260496 / 2.268929 (3.991568) | 3.094984 / 55.444624 (-52.349640) | 2.780629 / 6.876477 (-4.095847) | 2.947620 / 2.142072 (0.805548) | 1.002397 / 4.805227 (-3.802830) | 0.200502 / 6.500664 (-6.300162) | 0.076577 / 0.075469 (0.001107) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.505958 / 1.841788 (-0.335829) | 18.364986 / 8.074308 (10.290678) | 16.707214 / 10.191392 (6.515822) | 0.210976 / 0.680424 (-0.469447) | 0.022077 / 0.534201 (-0.512124) | 0.516174 / 0.579283 (-0.063109) | 0.502469 / 0.434364 (0.068105) | 0.626790 / 0.540337 (0.086453) | 0.747230 / 1.386936 (-0.639706) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc5fef5b6d91f009e4101684adcb374df2c170f6 \"CML watermark\")\n" ]

"2023-04-28T10:10:01Z"