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2402918335
I_kwDODunzps6POZ-_
7040
load `streaming=True` dataset with downloaded cache
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[ "When you pass `streaming=True`, the cache is ignored. The remote data URL is used instead and the data is streamed from the remote server." ]
2024-07-11T11:14:13
2024-07-11T11:54:18
NaT
NONE
nan
### Describe the bug We build a dataset which contains several hdf5 files and write a script using `h5py` to generate the dataset. The hdf5 files are large and the processed dataset cache takes more disk space. So we hope to try streaming iterable dataset. Unfortunately, `h5py` can't convert a remote URL into a hdf5 file descriptor. So we use `fsspec` as an interface like below: ```python def _generate_examples(self, filepath, split): for file in filepath: with fsspec.open(file, "rb") as fs: with h5py.File(fs, "r") as fp: # for event_id in sorted(list(fp.keys())): event_ids = list(fp.keys()) ...... ``` ### Steps to reproduce the bug The `fsspec` works, but it takes 10+ min to print the first 10 examples, which is even longer than the downloading time. I'm not sure if it just caches the whole hdf5 file and generates the examples. ### Expected behavior So does the following make sense so far? 1. download the files ```python dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True) ``` 2. load the iterable dataset faster (using the raw file cache at path `.cache/huggingface/datasets/downloads`) ```python dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True, streaming=true) ``` I made some tests, but the code above can't get the expected result. I'm not sure if this is supported. I also find the issue #6327 . It seemed similar to mine, but I couldn't find a solution. ### Environment info - `datasets` = 2.18.0 - `h5py` = 3.10.0 - `fsspec` = 2023.10.0
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PR_kwDODunzps51DgCY
7039
Fix export to JSON when dataset larger than batch size
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7039). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "The test before confirms the bug.\r\n\r\nThere are different possible solutions to this issue:\r\n- the easiest would be to write multiple JSON files, one for each batch; this solution can be done in parallel if `num_proc` is passed\r\n- alternatively, we could tweak the writing and remove the extra `[` and `]` characters; this solution will only be valid if `orient=\"records\"`\r\n- others?" ]
2024-07-11T06:52:22
2024-07-11T07:27:58
NaT
MEMBER
nan
Fix export to JSON (`files=False`) when dataset larger than batch size. Fix #7037.
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7038
Yes, can definitely elaborate:
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[ "This is the `datasets` repository, and the issue should be opened in the `transformers` repo instead." ]
2024-07-11T02:22:30
2024-07-11T05:28:39
2024-07-11 05:28:39+00:00
NONE
nan
Yes, can definitely elaborate: Say I want to use HF Trainer with an arbitrary PyTorch optimizer (`AdamW` here just as an example). Then I should intuitively extend `Trainer` like: ```python class CustomOptimizerTrainer(Trainer): @staticmethod def get_optimizer_cls_and_kwargs(args: HfTrainingArguments, model=None) -> tuple[type[torch.optim.Optimizer], dict[str, Any]]: optimizer = torch.optim.AdamW optimizer_kwargs = { "lr": 4e-3, "betas": (0.9, 0.999), "weight_decay": 0.05, } return optimizer, optimizer_kwargs ``` However, this won't take effect, because `Trainer.create_optimizer` hardcodes the `Trainer` class name when calling `get_optimizer_cls_and_kwargs`: https://github.com/huggingface/transformers/blob/6c1d0b069de22d7ed8aa83f733c25045eea0585d/src/transformers/trainer.py#L1076 `CustomOptimizerTrainer.get_optimizer_cls_and_kwargs` will never be called. So I could either: - also override the entire `create_optimizer` and rewrite `Trainer.get_optimizer_cls_and_kwargs` to `self.get_optimizer_cls_and_kwargs` (overkill) - or monkey-patch (not ideal): ```python class CustomOptimizerTrainer(Trainer): # def get_optimizer_cls_and_kwargs ... def create_optimizer(self): trainer_get_optimizer_fn = Trainer.get_optimizer_cls_and_kwargs Trainer.get_optimizer_cls_and_kwargs = self.get_optimizer_cls_and_kwargs optimizer = super().create_optimizer() Trainer.get_optimizer_cls_and_kwargs = trainer_get_optimizer_fn return optimizer ``` But I think the best fix is to change `Trainer.get_optimizer_cls_and_kwargs` to `self.get_optimizer_cls_and_kwargs` in the original source of `Trainer.create_optimizer`. I also made `get_optimizer_cls_and_kwargs` an instance method instead of a static method, but that probably doesn't matter as much and can be reverted. It breaks the syntax of the tests. Please let me know if that's clearer and if you agree! Thanks! _Originally posted by @apoorvkh in https://github.com/huggingface/transformers/issues/31875#issuecomment-2221491647_
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2400192419
I_kwDODunzps6PEAej
7037
A bug of Dataset.to_json() function
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[ "Thanks for reporting, @LinglingGreat.\r\n\r\nI confirm this is a bug." ]
2024-07-10T09:11:22
2024-07-10T13:07:44
NaT
NONE
nan
### Describe the bug When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again. The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size). ### Steps to reproduce the bug try this code: ```python from datasets import load_dataset import json train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"] output_path = "./harmless-base_hftojs.json" print(len(train_dataset)) train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2) with open(output_path, encoding="utf-8") as f: data = json.loads(f.read()) ``` it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709) Extra square brackets have appeared here: <img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc"> ### Expected behavior The code runs normally. ### Environment info datasets=2.20.0
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7036
Fix doc generation when NamedSplit is used as parameter default value
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7036). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
2024-07-10T07:58:46
2024-07-10T08:01:00
NaT
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Fix doc generation when `NamedSplit` is used as parameter default value. Fix #7035.
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7035
Docs are not generated when a parameter defaults to a NamedSplit value
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2024-07-10T07:51:24
2024-07-10T07:53:08
NaT
MEMBER
nan
While generating the docs, we get an error when some parameter defaults to a `NamedSplit` value, like: ```python def call_function(split=Split.TRAIN): ... ``` The error is: ValueError: Equality not supported between split train and <class 'inspect._empty'> See: https://github.com/huggingface/datasets/actions/runs/9869660902/job/27254359863?pr=7015 ``` Building the MDX files: 97%|█████████▋| 58/60 [00:00<00:00, 91.94it/s] Traceback (most recent call last): File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 197, in build_mdx_files content, new_anchors, source_files, errors = resolve_autodoc( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 123, in resolve_autodoc doc = autodoc( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 499, in autodoc method_doc, check = document_object( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 395, in document_object signature = format_signature(obj) File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 126, in format_signature if param.default != inspect._empty: File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 136, in __ne__ return not self.__eq__(other) File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 379, in __eq__ raise ValueError(f"Equality not supported between split {self} and {other}") ValueError: Equality not supported between split train and <class 'inspect._empty'> The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/runner/work/datasets/datasets/.venv/bin/doc-builder", line 8, in <module> sys.exit(main()) File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main args.func(args) File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/build.py", line 102, in build_command build_doc( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 367, in build_doc anchors_mapping, source_files_mapping = build_mdx_files( File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 230, in build_mdx_files raise type(e)(f"There was an error when converting {file} to the MDX format.\n" + e.args[0]) from e ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format. Equality not supported between split train and <class 'inspect._empty'> ```
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2397525974
PR_kwDODunzps50y-ya
7034
chore: fix typos in docs
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2024-07-09T08:35:05
2024-07-09T13:32:05
NaT
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I_kwDODunzps6O5bj4
7033
`from_generator` does not allow to specify the split name
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[ "Thanks for reporting, @pminervini.\r\n\r\nI agree we should give the option to define the split name.\r\n\r\nIndeed, there is a PR that addresses precisely this issue:\r\n- #7015\r\n\r\nI am reviewing it." ]
2024-07-09T07:47:58
2024-07-09T08:05:23
NaT
CONTRIBUTOR
nan
### Describe the bug I'm building train, dev, and test using `from_generator`; however, in all three cases, the logger prints `Generating train split:` It's not possible to change the split name since it seems to be hardcoded: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/generator/generator.py ### Steps to reproduce the bug ``` In [1]: from datasets import Dataset In [2]: def gen(): ...: yield {"pokemon": "bulbasaur", "type": "grass"} ...: In [3]: ds = Dataset.from_generator(gen) Generating train split: 1 examples [00:00, 133.89 examples/s] ``` ### Expected behavior It should be possible to specify any split name ### Environment info - `datasets` version: 2.19.2 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.5 - `huggingface_hub` version: 0.23.3 - PyArrow version: 15.0.0 - Pandas version: 2.0.3 - `fsspec` version: 2023.10.0
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2395531699
PR_kwDODunzps50sJTq
7032
Register `.zstd` extension for zstd-compressed files
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7032). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@albertvillanova hm I don't know tbh, it's just that \"mlfoundations/dclm-baseline-1.0\" dataset contains [files](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0/tree/main/global-shard_01_of_10/local-shard_0_of_10) with this extension and these files seem to be valid ", "not sure why CI is failing but seems to be unrelated to this pr? can I merge @lhoestq @albertvillanova ?", "yes you can merge, the CI failure is unrelated (surely an issue with hub-ci)", "ah why not, you could try opening a PR\r\n\r\nbtw there is a channel with them at (internal) https://app.slack.com/client/T1RCG4490/C079AKTV11P if you want to let them know", "@lhoestq, your previous comment was addressed to me or Polina?\r\n\r\n@polinaeterna let me know if it is OK for you." ]
2024-07-08T12:39:50
2024-07-11T08:15:17
NaT
CONTRIBUTOR
nan
For example, https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0 dataset files have `.zstd` extension which is currently ignored (only `.zst` is registered).
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7031
CI quality is broken: use ruff check instead
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2024-07-08T11:42:24
2024-07-08T11:47:29
2024-07-08 11:47:29+00:00
MEMBER
nan
CI quality is broken: https://github.com/huggingface/datasets/actions/runs/9838873879/job/27159697027 ``` error: `ruff <path>` has been removed. Use `ruff check <path>` instead. ```
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I_kwDODunzps6OqJAv
7030
Add option to disable progress bar when reading a dataset ("Loading dataset from disk")
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2024-07-06T05:43:37
2024-07-06T05:44:19
NaT
NONE
nan
### Feature request Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16. ### Motivation I am reading a lot of datasets that it creates lots of logs. <img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a"> ### Your contribution Seems like an easy fix to make. I can create a PR if necessary.
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I_kwDODunzps6OiVwo
7029
load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error
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2024-07-04T19:15:16
2024-07-04T19:17:46
NaT
NONE
nan
### Describe the bug I'm using AWS lambda to run a python application. I run the `load_dataset` function with cache_dir="/tmp" and is still throws the OSError(30, 'Read-only file system') error. Is even updated all the HF envs to point to /tmp dir but the issue still persists. I can confirm that the I can write to /tmp directory. ### Steps to reproduce the bug ```python d = load_dataset( path=hugging_face_link, split=split, token=token, cache_dir="/tmp/hugging_face_cache", ) ``` ### Expected behavior Everything written to the file system as part of the load_datasets function should be in the /tmp directory. ### Environment info datasets version: 2.16.1 Platform: Linux-5.10.216-225.855.amzn2.x86_64-x86_64-with-glibc2.26 Python version: 3.11.9 huggingface_hub version: 0.19.4 PyArrow version: 16.1.0 Pandas version: 2.2.2 fsspec version: 2023.10.0
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PR_kwDODunzps50dQ1w
7028
Fix ci
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7028). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005748 / 0.011353 (-0.005605) | 0.004109 / 0.011008 (-0.006899) | 0.067017 / 0.038508 (0.028509) | 0.031950 / 0.023109 (0.008841) | 0.239939 / 0.275898 (-0.035959) | 0.266339 / 0.323480 (-0.057141) | 0.003176 / 0.007986 (-0.004809) | 0.003556 / 0.004328 (-0.000773) | 0.050725 / 0.004250 (0.046475) | 0.047711 / 0.037052 (0.010658) | 0.251048 / 0.258489 (-0.007441) | 0.287049 / 0.293841 (-0.006792) | 0.029919 / 0.128546 (-0.098627) | 0.012562 / 0.075646 (-0.063085) | 0.212903 / 0.419271 (-0.206369) | 0.036570 / 0.043533 (-0.006963) | 0.240975 / 0.255139 (-0.014164) | 0.266473 / 0.283200 (-0.016726) | 0.019959 / 0.141683 (-0.121724) | 1.152224 / 1.452155 (-0.299931) | 1.186046 / 1.492716 (-0.306671) |\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) | 0.095836 / 0.018006 (0.077829) | 0.303402 / 0.000490 (0.302913) | 0.000210 / 0.000200 (0.000010) | 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) | 0.020552 / 0.037411 (-0.016859) | 0.063619 / 0.014526 (0.049093) | 0.076969 / 0.176557 (-0.099588) | 0.123368 / 0.737135 (-0.613767) | 0.077005 / 0.296338 (-0.219334) |\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) | 0.282005 / 0.215209 (0.066796) | 2.794144 / 2.077655 (0.716489) | 1.463569 / 1.504120 (-0.040551) | 1.334295 / 1.541195 (-0.206899) | 1.387198 / 1.468490 (-0.081292) | 0.707654 / 4.584777 (-3.877123) | 2.341698 / 3.745712 (-1.404014) | 2.865131 / 5.269862 (-2.404731) | 1.945168 / 4.565676 (-2.620509) | 0.077926 / 0.424275 (-0.346349) | 0.005470 / 0.007607 (-0.002137) | 0.336498 / 0.226044 (0.110454) | 3.330262 / 2.268929 (1.061334) | 1.865574 / 55.444624 (-53.579050) | 1.536932 / 6.876477 (-5.339545) | 1.720960 / 2.142072 (-0.421113) | 0.794753 / 4.805227 (-4.010475) | 0.133491 / 6.500664 (-6.367173) | 0.042437 / 0.075469 (-0.033032) |\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) | 0.976788 / 1.841788 (-0.865000) | 11.895137 / 8.074308 (3.820829) | 9.211969 / 10.191392 (-0.979423) | 0.141798 / 0.680424 (-0.538626) | 0.014354 / 0.534201 (-0.519847) | 0.306044 / 0.579283 (-0.273239) | 0.265016 / 0.434364 (-0.169348) | 0.340877 / 0.540337 (-0.199460) | 0.470449 / 1.386936 (-0.916487) |\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) | 0.006134 / 0.011353 (-0.005219) | 0.004023 / 0.011008 (-0.006985) | 0.050419 / 0.038508 (0.011911) | 0.033853 / 0.023109 (0.010744) | 0.266799 / 0.275898 (-0.009099) | 0.291248 / 0.323480 (-0.032232) | 0.004474 / 0.007986 (-0.003511) | 0.002847 / 0.004328 (-0.001481) | 0.049895 / 0.004250 (0.045645) | 0.041160 / 0.037052 (0.004108) | 0.278818 / 0.258489 (0.020329) | 0.314027 / 0.293841 (0.020186) | 0.032303 / 0.128546 (-0.096243) | 0.012367 / 0.075646 (-0.063279) | 0.061495 / 0.419271 (-0.357776) | 0.033512 / 0.043533 (-0.010021) | 0.266168 / 0.255139 (0.011029) | 0.283129 / 0.283200 (-0.000071) | 0.018674 / 0.141683 (-0.123009) | 1.124453 / 1.452155 (-0.327701) | 1.164527 / 1.492716 (-0.328189) |\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) | 0.098522 / 0.018006 (0.080516) | 0.315069 / 0.000490 (0.314579) | 0.000202 / 0.000200 (0.000002) | 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) | 0.022809 / 0.037411 (-0.014602) | 0.078409 / 0.014526 (0.063883) | 0.088558 / 0.176557 (-0.087998) | 0.130004 / 0.737135 (-0.607131) | 0.090507 / 0.296338 (-0.205832) |\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) | 0.291323 / 0.215209 (0.076114) | 2.836363 / 2.077655 (0.758708) | 1.548889 / 1.504120 (0.044769) | 1.423857 / 1.541195 (-0.117337) | 1.461667 / 1.468490 (-0.006823) | 0.714956 / 4.584777 (-3.869821) | 0.948170 / 3.745712 (-2.797542) | 3.036151 / 5.269862 (-2.233711) | 1.923824 / 4.565676 (-2.641853) | 0.078002 / 0.424275 (-0.346273) | 0.005198 / 0.007607 (-0.002409) | 0.337007 / 0.226044 (0.110963) | 3.310255 / 2.268929 (1.041327) | 1.910371 / 55.444624 (-53.534253) | 1.619855 / 6.876477 (-5.256622) | 1.682093 / 2.142072 (-0.459979) | 0.789903 / 4.805227 (-4.015324) | 0.132117 / 6.500664 (-6.368547) | 0.041312 / 0.075469 (-0.034157) |\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) | 0.997658 / 1.841788 (-0.844130) | 12.447878 / 8.074308 (4.373570) | 10.277662 / 10.191392 (0.086270) | 0.143580 / 0.680424 (-0.536844) | 0.016472 / 0.534201 (-0.517729) | 0.307235 / 0.579283 (-0.272048) | 0.125469 / 0.434364 (-0.308895) | 0.339525 / 0.540337 (-0.200813) | 0.427371 / 1.386936 (-0.959566) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#689447f8c86f777829a4db9ccc5d8133c12ec84c \"CML watermark\")\n" ]
2024-07-04T15:11:08
2024-07-04T15:26:35
2024-07-04 15:19:16+00:00
MEMBER
nan
...after last pr errors
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Missing line from previous pr
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7027). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005612 / 0.011353 (-0.005741) | 0.004023 / 0.011008 (-0.006985) | 0.065578 / 0.038508 (0.027070) | 0.030476 / 0.023109 (0.007367) | 0.237131 / 0.275898 (-0.038767) | 0.269388 / 0.323480 (-0.054092) | 0.003364 / 0.007986 (-0.004622) | 0.002938 / 0.004328 (-0.001390) | 0.050867 / 0.004250 (0.046617) | 0.049456 / 0.037052 (0.012403) | 0.249587 / 0.258489 (-0.008902) | 0.291132 / 0.293841 (-0.002709) | 0.029373 / 0.128546 (-0.099174) | 0.012266 / 0.075646 (-0.063380) | 0.206239 / 0.419271 (-0.213033) | 0.037192 / 0.043533 (-0.006340) | 0.244902 / 0.255139 (-0.010237) | 0.269779 / 0.283200 (-0.013421) | 0.019870 / 0.141683 (-0.121813) | 1.123697 / 1.452155 (-0.328458) | 1.181256 / 1.492716 (-0.311460) |\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) | 0.108535 / 0.018006 (0.090529) | 0.317838 / 0.000490 (0.317348) | 0.000216 / 0.000200 (0.000016) | 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) | 0.019097 / 0.037411 (-0.018315) | 0.063836 / 0.014526 (0.049310) | 0.075446 / 0.176557 (-0.101111) | 0.124503 / 0.737135 (-0.612632) | 0.077730 / 0.296338 (-0.218608) |\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) | 0.284688 / 0.215209 (0.069479) | 2.817832 / 2.077655 (0.740178) | 1.487342 / 1.504120 (-0.016778) | 1.354037 / 1.541195 (-0.187158) | 1.426904 / 1.468490 (-0.041586) | 0.728754 / 4.584777 (-3.856022) | 2.361140 / 3.745712 (-1.384573) | 2.926215 / 5.269862 (-2.343647) | 1.981767 / 4.565676 (-2.583909) | 0.079278 / 0.424275 (-0.344997) | 0.005567 / 0.007607 (-0.002040) | 0.336590 / 0.226044 (0.110546) | 3.371062 / 2.268929 (1.102134) | 1.845343 / 55.444624 (-53.599282) | 1.537699 / 6.876477 (-5.338777) | 1.731407 / 2.142072 (-0.410665) | 0.796148 / 4.805227 (-4.009079) | 0.133830 / 6.500664 (-6.366835) | 0.043117 / 0.075469 (-0.032352) |\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) | 0.980786 / 1.841788 (-0.861001) | 12.653553 / 8.074308 (4.579245) | 9.402636 / 10.191392 (-0.788756) | 0.143756 / 0.680424 (-0.536667) | 0.014896 / 0.534201 (-0.519304) | 0.328796 / 0.579283 (-0.250487) | 0.275108 / 0.434364 (-0.159255) | 0.343397 / 0.540337 (-0.196940) | 0.472301 / 1.386936 (-0.914635) |\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) | 0.005882 / 0.011353 (-0.005471) | 0.003982 / 0.011008 (-0.007026) | 0.050484 / 0.038508 (0.011976) | 0.035217 / 0.023109 (0.012108) | 0.271683 / 0.275898 (-0.004215) | 0.291498 / 0.323480 (-0.031982) | 0.004429 / 0.007986 (-0.003557) | 0.002928 / 0.004328 (-0.001401) | 0.049386 / 0.004250 (0.045136) | 0.040868 / 0.037052 (0.003815) | 0.280968 / 0.258489 (0.022479) | 0.314880 / 0.293841 (0.021039) | 0.032590 / 0.128546 (-0.095956) | 0.012319 / 0.075646 (-0.063327) | 0.060354 / 0.419271 (-0.358917) | 0.034138 / 0.043533 (-0.009394) | 0.267491 / 0.255139 (0.012352) | 0.283077 / 0.283200 (-0.000123) | 0.017784 / 0.141683 (-0.123899) | 1.154835 / 1.452155 (-0.297320) | 1.179271 / 1.492716 (-0.313446) |\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) | 0.100519 / 0.018006 (0.082513) | 0.309043 / 0.000490 (0.308553) | 0.000222 / 0.000200 (0.000022) | 0.000055 / 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) | 0.024056 / 0.037411 (-0.013356) | 0.077810 / 0.014526 (0.063284) | 0.092682 / 0.176557 (-0.083875) | 0.132101 / 0.737135 (-0.605034) | 0.091986 / 0.296338 (-0.204352) |\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) | 0.298186 / 0.215209 (0.082977) | 2.905134 / 2.077655 (0.827479) | 1.552364 / 1.504120 (0.048245) | 1.424644 / 1.541195 (-0.116551) | 1.457667 / 1.468490 (-0.010823) | 0.717606 / 4.584777 (-3.867171) | 0.944470 / 3.745712 (-2.801242) | 3.056236 / 5.269862 (-2.213626) | 1.946453 / 4.565676 (-2.619223) | 0.080525 / 0.424275 (-0.343750) | 0.005235 / 0.007607 (-0.002372) | 0.348561 / 0.226044 (0.122516) | 3.449350 / 2.268929 (1.180421) | 1.930165 / 55.444624 (-53.514459) | 1.620883 / 6.876477 (-5.255593) | 1.671963 / 2.142072 (-0.470109) | 0.801978 / 4.805227 (-4.003249) | 0.134494 / 6.500664 (-6.366170) | 0.041888 / 0.075469 (-0.033581) |\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) | 1.005961 / 1.841788 (-0.835826) | 12.687638 / 8.074308 (4.613330) | 10.398730 / 10.191392 (0.207338) | 0.134503 / 0.680424 (-0.545920) | 0.015839 / 0.534201 (-0.518362) | 0.307465 / 0.579283 (-0.271819) | 0.130805 / 0.434364 (-0.303559) | 0.349079 / 0.540337 (-0.191259) | 0.437609 / 1.386936 (-0.949327) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc6ac9e5f70811a450198203ddc077c0c7bff206 \"CML watermark\")\n" ]
2024-07-04T14:34:29
2024-07-04T14:40:46
2024-07-04 14:34:36+00:00
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2390983889
PR_kwDODunzps50c8Mf
7026
Fix check_library_imports
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7026). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005637 / 0.011353 (-0.005716) | 0.003967 / 0.011008 (-0.007041) | 0.064187 / 0.038508 (0.025679) | 0.031356 / 0.023109 (0.008246) | 0.239203 / 0.275898 (-0.036695) | 0.261033 / 0.323480 (-0.062447) | 0.003256 / 0.007986 (-0.004730) | 0.003416 / 0.004328 (-0.000913) | 0.049673 / 0.004250 (0.045423) | 0.047021 / 0.037052 (0.009969) | 0.252146 / 0.258489 (-0.006343) | 0.283663 / 0.293841 (-0.010178) | 0.030223 / 0.128546 (-0.098324) | 0.012342 / 0.075646 (-0.063304) | 0.213061 / 0.419271 (-0.206211) | 0.036867 / 0.043533 (-0.006665) | 0.242589 / 0.255139 (-0.012550) | 0.265584 / 0.283200 (-0.017616) | 0.019149 / 0.141683 (-0.122533) | 1.108909 / 1.452155 (-0.343246) | 1.148484 / 1.492716 (-0.344232) |\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) | 0.096815 / 0.018006 (0.078809) | 0.299633 / 0.000490 (0.299143) | 0.000212 / 0.000200 (0.000013) | 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) | 0.018947 / 0.037411 (-0.018464) | 0.061640 / 0.014526 (0.047114) | 0.074621 / 0.176557 (-0.101935) | 0.120830 / 0.737135 (-0.616305) | 0.075472 / 0.296338 (-0.220866) |\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) | 0.284626 / 0.215209 (0.069417) | 2.805299 / 2.077655 (0.727644) | 1.469879 / 1.504120 (-0.034241) | 1.355524 / 1.541195 (-0.185671) | 1.388246 / 1.468490 (-0.080244) | 0.726740 / 4.584777 (-3.858037) | 2.387461 / 3.745712 (-1.358251) | 2.834137 / 5.269862 (-2.435724) | 1.915750 / 4.565676 (-2.649927) | 0.079223 / 0.424275 (-0.345052) | 0.005489 / 0.007607 (-0.002118) | 0.335517 / 0.226044 (0.109473) | 3.299332 / 2.268929 (1.030403) | 1.817726 / 55.444624 (-53.626898) | 1.520834 / 6.876477 (-5.355642) | 1.696285 / 2.142072 (-0.445788) | 0.815147 / 4.805227 (-3.990080) | 0.136566 / 6.500664 (-6.364098) | 0.043482 / 0.075469 (-0.031987) |\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) | 0.981382 / 1.841788 (-0.860406) | 11.472890 / 8.074308 (3.398582) | 9.274181 / 10.191392 (-0.917211) | 0.133051 / 0.680424 (-0.547373) | 0.015417 / 0.534201 (-0.518784) | 0.306098 / 0.579283 (-0.273185) | 0.261424 / 0.434364 (-0.172940) | 0.338946 / 0.540337 (-0.201391) | 0.460776 / 1.386936 (-0.926160) |\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) | 0.005806 / 0.011353 (-0.005547) | 0.004274 / 0.011008 (-0.006734) | 0.050831 / 0.038508 (0.012323) | 0.033717 / 0.023109 (0.010607) | 0.280561 / 0.275898 (0.004663) | 0.302437 / 0.323480 (-0.021043) | 0.004543 / 0.007986 (-0.003442) | 0.002905 / 0.004328 (-0.001424) | 0.048897 / 0.004250 (0.044646) | 0.041089 / 0.037052 (0.004037) | 0.291439 / 0.258489 (0.032950) | 0.319762 / 0.293841 (0.025921) | 0.033178 / 0.128546 (-0.095368) | 0.012336 / 0.075646 (-0.063311) | 0.061033 / 0.419271 (-0.358238) | 0.034018 / 0.043533 (-0.009515) | 0.278514 / 0.255139 (0.023375) | 0.295648 / 0.283200 (0.012448) | 0.018621 / 0.141683 (-0.123062) | 1.160250 / 1.452155 (-0.291905) | 1.183867 / 1.492716 (-0.308850) |\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) | 0.096354 / 0.018006 (0.078348) | 0.301907 / 0.000490 (0.301417) | 0.000205 / 0.000200 (0.000006) | 0.000044 / 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) | 0.022357 / 0.037411 (-0.015054) | 0.076218 / 0.014526 (0.061692) | 0.088172 / 0.176557 (-0.088385) | 0.128621 / 0.737135 (-0.608515) | 0.089250 / 0.296338 (-0.207089) |\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) | 0.292633 / 0.215209 (0.077424) | 2.862456 / 2.077655 (0.784801) | 1.581967 / 1.504120 (0.077847) | 1.459822 / 1.541195 (-0.081373) | 1.475896 / 1.468490 (0.007406) | 0.728550 / 4.584777 (-3.856226) | 0.958819 / 3.745712 (-2.786893) | 3.011074 / 5.269862 (-2.258788) | 1.934393 / 4.565676 (-2.631283) | 0.079831 / 0.424275 (-0.344444) | 0.005249 / 0.007607 (-0.002358) | 0.346334 / 0.226044 (0.120290) | 3.438979 / 2.268929 (1.170051) | 1.935567 / 55.444624 (-53.509057) | 1.648723 / 6.876477 (-5.227754) | 1.685489 / 2.142072 (-0.456583) | 0.800992 / 4.805227 (-4.004236) | 0.139388 / 6.500664 (-6.361276) | 0.042518 / 0.075469 (-0.032951) |\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) | 1.031715 / 1.841788 (-0.810072) | 12.486711 / 8.074308 (4.412403) | 10.430191 / 10.191392 (0.238799) | 0.146884 / 0.680424 (-0.533540) | 0.015735 / 0.534201 (-0.518466) | 0.303938 / 0.579283 (-0.275346) | 0.140374 / 0.434364 (-0.293989) | 0.338508 / 0.540337 (-0.201830) | 0.429551 / 1.386936 (-0.957385) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e32336195f3ea69988148df5f129f9f59d3ab595 \"CML watermark\")\n" ]
2024-07-04T14:18:38
2024-07-04T14:28:36
2024-07-04 14:20:02+00:00
MEMBER
nan
move it to after the `trust_remote_code` check Note that it only affects local datasets that already exist on disk, not datasets loaded from HF directly
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PR_kwDODunzps50bSyD
7025
feat: support non streamable arrow file binary format
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[ "requesting review - @albertvillanova @lhoestq ", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7025). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thank you for the review.\r\n\r\n> Before we merge could you also add a test in tests/packaged_modules/test_arrow.py ?\r\n\r\n> I noticed it's pretty empty right now compared to test_json.py or test_csv.py though, maybe I can take care of it next week if needed\r\n\r\n@lhoestq Would you like to take that up? since it needs adding some test data and I see no supportive examples for similar data formats - parquet pandas etc. Thanks" ]
2024-07-04T10:11:12
2024-07-09T17:08:36
NaT
NONE
nan
Support Arrow files (`.arrow`) that are in non streamable binary file formats.
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7024
Streaming dataset not returning data
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2024-07-04T07:21:47
2024-07-04T07:21:47
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### Describe the bug I'm deciding to post here because I'm still not sure what the issue is, or if I am using IterableDatasets wrongly. I'm following the guide on here https://huggingface.co/learn/cookbook/en/fine_tuning_code_llm_on_single_gpu pretty much to a tee and have verified that it works when I'm fine-tuning on the provided dataset. However, I'm doing some data preprocessing steps (filtering out entries), when I try to swap out the dataset for mine, it fails to train. However, I eventually fixed this by simply setting `stream=False` in `load_dataset`. Coud this be some sort of network / firewall issue I'm facing? ### Steps to reproduce the bug I made a post with greater description about how I reproduced this problem before I found my workaround: https://discuss.huggingface.co/t/problem-with-custom-iterator-of-streaming-dataset-not-returning-anything/94551 Here is the problematic dataset snippet, which works when streaming=False (and with buffer keyword removed from shuffle) ``` commitpackft = load_dataset( "chargoddard/commitpack-ft-instruct", split="train", streaming=True ).filter(lambda example: example["language"] == "Python") def form_template(example): """Forms a template for each example following the alpaca format for CommitPack""" example["content"] = ( "### Human: " + example["instruction"] + " " + example["input"] + " ### Assistant: " + example["output"] ) return example dataset = commitpackft.map( form_template, remove_columns=["id", "language", "license", "instruction", "input", "output"], ).shuffle( seed=42, buffer_size=10000 ) # remove everything since its all inside "content" now validation_data = dataset.take(4000) train_data = dataset.skip(4000) ``` The annoying part about this is that it only fails during training and I don't know when it will fail, except that it always fails during evaluation. ### Expected behavior The expected behavior is that I should be able to get something from the iterator when called instead of getting nothing / stuck in a loop somewhere. ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.31 - Python version: 3.11.7 - `huggingface_hub` version: 0.23.4 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0
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7023
Remove dead code for pyarrow < 15.0.0
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7023). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005669 / 0.011353 (-0.005684) | 0.004233 / 0.011008 (-0.006775) | 0.063550 / 0.038508 (0.025041) | 0.031269 / 0.023109 (0.008160) | 0.234280 / 0.275898 (-0.041618) | 0.264517 / 0.323480 (-0.058963) | 0.003310 / 0.007986 (-0.004676) | 0.003640 / 0.004328 (-0.000688) | 0.050139 / 0.004250 (0.045889) | 0.046909 / 0.037052 (0.009856) | 0.253101 / 0.258489 (-0.005388) | 0.280281 / 0.293841 (-0.013560) | 0.029558 / 0.128546 (-0.098989) | 0.012537 / 0.075646 (-0.063110) | 0.209624 / 0.419271 (-0.209648) | 0.036857 / 0.043533 (-0.006676) | 0.236957 / 0.255139 (-0.018182) | 0.260510 / 0.283200 (-0.022689) | 0.019802 / 0.141683 (-0.121881) | 1.141747 / 1.452155 (-0.310407) | 1.172617 / 1.492716 (-0.320099) |\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) | 0.107381 / 0.018006 (0.089375) | 0.308401 / 0.000490 (0.307911) | 0.000227 / 0.000200 (0.000027) | 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) | 0.019504 / 0.037411 (-0.017907) | 0.063920 / 0.014526 (0.049394) | 0.075375 / 0.176557 (-0.101181) | 0.122707 / 0.737135 (-0.614428) | 0.080015 / 0.296338 (-0.216324) |\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) | 0.288716 / 0.215209 (0.073507) | 2.862022 / 2.077655 (0.784368) | 1.472510 / 1.504120 (-0.031610) | 1.332989 / 1.541195 (-0.208206) | 1.395140 / 1.468490 (-0.073350) | 0.728042 / 4.584777 (-3.856735) | 2.409914 / 3.745712 (-1.335799) | 2.912514 / 5.269862 (-2.357347) | 1.986980 / 4.565676 (-2.578697) | 0.078587 / 0.424275 (-0.345688) | 0.005601 / 0.007607 (-0.002006) | 0.342510 / 0.226044 (0.116466) | 3.354621 / 2.268929 (1.085692) | 1.852472 / 55.444624 (-53.592153) | 1.542567 / 6.876477 (-5.333910) | 1.726756 / 2.142072 (-0.415317) | 0.794567 / 4.805227 (-4.010660) | 0.135279 / 6.500664 (-6.365386) | 0.042591 / 0.075469 (-0.032878) |\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) | 0.968336 / 1.841788 (-0.873452) | 12.334614 / 8.074308 (4.260305) | 9.638775 / 10.191392 (-0.552617) | 0.143625 / 0.680424 (-0.536799) | 0.015475 / 0.534201 (-0.518726) | 0.313357 / 0.579283 (-0.265926) | 0.271257 / 0.434364 (-0.163107) | 0.362074 / 0.540337 (-0.178263) | 0.468595 / 1.386936 (-0.918341) |\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) | 0.006243 / 0.011353 (-0.005110) | 0.004496 / 0.011008 (-0.006512) | 0.051271 / 0.038508 (0.012763) | 0.035718 / 0.023109 (0.012609) | 0.272623 / 0.275898 (-0.003275) | 0.297060 / 0.323480 (-0.026420) | 0.004801 / 0.007986 (-0.003185) | 0.003060 / 0.004328 (-0.001269) | 0.049990 / 0.004250 (0.045740) | 0.042413 / 0.037052 (0.005360) | 0.281268 / 0.258489 (0.022779) | 0.327224 / 0.293841 (0.033383) | 0.033745 / 0.128546 (-0.094801) | 0.012777 / 0.075646 (-0.062869) | 0.061808 / 0.419271 (-0.357464) | 0.034428 / 0.043533 (-0.009105) | 0.272211 / 0.255139 (0.017072) | 0.327260 / 0.283200 (0.044061) | 0.019756 / 0.141683 (-0.121927) | 1.137768 / 1.452155 (-0.314387) | 1.220347 / 1.492716 (-0.272369) |\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) | 0.099737 / 0.018006 (0.081731) | 0.304627 / 0.000490 (0.304137) | 0.000210 / 0.000200 (0.000011) | 0.000052 / 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) | 0.023177 / 0.037411 (-0.014234) | 0.077505 / 0.014526 (0.062979) | 0.088957 / 0.176557 (-0.087599) | 0.129187 / 0.737135 (-0.607948) | 0.090386 / 0.296338 (-0.205953) |\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) | 0.291558 / 0.215209 (0.076349) | 2.874297 / 2.077655 (0.796642) | 1.562316 / 1.504120 (0.058196) | 1.439950 / 1.541195 (-0.101244) | 1.492316 / 1.468490 (0.023826) | 0.729885 / 4.584777 (-3.854892) | 0.985075 / 3.745712 (-2.760637) | 3.108313 / 5.269862 (-2.161549) | 1.998072 / 4.565676 (-2.567604) | 0.079367 / 0.424275 (-0.344908) | 0.005210 / 0.007607 (-0.002398) | 0.347335 / 0.226044 (0.121290) | 3.519375 / 2.268929 (1.250446) | 1.949395 / 55.444624 (-53.495229) | 1.650379 / 6.876477 (-5.226097) | 1.691606 / 2.142072 (-0.450466) | 0.816023 / 4.805227 (-3.989204) | 0.135318 / 6.500664 (-6.365346) | 0.041390 / 0.075469 (-0.034079) |\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) | 1.018964 / 1.841788 (-0.822823) | 13.120135 / 8.074308 (5.045827) | 10.618095 / 10.191392 (0.426703) | 0.134507 / 0.680424 (-0.545917) | 0.015895 / 0.534201 (-0.518306) | 0.302864 / 0.579283 (-0.276420) | 0.131117 / 0.434364 (-0.303247) | 0.342374 / 0.540337 (-0.197964) | 0.441640 / 1.386936 (-0.945296) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5fdb68cd12d069f05a3db8add8e6feab3c06930 \"CML watermark\")\n" ]
2024-07-03T09:05:03
2024-07-03T09:24:46
2024-07-03 09:17:35+00:00
MEMBER
nan
Remove dead code for pyarrow < 15.0.0. Code is dead since the merge of: - #6892 Fix #7022.
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7022
There is dead code after we require pyarrow >= 15.0.0
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2024-07-03T08:52:57
2024-07-03T09:17:36
2024-07-03 09:17:36+00:00
MEMBER
nan
There are code lines specific for pyarrow versions < 15.0.0. However, we require pyarrow >= 15.0.0 since the merge of PR: - #6892 Those code lines are now dead code and should be removed.
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7021
Fix casting list array to fixed size list
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7021). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005126 / 0.011353 (-0.006227) | 0.003417 / 0.011008 (-0.007591) | 0.063274 / 0.038508 (0.024766) | 0.030896 / 0.023109 (0.007787) | 0.246661 / 0.275898 (-0.029237) | 0.275037 / 0.323480 (-0.048443) | 0.003243 / 0.007986 (-0.004742) | 0.003460 / 0.004328 (-0.000868) | 0.049665 / 0.004250 (0.045414) | 0.045826 / 0.037052 (0.008773) | 0.254360 / 0.258489 (-0.004129) | 0.294934 / 0.293841 (0.001094) | 0.029115 / 0.128546 (-0.099431) | 0.011908 / 0.075646 (-0.063738) | 0.207429 / 0.419271 (-0.211842) | 0.036371 / 0.043533 (-0.007162) | 0.249127 / 0.255139 (-0.006012) | 0.273982 / 0.283200 (-0.009218) | 0.019318 / 0.141683 (-0.122365) | 1.108985 / 1.452155 (-0.343169) | 1.147234 / 1.492716 (-0.345482) |\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) | 0.104830 / 0.018006 (0.086824) | 0.313453 / 0.000490 (0.312964) | 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) | 0.019140 / 0.037411 (-0.018271) | 0.062160 / 0.014526 (0.047634) | 0.073537 / 0.176557 (-0.103020) | 0.119605 / 0.737135 (-0.617530) | 0.074707 / 0.296338 (-0.221632) |\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) | 0.282600 / 0.215209 (0.067391) | 2.805560 / 2.077655 (0.727906) | 1.471312 / 1.504120 (-0.032808) | 1.360920 / 1.541195 (-0.180275) | 1.361132 / 1.468490 (-0.107358) | 0.714791 / 4.584777 (-3.869986) | 2.405224 / 3.745712 (-1.340488) | 2.814498 / 5.269862 (-2.455363) | 1.896792 / 4.565676 (-2.668884) | 0.078138 / 0.424275 (-0.346137) | 0.005430 / 0.007607 (-0.002177) | 0.345529 / 0.226044 (0.119485) | 3.366205 / 2.268929 (1.097277) | 1.862820 / 55.444624 (-53.581805) | 1.555970 / 6.876477 (-5.320507) | 1.665102 / 2.142072 (-0.476970) | 0.798679 / 4.805227 (-4.006548) | 0.132601 / 6.500664 (-6.368064) | 0.041819 / 0.075469 (-0.033650) |\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) | 0.972545 / 1.841788 (-0.869242) | 11.250626 / 8.074308 (3.176318) | 9.211127 / 10.191392 (-0.980265) | 0.130818 / 0.680424 (-0.549605) | 0.014123 / 0.534201 (-0.520078) | 0.298384 / 0.579283 (-0.280899) | 0.269736 / 0.434364 (-0.164628) | 0.341322 / 0.540337 (-0.199015) | 0.466915 / 1.386936 (-0.920021) |\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) | 0.005884 / 0.011353 (-0.005469) | 0.003983 / 0.011008 (-0.007025) | 0.050295 / 0.038508 (0.011787) | 0.033906 / 0.023109 (0.010797) | 0.271364 / 0.275898 (-0.004534) | 0.290652 / 0.323480 (-0.032828) | 0.004503 / 0.007986 (-0.003483) | 0.002946 / 0.004328 (-0.001382) | 0.049336 / 0.004250 (0.045086) | 0.040987 / 0.037052 (0.003935) | 0.283088 / 0.258489 (0.024599) | 0.313132 / 0.293841 (0.019291) | 0.032545 / 0.128546 (-0.096001) | 0.012622 / 0.075646 (-0.063024) | 0.060574 / 0.419271 (-0.358698) | 0.033625 / 0.043533 (-0.009908) | 0.266765 / 0.255139 (0.011626) | 0.286164 / 0.283200 (0.002964) | 0.018840 / 0.141683 (-0.122843) | 1.167874 / 1.452155 (-0.284281) | 1.170767 / 1.492716 (-0.321950) |\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) | 0.102266 / 0.018006 (0.084260) | 0.309530 / 0.000490 (0.309040) | 0.000210 / 0.000200 (0.000010) | 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) | 0.023879 / 0.037411 (-0.013533) | 0.076837 / 0.014526 (0.062311) | 0.088718 / 0.176557 (-0.087839) | 0.129422 / 0.737135 (-0.607714) | 0.090051 / 0.296338 (-0.206287) |\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) | 0.287325 / 0.215209 (0.072116) | 2.844051 / 2.077655 (0.766397) | 1.552338 / 1.504120 (0.048218) | 1.422390 / 1.541195 (-0.118804) | 1.458580 / 1.468490 (-0.009910) | 0.712103 / 4.584777 (-3.872674) | 0.935116 / 3.745712 (-2.810596) | 2.891878 / 5.269862 (-2.377984) | 1.884683 / 4.565676 (-2.680994) | 0.077810 / 0.424275 (-0.346465) | 0.005087 / 0.007607 (-0.002520) | 0.337981 / 0.226044 (0.111937) | 3.346176 / 2.268929 (1.077248) | 1.892525 / 55.444624 (-53.552100) | 1.595472 / 6.876477 (-5.281004) | 1.595617 / 2.142072 (-0.546455) | 0.779581 / 4.805227 (-4.025647) | 0.131042 / 6.500664 (-6.369623) | 0.040665 / 0.075469 (-0.034804) |\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) | 1.063560 / 1.841788 (-0.778227) | 12.030321 / 8.074308 (3.956013) | 10.213963 / 10.191392 (0.022571) | 0.142954 / 0.680424 (-0.537470) | 0.015700 / 0.534201 (-0.518501) | 0.311536 / 0.579283 (-0.267747) | 0.127064 / 0.434364 (-0.307300) | 0.351636 / 0.540337 (-0.188702) | 0.442281 / 1.386936 (-0.944655) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9ccc1f3d533712baf15cb7a93182add3e5446165 \"CML watermark\")\n" ]
2024-07-03T07:58:57
2024-07-03T08:47:49
2024-07-03 08:41:55+00:00
MEMBER
nan
Fix casting list array to fixed size list. This bug was introduced in [datasets-2.17.0](https://github.com/huggingface/datasets/releases/tag/2.17.0) by PR: https://github.com/huggingface/datasets/pull/6283/files#diff-1cb2b66aa9311d729cfd83013dad56cf5afcda35b39dfd0bfe9c3813a049eab0R1899 - #6283 Fix #7020.
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7020
Casting list array to fixed size list raises error
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2024-07-03T07:54:49
2024-07-03T08:41:56
2024-07-03 08:41:56+00:00
MEMBER
nan
When trying to cast a list array to fixed size list, an AttributeError is raised: > AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length' Steps to reproduce the bug: ```python import pyarrow as pa from datasets.table import array_cast arr = pa.array([[0, 1]]) array_cast(arr, pa.list_(pa.int64(), 2)) ``` Stack trace: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-12-6cb90a1d8216> in <module> 3 4 arr = pa.array([[0, 1]]) ----> 5 array_cast(arr, pa.list_(pa.int64(), 2)) ~/huggingface/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs) 1802 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 1803 else: -> 1804 return func(array, *args, **kwargs) 1805 1806 return wrapper ~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str) 1920 else: 1921 array_values = array.values[ -> 1922 array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length 1923 ] 1924 return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size) AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length' ```
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7019
Allow Polars round trip by supporting pyarrow large list
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7019). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
2024-07-02T09:52:52
2024-07-08T15:33:31
NaT
MEMBER
nan
Allow Polars round trip by supporting pyarrow large list. Fix #6834, fix #6984. Supersede and close #4800, close #6835, close #6986.
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7018
`load_dataset` fails to load dataset saved by `save_to_disk`
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2024-07-01T12:19:19
2024-07-01T12:19:19
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### Describe the bug This code fails to load the dataset it just saved: ```python from datasets import load_dataset from transformers import AutoTokenizer MODEL = "google-bert/bert-base-cased" tokenizer = AutoTokenizer.from_pretrained(MODEL) dataset = load_dataset("yelp_review_full") def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", truncation=True) tokenized_datasets = dataset.map(tokenize_function, batched=True) tokenized_datasets.save_to_disk("dataset") tokenized_datasets = load_dataset("dataset/") # raises ``` It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`. I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON: ```shell $ ls -l dataset/test -rw-r--r-- 1 sliedes sliedes 191498784 Jul 1 13:55 data-00000-of-00001.arrow -rw-r--r-- 1 sliedes sliedes 1730 Jul 1 13:55 dataset_info.json -rw-r--r-- 1 sliedes sliedes 249 Jul 1 13:55 state.json ``` ### Steps to reproduce the bug Execute the code above. ### Expected behavior The dataset is loaded successfully. ### Environment info - `datasets` version: 2.20.0 - Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39 - Python version: 3.12.4 - `huggingface_hub` version: 0.23.4 - PyArrow version: 16.1.0 - Pandas version: 2.2.2 - `fsspec` version: 2024.5.0
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7017
Support fsspec 2024.6.1
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7017). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005520 / 0.011353 (-0.005832) | 0.004216 / 0.011008 (-0.006792) | 0.063465 / 0.038508 (0.024957) | 0.032116 / 0.023109 (0.009007) | 0.242486 / 0.275898 (-0.033412) | 0.262554 / 0.323480 (-0.060925) | 0.004218 / 0.007986 (-0.003768) | 0.003264 / 0.004328 (-0.001064) | 0.050306 / 0.004250 (0.046056) | 0.044995 / 0.037052 (0.007942) | 0.257797 / 0.258489 (-0.000693) | 0.284595 / 0.293841 (-0.009246) | 0.030623 / 0.128546 (-0.097924) | 0.012245 / 0.075646 (-0.063401) | 0.205496 / 0.419271 (-0.213775) | 0.039327 / 0.043533 (-0.004206) | 0.246834 / 0.255139 (-0.008305) | 0.269296 / 0.283200 (-0.013903) | 0.017714 / 0.141683 (-0.123969) | 1.127246 / 1.452155 (-0.324909) | 1.172147 / 1.492716 (-0.320569) |\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) | 0.137621 / 0.018006 (0.119615) | 0.299843 / 0.000490 (0.299353) | 0.000248 / 0.000200 (0.000048) | 0.000051 / 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) | 0.018968 / 0.037411 (-0.018443) | 0.062636 / 0.014526 (0.048111) | 0.074098 / 0.176557 (-0.102459) | 0.121139 / 0.737135 (-0.615996) | 0.075121 / 0.296338 (-0.221217) |\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) | 0.289907 / 0.215209 (0.074698) | 2.872250 / 2.077655 (0.794595) | 1.508635 / 1.504120 (0.004515) | 1.345356 / 1.541195 (-0.195839) | 1.361858 / 1.468490 (-0.106632) | 0.738961 / 4.584777 (-3.845816) | 2.414616 / 3.745712 (-1.331097) | 2.843464 / 5.269862 (-2.426398) | 1.953716 / 4.565676 (-2.611961) | 0.079063 / 0.424275 (-0.345212) | 0.005498 / 0.007607 (-0.002109) | 0.346211 / 0.226044 (0.120166) | 3.446294 / 2.268929 (1.177366) | 1.857191 / 55.444624 (-53.587433) | 1.536924 / 6.876477 (-5.339553) | 1.655782 / 2.142072 (-0.486290) | 0.800508 / 4.805227 (-4.004719) | 0.136116 / 6.500664 (-6.364548) | 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) | 0.964286 / 1.841788 (-0.877501) | 11.574645 / 8.074308 (3.500336) | 9.351631 / 10.191392 (-0.839761) | 0.139693 / 0.680424 (-0.540731) | 0.014368 / 0.534201 (-0.519833) | 0.303953 / 0.579283 (-0.275330) | 0.263302 / 0.434364 (-0.171062) | 0.342436 / 0.540337 (-0.197901) | 0.457195 / 1.386936 (-0.929741) |\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) | 0.005526 / 0.011353 (-0.005827) | 0.003959 / 0.011008 (-0.007050) | 0.049979 / 0.038508 (0.011471) | 0.032695 / 0.023109 (0.009586) | 0.269461 / 0.275898 (-0.006437) | 0.296622 / 0.323480 (-0.026858) | 0.004410 / 0.007986 (-0.003576) | 0.002708 / 0.004328 (-0.001621) | 0.048413 / 0.004250 (0.044163) | 0.040567 / 0.037052 (0.003515) | 0.278854 / 0.258489 (0.020364) | 0.318839 / 0.293841 (0.024998) | 0.031228 / 0.128546 (-0.097318) | 0.012411 / 0.075646 (-0.063236) | 0.060077 / 0.419271 (-0.359194) | 0.033072 / 0.043533 (-0.010461) | 0.275281 / 0.255139 (0.020142) | 0.292588 / 0.283200 (0.009388) | 0.018218 / 0.141683 (-0.123465) | 1.124877 / 1.452155 (-0.327278) | 1.164880 / 1.492716 (-0.327836) |\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) | 0.095098 / 0.018006 (0.077092) | 0.298341 / 0.000490 (0.297851) | 0.000225 / 0.000200 (0.000025) | 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) | 0.022502 / 0.037411 (-0.014909) | 0.076650 / 0.014526 (0.062124) | 0.088851 / 0.176557 (-0.087705) | 0.128261 / 0.737135 (-0.608875) | 0.089305 / 0.296338 (-0.207033) |\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) | 0.298704 / 0.215209 (0.083495) | 2.917605 / 2.077655 (0.839951) | 1.568964 / 1.504120 (0.064844) | 1.437668 / 1.541195 (-0.103527) | 1.458787 / 1.468490 (-0.009704) | 0.732347 / 4.584777 (-3.852430) | 0.960834 / 3.745712 (-2.784878) | 2.947899 / 5.269862 (-2.321963) | 1.885576 / 4.565676 (-2.680100) | 0.079093 / 0.424275 (-0.345182) | 0.005199 / 0.007607 (-0.002408) | 0.353754 / 0.226044 (0.127710) | 3.495197 / 2.268929 (1.226268) | 1.936840 / 55.444624 (-53.507785) | 1.622797 / 6.876477 (-5.253680) | 1.627132 / 2.142072 (-0.514940) | 0.804007 / 4.805227 (-4.001221) | 0.135990 / 6.500664 (-6.364674) | 0.041606 / 0.075469 (-0.033863) |\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) | 1.004860 / 1.841788 (-0.836928) | 12.027573 / 8.074308 (3.953265) | 10.478055 / 10.191392 (0.286663) | 0.143946 / 0.680424 (-0.536477) | 0.015538 / 0.534201 (-0.518663) | 0.302592 / 0.579283 (-0.276691) | 0.123177 / 0.434364 (-0.311187) | 0.340752 / 0.540337 (-0.199585) | 0.436536 / 1.386936 (-0.950400) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#100361d7ccae451a34c6bd9e48dee55d6a3c6006 \"CML watermark\")\n" ]
2024-07-01T11:57:15
2024-07-01T12:12:32
2024-07-01 12:06:24+00:00
MEMBER
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Support fsspec 2024.6.1.
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7016
`drop_duplicates` method
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[ "There is an open issue #2514 about this which also proposes solutions." ]
2024-07-01T09:01:06
2024-07-02T13:54:48
NaT
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### Feature request `drop_duplicates` method for huggingface datasets (similiar in simplicity to the `pandas` one) ### Motivation Ease of use ### Your contribution I don't think i am good enough to help
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7015
add split argument to Generator
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7015). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@albertvillanova thanks for the review, please take a look", "@albertvillanova please take a look" ]
2024-07-01T08:09:25
2024-07-11T08:20:16
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## Actual When creating a multi-split dataset using generators like ```python datasets.DatasetDict({ "val": datasets.Dataset.from_generator( generator=generator_val, features=features ), "test": datasets.Dataset.from_generator( generator=generator_test, features=features, ) }) ``` It displays (for both test and val) ``` Generating train split ``` ## Expected I would like to be able to improve this behavior by doing ```python datasets.DatasetDict({ "val": datasets.Dataset.from_generator( generator=generator_val, features=features, split="val" ), "test": datasets.Dataset.from_generator( generator=generator_test, features=features, split="test" ) }) ``` It would display ``` Generating val split ``` and ``` Generating test split ``` ## Proposal Current PR is adding an explicit `split` argument and replace the implicit "train" split in the following classes/function : * Generator * from_generator * AbstractDatasetInputStream * GeneratorDatasetInputStream Please share your feedbacks
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7014
Skip faiss tests on Windows to avoid running CI for 360 minutes
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7014). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "The failing CI tests are unrelated to this PR.\r\n\r\nWe can see that now the integration tests on Windows finish in a reasonable amount of time, e.g. 8m 10s.", "<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) | 0.005219 / 0.011353 (-0.006134) | 0.003825 / 0.011008 (-0.007183) | 0.063082 / 0.038508 (0.024574) | 0.031258 / 0.023109 (0.008149) | 0.232288 / 0.275898 (-0.043610) | 0.261140 / 0.323480 (-0.062340) | 0.003185 / 0.007986 (-0.004801) | 0.002807 / 0.004328 (-0.001522) | 0.049438 / 0.004250 (0.045188) | 0.045112 / 0.037052 (0.008059) | 0.245327 / 0.258489 (-0.013162) | 0.277941 / 0.293841 (-0.015900) | 0.029190 / 0.128546 (-0.099357) | 0.012071 / 0.075646 (-0.063575) | 0.204351 / 0.419271 (-0.214921) | 0.036546 / 0.043533 (-0.006987) | 0.235999 / 0.255139 (-0.019140) | 0.269069 / 0.283200 (-0.014131) | 0.019047 / 0.141683 (-0.122636) | 1.117213 / 1.452155 (-0.334941) | 1.202807 / 1.492716 (-0.289909) |\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) | 0.096680 / 0.018006 (0.078674) | 0.304513 / 0.000490 (0.304023) | 0.000211 / 0.000200 (0.000011) | 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) | 0.019526 / 0.037411 (-0.017885) | 0.062239 / 0.014526 (0.047713) | 0.073988 / 0.176557 (-0.102569) | 0.122156 / 0.737135 (-0.614980) | 0.075727 / 0.296338 (-0.220611) |\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) | 0.284125 / 0.215209 (0.068916) | 2.804235 / 2.077655 (0.726581) | 1.463729 / 1.504120 (-0.040390) | 1.337854 / 1.541195 (-0.203341) | 1.340435 / 1.468490 (-0.128055) | 0.711647 / 4.584777 (-3.873130) | 2.365194 / 3.745712 (-1.380518) | 2.839193 / 5.269862 (-2.430669) | 1.909730 / 4.565676 (-2.655947) | 0.077399 / 0.424275 (-0.346876) | 0.005432 / 0.007607 (-0.002175) | 0.332281 / 0.226044 (0.106236) | 3.301854 / 2.268929 (1.032925) | 1.836672 / 55.444624 (-53.607952) | 1.511144 / 6.876477 (-5.365333) | 1.624167 / 2.142072 (-0.517905) | 0.803453 / 4.805227 (-4.001775) | 0.132760 / 6.500664 (-6.367904) | 0.042323 / 0.075469 (-0.033146) |\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) | 0.951576 / 1.841788 (-0.890212) | 11.476809 / 8.074308 (3.402501) | 9.208285 / 10.191392 (-0.983107) | 0.131797 / 0.680424 (-0.548626) | 0.014362 / 0.534201 (-0.519839) | 0.316051 / 0.579283 (-0.263232) | 0.269250 / 0.434364 (-0.165114) | 0.366949 / 0.540337 (-0.173388) | 0.471047 / 1.386936 (-0.915889) |\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) | 0.005905 / 0.011353 (-0.005448) | 0.003892 / 0.011008 (-0.007116) | 0.050513 / 0.038508 (0.012005) | 0.030903 / 0.023109 (0.007794) | 0.268835 / 0.275898 (-0.007063) | 0.288825 / 0.323480 (-0.034655) | 0.004372 / 0.007986 (-0.003614) | 0.002805 / 0.004328 (-0.001523) | 0.048497 / 0.004250 (0.044246) | 0.040665 / 0.037052 (0.003613) | 0.279842 / 0.258489 (0.021352) | 0.310715 / 0.293841 (0.016874) | 0.032133 / 0.128546 (-0.096413) | 0.012288 / 0.075646 (-0.063358) | 0.059719 / 0.419271 (-0.359552) | 0.033825 / 0.043533 (-0.009708) | 0.264670 / 0.255139 (0.009531) | 0.283799 / 0.283200 (0.000599) | 0.017968 / 0.141683 (-0.123715) | 1.160578 / 1.452155 (-0.291577) | 1.198602 / 1.492716 (-0.294115) |\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) | 0.094388 / 0.018006 (0.076382) | 0.301861 / 0.000490 (0.301371) | 0.000212 / 0.000200 (0.000012) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022901 / 0.037411 (-0.014510) | 0.076816 / 0.014526 (0.062290) | 0.089203 / 0.176557 (-0.087354) | 0.129040 / 0.737135 (-0.608096) | 0.090758 / 0.296338 (-0.205580) |\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) | 0.301191 / 0.215209 (0.085982) | 2.962887 / 2.077655 (0.885232) | 1.607134 / 1.504120 (0.103014) | 1.477817 / 1.541195 (-0.063377) | 1.485984 / 1.468490 (0.017494) | 0.717358 / 4.584777 (-3.867419) | 0.976018 / 3.745712 (-2.769694) | 2.951509 / 5.269862 (-2.318352) | 1.910619 / 4.565676 (-2.655057) | 0.078579 / 0.424275 (-0.345697) | 0.005209 / 0.007607 (-0.002398) | 0.345287 / 0.226044 (0.119243) | 3.487012 / 2.268929 (1.218084) | 1.938104 / 55.444624 (-53.506521) | 1.639341 / 6.876477 (-5.237136) | 1.617874 / 2.142072 (-0.524198) | 0.793721 / 4.805227 (-4.011506) | 0.136834 / 6.500664 (-6.363830) | 0.041211 / 0.075469 (-0.034258) |\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) | 0.988106 / 1.841788 (-0.853682) | 12.035176 / 8.074308 (3.960868) | 10.594559 / 10.191392 (0.403167) | 0.149917 / 0.680424 (-0.530507) | 0.015913 / 0.534201 (-0.518288) | 0.307658 / 0.579283 (-0.271625) | 0.130645 / 0.434364 (-0.303719) | 0.348450 / 0.540337 (-0.191887) | 0.443559 / 1.386936 (-0.943377) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9af8dd3de7626183a9a9ec8973cebc672d690400 \"CML watermark\")\n" ]
2024-07-01T06:45:35
2024-07-01T07:16:36
2024-07-01 07:10:27+00:00
MEMBER
nan
Skip faiss tests on Windows to avoid running CI for 360 minutes. Fix #7013. Revert once the underlying issue is fixed.
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2382976738
I_kwDODunzps6OCVbi
7013
CI is broken for faiss tests on Windows: node down: Not properly terminated
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2024-07-01T06:40:03
2024-07-01T07:10:28
2024-07-01 07:10:28+00:00
MEMBER
nan
Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached. See: https://github.com/huggingface/datasets/actions/runs/9712659783 ``` test (integration, windows-latest, deps-minimum) The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes. test (integration, windows-latest, deps-latest) The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes. ``` ``` ____________________________ tests/test_search.py _____________________________ [gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index' ____________________________ tests/test_search.py _____________________________ [gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index' ``` ``` tests/test_search.py::IndexableDatasetTest::test_add_faiss_index [gw0] node down: Not properly terminated [gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index replacing crashed worker gw0 tests/test_search.py::IndexableDatasetTest::test_add_faiss_index [gw1] node down: Not properly terminated [gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index replacing crashed worker gw1 tests/test_search.py::IndexableDatasetTest::test_add_faiss_index [gw2] node down: Not properly terminated [gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index replacing crashed worker gw2 ```
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2380934047
PR_kwDODunzps5z61A3
7012
Raise an error when a nested object is expected to be a mapping that displays the object
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2024-06-28T18:10:59
2024-07-11T02:06:16
2024-07-11 02:06:16+00:00
NONE
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PR_kwDODunzps5z27Fs
7011
Re-enable raising error from huggingface-hub FutureWarning in CI
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7011). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<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) | 0.005589 / 0.011353 (-0.005764) | 0.003855 / 0.011008 (-0.007153) | 0.063445 / 0.038508 (0.024937) | 0.030815 / 0.023109 (0.007706) | 0.244052 / 0.275898 (-0.031846) | 0.269916 / 0.323480 (-0.053563) | 0.003130 / 0.007986 (-0.004856) | 0.003349 / 0.004328 (-0.000980) | 0.049338 / 0.004250 (0.045088) | 0.045314 / 0.037052 (0.008261) | 0.250646 / 0.258489 (-0.007844) | 0.295828 / 0.293841 (0.001987) | 0.029808 / 0.128546 (-0.098738) | 0.012299 / 0.075646 (-0.063347) | 0.204946 / 0.419271 (-0.214325) | 0.036387 / 0.043533 (-0.007146) | 0.244316 / 0.255139 (-0.010823) | 0.269308 / 0.283200 (-0.013892) | 0.019226 / 0.141683 (-0.122457) | 1.138739 / 1.452155 (-0.313416) | 1.155265 / 1.492716 (-0.337451) |\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) | 0.094085 / 0.018006 (0.076078) | 0.299764 / 0.000490 (0.299275) | 0.000205 / 0.000200 (0.000005) | 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) | 0.018361 / 0.037411 (-0.019050) | 0.062665 / 0.014526 (0.048139) | 0.075888 / 0.176557 (-0.100668) | 0.120915 / 0.737135 (-0.616221) | 0.075465 / 0.296338 (-0.220873) |\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) | 0.279698 / 0.215209 (0.064489) | 2.784544 / 2.077655 (0.706889) | 1.498441 / 1.504120 (-0.005679) | 1.379789 / 1.541195 (-0.161406) | 1.388480 / 1.468490 (-0.080011) | 0.724249 / 4.584777 (-3.860528) | 2.343139 / 3.745712 (-1.402573) | 2.816179 / 5.269862 (-2.453683) | 1.908737 / 4.565676 (-2.656940) | 0.077686 / 0.424275 (-0.346589) | 0.005444 / 0.007607 (-0.002163) | 0.344084 / 0.226044 (0.118039) | 3.367548 / 2.268929 (1.098619) | 1.849200 / 55.444624 (-53.595424) | 1.556390 / 6.876477 (-5.320087) | 1.672902 / 2.142072 (-0.469170) | 0.795457 / 4.805227 (-4.009770) | 0.133521 / 6.500664 (-6.367143) | 0.042883 / 0.075469 (-0.032586) |\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) | 0.959094 / 1.841788 (-0.882694) | 11.399783 / 8.074308 (3.325475) | 9.075784 / 10.191392 (-1.115608) | 0.142897 / 0.680424 (-0.537527) | 0.014765 / 0.534201 (-0.519436) | 0.302259 / 0.579283 (-0.277024) | 0.261148 / 0.434364 (-0.173216) | 0.340302 / 0.540337 (-0.200035) | 0.459203 / 1.386936 (-0.927733) |\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) | 0.005821 / 0.011353 (-0.005532) | 0.003964 / 0.011008 (-0.007044) | 0.049904 / 0.038508 (0.011396) | 0.031061 / 0.023109 (0.007952) | 0.270002 / 0.275898 (-0.005896) | 0.289489 / 0.323480 (-0.033991) | 0.004477 / 0.007986 (-0.003509) | 0.002800 / 0.004328 (-0.001528) | 0.048029 / 0.004250 (0.043779) | 0.040486 / 0.037052 (0.003434) | 0.278442 / 0.258489 (0.019953) | 0.312606 / 0.293841 (0.018765) | 0.032920 / 0.128546 (-0.095626) | 0.012572 / 0.075646 (-0.063075) | 0.060589 / 0.419271 (-0.358682) | 0.034147 / 0.043533 (-0.009386) | 0.275282 / 0.255139 (0.020143) | 0.314073 / 0.283200 (0.030873) | 0.017555 / 0.141683 (-0.124128) | 1.149974 / 1.452155 (-0.302181) | 1.183715 / 1.492716 (-0.309002) |\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) | 0.095616 / 0.018006 (0.077610) | 0.302101 / 0.000490 (0.301611) | 0.000201 / 0.000200 (0.000001) | 0.000051 / 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) | 0.022245 / 0.037411 (-0.015166) | 0.076890 / 0.014526 (0.062364) | 0.088471 / 0.176557 (-0.088085) | 0.128364 / 0.737135 (-0.608771) | 0.089907 / 0.296338 (-0.206431) |\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) | 0.302662 / 0.215209 (0.087453) | 2.979054 / 2.077655 (0.901399) | 1.576534 / 1.504120 (0.072414) | 1.443784 / 1.541195 (-0.097410) | 1.476000 / 1.468490 (0.007510) | 0.740580 / 4.584777 (-3.844197) | 0.953349 / 3.745712 (-2.792363) | 2.925619 / 5.269862 (-2.344243) | 1.904701 / 4.565676 (-2.660975) | 0.078404 / 0.424275 (-0.345872) | 0.005179 / 0.007607 (-0.002429) | 0.357217 / 0.226044 (0.131173) | 3.494812 / 2.268929 (1.225884) | 1.927345 / 55.444624 (-53.517280) | 1.627162 / 6.876477 (-5.249315) | 1.676748 / 2.142072 (-0.465324) | 0.798826 / 4.805227 (-4.006401) | 0.133617 / 6.500664 (-6.367047) | 0.041229 / 0.075469 (-0.034240) |\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) | 1.017046 / 1.841788 (-0.824742) | 12.045942 / 8.074308 (3.971634) | 10.430383 / 10.191392 (0.238991) | 0.144497 / 0.680424 (-0.535926) | 0.015809 / 0.534201 (-0.518392) | 0.304701 / 0.579283 (-0.274582) | 0.126496 / 0.434364 (-0.307868) | 0.340308 / 0.540337 (-0.200030) | 0.434917 / 1.386936 (-0.952019) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#054e57a8468af9fff5b75c08d2d6adf3e05fa763 \"CML watermark\")\n" ]
2024-06-28T07:28:32
2024-06-28T12:25:25
2024-06-28 12:19:28+00:00
MEMBER
nan
Re-enable raising error from huggingface-hub FutureWarning in tests, once that the fix in transformers - https://github.com/huggingface/transformers/pull/31007 was just released yesterday in transformers-4.42.0: https://github.com/huggingface/transformers/releases/tag/v4.42.0 Fix #7010.
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7010
Re-enable raising error from huggingface-hub FutureWarning in CI
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2024-06-28T07:23:40
2024-06-28T12:19:30
2024-06-28 12:19:29+00:00
MEMBER
nan
Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR: - #6876 Note that this can only be done once transformers releases the fix: - https://github.com/huggingface/transformers/pull/31007
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