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Add Hate Speech Dataset in Filipino
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"2020-12-06T02:01:56Z"
"2020-12-07T15:35:33Z"
"2020-12-07T15:35:33Z"
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This PR adds the Hate Speech Dataset, a text classification dataset in Filipino, consisting 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections. Link to the paper: https://pcj.csp.org.ph/index.php/pcj/issue/download/29/PCJ%20V14%20N1%20pp1-14%202019 Link to the dataset/repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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Minor fix in loading metrics docs
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"2021-06-29T07:55:11Z"
"2021-06-29T17:21:22Z"
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Make some minor fixes in "Loading metrics" docs.
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Iterable Dataset: rename column clashes with remove column
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[ "Column \"text\" doesn't exist anymore so you can't remove it", "You can get the expected result by fixing typos in the snippet :)\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset.features) - COLUMNS_TO_KEEP)\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Fixed code:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n#Β rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\ndataset_features = dataset.features.keys()\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP))\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Whoops πŸ˜… Thanks for the swift reply both! Works like a charm!" ]
"2023-12-08T16:11:30Z"
"2023-12-08T16:27:16Z"
"2023-12-08T16:27:04Z"
CONTRIBUTOR
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### Describe the bug Suppose I have a two iterable datasets, one with the features: * `{"audio", "text", "column_a"}` And the other with the features: * `{"audio", "sentence", "column_b"}` I want to combine both datasets using `interleave_datasets`, which requires me to unify the column names. I would typically do this by: 1. Renaming the common columns to the same name (e.g. `"text"` -> `"sentence"`) 2. Removing the unwanted columns (e.g. `"column_a"`, `"column_b"`) However, the process of renaming and removing columns in an iterable dataset doesn't work, since we need to preserve the original text column, meaning we can't combine the datasets. ### Steps to reproduce the bug ```python from datasets import load_dataset # load LS in streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # check original features dataset_features = dataset.features.keys() print("Original features: ", dataset_features) #Β rename "text" -> "sentence" dataset = dataset.rename_column("text", "sentence") # remove unwanted columns COLUMNS_TO_KEEP = {"audio", "sentence"} dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) # stream first sample, should return "audio" and "sentence" columns print(next(iter(dataset))) ``` Traceback: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[5], line 17 14 COLUMNS_TO_KEEP = {"audio", "sentence"} 15 dataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP)) ---> 17 print(next(iter(dataset))) File ~/datasets/src/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self) 1350 yield formatter.format_row(pa_table) 1351 return -> 1353 for key, example in ex_iterable: 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) File ~/datasets/src/datasets/iterable_dataset.py:652, in MappedExamplesIterable.__iter__(self) 650 yield from ArrowExamplesIterable(self._iter_arrow, {}) 651 else: --> 652 yield from self._iter() File ~/datasets/src/datasets/iterable_dataset.py:729, in MappedExamplesIterable._iter(self) 727 if self.remove_columns: 728 for c in self.remove_columns: --> 729 del transformed_example[c] 730 yield key, transformed_example 731 current_idx += 1 KeyError: 'text' ``` => we see that `datasets` is looking for the column "text", even though we've renamed this to "sentence" and then removed the un-wanted "text" column from our dataset. ### Expected behavior Should be able to rename and remove columns from iterable dataset. ### Environment info - `datasets` version: 2.15.1.dev0 - Platform: macOS-13.5.1-arm64-arm-64bit - Python version: 3.11.6 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2
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`wikipedia` pre-processed datasets
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[ "Hi @vpj, thanks for reporting.\r\n\r\nI'm sorry, but I can't reproduce your bug: I load \"20220301.simple\"in 9 seconds:\r\n```shell\r\ntime python -c \"from datasets import load_dataset; load_dataset('wikipedia', '20220301.simple')\"\r\n\r\nDownloading and preparing dataset wikipedia/20220301.simple (download: 228.58 MiB, generated: 224.18 MiB, post-processed: Unknown size, total: 452.76 MiB) to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...\r\nDownloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.66k/1.66k [00:00<00:00, 1.02MB/s]\r\nDownloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 235M/235M [00:02<00:00, 82.8MB/s]\r\nDataset wikipedia downloaded and prepared to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559. Subsequent calls will reuse this data.\r\n100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1/1 [00:00<00:00, 290.75it/s]\r\n\r\nreal\t0m9.693s\r\nuser\t0m6.002s\r\nsys\t0m3.260s\r\n```\r\n\r\nCould you please check your environment info, as requested when opening this issue?\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```\r\nMaybe you are using an old version of `datasets`...", "Downloading and processing `wikipedia simple` dataset completed in under 11sec on M1 Mac. Could you please check `dataset` version as mentioned by @albertvillanova? Also check system specs, if system is under load processing could take some time I guess." ]
"2022-05-12T11:25:42Z"
"2022-08-31T08:26:57Z"
"2022-08-31T08:26:57Z"
NONE
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## Describe the bug [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("wikipedia", "20220301.en") ``` ## Expected results To load the dataset ## Actual results Takes a very long time to load (after downloading) After `Downloading data files: 100%`. It takes hours and gets killed. Tried `wikipedia.simple` and it got processed after ~30mins.
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Added the HLGD dataset
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[ "Is there anything else needed from my end?", "Thanks Bhavitvya and Quentin, this was very streamlined!" ]
"2021-05-05T16:53:29Z"
"2021-05-12T14:55:13Z"
"2021-05-12T14:16:38Z"
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Added the Headline Grouping Dataset (HLGD), from the NAACL2021 paper: News Headline Grouping as a Challenging NLU Task Dataset Link: https://github.com/tingofurro/headline_grouping Paper link: https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf
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Fix misalignment in SQuAD
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"2021-07-05T06:42:20Z"
"2021-07-12T14:11:10Z"
"2021-07-07T13:18:51Z"
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Fix misalignment between: - the answer text and - the answer_start within the context by keeping original leading blank spaces in the context. Fix #2585.
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Set test cache config
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[ "> why a cache dir per test function does not work?\r\n\r\nProbably because we end up with multiple `datasets_module` in the python path. This breaks the import of all the datasets/metrics modules.\r\nIf you want to use one modules cache per test, you may need remove the `datasets_module` that was added to the python path during the test.\r\nIndeed if the module cache hasn't been initialized, then it's added to the python path by calling `init_dynamic_modules`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/ba76012a19193a35053b9e20243ff40c2b4204ab/src/datasets/load.py#L291-L291", "@lhoestq, for the moment, this PR avoids populating the `~/.cache` dir during training, which is already an improvement, isn't it?", "Yes we can merge it this way if you're fine with it !\r\nThis is a good improvement", "I will eventually try to implement a `cache_dir` per test function in another PR, but I think I should first fix some side effects in tests: each test function should be atomic and able to have its own `cache_dir` without being affected by the `cache_dir` set in other test functions.", "Yes this would be ideal !" ]
"2021-04-14T12:55:24Z"
"2021-04-15T19:11:25Z"
"2021-04-15T19:11:25Z"
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Currently, running the tests populates the default cache directory `"~/.cache"`. This PR monkey-patches the config to set the cache directory within the temporary test directory, avoiding side effects.
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Improve RedCaps dataset card
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I find this preprocessing a bit too specific to add it as a method to `datasets` as it's only useful in the context of CV (and we support multiple modalities). However, I agree it would be great to move this code to another lib to avoid code duplication. Maybe we should create a package with preprocessing functions/transforms for this purpose?" ]
"2022-04-05T15:57:14Z"
"2022-04-13T14:08:54Z"
"2022-04-13T14:02:26Z"
CONTRIBUTOR
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This PR modifies the RedCaps card to: * fix the formatting of the Point of Contact fields on the Hub * speed up the image fetching logic (aligns it with the [img2dataset](https://github.com/rom1504/img2dataset) tool) and make it more robust (return None if **any** exception is thrown)
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DataCollatorWithPadding: TypeError
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[ "\r\n> \r\n> Input:\r\n> \r\n> ```\r\n> tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n> ```\r\n> \r\n> Output:\r\n> \r\n> ```\r\n> TypeError Traceback (most recent call last)\r\n> /tmp/ipykernel_42/1563280798.py in <module>\r\n> 1 checkpoint = 'bert-base-uncased'\r\n> 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\n> TypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n> ```\r\n> \r\n\r\nThe issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n`# upgrade transformers and datasets to latest versions`\r\n`!pip install --upgrade transformers`\r\n`!pip install --upgrade datasets`\r\n\r\nCheers!" ]
"2021-11-20T11:59:55Z"
"2021-11-21T07:05:37Z"
"2021-11-21T07:05:37Z"
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Hi, I am following the HuggingFace course. I am now at Fine-tuning [https://huggingface.co/course/chapter3/3?fw=tf](https://huggingface.co/course/chapter3/3?fw=tf). When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device. Input: ```checkpoint = 'bert-base-uncased' tokenizer = AutoTokenizer.from_pretrained(checkpoint) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") ``` Output: ```--------------------------------------------------------------------------- TypeError Traceback (most recent call last) /tmp/ipykernel_42/1563280798.py in <module> 1 checkpoint = 'bert-base-uncased' 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint) ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt") TypeError: __init__() got an unexpected keyword argument 'return_tensors' ``` When I call `help` method, it too confirms that there is no argument `return_tensors`. Input: ``` help(DataCollatorWithPadding.__init__) ``` Output: ``` Help on function __init__ in module transformers.data.data_collator: __init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None ``` But, the source file *[Data Collator - docs](https://huggingface.co/transformers/main_classes/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors. Where do I miss? Please help me.
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007700 / 0.011353 (-0.003653) | 0.004680 / 0.011008 (-0.006328) | 0.098812 / 0.038508 (0.060304) | 0.085062 / 0.023109 (0.061952) | 0.371472 / 0.275898 (0.095574) | 0.412552 / 0.323480 (0.089072) | 0.004700 / 0.007986 (-0.003285) | 0.003765 / 0.004328 (-0.000564) | 0.074267 / 0.004250 (0.070017) | 0.063003 / 0.037052 (0.025951) | 0.391842 / 0.258489 (0.133353) | 0.436955 / 0.293841 (0.143114) | 0.035291 / 0.128546 (-0.093255) | 0.009309 / 0.075646 (-0.066338) | 0.313097 / 0.419271 (-0.106174) | 0.060098 / 0.043533 (0.016565) | 0.350726 / 0.255139 (0.095587) | 0.402692 / 0.283200 (0.119493) | 0.029321 / 0.141683 (-0.112361) | 1.671806 / 1.452155 (0.219651) | 1.743760 / 1.492716 (0.251044) |\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) | 0.242281 / 0.018006 (0.224275) | 0.505054 / 0.000490 (0.504564) | 0.006595 / 0.000200 (0.006395) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032174 / 0.037411 (-0.005238) | 0.094483 / 0.014526 (0.079957) | 0.108527 / 0.176557 (-0.068030) | 0.178983 / 0.737135 (-0.558152) | 0.113766 / 0.296338 (-0.182572) |\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) | 0.419764 / 0.215209 (0.204555) | 4.282650 / 2.077655 (2.204995) | 2.075325 / 1.504120 (0.571205) | 1.897668 / 1.541195 (0.356473) | 2.027109 / 1.468490 (0.558619) | 0.519983 / 4.584777 (-4.064794) | 4.134603 / 3.745712 (0.388891) | 6.586711 / 5.269862 (1.316849) | 3.811726 / 4.565676 (-0.753951) | 0.058628 / 0.424275 (-0.365647) | 0.007586 / 0.007607 (-0.000021) | 0.502180 / 0.226044 (0.276136) | 5.101588 / 2.268929 (2.832660) | 2.534295 / 55.444624 (-52.910330) | 2.220170 / 6.876477 (-4.656307) | 2.441110 / 2.142072 (0.299038) | 0.644775 / 4.805227 (-4.160452) | 0.144716 / 6.500664 (-6.355948) | 0.067018 / 0.075469 (-0.008451) |\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) | 1.431279 / 1.841788 (-0.410508) | 21.947814 / 8.074308 (13.873506) | 15.548236 / 10.191392 (5.356844) | 0.174774 / 0.680424 (-0.505650) | 0.021182 / 0.534201 (-0.513019) | 0.441320 / 0.579283 (-0.137963) | 0.476685 / 0.434364 (0.042321) | 0.506277 / 0.540337 (-0.034060) | 0.809943 / 1.386936 (-0.576993) |\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) | 0.007172 / 0.011353 (-0.004181) | 0.004358 / 0.011008 (-0.006650) | 0.068604 / 0.038508 (0.030096) | 0.083956 / 0.023109 (0.060847) | 0.402579 / 0.275898 (0.126681) | 0.444714 / 0.323480 (0.121235) | 0.005940 / 0.007986 (-0.002046) | 0.003607 / 0.004328 (-0.000722) | 0.073134 / 0.004250 (0.068883) | 0.061722 / 0.037052 (0.024669) | 0.410957 / 0.258489 (0.152468) | 0.458819 / 0.293841 (0.164978) | 0.033710 / 0.128546 (-0.094836) | 0.010230 / 0.075646 (-0.065417) | 0.084678 / 0.419271 (-0.334593) | 0.058203 / 0.043533 (0.014670) | 0.444972 / 0.255139 (0.189833) | 0.470962 / 0.283200 (0.187763) | 0.029222 / 0.141683 (-0.112461) | 1.671460 / 1.452155 (0.219306) | 1.759471 / 1.492716 (0.266754) |\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) | 0.238894 / 0.018006 (0.220888) | 0.493605 / 0.000490 (0.493115) | 0.001979 / 0.000200 (0.001780) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036498 / 0.037411 (-0.000913) | 0.095245 / 0.014526 (0.080719) | 0.112147 / 0.176557 (-0.064409) | 0.171128 / 0.737135 (-0.566007) | 0.115295 / 0.296338 (-0.181044) |\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) | 0.461067 / 0.215209 (0.245858) | 4.723932 / 2.077655 (2.646277) | 2.432697 / 1.504120 (0.928578) | 2.237302 / 1.541195 (0.696107) | 2.351320 / 1.468490 (0.882830) | 0.509963 / 4.584777 (-4.074813) | 4.194817 / 3.745712 (0.449105) | 6.689529 / 5.269862 (1.419667) | 3.351198 / 4.565676 (-1.214478) | 0.064563 / 0.424275 (-0.359712) | 0.008605 / 0.007607 (0.000998) | 0.575590 / 0.226044 (0.349546) | 5.644179 / 2.268929 (3.375250) | 3.021375 / 55.444624 (-52.423249) | 2.595305 / 6.876477 (-4.281172) | 2.839228 / 2.142072 (0.697156) | 0.657148 / 4.805227 (-4.148079) | 0.144831 / 6.500664 (-6.355834) | 0.067882 / 0.075469 (-0.007587) |\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) | 1.595580 / 1.841788 (-0.246208) | 22.431609 / 8.074308 (14.357301) | 15.700845 / 10.191392 (5.509453) | 0.164675 / 0.680424 (-0.515749) | 0.021322 / 0.534201 (-0.512879) | 0.455270 / 0.579283 (-0.124013) | 0.451547 / 0.434364 (0.017183) | 0.520955 / 0.540337 (-0.019383) | 0.687803 / 1.386936 (-0.699133) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d19574e9f44bd3b59a3e47ca7c4ea66305a8e6b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008171 / 0.011353 (-0.003182) | 0.005563 / 0.011008 (-0.005445) | 0.102265 / 0.038508 (0.063757) | 0.074755 / 0.023109 (0.051646) | 0.431317 / 0.275898 (0.155419) | 0.472179 / 0.323480 (0.148699) | 0.006153 / 0.007986 (-0.001833) | 0.003832 / 0.004328 (-0.000496) | 0.078480 / 0.004250 (0.074230) | 0.056250 / 0.037052 (0.019197) | 0.432938 / 0.258489 (0.174449) | 0.480983 / 0.293841 (0.187142) | 0.048861 / 0.128546 (-0.079685) | 0.016252 / 0.075646 (-0.059394) | 0.343508 / 0.419271 (-0.075763) | 0.065057 / 0.043533 (0.021524) | 0.468418 / 0.255139 (0.213279) | 0.463692 / 0.283200 (0.180492) | 0.032912 / 0.141683 (-0.108771) | 1.795194 / 1.452155 (0.343039) | 1.833047 / 1.492716 (0.340331) |\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) | 0.197980 / 0.018006 (0.179974) | 0.500662 / 0.000490 (0.500172) | 0.007380 / 0.000200 (0.007181) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028323 / 0.037411 (-0.009089) | 0.089817 / 0.014526 (0.075291) | 0.102923 / 0.176557 (-0.073633) | 0.173851 / 0.737135 (-0.563284) | 0.104006 / 0.296338 (-0.192333) |\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) | 0.580277 / 0.215209 (0.365068) | 5.878739 / 2.077655 (3.801085) | 2.404673 / 1.504120 (0.900553) | 2.071765 / 1.541195 (0.530571) | 2.106024 / 1.468490 (0.637534) | 0.855217 / 4.584777 (-3.729560) | 4.918602 / 3.745712 (1.172890) | 5.354984 / 5.269862 (0.085122) | 3.141288 / 4.565676 (-1.424389) | 0.099553 / 0.424275 (-0.324723) | 0.008152 / 0.007607 (0.000545) | 0.709857 / 0.226044 (0.483813) | 7.144602 / 2.268929 (4.875673) | 3.137637 / 55.444624 (-52.306987) | 2.379851 / 6.876477 (-4.496626) | 2.346426 / 2.142072 (0.204353) | 1.033416 / 4.805227 (-3.771811) | 0.213120 / 6.500664 (-6.287544) | 0.076037 / 0.075469 (0.000568) |\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) | 1.597742 / 1.841788 (-0.244046) | 21.745366 / 8.074308 (13.671058) | 20.830698 / 10.191392 (10.639306) | 0.238727 / 0.680424 (-0.441697) | 0.027923 / 0.534201 (-0.506278) | 0.466073 / 0.579283 (-0.113210) | 0.548647 / 0.434364 (0.114283) | 0.549245 / 0.540337 (0.008908) | 0.977148 / 1.386936 (-0.409788) |\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) | 0.008252 / 0.011353 (-0.003101) | 0.004653 / 0.011008 (-0.006356) | 0.084012 / 0.038508 (0.045504) | 0.077418 / 0.023109 (0.054309) | 0.440748 / 0.275898 (0.164850) | 0.464279 / 0.323480 (0.140799) | 0.005762 / 0.007986 (-0.002224) | 0.004909 / 0.004328 (0.000581) | 0.086441 / 0.004250 (0.082190) | 0.057883 / 0.037052 (0.020831) | 0.466655 / 0.258489 (0.208166) | 0.479751 / 0.293841 (0.185910) | 0.047166 / 0.128546 (-0.081380) | 0.014480 / 0.075646 (-0.061166) | 0.092599 / 0.419271 (-0.326672) | 0.062454 / 0.043533 (0.018921) | 0.449753 / 0.255139 (0.194614) | 0.461876 / 0.283200 (0.178676) | 0.034828 / 0.141683 (-0.106855) | 1.752249 / 1.452155 (0.300095) | 1.865449 / 1.492716 (0.372732) |\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) | 0.245028 / 0.018006 (0.227022) | 0.509564 / 0.000490 (0.509074) | 0.003930 / 0.000200 (0.003730) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034746 / 0.037411 (-0.002665) | 0.096563 / 0.014526 (0.082037) | 0.107581 / 0.176557 (-0.068975) | 0.184952 / 0.737135 (-0.552184) | 0.108747 / 0.296338 (-0.187591) |\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) | 0.613091 / 0.215209 (0.397882) | 5.994985 / 2.077655 (3.917330) | 2.711276 / 1.504120 (1.207156) | 2.415862 / 1.541195 (0.874668) | 2.391055 / 1.468490 (0.922565) | 0.868723 / 4.584777 (-3.716054) | 4.953992 / 3.745712 (1.208280) | 4.606542 / 5.269862 (-0.663319) | 2.942162 / 4.565676 (-1.623515) | 0.102737 / 0.424275 (-0.321538) | 0.008634 / 0.007607 (0.001027) | 0.722122 / 0.226044 (0.496078) | 7.245097 / 2.268929 (4.976168) | 3.428232 / 55.444624 (-52.016393) | 2.709539 / 6.876477 (-4.166938) | 2.857956 / 2.142072 (0.715884) | 1.045594 / 4.805227 (-3.759634) | 0.213344 / 6.500664 (-6.287320) | 0.073601 / 0.075469 (-0.001868) |\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) | 1.651954 / 1.841788 (-0.189834) | 22.458646 / 8.074308 (14.384338) | 19.583203 / 10.191392 (9.391811) | 0.246932 / 0.680424 (-0.433492) | 0.025730 / 0.534201 (-0.508471) | 0.473475 / 0.579283 (-0.105808) | 0.521411 / 0.434364 (0.087047) | 0.562038 / 0.540337 (0.021700) | 0.767673 / 1.386936 (-0.619263) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3869d99628329c696f6975377f65e625dd8ef3e0 \"CML watermark\")\n", "The CI error is unrelated.", "<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) | 0.006649 / 0.011353 (-0.004703) | 0.003963 / 0.011008 (-0.007045) | 0.084564 / 0.038508 (0.046056) | 0.075668 / 0.023109 (0.052559) | 0.314233 / 0.275898 (0.038335) | 0.343320 / 0.323480 (0.019841) | 0.005405 / 0.007986 (-0.002581) | 0.003356 / 0.004328 (-0.000973) | 0.065094 / 0.004250 (0.060844) | 0.058774 / 0.037052 (0.021722) | 0.320772 / 0.258489 (0.062283) | 0.353546 / 0.293841 (0.059705) | 0.030921 / 0.128546 (-0.097625) | 0.008463 / 0.075646 (-0.067184) | 0.287490 / 0.419271 (-0.131781) | 0.053188 / 0.043533 (0.009656) | 0.324023 / 0.255139 (0.068884) | 0.337828 / 0.283200 (0.054628) | 0.024764 / 0.141683 (-0.116918) | 1.458028 / 1.452155 (0.005873) | 1.521615 / 1.492716 (0.028899) |\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) | 0.209360 / 0.018006 (0.191353) | 0.461331 / 0.000490 (0.460841) | 0.000386 / 0.000200 (0.000186) | 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) | 0.028405 / 0.037411 (-0.009006) | 0.081074 / 0.014526 (0.066548) | 0.094868 / 0.176557 (-0.081689) | 0.151050 / 0.737135 (-0.586085) | 0.095854 / 0.296338 (-0.200484) |\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) | 0.393957 / 0.215209 (0.178748) | 3.938649 / 2.077655 (1.860994) | 1.938190 / 1.504120 (0.434070) | 1.766458 / 1.541195 (0.225263) | 1.818028 / 1.468490 (0.349538) | 0.483926 / 4.584777 (-4.100851) | 3.641957 / 3.745712 (-0.103755) | 4.883845 / 5.269862 (-0.386016) | 2.960300 / 4.565676 (-1.605377) | 0.057227 / 0.424275 (-0.367048) | 0.007285 / 0.007607 (-0.000322) | 0.475928 / 0.226044 (0.249884) | 4.756757 / 2.268929 (2.487828) | 2.502659 / 55.444624 (-52.941966) | 2.178067 / 6.876477 (-4.698410) | 2.378298 / 2.142072 (0.236226) | 0.578639 / 4.805227 (-4.226588) | 0.132512 / 6.500664 (-6.368152) | 0.059656 / 0.075469 (-0.015813) |\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) | 1.272673 / 1.841788 (-0.569115) | 19.266884 / 8.074308 (11.192576) | 14.272930 / 10.191392 (4.081538) | 0.165897 / 0.680424 (-0.514527) | 0.018436 / 0.534201 (-0.515765) | 0.395177 / 0.579283 (-0.184107) | 0.420134 / 0.434364 (-0.014229) | 0.460781 / 0.540337 (-0.079557) | 0.645376 / 1.386936 (-0.741560) |\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) | 0.006504 / 0.011353 (-0.004849) | 0.003942 / 0.011008 (-0.007066) | 0.064936 / 0.038508 (0.026428) | 0.075015 / 0.023109 (0.051905) | 0.396871 / 0.275898 (0.120973) | 0.423448 / 0.323480 (0.099968) | 0.005239 / 0.007986 (-0.002747) | 0.003265 / 0.004328 (-0.001063) | 0.064910 / 0.004250 (0.060660) | 0.055006 / 0.037052 (0.017953) | 0.392818 / 0.258489 (0.134329) | 0.429735 / 0.293841 (0.135894) | 0.031847 / 0.128546 (-0.096699) | 0.008626 / 0.075646 (-0.067021) | 0.071591 / 0.419271 (-0.347681) | 0.049006 / 0.043533 (0.005473) | 0.384913 / 0.255139 (0.129774) | 0.408969 / 0.283200 (0.125769) | 0.023573 / 0.141683 (-0.118110) | 1.490271 / 1.452155 (0.038117) | 1.564620 / 1.492716 (0.071904) |\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) | 0.225917 / 0.018006 (0.207911) | 0.450369 / 0.000490 (0.449880) | 0.000375 / 0.000200 (0.000175) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031196 / 0.037411 (-0.006215) | 0.090486 / 0.014526 (0.075960) | 0.102326 / 0.176557 (-0.074231) | 0.157483 / 0.737135 (-0.579653) | 0.103670 / 0.296338 (-0.192668) |\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) | 0.417577 / 0.215209 (0.202368) | 4.170798 / 2.077655 (2.093143) | 2.123689 / 1.504120 (0.619569) | 1.948231 / 1.541195 (0.407037) | 2.040277 / 1.468490 (0.571787) | 0.497919 / 4.584777 (-4.086858) | 3.633270 / 3.745712 (-0.112442) | 4.851698 / 5.269862 (-0.418164) | 2.691992 / 4.565676 (-1.873684) | 0.058641 / 0.424275 (-0.365634) | 0.007719 / 0.007607 (0.000112) | 0.500652 / 0.226044 (0.274607) | 4.988657 / 2.268929 (2.719728) | 2.604488 / 55.444624 (-52.840136) | 2.329829 / 6.876477 (-4.546648) | 2.468239 / 2.142072 (0.326167) | 0.598724 / 4.805227 (-4.206503) | 0.135959 / 6.500664 (-6.364706) | 0.061088 / 0.075469 (-0.014381) |\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) | 1.352107 / 1.841788 (-0.489681) | 19.973976 / 8.074308 (11.899668) | 14.292812 / 10.191392 (4.101420) | 0.163855 / 0.680424 (-0.516568) | 0.018402 / 0.534201 (-0.515799) | 0.393128 / 0.579283 (-0.186155) | 0.407379 / 0.434364 (-0.026985) | 0.462324 / 0.540337 (-0.078013) | 0.607501 / 1.386936 (-0.779435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae126ac974cad3050f90106e5909232140786811 \"CML watermark\")\n" ]
"2023-07-24T12:33:58Z"
"2023-07-24T14:13:20Z"
"2023-07-24T14:04:36Z"
CONTRIBUTOR
null
0
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Adds support for dill 3.7.
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PR_kwDODunzps46zfNa
4,627
fixed duplicate calculation of spearmanr function in metrics wrapper.
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[ "Great, can open a PR in `evaluate` as well to optimize this.\r\n\r\nRelatedly, I wanted to add a new metric, Kendall Tau (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kendalltau.html). If I were to open a PR with the wrapper, description, citation, docstrings, readme, etc. would it make more sense to do that in the `datasets` or `evaluate` repo (or both)?\r\n\r\nThanks!", "PR opened in`evaluate` library with same minor adjustment: https://github.com/huggingface/evaluate/pull/176 ", "> If I were to open a PR with the wrapper, description, citation, docstrings, readme, etc. would it make more sense to do that in the datasets or evaluate repo (or both)?\r\n\r\nI think you could just add it to `evaluate`, we're not adding new metrics in this repo anymore" ]
"2022-07-04T15:02:01Z"
"2022-07-07T12:41:09Z"
"2022-07-07T12:41:09Z"
CONTRIBUTOR
null
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During _compute, the scipy.stats spearmanr function was called twice, redundantly, once for calculating the score and once for calculating the p-value, under the conditional branch where return_pvalue=True. I adjusted the _compute function to execute the spearmanr function once, store the results tuple in a temporary variable, and then pass the indexed contents to the expected keys of the returned dictionary.
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1,086,908,508
PR_kwDODunzps4wMEwA
3,472
Fix `str(Path(...))` conversion in streaming on Linux
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"2021-12-22T15:06:03Z"
"2021-12-22T16:52:53Z"
"2021-12-22T16:52:52Z"
CONTRIBUTOR
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Fix `str(Path(...))` conversion in streaming on Linux. This should fix the streaming of the `beans` and `cats_vs_dogs` datasets.
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1,869
Remove outdated commands in favor of huggingface-cli
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"2021-02-12T11:28:10Z"
"2021-02-12T16:13:09Z"
"2021-02-12T16:13:08Z"
MEMBER
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Removing the old user commands since `huggingface_hub` is going to be used instead. cc @julien-c
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806
Quail dataset urls are out of date
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[ "Hi ! Thanks for reporting.\r\nWe should fix the urls and use quail 1.3.\r\nIf you want to contribute feel free to fix the urls and open a PR :) ", "Done! PR [https://github.com/huggingface/datasets/pull/820](https://github.com/huggingface/datasets/pull/820)\r\n\r\nUpdated links and also regenerated the metadata and dummy data for v1.3 in order to pass verifications as described here: [https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset](https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset). ", "Closing since #820 is merged.\r\nThanks again for fixing the urls :)" ]
"2020-11-05T19:40:19Z"
"2020-11-10T14:02:51Z"
"2020-11-10T14:02:51Z"
CONTRIBUTOR
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<h3>Code</h3> ``` from datasets import load_dataset quail = load_dataset('quail') ``` <h3>Error</h3> ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml ``` As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore.
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Inconsistent splits generation for datasets without loading script (packaged dataset puts everything into a single split)
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"2022-04-12T11:15:55Z"
"2022-04-28T21:02:44Z"
"2022-04-28T21:02:44Z"
CONTRIBUTOR
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## Describe the bug Splits for dataset loaders without scripts are prepared inconsistently. I think it might be confusing for users. ## Steps to reproduce the bug * If you load a packaged datasets from Hub, it infers splits from directory structure / filenames (check out the data [here](https://huggingface.co/datasets/nateraw/test-imagefolder-dataset)): ```python ds = load_dataset("nateraw/test-imagefolder-dataset") print(ds) ### Output: DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 6 }) test: Dataset({ features: ['image', 'label'], num_rows: 4 }) }) ``` * If you do the same from locally stored data specifying only directory path you'll get the same: ```python ds = load_dataset("/path/to/local/data/test-imagefolder-dataset") print(ds) ### Output: DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 6 }) test: Dataset({ features: ['image', 'label'], num_rows: 4 }) }) ``` * However, if you explicitely specify package name (like `imagefolder`, `csv`, `json`), all the data is put into a single split: ```python ds = load_dataset("imagefolder", data_dir="/path/to/local/data/test-imagefolder-dataset") print(ds) ### Output: DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 10 }) }) ``` ## Expected results For `load_dataset("imagefolder", data_dir="/path/to/local/data/test-imagefolder-dataset")` I expect the same output as of the two first options.
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Parallelize ETag requests
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"2021-07-19T13:30:42Z"
"2021-07-19T19:33:25Z"
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Since https://github.com/huggingface/datasets/pull/2628 we use the ETag or the remote data files to compute the directory in the cache where a dataset is saved. This is useful in order to reload the dataset from the cache only if the remote files haven't changed. In this I made the ETag requests parallel using multithreading. There is also a tqdm progress bar that shows up if there are more than 16 data files.
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Progress bars are not properly rendered in Jupyter notebook
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[ "To add my experience when trying to debug this issue:\r\n\r\nSeems like previously the workaround given [here](https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter/tqdm I still get terminal warnings that IPython tried to send a message from a forked process.", "Hi @mludv, thanks for the hint!!! :) \r\n\r\nWe will definitely take it into account to try to fix this issue... It seems somehow related to `multiprocessing` and `tqdm`..." ]
"2021-07-12T14:07:13Z"
"2022-02-03T15:55:33Z"
"2022-02-03T15:55:33Z"
MEMBER
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## Describe the bug The progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal). ## Steps to reproduce the bug ```python ds.map(tokenize, num_proc=10) ``` ## Expected results Jupyter widgets displaying the progress bars. ## Actual results Simple plane progress bars. cc: Reported by @thomwolf
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675
Add custom dataset to NLP?
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[ "Yes you can have a look here: https://huggingface.co/docs/datasets/loading_datasets.html#csv-files", "No activity, closing" ]
"2020-09-27T21:22:50Z"
"2020-10-20T09:08:49Z"
"2020-10-20T09:08:49Z"
CONTRIBUTOR
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Is it possible to add a custom dataset such as a .csv to the NLP library? Thanks.
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new dataset type: single-label and multi-label video classification
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[ "Hi ! You can in the `features` folder how we implemented the audio and image feature types.\r\n\r\nWe can have something similar to videos. What we need to decide:\r\n- the video loading library to use\r\n- the output format when a user accesses a video type object\r\n- what parameters a `Video()` feature type needs\r\n\r\nalso cc @nateraw who also took a look at what we can do for video", "@lhoestq @nateraw is there any progress on adding video classification datasets? ", "Hi ! I think we just missing which lib we're going to use to decode the videos + which parameters must go in the `Video` type", "Hmm. `decord` could be nice but it's no longer maintained [it seems](https://github.com/dmlc/decord/issues/214). ", "pytorchvideo uses [pyav](https://github.com/PyAV-Org/PyAV) as the default decoder: https://github.com/facebookresearch/pytorchvideo/blob/c8d23d8b7e597586a9e2d18f6ed31ad8aa379a7a/pytorchvideo/data/labeled_video_dataset.py#L37\r\n\r\nAlso it would be great if `optionally` audio can also be decoded from the video as in pytorchvideo: https://github.com/facebookresearch/pytorchvideo/blob/c8d23d8b7e597586a9e2d18f6ed31ad8aa379a7a/pytorchvideo/data/labeled_video_dataset.py#L35\r\n\r\nHere are the other decoders supported in pytorchvideo: https://github.com/facebookresearch/pytorchvideo/blob/c8d23d8b7e597586a9e2d18f6ed31ad8aa379a7a/pytorchvideo/data/encoded_video.py#L17\r\n", "@sayakpaul I did do quite a bit of work on [this PR](https://github.com/huggingface/datasets/pull/4532) a while back to add a video feature. It's outdated, but uses my `encoded_video` [package](https://github.com/nateraw/encoded-video) under the hood, which is basically a wrapper around PyAV stolen from [pytorchvideo](https://github.com/facebookresearch/pytorchvideo/) that gets rid of the `torch` dependency. \r\n\r\nwould be really great to get something like this in...it's just a really tricky and time consuming feature to add. " ]
"2022-09-27T19:40:11Z"
"2022-11-02T19:10:13Z"
null
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**Is your feature request related to a problem? Please describe.** In my research, I am dealing with multi-modal (audio+text+frame sequence) video classification. It would be great if the datasets library supported generating multi-modal batches from a video dataset. **Describe the solution you'd like** Assume I have video files having single/multiple labels. I want to train a single/multi-label video classification model. I want datasets to support generating multi-modal batches (audio+frame sequence) from video files. Audio waveform and frame sequence can be extracted from each video clip then I can use any audio, image and video model from transformers library to extract features which will be fed into my model. **Describe alternatives you've considered** Currently, I am using https://github.com/facebookresearch/pytorchvideo dataloaders. There seems to be not much alternative. **Additional context** I am wiling to open a PR but don't know where to start.
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Add kinnews_kirnews
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[ "merging since the CI is fixed on master" ]
"2020-12-10T12:29:08Z"
"2020-12-17T18:34:16Z"
"2020-12-17T18:34:16Z"
CONTRIBUTOR
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Add kinnews and kirnews
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44
[Tests] Fix tests for datasets with no config
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"2020-05-04T13:25:38Z"
"2020-05-04T13:28:04Z"
"2020-05-04T13:28:03Z"
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Forgot to fix `None` problem for datasets that have no config this in PR: https://github.com/huggingface/nlp/pull/42
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1,942
[experiment] missing default_experiment-1-0.arrow
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[ "Hi !\r\n\r\nThe cache at `~/.cache/huggingface/metrics` stores the users data for metrics computations (hence the arrow files).\r\n\r\nHowever python modules (i.e. dataset scripts, metric scripts) are stored in `~/.cache/huggingface/modules/datasets_modules`.\r\n\r\nIn particular the metrics are cached in `~/.cache/huggingface/modules/datasets_modules/metrics/`\r\n\r\nFeel free to take a look at your cache and let me know if you find any issue that would help explaining why you had an issue with `rouge` with no connection. I'm doing some tests on my side to try to reproduce the issue you have\r\n", "Thank you for clarifying that the metrics files are to be found elsewhere, @lhoestq \r\n\r\n> The cache at ~/.cache/huggingface/metrics stores the users data for metrics computations (hence the arrow files).\r\n\r\ncould it be renamed to reflect that? otherwise it misleadingly suggests that it's the metrics. Perhaps `~/.cache/huggingface/metrics-user-data`?\r\n\r\nAnd there are so many `.lock` files w/o corresponding files under `~/.cache/huggingface/metrics/`. Why are they there? \r\n\r\nfor example after I wipe out the dir completely and do one training I end up with:\r\n```\r\n~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock\r\n```\r\nwhat is that lock file locking when nothing is running?", "The lock files come from an issue with filelock (see comment in the code [here](https://github.com/benediktschmitt/py-filelock/blob/master/filelock.py#L394-L398)). Basically on unix there're always .lock files left behind. I haven't dove into this issue", "are you sure you need an external lock file? if it's a single purpose locking in the same scope you can lock the caller `__file__` instead, e.g. here is how one can `flock` the script file itself to ensure atomic printing:\r\n\r\n```\r\nimport fcntl\r\ndef printflock(*msgs):\r\n \"\"\" print in multiprocess env so that the outputs from different processes don't get interleaved \"\"\"\r\n with open(__file__, \"r\") as fh:\r\n fcntl.flock(fh, fcntl.LOCK_EX)\r\n try:\r\n print(*msgs)\r\n finally:\r\n fcntl.flock(fh, fcntl.LOCK_UN)\r\n```\r\n", "OK, this issue is not about caching but some internal conflict/race condition it seems, I have just run into it on my normal env:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 356, in _finalize\r\n self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 236, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 171, in _read_files\r\n pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 322, in read_table\r\n stream = stream_from(filename)\r\n File \"pyarrow/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '/home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"examples/seq2seq/run_seq2seq.py\", line 655, in <module>\r\n main()\r\n File \"examples/seq2seq/run_seq2seq.py\", line 619, in main\r\n test_results = trainer.predict(\r\n File \"/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer_seq2seq.py\", line 121, in predict\r\n return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)\r\n File \"/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer.py\", line 1706, in predict\r\n output = self.prediction_loop(\r\n File \"/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer.py\", line 1813, in prediction_loop\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n File \"examples/seq2seq/run_seq2seq.py\", line 556, in compute_metrics\r\n result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 388, in compute\r\n self._finalize()\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 358, in _finalize\r\n raise ValueError(\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```\r\n\r\nI'm just running `run_seq2seq.py` under DeepSpeed:\r\n\r\n```\r\nexport BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 deepspeed --num_gpus=2 examples/seq2seq/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --val_max_target_length 128 --warmup_steps 500 --max_train_samples 100 --max_val_samples 100 --max_test_samples 100 --dataset_name wmt16 --dataset_config ro-en --source_prefix \"translate English to Romanian: \" --deepspeed examples/tests/deepspeed/ds_config.json\r\n```\r\n\r\nIt finished the evaluation OK and crashed on the prediction part of the Trainer. But the eval / predict parts no longer run under Deepspeed, it's just plain ddp.\r\n\r\nIs this some kind of race condition? It happens intermittently - there is nothing else running at the same time.\r\n\r\nBut if 2 independent instances of the same script were to run at the same time it's clear to see that this problem would happen. Perhaps it'd help to create a unique hash which is shared between all processes in the group and use that as the default experiment id?\r\n", "When you're using metrics in a distributed setup, there are two cases:\r\n1. you're doing two completely different experiments (two evaluations) and the 2 metrics jobs have nothing to do with each other\r\n2. you're doing one experiment (one evaluation) but use multiple processes to feed the data to the metric.\r\n\r\nIn case 1. you just need to provide two different `experiment_id` so that the metrics don't collide.\r\nIn case 2. they must have the same experiment_id (or use the default one), but in this case you also need to provide the `num_processes` and `process_id`\r\n\r\nIf understand correctly you're in situation 2.\r\n\r\nIf so, you make sure that you instantiate the metrics with both the right `num_processes` and `process_id` parameters ?\r\n\r\nIf they're not set, then the cache files of the two metrics collide it can cause issues. For example if one metric finishes before the other, then the cache file is deleted and the other metric gets a FileNotFoundError\r\nThere's more information in the [documentation](https://huggingface.co/docs/datasets/loading_metrics.html#distributed-setups) if you want\r\n\r\nHope that helps !", "Thank you for explaining that in a great way, @lhoestq \r\n\r\nSo the bottom line is that the `transformers` examples are broken since they don't do any of that. At least `run_seq2seq.py` just does `metric = load_metric(metric_name)`\r\n\r\nWhat test would you recommend to reliably reproduce this bug in `examples/seq2seq/run_seq2seq.py`?", "To give more context, we are just using the metrics for the `comput_metric` function and nothing else. Is there something else we can use that just applies the function to the full arrays of predictions and labels? Because that's all we need, all the gathering has already been done because the datasets Metric multiprocessing relies on file storage and thus does not work in a multi-node distributed setup (whereas the Trainer does).\r\n\r\nOtherwise, we'll have to switch to something else to compute the metrics :-(", "OK, it definitely leads to a race condition in how it's used right now. Here is how you can reproduce it - by injecting a random sleep time different for each process before the locks are acquired. \r\n```\r\n--- a/src/datasets/metric.py\r\n+++ b/src/datasets/metric.py\r\n@@ -348,6 +348,16 @@ class Metric(MetricInfoMixin):\r\n\r\n elif self.process_id == 0:\r\n # Let's acquire a lock on each node files to be sure they are finished writing\r\n+\r\n+ import time\r\n+ import random\r\n+ import os\r\n+ pid = os.getpid()\r\n+ random.seed(pid)\r\n+ secs = random.randint(1, 15)\r\n+ time.sleep(secs)\r\n+ print(f\"sleeping {secs}\")\r\n+\r\n file_paths, filelocks = self._get_all_cache_files()\r\n\r\n # Read the predictions and references\r\n@@ -385,7 +395,10 @@ class Metric(MetricInfoMixin):\r\n\r\n if predictions is not None:\r\n self.add_batch(predictions=predictions, references=references)\r\n+ print(\"FINALIZE START\")\r\n+\r\n self._finalize()\r\n+ print(\"FINALIZE END\")\r\n\r\n self.cache_file_name = None\r\n self.filelock = None\r\n```\r\n\r\nthen run with 2 procs: `python -m torch.distributed.launch --nproc_per_node=2`\r\n```\r\nexport BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 examples/seq2seq/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --val_max_target_length 128 --warmup_steps 500 --max_train_samples 10 --max_val_samples 10 --max_test_samples 10 --dataset_name wmt16 --dataset_config ro-en --source_prefix \"translate English to Romanian: \"\r\n```\r\n\r\n```\r\n***** Running Evaluation *****\r\n Num examples = 10\r\n Batch size = 16\r\n 0%| | 0/1 [00:00<?, ?it/s]FINALIZE START\r\nFINALIZE START\r\nsleeping 11\r\nFINALIZE END\r\n100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1/1 [00:11<00:00, 11.06s/it]\r\nsleeping 11\r\nTraceback (most recent call last):\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 368, in _finalize\r\n self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 236, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 171, in _read_files\r\n pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 322, in read_table\r\n stream = stream_from(filename)\r\n File \"pyarrow/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '/home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"examples/seq2seq/run_seq2seq.py\", line 645, in <module>\r\n main()\r\n File \"examples/seq2seq/run_seq2seq.py\", line 601, in main\r\n metrics = trainer.evaluate(\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer_seq2seq.py\", line 74, in evaluate\r\n return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py\", line 1703, in evaluate\r\n output = self.prediction_loop(\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py\", line 1876, in prediction_loop\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n File \"examples/seq2seq/run_seq2seq.py\", line 556, in compute_metrics\r\n result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 402, in compute\r\n self._finalize()\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 370, in _finalize\r\n raise ValueError(\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```", "I tried to adjust `run_seq2seq.py` and trainer to use the suggested dist env:\r\n```\r\n import torch.distributed as dist\r\n metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())\r\n```\r\nand in `trainer.py` added to call just for rank 0:\r\n```\r\n if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n```\r\nand then the process hangs in a deadlock. \r\n\r\nHere is the tb:\r\n```\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/filelock.py\", line 275 in acquire\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 306 in _check_all_processes_locks\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 501 in _init_writer\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 440 in add_batch\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 397 in compute\r\n File \"examples/seq2seq/run_seq2seq.py\", line 558 in compute_metrics\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py\", line 1876 in prediction_loop\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py\", line 1703 in evaluate\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer_seq2seq.py\", line 74 in evaluate\r\n File \"examples/seq2seq/run_seq2seq.py\", line 603 in main\r\n File \"examples/seq2seq/run_seq2seq.py\", line 651 in <module>\r\n```\r\n\r\nBut this sounds right, since in the above diff I set up a distributed metric and only called one process - so it's blocking on waiting for other processes to do the same.\r\n\r\nSo one working solution is to leave:\r\n\r\n```\r\n metric = load_metric(metric_name)\r\n```\r\nalone, and only call `compute_metrics` from rank 0\r\n```\r\n if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n```\r\n\r\nso we now no longer use the distributed env as far as `datasets` is concerned, it's just a single process.\r\n\r\nAre there any repercussions/side-effects to this proposed change in Trainer? If it always gathers all inputs on rank 0 then this is how it should have been done in first place - i.e. only run for rank 0. It appears that currently it was re-calculating the metrics on all processes on the same data just to throw the results away other than for rank 0. Unless I missed something.\r\n", "But no, since \r\n`\r\n metric = load_metric(metric_name)\r\n`\r\nis called for each process, the race condition is still there. So still getting:\r\n\r\n```\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```\r\n\r\ni.e. the only way to fix this is to `load_metric` only for rank 0, but this requires huge changes in the code and all end users' code.\r\n", "OK, here is a workaround that works. The onus here is absolutely on the user:\r\n\r\n```\r\ndiff --git a/examples/seq2seq/run_seq2seq.py b/examples/seq2seq/run_seq2seq.py\r\nindex 2a060dac5..c82fd83ea 100755\r\n--- a/examples/seq2seq/run_seq2seq.py\r\n+++ b/examples/seq2seq/run_seq2seq.py\r\n@@ -520,7 +520,11 @@ def main():\r\n\r\n # Metric\r\n metric_name = \"rouge\" if data_args.task.startswith(\"summarization\") else \"sacrebleu\"\r\n- metric = load_metric(metric_name)\r\n+ import torch.distributed as dist\r\n+ if dist.is_initialized():\r\n+ metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())\r\n+ else:\r\n+ metric = load_metric(metric_name)\r\n\r\n def postprocess_text(preds, labels):\r\n preds = [pred.strip() for pred in preds]\r\n@@ -548,12 +552,17 @@ def main():\r\n # Some simple post-processing\r\n decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\r\n\r\n+ kwargs = dict(predictions=decoded_preds, references=decoded_labels)\r\n+ if metric_name == \"rouge\":\r\n+ kwargs.update(use_stemmer=True)\r\n+ result = metric.compute(**kwargs) # must call for all processes\r\n+ if result is None: # only process with rank-0 will return metrics, others None\r\n+ return {}\r\n+\r\n if metric_name == \"rouge\":\r\n- result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\r\n # Extract a few results from ROUGE\r\n result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\r\n else:\r\n- result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n result = {\"bleu\": result[\"score\"]}\r\n\r\n prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\r\n```\r\n\r\nThis is not user-friendly to say the least. And it's still wasteful as we don't need other processes to do anything.\r\n\r\nBut it solves the current race condition.\r\n\r\nClearly this calls for a design discussion as it's the responsibility of the Trainer to handle this and not user's. Perhaps in the `transformers` land?", "I don't see how this could be the responsibility of `Trainer`, who hasn't the faintest idea of what a `datasets.Metric` is. The trainer takes a function `compute_metrics` that goes from predictions + labels to metric results, there is nothing there. That computation is done on all processes \r\n\r\nThe fact a `datasets.Metric` object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in `datasets`. Especially since, as I mentioned before, the multiprocessing part of `datasets.Metric` has a deep flaw since it can't work in a multinode environment. So you actually need to do the job of gather predictions and labels yourself.\r\n\r\nThe changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels `number_of_processes` times I believe, which is not going to make the metric computation any faster.\r\n\r\n", "Right, to clarify, I meant it'd be good to have it sorted on the library side and not requiring the user to figure it out. This is too complex and error-prone and if not coded correctly the bug will be intermittent which is even worse.\r\n\r\nOh I guess I wasn't clear in my message - in no way I'm proposing that we use this workaround code - I was just showing what I had to do to make it work.\r\n\r\nWe are on the same page.\r\n\r\n> The changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels number_of_processes times I believe, which is not going to make the metric computation any faster.\r\n\r\nAnd yes, this is another problem that my workaround introduces. Thank you for pointing it out, @sgugger \r\n", "> The fact a datasets.Metric object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in datasets\r\n\r\nYes totally, this use case is supposed to be supported by `datasets`. And in this case there shouldn't be any collision between the metrics. I'm looking into it :)\r\nMy guess is that at one point the metric isn't using the right file name. It's supposed to use one with a unique uuid in order to avoid the collisions.", "I just opened #1966 to fix this :)\r\n@stas00 if have a chance feel free to try it !", "Thank you, @lhoestq - I will experiment and report back. \r\n\r\nedit: It works! Thank you", "Fixed in https://github.com/huggingface/datasets/pull/1966" ]
"2021-02-25T03:02:15Z"
"2022-10-05T13:08:45Z"
"2022-10-05T13:08:45Z"
CONTRIBUTOR
null
null
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the original report was pretty bad and incomplete - my apologies! Please see the complete version here: https://github.com/huggingface/datasets/issues/1942#issuecomment-786336481 ------------ As mentioned here https://github.com/huggingface/datasets/issues/1939 metrics don't get cached, looking at my local `~/.cache/huggingface/metrics` - there are many `*.arrow.lock` files but zero metrics files. w/o the network I get: ``` FileNotFoundError: [Errno 2] No such file or directory: '~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow ``` there is just `~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock` I did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind. this is with master. Thank you.
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756,720,643
MDExOlB1bGxSZXF1ZXN0NTMyMjIzNDEy
1,086
adding cdt dataset
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[ "> Thanks for adding this one !\r\n> \r\n> I left a few comments\r\n> \r\n> after the change you'll need to regenerate the dataset_infos.json file as well\r\n\r\ndataset_infos.json regenerated", "looks like this PR includes changes to many files other that the ones for CDT\r\ncould you create another branch and another PR please ?" ]
"2020-12-04T01:28:11Z"
"2020-12-04T15:04:02Z"
"2020-12-04T15:04:02Z"
CONTRIBUTOR
null
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- **Name:** *Cyberbullying Detection Task* - **Description:** *The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.* - **Data:** *https://github.com/ptaszynski/cyberbullying-Polish* - **Motivation:** *The KLEJ benchmark (Kompleksowa Lista Ewaluacji JΔ™zykowych) is a set of nine evaluation tasks for the Polish language understanding.*
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956,676,360
MDExOlB1bGxSZXF1ZXN0NzAwMjMzMzQy
2,732
Updated TTC4900 Dataset
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[ "@lhoestq, lütfen bu PR'ı gâzden geçirebilir misiniz?", "> Thanks ! This looks all good now :)\r\n\r\nThanks" ]
"2021-07-30T11:52:14Z"
"2021-07-30T16:00:51Z"
"2021-07-30T15:58:14Z"
CONTRIBUTOR
null
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- The source address of the TTC4900 dataset of [@savasy](https://github.com/savasy) has been updated for direct download. - Updated readme.
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1,162,386,592
PR_kwDODunzps40F3uN
3,853
add ontonotes_conll dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3853). All of your documentation changes will be reflected on that endpoint.", "The CI fail is unrelated to this dataset, merging :)" ]
"2022-03-08T08:53:42Z"
"2022-03-15T10:48:02Z"
"2022-03-15T10:48:02Z"
CONTRIBUTOR
null
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# Introduction of the dataset OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre, multilingual corpus manually annotated with syntactic, semantic and discourse information. This dataset is the version of OntoNotes v5.0 extended and used in the CoNLL-2012 shared task , includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only). This dataset is widely used in name entity recognition, coreference resolution, and semantic role labeling. In dataset loading script, I modify and use the code of [AllenNLP/Ontonotes](https://docs.allennlp.org/models/main/models/common/ontonotes/#ontonotes) to read the special conll files while don't get extra package dependency. # Some workarounds I did 1. task ids I add tasks that I can't find anywhere `semantic-role-labeling`, `lemmatization`, and `word-sense-disambiguation` to the task category `structure-prediction`, because they are related to "syntax". I feel there is another good name for the task category since some tasks mentioned aren't related to structure, but I have no good idea. 2. `dl_manage.extract` Since we'll get another zip after unzip the downloaded zip data, I have to use `dl_manager.extract` directly inside `_generate_examples`. But when testing dummy data, `dl_manager.extract` do nothing. So I make a conditional such that it manually extract data when testing dummy data. # Help Don't know how to fix the building doc error.
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connection issue while downloading data
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[ "could you tell me how I can avoid download, by pre-downloading the data first, put them in a folder so the code does not try to redownload? could you tell me the path to put the downloaded data, and how to do it? thanks\r\n@lhoestq ", "Does your instance have an internet connection ?\r\n\r\nIf you don't have an internet connection you'll need to have the dataset on the instance disk.\r\nTo do so first download the dataset on another machine using `load_dataset` and then you can save it in a folder using `my_dataset.save_to_disk(\"path/to/folder\")`. Once the folder is copied on your instance you can reload the dataset with `datasets.load_from_disk(\"path/to/folder\")`" ]
"2020-12-13T14:27:00Z"
"2022-10-05T12:33:29Z"
"2022-10-05T12:33:29Z"
NONE
null
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Hi I am running my codes on google cloud, and I am getting this error resulting in the failure of the codes when trying to download the data, could you assist me to solve this? also as a temporary solution, could you tell me how I can increase the number of retries and timeout to at least let the models run for now. thanks ``` Traceback (most recent call last): File "finetune_t5_trainer.py", line 361, in <module> main() File "finetune_t5_trainer.py", line 269, in main add_prefix=False if training_args.train_adapters else True) File "/workdir/seq2seq/data/tasks.py", line 70, in get_dataset dataset = self.load_dataset(split=split) File "/workdir/seq2seq/data/tasks.py", line 306, in load_dataset return datasets.load_dataset('glue', 'cola', split=split) File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module head_hf_s3(path, filename=name, dataset=dataset) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3 return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset)) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head return request('head', url, **kwargs) File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request return session.request(method=method, url=url, **kwargs) File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request resp = self.send(prep, **send_kwargs) File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send r = adapter.send(request, **kwargs) File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send raise ConnectTimeout(e, request=request) requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f47db511e80>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')) ```
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Use more efficient and idiomatic way to construct list.
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008156 / 0.011353 (-0.003197) | 0.005563 / 0.011008 (-0.005445) | 0.118319 / 0.038508 (0.079810) | 0.044305 / 0.023109 (0.021195) | 0.366221 / 0.275898 (0.090323) | 0.407585 / 0.323480 (0.084105) | 0.006961 / 0.007986 (-0.001024) | 0.004841 / 0.004328 (0.000513) | 0.089949 / 0.004250 (0.085698) | 0.062197 / 0.037052 (0.025144) | 0.360721 / 0.258489 (0.102232) | 0.415332 / 0.293841 (0.121491) | 0.035709 / 0.128546 (-0.092837) | 0.010617 / 0.075646 (-0.065030) | 0.397454 / 0.419271 (-0.021817) | 0.063490 / 0.043533 (0.019958) | 0.374289 / 0.255139 (0.119150) | 0.382827 / 0.283200 (0.099628) | 0.121014 / 0.141683 (-0.020669) | 1.729933 / 1.452155 (0.277779) | 1.896222 / 1.492716 (0.403506) |\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) | 0.254030 / 0.018006 (0.236023) | 0.491225 / 0.000490 (0.490736) | 0.018933 / 0.000200 (0.018734) | 0.000413 / 0.000054 (0.000358) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033085 / 0.037411 (-0.004327) | 0.132837 / 0.014526 (0.118311) | 0.143275 / 0.176557 (-0.033282) | 0.215800 / 0.737135 (-0.521335) | 0.149802 / 0.296338 (-0.146536) |\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) | 0.474688 / 0.215209 (0.259479) | 4.743223 / 2.077655 (2.665569) | 2.163107 / 1.504120 (0.658988) | 1.946396 / 1.541195 (0.405201) | 2.057538 / 1.468490 (0.589047) | 0.618836 / 4.584777 (-3.965941) | 4.605934 / 3.745712 (0.860222) | 2.201537 / 5.269862 (-3.068324) | 1.275758 / 4.565676 (-3.289919) | 0.077782 / 0.424275 (-0.346493) | 0.014830 / 0.007607 (0.007223) | 0.593372 / 0.226044 (0.367328) | 5.927000 / 2.268929 (3.658072) | 2.687293 / 55.444624 (-52.757331) | 2.301797 / 6.876477 (-4.574679) | 2.489928 / 2.142072 (0.347856) | 0.756779 / 4.805227 (-4.048449) | 0.168065 / 6.500664 (-6.332600) | 0.077276 / 0.075469 (0.001807) |\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) | 1.608169 / 1.841788 (-0.233619) | 19.048790 / 8.074308 (10.974482) | 16.100228 / 10.191392 (5.908836) | 0.215346 / 0.680424 (-0.465077) | 0.022293 / 0.534201 (-0.511907) | 0.535899 / 0.579283 (-0.043384) | 0.533729 / 0.434364 (0.099365) | 0.562697 / 0.540337 (0.022360) | 0.764082 / 1.386936 (-0.622854) |\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) | 0.010087 / 0.011353 (-0.001266) | 0.005357 / 0.011008 (-0.005651) | 0.092678 / 0.038508 (0.054170) | 0.041207 / 0.023109 (0.018098) | 0.437464 / 0.275898 (0.161566) | 0.527867 / 0.323480 (0.204387) | 0.006861 / 0.007986 (-0.001125) | 0.006131 / 0.004328 (0.001802) | 0.093741 / 0.004250 (0.089490) | 0.064142 / 0.037052 (0.027090) | 0.433577 / 0.258489 (0.175088) | 0.537148 / 0.293841 (0.243307) | 0.035339 / 0.128546 (-0.093207) | 0.010432 / 0.075646 (-0.065214) | 0.102838 / 0.419271 (-0.316434) | 0.057905 / 0.043533 (0.014372) | 0.437956 / 0.255139 (0.182817) | 0.509562 / 0.283200 (0.226362) | 0.120620 / 0.141683 (-0.021063) | 1.798686 / 1.452155 (0.346531) | 2.013290 / 1.492716 (0.520574) |\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) | 0.249067 / 0.018006 (0.231061) | 0.462219 / 0.000490 (0.461729) | 0.000476 / 0.000200 (0.000276) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033988 / 0.037411 (-0.003424) | 0.135863 / 0.014526 (0.121337) | 0.144082 / 0.176557 (-0.032474) | 0.201715 / 0.737135 (-0.535421) | 0.152079 / 0.296338 (-0.144259) |\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) | 0.522820 / 0.215209 (0.307611) | 5.216723 / 2.077655 (3.139068) | 2.582355 / 1.504120 (1.078235) | 2.352799 / 1.541195 (0.811604) | 2.451943 / 1.468490 (0.983453) | 0.620381 / 4.584777 (-3.964396) | 4.537841 / 3.745712 (0.792129) | 2.206431 / 5.269862 (-3.063431) | 1.269865 / 4.565676 (-3.295811) | 0.078744 / 0.424275 (-0.345531) | 0.014375 / 0.007607 (0.006768) | 0.648215 / 0.226044 (0.422171) | 6.482809 / 2.268929 (4.213881) | 3.210670 / 55.444624 (-52.233954) | 2.847485 / 6.876477 (-4.028992) | 2.820946 / 2.142072 (0.678873) | 0.762711 / 4.805227 (-4.042516) | 0.171235 / 6.500664 (-6.329429) | 0.080230 / 0.075469 (0.004761) |\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) | 1.646840 / 1.841788 (-0.194948) | 19.400451 / 8.074308 (11.326142) | 16.758845 / 10.191392 (6.567453) | 0.171377 / 0.680424 (-0.509046) | 0.020400 / 0.534201 (-0.513801) | 0.467675 / 0.579283 (-0.111608) | 0.529745 / 0.434364 (0.095381) | 0.605989 / 0.540337 (0.065652) | 0.694659 / 1.386936 (-0.692277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#006bf33ac5c308f9c70f4df4868abd539eb6c366 \"CML watermark\")\n", "It's faster because all the items are the same object, but this also means modifying one of them will alter each unless these items are immutable, and they are in this case (tuples). So we should be careful when using this idiom." ]
"2023-05-27T18:54:47Z"
"2023-05-31T15:37:11Z"
"2023-05-31T13:28:29Z"
CONTRIBUTOR
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Using `*` is ~2X faster according to [benchmark](https://colab.research.google.com/gist/ttsugriy/c964a2604edf70c41911b10335729b6a/for-vs-mult.ipynb) with just 4 patterns. This doesn't matter much since this tiny difference is not going to be noticeable, but why not?
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One or several metadata.jsonl were found, but not in the same directory or in a parent directory
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[ "Also, when generated from a zip archive, the dataset contains only a few images. In my case, 20 versus 2000+ contained in the archive. The generation from folders works as expected.", "Thanks for reporting, @blghtr.\r\n\r\nYou should include the `metadata.jsonl` in your ZIP archives, at the root level directory.\r\n\r\nI agree that our documentation is not clear enough. Maybe we could improve it.", "You can find a dummy dataset example here: https://huggingface.co/datasets/albertvillanova/tmp-imagefolder-metadata\r\n\r\n```\r\ntmp-imagefolder-metadata/\r\n└── data/\r\n β”œβ”€β”€ train.zip\r\n └── valid.zip\r\n```\r\nwhere, the directory structure within the `train.zip` archive is:\r\n```\r\nmetadata.jsonl\r\ntrain/\r\n β”œβ”€β”€ bharatanatyam/\r\n └── bharatanatyam_original_113.jpg_70c297a2-e2f2-4ed8-b93c-0c03d0809fe2.jpg\r\n └── kathak/\r\n └── kathak_original_10.jpg_2c4a2c3d-47fc-4b33-9c09-38b542826632.jpg\r\n```\r\nand the metadata file contains:\r\n```\r\n{\"file_name\": \"train/bharatanatyam/bharatanatyam_original_113.jpg_70c297a2-e2f2-4ed8-b93c-0c03d0809fe2.jpg\", \"text\": \"first\"}\r\n{\"file_name\": \"train/kathak/kathak_original_10.jpg_2c4a2c3d-47fc-4b33-9c09-38b542826632.jpg\", \"text\": \"second\"}\r\n```" ]
"2023-04-16T16:21:55Z"
"2023-04-19T11:53:24Z"
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### Describe the bug An attempt to generate a dataset from a zip archive using imagefolder and metadata.jsonl does not lead to the expected result. Tried all possible locations of the json file: the file in the archive is ignored (generated dataset contains only images), the file next to the archive like [here](https://huggingface.co/docs/datasets/image_dataset#imagefolder) leads to an error: ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1610, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1609 _time = time.time() -> 1610 for key, record in generator: 1611 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\packaged_modules\folder_based_builder\folder_based_builder.py:370, in FolderBasedBuilder._generate_examples(self, files, metadata_files, split_name, add_metadata, add_labels) 369 else: --> 370 raise ValueError( 371 f"One or several metadata.{metadata_ext} were found, but not in the same directory or in a parent directory of {downloaded_dir_file}." 372 ) 373 if metadata_dir is not None and downloaded_metadata_file is not None: ValueError: One or several metadata.jsonl were found, but not in the same directory or in a parent directory of C:\Users\User\.cache\huggingface\datasets\downloads\extracted\f7fb7de25fb28ae63089974524f2d271a39d83888bc456d04aa3b3d45f33e6a6\ff0745a0-a741-4d9e-b228-a93b851adf61.png. The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[3], line 1 ----> 1 dataset = load_dataset("imagefolder", data_dir=r'C:\Users\User\data') File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\load.py:1791, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 1788 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1790 # Download and prepare data -> 1791 builder_instance.download_and_prepare( 1792 download_config=download_config, 1793 download_mode=download_mode, 1794 verification_mode=verification_mode, 1795 try_from_hf_gcs=try_from_hf_gcs, 1796 num_proc=num_proc, 1797 storage_options=storage_options, 1798 ) 1800 # Build dataset for splits 1801 keep_in_memory = ( 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1803 ) File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:891, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 889 if num_proc is not None: 890 prepare_split_kwargs["num_proc"] = num_proc --> 891 self._download_and_prepare( 892 dl_manager=dl_manager, 893 verification_mode=verification_mode, 894 **prepare_split_kwargs, 895 **download_and_prepare_kwargs, 896 ) 897 # Sync info 898 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1651, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs) 1650 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs): -> 1651 super()._download_and_prepare( 1652 dl_manager, 1653 verification_mode, 1654 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS 1655 or verification_mode == VerificationMode.ALL_CHECKS, 1656 **prepare_splits_kwargs, 1657 ) File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:986, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 982 split_dict.add(split_generator.split_info) 984 try: 985 # Prepare split will record examples associated to the split --> 986 self._prepare_split(split_generator, **prepare_split_kwargs) 987 except OSError as e: 988 raise OSError( 989 "Cannot find data file. " 990 + (self.manual_download_instructions or "") 991 + "\nOriginal error:\n" 992 + str(e) 993 ) from None File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1490, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size) 1488 gen_kwargs = split_generator.gen_kwargs 1489 job_id = 0 -> 1490 for job_id, done, content in self._prepare_split_single( 1491 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1492 ): 1493 if done: 1494 result = content File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1646, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1644 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1645 e = e.__context__ -> 1646 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1648 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug 1. Organize directory structure like in the docs: folder/metadata.jsonl folder/train.zip 2. Run load_dataset("imagefolder", data_dir='folder/metadata.jsonl', split='train') ### Expected behavior Dataset generated with all additional features from metadata.jsonl ### Environment info - `datasets` version: 2.11.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.9.0 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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add Dataset.from_list
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[ "_The documentation is not available anymore as the PR was closed or merged._", "@albertvillanova it seems tests fail on pyarrow 6, perhaps from_pylist is a v7 method? How do you usually handle these version differences?\r\nAdded something that at least works" ]
"2022-08-25T09:05:58Z"
"2022-09-02T10:22:59Z"
"2022-09-02T10:20:33Z"
CONTRIBUTOR
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As discussed in #4885 I initially added this bit at the end, thinking filling this field was necessary as it is done in from_dict. However, it seems the constructor takes care of filling info when it is empty. ``` if info.features is None: info.features = Features( { col: generate_from_arrow_type(coldata.type) for col, coldata in zip(pa_table.column_names, pa_table.columns) } ) ```
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006761 / 0.011353 (-0.004592) | 0.004307 / 0.011008 (-0.006701) | 0.084682 / 0.038508 (0.046174) | 0.083994 / 0.023109 (0.060885) | 0.316612 / 0.275898 (0.040714) | 0.346157 / 0.323480 (0.022678) | 0.004490 / 0.007986 (-0.003495) | 0.003699 / 0.004328 (-0.000629) | 0.066144 / 0.004250 (0.061894) | 0.057958 / 0.037052 (0.020906) | 0.319018 / 0.258489 (0.060529) | 0.367597 / 0.293841 (0.073756) | 0.031146 / 0.128546 (-0.097401) | 0.008814 / 0.075646 (-0.066832) | 0.290971 / 0.419271 (-0.128301) | 0.052769 / 0.043533 (0.009236) | 0.313125 / 0.255139 (0.057986) | 0.330473 / 0.283200 (0.047273) | 0.025922 / 0.141683 (-0.115760) | 1.494989 / 1.452155 (0.042834) | 1.556140 / 1.492716 (0.063423) |\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) | 0.310580 / 0.018006 (0.292574) | 0.563600 / 0.000490 (0.563110) | 0.012344 / 0.000200 (0.012144) | 0.000382 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031468 / 0.037411 (-0.005943) | 0.084856 / 0.014526 (0.070331) | 0.101371 / 0.176557 (-0.075186) | 0.158735 / 0.737135 (-0.578400) | 0.102451 / 0.296338 (-0.193888) |\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) | 0.402288 / 0.215209 (0.187079) | 4.001351 / 2.077655 (1.923696) | 2.022710 / 1.504120 (0.518590) | 1.850236 / 1.541195 (0.309041) | 1.946779 / 1.468490 (0.478289) | 0.485828 / 4.584777 (-4.098949) | 3.584925 / 3.745712 (-0.160787) | 3.400815 / 5.269862 (-1.869046) | 2.123187 / 4.565676 (-2.442490) | 0.057373 / 0.424275 (-0.366902) | 0.007383 / 0.007607 (-0.000224) | 0.479773 / 0.226044 (0.253729) | 4.805342 / 2.268929 (2.536414) | 2.530151 / 55.444624 (-52.914473) | 2.190136 / 6.876477 (-4.686341) | 2.463666 / 2.142072 (0.321593) | 0.583512 / 4.805227 (-4.221715) | 0.134205 / 6.500664 (-6.366459) | 0.062021 / 0.075469 (-0.013448) |\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) | 1.239532 / 1.841788 (-0.602255) | 20.252941 / 8.074308 (12.178633) | 14.265697 / 10.191392 (4.074305) | 0.158745 / 0.680424 (-0.521679) | 0.018605 / 0.534201 (-0.515596) | 0.394246 / 0.579283 (-0.185037) | 0.415260 / 0.434364 (-0.019104) | 0.462636 / 0.540337 (-0.077701) | 0.645318 / 1.386936 (-0.741618) |\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) | 0.007063 / 0.011353 (-0.004290) | 0.004388 / 0.011008 (-0.006621) | 0.064997 / 0.038508 (0.026489) | 0.085135 / 0.023109 (0.062026) | 0.424349 / 0.275898 (0.148451) | 0.456033 / 0.323480 (0.132553) | 0.005745 / 0.007986 (-0.002241) | 0.003705 / 0.004328 (-0.000624) | 0.065835 / 0.004250 (0.061585) | 0.058366 / 0.037052 (0.021314) | 0.421654 / 0.258489 (0.163165) | 0.460334 / 0.293841 (0.166493) | 0.032828 / 0.128546 (-0.095718) | 0.008974 / 0.075646 (-0.066673) | 0.072524 / 0.419271 (-0.346747) | 0.048558 / 0.043533 (0.005025) | 0.413546 / 0.255139 (0.158407) | 0.435765 / 0.283200 (0.152565) | 0.023754 / 0.141683 (-0.117929) | 1.476884 / 1.452155 (0.024730) | 1.560011 / 1.492716 (0.067294) |\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) | 0.318279 / 0.018006 (0.300272) | 0.544990 / 0.000490 (0.544501) | 0.007118 / 0.000200 (0.006918) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033352 / 0.037411 (-0.004059) | 0.092921 / 0.014526 (0.078395) | 0.109028 / 0.176557 (-0.067528) | 0.161433 / 0.737135 (-0.575703) | 0.108445 / 0.296338 (-0.187893) |\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) | 0.438925 / 0.215209 (0.223716) | 4.400714 / 2.077655 (2.323059) | 2.403727 / 1.504120 (0.899607) | 2.236472 / 1.541195 (0.695277) | 2.319219 / 1.468490 (0.850729) | 0.490159 / 4.584777 (-4.094618) | 3.647474 / 3.745712 (-0.098238) | 3.433144 / 5.269862 (-1.836718) | 2.145367 / 4.565676 (-2.420310) | 0.057994 / 0.424275 (-0.366281) | 0.007452 / 0.007607 (-0.000155) | 0.513808 / 0.226044 (0.287763) | 5.130792 / 2.268929 (2.861863) | 2.861691 / 55.444624 (-52.582934) | 2.473292 / 6.876477 (-4.403185) | 2.756445 / 2.142072 (0.614372) | 0.586783 / 4.805227 (-4.218444) | 0.134170 / 6.500664 (-6.366494) | 0.061149 / 0.075469 (-0.014320) |\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) | 1.350144 / 1.841788 (-0.491644) | 21.003528 / 8.074308 (12.929220) | 15.174314 / 10.191392 (4.982922) | 0.186535 / 0.680424 (-0.493888) | 0.020821 / 0.534201 (-0.513380) | 0.399210 / 0.579283 (-0.180073) | 0.431942 / 0.434364 (-0.002422) | 0.475395 / 0.540337 (-0.064942) | 0.677457 / 1.386936 (-0.709479) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6aa5fc278324a253eab43ad1bc048e822e4ae5c7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007062 / 0.011353 (-0.004291) | 0.004299 / 0.011008 (-0.006710) | 0.086019 / 0.038508 (0.047511) | 0.085166 / 0.023109 (0.062057) | 0.355804 / 0.275898 (0.079906) | 0.381056 / 0.323480 (0.057577) | 0.005500 / 0.007986 (-0.002486) | 0.003496 / 0.004328 (-0.000833) | 0.064866 / 0.004250 (0.060615) | 0.057399 / 0.037052 (0.020346) | 0.357914 / 0.258489 (0.099425) | 0.395387 / 0.293841 (0.101546) | 0.031763 / 0.128546 (-0.096784) | 0.008665 / 0.075646 (-0.066981) | 0.290097 / 0.419271 (-0.129175) | 0.053297 / 0.043533 (0.009765) | 0.355659 / 0.255139 (0.100520) | 0.378232 / 0.283200 (0.095032) | 0.026015 / 0.141683 (-0.115668) | 1.437121 / 1.452155 (-0.015034) | 1.538798 / 1.492716 (0.046082) |\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) | 0.243518 / 0.018006 (0.225511) | 0.461361 / 0.000490 (0.460871) | 0.009529 / 0.000200 (0.009329) | 0.000473 / 0.000054 (0.000419) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030379 / 0.037411 (-0.007032) | 0.089851 / 0.014526 (0.075325) | 0.098278 / 0.176557 (-0.078278) | 0.157077 / 0.737135 (-0.580058) | 0.098997 / 0.296338 (-0.197341) |\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) | 0.382415 / 0.215209 (0.167206) | 3.801964 / 2.077655 (1.724309) | 1.887680 / 1.504120 (0.383560) | 1.775903 / 1.541195 (0.234709) | 1.851338 / 1.468490 (0.382848) | 0.483616 / 4.584777 (-4.101161) | 3.612977 / 3.745712 (-0.132736) | 3.397700 / 5.269862 (-1.872162) | 2.114572 / 4.565676 (-2.451105) | 0.057250 / 0.424275 (-0.367025) | 0.007362 / 0.007607 (-0.000245) | 0.456873 / 0.226044 (0.230829) | 4.567319 / 2.268929 (2.298391) | 2.399476 / 55.444624 (-53.045148) | 2.054542 / 6.876477 (-4.821935) | 2.343432 / 2.142072 (0.201359) | 0.582319 / 4.805227 (-4.222908) | 0.134045 / 6.500664 (-6.366619) | 0.062726 / 0.075469 (-0.012743) |\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) | 1.283390 / 1.841788 (-0.558398) | 20.358511 / 8.074308 (12.284202) | 14.933989 / 10.191392 (4.742597) | 0.164960 / 0.680424 (-0.515464) | 0.018625 / 0.534201 (-0.515576) | 0.394087 / 0.579283 (-0.185196) | 0.416761 / 0.434364 (-0.017603) | 0.466669 / 0.540337 (-0.073669) | 0.643161 / 1.386936 (-0.743775) |\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) | 0.007141 / 0.011353 (-0.004212) | 0.004185 / 0.011008 (-0.006824) | 0.066097 / 0.038508 (0.027588) | 0.088436 / 0.023109 (0.065327) | 0.401189 / 0.275898 (0.125291) | 0.440402 / 0.323480 (0.116922) | 0.005729 / 0.007986 (-0.002257) | 0.003527 / 0.004328 (-0.000801) | 0.065278 / 0.004250 (0.061027) | 0.060866 / 0.037052 (0.023813) | 0.407035 / 0.258489 (0.148546) | 0.443923 / 0.293841 (0.150083) | 0.032922 / 0.128546 (-0.095625) | 0.008739 / 0.075646 (-0.066907) | 0.071800 / 0.419271 (-0.347472) | 0.048994 / 0.043533 (0.005461) | 0.403736 / 0.255139 (0.148597) | 0.419566 / 0.283200 (0.136366) | 0.025369 / 0.141683 (-0.116314) | 1.474980 / 1.452155 (0.022825) | 1.553500 / 1.492716 (0.060784) |\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) | 0.225224 / 0.018006 (0.207218) | 0.462891 / 0.000490 (0.462401) | 0.006958 / 0.000200 (0.006758) | 0.000163 / 0.000054 (0.000108) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034431 / 0.037411 (-0.002980) | 0.100021 / 0.014526 (0.085495) | 0.108339 / 0.176557 (-0.068217) | 0.162762 / 0.737135 (-0.574374) | 0.108951 / 0.296338 (-0.187388) |\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) | 0.435966 / 0.215209 (0.220757) | 4.351744 / 2.077655 (2.274089) | 2.372307 / 1.504120 (0.868187) | 2.192146 / 1.541195 (0.650951) | 2.326839 / 1.468490 (0.858349) | 0.488292 / 4.584777 (-4.096485) | 3.745227 / 3.745712 (-0.000485) | 3.456306 / 5.269862 (-1.813556) | 2.159771 / 4.565676 (-2.405906) | 0.057953 / 0.424275 (-0.366322) | 0.007469 / 0.007607 (-0.000138) | 0.515116 / 0.226044 (0.289071) | 5.162871 / 2.268929 (2.893942) | 2.850336 / 55.444624 (-52.594288) | 2.514700 / 6.876477 (-4.361777) | 2.748843 / 2.142072 (0.606770) | 0.587687 / 4.805227 (-4.217540) | 0.134333 / 6.500664 (-6.366331) | 0.062097 / 0.075469 (-0.013372) |\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) | 1.377082 / 1.841788 (-0.464705) | 21.103127 / 8.074308 (13.028819) | 15.325275 / 10.191392 (5.133883) | 0.166225 / 0.680424 (-0.514199) | 0.020472 / 0.534201 (-0.513729) | 0.395866 / 0.579283 (-0.183417) | 0.444964 / 0.434364 (0.010600) | 0.475367 / 0.540337 (-0.064970) | 0.693325 / 1.386936 (-0.693611) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79b5bbbd52ffd90dd958c05b333d7c90a03756cc \"CML watermark\")\n" ]
"2023-10-11T21:51:12Z"
"2023-10-12T12:47:15Z"
"2023-10-12T12:38:19Z"
CONTRIBUTOR
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Changes in the doc READMe: * adds two new sections (to be aligned with `transformers` and `hfh`): "Previewing the documentation" and "Writing documentation examples" * replaces the mentions of `transformers` with `datasets` * fixes some dead links
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1,860
Add loading from the Datasets Hub + add relative paths in download manager
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[ "I just added the steps to share a dataset on the datasets hub. It's highly inspired by the steps to share a model in the `transformers` doc.\r\n\r\nMoreover once the new huggingface_hub is released we can update the version in the setup.py. We also need to update the command to create a dataset repo in the documentation\r\n\r\nI added a few more tests with the \"lhoestq/test\" dataset I added on the hub and it works fine :) ", "Here is the PR adding support for datasets repos in `huggingface_hub`: https://github.com/huggingface/huggingface_hub/pull/14" ]
"2021-02-10T13:24:11Z"
"2021-02-12T19:13:30Z"
"2021-02-12T19:13:29Z"
MEMBER
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With the new Datasets Hub on huggingface.co it's now possible to have a dataset repo with your own script and data. For example: https://huggingface.co/datasets/lhoestq/custom_squad/tree/main contains one script and two json files. You can load it using ```python from datasets import load_dataset d = load_dataset("lhoestq/custom_squad") ``` To be able to use the data files that live right next to the dataset script on the repo in the hub, I added relative paths support for the DownloadManager. For example in the repo mentioned above, there are two json files that can be downloaded via ```python _URLS = { "train": "train-v1.1.json", "dev": "dev-v1.1.json", } downloaded_files = dl_manager.download_and_extract(_URLS) ``` To make it work, I set the `base_path` of the DownloadManager to be the parent path of the dataset script (which comes from either a local path or a remote url). I also had to add the auth header of the requests to huggingface.co for private datasets repos. The token is fetched from [huggingface_hub](https://github.com/huggingface/huggingface_hub).
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4,993
fix: avoid casting tuples after Dataset.map
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
"2022-09-20T08:45:16Z"
"2022-09-20T16:11:27Z"
"2022-09-20T13:08:29Z"
CONTRIBUTOR
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This PR updates features.py to avoid casting tuples to lists when reading the results of Dataset.map as suggested by @lhoestq [here](https://github.com/huggingface/datasets/issues/4676#issuecomment-1187371367) in https://github.com/huggingface/datasets/issues/4676.
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1,349,181,448
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4,885
Create dataset from list of dicts
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[ "Hi @sanderland, thanks for your enhancement proposal.\r\n\r\nI agree with you that this would be useful.\r\n\r\nPlease note that under the hood, we use PyArrow tables as backend:\r\n- The implementation of `Dataset.from_dict` uses the PyArrow `Table.from_pydict`\r\n\r\nTherefore, I would suggest:\r\n- Implementing `Dataset.from_list` using the PyArrow `Table.from_pylist`\r\n\r\nWhat do you think?\r\nLet's see if other people have other suggestions...", "Thanks for the quick and positive reply @albertvillanova! \r\n`from_list` seems sensible. Have opened a PR so we can discuss details there.", "Resolved via #4890." ]
"2022-08-24T10:01:24Z"
"2022-09-08T16:02:52Z"
"2022-09-08T16:02:52Z"
CONTRIBUTOR
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I often find myself with data from a variety of sources, and a list of dicts is very common among these. However, converting this to a Dataset is a little awkward, requiring either ```Dataset.from_pandas(pd.DataFrame(formatted_training_data))``` Which can error out on some more exotic values as 2-d arrays for reasons that are not entirely clear > ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column labels with type object') Alternatively: ```Dataset.from_dict({k: [s[k] for s in formatted_training_data] for k in formatted_training_data[0].keys()})``` Which works, but is a little ugly. **Describe the solution you'd like** Either `.from_dict` accepting a list of dicts, or a `.from_records` function accepting such. I am happy to PR this, just wanted to check you are happy to accept this I haven't missed something obvious, and which of the solutions would be prefered.
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Illegal instruction (core dumped) at datasets import
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[ "It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 processors." ]
"2021-10-24T17:21:36Z"
"2021-11-18T19:07:04Z"
"2021-11-18T19:07:03Z"
CONTRIBUTOR
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## Describe the bug I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)" ## Steps to reproduce the bug ``` conda create --prefix path/to/env conda activate path/to/env conda install -c huggingface -c conda-forge datasets # exits with output "Illegal instruction (core dumped)" python -m datasets ``` ## Environment info When I run "datasets-cli env", I also get "Illegal instruction (core dumped)" If I run the following commands: ``` conda create --prefix path/to/another/new/env conda activate path/to/another/new/env conda install -c huggingface transformers transformers-cli env ``` Then I get: - `transformers` version: 4.11.3 - Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyTorch version (GPU?): not installed (NA) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: No - Using distributed or parallel set-up in script?: No Let me know what additional information you need in order to debug this issue. Thanks in advance!
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[Tests] Improved Error message for dummy folder structure
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"2020-05-06T18:11:48Z"
"2020-05-06T18:13:00Z"
"2020-05-06T18:12:59Z"
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Improved Error message
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Rerun pip install -e
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"2020-09-01T17:24:39Z"
"2020-09-01T17:24:51Z"
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Hopefully it fixes the github actions
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laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
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[ "~~You will see this error if the cache dir filepath contains relative `..` paths. Use `os.path.realpath(_CACHE_DIR)` before passing it to the `load_dataset` function.~~", "This is a real issue and not related to paths.", "Based on the StackOverflow answer, this causes the error to go away:\r\n```diff\r\ndiff --git a/table.py b/table.py\r\n--- a/table.py\t\r\n+++ b/table.py\t(date 1701824849806)\r\n@@ -47,7 +47,7 @@\r\n \r\n \r\n def _memory_mapped_record_batch_reader_from_file(filename: str) -> pa.RecordBatchStreamReader:\r\n- memory_mapped_stream = pa.memory_map(filename)\r\n+ memory_mapped_stream = pa.memory_map(filename, \"r+\")\r\n return pa.ipc.open_stream(memory_mapped_stream)\r\n```\r\nBut now loading the dataset goes very, very slowly, which is unexpected.", "I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...", "Hi! \r\n\r\nInstead of generating one (potentially large) Arrow file, we shard the generated data into 500 MB shards because memory-mapping large Arrow files can be problematic on some systems. Maybe deleting the dataset's cache and increasing the shard size (controlled with the `datasets.config.MAX_SHARD_SIZE` variable; e.g. to \"4GB\") can fix the issue for you.\r\n\r\n> I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...\r\n\r\nOur `.arrow` files are in the [Arrow streaming format](https://arrow.apache.org/docs/python/ipc.html#using-streams). To load them as a `polars` DataFrame, do the following:\r\n```python\r\ndf = pl.from_arrow(Dataset.from_from(path_to_arrow_file).data.table)\r\n```\r\n\r\nWe plan to switch to the IPC version eventually.\r\n", "Hmm, I have a feeling this works fine on Linux, and is a real bug for however `datasets` is doing the sharding on Windows. I will follow up, but I think this is a real bug." ]
"2023-12-06T00:07:34Z"
"2023-12-06T23:26:23Z"
null
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### Describe the bug I have downloaded laion2B-en, and I'm receiving the following error trying to load it: ``` Resolving data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 128/128 [00:00<00:00, 1173.79it/s] Traceback (most recent call last): File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <module> count = compute_frequencies() ^^^^^^^^^^^^^^^^^^^^^ File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 17, in compute_frequencies laion2b_dataset = load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\load.py", line 2165, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1187, in as_dataset datasets = map_nested( ^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\utils\py_utils.py", line 456, in map_nested return function(data_struct) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1217, in _build_single_dataset ds = self._as_dataset( ^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1291, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 66, in _memory_mapped_arrow_table_from_file pa_table = opened_stream.read_all() ^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow\ipc.pxi", line 757, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status OSError: [WinError 8] PrefetchVirtualMemory failed. Detail: [Windows error 8] Not enough memory resources are available to process this command. ``` This error is probably a red herring: https://stackoverflow.com/questions/50263929/numpy-memmap-returns-not-enough-memory-while-there-are-plenty-available In other words, the issue is related to asking for a memory mapping of length N > M the length of the file on Windows. This gracefully succeeds on Linux. I have 1024 arrow files in my cache instead of 128 like in the repository for it. Probably related. I don't know why `datasets` reorganized/rewrote the dataset in my cache to be 1024 slices instead of the original 128. ### Steps to reproduce the bug ``` # as a huggingface developer, you may already have laion2B-en somewhere _CACHE_DIR = "." from datasets import load_dataset load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False) ``` ### Expected behavior This should correctly load as a memory mapped Arrow dataset. ### Environment info - `datasets` version: 2.15.0 - Platform: Windows-10-10.0.20348-SP0 (this is windows 2022) - Python version: 3.11.4 - `huggingface_hub` version: 0.19.4 - PyArrow version: 14.0.1 - Pandas version: 2.1.2 - `fsspec` version: 2023.10.0
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[ "The CI failure is about some missing tags or sections in the dataset cards, and is unrelated to the part about non commercial use of this PR. Merging" ]
"2022-01-05T23:01:38Z"
"2022-01-06T18:58:20Z"
"2022-01-06T18:58:19Z"
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[ "`dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.\r\nAny idea ?", "> `dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.\r\n> Any idea ?\r\n\r\nWe should definitely find a way to make it work with only a few articles.\r\n\r\nIf it doesn't work right now for dummy data, I guess it's because it tries to load every single article file ?\r\n\r\nIf so, then maybe you can use `os.listdir` method to first check all the data files available in the path where the `articles.tgz` file is extracted. Then you can simply iter through the data files and depending on their ID, include them in the train or test set. With this method you should be able to have only a few articles files per split in the dummy data. Does that make sense ?", "fixed! so the issue was, `articles_ids` were prepared based on the number of files in articles dir, so for dummy data questions it was not able to load some articles due to incorrect ids and the test was failing" ]
"2020-11-30T17:45:22Z"
"2020-12-02T13:45:22Z"
"2020-12-02T13:40:13Z"
MEMBER
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0
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Adding inquisitive qg dataset More info: https://github.com/wjko2/INQUISITIVE
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Bad message when config name is missing
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"2021-07-22T09:47:23Z"
"2021-07-22T10:02:40Z"
"2021-07-22T10:02:40Z"
MEMBER
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When loading a dataset that have several configurations, we expect to see an error message if the user doesn't specify a config name. However in `datasets` 1.10.0 and 1.10.1 it doesn't show the right message: ```python import datasets datasets.load_dataset("glue") ``` raises ```python AttributeError: 'BuilderConfig' object has no attribute 'text_features' ``` instead of ```python ValueError: Config name is missing. Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax'] Example of usage: `load_dataset('glue', 'cola')` ```
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SAMSum dataset viewer not working
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[ "https://huggingface.co/datasets/samsum\r\n\r\n```\r\nStatus code: 400\r\nException: ValueError\r\nMessage: Cannot seek streaming HTTP file\r\n```", "Currently, only the datasets that can be streamed support the dataset viewer. Maybe @lhoestq @albertvillanova or @mariosasko could give more details about why the dataset cannot be streamed.", "It looks like the host (https://arxiv.org) doesn't allow HTTP Range requests, which is what we use to stream data.\r\n\r\nThis can be fix if we host the data ourselves, which is ok since the dataset is under CC BY-NC-ND 4.0" ]
"2022-04-11T16:22:57Z"
"2022-04-29T16:26:09Z"
"2022-04-29T16:26:09Z"
NONE
null
null
null
## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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4,817
Outdated Link for mkqa Dataset
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null
[ "Thanks for reporting @liaeh, we are investigating this. " ]
"2022-08-10T12:45:45Z"
"2022-08-11T09:37:52Z"
"2022-08-11T09:37:52Z"
NONE
null
null
null
## Describe the bug The URL used to download the mkqa dataset is outdated. It seems the URL to download the dataset is currently https://github.com/apple/ml-mkqa/blob/main/dataset/mkqa.jsonl.gz instead of https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz (master branch has been renamed to main). ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mkqa") ``` ## Expected results downloads the dataset ## Actual results ```python Downloading builder script: 4.79k/? [00:00<00:00, 201kB/s] Downloading metadata: 13.2k/? [00:00<00:00, 504kB/s] Downloading and preparing dataset mkqa/mkqa (download: 11.35 MiB, generated: 34.29 MiB, post-processed: Unknown size, total: 45.65 MiB) to /home/lhr/.cache/huggingface/datasets/mkqa/mkqa/1.0.0/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d... Downloading data files: 0% 0/1 [00:00<?, ?it/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Input In [3], in <cell line: 3>() 1 from datasets import load_dataset ----> 3 dataset = load_dataset("mkqa") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/load.py:1746, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1745 # Download and prepare data -> 1746 builder_instance.download_and_prepare( 1747 download_config=download_config, 1748 download_mode=download_mode, 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, 1751 use_auth_token=use_auth_token, 1752 ) 1754 # Build dataset for splits 1755 keep_in_memory = ( 1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1757 ) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mkqa/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d/mkqa.py:130, in Mkqa._split_generators(self, dl_manager) 128 # download and extract URLs 129 urls_to_download = _URLS --> 130 downloaded_files = dl_manager.download_and_extract(urls_to_download) 132 return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})] File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls) 415 def download_and_extract(self, url_or_urls): 416 """Download and extract given url_or_urls. 417 418 Is roughly equivalent to: (...) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:309, in DownloadManager.download(self, url_or_urls) 306 download_func = partial(self._download, download_config=download_config) 308 start_time = datetime.now() --> 309 downloaded_path_or_paths = map_nested( 310 download_func, 311 url_or_urls, 312 map_tuple=True, 313 num_proc=download_config.num_proc, 314 disable_tqdm=not is_progress_bar_enabled(), 315 desc="Downloading data files", 316 ) 317 duration = datetime.now() - start_time 318 logger.info(f"Downloading took {duration.total_seconds() // 60} min") File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:393, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: --> 393 mapped = [ 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:394, in <listcomp>(.0) 391 num_proc = 1 392 if num_proc <= 1 or len(iterable) <= num_proc: 393 mapped = [ --> 394 _single_map_nested((function, obj, types, None, True, None)) 395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 396 ] 397 else: 398 split_kwds = [] # We organize the splits ourselve (contiguous splits) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:330, in _single_map_nested(args) 328 # Singleton first to spare some computation 329 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 330 return function(data_struct) 332 # Reduce logging to keep things readable in multiprocessing with tqdm 333 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:335, in DownloadManager._download(self, url_or_filename, download_config) 332 if is_relative_path(url_or_filename): 333 # append the relative path to the base_path 334 url_or_filename = url_or_path_join(self._base_path, url_or_filename) --> 335 return cached_path(url_or_filename, download_config=download_config) File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:185, in cached_path(url_or_filename, download_config, **download_kwargs) 181 url_or_filename = str(url_or_filename) 183 if is_remote_url(url_or_filename): 184 # URL, so get it from the cache (downloading if necessary) --> 185 output_path = get_from_cache( 186 url_or_filename, 187 cache_dir=cache_dir, 188 force_download=download_config.force_download, 189 proxies=download_config.proxies, 190 resume_download=download_config.resume_download, 191 user_agent=download_config.user_agent, 192 local_files_only=download_config.local_files_only, 193 use_etag=download_config.use_etag, 194 max_retries=download_config.max_retries, 195 use_auth_token=download_config.use_auth_token, 196 ignore_url_params=download_config.ignore_url_params, 197 download_desc=download_config.download_desc, 198 ) 199 elif os.path.exists(url_or_filename): 200 # File, and it exists. 201 output_path = url_or_filename File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:530, in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc) 525 raise FileNotFoundError( 526 f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been" 527 " disabled. To enable file online look-ups, set 'local_files_only' to False." 528 ) 529 elif response is not None and response.status_code == 404: --> 530 raise FileNotFoundError(f"Couldn't find file at {url}") 531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 532 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz ``` ## Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 9.0.0 - Pandas version: 1.4.2
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fix: files counted twice in inferred structure
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[ "I see it creates some errors in the tests.\r\n\r\nAnother solution if needed is to add something like `data_files = list(set(data_files))` after [this line](https://github.com/huggingface/datasets/blob/8555197a3fe826e98bd0206c2d031c4488c53c5c/src/datasets/data_files.py#L511)", "Hi ! Thanks for the correction :)\r\n\r\nYour change seems right, let me look at the errors and try to fix this", "Not sure if it's due to this change but IΒ tested `load_dataset('dalle-mini/encoded-vqgan_imagenet_f16_16384', streaming=True)` and the `validation` set is empty.", "So indeed there was an issue with the patterns `*` and `**/*` that would return some files twice. This issue came from the fact that we were not using the right `glob`.\r\n\r\nIndeed we were using `Path.rglob` for local files and `Path.match` for remote files. Since these two methods don't have the same behavior for such patterns, I decided to change that.\r\n\r\nIn particular, we now use `glob.glob` (same as `fsspec` glob) as a reference for data files resolution from patterns. This is the same as dask for example.\r\n\r\n/!\\ Here are some behaviors specific to `glob.glob` that are different from Path.glob, Path.match or fnmatch:\r\n- '*' matches only first level files\r\n- '**/*' matches only at least second level files\r\n\r\nThis way we have a consistent behavior with respect to other python data libraries and there's no overlap anymore between the two patterns.\r\n\r\nSome implementations details:\r\n\r\nTo ensure that we have the same behavior for local files and for files in a remote dataset repository, I decided to use `fsspec` glob for both. This was made possible by implementing the `HfFileSystem` class as a `fsspec` filesystem.\r\n\r\nI pushed those changes directly to your PR - I hope you don't mind. I'm still fixing the remaining tests.\r\nPlease let me know if that solves your problem, and then we can merge !", "There's still an issue with fsspec's glob - I'll take a look this afternoon", "I just found out that actually glob.glob and fsspec glob are different haha\r\nglob.glob needs `**/*` and recursive=True to look into deep subdirectories, while fsspec only requires `**`\r\n\r\nI think we can go with fsspec glob for consistency with dask and since it's our main tool for filesystems management", "To recap:\r\n```\r\nWe use fsspec glob as a reference for data files resolution from patterns.\r\nThis is the same as dask for example.\r\n\r\n/!\\ Here are some behaviors specific to fsspec glob that are different from glob.glob, Path.glob, Path.match or fnmatch:\r\n- '*' matches only first level items\r\n- '**' matches all items\r\n- '**/*' matches all at least second level items\r\n\r\nMore generally:\r\n- `*`` matches any character except a forward-slash (to match just the file or directory name)\r\n- `**`` matches any character including a forward-slash /\r\n```", "lol Windows… Maybe `Pathlib` for the tests?\r\n\r\nI tested streaming a repo and it worked perfectly now!" ]
"2021-11-21T21:50:38Z"
"2021-11-23T17:00:58Z"
"2021-11-23T17:00:58Z"
CONTRIBUTOR
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Files were counted twice in a structure like: ``` my_dataset_local_path/ β”œβ”€β”€ README.md └── data/ β”œβ”€β”€ train/ β”‚ β”œβ”€β”€ shard_0.csv β”‚ β”œβ”€β”€ shard_1.csv β”‚ β”œβ”€β”€ shard_2.csv β”‚ └── shard_3.csv └── valid/ β”œβ”€β”€ shard_0.csv └── shard_1.csv ``` The reason is that they were matching both `*train*/*` and `*train*/**/*`. This PR fixes it. @lhoestq
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Windows test suite fails
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"2021-10-05T08:46:46Z"
"2021-10-05T09:58:27Z"
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MEMBER
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## Describe the bug There is an error during installation of tests dependencies for Windows: https://app.circleci.com/pipelines/github/huggingface/datasets/7981/workflows/9b6a0114-2b8e-4069-94e5-e844dbbdba4e/jobs/49206 ``` ERROR: Cannot uninstall 'ruamel-yaml'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall. ```
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Fix cats_vs_dogs
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
"2022-09-30T08:47:29Z"
"2022-09-30T10:23:22Z"
"2022-09-30T09:34:28Z"
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Reported in https://github.com/huggingface/datasets/pull/3878 I updated the number of examples
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Add tests for WMT datasets
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[ "Dummy data generation is deprecated now. Closing." ]
"2021-03-04T06:46:42Z"
"2022-11-04T14:19:16Z"
"2022-11-04T14:19:16Z"
MEMBER
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As requested in #1981, we need tests for WMT datasets, using dummy data.
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4,879
Fix Citation Information section in dataset cards
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4879). All of your documentation changes will be reflected on that endpoint." ]
"2022-08-23T18:06:43Z"
"2022-09-27T14:04:45Z"
"2022-08-24T04:09:07Z"
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Fix Citation Information section in dataset cards: - cc_news - conllpp - datacommons_factcheck - gnad10 - id_panl_bppt - jigsaw_toxicity_pred - kinnews_kirnews - kor_sarcasm - makhzan - reasoning_bg - ro_sts - ro_sts_parallel - sanskrit_classic - telugu_news - thaiqa_squad - wiki_movies This PR partially fixes the Citation Information section in dataset cards. Subsequent PRs will follow to complete this task.
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Fix manual download instructions
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[ "Verified that this works, thanks!", "But I get\r\n```python\r\nConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py\r\n```\r\nWhen I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).\r\n\r\n\r\nBoth machines can run\r\n```bash\r\naws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/\r\n```\r\nbut it seems one must be in the nlp directory to run the command?\r\n\r\n(I ran `pip install -e . ` on this branch in both situations.)\r\n\r\n\r\n", "`https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py` looks very weird.\r\n\r\n(Also, S3 is not a file-system, it's a flat key-value store)", "Good to merge I think @lhoestq ", "> But I get\r\n> \r\n> ```python\r\n> ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py\r\n> ```\r\n> \r\n> When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).\r\n> \r\n> Both machines can run\r\n> \r\n> ```shell\r\n> aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/\r\n> ```\r\n> \r\n> but it seems one must be in the nlp directory to run the command?\r\n> \r\n> (I ran `pip install -e . ` on this branch in both situations.)\r\n\r\nAs soon as it is on master, the dataset script wmt16.py will be synced on S3 and you'll be able to do `load_dataset(\"wmt16\")`" ]
"2020-06-18T15:59:57Z"
"2020-06-19T08:24:21Z"
"2020-06-19T08:24:19Z"
MEMBER
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This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`. Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs. After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed. Also this PR should unblock solves a bug with `wmt16 - ro-en` @sshleifer from this branch you should be able to succesfully run ```python import nlp ds = nlp.load_dataset('./datasets/wmt16', 'ro-en') ``` and once this PR is merged S3 should be synched so that ```python import nlp ds = nlp.load_dataset("wmt16", "ro-en") ``` works as well. **Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility.
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Better integration of BIG-bench
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[ "Hi, I made my version : https://huggingface.co/datasets/tasksource/bigbench" ]
"2023-01-03T15:37:57Z"
"2023-02-09T20:30:26Z"
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### Feature request Ideally, it would be nice to have a maintained PyPI package for `bigbench`. ### Motivation We'd like to allow anyone to access, explore and use any task. ### Your contribution @lhoestq has opened an issue in their repo: - https://github.com/google/BIG-bench/issues/906
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add openwebtext
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[ "BTW, is there a one-line command to make our building scripts pass flake8 test? (included code quality test), I got like trailing space or mixed space and tab warning and error, and fixed them manually.", "> BTW, is there a one-line command to make our building scripts pass flake8 test? (included code quality test), I got like trailing space or mixed space and tab warning and error, and fixed them manually.\r\n\r\nI don't think so.\r\nWe have a command for black and isort but not flake8 as far as I know.", "Thanks for your awesome work too.\r\nBTW a little reminder, this solves #132 " ]
"2020-09-22T12:05:22Z"
"2020-10-06T09:20:10Z"
"2020-09-28T09:07:26Z"
CONTRIBUTOR
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This adds [The OpenWebText Corpus](https://skylion007.github.io/OpenWebTextCorpus/), which is a clean and large text corpus for nlp pretraining. It is an open source effort to reproduce OpenAI’s WebText dataset used by GPT-2, and it is also needed to reproduce ELECTRA. It solves #132 . ### Besides dataset building script, I made some changes to the library. 1. Extract large amount of compressed files with multi processing I add a `num_proc` argument to `DownloadManager.extract` and pass this `num_proc` to `map_nested`. So I can decompress 20 thousands compressed files faster. `num_proc` I add is default to `None`, so it shouldn't break any other thing. 2. In `cached_path`, I change the order to deal with different kind of compressed files (zip, tar, gzip) Because there is no way to 100% detect a file is a zip file (see [this](https://stackoverflow.com/questions/18194688/how-can-i-determine-if-a-file-is-a-zip-file)), I found it wrongly detect `'./datasets/downloads/extracted/58764bd6898fa339b25d92e7fbbc3d8dbf64fb504edff1a30a1d7d99d1561027/openwebtext/urlsf_subset13-630_data.xz'` as a zip and try decompress it with zip, sure it will get error. So I made it detect wheter the file is tar or gzip first and detect zip in the last. 3. `MockDownloadManager.extract` Cuz I pass `num_proc` to `DownloadManager.extract`, I also have to make `MockDownloadManager.extract` to accept extra keywork arguments. So I make it `extract(path, *args, **kwargs)`, but just return the path as original implementation. **Note**: If there is better way for points mentioned above, thought I would like to help, unless we can solve point4 (make dataset building fast), I may not be able to afford rebuild the dataset again because of change of the dataset script (Building the dataset cost me 4 days). ### There is something I think we can improve 4. Long time to decompress compressed files Even I decompress those 20 thousands compressed files with 12 process on my 16 core 3.x Ghz server. It still took about 3 ~ 4days to complete dataset building. Most of time spent on decompress those files. ### Info about the source data The source data is an tar.xz file with following structure, files/directory beyond compressed file is what can we get after decompress it. ``` openwebtext.tar.xz |__ openwebtext |__subset000.xz | |__ ....txt | |__ ....txt | ... |__ subset001.xz | .... ``` And this the structure of dummy data, same as the original one. ``` dummy_data.zip |__ dummy_data |__ openwebtext |__fake_subset-1_data-dirxz # actually it is a directory | |__ ....txt | |__ ....txt |__ fake_subset-2_data-dirxz |__ ....txt |__ ....txt ```
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Drop labels in Image and Audio folders if files are on different levels in directory or if there is only one label
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "> Nit: maybe we can use the count_path_segments function from this file for counting (updated with your logic to make it faster).\r\n\r\n@mariosasko just to make sure I understood you correctly - are you okay with this change? (actually `os.path.normpath` is redundant here as paths from `data_files` should be already normalized but just in case)\r\nhttps://github.com/huggingface/datasets/pull/5192/files#diff-1f09f7a178211f7539b1499b64b69793bd53b30c8b7b34cfcc5835e25d31929fR33\r\nIf you are, we can merge.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5192). All of your documentation changes will be reflected on that endpoint.", "awesome ! :D" ]
"2022-11-02T14:01:41Z"
"2022-11-15T16:32:53Z"
"2022-11-15T16:31:07Z"
CONTRIBUTOR
null
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Will close https://github.com/huggingface/datasets/issues/5153 Drop labels by default (`drop_labels=None`) when: * there are files on different levels of directory hierarchy by checking their path depth * all files are in the same directory (=only one label was inferred) First one fixes cases like this: ``` repo image3.jpg image4.jpg data image1.jpg image2.jpg ``` Second one fixes cases like this: ``` repo image1.jpg image2.jpg image3.jpg ``` This is mostly to fix the viewer for people who just drop images in the Hub interface into the root dir. I added tests for both of the cases on local and remote files. **I also changed data files for old test on drop_labels** (`test_generate_examples_drop_labels`). The files I provide to `test_generate_examples_drop_labels` now has "canonical" classification structure (two dirs) in order not to change the logic of the test (=not to check these two cases addressed in the PR).
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Map cache does not detect function changes in another module
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[ "This issue is a duplicate of https://github.com/huggingface/datasets/issues/3297. This is a limitation of `dill`, a package we use for caching (non-`__main__` module objects are serialized by reference). You can find more info about it here: https://github.com/uqfoundation/dill/issues/424.\r\n\r\nIn your case, moving \r\n```\r\ndata = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train')\r\ndata = data.map(transform)\r\n``` \r\nto `test.py` and setting `transform.__module__ = None` at the end of `dataset.py` should fix the issue.", "I understand this may be a limitation of an upstream tool, but for a user for datasets this is very annoying, as when you have dozens of different datasets with different preprocessing functions you can't really move them all into the same file. It may be worth seeing if there is a way to specialize the dependency (eg. subclass it) and enforce behaviors that makes sense for your product.\r\n\r\nI was able to work around this for now by setting `__module__ = None`. If such workarounds are required for now it may be better to document it somewhere than a single obscure issue from a long time ago.\r\n\r\nAs this is a duplicate issue I'm closing it.\r\n\r\nI have another issue with the cache https://github.com/huggingface/datasets/issues/6179 can you take a look?" ]
"2023-08-25T22:59:14Z"
"2023-08-29T20:57:07Z"
"2023-08-29T20:56:49Z"
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```python # dataset.py import os import datasets if not os.path.exists('/tmp/test.json'): with open('/tmp/test.json', 'w') as file: file.write('[{"text": "hello"}]') def transform(example): text = example['text'] # text += ' world' return {'text': text} data = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train') data = data.map(transform) ``` ```python # test.py import dataset print(next(iter(dataset.data))) ``` Initialize cache ``` python3 test.py # {'text': 'hello'} ``` Edit dataset.py and uncomment the commented line, run again ``` python3 test.py # {'text': 'hello'} # expected: {'text': 'hello world'} ``` Clear cache and run again ``` rm -rf ~/.cache/huggingface/datasets/* python3 test.py # {'text': 'hello world'} ``` If instead the two files are combined, then changes to the function are detected correctly. But it's expected when working on any realistic codebase that things will be modularized into separate files.
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ModuleNotFoundError: No module named 'apache_beam', when specific languages.
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[ "Hi !\r\n\r\nApache Beam is a framework used to define data transformation pipelines. These pipeline can then be run in many runtimes: DataFlow, Spark, Flink, etc. There also exist a local runner called the DirectRunner.\r\nWikipedia is a dataset that requires some parsing, so to allow the processing to be run on this kind of runtime we're using Apache Beam.\r\n\r\nAt Hugging Face we've already processed certain versions of wikipedia (the `20200501.en` one for example) so that users can directly download the processed version instead of using Apache Beam to process it.\r\nHowever for the japanese language we haven't processed it so you'll have to run the processing on your side.\r\nSo you do need Apache Beam to process `20200501.ja`.\r\n\r\nYou can install Apache Beam with\r\n```\r\npip install apache-beam\r\n```\r\n\r\nI think we can probably improve the error message to let users know of this subtlety.\r\nWhat #498 implied is that Apache Beam is not needed when you process a dataset that doesn't use Apache Beam.", "Thanks for your reply! \r\nI understood.\r\n\r\nI tried again with installing apache-beam, add ` beam_runner=\"DirectRunner\"` and an anther `mwparserfromhell` is also required so I installed it.\r\nbut, it also failed. It exited 1 without error message.\r\n\r\n```py\r\nimport datasets\r\n# BTW, 20200501.ja doesn't exist at wikipedia, so I specified date argument\r\nwiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\"./datasets\", beam_runner=\"DirectRunner\")\r\nprint(wiki)\r\n```\r\nand its log is below\r\n```\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nKilled\r\n```\r\n\r\nI also tried on another machine because it may caused by insufficient resources.\r\n```\r\n$ python main.py\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to ./datasets/wikipedia/20210120.ja-date=20210120,language=ja/0.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\n\r\nTraceback (most recent call last):\r\n File \"main.py\", line 3, in <module>\r\n wiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\"./datasets\", beam_runner=\"DirectRunner\")\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/load.py\", line 609, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\", line 526, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/datasets/builder.py\", line 1069, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/pipeline.py\", line 561, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py\", line 126, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 182, in run_pipeline\r\n self._latest_run_result = self.run_via_runner_api(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 193, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 358, in run_stages\r\n stage_results = self._run_stage(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 549, in _run_stage\r\n last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 595, in _run_bundle\r\n result, splits = bundle_manager.process_bundle(\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 888, in process_bundle\r\n self._send_input_to_worker(process_bundle_id, transform_id, elements)\r\n File \"/home/miyamonz/.cache/pypoetry/virtualenvs/try-datasets-4t4JWXxu-py3.8/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 765, in _send_input_to_worker\r\n data_out.write(byte_stream)\r\n File \"apache_beam/coders/stream.pyx\", line 42, in apache_beam.coders.stream.OutputStream.write\r\n File \"apache_beam/coders/stream.pyx\", line 47, in apache_beam.coders.stream.OutputStream.write\r\n File \"apache_beam/coders/stream.pyx\", line 109, in apache_beam.coders.stream.OutputStream.extend\r\nAssertionError: OutputStream realloc failed.\r\n```\r\n\r\n", "Hi @miyamonz,\r\n\r\nI tried replicating this issue using the same snippet used by you. I am able to download the dataset without any issues, although I stopped it in the middle because the dataset is huge.\r\n\r\nBased on a similar issue [here](https://github.com/google-research/fixmatch/issues/23), it could be related to your environment setup, although I am just guessing here. Can you share these details?", "thanks for your reply and sorry for my late response.\r\n\r\n## environment\r\nmy local machine environment info\r\n- Ubuntu on WSL2\r\n\r\n`lsb_release -a`\r\n```\r\nNo LSB modules are available.\r\nDistributor ID: Ubuntu\r\nDescription: Ubuntu 20.04.2 LTS\r\nRelease: 20.04\r\nCodename: focal\r\n```\r\n\r\nRTX 2070 super\r\nInside WSL, there is no nvidia-msi command. I don't know why.\r\nBut, `torch.cuda.is_available()` is true and when I start something ML training code GPU usage is growing up, so I think it works.\r\n\r\nFrom PowerShell, there is nvidia-smi.exe and result is below.\r\n```\r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 470.05 Driver Version: 470.05 CUDA Version: 11.3 |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|===============================+======================+======================|\r\n| 0 NVIDIA GeForce ... WDDM | 00000000:09:00.0 On | N/A |\r\n| 0% 30C P8 19W / 175W | 523MiB / 8192MiB | 3% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=============================================================================|\r\n| 0 N/A N/A 1728 C+G Insufficient Permissions N/A |\r\n| 0 N/A N/A 3672 C+G ...ekyb3d8bbwe\\YourPhone.exe N/A |\r\n| 0 N/A N/A 6304 C+G ...2txyewy\\TextInputHost.exe N/A |\r\n| 0 N/A N/A 8648 C+G C:\\Windows\\explorer.exe N/A |\r\n| 0 N/A N/A 9536 C+G ...y\\ShellExperienceHost.exe N/A |\r\n| 0 N/A N/A 10668 C+G ...5n1h2txyewy\\SearchApp.exe N/A |\r\n| 0 N/A N/A 10948 C+G ...artMenuExperienceHost.exe N/A |\r\n| 0 N/A N/A 11988 C+G ...8wekyb3d8bbwe\\Cortana.exe N/A |\r\n| 0 N/A N/A 12464 C+G ...cw5n1h2txyewy\\LockApp.exe N/A |\r\n| 0 N/A N/A 13280 C+G ...upport\\CEF\\Max Helper.exe N/A |\r\n| 0 N/A N/A 15948 C+G ...t\\GoogleIMEJaRenderer.exe N/A |\r\n| 0 N/A N/A 16128 C+G ...ram Files\\Slack\\Slack.exe N/A |\r\n| 0 N/A N/A 19096 C+G ...8bbwe\\WindowsTerminal.exe N/A |\r\n+-----------------------------------------------------------------------------+\r\n```\r\n\r\nI don't know what should I show in such a case. If it's not enough, please tell me some commands.\r\n\r\n---\r\n## what I did\r\nI surveyed more and I found 2 issues.\r\n\r\nAbout the first one, I wrote it as a new issue.\r\nhttps://github.com/huggingface/datasets/issues/2031\r\n\r\nThe error I mentioned in the previous comment above, which occurred on my local machine, is no longer occurring.\r\n\r\nBut, it still failed. In the previous comment, I wrote `AssertionError: OutputStream realloc failed.` happen on another machine. It also happens on my local machine.\r\n\r\nHere's what I've tried.\r\n\r\nthe wikipedia.py downloads these xml.bz2 files based on dumpstatus.json\r\nIn Japanese Wikipedia dataset that I specified, it will download these 6 files.\r\n\r\n\r\n`https://dumps.wikimedia.org/jawiki/20210120/dumpstatus.json`\r\nand filtered json based on wikipedia.py is below.\r\n```json\r\n {\r\n \"jobs\": {\r\n \"articlesmultistreamdump\": {\r\n \"files\": {\r\n \"jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\"\r\n },\r\n \"jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\": {\r\n \"url\": \"/jawiki/20210120/jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\"\r\n }\r\n }\r\n }\r\n }\r\n }\r\n```\r\n\r\nSo, I tried running with fewer resources by modifying this line.\r\nhttps://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L524\r\nI changed it like this. just change filepaths list.\r\n` | \"Initialize\" >> beam.Create(filepaths[:1])`\r\n\r\nand I added a print line inside for the loop of _extract_content.\r\nlike this `if(i % 100000 == 0): print(i)`\r\n\r\nfirst, without modification, it always stops after all _extract_content is done.\r\n\r\n- `filepaths[:1]` then it succeeded.\r\n- `filepaths[:2]` then it failed.\r\nI don't try all patterns because each pattern takes a long time.\r\n\r\n### my opinion\r\nIt seems it's successful when the entire file size is small.\r\n \r\nso, at least it doesn't file-specific issue.\r\n\r\n\r\nI don't know it's true but I think when beam_writter writes into a file, it consumes memory depends on its entire file.\r\nbut It's correct Apache Beam's behavior? I'm not familiar with this library.\r\n", "I don't know if this is related, but there is this issue on the wikipedia processing that you reported at #2031 (open PR is at #2037 ) .\r\nDoes the fix your proposed at #2037 helps in your case ?\r\n\r\nAnd for information, the DirectRunner of Apache Beam is not optimized for memory intensive tasks, so you must be right when you say that it uses the memory for the entire file.", "the #2037 doesn't solve my problem directly, but I found the point!\r\n\r\nhttps://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/wikipedia/wikipedia.py#L523\r\nthis `beam.transforms.Reshuffle()` cause the memory error.\r\n\r\nit makes sense if I consider the shuffle means. Beam's reshuffle seems need put all data in memory.\r\nPreviously I doubt that this line causes error, but at that time another bug showed in #2037 made error, so I can't found it.\r\n\r\nAnyway, I comment out this line, and run load_dataset, then it works!\r\n\r\n```python\r\nwiki = datasets.load_dataset(\r\n \"./wikipedia.py\",\r\n cache_dir=\"./datasets\",\r\n beam_runner=\"DirectRunner\",\r\n language=\"ja\",\r\n date=\"20210120\",\r\n)[\"train\"]\r\n```\r\n![image](https://user-images.githubusercontent.com/6331508/112283369-6a9f3300-8ccb-11eb-82e5-827bf7fddfb9.png)\r\n\r\nDataset has already shuffle function. https://github.com/huggingface/datasets/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/arrow_dataset.py#L2069\r\nSo, though I don't know it's difference correctly, but I think Beam's reshuffle isn't be needed. How do you think?", "The reshuffle is needed when you use parallelism.\r\nThe objective is to redistribute the articles evenly on the workers, since the `_extract_content` step generated many articles per file. By using reshuffle, we can split the processing of the articles of one file into several workers. Without reshuffle, all the articles of one file would be processed on the same worker that read the file, making the whole process take a very long time.", "Maybe the reshuffle step can be added only if the runner is not a DirectRunner ?" ]
"2021-01-29T08:17:24Z"
"2021-03-25T12:10:51Z"
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```py import datasets wiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='./datasets') ``` then `ModuleNotFoundError: No module named 'apache_beam'` happend. The error doesn't appear when it's '20200501.en'. I don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?
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Add checksums
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[ "Looks good to me :-) \r\n\r\nJust would prefer to get rid of the `_DYNAMICALLY_IMPORTED_MODULE` attribute and replace it by a `get_imported_module()` function. Maybe there is something I'm not seeing here though - what do you think? ", "> * I'm not sure I understand the general organization of checksums. I see we have a checksum folder with potentially several checksum files but I also see that checksum files can potentially contain several checksums. Could you explain a bit more how this is organized?\r\n\r\nIt should look like this:\r\nsquad/\r\nβ”œβ”€β”€ squad.py/\r\n└── urls_checksums/\r\n...........└── checksums.txt\r\n\r\nIn checksums.txt, the format is one line per (url, size, checksum)\r\n\r\nI don't have a strong opinion between `urls_checksums/checksums.txt` or directly `checksums.txt` (not inside the `urls_checksums` folder), let me know what you think.\r\n\r\n\r\n> * Also regarding your comment on checksum files for \"canonical\" datasets. I understand we can just create these with `nlp-cli test` and then upload them manually to our S3, right?\r\n\r\nYes you're right", "Update of the commands:\r\n\r\n- nlp-cli test \\<dataset\\> : Run download_and_prepare and verify checksums\r\n * --name \\<name\\> : run only for the name\r\n * --all_configs : run for all configs\r\n * --save_checksums : instead of verifying checksums, compute and save them\r\n * --ignore_checksums : don't do checksums verification\r\n\r\n- nlp-cli upload \\<dataset_folder\\> : Upload a dataset\r\n * --upload_checksums : compute and upload checksums for uploaded files\r\n\r\nTODO:\r\n- don't overwrite checksums files on S3, to let the user upload a dataset in several steps if needed\r\n\r\nQuestion:\r\n- One idea from @patrickvonplaten : shall we upload checksums everytime we upload files ? (and therefore remove the upload_checksums parameter)", "Ok, ready to merge, then @lhoestq ?", "Yep :)" ]
"2020-04-29T13:37:29Z"
"2020-04-30T19:52:50Z"
"2020-04-30T19:52:49Z"
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### Checksums files They are stored next to the dataset script in urls_checksums/checksums.txt. They are used to check the integrity of the datasets downloaded files. I kept the same format as tensorflow-datasets. There is one checksums file for all configs. ### Load a dataset When you do `load("squad")`, it will also download the checksums file and put it next to the script in nlp/datasets/hash/urls_checksums/checksums.txt. It also verifies that the downloaded files checksums match the expected ones. You can ignore checksum tests with `load("squad", ignore_checksums=True)` (under the hood it just adds `ignore_checksums=True` in the `DownloadConfig`) ### Test a dataset There is a new command `nlp-cli test squad` that runs `download_and_prepare` to see if it runs ok, and that verifies that all the checksums match. Allowed arguments are `--name`, `--all_configs`, `--ignore_checksums` and `--register_checksums`. ### Register checksums 1. If the dataset has external dataset files The command `nlp-cli test squad --register_checksums --all_configs` runs `download_and_prepare` on all configs to see if it runs ok, and it creates the checksums file. You can also register one config at a time using `--name` instead ; the checksums file will be completed and not overwritten. If the script is a local script, the checksum file is moved to urls_checksums/checksums.txt next to the local script, to enable the user to upload both the script and the checksums file afterwards with `nlp-cli upload squad`. 2. If the dataset files are all inside the directory of the dataset script The user can directly do `nlp-cli upload squad --register_checksums`, as there is no need to download anything. In this case however, all the dataset must be uploaded at once. -- PS : it doesn't allow to register checksums for canonical datasets, the file has to be added manually on S3 for now (I guess ?) Also I feel like we must be sure that this processes would not constrain too much any user from uploading its dataset. Let me know what you think :)
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add web_questions
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[ "What does the `nlp-cli dummy_data` command returns ?", "`test.json` -> `test` \r\nand \r\n`train.json` -> `train`\r\n\r\nas shown by the `nlp-cli dummy_data` command ;-)", "LGTM for merge @lhoestq - I let you merge if you want to." ]
"2020-07-16T08:54:59Z"
"2020-08-06T06:16:20Z"
"2020-08-06T06:16:19Z"
CONTRIBUTOR
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add Web Question dataset #336 Maybe @patrickvonplaten you can help with the dummy_data structure? it still broken
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Don't memoize strings when hashing since two identical strings may have different python ids
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[ "This change slows down the hash computation a little bit but from my tests it doesn't look too impactful. So I think it's fine to merge this." ]
"2021-10-29T16:26:17Z"
"2021-11-02T09:35:38Z"
"2021-11-02T09:35:37Z"
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When hashing an object that has several times the same string, the hashing could return a different hash if the identical strings share the same python `id()` or not. Here is an example code that shows how the issue can affect the caching: ```python import json import pyarrow as pa from datasets.features import Features from datasets.fingerprint import Hasher schema = pa.schema([pa.field("some_string", pa.string()), pa.field("another_string", pa.string())]) features_from_schema = Features.from_arrow_schema(schema) Hasher.hash(features_from_schema) # dffa9dca9a73fd8c features_dict = json.loads('{"some_string": {"dtype": "string", "id": null, "_type": "Value"}, "another_string": {"dtype": "string", "id": null, "_type": "Value"}}') features_from_json = Features.from_dict(features_dict) Hasher.hash(features_from_json) # 3812e76b15e6420e features_from_schema == features_from_json # True ``` This is because in `features_dict`, some strings like "dtype" are repeated but don't share the same id, contrary to the ones in `features_from_schema`. I fixed that by disabling memoization for strings. This could be optimized in the future by implementing a smarter memoization with a special handling for strings.
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Cache miss during reload for datasets using image fetch utilities through map
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[ "Hi ! Maybe one of the objects in the function is not deterministic across sessions ? You can read more about it and how to investigate here: https://huggingface.co/docs/datasets/about_cache", "Hi @apsdehal! Can you verify that replacing\r\n```python\r\ndef fetch_single_image(image_url, timeout=None, retries=0):\r\n for _ in range(retries + 1):\r\n try:\r\n request = urllib.request.Request(\r\n image_url,\r\n data=None,\r\n headers={\"user-agent\": get_datasets_user_agent()},\r\n )\r\n with urllib.request.urlopen(request, timeout=timeout) as req:\r\n image = PIL.Image.open(io.BytesIO(req.read()))\r\n break\r\n except Exception:\r\n image = None\r\n return image\r\n```\r\nwith \r\n```python\r\nUSER_AGENT = get_datasets_user_agent()\r\n\r\ndef fetch_single_image(image_url, timeout=None, retries=0):\r\n for _ in range(retries + 1):\r\n try:\r\n request = urllib.request.Request(\r\n image_url,\r\n data=None,\r\n headers={\"user-agent\": USER_AGENT},\r\n )\r\n with urllib.request.urlopen(request, timeout=timeout) as req:\r\n image = PIL.Image.open(io.BytesIO(req.read()))\r\n break\r\n except Exception:\r\n image = None\r\n return image\r\n```\r\nfixes the issue?", "Thanks @mariosasko. That does fix the issue. In general, I think these image downloading utilities since they are being used by a lot of image dataset should be provided as a part of `datasets` library right to keep the logic consistent and READMEs smaller? If they already exists, that is also great, please point me to those. I saw that `http_get` does exist.", "You can find my rationale (and a proposed solution) for why these utilities are not a part of `datasets` here: https://github.com/huggingface/datasets/pull/4100#issuecomment-1097994003.", "Makes sense. But, I think as the number of image datasets as grow, more people are copying pasting original code from docs to work as it is while we make fixes to them later. I think we do need a central place for these to avoid that confusion as well as more easier access to image datasets. Should we restart that discussion, possible on slack?" ]
"2022-04-22T07:47:08Z"
"2022-04-26T17:00:32Z"
"2022-04-26T13:38:26Z"
CONTRIBUTOR
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## Describe the bug It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch. ## Steps to reproduce the bug Using the example provided in `red_caps` dataset. ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image import datasets from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"])) return batch def process_image_urls(batch): processed_batch_image_urls = [] for image_url in batch["image_url"]: processed_example_image_urls = [] image_url_splits = re.findall(r"http\S+", image_url) for image_url_split in image_url_splits: if "imgur" in image_url_split and "," in image_url_split: for image_url_part in image_url_split.split(","): if not image_url_part: continue image_url_part = image_url_part.strip() root, ext = os.path.splitext(image_url_part) if not root.startswith("http"): root = "http://i.imgur.com/" + root root = root.split("#")[0] if not ext: ext = ".jpg" ext = re.split(r"[?%]", ext)[0] image_url_part = root + ext processed_example_image_urls.append(image_url_part) else: processed_example_image_urls.append(image_url_split) processed_batch_image_urls.append(processed_example_image_urls) batch["image_url"] = processed_batch_image_urls return batch dset = load_dataset("red_caps", "jellyfish") dset = dset.map(process_image_urls, batched=True, num_proc=4) features = dset["train"].features.copy() features["image"] = datasets.Sequence(datasets.Image()) num_threads = 5 dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads}) ``` Run this in an interpretor or as a script twice and see that the cache is missed the second time. ## Expected results At reload there should not be any cache miss ## Actual results Every time script is run, cache is missed and dataset is built from scratch. ## Environment info - `datasets` version: 2.1.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
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removing unzipped hansards dummy data
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"2020-12-07T17:31:16Z"
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which were added by mistake
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Fake news english 4
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[ "Thanks for the PR @MisbahKhan789 !\r\n\r\nFew comments to help you along (I'm NOT a maintainer, just offering help to unblock the process) :-\r\n - Could you re-run `make style` and fix the errors related to code quality specific to your dataset in the `datasets/fake_news_english` folder?\r\n(These seem to show errors that need manual fixes on running `flake8 datasets/fake_news_english`)\r\n- Please run the local tests and check if they pass\r\n`RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_<your-dataset-name>`\r\n `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your-dataset-name>`\r\n(If not, you may have to regenerate the dummy data and the `dataset_infos.json` files)\r\n\r\n", "Hii please follow me", "> Thanks for the PR @MisbahKhan789 !\r\n> \r\n> Few comments to help you along (I'm NOT a maintainer, just offering help to unblock the process) :-\r\n> \r\n> * Could you re-run `make style` and fix the errors related to code quality specific to your dataset in the `datasets/fake_news_english` folder?\r\n> (These seem to show errors that need manual fixes on running `flake8 datasets/fake_news_english`)\r\n> * Please run the local tests and check if they pass\r\n> `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_<your-dataset-name>`\r\n> `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your-dataset-name>`\r\n> (If not, you may have to regenerate the dummy data and the `dataset_infos.json` files)\r\n\r\nHey Bharat, thanks for the reply. I actually submitted a new PR with the changes that are required :)\r\n" ]
"2020-12-11T21:10:35Z"
"2020-12-12T19:39:52Z"
"2020-12-12T19:38:09Z"
CONTRIBUTOR
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Add raw data files to the Hub with GitHub LFS for canonical dataset
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[ "Hi @zlucia, I would actually suggest hosting the dataset as a huggingface.co-hosted dataset.\r\n\r\nThe only difference with a \"canonical\"/legacy dataset is that it's nested under an organization (here `stanford` or `stanfordnlp` for instance – completely up to you) but then you can upload your data using git-lfs (unlike \"canonical\" datasets where we don't host the data)\r\n\r\nLet me know if this fits your use case!\r\n\r\ncc'ing @osanseviero @lhoestq and rest of the team πŸ€—", "Hi @zlucia,\r\n\r\nAs @julien-c pointed out, the way to store/host raw data files in our Hub is by using what we call \"community\" datasets:\r\n- either at your personal namespace: `load_dataset(\"zlucia/casehold\")`\r\n- or at an organization namespace: for example, if you create the organization `reglab`, then `load_dataset(\"reglab/casehold\")`\r\n\r\nPlease note that \"canonical\" datasets do not normally store/host their raw data at our Hub, but in a third-party server. For \"canonical\" datasets, we just host the \"loading script\", that is, a Python script that downloads the raw data from a third-party server, creates the HuggingFace dataset from it and caches it locally.\r\n\r\nIn order to create an organization namespace in our Hub, please follow this link: https://huggingface.co/organizations/new\r\n\r\nThere are already many organizations at our Hub (complete list here: https://huggingface.co/organizations), such as:\r\n- Stanford CRFM: https://huggingface.co/stanford-crfm\r\n- Stanford NLP: https://huggingface.co/stanfordnlp\r\n- Stanford CS329S: Machine Learning Systems Design: https://huggingface.co/stanford-cs329s\r\n\r\nAlso note that you in your organization namespace:\r\n- you can add any number of members\r\n- you can store both raw datasets and models, and those can be immediately accessed using `datasets` and `transformers`\r\n\r\nOnce you have created an organization, these are the steps to upload/host a raw dataset: \r\n- The no-code procedure: https://huggingface.co/docs/datasets/upload_dataset.html\r\n- Using the command line (terminal): https://huggingface.co/docs/datasets/share.html#add-a-community-dataset\r\n\r\nPlease, feel free to ping me if you have any further questions or need help.\r\n", "Ah I see, I think I was unclear whether there were benefits to uploading a canonical dataset vs. a community provided dataset. Thanks for clarifying. I'll see if we want to create an organization namespace and otherwise, will upload the dataset under my personal namespace." ]
"2021-10-25T23:28:21Z"
"2021-10-30T19:54:51Z"
"2021-10-30T19:54:51Z"
NONE
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I'm interested in sharing the CaseHOLD dataset (https://arxiv.org/abs/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset](https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!
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add woz_dialogue
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"2020-12-04T12:13:07Z"
"2020-12-05T15:41:23Z"
"2020-12-05T15:40:18Z"
MEMBER
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Adding Wizard-of-Oz task oriented dialogue dataset https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz https://arxiv.org/abs/1604.04562
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TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
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[ "Hi! Casting class labels from strings is currently not supported in the CSV loader, but you can get the same result with an additional map as follows:\r\n```python\r\nfrom datasets import load_dataset,Features,Value,ClassLabel\r\nclass_names = [\"cmn\",\"deu\",\"rus\",\"fra\",\"eng\",\"jpn\",\"spa\",\"ita\",\"kor\",\"vie\",\"nld\",\"epo\",\"por\",\"tur\",\"heb\",\"hun\",\"ell\",\"ind\",\"ara\",\"arz\",\"fin\",\"bul\",\"yue\",\"swe\",\"ukr\",\"bel\",\"que\",\"ces\",\"swh\",\"nno\",\"wuu\",\"nob\",\"zsm\",\"est\",\"kat\",\"pol\",\"lat\",\"urd\",\"sqi\",\"isl\",\"fry\",\"afr\",\"ron\",\"fao\",\"san\",\"bre\",\"tat\",\"yid\",\"uig\",\"uzb\",\"srp\",\"qya\",\"dan\",\"pes\",\"slk\",\"eus\",\"cycl\",\"acm\",\"tgl\",\"lvs\",\"kaz\",\"hye\",\"hin\",\"lit\",\"ben\",\"cat\",\"bos\",\"hrv\",\"tha\",\"orv\",\"cha\",\"mon\",\"lzh\",\"scn\",\"gle\",\"mkd\",\"slv\",\"frm\",\"glg\",\"vol\",\"ain\",\"jbo\",\"tok\",\"ina\",\"nds\",\"mal\",\"tlh\",\"roh\",\"ltz\",\"oss\",\"ido\",\"gla\",\"mlt\",\"sco\",\"ast\",\"jav\",\"oci\",\"ile\",\"ota\",\"xal\",\"tel\",\"sjn\",\"nov\",\"khm\",\"tpi\",\"ang\",\"aze\",\"tgk\",\"tuk\",\"chv\",\"hsb\",\"dsb\",\"bod\",\"sme\",\"cym\",\"mri\",\"ksh\",\"kmr\",\"ewe\",\"kab\",\"ber\",\"tpw\",\"udm\",\"lld\",\"pms\",\"lad\",\"grn\",\"mlg\",\"xho\",\"pnb\",\"grc\",\"hat\",\"lao\",\"npi\",\"cor\",\"nah\",\"avk\",\"mar\",\"guj\",\"pan\",\"kir\",\"myv\",\"prg\",\"sux\",\"crs\",\"ckt\",\"bak\",\"zlm\",\"hil\",\"cbk\",\"chr\",\"nav\",\"lkt\",\"enm\",\"arq\",\"lin\",\"abk\",\"pcd\",\"rom\",\"gsw\",\"tam\",\"zul\",\"awa\",\"wln\",\"amh\",\"bar\",\"hbo\",\"mhr\",\"bho\",\"mrj\",\"ckb\",\"osx\",\"pfl\",\"mgm\",\"sna\",\"mah\",\"hau\",\"kan\",\"nog\",\"sin\",\"glv\",\"dng\",\"kal\",\"liv\",\"vro\",\"apc\",\"jdt\",\"fur\",\"che\",\"haw\",\"yor\",\"crh\",\"pdc\",\"ppl\",\"kin\",\"shs\",\"mnw\",\"tet\",\"sah\",\"kum\",\"ngt\",\"nya\",\"pus\",\"hif\",\"mya\",\"moh\",\"wol\",\"tir\",\"ton\",\"lzz\",\"oar\",\"lug\",\"brx\",\"non\",\"mww\",\"hak\",\"nlv\",\"ngu\",\"bua\",\"aym\",\"vec\",\"ibo\",\"tkl\",\"bam\",\"kha\",\"ceb\",\"lou\",\"fuc\",\"smo\",\"gag\",\"lfn\",\"arg\",\"umb\",\"tyv\",\"kjh\",\"oji\",\"cyo\",\"urh\",\"kzj\",\"pam\",\"srd\",\"lmo\",\"swg\",\"mdf\",\"gil\",\"snd\",\"tso\",\"sot\",\"zza\",\"tsn\",\"pau\",\"som\",\"egl\",\"ady\",\"asm\",\"ori\",\"dtp\",\"cho\",\"max\",\"kam\",\"niu\",\"sag\",\"ilo\",\"kaa\",\"fuv\",\"nch\",\"hoc\",\"iba\",\"gbm\",\"sun\",\"war\",\"mvv\",\"pap\",\"ary\",\"kxi\",\"csb\",\"pag\",\"cos\",\"rif\",\"kek\",\"krc\",\"aii\",\"ban\",\"ssw\",\"tvl\",\"mfe\",\"tah\",\"bvy\",\"bcl\",\"hnj\",\"nau\",\"nst\",\"afb\",\"quc\",\"min\",\"tmw\",\"mad\",\"bjn\",\"mai\",\"cjy\",\"got\",\"hsn\",\"gan\",\"tzl\",\"dws\",\"ldn\",\"afh\",\"sgs\",\"krl\",\"vep\",\"rue\",\"tly\",\"mic\",\"ext\",\"izh\",\"sma\",\"jam\",\"cmo\",\"mwl\",\"kpv\",\"koi\",\"bis\",\"ike\",\"run\",\"evn\",\"ryu\",\"mnc\",\"aoz\",\"otk\",\"kas\",\"aln\",\"akl\",\"yua\",\"shy\",\"fkv\",\"gos\",\"fij\",\"thv\",\"zgh\",\"gcf\",\"cay\",\"xmf\",\"tig\",\"div\",\"lij\",\"rap\",\"hrx\",\"cpi\",\"tts\",\"gaa\",\"tmr\",\"iii\",\"ltg\",\"bzt\",\"syc\",\"emx\",\"gom\",\"chg\",\"osp\",\"stq\",\"frr\",\"fro\",\"nys\",\"toi\",\"new\",\"phn\",\"jpa\",\"rel\",\"drt\",\"chn\",\"pli\",\"laa\",\"bal\",\"hdn\",\"hax\",\"mik\",\"ajp\",\"xqa\",\"pal\",\"crk\",\"mni\",\"lut\",\"ayl\",\"ood\",\"sdh\",\"ofs\",\"nus\",\"kiu\",\"diq\",\"qxq\",\"alt\",\"bfz\",\"klj\",\"mus\",\"srn\",\"guc\",\"lim\",\"zea\",\"shi\",\"mnr\",\"bom\",\"sat\",\"szl\"]\r\nfeatures = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})\r\nnum_labels = features['label'].num_classes\r\ndata_files = { \"train\": \"train.csv\", \"test\": \"test.csv\" }\r\nsentences = load_dataset(\r\n \"loretoparisi/tatoeba-sentences\",\r\n data_files=data_files,\r\n delimiter='\\t', \r\n column_names=['label', 'text'],\r\n)\r\n# You can make this part faster with num_proc=<some int>\r\nsentences = sentences.map(lambda ex: features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None, features=features)\r\n```\r\n\r\n@lhoestq IIRC, I suggested adding `cast_to_storage` to `ClassLabel` + `table_cast` to the packaged loaders if the `ClassLabel`/`Image`/`Audio` type is present in `features` to avoid this kind of error, but your concern was speed. IMO shouldn't be a problem if we do `table_cast` only when these features are present.", "I agree packaged loaders should support `ClassLabel` feature without throwing an error.", "@albertvillanova @mariosasko thank you, with that change now I get\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n[<ipython-input-9-eeb68eeb9bec>](https://localhost:8080/#) in <module>()\r\n 11 )\r\n 12 # You can make this part faster with num_proc=<some int>\r\n---> 13 sentences = sentences.map(lambda ex: features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None, features=features)\r\n 14 sentences = sentences.shuffle()\r\n\r\n8 frames\r\n[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices)\r\n 2193 if processed_inputs is not None and not isinstance(processed_inputs, (Mapping, pa.Table)):\r\n 2194 raise TypeError(\r\n-> 2195 f\"Provided `function` which is applied to all elements of table returns a variable of type {type(processed_inputs)}. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects.\"\r\n 2196 )\r\n 2197 elif isinstance(indices, list) and isinstance(processed_inputs, Mapping):\r\n\r\nTypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'int'>. Make sure provided `function` returns a variable of type `dict` (or a pyarrow table) to update the dataset or `None` if you are only interested in side effects.\r\n```\r\n\r\nthe error is raised by [this](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2221)\r\n\r\n```\r\n[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in validate_function_output(processed_inputs, indices)\r\n```", "@mariosasko changed it like\r\n\r\n```python\r\nsentences = sentences.map(lambda ex: {\"label\" : features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None}, features=features)\r\n```\r\n\r\nto avoid the above errorr.", "Any update on this? Is this correct ?\r\n> @mariosasko changed it like\r\n> \r\n> ```python\r\n> sentences = sentences.map(lambda ex: {\"label\" : features[\"label\"].str2int(ex[\"label\"]) if ex[\"label\"] is not None else None}, features=features)\r\n> ```\r\n> \r\n> to avoid the above errorr.\r\n\r\n" ]
"2022-04-25T07:28:42Z"
"2022-05-31T12:16:31Z"
"2022-05-31T12:16:31Z"
NONE
null
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### System Info ```shell - `transformers` version: 4.18.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - Huggingface_hub version: 0.5.1 - PyTorch version (GPU?): 1.10.0+cu111 (True) - Tensorflow version (GPU?): 2.8.0 (True) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in> ``` ### Who can help? @LysandreJik ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction ```python from datasets import load_dataset,Features,Value,ClassLabel class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"] features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')}) num_labels = features['label'].num_classes data_files = { "train": "train.csv", "test": "test.csv" } sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'], features = features ``` ERROR: ``` ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None) Value(dtype='string', id=None) Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39 Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519... Downloading data files: 100% 2/2 [00:18<00:00, 8.06s/it] Downloading data: 100% 391M/391M [00:13<00:00, 35.3MB/s] Downloading data: 100% 92.4M/92.4M [00:02<00:00, 36.5MB/s] Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn' --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) 15 frames /usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() ValueError: invalid literal for int() with base 10: 'cmn' ``` while loading without `features` it loads without errors ``` sentences = load_dataset("loretoparisi/tatoeba-sentences", data_files=data_files, delimiter='\t', column_names=['label', 'text'] ) ``` but the `label` col seems to be wrong (without the `ClassLabel` object): ``` sentences['train'].features {'label': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None)} ``` The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences Dataset format is: ``` ces Nechci vΔ›dΔ›t, co je tam uvnitΕ™. ces Kdo o tom chce slyΕ‘et? deu Tom sagte, er fΓΌhle sich nicht wohl. ber Mel-iyi-d anida-t tura ? hun Gondom lesz rΓ‘ rΓΆgtΓΆn. ber Mel-iyi-d anida-tt tura ? deu Ich will dich nicht reden hΓΆren. ``` ### Expected behavior ```shell correctly load train and test files. ```
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cast_storage method from datasets.features
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[ "Hi! We've recently added a check to the `ClassLabel` type to ensure the values are in the valid label range `-1, 0, ..., num_classes-1` (-1 is used for missing values). The error in your case happens only if the `labels` column is of type `Sequence(ClassLabel(...))` before the `map` call and can be avoided by calling `dataset = dataset.cast_column(\"labels\", Sequence(Value(\"int\")))` beforehand. The token-classification examples in Transformers introduce a new `labels` column, so their type is also `Sequence(Value(\"int\"))`, which doesn't lead to an error as this type unbounded. ", "I'm fine with re-adding support for all negative values for unknown/missing labels @mariosasko, wdyt ?" ]
"2022-06-15T20:47:22Z"
"2022-06-16T13:54:07Z"
"2022-06-16T13:54:07Z"
NONE
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## Describe the bug A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets. ## Steps to reproduce the bug Steps are: - load whatever datset - write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification - map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features # Sample code to reproduce the bug def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length") labels = [] for i, label in enumerate(examples[f"labels"]): word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word. previous_word_idx = None label_ids = [] for word_idx in word_ids: # Set the special tokens to -100. if word_idx is None: label_ids.append(-100) elif word_idx != previous_word_idx: # Only label the first token of a given word. label_ids.append(label[word_idx]) else: label_ids.append(-100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") dt = dataset.map(tokenize_and_align_labels, batched=True) ## Expected results New dataset objects should load and do on older versions. ## Actual results "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features ## Environment info everything works fine on older installations of datasets/transformers Issue arises when installing datasets on google collab under python3.7 I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2
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Allow downloading/processing/caching only specific splits
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[ "> If you pass a dictionary like this:\r\n> \r\n> ```\r\n> {\"main_metadata\": url_to_main_data,\r\n> \"secondary_metadata\": url_to_sec_data,\r\n> \"train\": url_train_data,\r\n> \"test\": url_test_data}\r\n> ```\r\n> \r\n> then only the train or test keys will be kept, which I feel not intuitive.\r\n> \r\n> For example if the users asks to load the \"train\" split, then the main and secondary metadata won't be downloaded.\r\n> You can fix that by keeping all the keys except the splits to ignore\r\n\r\nHi @lhoestq, I have been thinking about this and I think it is worth that we discuss about it.\r\n\r\nWhen I created this PR, my first idea was to create a \"hack\" inside the download manager that will be able to filter some split(s) without touching any dataset script. Of course, the download manager does not know about splits logic, and thus this trick would only work for some very specific datasets: only the ones containing that pass a dict to the download manager containing only the keys \"train\", \"validation\", \"test\" (or the one passed by the user for advanced users knowing they can do it), e.g. the `natural_questions` dataset (which was one of the targets).\r\n\r\nThe big inconvenient of this approach is that it is not applicable to many datasets (or worse, it should be constantly tweaked to cope with exceptional cases). One exceptional case is the one you pointed out. But I see others:\r\n- the split keys can be different: train, test, dev, val, validation, eval,...\r\n- in `hope_edi` dataset, the split keys are: TRAIN_DOWNLOAD_URL, VALIDATION_DOWNLOAD_URL\r\n- in `few_rel` dataset, the split keys are: train_wiki, val_nyt, val_pubmed,..., pid2name\r\n- in `curiosity_dialogs`, the split keys are: train, val, test, test_zero; this means that for every split we pass, we will also get test_zero\r\n- in `deal_or_no_dialog`, each of the splits URL is passed separately to the download manager, so all splits would be always downloaded\r\n- etc.\r\n\r\nThen after discussing, another idea emerged: pass a `split` parameter to `_split_generators`, which know about the splits logic, so that it can handle which splits are passed to the download manager. This approach is more accurate and can be tweaked so that it works with all the datasets we want. The only inconvenient is that then for every target dataset, we must modify its corresponding `_split_generators` script method.\r\n\r\nMy point is that I don't think it is a good idea to implement both approaches. They could even interfere with each other! \r\n\r\nIf you agree, I would implement ONLY the second one, which is simpler, more consistent and stable and will avoid future problems.", "Hi @albertvillanova !\r\nYup I agree with you, implementing the 2nd approach seems to be the right solution" ]
"2021-04-22T17:51:44Z"
"2022-07-06T15:19:48Z"
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Allow downloading/processing/caching only specific splits without downloading/processing/caching the other splits. This PR implements two steps to handle only specific splits: - it allows processing/caching only specific splits into Arrow files - for some simple cases, it allows downloading only specific splits (which is more intricate as it depends on the user-defined method `_split_generators`) This PR makes several assumptions: - `DownloadConfig` contains the configuration settings for downloading - the parameter `split` passed to `load_dataset` is just a parameter for loading (from cache), not for downloading
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[ "Hi @cryoff, thanks for completing the dataset card.\r\n\r\nNow there is an automatic validation tool to assure that all dataset cards contain all the relevant information. This is the cause of the non-passing test on your Pull Request:\r\n```\r\nFound fields that are not non-empty list of strings: {'annotations_creators': [], 'language_creators': []}\r\n```" ]
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Provides description of data instances and dataset features
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"2023-12-15T10:10:21Z"
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### Describe the bug ![image](https://github.com/huggingface/datasets/assets/47747764/f58044fb-ddda-48b6-ba68-7bbfef781630) ### Steps to reproduce the bug just loading datasets ### Expected behavior how can I fix it ### Environment info pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl done
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Add missing columns to `blended_skill_talk`
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
"2022-06-02T14:16:26Z"
"2022-06-06T15:49:56Z"
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Adds the missing columns to `blended_skill_talk` to align the loading logic with [ParlAI](https://github.com/facebookresearch/ParlAI/blob/main/parlai/tasks/blended_skill_talk/build.py). Fix #4426
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"2022-05-31T08:47:45Z"
"2022-05-31T13:50:50Z"
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Fix #4422.
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File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'?
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[ "Instead of writing the loading script, you can use the built-in loader to [load JSON files](https://huggingface.co/docs/datasets/loading#json):\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"json\", data_files={\"train\": os.path.join(data_dir[\"train\"]), \"dev\": os.path.join(data_dir[\"dev\"])})\r\n```" ]
"2023-07-15T07:58:08Z"
"2023-07-24T11:54:15Z"
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Hi, I use the code below to load local file ``` def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive # urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(_URLs) print(data_dir) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir["train"]), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir["dev"]), "split": "dev", }, ), ] ``` and error occured ``` Traceback (most recent call last): File "/home/zhizhou/data1/zhanghao/huggingface/FineTuning_Transformer/load_local_dataset.py", line 2, in <module> dataset = load_dataset("./QA_script.py",data_files='/home/zhizhou/.cache/huggingface/datasets/conversatiom_corps/part_file.json') File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/load.py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 1670, in _download_and_prepare super()._download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'? ``` Could you help me?
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Update torch_formatter.py
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"2023-11-11T19:40:41Z"
"2023-11-11T19:41:53Z"
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Ensure PyTorch images are converted to (C, H, W) instead of (H, W, C). See #6394 for motivation.
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Fix sacrebleu parameter name
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"2021-07-19T07:07:26Z"
"2021-07-19T08:07:03Z"
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DONE: - Fix parameter name: `smooth` to `smooth_method`. - Improve kwargs description. - Align docs on using a metric. - Add example of passing additional arguments in using metrics. Related to #2669.
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Use prefix to allow exceed Windows MAX_PATH
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[ "Does this mean the `FileNotFoundError` that avoids infinite loop can be removed?", "Yes, I think so...", "Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?", " > Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?\r\n\r\nWhat about converting relative paths to absolute?", "Nice ! Have you had a chance to test it on a windows machine with the max path limit enabled ? Afaik the CI doesn't have the path limit", "Sure @lhoestq: I've tested on my machine... And this fixes most of the tests... πŸ˜… " ]
"2021-07-09T16:39:53Z"
"2021-07-16T15:28:12Z"
"2021-07-16T15:28:11Z"
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By using this prefix, you can exceed the Windows MAX_PATH limit. See: https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces Related to #2524, #2220.
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Add Flickr 30k Dataset
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[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/flickr30k-captions)." ]
"2022-07-07T01:59:08Z"
"2022-07-14T02:09:45Z"
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## Adding a Dataset - **Name:** *Flickr 30k* - **Description:** *To produce the denotation graph, we have created an image caption corpus consisting of 158,915 crowd-sourced captions describing 31,783 images. This is an extension of our previous Flickr 8k Dataset. The new images and captions focus on people involved in everyday activities and events.* - **Paper:** *https://transacl.org/ojs/index.php/tacl/article/view/229/33* - **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz* - **Motivation:** *Dataset for training and evaluating models of conversational response*
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Add CMU Hinglish DoG Dataset for MT
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[ "Hi @lhoestq, thanks a lot for the help. I have moved the part as suggested. \r\nAlthough still while running the dummy data script, I face this issue\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/ishan/anaconda3/bin/datasets-cli\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/datasets_cli.py\", line 33, in main\r\n service.run()\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py\", line 318, in run\r\n self._autogenerate_dummy_data(\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py\", line 363, in _autogenerate_dummy_data\r\n dataset_builder._prepare_split(split_generator)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/builder.py\", line 1103, in _prepare_split\r\n example = self.info.features.encode_example(record)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 981, in encode_example\r\n return encode_nested_example(self, example)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 775, in encode_nested_example\r\n return {\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 775, in <dictcomp>\r\n return {\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 99, in zip_dict\r\n yield key, tuple(d[key] for d in dicts)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 99, in <genexpr>\r\n yield key, tuple(d[key] for d in dicts)\r\nKeyError: 'status'\r\n```\r\nThis KeyError is at times different from 'status' also.\r\nwhen I run \r\n```\r\ndatasets-cli dummy_data datasets/cmu_hinglish_dog --auto_generate --json_field='history'\r\n```\r\nI have tried removing unnecessary feature type definition, but that didn't help. Please let me know if I am missing something, thanks!", "The CI fail is unrelated to this PR and fixed on master. Merging !" ]
"2021-10-22T16:17:25Z"
"2021-11-15T11:36:42Z"
"2021-11-15T10:27:45Z"
CONTRIBUTOR
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Address part of #2841 Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model. Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two. The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py) The dummy data part is not working properly, it shows ``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ``` when I run without ``--auto_generate``. Please let me know how I can fix that. Thanks
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Can't use custom feature description when loading a dataset
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[ "Forgot to actually convert the feature dict to a Feature object. Closing." ]
"2022-11-28T07:55:44Z"
"2022-11-28T08:11:45Z"
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### Describe the bug I have created a feature dictionary to describe my datasets' column types, to use when loading the dataset, following [the doc](https://huggingface.co/docs/datasets/main/en/about_dataset_features). It crashes at dataset load. ### Steps to reproduce the bug ```python # Creating features task_list = [f"motif_G{i}" for i in range(19, 53)] features = {t: Sequence(feature=Value(dtype="float64")) for t in task_list} for col_name in ["class_label"]: features[col_name] = Sequence(feature=Value(dtype="int64")) for col_name in ["num_nodes"]: features[col_name] = Value(dtype="int64") for col_name in ["num_bridges", "num_cycles", "avg_shortest_path_len"]: features[col_name] = Sequence(feature=Value(dtype="float64")) for col_name in ["edge_attr", "node_feat", "edge_index"]: features[col_name] = Sequence(feature=Sequence(feature=Value(dtype="int64"))) print(features) dataset = load_dataset(path=f"graphs-datasets/unbalanced-motifs-500K", split="train", features=features) ``` Last line will crash and say 'TypeError: argument of type 'Sequence' is not iterable'. Full stack: ``` Traceback (most recent call last): File "pretrain_tokengt.py", line 131, in <module> main(output_folder = "../workspace/pretraining", File "pretrain_tokengt.py", line 52, in main dataset = load_dataset(path=f"graphs-datasets/{dataset_name}", split="train", features=features) File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1718, in load_dataset builder_instance = load_dataset_builder( File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1514, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "huggingface_env/lib/python3.8/site-packages/datasets/builder.py", line 321, in __init__ info.update(self._info()) File "huggingface_env/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 62, in _info return datasets.DatasetInfo(features=self.config.features) File "<string>", line 20, in __init__ File "huggingface_env/lib/python3.8/site-packages/datasets/info.py", line 155, in __post_init__ self.features = Features.from_dict(self.features) File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1599, in from_dict obj = generate_from_dict(dic) File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1281, in generate_from_dict if "_type" not in obj or isinstance(obj["_type"], dict): TypeError: argument of type 'Sequence' is not iterable ``` ### Expected behavior For it not to crash. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3
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[ "Hi! \r\n\r\nIt's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.", "Yeah, I solved it. Problem was in osxkeychain. When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo contain other username. When I changed username in keychain - it works now." ]
"2023-03-02T12:08:39Z"
"2023-03-14T16:55:35Z"
"2023-03-14T16:55:34Z"
NONE
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### Describe the bug Get `Authorization error` when try to push data into hugginface datasets hub. ### Steps to reproduce the bug I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share), 1. `huggingface-cli login` with WRITE token 2. `git lfs install` 3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name` 4. ``` cp /somewhere/data/*.json . git lfs track *.json git add .gitattributes git add *.json git commit -m "add json files" ``` but when I execute `git push` I got the error: ``` Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done. batch response: Authorization error. error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news' ``` Size of data ~100Gb. I have five json files - different parts. ### Expected behavior All my data pushed into hub ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.10.10 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Add DER metric for SUPERB speaker diarization task
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[ "Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate" ]
"2021-08-06T09:12:36Z"
"2023-07-11T09:35:23Z"
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Add penn treebank dataset
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[ "@yjernite I have updated the PR to be language modeling task specific. Please review!\r\n", "Yes a line corresponds to a sentence in this data." ]
"2020-12-09T19:11:33Z"
"2020-12-16T09:38:23Z"
"2020-12-16T09:38:23Z"
CONTRIBUTOR
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Add QuALITY
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[ "Thanks for your contribution, @jaketae. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help." ]
"2021-12-29T10:58:46Z"
"2022-10-03T09:36:14Z"
"2022-10-03T09:36:14Z"
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Fixes #3441.
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[Feature Request] Add the OpenWebText dataset
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[ "We're experimenting with hosting the OpenWebText corpus on Zenodo for easier downloading. https://zenodo.org/record/3834942#.Xs1w8i-z2J8", "Closing since it's been added in #660 " ]
"2020-05-15T15:57:29Z"
"2020-10-07T14:22:48Z"
"2020-10-07T14:22:48Z"
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The OpenWebText dataset is an open clone of OpenAI's WebText dataset. It can be used to train ELECTRA as is specified in the [README](https://www.github.com/google-research/electra). More information and the download link are available [here](https://skylion007.github.io/OpenWebTextCorpus/).
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Index error in Dataset.map
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"2021-05-08T20:44:57Z"
"2021-05-10T13:26:12Z"
"2021-05-10T13:26:12Z"
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The following code, if executed on master, raises an IndexError (due to overflow): ```python >>> from datasets import * >>> d = load_dataset("bookcorpus", split="train") Reusing dataset bookcorpus (C:\Users\Mario\.cache\huggingface\datasets\bookcorpus\plain_text\1.0.0\44662c4a114441c35200992bea923b170e6f13f2f0beb7c14e43759cec498700) 2021-05-08 21:23:46.859818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll >>> d.map(lambda ex: ex) 0%|β–Ž | 289430/74004228 [00:13<58:41, 20935.33ex/s]c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py:84: RuntimeWarning: overflow encountered in int_scalars k = i + ((j - i) * (x - arr[i]) // (arr[j] - arr[i])) 0%|β–Ž | 290162/74004228 [00:13<59:11, 20757.23ex/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1498, in map new_fingerprint=new_fingerprint, File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 174, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\fingerprint.py", line 340, in wrapper out = func(self, *args, **kwargs) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1799, in _map_single for i, example in enumerate(pbar): File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\site-packages\tqdm\std.py", line 1133, in __iter__ for obj in iterable: File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1145, in __iter__ format_kwargs=format_kwargs, File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1337, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 368, in query_table pa_subtable = _query_table(table, key) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 79, in _query_table return table.fast_slice(key % table.num_rows, 1) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 128, in fast_slice i = _interpolation_search(self._offsets, offset) File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 91, in _interpolation_search raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.") IndexError: Invalid query '290162' for size 74004228. ``` Tested on Windows, can run on Linux if needed. EDIT: It seems like for this to happen, the default NumPy dtype has to be np.int32.
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More faiss control
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[ "> Ok, so we're getting rid of the `FaissGpuOptions`?\r\n\r\nWe support `device=...` because it's simple, but faiss GPU options can be used in so many ways (you can set different gpu options for the different parts of your index for example) that it's probably better to let the user create and configure its index and then use `custom_index=...`" ]
"2020-07-08T14:45:20Z"
"2020-07-09T09:54:54Z"
"2020-07-09T09:54:51Z"
MEMBER
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Allow users to specify a faiss index they created themselves, as sometimes indexes can be composite for examples
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Add twi wordsim353
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[ "Well actually it looks like it was already added in #1428 \r\n\r\nMaybe we can close this one ? Or you wanted to make changes to this dataset ?", "Thank you, it's just a modification of Readme. I added the missing citation.", "Indeed thanks" ]
"2020-12-28T19:31:55Z"
"2021-01-04T09:39:39Z"
"2021-01-04T09:39:38Z"
CONTRIBUTOR
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Added the citation information to the README file
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6,454
Refactor `dill` logic
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005490 / 0.011353 (-0.005863) | 0.003554 / 0.011008 (-0.007454) | 0.062183 / 0.038508 (0.023675) | 0.053093 / 0.023109 (0.029984) | 0.245370 / 0.275898 (-0.030528) | 0.271637 / 0.323480 (-0.051842) | 0.002997 / 0.007986 (-0.004989) | 0.002811 / 0.004328 (-0.001517) | 0.047874 / 0.004250 (0.043623) | 0.039673 / 0.037052 (0.002620) | 0.253219 / 0.258489 (-0.005271) | 0.280438 / 0.293841 (-0.013403) | 0.028393 / 0.128546 (-0.100153) | 0.010914 / 0.075646 (-0.064732) | 0.207491 / 0.419271 (-0.211781) | 0.037565 / 0.043533 (-0.005968) | 0.252382 / 0.255139 (-0.002757) | 0.272204 / 0.283200 (-0.010995) | 0.019007 / 0.141683 (-0.122676) | 1.099767 / 1.452155 (-0.352388) | 1.173220 / 1.492716 (-0.319496) |\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) | 0.098777 / 0.018006 (0.080771) | 0.325912 / 0.000490 (0.325422) | 0.000214 / 0.000200 (0.000014) | 0.000051 / 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) | 0.018815 / 0.037411 (-0.018596) | 0.070031 / 0.014526 (0.055506) | 0.075395 / 0.176557 (-0.101162) | 0.122633 / 0.737135 (-0.614502) | 0.077621 / 0.296338 (-0.218718) |\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) | 0.290830 / 0.215209 (0.075621) | 2.869214 / 2.077655 (0.791559) | 1.507337 / 1.504120 (0.003217) | 1.351391 / 1.541195 (-0.189804) | 1.386642 / 1.468490 (-0.081848) | 0.570318 / 4.584777 (-4.014459) | 2.423442 / 3.745712 (-1.322270) | 2.897812 / 5.269862 (-2.372050) | 1.796458 / 4.565676 (-2.769219) | 0.063649 / 0.424275 (-0.360626) | 0.005038 / 0.007607 (-0.002570) | 0.357819 / 0.226044 (0.131774) | 3.535478 / 2.268929 (1.266549) | 1.831764 / 55.444624 (-53.612861) | 1.545035 / 6.876477 (-5.331442) | 1.585919 / 2.142072 (-0.556154) | 0.643333 / 4.805227 (-4.161894) | 0.120319 / 6.500664 (-6.380345) | 0.043031 / 0.075469 (-0.032438) |\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) | 0.981155 / 1.841788 (-0.860633) | 12.136069 / 8.074308 (4.061760) | 10.579923 / 10.191392 (0.388531) | 0.152963 / 0.680424 (-0.527461) | 0.014783 / 0.534201 (-0.519418) | 0.289177 / 0.579283 (-0.290106) | 0.271784 / 0.434364 (-0.162580) | 0.322381 / 0.540337 (-0.217956) | 0.420034 / 1.386936 (-0.966902) |\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) | 0.005315 / 0.011353 (-0.006038) | 0.003584 / 0.011008 (-0.007424) | 0.048596 / 0.038508 (0.010088) | 0.055940 / 0.023109 (0.032830) | 0.277687 / 0.275898 (0.001789) | 0.301545 / 0.323480 (-0.021935) | 0.004150 / 0.007986 (-0.003836) | 0.002699 / 0.004328 (-0.001629) | 0.047661 / 0.004250 (0.043410) | 0.040618 / 0.037052 (0.003565) | 0.279173 / 0.258489 (0.020684) | 0.306105 / 0.293841 (0.012264) | 0.030099 / 0.128546 (-0.098447) | 0.010784 / 0.075646 (-0.064862) | 0.057418 / 0.419271 (-0.361853) | 0.032632 / 0.043533 (-0.010901) | 0.276064 / 0.255139 (0.020925) | 0.307194 / 0.283200 (0.023995) | 0.017416 / 0.141683 (-0.124267) | 1.107749 / 1.452155 (-0.344406) | 1.161104 / 1.492716 (-0.331612) |\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) | 0.102395 / 0.018006 (0.084389) | 0.316933 / 0.000490 (0.316443) | 0.000246 / 0.000200 (0.000046) | 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) | 0.022833 / 0.037411 (-0.014579) | 0.069372 / 0.014526 (0.054846) | 0.082139 / 0.176557 (-0.094418) | 0.121666 / 0.737135 (-0.615469) | 0.084039 / 0.296338 (-0.212300) |\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) | 0.298775 / 0.215209 (0.083566) | 2.973898 / 2.077655 (0.896244) | 1.614436 / 1.504120 (0.110316) | 1.476112 / 1.541195 (-0.065083) | 1.502031 / 1.468490 (0.033541) | 0.580626 / 4.584777 (-4.004151) | 2.493428 / 3.745712 (-1.252285) | 2.931050 / 5.269862 (-2.338811) | 1.823603 / 4.565676 (-2.742073) | 0.064736 / 0.424275 (-0.359539) | 0.004963 / 0.007607 (-0.002644) | 0.355096 / 0.226044 (0.129052) | 3.522801 / 2.268929 (1.253872) | 1.968690 / 55.444624 (-53.475935) | 1.698624 / 6.876477 (-5.177853) | 1.714166 / 2.142072 (-0.427906) | 0.681734 / 4.805227 (-4.123493) | 0.118940 / 6.500664 (-6.381724) | 0.041960 / 0.075469 (-0.033509) |\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) | 0.985311 / 1.841788 (-0.856476) | 12.785393 / 8.074308 (4.711085) | 11.289459 / 10.191392 (1.098067) | 0.145297 / 0.680424 (-0.535127) | 0.016125 / 0.534201 (-0.518076) | 0.289445 / 0.579283 (-0.289838) | 0.278974 / 0.434364 (-0.155390) | 0.322456 / 0.540337 (-0.217881) | 0.418218 / 1.386936 (-0.968718) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#66cef090c55d3561412468d94cb545b47fb000fb \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005142 / 0.011353 (-0.006211) | 0.004180 / 0.011008 (-0.006829) | 0.062647 / 0.038508 (0.024139) | 0.055072 / 0.023109 (0.031962) | 0.254681 / 0.275898 (-0.021217) | 0.282650 / 0.323480 (-0.040830) | 0.003950 / 0.007986 (-0.004035) | 0.002862 / 0.004328 (-0.001466) | 0.048420 / 0.004250 (0.044170) | 0.038447 / 0.037052 (0.001394) | 0.258160 / 0.258489 (-0.000329) | 0.288596 / 0.293841 (-0.005245) | 0.027898 / 0.128546 (-0.100648) | 0.011165 / 0.075646 (-0.064482) | 0.206844 / 0.419271 (-0.212427) | 0.036312 / 0.043533 (-0.007221) | 0.257957 / 0.255139 (0.002819) | 0.277387 / 0.283200 (-0.005812) | 0.018205 / 0.141683 (-0.123478) | 1.109870 / 1.452155 (-0.342284) | 1.175005 / 1.492716 (-0.317712) |\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) | 0.096692 / 0.018006 (0.078686) | 0.307463 / 0.000490 (0.306973) | 0.000218 / 0.000200 (0.000018) | 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) | 0.018602 / 0.037411 (-0.018809) | 0.061489 / 0.014526 (0.046964) | 0.072936 / 0.176557 (-0.103620) | 0.119863 / 0.737135 (-0.617272) | 0.073983 / 0.296338 (-0.222355) |\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) | 0.291444 / 0.215209 (0.076235) | 2.849024 / 2.077655 (0.771369) | 1.533121 / 1.504120 (0.029001) | 1.402148 / 1.541195 (-0.139046) | 1.406397 / 1.468490 (-0.062094) | 0.564241 / 4.584777 (-4.020536) | 2.402052 / 3.745712 (-1.343660) | 2.772639 / 5.269862 (-2.497223) | 1.732342 / 4.565676 (-2.833334) | 0.062361 / 0.424275 (-0.361914) | 0.004945 / 0.007607 (-0.002662) | 0.355841 / 0.226044 (0.129797) | 3.426931 / 2.268929 (1.158003) | 1.865412 / 55.444624 (-53.579212) | 1.592628 / 6.876477 (-5.283849) | 1.662364 / 2.142072 (-0.479708) | 0.653278 / 4.805227 (-4.151949) | 0.118626 / 6.500664 (-6.382038) | 0.042961 / 0.075469 (-0.032508) |\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) | 0.956279 / 1.841788 (-0.885509) | 11.635540 / 8.074308 (3.561232) | 10.719590 / 10.191392 (0.528198) | 0.130015 / 0.680424 (-0.550409) | 0.014424 / 0.534201 (-0.519777) | 0.288135 / 0.579283 (-0.291148) | 0.270819 / 0.434364 (-0.163545) | 0.320238 / 0.540337 (-0.220099) | 0.421044 / 1.386936 (-0.965892) |\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) | 0.005201 / 0.011353 (-0.006152) | 0.003467 / 0.011008 (-0.007541) | 0.048939 / 0.038508 (0.010431) | 0.051841 / 0.023109 (0.028732) | 0.273708 / 0.275898 (-0.002190) | 0.293491 / 0.323480 (-0.029988) | 0.004830 / 0.007986 (-0.003156) | 0.002696 / 0.004328 (-0.001632) | 0.047727 / 0.004250 (0.043476) | 0.041319 / 0.037052 (0.004266) | 0.273837 / 0.258489 (0.015348) | 0.309860 / 0.293841 (0.016019) | 0.029054 / 0.128546 (-0.099492) | 0.010410 / 0.075646 (-0.065237) | 0.058139 / 0.419271 (-0.361133) | 0.032682 / 0.043533 (-0.010850) | 0.273244 / 0.255139 (0.018105) | 0.291579 / 0.283200 (0.008380) | 0.018262 / 0.141683 (-0.123421) | 1.144590 / 1.452155 (-0.307565) | 1.202474 / 1.492716 (-0.290243) |\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) | 0.097110 / 0.018006 (0.079104) | 0.307344 / 0.000490 (0.306854) | 0.000229 / 0.000200 (0.000029) | 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) | 0.022263 / 0.037411 (-0.015148) | 0.070140 / 0.014526 (0.055614) | 0.081251 / 0.176557 (-0.095306) | 0.120839 / 0.737135 (-0.616297) | 0.083312 / 0.296338 (-0.213026) |\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) | 0.297381 / 0.215209 (0.082172) | 2.895530 / 2.077655 (0.817875) | 1.608442 / 1.504120 (0.104322) | 1.476237 / 1.541195 (-0.064958) | 1.491306 / 1.468490 (0.022816) | 0.567272 / 4.584777 (-4.017505) | 2.463543 / 3.745712 (-1.282170) | 2.814764 / 5.269862 (-2.455098) | 1.725845 / 4.565676 (-2.839831) | 0.064149 / 0.424275 (-0.360126) | 0.004953 / 0.007607 (-0.002654) | 0.359629 / 0.226044 (0.133585) | 3.482414 / 2.268929 (1.213486) | 1.949897 / 55.444624 (-53.494727) | 1.677383 / 6.876477 (-5.199094) | 1.683655 / 2.142072 (-0.458418) | 0.645671 / 4.805227 (-4.159557) | 0.115612 / 6.500664 (-6.385053) | 0.041013 / 0.075469 (-0.034456) |\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) | 0.967843 / 1.841788 (-0.873945) | 12.376877 / 8.074308 (4.302569) | 10.988174 / 10.191392 (0.796782) | 0.134660 / 0.680424 (-0.545764) | 0.015801 / 0.534201 (-0.518400) | 0.288699 / 0.579283 (-0.290584) | 0.284887 / 0.434364 (-0.149477) | 0.322000 / 0.540337 (-0.218337) | 0.412360 / 1.386936 (-0.974576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#148454d48b7c36507a283217c7c0e3bcc0539f75 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005407 / 0.011353 (-0.005946) | 0.003496 / 0.011008 (-0.007512) | 0.062730 / 0.038508 (0.024222) | 0.051882 / 0.023109 (0.028773) | 0.244766 / 0.275898 (-0.031132) | 0.257963 / 0.323480 (-0.065516) | 0.002894 / 0.007986 (-0.005092) | 0.002567 / 0.004328 (-0.001761) | 0.048756 / 0.004250 (0.044506) | 0.039024 / 0.037052 (0.001971) | 0.247303 / 0.258489 (-0.011186) | 0.278341 / 0.293841 (-0.015500) | 0.026725 / 0.128546 (-0.101821) | 0.010577 / 0.075646 (-0.065069) | 0.210483 / 0.419271 (-0.208789) | 0.035230 / 0.043533 (-0.008303) | 0.246125 / 0.255139 (-0.009014) | 0.264039 / 0.283200 (-0.019160) | 0.019881 / 0.141683 (-0.121802) | 1.113475 / 1.452155 (-0.338679) | 1.149606 / 1.492716 (-0.343110) |\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) | 0.092946 / 0.018006 (0.074940) | 0.299985 / 0.000490 (0.299495) | 0.000215 / 0.000200 (0.000016) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018421 / 0.037411 (-0.018991) | 0.060531 / 0.014526 (0.046005) | 0.074459 / 0.176557 (-0.102098) | 0.120369 / 0.737135 (-0.616766) | 0.075505 / 0.296338 (-0.220833) |\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) | 0.289497 / 0.215209 (0.074288) | 2.783139 / 2.077655 (0.705485) | 1.482533 / 1.504120 (-0.021587) | 1.371013 / 1.541195 (-0.170182) | 1.379114 / 1.468490 (-0.089376) | 0.563953 / 4.584777 (-4.020824) | 2.389996 / 3.745712 (-1.355716) | 2.788067 / 5.269862 (-2.481795) | 1.751772 / 4.565676 (-2.813904) | 0.062680 / 0.424275 (-0.361595) | 0.004901 / 0.007607 (-0.002706) | 0.365193 / 0.226044 (0.139149) | 3.389181 / 2.268929 (1.120252) | 1.861659 / 55.444624 (-53.582965) | 1.558899 / 6.876477 (-5.317577) | 1.591079 / 2.142072 (-0.550993) | 0.648300 / 4.805227 (-4.156927) | 0.117486 / 6.500664 (-6.383178) | 0.041961 / 0.075469 (-0.033508) |\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) | 0.944391 / 1.841788 (-0.897396) | 11.500823 / 8.074308 (3.426515) | 10.580430 / 10.191392 (0.389038) | 0.142845 / 0.680424 (-0.537579) | 0.014305 / 0.534201 (-0.519896) | 0.290723 / 0.579283 (-0.288560) | 0.266206 / 0.434364 (-0.168158) | 0.325482 / 0.540337 (-0.214856) | 0.416224 / 1.386936 (-0.970712) |\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) | 0.005363 / 0.011353 (-0.005990) | 0.003548 / 0.011008 (-0.007460) | 0.048704 / 0.038508 (0.010196) | 0.051025 / 0.023109 (0.027916) | 0.273037 / 0.275898 (-0.002861) | 0.297148 / 0.323480 (-0.026332) | 0.003985 / 0.007986 (-0.004001) | 0.002739 / 0.004328 (-0.001590) | 0.048108 / 0.004250 (0.043857) | 0.040244 / 0.037052 (0.003191) | 0.277825 / 0.258489 (0.019336) | 0.303704 / 0.293841 (0.009863) | 0.029460 / 0.128546 (-0.099086) | 0.010428 / 0.075646 (-0.065218) | 0.057022 / 0.419271 (-0.362249) | 0.032711 / 0.043533 (-0.010822) | 0.274462 / 0.255139 (0.019323) | 0.293499 / 0.283200 (0.010299) | 0.018266 / 0.141683 (-0.123417) | 1.158049 / 1.452155 (-0.294106) | 1.170097 / 1.492716 (-0.322620) |\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) | 0.093412 / 0.018006 (0.075406) | 0.301538 / 0.000490 (0.301049) | 0.000222 / 0.000200 (0.000022) | 0.000051 / 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) | 0.021698 / 0.037411 (-0.015713) | 0.068735 / 0.014526 (0.054209) | 0.083010 / 0.176557 (-0.093546) | 0.127491 / 0.737135 (-0.609644) | 0.083005 / 0.296338 (-0.213333) |\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) | 0.298299 / 0.215209 (0.083090) | 2.894209 / 2.077655 (0.816554) | 1.597455 / 1.504120 (0.093335) | 1.472953 / 1.541195 (-0.068241) | 1.491553 / 1.468490 (0.023063) | 0.556566 / 4.584777 (-4.028211) | 2.419429 / 3.745712 (-1.326283) | 2.788706 / 5.269862 (-2.481156) | 1.759888 / 4.565676 (-2.805789) | 0.062535 / 0.424275 (-0.361740) | 0.004959 / 0.007607 (-0.002648) | 0.345226 / 0.226044 (0.119182) | 3.438539 / 2.268929 (1.169611) | 1.943842 / 55.444624 (-53.500782) | 1.661080 / 6.876477 (-5.215397) | 1.687632 / 2.142072 (-0.454440) | 0.639971 / 4.805227 (-4.165256) | 0.116012 / 6.500664 (-6.384652) | 0.041723 / 0.075469 (-0.033746) |\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) | 0.965143 / 1.841788 (-0.876645) | 12.086547 / 8.074308 (4.012238) | 10.708787 / 10.191392 (0.517395) | 0.129506 / 0.680424 (-0.550918) | 0.015254 / 0.534201 (-0.518947) | 0.288326 / 0.579283 (-0.290957) | 0.271976 / 0.434364 (-0.162388) | 0.328402 / 0.540337 (-0.211936) | 0.418102 / 1.386936 (-0.968834) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b6f13ede3dccedf335bb2d8ff04db306dc710a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005375 / 0.011353 (-0.005978) | 0.003530 / 0.011008 (-0.007478) | 0.062521 / 0.038508 (0.024013) | 0.051514 / 0.023109 (0.028405) | 0.241623 / 0.275898 (-0.034275) | 0.269054 / 0.323480 (-0.054426) | 0.002877 / 0.007986 (-0.005109) | 0.002724 / 0.004328 (-0.001605) | 0.049045 / 0.004250 (0.044794) | 0.038560 / 0.037052 (0.001507) | 0.248437 / 0.258489 (-0.010052) | 0.276762 / 0.293841 (-0.017079) | 0.027522 / 0.128546 (-0.101024) | 0.010817 / 0.075646 (-0.064829) | 0.208686 / 0.419271 (-0.210585) | 0.035818 / 0.043533 (-0.007715) | 0.249398 / 0.255139 (-0.005741) | 0.268288 / 0.283200 (-0.014911) | 0.019039 / 0.141683 (-0.122644) | 1.135115 / 1.452155 (-0.317040) | 1.195531 / 1.492716 (-0.297185) |\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) | 0.093126 / 0.018006 (0.075120) | 0.301028 / 0.000490 (0.300539) | 0.000222 / 0.000200 (0.000023) | 0.000062 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018385 / 0.037411 (-0.019027) | 0.060902 / 0.014526 (0.046376) | 0.073168 / 0.176557 (-0.103389) | 0.119216 / 0.737135 (-0.617919) | 0.074225 / 0.296338 (-0.222114) |\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) | 0.283749 / 0.215209 (0.068540) | 2.741609 / 2.077655 (0.663954) | 1.483439 / 1.504120 (-0.020681) | 1.352896 / 1.541195 (-0.188299) | 1.378824 / 1.468490 (-0.089667) | 0.548731 / 4.584777 (-4.036046) | 2.342717 / 3.745712 (-1.402995) | 2.791592 / 5.269862 (-2.478269) | 1.740605 / 4.565676 (-2.825071) | 0.062059 / 0.424275 (-0.362216) | 0.005028 / 0.007607 (-0.002579) | 0.339205 / 0.226044 (0.113161) | 3.353386 / 2.268929 (1.084458) | 1.785717 / 55.444624 (-53.658907) | 1.523390 / 6.876477 (-5.353086) | 1.556999 / 2.142072 (-0.585073) | 0.636745 / 4.805227 (-4.168483) | 0.115821 / 6.500664 (-6.384843) | 0.042200 / 0.075469 (-0.033269) |\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) | 0.948678 / 1.841788 (-0.893110) | 11.588670 / 8.074308 (3.514362) | 10.897130 / 10.191392 (0.705738) | 0.140068 / 0.680424 (-0.540356) | 0.014565 / 0.534201 (-0.519636) | 0.286336 / 0.579283 (-0.292947) | 0.265292 / 0.434364 (-0.169072) | 0.324146 / 0.540337 (-0.216192) | 0.413463 / 1.386936 (-0.973473) |\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) | 0.005187 / 0.011353 (-0.006165) | 0.003471 / 0.011008 (-0.007537) | 0.048968 / 0.038508 (0.010460) | 0.051285 / 0.023109 (0.028176) | 0.283286 / 0.275898 (0.007388) | 0.307046 / 0.323480 (-0.016434) | 0.004017 / 0.007986 (-0.003969) | 0.002655 / 0.004328 (-0.001673) | 0.047762 / 0.004250 (0.043512) | 0.039855 / 0.037052 (0.002803) | 0.283101 / 0.258489 (0.024612) | 0.312905 / 0.293841 (0.019064) | 0.028188 / 0.128546 (-0.100358) | 0.010849 / 0.075646 (-0.064797) | 0.058112 / 0.419271 (-0.361159) | 0.032163 / 0.043533 (-0.011369) | 0.280825 / 0.255139 (0.025686) | 0.300946 / 0.283200 (0.017747) | 0.017409 / 0.141683 (-0.124274) | 1.127360 / 1.452155 (-0.324795) | 1.180409 / 1.492716 (-0.312307) |\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) | 0.093186 / 0.018006 (0.075180) | 0.300827 / 0.000490 (0.300338) | 0.000220 / 0.000200 (0.000020) | 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) | 0.021560 / 0.037411 (-0.015851) | 0.069158 / 0.014526 (0.054632) | 0.080953 / 0.176557 (-0.095603) | 0.119071 / 0.737135 (-0.618064) | 0.082817 / 0.296338 (-0.213521) |\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) | 0.307259 / 0.215209 (0.092050) | 2.996058 / 2.077655 (0.918404) | 1.627406 / 1.504120 (0.123286) | 1.500715 / 1.541195 (-0.040480) | 1.524278 / 1.468490 (0.055788) | 0.569711 / 4.584777 (-4.015066) | 2.436132 / 3.745712 (-1.309580) | 2.796995 / 5.269862 (-2.472866) | 1.760701 / 4.565676 (-2.804975) | 0.063521 / 0.424275 (-0.360754) | 0.004909 / 0.007607 (-0.002698) | 0.359129 / 0.226044 (0.133085) | 3.567278 / 2.268929 (1.298349) | 2.013821 / 55.444624 (-53.430804) | 1.708021 / 6.876477 (-5.168456) | 1.738959 / 2.142072 (-0.403114) | 0.648620 / 4.805227 (-4.156607) | 0.122016 / 6.500664 (-6.378648) | 0.041802 / 0.075469 (-0.033667) |\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) | 0.985208 / 1.841788 (-0.856579) | 12.307785 / 8.074308 (4.233477) | 10.587262 / 10.191392 (0.395870) | 0.130468 / 0.680424 (-0.549956) | 0.014912 / 0.534201 (-0.519289) | 0.293822 / 0.579283 (-0.285461) | 0.283021 / 0.434364 (-0.151343) | 0.329560 / 0.540337 (-0.210777) | 0.424741 / 1.386936 (-0.962195) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04426d9c8e0aa5c97af2826064287f8cab6bece0 \"CML watermark\")\n" ]
"2023-11-27T20:01:25Z"
"2023-11-28T16:29:58Z"
"2023-11-28T16:29:31Z"
CONTRIBUTOR
null
0
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Refactor the `dill` logic to make it easier to maintain (and fix some issues along the way) It makes the following improvements to the serialization API: * consistent order of a `dict`'s keys * support for hashing `torch.compile`-ed modules and functions * deprecates `datasets.fingerprint.hashregister` as the `hashregister`-ed reducers are never invoked anyways (does not support nested data as `pickle`/`dill` do) ~~TODO: optimize hashing of `pa.Table` and `datasets.table.Table`~~ The `pa_array.to_string` approach is faster for large arrays because it outputs the first 10 and last 10 elements (by default). The problem is that this can produce identical hashes for non-identical arrays if their differing elements get ellipsed... Fix https://github.com/huggingface/datasets/issues/6440, fix https://github.com/huggingface/datasets/issues/5839
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934,876,496
MDExOlB1bGxSZXF1ZXN0NjgxODg0OTgy
2,575
Add C4
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"2021-07-01T13:58:08Z"
"2021-07-02T14:50:23Z"
"2021-07-02T14:50:23Z"
MEMBER
null
0
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The old code for the C4 dataset was to generate the C4 with Apache Beam, as in Tensorflow Datasets. However AllenAI is now hosting the processed C4 dataset in this repo: https://huggingface.co/datasets/allenai/c4 Thanks a lot to them for their amazing work ! In this PR I changed the script to download and prepare the data directly from this repo. It has 4 variants: en, en.noblocklist, en.noclean, realnewslike You can load it with ```python from datasets import load_dataset c4 = load_dataset("c4", "en") ``` It also supports streaming, if you don't want to download hundreds of GB of data: ```python c4 = load_dataset("c4", "en", streaming=True) ``` Regarding the dataset_infos.json, I haven't added the infos for en.noclean. I will add them once I have them. Also we can work on the dataset card at https://huggingface.co/datasets/c4 For now I just added a link to https://huggingface.co/datasets/allenai/c4 as well as a few sections
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feat: 🎸 add paperswithcode id for qasper dataset
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"2021-07-20T13:22:29Z"
"2021-07-20T14:04:10Z"
"2021-07-20T14:04:10Z"
CONTRIBUTOR
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The reverse reference exists on paperswithcode: https://paperswithcode.com/dataset/qasper
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PR_kwDODunzps5JehbR
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Milvus integration for search
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5510). All of your documentation changes will be reflected on that endpoint.", "To the maintainer, sorry about the repeated run requests for formatting. Missed the `make style` outlined in contributing guidelines. ", "Anything I can do to get the workflow to run? @lhoestq ", "cc @mariosasko \r\n\r\n> Anything I can do to get the workflow to run?\r\n\r\nYou can merge `main` into your branch to fix code formatting (we switched from isort+flake8 to ruff this week), and then run `make style`", "I believe that should be good. @mariosasko" ]
"2023-02-07T23:30:26Z"
"2023-02-24T16:45:09Z"
null
NONE
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Signed-off-by: Filip Haltmayer <[email protected]>
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5,099
datasets doesn't support # in data paths
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[ "`datasets` doesn't seem to urlencode the directory names here\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/file_utils.py#L109-L111\r\n\r\nfor example we should have\r\n```python\r\nfrom datasets.utils.file_utils import hf_hub_url\r\n\r\nurl = hf_hub_url(\"loubnabnl/bigcode_csharp\", \"data/c#/data_0003.jsonl\")\r\nprint(url)\r\n# Currently returns\r\n# https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/main/data/c#/data_0003.jsonl\r\n# while it should be \r\n# https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/main/data/c%23/data_0003.jsonl\r\n```", "I'll work on this :)", "@loubnabnl The dataset you linked in the description of the bug does not work and returns a 404. Where can I find the dataset to reproduce the bug?", "I think you can create a dataset repository on the Hub with a dummy file containing a `#`", "Ah sorry it was private I just made it public, I can also help with this if needed", "@lhoestq Should I url encode also repo_id and revision parameters? I'm not sure what are the valid characters there.\r\n\r\nPersonally, I would be cautious and only url encode the path parameter.", "These are possible solutions (assuming `from urllib.parse import quote`):\r\n\r\n1) url encode only the path parameter:\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HUB_DATASETS_URL.format(repo_id=repo_id, path=quote(path), revision=revision)\r\n```\r\n2) url encode all parameters:\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HUB_DATASETS_URL.format(repo_id=quote(repo_id), path=quote(path), revision=quote(revision))\r\n```\r\n3) url encode the whole url:\r\n```\r\n# src/datasets/config.py\r\nHUB_DATASETS_PATH = \"/datasets/{repo_id}/resolve/{revision}/{path}\"\r\nHUB_DATASETS_URL = HF_ENDPOINT + HUB_DATASETS_PATH\r\n```\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HF_ENDPOINT + quote(config.HUB_DATASETS_PATH.format(repo_id=repo_id, path=path, revision=revision))\r\n```", "repo_id can only contain alphanumeric characters and _- so it doesn't need to be encoded.\r\n\r\nHowever I agree it's a good idea to also apply `quote` to the revision as well as in 2. !", "Should be fixed by https://github.com/huggingface/datasets/issues/5099 - we'll do a release later today" ]
"2022-10-11T10:05:32Z"
"2022-10-13T13:14:20Z"
"2022-10-13T13:14:20Z"
NONE
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## Describe the bug dataset files with `#` symbol their paths aren't read correctly. ## Steps to reproduce the bug The data in folder `c#`of this [dataset](https://huggingface.co/datasets/loubnabnl/bigcode_csharp) can't be loaded. While the folder `c_sharp` with the same data is loaded properly ```python ds = load_dataset('loubnabnl/bigcode_csharp', split="train", data_files=["data/c#/*"]) ``` ``` FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/27a3166cff4bb18e11919cafa6f169c0f57483de/data/c#/data_0003.jsonl ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.5.2 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.3 cc @lhoestq
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https://api.github.com/repos/huggingface/datasets/issues/5410
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https://github.com/huggingface/datasets/pull/5410
1,521,168,032
PR_kwDODunzps5GvnJH
5,410
Map-style Dataset to IterableDataset
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009812 / 0.011353 (-0.001540) | 0.005290 / 0.011008 (-0.005719) | 0.099728 / 0.038508 (0.061220) | 0.036712 / 0.023109 (0.013602) | 0.305924 / 0.275898 (0.030026) | 0.349844 / 0.323480 (0.026365) | 0.008353 / 0.007986 (0.000368) | 0.004464 / 0.004328 (0.000135) | 0.075329 / 0.004250 (0.071079) | 0.046146 / 0.037052 (0.009094) | 0.304197 / 0.258489 (0.045708) | 0.354245 / 0.293841 (0.060404) | 0.039270 / 0.128546 (-0.089276) | 0.012496 / 0.075646 (-0.063151) | 0.334390 / 0.419271 (-0.084882) | 0.049428 / 0.043533 (0.005896) | 0.297318 / 0.255139 (0.042179) | 0.315646 / 0.283200 (0.032447) | 0.106746 / 0.141683 (-0.034937) | 1.443562 / 1.452155 (-0.008593) | 1.546022 / 1.492716 (0.053305) |\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) | 0.303419 / 0.018006 (0.285413) | 0.536971 / 0.000490 (0.536481) | 0.001335 / 0.000200 (0.001135) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030484 / 0.037411 (-0.006927) | 0.110043 / 0.014526 (0.095518) | 0.125265 / 0.176557 (-0.051291) | 0.171410 / 0.737135 (-0.565725) | 0.128978 / 0.296338 (-0.167361) |\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) | 0.398354 / 0.215209 (0.183145) | 3.984180 / 2.077655 (1.906526) | 1.781134 / 1.504120 (0.277014) | 1.589656 / 1.541195 (0.048462) | 1.704192 / 1.468490 (0.235702) | 0.682271 / 4.584777 (-3.902506) | 3.731504 / 3.745712 (-0.014208) | 2.243520 / 5.269862 (-3.026342) | 1.511334 / 4.565676 (-3.054343) | 0.084243 / 0.424275 (-0.340032) | 0.012261 / 0.007607 (0.004654) | 0.507499 / 0.226044 (0.281454) | 5.066037 / 2.268929 (2.797109) | 2.246107 / 55.444624 (-53.198517) | 1.921032 / 6.876477 (-4.955444) | 2.144111 / 2.142072 (0.002039) | 0.845233 / 4.805227 (-3.959995) | 0.165392 / 6.500664 (-6.335272) | 0.064201 / 0.075469 (-0.011268) |\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) | 1.217649 / 1.841788 (-0.624138) | 15.890487 / 8.074308 (7.816179) | 14.772039 / 10.191392 (4.580647) | 0.192901 / 0.680424 (-0.487523) | 0.029119 / 0.534201 (-0.505082) | 0.442904 / 0.579283 (-0.136380) | 0.451035 / 0.434364 (0.016671) | 0.520788 / 0.540337 (-0.019550) | 0.623588 / 1.386936 (-0.763348) |\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) | 0.007452 / 0.011353 (-0.003901) | 0.005426 / 0.011008 (-0.005582) | 0.096488 / 0.038508 (0.057980) | 0.033575 / 0.023109 (0.010465) | 0.375688 / 0.275898 (0.099790) | 0.412393 / 0.323480 (0.088913) | 0.006050 / 0.007986 (-0.001936) | 0.004424 / 0.004328 (0.000095) | 0.073102 / 0.004250 (0.068852) | 0.052672 / 0.037052 (0.015620) | 0.379352 / 0.258489 (0.120862) | 0.436065 / 0.293841 (0.142224) | 0.036594 / 0.128546 (-0.091952) | 0.012380 / 0.075646 (-0.063266) | 0.332899 / 0.419271 (-0.086373) | 0.048859 / 0.043533 (0.005326) | 0.373215 / 0.255139 (0.118076) | 0.386990 / 0.283200 (0.103791) | 0.105166 / 0.141683 (-0.036517) | 1.490762 / 1.452155 (0.038607) | 1.611310 / 1.492716 (0.118593) |\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) | 0.333142 / 0.018006 (0.315136) | 0.537137 / 0.000490 (0.536647) | 0.000452 / 0.000200 (0.000252) | 0.000063 / 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) | 0.030368 / 0.037411 (-0.007043) | 0.109608 / 0.014526 (0.095083) | 0.124220 / 0.176557 (-0.052336) | 0.162834 / 0.737135 (-0.574301) | 0.128037 / 0.296338 (-0.168302) |\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) | 0.440991 / 0.215209 (0.225782) | 4.400825 / 2.077655 (2.323170) | 2.158768 / 1.504120 (0.654648) | 1.968158 / 1.541195 (0.426963) | 2.085115 / 1.468490 (0.616625) | 0.710757 / 4.584777 (-3.874020) | 3.835441 / 3.745712 (0.089729) | 2.204118 / 5.269862 (-3.065744) | 1.378909 / 4.565676 (-3.186767) | 0.089149 / 0.424275 (-0.335126) | 0.013066 / 0.007607 (0.005459) | 0.539165 / 0.226044 (0.313121) | 5.414176 / 2.268929 (3.145248) | 2.677020 / 55.444624 (-52.767604) | 2.328334 / 6.876477 (-4.548143) | 2.518933 / 2.142072 (0.376860) | 0.840902 / 4.805227 (-3.964325) | 0.170365 / 6.500664 (-6.330299) | 0.063909 / 0.075469 (-0.011561) |\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) | 1.237205 / 1.841788 (-0.604583) | 15.678776 / 8.074308 (7.604468) | 14.118576 / 10.191392 (3.927184) | 0.167236 / 0.680424 (-0.513188) | 0.018177 / 0.534201 (-0.516024) | 0.426680 / 0.579283 (-0.152603) | 0.425126 / 0.434364 (-0.009238) | 0.501755 / 0.540337 (-0.038582) | 0.592754 / 1.386936 (-0.794182) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008708 / 0.011353 (-0.002645) | 0.004462 / 0.011008 (-0.006546) | 0.100159 / 0.038508 (0.061651) | 0.029543 / 0.023109 (0.006434) | 0.304056 / 0.275898 (0.028158) | 0.367098 / 0.323480 (0.043618) | 0.007049 / 0.007986 (-0.000937) | 0.003294 / 0.004328 (-0.001034) | 0.076954 / 0.004250 (0.072703) | 0.036850 / 0.037052 (-0.000202) | 0.307556 / 0.258489 (0.049067) | 0.348327 / 0.293841 (0.054486) | 0.033520 / 0.128546 (-0.095026) | 0.011312 / 0.075646 (-0.064334) | 0.317588 / 0.419271 (-0.101684) | 0.040196 / 0.043533 (-0.003337) | 0.298330 / 0.255139 (0.043191) | 0.333821 / 0.283200 (0.050622) | 0.086584 / 0.141683 (-0.055099) | 1.480205 / 1.452155 (0.028050) | 1.520975 / 1.492716 (0.028259) |\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) | 0.186641 / 0.018006 (0.168635) | 0.414420 / 0.000490 (0.413930) | 0.003021 / 0.000200 (0.002821) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022953 / 0.037411 (-0.014458) | 0.097338 / 0.014526 (0.082812) | 0.104985 / 0.176557 (-0.071572) | 0.139208 / 0.737135 (-0.597927) | 0.108031 / 0.296338 (-0.188307) |\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) | 0.417969 / 0.215209 (0.202759) | 4.173189 / 2.077655 (2.095534) | 1.862813 / 1.504120 (0.358693) | 1.653226 / 1.541195 (0.112031) | 1.725917 / 1.468490 (0.257426) | 0.701038 / 4.584777 (-3.883739) | 3.350500 / 3.745712 (-0.395213) | 1.913156 / 5.269862 (-3.356705) | 1.267597 / 4.565676 (-3.298079) | 0.082197 / 0.424275 (-0.342078) | 0.012499 / 0.007607 (0.004892) | 0.520173 / 0.226044 (0.294128) | 5.219981 / 2.268929 (2.951053) | 2.306029 / 55.444624 (-53.138595) | 1.948169 / 6.876477 (-4.928307) | 2.013160 / 2.142072 (-0.128912) | 0.813325 / 4.805227 (-3.991902) | 0.149729 / 6.500664 (-6.350935) | 0.065492 / 0.075469 (-0.009977) |\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) | 1.194163 / 1.841788 (-0.647625) | 13.739562 / 8.074308 (5.665254) | 13.881988 / 10.191392 (3.690596) | 0.138180 / 0.680424 (-0.542244) | 0.029031 / 0.534201 (-0.505170) | 0.387858 / 0.579283 (-0.191425) | 0.395171 / 0.434364 (-0.039193) | 0.446349 / 0.540337 (-0.093988) | 0.527073 / 1.386936 (-0.859863) |\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) | 0.006504 / 0.011353 (-0.004849) | 0.004564 / 0.011008 (-0.006444) | 0.099108 / 0.038508 (0.060599) | 0.027420 / 0.023109 (0.004311) | 0.340712 / 0.275898 (0.064814) | 0.391613 / 0.323480 (0.068133) | 0.004977 / 0.007986 (-0.003009) | 0.003375 / 0.004328 (-0.000953) | 0.076403 / 0.004250 (0.072152) | 0.036650 / 0.037052 (-0.000402) | 0.341948 / 0.258489 (0.083459) | 0.392065 / 0.293841 (0.098224) | 0.031802 / 0.128546 (-0.096745) | 0.011659 / 0.075646 (-0.063987) | 0.320099 / 0.419271 (-0.099173) | 0.041615 / 0.043533 (-0.001918) | 0.342125 / 0.255139 (0.086986) | 0.372833 / 0.283200 (0.089633) | 0.089032 / 0.141683 (-0.052650) | 1.486691 / 1.452155 (0.034536) | 1.567326 / 1.492716 (0.074610) |\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) | 0.193123 / 0.018006 (0.175117) | 0.404062 / 0.000490 (0.403573) | 0.003460 / 0.000200 (0.003260) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024565 / 0.037411 (-0.012846) | 0.098958 / 0.014526 (0.084432) | 0.108701 / 0.176557 (-0.067855) | 0.142567 / 0.737135 (-0.594569) | 0.111048 / 0.296338 (-0.185290) |\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) | 0.474549 / 0.215209 (0.259340) | 4.753776 / 2.077655 (2.676121) | 2.435528 / 1.504120 (0.931409) | 2.234491 / 1.541195 (0.693297) | 2.269474 / 1.468490 (0.800984) | 0.695636 / 4.584777 (-3.889141) | 3.367816 / 3.745712 (-0.377896) | 1.854828 / 5.269862 (-3.415034) | 1.159729 / 4.565676 (-3.405948) | 0.082267 / 0.424275 (-0.342008) | 0.012483 / 0.007607 (0.004876) | 0.578490 / 0.226044 (0.352446) | 5.814490 / 2.268929 (3.545561) | 2.893310 / 55.444624 (-52.551314) | 2.540555 / 6.876477 (-4.335922) | 2.573705 / 2.142072 (0.431633) | 0.800545 / 4.805227 (-4.004682) | 0.151306 / 6.500664 (-6.349358) | 0.067925 / 0.075469 (-0.007544) |\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) | 1.294645 / 1.841788 (-0.547142) | 13.641842 / 8.074308 (5.567534) | 14.015200 / 10.191392 (3.823808) | 0.128829 / 0.680424 (-0.551595) | 0.016870 / 0.534201 (-0.517331) | 0.389137 / 0.579283 (-0.190146) | 0.388384 / 0.434364 (-0.045980) | 0.447711 / 0.540337 (-0.092627) | 0.540637 / 1.386936 (-0.846299) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45ad185b9040a68285080b6099ed3af58442ccb2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012282 / 0.011353 (0.000929) | 0.006328 / 0.011008 (-0.004680) | 0.129666 / 0.038508 (0.091158) | 0.039403 / 0.023109 (0.016294) | 0.375464 / 0.275898 (0.099566) | 0.463167 / 0.323480 (0.139687) | 0.010329 / 0.007986 (0.002344) | 0.005111 / 0.004328 (0.000782) | 0.108727 / 0.004250 (0.104476) | 0.047156 / 0.037052 (0.010103) | 0.381869 / 0.258489 (0.123380) | 0.441936 / 0.293841 (0.148095) | 0.054750 / 0.128546 (-0.073796) | 0.019809 / 0.075646 (-0.055837) | 0.436389 / 0.419271 (0.017118) | 0.066585 / 0.043533 (0.023052) | 0.402108 / 0.255139 (0.146969) | 0.424571 / 0.283200 (0.141371) | 0.118326 / 0.141683 (-0.023357) | 1.870175 / 1.452155 (0.418020) | 1.878720 / 1.492716 (0.386004) |\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) | 0.012863 / 0.018006 (-0.005144) | 0.528670 / 0.000490 (0.528181) | 0.006057 / 0.000200 (0.005857) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030091 / 0.037411 (-0.007320) | 0.136143 / 0.014526 (0.121618) | 0.148931 / 0.176557 (-0.027626) | 0.179578 / 0.737135 (-0.557558) | 0.144528 / 0.296338 (-0.151810) |\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) | 0.594080 / 0.215209 (0.378871) | 6.029101 / 2.077655 (3.951446) | 2.443084 / 1.504120 (0.938964) | 2.123949 / 1.541195 (0.582754) | 2.183021 / 1.468490 (0.714531) | 1.235453 / 4.584777 (-3.349324) | 5.585121 / 3.745712 (1.839408) | 3.208510 / 5.269862 (-2.061351) | 2.090334 / 4.565676 (-2.475342) | 0.150353 / 0.424275 (-0.273922) | 0.016787 / 0.007607 (0.009180) | 0.797561 / 0.226044 (0.571516) | 7.756291 / 2.268929 (5.487363) | 3.283638 / 55.444624 (-52.160986) | 2.527441 / 6.876477 (-4.349036) | 2.590765 / 2.142072 (0.448692) | 1.446818 / 4.805227 (-3.358409) | 0.250563 / 6.500664 (-6.250101) | 0.077919 / 0.075469 (0.002450) |\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) | 1.612022 / 1.841788 (-0.229765) | 18.363316 / 8.074308 (10.289008) | 22.578570 / 10.191392 (12.387178) | 0.232801 / 0.680424 (-0.447623) | 0.048232 / 0.534201 (-0.485969) | 0.549518 / 0.579283 (-0.029766) | 0.624663 / 0.434364 (0.190299) | 0.674745 / 0.540337 (0.134408) | 0.803489 / 1.386936 (-0.583447) |\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) | 0.009872 / 0.011353 (-0.001481) | 0.006593 / 0.011008 (-0.004415) | 0.139248 / 0.038508 (0.100740) | 0.035708 / 0.023109 (0.012598) | 0.551335 / 0.275898 (0.275437) | 0.544995 / 0.323480 (0.221515) | 0.007085 / 0.007986 (-0.000900) | 0.004742 / 0.004328 (0.000413) | 0.095823 / 0.004250 (0.091572) | 0.051674 / 0.037052 (0.014621) | 0.463405 / 0.258489 (0.204916) | 0.640392 / 0.293841 (0.346551) | 0.055242 / 0.128546 (-0.073304) | 0.022602 / 0.075646 (-0.053044) | 0.419171 / 0.419271 (-0.000100) | 0.062986 / 0.043533 (0.019453) | 0.503683 / 0.255139 (0.248544) | 0.568719 / 0.283200 (0.285519) | 0.113906 / 0.141683 (-0.027777) | 1.825248 / 1.452155 (0.373094) | 1.985667 / 1.492716 (0.492951) |\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) | 0.237478 / 0.018006 (0.219472) | 0.528861 / 0.000490 (0.528371) | 0.008507 / 0.000200 (0.008307) | 0.000158 / 0.000054 (0.000103) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033536 / 0.037411 (-0.003875) | 0.144202 / 0.014526 (0.129677) | 0.139472 / 0.176557 (-0.037084) | 0.184540 / 0.737135 (-0.552596) | 0.147818 / 0.296338 (-0.148520) |\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) | 0.671654 / 0.215209 (0.456445) | 6.616368 / 2.077655 (4.538713) | 2.805634 / 1.504120 (1.301514) | 2.482890 / 1.541195 (0.941695) | 2.547686 / 1.468490 (1.079195) | 1.289169 / 4.584777 (-3.295608) | 5.551436 / 3.745712 (1.805724) | 5.228500 / 5.269862 (-0.041362) | 2.456706 / 4.565676 (-2.108970) | 0.148556 / 0.424275 (-0.275720) | 0.015290 / 0.007607 (0.007683) | 0.837090 / 0.226044 (0.611045) | 8.373561 / 2.268929 (6.104632) | 3.663910 / 55.444624 (-51.780714) | 2.927117 / 6.876477 (-3.949360) | 2.976785 / 2.142072 (0.834712) | 1.501618 / 4.805227 (-3.303609) | 0.263321 / 6.500664 (-6.237343) | 0.082644 / 0.075469 (0.007175) |\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) | 1.707419 / 1.841788 (-0.134368) | 18.371117 / 8.074308 (10.296809) | 22.015154 / 10.191392 (11.823762) | 0.232066 / 0.680424 (-0.448357) | 0.027149 / 0.534201 (-0.507052) | 0.544450 / 0.579283 (-0.034833) | 0.605134 / 0.434364 (0.170770) | 0.656063 / 0.540337 (0.115725) | 0.788121 / 1.386936 (-0.598815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1e0ec31e07e4bc0469f4acfed601d9c71e9a459 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008952 / 0.011353 (-0.002401) | 0.005592 / 0.011008 (-0.005416) | 0.101138 / 0.038508 (0.062630) | 0.035573 / 0.023109 (0.012464) | 0.295959 / 0.275898 (0.020060) | 0.365347 / 0.323480 (0.041867) | 0.008136 / 0.007986 (0.000150) | 0.004479 / 0.004328 (0.000150) | 0.078806 / 0.004250 (0.074556) | 0.045180 / 0.037052 (0.008127) | 0.321687 / 0.258489 (0.063198) | 0.345874 / 0.293841 (0.052033) | 0.038720 / 0.128546 (-0.089826) | 0.012534 / 0.075646 (-0.063112) | 0.335571 / 0.419271 (-0.083700) | 0.049048 / 0.043533 (0.005515) | 0.294756 / 0.255139 (0.039617) | 0.327496 / 0.283200 (0.044296) | 0.109181 / 0.141683 (-0.032502) | 1.417068 / 1.452155 (-0.035087) | 1.455473 / 1.492716 (-0.037244) |\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) | 0.267774 / 0.018006 (0.249768) | 0.538546 / 0.000490 (0.538056) | 0.001755 / 0.000200 (0.001555) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026839 / 0.037411 (-0.010572) | 0.105862 / 0.014526 (0.091336) | 0.118278 / 0.176557 (-0.058279) | 0.157926 / 0.737135 (-0.579209) | 0.124700 / 0.296338 (-0.171638) |\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) | 0.399060 / 0.215209 (0.183851) | 3.991409 / 2.077655 (1.913754) | 1.763569 / 1.504120 (0.259449) | 1.579602 / 1.541195 (0.038407) | 1.652928 / 1.468490 (0.184438) | 0.692962 / 4.584777 (-3.891815) | 3.784635 / 3.745712 (0.038922) | 3.249341 / 5.269862 (-2.020521) | 1.815711 / 4.565676 (-2.749966) | 0.084384 / 0.424275 (-0.339891) | 0.012546 / 0.007607 (0.004939) | 0.521397 / 0.226044 (0.295352) | 5.075824 / 2.268929 (2.806895) | 2.258353 / 55.444624 (-53.186272) | 1.925220 / 6.876477 (-4.951256) | 2.002821 / 2.142072 (-0.139252) | 0.830507 / 4.805227 (-3.974720) | 0.165845 / 6.500664 (-6.334819) | 0.063905 / 0.075469 (-0.011565) |\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) | 1.198726 / 1.841788 (-0.643061) | 14.804448 / 8.074308 (6.730139) | 12.855167 / 10.191392 (2.663775) | 0.167932 / 0.680424 (-0.512492) | 0.028643 / 0.534201 (-0.505558) | 0.441224 / 0.579283 (-0.138059) | 0.434924 / 0.434364 (0.000560) | 0.516188 / 0.540337 (-0.024150) | 0.605017 / 1.386936 (-0.781919) |\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) | 0.007031 / 0.011353 (-0.004322) | 0.005157 / 0.011008 (-0.005851) | 0.086943 / 0.038508 (0.048434) | 0.031377 / 0.023109 (0.008268) | 0.334810 / 0.275898 (0.058912) | 0.368590 / 0.323480 (0.045110) | 0.005973 / 0.007986 (-0.002013) | 0.004173 / 0.004328 (-0.000155) | 0.067033 / 0.004250 (0.062783) | 0.054070 / 0.037052 (0.017018) | 0.332232 / 0.258489 (0.073743) | 0.384982 / 0.293841 (0.091141) | 0.034023 / 0.128546 (-0.094524) | 0.011301 / 0.075646 (-0.064345) | 0.295644 / 0.419271 (-0.123628) | 0.045589 / 0.043533 (0.002056) | 0.330739 / 0.255139 (0.075600) | 0.352841 / 0.283200 (0.069642) | 0.104829 / 0.141683 (-0.036854) | 1.329360 / 1.452155 (-0.122794) | 1.437956 / 1.492716 (-0.054760) |\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) | 0.299187 / 0.018006 (0.281181) | 0.563407 / 0.000490 (0.562917) | 0.004179 / 0.000200 (0.003979) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027405 / 0.037411 (-0.010006) | 0.097498 / 0.014526 (0.082972) | 0.114265 / 0.176557 (-0.062292) | 0.146823 / 0.737135 (-0.590313) | 0.117948 / 0.296338 (-0.178391) |\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) | 0.378756 / 0.215209 (0.163547) | 3.774804 / 2.077655 (1.697150) | 1.804149 / 1.504120 (0.300029) | 1.626312 / 1.541195 (0.085117) | 1.731111 / 1.468490 (0.262620) | 0.633493 / 4.584777 (-3.951284) | 3.488220 / 3.745712 (-0.257492) | 3.064710 / 5.269862 (-2.205151) | 1.690647 / 4.565676 (-2.875029) | 0.076093 / 0.424275 (-0.348182) | 0.010820 / 0.007607 (0.003213) | 0.465091 / 0.226044 (0.239046) | 4.676842 / 2.268929 (2.407913) | 2.297381 / 55.444624 (-53.147244) | 1.960355 / 6.876477 (-4.916122) | 1.983742 / 2.142072 (-0.158330) | 0.739525 / 4.805227 (-4.065702) | 0.152663 / 6.500664 (-6.348001) | 0.057316 / 0.075469 (-0.018153) |\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) | 1.104721 / 1.841788 (-0.737067) | 14.577171 / 8.074308 (6.502863) | 13.680402 / 10.191392 (3.489010) | 0.182234 / 0.680424 (-0.498190) | 0.018853 / 0.534201 (-0.515348) | 0.426194 / 0.579283 (-0.153089) | 0.429202 / 0.434364 (-0.005162) | 0.543125 / 0.540337 (0.002788) | 0.645887 / 1.386936 (-0.741049) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f830952573bdc59f8732b8f1a13f70d9187e0a65 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010055 / 0.011353 (-0.001298) | 0.005576 / 0.011008 (-0.005432) | 0.100059 / 0.038508 (0.061551) | 0.038535 / 0.023109 (0.015425) | 0.297538 / 0.275898 (0.021640) | 0.368117 / 0.323480 (0.044637) | 0.008540 / 0.007986 (0.000555) | 0.004469 / 0.004328 (0.000141) | 0.075801 / 0.004250 (0.071551) | 0.046604 / 0.037052 (0.009552) | 0.307242 / 0.258489 (0.048753) | 0.343949 / 0.293841 (0.050108) | 0.039353 / 0.128546 (-0.089194) | 0.012446 / 0.075646 (-0.063200) | 0.334628 / 0.419271 (-0.084643) | 0.051628 / 0.043533 (0.008095) | 0.298726 / 0.255139 (0.043587) | 0.316010 / 0.283200 (0.032810) | 0.120564 / 0.141683 (-0.021119) | 1.459396 / 1.452155 (0.007241) | 1.493682 / 1.492716 (0.000965) |\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) | 0.011702 / 0.018006 (-0.006304) | 0.570261 / 0.000490 (0.569771) | 0.003760 / 0.000200 (0.003560) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028806 / 0.037411 (-0.008605) | 0.112150 / 0.014526 (0.097625) | 0.123140 / 0.176557 (-0.053417) | 0.173055 / 0.737135 (-0.564080) | 0.130060 / 0.296338 (-0.166279) |\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) | 0.398216 / 0.215209 (0.183007) | 3.978677 / 2.077655 (1.901022) | 1.754229 / 1.504120 (0.250109) | 1.561892 / 1.541195 (0.020697) | 1.679138 / 1.468490 (0.210648) | 0.690254 / 4.584777 (-3.894523) | 3.817698 / 3.745712 (0.071986) | 2.177854 / 5.269862 (-3.092008) | 1.361860 / 4.565676 (-3.203816) | 0.084108 / 0.424275 (-0.340167) | 0.012640 / 0.007607 (0.005033) | 0.504385 / 0.226044 (0.278341) | 5.034103 / 2.268929 (2.765174) | 2.254032 / 55.444624 (-53.190593) | 1.910439 / 6.876477 (-4.966038) | 2.003515 / 2.142072 (-0.138558) | 0.839747 / 4.805227 (-3.965480) | 0.165654 / 6.500664 (-6.335010) | 0.063483 / 0.075469 (-0.011986) |\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) | 1.187521 / 1.841788 (-0.654267) | 15.381121 / 8.074308 (7.306812) | 14.579418 / 10.191392 (4.388026) | 0.199221 / 0.680424 (-0.481202) | 0.029335 / 0.534201 (-0.504866) | 0.443159 / 0.579283 (-0.136124) | 0.447772 / 0.434364 (0.013408) | 0.545071 / 0.540337 (0.004733) | 0.650494 / 1.386936 (-0.736442) |\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) | 0.007675 / 0.011353 (-0.003677) | 0.005364 / 0.011008 (-0.005644) | 0.097921 / 0.038508 (0.059413) | 0.033645 / 0.023109 (0.010536) | 0.404818 / 0.275898 (0.128920) | 0.429983 / 0.323480 (0.106503) | 0.006106 / 0.007986 (-0.001879) | 0.005281 / 0.004328 (0.000953) | 0.073762 / 0.004250 (0.069512) | 0.053065 / 0.037052 (0.016012) | 0.400657 / 0.258489 (0.142168) | 0.447743 / 0.293841 (0.153902) | 0.036782 / 0.128546 (-0.091765) | 0.012593 / 0.075646 (-0.063054) | 0.332825 / 0.419271 (-0.086446) | 0.049424 / 0.043533 (0.005891) | 0.400397 / 0.255139 (0.145258) | 0.414794 / 0.283200 (0.131594) | 0.106555 / 0.141683 (-0.035128) | 1.466917 / 1.452155 (0.014762) | 1.571351 / 1.492716 (0.078635) |\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) | 0.254337 / 0.018006 (0.236331) | 0.568360 / 0.000490 (0.567870) | 0.000445 / 0.000200 (0.000245) | 0.000059 / 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) | 0.031044 / 0.037411 (-0.006367) | 0.112282 / 0.014526 (0.097756) | 0.127205 / 0.176557 (-0.049352) | 0.166551 / 0.737135 (-0.570584) | 0.130520 / 0.296338 (-0.165818) |\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) | 0.442906 / 0.215209 (0.227697) | 4.430218 / 2.077655 (2.352563) | 2.287251 / 1.504120 (0.783132) | 2.112345 / 1.541195 (0.571150) | 2.240952 / 1.468490 (0.772462) | 0.713800 / 4.584777 (-3.870977) | 3.884161 / 3.745712 (0.138449) | 2.166901 / 5.269862 (-3.102960) | 1.374490 / 4.565676 (-3.191187) | 0.087548 / 0.424275 (-0.336727) | 0.012369 / 0.007607 (0.004761) | 0.540783 / 0.226044 (0.314739) | 5.396187 / 2.268929 (3.127258) | 2.779636 / 55.444624 (-52.664988) | 2.434220 / 6.876477 (-4.442257) | 2.508180 / 2.142072 (0.366107) | 0.852470 / 4.805227 (-3.952757) | 0.171266 / 6.500664 (-6.329398) | 0.065463 / 0.075469 (-0.010006) |\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) | 1.241720 / 1.841788 (-0.600067) | 15.332568 / 8.074308 (7.258260) | 13.688723 / 10.191392 (3.497331) | 0.145150 / 0.680424 (-0.535273) | 0.017694 / 0.534201 (-0.516507) | 0.426078 / 0.579283 (-0.153205) | 0.441189 / 0.434364 (0.006825) | 0.540284 / 0.540337 (-0.000054) | 0.657548 / 1.386936 (-0.729388) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c47ecf71362f6b6290b6471b30e77184a5e1df31 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008604 / 0.011353 (-0.002749) | 0.004566 / 0.011008 (-0.006442) | 0.099607 / 0.038508 (0.061099) | 0.029628 / 0.023109 (0.006519) | 0.300481 / 0.275898 (0.024583) | 0.342596 / 0.323480 (0.019116) | 0.007003 / 0.007986 (-0.000982) | 0.003408 / 0.004328 (-0.000920) | 0.079076 / 0.004250 (0.074826) | 0.034104 / 0.037052 (-0.002948) | 0.303856 / 0.258489 (0.045367) | 0.348729 / 0.293841 (0.054888) | 0.033752 / 0.128546 (-0.094794) | 0.011497 / 0.075646 (-0.064149) | 0.321568 / 0.419271 (-0.097704) | 0.041472 / 0.043533 (-0.002061) | 0.303396 / 0.255139 (0.048257) | 0.331121 / 0.283200 (0.047921) | 0.086203 / 0.141683 (-0.055480) | 1.476995 / 1.452155 (0.024840) | 1.539428 / 1.492716 (0.046712) |\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) | 0.215810 / 0.018006 (0.197803) | 0.414292 / 0.000490 (0.413802) | 0.000388 / 0.000200 (0.000188) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023441 / 0.037411 (-0.013970) | 0.098463 / 0.014526 (0.083938) | 0.105435 / 0.176557 (-0.071121) | 0.139736 / 0.737135 (-0.597399) | 0.109467 / 0.296338 (-0.186872) |\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) | 0.418244 / 0.215209 (0.203035) | 4.160693 / 2.077655 (2.083039) | 1.878895 / 1.504120 (0.374775) | 1.679338 / 1.541195 (0.138143) | 1.730384 / 1.468490 (0.261894) | 0.688603 / 4.584777 (-3.896174) | 3.393542 / 3.745712 (-0.352170) | 1.901337 / 5.269862 (-3.368525) | 1.447269 / 4.565676 (-3.118408) | 0.083003 / 0.424275 (-0.341272) | 0.012574 / 0.007607 (0.004967) | 0.526363 / 0.226044 (0.300318) | 5.275159 / 2.268929 (3.006230) | 2.323642 / 55.444624 (-53.120982) | 1.982929 / 6.876477 (-4.893548) | 2.014081 / 2.142072 (-0.127991) | 0.809466 / 4.805227 (-3.995761) | 0.149038 / 6.500664 (-6.351626) | 0.064394 / 0.075469 (-0.011075) |\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) | 1.207439 / 1.841788 (-0.634349) | 13.691048 / 8.074308 (5.616740) | 13.880965 / 10.191392 (3.689573) | 0.148553 / 0.680424 (-0.531871) | 0.028397 / 0.534201 (-0.505804) | 0.391818 / 0.579283 (-0.187465) | 0.407181 / 0.434364 (-0.027183) | 0.481163 / 0.540337 (-0.059175) | 0.570689 / 1.386936 (-0.816247) |\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) | 0.006361 / 0.011353 (-0.004992) | 0.004520 / 0.011008 (-0.006488) | 0.097679 / 0.038508 (0.059171) | 0.027223 / 0.023109 (0.004113) | 0.407966 / 0.275898 (0.132068) | 0.439868 / 0.323480 (0.116388) | 0.004625 / 0.007986 (-0.003360) | 0.004039 / 0.004328 (-0.000289) | 0.074548 / 0.004250 (0.070298) | 0.034957 / 0.037052 (-0.002095) | 0.412762 / 0.258489 (0.154273) | 0.449716 / 0.293841 (0.155875) | 0.031272 / 0.128546 (-0.097274) | 0.011598 / 0.075646 (-0.064049) | 0.320922 / 0.419271 (-0.098349) | 0.041250 / 0.043533 (-0.002283) | 0.411439 / 0.255139 (0.156300) | 0.429722 / 0.283200 (0.146523) | 0.087161 / 0.141683 (-0.054522) | 1.512573 / 1.452155 (0.060418) | 1.569385 / 1.492716 (0.076668) |\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) | 0.222612 / 0.018006 (0.204606) | 0.409086 / 0.000490 (0.408596) | 0.004246 / 0.000200 (0.004046) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024324 / 0.037411 (-0.013087) | 0.099055 / 0.014526 (0.084530) | 0.106809 / 0.176557 (-0.069748) | 0.141275 / 0.737135 (-0.595860) | 0.109426 / 0.296338 (-0.186913) |\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) | 0.469736 / 0.215209 (0.254527) | 4.686900 / 2.077655 (2.609246) | 2.413392 / 1.504120 (0.909272) | 2.217366 / 1.541195 (0.676171) | 2.266957 / 1.468490 (0.798467) | 0.698647 / 4.584777 (-3.886129) | 3.389317 / 3.745712 (-0.356395) | 1.862315 / 5.269862 (-3.407546) | 1.160931 / 4.565676 (-3.404746) | 0.082829 / 0.424275 (-0.341446) | 0.012627 / 0.007607 (0.005020) | 0.568027 / 0.226044 (0.341983) | 5.683220 / 2.268929 (3.414291) | 2.865701 / 55.444624 (-52.578924) | 2.522401 / 6.876477 (-4.354076) | 2.542395 / 2.142072 (0.400323) | 0.801224 / 4.805227 (-4.004003) | 0.149946 / 6.500664 (-6.350718) | 0.065447 / 0.075469 (-0.010023) |\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) | 1.283756 / 1.841788 (-0.558032) | 13.903662 / 8.074308 (5.829354) | 13.238389 / 10.191392 (3.046997) | 0.142304 / 0.680424 (-0.538120) | 0.016922 / 0.534201 (-0.517279) | 0.377797 / 0.579283 (-0.201487) | 0.382460 / 0.434364 (-0.051904) | 0.464645 / 0.540337 (-0.075692) | 0.556270 / 1.386936 (-0.830666) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#675cf2910c5e6f083ed6664a7bffba9a58f78309 \"CML watermark\")\n", "> I think this would be more of a Conceptual Guide doc since this is more explanatory and compares the differences between a Dataset and an IterableDataset\r\n\r\nsounds good to me !\r\n\r\n> There are definitely places in the docs where we can add a nice and link to this doc though to build up the user's understanding of this topic. For example, in the Know your dataset [tutorial](https://huggingface.co/docs/datasets/access), we only introduce the regular Dataset object and not the IterableDataset. We can add a section there for IterableDataset and then link to this doc that explains the difference between the two πŸ™‚\r\n\r\ngood idea, thanks :)", "I'll open a PR to add a section on `IterableDataset`'s in the tutorial, and once you're done editing this doc I can give it a final polish! πŸ˜„ ", "I moved the doc page to conceptual guides and took your suggestions into account :)\r\n\r\nI think this is ready for final review now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009890 / 0.011353 (-0.001463) | 0.005156 / 0.011008 (-0.005852) | 0.099493 / 0.038508 (0.060984) | 0.036671 / 0.023109 (0.013562) | 0.304686 / 0.275898 (0.028788) | 0.339070 / 0.323480 (0.015590) | 0.008466 / 0.007986 (0.000481) | 0.005863 / 0.004328 (0.001534) | 0.075082 / 0.004250 (0.070832) | 0.045926 / 0.037052 (0.008874) | 0.303157 / 0.258489 (0.044668) | 0.363710 / 0.293841 (0.069870) | 0.038497 / 0.128546 (-0.090049) | 0.012063 / 0.075646 (-0.063583) | 0.334463 / 0.419271 (-0.084808) | 0.048161 / 0.043533 (0.004628) | 0.300431 / 0.255139 (0.045292) | 0.330344 / 0.283200 (0.047145) | 0.105509 / 0.141683 (-0.036174) | 1.475242 / 1.452155 (0.023087) | 1.550624 / 1.492716 (0.057908) |\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) | 0.245749 / 0.018006 (0.227743) | 0.575091 / 0.000490 (0.574601) | 0.001556 / 0.000200 (0.001357) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030447 / 0.037411 (-0.006964) | 0.110982 / 0.014526 (0.096456) | 0.126760 / 0.176557 (-0.049797) | 0.173375 / 0.737135 (-0.563760) | 0.128799 / 0.296338 (-0.167539) |\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) | 0.392861 / 0.215209 (0.177651) | 3.911231 / 2.077655 (1.833576) | 1.757413 / 1.504120 (0.253293) | 1.563287 / 1.541195 (0.022093) | 1.658678 / 1.468490 (0.190188) | 0.677244 / 4.584777 (-3.907533) | 3.754917 / 3.745712 (0.009205) | 3.779417 / 5.269862 (-1.490444) | 1.993159 / 4.565676 (-2.572517) | 0.084425 / 0.424275 (-0.339850) | 0.012500 / 0.007607 (0.004893) | 0.501788 / 0.226044 (0.275743) | 5.003173 / 2.268929 (2.734244) | 2.273547 / 55.444624 (-53.171077) | 1.909766 / 6.876477 (-4.966711) | 1.968287 / 2.142072 (-0.173785) | 0.834895 / 4.805227 (-3.970332) | 0.165312 / 6.500664 (-6.335352) | 0.062202 / 0.075469 (-0.013267) |\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) | 1.203080 / 1.841788 (-0.638708) | 15.158284 / 8.074308 (7.083976) | 14.174484 / 10.191392 (3.983092) | 0.171540 / 0.680424 (-0.508883) | 0.028604 / 0.534201 (-0.505597) | 0.438379 / 0.579283 (-0.140904) | 0.429447 / 0.434364 (-0.004917) | 0.540979 / 0.540337 (0.000642) | 0.630322 / 1.386936 (-0.756614) |\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) | 0.007600 / 0.011353 (-0.003753) | 0.005400 / 0.011008 (-0.005608) | 0.097983 / 0.038508 (0.059475) | 0.033407 / 0.023109 (0.010297) | 0.384429 / 0.275898 (0.108531) | 0.415880 / 0.323480 (0.092400) | 0.006085 / 0.007986 (-0.001900) | 0.004330 / 0.004328 (0.000002) | 0.074654 / 0.004250 (0.070403) | 0.053076 / 0.037052 (0.016024) | 0.383958 / 0.258489 (0.125469) | 0.427289 / 0.293841 (0.133448) | 0.036710 / 0.128546 (-0.091836) | 0.012400 / 0.075646 (-0.063246) | 0.332712 / 0.419271 (-0.086560) | 0.058390 / 0.043533 (0.014857) | 0.377747 / 0.255139 (0.122608) | 0.398997 / 0.283200 (0.115798) | 0.117370 / 0.141683 (-0.024313) | 1.464211 / 1.452155 (0.012057) | 1.596465 / 1.492716 (0.103749) |\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) | 0.212989 / 0.018006 (0.194983) | 0.554968 / 0.000490 (0.554479) | 0.004305 / 0.000200 (0.004105) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029167 / 0.037411 (-0.008244) | 0.109156 / 0.014526 (0.094631) | 0.122575 / 0.176557 (-0.053982) | 0.163058 / 0.737135 (-0.574077) | 0.127431 / 0.296338 (-0.168908) |\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) | 0.445395 / 0.215209 (0.230185) | 4.447534 / 2.077655 (2.369879) | 2.259186 / 1.504120 (0.755066) | 2.082956 / 1.541195 (0.541761) | 2.259126 / 1.468490 (0.790636) | 0.692271 / 4.584777 (-3.892506) | 3.795759 / 3.745712 (0.050047) | 3.603000 / 5.269862 (-1.666862) | 1.948556 / 4.565676 (-2.617120) | 0.084589 / 0.424275 (-0.339687) | 0.012751 / 0.007607 (0.005144) | 0.544783 / 0.226044 (0.318738) | 5.452278 / 2.268929 (3.183349) | 2.809467 / 55.444624 (-52.635157) | 2.479297 / 6.876477 (-4.397180) | 2.587756 / 2.142072 (0.445683) | 0.832258 / 4.805227 (-3.972970) | 0.167424 / 6.500664 (-6.333240) | 0.066064 / 0.075469 (-0.009405) |\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) | 1.262719 / 1.841788 (-0.579069) | 15.917869 / 8.074308 (7.843561) | 13.879301 / 10.191392 (3.687909) | 0.187712 / 0.680424 (-0.492712) | 0.018175 / 0.534201 (-0.516026) | 0.425840 / 0.579283 (-0.153443) | 0.426164 / 0.434364 (-0.008200) | 0.527465 / 0.540337 (-0.012872) | 0.629478 / 1.386936 (-0.757458) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f7e178d6373e7d66a60662a22fd60af117f0885 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009064 / 0.011353 (-0.002289) | 0.004824 / 0.011008 (-0.006184) | 0.100869 / 0.038508 (0.062361) | 0.030803 / 0.023109 (0.007694) | 0.350880 / 0.275898 (0.074982) | 0.423816 / 0.323480 (0.100336) | 0.007581 / 0.007986 (-0.000405) | 0.003642 / 0.004328 (-0.000686) | 0.077682 / 0.004250 (0.073432) | 0.039856 / 0.037052 (0.002803) | 0.366097 / 0.258489 (0.107608) | 0.409226 / 0.293841 (0.115385) | 0.033698 / 0.128546 (-0.094848) | 0.011730 / 0.075646 (-0.063916) | 0.321683 / 0.419271 (-0.097588) | 0.041794 / 0.043533 (-0.001739) | 0.351175 / 0.255139 (0.096036) | 0.374328 / 0.283200 (0.091128) | 0.091833 / 0.141683 (-0.049850) | 1.507082 / 1.452155 (0.054927) | 1.543289 / 1.492716 (0.050572) |\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) | 0.010670 / 0.018006 (-0.007337) | 0.429674 / 0.000490 (0.429184) | 0.003246 / 0.000200 (0.003046) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025015 / 0.037411 (-0.012397) | 0.102155 / 0.014526 (0.087629) | 0.107010 / 0.176557 (-0.069546) | 0.144265 / 0.737135 (-0.592870) | 0.110635 / 0.296338 (-0.185703) |\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) | 0.414211 / 0.215209 (0.199002) | 4.125582 / 2.077655 (2.047928) | 1.997856 / 1.504120 (0.493736) | 1.847676 / 1.541195 (0.306481) | 1.994100 / 1.468490 (0.525610) | 0.694975 / 4.584777 (-3.889802) | 3.373629 / 3.745712 (-0.372083) | 2.863255 / 5.269862 (-2.406606) | 1.565723 / 4.565676 (-2.999953) | 0.082539 / 0.424275 (-0.341736) | 0.012650 / 0.007607 (0.005043) | 0.522989 / 0.226044 (0.296945) | 5.205720 / 2.268929 (2.936792) | 2.352292 / 55.444624 (-53.092332) | 2.080467 / 6.876477 (-4.796010) | 2.231014 / 2.142072 (0.088942) | 0.811252 / 4.805227 (-3.993975) | 0.149171 / 6.500664 (-6.351493) | 0.065207 / 0.075469 (-0.010262) |\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) | 1.203137 / 1.841788 (-0.638651) | 14.244903 / 8.074308 (6.170595) | 14.454368 / 10.191392 (4.262976) | 0.139090 / 0.680424 (-0.541334) | 0.028738 / 0.534201 (-0.505463) | 0.396394 / 0.579283 (-0.182889) | 0.407207 / 0.434364 (-0.027156) | 0.478036 / 0.540337 (-0.062302) | 0.568488 / 1.386936 (-0.818448) |\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) | 0.006878 / 0.011353 (-0.004475) | 0.004636 / 0.011008 (-0.006372) | 0.099118 / 0.038508 (0.060610) | 0.028076 / 0.023109 (0.004967) | 0.416097 / 0.275898 (0.140199) | 0.451722 / 0.323480 (0.128242) | 0.005364 / 0.007986 (-0.002622) | 0.003506 / 0.004328 (-0.000822) | 0.075791 / 0.004250 (0.071541) | 0.041373 / 0.037052 (0.004321) | 0.416358 / 0.258489 (0.157869) | 0.458440 / 0.293841 (0.164599) | 0.031870 / 0.128546 (-0.096676) | 0.011751 / 0.075646 (-0.063896) | 0.321748 / 0.419271 (-0.097524) | 0.041780 / 0.043533 (-0.001752) | 0.425037 / 0.255139 (0.169898) | 0.444169 / 0.283200 (0.160969) | 0.093145 / 0.141683 (-0.048538) | 1.472151 / 1.452155 (0.019996) | 1.542942 / 1.492716 (0.050226) |\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) | 0.224287 / 0.018006 (0.206281) | 0.415303 / 0.000490 (0.414813) | 0.003180 / 0.000200 (0.002980) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026377 / 0.037411 (-0.011035) | 0.106222 / 0.014526 (0.091696) | 0.113873 / 0.176557 (-0.062684) | 0.143255 / 0.737135 (-0.593880) | 0.112642 / 0.296338 (-0.183697) |\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) | 0.444149 / 0.215209 (0.228940) | 4.421434 / 2.077655 (2.343779) | 2.082198 / 1.504120 (0.578078) | 1.879909 / 1.541195 (0.338715) | 1.968526 / 1.468490 (0.500036) | 0.697230 / 4.584777 (-3.887546) | 3.430800 / 3.745712 (-0.314912) | 1.893353 / 5.269862 (-3.376509) | 1.173271 / 4.565676 (-3.392406) | 0.082636 / 0.424275 (-0.341639) | 0.012357 / 0.007607 (0.004750) | 0.544008 / 0.226044 (0.317964) | 5.465472 / 2.268929 (3.196543) | 2.530017 / 55.444624 (-52.914608) | 2.178462 / 6.876477 (-4.698014) | 2.279570 / 2.142072 (0.137498) | 0.804890 / 4.805227 (-4.000337) | 0.152091 / 6.500664 (-6.348573) | 0.069442 / 0.075469 (-0.006027) |\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) | 1.256722 / 1.841788 (-0.585065) | 14.554131 / 8.074308 (6.479823) | 13.499913 / 10.191392 (3.308521) | 0.144350 / 0.680424 (-0.536074) | 0.016977 / 0.534201 (-0.517224) | 0.378836 / 0.579283 (-0.200447) | 0.392004 / 0.434364 (-0.042360) | 0.468423 / 0.540337 (-0.071914) | 0.584711 / 1.386936 (-0.802225) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e4894fcdf2a82b3355bb6a2dc5557c8e23f8144 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008542 / 0.011353 (-0.002811) | 0.004552 / 0.011008 (-0.006456) | 0.100543 / 0.038508 (0.062035) | 0.029717 / 0.023109 (0.006608) | 0.301948 / 0.275898 (0.026050) | 0.360211 / 0.323480 (0.036731) | 0.006881 / 0.007986 (-0.001105) | 0.003433 / 0.004328 (-0.000896) | 0.077760 / 0.004250 (0.073510) | 0.037069 / 0.037052 (0.000017) | 0.314084 / 0.258489 (0.055595) | 0.347759 / 0.293841 (0.053918) | 0.033255 / 0.128546 (-0.095291) | 0.011487 / 0.075646 (-0.064160) | 0.323873 / 0.419271 (-0.095399) | 0.041203 / 0.043533 (-0.002330) | 0.298397 / 0.255139 (0.043258) | 0.327174 / 0.283200 (0.043974) | 0.088892 / 0.141683 (-0.052791) | 1.560114 / 1.452155 (0.107959) | 1.532475 / 1.492716 (0.039759) |\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) | 0.226080 / 0.018006 (0.208074) | 0.467492 / 0.000490 (0.467003) | 0.002198 / 0.000200 (0.001998) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023627 / 0.037411 (-0.013784) | 0.096696 / 0.014526 (0.082170) | 0.106196 / 0.176557 (-0.070360) | 0.140496 / 0.737135 (-0.596639) | 0.108859 / 0.296338 (-0.187480) |\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) | 0.422335 / 0.215209 (0.207126) | 4.214879 / 2.077655 (2.137224) | 1.865866 / 1.504120 (0.361747) | 1.660914 / 1.541195 (0.119719) | 1.691869 / 1.468490 (0.223379) | 0.688164 / 4.584777 (-3.896613) | 3.432708 / 3.745712 (-0.313004) | 1.856852 / 5.269862 (-3.413010) | 1.243685 / 4.565676 (-3.321991) | 0.081552 / 0.424275 (-0.342723) | 0.012491 / 0.007607 (0.004884) | 0.524331 / 0.226044 (0.298287) | 5.255090 / 2.268929 (2.986162) | 2.269705 / 55.444624 (-53.174919) | 1.936722 / 6.876477 (-4.939755) | 2.018958 / 2.142072 (-0.123114) | 0.800658 / 4.805227 (-4.004569) | 0.148665 / 6.500664 (-6.351999) | 0.064210 / 0.075469 (-0.011259) |\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) | 1.235422 / 1.841788 (-0.606365) | 14.156755 / 8.074308 (6.082447) | 14.005916 / 10.191392 (3.814524) | 0.150983 / 0.680424 (-0.529441) | 0.028500 / 0.534201 (-0.505701) | 0.393013 / 0.579283 (-0.186270) | 0.408191 / 0.434364 (-0.026173) | 0.481017 / 0.540337 (-0.059320) | 0.581711 / 1.386936 (-0.805225) |\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) | 0.006950 / 0.011353 (-0.004403) | 0.004575 / 0.011008 (-0.006434) | 0.076702 / 0.038508 (0.038194) | 0.028050 / 0.023109 (0.004941) | 0.342916 / 0.275898 (0.067018) | 0.378861 / 0.323480 (0.055381) | 0.005315 / 0.007986 (-0.002671) | 0.004822 / 0.004328 (0.000494) | 0.075560 / 0.004250 (0.071310) | 0.040441 / 0.037052 (0.003388) | 0.344284 / 0.258489 (0.085795) | 0.386519 / 0.293841 (0.092678) | 0.032122 / 0.128546 (-0.096424) | 0.011843 / 0.075646 (-0.063803) | 0.085798 / 0.419271 (-0.333473) | 0.043027 / 0.043533 (-0.000506) | 0.342910 / 0.255139 (0.087771) | 0.366618 / 0.283200 (0.083418) | 0.094766 / 0.141683 (-0.046917) | 1.492981 / 1.452155 (0.040827) | 1.566994 / 1.492716 (0.074278) |\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) | 0.166083 / 0.018006 (0.148076) | 0.409315 / 0.000490 (0.408826) | 0.003189 / 0.000200 (0.002989) | 0.000127 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024753 / 0.037411 (-0.012658) | 0.099112 / 0.014526 (0.084586) | 0.106668 / 0.176557 (-0.069889) | 0.142562 / 0.737135 (-0.594573) | 0.110648 / 0.296338 (-0.185690) |\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) | 0.452668 / 0.215209 (0.237459) | 4.501188 / 2.077655 (2.423534) | 2.086197 / 1.504120 (0.582077) | 1.873955 / 1.541195 (0.332761) | 1.935610 / 1.468490 (0.467120) | 0.708290 / 4.584777 (-3.876487) | 3.426986 / 3.745712 (-0.318726) | 2.805852 / 5.269862 (-2.464009) | 1.516918 / 4.565676 (-3.048759) | 0.084067 / 0.424275 (-0.340208) | 0.012776 / 0.007607 (0.005169) | 0.548853 / 0.226044 (0.322809) | 5.488198 / 2.268929 (3.219270) | 2.704464 / 55.444624 (-52.740161) | 2.377817 / 6.876477 (-4.498660) | 2.366152 / 2.142072 (0.224079) | 0.818192 / 4.805227 (-3.987035) | 0.152649 / 6.500664 (-6.348015) | 0.066914 / 0.075469 (-0.008555) |\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) | 1.273803 / 1.841788 (-0.567985) | 14.071633 / 8.074308 (5.997325) | 13.655586 / 10.191392 (3.464194) | 0.149471 / 0.680424 (-0.530953) | 0.016745 / 0.534201 (-0.517456) | 0.386850 / 0.579283 (-0.192434) | 0.393595 / 0.434364 (-0.040769) | 0.480396 / 0.540337 (-0.059942) | 0.573708 / 1.386936 (-0.813228) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b2c7de67b326a635c0dc39ea5dd1ae982c958d6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008173 / 0.011353 (-0.003180) | 0.004461 / 0.011008 (-0.006547) | 0.100284 / 0.038508 (0.061776) | 0.028900 / 0.023109 (0.005791) | 0.293639 / 0.275898 (0.017741) | 0.359450 / 0.323480 (0.035971) | 0.007567 / 0.007986 (-0.000418) | 0.003434 / 0.004328 (-0.000894) | 0.077913 / 0.004250 (0.073663) | 0.036313 / 0.037052 (-0.000740) | 0.308484 / 0.258489 (0.049995) | 0.347575 / 0.293841 (0.053734) | 0.033367 / 0.128546 (-0.095179) | 0.011508 / 0.075646 (-0.064138) | 0.323490 / 0.419271 (-0.095782) | 0.042285 / 0.043533 (-0.001248) | 0.295696 / 0.255139 (0.040557) | 0.332475 / 0.283200 (0.049276) | 0.089980 / 0.141683 (-0.051703) | 1.461851 / 1.452155 (0.009697) | 1.493030 / 1.492716 (0.000314) |\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) | 0.191068 / 0.018006 (0.173062) | 0.396768 / 0.000490 (0.396278) | 0.002355 / 0.000200 (0.002155) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023117 / 0.037411 (-0.014294) | 0.096155 / 0.014526 (0.081630) | 0.102424 / 0.176557 (-0.074132) | 0.142148 / 0.737135 (-0.594987) | 0.105954 / 0.296338 (-0.190384) |\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) | 0.421227 / 0.215209 (0.206018) | 4.200403 / 2.077655 (2.122748) | 1.899410 / 1.504120 (0.395290) | 1.684091 / 1.541195 (0.142896) | 1.698084 / 1.468490 (0.229594) | 0.696195 / 4.584777 (-3.888582) | 3.364116 / 3.745712 (-0.381596) | 1.899133 / 5.269862 (-3.370728) | 1.281405 / 4.565676 (-3.284272) | 0.082958 / 0.424275 (-0.341317) | 0.012433 / 0.007607 (0.004826) | 0.521856 / 0.226044 (0.295812) | 5.217626 / 2.268929 (2.948698) | 2.309228 / 55.444624 (-53.135396) | 1.956828 / 6.876477 (-4.919648) | 2.018964 / 2.142072 (-0.123108) | 0.816855 / 4.805227 (-3.988373) | 0.152867 / 6.500664 (-6.347798) | 0.064764 / 0.075469 (-0.010705) |\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) | 1.219020 / 1.841788 (-0.622768) | 13.509058 / 8.074308 (5.434750) | 13.637826 / 10.191392 (3.446434) | 0.156620 / 0.680424 (-0.523804) | 0.028518 / 0.534201 (-0.505683) | 0.399138 / 0.579283 (-0.180146) | 0.399931 / 0.434364 (-0.034433) | 0.482902 / 0.540337 (-0.057435) | 0.574089 / 1.386936 (-0.812847) |\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) | 0.006232 / 0.011353 (-0.005121) | 0.004467 / 0.011008 (-0.006542) | 0.075494 / 0.038508 (0.036986) | 0.026891 / 0.023109 (0.003782) | 0.356603 / 0.275898 (0.080705) | 0.371977 / 0.323480 (0.048497) | 0.004709 / 0.007986 (-0.003276) | 0.003230 / 0.004328 (-0.001099) | 0.074338 / 0.004250 (0.070088) | 0.035588 / 0.037052 (-0.001464) | 0.349554 / 0.258489 (0.091065) | 0.389672 / 0.293841 (0.095831) | 0.031524 / 0.128546 (-0.097022) | 0.011493 / 0.075646 (-0.064153) | 0.084584 / 0.419271 (-0.334688) | 0.041945 / 0.043533 (-0.001588) | 0.341057 / 0.255139 (0.085918) | 0.367876 / 0.283200 (0.084677) | 0.090113 / 0.141683 (-0.051569) | 1.507104 / 1.452155 (0.054949) | 1.567810 / 1.492716 (0.075094) |\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) | 0.210939 / 0.018006 (0.192933) | 0.392600 / 0.000490 (0.392110) | 0.002188 / 0.000200 (0.001988) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024294 / 0.037411 (-0.013118) | 0.100325 / 0.014526 (0.085799) | 0.104027 / 0.176557 (-0.072530) | 0.141189 / 0.737135 (-0.595947) | 0.107438 / 0.296338 (-0.188901) |\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) | 0.443314 / 0.215209 (0.228105) | 4.429612 / 2.077655 (2.351957) | 2.129275 / 1.504120 (0.625156) | 1.940016 / 1.541195 (0.398821) | 2.008975 / 1.468490 (0.540485) | 0.695434 / 4.584777 (-3.889343) | 3.355137 / 3.745712 (-0.390575) | 2.606262 / 5.269862 (-2.663600) | 1.451283 / 4.565676 (-3.114394) | 0.082875 / 0.424275 (-0.341400) | 0.012398 / 0.007607 (0.004791) | 0.544262 / 0.226044 (0.318218) | 5.450829 / 2.268929 (3.181900) | 2.582074 / 55.444624 (-52.862550) | 2.220037 / 6.876477 (-4.656439) | 2.232473 / 2.142072 (0.090401) | 0.802094 / 4.805227 (-4.003134) | 0.150188 / 6.500664 (-6.350476) | 0.066543 / 0.075469 (-0.008926) |\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) | 1.269098 / 1.841788 (-0.572690) | 13.764780 / 8.074308 (5.690472) | 13.461490 / 10.191392 (3.270098) | 0.143841 / 0.680424 (-0.536583) | 0.016687 / 0.534201 (-0.517514) | 0.388548 / 0.579283 (-0.190736) | 0.385229 / 0.434364 (-0.049135) | 0.478966 / 0.540337 (-0.061371) | 0.570355 / 1.386936 (-0.816581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0ba81f5b299f0918cb0c0c071412feadd0ea3ef5 \"CML watermark\")\n", "I took your comments into account :)\r\n\r\n> Regarding the docs, I think it would be better to add this info as notes/tips/sections to the existing docs (Process/Stream; e.g. a tip under Dataset.shuffle that explains how to make this operation more performant by using to_iterable + shuffle, etc.) rather than introducing a new doc page.\r\n\r\nI added a paragraph in the Dataset.shuffle docstring, and a note in the Process doc page", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010906 / 0.011353 (-0.000447) | 0.005995 / 0.011008 (-0.005014) | 0.120183 / 0.038508 (0.081675) | 0.042166 / 0.023109 (0.019057) | 0.350945 / 0.275898 (0.075046) | 0.433055 / 0.323480 (0.109575) | 0.009093 / 0.007986 (0.001107) | 0.004695 / 0.004328 (0.000366) | 0.090362 / 0.004250 (0.086112) | 0.051402 / 0.037052 (0.014350) | 0.368677 / 0.258489 (0.110188) | 0.410926 / 0.293841 (0.117086) | 0.044471 / 0.128546 (-0.084075) | 0.014051 / 0.075646 (-0.061595) | 0.397765 / 0.419271 (-0.021507) | 0.057227 / 0.043533 (0.013694) | 0.357587 / 0.255139 (0.102448) | 0.377470 / 0.283200 (0.094270) | 0.119482 / 0.141683 (-0.022201) | 1.719799 / 1.452155 (0.267645) | 1.758228 / 1.492716 (0.265511) |\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) | 0.224385 / 0.018006 (0.206379) | 0.505070 / 0.000490 (0.504580) | 0.004863 / 0.000200 (0.004663) | 0.000379 / 0.000054 (0.000324) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030366 / 0.037411 (-0.007046) | 0.130481 / 0.014526 (0.115955) | 0.136429 / 0.176557 (-0.040128) | 0.182263 / 0.737135 (-0.554872) | 0.142871 / 0.296338 (-0.153468) |\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) | 0.467623 / 0.215209 (0.252414) | 4.665522 / 2.077655 (2.587868) | 2.130885 / 1.504120 (0.626766) | 1.903810 / 1.541195 (0.362615) | 2.019077 / 1.468490 (0.550587) | 0.820868 / 4.584777 (-3.763909) | 4.543118 / 3.745712 (0.797406) | 2.491541 / 5.269862 (-2.778321) | 1.585377 / 4.565676 (-2.980299) | 0.101850 / 0.424275 (-0.322426) | 0.014737 / 0.007607 (0.007129) | 0.597241 / 0.226044 (0.371197) | 5.938445 / 2.268929 (3.669516) | 2.695799 / 55.444624 (-52.748825) | 2.286890 / 6.876477 (-4.589587) | 2.363064 / 2.142072 (0.220991) | 0.986670 / 4.805227 (-3.818557) | 0.194407 / 6.500664 (-6.306257) | 0.074767 / 0.075469 (-0.000702) |\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) | 1.420630 / 1.841788 (-0.421158) | 17.537702 / 8.074308 (9.463394) | 16.521804 / 10.191392 (6.330412) | 0.173622 / 0.680424 (-0.506802) | 0.033944 / 0.534201 (-0.500257) | 0.520461 / 0.579283 (-0.058822) | 0.541283 / 0.434364 (0.106919) | 0.651906 / 0.540337 (0.111569) | 0.771724 / 1.386936 (-0.615212) |\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) | 0.008448 / 0.011353 (-0.002905) | 0.005893 / 0.011008 (-0.005115) | 0.087995 / 0.038508 (0.049487) | 0.038602 / 0.023109 (0.015493) | 0.400048 / 0.275898 (0.124150) | 0.436998 / 0.323480 (0.113518) | 0.006414 / 0.007986 (-0.001572) | 0.004478 / 0.004328 (0.000149) | 0.086444 / 0.004250 (0.082194) | 0.056535 / 0.037052 (0.019483) | 0.402066 / 0.258489 (0.143577) | 0.458730 / 0.293841 (0.164889) | 0.041622 / 0.128546 (-0.086924) | 0.014014 / 0.075646 (-0.061632) | 0.101382 / 0.419271 (-0.317889) | 0.056986 / 0.043533 (0.013453) | 0.404527 / 0.255139 (0.149388) | 0.428105 / 0.283200 (0.144906) | 0.118321 / 0.141683 (-0.023361) | 1.716940 / 1.452155 (0.264785) | 1.834683 / 1.492716 (0.341967) |\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) | 0.252917 / 0.018006 (0.234910) | 0.485950 / 0.000490 (0.485461) | 0.000489 / 0.000200 (0.000289) | 0.000066 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035023 / 0.037411 (-0.002388) | 0.139055 / 0.014526 (0.124529) | 0.144165 / 0.176557 (-0.032392) | 0.189559 / 0.737135 (-0.547577) | 0.153213 / 0.296338 (-0.143126) |\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) | 0.505069 / 0.215209 (0.289860) | 5.024620 / 2.077655 (2.946965) | 2.429469 / 1.504120 (0.925349) | 2.186210 / 1.541195 (0.645015) | 2.275971 / 1.468490 (0.807481) | 0.829432 / 4.584777 (-3.755345) | 4.518600 / 3.745712 (0.772888) | 2.466418 / 5.269862 (-2.803443) | 1.558910 / 4.565676 (-3.006767) | 0.102017 / 0.424275 (-0.322258) | 0.015191 / 0.007607 (0.007584) | 0.619092 / 0.226044 (0.393048) | 6.241105 / 2.268929 (3.972176) | 3.044213 / 55.444624 (-52.400411) | 2.630194 / 6.876477 (-4.246282) | 2.723685 / 2.142072 (0.581613) | 0.994018 / 4.805227 (-3.811210) | 0.198722 / 6.500664 (-6.301942) | 0.075812 / 0.075469 (0.000343) |\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) | 1.545497 / 1.841788 (-0.296291) | 18.305250 / 8.074308 (10.230942) | 16.035275 / 10.191392 (5.843883) | 0.209339 / 0.680424 (-0.471085) | 0.020903 / 0.534201 (-0.513298) | 0.499909 / 0.579283 (-0.079374) | 0.488775 / 0.434364 (0.054411) | 0.581990 / 0.540337 (0.041653) | 0.697786 / 1.386936 (-0.689150) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78dca62e8aaddb9e0cf0212841f2c8d861fe74c8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011706 / 0.011353 (0.000353) | 0.008406 / 0.011008 (-0.002602) | 0.130887 / 0.038508 (0.092379) | 0.037468 / 0.023109 (0.014359) | 0.385043 / 0.275898 (0.109145) | 0.458837 / 0.323480 (0.135357) | 0.013400 / 0.007986 (0.005414) | 0.004885 / 0.004328 (0.000557) | 0.107156 / 0.004250 (0.102905) | 0.046958 / 0.037052 (0.009906) | 0.419314 / 0.258489 (0.160825) | 0.456061 / 0.293841 (0.162220) | 0.058859 / 0.128546 (-0.069687) | 0.016682 / 0.075646 (-0.058965) | 0.428401 / 0.419271 (0.009129) | 0.062908 / 0.043533 (0.019376) | 0.370902 / 0.255139 (0.115763) | 0.433897 / 0.283200 (0.150697) | 0.125672 / 0.141683 (-0.016011) | 1.818279 / 1.452155 (0.366124) | 1.935767 / 1.492716 (0.443050) |\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) | 0.011928 / 0.018006 (-0.006078) | 0.591995 / 0.000490 (0.591506) | 0.008416 / 0.000200 (0.008216) | 0.000122 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029640 / 0.037411 (-0.007772) | 0.121044 / 0.014526 (0.106518) | 0.141840 / 0.176557 (-0.034716) | 0.195856 / 0.737135 (-0.541280) | 0.146460 / 0.296338 (-0.149879) |\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) | 0.591838 / 0.215209 (0.376629) | 5.817309 / 2.077655 (3.739654) | 2.411864 / 1.504120 (0.907744) | 2.098517 / 1.541195 (0.557323) | 2.214609 / 1.468490 (0.746119) | 1.217542 / 4.584777 (-3.367235) | 5.658394 / 3.745712 (1.912682) | 5.155807 / 5.269862 (-0.114055) | 2.797313 / 4.565676 (-1.768363) | 0.141309 / 0.424275 (-0.282967) | 0.014462 / 0.007607 (0.006855) | 0.772274 / 0.226044 (0.546230) | 7.547357 / 2.268929 (5.278429) | 3.150178 / 55.444624 (-52.294446) | 2.500130 / 6.876477 (-4.376347) | 2.572036 / 2.142072 (0.429964) | 1.434498 / 4.805227 (-3.370729) | 0.257355 / 6.500664 (-6.243309) | 0.087491 / 0.075469 (0.012022) |\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) | 1.483899 / 1.841788 (-0.357889) | 17.990741 / 8.074308 (9.916433) | 20.398965 / 10.191392 (10.207573) | 0.239529 / 0.680424 (-0.440895) | 0.046118 / 0.534201 (-0.488083) | 0.528349 / 0.579283 (-0.050934) | 0.614333 / 0.434364 (0.179969) | 0.653621 / 0.540337 (0.113284) | 0.794654 / 1.386936 (-0.592282) |\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) | 0.008732 / 0.011353 (-0.002621) | 0.006432 / 0.011008 (-0.004576) | 0.090811 / 0.038508 (0.052303) | 0.030154 / 0.023109 (0.007045) | 0.407885 / 0.275898 (0.131987) | 0.452457 / 0.323480 (0.128977) | 0.006966 / 0.007986 (-0.001020) | 0.006449 / 0.004328 (0.002120) | 0.094439 / 0.004250 (0.090188) | 0.050628 / 0.037052 (0.013576) | 0.401815 / 0.258489 (0.143326) | 0.451814 / 0.293841 (0.157973) | 0.047456 / 0.128546 (-0.081090) | 0.019019 / 0.075646 (-0.056628) | 0.112941 / 0.419271 (-0.306331) | 0.057677 / 0.043533 (0.014145) | 0.406160 / 0.255139 (0.151021) | 0.434469 / 0.283200 (0.151269) | 0.110515 / 0.141683 (-0.031167) | 1.601393 / 1.452155 (0.149238) | 1.745581 / 1.492716 (0.252865) |\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) | 0.280264 / 0.018006 (0.262258) | 0.630074 / 0.000490 (0.629585) | 0.006900 / 0.000200 (0.006700) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027338 / 0.037411 (-0.010073) | 0.114772 / 0.014526 (0.100246) | 0.130436 / 0.176557 (-0.046121) | 0.168990 / 0.737135 (-0.568145) | 0.135842 / 0.296338 (-0.160496) |\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) | 0.666739 / 0.215209 (0.451530) | 6.212953 / 2.077655 (4.135298) | 2.781716 / 1.504120 (1.277596) | 2.369975 / 1.541195 (0.828781) | 2.338807 / 1.468490 (0.870317) | 1.174138 / 4.584777 (-3.410639) | 5.420297 / 3.745712 (1.674585) | 4.972669 / 5.269862 (-0.297192) | 2.214294 / 4.565676 (-2.351382) | 0.135429 / 0.424275 (-0.288846) | 0.013877 / 0.007607 (0.006270) | 0.750805 / 0.226044 (0.524761) | 7.145429 / 2.268929 (4.876500) | 3.215081 / 55.444624 (-52.229544) | 2.598307 / 6.876477 (-4.278170) | 2.690479 / 2.142072 (0.548406) | 1.344673 / 4.805227 (-3.460554) | 0.241536 / 6.500664 (-6.259128) | 0.075544 / 0.075469 (0.000074) |\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) | 1.473595 / 1.841788 (-0.368192) | 17.372237 / 8.074308 (9.297929) | 18.586588 / 10.191392 (8.395196) | 0.209300 / 0.680424 (-0.471124) | 0.030878 / 0.534201 (-0.503323) | 0.509131 / 0.579283 (-0.070152) | 0.617884 / 0.434364 (0.183520) | 0.633721 / 0.540337 (0.093383) | 0.727624 / 1.386936 (-0.659312) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87f2062d47fdbec3fadf5b39bab0801f59c0f4a3 \"CML watermark\")\n", "Took your last comments into account !\r\n\r\n> so maybe a better title for it would be \"Optimize processing\" (or \"Working with datasets at scale\" as I mentioned earlier on Slack)\r\n\r\nI think the content would be slightly different, e.g. focus more on multiprocessing/sharding or what data formats to use. This can be a complementary page IMO\r\n\r\n> PS: I think it would be a good idea to add links to the Guide pages for better discoverability and to somewhat \"justify their presence in the docs\" (from the tutorial/how-to pages to the guides; some guides are not referenced at all)\r\n\r\nAdded a link in the how-to stream page. We may want to include it in the tutorial at one point at well - right now none of the tutorials mention streaming", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009167 / 0.011353 (-0.002186) | 0.005345 / 0.011008 (-0.005663) | 0.098302 / 0.038508 (0.059794) | 0.035649 / 0.023109 (0.012540) | 0.295597 / 0.275898 (0.019699) | 0.358843 / 0.323480 (0.035364) | 0.008011 / 0.007986 (0.000025) | 0.004229 / 0.004328 (-0.000100) | 0.075123 / 0.004250 (0.070872) | 0.046098 / 0.037052 (0.009046) | 0.310581 / 0.258489 (0.052092) | 0.343230 / 0.293841 (0.049389) | 0.038318 / 0.128546 (-0.090229) | 0.011954 / 0.075646 (-0.063693) | 0.331056 / 0.419271 (-0.088216) | 0.052875 / 0.043533 (0.009342) | 0.302758 / 0.255139 (0.047619) | 0.340596 / 0.283200 (0.057396) | 0.113676 / 0.141683 (-0.028007) | 1.448272 / 1.452155 (-0.003883) | 1.498008 / 1.492716 (0.005291) |\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) | 0.240524 / 0.018006 (0.222518) | 0.555823 / 0.000490 (0.555333) | 0.003143 / 0.000200 (0.002943) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027764 / 0.037411 (-0.009647) | 0.105006 / 0.014526 (0.090480) | 0.120550 / 0.176557 (-0.056007) | 0.167052 / 0.737135 (-0.570084) | 0.124521 / 0.296338 (-0.171818) |\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) | 0.401758 / 0.215209 (0.186549) | 3.989629 / 2.077655 (1.911974) | 1.767307 / 1.504120 (0.263187) | 1.579451 / 1.541195 (0.038257) | 1.637642 / 1.468490 (0.169152) | 0.702524 / 4.584777 (-3.882253) | 3.714326 / 3.745712 (-0.031386) | 2.131829 / 5.269862 (-3.138033) | 1.487410 / 4.565676 (-3.078267) | 0.084901 / 0.424275 (-0.339374) | 0.012292 / 0.007607 (0.004685) | 0.505211 / 0.226044 (0.279166) | 5.074479 / 2.268929 (2.805551) | 2.243068 / 55.444624 (-53.201556) | 1.880199 / 6.876477 (-4.996278) | 2.003757 / 2.142072 (-0.138315) | 0.870719 / 4.805227 (-3.934508) | 0.167626 / 6.500664 (-6.333039) | 0.062024 / 0.075469 (-0.013445) |\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) | 1.192969 / 1.841788 (-0.648819) | 14.830812 / 8.074308 (6.756504) | 14.331178 / 10.191392 (4.139786) | 0.199222 / 0.680424 (-0.481202) | 0.029292 / 0.534201 (-0.504909) | 0.440427 / 0.579283 (-0.138857) | 0.437893 / 0.434364 (0.003529) | 0.547155 / 0.540337 (0.006818) | 0.645255 / 1.386936 (-0.741681) |\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) | 0.007465 / 0.011353 (-0.003888) | 0.005386 / 0.011008 (-0.005622) | 0.073609 / 0.038508 (0.035100) | 0.033550 / 0.023109 (0.010440) | 0.341730 / 0.275898 (0.065832) | 0.371518 / 0.323480 (0.048038) | 0.005986 / 0.007986 (-0.001999) | 0.004264 / 0.004328 (-0.000065) | 0.073749 / 0.004250 (0.069498) | 0.051452 / 0.037052 (0.014399) | 0.347385 / 0.258489 (0.088896) | 0.392284 / 0.293841 (0.098444) | 0.036981 / 0.128546 (-0.091566) | 0.012431 / 0.075646 (-0.063216) | 0.086421 / 0.419271 (-0.332850) | 0.053014 / 0.043533 (0.009481) | 0.336660 / 0.255139 (0.081521) | 0.359155 / 0.283200 (0.075956) | 0.107666 / 0.141683 (-0.034017) | 1.424324 / 1.452155 (-0.027830) | 1.543027 / 1.492716 (0.050310) |\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) | 0.260862 / 0.018006 (0.242855) | 0.552057 / 0.000490 (0.551567) | 0.000449 / 0.000200 (0.000249) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029184 / 0.037411 (-0.008227) | 0.108799 / 0.014526 (0.094274) | 0.125136 / 0.176557 (-0.051421) | 0.157436 / 0.737135 (-0.579699) | 0.126333 / 0.296338 (-0.170005) |\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) | 0.424054 / 0.215209 (0.208845) | 4.227847 / 2.077655 (2.150192) | 2.051102 / 1.504120 (0.546983) | 1.848651 / 1.541195 (0.307457) | 1.922728 / 1.468490 (0.454238) | 0.705903 / 4.584777 (-3.878874) | 3.800977 / 3.745712 (0.055265) | 2.099345 / 5.269862 (-3.170517) | 1.342919 / 4.565676 (-3.222757) | 0.086128 / 0.424275 (-0.338147) | 0.012539 / 0.007607 (0.004932) | 0.528767 / 0.226044 (0.302723) | 5.299989 / 2.268929 (3.031061) | 2.534280 / 55.444624 (-52.910345) | 2.229532 / 6.876477 (-4.646945) | 2.326704 / 2.142072 (0.184632) | 0.838533 / 4.805227 (-3.966694) | 0.168446 / 6.500664 (-6.332218) | 0.065158 / 0.075469 (-0.010311) |\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) | 1.250091 / 1.841788 (-0.591697) | 14.988651 / 8.074308 (6.914343) | 13.655103 / 10.191392 (3.463711) | 0.165079 / 0.680424 (-0.515345) | 0.017829 / 0.534201 (-0.516372) | 0.425903 / 0.579283 (-0.153381) | 0.419771 / 0.434364 (-0.014593) | 0.534309 / 0.540337 (-0.006028) | 0.635563 / 1.386936 (-0.751373) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7d17ccc9b9dde2d94803b1305226c5a58d916c5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010569 / 0.011353 (-0.000784) | 0.005790 / 0.011008 (-0.005218) | 0.118626 / 0.038508 (0.080118) | 0.040455 / 0.023109 (0.017346) | 0.342309 / 0.275898 (0.066411) | 0.411828 / 0.323480 (0.088349) | 0.008824 / 0.007986 (0.000839) | 0.005426 / 0.004328 (0.001098) | 0.088740 / 0.004250 (0.084489) | 0.050042 / 0.037052 (0.012990) | 0.352350 / 0.258489 (0.093861) | 0.396030 / 0.293841 (0.102189) | 0.043385 / 0.128546 (-0.085162) | 0.013805 / 0.075646 (-0.061841) | 0.396489 / 0.419271 (-0.022783) | 0.055667 / 0.043533 (0.012135) | 0.336165 / 0.255139 (0.081026) | 0.372912 / 0.283200 (0.089713) | 0.115343 / 0.141683 (-0.026340) | 1.656412 / 1.452155 (0.204257) | 1.708993 / 1.492716 (0.216277) |\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) | 0.011650 / 0.018006 (-0.006357) | 0.444415 / 0.000490 (0.443926) | 0.003985 / 0.000200 (0.003785) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031718 / 0.037411 (-0.005693) | 0.119640 / 0.014526 (0.105114) | 0.138519 / 0.176557 (-0.038037) | 0.188847 / 0.737135 (-0.548288) | 0.137891 / 0.296338 (-0.158448) |\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) | 0.447540 / 0.215209 (0.232331) | 4.577189 / 2.077655 (2.499534) | 2.106992 / 1.504120 (0.602872) | 1.889631 / 1.541195 (0.348436) | 1.972256 / 1.468490 (0.503766) | 0.778209 / 4.584777 (-3.806568) | 4.430279 / 3.745712 (0.684567) | 2.401226 / 5.269862 (-2.868636) | 1.481251 / 4.565676 (-3.084425) | 0.094244 / 0.424275 (-0.330031) | 0.013961 / 0.007607 (0.006354) | 0.570962 / 0.226044 (0.344917) | 5.809224 / 2.268929 (3.540295) | 2.663290 / 55.444624 (-52.781334) | 2.201228 / 6.876477 (-4.675249) | 2.319240 / 2.142072 (0.177168) | 0.938340 / 4.805227 (-3.866887) | 0.185546 / 6.500664 (-6.315118) | 0.069087 / 0.075469 (-0.006382) |\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) | 1.448597 / 1.841788 (-0.393191) | 17.188573 / 8.074308 (9.114265) | 16.197532 / 10.191392 (6.006140) | 0.194064 / 0.680424 (-0.486360) | 0.033694 / 0.534201 (-0.500507) | 0.507585 / 0.579283 (-0.071699) | 0.505470 / 0.434364 (0.071106) | 0.623270 / 0.540337 (0.082932) | 0.729964 / 1.386936 (-0.656972) |\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) | 0.008529 / 0.011353 (-0.002824) | 0.005705 / 0.011008 (-0.005304) | 0.085594 / 0.038508 (0.047086) | 0.038377 / 0.023109 (0.015268) | 0.384221 / 0.275898 (0.108323) | 0.414678 / 0.323480 (0.091199) | 0.006195 / 0.007986 (-0.001791) | 0.004549 / 0.004328 (0.000221) | 0.082710 / 0.004250 (0.078460) | 0.054899 / 0.037052 (0.017847) | 0.404017 / 0.258489 (0.145528) | 0.450309 / 0.293841 (0.156468) | 0.040620 / 0.128546 (-0.087926) | 0.013774 / 0.075646 (-0.061872) | 0.099231 / 0.419271 (-0.320041) | 0.057183 / 0.043533 (0.013650) | 0.390806 / 0.255139 (0.135667) | 0.419334 / 0.283200 (0.136134) | 0.116449 / 0.141683 (-0.025234) | 1.709124 / 1.452155 (0.256969) | 1.812769 / 1.492716 (0.320052) |\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) | 0.225206 / 0.018006 (0.207199) | 0.440530 / 0.000490 (0.440040) | 0.002982 / 0.000200 (0.002782) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032256 / 0.037411 (-0.005155) | 0.127086 / 0.014526 (0.112560) | 0.138133 / 0.176557 (-0.038424) | 0.176168 / 0.737135 (-0.560968) | 0.146072 / 0.296338 (-0.150267) |\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) | 0.474374 / 0.215209 (0.259165) | 4.785106 / 2.077655 (2.707452) | 2.319344 / 1.504120 (0.815225) | 2.075239 / 1.541195 (0.534045) | 2.179231 / 1.468490 (0.710741) | 0.832124 / 4.584777 (-3.752653) | 4.376302 / 3.745712 (0.630590) | 3.966837 / 5.269862 (-1.303024) | 1.820230 / 4.565676 (-2.745446) | 0.100692 / 0.424275 (-0.323583) | 0.014748 / 0.007607 (0.007141) | 0.568702 / 0.226044 (0.342657) | 5.771548 / 2.268929 (3.502619) | 2.747431 / 55.444624 (-52.697193) | 2.448482 / 6.876477 (-4.427994) | 2.497206 / 2.142072 (0.355133) | 0.960842 / 4.805227 (-3.844385) | 0.192855 / 6.500664 (-6.307809) | 0.072494 / 0.075469 (-0.002975) |\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) | 1.474542 / 1.841788 (-0.367245) | 17.344804 / 8.074308 (9.270496) | 15.336082 / 10.191392 (5.144690) | 0.200134 / 0.680424 (-0.480290) | 0.020728 / 0.534201 (-0.513473) | 0.488854 / 0.579283 (-0.090429) | 0.490781 / 0.434364 (0.056418) | 0.626288 / 0.540337 (0.085950) | 0.721130 / 1.386936 (-0.665806) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cd7877892aa48a2470b01f52013390c54aca8a49 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008542 / 0.011353 (-0.002811) | 0.004624 / 0.011008 (-0.006384) | 0.100749 / 0.038508 (0.062241) | 0.029587 / 0.023109 (0.006478) | 0.298680 / 0.275898 (0.022782) | 0.359659 / 0.323480 (0.036180) | 0.007001 / 0.007986 (-0.000984) | 0.003398 / 0.004328 (-0.000930) | 0.078654 / 0.004250 (0.074404) | 0.036440 / 0.037052 (-0.000612) | 0.313245 / 0.258489 (0.054756) | 0.342776 / 0.293841 (0.048936) | 0.033195 / 0.128546 (-0.095352) | 0.011500 / 0.075646 (-0.064146) | 0.323957 / 0.419271 (-0.095314) | 0.039878 / 0.043533 (-0.003655) | 0.298189 / 0.255139 (0.043050) | 0.325488 / 0.283200 (0.042289) | 0.087276 / 0.141683 (-0.054407) | 1.480846 / 1.452155 (0.028691) | 1.507016 / 1.492716 (0.014300) |\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) | 0.189570 / 0.018006 (0.171564) | 0.406407 / 0.000490 (0.405917) | 0.003062 / 0.000200 (0.002862) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022865 / 0.037411 (-0.014546) | 0.096103 / 0.014526 (0.081578) | 0.106462 / 0.176557 (-0.070094) | 0.140888 / 0.737135 (-0.596247) | 0.108172 / 0.296338 (-0.188167) |\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) | 0.415951 / 0.215209 (0.200742) | 4.172187 / 2.077655 (2.094532) | 1.842210 / 1.504120 (0.338090) | 1.636997 / 1.541195 (0.095802) | 1.706078 / 1.468490 (0.237588) | 0.695825 / 4.584777 (-3.888952) | 3.337354 / 3.745712 (-0.408358) | 1.877880 / 5.269862 (-3.391982) | 1.153882 / 4.565676 (-3.411794) | 0.082923 / 0.424275 (-0.341352) | 0.012814 / 0.007607 (0.005207) | 0.521793 / 0.226044 (0.295748) | 5.275980 / 2.268929 (3.007051) | 2.279230 / 55.444624 (-53.165394) | 1.941777 / 6.876477 (-4.934700) | 1.981297 / 2.142072 (-0.160775) | 0.809669 / 4.805227 (-3.995558) | 0.148753 / 6.500664 (-6.351911) | 0.064909 / 0.075469 (-0.010560) |\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) | 1.226757 / 1.841788 (-0.615031) | 13.717354 / 8.074308 (5.643046) | 12.925885 / 10.191392 (2.734493) | 0.137926 / 0.680424 (-0.542498) | 0.028788 / 0.534201 (-0.505413) | 0.396654 / 0.579283 (-0.182630) | 0.401931 / 0.434364 (-0.032432) | 0.460515 / 0.540337 (-0.079823) | 0.537903 / 1.386936 (-0.849033) |\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) | 0.006757 / 0.011353 (-0.004596) | 0.004474 / 0.011008 (-0.006534) | 0.076571 / 0.038508 (0.038063) | 0.027580 / 0.023109 (0.004471) | 0.348231 / 0.275898 (0.072333) | 0.398403 / 0.323480 (0.074923) | 0.005089 / 0.007986 (-0.002897) | 0.004676 / 0.004328 (0.000347) | 0.076444 / 0.004250 (0.072194) | 0.038508 / 0.037052 (0.001456) | 0.348515 / 0.258489 (0.090026) | 0.401456 / 0.293841 (0.107615) | 0.031630 / 0.128546 (-0.096916) | 0.011698 / 0.075646 (-0.063949) | 0.085805 / 0.419271 (-0.333467) | 0.041962 / 0.043533 (-0.001570) | 0.343415 / 0.255139 (0.088276) | 0.383001 / 0.283200 (0.099801) | 0.090231 / 0.141683 (-0.051452) | 1.488114 / 1.452155 (0.035960) | 1.569039 / 1.492716 (0.076323) |\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) | 0.261751 / 0.018006 (0.243745) | 0.411354 / 0.000490 (0.410865) | 0.015103 / 0.000200 (0.014903) | 0.000262 / 0.000054 (0.000208) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025423 / 0.037411 (-0.011988) | 0.101334 / 0.014526 (0.086808) | 0.108835 / 0.176557 (-0.067722) | 0.143995 / 0.737135 (-0.593140) | 0.111751 / 0.296338 (-0.184588) |\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) | 0.446507 / 0.215209 (0.231298) | 4.461543 / 2.077655 (2.383888) | 2.104648 / 1.504120 (0.600528) | 1.895900 / 1.541195 (0.354706) | 1.985481 / 1.468490 (0.516991) | 0.699029 / 4.584777 (-3.885748) | 3.371064 / 3.745712 (-0.374648) | 1.883445 / 5.269862 (-3.386416) | 1.166150 / 4.565676 (-3.399527) | 0.082639 / 0.424275 (-0.341636) | 0.012605 / 0.007607 (0.004998) | 0.544860 / 0.226044 (0.318815) | 5.513223 / 2.268929 (3.244294) | 2.570661 / 55.444624 (-52.873963) | 2.206066 / 6.876477 (-4.670411) | 2.256346 / 2.142072 (0.114273) | 0.801142 / 4.805227 (-4.004085) | 0.150412 / 6.500664 (-6.350252) | 0.067742 / 0.075469 (-0.007727) |\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) | 1.303477 / 1.841788 (-0.538310) | 14.287767 / 8.074308 (6.213458) | 13.525563 / 10.191392 (3.334171) | 0.148202 / 0.680424 (-0.532222) | 0.016868 / 0.534201 (-0.517333) | 0.380729 / 0.579283 (-0.198555) | 0.388177 / 0.434364 (-0.046187) | 0.477410 / 0.540337 (-0.062927) | 0.569343 / 1.386936 (-0.817593) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c18b77113da3f2e31af0570ec119877ca2a390 \"CML watermark\")\n", "> PS: I think it would be a good idea to add links to the Guide pages for better discoverability and to somewhat \"justify their presence in the docs\" (from the tutorial/how-to pages to the guides; some guides are not referenced at all)\r\n\r\nJust merged #5485, which references this new doc! Will look for other pages in the docs where it'd make sense to add them :)" ]
"2023-01-05T18:12:17Z"
"2023-02-01T18:11:45Z"
"2023-02-01T16:36:01Z"
MEMBER
null
0
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Added `ds.to_iterable()` to get an iterable dataset from a map-style arrow dataset. It also has a `num_shards` argument to split the dataset before converting to an iterable dataset. Sharding is important to enable efficient shuffling and parallel loading of iterable datasets. TODO: - [x] tests - [x] docs Fix https://github.com/huggingface/datasets/issues/5265
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5,693
[docs] Split pattern search order
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007841 / 0.011353 (-0.003512) | 0.005640 / 0.011008 (-0.005368) | 0.096465 / 0.038508 (0.057957) | 0.036476 / 0.023109 (0.013367) | 0.306431 / 0.275898 (0.030533) | 0.339545 / 0.323480 (0.016065) | 0.006064 / 0.007986 (-0.001922) | 0.004404 / 0.004328 (0.000076) | 0.073130 / 0.004250 (0.068879) | 0.052765 / 0.037052 (0.015713) | 0.309895 / 0.258489 (0.051406) | 0.354037 / 0.293841 (0.060196) | 0.037127 / 0.128546 (-0.091420) | 0.012387 / 0.075646 (-0.063260) | 0.333503 / 0.419271 (-0.085769) | 0.059799 / 0.043533 (0.016266) | 0.305496 / 0.255139 (0.050358) | 0.324122 / 0.283200 (0.040922) | 0.107007 / 0.141683 (-0.034676) | 1.416743 / 1.452155 (-0.035411) | 1.520772 / 1.492716 (0.028055) |\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) | 0.261233 / 0.018006 (0.243227) | 0.573806 / 0.000490 (0.573316) | 0.000390 / 0.000200 (0.000190) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027672 / 0.037411 (-0.009740) | 0.112803 / 0.014526 (0.098278) | 0.121085 / 0.176557 (-0.055471) | 0.176056 / 0.737135 (-0.561080) | 0.127171 / 0.296338 (-0.169167) |\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) | 0.414756 / 0.215209 (0.199547) | 4.148743 / 2.077655 (2.071088) | 1.883940 / 1.504120 (0.379820) | 1.698771 / 1.541195 (0.157576) | 1.811926 / 1.468490 (0.343436) | 0.708293 / 4.584777 (-3.876484) | 3.780456 / 3.745712 (0.034744) | 2.098556 / 5.269862 (-3.171306) | 1.323512 / 4.565676 (-3.242164) | 0.086253 / 0.424275 (-0.338022) | 0.012587 / 0.007607 (0.004980) | 0.514824 / 0.226044 (0.288779) | 5.157415 / 2.268929 (2.888487) | 2.382519 / 55.444624 (-53.062105) | 2.014539 / 6.876477 (-4.861938) | 2.215239 / 2.142072 (0.073166) | 0.847178 / 4.805227 (-3.958049) | 0.170053 / 6.500664 (-6.330611) | 0.066461 / 0.075469 (-0.009008) |\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) | 1.199056 / 1.841788 (-0.642732) | 15.244999 / 8.074308 (7.170691) | 14.661593 / 10.191392 (4.470201) | 0.168855 / 0.680424 (-0.511569) | 0.017889 / 0.534201 (-0.516312) | 0.424961 / 0.579283 (-0.154322) | 0.428632 / 0.434364 (-0.005732) | 0.502680 / 0.540337 (-0.037658) | 0.597827 / 1.386936 (-0.789109) |\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) | 0.007749 / 0.011353 (-0.003604) | 0.005527 / 0.011008 (-0.005482) | 0.074774 / 0.038508 (0.036266) | 0.035367 / 0.023109 (0.012258) | 0.340594 / 0.275898 (0.064696) | 0.373970 / 0.323480 (0.050490) | 0.006094 / 0.007986 (-0.001892) | 0.004428 / 0.004328 (0.000100) | 0.074120 / 0.004250 (0.069869) | 0.054852 / 0.037052 (0.017800) | 0.357173 / 0.258489 (0.098684) | 0.388877 / 0.293841 (0.095036) | 0.037002 / 0.128546 (-0.091545) | 0.012337 / 0.075646 (-0.063309) | 0.086962 / 0.419271 (-0.332310) | 0.050370 / 0.043533 (0.006837) | 0.342989 / 0.255139 (0.087850) | 0.358065 / 0.283200 (0.074865) | 0.111063 / 0.141683 (-0.030620) | 1.516704 / 1.452155 (0.064549) | 1.634359 / 1.492716 (0.141643) |\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) | 0.261493 / 0.018006 (0.243487) | 0.566288 / 0.000490 (0.565799) | 0.000439 / 0.000200 (0.000239) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030426 / 0.037411 (-0.006985) | 0.114606 / 0.014526 (0.100080) | 0.126134 / 0.176557 (-0.050423) | 0.175324 / 0.737135 (-0.561812) | 0.132766 / 0.296338 (-0.163573) |\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) | 0.426785 / 0.215209 (0.211576) | 4.243555 / 2.077655 (2.165900) | 2.089631 / 1.504120 (0.585511) | 1.994562 / 1.541195 (0.453367) | 2.140284 / 1.468490 (0.671794) | 0.698645 / 4.584777 (-3.886132) | 3.807471 / 3.745712 (0.061759) | 3.275343 / 5.269862 (-1.994519) | 1.796756 / 4.565676 (-2.768921) | 0.085986 / 0.424275 (-0.338289) | 0.012213 / 0.007607 (0.004606) | 0.536815 / 0.226044 (0.310771) | 5.344611 / 2.268929 (3.075683) | 2.498578 / 55.444624 (-52.946047) | 2.153260 / 6.876477 (-4.723217) | 2.251310 / 2.142072 (0.109237) | 0.839104 / 4.805227 (-3.966123) | 0.169639 / 6.500664 (-6.331025) | 0.065880 / 0.075469 (-0.009589) |\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) | 1.268610 / 1.841788 (-0.573178) | 15.624915 / 8.074308 (7.550606) | 15.163684 / 10.191392 (4.972292) | 0.172992 / 0.680424 (-0.507432) | 0.018154 / 0.534201 (-0.516047) | 0.440485 / 0.579283 (-0.138798) | 0.431949 / 0.434364 (-0.002415) | 0.547935 / 0.540337 (0.007597) | 0.662442 / 1.386936 (-0.724494) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5c8a6ba43c4aaa0ca0665d8dadd87ef33e28e8e4 \"CML watermark\")\n" ]
"2023-03-31T19:51:38Z"
"2023-04-03T18:43:30Z"
"2023-04-03T18:29:58Z"
MEMBER
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This PR addresses #5681 about the order of split patterns πŸ€— Datasets searches for when generating dataset splits.
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Update tasks.json
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think the only place where we need to add it is here https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts\r\n\r\nAnd I think we can remove tasks.json completely from this repo", "Isn't tasks.json used anymore in this repo?", "> I think the only place where we need to add it is here https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts\r\n> \r\n> And I think we can remove tasks.json completely from this repo\r\n\r\nWhat about the warning I mentioned in https://github.com/huggingface/datasets/issues/5255#issuecomment-1339013527? Also, the depth estimation entry is already present in https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts. ", "The update is based on what I received in the output of the export job (c.f. https://github.com/huggingface/datasets/issues/5255#issuecomment-1339107195). \r\n\r\nEdit: Oh, are you referring to the dataset card of NYU Depth V2?", "Yes, my suggestion was for the dataset card: you got the error message because you tried to set `depth-estimation` in `class_ids` instead of `class_categories`.", "> What about the warning I mentioned in https://github.com/huggingface/datasets/issues/5255#issuecomment-1339013527? Also, the depth estimation entry is already present in https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts.\r\n\r\nif you place it in `task_categories` you should be good :)", "yes i would suggest rm'ing tasks.json here for clarity", "Closing it. ", "It's not clear if we can remove it btw, since old versions of `evaluate` rely on it (see https://github.com/huggingface/evaluate/pull/309)\r\n\r\ncc @lvwerra ", "Actually it can be removed without incidence in old versions of evaluate since we kept an hardcoded `known_task_ids` that is marked \"DEPRECATED\"" ]
"2022-12-06T11:37:57Z"
"2023-09-24T10:06:42Z"
"2022-12-07T12:46:03Z"
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Context: * https://github.com/huggingface/datasets/issues/5255#issuecomment-1339107195 Cc: @osanseviero
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Fix code_search_net keys
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[ "Fix #2552." ]
"2021-06-28T13:40:23Z"
"2021-09-02T08:24:43Z"
"2021-06-28T14:10:35Z"
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There were duplicate keys in the `code_search_net` dataset, as reported in https://github.com/huggingface/datasets/issues/2552 I fixed the keys (it was an addition of the file and row indices, which was causing collisions) Fix #2552.
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Fix NestedDataStructure.data for empty dict
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[ "I validated that this fixed the problem, thank you, @albertvillanova!\r\n", "still facing the same issue or similar:\r\nfrom datasets import load_dataset\r\nwtm14_test = load_dataset('wmt14',\"de-en\",cache_dir='./datasets')\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\wmt14\\43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\\wmt_utils.py in _split_generators(self, dl_manager)\r\n 758 # Extract manually downloaded files.\r\n 759 manual_files = dl_manager.extract(manual_paths_dict)\r\n--> 760 extraction_map = dict(downloaded_files, **manual_files)\r\n 761 \r\n 762 for language in self.config.language_pair:\r\n\r\nTypeError: type object argument after ** must be a mapping, not list", "Hi @sabania \r\nWe released a patch version that fixes this issue (1.4.1), can you try with the new version please ?\r\n```\r\npip install --upgrade datasets\r\n```", "I re-validated with the hotfix and the problem is no more.", "It's working. thanks a lot." ]
"2021-03-03T20:16:51Z"
"2021-03-04T16:46:04Z"
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Fix #1981
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Support streaming compressed files
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"2021-08-11T09:02:06Z"
"2021-08-17T05:28:39Z"
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Add support to stream compressed files (current options in fsspec): - bz2 - lz4 - xz - zstd cc: @lewtun
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Fix VCTK encoding
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"2021-12-28T15:23:36Z"
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utf-8 encoding was missing in the VCTK dataset builder added in #3351
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Using disk for memory with the method `from_dict`
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[ "This issue was also causing an OOM in @nateraw 's workflow and shows again that behavior is confusing - we should definitely switch to using the disk IMO" ]
"2022-08-18T15:18:18Z"
"2023-01-26T18:36:28Z"
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**Is your feature request related to a problem? Please describe.** I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM error. **Describe the solution you'd like** The method `from_dict` loads the data in RAM. It could be good to add an option to use the disk instead. **Describe alternatives you've considered** To solve the problem, I have to do an intermediate step where I save the new datasets at each iteration with `save_to_disk`. Once it's done, I open them all and concatenate them.
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Fix BLEURT metrics for backward compatibility
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Fix #565
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4,359
Fix Version equality
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[ "_The documentation is not available anymore as the PR was closed or merged._" ]
"2022-05-16T13:19:26Z"
"2022-05-24T16:25:37Z"
"2022-05-24T16:17:14Z"
MEMBER
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I think `Version` equality should align with other similar cases in Python, like: ```python In [1]: "a" == 5, "a" == None Out[1]: (False, False) In [2]: "a" != 5, "a" != None Out[2]: (True, True) ``` With this PR, we will get: ```python In [3]: Version("1.0.0") == 5, Version("1.0.0") == None Out[3]: (False, False) In [4]: Version("1.0.0") != 5, Version("1.0.0") != None Out[4]: (True, True) ``` Note I found this issue when `doc-builder` tried to compare: ```python if param.default != inspect._empty ``` where `param.default` is an instance of `Version`.
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