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I_kwDODunzps53YhSi
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Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding
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[] | 2023-11-20T20:07:23Z
| 2023-11-20T20:07:37Z
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NONE
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### Describe the bug
I am working with a dataset I am trying to publish.
The path is Antreas/TALI.
It's a fairly large dataset, and contains images, video, audio and text.
I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process.
With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths.
Find the script I am using below:
```python
import multiprocessing as mp
import pathlib
from typing import Optional
import datasets
from rich import print
from tqdm import tqdm
def download_dataset_via_hub(
dataset_name: str,
dataset_download_path: pathlib.Path,
num_download_workers: int = mp.cpu_count(),
):
import huggingface_hub as hf_hub
download_folder = hf_hub.snapshot_download(
repo_id=dataset_name,
repo_type="dataset",
cache_dir=dataset_download_path,
resume_download=True,
max_workers=num_download_workers,
ignore_patterns=[],
)
return pathlib.Path(download_folder) / "data"
def load_dataset_via_hub(
dataset_download_path: pathlib.Path,
num_download_workers: int = mp.cpu_count(),
dataset_name: Optional[str] = None,
):
from dataclasses import dataclass, field
from datasets import ClassLabel, Features, Image, Sequence, Value
dataset_path = download_dataset_via_hub(
dataset_download_path=dataset_download_path,
num_download_workers=num_download_workers,
dataset_name=dataset_name,
)
# Building a list of file paths for validation set
train_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "train" in file.as_posix()
]
val_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "val" in file.as_posix()
]
test_files = [
file.as_posix()
for file in pathlib.Path(dataset_path).glob("*.parquet")
if "test" in file.as_posix()
]
print(
f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set"
)
data_files = {
"test": test_files,
"val": val_files,
"train": train_files,
}
features = Features(
{
"image": Image(
decode=True
), # Set `decode=True` if you want to decode the images, otherwise `decode=False`
"image_url": Value("string"),
"item_idx": Value("int64"),
"wit_features": Sequence(
{
"attribution_passes_lang_id": Value("bool"),
"caption_alt_text_description": Value("string"),
"caption_reference_description": Value("string"),
"caption_title_and_reference_description": Value("string"),
"context_page_description": Value("string"),
"context_section_description": Value("string"),
"hierarchical_section_title": Value("string"),
"is_main_image": Value("bool"),
"language": Value("string"),
"page_changed_recently": Value("bool"),
"page_title": Value("string"),
"page_url": Value("string"),
"section_title": Value("string"),
}
),
"wit_idx": Value("int64"),
"youtube_title_text": Value("string"),
"youtube_description_text": Value("string"),
"youtube_video_content": Value("binary"),
"youtube_video_starting_time": Value("string"),
"youtube_subtitle_text": Value("string"),
"youtube_video_size": Value("int64"),
"youtube_video_file_path": Value("string"),
}
)
dataset = datasets.load_dataset(
"parquet" if dataset_name is None else dataset_name,
data_files=data_files,
features=features,
num_proc=1,
cache_dir=dataset_download_path / "cache",
)
return dataset
if __name__ == "__main__":
dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/")
dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[
"test"
]
for sample in tqdm(dataset):
print(list(sample.keys()))
```
Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start!
### Steps to reproduce the bug
1. Run the code I provided to get a sense of how fast snapshot + manual is
2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP.
3. You should now have an appreciation of how long these things take.
### Expected behavior
The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.13
- Huggingface_hub version: 0.17.3
- PyArrow version: 13.0.0
- Pandas version: 2.1.1
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Workflow for Tabular data
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[
"I use below to load a dataset:\r\n```\r\ndataset = datasets.load_dataset(\"scikit-learn/auto-mpg\")\r\ndf = pd.DataFrame(dataset[\"train\"])\r\n```\r\nTBH as said, tabular folk split their own dataset, they sometimes have two splits, sometimes three. Maybe somehow avoiding it for tabular datasets might be good for later. (it's just UX improvement) ",
"is very slow batch access of a dataset (tabular, csv) with many columns to be expected?",
"Define \"many\" ? x)",
"~20k! I was surprised batch loading with as few as 32 samples was really slow. I was speculating the columnar format was the cause -- or do you see good performance with this approx size of tabular data?",
"20k can be a lot for a columnar format but maybe we can optimize a few things.\r\n\r\nIt would be cool to profile the code to see if there's an unoptimized part of the code that slows everything down.\r\n\r\n(it's also possible to kill the job when it accesses the batch, it often gives you the traceback at the location where the code was running)",
"FWIW I've worked with tabular data with 540k columns.",
"thats awesome, whats your secret? would love to see an example!",
"@wconnell I'm not sure what you mean by my secret, I load them into a numpy array 😁 \r\n\r\nAn example dataset is [here](https://portal.gdc.cancer.gov/repository?facetTab=files&filters=%7B%22content%22%3A%5B%7B%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-CESC%22%5D%7D%2C%22op%22%3A%22in%22%7D%2C%7B%22content%22%3A%7B%22field%22%3A%22files.data_category%22%2C%22value%22%3A%5B%22DNA%20Methylation%22%5D%7D%2C%22op%22%3A%22in%22%7D%5D%2C%22op%22%3A%22and%22%7D&searchTableTab=files) which is a dataset of DNA methylation reads. This dataset is about 950 rows and 450k columns. "
] | 2022-06-07T12:48:22Z
| 2023-03-06T08:53:55Z
| null |
MEMBER
| null | null | null |
Tabular data are treated very differently than data for NLP, audio, vision, etc. and therefore the worflow for tabular data in `datasets` is not ideal.
For example for tabular data, it is common to use pandas/spark/dask to process the data, and then load the data into X and y (X is an array of features and y an array of labels), then train_test_split and finally feed the data to a machine learning model.
In `datasets` the workflow is different: we use load_dataset, then map, then train_test_split (if we only have a train split) and we end up with columnar dataset splits, not formatted as X and y.
Right now, it is already possible to convert a dataset from and to pandas, but there are still many things that could improve the workflow for tabular data:
- be able to load the data into X and y
- be able to load a dataset from the output of spark or dask (as far as I know it's usually csv or parquet files on S3/GCS/HDFS etc.)
- support "unsplit" datasets explicitly, instead of putting everything in "train" by default
cc @adrinjalali @merveenoyan feel free to complete/correct this :)
Feel free to also share ideas of APIs that would be super intuitive in your opinion !
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Add QuALITY dataset
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[
"I'll take this one if no one hasn't yet!"
] | 2021-12-15T22:26:19Z
| 2021-12-28T15:17:05Z
| null |
MEMBER
| null | null | null |
## Adding a Dataset
- **Name:** QuALITY
- **Description:** A challenging question answering with very long contexts (Twitter [thread](https://twitter.com/sleepinyourhat/status/1471225421794529281?s=20))
- **Paper:** No ArXiv link yet, but draft is [here](https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf)
- **Data:** GitHub repo [here](https://github.com/nyu-mll/quality)
- **Motivation:** This dataset would serve as a nice way to benchmark long-range Transformer models like BigBird, Longformer and their descendants. In particular, it would be very interesting to see how the S4 model fares on this given it's impressive performance on the Long Range Arena
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Squad Metric Description & Feature Mismatch
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[
"Thanks for reporting !\r\nThere indeed a mismatch between the features and the kwargs description\r\n\r\nI believe `answer_start` was added to match the squad dataset format for consistency, even though it is not used in the metric computation. I think I'd rather keep it this way, so that you can just give `references=squad[\"answers\"]` to `.compute()`.\r\nMaybe we can just fix the description then.",
"But then providing the `answer_start` becomes mandatory since the format of the features is checked against the one provided in the squad [file](https://github.com/huggingface/datasets/pull/658/files)."
] | 2020-09-22T09:07:00Z
| 2020-10-13T02:16:56Z
| 2020-09-29T15:57:38Z
|
NONE
| null | null | null |
The [description](https://github.com/huggingface/datasets/blob/master/metrics/squad/squad.py#L39) doesn't mention `answer_start` in squad. However the `datasets.features` require [it](https://github.com/huggingface/datasets/blob/master/metrics/squad/squad.py#L68). It's also not used in the evaluation.
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Add missing features to commonsense_qa dataset
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"@albertvillanova it adds question_concept and id which is great. I suppose we'll talk about staying true to the format on another PR. ",
"Yes, let's merge this PR as it is: it adds missing features.\r\n\r\nA subsequent PR may address the request on changing the dataset feature structure."
] | 2022-05-04T14:24:26Z
| 2022-05-06T14:23:57Z
| 2022-05-06T14:16:46Z
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Fix partially #4275.
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label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator
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"Hi! Thanks for pointing out this inconsistency. Changing the default value at this point is probably not worth it, considering we've started discussing the state of the task API internally - we will most likely deprecate the current one and replace it with a more robust solution that relies on the `train_eval_index` field stored in the YAML section of the dataset cards.",
"The task templates API has been deprecated (will be removed in version 3.0), so I'm closing this issue."
] | 2023-01-13T09:40:07Z
| 2023-07-21T14:27:08Z
| 2023-07-21T14:27:08Z
|
NONE
| null | null | null |
### Describe the bug
When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem.
It is required to rename the column accordingly to the expected name : `label` or `label_ids`
### Steps to reproduce the bug
```python
from datasets import TextClassification, AutoTokenized, DataCollatorWithPadding
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0'))
print(ds_prepared)
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
ds_tokenized = ds_prepared.map(lambda x: tokenizer(x['text'], truncation=True), batched=True)
print(ds_tokenized)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
tf_data = model.prepare_tf_dataset(ds_tokenized, shuffle=True, batch_size=16, collate_fn=data_collator)
print(tf_data)
```
### Expected behavior
Without renaming the the column, the target column is not in the final tf_data since it is not in the column name expected by the data_collator.
To correct this, we have to rename the column:
```python
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0')).rename_column('labels', 'label')
```
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
- `transformers` version: 4.26.0.dev0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.11.1
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): 2.11.0 (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>
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Add `key` type and duplicates verification with hashing
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"@lhoestq The tests for key type and duplicate keys have been added and verified successfully.\r\nAfter generating with an intentionally faulty `mnist` script, when there is an incompatible key type, it shows:\r\n\r\n```\r\nDownloading and preparing dataset mnist/mnist (download: 11.06 MiB, generated: 60.62 MiB, post-processed: Unknown size, total: 71.67 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\mnist\\mnist\\1.0.0\\5064c25e57a1678f700d2dc798ffe8a6d519405cca7d33670fffda477857a994...\r\n0 examples [00:00, ? examples/s]2021-04-26 02:50:03.703836: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n\r\nFAILURE TO GENERATE DATASET: Invalid key type detected\r\nFound Key [0, 0] of type <class 'list'>\r\nKeys should be either str, int or bytes type\r\n```\r\n\r\nIn the case of duplicate keys, it now gives:\r\n\r\n```\r\nDownloading and preparing dataset mnist/mnist (download: 11.06 MiB, generated: 60.62 MiB, post-processed: Unknown size, total: 71.67 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\mnist\\mnist\\1.0.0\\5064c25e57a1678f700d2dc798ffe8a6d519405cca7d33670fffda477857a994...\r\n0 examples [00:00, ? examples/s]2021-04-26 02:53:13.498579: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\load.py\", line 746, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\builder.py\", line 587, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\builder.py\", line 665, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\builder.py\", line 1002, in _prepare_split\r\n writer.write(example, key)\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\arrow_writer.py\", line 321, in write\r\n self.check_duplicates()\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\arrow_writer.py\", line 331, in check_duplicates\r\n raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 234467\r\nKeys should be unique and deterministic in nature\r\n```\r\nPlease let me know if this is what we wanted to implement. Thanks!",
"This looks pretty cool !\r\nWe can make focus on the GeneratorBasedBuilder for now yes.\r\n\r\nDo you think we could make the ArrowWriter not look for duplicates by default ?\r\nThis way we can just enable duplicate detections when instantiating the writer in the GeneratorBasedBuilder for now.",
"Thank you @lhoestq\r\n\r\n\r\n\r\n> Do you think we could make the ArrowWriter not look for duplicates by default ?\r\n\r\nWe can definitely do that by including a `check_duplicates` argument while instantiating `ArrowWriter()`. \r\n\r\nHowever, since only `GeneratorBasedBuilder` uses the `write()` function (which includes the detection code) and the others like `ArrowBasedBuilder` use `write_table()` which remains as it was (without duplicate detection). I don't think it would be necessary.\r\n\r\nNonetheless, doing this would require just some small changes. Please let me know your thoughts on this. Thanks!",
"I like the idea of having the duplicate detection optional for other uses of the ArrowWriter.\r\nThis class is the main tool to write python data in arrow format so I'd expect it to be flexible.\r\nThat's why I think by default it shouldn't require users to provide keys or do any duplicates detection.\r\n\r\nAn alternative would be to subclass the writer to include duplicates detection in another class.\r\n\r\nBoth options are fine for me, let me know what you think !",
"> This class is the main tool to write python data in arrow format so I'd expect it to be flexible.\r\n> That's why I think by default it shouldn't require users to provide keys or do any duplicates detection.\r\n\r\nWell, that makes sense as the writer can indeed be used for other purposes as well.\r\n\r\n> We can definitely do that by including a `check_duplicates` argument while instantiating `ArrowWriter()`.\r\n\r\nI think that this would be the simplest and the more efficient option for achieving this as subclassing the writer only for this would lead to unnecessary complexity and code duplication (in case of `writer()`). \r\n\r\nI will be adding the changes soon. Thanks for the feedback @lhoestq!",
"@lhoestq I have pushed the final changes just now. \r\nNow, the keys and duplicate checking will be necessary only when the `ArrowWriter` is initialized with `check_duplicates=True` specifically (in this case, for `GeneratorBasedBuilders`)\r\n\r\nLet me know if this is what was required. Thanks!",
"@lhoestq Thanks for the feedback! I will be adding the tests for the same very soon. \r\n\r\nHowever, I'm not sure as to what exactly is causing the `segmentation fault` in the failing CI tests. It seems to be something from `test_concatenation_table_cast` from `test_table.py`, but I'm not sure as to what exactly. Would be great if you could help. Thanks!",
"You can merge master into your branch to fix this issue.\r\nBasically pyarrow 4.0.0 has a segfault issue (which has now been resolved on the master branch of pyarrow).\r\nSo until 4.0.1 comes out we changed to using `pyarrow<4.0.0` recently.",
"@lhoestq Thanks for the help with the CI failures. Apologies for the multiple merge commits. My local repo got messy while merging which led to this.\r\nWill be pushing the commit for the tests soon!",
"Hey @lhoestq, I've just added the required tests for checking key duplicates and invalid key data types.\r\nI think we have caught a nice little issue as 27 datasets are currently using non-unique keys (hence, the failing tests: All these datasets are giving `DuplicateKeysError` during testing). \r\nThese datasets were not detected earlier as there was no key checking when `num_examples < writer_batch_size` due to which they passed the dummy data generation test. This bug was fixed by adding the test to `writer.finalize()` method as well for checking any leftover examples from batches. \r\n\r\nI'd like to make changes to the faulty datasets' scripts. However, I was wondering if I should do that in this PR itself or open a new PR as this might get messy in the same PR. Let me know your thoughts on this. Thanks!",
"Hi ! Once https://github.com/huggingface/datasets/pull/2333 is merged, feel free to merge master into your branch to fix the CI :)",
"Thanks a lot for the help @lhoestq. Besides merging the new changes, I guess this PR is completed for now :)",
"I just merged the PR, feel free to merge `master` into your branch. It should fix most most of the CI issues. If there are some left we can fix them in this PR :)",
"@lhoestq Looks like the PR is completed now. Thanks for helping me out so much in this :)",
"Hey @lhoestq, I've added the test and corrected the Cl errors as well. Do let me know if this requires any change. Thanks!",
"Merging. I'll update the comment on the master branch (for some reason I can edit files on this branch)",
"@lhoestq Thank you for the help and feedback. Feels great to contribute!"
] | 2021-04-20T20:03:19Z
| 2021-05-10T18:04:37Z
| 2021-05-10T17:31:22Z
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Closes #2230
There is currently no verification for the data type and the uniqueness of the keys yielded by the `dataset_builder`.
This PR is currently a work in progress with the following goals:
- [x] Adding `hash_salt` to `ArrowWriter` so that the keys belonging to different splits have different hash
- [x] Add `key` arrtibute to `ArrowWriter.write()` for hashing
- [x] Add a hashing class which takes an input key of certain type (`str`/`int`/anything convertible to string) and produces a 128-bit hash using `hashlib.md5`
- [x] Creating a function giving a custom error message when non-unique keys are found
**[This will take care of type-checking for keys]**
- [x] Checking for duplicate keys in `writer.write()` for each batch
[**NOTE**: This PR is currently concerned with `GeneratorBasedBuilder` only, for simplification. A subsequent PR will be made in future for `ArrowBasedBuilder`]
@lhoestq Thank you for the feedback. It would be great to have your guidance on this!
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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) | 0.006013 / 0.011353 (-0.005340) | 0.003647 / 0.011008 (-0.007362) | 0.081781 / 0.038508 (0.043273) | 0.059020 / 0.023109 (0.035911) | 0.321823 / 0.275898 (0.045925) | 0.350159 / 0.323480 (0.026679) | 0.003599 / 0.007986 (-0.004386) | 0.002877 / 0.004328 (-0.001452) | 0.063941 / 0.004250 (0.059690) | 0.049460 / 0.037052 (0.012408) | 0.330185 / 0.258489 (0.071696) | 0.362220 / 0.293841 (0.068379) | 0.027613 / 0.128546 (-0.100934) | 0.007976 / 0.075646 (-0.067670) | 0.263386 / 0.419271 (-0.155885) | 0.045504 / 0.043533 (0.001971) | 0.321172 / 0.255139 (0.066033) | 0.345291 / 0.283200 (0.062091) | 0.023133 / 0.141683 (-0.118550) | 1.435816 / 1.452155 (-0.016339) | 1.557241 / 1.492716 (0.064524) |\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) | 0.222228 / 0.018006 (0.204222) | 0.420008 / 0.000490 (0.419518) | 0.008598 / 0.000200 (0.008398) | 0.000343 / 0.000054 (0.000288) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023725 / 0.037411 (-0.013686) | 0.073023 / 0.014526 (0.058497) | 0.814888 / 0.176557 (0.638332) | 0.294122 / 0.737135 (-0.443013) | 0.088945 / 0.296338 (-0.207393) |\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) | 0.393561 / 0.215209 (0.178352) | 3.946544 / 2.077655 (1.868890) | 1.916476 / 1.504120 (0.412356) | 1.721544 / 1.541195 (0.180349) | 1.768583 / 1.468490 (0.300093) | 0.508067 / 4.584777 (-4.076710) | 3.047832 / 3.745712 (-0.697880) | 2.952842 / 5.269862 (-2.317020) | 1.869337 / 4.565676 (-2.696339) | 0.057812 / 0.424275 (-0.366463) | 0.006694 / 0.007607 (-0.000913) | 0.463007 / 0.226044 (0.236963) | 4.635087 / 2.268929 (2.366158) | 2.419833 / 55.444624 (-53.024792) | 2.018519 / 6.876477 (-4.857958) | 2.043430 / 2.142072 (-0.098643) | 0.590895 / 4.805227 (-4.214333) | 0.126113 / 6.500664 (-6.374552) | 0.061045 / 0.075469 (-0.014424) |\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) | 1.226850 / 1.841788 (-0.614937) | 17.336630 / 8.074308 (9.262322) | 13.651049 / 10.191392 (3.459656) | 0.143308 / 0.680424 (-0.537116) | 0.016938 / 0.534201 (-0.517263) | 0.332829 / 0.579283 (-0.246454) | 0.368684 / 0.434364 (-0.065680) | 0.385848 / 0.540337 (-0.154489) | 0.546391 / 1.386936 (-0.840545) |\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) | 0.006149 / 0.011353 (-0.005204) | 0.003818 / 0.011008 (-0.007191) | 0.064012 / 0.038508 (0.025504) | 0.059846 / 0.023109 (0.036737) | 0.455928 / 0.275898 (0.180030) | 0.480736 / 0.323480 (0.157256) | 0.004874 / 0.007986 (-0.003111) | 0.002877 / 0.004328 (-0.001451) | 0.064195 / 0.004250 (0.059944) | 0.048146 / 0.037052 (0.011094) | 0.452638 / 0.258489 (0.194149) | 0.484339 / 0.293841 (0.190499) | 0.028832 / 0.128546 (-0.099715) | 0.008162 / 0.075646 (-0.067485) | 0.069855 / 0.419271 (-0.349417) | 0.041429 / 0.043533 (-0.002104) | 0.453282 / 0.255139 (0.198143) | 0.473812 / 0.283200 (0.190613) | 0.021186 / 0.141683 (-0.120497) | 1.465207 / 1.452155 (0.013052) | 1.508216 / 1.492716 (0.015500) |\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) | 0.242491 / 0.018006 (0.224485) | 0.421219 / 0.000490 (0.420730) | 0.011201 / 0.000200 (0.011001) | 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) | 0.027015 / 0.037411 (-0.010396) | 0.080465 / 0.014526 (0.065939) | 0.092622 / 0.176557 (-0.083934) | 0.146111 / 0.737135 (-0.591024) | 0.091546 / 0.296338 (-0.204793) |\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) | 0.458351 / 0.215209 (0.243142) | 4.591454 / 2.077655 (2.513799) | 2.508156 / 1.504120 (1.004037) | 2.328771 / 1.541195 (0.787576) | 2.423251 / 1.468490 (0.954761) | 0.508504 / 4.584777 (-4.076273) | 3.133789 / 3.745712 (-0.611923) | 2.862777 / 5.269862 (-2.407084) | 1.886327 / 4.565676 (-2.679350) | 0.058017 / 0.424275 (-0.366258) | 0.006496 / 0.007607 (-0.001111) | 0.529629 / 0.226044 (0.303585) | 5.310338 / 2.268929 (3.041409) | 2.973075 / 55.444624 (-52.471549) | 2.601313 / 6.876477 (-4.275163) | 2.777348 / 2.142072 (0.635275) | 0.593711 / 4.805227 (-4.211516) | 0.125453 / 6.500664 (-6.375211) | 0.061034 / 0.075469 (-0.014435) |\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) | 1.374391 / 1.841788 (-0.467397) | 18.768026 / 8.074308 (10.693718) | 15.053637 / 10.191392 (4.862245) | 0.158253 / 0.680424 (-0.522171) | 0.018126 / 0.534201 (-0.516075) | 0.337427 / 0.579283 (-0.241856) | 0.391678 / 0.434364 (-0.042686) | 0.398524 / 0.540337 (-0.141813) | 0.558629 / 1.386936 (-0.828307) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e0b79660f180c88517884f831eca620bc46a0957 \"CML watermark\")\n",
"I think https://github.com/huggingface/datasets/pull/6334 fixes it already no ?",
"<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) | 0.006432 / 0.011353 (-0.004921) | 0.003861 / 0.011008 (-0.007147) | 0.084132 / 0.038508 (0.045624) | 0.069391 / 0.023109 (0.046282) | 0.341081 / 0.275898 (0.065183) | 0.375975 / 0.323480 (0.052495) | 0.003962 / 0.007986 (-0.004024) | 0.003235 / 0.004328 (-0.001094) | 0.064927 / 0.004250 (0.060677) | 0.054190 / 0.037052 (0.017137) | 0.350719 / 0.258489 (0.092230) | 0.393216 / 0.293841 (0.099375) | 0.031002 / 0.128546 (-0.097544) | 0.008416 / 0.075646 (-0.067230) | 0.289268 / 0.419271 (-0.130003) | 0.052167 / 0.043533 (0.008634) | 0.347559 / 0.255139 (0.092420) | 0.370908 / 0.283200 (0.087709) | 0.022540 / 0.141683 (-0.119142) | 1.486297 / 1.452155 (0.034143) | 1.576968 / 1.492716 (0.084252) |\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) | 0.237048 / 0.018006 (0.219042) | 0.452065 / 0.000490 (0.451575) | 0.013963 / 0.000200 (0.013763) | 0.000242 / 0.000054 (0.000188) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028084 / 0.037411 (-0.009327) | 0.081271 / 0.014526 (0.066745) | 0.096490 / 0.176557 (-0.080067) | 0.152106 / 0.737135 (-0.585030) | 0.096174 / 0.296338 (-0.200164) |\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) | 0.386585 / 0.215209 (0.171375) | 3.854996 / 2.077655 (1.777342) | 1.832898 / 1.504120 (0.328778) | 1.662832 / 1.541195 (0.121638) | 1.730753 / 1.468490 (0.262263) | 0.485286 / 4.584777 (-4.099491) | 3.571410 / 3.745712 (-0.174302) | 3.373035 / 5.269862 (-1.896826) | 1.995570 / 4.565676 (-2.570107) | 0.056711 / 0.424275 (-0.367564) | 0.007447 / 0.007607 (-0.000160) | 0.462985 / 0.226044 (0.236941) | 4.617186 / 2.268929 (2.348257) | 2.313915 / 55.444624 (-53.130709) | 1.961697 / 6.876477 (-4.914780) | 1.990410 / 2.142072 (-0.151662) | 0.580536 / 4.805227 (-4.224692) | 0.146275 / 6.500664 (-6.354389) | 0.059458 / 0.075469 (-0.016011) |\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) | 1.274841 / 1.841788 (-0.566947) | 18.641853 / 8.074308 (10.567545) | 13.977525 / 10.191392 (3.786133) | 0.151469 / 0.680424 (-0.528955) | 0.018111 / 0.534201 (-0.516090) | 0.393243 / 0.579283 (-0.186040) | 0.412310 / 0.434364 (-0.022054) | 0.461646 / 0.540337 (-0.078692) | 0.633016 / 1.386936 (-0.753920) |\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) | 0.006496 / 0.011353 (-0.004857) | 0.003973 / 0.011008 (-0.007035) | 0.064527 / 0.038508 (0.026019) | 0.069390 / 0.023109 (0.046281) | 0.401162 / 0.275898 (0.125264) | 0.431031 / 0.323480 (0.107551) | 0.005244 / 0.007986 (-0.002741) | 0.003283 / 0.004328 (-0.001046) | 0.064931 / 0.004250 (0.060680) | 0.054402 / 0.037052 (0.017350) | 0.397917 / 0.258489 (0.139428) | 0.436728 / 0.293841 (0.142887) | 0.031932 / 0.128546 (-0.096614) | 0.008557 / 0.075646 (-0.067089) | 0.073336 / 0.419271 (-0.345935) | 0.047559 / 0.043533 (0.004026) | 0.395825 / 0.255139 (0.140686) | 0.423002 / 0.283200 (0.139802) | 0.021708 / 0.141683 (-0.119975) | 1.501140 / 1.452155 (0.048985) | 1.558376 / 1.492716 (0.065660) |\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) | 0.289522 / 0.018006 (0.271516) | 0.449078 / 0.000490 (0.448589) | 0.034174 / 0.000200 (0.033974) | 0.000396 / 0.000054 (0.000342) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032533 / 0.037411 (-0.004878) | 0.093398 / 0.014526 (0.078872) | 0.106930 / 0.176557 (-0.069626) | 0.158743 / 0.737135 (-0.578393) | 0.106904 / 0.296338 (-0.189435) |\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) | 0.427479 / 0.215209 (0.212270) | 4.271758 / 2.077655 (2.194103) | 2.298770 / 1.504120 (0.794650) | 2.134906 / 1.541195 (0.593712) | 2.220487 / 1.468490 (0.751996) | 0.490506 / 4.584777 (-4.094270) | 3.593876 / 3.745712 (-0.151836) | 3.225656 / 5.269862 (-2.044205) | 2.004434 / 4.565676 (-2.561243) | 0.058015 / 0.424275 (-0.366260) | 0.007221 / 0.007607 (-0.000387) | 0.504928 / 0.226044 (0.278884) | 5.049547 / 2.268929 (2.780618) | 2.743843 / 55.444624 (-52.700781) | 2.398399 / 6.876477 (-4.478078) | 2.562939 / 2.142072 (0.420867) | 0.597229 / 4.805227 (-4.207998) | 0.134664 / 6.500664 (-6.366001) | 0.059612 / 0.075469 (-0.015857) |\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) | 1.369692 / 1.841788 (-0.472095) | 19.065326 / 8.074308 (10.991018) | 14.404508 / 10.191392 (4.213116) | 0.175809 / 0.680424 (-0.504615) | 0.020137 / 0.534201 (-0.514064) | 0.394043 / 0.579283 (-0.185240) | 0.424772 / 0.434364 (-0.009592) | 0.475587 / 0.540337 (-0.064751) | 0.644275 / 1.386936 (-0.742661) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#224977971accd63d97ba0a90cc108c4754055ebb \"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) | 0.007259 / 0.011353 (-0.004094) | 0.004396 / 0.011008 (-0.006612) | 0.096456 / 0.038508 (0.057948) | 0.078752 / 0.023109 (0.055643) | 0.359215 / 0.275898 (0.083317) | 0.396927 / 0.323480 (0.073448) | 0.005611 / 0.007986 (-0.002375) | 0.003687 / 0.004328 (-0.000641) | 0.072794 / 0.004250 (0.068544) | 0.059794 / 0.037052 (0.022741) | 0.372352 / 0.258489 (0.113863) | 0.414038 / 0.293841 (0.120197) | 0.034490 / 0.128546 (-0.094056) | 0.009790 / 0.075646 (-0.065857) | 0.326338 / 0.419271 (-0.092934) | 0.058582 / 0.043533 (0.015049) | 0.354221 / 0.255139 (0.099082) | 0.386669 / 0.283200 (0.103469) | 0.025356 / 0.141683 (-0.116327) | 1.664104 / 1.452155 (0.211950) | 1.766825 / 1.492716 (0.274108) |\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) | 0.251107 / 0.018006 (0.233101) | 0.478833 / 0.000490 (0.478344) | 0.010776 / 0.000200 (0.010577) | 0.000292 / 0.000054 (0.000238) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032869 / 0.037411 (-0.004543) | 0.098449 / 0.014526 (0.083923) | 0.109954 / 0.176557 (-0.066602) | 0.176786 / 0.737135 (-0.560350) | 0.113477 / 0.296338 (-0.182862) |\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) | 0.431169 / 0.215209 (0.215960) | 4.303239 / 2.077655 (2.225585) | 2.088885 / 1.504120 (0.584765) | 1.895900 / 1.541195 (0.354706) | 1.997442 / 1.468490 (0.528952) | 0.541840 / 4.584777 (-4.042937) | 3.991982 / 3.745712 (0.246270) | 3.842421 / 5.269862 (-1.427440) | 2.281150 / 4.565676 (-2.284526) | 0.063851 / 0.424275 (-0.360425) | 0.008470 / 0.007607 (0.000863) | 0.515886 / 0.226044 (0.289841) | 5.202908 / 2.268929 (2.933980) | 2.662789 / 55.444624 (-52.781835) | 2.266731 / 6.876477 (-4.609746) | 2.343760 / 2.142072 (0.201688) | 0.641050 / 4.805227 (-4.164177) | 0.148236 / 6.500664 (-6.352428) | 0.067422 / 0.075469 (-0.008047) |\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) | 1.475729 / 1.841788 (-0.366059) | 22.401583 / 8.074308 (14.327274) | 15.886237 / 10.191392 (5.694845) | 0.171828 / 0.680424 (-0.508595) | 0.022161 / 0.534201 (-0.512040) | 0.465873 / 0.579283 (-0.113411) | 0.476386 / 0.434364 (0.042022) | 0.538317 / 0.540337 (-0.002020) | 0.754375 / 1.386936 (-0.632561) |\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) | 0.007429 / 0.011353 (-0.003924) | 0.004592 / 0.011008 (-0.006416) | 0.072315 / 0.038508 (0.033807) | 0.080806 / 0.023109 (0.057697) | 0.444607 / 0.275898 (0.168709) | 0.476970 / 0.323480 (0.153490) | 0.006030 / 0.007986 (-0.001956) | 0.003755 / 0.004328 (-0.000573) | 0.074602 / 0.004250 (0.070352) | 0.061846 / 0.037052 (0.024794) | 0.450928 / 0.258489 (0.192439) | 0.493932 / 0.293841 (0.200091) | 0.037398 / 0.128546 (-0.091148) | 0.009807 / 0.075646 (-0.065840) | 0.080531 / 0.419271 (-0.338741) | 0.054052 / 0.043533 (0.010519) | 0.453034 / 0.255139 (0.197895) | 0.464959 / 0.283200 (0.181760) | 0.024718 / 0.141683 (-0.116965) | 1.687552 / 1.452155 (0.235397) | 1.765746 / 1.492716 (0.273029) |\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) | 0.266998 / 0.018006 (0.248992) | 0.479832 / 0.000490 (0.479342) | 0.005429 / 0.000200 (0.005229) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038885 / 0.037411 (0.001474) | 0.105931 / 0.014526 (0.091405) | 0.120880 / 0.176557 (-0.055677) | 0.184006 / 0.737135 (-0.553130) | 0.120750 / 0.296338 (-0.175589) |\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) | 0.478626 / 0.215209 (0.263417) | 4.797355 / 2.077655 (2.719700) | 2.582758 / 1.504120 (1.078638) | 2.396488 / 1.541195 (0.855293) | 2.515597 / 1.468490 (1.047107) | 0.544541 / 4.584777 (-4.040236) | 4.150702 / 3.745712 (0.404990) | 3.676837 / 5.269862 (-1.593024) | 2.287275 / 4.565676 (-2.278402) | 0.064602 / 0.424275 (-0.359673) | 0.008253 / 0.007607 (0.000646) | 0.576201 / 0.226044 (0.350157) | 5.859839 / 2.268929 (3.590910) | 3.248603 / 55.444624 (-52.196021) | 2.841959 / 6.876477 (-4.034518) | 2.991120 / 2.142072 (0.849047) | 0.667755 / 4.805227 (-4.137472) | 0.151219 / 6.500664 (-6.349445) | 0.068990 / 0.075469 (-0.006479) |\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) | 1.572359 / 1.841788 (-0.269429) | 21.890279 / 8.074308 (13.815971) | 15.927473 / 10.191392 (5.736081) | 0.170388 / 0.680424 (-0.510036) | 0.023282 / 0.534201 (-0.510919) | 0.459371 / 0.579283 (-0.119912) | 0.468838 / 0.434364 (0.034475) | 0.546438 / 0.540337 (0.006101) | 0.746912 / 1.386936 (-0.640024) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8197ce872d2e24bd1ffbb07213faea25078f1386 \"CML watermark\")\n",
"Yes, @lhoestq, you are right. I think we cross-send fixing PRs in a 15 minute interval... :sweat_smile: \r\n\r\nI would say the code in this PR is simpler and easier to understand, but feel free to ignore it.",
"I think the correct way it to check if \"file\" in in the tuple if it's a tuple (in case someone adds another protocol name for the local filesystem)"
] | 2023-10-23T09:29:17Z
| 2023-11-14T14:18:12Z
| 2023-11-14T14:17:40Z
|
MEMBER
| null | 0
|
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Fix #6333.
|
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| 1,837,335,721
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I_kwDODunzps5tg4Sp
| 6,122
|
Upload README via `push_to_hub`
|
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"You can use `huggingface_hub`'s [Card API](https://huggingface.co/docs/huggingface_hub/package_reference/cards) to programmatically push a dataset card to the Hub."
] | 2023-08-04T21:00:27Z
| 2023-08-21T18:18:54Z
| 2023-08-21T18:18:54Z
|
NONE
| null | null | null |
### Feature request
`push_to_hub` now allows users to upload datasets programmatically. However, based on the latest doc, we still need to open the dataset page to add readme file manually.
However, I do discover snippets to intialize a README for every `push_to_hub`:
```
dataset_card = (
DatasetCard(
"---\n"
+ str(dataset_card_data)
+ "\n---\n"
+ f'# Dataset Card for "{repo_id.split("/")[-1]}"\n\n[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)'
)
if dataset_card is None
else dataset_card
)
HfApi(endpoint=config.HF_ENDPOINT).upload_file(
path_or_fileobj=str(dataset_card).encode(),
path_in_repo="README.md",
repo_id=repo_id,
token=token,
repo_type="dataset",
revision=branch,
)
```
So, if we can enable `push_to_hub` to upload a readme file by ourselves instead of using the auto generated ones, it can save ton of time, and will definitely alleviate the current "lack-of-dataset-card" situation.
### Motivation
as elabrated above.
### Your contribution
I might be able to make a pr.
|
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| 1,922
|
How to update the "wino_bias" dataset
|
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[
"Hi @JieyuZhao !\r\n\r\nYou can edit the dataset card of wino_bias to update the URL via a Pull Request. This would be really appreciated :)\r\n\r\nThe dataset card is the README.md file you can find at https://github.com/huggingface/datasets/tree/master/datasets/wino_bias\r\nAlso the homepage url is also mentioned in the wino_bias.py so feel free to update it there as well.\r\n\r\nYou can create a Pull Request directly from the github interface by editing the files you want and submit a PR, or from a local clone of the repository.\r\n\r\nThanks for noticing !"
] | 2021-02-22T05:39:39Z
| 2021-02-22T10:35:59Z
| null |
CONTRIBUTOR
| null | null | null |
Hi all,
Thanks for the efforts to collect all the datasets! But I think there is a problem with the wino_bias dataset. The current link is not correct. How can I update that?
Thanks!
|
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CI is broken: ModuleNotFoundError: jax requires jaxlib to be installed
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[] | 2023-03-23T09:39:43Z
| 2023-03-23T10:09:55Z
| 2023-03-23T10:09:55Z
|
MEMBER
| null | null | null |
CI test_py310 is broken: see https://github.com/huggingface/datasets/actions/runs/4498945505/jobs/7916194236?pr=5662
```
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_in_memory - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_jax_on_disk - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_audio - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_device - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_image - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/test_formatting.py::FormatterTest::test_jax_formatter_jnp_array_kwargs - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
FAILED tests/features/test_features.py::CastToPythonObjectsTest::test_cast_to_python_objects_jax - ModuleNotFoundError: jax requires jaxlib to be installed. See https://github.com/google/jax#installation for installation instructions.
===== 8 failed, 2147 passed, 10 skipped, 37 warnings in 228.69s (0:03:48) ======
```
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Finish deprecating the fs argument
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Thanks for the deprecation. Some minor suggested fixes below...\r\n> \r\n> Also note that the corresponding tests should be updated as well.\r\n\r\nThanks for the suggestions/typo fixes. I updated the failing test - passing locally now",
"Nice thanks !\r\n\r\nI believe you also need to update `_load_info` and `_save_info` in `builder.py` - they're still passing `fs=self._fs` instead of `storage_options=self._fs.storage_options`\r\n\r\nThis should remove the remaining warnings in the CI such as \r\n\r\n```python\r\ntests/test_builder.py::test_builder_with_filesystem_download_and_prepare_reload\r\ntests/test_load.py::test_load_dataset_local[False]\r\ntests/test_load.py::test_load_dataset_local[True]\r\ntests/test_load.py::test_load_dataset_zip_csv[csv_path-False]\r\ntests/test_load.py::test_load_dataset_then_move_then_reload\r\n /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/info.py:344: FutureWarning: 'fs' was deprecated in favor of 'storage_options' in version 2.9.0 and will be removed in 3.0.0.\r\n You can remove this warning by passing 'storage_options=fs.storage_options' instead.\r\n```",
"re: docstring, I assume passing in `storage_options=s3.storage_options` is correct/necessary to pass the secrets?",
"what about \r\nhttps://github.com/huggingface/datasets/blob/5b793dd8c43bf6e85f165238becb3c64f6cd3ed0/src/datasets/filesystems/__init__.py#L43-L54\r\nleave as is? Is this function no longer necessary?",
"<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) | 0.008877 / 0.011353 (-0.002475) | 0.004725 / 0.011008 (-0.006283) | 0.100738 / 0.038508 (0.062230) | 0.030251 / 0.023109 (0.007141) | 0.301483 / 0.275898 (0.025585) | 0.374161 / 0.323480 (0.050681) | 0.007225 / 0.007986 (-0.000761) | 0.003654 / 0.004328 (-0.000674) | 0.078400 / 0.004250 (0.074149) | 0.035786 / 0.037052 (-0.001267) | 0.309744 / 0.258489 (0.051255) | 0.355834 / 0.293841 (0.061994) | 0.034344 / 0.128546 (-0.094202) | 0.011584 / 0.075646 (-0.064062) | 0.321462 / 0.419271 (-0.097810) | 0.041201 / 0.043533 (-0.002332) | 0.298808 / 0.255139 (0.043669) | 0.332626 / 0.283200 (0.049426) | 0.089131 / 0.141683 (-0.052552) | 1.477888 / 1.452155 (0.025734) | 1.530365 / 1.492716 (0.037649) |\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) | 0.191647 / 0.018006 (0.173640) | 0.424339 / 0.000490 (0.423849) | 0.002941 / 0.000200 (0.002741) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023442 / 0.037411 (-0.013969) | 0.097264 / 0.014526 (0.082738) | 0.105655 / 0.176557 (-0.070901) | 0.145055 / 0.737135 (-0.592081) | 0.108750 / 0.296338 (-0.187588) |\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) | 0.422925 / 0.215209 (0.207716) | 4.216022 / 2.077655 (2.138367) | 1.876441 / 1.504120 (0.372322) | 1.665115 / 1.541195 (0.123920) | 1.711105 / 1.468490 (0.242615) | 0.701820 / 4.584777 (-3.882957) | 3.389319 / 3.745712 (-0.356393) | 1.909868 / 5.269862 (-3.359994) | 1.270482 / 4.565676 (-3.295195) | 0.083680 / 0.424275 (-0.340595) | 0.012347 / 0.007607 (0.004740) | 0.531076 / 0.226044 (0.305031) | 5.344045 / 2.268929 (3.075117) | 2.310897 / 55.444624 (-53.133728) | 1.971953 / 6.876477 (-4.904524) | 2.113748 / 2.142072 (-0.028325) | 0.823766 / 4.805227 (-3.981462) | 0.150864 / 6.500664 (-6.349800) | 0.066263 / 0.075469 (-0.009206) |\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) | 1.253190 / 1.841788 (-0.588598) | 13.757887 / 8.074308 (5.683579) | 13.888195 / 10.191392 (3.696803) | 0.137285 / 0.680424 (-0.543139) | 0.029151 / 0.534201 (-0.505050) | 0.387402 / 0.579283 (-0.191881) | 0.401673 / 0.434364 (-0.032691) | 0.450474 / 0.540337 (-0.089863) | 0.533757 / 1.386936 (-0.853179) |\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) | 0.006919 / 0.011353 (-0.004434) | 0.004655 / 0.011008 (-0.006353) | 0.096946 / 0.038508 (0.058438) | 0.028697 / 0.023109 (0.005588) | 0.420020 / 0.275898 (0.144122) | 0.460193 / 0.323480 (0.136713) | 0.005189 / 0.007986 (-0.002796) | 0.003425 / 0.004328 (-0.000904) | 0.074900 / 0.004250 (0.070649) | 0.041844 / 0.037052 (0.004792) | 0.421538 / 0.258489 (0.163049) | 0.468497 / 0.293841 (0.174656) | 0.032573 / 0.128546 (-0.095973) | 0.011731 / 0.075646 (-0.063916) | 0.320221 / 0.419271 (-0.099050) | 0.042113 / 0.043533 (-0.001420) | 0.422757 / 0.255139 (0.167618) | 0.445372 / 0.283200 (0.162172) | 0.090300 / 0.141683 (-0.051383) | 1.458598 / 1.452155 (0.006443) | 1.550060 / 1.492716 (0.057344) |\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) | 0.235489 / 0.018006 (0.217483) | 0.418207 / 0.000490 (0.417718) | 0.002511 / 0.000200 (0.002311) | 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) | 0.025603 / 0.037411 (-0.011808) | 0.100237 / 0.014526 (0.085711) | 0.108617 / 0.176557 (-0.067939) | 0.148417 / 0.737135 (-0.588719) | 0.110163 / 0.296338 (-0.186176) |\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) | 0.474804 / 0.215209 (0.259595) | 4.745370 / 2.077655 (2.667715) | 2.417819 / 1.504120 (0.913699) | 2.209892 / 1.541195 (0.668697) | 2.263296 / 1.468490 (0.794806) | 0.695537 / 4.584777 (-3.889240) | 3.381028 / 3.745712 (-0.364684) | 2.952271 / 5.269862 (-2.317591) | 1.507041 / 4.565676 (-3.058636) | 0.083334 / 0.424275 (-0.340941) | 0.012554 / 0.007607 (0.004947) | 0.578861 / 0.226044 (0.352817) | 5.795241 / 2.268929 (3.526313) | 2.858544 / 55.444624 (-52.586080) | 2.516270 / 6.876477 (-4.360207) | 2.557350 / 2.142072 (0.415278) | 0.801799 / 4.805227 (-4.003428) | 0.151579 / 6.500664 (-6.349085) | 0.068765 / 0.075469 (-0.006704) |\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) | 1.279935 / 1.841788 (-0.561853) | 14.049065 / 8.074308 (5.974757) | 13.972703 / 10.191392 (3.781311) | 0.140551 / 0.680424 (-0.539873) | 0.016831 / 0.534201 (-0.517370) | 0.383886 / 0.579283 (-0.195397) | 0.385661 / 0.434364 (-0.048703) | 0.444525 / 0.540337 (-0.095813) | 0.532197 / 1.386936 (-0.854739) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8d206848fb7afeafecf2a2581ca9a332bdedefa9 \"CML watermark\")\n"
] | 2022-12-28T15:33:17Z
| 2023-01-18T12:42:33Z
| 2023-01-18T12:35:32Z
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See #5385 for some discussion on this
The `fs=` arg was depcrecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in `2.8.0` (to be removed in `3.0.0`). There are a few other places where the `fs=` arg was still used (functions/methods in `datasets.info` and `datasets.load`). This PR adds a similar behavior, warnings and the `storage_options=` arg to these functions and methods.
One question: should the "deprecated" / "added" versions be `2.8.1` for the docs/warnings on these? Right now I'm going with "fs was deprecated in 2.8.0" but "storage_options= was added in 2.8.1" where appropriate.
@mariosasko
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Streaming Datasets don't work with Transformers Trainer when dataloader_num_workers>1
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"Hi, thanks for reporting. This could be related to https://github.com/huggingface/datasets/issues/3148 too\r\n\r\nWe should definitely make `TorchIterableDataset` picklable by moving it in the main code instead of inside a function. If you'd like to contribute, feel free to open a Pull Request :)\r\n\r\nI'm also taking a look at your second issue, which is more technical"
] | 2022-03-16T21:14:11Z
| 2022-06-10T20:47:26Z
| 2022-06-10T20:47:26Z
|
NONE
| null | null | null |
## Describe the bug
Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.
## Steps to reproduce the bug
```python
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("torch")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```
## Expected results
For this code I'd expect a crash related to not having preprocessed the data, but instead we get a pickling error.
## Actual results
```
0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 7, in <module>
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
for step, inputs in enumerate(epoch_iterator):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
return self._get_iterator()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 918, in __init__
w.start()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'iterable_dataset.<locals>.TorchIterableDataset'
0%| | 0/1000 [00:00<?, ?it/s]
```
This immediate crash can be fixed by not using a local class to make the `TorchIterableDataset` (Note that you have to do with_format("torch") or you get an exception because the dataset has no len) However, any lambdas etc used as maps will also trigger this crash. A more permanent fix would be to move away from multiprocessing and instead use something like pathos or multiprocessing_on_dill (https://stackoverflow.com/questions/19984152/what-can-multiprocessing-and-dill-do-together)
Note that if you bypass this crash you get another crash. (I'll file a separate bug).
## Environment info
- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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Add support for ".txm" format
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"Neat! Looks like you need a rebase and then should be good to go :) ",
"Done, @yjernite, @lhoestq.",
"If you agree, we could merge this.",
"Hi ! yes sure :) can you just merge master into this branch before we merge ?",
"Done @lhoestq "
] | 2020-12-03T06:52:08Z
| 2021-02-21T19:47:11Z
| 2021-02-21T19:47:11Z
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In dummy data generation, add support for XML-like ".txm" file format.
Also support filenames with additional compression extension: ".txm.gz".
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Release: 2.6.2
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| 2022-11-22T17:47:02Z
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[DOC] Mistaken docs on set_format
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CONTRIBUTOR
| null | null | null |
### Describe the bug
https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format
<img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png">
While actually running it will result in:
<img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png">
### Steps to reproduce the bug
_
### Expected behavior
_
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
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Use new hffs
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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) | 0.006665 / 0.011353 (-0.004688) | 0.004376 / 0.011008 (-0.006633) | 0.085529 / 0.038508 (0.047021) | 0.076372 / 0.023109 (0.053263) | 0.310019 / 0.275898 (0.034121) | 0.341404 / 0.323480 (0.017924) | 0.005666 / 0.007986 (-0.002320) | 0.003763 / 0.004328 (-0.000566) | 0.064678 / 0.004250 (0.060427) | 0.059283 / 0.037052 (0.022231) | 0.316194 / 0.258489 (0.057704) | 0.349397 / 0.293841 (0.055557) | 0.031199 / 0.128546 (-0.097347) | 0.008724 / 0.075646 (-0.066923) | 0.300236 / 0.419271 (-0.119035) | 0.068872 / 0.043533 (0.025339) | 0.308521 / 0.255139 (0.053382) | 0.331292 / 0.283200 (0.048092) | 0.028236 / 0.141683 (-0.113447) | 1.501365 / 1.452155 (0.049211) | 1.554334 / 1.492716 (0.061618) |\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) | 0.238291 / 0.018006 (0.220285) | 0.565069 / 0.000490 (0.564580) | 0.001626 / 0.000200 (0.001426) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029777 / 0.037411 (-0.007634) | 0.082873 / 0.014526 (0.068347) | 0.099619 / 0.176557 (-0.076937) | 0.156572 / 0.737135 (-0.580563) | 0.099887 / 0.296338 (-0.196452) |\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) | 0.401017 / 0.215209 (0.185808) | 3.827192 / 2.077655 (1.749537) | 1.861554 / 1.504120 (0.357434) | 1.699869 / 1.541195 (0.158674) | 1.720043 / 1.468490 (0.251553) | 0.486757 / 4.584777 (-4.098020) | 3.638125 / 3.745712 (-0.107587) | 5.844959 / 5.269862 (0.575097) | 3.454901 / 4.565676 (-1.110775) | 0.057650 / 0.424275 (-0.366625) | 0.007341 / 0.007607 (-0.000266) | 0.462698 / 0.226044 (0.236654) | 4.633472 / 2.268929 (2.364544) | 2.287607 / 55.444624 (-53.157017) | 2.057318 / 6.876477 (-4.819159) | 2.203657 / 2.142072 (0.061584) | 0.598136 / 4.805227 (-4.207091) | 0.134012 / 6.500664 (-6.366653) | 0.060824 / 0.075469 (-0.014645) |\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) | 1.277752 / 1.841788 (-0.564036) | 20.013398 / 8.074308 (11.939089) | 14.372993 / 10.191392 (4.181601) | 0.169991 / 0.680424 (-0.510433) | 0.018344 / 0.534201 (-0.515857) | 0.396985 / 0.579283 (-0.182299) | 0.416289 / 0.434364 (-0.018075) | 0.458658 / 0.540337 (-0.081680) | 0.692980 / 1.386936 (-0.693956) |\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) | 0.006689 / 0.011353 (-0.004664) | 0.004393 / 0.011008 (-0.006615) | 0.064069 / 0.038508 (0.025561) | 0.080717 / 0.023109 (0.057607) | 0.370090 / 0.275898 (0.094191) | 0.400432 / 0.323480 (0.076952) | 0.005613 / 0.007986 (-0.002372) | 0.003641 / 0.004328 (-0.000687) | 0.064771 / 0.004250 (0.060520) | 0.057555 / 0.037052 (0.020502) | 0.392156 / 0.258489 (0.133667) | 0.409842 / 0.293841 (0.116001) | 0.031500 / 0.128546 (-0.097047) | 0.008786 / 0.075646 (-0.066860) | 0.070342 / 0.419271 (-0.348929) | 0.048646 / 0.043533 (0.005113) | 0.360914 / 0.255139 (0.105775) | 0.387626 / 0.283200 (0.104426) | 0.022787 / 0.141683 (-0.118896) | 1.508915 / 1.452155 (0.056761) | 1.539719 / 1.492716 (0.047002) |\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) | 0.257985 / 0.018006 (0.239979) | 0.550990 / 0.000490 (0.550501) | 0.000407 / 0.000200 (0.000207) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030183 / 0.037411 (-0.007228) | 0.086882 / 0.014526 (0.072356) | 0.102382 / 0.176557 (-0.074175) | 0.154745 / 0.737135 (-0.582390) | 0.104008 / 0.296338 (-0.192331) |\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) | 0.426284 / 0.215209 (0.211075) | 4.240812 / 2.077655 (2.163158) | 2.261240 / 1.504120 (0.757120) | 2.085905 / 1.541195 (0.544710) | 2.160374 / 1.468490 (0.691883) | 0.481126 / 4.584777 (-4.103651) | 3.516234 / 3.745712 (-0.229478) | 3.325322 / 5.269862 (-1.944539) | 2.043307 / 4.565676 (-2.522369) | 0.056663 / 0.424275 (-0.367612) | 0.007786 / 0.007607 (0.000179) | 0.497614 / 0.226044 (0.271570) | 4.974529 / 2.268929 (2.705600) | 2.700018 / 55.444624 (-52.744606) | 2.393778 / 6.876477 (-4.482699) | 2.628202 / 2.142072 (0.486130) | 0.594316 / 4.805227 (-4.210911) | 0.147092 / 6.500664 (-6.353572) | 0.062207 / 0.075469 (-0.013262) |\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) | 1.315676 / 1.841788 (-0.526112) | 20.749251 / 8.074308 (12.674943) | 14.371553 / 10.191392 (4.180160) | 0.170249 / 0.680424 (-0.510175) | 0.018478 / 0.534201 (-0.515722) | 0.395710 / 0.579283 (-0.183573) | 0.409706 / 0.434364 (-0.024658) | 0.463454 / 0.540337 (-0.076884) | 0.615657 / 1.386936 (-0.771279) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5a752d8e8ca0a6ed118b024ba03c1b4a2881177 \"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) | 0.007224 / 0.011353 (-0.004129) | 0.004506 / 0.011008 (-0.006503) | 0.096729 / 0.038508 (0.058221) | 0.082394 / 0.023109 (0.059284) | 0.390954 / 0.275898 (0.115056) | 0.416647 / 0.323480 (0.093167) | 0.005894 / 0.007986 (-0.002092) | 0.003756 / 0.004328 (-0.000572) | 0.075800 / 0.004250 (0.071549) | 0.062683 / 0.037052 (0.025631) | 0.398959 / 0.258489 (0.140470) | 0.436624 / 0.293841 (0.142783) | 0.034650 / 0.128546 (-0.093896) | 0.009655 / 0.075646 (-0.065991) | 0.315761 / 0.419271 (-0.103511) | 0.060957 / 0.043533 (0.017424) | 0.385649 / 0.255139 (0.130510) | 0.394022 / 0.283200 (0.110822) | 0.024601 / 0.141683 (-0.117082) | 1.729586 / 1.452155 (0.277431) | 1.724153 / 1.492716 (0.231437) |\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) | 0.207070 / 0.018006 (0.189063) | 0.466502 / 0.000490 (0.466012) | 0.010739 / 0.000200 (0.010540) | 0.000214 / 0.000054 (0.000160) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031633 / 0.037411 (-0.005779) | 0.095345 / 0.014526 (0.080819) | 0.105399 / 0.176557 (-0.071157) | 0.174173 / 0.737135 (-0.562962) | 0.104207 / 0.296338 (-0.192132) |\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) | 0.435312 / 0.215209 (0.220103) | 4.265600 / 2.077655 (2.187946) | 2.056500 / 1.504120 (0.552380) | 1.848023 / 1.541195 (0.306828) | 1.946156 / 1.468490 (0.477666) | 0.557788 / 4.584777 (-4.026989) | 4.070289 / 3.745712 (0.324577) | 3.608027 / 5.269862 (-1.661835) | 2.214556 / 4.565676 (-2.351121) | 0.062623 / 0.424275 (-0.361652) | 0.008083 / 0.007607 (0.000476) | 0.491782 / 0.226044 (0.265738) | 4.989963 / 2.268929 (2.721035) | 2.575867 / 55.444624 (-52.868757) | 2.208045 / 6.876477 (-4.668431) | 2.364184 / 2.142072 (0.222112) | 0.633925 / 4.805227 (-4.171302) | 0.144323 / 6.500664 (-6.356341) | 0.067505 / 0.075469 (-0.007965) |\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) | 1.467219 / 1.841788 (-0.374569) | 22.334967 / 8.074308 (14.260659) | 15.715747 / 10.191392 (5.524355) | 0.175443 / 0.680424 (-0.504980) | 0.026165 / 0.534201 (-0.508036) | 0.490675 / 0.579283 (-0.088608) | 0.509211 / 0.434364 (0.074847) | 0.586303 / 0.540337 (0.045965) | 0.785052 / 1.386936 (-0.601884) |\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) | 0.007893 / 0.011353 (-0.003460) | 0.004577 / 0.011008 (-0.006431) | 0.075781 / 0.038508 (0.037273) | 0.095492 / 0.023109 (0.072382) | 0.433259 / 0.275898 (0.157361) | 0.469386 / 0.323480 (0.145906) | 0.006317 / 0.007986 (-0.001669) | 0.003708 / 0.004328 (-0.000621) | 0.074417 / 0.004250 (0.070167) | 0.068605 / 0.037052 (0.031552) | 0.448701 / 0.258489 (0.190212) | 0.469131 / 0.293841 (0.175290) | 0.036647 / 0.128546 (-0.091899) | 0.010077 / 0.075646 (-0.065570) | 0.082457 / 0.419271 (-0.336815) | 0.063255 / 0.043533 (0.019722) | 0.428144 / 0.255139 (0.173005) | 0.451872 / 0.283200 (0.168672) | 0.033953 / 0.141683 (-0.107730) | 1.781752 / 1.452155 (0.329597) | 1.869014 / 1.492716 (0.376297) |\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) | 0.223596 / 0.018006 (0.205590) | 0.470307 / 0.000490 (0.469818) | 0.005059 / 0.000200 (0.004859) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038804 / 0.037411 (0.001393) | 0.117879 / 0.014526 (0.103353) | 0.140701 / 0.176557 (-0.035855) | 0.194672 / 0.737135 (-0.542463) | 0.132806 / 0.296338 (-0.163533) |\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) | 0.510109 / 0.215209 (0.294900) | 4.729457 / 2.077655 (2.651803) | 2.512113 / 1.504120 (1.007993) | 2.302553 / 1.541195 (0.761358) | 2.420462 / 1.468490 (0.951972) | 0.531682 / 4.584777 (-4.053095) | 4.061208 / 3.745712 (0.315496) | 3.588542 / 5.269862 (-1.681320) | 2.203187 / 4.565676 (-2.362489) | 0.065791 / 0.424275 (-0.358484) | 0.008839 / 0.007607 (0.001232) | 0.562041 / 0.226044 (0.335997) | 5.702340 / 2.268929 (3.433412) | 3.127609 / 55.444624 (-52.317015) | 2.823060 / 6.876477 (-4.053417) | 2.898675 / 2.142072 (0.756603) | 0.659589 / 4.805227 (-4.145638) | 0.148798 / 6.500664 (-6.351866) | 0.070787 / 0.075469 (-0.004682) |\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) | 1.478317 / 1.841788 (-0.363471) | 21.995400 / 8.074308 (13.921092) | 16.770729 / 10.191392 (6.579337) | 0.226333 / 0.680424 (-0.454091) | 0.021835 / 0.534201 (-0.512366) | 0.460373 / 0.579283 (-0.118910) | 0.479494 / 0.434364 (0.045130) | 0.529470 / 0.540337 (-0.010868) | 0.718066 / 1.386936 (-0.668870) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9a717b8eb80b0e50b25818127f79a35e0866fb14 \"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) | 0.007824 / 0.011353 (-0.003529) | 0.004601 / 0.011008 (-0.006407) | 0.100025 / 0.038508 (0.061517) | 0.096046 / 0.023109 (0.072936) | 0.376226 / 0.275898 (0.100328) | 0.410905 / 0.323480 (0.087425) | 0.006048 / 0.007986 (-0.001938) | 0.003817 / 0.004328 (-0.000511) | 0.076624 / 0.004250 (0.072374) | 0.066390 / 0.037052 (0.029338) | 0.380098 / 0.258489 (0.121609) | 0.413603 / 0.293841 (0.119762) | 0.036546 / 0.128546 (-0.092001) | 0.009881 / 0.075646 (-0.065765) | 0.344338 / 0.419271 (-0.074934) | 0.061882 / 0.043533 (0.018350) | 0.368568 / 0.255139 (0.113429) | 0.397133 / 0.283200 (0.113934) | 0.027255 / 0.141683 (-0.114428) | 1.795099 / 1.452155 (0.342945) | 1.852443 / 1.492716 (0.359727) |\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) | 0.247436 / 0.018006 (0.229430) | 0.494119 / 0.000490 (0.493629) | 0.004359 / 0.000200 (0.004159) | 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) | 0.034765 / 0.037411 (-0.002647) | 0.104541 / 0.014526 (0.090015) | 0.113898 / 0.176557 (-0.062659) | 0.183634 / 0.737135 (-0.553501) | 0.116423 / 0.296338 (-0.179916) |\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) | 0.458747 / 0.215209 (0.243538) | 4.555740 / 2.077655 (2.478085) | 2.217240 / 1.504120 (0.713121) | 2.039879 / 1.541195 (0.498684) | 2.088581 / 1.468490 (0.620091) | 0.588063 / 4.584777 (-3.996714) | 4.238226 / 3.745712 (0.492514) | 4.768060 / 5.269862 (-0.501802) | 2.857117 / 4.565676 (-1.708560) | 0.068742 / 0.424275 (-0.355533) | 0.008667 / 0.007607 (0.001059) | 0.549294 / 0.226044 (0.323249) | 5.464635 / 2.268929 (3.195706) | 2.744435 / 55.444624 (-52.700189) | 2.347660 / 6.876477 (-4.528816) | 2.616816 / 2.142072 (0.474743) | 0.703701 / 4.805227 (-4.101526) | 0.159749 / 6.500664 (-6.340915) | 0.071990 / 0.075469 (-0.003479) |\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) | 1.486599 / 1.841788 (-0.355188) | 22.745438 / 8.074308 (14.671130) | 16.822332 / 10.191392 (6.630940) | 0.184730 / 0.680424 (-0.495694) | 0.021267 / 0.534201 (-0.512934) | 0.467108 / 0.579283 (-0.112176) | 0.472674 / 0.434364 (0.038311) | 0.548094 / 0.540337 (0.007756) | 0.735885 / 1.386936 (-0.651051) |\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) | 0.007746 / 0.011353 (-0.003607) | 0.004585 / 0.011008 (-0.006423) | 0.076943 / 0.038508 (0.038435) | 0.087473 / 0.023109 (0.064363) | 0.480099 / 0.275898 (0.204201) | 0.495271 / 0.323480 (0.171791) | 0.006348 / 0.007986 (-0.001638) | 0.003902 / 0.004328 (-0.000426) | 0.077586 / 0.004250 (0.073335) | 0.066467 / 0.037052 (0.029415) | 0.468741 / 0.258489 (0.210252) | 0.506778 / 0.293841 (0.212937) | 0.036877 / 0.128546 (-0.091669) | 0.010102 / 0.075646 (-0.065545) | 0.084419 / 0.419271 (-0.334852) | 0.058721 / 0.043533 (0.015188) | 0.453633 / 0.255139 (0.198494) | 0.481171 / 0.283200 (0.197971) | 0.028716 / 0.141683 (-0.112967) | 1.853048 / 1.452155 (0.400893) | 1.885847 / 1.492716 (0.393130) |\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) | 0.192136 / 0.018006 (0.174130) | 0.484481 / 0.000490 (0.483991) | 0.002951 / 0.000200 (0.002751) | 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) | 0.037949 / 0.037411 (0.000538) | 0.108364 / 0.014526 (0.093838) | 0.119542 / 0.176557 (-0.057014) | 0.188542 / 0.737135 (-0.548593) | 0.122011 / 0.296338 (-0.174327) |\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) | 0.483135 / 0.215209 (0.267926) | 4.849715 / 2.077655 (2.772060) | 2.497736 / 1.504120 (0.993616) | 2.314243 / 1.541195 (0.773048) | 2.412739 / 1.468490 (0.944249) | 0.564137 / 4.584777 (-4.020639) | 4.242273 / 3.745712 (0.496561) | 6.337843 / 5.269862 (1.067982) | 3.923250 / 4.565676 (-0.642426) | 0.066464 / 0.424275 (-0.357811) | 0.009217 / 0.007607 (0.001610) | 0.575667 / 0.226044 (0.349623) | 5.746187 / 2.268929 (3.477258) | 3.069655 / 55.444624 (-52.374969) | 2.674798 / 6.876477 (-4.201679) | 2.956535 / 2.142072 (0.814463) | 0.701043 / 4.805227 (-4.104185) | 0.157241 / 6.500664 (-6.343423) | 0.073175 / 0.075469 (-0.002294) |\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) | 1.609943 / 1.841788 (-0.231844) | 23.478594 / 8.074308 (15.404286) | 17.454437 / 10.191392 (7.263045) | 0.186422 / 0.680424 (-0.494002) | 0.021703 / 0.534201 (-0.512498) | 0.471704 / 0.579283 (-0.107579) | 0.480553 / 0.434364 (0.046189) | 0.552881 / 0.540337 (0.012544) | 0.722515 / 1.386936 (-0.664421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84645f80049cd00d9e0d4908faf3c3203fdcf21d \"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) | 0.007542 / 0.011353 (-0.003811) | 0.004692 / 0.011008 (-0.006316) | 0.099155 / 0.038508 (0.060647) | 0.089365 / 0.023109 (0.066256) | 0.370870 / 0.275898 (0.094972) | 0.422152 / 0.323480 (0.098673) | 0.006223 / 0.007986 (-0.001763) | 0.003852 / 0.004328 (-0.000476) | 0.075438 / 0.004250 (0.071188) | 0.065973 / 0.037052 (0.028921) | 0.381513 / 0.258489 (0.123024) | 0.416196 / 0.293841 (0.122355) | 0.035483 / 0.128546 (-0.093063) | 0.009884 / 0.075646 (-0.065762) | 0.341290 / 0.419271 (-0.077982) | 0.060546 / 0.043533 (0.017014) | 0.365101 / 0.255139 (0.109962) | 0.391058 / 0.283200 (0.107859) | 0.026325 / 0.141683 (-0.115358) | 1.815168 / 1.452155 (0.363013) | 1.834711 / 1.492716 (0.341994) |\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) | 0.222177 / 0.018006 (0.204171) | 0.501151 / 0.000490 (0.500662) | 0.010202 / 0.000200 (0.010002) | 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) | 0.034043 / 0.037411 (-0.003368) | 0.097884 / 0.014526 (0.083358) | 0.114022 / 0.176557 (-0.062534) | 0.186200 / 0.737135 (-0.550935) | 0.115555 / 0.296338 (-0.180783) |\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) | 0.485857 / 0.215209 (0.270648) | 4.959263 / 2.077655 (2.881608) | 2.501085 / 1.504120 (0.996965) | 2.234660 / 1.541195 (0.693465) | 2.238585 / 1.468490 (0.770095) | 0.645431 / 4.584777 (-3.939345) | 4.434311 / 3.745712 (0.688599) | 4.771491 / 5.269862 (-0.498371) | 2.778963 / 4.565676 (-1.786714) | 0.075615 / 0.424275 (-0.348660) | 0.009502 / 0.007607 (0.001895) | 0.546539 / 0.226044 (0.320495) | 5.464242 / 2.268929 (3.195314) | 2.894101 / 55.444624 (-52.550524) | 2.513761 / 6.876477 (-4.362715) | 2.719843 / 2.142072 (0.577770) | 0.678828 / 4.805227 (-4.126399) | 0.157839 / 6.500664 (-6.342825) | 0.071305 / 0.075469 (-0.004164) |\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) | 1.496879 / 1.841788 (-0.344909) | 22.214452 / 8.074308 (14.140144) | 17.707541 / 10.191392 (7.516149) | 0.197008 / 0.680424 (-0.483416) | 0.024883 / 0.534201 (-0.509318) | 0.493611 / 0.579283 (-0.085672) | 0.500677 / 0.434364 (0.066313) | 0.569381 / 0.540337 (0.029044) | 0.773950 / 1.386936 (-0.612986) |\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) | 0.007337 / 0.011353 (-0.004015) | 0.004572 / 0.011008 (-0.006436) | 0.091123 / 0.038508 (0.052615) | 0.079762 / 0.023109 (0.056652) | 0.450527 / 0.275898 (0.174629) | 0.525097 / 0.323480 (0.201617) | 0.005873 / 0.007986 (-0.002112) | 0.003797 / 0.004328 (-0.000532) | 0.076259 / 0.004250 (0.072009) | 0.062745 / 0.037052 (0.025692) | 0.465553 / 0.258489 (0.207064) | 0.546026 / 0.293841 (0.252186) | 0.035638 / 0.128546 (-0.092909) | 0.010086 / 0.075646 (-0.065560) | 0.109269 / 0.419271 (-0.310002) | 0.056765 / 0.043533 (0.013233) | 0.440887 / 0.255139 (0.185748) | 0.513325 / 0.283200 (0.230125) | 0.027206 / 0.141683 (-0.114476) | 1.863564 / 1.452155 (0.411409) | 1.918206 / 1.492716 (0.425490) |\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) | 0.266479 / 0.018006 (0.248473) | 0.487971 / 0.000490 (0.487481) | 0.012246 / 0.000200 (0.012046) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035281 / 0.037411 (-0.002130) | 0.102991 / 0.014526 (0.088465) | 0.114638 / 0.176557 (-0.061919) | 0.184117 / 0.737135 (-0.553018) | 0.117943 / 0.296338 (-0.178396) |\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) | 0.497897 / 0.215209 (0.282688) | 4.973806 / 2.077655 (2.896151) | 2.596146 / 1.504120 (1.092026) | 2.419694 / 1.541195 (0.878499) | 2.525784 / 1.468490 (1.057294) | 0.568021 / 4.584777 (-4.016756) | 4.296431 / 3.745712 (0.550719) | 3.690682 / 5.269862 (-1.579179) | 2.345965 / 4.565676 (-2.219712) | 0.066859 / 0.424275 (-0.357416) | 0.009093 / 0.007607 (0.001486) | 0.582616 / 0.226044 (0.356571) | 5.826528 / 2.268929 (3.557600) | 3.253222 / 55.444624 (-52.191403) | 2.798447 / 6.876477 (-4.078030) | 3.054609 / 2.142072 (0.912537) | 0.678816 / 4.805227 (-4.126411) | 0.157966 / 6.500664 (-6.342698) | 0.073797 / 0.075469 (-0.001672) |\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) | 1.599480 / 1.841788 (-0.242308) | 23.249738 / 8.074308 (15.175430) | 16.965406 / 10.191392 (6.774014) | 0.171390 / 0.680424 (-0.509034) | 0.021810 / 0.534201 (-0.512391) | 0.483339 / 0.579283 (-0.095944) | 0.496615 / 0.434364 (0.062251) | 0.583786 / 0.540337 (0.043448) | 0.741699 / 1.386936 (-0.645237) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7935cd2e564f5d1c66ed1acf731703724ba7a287 \"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) | 0.006054 / 0.011353 (-0.005299) | 0.003706 / 0.011008 (-0.007302) | 0.080060 / 0.038508 (0.041552) | 0.061479 / 0.023109 (0.038370) | 0.327981 / 0.275898 (0.052083) | 0.356930 / 0.323480 (0.033450) | 0.004671 / 0.007986 (-0.003315) | 0.002901 / 0.004328 (-0.001428) | 0.062425 / 0.004250 (0.058174) | 0.046310 / 0.037052 (0.009258) | 0.323657 / 0.258489 (0.065168) | 0.370130 / 0.293841 (0.076289) | 0.027151 / 0.128546 (-0.101395) | 0.007850 / 0.075646 (-0.067797) | 0.262300 / 0.419271 (-0.156971) | 0.045456 / 0.043533 (0.001923) | 0.325569 / 0.255139 (0.070430) | 0.352962 / 0.283200 (0.069762) | 0.020156 / 0.141683 (-0.121527) | 1.429404 / 1.452155 (-0.022750) | 1.615032 / 1.492716 (0.122316) |\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) | 0.187309 / 0.018006 (0.169303) | 0.428848 / 0.000490 (0.428358) | 0.003599 / 0.000200 (0.003399) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023260 / 0.037411 (-0.014151) | 0.072467 / 0.014526 (0.057941) | 0.082398 / 0.176557 (-0.094159) | 0.142573 / 0.737135 (-0.594562) | 0.082570 / 0.296338 (-0.213768) |\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) | 0.426503 / 0.215209 (0.211294) | 4.267875 / 2.077655 (2.190220) | 2.189762 / 1.504120 (0.685642) | 2.027992 / 1.541195 (0.486798) | 2.053211 / 1.468490 (0.584721) | 0.503850 / 4.584777 (-4.080927) | 3.086444 / 3.745712 (-0.659268) | 3.319492 / 5.269862 (-1.950370) | 2.070714 / 4.565676 (-2.494962) | 0.057591 / 0.424275 (-0.366684) | 0.006407 / 0.007607 (-0.001200) | 0.501145 / 0.226044 (0.275100) | 5.017753 / 2.268929 (2.748825) | 2.643145 / 55.444624 (-52.801479) | 2.327440 / 6.876477 (-4.549037) | 2.460250 / 2.142072 (0.318178) | 0.589397 / 4.805227 (-4.215830) | 0.124948 / 6.500664 (-6.375716) | 0.060450 / 0.075469 (-0.015020) |\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) | 1.279870 / 1.841788 (-0.561918) | 18.115908 / 8.074308 (10.041600) | 13.570032 / 10.191392 (3.378640) | 0.132981 / 0.680424 (-0.547442) | 0.016942 / 0.534201 (-0.517259) | 0.333591 / 0.579283 (-0.245692) | 0.358844 / 0.434364 (-0.075520) | 0.395748 / 0.540337 (-0.144590) | 0.546213 / 1.386936 (-0.840723) |\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) | 0.006062 / 0.011353 (-0.005291) | 0.003673 / 0.011008 (-0.007336) | 0.064726 / 0.038508 (0.026218) | 0.061854 / 0.023109 (0.038745) | 0.385343 / 0.275898 (0.109445) | 0.441284 / 0.323480 (0.117805) | 0.004830 / 0.007986 (-0.003156) | 0.002909 / 0.004328 (-0.001420) | 0.063874 / 0.004250 (0.059624) | 0.049331 / 0.037052 (0.012278) | 0.418484 / 0.258489 (0.159995) | 0.451397 / 0.293841 (0.157556) | 0.027665 / 0.128546 (-0.100881) | 0.008088 / 0.075646 (-0.067558) | 0.069625 / 0.419271 (-0.349646) | 0.043437 / 0.043533 (-0.000095) | 0.359789 / 0.255139 (0.104650) | 0.430206 / 0.283200 (0.147007) | 0.022308 / 0.141683 (-0.119375) | 1.461030 / 1.452155 (0.008875) | 1.513683 / 1.492716 (0.020966) |\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) | 0.230958 / 0.018006 (0.212952) | 0.417553 / 0.000490 (0.417063) | 0.000802 / 0.000200 (0.000602) | 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) | 0.025421 / 0.037411 (-0.011991) | 0.077156 / 0.014526 (0.062630) | 0.087533 / 0.176557 (-0.089024) | 0.138048 / 0.737135 (-0.599087) | 0.089358 / 0.296338 (-0.206981) |\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) | 0.439172 / 0.215209 (0.223963) | 4.409509 / 2.077655 (2.331854) | 2.491270 / 1.504120 (0.987150) | 2.308446 / 1.541195 (0.767252) | 2.378440 / 1.468490 (0.909950) | 0.499834 / 4.584777 (-4.084943) | 3.083168 / 3.745712 (-0.662544) | 2.867543 / 5.269862 (-2.402318) | 1.876354 / 4.565676 (-2.689323) | 0.057092 / 0.424275 (-0.367183) | 0.006955 / 0.007607 (-0.000653) | 0.513799 / 0.226044 (0.287754) | 5.126660 / 2.268929 (2.857731) | 2.917348 / 55.444624 (-52.527277) | 2.508035 / 6.876477 (-4.368441) | 2.698089 / 2.142072 (0.556016) | 0.586828 / 4.805227 (-4.218399) | 0.124740 / 6.500664 (-6.375924) | 0.062276 / 0.075469 (-0.013193) |\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) | 1.291624 / 1.841788 (-0.550164) | 18.199968 / 8.074308 (10.125660) | 13.888139 / 10.191392 (3.696747) | 0.162955 / 0.680424 (-0.517469) | 0.017343 / 0.534201 (-0.516858) | 0.334683 / 0.579283 (-0.244600) | 0.352708 / 0.434364 (-0.081656) | 0.400629 / 0.540337 (-0.139708) | 0.539497 / 1.386936 (-0.847439) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7976db7fe22c6b93a869488d07b8137ea6a0db4 \"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) | 0.007500 / 0.011353 (-0.003853) | 0.004498 / 0.011008 (-0.006510) | 0.100239 / 0.038508 (0.061731) | 0.083424 / 0.023109 (0.060315) | 0.366664 / 0.275898 (0.090766) | 0.406641 / 0.323480 (0.083161) | 0.004577 / 0.007986 (-0.003409) | 0.004809 / 0.004328 (0.000480) | 0.076898 / 0.004250 (0.072647) | 0.064021 / 0.037052 (0.026969) | 0.375836 / 0.258489 (0.117347) | 0.413008 / 0.293841 (0.119167) | 0.036010 / 0.128546 (-0.092537) | 0.009655 / 0.075646 (-0.065991) | 0.342595 / 0.419271 (-0.076677) | 0.061846 / 0.043533 (0.018313) | 0.376543 / 0.255139 (0.121404) | 0.395858 / 0.283200 (0.112659) | 0.026792 / 0.141683 (-0.114891) | 1.775569 / 1.452155 (0.323414) | 1.865077 / 1.492716 (0.372360) |\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) | 0.221521 / 0.018006 (0.203514) | 0.474604 / 0.000490 (0.474114) | 0.004354 / 0.000200 (0.004154) | 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) | 0.032947 / 0.037411 (-0.004464) | 0.100454 / 0.014526 (0.085928) | 0.111955 / 0.176557 (-0.064602) | 0.179752 / 0.737135 (-0.557383) | 0.114282 / 0.296338 (-0.182056) |\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) | 0.458261 / 0.215209 (0.243052) | 4.563536 / 2.077655 (2.485881) | 2.231928 / 1.504120 (0.727808) | 2.036751 / 1.541195 (0.495556) | 2.170413 / 1.468490 (0.701923) | 0.570825 / 4.584777 (-4.013952) | 4.505762 / 3.745712 (0.760050) | 5.033461 / 5.269862 (-0.236401) | 2.704989 / 4.565676 (-1.860687) | 0.067011 / 0.424275 (-0.357264) | 0.008568 / 0.007607 (0.000961) | 0.545151 / 0.226044 (0.319106) | 5.438984 / 2.268929 (3.170055) | 2.771818 / 55.444624 (-52.672806) | 2.393082 / 6.876477 (-4.483395) | 2.467173 / 2.142072 (0.325101) | 0.678849 / 4.805227 (-4.126379) | 0.160480 / 6.500664 (-6.340184) | 0.073681 / 0.075469 (-0.001788) |\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) | 1.532272 / 1.841788 (-0.309516) | 22.548741 / 8.074308 (14.474433) | 17.091044 / 10.191392 (6.899652) | 0.172100 / 0.680424 (-0.508324) | 0.022220 / 0.534201 (-0.511981) | 0.467871 / 0.579283 (-0.111412) | 0.491135 / 0.434364 (0.056771) | 0.548433 / 0.540337 (0.008096) | 0.733340 / 1.386936 (-0.653596) |\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) | 0.007593 / 0.011353 (-0.003760) | 0.004656 / 0.011008 (-0.006352) | 0.076940 / 0.038508 (0.038431) | 0.085183 / 0.023109 (0.062073) | 0.447178 / 0.275898 (0.171280) | 0.469545 / 0.323480 (0.146065) | 0.006023 / 0.007986 (-0.001962) | 0.003808 / 0.004328 (-0.000520) | 0.076767 / 0.004250 (0.072517) | 0.065713 / 0.037052 (0.028661) | 0.445573 / 0.258489 (0.187084) | 0.481689 / 0.293841 (0.187848) | 0.036893 / 0.128546 (-0.091654) | 0.009976 / 0.075646 (-0.065670) | 0.084443 / 0.419271 (-0.334829) | 0.058829 / 0.043533 (0.015297) | 0.429291 / 0.255139 (0.174152) | 0.454016 / 0.283200 (0.170816) | 0.027289 / 0.141683 (-0.114394) | 1.806786 / 1.452155 (0.354632) | 1.887680 / 1.492716 (0.394964) |\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) | 0.241012 / 0.018006 (0.223006) | 0.470629 / 0.000490 (0.470139) | 0.003213 / 0.000200 (0.003013) | 0.000107 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036896 / 0.037411 (-0.000515) | 0.106932 / 0.014526 (0.092406) | 0.120333 / 0.176557 (-0.056223) | 0.186271 / 0.737135 (-0.550865) | 0.121581 / 0.296338 (-0.174758) |\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) | 0.507782 / 0.215209 (0.292573) | 5.062932 / 2.077655 (2.985278) | 2.689539 / 1.504120 (1.185419) | 2.482978 / 1.541195 (0.941784) | 2.561320 / 1.468490 (1.092830) | 0.570664 / 4.584777 (-4.014113) | 4.346051 / 3.745712 (0.600339) | 6.479374 / 5.269862 (1.209513) | 4.096483 / 4.565676 (-0.469194) | 0.067564 / 0.424275 (-0.356711) | 0.009147 / 0.007607 (0.001540) | 0.596059 / 0.226044 (0.370015) | 5.963223 / 2.268929 (3.694295) | 3.201039 / 55.444624 (-52.243585) | 2.816581 / 6.876477 (-4.059896) | 3.047821 / 2.142072 (0.905748) | 0.687749 / 4.805227 (-4.117478) | 0.158174 / 6.500664 (-6.342490) | 0.073329 / 0.075469 (-0.002140) |\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) | 1.601346 / 1.841788 (-0.240441) | 23.712210 / 8.074308 (15.637902) | 16.567272 / 10.191392 (6.375880) | 0.224745 / 0.680424 (-0.455679) | 0.021662 / 0.534201 (-0.512539) | 0.471427 / 0.579283 (-0.107856) | 0.498751 / 0.434364 (0.064387) | 0.572047 / 0.540337 (0.031710) | 0.821868 / 1.386936 (-0.565068) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#34d0c9027c750adc89f3d04a6bf2e9cb95915da4 \"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) | 0.006371 / 0.011353 (-0.004981) | 0.003749 / 0.011008 (-0.007259) | 0.084155 / 0.038508 (0.045647) | 0.072450 / 0.023109 (0.049340) | 0.308002 / 0.275898 (0.032104) | 0.340471 / 0.323480 (0.016991) | 0.005054 / 0.007986 (-0.002931) | 0.003176 / 0.004328 (-0.001152) | 0.064867 / 0.004250 (0.060616) | 0.054305 / 0.037052 (0.017252) | 0.321047 / 0.258489 (0.062558) | 0.345999 / 0.293841 (0.052158) | 0.030507 / 0.128546 (-0.098039) | 0.008299 / 0.075646 (-0.067347) | 0.287682 / 0.419271 (-0.131590) | 0.052048 / 0.043533 (0.008515) | 0.308322 / 0.255139 (0.053183) | 0.333220 / 0.283200 (0.050020) | 0.022698 / 0.141683 (-0.118985) | 1.474033 / 1.452155 (0.021879) | 1.544790 / 1.492716 (0.052074) |\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) | 0.200612 / 0.018006 (0.182606) | 0.450934 / 0.000490 (0.450445) | 0.005383 / 0.000200 (0.005183) | 0.000200 / 0.000054 (0.000145) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027759 / 0.037411 (-0.009652) | 0.080935 / 0.014526 (0.066409) | 0.093041 / 0.176557 (-0.083516) | 0.148643 / 0.737135 (-0.588492) | 0.093463 / 0.296338 (-0.202876) |\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) | 0.381653 / 0.215209 (0.166444) | 3.810699 / 2.077655 (1.733044) | 1.866858 / 1.504120 (0.362738) | 1.716985 / 1.541195 (0.175790) | 1.788071 / 1.468490 (0.319581) | 0.481130 / 4.584777 (-4.103647) | 3.529798 / 3.745712 (-0.215914) | 3.982037 / 5.269862 (-1.287824) | 2.324866 / 4.565676 (-2.240811) | 0.056767 / 0.424275 (-0.367508) | 0.007306 / 0.007607 (-0.000301) | 0.459472 / 0.226044 (0.233428) | 4.602808 / 2.268929 (2.333879) | 2.332014 / 55.444624 (-53.112610) | 2.044858 / 6.876477 (-4.831619) | 2.204165 / 2.142072 (0.062093) | 0.577946 / 4.805227 (-4.227281) | 0.130900 / 6.500664 (-6.369764) | 0.059054 / 0.075469 (-0.016415) |\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) | 1.245211 / 1.841788 (-0.596576) | 19.176397 / 8.074308 (11.102089) | 13.995280 / 10.191392 (3.803888) | 0.171743 / 0.680424 (-0.508681) | 0.018038 / 0.534201 (-0.516163) | 0.392338 / 0.579283 (-0.186945) | 0.419370 / 0.434364 (-0.014994) | 0.477829 / 0.540337 (-0.062508) | 0.677409 / 1.386936 (-0.709527) |\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) | 0.006513 / 0.011353 (-0.004840) | 0.003984 / 0.011008 (-0.007024) | 0.064516 / 0.038508 (0.026008) | 0.070504 / 0.023109 (0.047395) | 0.384509 / 0.275898 (0.108611) | 0.410564 / 0.323480 (0.087084) | 0.005310 / 0.007986 (-0.002675) | 0.003268 / 0.004328 (-0.001061) | 0.064684 / 0.004250 (0.060433) | 0.055367 / 0.037052 (0.018315) | 0.399108 / 0.258489 (0.140619) | 0.422740 / 0.293841 (0.128900) | 0.031624 / 0.128546 (-0.096922) | 0.008617 / 0.075646 (-0.067030) | 0.070929 / 0.419271 (-0.348342) | 0.049146 / 0.043533 (0.005613) | 0.385492 / 0.255139 (0.130353) | 0.407434 / 0.283200 (0.124234) | 0.021972 / 0.141683 (-0.119711) | 1.496135 / 1.452155 (0.043980) | 1.533739 / 1.492716 (0.041023) |\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) | 0.226218 / 0.018006 (0.208211) | 0.443176 / 0.000490 (0.442686) | 0.000376 / 0.000200 (0.000176) | 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) | 0.030315 / 0.037411 (-0.007097) | 0.086416 / 0.014526 (0.071890) | 0.097725 / 0.176557 (-0.078831) | 0.150407 / 0.737135 (-0.586728) | 0.099914 / 0.296338 (-0.196424) |\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) | 0.409807 / 0.215209 (0.194598) | 4.099086 / 2.077655 (2.021431) | 2.103160 / 1.504120 (0.599040) | 1.927927 / 1.541195 (0.386733) | 1.977751 / 1.468490 (0.509261) | 0.476995 / 4.584777 (-4.107781) | 3.521835 / 3.745712 (-0.223877) | 3.237695 / 5.269862 (-2.032167) | 1.995953 / 4.565676 (-2.569724) | 0.056208 / 0.424275 (-0.368068) | 0.007660 / 0.007607 (0.000053) | 0.483537 / 0.226044 (0.257492) | 4.833974 / 2.268929 (2.565046) | 2.589115 / 55.444624 (-52.855510) | 2.228076 / 6.876477 (-4.648401) | 2.395271 / 2.142072 (0.253198) | 0.577534 / 4.805227 (-4.227694) | 0.131432 / 6.500664 (-6.369232) | 0.060999 / 0.075469 (-0.014471) |\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) | 1.356043 / 1.841788 (-0.485745) | 19.470401 / 8.074308 (11.396093) | 14.091266 / 10.191392 (3.899874) | 0.166809 / 0.680424 (-0.513615) | 0.018782 / 0.534201 (-0.515419) | 0.394916 / 0.579283 (-0.184367) | 0.411378 / 0.434364 (-0.022986) | 0.466886 / 0.540337 (-0.073451) | 0.617369 / 1.386936 (-0.769567) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#601ae6c7baff33a600fd10b12940966024fd2221 \"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) | 0.007590 / 0.011353 (-0.003762) | 0.004068 / 0.011008 (-0.006941) | 0.105479 / 0.038508 (0.066971) | 0.085614 / 0.023109 (0.062505) | 0.384325 / 0.275898 (0.108427) | 0.467867 / 0.323480 (0.144387) | 0.004652 / 0.007986 (-0.003333) | 0.005445 / 0.004328 (0.001117) | 0.079604 / 0.004250 (0.075353) | 0.066031 / 0.037052 (0.028978) | 0.426184 / 0.258489 (0.167695) | 0.480712 / 0.293841 (0.186871) | 0.037837 / 0.128546 (-0.090709) | 0.009765 / 0.075646 (-0.065882) | 0.351316 / 0.419271 (-0.067955) | 0.063634 / 0.043533 (0.020101) | 0.420297 / 0.255139 (0.165158) | 0.449169 / 0.283200 (0.165969) | 0.030947 / 0.141683 (-0.110736) | 1.840184 / 1.452155 (0.388029) | 1.934074 / 1.492716 (0.441357) |\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) | 0.223483 / 0.018006 (0.205477) | 0.521086 / 0.000490 (0.520596) | 0.000379 / 0.000200 (0.000179) | 0.000065 / 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) | 0.032011 / 0.037411 (-0.005400) | 0.101474 / 0.014526 (0.086948) | 0.108652 / 0.176557 (-0.067904) | 0.173340 / 0.737135 (-0.563796) | 0.114186 / 0.296338 (-0.182153) |\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) | 0.478020 / 0.215209 (0.262811) | 4.645400 / 2.077655 (2.567746) | 2.590763 / 1.504120 (1.086643) | 2.383002 / 1.541195 (0.841807) | 2.482550 / 1.468490 (1.014060) | 0.572417 / 4.584777 (-4.012360) | 4.233436 / 3.745712 (0.487724) | 4.858823 / 5.269862 (-0.411038) | 2.838913 / 4.565676 (-1.726764) | 0.070010 / 0.424275 (-0.354265) | 0.009602 / 0.007607 (0.001995) | 0.538735 / 0.226044 (0.312691) | 5.534340 / 2.268929 (3.265411) | 2.915006 / 55.444624 (-52.529619) | 2.625132 / 6.876477 (-4.251345) | 2.537838 / 2.142072 (0.395766) | 0.667870 / 4.805227 (-4.137357) | 0.146330 / 6.500664 (-6.354334) | 0.071631 / 0.075469 (-0.003838) |\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) | 1.594686 / 1.841788 (-0.247101) | 22.311113 / 8.074308 (14.236804) | 17.603983 / 10.191392 (7.412591) | 0.195995 / 0.680424 (-0.484428) | 0.022254 / 0.534201 (-0.511947) | 0.479661 / 0.579283 (-0.099622) | 0.463626 / 0.434364 (0.029262) | 0.483465 / 0.540337 (-0.056873) | 0.676141 / 1.386936 (-0.710795) |\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) | 0.006146 / 0.011353 (-0.005207) | 0.004856 / 0.011008 (-0.006152) | 0.067506 / 0.038508 (0.028998) | 0.073968 / 0.023109 (0.050859) | 0.470013 / 0.275898 (0.194115) | 0.479022 / 0.323480 (0.155542) | 0.005972 / 0.007986 (-0.002014) | 0.003846 / 0.004328 (-0.000483) | 0.075141 / 0.004250 (0.070890) | 0.058597 / 0.037052 (0.021544) | 0.481454 / 0.258489 (0.222965) | 0.515634 / 0.293841 (0.221793) | 0.034979 / 0.128546 (-0.093567) | 0.010385 / 0.075646 (-0.065261) | 0.072649 / 0.419271 (-0.346622) | 0.058183 / 0.043533 (0.014650) | 0.462138 / 0.255139 (0.206999) | 0.476093 / 0.283200 (0.192893) | 0.032918 / 0.141683 (-0.108765) | 1.820530 / 1.452155 (0.368375) | 1.626360 / 1.492716 (0.133644) |\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) | 0.208970 / 0.018006 (0.190964) | 0.492478 / 0.000490 (0.491988) | 0.005487 / 0.000200 (0.005287) | 0.000140 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037896 / 0.037411 (0.000484) | 0.089752 / 0.014526 (0.075227) | 0.107445 / 0.176557 (-0.069111) | 0.181260 / 0.737135 (-0.555876) | 0.105700 / 0.296338 (-0.190639) |\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) | 0.495031 / 0.215209 (0.279821) | 4.806939 / 2.077655 (2.729284) | 2.227928 / 1.504120 (0.723808) | 2.067117 / 1.541195 (0.525922) | 2.348982 / 1.468490 (0.880492) | 0.567201 / 4.584777 (-4.017576) | 4.166592 / 3.745712 (0.420880) | 3.654329 / 5.269862 (-1.615533) | 2.331092 / 4.565676 (-2.234584) | 0.062212 / 0.424275 (-0.362063) | 0.008775 / 0.007607 (0.001168) | 0.515413 / 0.226044 (0.289369) | 5.449300 / 2.268929 (3.180371) | 3.206574 / 55.444624 (-52.238050) | 2.600455 / 6.876477 (-4.276022) | 3.041162 / 2.142072 (0.899089) | 0.681899 / 4.805227 (-4.123328) | 0.155400 / 6.500664 (-6.345265) | 0.073933 / 0.075469 (-0.001537) |\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) | 1.572329 / 1.841788 (-0.269459) | 23.638519 / 8.074308 (15.564211) | 17.145663 / 10.191392 (6.954271) | 0.232690 / 0.680424 (-0.447734) | 0.028620 / 0.534201 (-0.505581) | 0.488105 / 0.579283 (-0.091178) | 0.490365 / 0.434364 (0.056001) | 0.599501 / 0.540337 (0.059164) | 0.708101 / 1.386936 (-0.678835) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a761315900880a25b347ad19b78bd567cfce1f0 \"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) | 0.005947 / 0.011353 (-0.005406) | 0.003577 / 0.011008 (-0.007431) | 0.081631 / 0.038508 (0.043122) | 0.058651 / 0.023109 (0.035541) | 0.342742 / 0.275898 (0.066843) | 0.384130 / 0.323480 (0.060650) | 0.004620 / 0.007986 (-0.003366) | 0.002885 / 0.004328 (-0.001444) | 0.063698 / 0.004250 (0.059448) | 0.048953 / 0.037052 (0.011901) | 0.367880 / 0.258489 (0.109391) | 0.407050 / 0.293841 (0.113209) | 0.027242 / 0.128546 (-0.101305) | 0.007914 / 0.075646 (-0.067733) | 0.262156 / 0.419271 (-0.157116) | 0.044750 / 0.043533 (0.001218) | 0.351613 / 0.255139 (0.096474) | 0.380284 / 0.283200 (0.097084) | 0.020080 / 0.141683 (-0.121603) | 1.498101 / 1.452155 (0.045946) | 1.543608 / 1.492716 (0.050892) |\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) | 0.180014 / 0.018006 (0.162008) | 0.436172 / 0.000490 (0.435682) | 0.003694 / 0.000200 (0.003494) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024389 / 0.037411 (-0.013022) | 0.072874 / 0.014526 (0.058348) | 0.083469 / 0.176557 (-0.093088) | 0.144600 / 0.737135 (-0.592536) | 0.084229 / 0.296338 (-0.212110) |\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) | 0.391636 / 0.215209 (0.176427) | 3.906941 / 2.077655 (1.829286) | 1.901944 / 1.504120 (0.397825) | 1.762702 / 1.541195 (0.221507) | 1.817970 / 1.468490 (0.349480) | 0.500345 / 4.584777 (-4.084432) | 3.011351 / 3.745712 (-0.734361) | 4.417763 / 5.269862 (-0.852098) | 2.689744 / 4.565676 (-1.875933) | 0.057765 / 0.424275 (-0.366511) | 0.006412 / 0.007607 (-0.001195) | 0.468156 / 0.226044 (0.242112) | 4.664975 / 2.268929 (2.396047) | 2.323355 / 55.444624 (-53.121270) | 1.984280 / 6.876477 (-4.892197) | 2.165215 / 2.142072 (0.023142) | 0.586950 / 4.805227 (-4.218278) | 0.124363 / 6.500664 (-6.376301) | 0.060702 / 0.075469 (-0.014767) |\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) | 1.238870 / 1.841788 (-0.602917) | 18.587360 / 8.074308 (10.513052) | 13.831674 / 10.191392 (3.640282) | 0.143542 / 0.680424 (-0.536882) | 0.016913 / 0.534201 (-0.517288) | 0.332314 / 0.579283 (-0.246969) | 0.345419 / 0.434364 (-0.088945) | 0.381257 / 0.540337 (-0.159081) | 0.537844 / 1.386936 (-0.849092) |\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) | 0.006294 / 0.011353 (-0.005059) | 0.003714 / 0.011008 (-0.007294) | 0.062684 / 0.038508 (0.024176) | 0.063520 / 0.023109 (0.040411) | 0.389591 / 0.275898 (0.113693) | 0.444278 / 0.323480 (0.120798) | 0.004825 / 0.007986 (-0.003160) | 0.003010 / 0.004328 (-0.001318) | 0.062767 / 0.004250 (0.058517) | 0.051739 / 0.037052 (0.014686) | 0.434299 / 0.258489 (0.175810) | 0.452003 / 0.293841 (0.158162) | 0.027375 / 0.128546 (-0.101171) | 0.008135 / 0.075646 (-0.067511) | 0.067401 / 0.419271 (-0.351871) | 0.042752 / 0.043533 (-0.000780) | 0.367633 / 0.255139 (0.112494) | 0.433039 / 0.283200 (0.149840) | 0.021086 / 0.141683 (-0.120597) | 1.488024 / 1.452155 (0.035870) | 1.507767 / 1.492716 (0.015050) |\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) | 0.230046 / 0.018006 (0.212040) | 0.428085 / 0.000490 (0.427595) | 0.002188 / 0.000200 (0.001988) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026705 / 0.037411 (-0.010706) | 0.082466 / 0.014526 (0.067940) | 0.089378 / 0.176557 (-0.087179) | 0.147287 / 0.737135 (-0.589849) | 0.090426 / 0.296338 (-0.205913) |\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) | 0.430882 / 0.215209 (0.215672) | 4.296224 / 2.077655 (2.218569) | 2.229982 / 1.504120 (0.725862) | 2.048506 / 1.541195 (0.507311) | 2.129514 / 1.468490 (0.661024) | 0.502964 / 4.584777 (-4.081813) | 3.048125 / 3.745712 (-0.697587) | 4.208636 / 5.269862 (-1.061226) | 2.594015 / 4.565676 (-1.971661) | 0.057967 / 0.424275 (-0.366308) | 0.006875 / 0.007607 (-0.000732) | 0.513872 / 0.226044 (0.287828) | 5.126435 / 2.268929 (2.857506) | 2.691278 / 55.444624 (-52.753346) | 2.361723 / 6.876477 (-4.514754) | 2.511213 / 2.142072 (0.369141) | 0.593558 / 4.805227 (-4.211670) | 0.129332 / 6.500664 (-6.371332) | 0.064051 / 0.075469 (-0.011418) |\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) | 1.289049 / 1.841788 (-0.552739) | 18.912363 / 8.074308 (10.838055) | 14.226500 / 10.191392 (4.035108) | 0.131392 / 0.680424 (-0.549032) | 0.016750 / 0.534201 (-0.517451) | 0.330078 / 0.579283 (-0.249205) | 0.347588 / 0.434364 (-0.086776) | 0.383234 / 0.540337 (-0.157103) | 0.510967 / 1.386936 (-0.875969) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7892beb30bab0633b84398c5ea43d7e69fe38cc \"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) | 0.005974 / 0.011353 (-0.005379) | 0.003691 / 0.011008 (-0.007317) | 0.079410 / 0.038508 (0.040902) | 0.061769 / 0.023109 (0.038660) | 0.323310 / 0.275898 (0.047412) | 0.354325 / 0.323480 (0.030845) | 0.004794 / 0.007986 (-0.003191) | 0.002899 / 0.004328 (-0.001430) | 0.062104 / 0.004250 (0.057854) | 0.048973 / 0.037052 (0.011921) | 0.326497 / 0.258489 (0.068008) | 0.361347 / 0.293841 (0.067506) | 0.026741 / 0.128546 (-0.101805) | 0.007936 / 0.075646 (-0.067710) | 0.259168 / 0.419271 (-0.160104) | 0.044859 / 0.043533 (0.001327) | 0.319342 / 0.255139 (0.064203) | 0.343711 / 0.283200 (0.060511) | 0.022298 / 0.141683 (-0.119384) | 1.451595 / 1.452155 (-0.000560) | 1.573730 / 1.492716 (0.081014) |\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) | 0.173086 / 0.018006 (0.155080) | 0.432400 / 0.000490 (0.431910) | 0.003739 / 0.000200 (0.003539) | 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) | 0.024477 / 0.037411 (-0.012934) | 0.073463 / 0.014526 (0.058937) | 0.083410 / 0.176557 (-0.093146) | 0.144760 / 0.737135 (-0.592376) | 0.084199 / 0.296338 (-0.212140) |\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) | 0.388251 / 0.215209 (0.173042) | 3.875375 / 2.077655 (1.797720) | 1.875515 / 1.504120 (0.371395) | 1.729282 / 1.541195 (0.188087) | 1.784732 / 1.468490 (0.316242) | 0.496985 / 4.584777 (-4.087792) | 3.030276 / 3.745712 (-0.715436) | 2.813192 / 5.269862 (-2.456669) | 1.868647 / 4.565676 (-2.697030) | 0.057376 / 0.424275 (-0.366899) | 0.006463 / 0.007607 (-0.001144) | 0.462153 / 0.226044 (0.236108) | 4.586583 / 2.268929 (2.317654) | 2.287730 / 55.444624 (-53.156894) | 1.972177 / 6.876477 (-4.904299) | 2.151592 / 2.142072 (0.009520) | 0.587169 / 4.805227 (-4.218058) | 0.127063 / 6.500664 (-6.373601) | 0.060297 / 0.075469 (-0.015172) |\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) | 1.267651 / 1.841788 (-0.574136) | 18.426011 / 8.074308 (10.351703) | 14.050470 / 10.191392 (3.859078) | 0.148063 / 0.680424 (-0.532361) | 0.017112 / 0.534201 (-0.517089) | 0.330051 / 0.579283 (-0.249232) | 0.358730 / 0.434364 (-0.075634) | 0.392365 / 0.540337 (-0.147972) | 0.534650 / 1.386936 (-0.852286) |\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) | 0.005936 / 0.011353 (-0.005417) | 0.003652 / 0.011008 (-0.007356) | 0.063066 / 0.038508 (0.024558) | 0.060617 / 0.023109 (0.037507) | 0.388293 / 0.275898 (0.112395) | 0.411422 / 0.323480 (0.087942) | 0.004691 / 0.007986 (-0.003295) | 0.002857 / 0.004328 (-0.001472) | 0.064198 / 0.004250 (0.059947) | 0.049124 / 0.037052 (0.012071) | 0.403601 / 0.258489 (0.145112) | 0.413619 / 0.293841 (0.119778) | 0.027279 / 0.128546 (-0.101267) | 0.008072 / 0.075646 (-0.067575) | 0.067890 / 0.419271 (-0.351381) | 0.041866 / 0.043533 (-0.001667) | 0.393438 / 0.255139 (0.138299) | 0.402865 / 0.283200 (0.119666) | 0.023381 / 0.141683 (-0.118302) | 1.496324 / 1.452155 (0.044170) | 1.538080 / 1.492716 (0.045364) |\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) | 0.212065 / 0.018006 (0.194059) | 0.410511 / 0.000490 (0.410021) | 0.001236 / 0.000200 (0.001036) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026012 / 0.037411 (-0.011399) | 0.076592 / 0.014526 (0.062066) | 0.085963 / 0.176557 (-0.090594) | 0.137803 / 0.737135 (-0.599332) | 0.087594 / 0.296338 (-0.208745) |\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) | 0.434283 / 0.215209 (0.219074) | 4.345478 / 2.077655 (2.267824) | 2.400954 / 1.504120 (0.896834) | 2.282024 / 1.541195 (0.740829) | 2.414247 / 1.468490 (0.945757) | 0.501855 / 4.584777 (-4.082922) | 3.059433 / 3.745712 (-0.686279) | 2.811288 / 5.269862 (-2.458574) | 1.856839 / 4.565676 (-2.708838) | 0.058017 / 0.424275 (-0.366258) | 0.006844 / 0.007607 (-0.000763) | 0.515376 / 0.226044 (0.289332) | 5.148775 / 2.268929 (2.879847) | 2.930807 / 55.444624 (-52.513817) | 2.520532 / 6.876477 (-4.355944) | 2.746299 / 2.142072 (0.604227) | 0.590102 / 4.805227 (-4.215125) | 0.125747 / 6.500664 (-6.374917) | 0.061873 / 0.075469 (-0.013597) |\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) | 1.306247 / 1.841788 (-0.535541) | 18.366048 / 8.074308 (10.291740) | 13.855617 / 10.191392 (3.664225) | 0.150124 / 0.680424 (-0.530300) | 0.017189 / 0.534201 (-0.517012) | 0.336285 / 0.579283 (-0.242998) | 0.344985 / 0.434364 (-0.089379) | 0.397973 / 0.540337 (-0.142364) | 0.536142 / 1.386936 (-0.850794) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ae24cf12054b4a512f198979b1ca7707bb99d56 \"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) | 0.006401 / 0.011353 (-0.004952) | 0.003789 / 0.011008 (-0.007219) | 0.079516 / 0.038508 (0.041008) | 0.068279 / 0.023109 (0.045170) | 0.295691 / 0.275898 (0.019793) | 0.327208 / 0.323480 (0.003728) | 0.005070 / 0.007986 (-0.002915) | 0.003044 / 0.004328 (-0.001285) | 0.061411 / 0.004250 (0.057161) | 0.053227 / 0.037052 (0.016175) | 0.297368 / 0.258489 (0.038879) | 0.334740 / 0.293841 (0.040899) | 0.029459 / 0.128546 (-0.099087) | 0.008080 / 0.075646 (-0.067566) | 0.267344 / 0.419271 (-0.151927) | 0.049877 / 0.043533 (0.006344) | 0.293853 / 0.255139 (0.038714) | 0.319819 / 0.283200 (0.036620) | 0.022593 / 0.141683 (-0.119089) | 1.459054 / 1.452155 (0.006900) | 1.471250 / 1.492716 (-0.021466) |\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) | 0.194326 / 0.018006 (0.176320) | 0.443565 / 0.000490 (0.443075) | 0.003745 / 0.000200 (0.003545) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026640 / 0.037411 (-0.010772) | 0.077630 / 0.014526 (0.063104) | 0.089364 / 0.176557 (-0.087192) | 0.147327 / 0.737135 (-0.589809) | 0.089603 / 0.296338 (-0.206735) |\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) | 0.373758 / 0.215209 (0.158549) | 3.746778 / 2.077655 (1.669123) | 1.814991 / 1.504120 (0.310871) | 1.645650 / 1.541195 (0.104455) | 1.690752 / 1.468490 (0.222262) | 0.472117 / 4.584777 (-4.112660) | 3.457346 / 3.745712 (-0.288367) | 3.138869 / 5.269862 (-2.130993) | 1.934924 / 4.565676 (-2.630753) | 0.055709 / 0.424275 (-0.368566) | 0.006680 / 0.007607 (-0.000927) | 0.446874 / 0.226044 (0.220829) | 4.458409 / 2.268929 (2.189480) | 2.253932 / 55.444624 (-53.190693) | 2.007240 / 6.876477 (-4.869237) | 2.081687 / 2.142072 (-0.060386) | 0.563379 / 4.805227 (-4.241848) | 0.128694 / 6.500664 (-6.371970) | 0.057409 / 0.075469 (-0.018060) |\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) | 1.212231 / 1.841788 (-0.629556) | 18.519121 / 8.074308 (10.444813) | 13.582243 / 10.191392 (3.390851) | 0.142488 / 0.680424 (-0.537936) | 0.017421 / 0.534201 (-0.516780) | 0.366864 / 0.579283 (-0.212419) | 0.401467 / 0.434364 (-0.032897) | 0.443659 / 0.540337 (-0.096679) | 0.618854 / 1.386936 (-0.768082) |\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) | 0.006121 / 0.011353 (-0.005232) | 0.003690 / 0.011008 (-0.007318) | 0.060340 / 0.038508 (0.021832) | 0.067215 / 0.023109 (0.044106) | 0.382846 / 0.275898 (0.106948) | 0.415774 / 0.323480 (0.092294) | 0.004868 / 0.007986 (-0.003118) | 0.003108 / 0.004328 (-0.001221) | 0.060572 / 0.004250 (0.056321) | 0.050453 / 0.037052 (0.013401) | 0.400494 / 0.258489 (0.142005) | 0.424368 / 0.293841 (0.130527) | 0.030279 / 0.128546 (-0.098267) | 0.008151 / 0.075646 (-0.067495) | 0.066707 / 0.419271 (-0.352564) | 0.046118 / 0.043533 (0.002585) | 0.386697 / 0.255139 (0.131558) | 0.410156 / 0.283200 (0.126957) | 0.020688 / 0.141683 (-0.120995) | 1.418162 / 1.452155 (-0.033993) | 1.463057 / 1.492716 (-0.029659) |\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) | 0.216081 / 0.018006 (0.198075) | 0.440541 / 0.000490 (0.440051) | 0.000371 / 0.000200 (0.000171) | 0.000054 / 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) | 0.027763 / 0.037411 (-0.009648) | 0.082316 / 0.014526 (0.067791) | 0.094086 / 0.176557 (-0.082471) | 0.144738 / 0.737135 (-0.592398) | 0.094837 / 0.296338 (-0.201501) |\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) | 0.396277 / 0.215209 (0.181068) | 3.958791 / 2.077655 (1.881136) | 2.021367 / 1.504120 (0.517247) | 1.860112 / 1.541195 (0.318917) | 1.886032 / 1.468490 (0.417541) | 0.468536 / 4.584777 (-4.116241) | 3.417950 / 3.745712 (-0.327762) | 4.849991 / 5.269862 (-0.419871) | 2.773935 / 4.565676 (-1.791742) | 0.055813 / 0.424275 (-0.368462) | 0.007053 / 0.007607 (-0.000554) | 0.470167 / 0.226044 (0.244122) | 4.702969 / 2.268929 (2.434041) | 2.474161 / 55.444624 (-52.970464) | 2.171256 / 6.876477 (-4.705220) | 2.315373 / 2.142072 (0.173301) | 0.589195 / 4.805227 (-4.216032) | 0.128237 / 6.500664 (-6.372427) | 0.058641 / 0.075469 (-0.016828) |\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) | 1.292947 / 1.841788 (-0.548841) | 18.851300 / 8.074308 (10.776992) | 14.089764 / 10.191392 (3.898372) | 0.164853 / 0.680424 (-0.515571) | 0.017281 / 0.534201 (-0.516920) | 0.359112 / 0.579283 (-0.220171) | 0.386696 / 0.434364 (-0.047668) | 0.428222 / 0.540337 (-0.112115) | 0.568659 / 1.386936 (-0.818277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#563864ded894b468e2ba3f677ef79c5ab3fe65df \"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) | 0.006051 / 0.011353 (-0.005301) | 0.003654 / 0.011008 (-0.007355) | 0.080081 / 0.038508 (0.041572) | 0.062925 / 0.023109 (0.039815) | 0.358097 / 0.275898 (0.082199) | 0.405728 / 0.323480 (0.082248) | 0.005359 / 0.007986 (-0.002627) | 0.002820 / 0.004328 (-0.001508) | 0.063108 / 0.004250 (0.058858) | 0.049627 / 0.037052 (0.012575) | 0.397870 / 0.258489 (0.139381) | 0.437157 / 0.293841 (0.143316) | 0.027707 / 0.128546 (-0.100839) | 0.007911 / 0.075646 (-0.067735) | 0.260991 / 0.419271 (-0.158280) | 0.044771 / 0.043533 (0.001238) | 0.340230 / 0.255139 (0.085091) | 0.384925 / 0.283200 (0.101725) | 0.021369 / 0.141683 (-0.120314) | 1.431439 / 1.452155 (-0.020715) | 1.478794 / 1.492716 (-0.013922) |\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) | 0.182626 / 0.018006 (0.164620) | 0.435551 / 0.000490 (0.435061) | 0.003015 / 0.000200 (0.002815) | 0.000064 / 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) | 0.024703 / 0.037411 (-0.012708) | 0.073640 / 0.014526 (0.059114) | 0.084598 / 0.176557 (-0.091959) | 0.145810 / 0.737135 (-0.591325) | 0.085125 / 0.296338 (-0.211213) |\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) | 0.394539 / 0.215209 (0.179330) | 3.945882 / 2.077655 (1.868227) | 1.947166 / 1.504120 (0.443046) | 1.763305 / 1.541195 (0.222111) | 1.816208 / 1.468490 (0.347718) | 0.498880 / 4.584777 (-4.085897) | 3.098283 / 3.745712 (-0.647429) | 2.823474 / 5.269862 (-2.446388) | 1.873993 / 4.565676 (-2.691684) | 0.058097 / 0.424275 (-0.366179) | 0.006488 / 0.007607 (-0.001119) | 0.466711 / 0.226044 (0.240667) | 4.671520 / 2.268929 (2.402592) | 2.363381 / 55.444624 (-53.081243) | 2.052092 / 6.876477 (-4.824385) | 2.209212 / 2.142072 (0.067140) | 0.594650 / 4.805227 (-4.210577) | 0.125604 / 6.500664 (-6.375060) | 0.061511 / 0.075469 (-0.013958) |\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) | 1.226564 / 1.841788 (-0.615224) | 18.583605 / 8.074308 (10.509297) | 13.993091 / 10.191392 (3.801699) | 0.146185 / 0.680424 (-0.534239) | 0.016839 / 0.534201 (-0.517362) | 0.334116 / 0.579283 (-0.245167) | 0.360780 / 0.434364 (-0.073584) | 0.386008 / 0.540337 (-0.154329) | 0.643278 / 1.386936 (-0.743658) |\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) | 0.006174 / 0.011353 (-0.005179) | 0.003658 / 0.011008 (-0.007350) | 0.063250 / 0.038508 (0.024742) | 0.063542 / 0.023109 (0.040433) | 0.366845 / 0.275898 (0.090947) | 0.409794 / 0.323480 (0.086314) | 0.005678 / 0.007986 (-0.002308) | 0.003061 / 0.004328 (-0.001268) | 0.063561 / 0.004250 (0.059311) | 0.052648 / 0.037052 (0.015596) | 0.378096 / 0.258489 (0.119607) | 0.410706 / 0.293841 (0.116865) | 0.027668 / 0.128546 (-0.100878) | 0.008045 / 0.075646 (-0.067601) | 0.068290 / 0.419271 (-0.350981) | 0.042602 / 0.043533 (-0.000930) | 0.364976 / 0.255139 (0.109837) | 0.395599 / 0.283200 (0.112400) | 0.022733 / 0.141683 (-0.118950) | 1.522473 / 1.452155 (0.070319) | 1.515891 / 1.492716 (0.023175) |\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) | 0.232554 / 0.018006 (0.214547) | 0.420702 / 0.000490 (0.420213) | 0.002161 / 0.000200 (0.001961) | 0.000064 / 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) | 0.026276 / 0.037411 (-0.011135) | 0.078504 / 0.014526 (0.063978) | 0.088989 / 0.176557 (-0.087567) | 0.144044 / 0.737135 (-0.593091) | 0.091074 / 0.296338 (-0.205265) |\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) | 0.420189 / 0.215209 (0.204980) | 4.189596 / 2.077655 (2.111941) | 2.316425 / 1.504120 (0.812305) | 2.186877 / 1.541195 (0.645682) | 2.259065 / 1.468490 (0.790575) | 0.502827 / 4.584777 (-4.081950) | 3.135266 / 3.745712 (-0.610446) | 2.838808 / 5.269862 (-2.431053) | 1.876519 / 4.565676 (-2.689158) | 0.057802 / 0.424275 (-0.366473) | 0.006824 / 0.007607 (-0.000784) | 0.500213 / 0.226044 (0.274168) | 4.999798 / 2.268929 (2.730869) | 2.627713 / 55.444624 (-52.816911) | 2.344263 / 6.876477 (-4.532214) | 2.415449 / 2.142072 (0.273376) | 0.593082 / 4.805227 (-4.212145) | 0.125787 / 6.500664 (-6.374877) | 0.062699 / 0.075469 (-0.012770) |\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) | 1.308219 / 1.841788 (-0.533569) | 18.703099 / 8.074308 (10.628791) | 13.976234 / 10.191392 (3.784842) | 0.144037 / 0.680424 (-0.536387) | 0.016592 / 0.534201 (-0.517609) | 0.333078 / 0.579283 (-0.246206) | 0.342317 / 0.434364 (-0.092047) | 0.396837 / 0.540337 (-0.143500) | 0.532641 / 1.386936 (-0.854295) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14f6edd9222e577dccb962ed5338b79b73502fa5 \"CML watermark\")\n"
] | 2023-07-13T15:41:44Z
| 2023-07-17T17:09:39Z
| 2023-07-17T17:01:00Z
|
MEMBER
| null | 0
|
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|
Thanks to @janineguo 's work in https://github.com/huggingface/datasets/pull/5919 which was needed to support HfFileSystem.
Switching to `HfFileSystem` will help implementing optimization in data files resolution
## Implementation details
I replaced all the from_hf_repo and from_local_or_remote in data_files.py to only use a new `from_patterns` which works for any fsspec path, including hf:// paths, https:// URLs and local paths. This simplifies the codebase since there is no logic duplication anymore when it comes to data files resolution.
I added `_prepare_path_and_storage_options` which returns the right storage_options to use given a path and a `DownloadConfig`. This is the only place where the logic depends on the filesystem type that must be used.
I also removed the `get_metadata_data_files_list ` and `get_patterns_and_data_files` functions added recently, since data files resolution is now handled using a common interface.
## New features
hf:// paths are now supported in data_files
## Breaking changes
DataFilesList and DataFilesDict:
- use `str` paths instead of `Union[Path, Url]`
- require posix paths for windows paths
close https://github.com/huggingface/datasets/issues/6017
|
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https://api.github.com/repos/huggingface/datasets/issues/6028/timeline
| null | null | true
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https://api.github.com/repos/huggingface/datasets/issues/142
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https://github.com/huggingface/datasets/pull/142
| 619,450,068
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MDExOlB1bGxSZXF1ZXN0NDE4OTU0OTc1
| 142
|
[WMT] Add all wmt
|
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This PR adds all wmt datasets scripts. At the moment the script is **not** functional for the language pairs "cs-en", "ru-en", "hi-en" because apparently it takes up to a week to get the manual data for these datasets: see http://ufal.mff.cuni.cz/czeng.
The datasets are fully functional though for the "big" language pairs "de-en" and "fr-en".
Overall I think the scripts are very messy and might need a big refactoring at some point.
For now I think there are good to merge (most dataset configs can be used). I will add "cs", "ru" and "hi" when the manual data is available.
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Add section in tutorial for IterableDataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008492 / 0.011353 (-0.002861) | 0.004717 / 0.011008 (-0.006292) | 0.101111 / 0.038508 (0.062602) | 0.029129 / 0.023109 (0.006019) | 0.307564 / 0.275898 (0.031666) | 0.367038 / 0.323480 (0.043558) | 0.007105 / 0.007986 (-0.000881) | 0.003622 / 0.004328 (-0.000706) | 0.078370 / 0.004250 (0.074120) | 0.036960 / 0.037052 (-0.000093) | 0.315612 / 0.258489 (0.057123) | 0.353601 / 0.293841 (0.059760) | 0.032900 / 0.128546 (-0.095647) | 0.011405 / 0.075646 (-0.064241) | 0.322331 / 0.419271 (-0.096940) | 0.040823 / 0.043533 (-0.002710) | 0.306734 / 0.255139 (0.051595) | 0.328155 / 0.283200 (0.044955) | 0.087169 / 0.141683 (-0.054514) | 1.460543 / 1.452155 (0.008389) | 1.498094 / 1.492716 (0.005378) |\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) | 0.011863 / 0.018006 (-0.006143) | 0.416315 / 0.000490 (0.415826) | 0.003463 / 0.000200 (0.003263) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023219 / 0.037411 (-0.014192) | 0.096469 / 0.014526 (0.081943) | 0.105960 / 0.176557 (-0.070596) | 0.148993 / 0.737135 (-0.588142) | 0.108112 / 0.296338 (-0.188226) |\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) | 0.415662 / 0.215209 (0.200453) | 4.155111 / 2.077655 (2.077456) | 1.834943 / 1.504120 (0.330823) | 1.622752 / 1.541195 (0.081557) | 1.701630 / 1.468490 (0.233140) | 0.690596 / 4.584777 (-3.894181) | 3.399385 / 3.745712 (-0.346327) | 3.140521 / 5.269862 (-2.129341) | 1.609152 / 4.565676 (-2.956524) | 0.082132 / 0.424275 (-0.342143) | 0.012343 / 0.007607 (0.004735) | 0.532715 / 0.226044 (0.306670) | 5.323032 / 2.268929 (3.054104) | 2.326625 / 55.444624 (-53.118000) | 1.944263 / 6.876477 (-4.932213) | 1.994015 / 2.142072 (-0.148058) | 0.813805 / 4.805227 (-3.991422) | 0.149233 / 6.500664 (-6.351431) | 0.065318 / 0.075469 (-0.010151) |\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) | 1.212441 / 1.841788 (-0.629347) | 13.979069 / 8.074308 (5.904761) | 14.003998 / 10.191392 (3.812606) | 0.146956 / 0.680424 (-0.533468) | 0.028564 / 0.534201 (-0.505637) | 0.392370 / 0.579283 (-0.186913) | 0.399695 / 0.434364 (-0.034669) | 0.473481 / 0.540337 (-0.066856) | 0.562625 / 1.386936 (-0.824311) |\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) | 0.006821 / 0.011353 (-0.004532) | 0.004570 / 0.011008 (-0.006438) | 0.076217 / 0.038508 (0.037709) | 0.028888 / 0.023109 (0.005779) | 0.345431 / 0.275898 (0.069533) | 0.389246 / 0.323480 (0.065766) | 0.005939 / 0.007986 (-0.002046) | 0.003356 / 0.004328 (-0.000973) | 0.075880 / 0.004250 (0.071629) | 0.041427 / 0.037052 (0.004374) | 0.344481 / 0.258489 (0.085992) | 0.398508 / 0.293841 (0.104667) | 0.031801 / 0.128546 (-0.096745) | 0.011763 / 0.075646 (-0.063884) | 0.085600 / 0.419271 (-0.333672) | 0.042656 / 0.043533 (-0.000876) | 0.345893 / 0.255139 (0.090754) | 0.376910 / 0.283200 (0.093711) | 0.092451 / 0.141683 (-0.049232) | 1.461222 / 1.452155 (0.009068) | 1.555822 / 1.492716 (0.063106) |\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) | 0.235781 / 0.018006 (0.217774) | 0.418485 / 0.000490 (0.417995) | 0.005560 / 0.000200 (0.005360) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025410 / 0.037411 (-0.012001) | 0.103780 / 0.014526 (0.089254) | 0.110183 / 0.176557 (-0.066374) | 0.151097 / 0.737135 (-0.586039) | 0.112539 / 0.296338 (-0.183799) |\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) | 0.436686 / 0.215209 (0.221477) | 4.341594 / 2.077655 (2.263940) | 2.062309 / 1.504120 (0.558190) | 1.857461 / 1.541195 (0.316267) | 1.947204 / 1.468490 (0.478713) | 0.699641 / 4.584777 (-3.885136) | 3.406983 / 3.745712 (-0.338729) | 3.294705 / 5.269862 (-1.975157) | 1.360582 / 4.565676 (-3.205095) | 0.083025 / 0.424275 (-0.341250) | 0.012461 / 0.007607 (0.004854) | 0.537767 / 0.226044 (0.311722) | 5.393316 / 2.268929 (3.124387) | 2.516692 / 55.444624 (-52.927932) | 2.163987 / 6.876477 (-4.712490) | 2.220480 / 2.142072 (0.078408) | 0.810648 / 4.805227 (-3.994579) | 0.151820 / 6.500664 (-6.348844) | 0.068080 / 0.075469 (-0.007389) |\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) | 1.279382 / 1.841788 (-0.562405) | 13.989947 / 8.074308 (5.915638) | 14.039229 / 10.191392 (3.847836) | 0.141071 / 0.680424 (-0.539352) | 0.017118 / 0.534201 (-0.517083) | 0.381558 / 0.579283 (-0.197725) | 0.390407 / 0.434364 (-0.043957) | 0.440920 / 0.540337 (-0.099418) | 0.525478 / 1.386936 (-0.861458) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeedb5167d150888a640cd70ca63d6d72bbe1043 \"CML watermark\")\n"
] | 2023-01-30T18:43:04Z
| 2023-02-01T18:15:38Z
| 2023-02-01T18:08:46Z
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Introduces an `IterableDataset` and how to access it in the tutorial section. It also adds a brief next step section at the end to provide a path for users who want more explanation and a path for users who want something more practical and learn how to preprocess these dataset types. It'll complement the awesome new doc introduced in:
- #5410
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MDExOlB1bGxSZXF1ZXN0NDg2ODIzMTY1
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|
Update GLUE URLs (now hosted on FB)
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NYU is switching dataset hosting from Google to FB. This PR closes https://github.com/huggingface/datasets/issues/608 and is necessary for https://github.com/jiant-dev/jiant/issues/161. This PR updates the data URLs based on changes made in https://github.com/nyu-mll/jiant/pull/1112.
Note: rebased on huggingface/datasets
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remove pathlib.Path with URIs
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"Thanks !\r\n`os.path.join` will use a backslash `\\` on windows which will also fail. You can use this instead in `load_from_disk`:\r\n```python\r\nfrom .filesystems import is_remote_filesystem\r\n\r\nis_local = not is_remote_filesystem(fs)\r\npath_join = os.path.join if is_local else posixpath.join\r\n```",
"Thank you ! I did a minor change to not have to define a new function and I ran the CI. If it's green we can merge :)",
"_The documentation is not available anymore as the PR was closed or merged._",
"> \r\n\r\n\r\n\r\n> Thank you ! I did a minor change to not have to define a new function and I ran the CI. If it's green we can merge :)\r\n\r\nlol it's a battle of +1 imports or +1 functions. LGTM, I was editing fast and swapped which branch gets os vs Path. Should be ok 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) | 0.012043 / 0.011353 (0.000690) | 0.006585 / 0.011008 (-0.004423) | 0.149007 / 0.038508 (0.110499) | 0.039514 / 0.023109 (0.016405) | 0.403893 / 0.275898 (0.127995) | 0.431252 / 0.323480 (0.107772) | 0.009218 / 0.007986 (0.001233) | 0.006108 / 0.004328 (0.001779) | 0.114666 / 0.004250 (0.110416) | 0.044962 / 0.037052 (0.007910) | 0.411592 / 0.258489 (0.153103) | 0.461561 / 0.293841 (0.167721) | 0.059958 / 0.128546 (-0.068589) | 0.029047 / 0.075646 (-0.046599) | 0.456000 / 0.419271 (0.036728) | 0.060744 / 0.043533 (0.017211) | 0.415816 / 0.255139 (0.160677) | 0.430488 / 0.283200 (0.147289) | 0.122477 / 0.141683 (-0.019205) | 1.862910 / 1.452155 (0.410755) | 1.974698 / 1.492716 (0.481981) |\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) | 0.257230 / 0.018006 (0.239224) | 0.606854 / 0.000490 (0.606364) | 0.006175 / 0.000200 (0.005975) | 0.000099 / 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) | 0.030533 / 0.037411 (-0.006879) | 0.130702 / 0.014526 (0.116177) | 0.143781 / 0.176557 (-0.032775) | 0.183272 / 0.737135 (-0.553863) | 0.151267 / 0.296338 (-0.145071) |\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) | 0.637422 / 0.215209 (0.422213) | 6.503535 / 2.077655 (4.425880) | 2.630387 / 1.504120 (1.126267) | 2.281180 / 1.541195 (0.739985) | 2.354341 / 1.468490 (0.885851) | 1.306497 / 4.584777 (-3.278280) | 5.837184 / 3.745712 (2.091472) | 3.257198 / 5.269862 (-2.012663) | 2.050681 / 4.565676 (-2.514995) | 0.146415 / 0.424275 (-0.277860) | 0.015386 / 0.007607 (0.007779) | 0.790146 / 0.226044 (0.564102) | 8.056137 / 2.268929 (5.787209) | 3.383566 / 55.444624 (-52.061059) | 2.707620 / 6.876477 (-4.168856) | 2.714857 / 2.142072 (0.572785) | 1.520847 / 4.805227 (-3.284380) | 0.266028 / 6.500664 (-6.234636) | 0.091422 / 0.075469 (0.015953) |\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) | 1.656148 / 1.841788 (-0.185640) | 18.833393 / 8.074308 (10.759085) | 21.360824 / 10.191392 (11.169432) | 0.227608 / 0.680424 (-0.452816) | 0.049018 / 0.534201 (-0.485183) | 0.593418 / 0.579283 (0.014135) | 0.656690 / 0.434364 (0.222326) | 0.709171 / 0.540337 (0.168833) | 0.828226 / 1.386936 (-0.558710) |\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) | 0.010112 / 0.011353 (-0.001241) | 0.006761 / 0.011008 (-0.004247) | 0.146723 / 0.038508 (0.108215) | 0.038451 / 0.023109 (0.015342) | 0.524267 / 0.275898 (0.248369) | 0.609484 / 0.323480 (0.286004) | 0.008502 / 0.007986 (0.000516) | 0.006964 / 0.004328 (0.002635) | 0.111396 / 0.004250 (0.107146) | 0.056839 / 0.037052 (0.019787) | 0.514649 / 0.258489 (0.256160) | 0.604212 / 0.293841 (0.310372) | 0.061410 / 0.128546 (-0.067137) | 0.020396 / 0.075646 (-0.055250) | 0.505026 / 0.419271 (0.085754) | 0.067280 / 0.043533 (0.023747) | 0.522249 / 0.255139 (0.267110) | 0.559484 / 0.283200 (0.276284) | 0.120943 / 0.141683 (-0.020740) | 2.124323 / 1.452155 (0.672169) | 2.153397 / 1.492716 (0.660681) |\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) | 0.216614 / 0.018006 (0.198608) | 0.594181 / 0.000490 (0.593692) | 0.004079 / 0.000200 (0.003879) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036925 / 0.037411 (-0.000486) | 0.131322 / 0.014526 (0.116797) | 0.148542 / 0.176557 (-0.028015) | 0.196045 / 0.737135 (-0.541090) | 0.156867 / 0.296338 (-0.139472) |\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) | 0.669722 / 0.215209 (0.454513) | 6.858856 / 2.077655 (4.781202) | 3.093969 / 1.504120 (1.589849) | 2.667385 / 1.541195 (1.126190) | 2.797192 / 1.468490 (1.328702) | 1.334759 / 4.584777 (-3.250018) | 6.024861 / 3.745712 (2.279149) | 3.257779 / 5.269862 (-2.012083) | 2.202816 / 4.565676 (-2.362860) | 0.147617 / 0.424275 (-0.276658) | 0.015451 / 0.007607 (0.007844) | 0.887015 / 0.226044 (0.660970) | 8.371288 / 2.268929 (6.102360) | 3.807451 / 55.444624 (-51.637173) | 3.079483 / 6.876477 (-3.796994) | 3.103321 / 2.142072 (0.961249) | 1.520272 / 4.805227 (-3.284955) | 0.273079 / 6.500664 (-6.227585) | 0.088613 / 0.075469 (0.013143) |\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) | 1.818913 / 1.841788 (-0.022875) | 19.274269 / 8.074308 (11.199960) | 19.871784 / 10.191392 (9.680392) | 0.250388 / 0.680424 (-0.430036) | 0.030562 / 0.534201 (-0.503638) | 0.560566 / 0.579283 (-0.018717) | 0.664701 / 0.434364 (0.230337) | 0.714513 / 0.540337 (0.174176) | 0.827227 / 1.386936 (-0.559710) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7a9bf823ea41b85313c0392388ec68b3033ef29 \"CML watermark\")\n"
] | 2023-01-26T03:25:45Z
| 2023-01-26T17:08:57Z
| 2023-01-26T16:59:11Z
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CONTRIBUTOR
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Pathlib will convert "//" to "/" which causes retry errors when downloading from cloud storage
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I_kwDODunzps5Q-sQ5
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Concatenate_datasets loads everything into RAM
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[] | 2022-09-01T10:25:17Z
| 2022-09-01T11:50:54Z
| 2022-09-01T11:50:54Z
|
NONE
| null | null | null |
## Describe the bug
When loading the datasets seperately and saving them on disk, I want to concatenate them. But `concatenate_datasets` is filling up my RAM and the process gets killed. Is there a way to prevent this from happening or is this intended behaviour? Thanks in advance
## Steps to reproduce the bug
```python
gcs = gcsfs.GCSFileSystem(project='project')
datasets = [load_from_disk(f'path/to/slice/of/data/{i}', fs=gcs, keep_in_memory=False) for i in range(10)]
dataset = concatenate_datasets(datasets)
```
## Expected results
A concatenated dataset which is stored on my disk.
## Actual results
Concatenated dataset gets loaded into RAM and overflows it which gets the process killed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 8.0.1
- Pandas version: 1.4.3
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Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows
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"Thanks for reporting. I'm looking at it",
" It's not related to the dataset viewer in itself. I can replicate the error with:\r\n\r\n```\r\n>>> import datasets as ds\r\n>>> d = ds.load_dataset('Pavithree/explainLikeImFive')\r\nUsing custom data configuration Pavithree--explainLikeImFive-b68b6d8112cd8a51\r\nDownloading and preparing dataset json/Pavithree--explainLikeImFive to /home/slesage/.cache/huggingface/datasets/json/Pavithree--explainLikeImFive-b68b6d8112cd8a51/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 305M/305M [00:03<00:00, 98.6MB/s]\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17.9M/17.9M [00:00<00:00, 75.7MB/s]\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11.9M/11.9M [00:00<00:00, 70.6MB/s]\r\nDownloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:05<00:00, 1.92s/it]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1948.42it/s]\r\nFailed to read file '/home/slesage/.cache/huggingface/datasets/downloads/5fee9c8819754df277aee6f252e4db6897d785231c21938407b8862ca871d246' with error <class 'pyarrow.lib.ArrowInvalid'>: Exceeded maximum rows\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 144, in _generate_tables\r\n dataset = json.load(f)\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py\", line 293, in load\r\n return loads(fp.read(),\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py\", line 357, in loads\r\n return _default_decoder.decode(s)\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/decoder.py\", line 340, in decode\r\n raise JSONDecodeError(\"Extra data\", s, end)\r\njson.decoder.JSONDecodeError: Extra data: line 1 column 916 (char 915)\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets/src/datasets/load.py\", line 1691, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 694, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 1151, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/tqdm/std.py\", line 1168, in __iter__\r\n for obj in iterable:\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 146, in _generate_tables\r\n raise e\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 122, in _generate_tables\r\n pa_table = paj.read_json(\r\n File \"pyarrow/_json.pyx\", line 246, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Exceeded maximum rows\r\n```\r\n\r\ncc @lhoestq @albertvillanova @mariosasko ",
"It seems that train.json is not a valid JSON Lines file: it has several JSON objects in the first line (the 915th character in the first line starts a new object, and there's no \"\\n\")\r\n\r\nYou need to have one JSON object per line",
"I'm closing this issue.\r\n\r\n@Pavithree, please, feel free to re-open it if fixing the JSON file does not solve it.",
"Thank you! that fixes the issue."
] | 2022-04-06T11:37:15Z
| 2022-04-08T07:13:07Z
| 2022-04-06T14:39:55Z
|
NONE
| null | null | null |
## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows
**Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive*
*This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error:
Status code: 400
Exception: ArrowInvalid
Message: Exceeded maximum rows
When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error*
Am I the one who added this dataset ? Yes
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| 869
|
Update ner datasets infos
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[
":+1: Thanks for fixing it!"
] | 2020-11-19T11:28:03Z
| 2020-11-19T14:14:18Z
| 2020-11-19T14:14:17Z
|
MEMBER
| null | 0
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Update the dataset_infos.json files for changes made in #850 regarding the ner datasets feature types (and the change to ClassLabel)
I also fixed the ner types of conll2003
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Error in metric.compute: missing `original_instructions` argument
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[] | 2020-06-13T06:26:54Z
| 2020-06-18T07:41:44Z
| 2020-06-18T07:41:44Z
|
NONE
| null | null | null |
I'm running into an error using metrics for computation in the latest master as well as version 0.2.1. Here is a minimal example:
```python
import nlp
rte_metric = nlp.load_metric('glue', name="rte")
rte_metric.compute(
[0, 0, 1, 1],
[0, 1, 0, 1],
)
```
```
181 # Read the predictions and references
182 reader = ArrowReader(path=self.data_dir, info=None)
--> 183 self.data = reader.read_files(node_files)
184
185 # Release all of our locks
TypeError: read_files() missing 1 required positional argument: 'original_instructions'
```
I believe this might have been introduced with cc8d2508b75f7ba0e5438d0686ee02dcec43c7f4, which added the `original_instructions` argument. Elsewhere, an empty-string default is provided--perhaps that could be done here too?
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Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix
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[
"I agree it would be nice to support this. It doesn't fit really well in the current data_files.py, where files of each splits are separated in different folder though, maybe we have to modify a bit the logic here. \r\n\r\nOne idea would be to extend `get_patterns_in_dataset_repository` and `get_patterns_locally` to additionally check for `metadata.json`, but feel free to comment if you have better ideas (I feel like we're reaching the limits of what the current implementation IMO, so we could think of a different way of resolving the data files if necessary)"
] | 2022-06-23T10:58:57Z
| 2022-06-30T10:15:32Z
| 2022-06-30T10:15:32Z
|
CONTRIBUTOR
| null | null | null |
If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored.
This happens when a directory is structured like as follows:
```
train/
file_1.jpg
file_2.jpg
test/
file_3.jpg
file_4.jpg
metadata.jsonl
```
or like as follows:
```
train_file_1.jpg
train_file_2.jpg
test_file_3.jpg
test_file_4.jpg
metadata.jsonl
```
The same for HF repos.
because it's ignored by the patterns [here](https://github.com/huggingface/datasets/blob/master/src/datasets/data_files.py#L29)
@lhoestq @mariosasko Do you think it's better to add this functionality in `data_files.py` or just specifically in imagefolder/audiofolder code? In `data_files.py` would me more general but I don't know if there are any other cases when that might be needed.
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Add dataset/mlsum
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[
"I think the problem is related to `wiki_dpr` dataset which is making the circle CI failed as you can see:\r\n```\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/dummy_psgs_w100_no_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/dummy_psgs_w100_with_nq_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/psgs_w100_no_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/psgs_w100_with_nq_embeddings\r\n\r\n```\r\nI'm facing the same issues with my last commits, I tried to rebase from master but it still not working. Maybe @lhoestq can help with.",
"Hello, I am confused about the next steps I need to do. Did the forced merge solve the issue ?",
"Hello :)\r\nI think you can just rebase from master and it should solve the CI error"
] | 2020-07-31T11:50:52Z
| 2020-08-24T14:54:42Z
| 2020-08-24T14:54:42Z
|
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New pull request that should correct the previous errors.
The load_real_data stills fails because it is looking for a default dataset URL that does not exists, this does not happen when loading the dataset with load_dataset
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`Proto_qa` hosting seems to be broken
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[
"@VictorSanh , I think @mariosasko is already working on it. "
] | 2021-06-08T16:16:32Z
| 2021-06-10T08:31:09Z
| 2021-06-10T08:31:09Z
|
MEMBER
| null | null | null |
## Describe the bug
The hosting (on Github) of the `proto_qa` dataset seems broken. I haven't investigated more yet, just flagging it for now.
@zaidalyafeai if you want to dive into it, I think it's just a matter of changing the links in `proto_qa.py`
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("proto_qa")
```
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset
use_auth_token=use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 630, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/hf/.cache/huggingface/modules/datasets_modules/datasets/proto_qa/445346efaad5c5f200ecda4aa7f0fb50ff1b55edde3003be424a2112c3e8102e/proto_qa.py", line 131, in _split_generators
train_fpath = dl_manager.download(_URLs[self.config.name]["train"])
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 199, in download
num_proc=download_config.num_proc,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 218, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/iesl/protoqa-data/master/data/train/protoqa_train.jsonl
```
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I_kwDODunzps4_IeTE
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nested sequence feature won't encode example if the first item of the outside sequence is an empty list
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[
"knock knock",
"Hi, thanks for reporting! I've linked a PR that should fix the issue.",
"I've checked the PR and it looks great, thanks a lot!"
] | 2021-11-20T16:57:54Z
| 2021-12-08T13:02:15Z
| 2021-12-08T13:02:15Z
|
NONE
| null | null | null |
## Describe the bug
As the title, nested sequence feature won't encode example if the first item of the outside sequence is an empty list.
## Steps to reproduce the bug
```python
from datasets import Features, Sequence, ClassLabel
features = Features({
'x': Sequence(Sequence(ClassLabel(names=['a', 'b']))),
})
print(features.encode_batch({
'x': [
[['a'], ['b']],
[[], ['b']],
]
}))
```
## Expected results
print `{'x': [[[0], [1]], [[], ['1']]]}`
## Actual results
print `{'x': [[[0], [1]], [[], ['b']]]}`
## Environment info
- `datasets` version: 1.15.1
- Platform: Linux-5.13.0-21-generic-x86_64-with-glibc2.34
- Python version: 3.9.7
- PyArrow version: 6.0.0
## Additional information
I think the issue stems from [here](https://github.com/huggingface/datasets/blob/8555197a3fe826e98bd0206c2d031c4488c53c5c/src/datasets/features/features.py#L847-L848).
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I_kwDODunzps4_2n6n
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|
Unable to load audio_features from common_voice dataset
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[
"Hi ! Feel free to access `batch[\"audio\"][\"array\"]` and `batch[\"audio\"][\"sampling_rate\"]` instead\r\n\r\n`datasets` 1.16 introduced some changes in `common_voice` and now the `path` field is no longer a path to a local file (but rather the path to the file in the archive it's extracted from)",
"Thanks for the information. It works.",
"Cool ! Closing this issue then"
] | 2021-12-04T19:59:11Z
| 2021-12-06T17:52:42Z
| 2021-12-06T17:52:42Z
|
NONE
| null | null | null |
## Describe the bug
I am not able to load audio features from common_voice dataset
## Steps to reproduce the bug
```
from datasets import load_dataset
import torchaudio
test_dataset = load_dataset("common_voice", "hi", split="test[:2%]")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
```
## Expected results
This piece of code should return test_dataset after loading audio features.
## Actual results
Reusing dataset common_voice (/home/jovyan/.cache/huggingface/datasets/common_voice/hi/6.1.0/b879a355caa529b11f2249400b61cadd0d9433f334d5c60f8c7216ccedfecfe1)
/opt/conda/lib/python3.7/site-packages/transformers/configuration_utils.py:341: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.
"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 "
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
0%| | 0/3 [00:00<?, ?ex/s]formats: can't open input file `common_voice_hi_23795358.mp3': No such file or directory
0%| | 0/3 [00:00<?, ?ex/s]
Traceback (most recent call last):
File "demo_file.py", line 23, in <module>
test_dataset = test_dataset.map(speech_file_to_array_fn)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2036, in map
desc=desc,
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 518, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 485, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py", line 411, in wrapper
out = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2368, in _map_single
example = apply_function_on_filtered_inputs(example, i, offset=offset)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2277, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1978, in decorated
result = f(decorated_item, *args, **kwargs)
File "demo_file.py", line 19, in speech_file_to_array_fn
speech_array, sampling_rate = torchaudio.load(batch["path"])
File "/opt/conda/lib/python3.7/site-packages/torchaudio/backend/sox_io_backend.py", line 154, in load
filepath, frame_offset, num_frames, normalize, channels_first, format)
RuntimeError: Error loading audio file: failed to open file common_voice_hi_23795358.mp3
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-4.14.243 with-debian-bullseye-sid
- Python version: 3.7.9
- PyArrow version: 6.0.1
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MDExOlB1bGxSZXF1ZXN0NTM3OTM0OTI1
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adding polsum
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[
"@lhoestq thanks for the comments! Should be fixed in the latest commit, I assume the CI errors are unrelated."
] | 2020-12-12T09:05:29Z
| 2020-12-18T09:43:43Z
| 2020-12-18T09:43:43Z
|
CONTRIBUTOR
| null | 0
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MDExOlB1bGxSZXF1ZXN0NDk2MjQxMjUw
| 693
|
Rachel ker add dataset/mlsum
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[
"It looks like an outdated PR (we've already added mlsum). Closing it"
] | 2020-10-01T13:01:10Z
| 2023-09-24T09:48:23Z
| 2020-10-01T17:01:13Z
|
NONE
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I_kwDODunzps5Kv_AS
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Errors when building dummy data if you use nested _URLS
|
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[] | 2022-05-31T16:10:57Z
| 2022-06-07T09:24:09Z
| 2022-06-07T09:24:09Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
When making dummy data with the `datasets-cli dummy_data` tool,
an error will be raised if you use a nested _URLS in your dataset script.
Traceback (most recent call last):
File "/home/name/LCCC/datasets/src/datasets/commands/datasets_cli.py", line 43, in <module>
main()
File "/home/name/LCCC/datasets/src/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 311, in run
self._autogenerate_dummy_data(
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 337, in _autogenerate_dummy_data
dataset_builder._split_generators(dl_manager)
File "/home/name/.cache/huggingface/modules/datasets_modules/datasets/personal_dialog/559332bced5eeafa7f7efc2a7c10ce02cee2a8116bbab4611c35a50ba2715b77/personal_dialog.py", line 108, in _split_generators
data_dir = dl_manager.download_and_extract(urls)
File "/home/name/LCCC/datasets/src/datasets/commands/dummy_data.py", line 56, in download_and_extract
dummy_output = self.mock_download_manager.download(url_or_urls)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 130, in download
return self.download_and_extract(data_url)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 122, in download_and_extract
return self.create_dummy_data_dict(dummy_file, data_url)
File "/home/name/LCCC/datasets/src/datasets/download/mock_download_manager.py", line 165, in create_dummy_data_dict
if isinstance(first_value, str) and len(set(dummy_data_dict.values())) < len(dummy_data_dict.values()):
TypeError: unhashable type: 'list'
## Steps to reproduce the bug
You can use my dataset script implemented here:
https://github.com/silverriver/datasets/blob/2ecd36760c40b8e29b1137cd19b5bad0e19c76fd/datasets/personal_dialog/personal_dialog.py
```python
datasets_cli dummy_data datasets/personal_dialog --auto_generate
```
You can change https://github.com/silverriver/datasets/blob/2ecd36760c40b8e29b1137cd19b5bad0e19c76fd/datasets/personal_dialog/personal_dialog.py#L54
to
```
"train": "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dev_random.jsonl.gz"
```
before runing the above script to avoid downloading a large training data.
## Expected results
The dummy data should be generated
## Actual results
An error is raised.
It seems that in https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/src/datasets/download/mock_download_manager.py#L165
We only check if the first item of dummy_data_dict.values() is str.
However, dummy_data_dict.values() may have the type of [str, list, list].
A simple fix would be changing https://github.com/huggingface/datasets/blob/12540dd75015678ec6019f258d811ee107439a73/src/datasets/download/mock_download_manager.py#L165 to
```python
if all([isinstance(value, str) for value in dummy_data_dict.values()]) and len(set(dummy_data_dict.values())) < len(dummy_data_dict.values()):
```
But I don't know if this kinds of change may bring any side effect since I am not sure about the detail logic here.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version: Python 3.9.10
- PyArrow version: 7.0.0
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PR_kwDODunzps4wiL8p
| 3,525
|
Adding license information for Openbookcorpus
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[
"The MIT license seems to be for the crawling code, no ? Then maybe we can also redirect users to the [terms of smashwords.com](https://www.smashwords.com/about/tos) regarding copyrights, in particular the paragraph 10 for end-users. In particular it seems that end users can download and use the content \"for their personal enjoyment in any reasonable non-commercial manner in compliance with copyright law\" and the smashwords end-users agreement.\r\n\r\nIt should be the same for https://github.com/huggingface/datasets/pull/3526 as well",
"May I merge this one ?",
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-01-04T23:20:36Z
| 2022-04-20T09:54:30Z
| 2022-04-20T09:48:10Z
|
CONTRIBUTOR
| null | 0
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Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE
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using torchrun, save_to_disk suddenly shows SIGTERM
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[] | 2023-12-08T13:22:03Z
| 2023-12-08T13:22:03Z
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NONE
| null | null | null |
### Describe the bug
When I run my code using the "torchrun" command, when the code reaches the "save_to_disk" part, suddenly I get the following warning and error messages:
Because the dataset is too large, the "save_to_disk" function splits it into 70 parts for saving. However, an error occurs suddenly when it reaches the 14th shard.
WARNING: torch.distributed.elastic.multiprocessing.api: Sending process 2224968 closing signal SIGTERM
ERROR: torch.distributed.elastic.multiprocessing.api: failed (exitcode: -7). traceback: Signal 7 (SIGBUS) received by PID 2224967.
### Steps to reproduce the bug
ds_shard = ds_shard.map(map_fn, *args, **kwargs)
ds_shard.save_to_disk(ds_shard_filepaths[rank])
Saving the dataset (14/70 shards): 20%|██ | 875350/4376702 [00:19<01:53, 30863.15 examples/s]
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 2224968 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -7) local_rank: 0 (pid: 2224967) of binary: /home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/python
Traceback (most recent call last):
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/torchrun", line 8, in <module>
sys.exit(main())
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main
run(args)
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
==========================================================
run.py FAILED
----------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
----------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-12-08_20:09:04
rank : 0 (local_rank: 0)
exitcode : -7 (pid: 2224967)
error_file: <N/A>
traceback : Signal 7 (SIGBUS) received by PID 2224967
### Expected behavior
I hope it can save successfully without any issues, but it seems there is a problem.
### Environment info
`datasets` version: 2.14.6
- Platform: Linux-4.19.90-24.4.v2101.ky10.aarch64-aarch64-with-glibc2.28
- Python version: 3.10.11
- Huggingface_hub version: 0.17.3
- PyArrow version: 14.0.0
- Pandas version: 2.1.2
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[Test] improve slow testing
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ALT dataset has repeating instances in all splits
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"Thanks for reporting. This looks like a very bad issue. I'm looking into it",
"I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch",
"Thanks!!! works perfectly in the bleading edge master version",
"Closed by #1899"
] | 2021-02-17T12:51:42Z
| 2021-02-19T06:18:46Z
| 2021-02-19T06:18:46Z
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NONE
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The [ALT](https://huggingface.co/datasets/alt) dataset has all the same instances within each split :/
Seemed like a great dataset for some experiments I wanted to carry out, especially since its medium-sized, and has all splits.
Would be great if this could be fixed :)
Added a snapshot of the contents from `explore-datset` feature, for quick reference.
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[data_files] Only match separated split names
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"_The documentation is not available anymore as the PR was closed or merged._",
"I ran a script to find affected datasets (just did it on non-private non-gated). Adding \"testing\" and \"evaluation\" fixes all of of them except one:\r\n- projecte-aina/cat_manynames:\thuman_annotated_testset.tsv\r\n\r\nLet me open a PR on their repository to fix it\r\nEDIT: pr [here](https://huggingface.co/datasets/projecte-aina/cat_manynames/discussions/2)",
"Feel free to merge @albertvillanova if it's all good to you :)",
"Thanks for the feedback @albertvillanova I took your comments into account :)\r\n- added numbers as supported delimiters\r\n- used list comprehension to create the patterns list\r\n- updated the docs and the tests according to your comments\r\n\r\nLet me know what you think !",
"I ended up removing the patching and the context manager :) merging"
] | 2022-07-05T14:18:11Z
| 2022-07-18T13:20:29Z
| 2022-07-18T13:07:33Z
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As reported in https://github.com/huggingface/datasets/issues/4477, the current pattern matching to infer which file goes into which split is too permissive. For example a file "contest.py" would be considered part of a test split (it contains "test") and "seqeval.py" as well (it contains "eval").
In this PR I made the pattern matching more robust by only matching split names **between separators**. The supported separators are dots, dashes, spaces and underscores.
I updated the docs accordingly.
One detail about the tests: I had to update one test because it was using `PurePath.match` as a reference for globbing, but it doesn't support the `[..]` glob pattern. Therefore I added a `mock_fs` context manager that can be used to easily define a dummy filesystem with certain files in it and run pattern matching tests. Its code comes mostly from test_streaming_download_manager.py
Close https://github.com/huggingface/datasets/issues/4477
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6477). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005383 / 0.011353 (-0.005969) | 0.003644 / 0.011008 (-0.007364) | 0.063375 / 0.038508 (0.024866) | 0.055567 / 0.023109 (0.032457) | 0.261376 / 0.275898 (-0.014522) | 0.283731 / 0.323480 (-0.039749) | 0.004022 / 0.007986 (-0.003964) | 0.002780 / 0.004328 (-0.001549) | 0.049407 / 0.004250 (0.045156) | 0.038208 / 0.037052 (0.001156) | 0.256275 / 0.258489 (-0.002214) | 0.293203 / 0.293841 (-0.000638) | 0.028411 / 0.128546 (-0.100135) | 0.010753 / 0.075646 (-0.064894) | 0.210420 / 0.419271 (-0.208851) | 0.036062 / 0.043533 (-0.007471) | 0.260455 / 0.255139 (0.005317) | 0.294991 / 0.283200 (0.011791) | 0.019020 / 0.141683 (-0.122662) | 1.118334 / 1.452155 (-0.333821) | 1.227391 / 1.492716 (-0.265325) |\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) | 0.094700 / 0.018006 (0.076694) | 0.302378 / 0.000490 (0.301888) | 0.000215 / 0.000200 (0.000015) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018745 / 0.037411 (-0.018667) | 0.061103 / 0.014526 (0.046578) | 0.075369 / 0.176557 (-0.101188) | 0.121573 / 0.737135 (-0.615563) | 0.076898 / 0.296338 (-0.219440) |\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) | 0.284143 / 0.215209 (0.068934) | 2.774298 / 2.077655 (0.696644) | 1.483557 / 1.504120 (-0.020563) | 1.365091 / 1.541195 (-0.176104) | 1.390170 / 1.468490 (-0.078320) | 0.561179 / 4.584777 (-4.023598) | 2.401654 / 3.745712 (-1.344058) | 2.782628 / 5.269862 (-2.487233) | 1.731497 / 4.565676 (-2.834179) | 0.061798 / 0.424275 (-0.362477) | 0.004998 / 0.007607 (-0.002609) | 0.336920 / 0.226044 (0.110875) | 3.371891 / 2.268929 (1.102963) | 1.832173 / 55.444624 (-53.612452) | 1.573515 / 6.876477 (-5.302962) | 1.595609 / 2.142072 (-0.546463) | 0.647652 / 4.805227 (-4.157575) | 0.118501 / 6.500664 (-6.382164) | 0.042521 / 0.075469 (-0.032948) |\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) | 0.939310 / 1.841788 (-0.902478) | 11.459855 / 8.074308 (3.385547) | 10.677954 / 10.191392 (0.486562) | 0.141029 / 0.680424 (-0.539395) | 0.014321 / 0.534201 (-0.519880) | 0.306679 / 0.579283 (-0.272604) | 0.262303 / 0.434364 (-0.172061) | 0.327422 / 0.540337 (-0.212915) | 0.436159 / 1.386936 (-0.950777) |\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) | 0.005430 / 0.011353 (-0.005923) | 0.003646 / 0.011008 (-0.007362) | 0.049272 / 0.038508 (0.010764) | 0.075367 / 0.023109 (0.052257) | 0.275959 / 0.275898 (0.000061) | 0.296317 / 0.323480 (-0.027163) | 0.004129 / 0.007986 (-0.003857) | 0.002731 / 0.004328 (-0.001597) | 0.048475 / 0.004250 (0.044225) | 0.041571 / 0.037052 (0.004518) | 0.277993 / 0.258489 (0.019504) | 0.298709 / 0.293841 (0.004868) | 0.033117 / 0.128546 (-0.095429) | 0.010914 / 0.075646 (-0.064732) | 0.057599 / 0.419271 (-0.361673) | 0.033354 / 0.043533 (-0.010179) | 0.275669 / 0.255139 (0.020530) | 0.288451 / 0.283200 (0.005251) | 0.019953 / 0.141683 (-0.121729) | 1.148608 / 1.452155 (-0.303547) | 1.184818 / 1.492716 (-0.307898) |\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) | 0.099566 / 0.018006 (0.081560) | 0.344935 / 0.000490 (0.344445) | 0.000221 / 0.000200 (0.000021) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021925 / 0.037411 (-0.015486) | 0.068623 / 0.014526 (0.054097) | 0.081533 / 0.176557 (-0.095024) | 0.120996 / 0.737135 (-0.616139) | 0.082495 / 0.296338 (-0.213844) |\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) | 0.294990 / 0.215209 (0.079781) | 2.892344 / 2.077655 (0.814690) | 1.611090 / 1.504120 (0.106970) | 1.496072 / 1.541195 (-0.045123) | 1.486069 / 1.468490 (0.017579) | 0.569769 / 4.584777 (-4.015008) | 2.477623 / 3.745712 (-1.268089) | 2.819576 / 5.269862 (-2.450286) | 1.745717 / 4.565676 (-2.819959) | 0.063763 / 0.424275 (-0.360512) | 0.004970 / 0.007607 (-0.002637) | 0.344879 / 0.226044 (0.118834) | 3.452795 / 2.268929 (1.183867) | 1.964468 / 55.444624 (-53.480156) | 1.674526 / 6.876477 (-5.201951) | 1.679716 / 2.142072 (-0.462356) | 0.650005 / 4.805227 (-4.155222) | 0.117019 / 6.500664 (-6.383646) | 0.048297 / 0.075469 (-0.027172) |\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) | 0.965422 / 1.841788 (-0.876366) | 11.989414 / 8.074308 (3.915106) | 10.938462 / 10.191392 (0.747070) | 0.140089 / 0.680424 (-0.540334) | 0.015533 / 0.534201 (-0.518668) | 0.292188 / 0.579283 (-0.287095) | 0.277903 / 0.434364 (-0.156461) | 0.326164 / 0.540337 (-0.214173) | 0.565674 / 1.386936 (-0.821262) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d78f07091bc42c41bea068bf1b6116e2bde46a6f \"CML watermark\")\n"
] | 2023-12-06T08:34:53Z
| 2023-12-06T09:24:11Z
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Fix #6476.
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Update builder hash with info
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6487). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Closing this one in favor of https://github.com/huggingface/datasets/pull/6458/commits/565c294fc12bc547730a023a610ed4f92313d8fb in https://github.com/huggingface/datasets/pull/6458"
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| 2023-12-11T11:41:34Z
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Currently if you change the `dataset_info` of a dataset (e.g. in the YAML part of the README.md), the cache ignores this change.
This is problematic because you want to regenerate a dataset if you change the features or the split sizes for example (e.g. after push_to_hub)
Ideally we should take the resolved files into account as well but this will be for another PR
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Push to hub in a pull request
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5528). All of your documentation changes will be reflected on that endpoint.",
"It seems that the parameter `create_pr` is available for [`0.8.0`](https://huggingface.co/docs/huggingface_hub/v0.8.1/en/package_reference/hf_api#huggingface_hub.HfApi.upload_file) (its not here: [`0.7.0`](https://huggingface.co/docs/huggingface_hub/v0.7.0.rc0/en/package_reference/hf_api#huggingface_hub.HfApi.upload_file)) and onwards. I included a warning, informing the user that no PR was created.",
"@nateraw you are completely right! Actually, the dataset shards is never added to the created pr, only the metadata, as the code is now. Ill look into you suggestion asap. Thank!",
"@nateraw Nothing more to add, that's a perfect usage of `huggingface_hub` as far as I can tell ! :fire: \r\n\r\nA very nit improvement would be to use the [for .. else ... python statement](https://book.pythontips.com/en/latest/for_-_else.html).\r\ni.e:\r\n\r\n```py\r\nif create_pr is True and revision is not None:\r\n for discussion in get_repo_discussions(repo_id, repo_type='dataset'):\r\n if discussion.is_pull_request and discussion.git_reference == revision:\r\n create_pr = False\r\n break\r\n else:\r\n raise ValueError(\"Provided revision not found\")\r\n```\r\nNo need for the `revision_found` temporary flag when do so. Yeah ok, it's niche :wink: ",
"I added the suggestions from @nateraw and @Wauplin .",
"> Thanks. Some comments/suggestions below...\r\n> \r\n> Why have you removed the test for create_pr? You could add it again and just add a pytest skipif when version of huggingface_hub is lower than 0.8.1.\r\n\r\nI have added the test again. I removed it because i kept getting errors when calling `create_pull_request` with `repo_id=ds_name` where `temporary_repo = ds_name`, and thought i might look more thoroughly at it later. I have added a test called `test_test` showing this, it gives:\r\n```\r\ntests/test_upstream_hub.py:360: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n return fn(*args, **kwargs)\r\n.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3451: in create_pull_request\r\n return self.create_discussion(\r\n.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n return fn(*args, **kwargs)\r\n.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3393: in create_discussion\r\n hf_raise_for_status(resp)\r\n(...)\r\nE huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-63ecd2cb-2cf2557a332c86ad27f687b3)\r\nE \r\nE Repository Not Found for url: https://huggingface.co/api/models/__DUMMY_TRANSFORMERS_USER__/test-16764648321590/discussions.\r\nE Please make sure you specified the correct `repo_id` and `repo_type`.\r\nE If you are trying to access a private or gated repo, make sure you are authenticated.\r\nE Invalid username or password.\r\n```",
"> > Thanks. Some comments/suggestions below...\r\n> > Why have you removed the test for create_pr? You could add it again and just add a pytest skipif when version of huggingface_hub is lower than 0.8.1.\r\n> \r\n> I have added the test again. I removed it because i kept getting errors when calling `create_pull_request` with `repo_id=ds_name` where `temporary_repo = ds_name`, and thought i might look more thoroughly at it later. I have added a test called `test_test` showing this, it gives:\r\n> \r\n> ```\r\n> tests/test_upstream_hub.py:360: \r\n> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n> return fn(*args, **kwargs)\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3451: in create_pull_request\r\n> return self.create_discussion(\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:124: in _inner_fn\r\n> return fn(*args, **kwargs)\r\n> .venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3393: in create_discussion\r\n> hf_raise_for_status(resp)\r\n> (...)\r\n> E huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-63ecd2cb-2cf2557a332c86ad27f687b3)\r\n> E \r\n> E Repository Not Found for url: https://huggingface.co/api/models/__DUMMY_TRANSFORMERS_USER__/test-16764648321590/discussions.\r\n> E Please make sure you specified the correct `repo_id` and `repo_type`.\r\n> E If you are trying to access a private or gated repo, make sure you are authenticated.\r\n> E Invalid username or password.\r\n> ```\r\n\r\n@albertvillanova, @lhoestq , FYI I have looked at this again, and i haven't figured it out, so the test`test_push_dataset_to_hub_with_pull_request` and the minimal example `test_test` are still failing locally, while the other tests succeed. Do you have any advice?",
"I tried to move all of the \"create pr safely\"-logic to a seperate function in `_hf_hub_fixes`. I looked at how the exceptions were raised before `huggingface_hub.utils.RepositoryNotFoundError`existed, and make changes accordingly. ",
"`create_pr` was set during `push_to_hub`, even though it was `None` from the outset, hence causing tests to fail for older versions of `huggingface_hub`. This is now fixed.\r\n\r\nWith the implementation of `_hf_hub_fixes.upload_file` the function call expected `commit_message`, `commit_description`. If these are not set we call the function without them, even though we are on a version of `huggingface_hub` where they are not available in `upload_file`.\r\n\r\nWhen `huggingface_hub < 0.5.0` we assume `repo_id` of them form `organisation/name`, so now that we are calling `create_repo` in the tests with `repo_id` not of this form, we need to handle this case, this is now done.\r\n\r\nMany tests failed for `dataset_dict` for the above reasons, so the fixes from `arrow_dataset.py` were also added to `dataset_dict.py`. \r\n\r\n**All tests are now passing locally for `huggingface_hub==0.2.0` and `huggingface_hub==0.12.1`…** Im sorry I should have downgraded and went through this a long time ago, but I didn’t realise the extend of these version fixes until recently…",
"Hi ! FYI bumped the `huggingface-hub` dependency to 0.11 and removed the `_hf_hub_fixes.py` - which should make this PR much easier",
"Just now finding this - seems like a cool issue to contribute to. If any more help is needed please ping me! @AJDERS "
] | 2023-02-13T11:43:47Z
| 2023-10-06T21:58:02Z
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Fixes #5492.
Introduce new kwarg `create_pr` in `push_to_hub`, which is passed to `HFapi.upload_file`.
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Updates LexGLUE and MultiEURLEX README.md files
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| 2021-10-18T10:13:40Z
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Updates LexGLUE and MultiEURLEX README.md files
- Fix leaderboard in LexGLUE.
- Fix an error in the CaseHOLD data example.
- Turn MultiEURLEX dataset statistics table into HTML to nicely render in HF website.
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[
"Fixed by #2847."
] | 2021-08-27T20:50:33Z
| 2021-09-10T11:51:07Z
| 2021-09-10T11:51:07Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
The load_dataset method do not accept a parquet file with a negative timezone, as it has the following regex:
```
"^(s|ms|us|ns),\s*tz=([a-zA-Z0-9/_+:]*)$"
```
So a valid timestap ```timestamp[us, tz=-03:00]``` returns an error when loading parquet files.
## Steps to reproduce the bug
```python
# Where the timestamp column has a tz of -03:00
datasets = load_dataset('parquet', data_files={'train': train_files, 'validation': validation_files,
'test': test_files}, cache_dir="./cache_teste/")
```
## Expected results
The -03:00 is a valid tz so the regex should accept this without raising an error.
## Actual results
As this regex disaproves a valid tz it raises the following error:
```python
raise ValueError(
f"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp."
f"Examples include timestamp[us] or timestamp[us, tz=America/New_York]"
f"See: https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp"
)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 5.0.0
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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) | 0.005594 / 0.011353 (-0.005759) | 0.003672 / 0.011008 (-0.007337) | 0.062796 / 0.038508 (0.024288) | 0.059432 / 0.023109 (0.036323) | 0.253976 / 0.275898 (-0.021922) | 0.281155 / 0.323480 (-0.042325) | 0.003023 / 0.007986 (-0.004962) | 0.003320 / 0.004328 (-0.001008) | 0.049059 / 0.004250 (0.044809) | 0.040252 / 0.037052 (0.003200) | 0.259526 / 0.258489 (0.001037) | 0.318798 / 0.293841 (0.024957) | 0.027883 / 0.128546 (-0.100663) | 0.010883 / 0.075646 (-0.064763) | 0.206948 / 0.419271 (-0.212323) | 0.036335 / 0.043533 (-0.007198) | 0.253209 / 0.255139 (-0.001930) | 0.275173 / 0.283200 (-0.008026) | 0.020365 / 0.141683 (-0.121318) | 1.121630 / 1.452155 (-0.330524) | 1.174680 / 1.492716 (-0.318036) |\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) | 0.098372 / 0.018006 (0.080366) | 0.309949 / 0.000490 (0.309460) | 0.000225 / 0.000200 (0.000025) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019495 / 0.037411 (-0.017916) | 0.062321 / 0.014526 (0.047795) | 0.074525 / 0.176557 (-0.102031) | 0.121832 / 0.737135 (-0.615303) | 0.077612 / 0.296338 (-0.218727) |\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) | 0.288156 / 0.215209 (0.072947) | 2.816411 / 2.077655 (0.738756) | 1.497926 / 1.504120 (-0.006193) | 1.378137 / 1.541195 (-0.163058) | 1.446466 / 1.468490 (-0.022024) | 0.566195 / 4.584777 (-4.018582) | 2.391933 / 3.745712 (-1.353780) | 2.929290 / 5.269862 (-2.340572) | 1.828215 / 4.565676 (-2.737462) | 0.063312 / 0.424275 (-0.360963) | 0.005199 / 0.007607 (-0.002408) | 0.342883 / 0.226044 (0.116838) | 3.378388 / 2.268929 (1.109459) | 1.865710 / 55.444624 (-53.578915) | 1.573442 / 6.876477 (-5.303035) | 1.631228 / 2.142072 (-0.510845) | 0.651614 / 4.805227 (-4.153613) | 0.118177 / 6.500664 (-6.382487) | 0.043303 / 0.075469 (-0.032166) |\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) | 0.950694 / 1.841788 (-0.891094) | 12.559851 / 8.074308 (4.485543) | 10.751123 / 10.191392 (0.559731) | 0.143107 / 0.680424 (-0.537317) | 0.014469 / 0.534201 (-0.519732) | 0.289531 / 0.579283 (-0.289752) | 0.267316 / 0.434364 (-0.167047) | 0.327748 / 0.540337 (-0.212590) | 0.437758 / 1.386936 (-0.949178) |\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) | 0.005669 / 0.011353 (-0.005684) | 0.003831 / 0.011008 (-0.007177) | 0.049096 / 0.038508 (0.010588) | 0.061408 / 0.023109 (0.038299) | 0.274571 / 0.275898 (-0.001327) | 0.299978 / 0.323480 (-0.023501) | 0.004216 / 0.007986 (-0.003769) | 0.002848 / 0.004328 (-0.001480) | 0.048755 / 0.004250 (0.044504) | 0.042576 / 0.037052 (0.005524) | 0.276781 / 0.258489 (0.018292) | 0.300903 / 0.293841 (0.007062) | 0.030243 / 0.128546 (-0.098303) | 0.010967 / 0.075646 (-0.064679) | 0.057879 / 0.419271 (-0.361392) | 0.033206 / 0.043533 (-0.010327) | 0.277620 / 0.255139 (0.022481) | 0.296263 / 0.283200 (0.013064) | 0.019022 / 0.141683 (-0.122660) | 1.125615 / 1.452155 (-0.326539) | 1.278016 / 1.492716 (-0.214700) |\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) | 0.096836 / 0.018006 (0.078830) | 0.307491 / 0.000490 (0.307001) | 0.000230 / 0.000200 (0.000030) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021552 / 0.037411 (-0.015859) | 0.071099 / 0.014526 (0.056573) | 0.082432 / 0.176557 (-0.094124) | 0.121826 / 0.737135 (-0.615310) | 0.084902 / 0.296338 (-0.211437) |\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) | 0.328113 / 0.215209 (0.112904) | 2.989613 / 2.077655 (0.911959) | 1.604904 / 1.504120 (0.100784) | 1.485459 / 1.541195 (-0.055735) | 1.524829 / 1.468490 (0.056339) | 0.580589 / 4.584777 (-4.004188) | 2.440087 / 3.745712 (-1.305625) | 2.944697 / 5.269862 (-2.325164) | 1.832728 / 4.565676 (-2.732949) | 0.064423 / 0.424275 (-0.359852) | 0.004991 / 0.007607 (-0.002616) | 0.357878 / 0.226044 (0.131834) | 3.515415 / 2.268929 (1.246487) | 1.964492 / 55.444624 (-53.480132) | 1.684058 / 6.876477 (-5.192418) | 1.730294 / 2.142072 (-0.411778) | 0.661228 / 4.805227 (-4.143999) | 0.122894 / 6.500664 (-6.377770) | 0.041776 / 0.075469 (-0.033693) |\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) | 0.969849 / 1.841788 (-0.871939) | 12.897067 / 8.074308 (4.822758) | 10.908200 / 10.191392 (0.716808) | 0.141139 / 0.680424 (-0.539285) | 0.015377 / 0.534201 (-0.518824) | 0.288625 / 0.579283 (-0.290658) | 0.279020 / 0.434364 (-0.155344) | 0.328386 / 0.540337 (-0.211951) | 0.590833 / 1.386936 (-0.796103) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#39ea60eaabb05d8ee38c072f375816cf87fce1a9 \"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) | 0.004986 / 0.011353 (-0.006367) | 0.003070 / 0.011008 (-0.007938) | 0.062433 / 0.038508 (0.023925) | 0.050639 / 0.023109 (0.027530) | 0.241807 / 0.275898 (-0.034091) | 0.262517 / 0.323480 (-0.060963) | 0.003826 / 0.007986 (-0.004160) | 0.002602 / 0.004328 (-0.001727) | 0.048508 / 0.004250 (0.044257) | 0.037276 / 0.037052 (0.000224) | 0.245757 / 0.258489 (-0.012732) | 0.272969 / 0.293841 (-0.020871) | 0.027139 / 0.128546 (-0.101407) | 0.010265 / 0.075646 (-0.065381) | 0.207279 / 0.419271 (-0.211992) | 0.035312 / 0.043533 (-0.008221) | 0.247535 / 0.255139 (-0.007604) | 0.260668 / 0.283200 (-0.022532) | 0.016496 / 0.141683 (-0.125187) | 1.137510 / 1.452155 (-0.314645) | 1.167870 / 1.492716 (-0.324847) |\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) | 0.091743 / 0.018006 (0.073736) | 0.298649 / 0.000490 (0.298159) | 0.000208 / 0.000200 (0.000009) | 0.000053 / 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) | 0.019053 / 0.037411 (-0.018359) | 0.060300 / 0.014526 (0.045774) | 0.072154 / 0.176557 (-0.104402) | 0.120293 / 0.737135 (-0.616842) | 0.073923 / 0.296338 (-0.222415) |\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) | 0.283058 / 0.215209 (0.067849) | 2.769503 / 2.077655 (0.691849) | 1.457016 / 1.504120 (-0.047104) | 1.335753 / 1.541195 (-0.205441) | 1.325986 / 1.468490 (-0.142504) | 0.562553 / 4.584777 (-4.022224) | 2.406144 / 3.745712 (-1.339568) | 2.778063 / 5.269862 (-2.491799) | 1.782199 / 4.565676 (-2.783477) | 0.062490 / 0.424275 (-0.361785) | 0.004912 / 0.007607 (-0.002695) | 0.338500 / 0.226044 (0.112456) | 3.309746 / 2.268929 (1.040818) | 1.819693 / 55.444624 (-53.624931) | 1.510295 / 6.876477 (-5.366182) | 1.578402 / 2.142072 (-0.563671) | 0.637517 / 4.805227 (-4.167710) | 0.117018 / 6.500664 (-6.383647) | 0.048149 / 0.075469 (-0.027320) |\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) | 0.939424 / 1.841788 (-0.902364) | 11.494891 / 8.074308 (3.420583) | 10.115194 / 10.191392 (-0.076198) | 0.126751 / 0.680424 (-0.553673) | 0.013567 / 0.534201 (-0.520634) | 0.282501 / 0.579283 (-0.296782) | 0.260594 / 0.434364 (-0.173770) | 0.325940 / 0.540337 (-0.214397) | 0.426186 / 1.386936 (-0.960750) |\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) | 0.005405 / 0.011353 (-0.005948) | 0.003557 / 0.011008 (-0.007451) | 0.051139 / 0.038508 (0.012631) | 0.053446 / 0.023109 (0.030337) | 0.268051 / 0.275898 (-0.007847) | 0.292343 / 0.323480 (-0.031136) | 0.004716 / 0.007986 (-0.003269) | 0.002677 / 0.004328 (-0.001651) | 0.047634 / 0.004250 (0.043384) | 0.041062 / 0.037052 (0.004009) | 0.269225 / 0.258489 (0.010736) | 0.297462 / 0.293841 (0.003621) | 0.029292 / 0.128546 (-0.099254) | 0.010947 / 0.075646 (-0.064699) | 0.057845 / 0.419271 (-0.361426) | 0.032793 / 0.043533 (-0.010740) | 0.265308 / 0.255139 (0.010169) | 0.288242 / 0.283200 (0.005043) | 0.018311 / 0.141683 (-0.123372) | 1.140957 / 1.452155 (-0.311197) | 1.204883 / 1.492716 (-0.287833) |\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) | 0.091375 / 0.018006 (0.073368) | 0.285922 / 0.000490 (0.285432) | 0.000238 / 0.000200 (0.000038) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021277 / 0.037411 (-0.016134) | 0.068853 / 0.014526 (0.054328) | 0.081002 / 0.176557 (-0.095555) | 0.120998 / 0.737135 (-0.616138) | 0.082741 / 0.296338 (-0.213598) |\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) | 0.299398 / 0.215209 (0.084189) | 2.909622 / 2.077655 (0.831967) | 1.624381 / 1.504120 (0.120261) | 1.501683 / 1.541195 (-0.039512) | 1.523045 / 1.468490 (0.054555) | 0.548960 / 4.584777 (-4.035817) | 2.413297 / 3.745712 (-1.332415) | 2.817852 / 5.269862 (-2.452010) | 1.754407 / 4.565676 (-2.811270) | 0.061912 / 0.424275 (-0.362363) | 0.004880 / 0.007607 (-0.002727) | 0.353989 / 0.226044 (0.127944) | 3.496147 / 2.268929 (1.227219) | 2.003026 / 55.444624 (-53.441598) | 1.702013 / 6.876477 (-5.174463) | 1.680935 / 2.142072 (-0.461137) | 0.630183 / 4.805227 (-4.175044) | 0.113786 / 6.500664 (-6.386878) | 0.040061 / 0.075469 (-0.035408) |\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) | 0.957218 / 1.841788 (-0.884569) | 11.914469 / 8.074308 (3.840160) | 10.488896 / 10.191392 (0.297504) | 0.129292 / 0.680424 (-0.551132) | 0.016603 / 0.534201 (-0.517598) | 0.287367 / 0.579283 (-0.291916) | 0.271332 / 0.434364 (-0.163032) | 0.325577 / 0.540337 (-0.214761) | 0.560553 / 1.386936 (-0.826383) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d31e434bbeafdf6a70cb80539342d8fe5f5fd27 \"CML watermark\")\n"
] | 2023-11-30T18:09:43Z
| 2023-11-30T18:36:40Z
| 2023-11-30T18:30:30Z
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continuation of https://github.com/huggingface/datasets/pull/6431
this should fix the CI in https://github.com/huggingface/datasets/pull/6458 too
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I_kwDODunzps5Z7YxR
| 5,387
|
Missing documentation page : improve-performance
|
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"Hi! Our documentation builder does not support links to sections, hence the bug. This is the link it should point to https://huggingface.co/docs/datasets/v2.8.0/en/cache#improve-performance."
] | 2022-12-23T01:12:57Z
| 2023-01-24T16:33:40Z
| 2023-01-24T16:33:40Z
|
NONE
| null | null | null |
### Describe the bug
Trying to access https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/cache#improve-performance, the page is missing.
The link is in here : https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/loading_methods#datasets.load_dataset.keep_in_memory
### Steps to reproduce the bug
Access the page and see it's missing.
### Expected behavior
Not missing page
### Environment info
Doesn't matter
|
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Add task templates for tydiqa and xquad
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[
"Just tested TydiQA and it works fine :)"
] | 2021-06-18T08:06:34Z
| 2021-06-18T15:01:17Z
| 2021-06-18T14:50:33Z
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This PR adds question-answering templates to the remaining datasets that are linked to a model on the Hub.
Notes:
* I could not test the tydiqa implementation since I don't have enough disk space 😢 . But I am confident the template works :)
* there exist other datasets like `fquad` and `mlqa` which are candidates for question-answering templates, but some work is needed to handle the ordering of nested column described in #2434
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PR_kwDODunzps40uOY6
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Fix main classes reference in docs
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3976). All of your documentation changes will be reflected on that endpoint.",
"Not sure why some section titles end with `[[datasets.xxx]]`, like this: https://moon-ci-docs.huggingface.co/docs/datasets/pr_3976/en/package_reference/main_classes#datasetdict[[datasets.datasetdict]]",
"Thanks ! I think this has been fixed already in https://github.com/huggingface/datasets/pull/3925 though\r\n\r\nI'm closing this one then if it's fine for you"
] | 2022-03-21T08:19:46Z
| 2022-04-12T14:19:39Z
| 2022-04-12T14:19:38Z
|
CONTRIBUTOR
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Currently the section index (on the page's right side) of the [main classes reference](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes) incorrectly displays `Tensor returned:`, this PR fixes this issue by wrapping code examples in this page with markdown code block.
There are other examples in datasets library having this issue.
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Doc red
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| 2020-07-06T12:10:39Z
| 2020-07-05T12:27:29Z
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Adding [DocRED](https://github.com/thunlp/DocRED) - a relation extraction dataset which tests document-level RE. A few implementation notes:
- There are 2 separate versions of the training set - *annotated* and *distant*. Instead of `nlp.Split.Train` I've used the splits `"train_annotated"` and `"train_distant"` to reflect this.
- As well as the relation id, the full relation name is mapped from `rel_info.json`
- I renamed the 'h', 'r', 't' keys to 'head', 'relation' and 'tail' to make them more readable.
- Used the fix from #319 to allow nested sequences of dicts.
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set dev version
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5958). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006232 / 0.011353 (-0.005121) | 0.003788 / 0.011008 (-0.007220) | 0.100014 / 0.038508 (0.061506) | 0.036488 / 0.023109 (0.013379) | 0.306255 / 0.275898 (0.030357) | 0.363337 / 0.323480 (0.039857) | 0.004765 / 0.007986 (-0.003221) | 0.002935 / 0.004328 (-0.001394) | 0.078897 / 0.004250 (0.074647) | 0.052221 / 0.037052 (0.015169) | 0.315169 / 0.258489 (0.056680) | 0.353050 / 0.293841 (0.059209) | 0.029059 / 0.128546 (-0.099488) | 0.008599 / 0.075646 (-0.067047) | 0.318770 / 0.419271 (-0.100502) | 0.046631 / 0.043533 (0.003098) | 0.303728 / 0.255139 (0.048589) | 0.332379 / 0.283200 (0.049180) | 0.021164 / 0.141683 (-0.120519) | 1.576963 / 1.452155 (0.124808) | 1.629575 / 1.492716 (0.136859) |\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) | 0.204246 / 0.018006 (0.186240) | 0.426600 / 0.000490 (0.426110) | 0.004336 / 0.000200 (0.004136) | 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) | 0.024039 / 0.037411 (-0.013372) | 0.098240 / 0.014526 (0.083715) | 0.108889 / 0.176557 (-0.067668) | 0.170827 / 0.737135 (-0.566308) | 0.111288 / 0.296338 (-0.185051) |\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) | 0.418103 / 0.215209 (0.202894) | 4.190759 / 2.077655 (2.113104) | 1.875978 / 1.504120 (0.371858) | 1.679198 / 1.541195 (0.138003) | 1.737965 / 1.468490 (0.269474) | 0.556660 / 4.584777 (-4.028117) | 3.413800 / 3.745712 (-0.331912) | 3.004999 / 5.269862 (-2.264862) | 1.464030 / 4.565676 (-3.101647) | 0.067338 / 0.424275 (-0.356937) | 0.011486 / 0.007607 (0.003879) | 0.522589 / 0.226044 (0.296544) | 5.214653 / 2.268929 (2.945724) | 2.316903 / 55.444624 (-53.127722) | 1.991941 / 6.876477 (-4.884536) | 2.110601 / 2.142072 (-0.031471) | 0.665400 / 4.805227 (-4.139828) | 0.135755 / 6.500664 (-6.364910) | 0.065980 / 0.075469 (-0.009489) |\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) | 1.197269 / 1.841788 (-0.644519) | 14.085205 / 8.074308 (6.010897) | 14.083360 / 10.191392 (3.891968) | 0.148054 / 0.680424 (-0.532369) | 0.016548 / 0.534201 (-0.517653) | 0.371538 / 0.579283 (-0.207745) | 0.391068 / 0.434364 (-0.043296) | 0.430589 / 0.540337 (-0.109748) | 0.529319 / 1.386936 (-0.857617) |\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) | 0.006214 / 0.011353 (-0.005138) | 0.003846 / 0.011008 (-0.007162) | 0.078559 / 0.038508 (0.040051) | 0.037855 / 0.023109 (0.014745) | 0.437479 / 0.275898 (0.161581) | 0.497588 / 0.323480 (0.174108) | 0.003491 / 0.007986 (-0.004494) | 0.003900 / 0.004328 (-0.000428) | 0.078443 / 0.004250 (0.074193) | 0.048019 / 0.037052 (0.010967) | 0.452076 / 0.258489 (0.193587) | 0.494597 / 0.293841 (0.200756) | 0.028127 / 0.128546 (-0.100419) | 0.008549 / 0.075646 (-0.067098) | 0.082977 / 0.419271 (-0.336295) | 0.043133 / 0.043533 (-0.000400) | 0.441342 / 0.255139 (0.186203) | 0.464339 / 0.283200 (0.181139) | 0.020110 / 0.141683 (-0.121573) | 1.485181 / 1.452155 (0.033026) | 1.532019 / 1.492716 (0.039302) |\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) | 0.228014 / 0.018006 (0.210007) | 0.416887 / 0.000490 (0.416397) | 0.001133 / 0.000200 (0.000933) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026452 / 0.037411 (-0.010960) | 0.104328 / 0.014526 (0.089802) | 0.110045 / 0.176557 (-0.066511) | 0.164725 / 0.737135 (-0.572410) | 0.116348 / 0.296338 (-0.179990) |\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) | 0.483502 / 0.215209 (0.268293) | 4.829814 / 2.077655 (2.752159) | 2.505271 / 1.504120 (1.001151) | 2.305819 / 1.541195 (0.764624) | 2.348633 / 1.468490 (0.880143) | 0.562316 / 4.584777 (-4.022461) | 3.426425 / 3.745712 (-0.319287) | 1.737934 / 5.269862 (-3.531927) | 1.042616 / 4.565676 (-3.523061) | 0.068088 / 0.424275 (-0.356187) | 0.011735 / 0.007607 (0.004128) | 0.586339 / 0.226044 (0.360295) | 5.861283 / 2.268929 (3.592354) | 2.953956 / 55.444624 (-52.490668) | 2.626611 / 6.876477 (-4.249865) | 2.687978 / 2.142072 (0.545906) | 0.672748 / 4.805227 (-4.132479) | 0.137231 / 6.500664 (-6.363433) | 0.068149 / 0.075469 (-0.007320) |\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) | 1.323139 / 1.841788 (-0.518649) | 14.503102 / 8.074308 (6.428794) | 14.092102 / 10.191392 (3.900710) | 0.165395 / 0.680424 (-0.515028) | 0.016898 / 0.534201 (-0.517303) | 0.366905 / 0.579283 (-0.212378) | 0.396671 / 0.434364 (-0.037692) | 0.421831 / 0.540337 (-0.118506) | 0.514075 / 1.386936 (-0.872861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d4238c132dd44b9a6e1dfe7101228bdeb538d57 \"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) | 0.007778 / 0.011353 (-0.003575) | 0.004624 / 0.011008 (-0.006384) | 0.123426 / 0.038508 (0.084918) | 0.052209 / 0.023109 (0.029100) | 0.341084 / 0.275898 (0.065186) | 0.421905 / 0.323480 (0.098425) | 0.005768 / 0.007986 (-0.002217) | 0.003647 / 0.004328 (-0.000682) | 0.085569 / 0.004250 (0.081319) | 0.070473 / 0.037052 (0.033421) | 0.356626 / 0.258489 (0.098136) | 0.407413 / 0.293841 (0.113572) | 0.038800 / 0.128546 (-0.089746) | 0.010289 / 0.075646 (-0.065357) | 0.462707 / 0.419271 (0.043436) | 0.060390 / 0.043533 (0.016858) | 0.349805 / 0.255139 (0.094666) | 0.355288 / 0.283200 (0.072088) | 0.025364 / 0.141683 (-0.116318) | 1.745720 / 1.452155 (0.293565) | 1.852764 / 1.492716 (0.360048) |\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) | 0.290582 / 0.018006 (0.272576) | 0.480044 / 0.000490 (0.479554) | 0.007658 / 0.000200 (0.007458) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031529 / 0.037411 (-0.005882) | 0.130441 / 0.014526 (0.115915) | 0.147653 / 0.176557 (-0.028904) | 0.215935 / 0.737135 (-0.521200) | 0.149871 / 0.296338 (-0.146467) |\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) | 0.461662 / 0.215209 (0.246453) | 4.570353 / 2.077655 (2.492698) | 2.104416 / 1.504120 (0.600297) | 1.936974 / 1.541195 (0.395779) | 2.139167 / 1.468490 (0.670677) | 0.645100 / 4.584777 (-3.939677) | 4.361536 / 3.745712 (0.615824) | 2.155960 / 5.269862 (-3.113902) | 1.207854 / 4.565676 (-3.357822) | 0.080162 / 0.424275 (-0.344113) | 0.014265 / 0.007607 (0.006658) | 0.606294 / 0.226044 (0.380250) | 5.928093 / 2.268929 (3.659165) | 2.701811 / 55.444624 (-52.742813) | 2.344490 / 6.876477 (-4.531987) | 2.435997 / 2.142072 (0.293925) | 0.761020 / 4.805227 (-4.044207) | 0.165860 / 6.500664 (-6.334804) | 0.075666 / 0.075469 (0.000197) |\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) | 1.427318 / 1.841788 (-0.414469) | 17.327468 / 8.074308 (9.253160) | 15.323065 / 10.191392 (5.131673) | 0.178518 / 0.680424 (-0.501905) | 0.020888 / 0.534201 (-0.513313) | 0.497891 / 0.579283 (-0.081393) | 0.487717 / 0.434364 (0.053353) | 0.581430 / 0.540337 (0.041093) | 0.703430 / 1.386936 (-0.683506) |\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) | 0.007954 / 0.011353 (-0.003399) | 0.004442 / 0.011008 (-0.006566) | 0.090950 / 0.038508 (0.052442) | 0.054282 / 0.023109 (0.031173) | 0.424474 / 0.275898 (0.148576) | 0.531770 / 0.323480 (0.208290) | 0.004492 / 0.007986 (-0.003493) | 0.004745 / 0.004328 (0.000416) | 0.088213 / 0.004250 (0.083962) | 0.063967 / 0.037052 (0.026914) | 0.454256 / 0.258489 (0.195767) | 0.502870 / 0.293841 (0.209029) | 0.038203 / 0.128546 (-0.090343) | 0.010327 / 0.075646 (-0.065319) | 0.097809 / 0.419271 (-0.321463) | 0.062136 / 0.043533 (0.018604) | 0.426148 / 0.255139 (0.171009) | 0.467812 / 0.283200 (0.184612) | 0.029148 / 0.141683 (-0.112535) | 1.762307 / 1.452155 (0.310152) | 1.814238 / 1.492716 (0.321521) |\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) | 0.195676 / 0.018006 (0.177670) | 0.475382 / 0.000490 (0.474892) | 0.003070 / 0.000200 (0.002870) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033945 / 0.037411 (-0.003466) | 0.134666 / 0.014526 (0.120140) | 0.147585 / 0.176557 (-0.028971) | 0.209472 / 0.737135 (-0.527664) | 0.154471 / 0.296338 (-0.141867) |\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) | 0.518132 / 0.215209 (0.302923) | 5.103423 / 2.077655 (3.025768) | 2.565207 / 1.504120 (1.061087) | 2.389454 / 1.541195 (0.848259) | 2.391706 / 1.468490 (0.923216) | 0.606463 / 4.584777 (-3.978314) | 4.392227 / 3.745712 (0.646515) | 2.067121 / 5.269862 (-3.202741) | 1.217551 / 4.565676 (-3.348125) | 0.074304 / 0.424275 (-0.349971) | 0.013418 / 0.007607 (0.005811) | 0.623327 / 0.226044 (0.397282) | 6.340233 / 2.268929 (4.071304) | 3.153948 / 55.444624 (-52.290677) | 2.824548 / 6.876477 (-4.051929) | 2.938402 / 2.142072 (0.796329) | 0.774305 / 4.805227 (-4.030922) | 0.170681 / 6.500664 (-6.329983) | 0.075895 / 0.075469 (0.000426) |\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) | 1.473491 / 1.841788 (-0.368296) | 17.372294 / 8.074308 (9.297986) | 15.550201 / 10.191392 (5.358809) | 0.191402 / 0.680424 (-0.489022) | 0.021401 / 0.534201 (-0.512800) | 0.484377 / 0.579283 (-0.094906) | 0.488844 / 0.434364 (0.054480) | 0.563336 / 0.540337 (0.022999) | 0.694210 / 1.386936 (-0.692726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96da7f51d81e52d7b587685f820b5e55f71e07d \"CML watermark\")\n"
] | 2023-06-14T16:26:34Z
| 2023-06-14T16:34:55Z
| 2023-06-14T16:26:51Z
|
MEMBER
| null | 0
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MDExOlB1bGxSZXF1ZXN0Njg5MTQ3NDE5
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Refactor patching to specific submodule
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[] | 2021-07-13T15:08:45Z
| 2021-07-13T16:52:49Z
| 2021-07-13T16:52:49Z
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Minor reorganization of the code, so that additional patching functions (not related to streaming) might be created.
In relation with the initial approach followed in #2631.
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Require torchaudio<0.12.0 to avoid RuntimeError
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-01T14:50:50Z
| 2022-08-02T17:35:14Z
| 2022-08-02T17:21:39Z
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Related to:
- https://github.com/huggingface/transformers/issues/18379
Fix partially #4776.
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Remove README link to deprecated Colab notebook
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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) | 0.006458 / 0.011353 (-0.004894) | 0.003895 / 0.011008 (-0.007114) | 0.084280 / 0.038508 (0.045772) | 0.071304 / 0.023109 (0.048195) | 0.313910 / 0.275898 (0.038012) | 0.344070 / 0.323480 (0.020590) | 0.005413 / 0.007986 (-0.002573) | 0.003308 / 0.004328 (-0.001021) | 0.064570 / 0.004250 (0.060320) | 0.056824 / 0.037052 (0.019771) | 0.321102 / 0.258489 (0.062613) | 0.355834 / 0.293841 (0.061993) | 0.031252 / 0.128546 (-0.097294) | 0.008427 / 0.075646 (-0.067219) | 0.287348 / 0.419271 (-0.131924) | 0.053261 / 0.043533 (0.009728) | 0.324892 / 0.255139 (0.069753) | 0.335847 / 0.283200 (0.052647) | 0.023453 / 0.141683 (-0.118230) | 1.485456 / 1.452155 (0.033301) | 1.531329 / 1.492716 (0.038612) |\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) | 0.201924 / 0.018006 (0.183918) | 0.447188 / 0.000490 (0.446698) | 0.005543 / 0.000200 (0.005343) | 0.000086 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027586 / 0.037411 (-0.009825) | 0.082412 / 0.014526 (0.067886) | 0.094851 / 0.176557 (-0.081706) | 0.151331 / 0.737135 (-0.585804) | 0.094475 / 0.296338 (-0.201863) |\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) | 0.399004 / 0.215209 (0.183795) | 3.974652 / 2.077655 (1.896997) | 1.991909 / 1.504120 (0.487789) | 1.811684 / 1.541195 (0.270489) | 1.869774 / 1.468490 (0.401283) | 0.487745 / 4.584777 (-4.097032) | 3.558945 / 3.745712 (-0.186768) | 5.530468 / 5.269862 (0.260606) | 3.293147 / 4.565676 (-1.272529) | 0.057531 / 0.424275 (-0.366744) | 0.007212 / 0.007607 (-0.000395) | 0.470325 / 0.226044 (0.244281) | 4.701652 / 2.268929 (2.432723) | 2.453020 / 55.444624 (-52.991605) | 2.110152 / 6.876477 (-4.766325) | 2.314669 / 2.142072 (0.172597) | 0.615039 / 4.805227 (-4.190189) | 0.133229 / 6.500664 (-6.367435) | 0.060821 / 0.075469 (-0.014648) |\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) | 1.296708 / 1.841788 (-0.545079) | 18.717251 / 8.074308 (10.642943) | 14.325305 / 10.191392 (4.133913) | 0.147680 / 0.680424 (-0.532744) | 0.018312 / 0.534201 (-0.515889) | 0.392766 / 0.579283 (-0.186517) | 0.403319 / 0.434364 (-0.031045) | 0.453696 / 0.540337 (-0.086641) | 0.622564 / 1.386936 (-0.764372) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006483 / 0.011353 (-0.004870) | 0.004018 / 0.011008 (-0.006991) | 0.064436 / 0.038508 (0.025928) | 0.072365 / 0.023109 (0.049256) | 0.387532 / 0.275898 (0.111634) | 0.418175 / 0.323480 (0.094695) | 0.005453 / 0.007986 (-0.002533) | 0.003368 / 0.004328 (-0.000961) | 0.064896 / 0.004250 (0.060645) | 0.057018 / 0.037052 (0.019966) | 0.406596 / 0.258489 (0.148107) | 0.431194 / 0.293841 (0.137353) | 0.031788 / 0.128546 (-0.096759) | 0.008532 / 0.075646 (-0.067114) | 0.070605 / 0.419271 (-0.348666) | 0.053317 / 0.043533 (0.009785) | 0.391930 / 0.255139 (0.136791) | 0.406071 / 0.283200 (0.122872) | 0.028652 / 0.141683 (-0.113030) | 1.487677 / 1.452155 (0.035522) | 1.546071 / 1.492716 (0.053355) |\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) | 0.220063 / 0.018006 (0.202056) | 0.441111 / 0.000490 (0.440621) | 0.006066 / 0.000200 (0.005867) | 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) | 0.035179 / 0.037411 (-0.002232) | 0.096745 / 0.014526 (0.082219) | 0.108171 / 0.176557 (-0.068386) | 0.164590 / 0.737135 (-0.572545) | 0.109425 / 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) | 0.408101 / 0.215209 (0.192892) | 4.062961 / 2.077655 (1.985306) | 2.101849 / 1.504120 (0.597730) | 1.935919 / 1.541195 (0.394724) | 1.993749 / 1.468490 (0.525259) | 0.487788 / 4.584777 (-4.096989) | 3.533972 / 3.745712 (-0.211740) | 3.218448 / 5.269862 (-2.051414) | 2.002322 / 4.565676 (-2.563355) | 0.057371 / 0.424275 (-0.366904) | 0.007704 / 0.007607 (0.000097) | 0.491695 / 0.226044 (0.265650) | 4.905009 / 2.268929 (2.636080) | 2.597879 / 55.444624 (-52.846745) | 2.252086 / 6.876477 (-4.624391) | 2.434439 / 2.142072 (0.292367) | 0.583071 / 4.805227 (-4.222156) | 0.133765 / 6.500664 (-6.366899) | 0.061276 / 0.075469 (-0.014193) |\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) | 1.403111 / 1.841788 (-0.438676) | 19.218886 / 8.074308 (11.144578) | 13.981775 / 10.191392 (3.790383) | 0.167784 / 0.680424 (-0.512640) | 0.018401 / 0.534201 (-0.515800) | 0.392038 / 0.579283 (-0.187245) | 0.414776 / 0.434364 (-0.019587) | 0.476221 / 0.540337 (-0.064117) | 0.632724 / 1.386936 (-0.754212) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#420dbd92c42840d6c91ecf5d3560c6799ee0cca1 \"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) | 0.007595 / 0.011353 (-0.003758) | 0.004540 / 0.011008 (-0.006468) | 0.099350 / 0.038508 (0.060842) | 0.087062 / 0.023109 (0.063953) | 0.415980 / 0.275898 (0.140082) | 0.466390 / 0.323480 (0.142910) | 0.005958 / 0.007986 (-0.002027) | 0.003671 / 0.004328 (-0.000657) | 0.075714 / 0.004250 (0.071463) | 0.066062 / 0.037052 (0.029010) | 0.426527 / 0.258489 (0.168038) | 0.473282 / 0.293841 (0.179441) | 0.035669 / 0.128546 (-0.092878) | 0.009729 / 0.075646 (-0.065918) | 0.344035 / 0.419271 (-0.075237) | 0.061153 / 0.043533 (0.017620) | 0.428607 / 0.255139 (0.173468) | 0.445951 / 0.283200 (0.162752) | 0.026373 / 0.141683 (-0.115310) | 1.788725 / 1.452155 (0.336570) | 1.871055 / 1.492716 (0.378339) |\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) | 0.230606 / 0.018006 (0.212600) | 0.489835 / 0.000490 (0.489345) | 0.005669 / 0.000200 (0.005469) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032197 / 0.037411 (-0.005214) | 0.099571 / 0.014526 (0.085045) | 0.112686 / 0.176557 (-0.063871) | 0.179478 / 0.737135 (-0.557658) | 0.112670 / 0.296338 (-0.183668) |\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) | 0.449606 / 0.215209 (0.234397) | 4.503356 / 2.077655 (2.425701) | 2.190480 / 1.504120 (0.686361) | 1.986054 / 1.541195 (0.444860) | 2.071594 / 1.468490 (0.603104) | 0.566301 / 4.584777 (-4.018475) | 4.088460 / 3.745712 (0.342748) | 4.840100 / 5.269862 (-0.429761) | 2.857697 / 4.565676 (-1.707980) | 0.066718 / 0.424275 (-0.357557) | 0.008642 / 0.007607 (0.001034) | 0.539785 / 0.226044 (0.313740) | 5.383252 / 2.268929 (3.114323) | 2.878177 / 55.444624 (-52.566447) | 2.374577 / 6.876477 (-4.501899) | 2.590500 / 2.142072 (0.448428) | 0.675196 / 4.805227 (-4.130031) | 0.153544 / 6.500664 (-6.347120) | 0.070958 / 0.075469 (-0.004511) |\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) | 1.490403 / 1.841788 (-0.351385) | 22.085740 / 8.074308 (14.011432) | 16.588093 / 10.191392 (6.396701) | 0.188598 / 0.680424 (-0.491826) | 0.021567 / 0.534201 (-0.512634) | 0.472594 / 0.579283 (-0.106689) | 0.472903 / 0.434364 (0.038539) | 0.545305 / 0.540337 (0.004968) | 0.736399 / 1.386936 (-0.650537) |\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) | 0.007635 / 0.011353 (-0.003718) | 0.004731 / 0.011008 (-0.006277) | 0.076482 / 0.038508 (0.037974) | 0.083666 / 0.023109 (0.060557) | 0.469596 / 0.275898 (0.193698) | 0.493068 / 0.323480 (0.169588) | 0.006014 / 0.007986 (-0.001971) | 0.003902 / 0.004328 (-0.000426) | 0.077142 / 0.004250 (0.072891) | 0.064355 / 0.037052 (0.027303) | 0.468859 / 0.258489 (0.210370) | 0.504002 / 0.293841 (0.210161) | 0.037606 / 0.128546 (-0.090940) | 0.010141 / 0.075646 (-0.065505) | 0.083790 / 0.419271 (-0.335482) | 0.060923 / 0.043533 (0.017390) | 0.464752 / 0.255139 (0.209613) | 0.500464 / 0.283200 (0.217264) | 0.031183 / 0.141683 (-0.110499) | 1.779294 / 1.452155 (0.327139) | 1.870848 / 1.492716 (0.378131) |\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) | 0.246567 / 0.018006 (0.228560) | 0.477182 / 0.000490 (0.476693) | 0.000426 / 0.000200 (0.000226) | 0.000067 / 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) | 0.035377 / 0.037411 (-0.002034) | 0.106042 / 0.014526 (0.091516) | 0.119237 / 0.176557 (-0.057320) | 0.182145 / 0.737135 (-0.554991) | 0.119537 / 0.296338 (-0.176801) |\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) | 0.491352 / 0.215209 (0.276143) | 4.824220 / 2.077655 (2.746565) | 2.652039 / 1.504120 (1.147919) | 2.535310 / 1.541195 (0.994116) | 2.620009 / 1.468490 (1.151519) | 0.567865 / 4.584777 (-4.016912) | 4.158795 / 3.745712 (0.413082) | 6.042582 / 5.269862 (0.772721) | 3.957193 / 4.565676 (-0.608484) | 0.066647 / 0.424275 (-0.357628) | 0.008893 / 0.007607 (0.001285) | 0.570137 / 0.226044 (0.344093) | 5.687126 / 2.268929 (3.418198) | 3.137605 / 55.444624 (-52.307019) | 2.655979 / 6.876477 (-4.220498) | 2.893338 / 2.142072 (0.751265) | 0.698388 / 4.805227 (-4.106840) | 0.154897 / 6.500664 (-6.345767) | 0.071208 / 0.075469 (-0.004261) |\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) | 1.619346 / 1.841788 (-0.222441) | 22.782510 / 8.074308 (14.708202) | 16.317395 / 10.191392 (6.126003) | 0.197630 / 0.680424 (-0.482794) | 0.021795 / 0.534201 (-0.512406) | 0.466982 / 0.579283 (-0.112302) | 0.468609 / 0.434364 (0.034245) | 0.574380 / 0.540337 (0.034043) | 0.759827 / 1.386936 (-0.627109) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c1c5d8268ae59a0dcaea47da825e87c3f9528b4 \"CML watermark\")\n"
] | 2023-07-26T15:27:49Z
| 2023-07-26T16:24:43Z
| 2023-07-26T16:14:34Z
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Add documentation about dataset viewer feature
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"In particular, include this somewhere in the docs: https://huggingface.co/docs/hub/datasets-viewer#access-the-parquet-files\r\n\r\nSee https://github.com/huggingface/hub-docs/issues/563"
] | 2021-11-05T08:11:19Z
| 2023-09-25T11:48:38Z
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Add to the docs more details about the dataset viewer feature in the Hub.
CC: @julien-c
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Add CLIMATE-FEVER dataset
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"Thank you @lhoestq for your comments! 😄 I added your suggested changes, ran the tests and regenerated `dataset_infos.json` and `dummy_data`."
] | 2020-12-22T13:34:05Z
| 2020-12-22T17:53:53Z
| 2020-12-22T17:53:53Z
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As suggested by @SBrandeis , fresh PR that adds CLIMATE-FEVER. Replaces PR #1579.
---
A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present.
More information can be found at:
* Homepage: http://climatefever.ai
* Paper: https://arxiv.org/abs/2012.00614
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Fix save_to_disk with relative path
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As noticed in #1919 and #1920 the target directory was not created using `makedirs` so saving to it raises `FileNotFoundError`. For absolute paths it works but not for the good reason. This is because the target path was the same as the temporary path where in-memory data are written as an intermediary step.
I added the `makedirs` call using `fs.makedirs` in order to support remote filesystems.
I also fixed the issue with the target path being the temporary path.
I added a test case for relative paths as well for save_to_disk.
Thanks to @M-Salti for reporting and investigating
|
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add eduge classification dataset
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"Closing this PR as I'll submit a new one - bug free"
] | 2021-06-13T04:37:01Z
| 2021-06-13T05:06:48Z
| 2021-06-13T05:06:38Z
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I_kwDODunzps5DfEn6
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Unable to load dataset 'big_patent'
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"Hi @ankitk2109,\r\n\r\nHave you tried passing the split name with the keyword `split=`? See e.g. an example in our Quick Start docs: https://huggingface.co/docs/datasets/quickstart.html#load-the-dataset-and-model\r\n```python\r\n ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")",
"Hi @albertvillanova,\r\n\r\nThanks for your response.\r\n\r\nYes, I tried the `split='validation'` as well. But getting the same issue. ",
"I'm sorry, but I can't reproduce your problem:\r\n```python\r\nIn [5]: ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")\r\nDownloading and preparing dataset big_patent/d (download: 6.01 GiB, generated: 169.61 MiB, post-processed: Unknown size, total: 6.17 GiB) to .../.cache/big_patent/d/1.0.0/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.45G/6.45G [27:36<00:00, 3.89MB/s]\r\nExtracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:18<00:00, 66.08s/it]\r\nDataset big_patent downloaded and prepared to .../.cache/big_patent/d/1.0.0/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c. Subsequent calls will reuse this data. \r\n\r\nIn [6]: ds\r\nOut[6]: \r\nDataset({\r\n features: ['description', 'abstract'],\r\n num_rows: 565\r\n})\r\n",
"Maybe you had a connection issue while downloading the file and this was corrupted?\r\nOur cache system uses the file you downloaded first time.\r\nIf so, you could try forcing redownload of the file with:\r\n```python\r\nds = load_dataset(\"big_patent\", \"d\", split=\"validation\", download_mode=\"force_redownload\")",
"I am able to download the dataset with ``` download_mode=\"force_redownload\"```. As you mentioned it was an issue with the cached version which was failed earlier due to a network issue. I am closing the issue now, once again thank you."
] | 2022-02-11T09:48:34Z
| 2022-02-14T15:26:03Z
| 2022-02-14T15:26:03Z
|
NONE
| null | null | null |
## Describe the bug
Unable to load the "big_patent" dataset
## Steps to reproduce the bug
```python
load_dataset('big_patent', 'd', 'validation')
```
## Expected results
Download big_patents' validation split from the 'd' subset
## Getting an error saying:
{FileNotFoundError}Local file ..\huggingface\datasets\downloads\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\bigPatentData\train.tar.gz doesn't exist
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:1.18.3
- Platform: Windows
- Python version:3.8
- PyArrow version:7.0.0
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Fix wrongly converted assert
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[
"Closes #3327 "
] | 2021-11-26T16:05:39Z
| 2021-11-26T16:44:12Z
| 2021-11-26T16:44:11Z
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Seems like this assertion was replaced by an exception but the condition got wrongly converted.
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Pin docutils for better doc
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The latest release of docutils make the navbar in the documentation weird and the Markdown wrongly interpreted:
We had the same problem in Transformers and solved it by pinning docutils (a dep of sphinx).
You can see the version after the change [here](https://32769-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html).
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Updating the DART file checksums in GEM
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The DART files were just updated on the source GitHub
https://github.com/Yale-LILY/dart/commit/34b3c872da4811523e334f1631e54ca8105dffab
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SNLI dataset has labels of -1
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"Hi @puzzler10, \r\nThose examples where `gold_label` field was empty, -1 label was alloted to it. In order to remove it you can filter the samples from train/val/test splits. Here's how you can drop those rows from the dataset:\r\n`dataset = load_dataset(\"snli\")`\r\n`dataset_test_filter = dataset['test'].filter(lambda example: example['label'] != -1)`\r\n\r\nI agree it should have been mentioned in the documentation. I'll raise a PR regarding the same. Thanks for pointing out!"
] | 2021-04-28T00:32:25Z
| 2021-05-17T13:34:18Z
| 2021-05-17T13:34:18Z
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NONE
| null | null | null |
There are a number of rows with a label of -1 in the SNLI dataset. The dataset descriptions [here](https://nlp.stanford.edu/projects/snli/) and [here](https://github.com/huggingface/datasets/tree/master/datasets/snli) don't list -1 as a label possibility, and neither does the dataset viewer. As examples, see index 107 or 124 of the test set.
It isn't clear what these labels mean. I found a [line of code](https://github.com/huggingface/datasets/blob/80e59ef178d3bb2090d091bc32315c655eb0633d/datasets/snli/snli.py#L94) that seems to put them in but it seems still unclear why they are there. The current workaround is to just drop the rows from any model being trained.
Perhaps the documentation should be updated.
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Help understanding how to build a dataset for language modeling as with the old TextDataset
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"\r\nI received an answer for this question on the HuggingFace Datasets forum by @lhoestq\r\n\r\nHi !\r\n\r\nIf you want to tokenize line by line, you can use this:\r\n\r\n```\r\nmax_seq_length = 512\r\nnum_proc = 4\r\n\r\ndef tokenize_function(examples):\r\n# Remove empty lines\r\nexamples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\nreturn tokenizer(\r\n examples[\"text\"],\r\n truncation=True,\r\n max_length=max_seq_length,\r\n)\r\n\r\ntokenized_dataset = dataset.map(\r\ntokenize_function,\r\nbatched=True,\r\nnum_proc=num_proc,\r\nremove_columns=[\"text\"],\r\n)\r\n```\r\n\r\nThough the TextDataset was doing a different processing by concatenating all the texts and building blocks of size 512. If you need this behavior, then you must apply an additional map function after the tokenization:\r\n\r\n```\r\n# Main data processing function that will concatenate all texts from\r\n# our dataset and generate chunks of max_seq_length.\r\ndef group_texts(examples):\r\n# Concatenate all texts.\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\ntotal_length = len(concatenated_examples[list(examples.keys())[0]])\r\n# We drop the small remainder, we could add padding if the model supported it instead of this drop,\r\n# you can customize this part to your needs.\r\ntotal_length = (total_length // max_seq_length) * max_seq_length\r\n# Split by chunks of max_len.\r\nresult = {\r\n k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n for k, t in concatenated_examples.items()\r\n}\r\nreturn result\r\n\r\n# Note that with `batched=True`, this map processes 1,000 texts together,\r\n# so group_texts throws away a remainder for each of those groups of 1,000 texts.\r\n# You can adjust that batch_size here but a higher value might be slower to preprocess.\r\n\r\ntokenized_dataset = tokenized_dataset.map(\r\ngroup_texts,\r\nbatched=True,\r\nnum_proc=num_proc,\r\n)\r\n```\r\n\r\nThis code comes from the processing of the run_mlm.py example script of transformers\r\n\r\n",
"Resolved"
] | 2021-04-29T13:16:45Z
| 2021-05-19T07:22:45Z
| 2021-05-19T07:22:39Z
|
NONE
| null | null | null |
Hello,
I am trying to load a custom dataset that I will then use for language modeling. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens limit of most tokenizers.
I would like to understand what is the process to build a text dataset that tokenizes each line, having previously split the documents in the dataset into lines of a "tokenizable" size, as the old TextDataset class would do, where you only had to do the following, and a tokenized dataset without text loss would be available to pass to a DataCollator:
```
model_checkpoint = 'distilbert-base-uncased'
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
from transformers import TextDataset
dataset = TextDataset(
tokenizer=tokenizer,
file_path="path/to/text_file.txt",
block_size=512,
)
```
For now, what I have is the following, which, of course, throws an error because each line is longer than the maximum block size in the tokenizer:
```
import datasets
dataset = datasets.load_dataset('path/to/text_file.txt')
model_checkpoint = 'distilbert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
def tokenize_function(examples):
return tokenizer(examples["text"])
tokenized_datasets = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])
tokenized_datasets
```
So what would be the "standard" way of creating a dataset in the way it was done before?
Thank you very much for the help :))
|
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| 860
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wmt16 cs-en does not donwload
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"We know host this file, so downloading should be more robust."
] | 2020-11-17T13:45:35Z
| 2022-10-05T12:27:00Z
| 2022-10-05T12:26:59Z
|
CONTRIBUTOR
| null | null | null |
Hi
I am trying with wmt16, cs-en pair, thanks for the help, perhaps similar to the ro-en issue. thanks
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "finetune_t5_trainer.py", line 109, in <dictcomp>
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "/home/rabeeh/internship/seq2seq/tasks/tasks.py", line 82, in get_dataset
dataset = load_dataset("wmt16", self.pair, split=split)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rabeeh/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz
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Add wiki_dpr
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[
"The two configurations don't have the same sizes, I may change that so that they both have 21015300 examples for convenience, even though it's supposed to have 21015324 examples in total.\r\n\r\nOne configuration only has 21015300 examples because it seems that the embeddings of the last 24 examples are missing.",
"It's ok to merge now imo. I'll make another PR if we find a way to have the missing embeddings"
] | 2020-07-01T17:12:00Z
| 2020-07-06T12:21:17Z
| 2020-07-06T12:21:16Z
|
MEMBER
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Presented in the [Dense Passage Retrieval paper](https://arxiv.org/pdf/2004.04906.pdf), this dataset consists in 21M passages from the english wikipedia along with their 768-dim embeddings computed using DPR's context encoder.
Note on the implementation:
- There are two configs: with and without the embeddings (73GB vs 14GB)
- I used a non-fixed-size sequence of floats to describe the feature format of the embeddings. I wanted to use fixed-size sequences but I had issues with reading the arrow file afterwards (for example `dataset[0]` was crashing)
- I added the case for lists of urls as input of the download_manager
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ImageFolder add option to ignore some folders like '.ipynb_checkpoints'
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[
"Maybe it would be nice to ignore private dirs like this one (ones starting with `.`) by default. \r\n\r\nCC @mariosasko ",
"Maybe we can add a `ignore_hidden_files` flag to the builder configs of our packaged loaders (to be consistent across all of them), wdyt @lhoestq @albertvillanova? ",
"I think they should always ignore them actually ! Not sure if adding a flag would be helpful",
"@lhoestq But what if the user explicitly requests those files via regex?\r\n\r\n`glob.glob` ignores hidden files (files starting with \".\") by default unless they are explicitly requested, but fsspec's `glob` doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo?",
"> @lhoestq But what if the user explicitly requests those files via regex?\r\n\r\nUsually hidden files are meant to be ignored. If they are data files, they must be placed outside a hidden directory in the first place right ? I think it's more sensible to explain this than adding a flag.\r\n\r\n> glob.glob ignores hidden files (files starting with \".\") by default unless they are explicitly requested, but fsspec's glob doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo?\r\n\r\nAfter globbing using `fsspec`, we already ignore files that start with a `.` in `_resolve_single_pattern_locally` and `_resolve_single_pattern_in_dataset_repository`, I guess we can just account for parent directories as well ?\r\n\r\nWe could open an issue on `fsspec` but I think they won't change this since it's an important breaking change for them."
] | 2022-04-06T17:29:43Z
| 2022-06-01T13:04:16Z
| 2022-06-01T13:04:16Z
|
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large.
**Describe the solution you'd like**
maybe have an option `ignore` or something .gitignore style
`dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")`
**Describe alternatives you've considered**
Could filter out manually
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[Tests] Test files locally
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"Super nice, good job @patrickvonplaten!"
] | 2020-05-08T15:02:43Z
| 2020-05-08T19:50:47Z
| 2020-05-08T15:17:00Z
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This PR adds a `aws` and a `local` decorator to the tests so that tests now run on the local datasets.
By default, the `aws` is deactivated and `local` is activated and `slow` is deactivated, so that only 1 test per dataset runs on circle ci.
**When local is activated all folders in `./datasets` are tested.**
**Important** When adding a dataset, we should no longer upload it to AWS. The steps are:
1. Open a PR
2. Add a dataset as described in `datasets/README.md`
3. If all tests pass, push to master
Currently we have 49 functional datasets in our code base.
We have 6 datasets "under-construction" that don't pass the tests - so I put them in a folder "datasets_under_construction" - it would be nice to open a PR to fix them and put them in the `datasets` folder.
**Important** when running tests locally, the datasets are cached so to rerun them delete your local cache via:
`rm -r ~/.cache/huggingface/datasets/*`
@thomwolf @mariamabarham @lhoestq
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Replace the old CircleCI badge with a new one for GH Actions.
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Fix evaluation metadata for ncbi_disease
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This PR fixes the task in the evaluation metadata and removes the metrics info as we've decided this is not a great way to propagate this information downstream.
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Update DBRD dataset card and download URL
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"not sure why the CI was not triggered though"
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| 2021-01-07T13:41:39Z
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I've added the Dutch Bood Review Dataset (DBRD) during the recent sprint. This pull request makes two minor changes:
1. I'm changing the download URL from Google Drive to the dataset's GitHub release package. This is now possible because of PR #1316.
2. I've updated the dataset card.
Cheers! 😄
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"Related to https://github.com/huggingface/datasets/issues/5245",
"#self-assign",
"Thanks @lhoestq and @alvarobartt!\r\n\r\nThis would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\r\n\r\n_c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377",
"> Thanks @lhoestq and @alvarobartt!\n> \n> \n> \n> This would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\n> \n> \n> \n> _c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377\n\nI'm almost done with at least a temporary fix to `rename_column`, `rename_columns`, and `remove_columns`, just trying to figure out how to extend it to the `map` function itself!\n\nI'll probably open the PR for review either tomorrow or Sunday hopefully! Glad I can help you and HuggingFace 🤗 ",
"Awesome - thank you so much for this PR @alvarobartt! Is much appreciated!",
"@sanchit-gandhi PR is ready and open for review at #5287, but there's still one issue I may need @lhoestq's input :hugs:",
"Let us know @sanchit-gandhi if you need a new release of `datasets` soon with this fix included :)",
"Thanks for the fix guys! We can direct people to install `datasets` from main if that's easier!",
"Hey guys, any update around this? I'm facing the same issue with a streamable dataset. ",
"Hi @asennoussi so this was already fixed and released as part of https://github.com/huggingface/datasets/releases/tag/2.8.0, so you should be able to install it as `pip install datasets==2.8.0` or just to use `pip install datasets --upgrade` to get the latest version, as of now, the https://github.com/huggingface/datasets/releases/tag/2.9.0 released last week! 🤗",
"Still facing the same issue though: \r\n```\r\nfrom datasets import IterableDatasetDict, load_dataset\r\n\r\nraw_datasets = vectorized_datasets = IterableDatasetDict()\r\n\r\n\r\nraw_datasets[\"train\"] = load_dataset(\"asennoussi/private\", split=\"train\", use_auth_token=True, streaming=True)\r\nraw_datasets[\"test\"] = load_dataset(\"asennoussi/private\", split=\"test\", use_auth_token=True, streaming=True)\r\n\r\nprint(\"Original features: \", raw_datasets['train'].features.keys())\r\n\r\n...\r\n\r\ndef prepare_dataset(batch):\r\n\r\n # load and (possibly) resample audio datato 16kHz\r\n audio = batch[\"audio\"]\r\n\r\n # compute log-Mel input features from input audio array \r\n batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\r\n # compute input length of audio sample in seconds\r\n batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\r\n \r\n # optional pre-processing steps\r\n transcription = batch[\"sentence\"]\r\n \r\n # encode target text to label ids\r\n batch[\"labels\"] = processor.tokenizer(transcription).input_ids\r\n batch[\"labels_length\"] = len(batch[\"labels\"])\r\n return batch\r\n...\r\nvectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\nprint(\"First sample:\", next(iter(vectorized_datasets['train'])))\r\n\r\n```\r\n\r\nOutput: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\n```",
"Hmm weird, could you try to print\r\n\r\n```python\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\n```\r\n\r\nagain after iterating over the `vectorized_datasets`? In the code above, should be last line :)",
"Didn't seem to fix it: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\nProcessed features: None\r\n```",
"Actually the culprit looks to be this one: \r\n`vectorized_datasets = raw_datasets.map(prepare_dataset).with_format(\"torch\")`\r\nWhen I remove this line: `vectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))`\r\n\r\nI still get \r\n```\r\nProcessed features: None\r\n```",
"The culprit is definitely `.map` \r\nJust validated it. \r\nAny idea please? ",
"> The culprit is definitely `.map` Just validated it. Any idea please?\r\n\r\nYes, indeed `.map` losses the features, because AFAIK pre-fetching the data to infer the features is expensive and not ideal, that's part of this issue https://github.com/huggingface/datasets/issues/3888\r\n\r\nAnyway, now you can pass the `features` as a param to `.map` as follows:\r\n\r\n```python\r\nfrom datasets import Features\r\nvectorized_datasets = raw_datasets.map(\r\n prepare_dataset,\r\n features=Features(\r\n {\"path\": raw_datasets[\"train\"].info.features[\"path\"], \"audio\": raw_datasets[\"train\"].info.features[\"audio\"], \"sentence\": raw_datasets[\"train\"].info.features[\"sentence\"]}\r\n ),\r\n).with_format(\"torch\")\r\n```\r\n\r\nAlso, to let you know, when calling `.remove_columns` over an `IterableDataset`, the `features` are not lost, as well as `.rename_column` and `rename_columns` :)\r\n\r\nMore information about the latter at https://github.com/huggingface/datasets/pull/5287",
"@asennoussi alternatively you can just call `._resolve_features()` from your `IterableDataset` and it will pre-fetch the data to resolve the features, but note that feature-inference is not as accurate as if you manually specify which features and feature-types the `IterableDataset` has, as mentioned in the comment above, the alternative is to provide `features` param to `.map` :hugs:",
"Got it thanks a lot! "
] | 2022-11-23T10:54:59Z
| 2023-02-02T09:05:51Z
| 2022-11-28T12:53:24Z
|
CONTRIBUTOR
| null | null | null |
### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# check original features
print("Original features: ", dataset.features.keys())
# define features to remove: we KEEP audio and text
COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id']
dataset = dataset.remove_columns(COLUMNS_TO_REMOVE)
# check processed features, uh-oh!
print("Processed features: ", dataset.features)
# streaming the first audio sample still works
print("First sample:", next(iter(ds)))
```
**Print Output:**
```
Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
Processed features: None
First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656,
-0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"}
```
### Expected behavior
The features should be those **not** removed by the `remove_column` method, i.e. audio and text.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
(Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml)
cc @polinaeterna @lhoestq
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Add `__add__()`, `__iadd__()` and similar to `Dataset` class
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"I was going through the codebase, and I believe the implementation of __add__() and __iadd__() will be similar to concatenate_datasets() after the elimination of code for arguments other than the list of datasets (info, split, axis). \r\n(Assuming elimination of axis means concatenating over axis 1.)",
"Most data frame libraries (Polars, Pandas, ...) override `__add__` to perform (mathematical) summation, so having different behavior here is a bad idea."
] | 2021-12-17T15:29:11Z
| 2023-07-25T15:33:57Z
| 2023-07-25T15:33:56Z
|
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
No.
**Describe the solution you'd like**
I would like to be able to concatenate datasets as follows:
```python
>>> dataset["train"] += dataset["validation"]
```
... instead of using `concatenate_datasets()`:
```python
>>> raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], raw_datasets["validation"]])
>>> del raw_datasets["validation"]
```
**Describe alternatives you've considered**
Well, I have considered `concatenate_datasets()` 😀
**Additional context**
N.a.
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[
"```python\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('compguesswhat', name='compguesswhat-original',split='train', streaming=True)\r\n>>> next(iter(dataset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 497, in __iter__\r\n for key, example in self._iter():\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 494, in _iter\r\n yield from ex_iterable\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py\", line 87, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/compguesswhat/4d08b9e0a8d1cf036c9626c93be4a759fdd9fcce050ea503ea14b075e830c799/compguesswhat.py\", line 251, in _generate_examples\r\n with gzip.open(filepath) as in_file:\r\n File \"/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/gzip.py\", line 58, in open\r\n binary_file = GzipFile(filename, gz_mode, compresslevel)\r\n File \"/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/gzip.py\", line 173, in __init__\r\n fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')\r\nFileNotFoundError: [Errno 2] No such file or directory: 'zip://compguesswhat-original/0.2.0/compguesswhat.train.jsonl.gz::https://www.dropbox.com/s/l0nc13udml6vs0w/compguesswhat-original.zip?dl=1'\r\n```\r\n\r\nIt's an issue with the streaming mode. Note that normal mode is used by the dataset viewer when streaming is failing, but only for the smallest datasets. This dataset is above the limit, hence the error.\r\n\r\nSame case as https://github.com/huggingface/datasets/issues/3186#issuecomment-1096549774.",
"cc @huggingface/datasets ",
"There is an issue with the URLs of their data files: https://www.dropbox.com/s/l0nc13udml6vs0w/compguesswhat-original.zip?dl=1\r\n> Dropbox Error: That didn't work for some reason\r\n\r\nError reported to their repo:\r\n- https://github.com/CompGuessWhat/compguesswhat.github.io/issues/1",
"Closed by:\r\n- #4968"
] | 2021-11-01T14:16:49Z
| 2022-09-12T08:02:29Z
| 2022-09-12T08:02:29Z
|
NONE
| null | null | null |
## Dataset viewer issue for '*compguesswhat*'
**Link:** https://huggingface.co/datasets/compguesswhat
File not found
Am I the one who added this dataset ? No
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add mutual friends conversational dataset
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| 2020-12-16T15:58:31Z
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Mutual friends dataset
WIP
TODO:
- scenario_kbs (bug with pyarrow conversion)
- download from codalab checksums bug
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Loading allenai/c4 in streaming mode does too many HEAD requests
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"https://github.com/huggingface/datasets/blob/6c766f9115d686182d76b1b937cb27e099c45d68/src/datasets/builder.py#L179-L186",
"Thanks a lot!!!"
] | 2021-08-31T21:11:04Z
| 2021-10-12T07:35:52Z
| 2021-10-11T11:05:51Z
|
MEMBER
| null | null | null |
This does 60,000+ HEAD requests to get all the ETags of all the data files:
```python
from datasets import load_dataset
load_dataset("allenai/c4", streaming=True)
```
It makes loading the dataset completely impractical.
The ETags are used to compute the config id (it must depend on the data files being used).
Instead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)
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Even though it is possible to manually get the tagset list with
```python
dset.features[field_name].feature.names
```
I think it is useful to have an overview of the used tagset on the dataset card. This is particularly useful in light of the **dataset viewer**: the tags are encoded, so it is not immediately obvious what they are for a given sample. Adding a label-int mapping should make it easier for visitors to get a grasp of what they mean.
From user-experience perspective, I would urge the full tagsets to always be available in the README's but I understand that that would take a lot of work, probably. Perhaps it can be automated?
closes #3189
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Actually disable dummy labels by default
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[] | 2021-09-27T14:50:20Z
| 2021-09-29T09:04:42Z
| 2021-09-29T09:04:41Z
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So I might have just changed the docstring instead of the actual default argument value and not realized.
@lhoestq I'm sorry >.>
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Document how to create a dataset loading script for audio/vision
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"I'm closing this issue as both the Audio and Image sections now have a \"Create dataset\" page that contains the info about writing the loading script version of a dataset."
] | 2022-07-19T08:03:40Z
| 2023-07-25T16:07:52Z
| 2023-07-25T16:07:52Z
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Currently, in our docs for Audio/Vision/Text, we explain how to:
- Load data
- Process data
However we only explain how to *Create a dataset loading script* for text data.
I think it would be useful that we add the same for Audio/Vision as these have some specificities different from Text.
See, for example:
- #4697
- and comment there: https://github.com/huggingface/datasets/issues/4697#issuecomment-1191502492
CC: @stevhliu
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Specify arguments as keywords in librosa.reshape to avoid future errors
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-18T14:58:47Z
| 2022-11-21T15:45:02Z
| 2022-11-21T15:41:57Z
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Fixes a warning and future deprecation from `librosa.reshape`:
```
FutureWarning: Pass orig_sr=16000, target_sr=48000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
```
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[BUG] No such file or directory
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| 2020-09-28T14:42:42Z
| 2020-09-28T14:42:42Z
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CONTRIBUTOR
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This happens when both
1. Huggingface datasets cache dir does not exist
2. Try to load a local dataset script
builder.py throws an error when trying to create a filelock in a directory (cache/datasets) that does not exist
https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L177
Tested on v1.0.2
@lhoestq
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Cache is not transportable
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[
"Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments.\r\n\r\nIn particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because some hashes used by caching are based on pickle dumps of the function you pass to `map`.\r\n\r\nFinally you may copy the cache to another machine, but all the `cached-*.arrow` files are unlikely to be reloaded.",
"OK good to know. Thanks @lhoestq !"
] | 2023-02-28T00:53:06Z
| 2023-02-28T21:26:52Z
| 2023-02-28T21:26:52Z
|
NONE
| null | null | null |
### Describe the bug
I would like to share cache between two machines (a Windows host machine and a WSL instance).
I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads.
I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL.
This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break.
A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place.
I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656
### Steps to reproduce the bug
View the cache directory in WSL/Windows.
### Expected behavior
Cache can be shared between (virtual) machines and be transportable.
It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location.
### Environment info
```
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- ```
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fix variable name typo
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[
"Good catch :)\r\nI think there is another occurence that needs to be fixed in the second gist (line 4924 of the notebook file):\r\n```python\r\nbleu = nlp.load_metric(...)\r\n```",
"Was fixed in e16f79b5f7fc12a6a30c777722be46897a272e6f\r\nClosing it."
] | 2020-07-01T19:13:50Z
| 2020-07-24T15:43:31Z
| 2020-07-24T08:32:16Z
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[
"Thanks for reporting @NitishkKarra.\r\n\r\nThe root source of the issue is that streaming mode is not supported out-of-the-box for that dataset, because it contains a TAR file.\r\n\r\nWe have opened a discussion in the corresponding Hub dataset page, pointing out this issue: https://huggingface.co/datasets/Theivaprakasham/wildreceipt/discussions/1\r\n\r\nI'm closing this issue here, so this discussion is transferred there instead."
] | 2022-08-01T09:46:17Z
| 2022-08-01T10:30:29Z
| 2022-08-01T10:30:29Z
|
NONE
| null | null | null |
### Link
_No response_
### Description
_No response_
### Owner
_No response_
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I_kwDODunzps5HFHWZ
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elena-soare/crawled-ecommerce: missing dataset
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[
"It's a bug! Thanks for reporting, I'm looking at it.",
"By the way, the error on our part is due to the huge size of every row (~90MB). The dataset viewer does not support such big dataset rows for the moment.\r\nAnyway, we're working to give a hint about this in the dataset viewer.",
"Fixed. See https://huggingface.co/datasets/elena-soare/crawled-ecommerce/viewer/elena-soare--crawled-ecommerce/train.\r\n\r\n<img width=\"1552\" alt=\"Capture d’écran 2022-04-12 à 11 23 51\" src=\"https://user-images.githubusercontent.com/1676121/162929722-2e2b80e2-154a-4b61-87bd-e341bd6c46e6.png\">\r\n\r\nThanks for reporting!"
] | 2022-04-05T02:25:19Z
| 2022-04-12T09:34:53Z
| 2022-04-12T09:34:53Z
|
NONE
| null | null | null |
elena-soare/crawled-ecommerce
**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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Update `unittest` assertions over tuples from `assertEqual` to `assertTupleEqual`
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[
"Hi! If the only goal is to improve readability, it's better to use `assertTupleEqual` than `assertSequenceEqual` for Python tuples. Also, note that this function is called internally by `assertEqual`, but I guess we can accept a PR to be more verbose.",
"Hi @mariosasko, right! I'll update the issue title/desc with `assertTupleEqual` even though as you said it seems to be internally using `assertEqual` so I'm not sure whether it's worth it or not...\r\n\r\nhttps://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual",
"I thought we were supposed to move gradually from `unittest` to `pytest`..."
] | 2022-05-30T12:13:18Z
| 2022-09-30T16:01:37Z
| 2022-09-30T16:01:37Z
|
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
So this is more a readability improvement rather than a proposal, wouldn't it be better to use `assertTupleEqual` over the tuples rather than `assertEqual`? As `unittest` added that function in `v3.1`, as detailed at https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual, so maybe it's worth updating.
Find an example of an `assertEqual` over a tuple in 🤗 `datasets` unit tests over an `ArrowDataset` at https://github.com/huggingface/datasets/blob/0bb47271910c8a0b628dba157988372307fca1d2/tests/test_arrow_dataset.py#L570
**Describe the solution you'd like**
Start slowly replacing all the `assertEqual` statements with `assertTupleEqual` if the assertion is done over a Python tuple, as we're doing with the Python lists using `assertListEqual` rather than `assertEqual`.
**Additional context**
If so, please let me know and I'll try to go over the tests and create a PR if applicable, otherwise, if you consider this should stay as `assertEqual` rather than `assertSequenceEqual` feel free to close this issue! Thanks 🤗
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MDU6SXNzdWU4NTk4MTA2MDI=
| 2,229
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`xnli` dataset creating a tuple key while yielding instead of `str` or `int`
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[
"Hi ! Sure sounds good. Also if you find other datasets that use tuples instead of str/int, you can also fix them !\r\nthanks :)",
"@lhoestq I have sent a PR for fixing the issue. Would be great if you could have a look! Thanks!"
] | 2021-04-16T13:21:53Z
| 2021-04-19T08:56:42Z
| 2021-04-19T08:56:42Z
|
CONTRIBUTOR
| null | null | null |
When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196
Since, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset.
I'm up for sending a fix for this, I think we can simply use `file_idx + "_" + row_idx` as a unique key instead of a tuple.
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Dataset Viewer issue for allocine
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[
"I removed my assignment as @huggingface/datasets should be able to answer better than me\r\n",
"Let me have a look...",
"Thanks for the quick fix @albertvillanova ",
"Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content *sequentially* (no random access).",
"> Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content _sequentially_ (no random access).\r\n\r\nAh thanks for the clarification! I'll look out for this next time and implement the fix myself :)"
] | 2022-06-24T13:50:38Z
| 2022-06-27T06:39:32Z
| 2022-06-24T16:44:41Z
|
MEMBER
| null | null | null |
### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No
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Support to load dataset with TSV files by passing only dataset name
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-04-28T07:30:15Z
| 2022-05-06T08:38:28Z
| 2022-05-06T08:14:07Z
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This PR implements support to load a dataset (w/o script) containing TSV files by passing only the dataset name (no need to pass `sep='\t'`):
```python
ds = load_dataset("dataset/name")
```
The refactoring allows for future builder kwargs customizations based on file extension.
Related to #4238.
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sp. Columbia => Colombia
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[
"The original openslr site mixed both names https://openslr.org/72/ :-)",
"Yeah, I filed the issue to have it fixed there last year, but it looks like they missed a few."
] | 2022-01-31T00:41:03Z
| 2022-02-09T16:55:25Z
| 2022-01-31T08:29:07Z
|
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"Columbia" is various places in North America. The country is "Colombia".
|
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Add RVL-CDIP dataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks a lot for inputs. I'll use the URL suggested and check.\r\n\r\n> we need to implement the streamable (can't use os.path.join) and the non-streamable versions of _generate_examples.\r\n\r\nSure. I will check the reference and try this out, will get back to you if I face any issues.\r\n\r\n> The labels-only data file URL doesn't work for me, so feel free to ask the authors whether they are OK with us hosting the file on the Hub/S3 (to speed up the streamable version)\r\n\r\nJust checked. The author (Adam Harley) has responded positively and allowed us to host the file. Do I share the file with you for hosting it on Hub/S3 ?",
"> Just checked. The author (Adam Harley) has responded positively and allowed us to host the file. Do I share the file with you for hosting it on Hub/S3 ?\r\n\r\nYes, feel free to e-mail me the file. Then I'll create a repo under my namespace and push the file there. We run a GH action on a GH dataset after merging to create its repo on the Hub, so after this PR is merged, I'll push the file to the \"official\" namespace and update the download link.",
"> You can use this URL to avoid manual download: https://drive.google.com/uc?export=download&id=0Bz1dfcnrpXM-MUt4cHNzUEFXcmc\r\n\r\nFor some reason, the direct download doesn't seem to work for me even with this URL. \r\n```\r\nDownloading and preparing dataset rvl_cdip/default to ~/.cache/huggingface/datasets/rvl_cdip/default/1.0.0/ea152149e06310d60a9ef3c3020199dd4780bb952a773ba5aac6b57d59f12628...\r\nDownloading data files: 100%|█████████████████████████████████████████████████████| 1/1 [00:00<00:00, 6307.22it/s]\r\n{'rvl-cdip': '~/.cache/huggingface/datasets/downloads/07ef956a33750078d570d76fefe9fed49f7dc32ecf6e872d690de11e66bbe869'}\r\n```\r\nAnd this directory does not exist. Am I doing something wrong ?\r\nTo verify, I tried using [gdown](https://github.com/wkentaro/gdown) for the above URL, we get the following : \r\n```\r\nAccess denied with the following error:\r\n\r\n Cannot retrieve the public link of the file. You may need to change\r\n the permission to 'Anyone with the link', or have had many accesses. \r\n\r\nYou may still be able to access the file from the browser:\r\n```\r\n----\r\n\r\n> Yes, feel free to e-mail me the file. Then I'll create a repo under my namespace and push the file there. We run a GH action on a GH dataset after merging to create its repo on the Hub, so after this PR is merged, I'll push the file to the \"official\" namespace and update the download link.\r\n\r\nGot it. I've sent you an email with the file. Thank you.",
"Actually this URL works for direct download :\r\n`https://drive.google.com/uc?export=download&confirm=pbef&id=0Bz1dfcnrpXM-MUt4cHNzUEFXcmc`\r\nRef : https://github.com/wkentaro/gdown/issues/146#issuecomment-1042382215\r\n\r\nI'm working on the streamable versions of _generate_examples as well, will update you regarding this.",
"Google Drive is a tricky host, and it's easy to exceed daily download quota limits, so if we are allowed to host the `rvl-cdip.tar.gz` file, I can push it to the Hub.",
"Just checked, the authors have agreed. He mentioned that he had complaints about the GDrive link.\r\nYou can push it to the Hub and share the link. :)",
"I have added :\r\n- streaming support for rvl-cdip.tar.gz file. [ Need to test this ]\r\n\r\nIs it possible for you to upload the train.txt, test.txt, val.txt files separately to the Hub instead of labels_only.tar.gz file.\r\nCurrently during the tests in stream mode, we get : \r\n`NotImplementedError: Extraction protocol for TAR archives like 'https://huggingface.co/datasets/mariosasko/rvl_cdip/resolve/main/labels_only.tar.gz' is not implemented in streaming mode. Please use dl_manager.iter_archive instead.`\r\nIf the label files are present as .txt files then we can directly use dl_manager.download.\r\n\r\n\r\n",
"The rvl-cdip.tar.gz archive and txt files with the labels are on the Hub!",
"- Added 🤗 Hub download links.\r\n- streamable and non-streamable versions of _generate_examples.\r\n- Updated dummy data, both real and dummy dataset tests have passed.\r\n\r\n",
"I've removed the extraction of the archive file locally as suggested. Let me know if any other changes are required. :)",
"The check for **Update Hub repositories / update-hub-repositories** has failed.\r\n\r\n> https://github.com/huggingface/datasets/runs/6116502392?check_suite_focus=true\r\n\r\n",
"Hi ! Thanks for reporting ;) yes this CI job has been failing for a few days. I'm working on fixing it, and I'm manually running it on my side in the meantime",
"Great. :D Thank you @lhoestq "
] | 2022-03-29T06:00:02Z
| 2022-04-22T09:55:07Z
| 2022-04-21T17:15:41Z
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Resolves #2762
Dataset Request : Add RVL-CDIP dataset [#2762](https://github.com/huggingface/datasets/issues/2762)
This PR adds the RVL-CDIP dataset.
The dataset contains Google Drive link for download and wasn't getting downloaded automatically, so I have provided manual_download_instructions.
- I have added the dummy_data.zip as well.
Needed inputs on how I can run the real data and the dummy data tests for datasets with manual download ?
Inputs and suggestions for improvement are welcome. Thank you.
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Raise error in `DatasetBuilder.as_dataset` when `file_format` is not `"arrow"`
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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) | 0.006416 / 0.011353 (-0.004937) | 0.004278 / 0.011008 (-0.006731) | 0.097562 / 0.038508 (0.059054) | 0.029488 / 0.023109 (0.006379) | 0.308648 / 0.275898 (0.032750) | 0.339879 / 0.323480 (0.016399) | 0.005288 / 0.007986 (-0.002697) | 0.005033 / 0.004328 (0.000704) | 0.074666 / 0.004250 (0.070416) | 0.034888 / 0.037052 (-0.002164) | 0.309960 / 0.258489 (0.051471) | 0.344276 / 0.293841 (0.050435) | 0.025564 / 0.128546 (-0.102982) | 0.008579 / 0.075646 (-0.067067) | 0.319796 / 0.419271 (-0.099476) | 0.044786 / 0.043533 (0.001253) | 0.308888 / 0.255139 (0.053749) | 0.334001 / 0.283200 (0.050802) | 0.089917 / 0.141683 (-0.051766) | 1.456696 / 1.452155 (0.004541) | 1.542273 / 1.492716 (0.049557) |\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) | 0.213236 / 0.018006 (0.195230) | 0.425139 / 0.000490 (0.424650) | 0.008831 / 0.000200 (0.008631) | 0.000209 / 0.000054 (0.000155) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023990 / 0.037411 (-0.013421) | 0.096787 / 0.014526 (0.082261) | 0.105783 / 0.176557 (-0.070774) | 0.167182 / 0.737135 (-0.569954) | 0.108896 / 0.296338 (-0.187442) |\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) | 0.419844 / 0.215209 (0.204635) | 4.201909 / 2.077655 (2.124254) | 1.910784 / 1.504120 (0.406664) | 1.685183 / 1.541195 (0.143988) | 1.716927 / 1.468490 (0.248437) | 0.548261 / 4.584777 (-4.036516) | 3.414168 / 3.745712 (-0.331544) | 1.695446 / 5.269862 (-3.574415) | 0.989668 / 4.565676 (-3.576008) | 0.067328 / 0.424275 (-0.356948) | 0.012084 / 0.007607 (0.004477) | 0.523799 / 0.226044 (0.297754) | 5.240589 / 2.268929 (2.971661) | 2.331618 / 55.444624 (-53.113007) | 1.996094 / 6.876477 (-4.880383) | 2.105450 / 2.142072 (-0.036623) | 0.654614 / 4.805227 (-4.150613) | 0.134721 / 6.500664 (-6.365943) | 0.066227 / 0.075469 (-0.009242) |\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) | 1.196266 / 1.841788 (-0.645521) | 13.990045 / 8.074308 (5.915737) | 13.928126 / 10.191392 (3.736734) | 0.142600 / 0.680424 (-0.537824) | 0.016462 / 0.534201 (-0.517739) | 0.363113 / 0.579283 (-0.216170) | 0.428590 / 0.434364 (-0.005773) | 0.452594 / 0.540337 (-0.087743) | 0.551678 / 1.386936 (-0.835258) |\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) | 0.005992 / 0.011353 (-0.005361) | 0.004161 / 0.011008 (-0.006847) | 0.076098 / 0.038508 (0.037589) | 0.028559 / 0.023109 (0.005450) | 0.411696 / 0.275898 (0.135798) | 0.444519 / 0.323480 (0.121040) | 0.004965 / 0.007986 (-0.003021) | 0.003452 / 0.004328 (-0.000876) | 0.075107 / 0.004250 (0.070857) | 0.037305 / 0.037052 (0.000252) | 0.429728 / 0.258489 (0.171239) | 0.444313 / 0.293841 (0.150472) | 0.025278 / 0.128546 (-0.103268) | 0.008527 / 0.075646 (-0.067120) | 0.081502 / 0.419271 (-0.337770) | 0.041237 / 0.043533 (-0.002296) | 0.417848 / 0.255139 (0.162709) | 0.426615 / 0.283200 (0.143415) | 0.094641 / 0.141683 (-0.047041) | 1.525141 / 1.452155 (0.072987) | 1.615608 / 1.492716 (0.122892) |\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) | 0.192867 / 0.018006 (0.174861) | 0.414979 / 0.000490 (0.414490) | 0.000815 / 0.000200 (0.000615) | 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) | 0.025354 / 0.037411 (-0.012058) | 0.102085 / 0.014526 (0.087559) | 0.107930 / 0.176557 (-0.068626) | 0.160483 / 0.737135 (-0.576652) | 0.112341 / 0.296338 (-0.183997) |\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) | 0.446938 / 0.215209 (0.231728) | 4.480057 / 2.077655 (2.402402) | 2.154825 / 1.504120 (0.650705) | 1.942774 / 1.541195 (0.401580) | 1.996418 / 1.468490 (0.527928) | 0.556728 / 4.584777 (-4.028049) | 3.441228 / 3.745712 (-0.304484) | 3.004179 / 5.269862 (-2.265683) | 1.314104 / 4.565676 (-3.251573) | 0.068670 / 0.424275 (-0.355606) | 0.011972 / 0.007607 (0.004365) | 0.556604 / 0.226044 (0.330560) | 5.561783 / 2.268929 (3.292855) | 2.631262 / 55.444624 (-52.813363) | 2.262143 / 6.876477 (-4.614333) | 2.364243 / 2.142072 (0.222170) | 0.660621 / 4.805227 (-4.144607) | 0.137371 / 6.500664 (-6.363293) | 0.069104 / 0.075469 (-0.006365) |\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) | 1.305706 / 1.841788 (-0.536081) | 14.015932 / 8.074308 (5.941624) | 14.353580 / 10.191392 (4.162187) | 0.146172 / 0.680424 (-0.534251) | 0.016699 / 0.534201 (-0.517502) | 0.357970 / 0.579283 (-0.221313) | 0.389067 / 0.434364 (-0.045297) | 0.415470 / 0.540337 (-0.124867) | 0.501359 / 1.386936 (-0.885577) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2b837b4e7267db9e32d2613d8bf8d70d2ce0b47 \"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) | 0.006800 / 0.011353 (-0.004552) | 0.004721 / 0.011008 (-0.006287) | 0.097760 / 0.038508 (0.059252) | 0.034192 / 0.023109 (0.011083) | 0.298240 / 0.275898 (0.022342) | 0.331119 / 0.323480 (0.007639) | 0.005826 / 0.007986 (-0.002160) | 0.003968 / 0.004328 (-0.000360) | 0.073833 / 0.004250 (0.069582) | 0.046288 / 0.037052 (0.009236) | 0.303018 / 0.258489 (0.044529) | 0.342163 / 0.293841 (0.048322) | 0.028504 / 0.128546 (-0.100042) | 0.009031 / 0.075646 (-0.066615) | 0.331617 / 0.419271 (-0.087655) | 0.060911 / 0.043533 (0.017379) | 0.304044 / 0.255139 (0.048905) | 0.328959 / 0.283200 (0.045759) | 0.113174 / 0.141683 (-0.028509) | 1.424652 / 1.452155 (-0.027502) | 1.531392 / 1.492716 (0.038676) |\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) | 0.206175 / 0.018006 (0.188169) | 0.435916 / 0.000490 (0.435426) | 0.002587 / 0.000200 (0.002387) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026996 / 0.037411 (-0.010415) | 0.106722 / 0.014526 (0.092196) | 0.117655 / 0.176557 (-0.058902) | 0.176969 / 0.737135 (-0.560166) | 0.122577 / 0.296338 (-0.173762) |\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) | 0.396086 / 0.215209 (0.180877) | 3.972465 / 2.077655 (1.894811) | 1.800798 / 1.504120 (0.296678) | 1.616747 / 1.541195 (0.075552) | 1.680711 / 1.468490 (0.212221) | 0.526479 / 4.584777 (-4.058298) | 3.791528 / 3.745712 (0.045816) | 2.989518 / 5.269862 (-2.280344) | 1.463221 / 4.565676 (-3.102455) | 0.065649 / 0.424275 (-0.358626) | 0.012155 / 0.007607 (0.004548) | 0.500241 / 0.226044 (0.274197) | 5.008895 / 2.268929 (2.739966) | 2.315288 / 55.444624 (-53.129336) | 1.959409 / 6.876477 (-4.917067) | 2.102371 / 2.142072 (-0.039701) | 0.639611 / 4.805227 (-4.165617) | 0.140101 / 6.500664 (-6.360563) | 0.063599 / 0.075469 (-0.011870) |\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) | 1.206729 / 1.841788 (-0.635059) | 15.127250 / 8.074308 (7.052942) | 14.397228 / 10.191392 (4.205836) | 0.148802 / 0.680424 (-0.531622) | 0.017628 / 0.534201 (-0.516573) | 0.396150 / 0.579283 (-0.183133) | 0.435826 / 0.434364 (0.001462) | 0.471215 / 0.540337 (-0.069122) | 0.559413 / 1.386936 (-0.827523) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006479 / 0.011353 (-0.004874) | 0.004520 / 0.011008 (-0.006488) | 0.074395 / 0.038508 (0.035887) | 0.033400 / 0.023109 (0.010291) | 0.388411 / 0.275898 (0.112513) | 0.396714 / 0.323480 (0.073234) | 0.005736 / 0.007986 (-0.002250) | 0.004038 / 0.004328 (-0.000291) | 0.073595 / 0.004250 (0.069345) | 0.045207 / 0.037052 (0.008155) | 0.378096 / 0.258489 (0.119607) | 0.417830 / 0.293841 (0.123989) | 0.028365 / 0.128546 (-0.100181) | 0.008887 / 0.075646 (-0.066760) | 0.080766 / 0.419271 (-0.338505) | 0.046923 / 0.043533 (0.003390) | 0.376190 / 0.255139 (0.121051) | 0.385875 / 0.283200 (0.102675) | 0.107542 / 0.141683 (-0.034141) | 1.409257 / 1.452155 (-0.042898) | 1.518475 / 1.492716 (0.025759) |\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) | 0.223299 / 0.018006 (0.205292) | 0.440640 / 0.000490 (0.440150) | 0.000397 / 0.000200 (0.000197) | 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) | 0.031388 / 0.037411 (-0.006024) | 0.113078 / 0.014526 (0.098552) | 0.124398 / 0.176557 (-0.052159) | 0.173802 / 0.737135 (-0.563333) | 0.129555 / 0.296338 (-0.166783) |\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) | 0.440220 / 0.215209 (0.225011) | 4.398052 / 2.077655 (2.320398) | 2.188396 / 1.504120 (0.684276) | 1.997811 / 1.541195 (0.456616) | 2.093338 / 1.468490 (0.624847) | 0.519597 / 4.584777 (-4.065180) | 3.885795 / 3.745712 (0.140083) | 2.896327 / 5.269862 (-2.373534) | 1.245785 / 4.565676 (-3.319891) | 0.065675 / 0.424275 (-0.358600) | 0.011729 / 0.007607 (0.004121) | 0.541526 / 0.226044 (0.315482) | 5.406763 / 2.268929 (3.137834) | 2.722914 / 55.444624 (-52.721711) | 2.471111 / 6.876477 (-4.405366) | 2.541488 / 2.142072 (0.399415) | 0.633566 / 4.805227 (-4.171661) | 0.139622 / 6.500664 (-6.361042) | 0.064220 / 0.075469 (-0.011249) |\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) | 1.296097 / 1.841788 (-0.545690) | 15.095320 / 8.074308 (7.021012) | 14.300821 / 10.191392 (4.109429) | 0.145470 / 0.680424 (-0.534954) | 0.017496 / 0.534201 (-0.516705) | 0.400589 / 0.579283 (-0.178694) | 0.423091 / 0.434364 (-0.011273) | 0.468258 / 0.540337 (-0.072079) | 0.570873 / 1.386936 (-0.816063) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aee6c67034d6ff298b2153a2fcdab97f14ee6d66 \"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) | 0.005918 / 0.011353 (-0.005435) | 0.004393 / 0.011008 (-0.006615) | 0.091677 / 0.038508 (0.053169) | 0.033546 / 0.023109 (0.010437) | 0.344682 / 0.275898 (0.068784) | 0.388906 / 0.323480 (0.065426) | 0.005412 / 0.007986 (-0.002574) | 0.004909 / 0.004328 (0.000580) | 0.082589 / 0.004250 (0.078339) | 0.045242 / 0.037052 (0.008190) | 0.339191 / 0.258489 (0.080702) | 0.349673 / 0.293841 (0.055832) | 0.026805 / 0.128546 (-0.101742) | 0.007529 / 0.075646 (-0.068117) | 0.319108 / 0.419271 (-0.100164) | 0.049482 / 0.043533 (0.005949) | 0.320013 / 0.255139 (0.064874) | 0.342059 / 0.283200 (0.058859) | 0.096623 / 0.141683 (-0.045060) | 1.458204 / 1.452155 (0.006049) | 1.571172 / 1.492716 (0.078455) |\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) | 0.235171 / 0.018006 (0.217165) | 0.479678 / 0.000490 (0.479188) | 0.006627 / 0.000200 (0.006427) | 0.000257 / 0.000054 (0.000202) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025716 / 0.037411 (-0.011696) | 0.107730 / 0.014526 (0.093204) | 0.111595 / 0.176557 (-0.064962) | 0.171316 / 0.737135 (-0.565819) | 0.118962 / 0.296338 (-0.177377) |\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) | 0.376318 / 0.215209 (0.161109) | 4.039484 / 2.077655 (1.961829) | 1.811548 / 1.504120 (0.307428) | 1.646728 / 1.541195 (0.105533) | 1.688071 / 1.468490 (0.219581) | 0.551256 / 4.584777 (-4.033520) | 4.153931 / 3.745712 (0.408218) | 3.424154 / 5.269862 (-1.845707) | 1.734860 / 4.565676 (-2.830816) | 0.067753 / 0.424275 (-0.356522) | 0.012699 / 0.007607 (0.005092) | 0.505722 / 0.226044 (0.279677) | 4.997321 / 2.268929 (2.728392) | 2.258755 / 55.444624 (-53.185869) | 1.954382 / 6.876477 (-4.922095) | 1.967545 / 2.142072 (-0.174527) | 0.630489 / 4.805227 (-4.174738) | 0.138738 / 6.500664 (-6.361926) | 0.064907 / 0.075469 (-0.010562) |\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) | 1.209634 / 1.841788 (-0.632154) | 15.055062 / 8.074308 (6.980754) | 12.721606 / 10.191392 (2.530214) | 0.164908 / 0.680424 (-0.515516) | 0.019528 / 0.534201 (-0.514673) | 0.400136 / 0.579283 (-0.179147) | 0.451640 / 0.434364 (0.017276) | 0.466272 / 0.540337 (-0.074065) | 0.553258 / 1.386936 (-0.833679) |\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) | 0.006341 / 0.011353 (-0.005011) | 0.004617 / 0.011008 (-0.006391) | 0.077953 / 0.038508 (0.039445) | 0.031104 / 0.023109 (0.007995) | 0.360328 / 0.275898 (0.084430) | 0.408403 / 0.323480 (0.084923) | 0.005704 / 0.007986 (-0.002282) | 0.003588 / 0.004328 (-0.000741) | 0.071441 / 0.004250 (0.067190) | 0.043520 / 0.037052 (0.006468) | 0.375798 / 0.258489 (0.117309) | 0.400955 / 0.293841 (0.107114) | 0.028166 / 0.128546 (-0.100381) | 0.008578 / 0.075646 (-0.067068) | 0.086673 / 0.419271 (-0.332598) | 0.046424 / 0.043533 (0.002891) | 0.367276 / 0.255139 (0.112137) | 0.414550 / 0.283200 (0.131351) | 0.097355 / 0.141683 (-0.044328) | 1.465191 / 1.452155 (0.013036) | 1.555028 / 1.492716 (0.062312) |\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) | 0.196642 / 0.018006 (0.178636) | 0.464221 / 0.000490 (0.463731) | 0.002726 / 0.000200 (0.002526) | 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) | 0.028078 / 0.037411 (-0.009333) | 0.110762 / 0.014526 (0.096236) | 0.122212 / 0.176557 (-0.054344) | 0.164758 / 0.737135 (-0.572377) | 0.133969 / 0.296338 (-0.162370) |\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) | 0.448134 / 0.215209 (0.232925) | 4.339335 / 2.077655 (2.261680) | 2.129209 / 1.504120 (0.625089) | 1.957805 / 1.541195 (0.416611) | 1.994038 / 1.468490 (0.525548) | 0.497101 / 4.584777 (-4.087676) | 4.114432 / 3.745712 (0.368720) | 3.437305 / 5.269862 (-1.832556) | 1.692810 / 4.565676 (-2.872866) | 0.071077 / 0.424275 (-0.353198) | 0.012735 / 0.007607 (0.005128) | 0.534393 / 0.226044 (0.308348) | 5.217445 / 2.268929 (2.948517) | 2.594858 / 55.444624 (-52.849766) | 2.317464 / 6.876477 (-4.559012) | 2.337974 / 2.142072 (0.195902) | 0.622291 / 4.805227 (-4.182936) | 0.144934 / 6.500664 (-6.355730) | 0.068524 / 0.075469 (-0.006945) |\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) | 1.310601 / 1.841788 (-0.531187) | 15.771527 / 8.074308 (7.697219) | 13.952032 / 10.191392 (3.760640) | 0.212473 / 0.680424 (-0.467951) | 0.017963 / 0.534201 (-0.516238) | 0.400755 / 0.579283 (-0.178528) | 0.439817 / 0.434364 (0.005453) | 0.472614 / 0.540337 (-0.067724) | 0.558410 / 1.386936 (-0.828526) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b51429d02a0da1ff798873afe655309136c5689 \"CML watermark\")\n"
] | 2023-05-30T14:27:55Z
| 2023-05-31T13:31:21Z
| 2023-05-31T13:23:54Z
|
CONTRIBUTOR
| null | 0
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Raise an error in `DatasetBuilder.as_dataset` when `file_format != "arrow"` (and fix the docstring)
Fix #5874
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I_kwDODunzps5mqVXy
| 5,889
|
Token Alignment for input and output data over train and test batch/dataset.
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[] | 2023-05-23T15:58:55Z
| 2023-05-23T15:58:55Z
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`data`
> DatasetDict({
train: Dataset({
features: ['input', 'output'],
num_rows: 4500
})
test: Dataset({
features: ['input', 'output'],
num_rows: 500
})
})
**# input (in-correct sentence)**
`data['train'][0]['input']`
**>>** 'We are meet sunday 10am12pmET in Crown Heights Brooklyn New York'
**# output (correct sentence)**
`data['train'][0]['output']`
**>>** 'We meet Sundays 10am-12pmET in Crown Heights, Brooklyn, New York.'
**I Want to align the output tokens with input**
```
`# tokenize both inputs and targets
def tokenize_fn(batch):
# tokenize the input sequence first
# this populates input_ids, attention_mask, etc.
tokenized_inputs = tokenizer(
batch['input']
)
labels_batch = tokenizer.tokenize(batch['output']) # original targets
aligned_labels_batch = []
for i, labels in enumerate(labels_batch):
word_ids = tokenized_inputs[i].word_ids()
aligned_labels_batch.append(align_targets(labels, word_ids)) # align_targets is another user defined function which is been called here
# recall: the 'target' must be stored in key called 'labels'
tokenized_inputs['labels'] = aligned_labels_batch
return tokenized_inputs`
```
```
data.map(
tokenize_fn,
batched=True,
remove_columns=data['train'].column_names,
)
```
When this user defined function is mapped to every records of train and test batch am getting following error:
**1.** **raise DatasetTransformationNotAllowedError(
3457 "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it."**
**2.** **TypeError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]]**
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MDU6SXNzdWU2MjMwNTQyNzA=
| 183
|
[Bug] labels of glue/ax are all -1
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[
"This is the test set given by the Glue benchmark. The labels are not provided, and therefore set to -1.",
"Ah, yeah. Why it didn’t occur to me. 😂\nThank you for your comment."
] | 2020-05-22T08:43:36Z
| 2020-05-22T22:14:05Z
| 2020-05-22T22:14:05Z
|
CONTRIBUTOR
| null | null | null |
```
ax = nlp.load_dataset('glue', 'ax')
for i in range(30): print(ax['test'][i]['label'], end=', ')
```
```
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
```
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I_kwDODunzps5wBn5R
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|
When calling load_dataset, raise error: pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
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[
"I solved the problem by modifying the \"self DEFAULT_WRITER_BATCH_SIZE\" in \"class MyDataset (datasets. GeneratorBasedBuilder) : __init__\""
] | 2023-09-04T04:14:00Z
| 2023-09-04T06:05:50Z
| 2023-09-04T06:05:49Z
|
NONE
| null | null | null |
### Describe the bug
When calling load_dataset, raise error
```
Traceback (most recent call last):
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1694, in _pre
pare_split_single
writer.write(example, key)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 490, in
write
self.write_examples_on_file()
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 448, in
write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 559, in
write_batch
self.write_table(pa_table, writer_batch_size)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 571, in
write_table
pa_table = pa_table.combine_chunks()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 3439, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
dataset = load_dataset(
^^^^^^^^^^^^^
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_da
taset
builder_instance.download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 954, in downl
oad_and_prepare
self._download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _dow
nload_and_prepare
super()._download_and_prepare(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _dow
nload_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _pre
pare_split
for job_id, done, content in self._prepare_split_single(
File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _pre
pare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
Setting num_proc from 8 back to 1 for the train split to disable multiprocessing as it only contains one shard.
09/04/2023 12:02:04 - WARNING - datasets.builder - Setting num_proc from 8 back to 1 for the train split to dis
able multiprocessing as it only contains one shard.
```
### Steps to reproduce the bug
Call load_dataset with the large image as feature
### Expected behavior
no error
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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PR_kwDODunzps43vuzF
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Eval metadata batch 3: Reddit, Rotten Tomatoes, SemEval 2010, Sentiment 140, SMS Spam, Snips, SQuAD, SQuAD v2, Timit ASR
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[
"Summary of CircleCI errors:\r\n\r\n- **sem_eval_2010_task_8**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n- **sms_spam**: `Data Instances` and`Data Splits` are empty.\r\n- **Quora** : Expected some content in section `Citation Information` but it is empty, missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n- **sentiment140**: missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n\r\nThere are also some timeout errors, I don't really understand the source though :confused: ",
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-12T20:52:02Z
| 2022-05-16T16:26:19Z
| 2022-05-16T16:18:30Z
|
NONE
| null | 0
|
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Adding evaluation metadata for:
- Reddit
- Rotten Tomatoes
- SemEval 2010
- Sentiment 140
- SMS Spam
- Snips
- SQuAD
- SQuAD v2
- Timit ASR
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I_kwDODunzps4-qbOs
| 3,253
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`GeneratorBasedBuilder` does not support `None` values
|
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[
"Hi,\r\n\r\nthanks for reporting and providing a minimal reproducible example. \r\n\r\nThis line of the PR I've linked in our discussion on the Forum will add support for `None` values:\r\nhttps://github.com/huggingface/datasets/blob/a53de01842aac65c66a49b2439e18fa93ff73ceb/src/datasets/features/features.py#L835\r\n\r\nI expect that PR to be merged soon."
] | 2021-11-11T19:51:21Z
| 2021-12-09T14:26:58Z
| 2021-12-09T14:26:58Z
|
NONE
| null | null | null |
## Describe the bug
`GeneratorBasedBuilder` does not support `None` values.
## Steps to reproduce the bug
See [this repository](https://github.com/pavel-lexyr/huggingface-datasets-bug-reproduction) for minimal reproduction.
## Expected results
Dataset is initialized with a `None` value in the `value` column.
## Actual results
```
Traceback (most recent call last):
File "main.py", line 3, in <module>
datasets.load_dataset("./bad-data")
File ".../datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File ".../datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File ".../datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File ".../datasets/builder.py", line 1103, in _prepare_split
example = self.info.features.encode_example(record)
File ".../datasets/features/features.py", line 1033, in encode_example
return encode_nested_example(self, example)
File ".../datasets/features/features.py", line 808, in encode_nested_example
return {
File ".../datasets/features/features.py", line 809, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File ".../datasets/features/features.py", line 855, in encode_nested_example
return schema.encode_example(obj)
File ".../datasets/features/features.py", line 299, in encode_example
return float(value)
TypeError: float() argument must be a string or a number, not 'NoneType'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.0
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MDExOlB1bGxSZXF1ZXN0NzA3MTgwNTgw
| 2,779
|
Fix sacrebleu tokenizers
|
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[] | 2021-08-10T09:24:27Z
| 2021-08-10T11:03:08Z
| 2021-08-10T10:57:54Z
|
MEMBER
| null | 0
|
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Last `sacrebleu` release (v2.0.0) has removed `sacrebleu.TOKENIZERS`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15
This PR makes a hot fix of the bug by using a private function in `sacrebleu`: `sacrebleu.metrics.bleu._get_tokenizer()`.
Eventually, this should be further fixed in order to use only public functions.
This is a partial hotfix of #2781.
|
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I_kwDODunzps5dayCF
| 5,496
|
Add a `reduce` method
|
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[] | null |
[
"Hi! Sure, feel free to open a PR, so we can see the API you have in mind.",
"I would like to give it a go! #self-assign",
"Closing as `Dataset.map` can be used instead (see https://github.com/huggingface/datasets/pull/5533#issuecomment-1440571658 and https://github.com/huggingface/datasets/pull/5533#issuecomment-1446403263)"
] | 2023-02-02T04:30:22Z
| 2023-07-21T14:24:32Z
| 2023-07-21T14:24:32Z
|
NONE
| null | null | null |
### Feature request
Right now the `Dataset` class implements `map()` and `filter()`, but leaves out the third functional idiom popular among Python users: `reduce`.
### Motivation
A `reduce` method is often useful when calculating dataset statistics, for example, the occurrence of a particular n-gram or the average line length of a code dataset.
### Your contribution
I haven't contributed to `datasets` before, but I don't expect this will be too difficult, since the implementation will closely follow that of `map` and `filter`. I could have a crack over the weekend.
|
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