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https://api.github.com/repos/huggingface/datasets/issues/2823
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HF_DATASETS_CACHE variable in Windows
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"Agh - I'm a muppet. No quote marks are needed.\r\nset HF_DATASETS_CACHE = C:\\Datasets\r\nworks as intended."
] | 2021-08-21T13:17:44Z
| 2021-08-21T13:20:11Z
| 2021-08-21T13:20:11Z
|
NONE
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I can't seem to use a custom Cache directory in Windows. I have tried:
set HF_DATASETS_CACHE = "C:\Datasets"
set HF_DATASETS_CACHE = "C:/Datasets"
set HF_DATASETS_CACHE = "C:\\Datasets"
set HF_DATASETS_CACHE = "r'C:\Datasets'"
set HF_DATASETS_CACHE = "\Datasets"
set HF_DATASETS_CACHE = "/Datasets"
In each instance I get the "[WinError 123] The filename, directory name, or volume label syntax is incorrect" error when attempting to load a dataset
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Jigsaw toxicity pred
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Managed to mess up my original pull request, opening a fresh one incorporating the changes suggested by @lhoestq.
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MDExOlB1bGxSZXF1ZXN0NDcwNTI4MDE0
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|
Transform references for sacrebleu
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"I think I agree @lhoestq so I pushed a change.\r\nThanks for your work on the library!"
] | 2020-08-20T00:26:55Z
| 2020-08-20T09:30:54Z
| 2020-08-20T09:30:53Z
|
CONTRIBUTOR
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Currently it is impossible to use sacrebleu when len(predictions) != the number of references per prediction (very uncommon), due to a strange format expected by sacrebleu. If one passes in the data to `nlp.metric.compute()` in sacrebleu format, `nlp` throws an error due to mismatching lengths between predictions and references. If one uses a more standard format where predictions and references are lists of the same length, sacrebleu throws an error.
This PR transforms reference data in a more standard format into the [unusual format](https://github.com/mjpost/sacreBLEU#using-sacrebleu-from-python) expected by sacrebleu.
|
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Add splits to MBPP dataset
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[
"```\r\n(env) cwarny@Cedrics-Air datasets % RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_mbpp\r\n================================================================================================ test session starts =================================================================================================\r\nplatform darwin -- Python 3.8.13, pytest-7.1.3, pluggy-1.0.0\r\nrootdir: /Users/cwarny/datasets, configfile: setup.cfg\r\ncollected 1 item \r\n\r\ntests/test_dataset_common.py . [100%]\r\n\r\n================================================================================================= 1 passed in 1.12s ==================================================================================================\r\n(env) cwarny@Cedrics-Air datasets % RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_mbpp \r\n================================================================================================ test session starts =================================================================================================\r\nplatform darwin -- Python 3.8.13, pytest-7.1.3, pluggy-1.0.0\r\nrootdir: /Users/cwarny/datasets, configfile: setup.cfg\r\ncollected 1 item \r\n\r\ntests/test_dataset_common.py . [100%]\r\n\r\n================================================================================================= 1 passed in 0.35s ==================================================================================================\r\n\r\n```",
"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @cwarny ! Thanks for adding the correct splits :)\r\n\r\nYou can fix the CI error by running `make style` - this should reformat the dataset script",
"done"
] | 2022-09-07T01:18:31Z
| 2022-09-13T12:29:19Z
| 2022-09-13T12:27:21Z
|
CONTRIBUTOR
| null | 0
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This PR addresses https://github.com/huggingface/datasets/issues/4795
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asserts replaced with exception for image classification task, csv, json
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| 2021-11-15T11:08:37Z
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Fixes for csv, json in io module and image_classification task with tests referenced in https://github.com/huggingface/datasets/issues/3171
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Created wiki_movies dataset.
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[
"looks like your PR includes changes about many other files than the ones for wiki_movies\r\n\r\nCan you create another branch and another PR please ?",
"I'm happy to. What's the best way to do that (sorry, I'm new to PRs etc.)?",
"Sure !\r\n\r\nFirst please save your new dataset files somewhere.\r\nThen you can do in this order:\r\n```\r\ngit checkout master\r\ngit fetch upstream\r\ngit rebase upstream/master\r\ngit push\r\ngit checkout -b my-new-branch-name\r\n```\r\nThis will create a new branch from the updated master branch.\r\nThen you can re-add your files and commit + push them\r\n\r\nOnce it's done you should be able to create a new PR using your new branch :) ",
"Done!",
"closing in favor of #1485 "
] | 2020-12-07T23:38:54Z
| 2020-12-14T13:56:49Z
| 2020-12-14T13:56:49Z
|
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First PR (ever). Hopefully this movies dataset is useful to others!
|
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Support latest Docker image in CI benchmarks
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"Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).\r\n\r\n[^1]: More or less, see https://github.com/yargs/yargs/issues/873.",
"Opened https://github.com/huggingface/datasets/pull/5436 unpinning again the container image.",
"Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!\r\n\r\nWe are reviewing your PR to unpin the container image."
] | 2023-01-17T09:06:08Z
| 2023-01-18T06:29:08Z
| 2023-01-18T06:29:08Z
|
MEMBER
| null | null | null |
Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432
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[Bug] FileLock dependency incompatible with filesystem
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"Hi, can you give details on your environment/os/packages versions/etc?",
"Environment is Ubuntu 18.04, Python 3.7.5, nlp==0.3.0, filelock=3.0.12.\r\n\r\nThe external volume is Amazon FSx for Lustre, and it by default creates files with limited permissions. My working theory is that FileLock creates a lockfile that isn't writable, and thus there's no way to acquire it by removing the .lock file. But Python is able to create new files and write to them outside of the FileLock package.\r\n\r\nWhen I attempt to use FileLock within a Docker container by writing to `/root/.cache/hello.txt`, it succeeds. So there's some permissions issue. But it's not a Docker configuration issue; I've replicated it without Docker.\r\n```bash\r\necho \"hello world\" >> hello.txt\r\nls -l\r\n\r\n-rw-rw-r-- 1 ubuntu ubuntu 10 Jun 30 19:52 hello.txt\r\n```",
"Looks like the `flock` syscall does not work on Lustre filesystems by default: https://github.com/benediktschmitt/py-filelock/issues/67.\r\n\r\nI added the `-o flock` option when mounting the filesystem, as [described here](https://docs.aws.amazon.com/fsx/latest/LustreGuide/getting-started-step2.html), which fixed the issue.",
"Awesome, thanks a lot for sharing your fix!",
"I'm wondering if this can be revisited. In some managed environments the same person using HF cannot change the file-system mount flags, (and the organization may be unwilling to change these flags due to other concerns) but can ensure that there won't be concurrent writes, for example because HF is offline and the models/datasets were downloaded earlier. \r\n\r\nThe real fix would be to FileLock itself, which does not seem very active and seems to not deal with failed system flock calls , which would be one way to fix this, as they mention in the issue below also raised by @jarednielsen \r\n\r\nhttps://github.com/tox-dev/py-filelock/issues/67",
"> I'm wondering if this can be revisited. In some managed environments the same person using HF cannot change the file-system mount flags, (and the organization may be unwilling to change these flags due to other concerns) but can ensure that there won't be concurrent writes, for example because HF is offline and the models/datasets were downloaded earlier.\r\n\r\nI am one of those users. Is there a work around for this?\r\n",
"The machines I use have a shared FS which has the filelock problem as well as a local one that does not. Using some env vars (HF_HOME, which controls both models and datasets, and HF_DATASETS_OFFLINE) for both transformers and datasets library one can influence where these downloads happen, and whether the locks get taken. I think some of the relevant documentation is here https://huggingface.co/docs/transformers/installation#cache-setup. I do end up using different settings when I download the models and when I use them, and have to rsync the models to the local file system using a separate script. ",
"Thanks @orm011 . These filesystems are such a pain. I'll dig around, looks like setting `cache_dir` to a non-lustre filesystem works for `transformers` but not `datasets`.",
"Note I `export HF_HOME=` in the shell prior to running python (I do not use the `cache_dir` argument, I think I ran into similar issues with it, nor `HF_DATASETS_CACHE` , though maybe that works, or maybe you can set it in python prior to importing the library ), and I change no other variables. Then `datasets.load_dataset()` works without any additional flags, and they go into `HF_HOME/datasets/` and the models go into `HF_HOME/transformers/` (and the lock files are all there as well). ",
"I am using a shared cluster with a lustre system that I can't change. I am unable to download or load datsets onto the filesystem because of file lock. @thomwolf can this issue be reopened? "
] | 2020-06-30T19:45:31Z
| 2023-10-07T17:07:53Z
| 2020-06-30T21:33:06Z
|
CONTRIBUTOR
| null | null | null |
I'm downloading a dataset successfully with
`load_dataset("wikitext", "wikitext-2-raw-v1")`
But when I attempt to cache it on an external volume, it hangs indefinitely:
`load_dataset("wikitext", "wikitext-2-raw-v1", cache_dir="/fsx") # /fsx is an external volume mount`
The filesystem when hanging looks like this:
```bash
/fsx
----downloads
----94be...73.lock
----wikitext
----wikitext-2-raw
----wikitext-2-raw-1.0.0.incomplete
```
It appears that on this filesystem, the FileLock object is forever stuck in its "acquire" stage. I have verified that the issue lies specifically with the `filelock` dependency:
```python
open("/fsx/hello.txt").write("hello") # succeeds
from filelock import FileLock
with FileLock("/fsx/hello.lock"):
open("/fsx/hello.txt").write("hello") # hangs indefinitely
```
Has anyone else run into this issue? I'd raise it directly on the FileLock repo, but that project appears abandoned with the last update over a year ago. Or if there's a solution that would remove the FileLock dependency from the project, I would appreciate that.
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Fix header level in Audio docs
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-05T20:22:44Z
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Fixes header level so `Dataset features` is the doc title instead of `The Audio type`:
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Remove decode: true for image feature in head_qa
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This was erroneously added in https://github.com/huggingface/datasets/commit/701f128de2594e8dc06c0b0427c0ba1e08be3054. This PR removes it.
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Some question about raw dataset download info in the project .
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[
"Hi ! The `dl_manager` is a `DownloadManager` object and is responsible for downloading the raw data files.\r\nIt is used by dataset builders in their `_split_generators` method to download the raw data files that are necessary to build the datasets splits.\r\n\r\nThe `Conll2003` class is a dataset builder, and so you can download all the raw data files by calling `_split_generators` with a download manager:\r\n```python\r\nfrom datasets import DownloadManager\r\nfrom datasets.load import import_main_class\r\n\r\nconll2003_builder = import_main_class(...)\r\n\r\ndl_manager = DownloadManager()\r\nsplis_generators = conll2003_builder._split_generators(dl_manager)\r\n```\r\n\r\nThen you can see what files have been downloaded with\r\n```python\r\ndl_manager.get_recorded_sizes_checksums()\r\n```\r\nIt returns a dictionary with the format {url: {num_bytes: int, checksum: str}}\r\n\r\nThen you can get the actual location of the downloaded files with\r\n```python\r\nfrom datasets import cached_path\r\n\r\nlocal_path_to_downloaded_file = cached_path(url)\r\n```\r\n\r\n------------------\r\n\r\nNote that you can also get the urls from the Dataset object:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nconll2003 = load_dataset(\"conll2003\")\r\nprint(conll2003[\"train\"].download_checksums)\r\n```\r\nIt returns the same dictionary with the format {url: {num_bytes: int, checksum: str}}",
"I am afraid that there is not a very straightforward way to get that location.\r\n\r\nAnother option, from _split_generators would be to use:\r\n- `dl_manager._download_config.cache_dir` to get the directory where all the raw downloaded files are:\r\n ```python\r\n download_dir = dl_manager._download_config.cache_dir\r\n ```\r\n- the function `datasets.utils.file_utils.hash_url_to_filename` to get the filenames of the raw downloaded files:\r\n ```python\r\n filenames = [hash_url_to_filename(url) for url in urls_to_download.values()]\r\n ```\r\nTherefore the complete path to the raw downloaded files would be the join of both:\r\n```python\r\ndownloaded_paths = [os.path.join(download_dir, filename) for filename in filenames]\r\n```\r\n\r\nMaybe it would be interesting to make these paths accessible more easily. I could work on this. What do you think, @lhoestq ?",
"Sure it would be nice to have an easier access to these paths !\r\nThe dataset builder could have a method to return those, what do you think ?\r\nFeel free to work on this @albertvillanova , it would be a nice addition :) \r\n\r\nYour suggestion does work as well @albertvillanova if you complete it by specifying `etag=` to `hash_url_to_filename`.\r\n\r\nThe ETag is obtained by a HEAD request and is used to know if the file on the remote host has changed. Therefore if a file is updated on the remote host, then the hash returned by `hash_url_to_filename` is different.",
"Once #1846 will be merged, the paths to the raw downloaded files will be accessible as:\r\n```python\r\nbuilder_instance.dl_manager.downloaded_paths\r\n``` "
] | 2021-02-07T05:33:36Z
| 2021-02-25T14:10:18Z
| 2021-02-25T14:10:18Z
|
NONE
| null | null | null |
Hi , i review the code in
https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class in load_dataset function
My question is that , with this logic it seems that i can not have the raw dataset download location
in variable in downloaded_files in _split_generators.
If someone also want use huggingface datasets as raw dataset downloader,
how can he retrieve the raw dataset download path from attributes in
datasets.dataset_dict.DatasetDict ?
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"I believe the issue is in `codeparrot/github-code`. `base_path` param is missing - https://huggingface.co/datasets/codeparrot/github-code/blob/main/github-code.py#L169\r\n\r\nFunction definition has changed.\r\nhttps://github.com/huggingface/datasets/blob/0e1c629cfb9f9ba124537ba294a0ec451584da5f/src/datasets/data_files.py#L547\r\n\r\n@mariosasko could you please confirm my finding? And are there any changes that need to be done from my side?",
"Good catch ! We recently did a breaking change in `get_patterns_in_dataset_repository`, I think we can revert it",
"> Good catch ! We recently did a breaking change in `get_patterns_in_dataset_repository`, I think we can revert it\n\nI can't wait for that releasee. Broke my application",
"This simple workaround should fix: https://huggingface.co/datasets/codeparrot/github-code/discussions/2\r\n\r\n`get_patterns_in_dataset_repository` can treat whether `base_path=None`, so we just need to make sure that codeparrot/github-code `_split_generators` calls with such an argument.",
"I am afraid your suggested change @gugarosa will break compatibility with older datasets versions that don't have `base_path` argument in `get_patterns_in_dataset_repository`, as a workaround while the issue gets resolved in `datasets` can you downgrade your datasets version to `<=2.1.0` ? \r\n@lvwerra do you think we should adapt the script to check the datasets version before calling `get_patterns_in_dataset_repository`?",
"Actually I think it's just simpler to fix it in the dataset itself, let me open a PR\r\n\r\nEDIT: PR opened here: https://huggingface.co/datasets/codeparrot/github-code/discussions/3",
"PR is merged, it's working now ! Closing this one :)",
"> I am afraid your suggested change @gugarosa will break compatibility with older datasets versions that don't have `base_path` argument in `get_patterns_in_dataset_repository`, as a workaround while the issue gets resolved in `datasets` can you downgrade your datasets version to `<=2.1.0` ?\r\n> @lvwerra do you think we should adapt the script to check the datasets version before calling `get_patterns_in_dataset_repository`?\r\n\r\nYou are definitely right, sorry about it. I always keep forgetting that we need to keep in mind users from past versions, my bad."
] | 2022-06-30T20:24:48Z
| 2022-07-05T14:24:13Z
| 2022-07-05T09:19:56Z
|
NONE
| null | null | null |
## Describe the bug
codeparrot/github-code fails to load with a `TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'`
## Steps to reproduce the bug
```python
from datasets import load_dataset
```
## Expected results
loaded dataset object
## Actual results
```python
[3]: dataset = load_dataset("codeparrot/github-code")
No config specified, defaulting to: github-code/all-all
Downloading and preparing dataset github-code/all-all to /home/bebr/.cache/huggingface/datasets/codeparrot___github-code/all-all/0.0.0/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817...
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [3], in <cell line: 1>()
----> 1 dataset = load_dataset("codeparrot/github-code")
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:1221, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1220 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1221 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/miniconda3/envs/fastapi-kube/lib/python3.10/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/codeparrot--github-code/a55513bc0f81db773f9896c7aac225af0cff5b323bb9d2f68124f0a8cc3fb817/github-code.py:169, in GithubCode._split_generators(self, dl_manager)
162 def _split_generators(self, dl_manager):
164 hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
165 _REPO_NAME,
166 timeout=100.0,
167 )
--> 169 patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
170 data_files = datasets.data_files.DataFilesDict.from_hf_repo(
171 patterns,
172 dataset_info=hfh_dataset_info,
173 )
175 files = dl_manager.download_and_extract(data_files["train"])
TypeError: get_patterns_in_dataset_repository() missing 1 required positional argument: 'base_path'
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.18.7-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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[
"I think it's ready now @stas00, did you want to add something else ?\r\nThis PR includes your changes but with the level set to warning",
"LGTM, thank you, @lhoestq "
] | 2020-09-16T14:37:53Z
| 2020-09-17T09:52:19Z
| 2020-09-17T09:52:18Z
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Continuation of #618
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IndexError when accessing first element of a Dataset if first RecordBatch is empty
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[] | 2021-08-24T16:49:20Z
| 2021-08-24T17:21:17Z
| 2021-08-24T17:21:17Z
|
MEMBER
| null | null | null |
The computation of the offsets of the underlying Table of a Dataset has some issues if the first RecordBatch is empty.
```python
from datasets import Dataset
import pyarrow as pa
pa_table = pa.Table.from_pydict({"a": [1]})
pa_table2 = pa.Table.from_pydict({"a": []}, schema=pa_table.schema)
ds_table = pa.concat_tables([pa_table2, pa_table])
dataset = Dataset(ds_table)
print([len(b) for b in dataset.data._batches])
# [0, 1]
print(dataset.data._offsets)
# [0 0 1] (should be [0, 1])
dataset[0]
```
raises
```python
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/table.py in _interpolation_search(arr, x)
90 else:
91 i, j = i, k
---> 92 raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.")
93
94
IndexError: Invalid query '0' for size 1.
```
This can be fixed by ignoring empty batches when computing `table._batches` and `table._offsets`
cc @SaulLu
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007342 / 0.011353 (-0.004011) | 0.004586 / 0.011008 (-0.006422) | 0.100430 / 0.038508 (0.061922) | 0.081053 / 0.023109 (0.057944) | 0.368130 / 0.275898 (0.092232) | 0.402852 / 0.323480 (0.079372) | 0.004504 / 0.007986 (-0.003482) | 0.003824 / 0.004328 (-0.000505) | 0.075326 / 0.004250 (0.071076) | 0.063329 / 0.037052 (0.026277) | 0.372837 / 0.258489 (0.114348) | 0.437857 / 0.293841 (0.144017) | 0.035512 / 0.128546 (-0.093034) | 0.009756 / 0.075646 (-0.065890) | 0.341035 / 0.419271 (-0.078236) | 0.060503 / 0.043533 (0.016970) | 0.362555 / 0.255139 (0.107416) | 0.409216 / 0.283200 (0.126017) | 0.030093 / 0.141683 (-0.111590) | 1.751550 / 1.452155 (0.299395) | 1.848676 / 1.492716 (0.355959) |\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) | 0.229448 / 0.018006 (0.211442) | 0.500300 / 0.000490 (0.499811) | 0.005195 / 0.000200 (0.004995) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031753 / 0.037411 (-0.005658) | 0.096075 / 0.014526 (0.081549) | 0.111476 / 0.176557 (-0.065081) | 0.179236 / 0.737135 (-0.557899) | 0.113599 / 0.296338 (-0.182739) |\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) | 0.472817 / 0.215209 (0.257608) | 4.715029 / 2.077655 (2.637374) | 2.417934 / 1.504120 (0.913814) | 2.235014 / 1.541195 (0.693819) | 2.323588 / 1.468490 (0.855098) | 0.553751 / 4.584777 (-4.031026) | 4.153467 / 3.745712 (0.407755) | 3.858836 / 5.269862 (-1.411025) | 2.377499 / 4.565676 (-2.188178) | 0.066528 / 0.424275 (-0.357747) | 0.008979 / 0.007607 (0.001372) | 0.561076 / 0.226044 (0.335032) | 5.609817 / 2.268929 (3.340888) | 3.011098 / 55.444624 (-52.433526) | 2.594162 / 6.876477 (-4.282314) | 2.863597 / 2.142072 (0.721525) | 0.681135 / 4.805227 (-4.124092) | 0.158863 / 6.500664 (-6.341801) | 0.072551 / 0.075469 (-0.002918) |\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) | 1.492230 / 1.841788 (-0.349558) | 23.028828 / 8.074308 (14.954519) | 16.663265 / 10.191392 (6.471873) | 0.173146 / 0.680424 (-0.507278) | 0.021635 / 0.534201 (-0.512566) | 0.478919 / 0.579283 (-0.100364) | 0.472908 / 0.434364 (0.038544) | 0.547248 / 0.540337 (0.006910) | 0.770288 / 1.386936 (-0.616648) |\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) | 0.007728 / 0.011353 (-0.003625) | 0.004477 / 0.011008 (-0.006531) | 0.074858 / 0.038508 (0.036350) | 0.084266 / 0.023109 (0.061157) | 0.420280 / 0.275898 (0.144382) | 0.466835 / 0.323480 (0.143356) | 0.005980 / 0.007986 (-0.002006) | 0.003600 / 0.004328 (-0.000729) | 0.074941 / 0.004250 (0.070691) | 0.066414 / 0.037052 (0.029361) | 0.425949 / 0.258489 (0.167460) | 0.473236 / 0.293841 (0.179395) | 0.037213 / 0.128546 (-0.091333) | 0.009743 / 0.075646 (-0.065903) | 0.083758 / 0.419271 (-0.335513) | 0.057916 / 0.043533 (0.014383) | 0.423031 / 0.255139 (0.167892) | 0.451107 / 0.283200 (0.167907) | 0.028577 / 0.141683 (-0.113106) | 1.810509 / 1.452155 (0.358354) | 1.875579 / 1.492716 (0.382863) |\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) | 0.296052 / 0.018006 (0.278046) | 0.496618 / 0.000490 (0.496128) | 0.028667 / 0.000200 (0.028467) | 0.000140 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036694 / 0.037411 (-0.000717) | 0.110873 / 0.014526 (0.096347) | 0.126550 / 0.176557 (-0.050007) | 0.182924 / 0.737135 (-0.554212) | 0.123793 / 0.296338 (-0.172545) |\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) | 0.509881 / 0.215209 (0.294672) | 5.067402 / 2.077655 (2.989747) | 2.696028 / 1.504120 (1.191908) | 2.489861 / 1.541195 (0.948666) | 2.563400 / 1.468490 (1.094910) | 0.571184 / 4.584777 (-4.013593) | 4.154231 / 3.745712 (0.408519) | 3.891004 / 5.269862 (-1.378858) | 2.435290 / 4.565676 (-2.130387) | 0.065825 / 0.424275 (-0.358450) | 0.008460 / 0.007607 (0.000853) | 0.597579 / 0.226044 (0.371534) | 5.914954 / 2.268929 (3.646025) | 3.219305 / 55.444624 (-52.225319) | 2.843548 / 6.876477 (-4.032929) | 3.070300 / 2.142072 (0.928228) | 0.686018 / 4.805227 (-4.119209) | 0.160077 / 6.500664 (-6.340587) | 0.074058 / 0.075469 (-0.001411) |\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) | 1.598748 / 1.841788 (-0.243039) | 23.475685 / 8.074308 (15.401377) | 17.257831 / 10.191392 (7.066439) | 0.176539 / 0.680424 (-0.503885) | 0.021969 / 0.534201 (-0.512232) | 0.473565 / 0.579283 (-0.105718) | 0.465471 / 0.434364 (0.031107) | 0.567107 / 0.540337 (0.026769) | 0.783757 / 1.386936 (-0.603179) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2f6bb450b4a3065a7d5fc50ea67711082749a337 \"CML watermark\")\n",
"Note that the https://github.com/huggingface/datasets-server/ explicitly relies on the fact that a split cannot contain a hyphen. cc @lhoestq ",
"We can't enable this that easily unfortunately because it could make arrow file names ambiguous in the cache.\r\n\r\ne.g. dataset_name-train-0000-of-0008.arrow",
"Oh, this would indeed make the caching for the multi-proc case ambiguous. Implementing this is only worth it if we get more requests, so I'm closing this PR for now."
] | 2023-08-22T13:30:59Z
| 2023-08-22T15:39:24Z
| 2023-08-22T15:38:53Z
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To fix https://discuss.huggingface.co/t/error-when-setting-up-the-dataset-viewer-streamingrowserror/51276.
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Update CommonVoice with new release
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"cc @patrickvonplaten?",
"Does anybody know if there is a bundled link, which would allow direct data download instead of manual? \r\nSomething similar to: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ab.tar.gz` ? cc @patil-suraj \r\n",
"Also see: https://github.com/common-voice/common-voice-bundler/issues/15"
] | 2021-07-29T15:59:59Z
| 2021-08-07T16:19:19Z
| null |
MEMBER
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## Adding a Dataset
- **Name:** CommonVoice mid-2021 release
- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).
- **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812
- **Data:** https://commonvoice.mozilla.org/en/datasets
- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add packaged builder configs to the documentation
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-10T13:34:19Z
| 2022-05-10T14:03:50Z
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Add the packaged builders configurations to the docs reference is useful to show the list of all parameters one can use when loading data in many formats: CSV, JSON, etc.
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Unable to Download CNN-Dailymail Dataset
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[
"#self-assign",
"@AngadSethi thanks for reporting and thanks for your PR!",
"Glad to help @albertvillanova! Just fine-tuning the PR, will comment once I am able to get it up and running 😀",
"Fixed by:\r\n- #3787"
] | 2022-02-25T05:24:47Z
| 2022-03-03T14:05:17Z
| 2022-03-03T14:05:17Z
|
NONE
| null | null | null |
## Describe the bug
I am unable to download the CNN-Dailymail dataset. Upon closer investigation, I realised why this was happening:
- The dataset sits in Google Drive, and both the CNN and DM datasets are large.
- Google is unable to scan the folder for viruses, **so the link which would originally download the dataset, now downloads the source code of this web page:**
- **This leads to the following error**:
```python
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
## Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train")
```
## Expected results
That the dataset is downloaded and processed just like other datasets.
## Actual results
Hit with this error:
```python
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1
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I_kwDODunzps5eouB0
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load_dataset in seaborn is not working for me. getting this error.
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[
"Hi! `seaborn`'s `load_dataset` pulls datasets from [here](https://github.com/mwaskom/seaborn-data) and not from our Hub, so this issue is not related to our library in any way and should be reported in their repo instead."
] | 2023-02-16T14:01:58Z
| 2023-02-16T14:44:36Z
| 2023-02-16T14:44:36Z
|
NONE
| null | null | null |
TimeoutError Traceback (most recent call last)
~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
1345 try:
-> 1346 h.request(req.get_method(), req.selector, req.data, headers,
1347 encode_chunked=req.has_header('Transfer-encoding'))
~\anaconda3\lib\http\client.py in request(self, method, url, body, headers, encode_chunked)
1278 """Send a complete request to the server."""
-> 1279 self._send_request(method, url, body, headers, encode_chunked)
1280
~\anaconda3\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked)
1324 body = _encode(body, 'body')
-> 1325 self.endheaders(body, encode_chunked=encode_chunked)
1326
~\anaconda3\lib\http\client.py in endheaders(self, message_body, encode_chunked)
1273 raise CannotSendHeader()
-> 1274 self._send_output(message_body, encode_chunked=encode_chunked)
1275
~\anaconda3\lib\http\client.py in _send_output(self, message_body, encode_chunked)
1033 del self._buffer[:]
-> 1034 self.send(msg)
1035
~\anaconda3\lib\http\client.py in send(self, data)
973 if self.auto_open:
--> 974 self.connect()
975 else:
~\anaconda3\lib\http\client.py in connect(self)
1440
-> 1441 super().connect()
1442
~\anaconda3\lib\http\client.py in connect(self)
944 """Connect to the host and port specified in __init__."""
--> 945 self.sock = self._create_connection(
946 (self.host,self.port), self.timeout, self.source_address)
~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address)
843 try:
--> 844 raise err
845 finally:
~\anaconda3\lib\socket.py in create_connection(address, timeout, source_address)
831 sock.bind(source_address)
--> 832 sock.connect(sa)
833 # Break explicitly a reference cycle
TimeoutError: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond
During handling of the above exception, another exception occurred:
URLError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_12220/2927704185.py in <module>
1 import seaborn as sn
----> 2 iris = sn.load_dataset('iris')
~\anaconda3\lib\site-packages\seaborn\utils.py in load_dataset(name, cache, data_home, **kws)
594 if name not in get_dataset_names():
595 raise ValueError(f"'{name}' is not one of the example datasets.")
--> 596 urlretrieve(url, cache_path)
597 full_path = cache_path
598 else:
~\anaconda3\lib\urllib\request.py in urlretrieve(url, filename, reporthook, data)
237 url_type, path = _splittype(url)
238
--> 239 with contextlib.closing(urlopen(url, data)) as fp:
240 headers = fp.info()
241
~\anaconda3\lib\urllib\request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
212 else:
213 opener = _opener
--> 214 return opener.open(url, data, timeout)
215
216 def install_opener(opener):
~\anaconda3\lib\urllib\request.py in open(self, fullurl, data, timeout)
515
516 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
--> 517 response = self._open(req, data)
518
519 # post-process response
~\anaconda3\lib\urllib\request.py in _open(self, req, data)
532
533 protocol = req.type
--> 534 result = self._call_chain(self.handle_open, protocol, protocol +
535 '_open', req)
536 if result:
~\anaconda3\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args)
492 for handler in handlers:
493 func = getattr(handler, meth_name)
--> 494 result = func(*args)
495 if result is not None:
496 return result
~\anaconda3\lib\urllib\request.py in https_open(self, req)
1387
1388 def https_open(self, req):
-> 1389 return self.do_open(http.client.HTTPSConnection, req,
1390 context=self._context, check_hostname=self._check_hostname)
1391
~\anaconda3\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
1347 encode_chunked=req.has_header('Transfer-encoding'))
1348 except OSError as err: # timeout error
-> 1349 raise URLError(err)
1350 r = h.getresponse()
1351 except:
URLError: <urlopen error [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond>
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|
Issues: Adding a FAISS or Elastic Search index to a Dataset
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[
"`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https://huggingface.co/transformers/master/model_doc/dpr.html).\r\n\r\nMoreover all the indexing features will also be available in the next release of `nlp`.",
"@lhoestq Thanks for the info ",
"@lhoestq I tried installing transformer from the master branch. Python imports for DPR again didnt' work. Anyways, Looking forward to trying it in the next release of nlp ",
"@nsankar have you tried with the latest version of the library?",
"@yjernite it worked. Thanks"
] | 2020-07-27T04:25:17Z
| 2020-10-28T01:46:24Z
| 2020-10-28T01:46:24Z
|
NONE
| null | null | null |
It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https://huggingface.co/nlp/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab. Is there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ?
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PR_kwDODunzps46oeju
| 4,604
|
Update CI Windows orb
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-30T11:00:31Z
| 2022-06-30T13:33:11Z
| 2022-06-30T13:22:26Z
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MEMBER
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This PR tries to fix recurrent random CI failures on Windows.
After 2 runs, it seems to have fixed the issue.
Fix #4603.
|
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| 3,358
|
add new field, and get errors
|
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[
"Hi, \r\n\r\ncould you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests? ",
"> Hi,\r\n> \r\n> could you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests?\r\n\r\nok."
] | 2021-12-01T16:35:38Z
| 2021-12-02T02:26:22Z
| 2021-12-02T02:26:22Z
|
NONE
| null | null | null |
after adding new field **tokenized_examples["example_id"]**, and get errors below,
I think it is due to changing data to tensor, and **tokenized_examples["example_id"]** is string list
**all fields**
```
***************** train_dataset 1: Dataset({
features: ['attention_mask', 'end_positions', 'example_id', 'input_ids', 'start_positions', 'token_type_ids'],
num_rows: 87714
})
```
**Errors**
```
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors
tensor = as_tensor(value)
ValueError: too many dimensions 'str'
```
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[LibriSpeech] Fix dev split local_extracted_archive for 'all' config
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"_The documentation is not available anymore as the PR was closed or merged._",
"This PR fixes a bug introduced in:\r\n- #4184"
] | 2022-08-27T10:04:57Z
| 2022-08-30T10:06:21Z
| 2022-08-30T10:03:25Z
|
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We define the keys for the `_DL_URLS` of the dev split as `dev.clean` and `dev.other`:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L60-L61
These keys get forwarded to the `dl_manager` and thus the `local_extracted_archive`.
However, when calling `SplitGenerator` for the dev sets, we query the `local_extracted_archive` keys `validation.clean` and `validation.other`:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L212
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L219
The consequence of this is that the `local_extracted_archive` arg passed to `_generate_examples` is always `None`, as the keys `validation.clean` and `validation.other` do not exists in the `local_extracted_archive`.
When defining the `audio_file` in `_generate_examples`, since `local_extracted_archive` is always `None`, we always omit the `local_extracted_archive` path from the `audio_file` path, **even** if in non-streaming mode:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L259-L263
Thus, `audio_file` will only ever be the streaming path (`audio_file`, not `os.path.join(local_extracted_archive, audio_file)`).
This PR fixes the `.get()` keys for the `local_extracted_archive` for the dev splits.
|
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Update dataset URL
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"Superseded by PR #2864.\r\n\r\n@mrm8488 next time you would like to work on an issue, you can first self-assign it to you (by writing `#self-assign` in a comment on the issue). That way, other people can see you are already working on it and there are not multiple people working on the same issue. 😉 "
] | 2021-09-02T05:22:18Z
| 2021-09-02T08:10:50Z
| 2021-09-02T08:10:50Z
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Config name / split name lost after map with multiproc
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[
"This must be due to DatasetInfo.from_merge which drops them and is used in `concatenate_datasets`.\r\n\r\nAnd you're experiencing this issue because multiprocessing does concatenate the resulting datasets from each process.\r\n\r\nMaybe they should be kept if all the subdatasets share the same values for config_name and split",
"That sounds like a clean workaround!"
] | 2023-06-19T17:27:36Z
| 2023-06-28T08:55:25Z
| null |
CONTRIBUTOR
| null | null | null |
### Describe the bug
Performing a `.map` method on a dataset loses it's config name / split name only if run with multiproc
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
from transformers import AutoFeatureExtractor
import numpy as np
# load dummy dataset
libri = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
# make train / test splits
libri = libri["validation"].train_test_split(seed=42, shuffle=True, test_size=0.1)
# example feature extractor
model_id = "ntu-spml/distilhubert"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True, return_attention_mask=True)
sampling_rate = feature_extractor.sampling_rate
libri = libri.cast_column("audio", Audio(sampling_rate=sampling_rate))
max_duration = 30.0
def preprocess_function(examples):
audio_arrays = [x["array"] for x in examples["audio"]]
inputs = feature_extractor(
audio_arrays,
sampling_rate=feature_extractor.sampling_rate,
max_length=int(feature_extractor.sampling_rate * max_duration),
truncation=True,
return_attention_mask=True,
)
return inputs
# single proc map
libri_encoded = libri.map(
preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=1
)
print(10 * "=" ,"Single processing", 10 * "=")
print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split)
print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split)
# multi proc map
libri_encoded = libri.map(
preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=2
)
print(10 * "=" ,"Multi processing", 10 * "=")
print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split)
print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split)
```
**Print Output:**
```
========== Single processing ==========
Config name before: clean Split name before: validation
Config name after: clean Split name after: validation
========== Multi processing ==========
Config name before: clean Split name before: validation
Config name after: None Split name after: None
```
=> we can see that the config/split names are lost in the multiprocessing setting
### Expected behavior
Should retain both config / split names in the multiproc setting
### Environment info
- `datasets` version: 2.13.1.dev0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2
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Import error installing with ppc64le
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] | 2021-10-13T21:37:23Z
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|
NONE
| null | null | null |
## Describe the bug
Installing the datasets library with a computer running with ppc64le seems to cause an issue when importing the datasets library.
```
python
Python 3.6.13 | packaged by conda-forge | (default, Sep 23 2021, 07:37:44)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import datasets
Illegal instruction (core dumped)
```
Error when importing
`Illegal instruction (core dumped)`
## Steps to reproduce the bug
I get this error when installing the library by using conda. I can't install with pip I believe because pyarrow only has the ppc64le library on conda forge
```
conda create --name transformers_py36_v2 python=3.6
conda activate transformers_py36_v2
conda install datasets
```
## Tracebacks
conda create --name transformers_py36_v2 python=3.6
```
Collecting package metadata (current_repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.9.2
latest version: 4.10.3
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2
added / updated specs:
- python=3.6
The following NEW packages will be INSTALLED:
_libgcc_mutex conda-forge/linux-ppc64le::_libgcc_mutex-0.1-conda_forge
_openmp_mutex conda-forge/linux-ppc64le::_openmp_mutex-4.5-1_gnu
ca-certificates conda-forge/linux-ppc64le::ca-certificates-2021.10.8-h1084571_0
certifi pkgs/main/linux-ppc64le::certifi-2020.12.5-py36h6ffa863_0
ld_impl_linux-ppc~ conda-forge/linux-ppc64le::ld_impl_linux-ppc64le-2.36.1-ha35d02b_2
libffi conda-forge/linux-ppc64le::libffi-3.4.2-h3b9df90_4
libgcc-ng conda-forge/linux-ppc64le::libgcc-ng-11.2.0-h7698a5e_11
libgomp conda-forge/linux-ppc64le::libgomp-11.2.0-h7698a5e_11
libstdcxx-ng conda-forge/linux-ppc64le::libstdcxx-ng-11.2.0-habdf983_11
libzlib conda-forge/linux-ppc64le::libzlib-1.2.11-h339bb43_1013
ncurses conda-forge/linux-ppc64le::ncurses-6.2-hea85c5d_4
openssl conda-forge/linux-ppc64le::openssl-1.1.1l-h4e0d66e_0
pip conda-forge/noarch::pip-21.3-pyhd8ed1ab_0
python conda-forge/linux-ppc64le::python-3.6.13-h57873ef_2_cpython
readline conda-forge/linux-ppc64le::readline-8.1-h5c45dff_0
setuptools pkgs/main/linux-ppc64le::setuptools-58.0.4-py36h6ffa863_0
sqlite conda-forge/linux-ppc64le::sqlite-3.36.0-h4e2196e_2
tk conda-forge/linux-ppc64le::tk-8.6.11-h41c6715_1
wheel conda-forge/noarch::wheel-0.37.0-pyhd8ed1ab_1
xz conda-forge/linux-ppc64le::xz-5.2.5-h6eb9509_1
zlib conda-forge/linux-ppc64le::zlib-1.2.11-h339bb43_1013
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate transformers_py36_v2
#
# To deactivate an active environment, use
#
# $ conda deactivate
```
conda activate transformers_py36_v2
conda install datasets
```
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.9.2
latest version: 4.10.3
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: /p/home/gerryc/.conda/envs/transformers_py36_v2
added / updated specs:
- datasets
The following NEW packages will be INSTALLED:
abseil-cpp conda-forge/linux-ppc64le::abseil-cpp-20210324.2-h3b9df90_0
aiohttp conda-forge/linux-ppc64le::aiohttp-3.7.4.post0-py36hc33305d_0
arrow-cpp conda-forge/linux-ppc64le::arrow-cpp-5.0.0-py36hf9cf308_8_cpu
async-timeout conda-forge/noarch::async-timeout-3.0.1-py_1000
attrs conda-forge/noarch::attrs-21.2.0-pyhd8ed1ab_0
aws-c-cal conda-forge/linux-ppc64le::aws-c-cal-0.5.11-hb3fac3d_0
aws-c-common conda-forge/linux-ppc64le::aws-c-common-0.6.2-h4e0d66e_0
aws-c-event-stream conda-forge/linux-ppc64le::aws-c-event-stream-0.2.7-h76da5f2_13
aws-c-io conda-forge/linux-ppc64le::aws-c-io-0.10.5-hf6a6c7c_0
aws-checksums conda-forge/linux-ppc64le::aws-checksums-0.1.11-hfe76d68_7
aws-sdk-cpp conda-forge/linux-ppc64le::aws-sdk-cpp-1.8.186-h90855e8_3
brotlipy conda-forge/linux-ppc64le::brotlipy-0.7.0-py36hc33305d_1001
bzip2 conda-forge/linux-ppc64le::bzip2-1.0.8-h4e0d66e_4
c-ares conda-forge/linux-ppc64le::c-ares-1.17.2-h4e0d66e_0
cffi conda-forge/linux-ppc64le::cffi-1.14.6-py36h021ab3c_1
chardet conda-forge/linux-ppc64le::chardet-4.0.0-py36h270354c_1
colorama conda-forge/noarch::colorama-0.4.4-pyh9f0ad1d_0
cryptography conda-forge/linux-ppc64le::cryptography-3.4.7-py36hc71b123_0
dataclasses conda-forge/noarch::dataclasses-0.8-pyh787bdff_2
datasets conda-forge/noarch::datasets-1.12.1-pyhd8ed1ab_1
dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0
filelock conda-forge/noarch::filelock-3.3.0-pyhd8ed1ab_0
fsspec conda-forge/noarch::fsspec-2021.10.0-pyhd8ed1ab_0
gflags conda-forge/linux-ppc64le::gflags-2.2.2-hb209c28_1004
glog conda-forge/linux-ppc64le::glog-0.5.0-h4040248_0
grpc-cpp conda-forge/linux-ppc64le::grpc-cpp-1.40.0-h2bf711c_2
huggingface_hub conda-forge/noarch::huggingface_hub-0.0.19-pyhd8ed1ab_0
idna conda-forge/noarch::idna-2.10-pyh9f0ad1d_0
idna_ssl conda-forge/noarch::idna_ssl-1.0.0-0
importlib-metadata conda-forge/linux-ppc64le::importlib-metadata-4.8.1-py36h270354c_0
importlib_metadata conda-forge/noarch::importlib_metadata-4.8.1-hd8ed1ab_0
krb5 conda-forge/linux-ppc64le::krb5-1.19.2-haf43566_2
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libnghttp2 conda-forge/linux-ppc64le::libnghttp2-1.43.0-h42039ad_1
libopenblas conda-forge/linux-ppc64le::libopenblas-0.3.17-pthreads_h486567c_1
libprotobuf conda-forge/linux-ppc64le::libprotobuf-3.18.1-h690f14c_0
libssh2 conda-forge/linux-ppc64le::libssh2-1.10.0-ha5a9321_2
libthrift conda-forge/linux-ppc64le::libthrift-0.15.0-h54f692e_1
libutf8proc conda-forge/linux-ppc64le::libutf8proc-2.6.1-h4e0d66e_0
lz4-c conda-forge/linux-ppc64le::lz4-c-1.9.3-h3b9df90_1
multidict conda-forge/linux-ppc64le::multidict-5.2.0-py36hc33305d_0
multiprocess conda-forge/linux-ppc64le::multiprocess-0.70.12.2-py36hc33305d_0
numpy conda-forge/linux-ppc64le::numpy-1.19.5-py36h86665d4_1
orc conda-forge/linux-ppc64le::orc-1.7.0-hae6b4bd_0
packaging conda-forge/noarch::packaging-21.0-pyhd8ed1ab_0
pandas conda-forge/linux-ppc64le::pandas-1.1.5-py36hab1a6e6_0
parquet-cpp conda-forge/noarch::parquet-cpp-1.5.1-2
pyarrow conda-forge/linux-ppc64le::pyarrow-5.0.0-py36h7a46c7e_8_cpu
pycparser conda-forge/noarch::pycparser-2.20-pyh9f0ad1d_2
pyopenssl conda-forge/noarch::pyopenssl-21.0.0-pyhd8ed1ab_0
pyparsing conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0
pysocks conda-forge/linux-ppc64le::pysocks-1.7.1-py36h270354c_3
python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0
python-xxhash conda-forge/linux-ppc64le::python-xxhash-2.0.2-py36hc33305d_0
python_abi conda-forge/linux-ppc64le::python_abi-3.6-2_cp36m
pytz conda-forge/noarch::pytz-2021.3-pyhd8ed1ab_0
pyyaml conda-forge/linux-ppc64le::pyyaml-5.4.1-py36hc33305d_1
re2 conda-forge/linux-ppc64le::re2-2021.09.01-h3b9df90_0
requests conda-forge/noarch::requests-2.25.1-pyhd3deb0d_0
s2n conda-forge/linux-ppc64le::s2n-1.0.10-h97db324_0
six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0
snappy conda-forge/linux-ppc64le::snappy-1.1.8-hb209c28_3
tqdm conda-forge/noarch::tqdm-4.62.3-pyhd8ed1ab_0
typing-extensions conda-forge/noarch::typing-extensions-3.10.0.2-hd8ed1ab_0
typing_extensions conda-forge/noarch::typing_extensions-3.10.0.2-pyha770c72_0
urllib3 conda-forge/noarch::urllib3-1.26.7-pyhd8ed1ab_0
xxhash conda-forge/linux-ppc64le::xxhash-0.8.0-h4e0d66e_3
yaml conda-forge/linux-ppc64le::yaml-0.2.5-h6eb9509_0
yarl conda-forge/linux-ppc64le::yarl-1.6.3-py36hc33305d_2
zipp conda-forge/noarch::zipp-3.6.0-pyhd8ed1ab_0
zstd conda-forge/linux-ppc64le::zstd-1.5.0-h65c4b1a_0
The following packages will be UPDATED:
certifi pkgs/main::certifi-2020.12.5-py36h6ff~ --> conda-forge::certifi-2021.5.30-py36h270354c_0
Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Red Hat Enterprise Linux 8.2 (Ootpa)
- Python version: 3.6
- PyArrow version: pyarrow - 5.0.0 - py36h7a46c7e_8_cpu - conda-forge
Any help would be appreciated! I've been struggling on installing datasets on this machine.
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Fix shard retry mechanism in `push_to_hub`
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[
"@Wauplin Maybe `504` should be added to the `retry_on_status_codes` tuple [here](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300) to guard against https://github.com/huggingface/datasets/issues/3872",
"We could but I'm not sure to have witness a 504 on S3 before. The issue reported in https://github.com/huggingface/datasets/issues/3872 is a 504 on the `/upload` endpoint on the Hub and this is not an endpoint that is retried on [this line](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300).",
"<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) | 0.005110 / 0.011353 (-0.006243) | 0.003307 / 0.011008 (-0.007701) | 0.062601 / 0.038508 (0.024093) | 0.049644 / 0.023109 (0.026534) | 0.243195 / 0.275898 (-0.032703) | 0.273543 / 0.323480 (-0.049936) | 0.003862 / 0.007986 (-0.004123) | 0.002624 / 0.004328 (-0.001705) | 0.048273 / 0.004250 (0.044023) | 0.037820 / 0.037052 (0.000768) | 0.249134 / 0.258489 (-0.009355) | 0.319359 / 0.293841 (0.025518) | 0.027816 / 0.128546 (-0.100730) | 0.010422 / 0.075646 (-0.065225) | 0.206607 / 0.419271 (-0.212665) | 0.035719 / 0.043533 (-0.007814) | 0.250300 / 0.255139 (-0.004839) | 0.290377 / 0.283200 (0.007177) | 0.018459 / 0.141683 (-0.123224) | 1.114664 / 1.452155 (-0.337490) | 1.171429 / 1.492716 (-0.321288) |\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) | 0.091483 / 0.018006 (0.073477) | 0.302770 / 0.000490 (0.302281) | 0.000203 / 0.000200 (0.000003) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018870 / 0.037411 (-0.018541) | 0.062692 / 0.014526 (0.048166) | 0.075381 / 0.176557 (-0.101176) | 0.122338 / 0.737135 (-0.614797) | 0.075608 / 0.296338 (-0.220730) |\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) | 0.288115 / 0.215209 (0.072906) | 2.816183 / 2.077655 (0.738528) | 1.535601 / 1.504120 (0.031481) | 1.409546 / 1.541195 (-0.131648) | 1.438569 / 1.468490 (-0.029921) | 0.561797 / 4.584777 (-4.022980) | 2.373921 / 3.745712 (-1.371791) | 2.739437 / 5.269862 (-2.530424) | 1.750921 / 4.565676 (-2.814755) | 0.062114 / 0.424275 (-0.362161) | 0.004965 / 0.007607 (-0.002642) | 0.348614 / 0.226044 (0.122569) | 3.519631 / 2.268929 (1.250703) | 1.910797 / 55.444624 (-53.533827) | 1.610541 / 6.876477 (-5.265936) | 1.617972 / 2.142072 (-0.524100) | 0.639421 / 4.805227 (-4.165806) | 0.117371 / 6.500664 (-6.383293) | 0.041851 / 0.075469 (-0.033618) |\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) | 0.945563 / 1.841788 (-0.896224) | 11.362399 / 8.074308 (3.288090) | 10.468468 / 10.191392 (0.277075) | 0.128925 / 0.680424 (-0.551499) | 0.013892 / 0.534201 (-0.520309) | 0.285487 / 0.579283 (-0.293796) | 0.269295 / 0.434364 (-0.165069) | 0.324843 / 0.540337 (-0.215495) | 0.438452 / 1.386936 (-0.948484) |\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) | 0.005303 / 0.011353 (-0.006050) | 0.003162 / 0.011008 (-0.007846) | 0.048177 / 0.038508 (0.009669) | 0.048708 / 0.023109 (0.025599) | 0.271663 / 0.275898 (-0.004235) | 0.289948 / 0.323480 (-0.033532) | 0.003955 / 0.007986 (-0.004030) | 0.002616 / 0.004328 (-0.001713) | 0.047510 / 0.004250 (0.043260) | 0.039938 / 0.037052 (0.002886) | 0.277449 / 0.258489 (0.018960) | 0.300315 / 0.293841 (0.006474) | 0.029263 / 0.128546 (-0.099283) | 0.010403 / 0.075646 (-0.065244) | 0.056682 / 0.419271 (-0.362590) | 0.032757 / 0.043533 (-0.010776) | 0.273291 / 0.255139 (0.018152) | 0.289023 / 0.283200 (0.005824) | 0.017843 / 0.141683 (-0.123840) | 1.124762 / 1.452155 (-0.327393) | 1.176646 / 1.492716 (-0.316070) |\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) | 0.004568 / 0.018006 (-0.013438) | 0.300715 / 0.000490 (0.300225) | 0.000212 / 0.000200 (0.000012) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021528 / 0.037411 (-0.015883) | 0.068317 / 0.014526 (0.053792) | 0.081358 / 0.176557 (-0.095199) | 0.119297 / 0.737135 (-0.617838) | 0.082445 / 0.296338 (-0.213893) |\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) | 0.289681 / 0.215209 (0.074472) | 2.843862 / 2.077655 (0.766208) | 1.574257 / 1.504120 (0.070137) | 1.454026 / 1.541195 (-0.087169) | 1.478379 / 1.468490 (0.009889) | 0.558259 / 4.584777 (-4.026518) | 2.513261 / 3.745712 (-1.232451) | 2.759751 / 5.269862 (-2.510111) | 1.730335 / 4.565676 (-2.835341) | 0.063805 / 0.424275 (-0.360470) | 0.004991 / 0.007607 (-0.002616) | 0.346586 / 0.226044 (0.120542) | 3.369163 / 2.268929 (1.100234) | 1.934734 / 55.444624 (-53.509890) | 1.658864 / 6.876477 (-5.217613) | 1.645621 / 2.142072 (-0.496452) | 0.636633 / 4.805227 (-4.168594) | 0.116839 / 6.500664 (-6.383825) | 0.040863 / 0.075469 (-0.034606) |\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) | 0.960925 / 1.841788 (-0.880863) | 11.769189 / 8.074308 (3.694881) | 10.713662 / 10.191392 (0.522270) | 0.140510 / 0.680424 (-0.539914) | 0.015424 / 0.534201 (-0.518777) | 0.288039 / 0.579283 (-0.291244) | 0.277623 / 0.434364 (-0.156741) | 0.322622 / 0.540337 (-0.217716) | 0.539805 / 1.386936 (-0.847131) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07ad81c15bd3b954defe779fc37ba5f432f5ff2a \"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) | 0.005501 / 0.011353 (-0.005852) | 0.003754 / 0.011008 (-0.007254) | 0.062628 / 0.038508 (0.024120) | 0.059951 / 0.023109 (0.036842) | 0.254851 / 0.275898 (-0.021047) | 0.272133 / 0.323480 (-0.051347) | 0.003962 / 0.007986 (-0.004024) | 0.002759 / 0.004328 (-0.001569) | 0.048412 / 0.004250 (0.044161) | 0.039349 / 0.037052 (0.002297) | 0.253093 / 0.258489 (-0.005397) | 0.287048 / 0.293841 (-0.006793) | 0.027197 / 0.128546 (-0.101349) | 0.010828 / 0.075646 (-0.064819) | 0.206371 / 0.419271 (-0.212901) | 0.035881 / 0.043533 (-0.007652) | 0.254905 / 0.255139 (-0.000234) | 0.273819 / 0.283200 (-0.009381) | 0.018041 / 0.141683 (-0.123642) | 1.103970 / 1.452155 (-0.348185) | 1.166340 / 1.492716 (-0.326377) |\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) | 0.093196 / 0.018006 (0.075190) | 0.302690 / 0.000490 (0.302200) | 0.000219 / 0.000200 (0.000019) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019552 / 0.037411 (-0.017860) | 0.062337 / 0.014526 (0.047811) | 0.074070 / 0.176557 (-0.102486) | 0.120998 / 0.737135 (-0.616137) | 0.076265 / 0.296338 (-0.220074) |\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) | 0.272637 / 0.215209 (0.057427) | 2.693350 / 2.077655 (0.615696) | 1.398020 / 1.504120 (-0.106100) | 1.285706 / 1.541195 (-0.255488) | 1.342810 / 1.468490 (-0.125680) | 0.565378 / 4.584777 (-4.019399) | 2.390131 / 3.745712 (-1.355581) | 2.892137 / 5.269862 (-2.377725) | 1.819840 / 4.565676 (-2.745836) | 0.062789 / 0.424275 (-0.361486) | 0.004920 / 0.007607 (-0.002687) | 0.329281 / 0.226044 (0.103237) | 3.261664 / 2.268929 (0.992735) | 1.775102 / 55.444624 (-53.669523) | 1.514341 / 6.876477 (-5.362136) | 1.530805 / 2.142072 (-0.611267) | 0.641009 / 4.805227 (-4.164218) | 0.118626 / 6.500664 (-6.382038) | 0.042732 / 0.075469 (-0.032737) |\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) | 0.933179 / 1.841788 (-0.908609) | 12.085247 / 8.074308 (4.010939) | 10.541596 / 10.191392 (0.350204) | 0.140141 / 0.680424 (-0.540283) | 0.014646 / 0.534201 (-0.519555) | 0.289640 / 0.579283 (-0.289643) | 0.281042 / 0.434364 (-0.153322) | 0.326462 / 0.540337 (-0.213876) | 0.441981 / 1.386936 (-0.944955) |\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) | 0.005259 / 0.011353 (-0.006094) | 0.003766 / 0.011008 (-0.007242) | 0.048782 / 0.038508 (0.010273) | 0.064946 / 0.023109 (0.041836) | 0.264529 / 0.275898 (-0.011369) | 0.289675 / 0.323480 (-0.033805) | 0.004057 / 0.007986 (-0.003928) | 0.002805 / 0.004328 (-0.001523) | 0.047709 / 0.004250 (0.043459) | 0.041149 / 0.037052 (0.004096) | 0.271254 / 0.258489 (0.012765) | 0.296685 / 0.293841 (0.002844) | 0.029486 / 0.128546 (-0.099060) | 0.010608 / 0.075646 (-0.065038) | 0.056392 / 0.419271 (-0.362879) | 0.033181 / 0.043533 (-0.010352) | 0.267029 / 0.255139 (0.011890) | 0.284987 / 0.283200 (0.001787) | 0.018045 / 0.141683 (-0.123637) | 1.137358 / 1.452155 (-0.314796) | 1.184007 / 1.492716 (-0.308709) |\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) | 0.004603 / 0.018006 (-0.013403) | 0.303901 / 0.000490 (0.303411) | 0.000225 / 0.000200 (0.000025) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021957 / 0.037411 (-0.015454) | 0.069427 / 0.014526 (0.054901) | 0.082394 / 0.176557 (-0.094163) | 0.120745 / 0.737135 (-0.616390) | 0.084571 / 0.296338 (-0.211767) |\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) | 0.292832 / 0.215209 (0.077623) | 2.824295 / 2.077655 (0.746640) | 1.563273 / 1.504120 (0.059153) | 1.440202 / 1.541195 (-0.100992) | 1.489810 / 1.468490 (0.021320) | 0.561120 / 4.584777 (-4.023657) | 2.439045 / 3.745712 (-1.306667) | 2.867139 / 5.269862 (-2.402722) | 1.793812 / 4.565676 (-2.771865) | 0.062797 / 0.424275 (-0.361478) | 0.005033 / 0.007607 (-0.002574) | 0.343648 / 0.226044 (0.117604) | 3.432285 / 2.268929 (1.163357) | 1.918175 / 55.444624 (-53.526449) | 1.637245 / 6.876477 (-5.239232) | 1.709246 / 2.142072 (-0.432826) | 0.634744 / 4.805227 (-4.170483) | 0.115782 / 6.500664 (-6.384882) | 0.041228 / 0.075469 (-0.034241) |\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) | 0.962369 / 1.841788 (-0.879418) | 12.750819 / 8.074308 (4.676511) | 10.927356 / 10.191392 (0.735964) | 0.143454 / 0.680424 (-0.536970) | 0.015348 / 0.534201 (-0.518853) | 0.291207 / 0.579283 (-0.288076) | 0.276924 / 0.434364 (-0.157440) | 0.327287 / 0.540337 (-0.213050) | 0.577439 / 1.386936 (-0.809497) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#544ad95f6b6da7fee44a2bc838e15a5e0156c946 \"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) | 0.005070 / 0.011353 (-0.006283) | 0.003475 / 0.011008 (-0.007533) | 0.061985 / 0.038508 (0.023477) | 0.048539 / 0.023109 (0.025430) | 0.229935 / 0.275898 (-0.045963) | 0.255247 / 0.323480 (-0.068233) | 0.003919 / 0.007986 (-0.004066) | 0.002664 / 0.004328 (-0.001664) | 0.048892 / 0.004250 (0.044642) | 0.037381 / 0.037052 (0.000328) | 0.238517 / 0.258489 (-0.019972) | 0.284069 / 0.293841 (-0.009772) | 0.027513 / 0.128546 (-0.101033) | 0.010778 / 0.075646 (-0.064868) | 0.205004 / 0.419271 (-0.214268) | 0.035553 / 0.043533 (-0.007980) | 0.230117 / 0.255139 (-0.025022) | 0.251150 / 0.283200 (-0.032050) | 0.017951 / 0.141683 (-0.123732) | 1.145548 / 1.452155 (-0.306607) | 1.191659 / 1.492716 (-0.301057) |\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) | 0.092335 / 0.018006 (0.074329) | 0.300264 / 0.000490 (0.299774) | 0.000206 / 0.000200 (0.000006) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018608 / 0.037411 (-0.018804) | 0.060376 / 0.014526 (0.045850) | 0.073551 / 0.176557 (-0.103006) | 0.118840 / 0.737135 (-0.618295) | 0.074447 / 0.296338 (-0.221892) |\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) | 0.287033 / 0.215209 (0.071824) | 2.770958 / 2.077655 (0.693303) | 1.443986 / 1.504120 (-0.060134) | 1.314627 / 1.541195 (-0.226567) | 1.342287 / 1.468490 (-0.126203) | 0.559607 / 4.584777 (-4.025170) | 2.409678 / 3.745712 (-1.336034) | 2.772566 / 5.269862 (-2.497295) | 1.743511 / 4.565676 (-2.822165) | 0.062277 / 0.424275 (-0.361998) | 0.004952 / 0.007607 (-0.002655) | 0.330581 / 0.226044 (0.104537) | 3.280385 / 2.268929 (1.011456) | 1.809599 / 55.444624 (-53.635025) | 1.532186 / 6.876477 (-5.344290) | 1.529689 / 2.142072 (-0.612383) | 0.645213 / 4.805227 (-4.160014) | 0.117564 / 6.500664 (-6.383100) | 0.041657 / 0.075469 (-0.033812) |\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) | 0.943912 / 1.841788 (-0.897876) | 11.414317 / 8.074308 (3.340009) | 10.394915 / 10.191392 (0.203523) | 0.129271 / 0.680424 (-0.551153) | 0.013934 / 0.534201 (-0.520267) | 0.288217 / 0.579283 (-0.291066) | 0.267171 / 0.434364 (-0.167193) | 0.327112 / 0.540337 (-0.213225) | 0.446680 / 1.386936 (-0.940256) |\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) | 0.005200 / 0.011353 (-0.006152) | 0.003453 / 0.011008 (-0.007555) | 0.048736 / 0.038508 (0.010228) | 0.051073 / 0.023109 (0.027964) | 0.276591 / 0.275898 (0.000693) | 0.294495 / 0.323480 (-0.028985) | 0.004069 / 0.007986 (-0.003917) | 0.002945 / 0.004328 (-0.001383) | 0.047090 / 0.004250 (0.042839) | 0.040445 / 0.037052 (0.003393) | 0.278464 / 0.258489 (0.019975) | 0.304020 / 0.293841 (0.010179) | 0.028811 / 0.128546 (-0.099736) | 0.010388 / 0.075646 (-0.065259) | 0.057214 / 0.419271 (-0.362057) | 0.032588 / 0.043533 (-0.010945) | 0.277694 / 0.255139 (0.022555) | 0.294979 / 0.283200 (0.011779) | 0.018384 / 0.141683 (-0.123299) | 1.162332 / 1.452155 (-0.289822) | 1.188355 / 1.492716 (-0.304361) |\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) | 0.090501 / 0.018006 (0.072495) | 0.303122 / 0.000490 (0.302632) | 0.000222 / 0.000200 (0.000022) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022536 / 0.037411 (-0.014876) | 0.068452 / 0.014526 (0.053926) | 0.080932 / 0.176557 (-0.095625) | 0.119185 / 0.737135 (-0.617950) | 0.081513 / 0.296338 (-0.214825) |\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) | 0.291522 / 0.215209 (0.076313) | 2.849467 / 2.077655 (0.771812) | 1.597395 / 1.504120 (0.093275) | 1.512872 / 1.541195 (-0.028323) | 1.488144 / 1.468490 (0.019654) | 0.572436 / 4.584777 (-4.012341) | 2.440129 / 3.745712 (-1.305583) | 2.788045 / 5.269862 (-2.481817) | 1.754246 / 4.565676 (-2.811430) | 0.066706 / 0.424275 (-0.357569) | 0.005035 / 0.007607 (-0.002573) | 0.336621 / 0.226044 (0.110576) | 3.322820 / 2.268929 (1.053891) | 1.940494 / 55.444624 (-53.504130) | 1.670022 / 6.876477 (-5.206454) | 1.666353 / 2.142072 (-0.475720) | 0.646180 / 4.805227 (-4.159047) | 0.116676 / 6.500664 (-6.383988) | 0.040559 / 0.075469 (-0.034910) |\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) | 0.971396 / 1.841788 (-0.870392) | 11.782426 / 8.074308 (3.708118) | 10.672034 / 10.191392 (0.480642) | 0.137658 / 0.680424 (-0.542766) | 0.016210 / 0.534201 (-0.517991) | 0.288302 / 0.579283 (-0.290981) | 0.280775 / 0.434364 (-0.153589) | 0.326962 / 0.540337 (-0.213375) | 0.558511 / 1.386936 (-0.828425) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76020180407d7ea9a0b535758d8d1b241fd19d8c \"CML watermark\")\n"
] | 2023-11-30T14:57:14Z
| 2023-12-01T17:57:39Z
| 2023-12-01T17:51:33Z
|
CONTRIBUTOR
| null | 0
|
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When it fails, `preupload_lfs_files` throws a [`RuntimeError`](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/_commit_api.py#L402) error and chains the original HTTP error. This PR modifies the retry mechanism's error handling to account for that.
Fix https://github.com/huggingface/datasets/issues/6392
|
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MDExOlB1bGxSZXF1ZXN0NTM2MjA3NDcx
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adding snow_simplified_japanese_corpus
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[
"merging since the CI is fixed on master",
"Thank you for the updates and merging!"
] | 2020-12-10T19:45:03Z
| 2020-12-17T13:22:48Z
| 2020-12-17T11:25:34Z
|
CONTRIBUTOR
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Adding simplified Japanese corpus "SNOW T15" and "SNOW T23".
They contain original Japanese, simplified Japanese, and original English (the original text is gotten from en-ja translation corpus). Hence, it can be used not only for Japanese simplification but also for en-ja translation.
- http://www.jnlp.org/SNOW/T15
- http://www.jnlp.org/SNOW/T23
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PR_kwDODunzps4-S0we
| 4,927
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fix BLEU metric card
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[] | 2022-09-02T17:00:56Z
| 2022-09-09T16:28:15Z
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CONTRIBUTOR
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I've fixed some typos in BLEU metric card.
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PR_kwDODunzps5POJo8
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Fix spark imports
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010844 / 0.011353 (-0.000509) | 0.007329 / 0.011008 (-0.003680) | 0.133764 / 0.038508 (0.095256) | 0.040213 / 0.023109 (0.017103) | 0.413466 / 0.275898 (0.137568) | 0.452860 / 0.323480 (0.129380) | 0.008109 / 0.007986 (0.000123) | 0.005773 / 0.004328 (0.001444) | 0.109969 / 0.004250 (0.105718) | 0.053001 / 0.037052 (0.015949) | 0.416377 / 0.258489 (0.157888) | 0.477486 / 0.293841 (0.183645) | 0.056556 / 0.128546 (-0.071990) | 0.024322 / 0.075646 (-0.051324) | 0.437750 / 0.419271 (0.018479) | 0.087732 / 0.043533 (0.044199) | 0.421540 / 0.255139 (0.166401) | 0.429143 / 0.283200 (0.145944) | 0.144864 / 0.141683 (0.003181) | 1.882785 / 1.452155 (0.430631) | 1.980721 / 1.492716 (0.488005) |\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) | 0.285497 / 0.018006 (0.267491) | 0.601820 / 0.000490 (0.601331) | 0.005003 / 0.000200 (0.004804) | 0.000122 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030673 / 0.037411 (-0.006739) | 0.126883 / 0.014526 (0.112357) | 0.137677 / 0.176557 (-0.038880) | 0.211504 / 0.737135 (-0.525632) | 0.144752 / 0.296338 (-0.151587) |\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) | 0.665845 / 0.215209 (0.450636) | 6.369040 / 2.077655 (4.291385) | 2.708979 / 1.504120 (1.204859) | 2.370842 / 1.541195 (0.829647) | 2.445987 / 1.468490 (0.977497) | 1.260806 / 4.584777 (-3.323971) | 5.979216 / 3.745712 (2.233504) | 3.334350 / 5.269862 (-1.935512) | 2.187298 / 4.565676 (-2.378379) | 0.155494 / 0.424275 (-0.268781) | 0.017351 / 0.007607 (0.009744) | 0.853626 / 0.226044 (0.627581) | 8.375001 / 2.268929 (6.106072) | 3.528312 / 55.444624 (-51.916313) | 2.890509 / 6.876477 (-3.985968) | 3.051016 / 2.142072 (0.908944) | 1.529811 / 4.805227 (-3.275416) | 0.273883 / 6.500664 (-6.226781) | 0.086617 / 0.075469 (0.011148) |\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) | 1.648231 / 1.841788 (-0.193557) | 19.487109 / 8.074308 (11.412801) | 23.474621 / 10.191392 (13.283229) | 0.221392 / 0.680424 (-0.459032) | 0.028878 / 0.534201 (-0.505323) | 0.582302 / 0.579283 (0.003019) | 0.615059 / 0.434364 (0.180695) | 0.656082 / 0.540337 (0.115745) | 0.740544 / 1.386936 (-0.646392) |\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) | 0.010687 / 0.011353 (-0.000665) | 0.007114 / 0.011008 (-0.003894) | 0.135426 / 0.038508 (0.096918) | 0.041027 / 0.023109 (0.017918) | 0.466441 / 0.275898 (0.190543) | 0.503545 / 0.323480 (0.180065) | 0.009418 / 0.007986 (0.001432) | 0.004976 / 0.004328 (0.000647) | 0.101342 / 0.004250 (0.097092) | 0.058289 / 0.037052 (0.021237) | 0.473715 / 0.258489 (0.215226) | 0.539556 / 0.293841 (0.245715) | 0.063138 / 0.128546 (-0.065408) | 0.020429 / 0.075646 (-0.055217) | 0.124179 / 0.419271 (-0.295093) | 0.066400 / 0.043533 (0.022867) | 0.450793 / 0.255139 (0.195654) | 0.494163 / 0.283200 (0.210964) | 0.131179 / 0.141683 (-0.010504) | 1.876396 / 1.452155 (0.424241) | 1.974148 / 1.492716 (0.481432) |\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) | 0.313362 / 0.018006 (0.295356) | 0.602618 / 0.000490 (0.602129) | 0.008279 / 0.000200 (0.008079) | 0.000155 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037250 / 0.037411 (-0.000161) | 0.144151 / 0.014526 (0.129625) | 0.155733 / 0.176557 (-0.020824) | 0.214334 / 0.737135 (-0.522801) | 0.167124 / 0.296338 (-0.129214) |\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) | 0.686471 / 0.215209 (0.471262) | 6.749174 / 2.077655 (4.671520) | 3.024941 / 1.504120 (1.520821) | 2.553363 / 1.541195 (1.012168) | 2.679107 / 1.468490 (1.210617) | 1.317212 / 4.584777 (-3.267565) | 5.917575 / 3.745712 (2.171862) | 3.412715 / 5.269862 (-1.857146) | 2.203478 / 4.565676 (-2.362198) | 0.150387 / 0.424275 (-0.273888) | 0.015977 / 0.007607 (0.008370) | 0.862999 / 0.226044 (0.636954) | 8.706459 / 2.268929 (6.437530) | 3.762648 / 55.444624 (-51.681977) | 2.992544 / 6.876477 (-3.883933) | 3.135796 / 2.142072 (0.993724) | 1.504140 / 4.805227 (-3.301088) | 0.268265 / 6.500664 (-6.232399) | 0.083297 / 0.075469 (0.007828) |\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) | 1.690193 / 1.841788 (-0.151594) | 19.912854 / 8.074308 (11.838546) | 23.568217 / 10.191392 (13.376825) | 0.285125 / 0.680424 (-0.395299) | 0.030593 / 0.534201 (-0.503608) | 0.565305 / 0.579283 (-0.013978) | 0.659283 / 0.434364 (0.224919) | 0.678864 / 0.540337 (0.138527) | 0.793634 / 1.386936 (-0.593302) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d0edbe3f3258b7e580d1b58c0eea6637b5e22b2 \"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) | 0.011615 / 0.011353 (0.000262) | 0.006716 / 0.011008 (-0.004292) | 0.146868 / 0.038508 (0.108360) | 0.037621 / 0.023109 (0.014512) | 0.425563 / 0.275898 (0.149664) | 0.483217 / 0.323480 (0.159737) | 0.007830 / 0.007986 (-0.000156) | 0.005940 / 0.004328 (0.001612) | 0.100771 / 0.004250 (0.096521) | 0.063907 / 0.037052 (0.026854) | 0.422993 / 0.258489 (0.164503) | 0.496514 / 0.293841 (0.202673) | 0.056004 / 0.128546 (-0.072542) | 0.021441 / 0.075646 (-0.054206) | 0.453589 / 0.419271 (0.034317) | 0.067555 / 0.043533 (0.024022) | 0.442490 / 0.255139 (0.187351) | 0.503941 / 0.283200 (0.220742) | 0.134023 / 0.141683 (-0.007660) | 1.886329 / 1.452155 (0.434175) | 2.030867 / 1.492716 (0.538150) |\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) | 0.288063 / 0.018006 (0.270057) | 0.627177 / 0.000490 (0.626687) | 0.006335 / 0.000200 (0.006135) | 0.000171 / 0.000054 (0.000116) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032424 / 0.037411 (-0.004987) | 0.132749 / 0.014526 (0.118223) | 0.144727 / 0.176557 (-0.031829) | 0.232577 / 0.737135 (-0.504558) | 0.157315 / 0.296338 (-0.139024) |\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) | 0.623058 / 0.215209 (0.407849) | 6.272447 / 2.077655 (4.194792) | 2.506778 / 1.504120 (1.002658) | 2.203094 / 1.541195 (0.661899) | 2.346972 / 1.468490 (0.878482) | 1.358498 / 4.584777 (-3.226279) | 5.879670 / 3.745712 (2.133958) | 5.818406 / 5.269862 (0.548545) | 3.231936 / 4.565676 (-1.333741) | 0.154013 / 0.424275 (-0.270263) | 0.021541 / 0.007607 (0.013934) | 0.823746 / 0.226044 (0.597702) | 8.140304 / 2.268929 (5.871375) | 3.366911 / 55.444624 (-52.077714) | 2.696856 / 6.876477 (-4.179621) | 2.845743 / 2.142072 (0.703671) | 1.522363 / 4.805227 (-3.282864) | 0.278938 / 6.500664 (-6.221726) | 0.085044 / 0.075469 (0.009575) |\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) | 1.681348 / 1.841788 (-0.160440) | 19.686703 / 8.074308 (11.612395) | 22.995655 / 10.191392 (12.804263) | 0.218876 / 0.680424 (-0.461548) | 0.029334 / 0.534201 (-0.504867) | 0.560846 / 0.579283 (-0.018438) | 0.645210 / 0.434364 (0.210846) | 0.697842 / 0.540337 (0.157505) | 0.832875 / 1.386936 (-0.554061) |\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) | 0.009509 / 0.011353 (-0.001844) | 0.006471 / 0.011008 (-0.004537) | 0.101477 / 0.038508 (0.062969) | 0.035281 / 0.023109 (0.012171) | 0.470032 / 0.275898 (0.194134) | 0.501475 / 0.323480 (0.177995) | 0.007641 / 0.007986 (-0.000344) | 0.006784 / 0.004328 (0.002455) | 0.096111 / 0.004250 (0.091861) | 0.055199 / 0.037052 (0.018146) | 0.470095 / 0.258489 (0.211606) | 0.530955 / 0.293841 (0.237114) | 0.056161 / 0.128546 (-0.072385) | 0.022055 / 0.075646 (-0.053591) | 0.121585 / 0.419271 (-0.297686) | 0.063736 / 0.043533 (0.020203) | 0.470771 / 0.255139 (0.215632) | 0.490546 / 0.283200 (0.207346) | 0.128825 / 0.141683 (-0.012858) | 1.898639 / 1.452155 (0.446484) | 2.052305 / 1.492716 (0.559589) |\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) | 0.322526 / 0.018006 (0.304520) | 0.628096 / 0.000490 (0.627607) | 0.006837 / 0.000200 (0.006637) | 0.000199 / 0.000054 (0.000145) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033830 / 0.037411 (-0.003581) | 0.136217 / 0.014526 (0.121691) | 0.147006 / 0.176557 (-0.029551) | 0.203950 / 0.737135 (-0.533185) | 0.150327 / 0.296338 (-0.146011) |\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) | 0.654287 / 0.215209 (0.439078) | 6.430306 / 2.077655 (4.352651) | 2.881750 / 1.504120 (1.377630) | 2.489505 / 1.541195 (0.948310) | 2.543037 / 1.468490 (1.074547) | 1.226682 / 4.584777 (-3.358094) | 5.902076 / 3.745712 (2.156364) | 3.335344 / 5.269862 (-1.934518) | 2.156738 / 4.565676 (-2.408939) | 0.151804 / 0.424275 (-0.272472) | 0.015238 / 0.007607 (0.007631) | 0.816364 / 0.226044 (0.590319) | 8.126367 / 2.268929 (5.857438) | 3.653222 / 55.444624 (-51.791402) | 2.886667 / 6.876477 (-3.989809) | 3.120852 / 2.142072 (0.978779) | 1.421423 / 4.805227 (-3.383804) | 0.264590 / 6.500664 (-6.236074) | 0.085716 / 0.075469 (0.010247) |\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) | 1.745258 / 1.841788 (-0.096530) | 19.379253 / 8.074308 (11.304945) | 23.827046 / 10.191392 (13.635654) | 0.267702 / 0.680424 (-0.412722) | 0.030253 / 0.534201 (-0.503948) | 0.542037 / 0.579283 (-0.037246) | 0.655946 / 0.434364 (0.221582) | 0.683525 / 0.540337 (0.143188) | 0.831333 / 1.386936 (-0.555603) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b011a258329375aa4dc7b414bd4e7b6363c5357 \"CML watermark\")\n"
] | 2023-04-26T17:09:32Z
| 2023-04-26T17:49:03Z
| 2023-04-26T17:39:12Z
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6047). 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) | 0.006384 / 0.011353 (-0.004969) | 0.003872 / 0.011008 (-0.007136) | 0.083454 / 0.038508 (0.044946) | 0.069120 / 0.023109 (0.046011) | 0.312573 / 0.275898 (0.036675) | 0.345814 / 0.323480 (0.022334) | 0.005729 / 0.007986 (-0.002257) | 0.003225 / 0.004328 (-0.001103) | 0.063950 / 0.004250 (0.059700) | 0.053998 / 0.037052 (0.016946) | 0.316492 / 0.258489 (0.058003) | 0.350738 / 0.293841 (0.056897) | 0.030770 / 0.128546 (-0.097776) | 0.008474 / 0.075646 (-0.067173) | 0.286989 / 0.419271 (-0.132282) | 0.052473 / 0.043533 (0.008940) | 0.314361 / 0.255139 (0.059222) | 0.335170 / 0.283200 (0.051970) | 0.022885 / 0.141683 (-0.118798) | 1.465430 / 1.452155 (0.013275) | 1.527799 / 1.492716 (0.035083) |\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) | 0.209377 / 0.018006 (0.191371) | 0.455583 / 0.000490 (0.455094) | 0.003352 / 0.000200 (0.003152) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026284 / 0.037411 (-0.011127) | 0.080710 / 0.014526 (0.066185) | 0.091741 / 0.176557 (-0.084816) | 0.147602 / 0.737135 (-0.589534) | 0.091173 / 0.296338 (-0.205166) |\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) | 0.386592 / 0.215209 (0.171383) | 3.856665 / 2.077655 (1.779011) | 1.835745 / 1.504120 (0.331625) | 1.671814 / 1.541195 (0.130619) | 1.711224 / 1.468490 (0.242734) | 0.484704 / 4.584777 (-4.100073) | 3.649239 / 3.745712 (-0.096473) | 3.784051 / 5.269862 (-1.485810) | 2.241195 / 4.565676 (-2.324482) | 0.056613 / 0.424275 (-0.367662) | 0.007140 / 0.007607 (-0.000467) | 0.464585 / 0.226044 (0.238540) | 4.616537 / 2.268929 (2.347609) | 2.371969 / 55.444624 (-53.072656) | 1.977754 / 6.876477 (-4.898723) | 2.083385 / 2.142072 (-0.058687) | 0.582330 / 4.805227 (-4.222897) | 0.132744 / 6.500664 (-6.367920) | 0.059822 / 0.075469 (-0.015647) |\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) | 1.259566 / 1.841788 (-0.582221) | 18.990166 / 8.074308 (10.915858) | 13.992069 / 10.191392 (3.800677) | 0.160001 / 0.680424 (-0.520423) | 0.018622 / 0.534201 (-0.515579) | 0.392921 / 0.579283 (-0.186362) | 0.418225 / 0.434364 (-0.016139) | 0.471252 / 0.540337 (-0.069086) | 0.653227 / 1.386936 (-0.733709) |\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) | 0.006641 / 0.011353 (-0.004712) | 0.003738 / 0.011008 (-0.007271) | 0.064053 / 0.038508 (0.025545) | 0.069467 / 0.023109 (0.046357) | 0.360625 / 0.275898 (0.084727) | 0.394291 / 0.323480 (0.070811) | 0.005236 / 0.007986 (-0.002750) | 0.003304 / 0.004328 (-0.001024) | 0.064078 / 0.004250 (0.059827) | 0.054605 / 0.037052 (0.017552) | 0.374567 / 0.258489 (0.116078) | 0.411227 / 0.293841 (0.117386) | 0.031614 / 0.128546 (-0.096933) | 0.008323 / 0.075646 (-0.067324) | 0.070616 / 0.419271 (-0.348656) | 0.050077 / 0.043533 (0.006544) | 0.362229 / 0.255139 (0.107090) | 0.388310 / 0.283200 (0.105110) | 0.024053 / 0.141683 (-0.117630) | 1.508913 / 1.452155 (0.056759) | 1.562140 / 1.492716 (0.069423) |\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) | 0.230172 / 0.018006 (0.212165) | 0.449363 / 0.000490 (0.448873) | 0.002374 / 0.000200 (0.002174) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029813 / 0.037411 (-0.007598) | 0.087298 / 0.014526 (0.072772) | 0.096712 / 0.176557 (-0.079845) | 0.152864 / 0.737135 (-0.584271) | 0.098204 / 0.296338 (-0.198135) |\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) | 0.408664 / 0.215209 (0.193455) | 4.075068 / 2.077655 (1.997414) | 2.096365 / 1.504120 (0.592245) | 1.936096 / 1.541195 (0.394901) | 1.961872 / 1.468490 (0.493382) | 0.483383 / 4.584777 (-4.101394) | 3.686926 / 3.745712 (-0.058787) | 4.798824 / 5.269862 (-0.471037) | 2.652279 / 4.565676 (-1.913398) | 0.056695 / 0.424275 (-0.367580) | 0.007592 / 0.007607 (-0.000016) | 0.484710 / 0.226044 (0.258665) | 4.842153 / 2.268929 (2.573225) | 2.636828 / 55.444624 (-52.807796) | 2.243666 / 6.876477 (-4.632811) | 2.375972 / 2.142072 (0.233899) | 0.578544 / 4.805227 (-4.226683) | 0.132579 / 6.500664 (-6.368085) | 0.061287 / 0.075469 (-0.014182) |\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) | 1.360287 / 1.841788 (-0.481501) | 19.464110 / 8.074308 (11.389802) | 14.530875 / 10.191392 (4.339483) | 0.149479 / 0.680424 (-0.530944) | 0.018471 / 0.534201 (-0.515730) | 0.395399 / 0.579283 (-0.183884) | 0.412897 / 0.434364 (-0.021467) | 0.465194 / 0.540337 (-0.075144) | 0.611752 / 1.386936 (-0.775184) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79a535de98b590da7bc223a6498c59790882f14a \"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) | 0.008986 / 0.011353 (-0.002367) | 0.005104 / 0.011008 (-0.005905) | 0.108371 / 0.038508 (0.069863) | 0.091655 / 0.023109 (0.068546) | 0.430183 / 0.275898 (0.154285) | 0.481387 / 0.323480 (0.157907) | 0.006662 / 0.007986 (-0.001324) | 0.004681 / 0.004328 (0.000353) | 0.089325 / 0.004250 (0.085075) | 0.065096 / 0.037052 (0.028044) | 0.435021 / 0.258489 (0.176532) | 0.478635 / 0.293841 (0.184794) | 0.047628 / 0.128546 (-0.080918) | 0.013496 / 0.075646 (-0.062150) | 0.389661 / 0.419271 (-0.029611) | 0.082260 / 0.043533 (0.038727) | 0.474165 / 0.255139 (0.219026) | 0.464877 / 0.283200 (0.181677) | 0.039784 / 0.141683 (-0.101899) | 1.874694 / 1.452155 (0.422539) | 1.980183 / 1.492716 (0.487467) |\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) | 0.254044 / 0.018006 (0.236038) | 0.631495 / 0.000490 (0.631005) | 0.000628 / 0.000200 (0.000428) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038773 / 0.037411 (0.001362) | 0.103681 / 0.014526 (0.089156) | 0.125081 / 0.176557 (-0.051476) | 0.198345 / 0.737135 (-0.538790) | 0.122217 / 0.296338 (-0.174121) |\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) | 0.611677 / 0.215209 (0.396468) | 6.220790 / 2.077655 (4.143135) | 2.729858 / 1.504120 (1.225739) | 2.351944 / 1.541195 (0.810749) | 2.449137 / 1.468490 (0.980647) | 0.896842 / 4.584777 (-3.687935) | 5.537491 / 3.745712 (1.791778) | 8.480182 / 5.269862 (3.210320) | 5.251404 / 4.565676 (0.685728) | 0.100449 / 0.424275 (-0.323826) | 0.009008 / 0.007607 (0.001401) | 0.750060 / 0.226044 (0.524016) | 7.390940 / 2.268929 (5.122011) | 3.478256 / 55.444624 (-51.966369) | 2.883597 / 6.876477 (-3.992880) | 3.082256 / 2.142072 (0.940183) | 1.114339 / 4.805227 (-3.690889) | 0.225389 / 6.500664 (-6.275275) | 0.083972 / 0.075469 (0.008503) |\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) | 1.741522 / 1.841788 (-0.100266) | 25.674700 / 8.074308 (17.600392) | 24.324412 / 10.191392 (14.133020) | 0.257878 / 0.680424 (-0.422546) | 0.038384 / 0.534201 (-0.495817) | 0.508302 / 0.579283 (-0.070981) | 0.612979 / 0.434364 (0.178615) | 0.584366 / 0.540337 (0.044029) | 0.881115 / 1.386936 (-0.505821) |\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) | 0.009114 / 0.011353 (-0.002239) | 0.005333 / 0.011008 (-0.005675) | 0.094944 / 0.038508 (0.056436) | 0.099178 / 0.023109 (0.076068) | 0.529813 / 0.275898 (0.253915) | 0.551282 / 0.323480 (0.227802) | 0.006442 / 0.007986 (-0.001543) | 0.004283 / 0.004328 (-0.000045) | 0.084257 / 0.004250 (0.080007) | 0.067557 / 0.037052 (0.030504) | 0.514733 / 0.258489 (0.256244) | 0.568200 / 0.293841 (0.274359) | 0.050969 / 0.128546 (-0.077577) | 0.014495 / 0.075646 (-0.061151) | 0.097089 / 0.419271 (-0.322182) | 0.063142 / 0.043533 (0.019609) | 0.513327 / 0.255139 (0.258188) | 0.520593 / 0.283200 (0.237394) | 0.036824 / 0.141683 (-0.104859) | 1.954875 / 1.452155 (0.502720) | 1.976307 / 1.492716 (0.483591) |\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) | 0.304070 / 0.018006 (0.286063) | 0.611073 / 0.000490 (0.610583) | 0.005027 / 0.000200 (0.004827) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037993 / 0.037411 (0.000582) | 0.115876 / 0.014526 (0.101350) | 0.118087 / 0.176557 (-0.058469) | 0.186437 / 0.737135 (-0.550699) | 0.129883 / 0.296338 (-0.166456) |\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) | 0.658292 / 0.215209 (0.443083) | 6.618257 / 2.077655 (4.540602) | 3.203786 / 1.504120 (1.699667) | 2.858714 / 1.541195 (1.317519) | 2.940974 / 1.468490 (1.472484) | 0.856238 / 4.584777 (-3.728538) | 5.427708 / 3.745712 (1.681996) | 4.810048 / 5.269862 (-0.459813) | 3.120006 / 4.565676 (-1.445671) | 0.098098 / 0.424275 (-0.326177) | 0.010077 / 0.007607 (0.002470) | 0.790890 / 0.226044 (0.564845) | 7.956679 / 2.268929 (5.687750) | 3.955710 / 55.444624 (-51.488914) | 3.446419 / 6.876477 (-3.430057) | 3.541228 / 2.142072 (1.399156) | 1.013420 / 4.805227 (-3.791808) | 0.213741 / 6.500664 (-6.286923) | 0.080857 / 0.075469 (0.005388) |\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) | 1.813265 / 1.841788 (-0.028522) | 25.965199 / 8.074308 (17.890891) | 21.892761 / 10.191392 (11.701369) | 0.257843 / 0.680424 (-0.422580) | 0.029388 / 0.534201 (-0.504813) | 0.510609 / 0.579283 (-0.068674) | 0.626579 / 0.434364 (0.192215) | 0.576865 / 0.540337 (0.036528) | 0.826610 / 1.386936 (-0.560326) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a9c00249b330f97f66ceb86c2939261091f4fe \"CML watermark\")\n"
] | 2023-07-18T10:15:39Z
| 2023-07-18T10:28:01Z
| 2023-07-18T10:15:52Z
|
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workaround to fix an issue with transformers CI
https://github.com/huggingface/transformers/pull/24867#discussion_r1266519626
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CI tests are broken: SchemaInferenceError
|
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[] | 2023-01-16T16:02:07Z
| 2023-06-02T06:40:32Z
| 2023-01-16T16:49:04Z
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CI test (unit, ubuntu-latest, deps-minimum) is broken, raising a `SchemaInferenceError`: see https://github.com/huggingface/datasets/actions/runs/3930901593/jobs/6721492004
```
FAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare_sharded - datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
```
Stack trace:
```
______________ BeamBuilderTest.test_download_and_prepare_sharded _______________
[gw1] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
self = <tests.test_beam.BeamBuilderTest testMethod=test_download_and_prepare_sharded>
@require_beam
def test_download_and_prepare_sharded(self):
import apache_beam as beam
original_write_parquet = beam.io.parquetio.WriteToParquet
expected_num_examples = len(get_test_dummy_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
with patch("apache_beam.io.parquetio.WriteToParquet") as write_parquet_mock:
write_parquet_mock.side_effect = partial(original_write_parquet, num_shards=2)
> builder.download_and_prepare()
tests/test_beam.py:97:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:864: in download_and_prepare
**download_and_prepare_kwargs,
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:1976: in _download_and_prepare
num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter))
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:694: in finalize
shard_num_bytes, _ = parquet_to_arrow(source, destination)
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:740: in parquet_to_arrow
num_bytes, num_examples = writer.finalize()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <datasets.arrow_writer.ArrowWriter object at 0x7f6dcbb3e810>
close_stream = True
def finalize(self, close_stream=True):
self.write_rows_on_file()
# In case current_examples < writer_batch_size, but user uses finalize()
if self._check_duplicates:
self.check_duplicate_keys()
# Re-intializing to empty list for next batch
self.hkey_record = []
self.write_examples_on_file()
# If schema is known, infer features even if no examples were written
if self.pa_writer is None and self.schema:
self._build_writer(self.schema)
if self.pa_writer is not None:
self.pa_writer.close()
self.pa_writer = None
if close_stream:
self.stream.close()
else:
if close_stream:
self.stream.close()
> raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:593: SchemaInferenceError
```
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Add Dataset: Qanta
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[
"@lhoestq - the config name is rather special here: *E.g.* `mode=first,char_skip=25`. It includes `=` and `,` - will that be a problem for windows folders, you think? \r\n\r\nApart from that good to merge for me.",
"It's ok to have `=` and `,`.\r\nWindows doesn't like things like `?`, `:`, `/` etc.\r\n\r\nI'll add some lines to raise an error if the config name is invalid.",
"Thanks for fixing things up! I'm curious to take a look at the zip files now to know the format for future reference."
] | 2020-05-26T12:44:35Z
| 2020-05-26T16:58:17Z
| 2020-05-26T13:16:20Z
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Fixes dummy data for #169 @EntilZha
|
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dataset(ngt): add ngt dataset initial loading script
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| 2021-03-23T06:19:38Z
| 2021-03-23T06:19:38Z
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Currently only making the paths to the annotation ELAN (eaf) file and videos available.
This is the first accessible way to download this dataset, which is not manual file-by-file.
Only downloading the necessary files, the annotation files are very small, 20MB for all of them, but the video files are large, 100GB in total, saved in `mpg` format.
I do not intend to actually store these as an uncompressed array of frames, because it will be huge.
Future updates may add pose estimation files for all videos, making it easier to work with this data
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Fixed bug in the shape property
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| 2021-01-02T23:22:52Z
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Fix to the bug reported in issue #1622. Just replaced `return tuple(self._indices.num_rows, self._data.num_columns)` by `return (self._indices.num_rows, self._data.num_columns)`.
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Add WMT20 MLQE 3 shared tasks
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"Thanks for the comments Quentin!\r\nI integrated them",
"It should be ok now!\r\nSorry I wasn't attentive enough.\r\n(tests are currently failing, I understand it's from other datasets)",
"merging since the CI is fixed on master"
] | 2020-12-06T19:59:12Z
| 2020-12-15T15:27:30Z
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3 tasks for the WMT 20 MLQE shared tasks -> 3 different datasets
(I re-created #1137 because it was too messy).
Note that in L199 `task3.py`, I used `logging.warning` to print some missing data in the train set.
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typo
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Improve Wikipedia Loading Script
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"I wanted to flag a change from since we discussed this: I initially wrote a function for using the Wikimedia APIs to collect namespace aliases, but decided that adding in more http requests to the script wasn't a great idea so instead used that code to build a static list that I just added directly to the code.\r\n\r\nAlso, an FYI that python library dependencies weren't working on my local end so I wasn't able to directly test the code. I tested a copy with the problematic elements stripped (beam etc.) that worked fine, but someone with a working local copy may want to test just to make sure I didn't accidentally break anything.",
"Also, while I would argue more strongly for some of the changes in this code, they are five distinct changes so not so hard to remove one or two if other folks think they aren't worth the overhead etc.",
"I also add a comment by @geohci in the Issue page:\r\n> See https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details / some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)",
"Hi ! Thanks a lot, this is very cool ! Note that unfortunately if we change the processing right now, users won't be able to load the \"big\" languages like english anymore, because it requires an Apache Beam runtime to process them. Some Wikipedia dumps have been processed by Hugging Face so that users don't need to run Apache Beam stuff.\r\n\r\nTherefore, we can merge this change after we have processed dumps using this new processing, and host them on the Hugging Face google storage.\r\n\r\nI think we can take care of this and let you know once this is ready ? What do you think @albertvillanova ?\r\n\r\nThis is also an opportunity to have the latest dumps ready, the current ones are from 2020",
"Related PR on updating to the latest dates: https://github.com/huggingface/datasets/pull/3612",
"@lhoestq if the additional processing steps are validated, we could go on generating the processed datasets for the big languages.\r\n\r\nThe only thing before doing that is that we should also validate other change (so that we include it also in the processed datasets):\r\n- #3398 ",
"> @lhoestq if the additional processing steps are validated, we could go on generating the processed datasets for the big languages.\r\n\r\nCool ! Looking forward to it :)\r\n\r\n> The only thing before doing that is that we should also validate other change (so that we include it also in the processed datasets):\r\n> \r\n> https://github.com/huggingface/datasets/issues/3398\r\n\r\nSounds good ! We can definitely add the URL as asked by the Wikipedia to provide credits to the authors.",
"@geohci I do not have push rights to this PR. See: [Enabling repository maintainer permissions on existing pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests).\r\n\r\nI would like to merge the master branch so that all tests pass. Once done, I will be able approve this PR.",
"> @geohci I do not have push rights to this PR. See: [Enabling repository maintainer permissions on existing pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests).\r\n> \r\n> I would like to merge the master branch so that all tests pass. Once done, I will be able approve this PR.\r\n\r\n@albertvillanova the `Allow edits by maintainers` box was already checked (what your instructions indicated) and indicates `If checked, users with write access to huggingface/datasets can add new commits to your wikipedia-updates branch. You can always change this setting later.` so you should have permissions already. If there's something else I'm missing or can do, please let me know. If it's not easy to resolve, I am plenty comfortable with you creating a new PR with these changes under your account too."
] | 2021-12-15T13:30:06Z
| 2022-03-04T08:16:00Z
| 2022-03-04T08:16:00Z
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* More structured approach to detecting redirects
* Remove redundant template filter code (covered by strip_code)
* Add language-specific lists of additional media namespace aliases for filtering
* Add language-specific lists of category namespace aliases for new link text cleaning step
* Remove magic words (parser directions like __TOC__ that occasionally occur in text)
Fix #3400
With support from @albertvillanova
CC @yjernite
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Feature Request: Dataset.add_item
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"Hi @sshleifer.\r\n\r\nI am not sure of understanding the need of the `add_item` approach...\r\n\r\nBy just reading your \"Desired API\" section, I would say you could (nearly) get it with a 1-column Dataset:\r\n```python\r\ndata = {\"input_ids\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\r\nds = Dataset.from_dict(data)\r\nassert (ds[\"input_ids\"][0] == np.array([4,4,2])).all()\r\n```",
"Hi @sshleifer :) \r\n\r\nWe don't have methods like `Dataset.add_batch` or `Dataset.add_entry/add_item` yet.\r\nBut that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset\r\n\r\ntokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\n# API suggestion (not available yet)\r\nd = Dataset()\r\nfor input_ids in tokenized:\r\n d.add_item({\"input_ids\": input_ids})\r\n\r\nprint(d[0][\"input_ids\"])\r\n# [4, 4, 2]\r\n```\r\n\r\nCurrently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).",
"Your API looks perfect @lhoestq, thanks!"
] | 2021-02-10T06:06:00Z
| 2021-04-23T10:01:30Z
| 2021-04-23T10:01:30Z
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I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.
Is this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.
### Desired API
```python
import numpy as np
tokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])
def build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:
"""FIXME"""
dataset = EmptyDataset()
for t in tokenized: dataset.append(t)
return dataset
ds = build_dataset_from_tokenized(tokenized)
assert (ds[0] == np.array([4,4,2])).all()
```
### What I tried
grep, google for "add one entry at a time", "datasets.append"
### Current Code
This code achieves the same result but doesn't fit into the `add_item` abstraction.
```python
dataset = load_dataset('text', data_files={'train': 'train.txt'})
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)
def tokenize_function(examples):
ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']
return {'input_ids': [x[1:] for x in ids]}
ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)
print(ds['train'][0]) => np array
```
Thanks in advance!
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Generics kb new branch
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Datasets need manual downloads. Have thus created dummy data as well. But pytest on real and dummy data are failing.
I have completed the readme , tags and other required things. I need to create the metadata json once tests get successful.
Opening a PR while working with Yacine Jernite to resolve my pytest issues.
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Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays`
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[
"Also, I had a doubt while checking the code related to the indices... \r\n\r\n@lhoestq, there's a value in `config.py` named `DATASET_INDICES_FILENAME` which has the arrow extension (which I assume it should be `indices.faiss`, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an `ArrowDataset` in disk, but since those indices are never stored AFAIK, is that actually required?\r\n\r\nhttps://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/config.py#L183\r\n\r\nhttps://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/arrow_dataset.py#L1079-L1092\r\n\r\nSo should I also remove that?\r\n\r\nP.S. I also edited the following code comment which I found misleading as it's not actually storing the indices.\r\n\r\nhttps://github.com/huggingface/datasets/blob/8ddc4bbeb1e2bd307b21f5d21f884649aa2bf640/src/datasets/arrow_dataset.py#L1122",
"_The documentation is not available anymore as the PR was closed or merged._",
"> @lhoestq, there's a value in config.py named DATASET_INDICES_FILENAME which has the arrow extension (which I assume it should be indices.faiss, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an ArrowDataset in disk, but since those indices are never stored AFAIK, is that actually required?\r\n\r\nThe arrow file is used to store an indices mapping (when you shuffle the dataset for example) - not for a faiss index ;)",
"Ok cool thanks a lot for the explanation @lhoestq I was not sure about that :+1: I'll also add it there as you suggested!",
"CI failures are unrelated to this PR and fixed on master, merging"
] | 2022-06-21T12:18:49Z
| 2022-06-27T16:25:09Z
| 2022-06-27T16:14:36Z
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| null | 0
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Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`.
This is useful so as to tweak the `batch_size` according to the VM specifications.
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Add MLSUM
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[
"Could you test to run the test using the changes in #527 and let me know if it fixes the issue ? If so I'll merge it and we'll be good to go :)",
"Hello, it does work on the fixing real dataset branch. Merci Quentin :)",
"Nice, glad to hear that :)\r\nde rien !"
] | 2020-08-24T16:18:35Z
| 2020-08-26T08:04:11Z
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Hello (again :) !),
So, I started a new branch because of a [rebase issue](https://github.com/huggingface/nlp/pull/463), sorry for the mess.
However, the command `pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_mlsum` still fails because there is no default language dataset : the script throws an error as a specific config language is necessary.
I think that setting a default language would be a bad workaround for this so I kept it as it is. Putting all the train files across languages together would also be a bad idea because of the size.
Thanks for your help,
Rachel
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Once `rouge-score` has made the 0.1.2 release to fix their issue https://github.com/google-research/google-research/issues/1212, we can unpin it.
Related to:
- #4735
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Add Spearmanr Metric Card
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"_The documentation is not available anymore as the PR was closed or merged._",
"changes made! @lhoestq let me know what you think ",
"The CI fail is unrelated to this PR and fixed on master, feel free to merge :)"
] | 2022-04-06T12:57:53Z
| 2022-05-03T16:50:26Z
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Minor documentation fix
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| 2021-07-21T13:04:55Z
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Currently, [Writing a dataset loading script](https://huggingface.co/docs/datasets/add_dataset.html) page has a small error. A link to `matinf` dataset in [_Dataset scripts of reference_](https://huggingface.co/docs/datasets/add_dataset.html#dataset-scripts-of-reference) section actually leads to `xsquad`, instead. This PR fixes that.
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Converting TensorFlow dataset example
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[
"Do you want to convert a dataset script to the tfds format ?\r\nIf so, we currently have a comversion script nlp/commands/convert.py but it is a conversion script that goes from tfds to nlp.\r\nI think it shouldn't be too hard to do the changes in reverse (at some manual adjustments).\r\nIf you manage to make it work in reverse, feel free to open a PR to share it with the community :)",
"In our docs: [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)."
] | 2020-08-16T08:05:20Z
| 2021-08-03T06:01:18Z
| 2021-08-03T06:01:17Z
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NONE
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Hi,
I want to use TensorFlow datasets with this repo, I noticed you made some conversion script,
can you give a simple example of using it?
Thanks
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Ollie dataset
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This is the dataset used to train the Ollie open information extraction algorithm. It has over 21M sentences. See http://knowitall.github.io/ollie/ for more details.
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Fix streaming for id_newspapers_2018
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To be compatible with streaming, this dataset must use `dl_manager.iter_archive` since the data are in a .tgz file
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benchmarking against MMapIndexedDataset
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"Hi sam !\r\nIndeed we can expect the performances to be very close since both MMapIndexedDataset and the `datasets` implem use memory mapping. With memory mapping what determines the I/O performance is the speed of your hard drive/SSD.\r\n\r\nIn terms of performance we're pretty close to the optimal speed for reading text, even though I found recently that we could still slightly improve speed for big datasets (see [here](https://github.com/huggingface/datasets/issues/1803)).\r\n\r\nIn terms of number of examples and example sizes, the only limit is the available disk space you have.\r\n\r\nI haven't used `psrecord` yet but it seems to be a very interesting tool for benchmarking. Currently for benchmarks we only have github actions to avoid regressions in terms of speed. But it would be cool to have benchmarks with comparisons with other dataset tools ! This would be useful to many people",
"Also I would be interested to know what data types `MMapIndexedDataset` supports. Is there some documentation somewhere ?",
"no docs haha, it's written to support integer numpy arrays.\r\n\r\nYou can build one in fairseq with, roughly:\r\n```bash\r\n\r\nwget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip\r\nunzip wikitext-103-raw-v1.zip\r\nexport dd=$HOME/fairseq-py/wikitext-103-raw\r\n\r\nexport mm_dir=$HOME/mmap_wikitext2\r\nmkdir -p gpt2_bpe\r\nwget -O gpt2_bpe/encoder.json https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json\r\nwget -O gpt2_bpe/vocab.bpe https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe\r\nwget -O gpt2_bpe/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt\r\nfor SPLIT in train valid; do \\\r\n python -m examples.roberta.multiprocessing_bpe_encoder \\\r\n --encoder-json gpt2_bpe/encoder.json \\\r\n --vocab-bpe gpt2_bpe/vocab.bpe \\\r\n --inputs /scratch/stories_small/${SPLIT}.txt \\\r\n --outputs /scratch/stories_small/${SPLIT}.bpe \\\r\n --keep-empty \\\r\n --workers 60; \\\r\ndone\r\n\r\nmkdir -p $mm_dir\r\nfairseq-preprocess \\\r\n --only-source \\\r\n --srcdict gpt2_bpe/dict.txt \\\r\n --trainpref $dd/wiki.train.bpe \\\r\n --validpref $dd/wiki.valid.bpe \\\r\n --destdir $mm_dir \\\r\n --workers 60 \\\r\n --dataset-impl mmap\r\n```\r\n\r\nI'm noticing in my benchmarking that it's much smaller on disk than arrow (200mb vs 900mb), and that both incur significant cost by increasing the number of data loader workers. \r\nThis somewhat old [post](https://ray-project.github.io/2017/10/15/fast-python-serialization-with-ray-and-arrow.html) suggests there are some gains to be had from using `pyarrow.serialize(array).tobuffer()`. I haven't yet figured out how much of this stuff `pa.Table` does under the hood.\r\n\r\nThe `MMapIndexedDataset` bottlenecks we are working on improving (by using arrow) are:\r\n1) `MMapIndexedDataset`'s index, which stores offsets, basically gets read in its entirety by each dataloading process.\r\n2) we have separate, identical, `MMapIndexedDatasets` on each dataloading worker, so there's redundancy there; we wonder if there is a way that arrow can somehow dedupe these in shared memory.\r\n\r\nIt will take me a few hours to get `MMapIndexedDataset` benchmarks out of `fairseq`/onto a branch in this repo, but I'm happy to invest the time if you're interested in collaborating on some performance hacking."
] | 2021-02-16T20:04:58Z
| 2021-02-17T18:52:28Z
| null |
CONTRIBUTOR
| null | null | null |
I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).
Questions:
1) Is this (basically identical) performance expected?
2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples/larger examples?)
3) Should I be using different benchmarking tools than `psrecord`/how do you guys do benchmarks?
Thanks in advance! Sam
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fix: dataset path should be absolute
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[
"Good catch thanks ! Have you tried to use the absolue path in `MemoryMappedTable.__init__` in `table.py`?\r\n\r\nI think it can fix issues with relative paths at more levels than just fixing it `load_from_disk`. If it works I think it would be a more robust fix to this issue",
"@lhoestq right, that actually fixed it indeed. I've pushed the changes (one-liner). lemme know if there's anything else you need for this fix",
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-14T12:47:40Z
| 2022-12-07T23:49:22Z
| 2022-12-07T23:46:34Z
|
CONTRIBUTOR
| null | 0
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|
cache_file_name depends on dataset's path.
A simple way where this could cause a problem:
```
import os
import datasets
def add_prefix(example):
example["text"] = "Review: " + example["text"]
return example
ds = datasets.load_from_disk("a/relative/path")
os.chdir("/tmp")
ds_1 = ds.map(add_prefix)
```
while it may feel that the `chdir` is quite constructed, there are many scenarios when the current working dir can/will change...
|
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Adding Text-based NP Enrichment (TNE) dataset
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[
"Hey @lhoestq, can you please have a look? 🙏",
"Great, thanks again @lhoestq! I think we're good to go now",
"Done"
] | 2022-04-12T15:47:03Z
| 2022-05-03T14:05:48Z
| 2022-05-03T14:05:48Z
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Added the [TNE](https://github.com/yanaiela/TNE) dataset to the library
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MDExOlB1bGxSZXF1ZXN0NTg5NzEzNDAy
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Remove print statement from mnist.py
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[
"Thanks for noticing !\r\n#2020 fixed this earlier today though ^^'\r\n\r\nClosing this one"
] | 2021-03-10T14:39:58Z
| 2021-03-11T18:03:52Z
| 2021-03-11T18:03:51Z
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I_kwDODunzps5w-LyG
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|
Load local audio data doesn't work
|
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[
"I think this is the same issue as https://github.com/huggingface/datasets/issues/4776. Maybe installing `ffmpeg` can fix it:\r\n```python\r\nadd-apt-repository -y ppa:savoury1/ffmpeg4\r\napt-get -qq install -y ffmpeg\r\n```\r\n\r\nHowever, the best solution is to use a newer version of `datasets`. In the recent releases, we've replaced `torchaudio` with `soundfile`, which is easier to install and faster.",
"@mariosasko \r\nThanks for your help"
] | 2023-09-13T22:30:01Z
| 2023-09-15T14:32:10Z
| 2023-09-15T14:32:10Z
|
NONE
| null | null | null |
### Describe the bug
I get a RuntimeError from the following code:
```python
audio_dataset = Dataset.from_dict({"audio": ["/kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3"]}).cast_column("audio", Audio())
audio_dataset[0]
```
### Traceback
<details>
```python
RuntimeError Traceback (most recent call last)
Cell In[33], line 1
----> 1 train_dataset[0]
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key)
1762 def __getitem__(self, key): # noqa: F811
1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 1764 return self._getitem(
1765 key,
1766 )
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs)
1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 1749 formatted_output = format_table(
1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1751 )
1752 return formatted_output
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table)
310 row = self.python_arrow_extractor().extract_row(pa_table)
311 if self.decoded:
--> 312 row = self.python_features_decoder.decode_row(row)
313 return row
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row)
220 def decode_row(self, row: dict) -> dict:
--> 221 return self.features.decode_example(row) if self.features else row
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1386, in Features.decode_example(self, example)
1376 def decode_example(self, example: dict):
1377 """Decode example with custom feature decoding.
1378
1379 Args:
(...)
1383 :obj:`dict[str, Any]`
1384 """
-> 1386 return {
1387 column_name: decode_nested_example(feature, value)
1388 if self._column_requires_decoding[column_name]
1389 else value
1390 for column_name, (feature, value) in zip_dict(
1391 {key: value for key, value in self.items() if key in example}, example
1392 )
1393 }
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1387, in <dictcomp>(.0)
1376 def decode_example(self, example: dict):
1377 """Decode example with custom feature decoding.
1378
1379 Args:
(...)
1383 :obj:`dict[str, Any]`
1384 """
1386 return {
-> 1387 column_name: decode_nested_example(feature, value)
1388 if self._column_requires_decoding[column_name]
1389 else value
1390 for column_name, (feature, value) in zip_dict(
1391 {key: value for key, value in self.items() if key in example}, example
1392 )
1393 }
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1087, in decode_nested_example(schema, obj)
1085 # Object with special decoding:
1086 elif isinstance(schema, (Audio, Image)):
-> 1087 return schema.decode_example(obj) if obj is not None else None
1088 return obj
File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:103, in Audio.decode_example(self, value)
101 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.")
102 elif path is not None and path.endswith("mp3"):
--> 103 array, sampling_rate = self._decode_mp3(file if file else path)
104 elif path is not None and path.endswith("opus"):
105 if file:
File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:241, in Audio._decode_mp3(self, path_or_file)
238 except RuntimeError as err:
239 raise ImportError("To support decoding 'mp3' audio files, please install 'sox'.") from err
--> 241 array, sampling_rate = torchaudio.load(path_or_file, format="mp3")
242 if self.sampling_rate and self.sampling_rate != sampling_rate:
243 if not hasattr(self, "_resampler") or self._resampler.orig_freq != sampling_rate:
File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:256, in load(filepath, frame_offset, num_frames, normalize, channels_first, format)
254 if ret is not None:
255 return ret
--> 256 return _fallback_load(filepath, frame_offset, num_frames, normalize, channels_first, format)
File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:30, in _fail_load(filepath, frame_offset, num_frames, normalize, channels_first, format)
22 def _fail_load(
23 filepath: str,
24 frame_offset: int = 0,
(...)
28 format: Optional[str] = None,
29 ) -> Tuple[torch.Tensor, int]:
---> 30 raise RuntimeError("Failed to load audio from {}".format(filepath))
RuntimeError: Failed to load audio from /kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3
```
</details>
### Steps to reproduce the bug
1. - Create a custom dataset using Local files of type mp3.
3. - Try to read the first audio item.
### Expected behavior
Expected output
```python
audio_dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': 'path/to/audio_1',
'sampling_rate': 16000}
```
### Environment info
N/A
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Perplexity Speedup
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[
"WRT the high values, can you add some unit tests with some [string, model] pairs and their resulting perplexity code, and @TristanThrush can run the same pairs through his version of the code?",
"_The documentation is not available anymore as the PR was closed or merged._",
"I thought that the perplexity metric should output the average perplexity value of all the strings that it gets as input (not a perplexity value per string, as the new version does).\r\n@lhoestq , @TristanThrush thoughts?",
"> I thought that the perplexity metric should output the average perplexity value of all the strings that it gets as input (not a perplexity value per string, as the new version does). @lhoestq , @TristanThrush thoughts?\r\n\r\nI support this change from Emi. If we have a perplexity function that loads GPT2 and then returns an average over all of the strings, then it is impossible to get multiple perplexities of a batch of strings efficiently. If we have this new perplexity function that is built for batching, then it is possible to get a batch of perplexities efficiently and you can still compute the average efficiently afterwards.",
"Thanks a lot for working on this @emibaylor @TristanThrush :)\r\n\r\nFor consistency with the other metrics, I think it's nice if we return the mean perplexity. Though I agree that having the separate perplexities per sample can also be useful. What do you think about returning both ?\r\n```python\r\nreturn {\"perplexities\": ppls, \"mean_perplexity\": np.mean(ppls)}\r\n```\r\nwe're also doing this for the COMET metric.",
"> Thanks a lot for working on this @emibaylor @TristanThrush :)\r\n> \r\n> For consistency with the other metrics, I think it's nice if we return the mean perplexity. Though I agree that having the separate perplexities per sample can also be useful. What do you think about returning both ?\r\n> \r\n> ```python\r\n> return {\"perplexities\": ppls, \"mean_perplexity\": np.mean(ppls)}\r\n> ```\r\n> \r\n> we're also doing this for the COMET metric.\r\n\r\nThanks! Sounds great to me.",
"The CI fail is unrelated to your PR and has been fixed on master, feel free to merge the master branch into your PR to fix the CI ;)"
] | 2022-04-06T12:57:21Z
| 2022-04-20T13:00:54Z
| 2022-04-20T12:54:42Z
|
CONTRIBUTOR
| null | 0
|
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This PR makes necessary changes to perplexity such that:
- it runs much faster (via batching)
- it throws an error when input is empty, or when input is one word without <BOS> token
- it adds the option to add a <BOS> token
Issues:
- The values returned are extremely high, and I'm worried they aren't correct. Even if they are correct, they are sometimes returned as `inf`, which is not very useful (see [comment below](https://github.com/huggingface/datasets/pull/4108#discussion_r843931094) for some of the output values).
- If the values are not correct, can you help me find the error?
- If the values are correct, it might be worth it to measure something like perplexity per word, which would allow us to get actual values for the larger perplexities, instead of just `inf`
Future:
- `stride` is not currently implemented here. I have some thoughts on how to make it happen with batching, but I think it would be better to get another set of eyes to look at any possible errors causing such large values now rather than later.
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PR_kwDODunzps5fZZ2l
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Set `usedforsecurity=False` in hashlib methods (FIPS compliance)
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[
"<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) | 0.008434 / 0.011353 (-0.002919) | 0.006755 / 0.011008 (-0.004253) | 0.106169 / 0.038508 (0.067661) | 0.049329 / 0.023109 (0.026220) | 0.433610 / 0.275898 (0.157712) | 0.441993 / 0.323480 (0.118513) | 0.004703 / 0.007986 (-0.003282) | 0.006996 / 0.004328 (0.002667) | 0.080330 / 0.004250 (0.076080) | 0.066098 / 0.037052 (0.029045) | 0.435444 / 0.258489 (0.176955) | 0.490442 / 0.293841 (0.196601) | 0.047050 / 0.128546 (-0.081496) | 0.014520 / 0.075646 (-0.061127) | 0.339805 / 0.419271 (-0.079467) | 0.101161 / 0.043533 (0.057629) | 0.423236 / 0.255139 (0.168097) | 0.455627 / 0.283200 (0.172427) | 0.036218 / 0.141683 (-0.105465) | 1.766128 / 1.452155 (0.313973) | 1.923919 / 1.492716 (0.431203) |\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) | 0.242939 / 0.018006 (0.224933) | 0.515582 / 0.000490 (0.515093) | 0.020271 / 0.000200 (0.020071) | 0.000383 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030927 / 0.037411 (-0.006484) | 0.093951 / 0.014526 (0.079425) | 0.109028 / 0.176557 (-0.067529) | 0.174947 / 0.737135 (-0.562188) | 0.120538 / 0.296338 (-0.175800) |\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) | 0.553884 / 0.215209 (0.338675) | 5.424566 / 2.077655 (3.346911) | 2.439420 / 1.504120 (0.935301) | 2.019324 / 1.541195 (0.478129) | 2.170781 / 1.468490 (0.702290) | 0.924424 / 4.584777 (-3.660353) | 5.706029 / 3.745712 (1.960317) | 5.096911 / 5.269862 (-0.172951) | 3.168261 / 4.565676 (-1.397416) | 0.094336 / 0.424275 (-0.329940) | 0.015899 / 0.007607 (0.008292) | 0.709684 / 0.226044 (0.483639) | 7.476865 / 2.268929 (5.207936) | 3.350983 / 55.444624 (-52.093641) | 2.653419 / 6.876477 (-4.223058) | 2.802201 / 2.142072 (0.660129) | 1.081442 / 4.805227 (-3.723785) | 0.217025 / 6.500664 (-6.283639) | 0.077248 / 0.075469 (0.001779) |\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) | 1.598621 / 1.841788 (-0.243167) | 23.490338 / 8.074308 (15.416030) | 21.853488 / 10.191392 (11.662096) | 0.209625 / 0.680424 (-0.470799) | 0.028166 / 0.534201 (-0.506035) | 0.473883 / 0.579283 (-0.105400) | 0.584226 / 0.434364 (0.149862) | 0.538605 / 0.540337 (-0.001732) | 0.837060 / 1.386936 (-0.549876) |\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) | 0.009029 / 0.011353 (-0.002324) | 0.004945 / 0.011008 (-0.006063) | 0.084539 / 0.038508 (0.046031) | 0.081014 / 0.023109 (0.057905) | 0.431291 / 0.275898 (0.155393) | 0.478913 / 0.323480 (0.155433) | 0.006107 / 0.007986 (-0.001879) | 0.003939 / 0.004328 (-0.000390) | 0.079932 / 0.004250 (0.075682) | 0.057936 / 0.037052 (0.020884) | 0.437295 / 0.258489 (0.178806) | 0.489790 / 0.293841 (0.195949) | 0.049544 / 0.128546 (-0.079003) | 0.013675 / 0.075646 (-0.061972) | 0.093143 / 0.419271 (-0.326128) | 0.064104 / 0.043533 (0.020571) | 0.444699 / 0.255139 (0.189560) | 0.443688 / 0.283200 (0.160489) | 0.034331 / 0.141683 (-0.107352) | 1.753014 / 1.452155 (0.300859) | 1.877274 / 1.492716 (0.384558) |\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) | 0.250460 / 0.018006 (0.232454) | 0.527241 / 0.000490 (0.526752) | 0.007679 / 0.000200 (0.007479) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033269 / 0.037411 (-0.004142) | 0.111262 / 0.014526 (0.096736) | 0.133503 / 0.176557 (-0.043053) | 0.177998 / 0.737135 (-0.559137) | 0.117899 / 0.296338 (-0.178440) |\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) | 0.633588 / 0.215209 (0.418379) | 6.105283 / 2.077655 (4.027628) | 2.779309 / 1.504120 (1.275189) | 2.445788 / 1.541195 (0.904594) | 2.396443 / 1.468490 (0.927953) | 0.925928 / 4.584777 (-3.658849) | 5.266142 / 3.745712 (1.520430) | 4.868830 / 5.269862 (-0.401031) | 2.998768 / 4.565676 (-1.566909) | 0.103135 / 0.424275 (-0.321140) | 0.008059 / 0.007607 (0.000452) | 0.753159 / 0.226044 (0.527115) | 7.532170 / 2.268929 (5.263242) | 3.563941 / 55.444624 (-51.880683) | 2.829208 / 6.876477 (-4.047269) | 2.913954 / 2.142072 (0.771881) | 1.085843 / 4.805227 (-3.719384) | 0.214195 / 6.500664 (-6.286469) | 0.071509 / 0.075469 (-0.003960) |\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) | 1.544819 / 1.841788 (-0.296968) | 23.790149 / 8.074308 (15.715841) | 23.086019 / 10.191392 (12.894627) | 0.242695 / 0.680424 (-0.437729) | 0.041706 / 0.534201 (-0.492495) | 0.552402 / 0.579283 (-0.026881) | 0.652518 / 0.434364 (0.218154) | 0.581876 / 0.540337 (0.041539) | 0.795425 / 1.386936 (-0.591511) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#117fdfccc8523fe150521ad74e478459fe2f297c \"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) | 0.004573 / 0.011353 (-0.006780) | 0.002965 / 0.011008 (-0.008043) | 0.061913 / 0.038508 (0.023405) | 0.029474 / 0.023109 (0.006365) | 0.258117 / 0.275898 (-0.017781) | 0.279854 / 0.323480 (-0.043626) | 0.003954 / 0.007986 (-0.004031) | 0.002479 / 0.004328 (-0.001850) | 0.048685 / 0.004250 (0.044434) | 0.044733 / 0.037052 (0.007681) | 0.256659 / 0.258489 (-0.001830) | 0.285235 / 0.293841 (-0.008606) | 0.023566 / 0.128546 (-0.104981) | 0.007291 / 0.075646 (-0.068355) | 0.202701 / 0.419271 (-0.216570) | 0.055706 / 0.043533 (0.012173) | 0.258790 / 0.255139 (0.003651) | 0.278675 / 0.283200 (-0.004525) | 0.018574 / 0.141683 (-0.123109) | 1.109359 / 1.452155 (-0.342796) | 1.184434 / 1.492716 (-0.308282) |\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) | 0.095048 / 0.018006 (0.077042) | 0.305027 / 0.000490 (0.304537) | 0.000310 / 0.000200 (0.000110) | 0.000066 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018183 / 0.037411 (-0.019228) | 0.066130 / 0.014526 (0.051604) | 0.073948 / 0.176557 (-0.102608) | 0.120458 / 0.737135 (-0.616678) | 0.075995 / 0.296338 (-0.220343) |\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) | 0.279419 / 0.215209 (0.064210) | 2.728591 / 2.077655 (0.650936) | 1.439016 / 1.504120 (-0.065104) | 1.325798 / 1.541195 (-0.215397) | 1.352050 / 1.468490 (-0.116440) | 0.395041 / 4.584777 (-4.189736) | 2.377651 / 3.745712 (-1.368061) | 2.618473 / 5.269862 (-2.651389) | 1.587580 / 4.565676 (-2.978096) | 0.045910 / 0.424275 (-0.378365) | 0.004843 / 0.007607 (-0.002764) | 0.335491 / 0.226044 (0.109447) | 3.378441 / 2.268929 (1.109512) | 1.827757 / 55.444624 (-53.616868) | 1.502360 / 6.876477 (-5.374117) | 1.508460 / 2.142072 (-0.633612) | 0.471309 / 4.805227 (-4.333918) | 0.098934 / 6.500664 (-6.401730) | 0.041705 / 0.075469 (-0.033764) |\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) | 0.945067 / 1.841788 (-0.896720) | 11.548209 / 8.074308 (3.473900) | 10.422628 / 10.191392 (0.231236) | 0.141494 / 0.680424 (-0.538929) | 0.014345 / 0.534201 (-0.519856) | 0.267750 / 0.579283 (-0.311533) | 0.261488 / 0.434364 (-0.172876) | 0.307192 / 0.540337 (-0.233145) | 0.427926 / 1.386936 (-0.959010) |\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) | 0.004831 / 0.011353 (-0.006522) | 0.002876 / 0.011008 (-0.008132) | 0.048629 / 0.038508 (0.010121) | 0.055090 / 0.023109 (0.031981) | 0.271381 / 0.275898 (-0.004517) | 0.292350 / 0.323480 (-0.031130) | 0.004001 / 0.007986 (-0.003985) | 0.002389 / 0.004328 (-0.001939) | 0.047527 / 0.004250 (0.043277) | 0.038065 / 0.037052 (0.001012) | 0.277387 / 0.258489 (0.018898) | 0.307209 / 0.293841 (0.013368) | 0.025136 / 0.128546 (-0.103411) | 0.007309 / 0.075646 (-0.068338) | 0.054483 / 0.419271 (-0.364789) | 0.032807 / 0.043533 (-0.010726) | 0.274364 / 0.255139 (0.019225) | 0.290280 / 0.283200 (0.007080) | 0.017855 / 0.141683 (-0.123828) | 1.185912 / 1.452155 (-0.266243) | 1.228141 / 1.492716 (-0.264576) |\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) | 0.094787 / 0.018006 (0.076781) | 0.314191 / 0.000490 (0.313701) | 0.000217 / 0.000200 (0.000017) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020920 / 0.037411 (-0.016491) | 0.070446 / 0.014526 (0.055920) | 0.081371 / 0.176557 (-0.095186) | 0.119127 / 0.737135 (-0.618009) | 0.085658 / 0.296338 (-0.210680) |\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) | 0.290601 / 0.215209 (0.075392) | 2.874091 / 2.077655 (0.796436) | 1.598934 / 1.504120 (0.094814) | 1.464329 / 1.541195 (-0.076866) | 1.504943 / 1.468490 (0.036453) | 0.410457 / 4.584777 (-4.174320) | 2.428706 / 3.745712 (-1.317006) | 2.596510 / 5.269862 (-2.673352) | 1.547084 / 4.565676 (-3.018592) | 0.047546 / 0.424275 (-0.376729) | 0.004740 / 0.007607 (-0.002867) | 0.351168 / 0.226044 (0.125123) | 3.424554 / 2.268929 (1.155626) | 1.969792 / 55.444624 (-53.474832) | 1.676731 / 6.876477 (-5.199745) | 1.668769 / 2.142072 (-0.473304) | 0.482486 / 4.805227 (-4.322741) | 0.100018 / 6.500664 (-6.400646) | 0.040956 / 0.075469 (-0.034513) |\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) | 0.966306 / 1.841788 (-0.875482) | 12.158909 / 8.074308 (4.084601) | 10.926447 / 10.191392 (0.735055) | 0.130359 / 0.680424 (-0.550065) | 0.016162 / 0.534201 (-0.518039) | 0.269977 / 0.579283 (-0.309306) | 0.283366 / 0.434364 (-0.150997) | 0.304517 / 0.540337 (-0.235821) | 0.410398 / 1.386936 (-0.976539) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d5d6e57913465c22bb8074b0c0f968252cb12b \"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) | 0.004686 / 0.011353 (-0.006667) | 0.002764 / 0.011008 (-0.008244) | 0.061411 / 0.038508 (0.022902) | 0.030450 / 0.023109 (0.007341) | 0.247648 / 0.275898 (-0.028250) | 0.278033 / 0.323480 (-0.045447) | 0.002903 / 0.007986 (-0.005082) | 0.002350 / 0.004328 (-0.001979) | 0.047514 / 0.004250 (0.043264) | 0.044446 / 0.037052 (0.007393) | 0.256170 / 0.258489 (-0.002319) | 0.285977 / 0.293841 (-0.007864) | 0.023407 / 0.128546 (-0.105139) | 0.007223 / 0.075646 (-0.068423) | 0.201274 / 0.419271 (-0.217997) | 0.054022 / 0.043533 (0.010489) | 0.253841 / 0.255139 (-0.001298) | 0.278219 / 0.283200 (-0.004980) | 0.017796 / 0.141683 (-0.123886) | 1.105950 / 1.452155 (-0.346205) | 1.182021 / 1.492716 (-0.310695) |\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) | 0.089584 / 0.018006 (0.071578) | 0.299338 / 0.000490 (0.298849) | 0.000202 / 0.000200 (0.000003) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018974 / 0.037411 (-0.018437) | 0.062352 / 0.014526 (0.047826) | 0.073667 / 0.176557 (-0.102889) | 0.119225 / 0.737135 (-0.617911) | 0.075393 / 0.296338 (-0.220945) |\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) | 0.282749 / 0.215209 (0.067540) | 2.795822 / 2.077655 (0.718167) | 1.492946 / 1.504120 (-0.011174) | 1.382340 / 1.541195 (-0.158855) | 1.377281 / 1.468490 (-0.091209) | 0.397361 / 4.584777 (-4.187415) | 2.379416 / 3.745712 (-1.366296) | 2.552967 / 5.269862 (-2.716895) | 1.546347 / 4.565676 (-3.019330) | 0.045851 / 0.424275 (-0.378424) | 0.004830 / 0.007607 (-0.002777) | 0.351194 / 0.226044 (0.125150) | 3.407406 / 2.268929 (1.138478) | 1.852983 / 55.444624 (-53.591641) | 1.536381 / 6.876477 (-5.340095) | 1.542786 / 2.142072 (-0.599287) | 0.471960 / 4.805227 (-4.333267) | 0.098336 / 6.500664 (-6.402328) | 0.041569 / 0.075469 (-0.033900) |\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) | 0.912718 / 1.841788 (-0.929070) | 11.339404 / 8.074308 (3.265095) | 10.480593 / 10.191392 (0.289201) | 0.139508 / 0.680424 (-0.540916) | 0.014210 / 0.534201 (-0.519991) | 0.268152 / 0.579283 (-0.311131) | 0.260503 / 0.434364 (-0.173860) | 0.304735 / 0.540337 (-0.235602) | 0.422155 / 1.386936 (-0.964781) |\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) | 0.004714 / 0.011353 (-0.006638) | 0.002638 / 0.011008 (-0.008370) | 0.047967 / 0.038508 (0.009459) | 0.050758 / 0.023109 (0.027649) | 0.265619 / 0.275898 (-0.010279) | 0.286920 / 0.323480 (-0.036560) | 0.003936 / 0.007986 (-0.004050) | 0.002351 / 0.004328 (-0.001977) | 0.047642 / 0.004250 (0.043392) | 0.038412 / 0.037052 (0.001360) | 0.269561 / 0.258489 (0.011072) | 0.302057 / 0.293841 (0.008216) | 0.023893 / 0.128546 (-0.104653) | 0.006793 / 0.075646 (-0.068854) | 0.053091 / 0.419271 (-0.366180) | 0.032228 / 0.043533 (-0.011305) | 0.267110 / 0.255139 (0.011971) | 0.287211 / 0.283200 (0.004011) | 0.017945 / 0.141683 (-0.123738) | 1.191770 / 1.452155 (-0.260384) | 1.269644 / 1.492716 (-0.223072) |\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) | 0.088067 / 0.018006 (0.070061) | 0.298383 / 0.000490 (0.297893) | 0.000202 / 0.000200 (0.000002) | 0.000048 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020685 / 0.037411 (-0.016726) | 0.069883 / 0.014526 (0.055357) | 0.080107 / 0.176557 (-0.096450) | 0.119311 / 0.737135 (-0.617825) | 0.080791 / 0.296338 (-0.215548) |\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) | 0.295781 / 0.215209 (0.080572) | 2.905536 / 2.077655 (0.827881) | 1.579184 / 1.504120 (0.075064) | 1.475937 / 1.541195 (-0.065258) | 1.533708 / 1.468490 (0.065218) | 0.409851 / 4.584777 (-4.174926) | 2.443217 / 3.745712 (-1.302496) | 2.543980 / 5.269862 (-2.725882) | 1.512187 / 4.565676 (-3.053489) | 0.046390 / 0.424275 (-0.377885) | 0.004762 / 0.007607 (-0.002845) | 0.345066 / 0.226044 (0.119021) | 3.485133 / 2.268929 (1.216204) | 1.954690 / 55.444624 (-53.489934) | 1.671104 / 6.876477 (-5.205372) | 1.655330 / 2.142072 (-0.486743) | 0.487910 / 4.805227 (-4.317317) | 0.097707 / 6.500664 (-6.402957) | 0.040379 / 0.075469 (-0.035090) |\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) | 0.981620 / 1.841788 (-0.860168) | 11.806530 / 8.074308 (3.732222) | 10.868275 / 10.191392 (0.676883) | 0.141230 / 0.680424 (-0.539194) | 0.015785 / 0.534201 (-0.518416) | 0.271416 / 0.579283 (-0.307867) | 0.276048 / 0.434364 (-0.158316) | 0.310988 / 0.540337 (-0.229349) | 0.410078 / 1.386936 (-0.976858) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ec565740dee10c466ade16f81dee2783e442ba55 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004803 / 0.011353 (-0.006550) | 0.002961 / 0.011008 (-0.008047) | 0.061431 / 0.038508 (0.022923) | 0.030189 / 0.023109 (0.007080) | 0.255755 / 0.275898 (-0.020143) | 0.277841 / 0.323480 (-0.045639) | 0.003083 / 0.007986 (-0.004902) | 0.002432 / 0.004328 (-0.001896) | 0.047674 / 0.004250 (0.043424) | 0.045066 / 0.037052 (0.008014) | 0.268701 / 0.258489 (0.010211) | 0.286673 / 0.293841 (-0.007168) | 0.023663 / 0.128546 (-0.104883) | 0.007148 / 0.075646 (-0.068499) | 0.201962 / 0.419271 (-0.217310) | 0.054953 / 0.043533 (0.011420) | 0.257155 / 0.255139 (0.002016) | 0.277769 / 0.283200 (-0.005431) | 0.017803 / 0.141683 (-0.123880) | 1.100270 / 1.452155 (-0.351884) | 1.146975 / 1.492716 (-0.345741) |\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) | 0.092776 / 0.018006 (0.074770) | 0.303786 / 0.000490 (0.303296) | 0.000237 / 0.000200 (0.000037) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019647 / 0.037411 (-0.017765) | 0.063211 / 0.014526 (0.048686) | 0.076684 / 0.176557 (-0.099873) | 0.121952 / 0.737135 (-0.615184) | 0.077202 / 0.296338 (-0.219137) |\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) | 0.282087 / 0.215209 (0.066878) | 2.789204 / 2.077655 (0.711550) | 1.510376 / 1.504120 (0.006256) | 1.384241 / 1.541195 (-0.156954) | 1.414949 / 1.468490 (-0.053541) | 0.402206 / 4.584777 (-4.182570) | 2.377601 / 3.745712 (-1.368111) | 2.585354 / 5.269862 (-2.684508) | 1.592937 / 4.565676 (-2.972740) | 0.045217 / 0.424275 (-0.379058) | 0.004772 / 0.007607 (-0.002835) | 0.339584 / 0.226044 (0.113539) | 3.373184 / 2.268929 (1.104256) | 1.855196 / 55.444624 (-53.589428) | 1.599559 / 6.876477 (-5.276918) | 1.604421 / 2.142072 (-0.537651) | 0.467754 / 4.805227 (-4.337474) | 0.098244 / 6.500664 (-6.402420) | 0.042631 / 0.075469 (-0.032838) |\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) | 0.947680 / 1.841788 (-0.894108) | 11.539875 / 8.074308 (3.465567) | 10.340830 / 10.191392 (0.149438) | 0.145591 / 0.680424 (-0.534833) | 0.014367 / 0.534201 (-0.519834) | 0.270506 / 0.579283 (-0.308777) | 0.268825 / 0.434364 (-0.165539) | 0.308372 / 0.540337 (-0.231966) | 0.425039 / 1.386936 (-0.961897) |\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) | 0.004813 / 0.011353 (-0.006540) | 0.002931 / 0.011008 (-0.008078) | 0.047997 / 0.038508 (0.009489) | 0.050753 / 0.023109 (0.027644) | 0.272704 / 0.275898 (-0.003194) | 0.294045 / 0.323480 (-0.029435) | 0.004059 / 0.007986 (-0.003927) | 0.002491 / 0.004328 (-0.001838) | 0.047621 / 0.004250 (0.043371) | 0.038824 / 0.037052 (0.001772) | 0.275322 / 0.258489 (0.016833) | 0.306447 / 0.293841 (0.012606) | 0.024402 / 0.128546 (-0.104145) | 0.007252 / 0.075646 (-0.068394) | 0.053346 / 0.419271 (-0.365925) | 0.032224 / 0.043533 (-0.011309) | 0.271468 / 0.255139 (0.016329) | 0.289429 / 0.283200 (0.006229) | 0.018285 / 0.141683 (-0.123398) | 1.116743 / 1.452155 (-0.335412) | 1.182724 / 1.492716 (-0.309993) |\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) | 0.091899 / 0.018006 (0.073893) | 0.299161 / 0.000490 (0.298671) | 0.000224 / 0.000200 (0.000024) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021823 / 0.037411 (-0.015588) | 0.071227 / 0.014526 (0.056701) | 0.080503 / 0.176557 (-0.096053) | 0.120243 / 0.737135 (-0.616892) | 0.082328 / 0.296338 (-0.214010) |\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) | 0.324951 / 0.215209 (0.109742) | 2.842358 / 2.077655 (0.764703) | 1.602317 / 1.504120 (0.098197) | 1.481103 / 1.541195 (-0.060091) | 1.497557 / 1.468490 (0.029067) | 0.406523 / 4.584777 (-4.178254) | 2.402743 / 3.745712 (-1.342970) | 2.545435 / 5.269862 (-2.724427) | 1.534071 / 4.565676 (-3.031605) | 0.046914 / 0.424275 (-0.377361) | 0.004728 / 0.007607 (-0.002879) | 0.341544 / 0.226044 (0.115499) | 3.412017 / 2.268929 (1.143089) | 1.937442 / 55.444624 (-53.507182) | 1.668774 / 6.876477 (-5.207703) | 1.668908 / 2.142072 (-0.473165) | 0.477398 / 4.805227 (-4.327829) | 0.098531 / 6.500664 (-6.402133) | 0.041077 / 0.075469 (-0.034392) |\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) | 0.983888 / 1.841788 (-0.857900) | 12.072703 / 8.074308 (3.998395) | 11.028622 / 10.191392 (0.837230) | 0.148097 / 0.680424 (-0.532327) | 0.015869 / 0.534201 (-0.518332) | 0.267609 / 0.579283 (-0.311674) | 0.272345 / 0.434364 (-0.162019) | 0.303840 / 0.540337 (-0.236497) | 0.409199 / 1.386936 (-0.977737) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1487df064580bd23458234fab2e85876d9364e03 \"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) | 0.005016 / 0.011353 (-0.006337) | 0.002931 / 0.011008 (-0.008077) | 0.062142 / 0.038508 (0.023634) | 0.030758 / 0.023109 (0.007648) | 0.251689 / 0.275898 (-0.024209) | 0.272114 / 0.323480 (-0.051366) | 0.004102 / 0.007986 (-0.003884) | 0.002500 / 0.004328 (-0.001828) | 0.049187 / 0.004250 (0.044937) | 0.047150 / 0.037052 (0.010098) | 0.256497 / 0.258489 (-0.001992) | 0.288069 / 0.293841 (-0.005772) | 0.023915 / 0.128546 (-0.104632) | 0.007204 / 0.075646 (-0.068442) | 0.204257 / 0.419271 (-0.215015) | 0.063879 / 0.043533 (0.020346) | 0.253008 / 0.255139 (-0.002131) | 0.266554 / 0.283200 (-0.016645) | 0.018929 / 0.141683 (-0.122754) | 1.140547 / 1.452155 (-0.311608) | 1.197049 / 1.492716 (-0.295668) |\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) | 0.094111 / 0.018006 (0.076105) | 0.301618 / 0.000490 (0.301128) | 0.000219 / 0.000200 (0.000019) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018614 / 0.037411 (-0.018797) | 0.062426 / 0.014526 (0.047900) | 0.073079 / 0.176557 (-0.103477) | 0.120313 / 0.737135 (-0.616823) | 0.076445 / 0.296338 (-0.219894) |\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) | 0.285151 / 0.215209 (0.069942) | 2.754272 / 2.077655 (0.676617) | 1.485254 / 1.504120 (-0.018866) | 1.368412 / 1.541195 (-0.172783) | 1.402819 / 1.468490 (-0.065671) | 0.396561 / 4.584777 (-4.188216) | 2.375708 / 3.745712 (-1.370004) | 2.656088 / 5.269862 (-2.613773) | 1.588676 / 4.565676 (-2.977001) | 0.048662 / 0.424275 (-0.375613) | 0.004963 / 0.007607 (-0.002644) | 0.339747 / 0.226044 (0.113702) | 3.315841 / 2.268929 (1.046912) | 1.841439 / 55.444624 (-53.603186) | 1.547803 / 6.876477 (-5.328674) | 1.601872 / 2.142072 (-0.540200) | 0.468637 / 4.805227 (-4.336591) | 0.099423 / 6.500664 (-6.401241) | 0.041926 / 0.075469 (-0.033543) |\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) | 0.933058 / 1.841788 (-0.908730) | 11.680870 / 8.074308 (3.606561) | 10.239009 / 10.191392 (0.047617) | 0.129974 / 0.680424 (-0.550450) | 0.014081 / 0.534201 (-0.520120) | 0.273076 / 0.579283 (-0.306207) | 0.261914 / 0.434364 (-0.172450) | 0.305982 / 0.540337 (-0.234356) | 0.430623 / 1.386936 (-0.956313) |\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) | 0.004969 / 0.011353 (-0.006384) | 0.003084 / 0.011008 (-0.007924) | 0.048686 / 0.038508 (0.010178) | 0.057234 / 0.023109 (0.034125) | 0.295408 / 0.275898 (0.019510) | 0.323774 / 0.323480 (0.000294) | 0.004014 / 0.007986 (-0.003972) | 0.002423 / 0.004328 (-0.001905) | 0.048000 / 0.004250 (0.043749) | 0.039872 / 0.037052 (0.002820) | 0.294717 / 0.258489 (0.036228) | 0.331149 / 0.293841 (0.037309) | 0.027884 / 0.128546 (-0.100662) | 0.007155 / 0.075646 (-0.068491) | 0.053812 / 0.419271 (-0.365460) | 0.032483 / 0.043533 (-0.011050) | 0.293402 / 0.255139 (0.038263) | 0.312553 / 0.283200 (0.029354) | 0.017848 / 0.141683 (-0.123835) | 1.125600 / 1.452155 (-0.326554) | 1.189469 / 1.492716 (-0.303248) |\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) | 0.096198 / 0.018006 (0.078191) | 0.305096 / 0.000490 (0.304607) | 0.000229 / 0.000200 (0.000029) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021992 / 0.037411 (-0.015419) | 0.072082 / 0.014526 (0.057556) | 0.082704 / 0.176557 (-0.093853) | 0.124512 / 0.737135 (-0.612624) | 0.084541 / 0.296338 (-0.211797) |\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) | 0.296440 / 0.215209 (0.081231) | 2.923392 / 2.077655 (0.845738) | 1.599057 / 1.504120 (0.094937) | 1.480473 / 1.541195 (-0.060722) | 1.551837 / 1.468490 (0.083347) | 0.418618 / 4.584777 (-4.166159) | 2.472727 / 3.745712 (-1.272985) | 2.796141 / 5.269862 (-2.473721) | 1.629139 / 4.565676 (-2.936538) | 0.047703 / 0.424275 (-0.376572) | 0.004971 / 0.007607 (-0.002636) | 0.354453 / 0.226044 (0.128408) | 3.514861 / 2.268929 (1.245932) | 1.993597 / 55.444624 (-53.451028) | 1.694386 / 6.876477 (-5.182090) | 1.748562 / 2.142072 (-0.393510) | 0.487158 / 4.805227 (-4.318070) | 0.102021 / 6.500664 (-6.398643) | 0.042648 / 0.075469 (-0.032821) |\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) | 0.974950 / 1.841788 (-0.866837) | 13.391204 / 8.074308 (5.316896) | 11.474696 / 10.191392 (1.283304) | 0.142618 / 0.680424 (-0.537806) | 0.016163 / 0.534201 (-0.518038) | 0.271453 / 0.579283 (-0.307830) | 0.287049 / 0.434364 (-0.147315) | 0.309069 / 0.540337 (-0.231268) | 0.417117 / 1.386936 (-0.969819) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35a3422cfcebfef5b09ae70c22843ffadaf44c46 \"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) | 0.004974 / 0.011353 (-0.006379) | 0.002950 / 0.011008 (-0.008058) | 0.061856 / 0.038508 (0.023348) | 0.030539 / 0.023109 (0.007429) | 0.250105 / 0.275898 (-0.025793) | 0.276687 / 0.323480 (-0.046793) | 0.003077 / 0.007986 (-0.004908) | 0.002412 / 0.004328 (-0.001916) | 0.048336 / 0.004250 (0.044086) | 0.045849 / 0.037052 (0.008797) | 0.251757 / 0.258489 (-0.006732) | 0.284914 / 0.293841 (-0.008927) | 0.024033 / 0.128546 (-0.104513) | 0.007343 / 0.075646 (-0.068303) | 0.202867 / 0.419271 (-0.216405) | 0.061294 / 0.043533 (0.017762) | 0.263590 / 0.255139 (0.008451) | 0.272744 / 0.283200 (-0.010455) | 0.019613 / 0.141683 (-0.122070) | 1.104263 / 1.452155 (-0.347892) | 1.164128 / 1.492716 (-0.328588) |\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) | 0.094261 / 0.018006 (0.076255) | 0.303340 / 0.000490 (0.302850) | 0.000215 / 0.000200 (0.000015) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018381 / 0.037411 (-0.019030) | 0.062727 / 0.014526 (0.048201) | 0.074955 / 0.176557 (-0.101602) | 0.124810 / 0.737135 (-0.612326) | 0.074335 / 0.296338 (-0.222004) |\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) | 0.279368 / 0.215209 (0.064159) | 2.721641 / 2.077655 (0.643986) | 1.510773 / 1.504120 (0.006653) | 1.364349 / 1.541195 (-0.176845) | 1.386044 / 1.468490 (-0.082446) | 0.403051 / 4.584777 (-4.181726) | 2.416525 / 3.745712 (-1.329187) | 2.623198 / 5.269862 (-2.646663) | 1.560869 / 4.565676 (-3.004808) | 0.046613 / 0.424275 (-0.377662) | 0.004861 / 0.007607 (-0.002746) | 0.337875 / 0.226044 (0.111830) | 3.289956 / 2.268929 (1.021028) | 1.851707 / 55.444624 (-53.592917) | 1.571092 / 6.876477 (-5.305385) | 1.600328 / 2.142072 (-0.541745) | 0.480766 / 4.805227 (-4.324461) | 0.099138 / 6.500664 (-6.401526) | 0.041691 / 0.075469 (-0.033779) |\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) | 0.941162 / 1.841788 (-0.900626) | 11.745335 / 8.074308 (3.671027) | 10.645509 / 10.191392 (0.454117) | 0.132506 / 0.680424 (-0.547918) | 0.015192 / 0.534201 (-0.519009) | 0.272483 / 0.579283 (-0.306800) | 0.270269 / 0.434364 (-0.164094) | 0.309580 / 0.540337 (-0.230758) | 0.431513 / 1.386936 (-0.955423) |\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) | 0.005068 / 0.011353 (-0.006285) | 0.003069 / 0.011008 (-0.007939) | 0.048605 / 0.038508 (0.010097) | 0.059557 / 0.023109 (0.036448) | 0.275092 / 0.275898 (-0.000806) | 0.298910 / 0.323480 (-0.024570) | 0.004198 / 0.007986 (-0.003788) | 0.002499 / 0.004328 (-0.001830) | 0.048248 / 0.004250 (0.043997) | 0.040302 / 0.037052 (0.003249) | 0.279539 / 0.258489 (0.021050) | 0.312500 / 0.293841 (0.018659) | 0.025407 / 0.128546 (-0.103140) | 0.007364 / 0.075646 (-0.068282) | 0.053086 / 0.419271 (-0.366186) | 0.033291 / 0.043533 (-0.010242) | 0.276521 / 0.255139 (0.021382) | 0.292943 / 0.283200 (0.009743) | 0.019416 / 0.141683 (-0.122267) | 1.151734 / 1.452155 (-0.300421) | 1.205021 / 1.492716 (-0.287695) |\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) | 0.094112 / 0.018006 (0.076106) | 0.309534 / 0.000490 (0.309044) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021539 / 0.037411 (-0.015872) | 0.070325 / 0.014526 (0.055799) | 0.080468 / 0.176557 (-0.096089) | 0.121095 / 0.737135 (-0.616040) | 0.082008 / 0.296338 (-0.214331) |\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) | 0.302591 / 0.215209 (0.087382) | 2.943475 / 2.077655 (0.865820) | 1.597970 / 1.504120 (0.093850) | 1.468774 / 1.541195 (-0.072421) | 1.504812 / 1.468490 (0.036322) | 0.413715 / 4.584777 (-4.171062) | 2.418319 / 3.745712 (-1.327393) | 2.616656 / 5.269862 (-2.653206) | 1.558165 / 4.565676 (-3.007512) | 0.047169 / 0.424275 (-0.377106) | 0.004761 / 0.007607 (-0.002846) | 0.347225 / 0.226044 (0.121180) | 3.479624 / 2.268929 (1.210696) | 1.961253 / 55.444624 (-53.483371) | 1.673532 / 6.876477 (-5.202944) | 1.698900 / 2.142072 (-0.443172) | 0.488373 / 4.805227 (-4.316855) | 0.098322 / 6.500664 (-6.402342) | 0.040832 / 0.075469 (-0.034637) |\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) | 1.009133 / 1.841788 (-0.832655) | 13.373258 / 8.074308 (5.298949) | 11.327360 / 10.191392 (1.135968) | 0.135778 / 0.680424 (-0.544646) | 0.015813 / 0.534201 (-0.518388) | 0.275404 / 0.579283 (-0.303879) | 0.282564 / 0.434364 (-0.151799) | 0.311830 / 0.540337 (-0.228507) | 0.419008 / 1.386936 (-0.967928) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4592709e5399f91b5b392f4fd73687985365c909 \"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) | 0.004899 / 0.011353 (-0.006454) | 0.002780 / 0.011008 (-0.008229) | 0.061997 / 0.038508 (0.023489) | 0.029909 / 0.023109 (0.006800) | 0.233445 / 0.275898 (-0.042453) | 0.254128 / 0.323480 (-0.069351) | 0.002927 / 0.007986 (-0.005058) | 0.002396 / 0.004328 (-0.001932) | 0.048118 / 0.004250 (0.043868) | 0.044520 / 0.037052 (0.007468) | 0.237594 / 0.258489 (-0.020895) | 0.268407 / 0.293841 (-0.025434) | 0.023517 / 0.128546 (-0.105029) | 0.007035 / 0.075646 (-0.068612) | 0.202803 / 0.419271 (-0.216469) | 0.057692 / 0.043533 (0.014159) | 0.237058 / 0.255139 (-0.018081) | 0.252966 / 0.283200 (-0.030233) | 0.017934 / 0.141683 (-0.123748) | 1.096406 / 1.452155 (-0.355749) | 1.153509 / 1.492716 (-0.339207) |\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) | 0.091812 / 0.018006 (0.073806) | 0.298410 / 0.000490 (0.297920) | 0.000228 / 0.000200 (0.000028) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018333 / 0.037411 (-0.019078) | 0.062685 / 0.014526 (0.048159) | 0.073295 / 0.176557 (-0.103261) | 0.119234 / 0.737135 (-0.617901) | 0.074603 / 0.296338 (-0.221736) |\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) | 0.279078 / 0.215209 (0.063869) | 2.768535 / 2.077655 (0.690880) | 1.457049 / 1.504120 (-0.047071) | 1.326870 / 1.541195 (-0.214325) | 1.349657 / 1.468490 (-0.118833) | 0.405003 / 4.584777 (-4.179774) | 2.428726 / 3.745712 (-1.316986) | 2.595776 / 5.269862 (-2.674086) | 1.557879 / 4.565676 (-3.007797) | 0.045985 / 0.424275 (-0.378291) | 0.004854 / 0.007607 (-0.002753) | 0.336437 / 0.226044 (0.110392) | 3.317330 / 2.268929 (1.048401) | 1.784525 / 55.444624 (-53.660100) | 1.500295 / 6.876477 (-5.376182) | 1.529869 / 2.142072 (-0.612203) | 0.473426 / 4.805227 (-4.331801) | 0.099609 / 6.500664 (-6.401055) | 0.042054 / 0.075469 (-0.033415) |\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) | 0.937154 / 1.841788 (-0.904633) | 11.482383 / 8.074308 (3.408075) | 10.468769 / 10.191392 (0.277377) | 0.132724 / 0.680424 (-0.547700) | 0.015242 / 0.534201 (-0.518959) | 0.281124 / 0.579283 (-0.298159) | 0.268603 / 0.434364 (-0.165761) | 0.311410 / 0.540337 (-0.228928) | 0.431817 / 1.386936 (-0.955119) |\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) | 0.004695 / 0.011353 (-0.006658) | 0.002873 / 0.011008 (-0.008135) | 0.048133 / 0.038508 (0.009625) | 0.052505 / 0.023109 (0.029396) | 0.271679 / 0.275898 (-0.004219) | 0.292530 / 0.323480 (-0.030950) | 0.003844 / 0.007986 (-0.004142) | 0.002417 / 0.004328 (-0.001912) | 0.048619 / 0.004250 (0.044369) | 0.039152 / 0.037052 (0.002100) | 0.276575 / 0.258489 (0.018086) | 0.307836 / 0.293841 (0.013995) | 0.023877 / 0.128546 (-0.104669) | 0.006897 / 0.075646 (-0.068749) | 0.053241 / 0.419271 (-0.366031) | 0.032487 / 0.043533 (-0.011046) | 0.274205 / 0.255139 (0.019066) | 0.289701 / 0.283200 (0.006502) | 0.018250 / 0.141683 (-0.123432) | 1.137902 / 1.452155 (-0.314253) | 1.202043 / 1.492716 (-0.290673) |\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) | 0.091453 / 0.018006 (0.073446) | 0.297032 / 0.000490 (0.296543) | 0.000224 / 0.000200 (0.000024) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021062 / 0.037411 (-0.016349) | 0.069848 / 0.014526 (0.055322) | 0.084337 / 0.176557 (-0.092219) | 0.119951 / 0.737135 (-0.617184) | 0.082805 / 0.296338 (-0.213533) |\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) | 0.297056 / 0.215209 (0.081846) | 2.890110 / 2.077655 (0.812456) | 1.609918 / 1.504120 (0.105798) | 1.491184 / 1.541195 (-0.050011) | 1.529433 / 1.468490 (0.060943) | 0.396081 / 4.584777 (-4.188696) | 2.408310 / 3.745712 (-1.337402) | 2.567905 / 5.269862 (-2.701957) | 1.514465 / 4.565676 (-3.051212) | 0.045329 / 0.424275 (-0.378946) | 0.004738 / 0.007607 (-0.002869) | 0.344373 / 0.226044 (0.118328) | 3.428333 / 2.268929 (1.159404) | 1.981401 / 55.444624 (-53.463223) | 1.688007 / 6.876477 (-5.188470) | 1.685542 / 2.142072 (-0.456531) | 0.478045 / 4.805227 (-4.327182) | 0.096664 / 6.500664 (-6.404001) | 0.040335 / 0.075469 (-0.035135) |\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) | 0.972912 / 1.841788 (-0.868876) | 12.055045 / 8.074308 (3.980737) | 10.821073 / 10.191392 (0.629681) | 0.139177 / 0.680424 (-0.541247) | 0.015046 / 0.534201 (-0.519155) | 0.275670 / 0.579283 (-0.303613) | 0.280366 / 0.434364 (-0.153998) | 0.315781 / 0.540337 (-0.224556) | 0.424536 / 1.386936 (-0.962400) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0684b471d6ca8a235162f5575f624b6eda7956c5 \"CML watermark\")\n",
"I'm finally merging as `transformers`/`tokenizers` dependency pins have been removed + `huggingface_hub 0.19.4` has fixed the deps incompatibility issue. All good now :)",
"<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) | 0.004435 / 0.011353 (-0.006918) | 0.002924 / 0.011008 (-0.008084) | 0.062159 / 0.038508 (0.023651) | 0.029639 / 0.023109 (0.006529) | 0.237470 / 0.275898 (-0.038428) | 0.269641 / 0.323480 (-0.053839) | 0.004124 / 0.007986 (-0.003862) | 0.002528 / 0.004328 (-0.001800) | 0.048114 / 0.004250 (0.043864) | 0.046055 / 0.037052 (0.009002) | 0.245844 / 0.258489 (-0.012645) | 0.278085 / 0.293841 (-0.015756) | 0.023152 / 0.128546 (-0.105394) | 0.007194 / 0.075646 (-0.068452) | 0.206493 / 0.419271 (-0.212778) | 0.055687 / 0.043533 (0.012155) | 0.243301 / 0.255139 (-0.011838) | 0.267645 / 0.283200 (-0.015555) | 0.017413 / 0.141683 (-0.124270) | 1.113071 / 1.452155 (-0.339083) | 1.201436 / 1.492716 (-0.291280) |\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) | 0.092576 / 0.018006 (0.074570) | 0.303516 / 0.000490 (0.303027) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019108 / 0.037411 (-0.018303) | 0.062326 / 0.014526 (0.047800) | 0.073711 / 0.176557 (-0.102846) | 0.120414 / 0.737135 (-0.616721) | 0.075837 / 0.296338 (-0.220501) |\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) | 0.278267 / 0.215209 (0.063058) | 2.766231 / 2.077655 (0.688576) | 1.455613 / 1.504120 (-0.048507) | 1.337128 / 1.541195 (-0.204066) | 1.357659 / 1.468490 (-0.110831) | 0.404549 / 4.584777 (-4.180228) | 2.409084 / 3.745712 (-1.336628) | 2.645000 / 5.269862 (-2.624861) | 1.600475 / 4.565676 (-2.965201) | 0.046680 / 0.424275 (-0.377595) | 0.004887 / 0.007607 (-0.002720) | 0.340338 / 0.226044 (0.114294) | 3.332647 / 2.268929 (1.063719) | 1.852529 / 55.444624 (-53.592096) | 1.532442 / 6.876477 (-5.344035) | 1.550383 / 2.142072 (-0.591689) | 0.482702 / 4.805227 (-4.322525) | 0.101067 / 6.500664 (-6.399597) | 0.042132 / 0.075469 (-0.033337) |\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) | 0.945481 / 1.841788 (-0.896307) | 11.886240 / 8.074308 (3.811932) | 10.484620 / 10.191392 (0.293228) | 0.130906 / 0.680424 (-0.549518) | 0.014880 / 0.534201 (-0.519321) | 0.268836 / 0.579283 (-0.310447) | 0.268112 / 0.434364 (-0.166251) | 0.304300 / 0.540337 (-0.236038) | 0.440262 / 1.386936 (-0.946674) |\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) | 0.005028 / 0.011353 (-0.006325) | 0.002937 / 0.011008 (-0.008071) | 0.049038 / 0.038508 (0.010530) | 0.057763 / 0.023109 (0.034653) | 0.273196 / 0.275898 (-0.002702) | 0.295519 / 0.323480 (-0.027961) | 0.004102 / 0.007986 (-0.003883) | 0.002487 / 0.004328 (-0.001841) | 0.049148 / 0.004250 (0.044898) | 0.040303 / 0.037052 (0.003251) | 0.279187 / 0.258489 (0.020698) | 0.311086 / 0.293841 (0.017245) | 0.024961 / 0.128546 (-0.103585) | 0.007264 / 0.075646 (-0.068382) | 0.055711 / 0.419271 (-0.363561) | 0.032355 / 0.043533 (-0.011178) | 0.274304 / 0.255139 (0.019165) | 0.290953 / 0.283200 (0.007753) | 0.018358 / 0.141683 (-0.123325) | 1.115984 / 1.452155 (-0.336170) | 1.190409 / 1.492716 (-0.302308) |\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) | 0.095765 / 0.018006 (0.077759) | 0.287947 / 0.000490 (0.287457) | 0.000242 / 0.000200 (0.000042) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022165 / 0.037411 (-0.015246) | 0.070465 / 0.014526 (0.055940) | 0.082078 / 0.176557 (-0.094479) | 0.120209 / 0.737135 (-0.616926) | 0.084573 / 0.296338 (-0.211765) |\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) | 0.298492 / 0.215209 (0.083283) | 2.924981 / 2.077655 (0.847327) | 1.597326 / 1.504120 (0.093206) | 1.459132 / 1.541195 (-0.082062) | 1.511471 / 1.468490 (0.042981) | 0.406671 / 4.584777 (-4.178106) | 2.443154 / 3.745712 (-1.302558) | 2.591131 / 5.269862 (-2.678731) | 1.549931 / 4.565676 (-3.015745) | 0.047042 / 0.424275 (-0.377234) | 0.004891 / 0.007607 (-0.002716) | 0.346274 / 0.226044 (0.120230) | 3.456050 / 2.268929 (1.187121) | 1.959328 / 55.444624 (-53.485296) | 1.647631 / 6.876477 (-5.228845) | 1.692024 / 2.142072 (-0.450049) | 0.478307 / 4.805227 (-4.326920) | 0.098738 / 6.500664 (-6.401926) | 0.041743 / 0.075469 (-0.033726) |\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) | 0.984619 / 1.841788 (-0.857168) | 12.403984 / 8.074308 (4.329676) | 10.974347 / 10.191392 (0.782955) | 0.132893 / 0.680424 (-0.547530) | 0.015504 / 0.534201 (-0.518697) | 0.275354 / 0.579283 (-0.303929) | 0.283312 / 0.434364 (-0.151052) | 0.313661 / 0.540337 (-0.226677) | 0.419065 / 1.386936 (-0.967871) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65315e4a8308f04fcb025039afe2a2e43b5684e \"CML watermark\")\n"
] | 2023-11-14T10:47:09Z
| 2023-11-17T14:23:20Z
| 2023-11-17T14:17:00Z
|
CONTRIBUTOR
| null | 0
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Related to https://github.com/huggingface/transformers/issues/27034 and https://github.com/huggingface/huggingface_hub/pull/1782.
**TL;DR:** `hashlib` is not a secure library for cryptography-related stuff. We are only using `hashlib` for non-security-related purposes in `datasets` so it's fine. From Python 3.9 we set can `usedforsecurity=False` in any `hashlib` method which is mandatory for companies that forbid the use of `hashlib` for security purposes. This PR fixes that.
**Note:** before merging this we need to release a new tokenizers version that would allow the newest `huggingface_hub` version (see https://github.com/huggingface/tokenizers/pull/1385). Otherwise it might create friction to users that want to install `datasets` + `tokenizers` at the same time.
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PR_kwDODunzps4tCxfK
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Update summary on PyPi beyond NLP
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| 2021-10-13T08:55:54Z
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More than just NLP now
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PR_kwDODunzps5eYsZc
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Fix typo in `Dataset.map` docstring
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006709 / 0.011353 (-0.004643) | 0.004102 / 0.011008 (-0.006906) | 0.084449 / 0.038508 (0.045941) | 0.076078 / 0.023109 (0.052969) | 0.319831 / 0.275898 (0.043933) | 0.359918 / 0.323480 (0.036438) | 0.006092 / 0.007986 (-0.001894) | 0.003402 / 0.004328 (-0.000926) | 0.064715 / 0.004250 (0.060465) | 0.054541 / 0.037052 (0.017488) | 0.330394 / 0.258489 (0.071905) | 0.366048 / 0.293841 (0.072207) | 0.031594 / 0.128546 (-0.096952) | 0.008591 / 0.075646 (-0.067056) | 0.292983 / 0.419271 (-0.126288) | 0.052986 / 0.043533 (0.009453) | 0.322253 / 0.255139 (0.067114) | 0.340082 / 0.283200 (0.056882) | 0.023390 / 0.141683 (-0.118293) | 1.459038 / 1.452155 (0.006883) | 1.536256 / 1.492716 (0.043540) |\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) | 0.233527 / 0.018006 (0.215521) | 0.459145 / 0.000490 (0.458655) | 0.007471 / 0.000200 (0.007271) | 0.000281 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028158 / 0.037411 (-0.009253) | 0.083079 / 0.014526 (0.068553) | 0.097159 / 0.176557 (-0.079397) | 0.151927 / 0.737135 (-0.585208) | 0.098024 / 0.296338 (-0.198314) |\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) | 0.386882 / 0.215209 (0.171673) | 3.849635 / 2.077655 (1.771981) | 1.832885 / 1.504120 (0.328765) | 1.668356 / 1.541195 (0.127162) | 1.745066 / 1.468490 (0.276576) | 0.484476 / 4.584777 (-4.100301) | 3.547604 / 3.745712 (-0.198108) | 3.480338 / 5.269862 (-1.789523) | 2.066837 / 4.565676 (-2.498840) | 0.056755 / 0.424275 (-0.367520) | 0.007747 / 0.007607 (0.000140) | 0.467999 / 0.226044 (0.241955) | 4.678875 / 2.268929 (2.409946) | 2.341930 / 55.444624 (-53.102695) | 1.985632 / 6.876477 (-4.890844) | 2.046998 / 2.142072 (-0.095074) | 0.579860 / 4.805227 (-4.225367) | 0.131488 / 6.500664 (-6.369176) | 0.060193 / 0.075469 (-0.015276) |\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) | 1.249656 / 1.841788 (-0.592132) | 19.079517 / 8.074308 (11.005209) | 14.328827 / 10.191392 (4.137435) | 0.173707 / 0.680424 (-0.506717) | 0.018250 / 0.534201 (-0.515951) | 0.392225 / 0.579283 (-0.187058) | 0.413920 / 0.434364 (-0.020444) | 0.464124 / 0.540337 (-0.076214) | 0.640283 / 1.386936 (-0.746653) |\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) | 0.006859 / 0.011353 (-0.004494) | 0.004068 / 0.011008 (-0.006940) | 0.063936 / 0.038508 (0.025428) | 0.077187 / 0.023109 (0.054078) | 0.365098 / 0.275898 (0.089200) | 0.391003 / 0.323480 (0.067523) | 0.005571 / 0.007986 (-0.002415) | 0.003425 / 0.004328 (-0.000904) | 0.063220 / 0.004250 (0.058970) | 0.056964 / 0.037052 (0.019912) | 0.367793 / 0.258489 (0.109304) | 0.398776 / 0.293841 (0.104935) | 0.033182 / 0.128546 (-0.095364) | 0.008601 / 0.075646 (-0.067045) | 0.070276 / 0.419271 (-0.348996) | 0.048383 / 0.043533 (0.004850) | 0.360414 / 0.255139 (0.105275) | 0.368171 / 0.283200 (0.084971) | 0.023114 / 0.141683 (-0.118569) | 1.503503 / 1.452155 (0.051349) | 1.567279 / 1.492716 (0.074562) |\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) | 0.224296 / 0.018006 (0.206290) | 0.455138 / 0.000490 (0.454648) | 0.004014 / 0.000200 (0.003814) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032337 / 0.037411 (-0.005074) | 0.094385 / 0.014526 (0.079859) | 0.109870 / 0.176557 (-0.066687) | 0.156978 / 0.737135 (-0.580157) | 0.107559 / 0.296338 (-0.188780) |\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) | 0.427409 / 0.215209 (0.212200) | 4.261772 / 2.077655 (2.184117) | 2.276106 / 1.504120 (0.771986) | 2.115232 / 1.541195 (0.574038) | 2.192048 / 1.468490 (0.723558) | 0.488459 / 4.584777 (-4.096318) | 3.675463 / 3.745712 (-0.070249) | 3.322475 / 5.269862 (-1.947387) | 2.072253 / 4.565676 (-2.493424) | 0.058259 / 0.424275 (-0.366017) | 0.007319 / 0.007607 (-0.000288) | 0.499513 / 0.226044 (0.273469) | 4.994774 / 2.268929 (2.725845) | 2.760927 / 55.444624 (-52.683697) | 2.391947 / 6.876477 (-4.484530) | 2.600557 / 2.142072 (0.458484) | 0.587597 / 4.805227 (-4.217630) | 0.131444 / 6.500664 (-6.369220) | 0.057334 / 0.075469 (-0.018135) |\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) | 1.354636 / 1.841788 (-0.487152) | 19.685735 / 8.074308 (11.611427) | 14.295920 / 10.191392 (4.104528) | 0.171921 / 0.680424 (-0.508503) | 0.019926 / 0.534201 (-0.514274) | 0.395216 / 0.579283 (-0.184068) | 0.432791 / 0.434364 (-0.001573) | 0.473055 / 0.540337 (-0.067282) | 0.638633 / 1.386936 (-0.748303) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fad7c899ec9218a717311223aa6ef5c09a6c7885 \"CML watermark\")\n"
] | 2023-11-02T01:36:49Z
| 2023-11-02T15:18:22Z
| 2023-11-02T10:11:38Z
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I_kwDODunzps5JnaC2
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GH Action to build documentation never ends
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[] | 2022-05-13T10:44:44Z
| 2022-05-13T11:22:00Z
| 2022-05-13T11:22:00Z
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MEMBER
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## Describe the bug
See: https://github.com/huggingface/datasets/runs/6418035586?check_suite_focus=true
I finally forced the cancel of the workflow.
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Don't skip hidden files in `dl_manager.iter_files` when they are given as input
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"<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) | 0.005894 / 0.011353 (-0.005459) | 0.003621 / 0.011008 (-0.007387) | 0.080446 / 0.038508 (0.041938) | 0.056800 / 0.023109 (0.033691) | 0.326485 / 0.275898 (0.050587) | 0.376207 / 0.323480 (0.052727) | 0.004640 / 0.007986 (-0.003346) | 0.002795 / 0.004328 (-0.001533) | 0.062815 / 0.004250 (0.058565) | 0.045761 / 0.037052 (0.008709) | 0.341417 / 0.258489 (0.082928) | 0.373129 / 0.293841 (0.079288) | 0.027226 / 0.128546 (-0.101321) | 0.007873 / 0.075646 (-0.067774) | 0.261737 / 0.419271 (-0.157535) | 0.044648 / 0.043533 (0.001115) | 0.320195 / 0.255139 (0.065056) | 0.381892 / 0.283200 (0.098692) | 0.020431 / 0.141683 (-0.121252) | 1.405332 / 1.452155 (-0.046823) | 1.455592 / 1.492716 (-0.037125) |\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) | 0.191539 / 0.018006 (0.173533) | 0.423655 / 0.000490 (0.423165) | 0.002741 / 0.000200 (0.002541) | 0.000069 / 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) | 0.023952 / 0.037411 (-0.013459) | 0.073387 / 0.014526 (0.058861) | 0.083746 / 0.176557 (-0.092810) | 0.144977 / 0.737135 (-0.592159) | 0.083808 / 0.296338 (-0.212530) |\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) | 0.436228 / 0.215209 (0.221019) | 4.370510 / 2.077655 (2.292855) | 2.340426 / 1.504120 (0.836306) | 2.202215 / 1.541195 (0.661021) | 2.258528 / 1.468490 (0.790037) | 0.503455 / 4.584777 (-4.081322) | 3.043695 / 3.745712 (-0.702017) | 2.784033 / 5.269862 (-2.485829) | 1.847956 / 4.565676 (-2.717721) | 0.057702 / 0.424275 (-0.366573) | 0.006703 / 0.007607 (-0.000904) | 0.510628 / 0.226044 (0.284583) | 5.101890 / 2.268929 (2.832961) | 2.816469 / 55.444624 (-52.628155) | 2.474220 / 6.876477 (-4.402257) | 2.617851 / 2.142072 (0.475779) | 0.593585 / 4.805227 (-4.211642) | 0.125895 / 6.500664 (-6.374769) | 0.062170 / 0.075469 (-0.013299) |\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) | 1.238792 / 1.841788 (-0.602996) | 18.096417 / 8.074308 (10.022108) | 13.548778 / 10.191392 (3.357386) | 0.144878 / 0.680424 (-0.535546) | 0.016644 / 0.534201 (-0.517557) | 0.334556 / 0.579283 (-0.244728) | 0.343680 / 0.434364 (-0.090684) | 0.383093 / 0.540337 (-0.157244) | 0.525075 / 1.386936 (-0.861861) |\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) | 0.006125 / 0.011353 (-0.005228) | 0.003668 / 0.011008 (-0.007340) | 0.062650 / 0.038508 (0.024142) | 0.058882 / 0.023109 (0.035772) | 0.454643 / 0.275898 (0.178745) | 0.486659 / 0.323480 (0.163179) | 0.005558 / 0.007986 (-0.002427) | 0.002858 / 0.004328 (-0.001471) | 0.062603 / 0.004250 (0.058353) | 0.049701 / 0.037052 (0.012649) | 0.455903 / 0.258489 (0.197413) | 0.491544 / 0.293841 (0.197703) | 0.028581 / 0.128546 (-0.099965) | 0.008040 / 0.075646 (-0.067607) | 0.068314 / 0.419271 (-0.350957) | 0.040637 / 0.043533 (-0.002896) | 0.450288 / 0.255139 (0.195149) | 0.476330 / 0.283200 (0.193131) | 0.018989 / 0.141683 (-0.122693) | 1.455122 / 1.452155 (0.002967) | 1.496941 / 1.492716 (0.004225) |\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) | 0.227382 / 0.018006 (0.209376) | 0.432637 / 0.000490 (0.432147) | 0.002727 / 0.000200 (0.002527) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026125 / 0.037411 (-0.011286) | 0.081342 / 0.014526 (0.066817) | 0.091227 / 0.176557 (-0.085329) | 0.145175 / 0.737135 (-0.591960) | 0.091988 / 0.296338 (-0.204351) |\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) | 0.454293 / 0.215209 (0.239083) | 4.537912 / 2.077655 (2.460257) | 2.489146 / 1.504120 (0.985026) | 2.307166 / 1.541195 (0.765971) | 2.380866 / 1.468490 (0.912376) | 0.509015 / 4.584777 (-4.075762) | 3.111069 / 3.745712 (-0.634644) | 2.839181 / 5.269862 (-2.430681) | 1.874630 / 4.565676 (-2.691047) | 0.058540 / 0.424275 (-0.365735) | 0.006693 / 0.007607 (-0.000914) | 0.528408 / 0.226044 (0.302363) | 5.285802 / 2.268929 (3.016874) | 2.952090 / 55.444624 (-52.492534) | 2.591496 / 6.876477 (-4.284980) | 2.741080 / 2.142072 (0.599007) | 0.595610 / 4.805227 (-4.209617) | 0.124387 / 6.500664 (-6.376277) | 0.061032 / 0.075469 (-0.014437) |\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) | 1.365816 / 1.841788 (-0.475972) | 18.684534 / 8.074308 (10.610226) | 14.540438 / 10.191392 (4.349046) | 0.146793 / 0.680424 (-0.533631) | 0.018165 / 0.534201 (-0.516036) | 0.333794 / 0.579283 (-0.245489) | 0.345533 / 0.434364 (-0.088830) | 0.384453 / 0.540337 (-0.155885) | 0.529104 / 1.386936 (-0.857832) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6c884967dd5f4e8aa3d1f3c2e3a414ae53afe261 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006121 / 0.011353 (-0.005232) | 0.003683 / 0.011008 (-0.007325) | 0.083329 / 0.038508 (0.044821) | 0.063350 / 0.023109 (0.040241) | 0.329959 / 0.275898 (0.054061) | 0.396111 / 0.323480 (0.072631) | 0.003554 / 0.007986 (-0.004432) | 0.002907 / 0.004328 (-0.001421) | 0.064152 / 0.004250 (0.059902) | 0.049182 / 0.037052 (0.012130) | 0.343862 / 0.258489 (0.085373) | 0.414568 / 0.293841 (0.120727) | 0.027157 / 0.128546 (-0.101389) | 0.007957 / 0.075646 (-0.067689) | 0.261868 / 0.419271 (-0.157404) | 0.044938 / 0.043533 (0.001405) | 0.318470 / 0.255139 (0.063331) | 0.393319 / 0.283200 (0.110119) | 0.022848 / 0.141683 (-0.118835) | 1.419916 / 1.452155 (-0.032238) | 1.508783 / 1.492716 (0.016067) |\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) | 0.200530 / 0.018006 (0.182523) | 0.433586 / 0.000490 (0.433097) | 0.002063 / 0.000200 (0.001863) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024803 / 0.037411 (-0.012609) | 0.075894 / 0.014526 (0.061368) | 0.086488 / 0.176557 (-0.090069) | 0.149058 / 0.737135 (-0.588077) | 0.087046 / 0.296338 (-0.209292) |\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) | 0.390771 / 0.215209 (0.175562) | 3.886178 / 2.077655 (1.808523) | 1.868626 / 1.504120 (0.364506) | 1.708532 / 1.541195 (0.167338) | 1.788491 / 1.468490 (0.320001) | 0.505706 / 4.584777 (-4.079071) | 3.062094 / 3.745712 (-0.683618) | 2.898559 / 5.269862 (-2.371302) | 1.901225 / 4.565676 (-2.664452) | 0.058366 / 0.424275 (-0.365909) | 0.006851 / 0.007607 (-0.000756) | 0.465382 / 0.226044 (0.239337) | 4.650187 / 2.268929 (2.381258) | 2.316152 / 55.444624 (-53.128472) | 1.989597 / 6.876477 (-4.886879) | 2.169266 / 2.142072 (0.027194) | 0.593257 / 4.805227 (-4.211970) | 0.126440 / 6.500664 (-6.374224) | 0.062227 / 0.075469 (-0.013242) |\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) | 1.283591 / 1.841788 (-0.558197) | 18.384667 / 8.074308 (10.310358) | 14.079611 / 10.191392 (3.888219) | 0.150453 / 0.680424 (-0.529971) | 0.017100 / 0.534201 (-0.517101) | 0.330503 / 0.579283 (-0.248780) | 0.348134 / 0.434364 (-0.086230) | 0.385726 / 0.540337 (-0.154612) | 0.529147 / 1.386936 (-0.857789) |\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) | 0.006168 / 0.011353 (-0.005185) | 0.003801 / 0.011008 (-0.007208) | 0.063168 / 0.038508 (0.024660) | 0.062331 / 0.023109 (0.039221) | 0.448321 / 0.275898 (0.172423) | 0.484416 / 0.323480 (0.160937) | 0.004827 / 0.007986 (-0.003159) | 0.002848 / 0.004328 (-0.001480) | 0.062736 / 0.004250 (0.058486) | 0.049128 / 0.037052 (0.012075) | 0.449276 / 0.258489 (0.190787) | 0.499035 / 0.293841 (0.205194) | 0.028577 / 0.128546 (-0.099969) | 0.008114 / 0.075646 (-0.067532) | 0.068297 / 0.419271 (-0.350974) | 0.040835 / 0.043533 (-0.002698) | 0.453556 / 0.255139 (0.198417) | 0.475420 / 0.283200 (0.192220) | 0.020292 / 0.141683 (-0.121390) | 1.472226 / 1.452155 (0.020071) | 1.523809 / 1.492716 (0.031093) |\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) | 0.230662 / 0.018006 (0.212655) | 0.439697 / 0.000490 (0.439207) | 0.009899 / 0.000200 (0.009699) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026418 / 0.037411 (-0.010993) | 0.082188 / 0.014526 (0.067662) | 0.091039 / 0.176557 (-0.085518) | 0.146646 / 0.737135 (-0.590489) | 0.091693 / 0.296338 (-0.204645) |\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) | 0.462086 / 0.215209 (0.246877) | 4.620925 / 2.077655 (2.543271) | 2.539234 / 1.504120 (1.035114) | 2.371178 / 1.541195 (0.829983) | 2.440538 / 1.468490 (0.972048) | 0.511047 / 4.584777 (-4.073730) | 3.082088 / 3.745712 (-0.663624) | 2.918162 / 5.269862 (-2.351700) | 1.899651 / 4.565676 (-2.666025) | 0.059003 / 0.424275 (-0.365272) | 0.006746 / 0.007607 (-0.000861) | 0.537863 / 0.226044 (0.311819) | 5.382355 / 2.268929 (3.113426) | 3.060091 / 55.444624 (-52.384534) | 2.754969 / 6.876477 (-4.121507) | 2.863156 / 2.142072 (0.721084) | 0.606888 / 4.805227 (-4.198339) | 0.127448 / 6.500664 (-6.373216) | 0.062975 / 0.075469 (-0.012494) |\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) | 1.336065 / 1.841788 (-0.505722) | 19.019902 / 8.074308 (10.945594) | 15.057979 / 10.191392 (4.866587) | 0.160646 / 0.680424 (-0.519778) | 0.018340 / 0.534201 (-0.515861) | 0.341664 / 0.579283 (-0.237619) | 0.356536 / 0.434364 (-0.077828) | 0.393974 / 0.540337 (-0.146363) | 0.546036 / 1.386936 (-0.840900) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd04e445bd36d7eb4af4d5a6b8519ab8e306ecf5 \"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) | 0.007220 / 0.011353 (-0.004132) | 0.004537 / 0.011008 (-0.006471) | 0.087333 / 0.038508 (0.048825) | 0.095637 / 0.023109 (0.072528) | 0.323819 / 0.275898 (0.047921) | 0.358838 / 0.323480 (0.035358) | 0.005910 / 0.007986 (-0.002076) | 0.003781 / 0.004328 (-0.000548) | 0.064565 / 0.004250 (0.060315) | 0.062818 / 0.037052 (0.025766) | 0.322595 / 0.258489 (0.064106) | 0.371865 / 0.293841 (0.078024) | 0.031667 / 0.128546 (-0.096880) | 0.009068 / 0.075646 (-0.066579) | 0.290574 / 0.419271 (-0.128697) | 0.054618 / 0.043533 (0.011085) | 0.314708 / 0.255139 (0.059569) | 0.336647 / 0.283200 (0.053447) | 0.027070 / 0.141683 (-0.114613) | 1.500640 / 1.452155 (0.048485) | 1.586775 / 1.492716 (0.094059) |\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) | 0.294461 / 0.018006 (0.276455) | 0.580125 / 0.000490 (0.579635) | 0.008165 / 0.000200 (0.007965) | 0.000320 / 0.000054 (0.000266) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032352 / 0.037411 (-0.005059) | 0.092187 / 0.014526 (0.077661) | 0.104993 / 0.176557 (-0.071564) | 0.162738 / 0.737135 (-0.574397) | 0.103242 / 0.296338 (-0.193096) |\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) | 0.396732 / 0.215209 (0.181523) | 3.955049 / 2.077655 (1.877394) | 1.876762 / 1.504120 (0.372642) | 1.698477 / 1.541195 (0.157282) | 1.847086 / 1.468490 (0.378596) | 0.488306 / 4.584777 (-4.096471) | 3.658922 / 3.745712 (-0.086790) | 3.559050 / 5.269862 (-1.710812) | 2.187363 / 4.565676 (-2.378313) | 0.059795 / 0.424275 (-0.364480) | 0.008966 / 0.007607 (0.001359) | 0.474212 / 0.226044 (0.248168) | 4.732540 / 2.268929 (2.463611) | 2.466370 / 55.444624 (-52.978254) | 2.112105 / 6.876477 (-4.764372) | 2.414624 / 2.142072 (0.272552) | 0.595447 / 4.805227 (-4.209780) | 0.136705 / 6.500664 (-6.363959) | 0.062267 / 0.075469 (-0.013202) |\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) | 1.266518 / 1.841788 (-0.575270) | 21.009975 / 8.074308 (12.935666) | 14.823960 / 10.191392 (4.632568) | 0.165630 / 0.680424 (-0.514793) | 0.018499 / 0.534201 (-0.515702) | 0.396720 / 0.579283 (-0.182563) | 0.424807 / 0.434364 (-0.009557) | 0.463326 / 0.540337 (-0.077011) | 0.653132 / 1.386936 (-0.733804) |\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) | 0.007789 / 0.011353 (-0.003564) | 0.004720 / 0.011008 (-0.006288) | 0.066656 / 0.038508 (0.028148) | 0.094219 / 0.023109 (0.071109) | 0.414965 / 0.275898 (0.139067) | 0.454808 / 0.323480 (0.131328) | 0.006088 / 0.007986 (-0.001898) | 0.003980 / 0.004328 (-0.000349) | 0.066048 / 0.004250 (0.061797) | 0.065875 / 0.037052 (0.028823) | 0.419994 / 0.258489 (0.161505) | 0.462001 / 0.293841 (0.168160) | 0.033534 / 0.128546 (-0.095013) | 0.009010 / 0.075646 (-0.066636) | 0.072778 / 0.419271 (-0.346493) | 0.049834 / 0.043533 (0.006301) | 0.411003 / 0.255139 (0.155864) | 0.430918 / 0.283200 (0.147718) | 0.025664 / 0.141683 (-0.116019) | 1.526771 / 1.452155 (0.074616) | 1.634767 / 1.492716 (0.142051) |\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) | 0.271180 / 0.018006 (0.253174) | 0.576704 / 0.000490 (0.576214) | 0.004362 / 0.000200 (0.004162) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035648 / 0.037411 (-0.001763) | 0.102407 / 0.014526 (0.087881) | 0.111613 / 0.176557 (-0.064944) | 0.166173 / 0.737135 (-0.570962) | 0.113371 / 0.296338 (-0.182967) |\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) | 0.436031 / 0.215209 (0.220822) | 4.347071 / 2.077655 (2.269416) | 2.366937 / 1.504120 (0.862817) | 2.216356 / 1.541195 (0.675161) | 2.335933 / 1.468490 (0.867443) | 0.490484 / 4.584777 (-4.094293) | 3.730656 / 3.745712 (-0.015056) | 3.497248 / 5.269862 (-1.772613) | 2.215729 / 4.565676 (-2.349947) | 0.057905 / 0.424275 (-0.366370) | 0.007983 / 0.007607 (0.000376) | 0.510413 / 0.226044 (0.284369) | 5.114502 / 2.268929 (2.845574) | 2.871599 / 55.444624 (-52.573026) | 2.537514 / 6.876477 (-4.338962) | 2.819135 / 2.142072 (0.677063) | 0.588397 / 4.805227 (-4.216830) | 0.134665 / 6.500664 (-6.365999) | 0.063349 / 0.075469 (-0.012120) |\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) | 1.352962 / 1.841788 (-0.488826) | 21.628664 / 8.074308 (13.554356) | 15.962105 / 10.191392 (5.770713) | 0.167781 / 0.680424 (-0.512643) | 0.020965 / 0.534201 (-0.513236) | 0.402809 / 0.579283 (-0.176474) | 0.435153 / 0.434364 (0.000789) | 0.481394 / 0.540337 (-0.058944) | 0.658068 / 1.386936 (-0.728868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12adf38b90fde8e2a4e46fcbb023ee23b5c4e98c \"CML watermark\")\n"
] | 2023-09-11T13:29:19Z
| 2023-09-13T18:21:28Z
| 2023-09-13T18:12:09Z
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CONTRIBUTOR
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Required for `load_dataset(<format>, data_files=["path/to/.hidden_file"])` to work as expected
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Coc
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[] | 2021-09-10T07:32:07Z
| 2021-09-10T11:45:54Z
| 2021-09-10T11:45:54Z
|
NONE
| null | null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Fix filter method for batched=True
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[] | 2021-11-09T14:30:59Z
| 2021-11-09T15:52:58Z
| 2021-11-09T15:52:57Z
|
CONTRIBUTOR
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Raise FileNotFoundError in WindowsFileLock
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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
|
CONTRIBUTOR
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Closes #2443
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metric = metric_cls( TypeError: 'NoneType' object is not callable
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[
"Hi @klyuhang9,\r\n\r\nI'm sorry but I can't reproduce your problem:\r\n```python\r\nIn [2]: metric = load_metric('glue', 'rte')\r\nDownloading builder script: 5.76kB [00:00, 2.40MB/s]\r\n```\r\n\r\nCould you please, retry to load the metric? Sometimes there are temporary connectivity issues.\r\n\r\nFeel free to re-open this issue of the problem persists."
] | 2022-03-29T07:43:08Z
| 2022-03-29T14:06:01Z
| 2022-03-29T14:06:01Z
|
NONE
| null | null | null |
Hi, friend. I meet a problem.
When I run the code:
`metric = load_metric('glue', 'rte')`
There is a problem raising:
`metric = metric_cls(
TypeError: 'NoneType' object is not callable `
I don't know why. Thanks for your help!
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I_kwDODunzps5oavCx
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Load Data Sets Too Slow In Train Seq2seq Model
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[
"Hi ! you can speed it up using multiprocessing by passing `num_proc=` to `load_dataset()`",
"already did，but not useful for step Generating train split，it works in step \"Resolving data files\" & \"Downloading data files\" ",
"@mariosasko some advice ， thanks！",
"I met the same problem, terrible experience",
"@mariosasko ",
"We need more info about the issue to provide help. \r\n\r\nCan you interrupt the process (with `num_proc=None`) after the `load_dataset` call when the slowdown occurs? So we can know what part of the code is causing it.\r\n\r\nThe `audiofolder` \\ `imagefolder` with metadata is not performant for large datasets. Luckily, we can make them much faster if drop the nested metadata files feature (not that useful). I plan to work on this soon.\r\n\r\nIn the meantime, it's better to use `Dataset.from_generator` (requires replacing the `load_dataset` calls in the transformers script with `Dataset.from_generator`) or write a dataset loading script for large datasets.",
"Can you interrupt the process (with num_proc=None) after the load_dataset call when the slowdown occurs? So we can know what part of the code is causing it.\r\n（I'll try this operation）\r\nThe audiofolder \\ imagefolder with metadata is not performant for large datasets. Luckily, we can make them much faster if drop the nested metadata files feature (not that useful). I plan to work on this soon.\r\n(My data is indeed a bit large, exceeding 10000 hours of audio data. Looking forward to your improvement work very much)\r\n\r\nIn the meantime, it's better to use Dataset.from_generator (requires replacing the load_dataset calls in the transformers script with Dataset.from_generator) or write a dataset loading script for large datasets.\r\n（I want to use Dataset.from_generator instead of load_dataset ，where can i found sample code to load audio&label dataset， I was to do asr task）",
"Can you interrupt the process (with num_proc=None) after the load_dataset call when the slowdown occurs? So we can know what part of the code is causing it.\r\n================================================================================\r\nHere is the log：\r\n[load_dataset.log](https://github.com/huggingface/datasets/files/12169362/load_dataset.log)\r\n（The larger my training data, the slower it loads）\r\n\r\n\r\n",
"In the meantime, it's better to use Dataset.from_generator (requires replacing the load_dataset calls in the transformers script with Dataset.from_generator) or write a dataset loading script for large datasets.\r\n================================================================================\r\nI tried ‘Dataset. from_generator’ implements data loading, but the testing results show no improvement",
"I have already solved this problem, referring to #5990 : read audio frist, then use data_generator to change format ."
] | 2023-06-12T03:58:43Z
| 2023-08-15T02:52:22Z
| 2023-08-15T02:52:22Z
|
NONE
| null | null | null |
### Describe the bug
step 'Generating train split' in load_dataset is too slow：
### Steps to reproduce the bug
Data： own data，16K16B Mono wav
Oficial Script:[ run_speech_recognition_seq2seq.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py)
Add Code：
if data_args.data_path is not None:
print(data_args.data_path)
raw_datasets = load_dataset("audiofolder", data_dir=data_args.data_path, cache_dir=model_args.cache_dir)
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
raw_datasets = raw_datasets["train"].train_test_split(test_size=0.005, shuffle=True)
（change cache_dir to other path ，ex:/DATA/cache）
### Expected behavior
load data fast,at least 1000+
`Generating train split: 387875 examples [32:24:45, 1154.83 examples/s]`
### Environment info
- `transformers` version: 4.28.0.dev0
- Platform: Linux-5.4.0-149-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.16
- Huggingface_hub version: 0.13.2
- PyTorch version (GPU?): 1.13.1+cu116 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
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| 691
|
Add UI filter to filter datasets based on task
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[
"Already supported."
] | 2020-10-01T00:56:18Z
| 2022-02-15T10:46:50Z
| 2022-02-15T10:46:50Z
|
NONE
| null | null | null |
This is great work, so huge shoutout to contributors and huggingface.
The [/nlp/viewer](https://huggingface.co/nlp/viewer/) is great and the [/datasets](https://huggingface.co/datasets) page is great. I was wondering if in both or either places we can have a filter that selects if a dataset is good for the following tasks (non exhaustive list)
- Classification
- Multi label
- Multi class
- Q&A
- Summarization
- Translation
I believe this feature might have some value, for folks trying to find datasets for a particular task, and then testing their model capabilities.
Thank you :)
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TypeError: <lambda>() takes 0 positional arguments but 1 was given
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[
"This looks like a problem with your environment rather than `datasets`."
] | 2023-11-19T13:10:20Z
| 2023-11-29T16:28:34Z
| 2023-11-29T16:28:34Z
|
NONE
| null | null | null |
### Describe the bug
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from datasets import Dataset
9 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.15.0"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
61 import pyarrow.compute as pc
62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi
---> 63 from multiprocess import Pool
64 from requests import HTTPError
65
[/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module>
31
32 import sys
---> 33 from . import context
34
35 #
[/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module>
4
5 from . import process
----> 6 from . import reduction
7
8 __all__ = ()
[/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module>
14 import os
15 try:
---> 16 import dill as pickle
17 except ImportError:
18 import pickle
[/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module>
24
25
---> 26 from ._dill import (
27 dump, dumps, load, loads, copy,
28 Pickler, Unpickler, register, pickle, pickles, check,
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module>
166 try:
167 from _pyio import open as _open
--> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open)
169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open)
170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open)
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs)
154 def get_file_type(*args, **kwargs):
155 open = kwargs.pop("open", __builtin__.open)
--> 156 f = open(os.devnull, *args, **kwargs)
157 t = type(f)
158 f.close()
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener)
280 return result
281 encoding = text_encoding(encoding)
--> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering)
283 result = text
284 text.mode = mode
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through)
2043 encoding = "utf-8"
2044 else:
-> 2045 encoding = locale.getpreferredencoding(False)
2046
2047 if not isinstance(encoding, str):
TypeError: <lambda>() takes 0 positional arguments but 1 was given
```
or
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>()
----> 1 import datasets
9 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.15.0"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
61 import pyarrow.compute as pc
62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi
---> 63 from multiprocess import Pool
64 from requests import HTTPError
65
[/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module>
31
32 import sys
---> 33 from . import context
34
35 #
[/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module>
4
5 from . import process
----> 6 from . import reduction
7
8 __all__ = ()
[/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module>
14 import os
15 try:
---> 16 import dill as pickle
17 except ImportError:
18 import pickle
[/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module>
24
25
---> 26 from ._dill import (
27 dump, dumps, load, loads, copy,
28 Pickler, Unpickler, register, pickle, pickles, check,
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module>
166 try:
167 from _pyio import open as _open
--> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open)
169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open)
170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open)
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs)
154 def get_file_type(*args, **kwargs):
155 open = kwargs.pop("open", __builtin__.open)
--> 156 f = open(os.devnull, *args, **kwargs)
157 t = type(f)
158 f.close()
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener)
280 return result
281 encoding = text_encoding(encoding)
--> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering)
283 result = text
284 text.mode = mode
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through)
2043 encoding = "utf-8"
2044 else:
-> 2045 encoding = locale.getpreferredencoding(False)
2046
2047 if not isinstance(encoding, str):
TypeError: <lambda>() takes 0 positional arguments but 1 was given
```
### Steps to reproduce the bug
`import datasets` on colab
### Expected behavior
work fine
### Environment info
colab
`!pip install datasets`
|
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Add a parallel implementation of to_tf_dataset()
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"_The documentation is not available anymore as the PR was closed or merged._",
"Failing because the test server uses Py3.7 but the `SharedMemory` features require Py3.8! I forgot we still support 3.7 for another couple of months. I'm not sure exactly how to proceed, whether I should leave this PR until then, or just gate the feature behind a version check and skip the tests until the Python version catches up.",
"I haven't played with `NumpyMultiprocessingGenerator` so I can't really help here, but this sounds promising :) Otherwise I think it's also fine to allow `num_workers` only for py>=3.8 for now. You can skip the test on 3.7 and make sure to raise an informative error if someone wants to use `num_workers` with 3.7",
"Lots of comments here - I'll reply to the specific code comments underneath them, but in response to the general comments:\r\n\r\n@gante: I think this approach is much more performant than a `multiprocessing.Pool`. The reason is that when results are returned from a process `Pool`, the returned Python objects are pickled by the child processes, sent down a pipe and unpickled by the parent process. This creates a huge single-process bottleneck as the parent has to unpickle lots of large NumPy arrays, which is quite slow.\r\n\r\nWhen you use a `SharedMemory` approach, the data is just **there** for the parent process - the child and the parent are writing to exactly the same array in memory, and no pickling or unpickling occurs. This means the parent can just immediately copy the array (which is much faster than unpickling) and yield it to `tf.data`. We're taking advantage of the fact that we know the data is just big NumPy arrays and we don't need the full generality of `pickle`.\r\n\r\n@lhoestq: Sounds good! I'll add a clear error and skip the tests on Py<=3.7.",
"Also, an extra technicality, just for information in case anyone looks at this PR later: Recent versions of Python allow [pickled objects to store out-of-band data](https://peps.python.org/pep-0574/). This allows for very efficient zero-copy unpickling of objects like NumPy arrays, with the unpickled object having a view on the same memory as the original. \r\n\r\nHowever, this explicitly does **not** work when the object is unpickled by a different process than the one that created it. For this to work you must explicitly allocate shared memory and create the array there, which pickle cannot handle for you. As a result, if you just benchmark unpickling vs copying of NumPy arrays it can seem like unpickling is very fast - but this is only true when the pickle was created in the unpickling process!",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008666 / 0.011353 (-0.002687) | 0.004624 / 0.011008 (-0.006384) | 0.099247 / 0.038508 (0.060739) | 0.029766 / 0.023109 (0.006657) | 0.303347 / 0.275898 (0.027449) | 0.370022 / 0.323480 (0.046542) | 0.007128 / 0.007986 (-0.000857) | 0.003446 / 0.004328 (-0.000883) | 0.076670 / 0.004250 (0.072420) | 0.038892 / 0.037052 (0.001840) | 0.313035 / 0.258489 (0.054546) | 0.350503 / 0.293841 (0.056662) | 0.033732 / 0.128546 (-0.094815) | 0.011644 / 0.075646 (-0.064003) | 0.323295 / 0.419271 (-0.095977) | 0.040336 / 0.043533 (-0.003196) | 0.302253 / 0.255139 (0.047114) | 0.337199 / 0.283200 (0.053999) | 0.089454 / 0.141683 (-0.052229) | 1.624906 / 1.452155 (0.172752) | 1.546187 / 1.492716 (0.053470) |\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) | 0.184614 / 0.018006 (0.166608) | 0.427397 / 0.000490 (0.426907) | 0.003342 / 0.000200 (0.003142) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023684 / 0.037411 (-0.013727) | 0.100095 / 0.014526 (0.085569) | 0.104996 / 0.176557 (-0.071560) | 0.144719 / 0.737135 (-0.592416) | 0.110759 / 0.296338 (-0.185579) |\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) | 0.421108 / 0.215209 (0.205899) | 4.214094 / 2.077655 (2.136440) | 1.906231 / 1.504120 (0.402111) | 1.698000 / 1.541195 (0.156806) | 1.744856 / 1.468490 (0.276366) | 0.693671 / 4.584777 (-3.891106) | 3.362522 / 3.745712 (-0.383190) | 1.878470 / 5.269862 (-3.391392) | 1.167563 / 4.565676 (-3.398113) | 0.082455 / 0.424275 (-0.341820) | 0.012261 / 0.007607 (0.004654) | 0.525196 / 0.226044 (0.299152) | 5.257553 / 2.268929 (2.988624) | 2.298286 / 55.444624 (-53.146339) | 1.956106 / 6.876477 (-4.920371) | 2.006308 / 2.142072 (-0.135764) | 0.811069 / 4.805227 (-3.994158) | 0.150368 / 6.500664 (-6.350296) | 0.065699 / 0.075469 (-0.009771) |\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) | 1.224516 / 1.841788 (-0.617272) | 13.619084 / 8.074308 (5.544776) | 14.096666 / 10.191392 (3.905274) | 0.151068 / 0.680424 (-0.529356) | 0.028819 / 0.534201 (-0.505382) | 0.402071 / 0.579283 (-0.177212) | 0.408647 / 0.434364 (-0.025717) | 0.466605 / 0.540337 (-0.073733) | 0.547094 / 1.386936 (-0.839842) |\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) | 0.006935 / 0.011353 (-0.004418) | 0.004590 / 0.011008 (-0.006419) | 0.099398 / 0.038508 (0.060890) | 0.028145 / 0.023109 (0.005036) | 0.426582 / 0.275898 (0.150684) | 0.465712 / 0.323480 (0.142233) | 0.005254 / 0.007986 (-0.002731) | 0.004956 / 0.004328 (0.000627) | 0.075616 / 0.004250 (0.071365) | 0.039871 / 0.037052 (0.002819) | 0.428859 / 0.258489 (0.170370) | 0.470839 / 0.293841 (0.176998) | 0.032150 / 0.128546 (-0.096396) | 0.011778 / 0.075646 (-0.063868) | 0.322358 / 0.419271 (-0.096913) | 0.041974 / 0.043533 (-0.001559) | 0.427459 / 0.255139 (0.172320) | 0.446685 / 0.283200 (0.163485) | 0.092000 / 0.141683 (-0.049683) | 1.509231 / 1.452155 (0.057076) | 1.578950 / 1.492716 (0.086234) |\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) | 0.168047 / 0.018006 (0.150041) | 0.418993 / 0.000490 (0.418503) | 0.002855 / 0.000200 (0.002655) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025652 / 0.037411 (-0.011759) | 0.100141 / 0.014526 (0.085616) | 0.107293 / 0.176557 (-0.069264) | 0.142857 / 0.737135 (-0.594278) | 0.110933 / 0.296338 (-0.185406) |\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) | 0.477556 / 0.215209 (0.262347) | 4.777951 / 2.077655 (2.700296) | 2.461885 / 1.504120 (0.957765) | 2.252307 / 1.541195 (0.711112) | 2.307983 / 1.468490 (0.839493) | 0.697570 / 4.584777 (-3.887207) | 3.370323 / 3.745712 (-0.375389) | 3.131333 / 5.269862 (-2.138529) | 1.594839 / 4.565676 (-2.970838) | 0.082333 / 0.424275 (-0.341942) | 0.012574 / 0.007607 (0.004967) | 0.583704 / 0.226044 (0.357660) | 5.817675 / 2.268929 (3.548746) | 2.927054 / 55.444624 (-52.517570) | 2.582929 / 6.876477 (-4.293548) | 2.634275 / 2.142072 (0.492202) | 0.806407 / 4.805227 (-3.998821) | 0.151438 / 6.500664 (-6.349226) | 0.067429 / 0.075469 (-0.008040) |\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) | 1.267011 / 1.841788 (-0.574776) | 13.989515 / 8.074308 (5.915207) | 14.087968 / 10.191392 (3.896576) | 0.142130 / 0.680424 (-0.538293) | 0.017201 / 0.534201 (-0.517000) | 0.383394 / 0.579283 (-0.195889) | 0.381921 / 0.434364 (-0.052443) | 0.439169 / 0.540337 (-0.101168) | 0.524215 / 1.386936 (-0.862721) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be2ebc8f3cfeb532c933be2443094603bafcab04 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008489 / 0.011353 (-0.002864) | 0.004617 / 0.011008 (-0.006391) | 0.102035 / 0.038508 (0.063527) | 0.029850 / 0.023109 (0.006741) | 0.296789 / 0.275898 (0.020891) | 0.367270 / 0.323480 (0.043790) | 0.006934 / 0.007986 (-0.001052) | 0.004923 / 0.004328 (0.000595) | 0.079150 / 0.004250 (0.074900) | 0.036884 / 0.037052 (-0.000169) | 0.305747 / 0.258489 (0.047258) | 0.348510 / 0.293841 (0.054669) | 0.034074 / 0.128546 (-0.094472) | 0.011650 / 0.075646 (-0.063997) | 0.324226 / 0.419271 (-0.095045) | 0.041763 / 0.043533 (-0.001770) | 0.300887 / 0.255139 (0.045748) | 0.333393 / 0.283200 (0.050193) | 0.093838 / 0.141683 (-0.047844) | 1.499801 / 1.452155 (0.047646) | 1.505988 / 1.492716 (0.013272) |\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) | 0.198610 / 0.018006 (0.180604) | 0.407380 / 0.000490 (0.406891) | 0.000367 / 0.000200 (0.000167) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022858 / 0.037411 (-0.014554) | 0.095727 / 0.014526 (0.081202) | 0.104014 / 0.176557 (-0.072543) | 0.138764 / 0.737135 (-0.598371) | 0.105860 / 0.296338 (-0.190478) |\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) | 0.416352 / 0.215209 (0.201143) | 4.150007 / 2.077655 (2.072352) | 1.878727 / 1.504120 (0.374607) | 1.678978 / 1.541195 (0.137783) | 1.711990 / 1.468490 (0.243500) | 0.691722 / 4.584777 (-3.893055) | 3.386466 / 3.745712 (-0.359246) | 1.835730 / 5.269862 (-3.434132) | 1.149975 / 4.565676 (-3.415702) | 0.081914 / 0.424275 (-0.342362) | 0.012238 / 0.007607 (0.004631) | 0.522945 / 0.226044 (0.296900) | 5.251793 / 2.268929 (2.982864) | 2.306907 / 55.444624 (-53.137717) | 1.968400 / 6.876477 (-4.908076) | 1.981154 / 2.142072 (-0.160919) | 0.810126 / 4.805227 (-3.995101) | 0.147876 / 6.500664 (-6.352788) | 0.064042 / 0.075469 (-0.011428) |\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) | 1.199150 / 1.841788 (-0.642637) | 13.913473 / 8.074308 (5.839165) | 14.079132 / 10.191392 (3.887740) | 0.137387 / 0.680424 (-0.543037) | 0.028456 / 0.534201 (-0.505745) | 0.394162 / 0.579283 (-0.185122) | 0.402051 / 0.434364 (-0.032313) | 0.461944 / 0.540337 (-0.078394) | 0.542648 / 1.386936 (-0.844288) |\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) | 0.006393 / 0.011353 (-0.004960) | 0.004599 / 0.011008 (-0.006409) | 0.097389 / 0.038508 (0.058881) | 0.027719 / 0.023109 (0.004610) | 0.341060 / 0.275898 (0.065162) | 0.379604 / 0.323480 (0.056124) | 0.004955 / 0.007986 (-0.003030) | 0.003369 / 0.004328 (-0.000959) | 0.075390 / 0.004250 (0.071139) | 0.038518 / 0.037052 (0.001466) | 0.347085 / 0.258489 (0.088596) | 0.393468 / 0.293841 (0.099627) | 0.031482 / 0.128546 (-0.097064) | 0.011585 / 0.075646 (-0.064061) | 0.317969 / 0.419271 (-0.101302) | 0.041389 / 0.043533 (-0.002144) | 0.343812 / 0.255139 (0.088673) | 0.371047 / 0.283200 (0.087848) | 0.090020 / 0.141683 (-0.051663) | 1.461690 / 1.452155 (0.009536) | 1.552458 / 1.492716 (0.059741) |\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) | 0.188691 / 0.018006 (0.170684) | 0.415635 / 0.000490 (0.415145) | 0.005285 / 0.000200 (0.005085) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024695 / 0.037411 (-0.012716) | 0.098939 / 0.014526 (0.084413) | 0.108472 / 0.176557 (-0.068085) | 0.152635 / 0.737135 (-0.584501) | 0.109947 / 0.296338 (-0.186391) |\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) | 0.471975 / 0.215209 (0.256766) | 4.716437 / 2.077655 (2.638782) | 2.420148 / 1.504120 (0.916028) | 2.219864 / 1.541195 (0.678669) | 2.238647 / 1.468490 (0.770157) | 0.697628 / 4.584777 (-3.887149) | 3.530720 / 3.745712 (-0.214993) | 3.327354 / 5.269862 (-1.942508) | 1.665877 / 4.565676 (-2.899800) | 0.082650 / 0.424275 (-0.341625) | 0.012593 / 0.007607 (0.004986) | 0.576109 / 0.226044 (0.350065) | 5.744691 / 2.268929 (3.475762) | 2.863473 / 55.444624 (-52.581152) | 2.529616 / 6.876477 (-4.346861) | 2.562802 / 2.142072 (0.420730) | 0.805631 / 4.805227 (-3.999597) | 0.150788 / 6.500664 (-6.349876) | 0.065743 / 0.075469 (-0.009726) |\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) | 1.295134 / 1.841788 (-0.546654) | 14.096046 / 8.074308 (6.021738) | 13.901399 / 10.191392 (3.710007) | 0.127481 / 0.680424 (-0.552943) | 0.016666 / 0.534201 (-0.517535) | 0.381819 / 0.579283 (-0.197464) | 0.382629 / 0.434364 (-0.051735) | 0.439354 / 0.540337 (-0.100984) | 0.527662 / 1.386936 (-0.859274) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fe2ad43f59e65d39f2f2ce7442c76990493deb7 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008509 / 0.011353 (-0.002844) | 0.004523 / 0.011008 (-0.006485) | 0.100616 / 0.038508 (0.062108) | 0.029573 / 0.023109 (0.006464) | 0.306414 / 0.275898 (0.030516) | 0.377034 / 0.323480 (0.053554) | 0.007621 / 0.007986 (-0.000365) | 0.003335 / 0.004328 (-0.000993) | 0.078598 / 0.004250 (0.074348) | 0.036902 / 0.037052 (-0.000150) | 0.318146 / 0.258489 (0.059657) | 0.355626 / 0.293841 (0.061785) | 0.033441 / 0.128546 (-0.095105) | 0.011552 / 0.075646 (-0.064094) | 0.322973 / 0.419271 (-0.096299) | 0.040564 / 0.043533 (-0.002968) | 0.306451 / 0.255139 (0.051312) | 0.337591 / 0.283200 (0.054392) | 0.086822 / 0.141683 (-0.054861) | 1.484601 / 1.452155 (0.032447) | 1.542777 / 1.492716 (0.050061) |\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) | 0.201711 / 0.018006 (0.183705) | 0.418387 / 0.000490 (0.417898) | 0.002753 / 0.000200 (0.002553) | 0.000263 / 0.000054 (0.000209) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023016 / 0.037411 (-0.014395) | 0.097313 / 0.014526 (0.082787) | 0.103435 / 0.176557 (-0.073122) | 0.142665 / 0.737135 (-0.594470) | 0.107397 / 0.296338 (-0.188942) |\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) | 0.422739 / 0.215209 (0.207530) | 4.220126 / 2.077655 (2.142471) | 1.865447 / 1.504120 (0.361327) | 1.649647 / 1.541195 (0.108453) | 1.711655 / 1.468490 (0.243165) | 0.704269 / 4.584777 (-3.880508) | 3.407390 / 3.745712 (-0.338322) | 1.929224 / 5.269862 (-3.340638) | 1.281225 / 4.565676 (-3.284452) | 0.082924 / 0.424275 (-0.341351) | 0.012588 / 0.007607 (0.004981) | 0.531025 / 0.226044 (0.304980) | 5.339441 / 2.268929 (3.070512) | 2.298969 / 55.444624 (-53.145656) | 1.952145 / 6.876477 (-4.924332) | 2.034754 / 2.142072 (-0.107318) | 0.823672 / 4.805227 (-3.981555) | 0.151465 / 6.500664 (-6.349199) | 0.066663 / 0.075469 (-0.008807) |\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) | 1.258981 / 1.841788 (-0.582807) | 13.791640 / 8.074308 (5.717332) | 14.001514 / 10.191392 (3.810122) | 0.149805 / 0.680424 (-0.530619) | 0.028614 / 0.534201 (-0.505587) | 0.400266 / 0.579283 (-0.179017) | 0.405891 / 0.434364 (-0.028473) | 0.471903 / 0.540337 (-0.068435) | 0.563656 / 1.386936 (-0.823280) |\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) | 0.006751 / 0.011353 (-0.004601) | 0.004665 / 0.011008 (-0.006343) | 0.098362 / 0.038508 (0.059854) | 0.027451 / 0.023109 (0.004342) | 0.421859 / 0.275898 (0.145961) | 0.458089 / 0.323480 (0.134609) | 0.004885 / 0.007986 (-0.003101) | 0.003459 / 0.004328 (-0.000870) | 0.075871 / 0.004250 (0.071621) | 0.036591 / 0.037052 (-0.000462) | 0.423307 / 0.258489 (0.164818) | 0.467040 / 0.293841 (0.173199) | 0.031837 / 0.128546 (-0.096710) | 0.011604 / 0.075646 (-0.064042) | 0.321132 / 0.419271 (-0.098140) | 0.041806 / 0.043533 (-0.001727) | 0.421653 / 0.255139 (0.166514) | 0.445896 / 0.283200 (0.162696) | 0.087998 / 0.141683 (-0.053685) | 1.475818 / 1.452155 (0.023664) | 1.559487 / 1.492716 (0.066770) |\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) | 0.203096 / 0.018006 (0.185090) | 0.401381 / 0.000490 (0.400892) | 0.004037 / 0.000200 (0.003837) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023757 / 0.037411 (-0.013654) | 0.099919 / 0.014526 (0.085393) | 0.108384 / 0.176557 (-0.068173) | 0.143780 / 0.737135 (-0.593355) | 0.111528 / 0.296338 (-0.184811) |\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) | 0.475896 / 0.215209 (0.260686) | 4.754567 / 2.077655 (2.676912) | 2.444986 / 1.504120 (0.940866) | 2.231055 / 1.541195 (0.689860) | 2.283646 / 1.468490 (0.815156) | 0.701303 / 4.584777 (-3.883474) | 3.381597 / 3.745712 (-0.364115) | 1.878714 / 5.269862 (-3.391148) | 1.171566 / 4.565676 (-3.394111) | 0.083106 / 0.424275 (-0.341169) | 0.012575 / 0.007607 (0.004967) | 0.582570 / 0.226044 (0.356526) | 5.813677 / 2.268929 (3.544748) | 2.908578 / 55.444624 (-52.536046) | 2.548459 / 6.876477 (-4.328017) | 2.581211 / 2.142072 (0.439139) | 0.807925 / 4.805227 (-3.997302) | 0.153516 / 6.500664 (-6.347148) | 0.068763 / 0.075469 (-0.006706) |\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) | 1.249595 / 1.841788 (-0.592193) | 14.208573 / 8.074308 (6.134265) | 14.179174 / 10.191392 (3.987781) | 0.156005 / 0.680424 (-0.524419) | 0.017045 / 0.534201 (-0.517156) | 0.377414 / 0.579283 (-0.201869) | 0.395291 / 0.434364 (-0.039073) | 0.444642 / 0.540337 (-0.095695) | 0.531626 / 1.386936 (-0.855311) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52888645daa6854928474df6308bd997c8878ced \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008871 / 0.011353 (-0.002482) | 0.004616 / 0.011008 (-0.006392) | 0.100910 / 0.038508 (0.062402) | 0.030381 / 0.023109 (0.007272) | 0.304636 / 0.275898 (0.028737) | 0.384258 / 0.323480 (0.060778) | 0.007019 / 0.007986 (-0.000966) | 0.004262 / 0.004328 (-0.000066) | 0.077082 / 0.004250 (0.072832) | 0.035235 / 0.037052 (-0.001817) | 0.318293 / 0.258489 (0.059804) | 0.356578 / 0.293841 (0.062737) | 0.033568 / 0.128546 (-0.094978) | 0.011583 / 0.075646 (-0.064063) | 0.322442 / 0.419271 (-0.096830) | 0.041941 / 0.043533 (-0.001592) | 0.310469 / 0.255139 (0.055330) | 0.335626 / 0.283200 (0.052427) | 0.088195 / 0.141683 (-0.053487) | 1.466778 / 1.452155 (0.014623) | 1.512459 / 1.492716 (0.019743) |\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) | 0.184126 / 0.018006 (0.166120) | 0.413392 / 0.000490 (0.412902) | 0.002191 / 0.000200 (0.001992) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023426 / 0.037411 (-0.013985) | 0.096240 / 0.014526 (0.081715) | 0.105908 / 0.176557 (-0.070648) | 0.146331 / 0.737135 (-0.590804) | 0.107441 / 0.296338 (-0.188898) |\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) | 0.420018 / 0.215209 (0.204809) | 4.198129 / 2.077655 (2.120474) | 1.998726 / 1.504120 (0.494606) | 1.870410 / 1.541195 (0.329215) | 1.925160 / 1.468490 (0.456670) | 0.688790 / 4.584777 (-3.895987) | 3.430629 / 3.745712 (-0.315083) | 2.875616 / 5.269862 (-2.394246) | 1.566269 / 4.565676 (-2.999408) | 0.082431 / 0.424275 (-0.341844) | 0.012409 / 0.007607 (0.004802) | 0.536178 / 0.226044 (0.310134) | 5.342918 / 2.268929 (3.073989) | 2.410814 / 55.444624 (-53.033811) | 2.056518 / 6.876477 (-4.819958) | 2.240148 / 2.142072 (0.098075) | 0.804848 / 4.805227 (-4.000379) | 0.147325 / 6.500664 (-6.353340) | 0.064217 / 0.075469 (-0.011252) |\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) | 1.285725 / 1.841788 (-0.556063) | 13.909739 / 8.074308 (5.835431) | 14.025774 / 10.191392 (3.834382) | 0.142413 / 0.680424 (-0.538011) | 0.028390 / 0.534201 (-0.505811) | 0.402345 / 0.579283 (-0.176939) | 0.404341 / 0.434364 (-0.030023) | 0.463055 / 0.540337 (-0.077282) | 0.556811 / 1.386936 (-0.830125) |\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) | 0.006557 / 0.011353 (-0.004795) | 0.004668 / 0.011008 (-0.006340) | 0.098839 / 0.038508 (0.060331) | 0.027618 / 0.023109 (0.004508) | 0.409338 / 0.275898 (0.133440) | 0.444048 / 0.323480 (0.120568) | 0.004881 / 0.007986 (-0.003105) | 0.003434 / 0.004328 (-0.000895) | 0.076497 / 0.004250 (0.072247) | 0.038932 / 0.037052 (0.001880) | 0.411419 / 0.258489 (0.152930) | 0.451167 / 0.293841 (0.157326) | 0.031649 / 0.128546 (-0.096897) | 0.011691 / 0.075646 (-0.063955) | 0.321586 / 0.419271 (-0.097685) | 0.041984 / 0.043533 (-0.001549) | 0.407717 / 0.255139 (0.152578) | 0.434687 / 0.283200 (0.151487) | 0.086419 / 0.141683 (-0.055264) | 1.491755 / 1.452155 (0.039601) | 1.569081 / 1.492716 (0.076364) |\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) | 0.231746 / 0.018006 (0.213739) | 0.412271 / 0.000490 (0.411781) | 0.000403 / 0.000200 (0.000203) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024264 / 0.037411 (-0.013147) | 0.100478 / 0.014526 (0.085952) | 0.107065 / 0.176557 (-0.069491) | 0.140724 / 0.737135 (-0.596412) | 0.110631 / 0.296338 (-0.185707) |\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) | 0.472476 / 0.215209 (0.257267) | 4.738919 / 2.077655 (2.661265) | 2.438049 / 1.504120 (0.933929) | 2.237855 / 1.541195 (0.696660) | 2.282885 / 1.468490 (0.814395) | 0.690420 / 4.584777 (-3.894357) | 3.426487 / 3.745712 (-0.319225) | 1.842443 / 5.269862 (-3.427418) | 1.154466 / 4.565676 (-3.411210) | 0.082166 / 0.424275 (-0.342109) | 0.012309 / 0.007607 (0.004701) | 0.574730 / 0.226044 (0.348686) | 5.737566 / 2.268929 (3.468638) | 2.882405 / 55.444624 (-52.562220) | 2.540276 / 6.876477 (-4.336201) | 2.552356 / 2.142072 (0.410283) | 0.796413 / 4.805227 (-4.008815) | 0.152705 / 6.500664 (-6.347959) | 0.068273 / 0.075469 (-0.007196) |\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) | 1.244423 / 1.841788 (-0.597365) | 13.827750 / 8.074308 (5.753442) | 14.074083 / 10.191392 (3.882691) | 0.140291 / 0.680424 (-0.540133) | 0.017337 / 0.534201 (-0.516864) | 0.389314 / 0.579283 (-0.189969) | 0.390914 / 0.434364 (-0.043450) | 0.450333 / 0.540337 (-0.090004) | 0.543860 / 1.386936 (-0.843076) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cdcddc51d3cda24c2d79ad137af9e55d0a38044 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009490 / 0.011353 (-0.001863) | 0.005211 / 0.011008 (-0.005798) | 0.100884 / 0.038508 (0.062376) | 0.035834 / 0.023109 (0.012725) | 0.293623 / 0.275898 (0.017724) | 0.378118 / 0.323480 (0.054638) | 0.008106 / 0.007986 (0.000120) | 0.005339 / 0.004328 (0.001010) | 0.076311 / 0.004250 (0.072061) | 0.045954 / 0.037052 (0.008902) | 0.308163 / 0.258489 (0.049674) | 0.353470 / 0.293841 (0.059629) | 0.038539 / 0.128546 (-0.090008) | 0.012174 / 0.075646 (-0.063472) | 0.334875 / 0.419271 (-0.084396) | 0.048602 / 0.043533 (0.005069) | 0.295803 / 0.255139 (0.040664) | 0.318894 / 0.283200 (0.035695) | 0.105487 / 0.141683 (-0.036195) | 1.433628 / 1.452155 (-0.018526) | 1.466843 / 1.492716 (-0.025873) |\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) | 0.203426 / 0.018006 (0.185419) | 0.456877 / 0.000490 (0.456387) | 0.001452 / 0.000200 (0.001252) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028308 / 0.037411 (-0.009103) | 0.108965 / 0.014526 (0.094439) | 0.119552 / 0.176557 (-0.057005) | 0.156371 / 0.737135 (-0.580765) | 0.124141 / 0.296338 (-0.172197) |\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) | 0.400183 / 0.215209 (0.184973) | 3.990983 / 2.077655 (1.913329) | 1.806729 / 1.504120 (0.302609) | 1.611944 / 1.541195 (0.070750) | 1.740019 / 1.468490 (0.271529) | 0.699600 / 4.584777 (-3.885177) | 3.868711 / 3.745712 (0.122999) | 3.249758 / 5.269862 (-2.020103) | 1.832213 / 4.565676 (-2.733463) | 0.085282 / 0.424275 (-0.338993) | 0.012726 / 0.007607 (0.005119) | 0.509385 / 0.226044 (0.283341) | 5.066913 / 2.268929 (2.797984) | 2.325710 / 55.444624 (-53.118914) | 1.962238 / 6.876477 (-4.914239) | 2.017576 / 2.142072 (-0.124496) | 0.839444 / 4.805227 (-3.965783) | 0.166936 / 6.500664 (-6.333728) | 0.064546 / 0.075469 (-0.010923) |\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) | 1.196396 / 1.841788 (-0.645392) | 15.077063 / 8.074308 (7.002755) | 14.268103 / 10.191392 (4.076711) | 0.163782 / 0.680424 (-0.516642) | 0.028794 / 0.534201 (-0.505407) | 0.440564 / 0.579283 (-0.138719) | 0.439826 / 0.434364 (0.005463) | 0.514786 / 0.540337 (-0.025551) | 0.603353 / 1.386936 (-0.783583) |\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) | 0.007874 / 0.011353 (-0.003479) | 0.005347 / 0.011008 (-0.005661) | 0.099461 / 0.038508 (0.060953) | 0.034010 / 0.023109 (0.010901) | 0.384650 / 0.275898 (0.108752) | 0.423827 / 0.323480 (0.100347) | 0.006201 / 0.007986 (-0.001784) | 0.004212 / 0.004328 (-0.000117) | 0.074354 / 0.004250 (0.070104) | 0.051675 / 0.037052 (0.014623) | 0.392488 / 0.258489 (0.133999) | 0.425828 / 0.293841 (0.131987) | 0.037444 / 0.128546 (-0.091103) | 0.012388 / 0.075646 (-0.063258) | 0.334482 / 0.419271 (-0.084789) | 0.050715 / 0.043533 (0.007182) | 0.378323 / 0.255139 (0.123184) | 0.395450 / 0.283200 (0.112250) | 0.108403 / 0.141683 (-0.033280) | 1.426803 / 1.452155 (-0.025352) | 1.532417 / 1.492716 (0.039701) |\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) | 0.219989 / 0.018006 (0.201982) | 0.454101 / 0.000490 (0.453611) | 0.000407 / 0.000200 (0.000207) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030590 / 0.037411 (-0.006822) | 0.113483 / 0.014526 (0.098957) | 0.122603 / 0.176557 (-0.053954) | 0.161031 / 0.737135 (-0.576104) | 0.128039 / 0.296338 (-0.168300) |\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) | 0.430458 / 0.215209 (0.215249) | 4.286594 / 2.077655 (2.208940) | 2.056666 / 1.504120 (0.552546) | 1.861142 / 1.541195 (0.319948) | 1.937185 / 1.468490 (0.468695) | 0.701881 / 4.584777 (-3.882896) | 3.970144 / 3.745712 (0.224432) | 2.107118 / 5.269862 (-3.162744) | 1.351561 / 4.565676 (-3.214115) | 0.085470 / 0.424275 (-0.338805) | 0.012366 / 0.007607 (0.004759) | 0.525212 / 0.226044 (0.299168) | 5.301553 / 2.268929 (3.032625) | 2.593862 / 55.444624 (-52.850763) | 2.287315 / 6.876477 (-4.589161) | 2.368249 / 2.142072 (0.226176) | 0.855656 / 4.805227 (-3.949571) | 0.167846 / 6.500664 (-6.332818) | 0.064521 / 0.075469 (-0.010948) |\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) | 1.237008 / 1.841788 (-0.604779) | 15.784303 / 8.074308 (7.709995) | 14.613081 / 10.191392 (4.421689) | 0.161012 / 0.680424 (-0.519412) | 0.017928 / 0.534201 (-0.516273) | 0.423905 / 0.579283 (-0.155378) | 0.428316 / 0.434364 (-0.006048) | 0.500226 / 0.540337 (-0.040112) | 0.606725 / 1.386936 (-0.780211) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08473e2ee66acb7e6f82d3591bb9b03924a661ed \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008874 / 0.011353 (-0.002479) | 0.004581 / 0.011008 (-0.006428) | 0.100180 / 0.038508 (0.061672) | 0.029990 / 0.023109 (0.006880) | 0.301616 / 0.275898 (0.025718) | 0.343662 / 0.323480 (0.020183) | 0.007111 / 0.007986 (-0.000875) | 0.003428 / 0.004328 (-0.000900) | 0.078031 / 0.004250 (0.073780) | 0.037332 / 0.037052 (0.000279) | 0.301977 / 0.258489 (0.043488) | 0.345581 / 0.293841 (0.051740) | 0.034305 / 0.128546 (-0.094241) | 0.011660 / 0.075646 (-0.063986) | 0.322289 / 0.419271 (-0.096982) | 0.041488 / 0.043533 (-0.002045) | 0.301612 / 0.255139 (0.046473) | 0.328174 / 0.283200 (0.044974) | 0.085561 / 0.141683 (-0.056122) | 1.482114 / 1.452155 (0.029959) | 1.556194 / 1.492716 (0.063478) |\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) | 0.186989 / 0.018006 (0.168983) | 0.421499 / 0.000490 (0.421009) | 0.001193 / 0.000200 (0.000993) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023551 / 0.037411 (-0.013861) | 0.099868 / 0.014526 (0.085343) | 0.105233 / 0.176557 (-0.071324) | 0.141628 / 0.737135 (-0.595507) | 0.109004 / 0.296338 (-0.187335) |\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) | 0.415189 / 0.215209 (0.199979) | 4.145716 / 2.077655 (2.068061) | 1.837917 / 1.504120 (0.333797) | 1.635043 / 1.541195 (0.093848) | 1.683299 / 1.468490 (0.214809) | 0.688538 / 4.584777 (-3.896239) | 3.412628 / 3.745712 (-0.333084) | 1.877456 / 5.269862 (-3.392405) | 1.154129 / 4.565676 (-3.411547) | 0.081850 / 0.424275 (-0.342425) | 0.012309 / 0.007607 (0.004702) | 0.522830 / 0.226044 (0.296785) | 5.238685 / 2.268929 (2.969756) | 2.277840 / 55.444624 (-53.166784) | 1.941787 / 6.876477 (-4.934690) | 1.999688 / 2.142072 (-0.142385) | 0.807590 / 4.805227 (-3.997637) | 0.148157 / 6.500664 (-6.352507) | 0.064898 / 0.075469 (-0.010571) |\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) | 1.253859 / 1.841788 (-0.587929) | 13.676097 / 8.074308 (5.601789) | 14.237837 / 10.191392 (4.046444) | 0.137178 / 0.680424 (-0.543246) | 0.028971 / 0.534201 (-0.505230) | 0.400380 / 0.579283 (-0.178903) | 0.409990 / 0.434364 (-0.024374) | 0.462552 / 0.540337 (-0.077786) | 0.552153 / 1.386936 (-0.834783) |\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) | 0.006831 / 0.011353 (-0.004522) | 0.004627 / 0.011008 (-0.006381) | 0.099883 / 0.038508 (0.061375) | 0.028072 / 0.023109 (0.004962) | 0.343556 / 0.275898 (0.067658) | 0.386792 / 0.323480 (0.063312) | 0.005080 / 0.007986 (-0.002906) | 0.003508 / 0.004328 (-0.000820) | 0.077803 / 0.004250 (0.073552) | 0.040038 / 0.037052 (0.002985) | 0.345089 / 0.258489 (0.086600) | 0.396078 / 0.293841 (0.102238) | 0.032241 / 0.128546 (-0.096305) | 0.011711 / 0.075646 (-0.063935) | 0.320531 / 0.419271 (-0.098740) | 0.043658 / 0.043533 (0.000125) | 0.344696 / 0.255139 (0.089557) | 0.389847 / 0.283200 (0.106648) | 0.092328 / 0.141683 (-0.049355) | 1.477290 / 1.452155 (0.025136) | 1.548698 / 1.492716 (0.055982) |\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) | 0.236073 / 0.018006 (0.218067) | 0.422113 / 0.000490 (0.421624) | 0.000431 / 0.000200 (0.000231) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024738 / 0.037411 (-0.012673) | 0.100546 / 0.014526 (0.086020) | 0.107550 / 0.176557 (-0.069006) | 0.146056 / 0.737135 (-0.591079) | 0.112665 / 0.296338 (-0.183674) |\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) | 0.490259 / 0.215209 (0.275050) | 4.907994 / 2.077655 (2.830339) | 2.547175 / 1.504120 (1.043055) | 2.344419 / 1.541195 (0.803224) | 2.403985 / 1.468490 (0.935495) | 0.696011 / 4.584777 (-3.888766) | 3.442426 / 3.745712 (-0.303286) | 1.878702 / 5.269862 (-3.391159) | 1.158280 / 4.565676 (-3.407396) | 0.082300 / 0.424275 (-0.341975) | 0.012513 / 0.007607 (0.004906) | 0.602696 / 0.226044 (0.376651) | 6.014592 / 2.268929 (3.745663) | 3.014466 / 55.444624 (-52.430159) | 2.669376 / 6.876477 (-4.207101) | 2.724485 / 2.142072 (0.582412) | 0.799795 / 4.805227 (-4.005432) | 0.151220 / 6.500664 (-6.349444) | 0.067486 / 0.075469 (-0.007983) |\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) | 1.281265 / 1.841788 (-0.560523) | 14.362284 / 8.074308 (6.287976) | 14.313690 / 10.191392 (4.122298) | 0.142870 / 0.680424 (-0.537554) | 0.017206 / 0.534201 (-0.516995) | 0.380084 / 0.579283 (-0.199199) | 0.388161 / 0.434364 (-0.046203) | 0.442617 / 0.540337 (-0.097721) | 0.528487 / 1.386936 (-0.858449) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#452b7f8ae78967dc662f5436e751233d46c62e78 \"CML watermark\")\n",
"@lhoestq @amyeroberts @gante I did a substantial rewrite and all tests are passing now (Windows seems to time out or something and I can't figure out why - not sure if that's related to this PR!). I also confirmed tests are passing locally with Py==3.10. \r\n\r\nAside from incorporating everyone's comments, I also made a context manager to create and handle shared memory - this ensures that shared memory is cleaned up even if execution is interrupted. Also, shared memory names include a UUID string now to avoid collisions. Finally, string arrays are now split up into fixed-width character arrays in the workers so that they can be passed through shared memory, and the parent process reconstructs them into string arrays.",
"Update: `test_arrow_dataset.py` ran fine in this branch on my Windows machine (Py 3.10), so I have no idea what's up with those tests",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008852 / 0.011353 (-0.002500) | 0.004545 / 0.011008 (-0.006464) | 0.099814 / 0.038508 (0.061306) | 0.030314 / 0.023109 (0.007205) | 0.310426 / 0.275898 (0.034528) | 0.366893 / 0.323480 (0.043413) | 0.007183 / 0.007986 (-0.000802) | 0.003476 / 0.004328 (-0.000853) | 0.077566 / 0.004250 (0.073315) | 0.038269 / 0.037052 (0.001217) | 0.319133 / 0.258489 (0.060644) | 0.352399 / 0.293841 (0.058558) | 0.033847 / 0.128546 (-0.094700) | 0.011568 / 0.075646 (-0.064078) | 0.321355 / 0.419271 (-0.097917) | 0.040719 / 0.043533 (-0.002814) | 0.304812 / 0.255139 (0.049673) | 0.329512 / 0.283200 (0.046312) | 0.088045 / 0.141683 (-0.053638) | 1.514182 / 1.452155 (0.062027) | 1.529459 / 1.492716 (0.036742) |\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) | 0.216749 / 0.018006 (0.198743) | 0.409909 / 0.000490 (0.409419) | 0.002790 / 0.000200 (0.002590) | 0.000081 / 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) | 0.023390 / 0.037411 (-0.014021) | 0.095955 / 0.014526 (0.081430) | 0.104749 / 0.176557 (-0.071807) | 0.143414 / 0.737135 (-0.593721) | 0.109011 / 0.296338 (-0.187328) |\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) | 0.420410 / 0.215209 (0.205201) | 4.185745 / 2.077655 (2.108090) | 1.910207 / 1.504120 (0.406087) | 1.679330 / 1.541195 (0.138135) | 1.727134 / 1.468490 (0.258644) | 0.692379 / 4.584777 (-3.892398) | 3.358731 / 3.745712 (-0.386982) | 2.914657 / 5.269862 (-2.355205) | 1.506083 / 4.565676 (-3.059594) | 0.081922 / 0.424275 (-0.342353) | 0.012691 / 0.007607 (0.005084) | 0.530942 / 0.226044 (0.304897) | 5.357642 / 2.268929 (3.088714) | 2.387347 / 55.444624 (-53.057277) | 2.030001 / 6.876477 (-4.846476) | 2.026405 / 2.142072 (-0.115667) | 0.809406 / 4.805227 (-3.995821) | 0.149003 / 6.500664 (-6.351661) | 0.066910 / 0.075469 (-0.008559) |\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) | 1.278160 / 1.841788 (-0.563627) | 13.632742 / 8.074308 (5.558434) | 13.995537 / 10.191392 (3.804145) | 0.136507 / 0.680424 (-0.543917) | 0.028817 / 0.534201 (-0.505384) | 0.394842 / 0.579283 (-0.184441) | 0.399526 / 0.434364 (-0.034838) | 0.459174 / 0.540337 (-0.081163) | 0.536877 / 1.386936 (-0.850059) |\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) | 0.006814 / 0.011353 (-0.004539) | 0.004456 / 0.011008 (-0.006552) | 0.098386 / 0.038508 (0.059878) | 0.028124 / 0.023109 (0.005015) | 0.409004 / 0.275898 (0.133106) | 0.446746 / 0.323480 (0.123266) | 0.005108 / 0.007986 (-0.002877) | 0.004807 / 0.004328 (0.000479) | 0.075751 / 0.004250 (0.071500) | 0.039297 / 0.037052 (0.002244) | 0.413198 / 0.258489 (0.154709) | 0.452124 / 0.293841 (0.158283) | 0.032534 / 0.128546 (-0.096012) | 0.011689 / 0.075646 (-0.063957) | 0.325465 / 0.419271 (-0.093806) | 0.041347 / 0.043533 (-0.002185) | 0.411489 / 0.255139 (0.156350) | 0.447120 / 0.283200 (0.163920) | 0.093058 / 0.141683 (-0.048625) | 1.489903 / 1.452155 (0.037748) | 1.580771 / 1.492716 (0.088055) |\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) | 0.192619 / 0.018006 (0.174613) | 0.399201 / 0.000490 (0.398711) | 0.002894 / 0.000200 (0.002694) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025120 / 0.037411 (-0.012292) | 0.100126 / 0.014526 (0.085600) | 0.108669 / 0.176557 (-0.067887) | 0.148687 / 0.737135 (-0.588448) | 0.112286 / 0.296338 (-0.184052) |\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) | 0.438866 / 0.215209 (0.223657) | 4.382418 / 2.077655 (2.304764) | 2.106450 / 1.504120 (0.602330) | 1.885105 / 1.541195 (0.343910) | 1.922948 / 1.468490 (0.454458) | 0.693145 / 4.584777 (-3.891632) | 3.378206 / 3.745712 (-0.367506) | 1.867295 / 5.269862 (-3.402566) | 1.164999 / 4.565676 (-3.400678) | 0.081918 / 0.424275 (-0.342357) | 0.012225 / 0.007607 (0.004618) | 0.547114 / 0.226044 (0.321069) | 5.454208 / 2.268929 (3.185279) | 2.532112 / 55.444624 (-52.912512) | 2.192573 / 6.876477 (-4.683904) | 2.225364 / 2.142072 (0.083291) | 0.797165 / 4.805227 (-4.008062) | 0.151185 / 6.500664 (-6.349480) | 0.067512 / 0.075469 (-0.007957) |\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) | 1.303905 / 1.841788 (-0.537883) | 14.107678 / 8.074308 (6.033370) | 14.147630 / 10.191392 (3.956238) | 0.156597 / 0.680424 (-0.523827) | 0.017037 / 0.534201 (-0.517164) | 0.383202 / 0.579283 (-0.196081) | 0.385340 / 0.434364 (-0.049024) | 0.443338 / 0.540337 (-0.097000) | 0.542345 / 1.386936 (-0.844591) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#38228533a03767aab713a3806aac0e8503668c68 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009982 / 0.011353 (-0.001371) | 0.005327 / 0.011008 (-0.005681) | 0.099092 / 0.038508 (0.060584) | 0.035824 / 0.023109 (0.012715) | 0.303258 / 0.275898 (0.027360) | 0.335379 / 0.323480 (0.011899) | 0.008192 / 0.007986 (0.000207) | 0.004242 / 0.004328 (-0.000087) | 0.076277 / 0.004250 (0.072026) | 0.043851 / 0.037052 (0.006799) | 0.307750 / 0.258489 (0.049261) | 0.348459 / 0.293841 (0.054618) | 0.038943 / 0.128546 (-0.089604) | 0.012128 / 0.075646 (-0.063519) | 0.334143 / 0.419271 (-0.085128) | 0.047865 / 0.043533 (0.004332) | 0.300909 / 0.255139 (0.045770) | 0.320879 / 0.283200 (0.037680) | 0.103812 / 0.141683 (-0.037871) | 1.468646 / 1.452155 (0.016491) | 1.557660 / 1.492716 (0.064944) |\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) | 0.244108 / 0.018006 (0.226102) | 0.554895 / 0.000490 (0.554405) | 0.005311 / 0.000200 (0.005111) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028771 / 0.037411 (-0.008640) | 0.108133 / 0.014526 (0.093608) | 0.120098 / 0.176557 (-0.056458) | 0.159815 / 0.737135 (-0.577320) | 0.125437 / 0.296338 (-0.170901) |\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) | 0.397675 / 0.215209 (0.182466) | 3.975839 / 2.077655 (1.898184) | 1.797803 / 1.504120 (0.293683) | 1.612517 / 1.541195 (0.071322) | 1.659086 / 1.468490 (0.190596) | 0.679822 / 4.584777 (-3.904955) | 3.688321 / 3.745712 (-0.057391) | 2.155285 / 5.269862 (-3.114576) | 1.466453 / 4.565676 (-3.099223) | 0.084102 / 0.424275 (-0.340173) | 0.012074 / 0.007607 (0.004467) | 0.503744 / 0.226044 (0.277699) | 5.075599 / 2.268929 (2.806670) | 2.312149 / 55.444624 (-53.132476) | 1.975028 / 6.876477 (-4.901449) | 2.069554 / 2.142072 (-0.072519) | 0.828329 / 4.805227 (-3.976898) | 0.162816 / 6.500664 (-6.337849) | 0.063813 / 0.075469 (-0.011656) |\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) | 1.173327 / 1.841788 (-0.668461) | 15.281584 / 8.074308 (7.207276) | 14.450851 / 10.191392 (4.259459) | 0.165621 / 0.680424 (-0.514802) | 0.028779 / 0.534201 (-0.505422) | 0.438483 / 0.579283 (-0.140800) | 0.438477 / 0.434364 (0.004113) | 0.517703 / 0.540337 (-0.022634) | 0.615119 / 1.386936 (-0.771817) |\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) | 0.007013 / 0.011353 (-0.004340) | 0.005272 / 0.011008 (-0.005736) | 0.097203 / 0.038508 (0.058695) | 0.033103 / 0.023109 (0.009994) | 0.380203 / 0.275898 (0.104305) | 0.414868 / 0.323480 (0.091388) | 0.006326 / 0.007986 (-0.001659) | 0.005433 / 0.004328 (0.001104) | 0.074299 / 0.004250 (0.070049) | 0.049418 / 0.037052 (0.012366) | 0.388771 / 0.258489 (0.130282) | 0.435169 / 0.293841 (0.141328) | 0.036170 / 0.128546 (-0.092377) | 0.012452 / 0.075646 (-0.063195) | 0.331215 / 0.419271 (-0.088056) | 0.048577 / 0.043533 (0.005044) | 0.381491 / 0.255139 (0.126352) | 0.396731 / 0.283200 (0.113531) | 0.106435 / 0.141683 (-0.035248) | 1.446437 / 1.452155 (-0.005718) | 1.542337 / 1.492716 (0.049621) |\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) | 0.216714 / 0.018006 (0.198707) | 0.562460 / 0.000490 (0.561970) | 0.003636 / 0.000200 (0.003436) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028726 / 0.037411 (-0.008686) | 0.111993 / 0.014526 (0.097467) | 0.125325 / 0.176557 (-0.051232) | 0.157779 / 0.737135 (-0.579356) | 0.130633 / 0.296338 (-0.165705) |\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) | 0.440520 / 0.215209 (0.225311) | 4.396283 / 2.077655 (2.318628) | 2.204714 / 1.504120 (0.700594) | 2.011667 / 1.541195 (0.470473) | 2.050518 / 1.468490 (0.582028) | 0.695204 / 4.584777 (-3.889573) | 3.779699 / 3.745712 (0.033987) | 2.096064 / 5.269862 (-3.173798) | 1.325446 / 4.565676 (-3.240230) | 0.085315 / 0.424275 (-0.338960) | 0.012178 / 0.007607 (0.004570) | 0.550478 / 0.226044 (0.324434) | 5.471872 / 2.268929 (3.202943) | 2.687147 / 55.444624 (-52.757478) | 2.348465 / 6.876477 (-4.528011) | 2.409700 / 2.142072 (0.267628) | 0.839468 / 4.805227 (-3.965760) | 0.167030 / 6.500664 (-6.333635) | 0.063243 / 0.075469 (-0.012226) |\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) | 1.257347 / 1.841788 (-0.584441) | 15.157821 / 8.074308 (7.083512) | 14.646381 / 10.191392 (4.454989) | 0.185550 / 0.680424 (-0.494874) | 0.018441 / 0.534201 (-0.515760) | 0.423330 / 0.579283 (-0.155954) | 0.426204 / 0.434364 (-0.008160) | 0.498985 / 0.540337 (-0.041352) | 0.608432 / 1.386936 (-0.778504) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f96e349ec5665e1e4135b5a108ba5db227bd3b1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.010856 / 0.011353 (-0.000497) | 0.005897 / 0.011008 (-0.005111) | 0.117826 / 0.038508 (0.079317) | 0.041899 / 0.023109 (0.018790) | 0.353804 / 0.275898 (0.077906) | 0.431021 / 0.323480 (0.107541) | 0.009288 / 0.007986 (0.001303) | 0.004556 / 0.004328 (0.000227) | 0.089344 / 0.004250 (0.085094) | 0.052224 / 0.037052 (0.015172) | 0.373242 / 0.258489 (0.114753) | 0.420667 / 0.293841 (0.126826) | 0.044191 / 0.128546 (-0.084355) | 0.014083 / 0.075646 (-0.061564) | 0.400373 / 0.419271 (-0.018898) | 0.056119 / 0.043533 (0.012586) | 0.363302 / 0.255139 (0.108163) | 0.382073 / 0.283200 (0.098873) | 0.118646 / 0.141683 (-0.023037) | 1.696576 / 1.452155 (0.244422) | 1.756518 / 1.492716 (0.263802) |\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) | 0.216388 / 0.018006 (0.198382) | 0.485732 / 0.000490 (0.485242) | 0.004012 / 0.000200 (0.003812) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032095 / 0.037411 (-0.005316) | 0.128954 / 0.014526 (0.114429) | 0.137564 / 0.176557 (-0.038993) | 0.184315 / 0.737135 (-0.552820) | 0.144707 / 0.296338 (-0.151631) |\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) | 0.472792 / 0.215209 (0.257583) | 4.723044 / 2.077655 (2.645390) | 2.115075 / 1.504120 (0.610955) | 1.898993 / 1.541195 (0.357798) | 1.972894 / 1.468490 (0.504404) | 0.807210 / 4.584777 (-3.777567) | 4.493139 / 3.745712 (0.747427) | 2.501053 / 5.269862 (-2.768808) | 1.686121 / 4.565676 (-2.879556) | 0.099545 / 0.424275 (-0.324730) | 0.014360 / 0.007607 (0.006753) | 0.596235 / 0.226044 (0.370191) | 5.944285 / 2.268929 (3.675357) | 2.654944 / 55.444624 (-52.789681) | 2.281451 / 6.876477 (-4.595026) | 2.448407 / 2.142072 (0.306334) | 1.000512 / 4.805227 (-3.804716) | 0.196413 / 6.500664 (-6.304251) | 0.075810 / 0.075469 (0.000341) |\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) | 1.435707 / 1.841788 (-0.406081) | 17.931070 / 8.074308 (9.856762) | 16.635522 / 10.191392 (6.444130) | 0.189119 / 0.680424 (-0.491304) | 0.034392 / 0.534201 (-0.499809) | 0.519041 / 0.579283 (-0.060242) | 0.516159 / 0.434364 (0.081795) | 0.601180 / 0.540337 (0.060843) | 0.713180 / 1.386936 (-0.673756) |\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) | 0.008741 / 0.011353 (-0.002612) | 0.006102 / 0.011008 (-0.004906) | 0.114787 / 0.038508 (0.076279) | 0.039610 / 0.023109 (0.016501) | 0.451730 / 0.275898 (0.175832) | 0.488820 / 0.323480 (0.165340) | 0.006979 / 0.007986 (-0.001006) | 0.006458 / 0.004328 (0.002130) | 0.086505 / 0.004250 (0.082254) | 0.057684 / 0.037052 (0.020632) | 0.451354 / 0.258489 (0.192865) | 0.523143 / 0.293841 (0.229302) | 0.043224 / 0.128546 (-0.085323) | 0.014671 / 0.075646 (-0.060975) | 0.398030 / 0.419271 (-0.021241) | 0.063650 / 0.043533 (0.020117) | 0.448324 / 0.255139 (0.193185) | 0.476560 / 0.283200 (0.193361) | 0.125772 / 0.141683 (-0.015911) | 1.801051 / 1.452155 (0.348896) | 1.872736 / 1.492716 (0.380020) |\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) | 0.256146 / 0.018006 (0.238139) | 0.486915 / 0.000490 (0.486425) | 0.000513 / 0.000200 (0.000313) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035242 / 0.037411 (-0.002170) | 0.134322 / 0.014526 (0.119797) | 0.144786 / 0.176557 (-0.031770) | 0.188786 / 0.737135 (-0.548349) | 0.151737 / 0.296338 (-0.144602) |\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) | 0.506047 / 0.215209 (0.290838) | 5.028253 / 2.077655 (2.950598) | 2.393070 / 1.504120 (0.888950) | 2.157847 / 1.541195 (0.616652) | 2.229412 / 1.468490 (0.760922) | 0.828973 / 4.584777 (-3.755804) | 4.741470 / 3.745712 (0.995758) | 4.048118 / 5.269862 (-1.221744) | 2.573818 / 4.565676 (-1.991859) | 0.101019 / 0.424275 (-0.323256) | 0.014640 / 0.007607 (0.007033) | 0.632591 / 0.226044 (0.406546) | 6.289153 / 2.268929 (4.020224) | 2.977261 / 55.444624 (-52.467363) | 2.554396 / 6.876477 (-4.322081) | 2.619446 / 2.142072 (0.477374) | 0.988376 / 4.805227 (-3.816851) | 0.196895 / 6.500664 (-6.303769) | 0.076355 / 0.075469 (0.000886) |\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) | 1.493570 / 1.841788 (-0.348218) | 18.422758 / 8.074308 (10.348449) | 17.007352 / 10.191392 (6.815960) | 0.191903 / 0.680424 (-0.488521) | 0.020974 / 0.534201 (-0.513227) | 0.500573 / 0.579283 (-0.078710) | 0.489381 / 0.434364 (0.055017) | 0.580765 / 0.540337 (0.040428) | 0.698907 / 1.386936 (-0.688029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa9baa268a6d285ab0a61cc37413392c94cfe2e8 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008979 / 0.011353 (-0.002374) | 0.004497 / 0.011008 (-0.006511) | 0.102227 / 0.038508 (0.063719) | 0.031302 / 0.023109 (0.008193) | 0.298488 / 0.275898 (0.022590) | 0.372589 / 0.323480 (0.049109) | 0.007261 / 0.007986 (-0.000725) | 0.003542 / 0.004328 (-0.000786) | 0.078503 / 0.004250 (0.074253) | 0.039474 / 0.037052 (0.002422) | 0.310991 / 0.258489 (0.052502) | 0.353245 / 0.293841 (0.059404) | 0.033798 / 0.128546 (-0.094749) | 0.011634 / 0.075646 (-0.064012) | 0.321141 / 0.419271 (-0.098131) | 0.041264 / 0.043533 (-0.002268) | 0.300900 / 0.255139 (0.045761) | 0.326255 / 0.283200 (0.043055) | 0.092477 / 0.141683 (-0.049205) | 1.478921 / 1.452155 (0.026766) | 1.514915 / 1.492716 (0.022198) |\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) | 0.184415 / 0.018006 (0.166408) | 0.428986 / 0.000490 (0.428497) | 0.002590 / 0.000200 (0.002390) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023730 / 0.037411 (-0.013681) | 0.099846 / 0.014526 (0.085320) | 0.107075 / 0.176557 (-0.069482) | 0.147475 / 0.737135 (-0.589661) | 0.111802 / 0.296338 (-0.184537) |\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) | 0.413704 / 0.215209 (0.198494) | 4.144498 / 2.077655 (2.066843) | 1.855900 / 1.504120 (0.351780) | 1.647958 / 1.541195 (0.106763) | 1.712437 / 1.468490 (0.243947) | 0.688382 / 4.584777 (-3.896395) | 3.432136 / 3.745712 (-0.313576) | 2.837211 / 5.269862 (-2.432651) | 1.519004 / 4.565676 (-3.046672) | 0.082429 / 0.424275 (-0.341846) | 0.012610 / 0.007607 (0.005003) | 0.525078 / 0.226044 (0.299034) | 5.272932 / 2.268929 (3.004003) | 2.340482 / 55.444624 (-53.104143) | 2.007372 / 6.876477 (-4.869104) | 2.060567 / 2.142072 (-0.081506) | 0.806476 / 4.805227 (-3.998752) | 0.149421 / 6.500664 (-6.351243) | 0.066252 / 0.075469 (-0.009218) |\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) | 1.235078 / 1.841788 (-0.606710) | 13.870758 / 8.074308 (5.796450) | 14.104582 / 10.191392 (3.913190) | 0.159375 / 0.680424 (-0.521049) | 0.029233 / 0.534201 (-0.504968) | 0.392184 / 0.579283 (-0.187099) | 0.407909 / 0.434364 (-0.026455) | 0.458757 / 0.540337 (-0.081581) | 0.547681 / 1.386936 (-0.839255) |\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) | 0.007194 / 0.011353 (-0.004159) | 0.004578 / 0.011008 (-0.006431) | 0.098936 / 0.038508 (0.060428) | 0.029639 / 0.023109 (0.006530) | 0.347241 / 0.275898 (0.071343) | 0.378838 / 0.323480 (0.055358) | 0.005632 / 0.007986 (-0.002353) | 0.003469 / 0.004328 (-0.000860) | 0.075536 / 0.004250 (0.071285) | 0.043301 / 0.037052 (0.006249) | 0.348091 / 0.258489 (0.089602) | 0.388595 / 0.293841 (0.094754) | 0.033512 / 0.128546 (-0.095034) | 0.011754 / 0.075646 (-0.063892) | 0.321003 / 0.419271 (-0.098268) | 0.044634 / 0.043533 (0.001101) | 0.346688 / 0.255139 (0.091549) | 0.366346 / 0.283200 (0.083147) | 0.093650 / 0.141683 (-0.048033) | 1.509913 / 1.452155 (0.057759) | 1.596414 / 1.492716 (0.103698) |\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) | 0.230466 / 0.018006 (0.212459) | 0.417106 / 0.000490 (0.416617) | 0.000959 / 0.000200 (0.000759) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025581 / 0.037411 (-0.011830) | 0.105246 / 0.014526 (0.090720) | 0.108997 / 0.176557 (-0.067560) | 0.144342 / 0.737135 (-0.592794) | 0.113911 / 0.296338 (-0.182427) |\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) | 0.479608 / 0.215209 (0.264399) | 4.766081 / 2.077655 (2.688426) | 2.446597 / 1.504120 (0.942477) | 2.228278 / 1.541195 (0.687083) | 2.289943 / 1.468490 (0.821453) | 0.703146 / 4.584777 (-3.881631) | 3.414150 / 3.745712 (-0.331562) | 2.957730 / 5.269862 (-2.312132) | 1.531524 / 4.565676 (-3.034152) | 0.083449 / 0.424275 (-0.340826) | 0.012684 / 0.007607 (0.005077) | 0.587622 / 0.226044 (0.361578) | 5.888791 / 2.268929 (3.619863) | 2.884200 / 55.444624 (-52.560424) | 2.543739 / 6.876477 (-4.332737) | 2.596245 / 2.142072 (0.454173) | 0.813070 / 4.805227 (-3.992157) | 0.152706 / 6.500664 (-6.347958) | 0.069257 / 0.075469 (-0.006212) |\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) | 1.302945 / 1.841788 (-0.538842) | 14.484051 / 8.074308 (6.409743) | 14.216143 / 10.191392 (4.024751) | 0.154537 / 0.680424 (-0.525886) | 0.016909 / 0.534201 (-0.517292) | 0.389433 / 0.579283 (-0.189850) | 0.393280 / 0.434364 (-0.041084) | 0.446884 / 0.540337 (-0.093453) | 0.534394 / 1.386936 (-0.852542) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bcdeb952c57c5f22643061d49d16014a7b6426a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008822 / 0.011353 (-0.002530) | 0.004826 / 0.011008 (-0.006182) | 0.102710 / 0.038508 (0.064202) | 0.030353 / 0.023109 (0.007244) | 0.297224 / 0.275898 (0.021326) | 0.371861 / 0.323480 (0.048381) | 0.007266 / 0.007986 (-0.000720) | 0.003632 / 0.004328 (-0.000696) | 0.079960 / 0.004250 (0.075710) | 0.036908 / 0.037052 (-0.000144) | 0.309582 / 0.258489 (0.051093) | 0.350108 / 0.293841 (0.056267) | 0.034280 / 0.128546 (-0.094266) | 0.011739 / 0.075646 (-0.063907) | 0.323217 / 0.419271 (-0.096054) | 0.043491 / 0.043533 (-0.000042) | 0.298454 / 0.255139 (0.043315) | 0.326735 / 0.283200 (0.043535) | 0.093955 / 0.141683 (-0.047728) | 1.494313 / 1.452155 (0.042159) | 1.562104 / 1.492716 (0.069388) |\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) | 0.182796 / 0.018006 (0.164790) | 0.420133 / 0.000490 (0.419643) | 0.002537 / 0.000200 (0.002337) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023143 / 0.037411 (-0.014269) | 0.098560 / 0.014526 (0.084034) | 0.105060 / 0.176557 (-0.071496) | 0.140269 / 0.737135 (-0.596866) | 0.109120 / 0.296338 (-0.187219) |\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) | 0.419907 / 0.215209 (0.204698) | 4.196179 / 2.077655 (2.118524) | 1.887663 / 1.504120 (0.383543) | 1.686232 / 1.541195 (0.145037) | 1.741741 / 1.468490 (0.273251) | 0.696222 / 4.584777 (-3.888555) | 3.400250 / 3.745712 (-0.345462) | 1.875058 / 5.269862 (-3.394803) | 1.159466 / 4.565676 (-3.406211) | 0.082520 / 0.424275 (-0.341755) | 0.012408 / 0.007607 (0.004801) | 0.525212 / 0.226044 (0.299168) | 5.283691 / 2.268929 (3.014762) | 2.314487 / 55.444624 (-53.130138) | 1.966212 / 6.876477 (-4.910265) | 2.023458 / 2.142072 (-0.118615) | 0.808896 / 4.805227 (-3.996331) | 0.148973 / 6.500664 (-6.351691) | 0.065378 / 0.075469 (-0.010091) |\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) | 1.223833 / 1.841788 (-0.617955) | 14.053651 / 8.074308 (5.979343) | 14.072165 / 10.191392 (3.880773) | 0.156006 / 0.680424 (-0.524418) | 0.028665 / 0.534201 (-0.505536) | 0.392099 / 0.579283 (-0.187184) | 0.401460 / 0.434364 (-0.032904) | 0.462184 / 0.540337 (-0.078153) | 0.540459 / 1.386936 (-0.846477) |\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) | 0.006907 / 0.011353 (-0.004446) | 0.004585 / 0.011008 (-0.006423) | 0.099027 / 0.038508 (0.060519) | 0.028317 / 0.023109 (0.005208) | 0.421068 / 0.275898 (0.145170) | 0.450712 / 0.323480 (0.127233) | 0.005229 / 0.007986 (-0.002756) | 0.004873 / 0.004328 (0.000545) | 0.077374 / 0.004250 (0.073124) | 0.042530 / 0.037052 (0.005477) | 0.417392 / 0.258489 (0.158903) | 0.462605 / 0.293841 (0.168764) | 0.032195 / 0.128546 (-0.096351) | 0.011777 / 0.075646 (-0.063870) | 0.321927 / 0.419271 (-0.097344) | 0.041999 / 0.043533 (-0.001533) | 0.419402 / 0.255139 (0.164263) | 0.437179 / 0.283200 (0.153979) | 0.089549 / 0.141683 (-0.052134) | 1.469525 / 1.452155 (0.017370) | 1.586407 / 1.492716 (0.093691) |\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) | 0.209533 / 0.018006 (0.191526) | 0.413886 / 0.000490 (0.413396) | 0.003357 / 0.000200 (0.003157) | 0.000121 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026133 / 0.037411 (-0.011278) | 0.103128 / 0.014526 (0.088602) | 0.110604 / 0.176557 (-0.065952) | 0.153055 / 0.737135 (-0.584080) | 0.112257 / 0.296338 (-0.184081) |\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) | 0.471281 / 0.215209 (0.256072) | 4.708361 / 2.077655 (2.630706) | 2.572681 / 1.504120 (1.068561) | 2.370536 / 1.541195 (0.829341) | 2.456010 / 1.468490 (0.987520) | 0.694173 / 4.584777 (-3.890603) | 3.434511 / 3.745712 (-0.311201) | 1.877169 / 5.269862 (-3.392693) | 1.158387 / 4.565676 (-3.407289) | 0.081849 / 0.424275 (-0.342426) | 0.012176 / 0.007607 (0.004569) | 0.581736 / 0.226044 (0.355692) | 5.803173 / 2.268929 (3.534245) | 3.040003 / 55.444624 (-52.404621) | 2.704698 / 6.876477 (-4.171779) | 2.760138 / 2.142072 (0.618065) | 0.802557 / 4.805227 (-4.002671) | 0.151397 / 6.500664 (-6.349268) | 0.068308 / 0.075469 (-0.007161) |\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) | 1.304062 / 1.841788 (-0.537725) | 14.364809 / 8.074308 (6.290501) | 14.192131 / 10.191392 (4.000739) | 0.150025 / 0.680424 (-0.530399) | 0.017020 / 0.534201 (-0.517181) | 0.389235 / 0.579283 (-0.190048) | 0.387557 / 0.434364 (-0.046807) | 0.454636 / 0.540337 (-0.085702) | 0.558182 / 1.386936 (-0.828754) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#663e5eddca188abbb37e2f803846f02fe4ca0d9b \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008519 / 0.011353 (-0.002834) | 0.004538 / 0.011008 (-0.006470) | 0.102066 / 0.038508 (0.063558) | 0.029700 / 0.023109 (0.006591) | 0.304573 / 0.275898 (0.028675) | 0.366232 / 0.323480 (0.042752) | 0.007154 / 0.007986 (-0.000832) | 0.003497 / 0.004328 (-0.000831) | 0.079119 / 0.004250 (0.074868) | 0.036088 / 0.037052 (-0.000964) | 0.311076 / 0.258489 (0.052587) | 0.352205 / 0.293841 (0.058364) | 0.033706 / 0.128546 (-0.094840) | 0.011657 / 0.075646 (-0.063990) | 0.324024 / 0.419271 (-0.095247) | 0.040777 / 0.043533 (-0.002756) | 0.302661 / 0.255139 (0.047522) | 0.329091 / 0.283200 (0.045891) | 0.086774 / 0.141683 (-0.054909) | 1.485874 / 1.452155 (0.033720) | 1.535726 / 1.492716 (0.043009) |\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) | 0.194284 / 0.018006 (0.176277) | 0.412875 / 0.000490 (0.412385) | 0.003348 / 0.000200 (0.003148) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022432 / 0.037411 (-0.014979) | 0.095008 / 0.014526 (0.080482) | 0.103268 / 0.176557 (-0.073288) | 0.140121 / 0.737135 (-0.597014) | 0.106619 / 0.296338 (-0.189719) |\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) | 0.414786 / 0.215209 (0.199577) | 4.146345 / 2.077655 (2.068690) | 1.873703 / 1.504120 (0.369583) | 1.673498 / 1.541195 (0.132303) | 1.716993 / 1.468490 (0.248502) | 0.692098 / 4.584777 (-3.892679) | 3.380991 / 3.745712 (-0.364721) | 1.846811 / 5.269862 (-3.423050) | 1.159617 / 4.565676 (-3.406059) | 0.081867 / 0.424275 (-0.342408) | 0.012371 / 0.007607 (0.004764) | 0.526228 / 0.226044 (0.300184) | 5.273139 / 2.268929 (3.004211) | 2.327147 / 55.444624 (-53.117477) | 1.968366 / 6.876477 (-4.908111) | 2.018053 / 2.142072 (-0.124019) | 0.816098 / 4.805227 (-3.989130) | 0.149438 / 6.500664 (-6.351226) | 0.065000 / 0.075469 (-0.010469) |\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) | 1.244408 / 1.841788 (-0.597380) | 13.774354 / 8.074308 (5.700046) | 14.178923 / 10.191392 (3.987531) | 0.150032 / 0.680424 (-0.530392) | 0.029736 / 0.534201 (-0.504465) | 0.399134 / 0.579283 (-0.180149) | 0.404214 / 0.434364 (-0.030150) | 0.462096 / 0.540337 (-0.078242) | 0.542256 / 1.386936 (-0.844680) |\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) | 0.006776 / 0.011353 (-0.004577) | 0.004586 / 0.011008 (-0.006422) | 0.097658 / 0.038508 (0.059150) | 0.027627 / 0.023109 (0.004517) | 0.423794 / 0.275898 (0.147896) | 0.447443 / 0.323480 (0.123963) | 0.005099 / 0.007986 (-0.002886) | 0.004846 / 0.004328 (0.000517) | 0.075135 / 0.004250 (0.070884) | 0.038068 / 0.037052 (0.001016) | 0.420999 / 0.258489 (0.162510) | 0.460368 / 0.293841 (0.166527) | 0.032107 / 0.128546 (-0.096439) | 0.011775 / 0.075646 (-0.063871) | 0.323854 / 0.419271 (-0.095418) | 0.045538 / 0.043533 (0.002005) | 0.420949 / 0.255139 (0.165810) | 0.441906 / 0.283200 (0.158706) | 0.091955 / 0.141683 (-0.049728) | 1.523736 / 1.452155 (0.071581) | 1.587865 / 1.492716 (0.095148) |\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) | 0.263297 / 0.018006 (0.245290) | 0.416170 / 0.000490 (0.415680) | 0.023161 / 0.000200 (0.022961) | 0.000243 / 0.000054 (0.000188) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024000 / 0.037411 (-0.013412) | 0.097787 / 0.014526 (0.083262) | 0.106884 / 0.176557 (-0.069672) | 0.140861 / 0.737135 (-0.596274) | 0.108228 / 0.296338 (-0.188111) |\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) | 0.477222 / 0.215209 (0.262013) | 4.774729 / 2.077655 (2.697074) | 2.451575 / 1.504120 (0.947455) | 2.251255 / 1.541195 (0.710060) | 2.281154 / 1.468490 (0.812664) | 0.699394 / 4.584777 (-3.885383) | 3.421575 / 3.745712 (-0.324137) | 2.704713 / 5.269862 (-2.565148) | 1.508464 / 4.565676 (-3.057212) | 0.082199 / 0.424275 (-0.342076) | 0.012586 / 0.007607 (0.004979) | 0.588783 / 0.226044 (0.362739) | 5.878434 / 2.268929 (3.609505) | 2.927422 / 55.444624 (-52.517202) | 2.574357 / 6.876477 (-4.302120) | 2.603626 / 2.142072 (0.461554) | 0.804706 / 4.805227 (-4.000521) | 0.152919 / 6.500664 (-6.347745) | 0.069316 / 0.075469 (-0.006153) |\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) | 1.280025 / 1.841788 (-0.561763) | 13.968407 / 8.074308 (5.894099) | 13.874506 / 10.191392 (3.683114) | 0.154711 / 0.680424 (-0.525713) | 0.016827 / 0.534201 (-0.517374) | 0.377775 / 0.579283 (-0.201508) | 0.393035 / 0.434364 (-0.041329) | 0.439405 / 0.540337 (-0.100932) | 0.528135 / 1.386936 (-0.858801) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00b27a59b8af9075967b800e3b0f1de8616aa0ce \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009035 / 0.011353 (-0.002318) | 0.004518 / 0.011008 (-0.006490) | 0.102077 / 0.038508 (0.063569) | 0.030169 / 0.023109 (0.007060) | 0.297713 / 0.275898 (0.021815) | 0.364976 / 0.323480 (0.041496) | 0.007079 / 0.007986 (-0.000906) | 0.003438 / 0.004328 (-0.000890) | 0.079667 / 0.004250 (0.075416) | 0.035890 / 0.037052 (-0.001162) | 0.306065 / 0.258489 (0.047576) | 0.352133 / 0.293841 (0.058292) | 0.033800 / 0.128546 (-0.094746) | 0.011613 / 0.075646 (-0.064034) | 0.322917 / 0.419271 (-0.096354) | 0.040973 / 0.043533 (-0.002560) | 0.300896 / 0.255139 (0.045757) | 0.331540 / 0.283200 (0.048341) | 0.089579 / 0.141683 (-0.052103) | 1.466755 / 1.452155 (0.014600) | 1.522120 / 1.492716 (0.029404) |\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) | 0.193172 / 0.018006 (0.175166) | 0.408878 / 0.000490 (0.408389) | 0.001586 / 0.000200 (0.001386) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023496 / 0.037411 (-0.013915) | 0.098046 / 0.014526 (0.083520) | 0.104599 / 0.176557 (-0.071957) | 0.139054 / 0.737135 (-0.598081) | 0.111163 / 0.296338 (-0.185175) |\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) | 0.417374 / 0.215209 (0.202165) | 4.145808 / 2.077655 (2.068153) | 1.847101 / 1.504120 (0.342981) | 1.637207 / 1.541195 (0.096012) | 1.676906 / 1.468490 (0.208416) | 0.689851 / 4.584777 (-3.894926) | 3.402099 / 3.745712 (-0.343614) | 1.896808 / 5.269862 (-3.373054) | 1.257876 / 4.565676 (-3.307801) | 0.081744 / 0.424275 (-0.342531) | 0.012206 / 0.007607 (0.004599) | 0.524830 / 0.226044 (0.298786) | 5.251344 / 2.268929 (2.982416) | 2.277907 / 55.444624 (-53.166717) | 1.933985 / 6.876477 (-4.942491) | 2.038500 / 2.142072 (-0.103573) | 0.808696 / 4.805227 (-3.996532) | 0.149488 / 6.500664 (-6.351176) | 0.065323 / 0.075469 (-0.010146) |\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) | 1.204294 / 1.841788 (-0.637493) | 13.696526 / 8.074308 (5.622218) | 13.947195 / 10.191392 (3.755802) | 0.136812 / 0.680424 (-0.543611) | 0.028625 / 0.534201 (-0.505576) | 0.397662 / 0.579283 (-0.181621) | 0.403423 / 0.434364 (-0.030941) | 0.465288 / 0.540337 (-0.075049) | 0.551919 / 1.386936 (-0.835017) |\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) | 0.006467 / 0.011353 (-0.004886) | 0.004562 / 0.011008 (-0.006447) | 0.097514 / 0.038508 (0.059006) | 0.027471 / 0.023109 (0.004362) | 0.425504 / 0.275898 (0.149606) | 0.458856 / 0.323480 (0.135376) | 0.004816 / 0.007986 (-0.003169) | 0.003264 / 0.004328 (-0.001065) | 0.074947 / 0.004250 (0.070697) | 0.037147 / 0.037052 (0.000095) | 0.429513 / 0.258489 (0.171024) | 0.463971 / 0.293841 (0.170130) | 0.031638 / 0.128546 (-0.096908) | 0.011545 / 0.075646 (-0.064101) | 0.320261 / 0.419271 (-0.099010) | 0.041570 / 0.043533 (-0.001963) | 0.424809 / 0.255139 (0.169670) | 0.447158 / 0.283200 (0.163959) | 0.088418 / 0.141683 (-0.053265) | 1.492242 / 1.452155 (0.040087) | 1.545523 / 1.492716 (0.052807) |\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) | 0.217865 / 0.018006 (0.199859) | 0.399925 / 0.000490 (0.399436) | 0.004853 / 0.000200 (0.004653) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024275 / 0.037411 (-0.013137) | 0.098249 / 0.014526 (0.083723) | 0.107110 / 0.176557 (-0.069446) | 0.143870 / 0.737135 (-0.593265) | 0.108796 / 0.296338 (-0.187542) |\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) | 0.470856 / 0.215209 (0.255647) | 4.687921 / 2.077655 (2.610266) | 2.448631 / 1.504120 (0.944511) | 2.247748 / 1.541195 (0.706553) | 2.287713 / 1.468490 (0.819223) | 0.687534 / 4.584777 (-3.897243) | 3.421099 / 3.745712 (-0.324613) | 2.977280 / 5.269862 (-2.292582) | 1.274837 / 4.565676 (-3.290839) | 0.081611 / 0.424275 (-0.342664) | 0.012603 / 0.007607 (0.004996) | 0.574600 / 0.226044 (0.348556) | 5.802826 / 2.268929 (3.533898) | 2.913178 / 55.444624 (-52.531446) | 2.589486 / 6.876477 (-4.286991) | 2.630004 / 2.142072 (0.487932) | 0.790087 / 4.805227 (-4.015140) | 0.150019 / 6.500664 (-6.350645) | 0.067346 / 0.075469 (-0.008123) |\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) | 1.266521 / 1.841788 (-0.575267) | 13.818770 / 8.074308 (5.744462) | 13.872277 / 10.191392 (3.680885) | 0.147375 / 0.680424 (-0.533049) | 0.016837 / 0.534201 (-0.517363) | 0.376421 / 0.579283 (-0.202862) | 0.400236 / 0.434364 (-0.034128) | 0.436623 / 0.540337 (-0.103714) | 0.527173 / 1.386936 (-0.859763) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f347cf8443aa35401ba6a4159600b92bc6a156b \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009341 / 0.011353 (-0.002012) | 0.005188 / 0.011008 (-0.005820) | 0.101831 / 0.038508 (0.063323) | 0.035141 / 0.023109 (0.012032) | 0.299324 / 0.275898 (0.023426) | 0.334749 / 0.323480 (0.011269) | 0.007958 / 0.007986 (-0.000027) | 0.005482 / 0.004328 (0.001153) | 0.077070 / 0.004250 (0.072820) | 0.044733 / 0.037052 (0.007680) | 0.310398 / 0.258489 (0.051909) | 0.347925 / 0.293841 (0.054084) | 0.038141 / 0.128546 (-0.090405) | 0.012135 / 0.075646 (-0.063512) | 0.333799 / 0.419271 (-0.085472) | 0.048881 / 0.043533 (0.005348) | 0.301336 / 0.255139 (0.046197) | 0.314592 / 0.283200 (0.031393) | 0.103635 / 0.141683 (-0.038048) | 1.437321 / 1.452155 (-0.014833) | 1.598781 / 1.492716 (0.106065) |\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) | 0.248911 / 0.018006 (0.230905) | 0.528932 / 0.000490 (0.528442) | 0.002495 / 0.000200 (0.002295) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027903 / 0.037411 (-0.009509) | 0.106716 / 0.014526 (0.092190) | 0.122650 / 0.176557 (-0.053907) | 0.162481 / 0.737135 (-0.574654) | 0.126402 / 0.296338 (-0.169937) |\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) | 0.352819 / 0.215209 (0.137610) | 3.522761 / 2.077655 (1.445106) | 1.576761 / 1.504120 (0.072641) | 1.411631 / 1.541195 (-0.129563) | 1.449689 / 1.468490 (-0.018801) | 0.608987 / 4.584777 (-3.975790) | 3.705121 / 3.745712 (-0.040592) | 2.085071 / 5.269862 (-3.184790) | 1.308653 / 4.565676 (-3.257024) | 0.083763 / 0.424275 (-0.340512) | 0.011957 / 0.007607 (0.004350) | 0.502182 / 0.226044 (0.276137) | 5.008829 / 2.268929 (2.739900) | 2.244687 / 55.444624 (-53.199937) | 1.891411 / 6.876477 (-4.985065) | 1.940789 / 2.142072 (-0.201284) | 0.825966 / 4.805227 (-3.979261) | 0.165267 / 6.500664 (-6.335397) | 0.063020 / 0.075469 (-0.012449) |\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) | 1.196707 / 1.841788 (-0.645081) | 14.236877 / 8.074308 (6.162569) | 14.872954 / 10.191392 (4.681562) | 0.168560 / 0.680424 (-0.511864) | 0.029038 / 0.534201 (-0.505163) | 0.440192 / 0.579283 (-0.139091) | 0.437021 / 0.434364 (0.002657) | 0.519612 / 0.540337 (-0.020725) | 0.612013 / 1.386936 (-0.774923) |\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) | 0.007170 / 0.011353 (-0.004183) | 0.005303 / 0.011008 (-0.005705) | 0.098503 / 0.038508 (0.059995) | 0.032573 / 0.023109 (0.009463) | 0.398203 / 0.275898 (0.122305) | 0.446075 / 0.323480 (0.122595) | 0.005712 / 0.007986 (-0.002274) | 0.004165 / 0.004328 (-0.000164) | 0.074273 / 0.004250 (0.070023) | 0.049587 / 0.037052 (0.012534) | 0.399458 / 0.258489 (0.140969) | 0.459167 / 0.293841 (0.165327) | 0.036063 / 0.128546 (-0.092483) | 0.012394 / 0.075646 (-0.063253) | 0.332559 / 0.419271 (-0.086713) | 0.048499 / 0.043533 (0.004967) | 0.404044 / 0.255139 (0.148905) | 0.410462 / 0.283200 (0.127262) | 0.104104 / 0.141683 (-0.037579) | 1.488141 / 1.452155 (0.035986) | 1.535517 / 1.492716 (0.042801) |\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) | 0.292976 / 0.018006 (0.274970) | 0.569139 / 0.000490 (0.568649) | 0.000553 / 0.000200 (0.000353) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030144 / 0.037411 (-0.007267) | 0.098699 / 0.014526 (0.084173) | 0.114437 / 0.176557 (-0.062120) | 0.156657 / 0.737135 (-0.580478) | 0.117449 / 0.296338 (-0.178890) |\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) | 0.441921 / 0.215209 (0.226712) | 4.413090 / 2.077655 (2.335435) | 2.190458 / 1.504120 (0.686338) | 2.008919 / 1.541195 (0.467724) | 2.049657 / 1.468490 (0.581167) | 0.691751 / 4.584777 (-3.893026) | 3.767524 / 3.745712 (0.021812) | 3.395564 / 5.269862 (-1.874297) | 1.633480 / 4.565676 (-2.932196) | 0.084880 / 0.424275 (-0.339395) | 0.012133 / 0.007607 (0.004526) | 0.555372 / 0.226044 (0.329327) | 5.522820 / 2.268929 (3.253892) | 2.723331 / 55.444624 (-52.721293) | 2.337583 / 6.876477 (-4.538894) | 2.368746 / 2.142072 (0.226674) | 0.830127 / 4.805227 (-3.975100) | 0.166239 / 6.500664 (-6.334425) | 0.064279 / 0.075469 (-0.011190) |\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) | 1.123421 / 1.841788 (-0.718367) | 14.413392 / 8.074308 (6.339084) | 12.865143 / 10.191392 (2.673751) | 0.132198 / 0.680424 (-0.548226) | 0.016138 / 0.534201 (-0.518063) | 0.380760 / 0.579283 (-0.198523) | 0.387223 / 0.434364 (-0.047141) | 0.445574 / 0.540337 (-0.094764) | 0.535658 / 1.386936 (-0.851278) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a89564d3d17b5960db2435662cb9c49f8ad7488a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008316 / 0.011353 (-0.003037) | 0.004503 / 0.011008 (-0.006505) | 0.100565 / 0.038508 (0.062057) | 0.030388 / 0.023109 (0.007279) | 0.304417 / 0.275898 (0.028519) | 0.369655 / 0.323480 (0.046175) | 0.007796 / 0.007986 (-0.000190) | 0.003450 / 0.004328 (-0.000878) | 0.078694 / 0.004250 (0.074443) | 0.038068 / 0.037052 (0.001016) | 0.316353 / 0.258489 (0.057864) | 0.352344 / 0.293841 (0.058503) | 0.033271 / 0.128546 (-0.095276) | 0.011427 / 0.075646 (-0.064220) | 0.322367 / 0.419271 (-0.096904) | 0.041497 / 0.043533 (-0.002036) | 0.305876 / 0.255139 (0.050737) | 0.332279 / 0.283200 (0.049079) | 0.086719 / 0.141683 (-0.054964) | 1.488367 / 1.452155 (0.036212) | 1.528943 / 1.492716 (0.036227) |\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) | 0.171072 / 0.018006 (0.153066) | 0.421048 / 0.000490 (0.420558) | 0.003622 / 0.000200 (0.003422) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022632 / 0.037411 (-0.014779) | 0.095304 / 0.014526 (0.080778) | 0.106254 / 0.176557 (-0.070302) | 0.138437 / 0.737135 (-0.598698) | 0.107258 / 0.296338 (-0.189080) |\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) | 0.423201 / 0.215209 (0.207992) | 4.208397 / 2.077655 (2.130742) | 1.899800 / 1.504120 (0.395680) | 1.682782 / 1.541195 (0.141587) | 1.708840 / 1.468490 (0.240350) | 0.694492 / 4.584777 (-3.890285) | 3.380369 / 3.745712 (-0.365344) | 1.851731 / 5.269862 (-3.418130) | 1.151615 / 4.565676 (-3.414061) | 0.082446 / 0.424275 (-0.341829) | 0.012483 / 0.007607 (0.004876) | 0.533688 / 0.226044 (0.307643) | 5.373434 / 2.268929 (3.104505) | 2.346403 / 55.444624 (-53.098221) | 1.978505 / 6.876477 (-4.897971) | 2.005875 / 2.142072 (-0.136198) | 0.820785 / 4.805227 (-3.984442) | 0.150728 / 6.500664 (-6.349936) | 0.065761 / 0.075469 (-0.009708) |\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) | 1.244550 / 1.841788 (-0.597237) | 13.219096 / 8.074308 (5.144788) | 13.960463 / 10.191392 (3.769071) | 0.135572 / 0.680424 (-0.544852) | 0.028746 / 0.534201 (-0.505455) | 0.393082 / 0.579283 (-0.186201) | 0.402852 / 0.434364 (-0.031512) | 0.461191 / 0.540337 (-0.079147) | 0.543500 / 1.386936 (-0.843436) |\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) | 0.006316 / 0.011353 (-0.005037) | 0.004394 / 0.011008 (-0.006615) | 0.096478 / 0.038508 (0.057970) | 0.026965 / 0.023109 (0.003855) | 0.340371 / 0.275898 (0.064473) | 0.368334 / 0.323480 (0.044854) | 0.004744 / 0.007986 (-0.003242) | 0.004652 / 0.004328 (0.000324) | 0.074479 / 0.004250 (0.070228) | 0.036358 / 0.037052 (-0.000694) | 0.342968 / 0.258489 (0.084479) | 0.383675 / 0.293841 (0.089834) | 0.031439 / 0.128546 (-0.097107) | 0.011529 / 0.075646 (-0.064117) | 0.319560 / 0.419271 (-0.099711) | 0.041370 / 0.043533 (-0.002163) | 0.342594 / 0.255139 (0.087455) | 0.363237 / 0.283200 (0.080038) | 0.087316 / 0.141683 (-0.054367) | 1.468690 / 1.452155 (0.016535) | 1.553974 / 1.492716 (0.061257) |\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) | 0.198366 / 0.018006 (0.180360) | 0.401581 / 0.000490 (0.401091) | 0.000400 / 0.000200 (0.000200) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023150 / 0.037411 (-0.014261) | 0.097797 / 0.014526 (0.083271) | 0.106198 / 0.176557 (-0.070359) | 0.139599 / 0.737135 (-0.597536) | 0.108361 / 0.296338 (-0.187978) |\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) | 0.472962 / 0.215209 (0.257753) | 4.702688 / 2.077655 (2.625033) | 2.401002 / 1.504120 (0.896882) | 2.193857 / 1.541195 (0.652663) | 2.219188 / 1.468490 (0.750697) | 0.689993 / 4.584777 (-3.894784) | 3.369409 / 3.745712 (-0.376304) | 1.824801 / 5.269862 (-3.445061) | 1.150815 / 4.565676 (-3.414862) | 0.082197 / 0.424275 (-0.342078) | 0.012287 / 0.007607 (0.004679) | 0.581963 / 0.226044 (0.355918) | 5.786943 / 2.268929 (3.518015) | 2.871235 / 55.444624 (-52.573389) | 2.516009 / 6.876477 (-4.360468) | 2.535669 / 2.142072 (0.393597) | 0.804733 / 4.805227 (-4.000494) | 0.150545 / 6.500664 (-6.350119) | 0.066964 / 0.075469 (-0.008505) |\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) | 1.285431 / 1.841788 (-0.556356) | 14.097108 / 8.074308 (6.022800) | 13.821497 / 10.191392 (3.630105) | 0.141922 / 0.680424 (-0.538502) | 0.016964 / 0.534201 (-0.517237) | 0.374784 / 0.579283 (-0.204500) | 0.381034 / 0.434364 (-0.053330) | 0.435487 / 0.540337 (-0.104850) | 0.521894 / 1.386936 (-0.865042) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#462000c2b12a11f1fc26853e842d3f6e40287737 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009486 / 0.011353 (-0.001867) | 0.005363 / 0.011008 (-0.005645) | 0.101008 / 0.038508 (0.062500) | 0.036355 / 0.023109 (0.013246) | 0.290575 / 0.275898 (0.014677) | 0.391634 / 0.323480 (0.068154) | 0.009085 / 0.007986 (0.001099) | 0.005780 / 0.004328 (0.001451) | 0.077848 / 0.004250 (0.073598) | 0.049062 / 0.037052 (0.012009) | 0.310900 / 0.258489 (0.052411) | 0.358224 / 0.293841 (0.064383) | 0.038838 / 0.128546 (-0.089708) | 0.012244 / 0.075646 (-0.063402) | 0.333701 / 0.419271 (-0.085570) | 0.048021 / 0.043533 (0.004488) | 0.289584 / 0.255139 (0.034445) | 0.317556 / 0.283200 (0.034356) | 0.109807 / 0.141683 (-0.031876) | 1.465966 / 1.452155 (0.013811) | 1.526341 / 1.492716 (0.033625) |\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) | 0.246221 / 0.018006 (0.228215) | 0.580659 / 0.000490 (0.580169) | 0.000627 / 0.000200 (0.000427) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028352 / 0.037411 (-0.009059) | 0.110569 / 0.014526 (0.096043) | 0.126456 / 0.176557 (-0.050100) | 0.163633 / 0.737135 (-0.573503) | 0.128252 / 0.296338 (-0.168087) |\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) | 0.397271 / 0.215209 (0.182062) | 3.975336 / 2.077655 (1.897682) | 1.786957 / 1.504120 (0.282837) | 1.598468 / 1.541195 (0.057273) | 1.645299 / 1.468490 (0.176809) | 0.686221 / 4.584777 (-3.898556) | 3.753184 / 3.745712 (0.007472) | 2.089505 / 5.269862 (-3.180356) | 1.325799 / 4.565676 (-3.239878) | 0.084608 / 0.424275 (-0.339667) | 0.012343 / 0.007607 (0.004736) | 0.509951 / 0.226044 (0.283907) | 5.092102 / 2.268929 (2.823174) | 2.297551 / 55.444624 (-53.147073) | 1.938177 / 6.876477 (-4.938300) | 2.012448 / 2.142072 (-0.129625) | 0.835206 / 4.805227 (-3.970021) | 0.166373 / 6.500664 (-6.334291) | 0.063996 / 0.075469 (-0.011473) |\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) | 1.212936 / 1.841788 (-0.628851) | 15.067370 / 8.074308 (6.993062) | 14.165214 / 10.191392 (3.973822) | 0.157041 / 0.680424 (-0.523383) | 0.029612 / 0.534201 (-0.504589) | 0.440006 / 0.579283 (-0.139277) | 0.439165 / 0.434364 (0.004801) | 0.524970 / 0.540337 (-0.015368) | 0.608305 / 1.386936 (-0.778631) |\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) | 0.007433 / 0.011353 (-0.003920) | 0.005310 / 0.011008 (-0.005698) | 0.097194 / 0.038508 (0.058686) | 0.033265 / 0.023109 (0.010156) | 0.369908 / 0.275898 (0.094010) | 0.411508 / 0.323480 (0.088028) | 0.006000 / 0.007986 (-0.001986) | 0.005647 / 0.004328 (0.001319) | 0.075597 / 0.004250 (0.071347) | 0.051951 / 0.037052 (0.014899) | 0.378469 / 0.258489 (0.119980) | 0.424849 / 0.293841 (0.131008) | 0.036700 / 0.128546 (-0.091846) | 0.012535 / 0.075646 (-0.063111) | 0.333197 / 0.419271 (-0.086074) | 0.049046 / 0.043533 (0.005513) | 0.381845 / 0.255139 (0.126706) | 0.397846 / 0.283200 (0.114646) | 0.109152 / 0.141683 (-0.032531) | 1.432407 / 1.452155 (-0.019748) | 1.555509 / 1.492716 (0.062793) |\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) | 0.265433 / 0.018006 (0.247427) | 0.559590 / 0.000490 (0.559100) | 0.000492 / 0.000200 (0.000292) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029748 / 0.037411 (-0.007663) | 0.110490 / 0.014526 (0.095964) | 0.124125 / 0.176557 (-0.052431) | 0.160089 / 0.737135 (-0.577046) | 0.128755 / 0.296338 (-0.167583) |\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) | 0.443976 / 0.215209 (0.228767) | 4.416960 / 2.077655 (2.339305) | 2.239408 / 1.504120 (0.735288) | 2.055341 / 1.541195 (0.514147) | 2.093479 / 1.468490 (0.624988) | 0.688846 / 4.584777 (-3.895930) | 3.797526 / 3.745712 (0.051814) | 3.578137 / 5.269862 (-1.691725) | 2.015073 / 4.565676 (-2.550603) | 0.084126 / 0.424275 (-0.340149) | 0.012581 / 0.007607 (0.004974) | 0.549774 / 0.226044 (0.323730) | 5.492185 / 2.268929 (3.223256) | 2.739851 / 55.444624 (-52.704773) | 2.371091 / 6.876477 (-4.505386) | 2.400178 / 2.142072 (0.258105) | 0.831227 / 4.805227 (-3.974001) | 0.166156 / 6.500664 (-6.334508) | 0.063901 / 0.075469 (-0.011568) |\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) | 1.236127 / 1.841788 (-0.605660) | 15.236884 / 8.074308 (7.162576) | 14.434351 / 10.191392 (4.242959) | 0.163725 / 0.680424 (-0.516699) | 0.018009 / 0.534201 (-0.516192) | 0.430612 / 0.579283 (-0.148671) | 0.420426 / 0.434364 (-0.013938) | 0.497062 / 0.540337 (-0.043275) | 0.590924 / 1.386936 (-0.796012) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#63377dc53fc94f19bc2b0bbfb118a90d01a1d020 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.010862 / 0.011353 (-0.000491) | 0.005741 / 0.011008 (-0.005267) | 0.111911 / 0.038508 (0.073403) | 0.042316 / 0.023109 (0.019207) | 0.347665 / 0.275898 (0.071767) | 0.377335 / 0.323480 (0.053855) | 0.009400 / 0.007986 (0.001414) | 0.006814 / 0.004328 (0.002486) | 0.087194 / 0.004250 (0.082943) | 0.046878 / 0.037052 (0.009826) | 0.348920 / 0.258489 (0.090430) | 0.393347 / 0.293841 (0.099507) | 0.044212 / 0.128546 (-0.084334) | 0.013925 / 0.075646 (-0.061722) | 0.386076 / 0.419271 (-0.033195) | 0.054195 / 0.043533 (0.010662) | 0.358486 / 0.255139 (0.103347) | 0.360132 / 0.283200 (0.076932) | 0.109783 / 0.141683 (-0.031900) | 1.679875 / 1.452155 (0.227720) | 1.794379 / 1.492716 (0.301663) |\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) | 0.221927 / 0.018006 (0.203921) | 0.487352 / 0.000490 (0.486863) | 0.003494 / 0.000200 (0.003294) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032201 / 0.037411 (-0.005210) | 0.125861 / 0.014526 (0.111335) | 0.133905 / 0.176557 (-0.042652) | 0.183319 / 0.737135 (-0.553817) | 0.142646 / 0.296338 (-0.153693) |\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) | 0.442720 / 0.215209 (0.227511) | 4.602619 / 2.077655 (2.524964) | 2.050214 / 1.504120 (0.546094) | 1.837968 / 1.541195 (0.296773) | 1.961199 / 1.468490 (0.492709) | 0.793426 / 4.584777 (-3.791351) | 4.472078 / 3.745712 (0.726366) | 2.364903 / 5.269862 (-2.904959) | 1.515076 / 4.565676 (-3.050600) | 0.103087 / 0.424275 (-0.321188) | 0.014676 / 0.007607 (0.007068) | 0.576887 / 0.226044 (0.350843) | 5.785525 / 2.268929 (3.516596) | 2.765231 / 55.444624 (-52.679393) | 2.365364 / 6.876477 (-4.511113) | 2.448335 / 2.142072 (0.306262) | 0.978726 / 4.805227 (-3.826501) | 0.191417 / 6.500664 (-6.309247) | 0.073295 / 0.075469 (-0.002174) |\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) | 1.378995 / 1.841788 (-0.462792) | 16.583655 / 8.074308 (8.509347) | 14.944731 / 10.191392 (4.753339) | 0.168916 / 0.680424 (-0.511508) | 0.035272 / 0.534201 (-0.498928) | 0.489729 / 0.579283 (-0.089554) | 0.496231 / 0.434364 (0.061867) | 0.576218 / 0.540337 (0.035880) | 0.673558 / 1.386936 (-0.713378) |\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) | 0.008104 / 0.011353 (-0.003249) | 0.005179 / 0.011008 (-0.005829) | 0.103908 / 0.038508 (0.065400) | 0.034661 / 0.023109 (0.011552) | 0.398119 / 0.275898 (0.122221) | 0.411765 / 0.323480 (0.088286) | 0.006016 / 0.007986 (-0.001970) | 0.005637 / 0.004328 (0.001308) | 0.073662 / 0.004250 (0.069412) | 0.052411 / 0.037052 (0.015359) | 0.391826 / 0.258489 (0.133337) | 0.455217 / 0.293841 (0.161376) | 0.039924 / 0.128546 (-0.088622) | 0.013390 / 0.075646 (-0.062256) | 0.390319 / 0.419271 (-0.028953) | 0.054312 / 0.043533 (0.010779) | 0.395492 / 0.255139 (0.140353) | 0.446324 / 0.283200 (0.163124) | 0.116461 / 0.141683 (-0.025222) | 1.502163 / 1.452155 (0.050008) | 1.731541 / 1.492716 (0.238825) |\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) | 0.282612 / 0.018006 (0.264606) | 0.503170 / 0.000490 (0.502680) | 0.005307 / 0.000200 (0.005107) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029071 / 0.037411 (-0.008340) | 0.123831 / 0.014526 (0.109306) | 0.133284 / 0.176557 (-0.043272) | 0.172029 / 0.737135 (-0.565106) | 0.140639 / 0.296338 (-0.155700) |\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) | 0.496812 / 0.215209 (0.281603) | 4.958915 / 2.077655 (2.881260) | 2.559188 / 1.504120 (1.055068) | 2.262434 / 1.541195 (0.721240) | 2.371126 / 1.468490 (0.902636) | 0.780150 / 4.584777 (-3.804627) | 4.417060 / 3.745712 (0.671348) | 2.401909 / 5.269862 (-2.867953) | 1.527943 / 4.565676 (-3.037733) | 0.100074 / 0.424275 (-0.324201) | 0.014853 / 0.007607 (0.007246) | 0.630192 / 0.226044 (0.404147) | 6.409685 / 2.268929 (4.140757) | 3.224718 / 55.444624 (-52.219906) | 2.795301 / 6.876477 (-4.081176) | 2.927205 / 2.142072 (0.785132) | 0.989537 / 4.805227 (-3.815690) | 0.199775 / 6.500664 (-6.300889) | 0.076725 / 0.075469 (0.001256) |\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) | 1.433504 / 1.841788 (-0.408284) | 17.117134 / 8.074308 (9.042825) | 16.606367 / 10.191392 (6.414975) | 0.165653 / 0.680424 (-0.514771) | 0.020818 / 0.534201 (-0.513383) | 0.496782 / 0.579283 (-0.082501) | 0.473895 / 0.434364 (0.039531) | 0.576796 / 0.540337 (0.036459) | 0.703272 / 1.386936 (-0.683664) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6627fb6f2639ac3b1435b3386545612db038a42e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012501 / 0.011353 (0.001148) | 0.006437 / 0.011008 (-0.004571) | 0.129387 / 0.038508 (0.090878) | 0.035847 / 0.023109 (0.012737) | 0.339243 / 0.275898 (0.063345) | 0.423274 / 0.323480 (0.099794) | 0.008489 / 0.007986 (0.000503) | 0.004596 / 0.004328 (0.000268) | 0.103322 / 0.004250 (0.099071) | 0.043570 / 0.037052 (0.006517) | 0.357004 / 0.258489 (0.098515) | 0.426511 / 0.293841 (0.132670) | 0.062923 / 0.128546 (-0.065623) | 0.021168 / 0.075646 (-0.054478) | 0.387485 / 0.419271 (-0.031787) | 0.059745 / 0.043533 (0.016213) | 0.341101 / 0.255139 (0.085962) | 0.365530 / 0.283200 (0.082331) | 0.102110 / 0.141683 (-0.039573) | 1.729408 / 1.452155 (0.277253) | 1.759510 / 1.492716 (0.266794) |\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) | 0.187065 / 0.018006 (0.169059) | 0.499685 / 0.000490 (0.499196) | 0.004677 / 0.000200 (0.004478) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025827 / 0.037411 (-0.011584) | 0.113780 / 0.014526 (0.099255) | 0.146060 / 0.176557 (-0.030496) | 0.158169 / 0.737135 (-0.578966) | 0.136133 / 0.296338 (-0.160206) |\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) | 0.608421 / 0.215209 (0.393211) | 5.907395 / 2.077655 (3.829741) | 2.193140 / 1.504120 (0.689021) | 1.870315 / 1.541195 (0.329120) | 1.885660 / 1.468490 (0.417170) | 1.227637 / 4.584777 (-3.357140) | 5.319242 / 3.745712 (1.573530) | 2.991595 / 5.269862 (-2.278267) | 2.043906 / 4.565676 (-2.521771) | 0.151829 / 0.424275 (-0.272447) | 0.018974 / 0.007607 (0.011367) | 0.778035 / 0.226044 (0.551991) | 7.705796 / 2.268929 (5.436868) | 2.990156 / 55.444624 (-52.454468) | 2.372643 / 6.876477 (-4.503834) | 2.240847 / 2.142072 (0.098775) | 1.407209 / 4.805227 (-3.398018) | 0.242336 / 6.500664 (-6.258328) | 0.069847 / 0.075469 (-0.005622) |\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) | 1.445817 / 1.841788 (-0.395970) | 16.059632 / 8.074308 (7.985324) | 18.541971 / 10.191392 (8.350579) | 0.237830 / 0.680424 (-0.442594) | 0.041060 / 0.534201 (-0.493141) | 0.496765 / 0.579283 (-0.082518) | 0.609666 / 0.434364 (0.175302) | 0.584614 / 0.540337 (0.044277) | 0.680858 / 1.386936 (-0.706078) |\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) | 0.009037 / 0.011353 (-0.002315) | 0.005961 / 0.011008 (-0.005047) | 0.127204 / 0.038508 (0.088696) | 0.030664 / 0.023109 (0.007555) | 0.417968 / 0.275898 (0.142070) | 0.515316 / 0.323480 (0.191836) | 0.006549 / 0.007986 (-0.001436) | 0.004456 / 0.004328 (0.000128) | 0.083715 / 0.004250 (0.079464) | 0.043701 / 0.037052 (0.006648) | 0.521153 / 0.258489 (0.262664) | 0.565456 / 0.293841 (0.271615) | 0.055298 / 0.128546 (-0.073248) | 0.018103 / 0.075646 (-0.057544) | 0.403990 / 0.419271 (-0.015282) | 0.060162 / 0.043533 (0.016629) | 0.486383 / 0.255139 (0.231244) | 0.470342 / 0.283200 (0.187142) | 0.102269 / 0.141683 (-0.039414) | 1.643241 / 1.452155 (0.191086) | 1.763850 / 1.492716 (0.271133) |\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) | 0.185602 / 0.018006 (0.167596) | 0.489163 / 0.000490 (0.488674) | 0.000426 / 0.000200 (0.000226) | 0.000086 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026689 / 0.037411 (-0.010722) | 0.111520 / 0.014526 (0.096994) | 0.119838 / 0.176557 (-0.056719) | 0.153698 / 0.737135 (-0.583437) | 0.130969 / 0.296338 (-0.165370) |\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) | 0.616170 / 0.215209 (0.400961) | 6.219702 / 2.077655 (4.142048) | 2.533554 / 1.504120 (1.029434) | 2.256009 / 1.541195 (0.714815) | 2.217617 / 1.468490 (0.749127) | 1.156920 / 4.584777 (-3.427857) | 5.175759 / 3.745712 (1.430046) | 2.848419 / 5.269862 (-2.421442) | 1.943864 / 4.565676 (-2.621813) | 0.138342 / 0.424275 (-0.285933) | 0.013140 / 0.007607 (0.005533) | 0.782105 / 0.226044 (0.556060) | 7.602003 / 2.268929 (5.333075) | 3.629577 / 55.444624 (-51.815047) | 2.713849 / 6.876477 (-4.162628) | 2.663888 / 2.142072 (0.521816) | 1.418381 / 4.805227 (-3.386847) | 0.250649 / 6.500664 (-6.250015) | 0.073564 / 0.075469 (-0.001905) |\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) | 1.483739 / 1.841788 (-0.358049) | 16.386204 / 8.074308 (8.311896) | 20.685262 / 10.191392 (10.493870) | 0.237084 / 0.680424 (-0.443340) | 0.039097 / 0.534201 (-0.495104) | 0.525399 / 0.579283 (-0.053884) | 0.587541 / 0.434364 (0.153177) | 0.566605 / 0.540337 (0.026268) | 0.677384 / 1.386936 (-0.709552) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3b67d42733dabb15ce4997c8324f8e047ce12bd \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.014050 / 0.011353 (0.002697) | 0.005981 / 0.011008 (-0.005028) | 0.126307 / 0.038508 (0.087799) | 0.035400 / 0.023109 (0.012290) | 0.387821 / 0.275898 (0.111923) | 0.462785 / 0.323480 (0.139305) | 0.009427 / 0.007986 (0.001441) | 0.005081 / 0.004328 (0.000753) | 0.097273 / 0.004250 (0.093023) | 0.044699 / 0.037052 (0.007647) | 0.396025 / 0.258489 (0.137536) | 0.450137 / 0.293841 (0.156296) | 0.055660 / 0.128546 (-0.072886) | 0.022710 / 0.075646 (-0.052936) | 0.443784 / 0.419271 (0.024513) | 0.065756 / 0.043533 (0.022223) | 0.379350 / 0.255139 (0.124211) | 0.396783 / 0.283200 (0.113583) | 0.114088 / 0.141683 (-0.027594) | 1.856834 / 1.452155 (0.404679) | 1.839292 / 1.492716 (0.346576) |\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) | 0.206748 / 0.018006 (0.188742) | 0.517711 / 0.000490 (0.517222) | 0.008302 / 0.000200 (0.008102) | 0.000494 / 0.000054 (0.000440) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033987 / 0.037411 (-0.003424) | 0.131067 / 0.014526 (0.116542) | 0.155539 / 0.176557 (-0.021018) | 0.188598 / 0.737135 (-0.548537) | 0.156000 / 0.296338 (-0.140338) |\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) | 0.641413 / 0.215209 (0.426204) | 6.156680 / 2.077655 (4.079025) | 2.428858 / 1.504120 (0.924738) | 2.086195 / 1.541195 (0.545000) | 2.109604 / 1.468490 (0.641114) | 1.209426 / 4.584777 (-3.375351) | 5.139398 / 3.745712 (1.393686) | 3.041337 / 5.269862 (-2.228524) | 2.294809 / 4.565676 (-2.270868) | 0.142206 / 0.424275 (-0.282069) | 0.015167 / 0.007607 (0.007560) | 0.816269 / 0.226044 (0.590224) | 7.953931 / 2.268929 (5.685002) | 3.201793 / 55.444624 (-52.242832) | 2.448620 / 6.876477 (-4.427857) | 2.521670 / 2.142072 (0.379597) | 1.484094 / 4.805227 (-3.321133) | 0.255069 / 6.500664 (-6.245595) | 0.076031 / 0.075469 (0.000561) |\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) | 1.590951 / 1.841788 (-0.250836) | 17.661353 / 8.074308 (9.587045) | 21.097837 / 10.191392 (10.906445) | 0.229265 / 0.680424 (-0.451159) | 0.042618 / 0.534201 (-0.491583) | 0.535942 / 0.579283 (-0.043342) | 0.590195 / 0.434364 (0.155831) | 0.623985 / 0.540337 (0.083648) | 0.742637 / 1.386936 (-0.644299) |\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) | 0.009264 / 0.011353 (-0.002088) | 0.008798 / 0.011008 (-0.002210) | 0.122208 / 0.038508 (0.083700) | 0.034835 / 0.023109 (0.011726) | 0.462618 / 0.275898 (0.186720) | 0.505632 / 0.323480 (0.182152) | 0.006320 / 0.007986 (-0.001665) | 0.005383 / 0.004328 (0.001054) | 0.091229 / 0.004250 (0.086979) | 0.045828 / 0.037052 (0.008775) | 0.477507 / 0.258489 (0.219018) | 0.539616 / 0.293841 (0.245775) | 0.061913 / 0.128546 (-0.066633) | 0.019390 / 0.075646 (-0.056257) | 0.420016 / 0.419271 (0.000745) | 0.065958 / 0.043533 (0.022425) | 0.468603 / 0.255139 (0.213464) | 0.486246 / 0.283200 (0.203046) | 0.107924 / 0.141683 (-0.033759) | 1.843614 / 1.452155 (0.391459) | 1.988159 / 1.492716 (0.495442) |\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) | 0.247043 / 0.018006 (0.229037) | 0.515580 / 0.000490 (0.515090) | 0.005630 / 0.000200 (0.005430) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030674 / 0.037411 (-0.006737) | 0.130783 / 0.014526 (0.116258) | 0.147669 / 0.176557 (-0.028888) | 0.175656 / 0.737135 (-0.561479) | 0.138317 / 0.296338 (-0.158022) |\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) | 0.727119 / 0.215209 (0.511909) | 6.848208 / 2.077655 (4.770553) | 3.121418 / 1.504120 (1.617298) | 2.701799 / 1.541195 (1.160604) | 2.749179 / 1.468490 (1.280689) | 1.312058 / 4.584777 (-3.272719) | 5.400562 / 3.745712 (1.654850) | 3.058142 / 5.269862 (-2.211719) | 2.076361 / 4.565676 (-2.489316) | 0.142169 / 0.424275 (-0.282106) | 0.014340 / 0.007607 (0.006733) | 0.853534 / 0.226044 (0.627490) | 8.734484 / 2.268929 (6.465556) | 3.968130 / 55.444624 (-51.476495) | 3.118032 / 6.876477 (-3.758444) | 3.078757 / 2.142072 (0.936684) | 1.460694 / 4.805227 (-3.344533) | 0.261858 / 6.500664 (-6.238806) | 0.081089 / 0.075469 (0.005620) |\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) | 1.611473 / 1.841788 (-0.230315) | 17.660545 / 8.074308 (9.586237) | 20.526023 / 10.191392 (10.334631) | 0.223320 / 0.680424 (-0.457103) | 0.027939 / 0.534201 (-0.506261) | 0.542704 / 0.579283 (-0.036579) | 0.563826 / 0.434364 (0.129462) | 0.639936 / 0.540337 (0.099599) | 0.755974 / 1.386936 (-0.630962) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#942141e13ba2be853e2231d9edbfa38044e2632d \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008776 / 0.011353 (-0.002577) | 0.004532 / 0.011008 (-0.006476) | 0.100373 / 0.038508 (0.061865) | 0.029706 / 0.023109 (0.006597) | 0.304374 / 0.275898 (0.028476) | 0.337223 / 0.323480 (0.013743) | 0.007021 / 0.007986 (-0.000965) | 0.003420 / 0.004328 (-0.000908) | 0.077754 / 0.004250 (0.073504) | 0.034411 / 0.037052 (-0.002642) | 0.302926 / 0.258489 (0.044437) | 0.342654 / 0.293841 (0.048813) | 0.034528 / 0.128546 (-0.094018) | 0.011926 / 0.075646 (-0.063721) | 0.322971 / 0.419271 (-0.096301) | 0.041384 / 0.043533 (-0.002149) | 0.306433 / 0.255139 (0.051294) | 0.332293 / 0.283200 (0.049093) | 0.084972 / 0.141683 (-0.056711) | 1.493426 / 1.452155 (0.041271) | 1.570446 / 1.492716 (0.077729) |\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) | 0.189090 / 0.018006 (0.171084) | 0.433904 / 0.000490 (0.433414) | 0.001323 / 0.000200 (0.001124) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023531 / 0.037411 (-0.013880) | 0.097774 / 0.014526 (0.083248) | 0.106383 / 0.176557 (-0.070174) | 0.139158 / 0.737135 (-0.597977) | 0.109443 / 0.296338 (-0.186896) |\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) | 0.419078 / 0.215209 (0.203869) | 4.182657 / 2.077655 (2.105002) | 1.887276 / 1.504120 (0.383156) | 1.679542 / 1.541195 (0.138347) | 1.718035 / 1.468490 (0.249545) | 0.692628 / 4.584777 (-3.892149) | 3.361354 / 3.745712 (-0.384358) | 1.928583 / 5.269862 (-3.341278) | 1.317291 / 4.565676 (-3.248386) | 0.081799 / 0.424275 (-0.342476) | 0.012318 / 0.007607 (0.004711) | 0.525927 / 0.226044 (0.299883) | 5.285905 / 2.268929 (3.016977) | 2.317524 / 55.444624 (-53.127100) | 1.966478 / 6.876477 (-4.909998) | 2.054869 / 2.142072 (-0.087204) | 0.807579 / 4.805227 (-3.997649) | 0.149854 / 6.500664 (-6.350810) | 0.065285 / 0.075469 (-0.010184) |\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) | 1.180516 / 1.841788 (-0.661271) | 13.889734 / 8.074308 (5.815426) | 14.076163 / 10.191392 (3.884771) | 0.156276 / 0.680424 (-0.524148) | 0.029187 / 0.534201 (-0.505013) | 0.403859 / 0.579283 (-0.175424) | 0.404998 / 0.434364 (-0.029366) | 0.471467 / 0.540337 (-0.068871) | 0.564526 / 1.386936 (-0.822410) |\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) | 0.006739 / 0.011353 (-0.004614) | 0.004644 / 0.011008 (-0.006364) | 0.097326 / 0.038508 (0.058818) | 0.027728 / 0.023109 (0.004619) | 0.413537 / 0.275898 (0.137639) | 0.452012 / 0.323480 (0.128532) | 0.005346 / 0.007986 (-0.002639) | 0.003338 / 0.004328 (-0.000991) | 0.075670 / 0.004250 (0.071420) | 0.038825 / 0.037052 (0.001772) | 0.415612 / 0.258489 (0.157123) | 0.454680 / 0.293841 (0.160839) | 0.031866 / 0.128546 (-0.096680) | 0.011616 / 0.075646 (-0.064031) | 0.319527 / 0.419271 (-0.099745) | 0.041283 / 0.043533 (-0.002250) | 0.412046 / 0.255139 (0.156907) | 0.435244 / 0.283200 (0.152044) | 0.088400 / 0.141683 (-0.053283) | 1.478125 / 1.452155 (0.025970) | 1.553677 / 1.492716 (0.060960) |\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) | 0.229919 / 0.018006 (0.211913) | 0.415446 / 0.000490 (0.414956) | 0.000386 / 0.000200 (0.000186) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024365 / 0.037411 (-0.013046) | 0.098225 / 0.014526 (0.083699) | 0.106674 / 0.176557 (-0.069883) | 0.144755 / 0.737135 (-0.592380) | 0.109221 / 0.296338 (-0.187117) |\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) | 0.457665 / 0.215209 (0.242456) | 4.597849 / 2.077655 (2.520195) | 2.171275 / 1.504120 (0.667155) | 1.945547 / 1.541195 (0.404352) | 2.014043 / 1.468490 (0.545553) | 0.699732 / 4.584777 (-3.885045) | 3.420711 / 3.745712 (-0.325001) | 3.298702 / 5.269862 (-1.971159) | 1.390324 / 4.565676 (-3.175353) | 0.082668 / 0.424275 (-0.341607) | 0.012556 / 0.007607 (0.004949) | 0.550406 / 0.226044 (0.324361) | 5.501060 / 2.268929 (3.232132) | 2.659841 / 55.444624 (-52.784783) | 2.243443 / 6.876477 (-4.633034) | 2.266006 / 2.142072 (0.123934) | 0.806295 / 4.805227 (-3.998933) | 0.151399 / 6.500664 (-6.349265) | 0.067048 / 0.075469 (-0.008421) |\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) | 1.291404 / 1.841788 (-0.550384) | 14.164728 / 8.074308 (6.090419) | 13.980219 / 10.191392 (3.788827) | 0.140599 / 0.680424 (-0.539824) | 0.016880 / 0.534201 (-0.517321) | 0.379073 / 0.579283 (-0.200210) | 0.385770 / 0.434364 (-0.048594) | 0.442516 / 0.540337 (-0.097822) | 0.533569 / 1.386936 (-0.853367) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29fa15df972353f51fc434cf8eceb574b60a415f \"CML watermark\")\n",
"Tests seem to be failing for unrelated reasons.",
"Tests are failing because of a bug on the Hub side - this is being fixed :)\r\n\r\nlmk once the TF documentation page is updated and we can merge !",
"@lhoestq Docs updated!"
] | 2022-12-19T19:40:27Z
| 2023-01-25T16:28:44Z
| 2023-01-25T16:21:40Z
|
MEMBER
| null | 0
|
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Hey all! Here's a first draft of the PR to add a multiprocessing implementation for `to_tf_dataset()`. It worked in some quick testing for me, but obviously I need to do some much more rigorous testing/benchmarking, and add some proper library tests.
The core idea is that we do everything using `multiprocessing` and `numpy`, and just wrap a `tf.data.Dataset` around the output. We could also rewrite the existing single-threaded implementation based on this code, which might simplify it a bit.
Checklist:
- [X] Add initial draft
- [x] Check that it works regardless of whether the `collate_fn` or dataset returns `tf` or `np` arrays
- [x] Check that it works with `tf.string` return data
- [x] Check indices are correctly reshuffled each epoch
- [x] Make sure workers don't try to initialize a GPU device!!
- [x] Check `fit()` with multiple epochs works fine and that the progress bar is correct
- [x] Check there are no memory leaks or zombie processes
- [x] Benchmark performance
- [x] Tweak params for dataset inference - can we speed things up there a bit?
- [x] Add tests to the library
- [x] Add a PR to `transformers` to expose the `num_workers` argument via `prepare_tf_dataset` (will merge after this one is released)
- [x] Stop TF console spam!! (almost)
- [x] Add a method for creating SHM that doesn't crash if it was left and still linked
- [x] Add a barrier for Py <= 3.7 because it doesn't support SharedMemory
- [x] Support string dtypes by converting them into fixed-width character arrays
|
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MDExOlB1bGxSZXF1ZXN0NTI5ODUyMTI1
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Add NumerSense
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| 2020-12-01T20:25:50Z
| 2020-12-01T19:51:56Z
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Adds the NumerSense dataset
- Webpage/leaderboard: https://inklab.usc.edu/NumerSense/
- Paper: https://arxiv.org/abs/2005.00683
- Description: NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes. Basically, it's a benchmark to see whether your MLM can figure out the right number in a fill-in-the-blank task based on commonsense knowledge (a bird has **two** legs)
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I_kwDODunzps5sR1To
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Dataset.map() execute twice when in PyTorch DDP mode
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"Sorry for asking a duplicate question about `num_proc`, I searched the forum and find the solution.\r\n\r\nBut I still can't make the trick with `torch.distributed.barrier()` to only map at the main process work. The [post on forum]( https://discuss.huggingface.co/t/slow-processing-with-map-when-using-deepspeed-or-fairscale/7229/7) didn't help.",
"If it does the `map` twice then it means the hash of your map function is not some same between your two processes.\r\n\r\nCan you make sure your map functions have the same hash in different processes ?\r\n\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nprint(Hasher.hash(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True)))\r\nprint(Hasher.hash(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16)))\r\n```\r\n\r\nYou can also set the fingerprint used to reload the resulting dataset by passing `new_finegrprint=` in `map`, see https://huggingface.co/docs/datasets/v2.13.1/en/about_cache#the-cache. This will force the different processes to use the same fingerprint used to locate the resulting dataset in the cache.",
"Thanks for help! I find the fingerprint between processes don't have same hash:\r\n```\r\nRank 0: Gpu 0 cut_reorder_keys fingerprint c7f47f40e9a67657\r\nRank 0: Gpu 0 random_shift fingerprint 240a0ce79831e7d4\r\n\r\nRank 1: Gpu 1 cut_reorder_keys fingerprint 20edd3d9cf284001\r\nRank 1: Gpu 1 random_shift fingerprint 819f7c1c18e7733f\r\n```\r\nBut my functions only process the example one by one and don't need rank or other arguments. After all it can work in the test for dataset and dataloader.\r\nI'll try to set `new_fingerprint` to see if it works and figure out the reason of different hash."
] | 2023-07-22T05:06:43Z
| 2023-07-24T19:29:55Z
| null |
NONE
| null | null | null |
### Describe the bug
I use `torchrun --standalone --nproc_per_node=2 train.py` to start training. And write the code following the [docs](https://huggingface.co/docs/datasets/process#distributed-usage). The trick about using `torch.distributed.barrier()` to only execute map at the main process doesn't always work. When I am training model, it will map twice. When I am running a test for dataset and dataloader (just print the batches), it can work. Their code about loading dataset are same.
And on another server with 30 CPU cores, I use 2 GPUs and it can't work neither.
I have tried to use `rank` and `local_rank` to check, they all didn't make sense.
### Steps to reproduce the bug
use `torchrun --standalone --nproc_per_node=2 train.py` or `torchrun --standalone train.py` to run
This is my code:
```python
if args.distributed and world_size > 1:
if args.local_rank > 0:
print(f"Rank {args.rank}: Gpu {args.gpu} waiting for main process to perform the mapping", force=True)
torch.distributed.barrier()
print("Mapping dataset")
dataset = dataset.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True), num_proc=8, desc="cut_reorder_keys")
dataset = dataset.map(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16), num_proc=8, desc="random_shift")
dataset_test = dataset_test.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=False), num_proc=8, desc="cut_reorder_keys")
if args.local_rank == 0:
print("Mapping finished, loading results from main process")
torch.distributed.barrier()
```
### Expected behavior
Only the main process will execute `map`, while the sub process will load cache from disk.
### Environment info
server with 64 CPU cores (AMD Ryzen Threadripper PRO 5995WX 64-Cores) and 2 RTX 4090
- `python==3.9.16`
- `datasets==2.13.1`
- `torch==2.0.1+cu117`
- `22.04.1-Ubuntu`
server with 30 CPU cores (Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz) and 2 RTX 4090
- `python==3.9.0`
- `datasets==2.13.1`
- `torch==2.0.1+cu117`
- `Ubuntu 20.04`
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I_kwDODunzps4_DxxM
| 3,295
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Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk
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"Hi ! Good catch and thanks for opening a PR :)\r\n\r\nI just responded in your PR"
] | 2021-11-18T23:24:02Z
| 2021-12-06T10:45:04Z
| 2021-12-06T10:45:04Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
When trying to build a temporary dataset path from a remote URI in this block of code:
https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042
the result is not the expected when passing an absolute path in an URI like `hdfs:///absolute/path`.
## Steps to reproduce the bug
```python
dataset_path = "hdfs:///absolute/path"
src_dataset_path = extract_path_from_uri(dataset_path)
tmp_dir = get_temporary_cache_files_directory()
dataset_path = Path(tmp_dir, src_dataset_path)
print(dataset_path)
```
## Expected results
With the code above, we would expect a value in `dataset_path` similar to:
`/tmp/tmpnwxyvao5/absolute/path`
## Actual results
However, we get a `dataset_path` value like:
`/absolute/path`
This is because this line here: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1041
returns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments.
## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
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Remove unnecessary 'r' arg in
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"The CI failure is only because of the datasets is missing some sections in their cards - we can ignore that since it's unrelated to this PR"
] | 2022-02-01T17:29:27Z
| 2022-02-07T16:57:27Z
| 2022-02-07T16:02:42Z
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Originally from #3489
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Adding NewsQA dataset
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[
"Generate the dummy dataset then regenerate the dataset_info.json file, ",
"> Generate the dummy dataset then regenerate the dataset_info.json file,\r\n\r\nThe pytest scripts do not accept manual directory inputs for the data provided manually. This is why the tests fail. ",
"don't use the --auto-generate argument and you will get a brief instructions on how to create dummy data for your dataset,\r\nalso you dont have to run the pytest for main dataset if your data is needed to be downloaded manually, just run the pytest for dummy dataset, and when you will create the json you need to provide the main data directory path by using this argument --data_dir",
"Thanks for your help, @tanmoyio \r\nI tried with and without --auto_generate flag. \r\nHere are the issues. \r\n\r\n**With --auto_generate**\r\n`python datasets-cli dummy_data datasets/newsqa/ --auto_generate `\r\n`Traceback (most recent call last):\r\n File \"datasets-cli\", line 36, in <module>\r\n service.run()\r\n File \"/Users/sanjaykamath/Python_Projects/HuggingFace/datasets/src/datasets/commands/dummy_data.py\", line 321, in run\r\n keep_uncompressed=self._keep_uncompressed,\r\n File \"/Users/sanjaykamath/Python_Projects/HuggingFace/datasets/src/datasets/commands/dummy_data.py\", line 340, in _autogenerate_dummy_data\r\n dataset_builder._split_generators(dl_manager)\r\n File \"/Users/sanjaykamath/.cache/huggingface/modules/datasets_modules/datasets/newsqa/7a565b204506c1fd91047290073be54d3ae05fa2b0ab17ae0bc6f709350fcbca/newsqa.py\", line 180, in _split_generators\r\n path_to_manual_folder = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))\r\n File \"/Users/sanjaykamath/anaconda3/envs/huggingface/lib/python3.7/posixpath.py\", line 235, in expanduser\r\n path = os.fspath(path)\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n`\r\n\r\n\r\n**Without --auto_generate**\r\n`python datasets-cli dummy_data datasets/newsqa/`\r\n`Dataset with config BuilderConfig(name='combined-csv', version=1.0.0, data_dir=None, data_files=None, description='This part of the dataset covers the whole dataset in the combined format of CSV as mentioned here: https://github.com/Maluuba/newsqa#csv') seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has to be created with less guidance. Make sure you create the file None.\r\nTraceback (most recent call last):\r\n File \"datasets-cli\", line 36, in <module>\r\n service.run()\r\n File \"/Users/sanjaykamath/Python_Projects/HuggingFace/datasets/src/datasets/commands/dummy_data.py\", line 326, in run\r\n dataset_builder=dataset_builder, mock_dl_manager=mock_dl_manager\r\n File \"/Users/sanjaykamath/Python_Projects/HuggingFace/datasets/src/datasets/commands/dummy_data.py\", line 406, in _print_dummy_data_instructions\r\n for split in generator_splits:\r\nUnboundLocalError: local variable 'generator_splits' referenced before assignment\r\n`\r\n",
"Excellent comments. Thanks @lhoestq for your valuable comments. \r\nI've changed everything you had mentioned and the tests pass now. \r\nLet me know if something still needs to be changed. ",
"Thank you very much @lhoestq @tanmoyio @yjernite @thomwolf for all your support :) "
] | 2020-12-10T17:01:10Z
| 2020-12-17T18:29:03Z
| 2020-12-17T18:27:36Z
|
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Since the dataset has legal restrictions to circulate the original data. It has to be manually downloaded by the user and loaded to the library.
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008578 / 0.011353 (-0.002775) | 0.004535 / 0.011008 (-0.006473) | 0.100694 / 0.038508 (0.062186) | 0.029570 / 0.023109 (0.006460) | 0.296384 / 0.275898 (0.020486) | 0.354405 / 0.323480 (0.030925) | 0.006962 / 0.007986 (-0.001024) | 0.003405 / 0.004328 (-0.000924) | 0.077275 / 0.004250 (0.073025) | 0.036623 / 0.037052 (-0.000429) | 0.309844 / 0.258489 (0.051355) | 0.340343 / 0.293841 (0.046502) | 0.033626 / 0.128546 (-0.094920) | 0.011433 / 0.075646 (-0.064214) | 0.322659 / 0.419271 (-0.096612) | 0.040509 / 0.043533 (-0.003024) | 0.294002 / 0.255139 (0.038863) | 0.323259 / 0.283200 (0.040059) | 0.088023 / 0.141683 (-0.053660) | 1.462039 / 1.452155 (0.009885) | 1.495401 / 1.492716 (0.002684) |\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) | 0.218614 / 0.018006 (0.200608) | 0.482359 / 0.000490 (0.481869) | 0.001216 / 0.000200 (0.001016) | 0.000081 / 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) | 0.023167 / 0.037411 (-0.014245) | 0.098468 / 0.014526 (0.083942) | 0.108273 / 0.176557 (-0.068284) | 0.139991 / 0.737135 (-0.597144) | 0.109032 / 0.296338 (-0.187307) |\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) | 0.421526 / 0.215209 (0.206317) | 4.216808 / 2.077655 (2.139153) | 1.860550 / 1.504120 (0.356431) | 1.654518 / 1.541195 (0.113323) | 1.699064 / 1.468490 (0.230574) | 0.691489 / 4.584777 (-3.893287) | 3.401885 / 3.745712 (-0.343827) | 2.792860 / 5.269862 (-2.477001) | 1.516269 / 4.565676 (-3.049408) | 0.081627 / 0.424275 (-0.342648) | 0.012556 / 0.007607 (0.004949) | 0.531535 / 0.226044 (0.305491) | 5.320752 / 2.268929 (3.051823) | 2.314502 / 55.444624 (-53.130123) | 1.967118 / 6.876477 (-4.909359) | 2.008252 / 2.142072 (-0.133821) | 0.809730 / 4.805227 (-3.995497) | 0.148112 / 6.500664 (-6.352552) | 0.064821 / 0.075469 (-0.010648) |\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) | 1.269754 / 1.841788 (-0.572033) | 13.884200 / 8.074308 (5.809892) | 13.914390 / 10.191392 (3.722998) | 0.150176 / 0.680424 (-0.530248) | 0.028463 / 0.534201 (-0.505738) | 0.398723 / 0.579283 (-0.180561) | 0.400433 / 0.434364 (-0.033931) | 0.485169 / 0.540337 (-0.055169) | 0.565995 / 1.386936 (-0.820941) |\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) | 0.006479 / 0.011353 (-0.004874) | 0.004504 / 0.011008 (-0.006504) | 0.097905 / 0.038508 (0.059397) | 0.027140 / 0.023109 (0.004031) | 0.408742 / 0.275898 (0.132844) | 0.448707 / 0.323480 (0.125228) | 0.004819 / 0.007986 (-0.003166) | 0.004761 / 0.004328 (0.000433) | 0.075456 / 0.004250 (0.071205) | 0.036282 / 0.037052 (-0.000771) | 0.405961 / 0.258489 (0.147472) | 0.449411 / 0.293841 (0.155570) | 0.031159 / 0.128546 (-0.097387) | 0.011693 / 0.075646 (-0.063954) | 0.321124 / 0.419271 (-0.098147) | 0.041369 / 0.043533 (-0.002164) | 0.408070 / 0.255139 (0.152931) | 0.428704 / 0.283200 (0.145504) | 0.086839 / 0.141683 (-0.054844) | 1.477772 / 1.452155 (0.025617) | 1.555913 / 1.492716 (0.063197) |\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) | 0.239494 / 0.018006 (0.221488) | 0.410785 / 0.000490 (0.410295) | 0.000989 / 0.000200 (0.000789) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023805 / 0.037411 (-0.013607) | 0.097904 / 0.014526 (0.083378) | 0.106437 / 0.176557 (-0.070120) | 0.140555 / 0.737135 (-0.596580) | 0.107169 / 0.296338 (-0.189170) |\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) | 0.470233 / 0.215209 (0.255024) | 4.700451 / 2.077655 (2.622797) | 2.391712 / 1.504120 (0.887592) | 2.191125 / 1.541195 (0.649930) | 2.268924 / 1.468490 (0.800434) | 0.692421 / 4.584777 (-3.892356) | 3.387117 / 3.745712 (-0.358595) | 1.881731 / 5.269862 (-3.388130) | 1.155759 / 4.565676 (-3.409917) | 0.082040 / 0.424275 (-0.342236) | 0.012687 / 0.007607 (0.005080) | 0.567556 / 0.226044 (0.341511) | 5.701408 / 2.268929 (3.432480) | 2.864368 / 55.444624 (-52.580256) | 2.512073 / 6.876477 (-4.364404) | 2.546078 / 2.142072 (0.404005) | 0.795939 / 4.805227 (-4.009288) | 0.150078 / 6.500664 (-6.350586) | 0.067644 / 0.075469 (-0.007825) |\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) | 1.281681 / 1.841788 (-0.560107) | 13.967107 / 8.074308 (5.892799) | 13.293648 / 10.191392 (3.102256) | 0.128027 / 0.680424 (-0.552397) | 0.016791 / 0.534201 (-0.517410) | 0.379400 / 0.579283 (-0.199884) | 0.386847 / 0.434364 (-0.047517) | 0.469859 / 0.540337 (-0.070478) | 0.564203 / 1.386936 (-0.822733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#90832b5e33774ea8ec35ccb92ac14649a345bdbe \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008701 / 0.011353 (-0.002652) | 0.004564 / 0.011008 (-0.006444) | 0.100578 / 0.038508 (0.062070) | 0.029209 / 0.023109 (0.006100) | 0.315308 / 0.275898 (0.039410) | 0.381022 / 0.323480 (0.057542) | 0.007152 / 0.007986 (-0.000834) | 0.003511 / 0.004328 (-0.000817) | 0.078361 / 0.004250 (0.074110) | 0.035394 / 0.037052 (-0.001658) | 0.331076 / 0.258489 (0.072586) | 0.366613 / 0.293841 (0.072772) | 0.033466 / 0.128546 (-0.095080) | 0.011521 / 0.075646 (-0.064126) | 0.322178 / 0.419271 (-0.097093) | 0.040891 / 0.043533 (-0.002641) | 0.320418 / 0.255139 (0.065279) | 0.345199 / 0.283200 (0.062000) | 0.087906 / 0.141683 (-0.053777) | 1.476801 / 1.452155 (0.024646) | 1.497738 / 1.492716 (0.005022) |\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) | 0.178094 / 0.018006 (0.160087) | 0.408317 / 0.000490 (0.407827) | 0.001825 / 0.000200 (0.001625) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022402 / 0.037411 (-0.015010) | 0.097104 / 0.014526 (0.082578) | 0.105361 / 0.176557 (-0.071196) | 0.139728 / 0.737135 (-0.597407) | 0.109613 / 0.296338 (-0.186725) |\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) | 0.418245 / 0.215209 (0.203036) | 4.155655 / 2.077655 (2.078000) | 1.865892 / 1.504120 (0.361772) | 1.659003 / 1.541195 (0.117809) | 1.725649 / 1.468490 (0.257159) | 0.688733 / 4.584777 (-3.896044) | 3.323529 / 3.745712 (-0.422184) | 1.867807 / 5.269862 (-3.402054) | 1.157740 / 4.565676 (-3.407936) | 0.081947 / 0.424275 (-0.342329) | 0.012471 / 0.007607 (0.004864) | 0.529333 / 0.226044 (0.303288) | 5.284898 / 2.268929 (3.015970) | 2.321741 / 55.444624 (-53.122883) | 1.975683 / 6.876477 (-4.900794) | 2.029691 / 2.142072 (-0.112381) | 0.810212 / 4.805227 (-3.995015) | 0.148185 / 6.500664 (-6.352479) | 0.064594 / 0.075469 (-0.010875) |\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) | 1.183391 / 1.841788 (-0.658396) | 13.574760 / 8.074308 (5.500452) | 14.215015 / 10.191392 (4.023623) | 0.150776 / 0.680424 (-0.529648) | 0.029058 / 0.534201 (-0.505143) | 0.404071 / 0.579283 (-0.175212) | 0.401289 / 0.434364 (-0.033075) | 0.490946 / 0.540337 (-0.049392) | 0.582292 / 1.386936 (-0.804644) |\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) | 0.006695 / 0.011353 (-0.004658) | 0.004499 / 0.011008 (-0.006510) | 0.097633 / 0.038508 (0.059125) | 0.027606 / 0.023109 (0.004496) | 0.413191 / 0.275898 (0.137293) | 0.441896 / 0.323480 (0.118416) | 0.005703 / 0.007986 (-0.002283) | 0.004608 / 0.004328 (0.000280) | 0.074392 / 0.004250 (0.070141) | 0.037966 / 0.037052 (0.000913) | 0.410736 / 0.258489 (0.152247) | 0.448581 / 0.293841 (0.154740) | 0.031594 / 0.128546 (-0.096952) | 0.011597 / 0.075646 (-0.064049) | 0.319632 / 0.419271 (-0.099639) | 0.041189 / 0.043533 (-0.002343) | 0.407120 / 0.255139 (0.151981) | 0.433416 / 0.283200 (0.150216) | 0.089932 / 0.141683 (-0.051751) | 1.453919 / 1.452155 (0.001764) | 1.545892 / 1.492716 (0.053176) |\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) | 0.224302 / 0.018006 (0.206296) | 0.415519 / 0.000490 (0.415029) | 0.000407 / 0.000200 (0.000207) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024104 / 0.037411 (-0.013307) | 0.098202 / 0.014526 (0.083676) | 0.106416 / 0.176557 (-0.070140) | 0.141090 / 0.737135 (-0.596045) | 0.110188 / 0.296338 (-0.186150) |\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) | 0.478252 / 0.215209 (0.263043) | 4.739684 / 2.077655 (2.662029) | 2.419040 / 1.504120 (0.914920) | 2.217705 / 1.541195 (0.676510) | 2.303288 / 1.468490 (0.834798) | 0.696682 / 4.584777 (-3.888095) | 3.401962 / 3.745712 (-0.343750) | 1.886015 / 5.269862 (-3.383846) | 1.175084 / 4.565676 (-3.390592) | 0.083064 / 0.424275 (-0.341211) | 0.012613 / 0.007607 (0.005006) | 0.579105 / 0.226044 (0.353060) | 5.792119 / 2.268929 (3.523191) | 2.889778 / 55.444624 (-52.554846) | 2.537438 / 6.876477 (-4.339039) | 2.574814 / 2.142072 (0.432741) | 0.803438 / 4.805227 (-4.001789) | 0.151912 / 6.500664 (-6.348752) | 0.068291 / 0.075469 (-0.007178) |\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) | 1.286002 / 1.841788 (-0.555786) | 14.179443 / 8.074308 (6.105135) | 13.443939 / 10.191392 (3.252547) | 0.152427 / 0.680424 (-0.527996) | 0.017248 / 0.534201 (-0.516953) | 0.378734 / 0.579283 (-0.200549) | 0.382276 / 0.434364 (-0.052087) | 0.465323 / 0.540337 (-0.075014) | 0.556454 / 1.386936 (-0.830482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b5672a956d5de864e6f5550e493527d962d6ae55 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008675 / 0.011353 (-0.002678) | 0.004537 / 0.011008 (-0.006471) | 0.100179 / 0.038508 (0.061671) | 0.029307 / 0.023109 (0.006198) | 0.294687 / 0.275898 (0.018789) | 0.356868 / 0.323480 (0.033388) | 0.006992 / 0.007986 (-0.000994) | 0.003380 / 0.004328 (-0.000949) | 0.076961 / 0.004250 (0.072710) | 0.036047 / 0.037052 (-0.001005) | 0.308037 / 0.258489 (0.049548) | 0.341089 / 0.293841 (0.047248) | 0.033416 / 0.128546 (-0.095131) | 0.011534 / 0.075646 (-0.064112) | 0.322976 / 0.419271 (-0.096296) | 0.040894 / 0.043533 (-0.002639) | 0.296501 / 0.255139 (0.041362) | 0.324605 / 0.283200 (0.041405) | 0.086713 / 0.141683 (-0.054970) | 1.502784 / 1.452155 (0.050630) | 1.535013 / 1.492716 (0.042297) |\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) | 0.186647 / 0.018006 (0.168641) | 0.411003 / 0.000490 (0.410514) | 0.003594 / 0.000200 (0.003394) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023704 / 0.037411 (-0.013707) | 0.096154 / 0.014526 (0.081629) | 0.103671 / 0.176557 (-0.072885) | 0.138878 / 0.737135 (-0.598258) | 0.106947 / 0.296338 (-0.189391) |\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) | 0.417180 / 0.215209 (0.201970) | 4.149579 / 2.077655 (2.071925) | 1.865763 / 1.504120 (0.361643) | 1.669722 / 1.541195 (0.128527) | 1.722345 / 1.468490 (0.253855) | 0.695910 / 4.584777 (-3.888867) | 3.342266 / 3.745712 (-0.403446) | 1.884568 / 5.269862 (-3.385294) | 1.265013 / 4.565676 (-3.300664) | 0.081836 / 0.424275 (-0.342439) | 0.012371 / 0.007607 (0.004764) | 0.522997 / 0.226044 (0.296953) | 5.225434 / 2.268929 (2.956506) | 2.304701 / 55.444624 (-53.139924) | 1.949067 / 6.876477 (-4.927410) | 2.016347 / 2.142072 (-0.125725) | 0.809850 / 4.805227 (-3.995377) | 0.148396 / 6.500664 (-6.352268) | 0.063340 / 0.075469 (-0.012129) |\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) | 1.224621 / 1.841788 (-0.617167) | 13.814223 / 8.074308 (5.739915) | 13.879728 / 10.191392 (3.688336) | 0.149530 / 0.680424 (-0.530894) | 0.028439 / 0.534201 (-0.505762) | 0.392726 / 0.579283 (-0.186557) | 0.396894 / 0.434364 (-0.037469) | 0.474395 / 0.540337 (-0.065943) | 0.569090 / 1.386936 (-0.817847) |\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) | 0.006483 / 0.011353 (-0.004870) | 0.004527 / 0.011008 (-0.006481) | 0.098038 / 0.038508 (0.059530) | 0.027239 / 0.023109 (0.004130) | 0.441773 / 0.275898 (0.165875) | 0.471448 / 0.323480 (0.147968) | 0.005034 / 0.007986 (-0.002951) | 0.004732 / 0.004328 (0.000403) | 0.075036 / 0.004250 (0.070785) | 0.036711 / 0.037052 (-0.000341) | 0.442634 / 0.258489 (0.184145) | 0.476479 / 0.293841 (0.182638) | 0.031303 / 0.128546 (-0.097243) | 0.011642 / 0.075646 (-0.064005) | 0.320750 / 0.419271 (-0.098521) | 0.048698 / 0.043533 (0.005165) | 0.441205 / 0.255139 (0.186066) | 0.464845 / 0.283200 (0.181645) | 0.092716 / 0.141683 (-0.048967) | 1.510028 / 1.452155 (0.057874) | 1.574065 / 1.492716 (0.081349) |\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) | 0.220756 / 0.018006 (0.202750) | 0.393971 / 0.000490 (0.393482) | 0.002506 / 0.000200 (0.002306) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024455 / 0.037411 (-0.012956) | 0.100164 / 0.014526 (0.085638) | 0.108053 / 0.176557 (-0.068504) | 0.142973 / 0.737135 (-0.594163) | 0.110108 / 0.296338 (-0.186231) |\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) | 0.473639 / 0.215209 (0.258430) | 4.737521 / 2.077655 (2.659866) | 2.466208 / 1.504120 (0.962088) | 2.272608 / 1.541195 (0.731413) | 2.349255 / 1.468490 (0.880764) | 0.699928 / 4.584777 (-3.884849) | 3.348443 / 3.745712 (-0.397269) | 2.604611 / 5.269862 (-2.665250) | 1.543080 / 4.565676 (-3.022597) | 0.082627 / 0.424275 (-0.341648) | 0.012251 / 0.007607 (0.004644) | 0.569949 / 0.226044 (0.343905) | 5.732316 / 2.268929 (3.463388) | 2.913541 / 55.444624 (-52.531084) | 2.560584 / 6.876477 (-4.315892) | 2.615192 / 2.142072 (0.473120) | 0.803822 / 4.805227 (-4.001406) | 0.150821 / 6.500664 (-6.349843) | 0.067128 / 0.075469 (-0.008341) |\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) | 1.272278 / 1.841788 (-0.569510) | 13.783339 / 8.074308 (5.709030) | 13.243601 / 10.191392 (3.052209) | 0.136421 / 0.680424 (-0.544003) | 0.016565 / 0.534201 (-0.517636) | 0.381102 / 0.579283 (-0.198181) | 0.386166 / 0.434364 (-0.048197) | 0.474249 / 0.540337 (-0.066089) | 0.566826 / 1.386936 (-0.820110) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b5672a956d5de864e6f5550e493527d962d6ae55 \"CML watermark\")\n"
] | 2023-01-26T19:29:42Z
| 2023-01-26T19:40:44Z
| 2023-01-26T19:33:00Z
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| 409
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train_test_split error: 'dict' object has no attribute 'deepcopy'
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[
"It was fixed in 2ddd18d139d3047c9c3abe96e1e7d05bb360132c.\r\nCould you pull the latest changes from master @morganmcg1 ?",
"Thanks @lhoestq, works fine now!"
] | 2020-07-17T10:36:28Z
| 2020-07-21T14:34:52Z
| 2020-07-21T14:34:52Z
|
NONE
| null | null | null |
`train_test_split` is giving me an error when I try and call it:
`'dict' object has no attribute 'deepcopy'`
## To reproduce
```
dataset = load_dataset('glue', 'mrpc', split='train')
dataset = dataset.train_test_split(test_size=0.2)
```
## Full Stacktrace
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-feb740dbec9a> in <module>
1 dataset = load_dataset('glue', 'mrpc', split='train')
----> 2 dataset = dataset.train_test_split(test_size=0.2)
~/anaconda3/envs/fastai2_me/lib/python3.7/site-packages/nlp/arrow_dataset.py in train_test_split(self, test_size, train_size, shuffle, seed, generator, keep_in_memory, load_from_cache_file, train_cache_file_name, test_cache_file_name, writer_batch_size)
1032 "writer_batch_size": writer_batch_size,
1033 }
-> 1034 train_kwargs = cache_kwargs.deepcopy()
1035 train_kwargs["split"] = "train"
1036 test_kwargs = cache_kwargs.deepcopy()
AttributeError: 'dict' object has no attribute 'deepcopy'
```
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TIMIT won't load after manual download: Errors about files that don't exist
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[
"To have some context, please see:\r\n- #4145\r\n\r\nPlease, also note that we have recently made some fixes to the script, which are in our GitHub master branch but not yet released:\r\n- #4422\r\n- #4425 \r\n- #4436",
"Thanks Albert! I'll try pulling `datasets` from the git repo instead of PyPI, and/or just wait for the next release.\r\n",
"I'm closing this issue then. Please, feel free to reopen it again if the problem persists."
] | 2022-06-02T16:35:56Z
| 2022-06-03T08:44:17Z
| 2022-06-03T08:44:16Z
|
NONE
| null | null | null |
## Describe the bug
I get the message from HuggingFace that it must be downloaded manually. From the URL provided in the message, I got to UPenn page for manual download. (UPenn apparently want $250? for the dataset??) ...So, ok, I obtained a copy from a friend and also a smaller version from Kaggle. But in both cases the HF dataloader fails; it is looking for files that don't exist anywhere in the dataset: it is looking for files with lower-case letters like "**test*" (all the filenames in both my copies are uppercase) and certain file extensions that exclude the .DOC which is provided in TIMIT:
## Steps to reproduce the bug
```python
data = load_dataset('timit_asr', 'clean')['train']
```
## Expected results
The dataset should load with no errors.
## Actual results
This error message:
```
File "/home/ubuntu/envs/data2vec/lib/python3.9/site-packages/datasets/data_files.py", line 201, in resolve_patterns_locally_or_by_urls
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to resolve any data file that matches '['**test*', '**eval*']' at /home/ubuntu/datasets/timit with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
But this is a strange sort of error: why is it looking for lower-case file names when all the TIMIT dataset filenames are uppercase? Why does it exclude .DOC files when the only parts of the TIMIT data set with "TEST" in them have ".DOC" extensions? ...I wonder, how was anyone able to get this to work in the first place?
The files in the dataset look like the following:
```
³ PHONCODE.DOC
³ PROMPTS.TXT
³ SPKRINFO.TXT
³ SPKRSENT.TXT
³ TESTSET.DOC
```
...so why are these being excluded by the dataset loader?
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-1060-aws-x86_64-with-glibc2.27
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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I_kwDODunzps5S_g-6
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|
Restore saved format state in `load_from_disk`
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[
"Hi, can I work on this?",
"Hi, sure! Let us know if you need some pointers/help."
] | 2022-09-30T12:40:07Z
| 2022-10-11T16:49:24Z
| 2022-10-11T16:49:24Z
|
CONTRIBUTOR
| null | null | null |
Even though we save the `format` state in `save_to_disk`, we don't restore it in `load_from_disk`. We should fix that.
Reported here: https://discuss.huggingface.co/t/save-to-disk-loses-formatting-information/23815
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|
dataset viewer does not work anymore
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[
"Thanks for reporting :) We're looking into it",
"Back up. "
] | 2021-03-26T13:22:13Z
| 2021-03-26T15:52:22Z
| 2021-03-26T15:52:22Z
|
NONE
| null | null | null |
Hi
I normally use this link to see all datasets and how I can load them
https://huggingface.co/datasets/viewer/
Now I am getting
502 Bad Gateway
nginx/1.18.0 (Ubuntu)
could you bring this webpage back ? this was very helpful @lhoestq
thanks for your help
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PR_kwDODunzps4weGWl
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Add desc parameter to filter
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Fix #3317
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PR_kwDODunzps5DASLI
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Release: 2.7.0
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Align remove columns behavior and input dict mutation in `map` with previous behavior
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Align the `remove_columns` behavior and input dict mutation in `map` with the behavior before https://github.com/huggingface/datasets/pull/5252.
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Streaming for the Pandas loader
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It was not using open in the builder. Therefore pd.read_pickle could fail when streaming from a private repo for example.
Indeed, when streaming, open is extended to support reading from remote files and handles authentication to the HF Hub
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MDExOlB1bGxSZXF1ZXN0NDc5MTgyNDU3
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Add CLUE Benchmark (11 datasets)
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[
"Thanks, @lhoestq! I've addressed the comments. \r\nAlso, I have tried to use `ClassLabel` [when possible](https://github.com/huggingface/nlp/pull/572/files#diff-1026ac7d7b78bf029cb0ebe63162c77dR297). Is there still somewhere else we can use `ClassLabel`? ",
"I believe CI failure is unrelated.",
"Great job! "
] | 2020-09-04T01:57:40Z
| 2020-09-07T09:59:11Z
| 2020-09-07T09:59:10Z
|
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Add 11 tasks of [CLUE](https://github.com/CLUEbenchmark/CLUE).
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PR_kwDODunzps49sRtv
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Fix language tags resource file
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4882). All of your documentation changes will be reflected on that endpoint."
] | 2022-08-24T06:06:01Z
| 2022-08-24T13:58:33Z
| 2022-08-24T13:58:30Z
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This PR fixes/updates/adds ALL language tags from IANA (as of 2022-08-08).
This PR also removes all BCP47 suffixes (the languages file only contains language subtags, i.e. ISO 639 1 or 2 codes; no script/region/variant suffixes). See:
- #4753
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Add OpenPI Dataset
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"Hi @Bharat123rox ! It looks like some of the dummy data is broken or missing. Did you auto-generate it? Does the local test pass for you?",
"@yjernite requesting you to have a look as to why the tests are failing only on Windows, there seems to be a backslash error somewhere, could it be the result of `os.path.join` and what should be the fix for this?",
"This is the `black` output locally:\r\n```\r\n(datasets_env) datasets (openpi) > black --check --line-length 119 --target-version py36 datasets/openpi/\r\nAll done! ✨ 🍰 ✨\r\n1 file would be left unchanged.\r\n```",
"Can you check your version of black (should be `20.8b1`) and run `make style again`? (And don't forget to rebase before pushing ;) )\r\n\r\nThe other test was a time-out error so should be good on the next commit",
"Thanks @yjernite the CI tests finally passed!!",
"Hi @Bharat123rox did you manage to join the different config into one using the IDs ?\r\n\r\nFeel free to ping me when you're ready for the next review :) ",
"> Hi @Bharat123rox did you manage to join the different config into one using the IDs ?\n> \n> Feel free to ping me when you're ready for the next review :) \n\nNot yet @lhoestq still working on this! Meanwhile please review #1507 where I added the SelQA dataset :)",
"Ok ! Let me review SelQA then :) \r\nThanks for your help !",
"Apologies for the very late response. Here is the openpi dataset file with a single file per partition after merging `id_answers, answers.jsonl, question.jsonl , question_metadata.jsonl`\r\n\r\nhttps://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl",
"Nice thank you @nikett !",
"Hi @Bharat123rox , when you get a chance, please feel free to use the dataset from the repo ( [Link](https://github.com/allenai/openpi-dataset/blob/main/data/gold-v1.1/dev.jsonl) ) . Please let me know if any file is missing! Thank you ",
"Hi @Bharat123rox are you working on this? ",
"@nikett Sorry I'm no longer working on this as I'm out of time for it, please feel free to raise a new PR for this\r\n\r\n",
"We are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest to create this dataset there. Please, feel free to tell us if you need some help."
] | 2020-12-05T20:54:06Z
| 2022-10-03T09:39:54Z
| 2022-10-03T09:39:54Z
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Add the OpenPI Dataset by AI2 (AllenAI)
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MDExOlB1bGxSZXF1ZXN0NDgwNzM3NDQ1
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Fix the code block in doc
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| 2020-09-07T07:37:32Z
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500 internal server error when trying to open a dataset composed of Zarr stores
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"Hi @jacobbieker, thanks for reporting!\r\n\r\nI have transferred this issue to our Hub team and they are investigating it. I keep you informed. ",
"Hi @jacobbieker, we are investigating this issue on our side and we'll see if we can fix it, but please note that your repo is considered problematic for git. Here are the results of running https://github.com/github/git-sizer on it:\r\n\r\n```\r\nProcessing blobs: 147448 \r\nProcessing trees: 27 \r\nProcessing commits: 4 \r\nMatching commits to trees: 4 \r\nProcessing annotated tags: 0 \r\nProcessing references: 3 \r\n| Name | Value | Level of concern |\r\n| ---------------------------- | --------- | ------------------------------ |\r\n| Biggest objects | | |\r\n| * Trees | | |\r\n| * Maximum entries [1] | 167 k | !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! |\r\n| | | |\r\n| Biggest checkouts | | |\r\n| * Number of files [2] | 189 k | *** |\r\n\r\n[1] aa057d2667c34c70c6146efc631f5c9917ff326e (refs/heads/main:2016.zarr/unknown)\r\n[2] 6897b7bf6440fdd16b2c39d08085a669e7eaa59d (refs/heads/main^{tree})\r\n```\r\n\r\nYou can check https://github.com/github/git-sizer for more information on how to avoid such pathological structures.",
"Hi, thanks for getting back to me so quick! And yeah, I figured that was probably the problem. I was going to try to delete the repo, but couldn't through the website, so if that's the easiest way to solve it, I can regenerate the dataset in a different format with less tiny files, and you guys can delete the repo as it is. Zarr just saves everything as lots of small files to make chunks easy to load, which is why I was preferring that format, but maybne that just doesn't work well for HF datasets.",
"Hi @jacobbieker,\r\n\r\nFor future use cases, our Hub team is still pondering whether to limit the maximum number of files per repo to avoid technical issues...\r\n\r\nOn the meantime, they have made a fix and your dataset is working: https://huggingface.co/datasets/openclimatefix/mrms"
] | 2022-03-04T10:37:14Z
| 2022-03-08T09:47:39Z
| 2022-03-08T09:47:39Z
|
NONE
| null | null | null |
## Describe the bug
The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code.
The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine.
In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format?
For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("openclimatefix/mrms")
```
## Expected results
The dataset should be downloaded or open up
## Actual results
A 500 internal server error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35
- Python version: 3.9.10
- PyArrow version: 7.0.0
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Add installation instructions to image_process doc
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-03-31T15:29:37Z
| 2022-03-31T17:05:46Z
| 2022-03-31T17:00:19Z
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This PR adds the installation instructions for the Image feature to the image process doc.
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Search qa
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"Could you rebase from master just to make sure we won't break anything for `fever` pls @mariamabarham ?"
] | 2020-07-06T12:23:16Z
| 2020-07-16T08:58:16Z
| 2020-07-16T08:58:16Z
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This PR adds the Search QA dataset used in **SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine**. The dataset has the following config name:
- raw_jeopardy: raw data
- train_test_val: which is the splitted version
#336
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Language names and language codes: connecting to a big database (rather than slow enrichment of custom list)
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"Thanks for opening this discussion, @alexis-michaud.\r\n\r\nAs the language validation procedure is shared with other Hugging Face projects, I'm tagging them as well.\r\n\r\nCC: @huggingface/moon-landing ",
"on the Hub side, there is not fine grained validation we just check that `language:` contains an array of lowercase strings between 2 and 3 characters long =)\r\n\r\nand for `language_bcp47:` we just check it's an array of strings.\r\n\r\nThe only page where we have a hardcoded list of languages is https://huggingface.co/languages and I've been thinking of hooking that page on an external database of languages (so any suggestion is super interesting), but it's not used for validation.\r\n\r\nThat being said, in `datasets` this file https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json is not really used no? Or just in the tagging tool? What about just removing it?\r\n\r\nalso cc'ing @lbourdois who's been active and helpful on those subjects in the past!",
"PS @alexis-michaud is there a DB of language codes you would recommend? That would contain all `ISO 639-1, 639-2 or 639-3 codes` and be kept up to date, and ideally that would be accessible as a Node.js npm package?\r\n\r\ncc @albertvillanova too",
"> PS @alexis-michaud is there a DB of language codes you would recommend? That would contain all `ISO 639-1, 639-2 or 639-3 codes` and be kept up to date, and ideally that would be accessible as a Node.js npm package?\r\n> \r\n> cc @albertvillanova too\r\n\r\nMany thanks for your answer! \r\n\r\nThe Glottolog database is kept up to date, and has information on the closest ISO code for each Glottocode. So providing a clean table with equivalences sounds (to me) like something perfectly reasonable to expect from their team. \r\nTo what extent would [pyglottolog](https://github.com/glottolog/pyglottolog) fit the bill / do the job? (API documentation [here](https://pyglottolog.readthedocs.io/en/latest/index.html)) I'm reaching my technical limitations here: I can't assess the distance between what they offer and what the HF team needs. \r\nI have opened an Issue in [their repo](https://github.com/glottolog/glottolog-cldf/issues/13). \r\n\r\nVery interested to see where it goes from there.",
"I just tried pyglottolog to generate a file with all the current IDs (first column).\r\n\r\n`glottolog languoids` inside the `glottolog` repository.\r\n\r\n[glottolog-languoids-v4.6-10-g5c66eec874.csv](https://github.com/huggingface/datasets/files/9417456/glottolog-languoids-v4.6-10-g5c66eec874.csv)\r\n\r\n",
"Greetings @alexis-michaud and others,\r\nI think perhaps a standards-based approach here would help everyone out both at the technical and social layers of technical innovations. \r\n\r\nLet me say a few things: \r\n1. there are multiple kinds of assets in AI that should have associated language codes. \r\n * AI Training Data sets\r\n * AI models\r\n * AI outputs\r\nThese are all distinct components which should be tagged for the language and encoding methods they operate on or enhance. For example, an AI based cross-language tool from French to English (UK variety) still needs to consider if it is operating on oral language speech or written text. This is where [IANA language sub-tags](https://www.iana.org/assignments/language-subtag-registry/language-subtag-registry) come in and are so important. I link to the official source. If one wants to use middleware such as a python package or npm package to manage strings then please make sure those packages are updating codes as they are being revised. I see that @julien-c mentioned BCP-47. BCP-47 is the current standard for language tagging. Following it will make the resources you create more findable and let future users better understand or expect any biases which may have been introduced in the different AI based products.\r\n2. BCP-47 is a technical read. However, you will notice that it identifies when to use an ISO 639-1, ISO 639-2, or ISO 639-3. code. This is important for interoperability with many systems. If you are using library systems then you should likely just stick with ISO 639-3 codes.\r\n3. If you are going to use Glottolog codes use them after an `-x-` tag in the BCP-47 format to maintain BCP-47 validity. \r\n4. You should source ISO 639-3 codes directly from the [ISO 639-3 registrar](https://iso639-3.sil.org/code_tables/639/data) as these codes are updated annually, usually in February or March. ISO 639-3 codes have multiple classes: `Active`, `Deprecated`, and `Unassigned`. This means that string length checking is not a sufficient strategy for validation.\r\n5. The names of smaller languages often change depending on the language used to describe them. The [ISO639-2 documentation](https://www.loc.gov/standards/iso639-2/php/code_list.php) has a list of language names for languages with smaller populations for languages in which descriptions about these languages are often written. For example, ISO 639-2's documentation contains the names of languages as they are used in French, German, and English. ISO 639-2 rarely is updated as it is now tied to ISO 639-3's evolution and modern systems should just use ISO 639-3, but these additional names of languages in other languages may not appear in the ISO 369-3 tables.\r\n6. Glottolog codes are also updated at least annually. Usually sometime after ISO 639-3 updates.\r\n7. Please, if the material is in a written mode, please indicate which script is used unless the IANA field has a `suppress script` value. Please use the script tag that BCP-47 calls for from [ISO 15924](https://unicode.org/iso15924/iso15924-codes.html). This also updates at least annually. \r\n8. Another great place to look for language names is the [Unicode CLDR database for locales](https://cldr.unicode.org/translation/displaynames/languagelocale-names). These ought to be congruent with ISO 639-3 but, sometimes CLDR has additional references to languages (such as the french name for a language) which is not contained in ISO 639-2 or ISO 639-3.\r\n9. Wikidata for language names is not always a great source of authoritative information. Language names are asymmetrical. Many times they are contrived because there is no actual name for the language in the language referring... e.g. French doesn't have a name for every language in the world, often they say something like: the language of 'x' people. — English does the same. When a language name standard does not have the best name for a language the best way to handle that is to make a change request with the standards registrar. Keeping track of the source list and the version of your source list for your language codes is very important. \r\n10. Finally, It would be a great service to technologist, minority language communities, and linguists if for all resources of the three types mentioned in number 1 above you added a record to [OLAC](http://www.language-archives.org/). — I can help you with that. OLAC is a search interface for language resources.\r\n",
"Hi everybody!\r\n\r\nAbout the point:\r\n> also cc'ing @lbourdois who's been active and helpful on those subjects in the past!\r\n\r\nDiscussions on the need to improve the Hub's tagging system (applying to both datasets and models) can be found in the following discussion: https://github.com/huggingface/hub-docs/issues/193\r\nOnce this system has been redone and satisfies the identified needs, a redesign of the [Languages page](https://huggingface.co/languages) would also be relevant: https://github.com/huggingface/hub-docs/issues/194. \r\nI invite you to read them. But as a quick summary, the exchanges were oriented towards the ISO standard (the first HF system was based on it and it is generally the standard indicated in AI/DL papers) by favouring ISO 639-1 if it exists, and fallback to ISO 639-2 or ISO 639-3 if it doesn't. In addition, it is possible to add BCP-47 tags to consider existing varieties/regionalisms within a language (https://huggingface.co/datasets/AmazonScience/massive/discussions/1). If a language does not belong to either of these two standards, then a request should be made to the HF team to add it manually.\r\n\r\n\r\nTo return to the present discussion, thank you for the various databases and methodologies you mention. It makes a big difference to have linguists in the loop 🚀.\r\n\r\nI have a couple of questions where I think an expert perspective would be appreciated:\r\n- Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\nFor example (I'm taking the case of Hebrew but this has happened for other languages) I tagged Google models with the \"iw\" [tag](https://huggingface.co/models?language=iw&sort=downloads) because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\n\r\n- When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\n- On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone \r\nIs there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\nBased on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\n- Are there any databases that take into account all the existing sign languages in the world?\r\nIt would be nice to have them included on the Hub.\r\n\r\n- Is there an international classification of languages?\r\nA bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later. \r\n\r\n- Finally for the CNRS team, when can we expect to see all the datasets of [Pangloss](https://pangloss.cnrs.fr/) on HF? 👀 And I don't know if you have a way to help to add also the datasets of [CoCoON](https://cocoon.huma-num.fr/exist/crdo/).",
"> I invite you to read them. But as a quick summary, the exchanges were oriented towards the ISO standard (the first HF system was based on it and it is generally the standard indicated in AI/DL papers) by favouring ISO 639-1 if it exists, and fallback to ISO 639-2 or ISO 639-3 if it doesn't. In addition, it is possible to add BCP-47 tags to consider existing varieties/regionalisms within a language (https://huggingface.co/datasets/AmazonScience/massive/discussions/1). If a language does not belong to either of these two standards, then a request should be made to the HF team to add it manually.\r\n\r\nOne comment on this fall back system (which generally follows the BCP-47 process). ISO 639-2 has some codes which refer to a language ambiguously. For example, I believe code `ara` is used for arabic. In some contexts arabic is considered a single language, however, Egyptian Arabic is quite different from Moroccan Arabic, which are both considered separate languages. These ambiguous codes are valid ISO 639-3 codes but they have a special status. They are called `macro codes`. They exist inside the ISO 639-3 standard to provide absolute fallback compatibility between ISO 639-2 and ISO 639-3. However, when considering AI and MT applications with language data, the unforeseen potential applications and the potential for bias using macro codes should be avoided for new applications of language tags to resources. For historical cases where it is not clear what resources were used to create the AI tools or datasets then I understand the use of ambiguous tag uses. So for clarity in language tagging I suggest:\r\n\r\n1. Strictly following BCP-47\r\n2. Whenever possible avoid the use of macro tags in the ISO 639-3 standard. These are BCP-47 valid, but could introduce biases in the application of their use in society. (Generally there are more specific tags available to use in the ISO 639-3 standard.)",
"> * Are there any databases that take into account all the existing sign languages in the world?\r\n> It would be nice to have them included on the Hub.\r\n\r\nSign Languages present an interesting case. As I understand the situation. The identification of sign languages has been identified as a component of their endangerment. Some sign languages do exist in ISO 639-3. For further discussion on the issue I refer readers to the following publications: \r\n\r\n* https://doi.org/10.3390/languages7010049\r\n* https://www.academia.edu/35870983/The_ethics_of_of_language_identification_and_ISO_639\r\n\r\nOne way to be BCP-47 compliant and identify a sign language which is not identified in any of the BCP-47 referenced standards is to use the ISO 639-3 code for undetermined language `und` and then apply a custom suffix indicator (as explained in BCP-47) `-x-` and a custom code, such as the ones used in https://doi.org/10.3390/languages7010049",
"> * Is there an international classification of languages?\r\n> A bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later.\r\n\r\nYes that would be the function of ISO 639-3. It is the reference standard for languages. It includes a code and its name and the status of the code. Many technical metadata standards for file and computer interoperability reference it, many technical library metadata standards reference it. Some linguists use it. Many governments reference it. \r\n\r\nIndexing diseases are different from indexing languages in several ways, one way is that diseases are the impact of a pathogen not the pathogen itself. If we take COVID-19 as an example, there are many varieties of the pathogen but broadly speaking there is only one disease — with many symptoms.\r\n\r\n",
">* When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\nWhile these do appear on wikipedia, I don't know of any information system which uses these codes. I do know that glottolog did import ELP data at one time and its database does contain ELP data I'm not sure if Glottolog regularly ingests new versions of ELP data. I suspect that the use of Linguasphere data may be relevant to users of wikidata as a linked data attribute but I haven't heard of any linked data projects using Linguasphere data for analysis or product development. My impression is that it is fairly unused.",
"> * Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\n>For example (I'm taking the case of Hebrew but this has happened for other languages) I [tag](https://huggingface.co/models?language=iw&sort=downloads)ged Google models with the \"iw\" tag because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\n\r\nYes. You can parse the IANA file linked to above (it is regularly updated). All deprecated tags are marked as such in that file. The new prefered tag if there is one, is indicated. ISO 639-3 also indicates a code's status but their list is relevant only codes within their domain (ISO 639-3).",
"> * On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\nIs there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n\r\nI would interpret `en-fr` as english as spoken in France. `fr`in this position refers to the geo-political entity not a second language. I see no reason that other linguists should have a different option after having read BCP-47 and understood how it works.\r\n\r\nThe functional goal here is to tag a language resource as being produced by nonnative speakers, while tagging both languages. There are several problems here. The first is that BCP-47 has no way explicit way to do this. One could use the sub code `x-` with a private use code to indicate a second language and infer some meaning as to that language's role. However, there is another problem here which complexifies the situation greatly... how do we know that those english speakers (in France, or from France, or who were native French speakers) were not speaking their third or fourth language rather than their second language. So to conceptualize a sub-tag which indicates the first language of a speech act for speakers in a second (or other) language would need to be carefully crafted. It might then be proposed to the appropriate authorities. For example three sub-tags exist.\r\n\r\nThere are three registered sub-tags out of a BCP-47 allowed 35. These are `x-`, `u-`, and `t-`. `u-` and `t-` are defined in [RFC6067 ](https://www.rfc-editor.org/rfc/rfc6067)and [RFC6497](https://www.rfc-editor.org/rfc/rfc6497) . For more information see the [Unicode CLDR documentation](https://cldr.unicode.org/index/bcp47-extension) where it says: \r\n\r\n\r\n>[IETF BCP 47 ](http://www.google.com/url?q=http%3A%2F%2Ftools.ietf.org%2Fhtml%2Fbcp47&sa=D&sntz=1&usg=AOvVaw1DoMN1IBGg-JHgECBvdW1t)[Tags for Identifying Languages](http://www.google.com/url?q=http%3A%2F%2Ftools.ietf.org%2Fhtml%2Fbcp47&sa=D&sntz=1&usg=AOvVaw1DoMN1IBGg-JHgECBvdW1t) defines the language identifiers (tags) used on the Internet and in many standards. It has an extension mechanism that allows additional information to be included. The Unicode Consortium is the maintainer of the extension ‘u’ for Locale Extensions, as described in [rfc6067](https://www.google.com/url?q=https%3A%2F%2Ftools.ietf.org%2Fhtml%2Frfc6067&sa=D&sntz=1&usg=AOvVaw0gGWi0EjHfy1WId8k8oKAi), and the extension 't' for Transformed Content, as described in [rfc6497](https://www.google.com/url?q=https%3A%2F%2Ftools.ietf.org%2Fhtml%2Frfc6497&sa=D&sntz=1&usg=AOvVaw0w-OUsFX1PtaKYIq31P64I).\r\n>\r\n>The subtags available for use in the 'u' extension provide language tag extensions that provide for additional information needed for identifying locales. The 'u' subtags consist of a set of keys and associated values (types). For example, a locale identifier for British English with numeric collation has the following form: en-GB-u-kn-true\r\n>\r\n>The subtags available for use in the 't' extension provide language tag extensions that provide for additional information needed for identifying transformed content, or a request to transform content in a certain way. For example, the language tag \"ja-Kana-t-it\" can be used as a content tag indicates Japanese Katakana transformed from Italian. It can also be used as a request for a given transformation.\r\n>\r\n>For more details on the valid subtags for these extensions, their syntax, and their meanings, see LDML Section 3.7 [Unicode BCP 47 Extension Data](http://www.google.com/url?q=http%3A%2F%2Fwww.unicode.org%2Freports%2Ftr35%2F%23Locale_Extension_Key_and_Type_Data&sa=D&sntz=1&usg=AOvVaw0lMthb9KbTJtoOd5mvv3Ha).",
"Hi @lbourdois ! Many thanks for the detailed information.\r\n\r\n> Discussions on the need to improve the Hub's tagging system (applying to both datasets and models) can be found in the following discussion: [huggingface/hub-docs#193](https://github.com/huggingface/hub-docs/issues/193) \r\nFascinating topic! To me, the following suggestion has a lot of appeal:\r\n\"if consider that it was necessary to create an ISO 639-3 because ISO 639-1 was deficient, it would be to do the reverse and thus convert the tags from ISO 639-1 to ISO 639-2 or 3 (https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes or https://iso639-3.sil.org/code_tables/639/data).\"\r\n\r\nYes, ISO 639-1 is unsuitable because it has so few codes: less than 200. To address linguistic diversity in 'unrestricted mode', a list of all languages is wanted. \r\n\r\nThe idea of letting people use their favourite nomenclature and automatically adding the ISO 639-3 three-letter code as a tag is appealing. Thus all the HF datasets would have three-letter language tags (handy for basic search), alongside the authors' preferred tags and language names (including Glottolog tags as well as ISO 639-{1, 2}, and all other options allowed by BCP-47). \r\n\r\nRetaining the authors' original tags and language names would be best. \r\n* For language names: some people favour one name over another and it is important to respect their choice. In the case of Yongning Na: alternative names include 'Mosuo', 'Narua', 'Eastern Naxi'... and the names carry implications: people have been reported to come to blows about the use of the term 'Mosuo'. \r\n* For language tags: Glottocodes can be more fine-grained than Ethnologue (ISO 639-3), and some colleagues feel strongly about those. \r\n\r\nThus there would be a BCP-47 tag (sounds like a solid technical choice, though not 'passer-by-friendly': requiring some expertise to interpret) **plus** an ISO 639-3 tag that could be grabbed easily, and (last but not least) language names spelled out in full. Searches would be easier. No information would be lost. \r\n\r\nAre industry practices so conservative that many people are happy with two-letter codes, and consider ISO 639-3 three-letter codes an unnecessary complication? That would be a pity, since there are so many advantages to using longer lists. (Somewhat like the transition to Unicode: sooo much better!) But maybe that conservative attitude _is_ widespread, and it would then need to be taken into account. In which case, one could consider offering two-letter codes as a search option. Internally, the search engine would look up the corresponding 3-letter codes, and produce the search results accordingly. \r\n\r\nNow to the other questions:\r\n\r\n> * Do you think it's possible to easily handle tags that have been deprecated potentially for decades?\r\n> For example (I'm taking the case of Hebrew but this has happened for other languages) I tagged Google models with the \"iw\" [tag](https://huggingface.co/models?language=iw&sort=downloads) because I based it on what the authors gave in their [paper](https://arxiv.org/pdf/2010.11934.pdf) see table 6 page 12). It turns out that this ISO tag has in fact been deprecated since 1989 in favour of the \"he\" tag. It would therefore be necessary to have a verification that transforms the old tags into the most recent ones.\r\nI guess that the above suggestion takes care of this case. The original tag (in this example, \"iw\") is retained (facilitating cross-reference with the published paper, and respecting the real: the way the dataset was originally tagged). This old tag goes into the `BCP-47` field of the dataset, which can handle quirks & oddities like this one. And a new tag is added in the `ISO 639-3` field: the 3-letter code \"heb\". \r\n\r\n> * When you look up a language on Wikipedia, it usually shows, in addition to the ISO standard, the codes in the Glottolog (which you have already mentioned), [ELP](https://www.endangeredlanguages.com/?hl=en) and [Linguasphere](http://www.linguasphere.info/jr/index.php?l1=home&l2=welcome) databases. Would you have any opinion about these two other databases?\r\n\r\nI'm afraid I never heard about Linguasphere. The [online register for Linguasphere (PDF)](http://www.linguasphere.info/jr/pdf/index/LS_index_n-n.pdf) seems to be from 1999-2000. It seems that the level of interoperability is not very high right now. (By contrast, Glottolog has [pyglottolog](https://github.com/glottolog/pyglottolog) and in my experience contacts flow well.) \r\n\r\nThe Endangered Languages Project is something Google started but initially did not 'push' very strongly, it seems. Just airing an opinion on the public Internet, it seems that the project is now solidly rooted at University of Hawaiʻi at Mānoa. It seems that they do not generate codes of their own. They refer to ISO 639-3 (Ethnologue) as a code authority when applicable, and otherwise provide comments in so many words, such as that language L currently lacks an Ethnologue code of its own (example [here](https://www.endangeredlanguages.com/lang/10624)). \r\n\r\n> * On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\n> Is there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n> Based on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\nYes, you noted the difficulty here: that there are so many possible situations. Eventually, each dataset would required descriptors of its own. @BenjaminGalliot points out that, in addition to specifying the speakers' native languages, the degree of language proficiency would also be relevant. How many years did the speakers spend in which area? Talking which languages? In what chronological order? Etc. The complexity defies encoding. The purpose of language codes is to allow for searches that group resources into sets that make sense. Additional information is very important, but would seem to be a matter for 'comments' fields. \r\n\r\n> * Is there an international classification of languages?\r\n> A bit like the [International Classification of Diseases](https://en.wikipedia.org/wiki/International_Classification_of_Diseases) in medicine, which is established by the WHO and used as a reference throughout the world. The idea would be to have a precise number of languages to which we would then have to assign a unique tag in order to find them later.\r\n\r\nAs I understand, Ethnologue and Glottolog both try to do that, each in its own way. The simile with diseases seems interesting, to some extent: in both cases it's about human classification of phenomena that have complexity (though some diseases are simpler than others, whereas all languages have much complexity, in different ways).\r\n\r\n> * Finally, when can we expect to see all the datasets of [Pangloss](https://pangloss.cnrs.fr/) on HF? eyes And I don't know if you have a way to help to add also the datasets of [CoCoON](https://cocoon.huma-num.fr/exist/crdo/).\r\n\r\nThree concerns: (i) Technical specifications: we have not yet received feedback on the Japhug and Na datasets in HF. There may be technical considerations that we have not yet thought of and that would need to be taken into account before 'bulk upload'. (ii) Would there be a way to automate the process? The way @BenjaminGalliot did it for Japhug and Na, there was a manual component involved, and doing it by hand for all 200 datasets would not be an ideal workflow, given that the metadata are all clearly arranged. (iii) Some datasets are currently under a 'No derivatives' CreativeCommons license. We could go back to the depositors and argue that the 'No derivatives' mention were best omitted (see [here a similar argument about publications](https://creativecommons.org/2020/04/21/academic-publications-under-no-derivatives-licenses-is-misguided/)): again, we'd want to be sure about the way forward before we set the process into motion.\r\n\r\nOur hope would be that some colleagues try out the [OutilsPangloss](https://gitlab.com/lacito/outilspangloss) download tool, assemble datasets from Pangloss/Cocoon as they wish, then deposit them to HF.",
"> The idea of letting people use their favourite nomenclature and automatically adding the ISO 639-3 three-letter code as a tag is appealing. Thus all the HF datasets would have three-letter language tags (handy for basic search), alongside the authors' preferred tags and language names (including Glottolog tags as well as ISO 639-{1, 2}, and all other options allowed by BCP-47).\r\n> \r\n> Retaining the authors' original tags and language names would be best.\r\n> \r\n> * For language names: some people favour one name over another and it is important to respect their choice. In the case of Yongning Na: alternative names include 'Mosuo', 'Narua', 'Eastern Naxi'... and the names carry implications: people have been reported to come to blows about the use of the term 'Mosuo'.\r\n> * For language tags: Glottocodes can be more fine-grained than Ethnologue (ISO 639-3), and some colleagues feel strongly about those.\r\n> \r\n> Thus there would be a BCP-47 tag (sounds like a solid technical choice, though not 'passer-by-friendly': requiring some expertise to interpret) **plus** an ISO 639-3 tag that could be grabbed easily, and (last but not least) language names spelled out in full. Searches would be easier. No information would be lost.\r\n\r\n@alexis-michaud raises an excellent point. Language Resource users have varying search habits (or approaches). This includes cases where two or more language names refer to a single language. A search utility/interface needs to be flexible and able to present results from various kinds of input in the search process. This could be like how the terms French/Français/Franzosisch (en/fr/de) are names for the same language or it could be a variety of the following: autoglottonyms (how the speakers of the language refer to their language), or exoglottonyms (how others refer to the language). Additionally, in web based searches I have also needed to implement diacritic sensitive and insensitive logic so that users can type with or without diacritics and not have results unnecessarily excluded. \r\n\r\nDepending on how detailed of a search problem HF seeks to solve. It may be better to off load complex search to search engines like OLAC which aggregate a lot of language resources. — as I mentioned above I can assist with the informatics on creating an OLAC feed.\r\n\r\nAbstracting search logic from actual metadata may prove a useful way to lower the technical debt overhead. Technical tools and library standards use ISO and BCP-47 Standards. So, from a bibliographic metadata perspective this seems to be the way forward with the widest set of use cases. ",
"To get a visual idea of these first exchanges, I coded a Streamlit app that I put online on Spaces: https://huggingface.co/spaces/lbourdois/Language-tags-demo. \r\nThe code is in Python so I don't know if it can be used by HF who seems to need something in Node.js but it serves as a proof of concept. The advantage is also that you can directly test ideas by enter things in a search bar and see what comes up. \r\n\r\nThis application is divided into 3 points:\r\n- The first is to enter a language in natural language to get its code which can then be filled in the YAML file of the README.MD files of the HF datasets or models in order to be referenced and found by everyone.\r\nIn practice, enter the language (e.g: `English`) you are interested in to get its associated tag (e.g: `en`). You can enter several languages by separating them with a comma (e.g `French,English,German`). You will be given priority to the ISO 639-3 code if it exists otherwise the Glottocode or the BCP47 code (for varieties in particular). If none of these codes are available, it links to a page where the user can contact HF to request to add this tag. \r\nIf you enter a BCP47 code, it must be entered as follows: `Language(Territory)`, for example `French(Canada)`. Attention! If you enter a BCP-47 language, it must be entered first, otherwise the plant code will be displayed. I have to fix this problem but I am moving to a new place, I don't have an internet connection when I want and I prefer to push this first version so that you can already test things now and not have to wait days or weeks.\r\nThis point is intended to simulate the user's side of the equation, which wonders which tag he should fill in for his language.\r\n\r\n\r\n- The second is to enter a language code to obtain the name of the language in natural language.\r\nIn practice, enter the tag (ISO 639-1/2/3, Glottolog or BCP-47) you are interested in (e.g: `fra`) to get its associated language (e.g: French). You can enter several languages by separating them with a comma (e.g `fra,eng,deu`). Attention! If you enter a BCP-47 code, it must be entered first, otherwise the plant code will be displayed. Same as the other bug above (it's actually the same one).\r\nThis point is intended to simulate the side of HF that for a given tag must return the correct language.\r\n\r\n\r\n\r\nTo code these two points, I tested two approaches. \r\n\r\n1. The first one (internal DB in the app) consists in querying a database that HF would have locally at their place. To create this database, I merged the ISO 639 database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab) and the Glottolog database (https://glottolog.org/meta/downloads). The result of this merge is visible in the 3rd point of the application qui is an overview of the database.\r\nIn the image below, on line 1 of the database, we can see that the Glottocode database gives an ISO 639-3 code (column ISO639P3code) but not the ISO 639 database (column 639-3). Do you have an explanation for this phenomenon?\r\n\r\n\r\n\r\nFor BCP 47 codes of the type `fr-CA`, I have retrieved the ISO-3166 alpha 1 codes of the territories (https://www.iso.org/iso-3166-country-codes.html).\r\nIn practice, what I do is if we enter `fr-CA` is that the letters before the `-` refer to a language in the `Name` column for a `639-1` == `fr` (`639-3` for `fra` or `fre`) in the base of my image above. Then I look at the letters after the `-` which refers to a territory. It comes out `French (Canada)`. I used https://cldr.unicode.org/translation/displaynames/languagelocale-name-patterns for the pattern that came up.\r\n\r\n\r\n2. The second approach (with langcodes lib in the app) consists in using the Python `langcodes` library (https://github.com/rspeer/langcodes) which offers a lot of features in ready-made functions. It manages for example deprecated codes, the validity of an entered code, gives languages from code in the language of your choice (by default in English, but also autoglottonyms), etc. I invite you to read the README of the library. The only negative point is that it hasn't been updated for 10 months so basing your tag system on an external tool that isn't necessarily up to date can cause problems in the long run. But it is certainly an interesting source.\r\n\r\nFinally, I have added some information on the number of people speaking/reading the language(s) searched (figures provided by langcodes which are based on those given by ISO). This is not relevant for our topic but it could be figures that could be added as information on the https://huggingface.co/languages page. \r\n\r\n\r\n\r\nWhat could be done to improve the app if I have time:\r\n- Write the text for the app's homepage to describe what it does. This could serve as a basis for a documentation that I think will be necessary to add somewhere on the HF website to explain how the language tagging system works.\r\n- Deal with the bug mentioned above\r\n- Integrate ISO 3166-1 alpha 2 territories (https://www.iso.org/obp/ui#iso:pub:PUB500001:en)? They offer a finer granularity than ISO 3166-1 alpha 1 which is limited to the country level, but they are very administrative (for French, ISO 3166-1 alpha 2 gives us the \"départements\" for example).\r\n- Add autoglottonyms? (I only handle English language names for the moment)\r\n- For each language indicate to which family it belongs, in practice this could help to make data augmentation, but especially to classify the languages and find them more easily on the page https://huggingface.co/languages.",
"Very impressive! Using the prompt 'Japhug' (a language name), the app finds the intended language:\r\n\r\n\r\nA first question: based on the Glottocode, would it be possible to grab the closest ISO639-3 code? In case there is no match for the exact language variety, one needs to explore the higher-level groupings, level by level. For this language (Japhug), the information provided in the extracted CSV file (`glottolog-languoids-v4.6.csv`) is: \r\n`sino1245/burm1265/naqi1236/qian1263/rgya1241/core1262/jiar1240` \r\nOne need not look further than the first higher-level grouping, [`jiar1240`](https://glottolog.org/resource/languoid/id/jiar1240), to get an ISO639-3 code, namely `jya`.\r\n\r\nThus users searching by language names would get ISO639-3 (often less fine-grained than Glottolog) as a bonus.\r\nIt might be possible to ask the Glottolog team to provide this piece of information as part of an export from their database.",
"> on line 1 of the database, we can see that the Glottocode database gives an ISO 639-3 code (column ISO639P3code) but not the ISO 639 database (column 639-3). Do you have an explanation for this phenomenon?\r\n\r\nThat is because the language name 'Aewa' is not found in the Ethnologue (ISO 639-3) export that you are using. [This export in table form](https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab) only has one reference name (`Ref_Name`). For the language at issue, it is not 'Aewa' but ['Awishira'](https://www.ethnologue.com/language/ash).\r\n\r\nBy contrast, the language on line 0 of the database is called 'Abinomn' by both Ethnologue and Glottolog, and accordingly, columns `ISO639P3code` and `639-3` both contain the ISO 639-3 code, `bsa`.\r\n \r\nThe full Ethnologue database records alternate names for each language, and I'd bet that 'Aewa' is recorded among alternate names for the 'Ashiwira' language. I can't check because the full Ethnologue database is paywalled. \r\n\r\n\r\n[Glottolog](https://glottolog.org/resource/languoid/id/abis1238) does provide the corresponding ISO 639-3 code for 'Aewa', `ash`, which is an exact match (it refers to the same variety as Glottolog `abis1238`).\r\nIn this specific case, Glottolog provides all the relevant information. I'd say that Glottolog can be trusted for all the codes they provide, including ISO 639-3 codes: they only include them when the match is good. \r\n\r\nSee previous comment about the cases where there is no exact match between Glottolog and ISO 639-3 (suggested workaround: look at a higher-level grouping to get an ISO 639-3 code).",
"I will add these two points to my TODO list.\r\n- Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n- For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of `Japhug` , should it be just `jya`, or `jya-japh1234` or `jya-Japhug`?",
"> * Integrate ISO 3166-1 alpha 2 territories (https://www.iso.org/obp/ui#iso:pub:PUB500001:en)? They offer a finer granularity than ISO 3166-1 alpha 1 which is limited to the country level, but they are very administrative (for French, ISO 3166-1 alpha 2 gives us the \"départements\" for example).\r\n\r\nI'm concerned with this sort of exploration. Not because I am against innovation. In fact this is an interesting thought exercise. However, to explore this thought further creates cognitive dissidence between BCP-47 authorized codes and other code sets which are not BP-47 compliant. For that reason, I think adding additional codes is a waste of time both for HF devs and for future users who get a confusing idea about language tagging. ",
"Good job for the application!\r\n\r\n> On the Hub, there is the following dataset where French people speak in English: https://huggingface.co/datasets/Datatang/French_Speaking_English_Speech_Data_by_Mobile_Phone\r\n Is there a database to take this case into account? I have not found any code in the Glottolog database. If based on an IETF BCP-47 standard, I would tend to tag the dataset with \"en-fr\" but would this be something accepted by linguists?\r\n Based on the first post in this thread that there are about 8000 languages, if one considers that a given language can be pronounced by a speaker of the other 7999, that would theoretically make about 64 million BCP-47 language1-language2 codes existing. And even much more if we consider regionalists with language1_regionalism_x-language2_regionalism_y. I guess there is no such database.\r\n\r\n> Yes, you noted the difficulty here: that there are so many possible situations. Eventually, each dataset would required descriptors of its own. @BenjaminGalliot points out that, in addition to specifying the speakers' native languages, the degree of language proficiency would also be relevant. How many years did the speakers spend in which area? Talking which languages? In what chronological order? Etc. The complexity defies encoding. The purpose of language codes is to allow for searches that group resources into sets that make sense. Additional information is very important, but would seem to be a matter for 'comments' fields.\r\n\r\nTo briefly complete what I said on this subject in a private discussion group, there is a lot of (meta)data associated with each element of a corpus (which language level, according to which criteria, knowing that even among native speakers there are differences, some of which may go beyond what seems obvious to us from a linguistic point of view, such as socio-professional category, life history, environment in the broad sense, etc.), which can be placed in ad-hoc columns, or more freely in a comment/note column. And it is the role of the researcher (in this case a linguist, in all likelihood) to do analyses (statistics...) to determine the relevant data, including criteria that may justify separating different languages (in the broad sense), making separate corpora, etc. Putting this information in the language code is in my opinion doing the job in the opposite and wrong direction, as well as bringing other problems, like where to stop in the list of multidimensional criteria to be integrated, so in my opinion, here, the minimum is the best (the important thing is in my opinion to have well-documented data, globally, by sub-corpus or by line)...\r\n\r\n> If you are going to use Glottolog codes use them after an -x- tag in the BCP-47 format to maintain BCP-47 validity.\r\n\r\nYes, for the current corpora, I have written:\r\n\r\n```\r\nlanguage:\r\n- jya\r\n- nru\r\nlanguage_bcp47:\r\n- x-japh1234\r\n- x-yong1288\r\n```\r\n\r\n> * Add autoglottonyms? (I only handle English language names for the moment)\r\n\r\nAutoglossonyms are useful (I use them prior to other glossonyms), but I'm not sure there is an easy way to retrieve them. We can find some of them in the \"Alternative Names\" panel of Glottolog, but even if we have an API to retrieve them easily, their associated language code will often not be the one we are in (hence the need to do several cycles to find one, which might not be the right one...). Maybe this problem needs more investigation...\r\n\r\n> For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of Japhug , should it be just jya, or jya-japh1234 or jya-Japhug?\r\n\r\nI strongly insist not to add **a** language name after the code, it would restart a spiral of problems, notably the choice of the language in question:\r\n* the autoglossonym: in my opinion the best choice, but you have to know it…\r\n* the English name: iniquitous,\r\n* the name in the administratively/politically dominant language of the target language if it is relevant (strictly localized without overlapping, for example): iniquitous and tendentious (and in a way a special case of the previous one)...\r\n* etc.\r\n",
"> To get a visual idea of these first exchanges, I coded a Streamlit app that I put online on Spaces: https://huggingface.co/spaces/lbourdois/Language-tags-demo.\r\n> The code is in Python so I don't know if it can be used by HF who seems to need something in Node.js but it serves as a proof of concept. The advantage is also that you can directly test ideas by enter things in a search bar and see what comes up.\r\n\r\nThis is really great. You're doing a fantastic job. I love watching the creative process evolve. It is exciting. Let me provide some links to some search interfaces for further inspiration. I always find it helpful to know how others have approached a problem when figuring out my approach. I will link to three examples Glottolog, r12a's language sub-tag chooser, and the FLEx project builder wizard. The first two are online, but the last one is in an application which must be downloaded and works only on windows or linux. I have placed some notes on each of the screenshots.\r\n\r\n* **[Glottolog](https://glottolog.org/)** | [Search Query](https://glottolog.org/glottolog?name=en&namequerytype=part&multilingual=on#2/20.9/150.0) \r\n\r\n\r\n\r\n\r\n\r\n* **[r12a language sub-tag chooser](https://r12a.github.io/app-subtags/)** | [Code on github](https://github.com/r12a/app-subtags)\r\n\r\n\r\n\r\n\r\n* **FLEx Language Chooser** | [application page](https://software.sil.org/fieldworks/)\r\n\r\n\r\n",
"> In practice, what I do is if we enter `fr-CA` is that the letters before the `-` refer to a language in the `Name` column for a `639-1` == `fr` (`639-3` for `fra` or `fre`) in the base of my image above. Then I look at the letters after the `-` which refers to a territory. It comes out `French (Canada)`. I used https://cldr.unicode.org/translation/displaynames/languagelocale-name-patterns for the pattern that came up.\r\n\r\nWhat you are doing is looking at the algorithm for Locale generation rather than BCP-47's original documentation. I'm not sure there are difference, there might be. I know that locale IDs generally follow BCP-47 But I think there are some differences such as the use of `_` vs. `-`. ",
"> A first question: based on the Glottocode, would it be possible to grab the closest ISO639-3 code? In case there is no match for the exact language variety, one needs to explore the higher-level groupings, level by level. For this language (Japhug), the information provided in the extracted CSV file (`glottolog-languoids-v4.6.csv`) is: `sino1245/burm1265/naqi1236/qian1263/rgya1241/core1262/jiar1240` One need not look further than the first higher-level grouping, [`jiar1240`](https://glottolog.org/resource/languoid/id/jiar1240), to get an ISO639-3 code, namely `jya`.\r\n> \r\n> Thus users searching by language names would get ISO639-3 (often less fine-grained than Glottolog) as a bonus. It might be possible to ask the Glottolog team to provide this piece of information as part of an export from their database.\r\n\r\nThis is logical, but the fine grained assertions are not the same. That is just because they are in a hierarchical structure today doesn't mean they will be tomorrow. In some cases the glottolog is clearly referring to sub-language variants which will never receive full language status, whereas in other cases glottolog is referencing to unequal entities one or more of which should be a language. Many of the codes in glottolog have no associated documentation indicating what sort of speech variety they are. ",
"@lbourdois \r\n> * Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n\r\nI'm confused here... if there is no ISO639-3 code in the official database from the registrar, why would you look for an \"unofficial\" code from someone else? What is the use case here?",
"> For the point of adding the closest ISO 639-3 code for a Glottolog code, what convention should be adopted for the output? Just the ISO 639-3 code, or the ISO 639-3 code - Glottolog code, or the ISO 639-3 code - language name?\r\nTo use the example of Japhug , should it be just jya, or jya-japh1234 or jya-Japhug?\r\n\r\n(answer edited in view of [Benjamin Galliot's comment](https://github.com/huggingface/datasets/issues/4881#issuecomment-1237420600) \r\nEasy part of the answer first: jya-Japhug is out, because, as @BenjaminGalliot pointed out above, mixing language names with language codes will make trouble. For Japhug, `jya-Japhug` looks rather good: the pair looks nice, the one (`jya`) packed together, the other (`Japhug`) good and complete while still pretty compact. But think about languages like 'Yongning Na' or 'Yucatán Maya': a code with a space in the middle, like `nru-Yongning Na`, is really unsightly and unwieldy, not?\r\n\r\nSome [principles for language naming in English](http://hdl.handle.net/10125/24725) have been put forward, with some linguistic arguments, but always supposing that such standardization is desirable, actual standardization of language names in English may well never happen.\r\n\r\nAs for `jya-japh1234`: again, at first sight it seems cute, combining two fierce competitors (Ethnologue and Glottolog) into something that gets the best of both worlds. \r\nBut @HughP has a point: _adding additional codes is a waste of time both for HF devs and for future users who get a confusing idea about language tagging_ Strong wording, for an important comment: better stick with BCP 47. \r\n\r\nSo the solution pointed out by Benjamin, from Frances Gillis-Webber and Sabine Tittel, looks attractive: \r\njya-x-japh1234\r\n\r\nOn the other hand, if the idea for HF Datasets is simply to add the closest ISO 639-3 code for a Glottolog code, maybe it could be provided simply in three letters: providing the 'raw' ISO 639-3 code `jya`. Availability of 'straight' ISO 639-3 codes could save trouble for some users, and those who want more detail could look at the rest of the metadata and general information associated with datasets.",
"The problem seems to have already been raised here: https://drops.dagstuhl.de/opus/volltexte/2019/10368/pdf/OASIcs-LDK-2019-4.pdf\r\n\r\nAn example can be seen here :\r\n\r\n> 3.1.2 The use of privateuse sub-tag\r\nIn light of unambiguous language codes being available for the two Khoisan varieties, we\r\npropose to combine the ISO 639-3 code for the parent language N‖ng, i.e., ‘ngh’, with the\r\nprivateuse sub-tag ‘x-’ and the respective Glottocodes stated above.\r\nThe language tags for N|uu and ‖’Au can then be defined accordingly:\r\nN|uu: ngh-x-nuuu1242\r\n‖’Au: ngh-x-auni1243\r\n\r\nBy the way, while searching for this, I came across this application: https://huggingface.co/spaces/cdleong/langcode-search",
"> > * Since Glottolog can be trust, I will add a condition to the code that if there is no ISO 639-3 code in the \"official\" database (https://iso639-3.sil.org/sites/iso639-3/files/downloads/iso-639-3.tab), look for it in the \"ISO639P3code\" column of Glottolog.\r\n> \r\n> I'm confused here... if there is no ISO639-3 code in the official database from the registrar, why would you look for an \"unofficial\" code from someone else? What is the use case here?\r\n\r\nHi @HughP, I'm happy to clear what confusion may exist here :innocent: Here is the use case. \r\nGuillaume Jacques (@rgyalrong) put together a sizeable corpus of the Japhug language. It is up on HF Datasets ([here](https://huggingface.co/datasets/Lacito/pangloss/viewer/japh1234)) as well as on Zenodo. \r\n\r\nZenodo is an all-purpose repository without adequate domain-specific metadata (\"[métadonnées métier](https://www.cines.fr/archivage/des-expertises/les-metadonnees/metadonnees-metier/)\"), and the deposits in there are not easy to locate. The Zenodo deposit is intended for a highly specific user case: someone reads about the dataset in a paper, goes to the address on Zenodo and grabs the dataset at one go. \r\n\r\nHF Datasets, on the other hand, allows users to look around among corpora. The Japhug corpus needs proper tagging so that HF Datasets users can find out about it. \r\nJaphug has an entry of its own in Glottolog, whereas it lacks an entry of its own in Ethnologue. Hence the practical usefulness of Glottolog. Ethnologue pools together, under the code `jya`, three different languages (Japhug, Tshobdun `tsho1240` and Zbu `zbua1234`). \r\n\r\nI hope that this helps.",
"> By the way, while searching for this, I came across this application: https://huggingface.co/spaces/cdleong/langcode-search\r\n\r\nReally relevant Space, so tagging its author @cdleong, just in case!",
"@cdleong A one-stop shop for language codes: terrific!\r\nHow do you feel about the use of Glottocodes? When searching the language names 'Japhug' and 'Yongning Na' (real examples, related to a HF Datasets deposit & various research projects), the relevant Glottocodes are retrieved, and that is great (and not that easy, notably with the space in the middle of 'Yongning Na'). But this positive result is 'hidden' in the results page. Specifically: \r\n\r\n- for Japhug: when searching by language name ('Japhug'), the result in big print is 'Failure', even though there is an available Glottocode (at bottom).\r\n\r\nWhen searching by Glottocode (japh1234), same outcome: 'Result: failure!' (even though this _is_ the right Glottocode\r\nWhen searching by x-japh1234 (Glottocode encapsulated in BCP 47 syntax), one gets the message \r\n\r\n> ''x-japh1234' parses meaningfully as a language tag according to IANA\"\r\n\r\nbut there is paradoxically no link provided to Glottolog: the 'Glottolog' part of the results page is empty\r\n\r\n\r\n- Yongning Na: the correct code is identified (yong1288) but instead of foregrounding this exact match, the first result that comes up is a completely different language, called 'Yong'. \r\n\r\nTrying to formulate a conclusion (admittedly, this note is not based on intensive testing, it is just feedback on initial contact): from a user perspective, it seems that the tool could make more extensive use of Glottolog. `langcode-search` does a great job querying Glottolog, why not make more extensive use of that information? (including: to arrive at the nearest ISO 639-3 code)"
] | 2022-08-23T20:14:24Z
| 2023-01-03T08:32:35Z
| null |
NONE
| null | null | null |
**The problem:**
Language diversity is an important dimension of the diversity of datasets. To find one's way around datasets, being able to search by language name and by standardized codes appears crucial.
Currently the list of language codes is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json), right? At about 1,500 entries, it is roughly at 1/4th of the world's diversity of extant languages. (Probably less, as the list of 1,418 contains variants that are linguistically very close: 108 varieties of English, for instance.)
Looking forward to ever increasing coverage, how will the list of language names and language codes improve over time?
Enrichment of the custom list by HFT contributors (like [here](https://github.com/huggingface/datasets/pull/4880)) has several issues:
* progress is likely to be slow:
(input required from reviewers, etc.)
* the more contributors, the less consistency can be expected among contributions. No need to elaborate on how much confusion is likely to ensue as datasets accumulate.
* there is no information on which language relates with which: no encoding of the special closeness between the languages of the Northwestern Germanic branch (English+Dutch+German etc.), for instance. Information on phylogenetic closeness can be relevant to run experiments on transfer of technology from one language to its close relatives.
**A solution that seems desirable:**
Connecting to an established database that (i) aims at full coverage of the world's languages and (ii) has information on higher-level groupings, alternative names, etc.
It takes a lot of hard work to do such databases. Two important initiatives are [Ethnologue](https://www.ethnologue.com/) (ISO standard) and [Glottolog](https://glottolog.org/). Both have pros and cons. Glottolog contains references to Ethnologue identifiers, so adopting Glottolog entails getting the advantages of both sets of language codes.
Both seem technically accessible & 'developer-friendly'. Glottolog has a [GitHub repo](https://github.com/glottolog/glottolog). For Ethnologue, harvesting tools have been devised (see [here](https://github.com/lyy1994/ethnologue); I did not try it out).
In case a conversation with linguists seemed in order here, I'd be happy to participate ('pro bono', of course), & to rustle up more colleagues as useful, to help this useful development happen.
With appreciation of HFT,
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Add Speech Commands dataset
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"#self-assign"
] | 2021-11-16T22:39:56Z
| 2021-12-10T10:30:15Z
| 2021-12-10T10:30:15Z
|
MEMBER
| null | null | null |
## Adding a Dataset
- **Name:** Speech commands
- **Description:** A Dataset for Limited-Vocabulary Speech Recognition
- **Paper:** https://arxiv.org/abs/1804.03209
- **Data:** https://www.tensorflow.org/datasets/catalog/speech_commands, Available:
http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz
- **Motivation:** Nice dataset for audio classification training
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Support sliced list arrays in cast
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[] | 2021-06-08T17:38:47Z
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There is this issue in pyarrow:
```python
import pyarrow as pa
arr = pa.array([[i * 10] for i in range(4)])
arr.cast(pa.list_(pa.int32())) # works
arr = arr.slice(1)
arr.cast(pa.list_(pa.int32())) # fails
# ArrowNotImplementedError("Casting sliced lists (non-zero offset) not yet implemented")
```
However in `Dataset.cast` we slice tables to cast their types (it's memory intensive), so we have the same issue.
Because of this it is currently not possible to cast a Dataset with a Sequence feature type (unless the table is small enough to not be sliced).
In this PR I fixed this by resetting the offset of `pyarrow.ListArray` arrays to zero in the table before casting.
I used `pyarrow.compute.subtract` function to update the offsets of the ListArray.
cc @abhi1thakur @SBrandeis
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Use full released xsum dataset
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[
"@lhoestq I took a shot at addressing your comments but the build scripts seem to be complaining about not being able to open dummy files. How do I resolve those errors without copying the full dataset into the dummy dir?",
"Could you check that the names of the dummy data files are right ?\r\nYou can use \r\n```\r\ndatasets-cli dummy_data ./datasets/xum\r\n```\r\nto print the expected file names",
"Ok @lhoestq looks like I got the tests to pass :)"
] | 2020-10-23T03:29:49Z
| 2021-01-01T03:11:56Z
| 2020-10-26T12:56:58Z
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CONTRIBUTOR
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#672 Fix xsum to expand coverage and include IDs
Code based on parser from older version of `datasets/xsum/xsum.py`
@lhoestq
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Update Coached Conv Pref DatasetCard
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[
"Really cool!\r\n\r\nCan you add some task tags for `dialogue-modeling` (under `sequence-modeling`) and `parsing` (under `structured-prediction`)?"
] | 2021-01-07T09:07:16Z
| 2021-01-08T17:04:33Z
| 2021-01-08T17:04:32Z
|
CONTRIBUTOR
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Update Coached Conversation Preferance DatasetCard
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| 1,053,516,479
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I_kwDODunzps4-y2K_
| 3,272
|
Make iter_archive work with ZIP files
|
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[
"Hello, is this issue open for any contributor ? can I work on it ?\r\n\r\n",
"Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.\r\n\r\nTo begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:\r\n\r\nIn the `DownloadManager` for local dowloads:\r\nhttps://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/download_manager.py#L218-L242\r\n\r\nIn the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:\r\nhttps://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/streaming_download_manager.py#L502-L526\r\n\r\nNotice the call to `xopen` that opens and streams a file given either an URL or a local path :)",
"Okay thank you for the information. I will work on this :) ",
"#self-assign"
] | 2021-11-15T10:50:42Z
| 2021-11-25T00:08:47Z
| null |
MEMBER
| null | null | null |
Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive.
It would be nice if it could work with ZIP files too !
|
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MDExOlB1bGxSZXF1ZXN0NTQ2Mjg4Njg4
| 1,658
|
brwac dataset: add instances and data splits info
|
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[] | 2020-12-29T01:24:45Z
| 2020-12-30T16:54:26Z
| 2020-12-30T16:54:26Z
|
CONTRIBUTOR
| null | 0
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MDExOlB1bGxSZXF1ZXN0NDEwMTAyMjU3
| 22
|
adding bleu score code
|
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[] | 2020-04-28T13:00:50Z
| 2020-04-28T17:48:20Z
| 2020-04-28T17:48:08Z
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CONTRIBUTOR
| null | 0
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this PR add the BLEU score metric to the lib. It can be tested by running the following code.
` from nlp.metrics import bleu
hyp1 = "It is a guide to action which ensures that the military always obeys the commands of the party"
ref1a = "It is a guide to action that ensures that the military forces always being under the commands of the party "
ref1b = "It is the guiding principle which guarantees the military force always being under the command of the Party"
ref1c = "It is the practical guide for the army always to heed the directions of the party"
list_of_references = [[ref1a, ref1b, ref1c]]
hypotheses = [hyp1]
bleu = bleu.bleu_score(list_of_references, hypotheses,4, smooth=True)
print(bleu) `
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