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Wrong example in opus_gnome dataset card
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CONTRIBUTOR
| null | null | null |
## Describe the bug
I found that [the example on opus_gone dataset ](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary) doesn't work.
## Steps to reproduce the bug
```python
load_dataset("gnome", lang1="it", lang2="pl")
```
`"gnome"` should be `"opus_gnome"`
## Expected results
```bash
100%
1/1 [00:00<00:00, 42.09it/s]
DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 8368
})
})
```
## Actual results
```bash
Couldn't find 'gnome' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/gnome/gnome.py
```
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.27
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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Enable splits during streaming the dataset
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[] | 2021-09-24T15:01:29Z
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CONTRIBUTOR
| null | null | null |
## Describe the Problem
I'd like to stream only a specific percentage or part of the dataset.
I want to do splitting when I'm streaming dataset as well.
## Solution
Enabling splits when `streaming = True` as well.
`e.g. dataset = load_dataset('dataset', split='train[:100]', streaming = True)`
## Alternatives
Below is the alternative of doing it.
`dataset = load_dataset("dataset", split='train', streaming = True).take(100)`
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Fix: Bypass Virus Checks in Google Drive Links (CNN-DM dataset)
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[
"Thank you, @albertvillanova!",
"Got it. Thanks for explaining this, @albertvillanova!\r\n\r\n> On the other hand, the tests are not passing because the dummy data should also be fixed. Once done, this PR will be able to be merged into master.\r\n\r\nWill do this 👍",
"Hi ! I think we need to fix the issue for every dataset. This can be done simply by fixing how we handle Google Drive links, see my comment https://github.com/huggingface/datasets/pull/3775#issuecomment-1050970157",
"Hi @lhoestq! I think @albertvillanova has already fixed this in #3787",
"Cool ! I missed this one :) thanks",
"No problem!",
"Hi, @AngadSethi, I think that once:\r\n- #3787 \r\n\r\nwas merged, issue:\r\n- #3784 \r\n\r\nwas also fixed.\r\n\r\nTherefore, I think this PR is no longer necessary. I'm closing it. Let me know if you agree.",
"Yes, absolutely @albertvillanova! I agree :)"
] | 2022-02-25T05:48:57Z
| 2022-03-03T16:43:47Z
| 2022-03-03T14:03:37Z
|
NONE
| null | 0
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This commit fixes the issue described in #3784. By adding an extra parameter to the end of Google Drive links, we are able to bypass the virus check and download the datasets.
So, if the original link looked like https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ
The new link now looks like https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ&confirm=t
Fixes #3784
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`push_to_hub` not propagating `token` through `DownloadConfig`
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[
"#self-assign",
"@lhoestq can you close this issue as part of the recent #5205 merge? Thanks 🤗 ",
"Thank you :)"
] | 2022-11-05T23:32:20Z
| 2022-11-08T10:12:09Z
| 2022-11-08T10:12:08Z
|
CONTRIBUTOR
| null | null | null |
### Describe the bug
When trying to upload a new 🤗 Dataset to the Hub via Python, and providing the `token` as a parameter to the `Dataset.push_to_hub` function, it just works for the first time, assuming that the dataset didn't exist before.
But when trying to run `Dataset.push_to_hub` again over the same dataset, instead of updating it, it throws a `ConnectionError` when trying to retrieve the `README.md` that may contain some metadata about the dataset, so as to also update it, but since the `token` is not propagated, the `DownloadConfig` provided to the `datasets.utils.file_utils.get_from_cache` function doesn't contain the `use_auth_token` value set to `token`, it's just using the default one which is None/False.
So on, when uploading a dataset via Python with `push_to_hub` with the `token` as a parameter with the HuggingFace API Token as value, it can just be uploaded when the dataset is new, otherwise it fails with to `ConnectionError` due to the `token` not being propagated as `use_auth_token`.
### Steps to reproduce the bug
Let's create a new dataset in our HF account via Python as:
```python
from datasets import Dataset
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
```
When we create the `Dataset` for the first time it works and there are no issues, but when trying to actually upload a new version of the same dataset (same name under the same username), we encounter the following issue:
```python
from datasets import Dataset
data = {"a": [1, 2, 3], "b": [4, 5, 6]}
ds = Dataset.from_dict(data)
ds.push_to_hub(repo_id=<HF_USERNAME>/<HF_DATASET>, private=private, token=<HF_TOKEN_HERE>)
>>> ConnectionError: Couldn't reach https://huggingface.co/datasets/alvarobartt/demo/resolve/main/README.md (ConnectionError('Unauthorized for URL https://huggingface.co/datasets/<HF_USERNAME>/<HF_DATASET>/resolve/main/README.md. Please use the parameter `use_auth_token=True` after logging in with `huggingface-cli login`'))
```
### Expected behavior
Ideally, the `token` parameter provided to `push_to_hub` should be propagated and used to download the `README.md` when trying to update a `Dataset`, instead of throwing that exception, so that the authentication can be done directly through code without running `huggingface-cli login`as mentioned at https://huggingface.co/docs/datasets/upload_dataset#upload-with-python.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-13.0-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.0
- Pandas version: 1.5.1
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❓ How to remove specific rows of a dataset ?
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[
"Hi, you can't do that at the moment.",
"Can you do it by now? Coz it would be awfully helpful!",
"you can convert dataset object to pandas and remove a feature and convert back to dataset .",
"That's what I ended up doing too. but it feels like a workaround to a feature that should be added to the datasets class."
] | 2020-05-15T01:25:06Z
| 2022-07-15T08:36:44Z
| 2020-05-15T07:04:32Z
|
NONE
| null | null | null |
I saw on the [example notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb#scrollTo=efFhDWhlvSVC) how to remove a specific column :
```python
dataset.drop('id')
```
But I didn't find how to remove a specific row.
**For example, how can I remove all sample with `id` < 10 ?**
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`.map` not hashing under python 3.9
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"Tried to upgrade Python to 3.11 - still get this message. A partial solution is to NOT use `num_proc` at all. It will be considerably longer to finish the job.",
"Hi! The `model = torch.compile(model)` line is problematic for our hashing logic. We would have to merge https://github.com/huggingface/datasets/pull/5867 to support hashing `torch.compile`-ed models/functions. \r\n\r\nI've started refactoring the hashing logic and plan to incorporate a fix for `torch.compile` as part of it, so this should be addressed soon (probably this or next week). "
] | 2023-11-21T15:14:54Z
| 2023-11-28T16:29:33Z
| 2023-11-28T16:29:33Z
|
NONE
| null | null | null |
### Describe the bug
The `.map` function cannot hash under python 3.9. Tried to use [the solution here](https://github.com/huggingface/datasets/issues/4521#issuecomment-1205166653), but still get the same message:
`Parameter 'function'=<function map_to_pred at 0x7fa0b49ead30> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.`
### Steps to reproduce the bug
```python
def map_to_pred(batch):
"""
Perform inference on an audio batch
Parameters:
batch (dict): A dictionary containing audio data and other related information.
Returns:
dict: The input batch dictionary with added prediction and transcription fields.
"""
audio = batch['audio']
input_features = processor(
audio['array'], sampling_rate=audio['sampling_rate'], return_tensors="pt").input_features
input_features = input_features.to('cuda')
with torch.no_grad():
predicted_ids = model.generate(input_features)
preds = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
batch['prediction'] = processor.tokenizer._normalize(preds)
batch["transcription"] = processor.tokenizer._normalize(batch['transcription'])
return batch
MODEL_CARD = "openai/whisper-small"
MODEL_NAME = MODEL_CARD.rsplit('/', maxsplit=1)[-1]
model = WhisperForConditionalGeneration.from_pretrained(MODEL_CARD)
processor = AutoProcessor.from_pretrained(
MODEL_CARD, language="english", task="transcribe")
model = torch.compile(model)
dt = load_dataset("audiofolder", data_dir=config['DATA']['dataset'], split="test")
dt = dt.cast_column("audio", Audio(sampling_rate=16000))
result = coraal_dt.map(map_to_pred, num_proc=16)
```
### Expected behavior
Hashed and cached dataset starts inferencing
### Environment info
- `transformers` version: 4.35.0
- Platform: Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.18
- Huggingface_hub version: 0.17.3
- Safetensors version: 0.4.0
- Accelerate version: 0.24.1
- Accelerate config: not found
- PyTorch version (GPU?): 2.1.0 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no
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Add GoEmotions
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"Whoops, didn't mean for that to be merged yet (my bad). I'm reaching out to the authors since we'd like their feedback on the best way to have the `author` field anonymized or removed. Will send a patch once they get back to me."
] | 2020-12-03T12:49:53Z
| 2020-12-03T17:37:45Z
| 2020-12-03T17:30:08Z
|
CONTRIBUTOR
| null | 0
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Adds the GoEmotions dataset, a nice emotion classification dataset with 27 (multi-)label annotations on reddit comments. Includes both a large raw version and a narrowed version with predefined train/test/val splits, which I've included as separate configs with the latter as a default.
- Webpage/repo: https://github.com/google-research/google-research/tree/master/goemotions
- Paper: https://arxiv.org/abs/2005.00547
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always requiring the username in the dataset name when there is one
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[
"From what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?",
"I don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nMoreover, if someone were to create `openwebtext-10k` w/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas/openwebtext-10k`.\r\n\r\nAnd if there are 2 users with the same dataset name `foo/ds` and `bar/ds` - currently this won't work to get the correct dataset.\r\n\r\nSo really there 3 unrelated issues hiding in the current behavior.",
"This has been fixed now, and we'll do a new release of the library today.\r\n\r\nNow the stas/openwebtext-10k dataset is cached at `.cache/huggingface/datasets/stas___openwebtext10k` and openwebtext-10k would be at `.cache/huggingface/datasets/openwebtext10k`. Since they are different, the cache won't fall back on loading the wrong one anymore.\r\n\r\nSame for the python script used to generate the dataset: stas/openwebtext-10k is cached at `.cache/huggingface/modules/datasets_modules/datasets/stas___openwebtext10k` and openwebtext-10k would be at `.cache/huggingface/modules/datasets_modules/datasets/openwebtext10k`",
"Amazing! Thank you for adding this improvement, @lhoestq!",
"(can be closed?)",
"Yes indeed :) thanks"
] | 2021-08-26T23:31:53Z
| 2021-10-22T09:43:35Z
| 2021-10-22T09:43:35Z
|
CONTRIBUTOR
| null | null | null |
Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.
So both of us started with `stas/openwebtext-10k`, somewhere along the lines lost `stas/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`
So this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w/o it.
The same in code:
```
# first run
python -c "from datasets import load_dataset; load_dataset('stas/openwebtext-10k')"
# now run immediately
python -c "from datasets import load_dataset; load_dataset('openwebtext-10k')"
# the second command should fail, but it doesn't fail now.
```
Please let me know if I explained myself clearly.
Thank you!
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|
Fix typo in doc build yml
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3819). All of your documentation changes will be reflected on that endpoint."
] | 2022-03-03T20:08:44Z
| 2022-03-04T13:07:41Z
| 2022-03-04T13:07:41Z
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cc: @lhoestq
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Update format columns in Dataset.rename_columns
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| 2021-03-11T14:38:40Z
| 2021-03-11T14:38:40Z
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Fixes #2026
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Saving a dataset after setting format to torch doesn't work, but only if filtering
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"Hey, I'm a research engineer working on language modelling wanting to contribute to open source. I was wondering if I could give it a shot?",
"Hi! This issue was fixed in https://github.com/huggingface/datasets/pull/4972, so please install `datasets>=2.5.0` to avoid it."
] | 2023-02-06T21:08:58Z
| 2023-02-09T14:55:26Z
| 2023-02-09T14:55:26Z
|
NONE
| null | null | null |
### Describe the bug
Saving a dataset after setting format to torch doesn't work, but only if filtering
### Steps to reproduce the bug
```
a = Dataset.from_dict({"b": [1, 2]})
a.set_format('torch')
a.save_to_disk("test_save") # saves successfully
a.filter(None).save_to_disk("test_save_filter") # does not
>> [...] TypeError: Provided `function` which is applied to all elements of table returns a `dict` of types [<class 'torch.Tensor'>]. When using `batched=True`, make sure provided `function` returns a `dict` of types like `(<class 'list'>, <class 'numpy.ndarray'>)`.
# note: skipping the format change to torch lets this work.
### Expected behavior
Saving to work
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-6.1.9-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.9
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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add tests
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Tests for py_utils functions and for the BaseReader used to read from arrow and parquet.
I also removed unused utils functions.
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Add logo img
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3865). All of your documentation changes will be reflected on that endpoint.",
"Superceded by https://github.com/huggingface/datasets/pull/3866"
] | 2022-03-08T15:50:59Z
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Fix memory issue in multiprocessing: Don't pickle table index
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"The code quality check is going to be fixed by #2265 ",
"The memory issue didn't come from `self.__dict__.copy()` but from the fact that this dict contains `_batches` which has all the batches of the table in it.\r\nTherefore for a MemoryMappedTable all the data in `_batches` were copied in memory when pickling and this is the issue.",
"I'm still investigating why we didn't catch this issue in the tests.\r\nThis test should have caught it but didn't:\r\n\r\nhttps://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/tests/test_table.py#L350-L353",
"I'll focus on the patch release and fix the test in another PR after the release",
"Yes, I think it is better that way..."
] | 2021-04-26T09:21:35Z
| 2021-04-26T10:30:28Z
| 2021-04-26T10:08:14Z
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The table index is currently being pickled when doing multiprocessing, which brings all the record batches of the dataset in memory.
I fixed that by not pickling the index attributes. Therefore each process has to rebuild the index when unpickling the table.
Fix issue #2256
We'll do a patch release asap !
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wi_locness: reference latest leaderboard on codalab
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The dataset's author asked me to put this codalab link into the dataset's README.
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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) | 0.009491 / 0.011353 (-0.001862) | 0.004690 / 0.011008 (-0.006319) | 0.111904 / 0.038508 (0.073396) | 0.030781 / 0.023109 (0.007671) | 0.309442 / 0.275898 (0.033544) | 0.389511 / 0.323480 (0.066031) | 0.007277 / 0.007986 (-0.000709) | 0.004364 / 0.004328 (0.000036) | 0.074501 / 0.004250 (0.070250) | 0.036799 / 0.037052 (-0.000254) | 0.320279 / 0.258489 (0.061790) | 0.353887 / 0.293841 (0.060046) | 0.047969 / 0.128546 (-0.080577) | 0.017281 / 0.075646 (-0.058366) | 0.339655 / 0.419271 (-0.079617) | 0.049317 / 0.043533 (0.005784) | 0.321221 / 0.255139 (0.066082) | 0.354743 / 0.283200 (0.071544) | 0.098634 / 0.141683 (-0.043049) | 1.408640 / 1.452155 (-0.043515) | 1.488361 / 1.492716 (-0.004356) |\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) | 0.233677 / 0.018006 (0.215671) | 0.604424 / 0.000490 (0.603934) | 0.003834 / 0.000200 (0.003634) | 0.000103 / 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) | 0.022682 / 0.037411 (-0.014729) | 0.103800 / 0.014526 (0.089274) | 0.113868 / 0.176557 (-0.062689) | 0.155111 / 0.737135 (-0.582025) | 0.111862 / 0.296338 (-0.184476) |\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) | 0.474992 / 0.215209 (0.259783) | 4.755325 / 2.077655 (2.677670) | 1.889754 / 1.504120 (0.385634) | 1.597009 / 1.541195 (0.055814) | 1.639570 / 1.468490 (0.171080) | 0.970681 / 4.584777 (-3.614096) | 4.782567 / 3.745712 (1.036855) | 4.350465 / 5.269862 (-0.919397) | 2.413533 / 4.565676 (-2.152144) | 0.115510 / 0.424275 (-0.308765) | 0.011663 / 0.007607 (0.004055) | 0.626450 / 0.226044 (0.400406) | 6.238147 / 2.268929 (3.969218) | 2.603070 / 55.444624 (-52.841555) | 2.030378 / 6.876477 (-4.846099) | 1.996883 / 2.142072 (-0.145190) | 1.206436 / 4.805227 (-3.598792) | 0.203018 / 6.500664 (-6.297646) | 0.060550 / 0.075469 (-0.014919) |\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) | 1.259850 / 1.841788 (-0.581937) | 14.079936 / 8.074308 (6.005628) | 16.036329 / 10.191392 (5.844937) | 0.221546 / 0.680424 (-0.458878) | 0.042416 / 0.534201 (-0.491785) | 0.438851 / 0.579283 (-0.140432) | 0.507053 / 0.434364 (0.072689) | 0.518672 / 0.540337 (-0.021665) | 0.585278 / 1.386936 (-0.801659) |\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) | 0.010718 / 0.011353 (-0.000635) | 0.005469 / 0.011008 (-0.005539) | 0.075624 / 0.038508 (0.037116) | 0.029103 / 0.023109 (0.005994) | 0.353294 / 0.275898 (0.077395) | 0.353674 / 0.323480 (0.030194) | 0.005678 / 0.007986 (-0.002308) | 0.004610 / 0.004328 (0.000282) | 0.075213 / 0.004250 (0.070963) | 0.040032 / 0.037052 (0.002980) | 0.344363 / 0.258489 (0.085874) | 0.376861 / 0.293841 (0.083020) | 0.043718 / 0.128546 (-0.084828) | 0.016057 / 0.075646 (-0.059589) | 0.087746 / 0.419271 (-0.331526) | 0.051380 / 0.043533 (0.007848) | 0.336904 / 0.255139 (0.081765) | 0.357636 / 0.283200 (0.074436) | 0.089425 / 0.141683 (-0.052258) | 1.377462 / 1.452155 (-0.074692) | 1.448844 / 1.492716 (-0.043872) |\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) | 0.259038 / 0.018006 (0.241031) | 0.512284 / 0.000490 (0.511794) | 0.005666 / 0.000200 (0.005466) | 0.000123 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023669 / 0.037411 (-0.013742) | 0.097979 / 0.014526 (0.083453) | 0.117947 / 0.176557 (-0.058610) | 0.140764 / 0.737135 (-0.596372) | 0.114700 / 0.296338 (-0.181638) |\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) | 0.528844 / 0.215209 (0.313635) | 5.073828 / 2.077655 (2.996173) | 2.088738 / 1.504120 (0.584618) | 1.855820 / 1.541195 (0.314626) | 1.838639 / 1.468490 (0.370149) | 0.968228 / 4.584777 (-3.616549) | 4.589792 / 3.745712 (0.844079) | 2.586149 / 5.269862 (-2.683712) | 1.714241 / 4.565676 (-2.851435) | 0.124502 / 0.424275 (-0.299774) | 0.012115 / 0.007607 (0.004507) | 0.679539 / 0.226044 (0.453494) | 6.541335 / 2.268929 (4.272407) | 2.749153 / 55.444624 (-52.695471) | 2.124164 / 6.876477 (-4.752313) | 2.181249 / 2.142072 (0.039177) | 1.196846 / 4.805227 (-3.608381) | 0.213352 / 6.500664 (-6.287312) | 0.075021 / 0.075469 (-0.000448) |\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) | 1.254301 / 1.841788 (-0.587487) | 14.494254 / 8.074308 (6.419946) | 16.619679 / 10.191392 (6.428287) | 0.205158 / 0.680424 (-0.475266) | 0.022181 / 0.534201 (-0.512019) | 0.422928 / 0.579283 (-0.156355) | 0.539825 / 0.434364 (0.105461) | 0.523165 / 0.540337 (-0.017173) | 0.615014 / 1.386936 (-0.771922) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4d8a3d43569d61e73f7ab12ff3a6b48466afa8d \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011522 / 0.011353 (0.000169) | 0.006906 / 0.011008 (-0.004102) | 0.114692 / 0.038508 (0.076184) | 0.037686 / 0.023109 (0.014577) | 0.393662 / 0.275898 (0.117764) | 0.377730 / 0.323480 (0.054250) | 0.008212 / 0.007986 (0.000226) | 0.005470 / 0.004328 (0.001142) | 0.086962 / 0.004250 (0.082712) | 0.039085 / 0.037052 (0.002033) | 0.357565 / 0.258489 (0.099076) | 0.404384 / 0.293841 (0.110543) | 0.055523 / 0.128546 (-0.073023) | 0.018277 / 0.075646 (-0.057369) | 0.389812 / 0.419271 (-0.029459) | 0.058706 / 0.043533 (0.015173) | 0.344735 / 0.255139 (0.089597) | 0.395734 / 0.283200 (0.112535) | 0.096098 / 0.141683 (-0.045584) | 1.546654 / 1.452155 (0.094499) | 1.665314 / 1.492716 (0.172597) |\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) | 0.255893 / 0.018006 (0.237887) | 0.589563 / 0.000490 (0.589074) | 0.005890 / 0.000200 (0.005690) | 0.000123 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029167 / 0.037411 (-0.008245) | 0.113561 / 0.014526 (0.099036) | 0.125361 / 0.176557 (-0.051195) | 0.182225 / 0.737135 (-0.554910) | 0.125147 / 0.296338 (-0.171192) |\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) | 0.596859 / 0.215209 (0.381650) | 5.797725 / 2.077655 (3.720071) | 2.238420 / 1.504120 (0.734300) | 1.933177 / 1.541195 (0.391982) | 2.030750 / 1.468490 (0.562260) | 1.122655 / 4.584777 (-3.462122) | 5.247913 / 3.745712 (1.502201) | 2.792742 / 5.269862 (-2.477120) | 1.861487 / 4.565676 (-2.704190) | 0.133009 / 0.424275 (-0.291266) | 0.013219 / 0.007607 (0.005612) | 0.696905 / 0.226044 (0.470861) | 6.961298 / 2.268929 (4.692369) | 2.895352 / 55.444624 (-52.549273) | 2.353677 / 6.876477 (-4.522799) | 2.458804 / 2.142072 (0.316731) | 1.271905 / 4.805227 (-3.533322) | 0.224850 / 6.500664 (-6.275814) | 0.083773 / 0.075469 (0.008304) |\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) | 1.502425 / 1.841788 (-0.339363) | 16.959241 / 8.074308 (8.884933) | 19.865569 / 10.191392 (9.674177) | 0.228608 / 0.680424 (-0.451816) | 0.044035 / 0.534201 (-0.490166) | 0.545172 / 0.579283 (-0.034112) | 0.677193 / 0.434364 (0.242829) | 0.608988 / 0.540337 (0.068650) | 0.719210 / 1.386936 (-0.667726) |\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) | 0.008297 / 0.011353 (-0.003056) | 0.005729 / 0.011008 (-0.005280) | 0.084762 / 0.038508 (0.046254) | 0.030622 / 0.023109 (0.007512) | 0.408017 / 0.275898 (0.132119) | 0.432114 / 0.323480 (0.108634) | 0.006965 / 0.007986 (-0.001021) | 0.004830 / 0.004328 (0.000502) | 0.087375 / 0.004250 (0.083124) | 0.048110 / 0.037052 (0.011058) | 0.414978 / 0.258489 (0.156489) | 0.446136 / 0.293841 (0.152295) | 0.064351 / 0.128546 (-0.064195) | 0.018273 / 0.075646 (-0.057374) | 0.114853 / 0.419271 (-0.304418) | 0.056962 / 0.043533 (0.013429) | 0.427791 / 0.255139 (0.172652) | 0.428829 / 0.283200 (0.145629) | 0.108004 / 0.141683 (-0.033679) | 1.639285 / 1.452155 (0.187130) | 1.652106 / 1.492716 (0.159390) |\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) | 0.359744 / 0.018006 (0.341738) | 0.596060 / 0.000490 (0.595570) | 0.025448 / 0.000200 (0.025248) | 0.000158 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026348 / 0.037411 (-0.011064) | 0.119153 / 0.014526 (0.104628) | 0.129304 / 0.176557 (-0.047253) | 0.195670 / 0.737135 (-0.541465) | 0.135559 / 0.296338 (-0.160780) |\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) | 0.588963 / 0.215209 (0.373754) | 5.682957 / 2.077655 (3.605302) | 2.380178 / 1.504120 (0.876059) | 2.131299 / 1.541195 (0.590104) | 2.167839 / 1.468490 (0.699349) | 1.126418 / 4.584777 (-3.458359) | 5.289104 / 3.745712 (1.543392) | 2.952128 / 5.269862 (-2.317734) | 1.922974 / 4.565676 (-2.642702) | 0.143874 / 0.424275 (-0.280401) | 0.015399 / 0.007607 (0.007792) | 0.815675 / 0.226044 (0.589631) | 7.320146 / 2.268929 (5.051217) | 3.453670 / 55.444624 (-51.990954) | 2.579133 / 6.876477 (-4.297344) | 2.532331 / 2.142072 (0.390258) | 1.345881 / 4.805227 (-3.459347) | 0.242448 / 6.500664 (-6.258216) | 0.070007 / 0.075469 (-0.005462) |\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) | 1.433173 / 1.841788 (-0.408614) | 17.127287 / 8.074308 (9.052979) | 17.953878 / 10.191392 (7.762486) | 0.220035 / 0.680424 (-0.460389) | 0.028660 / 0.534201 (-0.505541) | 0.496233 / 0.579283 (-0.083050) | 0.591587 / 0.434364 (0.157223) | 0.635204 / 0.540337 (0.094867) | 0.702143 / 1.386936 (-0.684793) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7cfac43b980ab9e4a69c2328f085770996323005 \"CML watermark\")\n"
] | 2023-02-02T08:56:15Z
| 2023-02-02T11:26:08Z
| 2023-02-02T11:17:15Z
|
MEMBER
| null | 0
|
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Using `use_auth_token=True` is not needed anymore. If a user logged in, the token will be automatically retrieved. Also include a mention for gated repos
See https://github.com/huggingface/huggingface_hub/pull/1064
|
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https://api.github.com/repos/huggingface/datasets/issues/6044
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https://github.com/huggingface/datasets/pull/6044
| 1,808,057,906
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PR_kwDODunzps5Vr7jr
| 6,044
|
Rename "pattern" to "path" in YAML data_files configs
|
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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) | 0.006543 / 0.011353 (-0.004809) | 0.004085 / 0.011008 (-0.006924) | 0.083989 / 0.038508 (0.045481) | 0.074733 / 0.023109 (0.051623) | 0.310839 / 0.275898 (0.034941) | 0.333540 / 0.323480 (0.010060) | 0.005566 / 0.007986 (-0.002419) | 0.003461 / 0.004328 (-0.000868) | 0.065194 / 0.004250 (0.060943) | 0.057007 / 0.037052 (0.019954) | 0.325633 / 0.258489 (0.067144) | 0.351665 / 0.293841 (0.057824) | 0.030561 / 0.128546 (-0.097985) | 0.008579 / 0.075646 (-0.067068) | 0.287457 / 0.419271 (-0.131815) | 0.063554 / 0.043533 (0.020021) | 0.309182 / 0.255139 (0.054043) | 0.327809 / 0.283200 (0.044609) | 0.034470 / 0.141683 (-0.107213) | 1.452098 / 1.452155 (-0.000057) | 1.527130 / 1.492716 (0.034414) |\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) | 0.241736 / 0.018006 (0.223729) | 0.552432 / 0.000490 (0.551943) | 0.004085 / 0.000200 (0.003885) | 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) | 0.027290 / 0.037411 (-0.010121) | 0.081250 / 0.014526 (0.066724) | 0.094739 / 0.176557 (-0.081818) | 0.150424 / 0.737135 (-0.586711) | 0.095488 / 0.296338 (-0.200851) |\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) | 0.377245 / 0.215209 (0.162036) | 3.781021 / 2.077655 (1.703366) | 1.820092 / 1.504120 (0.315972) | 1.654420 / 1.541195 (0.113225) | 1.751256 / 1.468490 (0.282766) | 0.475161 / 4.584777 (-4.109616) | 3.603462 / 3.745712 (-0.142251) | 5.437837 / 5.269862 (0.167975) | 3.305598 / 4.565676 (-1.260079) | 0.055856 / 0.424275 (-0.368419) | 0.007259 / 0.007607 (-0.000348) | 0.454205 / 0.226044 (0.228161) | 4.544157 / 2.268929 (2.275229) | 2.296776 / 55.444624 (-53.147848) | 1.951017 / 6.876477 (-4.925459) | 2.128759 / 2.142072 (-0.013313) | 0.590354 / 4.805227 (-4.214873) | 0.129974 / 6.500664 (-6.370690) | 0.059506 / 0.075469 (-0.015963) |\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) | 1.285866 / 1.841788 (-0.555921) | 19.419446 / 8.074308 (11.345138) | 13.985108 / 10.191392 (3.793716) | 0.146803 / 0.680424 (-0.533620) | 0.018176 / 0.534201 (-0.516025) | 0.392345 / 0.579283 (-0.186938) | 0.405394 / 0.434364 (-0.028970) | 0.454649 / 0.540337 (-0.085688) | 0.633075 / 1.386936 (-0.753861) |\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) | 0.006497 / 0.011353 (-0.004855) | 0.004092 / 0.011008 (-0.006916) | 0.064908 / 0.038508 (0.026400) | 0.073494 / 0.023109 (0.050385) | 0.382227 / 0.275898 (0.106329) | 0.407320 / 0.323480 (0.083840) | 0.005653 / 0.007986 (-0.002332) | 0.003500 / 0.004328 (-0.000829) | 0.064570 / 0.004250 (0.060320) | 0.058733 / 0.037052 (0.021681) | 0.385702 / 0.258489 (0.127213) | 0.426463 / 0.293841 (0.132622) | 0.031073 / 0.128546 (-0.097473) | 0.008710 / 0.075646 (-0.066936) | 0.071378 / 0.419271 (-0.347893) | 0.050141 / 0.043533 (0.006608) | 0.377769 / 0.255139 (0.122630) | 0.395016 / 0.283200 (0.111816) | 0.025158 / 0.141683 (-0.116525) | 1.470503 / 1.452155 (0.018348) | 1.532742 / 1.492716 (0.040026) |\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) | 0.214249 / 0.018006 (0.196243) | 0.583580 / 0.000490 (0.583090) | 0.004027 / 0.000200 (0.003828) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030186 / 0.037411 (-0.007226) | 0.086927 / 0.014526 (0.072401) | 0.102060 / 0.176557 (-0.074497) | 0.156281 / 0.737135 (-0.580855) | 0.100825 / 0.296338 (-0.195514) |\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) | 0.419942 / 0.215209 (0.204733) | 4.183797 / 2.077655 (2.106142) | 2.205079 / 1.504120 (0.700959) | 2.071219 / 1.541195 (0.530024) | 2.194047 / 1.468490 (0.725557) | 0.478768 / 4.584777 (-4.106009) | 3.584864 / 3.745712 (-0.160848) | 3.371635 / 5.269862 (-1.898227) | 2.022134 / 4.565676 (-2.543542) | 0.056553 / 0.424275 (-0.367722) | 0.007231 / 0.007607 (-0.000376) | 0.493158 / 0.226044 (0.267113) | 4.934370 / 2.268929 (2.665441) | 2.699593 / 55.444624 (-52.745031) | 2.396371 / 6.876477 (-4.480105) | 2.438052 / 2.142072 (0.295979) | 0.589578 / 4.805227 (-4.215649) | 0.147234 / 6.500664 (-6.353430) | 0.062049 / 0.075469 (-0.013420) |\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) | 1.318246 / 1.841788 (-0.523542) | 19.829025 / 8.074308 (11.754717) | 14.314825 / 10.191392 (4.123433) | 0.168309 / 0.680424 (-0.512115) | 0.018596 / 0.534201 (-0.515605) | 0.397540 / 0.579283 (-0.181743) | 0.421280 / 0.434364 (-0.013084) | 0.479917 / 0.540337 (-0.060421) | 0.643494 / 1.386936 (-0.743442) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5be59becaa65f1fa08129091b8c778823e4a50ac \"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) | 0.008349 / 0.011353 (-0.003004) | 0.005362 / 0.011008 (-0.005646) | 0.100777 / 0.038508 (0.062269) | 0.078719 / 0.023109 (0.055609) | 0.398105 / 0.275898 (0.122207) | 0.444189 / 0.323480 (0.120709) | 0.006834 / 0.007986 (-0.001152) | 0.004642 / 0.004328 (0.000314) | 0.076284 / 0.004250 (0.072034) | 0.062738 / 0.037052 (0.025685) | 0.409532 / 0.258489 (0.151043) | 0.447218 / 0.293841 (0.153377) | 0.052996 / 0.128546 (-0.075550) | 0.012977 / 0.075646 (-0.062669) | 0.347687 / 0.419271 (-0.071585) | 0.068076 / 0.043533 (0.024543) | 0.394526 / 0.255139 (0.139387) | 0.434110 / 0.283200 (0.150910) | 0.041719 / 0.141683 (-0.099963) | 1.759109 / 1.452155 (0.306955) | 1.866049 / 1.492716 (0.373333) |\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) | 0.287633 / 0.018006 (0.269627) | 0.611540 / 0.000490 (0.611051) | 0.005388 / 0.000200 (0.005188) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027394 / 0.037411 (-0.010017) | 0.089796 / 0.014526 (0.075270) | 0.106931 / 0.176557 (-0.069625) | 0.173560 / 0.737135 (-0.563575) | 0.106948 / 0.296338 (-0.189391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.575156 / 0.215209 (0.359947) | 5.674170 / 2.077655 (3.596516) | 2.463090 / 1.504120 (0.958971) | 2.128245 / 1.541195 (0.587050) | 2.118982 / 1.468490 (0.650492) | 0.876976 / 4.584777 (-3.707801) | 5.238229 / 3.745712 (1.492517) | 4.548788 / 5.269862 (-0.721074) | 2.905243 / 4.565676 (-1.660433) | 0.090750 / 0.424275 (-0.333525) | 0.008266 / 0.007607 (0.000659) | 0.693305 / 0.226044 (0.467260) | 7.126970 / 2.268929 (4.858041) | 3.152131 / 55.444624 (-52.292494) | 2.532118 / 6.876477 (-4.344359) | 2.678442 / 2.142072 (0.536369) | 0.932745 / 4.805227 (-3.872483) | 0.196290 / 6.500664 (-6.304374) | 0.074082 / 0.075469 (-0.001387) |\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) | 1.599636 / 1.841788 (-0.242152) | 23.271435 / 8.074308 (15.197127) | 19.696709 / 10.191392 (9.505317) | 0.222668 / 0.680424 (-0.457756) | 0.029088 / 0.534201 (-0.505113) | 0.492477 / 0.579283 (-0.086806) | 0.580578 / 0.434364 (0.146214) | 0.558852 / 0.540337 (0.018514) | 0.762083 / 1.386936 (-0.624853) |\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) | 0.009021 / 0.011353 (-0.002332) | 0.005011 / 0.011008 (-0.005997) | 0.076504 / 0.038508 (0.037996) | 0.077303 / 0.023109 (0.054193) | 0.480660 / 0.275898 (0.204762) | 0.493944 / 0.323480 (0.170464) | 0.006339 / 0.007986 (-0.001646) | 0.004302 / 0.004328 (-0.000026) | 0.076228 / 0.004250 (0.071978) | 0.060805 / 0.037052 (0.023753) | 0.477539 / 0.258489 (0.219050) | 0.496799 / 0.293841 (0.202958) | 0.049495 / 0.128546 (-0.079052) | 0.013333 / 0.075646 (-0.062313) | 0.087217 / 0.419271 (-0.332055) | 0.061451 / 0.043533 (0.017918) | 0.485169 / 0.255139 (0.230030) | 0.487348 / 0.283200 (0.204149) | 0.035874 / 0.141683 (-0.105809) | 1.829137 / 1.452155 (0.376982) | 1.906151 / 1.492716 (0.413435) |\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) | 0.304526 / 0.018006 (0.286520) | 0.627499 / 0.000490 (0.627009) | 0.003786 / 0.000200 (0.003586) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035512 / 0.037411 (-0.001899) | 0.096684 / 0.014526 (0.082158) | 0.111879 / 0.176557 (-0.064678) | 0.171489 / 0.737135 (-0.565647) | 0.112175 / 0.296338 (-0.184164) |\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) | 0.604791 / 0.215209 (0.389582) | 6.089137 / 2.077655 (4.011482) | 2.883237 / 1.504120 (1.379117) | 2.561109 / 1.541195 (1.019914) | 2.542400 / 1.468490 (1.073910) | 0.852828 / 4.584777 (-3.731949) | 5.236812 / 3.745712 (1.491100) | 4.756429 / 5.269862 (-0.513432) | 2.885660 / 4.565676 (-1.680016) | 0.095643 / 0.424275 (-0.328632) | 0.008403 / 0.007607 (0.000796) | 0.727707 / 0.226044 (0.501663) | 7.428002 / 2.268929 (5.159074) | 3.816051 / 55.444624 (-51.628573) | 2.971057 / 6.876477 (-3.905420) | 2.915965 / 2.142072 (0.773893) | 1.006553 / 4.805227 (-3.798674) | 0.201840 / 6.500664 (-6.298824) | 0.080795 / 0.075469 (0.005326) |\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) | 1.794951 / 1.841788 (-0.046837) | 23.624556 / 8.074308 (15.550248) | 21.856195 / 10.191392 (11.664802) | 0.253043 / 0.680424 (-0.427381) | 0.031201 / 0.534201 (-0.503000) | 0.461641 / 0.579283 (-0.117642) | 0.577789 / 0.434364 (0.143425) | 0.569197 / 0.540337 (0.028860) | 0.780111 / 1.386936 (-0.606825) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4904f14459c862f0ab525ec034a636177be5dee4 \"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) | 0.007646 / 0.011353 (-0.003707) | 0.004750 / 0.011008 (-0.006258) | 0.097981 / 0.038508 (0.059473) | 0.088989 / 0.023109 (0.065880) | 0.377732 / 0.275898 (0.101834) | 0.406805 / 0.323480 (0.083325) | 0.006389 / 0.007986 (-0.001597) | 0.003854 / 0.004328 (-0.000474) | 0.073977 / 0.004250 (0.069727) | 0.066497 / 0.037052 (0.029444) | 0.371498 / 0.258489 (0.113009) | 0.417352 / 0.293841 (0.123511) | 0.036326 / 0.128546 (-0.092220) | 0.009876 / 0.075646 (-0.065770) | 0.330142 / 0.419271 (-0.089130) | 0.062423 / 0.043533 (0.018890) | 0.369375 / 0.255139 (0.114236) | 0.406048 / 0.283200 (0.122848) | 0.026564 / 0.141683 (-0.115119) | 1.713295 / 1.452155 (0.261140) | 1.797493 / 1.492716 (0.304777) |\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) | 0.231889 / 0.018006 (0.213882) | 0.512497 / 0.000490 (0.512007) | 0.000390 / 0.000200 (0.000190) | 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) | 0.033978 / 0.037411 (-0.003433) | 0.100117 / 0.014526 (0.085592) | 0.112460 / 0.176557 (-0.064097) | 0.179936 / 0.737135 (-0.557200) | 0.114277 / 0.296338 (-0.182061) |\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) | 0.461320 / 0.215209 (0.246111) | 4.563180 / 2.077655 (2.485526) | 2.249474 / 1.504120 (0.745354) | 2.100450 / 1.541195 (0.559255) | 2.231080 / 1.468490 (0.762590) | 0.567907 / 4.584777 (-4.016870) | 4.117233 / 3.745712 (0.371521) | 4.943159 / 5.269862 (-0.326703) | 3.112299 / 4.565676 (-1.453377) | 0.065500 / 0.424275 (-0.358775) | 0.008407 / 0.007607 (0.000800) | 0.545928 / 0.226044 (0.319883) | 5.508058 / 2.268929 (3.239129) | 2.834645 / 55.444624 (-52.609980) | 2.440328 / 6.876477 (-4.436148) | 2.680483 / 2.142072 (0.538410) | 0.697191 / 4.805227 (-4.108036) | 0.176646 / 6.500664 (-6.324018) | 0.073608 / 0.075469 (-0.001861) |\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) | 1.451865 / 1.841788 (-0.389922) | 22.752595 / 8.074308 (14.678287) | 15.543338 / 10.191392 (5.351946) | 0.214644 / 0.680424 (-0.465780) | 0.022050 / 0.534201 (-0.512151) | 0.463898 / 0.579283 (-0.115385) | 0.481691 / 0.434364 (0.047327) | 0.549715 / 0.540337 (0.009378) | 0.773595 / 1.386936 (-0.613341) |\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) | 0.007541 / 0.011353 (-0.003812) | 0.004715 / 0.011008 (-0.006293) | 0.076782 / 0.038508 (0.038274) | 0.086242 / 0.023109 (0.063133) | 0.458053 / 0.275898 (0.182155) | 0.503097 / 0.323480 (0.179617) | 0.006262 / 0.007986 (-0.001724) | 0.003882 / 0.004328 (-0.000447) | 0.075669 / 0.004250 (0.071419) | 0.066004 / 0.037052 (0.028952) | 0.469439 / 0.258489 (0.210950) | 0.529744 / 0.293841 (0.235903) | 0.037228 / 0.128546 (-0.091319) | 0.009794 / 0.075646 (-0.065852) | 0.082464 / 0.419271 (-0.336808) | 0.058797 / 0.043533 (0.015264) | 0.452069 / 0.255139 (0.196930) | 0.488246 / 0.283200 (0.205046) | 0.029324 / 0.141683 (-0.112359) | 1.742237 / 1.452155 (0.290082) | 1.839676 / 1.492716 (0.346959) |\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) | 0.228106 / 0.018006 (0.210100) | 0.491632 / 0.000490 (0.491142) | 0.004993 / 0.000200 (0.004793) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035413 / 0.037411 (-0.001999) | 0.104617 / 0.014526 (0.090091) | 0.121948 / 0.176557 (-0.054609) | 0.186233 / 0.737135 (-0.550902) | 0.121574 / 0.296338 (-0.174764) |\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) | 0.473849 / 0.215209 (0.258640) | 4.788312 / 2.077655 (2.710657) | 2.470535 / 1.504120 (0.966415) | 2.270393 / 1.541195 (0.729198) | 2.361096 / 1.468490 (0.892606) | 0.556184 / 4.584777 (-4.028593) | 4.216852 / 3.745712 (0.471140) | 3.901718 / 5.269862 (-1.368143) | 2.355209 / 4.565676 (-2.210467) | 0.066708 / 0.424275 (-0.357567) | 0.008709 / 0.007607 (0.001102) | 0.571714 / 0.226044 (0.345669) | 5.663150 / 2.268929 (3.394221) | 3.025769 / 55.444624 (-52.418855) | 2.652554 / 6.876477 (-4.223923) | 2.750555 / 2.142072 (0.608483) | 0.681536 / 4.805227 (-4.123691) | 0.157187 / 6.500664 (-6.343477) | 0.073533 / 0.075469 (-0.001936) |\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) | 1.604630 / 1.841788 (-0.237158) | 22.735629 / 8.074308 (14.661321) | 16.762347 / 10.191392 (6.570955) | 0.175514 / 0.680424 (-0.504910) | 0.021497 / 0.534201 (-0.512704) | 0.461438 / 0.579283 (-0.117845) | 0.476184 / 0.434364 (0.041820) | 0.571048 / 0.540337 (0.030710) | 0.747086 / 1.386936 (-0.639850) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ea38fc40ee2b10d3b5c6df09b09ad05e02a2cff \"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) | 0.006889 / 0.011353 (-0.004464) | 0.004241 / 0.011008 (-0.006767) | 0.084542 / 0.038508 (0.046034) | 0.080484 / 0.023109 (0.057374) | 0.309356 / 0.275898 (0.033458) | 0.338548 / 0.323480 (0.015068) | 0.004904 / 0.007986 (-0.003082) | 0.005220 / 0.004328 (0.000892) | 0.065501 / 0.004250 (0.061251) | 0.062095 / 0.037052 (0.025043) | 0.317332 / 0.258489 (0.058843) | 0.364797 / 0.293841 (0.070956) | 0.030492 / 0.128546 (-0.098054) | 0.008991 / 0.075646 (-0.066656) | 0.288274 / 0.419271 (-0.130998) | 0.052582 / 0.043533 (0.009049) | 0.310838 / 0.255139 (0.055699) | 0.346304 / 0.283200 (0.063104) | 0.027968 / 0.141683 (-0.113715) | 1.509727 / 1.452155 (0.057573) | 1.577410 / 1.492716 (0.084694) |\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) | 0.269725 / 0.018006 (0.251719) | 0.627685 / 0.000490 (0.627195) | 0.000419 / 0.000200 (0.000219) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031022 / 0.037411 (-0.006389) | 0.081858 / 0.014526 (0.067332) | 0.099477 / 0.176557 (-0.077080) | 0.162981 / 0.737135 (-0.574154) | 0.101987 / 0.296338 (-0.194351) |\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) | 0.386297 / 0.215209 (0.171088) | 3.845321 / 2.077655 (1.767666) | 1.834446 / 1.504120 (0.330326) | 1.699730 / 1.541195 (0.158536) | 1.764342 / 1.468490 (0.295852) | 0.486423 / 4.584777 (-4.098354) | 3.527595 / 3.745712 (-0.218117) | 4.137034 / 5.269862 (-1.132827) | 2.590457 / 4.565676 (-1.975219) | 0.057598 / 0.424275 (-0.366677) | 0.007318 / 0.007607 (-0.000289) | 0.460775 / 0.226044 (0.234730) | 4.627576 / 2.268929 (2.358647) | 2.402566 / 55.444624 (-53.042059) | 2.011392 / 6.876477 (-4.865085) | 2.223915 / 2.142072 (0.081842) | 0.623217 / 4.805227 (-4.182011) | 0.148875 / 6.500664 (-6.351789) | 0.059799 / 0.075469 (-0.015671) |\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) | 1.290768 / 1.841788 (-0.551020) | 20.455083 / 8.074308 (12.380775) | 13.469846 / 10.191392 (3.278454) | 0.170329 / 0.680424 (-0.510095) | 0.018409 / 0.534201 (-0.515792) | 0.394356 / 0.579283 (-0.184927) | 0.422685 / 0.434364 (-0.011679) | 0.476241 / 0.540337 (-0.064096) | 0.662682 / 1.386936 (-0.724254) |\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) | 0.006724 / 0.011353 (-0.004629) | 0.004508 / 0.011008 (-0.006500) | 0.065304 / 0.038508 (0.026796) | 0.080243 / 0.023109 (0.057133) | 0.384545 / 0.275898 (0.108647) | 0.415234 / 0.323480 (0.091754) | 0.006361 / 0.007986 (-0.001624) | 0.004193 / 0.004328 (-0.000135) | 0.065940 / 0.004250 (0.061689) | 0.063633 / 0.037052 (0.026581) | 0.392799 / 0.258489 (0.134310) | 0.443618 / 0.293841 (0.149777) | 0.031134 / 0.128546 (-0.097412) | 0.009058 / 0.075646 (-0.066588) | 0.071051 / 0.419271 (-0.348221) | 0.049096 / 0.043533 (0.005563) | 0.379526 / 0.255139 (0.124387) | 0.403370 / 0.283200 (0.120171) | 0.026378 / 0.141683 (-0.115305) | 1.457879 / 1.452155 (0.005724) | 1.562890 / 1.492716 (0.070174) |\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) | 0.304416 / 0.018006 (0.286410) | 0.626046 / 0.000490 (0.625557) | 0.000469 / 0.000200 (0.000269) | 0.000057 / 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) | 0.032979 / 0.037411 (-0.004433) | 0.086769 / 0.014526 (0.072243) | 0.108188 / 0.176557 (-0.068369) | 0.163077 / 0.737135 (-0.574058) | 0.106276 / 0.296338 (-0.190062) |\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) | 0.406922 / 0.215209 (0.191713) | 4.052828 / 2.077655 (1.975174) | 2.084802 / 1.504120 (0.580682) | 1.927263 / 1.541195 (0.386069) | 1.956078 / 1.468490 (0.487587) | 0.480110 / 4.584777 (-4.104667) | 3.553022 / 3.745712 (-0.192691) | 3.554450 / 5.269862 (-1.715411) | 2.082681 / 4.565676 (-2.482995) | 0.056711 / 0.424275 (-0.367564) | 0.007374 / 0.007607 (-0.000234) | 0.480555 / 0.226044 (0.254510) | 4.795851 / 2.268929 (2.526923) | 2.606675 / 55.444624 (-52.837949) | 2.249964 / 6.876477 (-4.626512) | 2.274234 / 2.142072 (0.132162) | 0.571767 / 4.805227 (-4.233461) | 0.133312 / 6.500664 (-6.367352) | 0.061703 / 0.075469 (-0.013766) |\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) | 1.354308 / 1.841788 (-0.487479) | 20.959352 / 8.074308 (12.885044) | 14.158420 / 10.191392 (3.967028) | 0.197959 / 0.680424 (-0.482465) | 0.018412 / 0.534201 (-0.515789) | 0.394307 / 0.579283 (-0.184976) | 0.402455 / 0.434364 (-0.031909) | 0.463314 / 0.540337 (-0.077024) | 0.621050 / 1.386936 (-0.765886) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7298d4d1b169442a8d0bc8c1667298bb89ca501 \"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) | 0.007179 / 0.011353 (-0.004174) | 0.004318 / 0.011008 (-0.006690) | 0.085209 / 0.038508 (0.046701) | 0.089989 / 0.023109 (0.066880) | 0.328188 / 0.275898 (0.052290) | 0.346027 / 0.323480 (0.022547) | 0.005711 / 0.007986 (-0.002275) | 0.003703 / 0.004328 (-0.000625) | 0.065419 / 0.004250 (0.061169) | 0.065354 / 0.037052 (0.028301) | 0.314531 / 0.258489 (0.056042) | 0.354357 / 0.293841 (0.060516) | 0.030918 / 0.128546 (-0.097628) | 0.008632 / 0.075646 (-0.067015) | 0.286817 / 0.419271 (-0.132455) | 0.065267 / 0.043533 (0.021735) | 0.310918 / 0.255139 (0.055779) | 0.330497 / 0.283200 (0.047298) | 0.035695 / 0.141683 (-0.105988) | 1.471101 / 1.452155 (0.018947) | 1.538658 / 1.492716 (0.045942) |\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) | 0.254314 / 0.018006 (0.236308) | 0.591413 / 0.000490 (0.590923) | 0.006082 / 0.000200 (0.005882) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031843 / 0.037411 (-0.005568) | 0.089968 / 0.014526 (0.075442) | 0.101838 / 0.176557 (-0.074718) | 0.164401 / 0.737135 (-0.572734) | 0.103785 / 0.296338 (-0.192554) |\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) | 0.380486 / 0.215209 (0.165277) | 3.798868 / 2.077655 (1.721213) | 1.824645 / 1.504120 (0.320525) | 1.660804 / 1.541195 (0.119610) | 1.784793 / 1.468490 (0.316303) | 0.487222 / 4.584777 (-4.097555) | 3.560580 / 3.745712 (-0.185132) | 5.392662 / 5.269862 (0.122800) | 3.295327 / 4.565676 (-1.270350) | 0.057699 / 0.424275 (-0.366576) | 0.007559 / 0.007607 (-0.000048) | 0.459655 / 0.226044 (0.233611) | 4.587583 / 2.268929 (2.318654) | 2.304845 / 55.444624 (-53.139779) | 1.966433 / 6.876477 (-4.910044) | 2.254591 / 2.142072 (0.112519) | 0.582978 / 4.805227 (-4.222250) | 0.133455 / 6.500664 (-6.367210) | 0.061924 / 0.075469 (-0.013546) |\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) | 1.275685 / 1.841788 (-0.566103) | 20.814545 / 8.074308 (12.740237) | 13.753567 / 10.191392 (3.562175) | 0.164076 / 0.680424 (-0.516348) | 0.018768 / 0.534201 (-0.515433) | 0.390991 / 0.579283 (-0.188293) | 0.404417 / 0.434364 (-0.029947) | 0.457522 / 0.540337 (-0.082815) | 0.624654 / 1.386936 (-0.762282) |\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) | 0.007435 / 0.011353 (-0.003918) | 0.004255 / 0.011008 (-0.006754) | 0.066134 / 0.038508 (0.027626) | 0.086035 / 0.023109 (0.062925) | 0.364688 / 0.275898 (0.088790) | 0.403895 / 0.323480 (0.080415) | 0.005868 / 0.007986 (-0.002117) | 0.003634 / 0.004328 (-0.000694) | 0.065803 / 0.004250 (0.061553) | 0.065113 / 0.037052 (0.028061) | 0.370057 / 0.258489 (0.111568) | 0.412634 / 0.293841 (0.118793) | 0.031660 / 0.128546 (-0.096886) | 0.008699 / 0.075646 (-0.066947) | 0.070618 / 0.419271 (-0.348654) | 0.050814 / 0.043533 (0.007281) | 0.362320 / 0.255139 (0.107181) | 0.383863 / 0.283200 (0.100663) | 0.027980 / 0.141683 (-0.113703) | 1.486389 / 1.452155 (0.034234) | 1.595534 / 1.492716 (0.102817) |\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) | 0.300991 / 0.018006 (0.282985) | 0.565265 / 0.000490 (0.564775) | 0.000400 / 0.000200 (0.000200) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034942 / 0.037411 (-0.002470) | 0.092498 / 0.014526 (0.077972) | 0.106737 / 0.176557 (-0.069819) | 0.165400 / 0.737135 (-0.571735) | 0.107809 / 0.296338 (-0.188529) |\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) | 0.412156 / 0.215209 (0.196947) | 4.116747 / 2.077655 (2.039092) | 2.199612 / 1.504120 (0.695492) | 2.049310 / 1.541195 (0.508115) | 2.174342 / 1.468490 (0.705852) | 0.482794 / 4.584777 (-4.101983) | 3.561344 / 3.745712 (-0.184368) | 3.465935 / 5.269862 (-1.803926) | 2.076595 / 4.565676 (-2.489081) | 0.056242 / 0.424275 (-0.368033) | 0.007371 / 0.007607 (-0.000236) | 0.489135 / 0.226044 (0.263091) | 4.895691 / 2.268929 (2.626763) | 2.626936 / 55.444624 (-52.817688) | 2.306658 / 6.876477 (-4.569818) | 2.421705 / 2.142072 (0.279633) | 0.599547 / 4.805227 (-4.205680) | 0.133627 / 6.500664 (-6.367037) | 0.063830 / 0.075469 (-0.011639) |\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) | 1.383039 / 1.841788 (-0.458748) | 21.005346 / 8.074308 (12.931038) | 14.911083 / 10.191392 (4.719691) | 0.190995 / 0.680424 (-0.489429) | 0.018510 / 0.534201 (-0.515691) | 0.396346 / 0.579283 (-0.182937) | 0.411496 / 0.434364 (-0.022868) | 0.470972 / 0.540337 (-0.069366) | 0.615670 / 1.386936 (-0.771266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6d2ba47759d8acbf3d750b1cc4d89b195b1f9c9 \"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) | 0.007249 / 0.011353 (-0.004104) | 0.004261 / 0.011008 (-0.006747) | 0.100645 / 0.038508 (0.062137) | 0.078522 / 0.023109 (0.055413) | 0.423526 / 0.275898 (0.147628) | 0.439541 / 0.323480 (0.116061) | 0.005812 / 0.007986 (-0.002173) | 0.003615 / 0.004328 (-0.000713) | 0.075908 / 0.004250 (0.071658) | 0.062490 / 0.037052 (0.025437) | 0.414941 / 0.258489 (0.156452) | 0.447267 / 0.293841 (0.153426) | 0.035127 / 0.128546 (-0.093419) | 0.009642 / 0.075646 (-0.066004) | 0.354093 / 0.419271 (-0.065179) | 0.060970 / 0.043533 (0.017437) | 0.418579 / 0.255139 (0.163440) | 0.427972 / 0.283200 (0.144772) | 0.025838 / 0.141683 (-0.115845) | 1.778349 / 1.452155 (0.326194) | 1.845965 / 1.492716 (0.353249) |\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) | 0.227304 / 0.018006 (0.209298) | 0.571833 / 0.000490 (0.571343) | 0.001328 / 0.000200 (0.001128) | 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) | 0.031343 / 0.037411 (-0.006068) | 0.096400 / 0.014526 (0.081875) | 0.106881 / 0.176557 (-0.069676) | 0.175449 / 0.737135 (-0.561686) | 0.108751 / 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) | 0.480204 / 0.215209 (0.264995) | 4.622063 / 2.077655 (2.544408) | 2.211505 / 1.504120 (0.707385) | 2.065154 / 1.541195 (0.523959) | 2.159446 / 1.468490 (0.690956) | 0.584571 / 4.584777 (-4.000206) | 4.392449 / 3.745712 (0.646737) | 4.790166 / 5.269862 (-0.479695) | 2.840615 / 4.565676 (-1.725062) | 0.070845 / 0.424275 (-0.353430) | 0.009112 / 0.007607 (0.001505) | 0.580251 / 0.226044 (0.354207) | 5.660311 / 2.268929 (3.391382) | 2.836136 / 55.444624 (-52.608489) | 2.412859 / 6.876477 (-4.463618) | 2.556710 / 2.142072 (0.414637) | 0.691946 / 4.805227 (-4.113282) | 0.160123 / 6.500664 (-6.340541) | 0.072593 / 0.075469 (-0.002876) |\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) | 1.547339 / 1.841788 (-0.294448) | 21.724793 / 8.074308 (13.650485) | 16.315304 / 10.191392 (6.123912) | 0.188733 / 0.680424 (-0.491690) | 0.022109 / 0.534201 (-0.512092) | 0.481623 / 0.579283 (-0.097660) | 0.464316 / 0.434364 (0.029952) | 0.557953 / 0.540337 (0.017615) | 0.756023 / 1.386936 (-0.630913) |\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) | 0.008637 / 0.011353 (-0.002716) | 0.005286 / 0.011008 (-0.005723) | 0.091387 / 0.038508 (0.052879) | 0.114092 / 0.023109 (0.090983) | 0.457547 / 0.275898 (0.181649) | 0.506878 / 0.323480 (0.183398) | 0.006849 / 0.007986 (-0.001137) | 0.004255 / 0.004328 (-0.000073) | 0.079556 / 0.004250 (0.075306) | 0.077729 / 0.037052 (0.040677) | 0.454094 / 0.258489 (0.195605) | 0.515812 / 0.293841 (0.221971) | 0.038271 / 0.128546 (-0.090275) | 0.010110 / 0.075646 (-0.065536) | 0.094254 / 0.419271 (-0.325017) | 0.065392 / 0.043533 (0.021860) | 0.459749 / 0.255139 (0.204610) | 0.489829 / 0.283200 (0.206629) | 0.040393 / 0.141683 (-0.101290) | 1.810414 / 1.452155 (0.358259) | 1.913212 / 1.492716 (0.420496) |\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) | 0.236898 / 0.018006 (0.218891) | 0.513118 / 0.000490 (0.512628) | 0.004432 / 0.000200 (0.004232) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035074 / 0.037411 (-0.002337) | 0.102384 / 0.014526 (0.087858) | 0.117326 / 0.176557 (-0.059231) | 0.182596 / 0.737135 (-0.554539) | 0.116384 / 0.296338 (-0.179955) |\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) | 0.514544 / 0.215209 (0.299335) | 5.152930 / 2.077655 (3.075275) | 2.624477 / 1.504120 (1.120357) | 2.363209 / 1.541195 (0.822014) | 2.436060 / 1.468490 (0.967570) | 0.592523 / 4.584777 (-3.992254) | 4.209668 / 3.745712 (0.463956) | 6.284372 / 5.269862 (1.014511) | 3.667303 / 4.565676 (-0.898374) | 0.067017 / 0.424275 (-0.357259) | 0.008607 / 0.007607 (0.001000) | 0.600840 / 0.226044 (0.374796) | 5.992630 / 2.268929 (3.723701) | 3.114532 / 55.444624 (-52.330093) | 2.693242 / 6.876477 (-4.183235) | 2.767187 / 2.142072 (0.625115) | 0.687591 / 4.805227 (-4.117636) | 0.158477 / 6.500664 (-6.342187) | 0.075504 / 0.075469 (0.000034) |\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) | 1.605039 / 1.841788 (-0.236749) | 21.524730 / 8.074308 (13.450422) | 17.014643 / 10.191392 (6.823251) | 0.201580 / 0.680424 (-0.478843) | 0.023028 / 0.534201 (-0.511173) | 0.483801 / 0.579283 (-0.095482) | 0.490221 / 0.434364 (0.055857) | 0.589292 / 0.540337 (0.048955) | 0.758532 / 1.386936 (-0.628404) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c9c24d1d90f0c2db043ae2bc39f7c292454a58c \"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) | 0.008080 / 0.011353 (-0.003273) | 0.004859 / 0.011008 (-0.006149) | 0.101895 / 0.038508 (0.063387) | 0.091168 / 0.023109 (0.068059) | 0.378914 / 0.275898 (0.103016) | 0.417172 / 0.323480 (0.093692) | 0.006314 / 0.007986 (-0.001672) | 0.004069 / 0.004328 (-0.000259) | 0.076566 / 0.004250 (0.072315) | 0.070986 / 0.037052 (0.033934) | 0.380935 / 0.258489 (0.122446) | 0.417131 / 0.293841 (0.123290) | 0.036343 / 0.128546 (-0.092203) | 0.009996 / 0.075646 (-0.065650) | 0.346386 / 0.419271 (-0.072886) | 0.063162 / 0.043533 (0.019630) | 0.372620 / 0.255139 (0.117481) | 0.404902 / 0.283200 (0.121702) | 0.028217 / 0.141683 (-0.113466) | 1.793875 / 1.452155 (0.341721) | 1.836284 / 1.492716 (0.343568) |\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) | 0.223830 / 0.018006 (0.205823) | 0.503643 / 0.000490 (0.503153) | 0.004957 / 0.000200 (0.004757) | 0.000107 / 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) | 0.035455 / 0.037411 (-0.001957) | 0.108015 / 0.014526 (0.093489) | 0.116887 / 0.176557 (-0.059669) | 0.188174 / 0.737135 (-0.548961) | 0.117217 / 0.296338 (-0.179121) |\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) | 0.471681 / 0.215209 (0.256472) | 4.694509 / 2.077655 (2.616855) | 2.369539 / 1.504120 (0.865419) | 2.176839 / 1.541195 (0.635644) | 2.300536 / 1.468490 (0.832045) | 0.575689 / 4.584777 (-4.009088) | 4.232765 / 3.745712 (0.487053) | 4.766775 / 5.269862 (-0.503087) | 2.864667 / 4.565676 (-1.701010) | 0.069390 / 0.424275 (-0.354885) | 0.008822 / 0.007607 (0.001214) | 0.559620 / 0.226044 (0.333576) | 5.580401 / 2.268929 (3.311472) | 2.920293 / 55.444624 (-52.524331) | 2.552166 / 6.876477 (-4.324311) | 2.795890 / 2.142072 (0.653818) | 0.687863 / 4.805227 (-4.117364) | 0.159129 / 6.500664 (-6.341535) | 0.073475 / 0.075469 (-0.001994) |\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) | 1.505892 / 1.841788 (-0.335896) | 24.127650 / 8.074308 (16.053342) | 16.758238 / 10.191392 (6.566846) | 0.200555 / 0.680424 (-0.479869) | 0.021596 / 0.534201 (-0.512605) | 0.480668 / 0.579283 (-0.098615) | 0.483528 / 0.434364 (0.049164) | 0.571241 / 0.540337 (0.030903) | 0.790547 / 1.386936 (-0.596390) |\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) | 0.007997 / 0.011353 (-0.003356) | 0.004842 / 0.011008 (-0.006166) | 0.077190 / 0.038508 (0.038681) | 0.092765 / 0.023109 (0.069656) | 0.457475 / 0.275898 (0.181577) | 0.523914 / 0.323480 (0.200434) | 0.006349 / 0.007986 (-0.001637) | 0.003902 / 0.004328 (-0.000427) | 0.075860 / 0.004250 (0.071609) | 0.069708 / 0.037052 (0.032656) | 0.459612 / 0.258489 (0.201123) | 0.555028 / 0.293841 (0.261187) | 0.036854 / 0.128546 (-0.091692) | 0.010078 / 0.075646 (-0.065568) | 0.083871 / 0.419271 (-0.335400) | 0.061221 / 0.043533 (0.017689) | 0.435737 / 0.255139 (0.180598) | 0.509700 / 0.283200 (0.226500) | 0.038091 / 0.141683 (-0.103592) | 1.777161 / 1.452155 (0.325006) | 1.859603 / 1.492716 (0.366886) |\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) | 0.250020 / 0.018006 (0.232014) | 0.486198 / 0.000490 (0.485708) | 0.007080 / 0.000200 (0.006880) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038163 / 0.037411 (0.000751) | 0.110812 / 0.014526 (0.096286) | 0.122489 / 0.176557 (-0.054068) | 0.188215 / 0.737135 (-0.548920) | 0.122375 / 0.296338 (-0.173963) |\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) | 0.484534 / 0.215209 (0.269325) | 4.828654 / 2.077655 (2.751000) | 2.545102 / 1.504120 (1.040982) | 2.368867 / 1.541195 (0.827672) | 2.458042 / 1.468490 (0.989552) | 0.576372 / 4.584777 (-4.008404) | 4.814033 / 3.745712 (1.068321) | 6.175972 / 5.269862 (0.906110) | 4.033422 / 4.565676 (-0.532254) | 0.068544 / 0.424275 (-0.355731) | 0.008906 / 0.007607 (0.001299) | 0.581767 / 0.226044 (0.355723) | 5.808623 / 2.268929 (3.539695) | 3.120312 / 55.444624 (-52.324313) | 2.774834 / 6.876477 (-4.101642) | 2.770413 / 2.142072 (0.628340) | 0.692715 / 4.805227 (-4.112512) | 0.158883 / 6.500664 (-6.341782) | 0.075894 / 0.075469 (0.000425) |\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) | 1.631250 / 1.841788 (-0.210538) | 24.693250 / 8.074308 (16.618942) | 17.434790 / 10.191392 (7.243398) | 0.196456 / 0.680424 (-0.483968) | 0.022505 / 0.534201 (-0.511696) | 0.474788 / 0.579283 (-0.104495) | 0.500947 / 0.434364 (0.066583) | 0.553596 / 0.540337 (0.013259) | 0.737767 / 1.386936 (-0.649169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f87d6e6394bf4b390ccc82235eb7667f874e5d43 \"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) | 0.006629 / 0.011353 (-0.004724) | 0.004115 / 0.011008 (-0.006894) | 0.083934 / 0.038508 (0.045426) | 0.074952 / 0.023109 (0.051843) | 0.313069 / 0.275898 (0.037171) | 0.345878 / 0.323480 (0.022398) | 0.006034 / 0.007986 (-0.001952) | 0.003413 / 0.004328 (-0.000916) | 0.065130 / 0.004250 (0.060880) | 0.057363 / 0.037052 (0.020310) | 0.314483 / 0.258489 (0.055994) | 0.352626 / 0.293841 (0.058785) | 0.031325 / 0.128546 (-0.097221) | 0.008577 / 0.075646 (-0.067069) | 0.288137 / 0.419271 (-0.131135) | 0.053651 / 0.043533 (0.010118) | 0.313006 / 0.255139 (0.057867) | 0.338668 / 0.283200 (0.055468) | 0.023709 / 0.141683 (-0.117974) | 1.481209 / 1.452155 (0.029054) | 1.559801 / 1.492716 (0.067085) |\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) | 0.211543 / 0.018006 (0.193537) | 0.452185 / 0.000490 (0.451696) | 0.003177 / 0.000200 (0.002977) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028821 / 0.037411 (-0.008591) | 0.083290 / 0.014526 (0.068765) | 0.097478 / 0.176557 (-0.079079) | 0.153506 / 0.737135 (-0.583629) | 0.097054 / 0.296338 (-0.199284) |\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) | 0.385847 / 0.215209 (0.170638) | 3.835629 / 2.077655 (1.757974) | 1.880938 / 1.504120 (0.376819) | 1.711848 / 1.541195 (0.170653) | 1.785099 / 1.468490 (0.316609) | 0.486256 / 4.584777 (-4.098521) | 3.629026 / 3.745712 (-0.116686) | 3.321578 / 5.269862 (-1.948283) | 2.024314 / 4.565676 (-2.541363) | 0.058097 / 0.424275 (-0.366179) | 0.007724 / 0.007607 (0.000117) | 0.458293 / 0.226044 (0.232249) | 4.581314 / 2.268929 (2.312386) | 2.314379 / 55.444624 (-53.130246) | 1.966089 / 6.876477 (-4.910387) | 2.203824 / 2.142072 (0.061752) | 0.611581 / 4.805227 (-4.193647) | 0.149166 / 6.500664 (-6.351498) | 0.059825 / 0.075469 (-0.015644) |\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) | 1.235546 / 1.841788 (-0.606242) | 19.747439 / 8.074308 (11.673131) | 14.628383 / 10.191392 (4.436991) | 0.193074 / 0.680424 (-0.487350) | 0.020327 / 0.534201 (-0.513874) | 0.397051 / 0.579283 (-0.182232) | 0.418491 / 0.434364 (-0.015873) | 0.462055 / 0.540337 (-0.078282) | 0.637524 / 1.386936 (-0.749412) |\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) | 0.007069 / 0.011353 (-0.004284) | 0.004106 / 0.011008 (-0.006902) | 0.065818 / 0.038508 (0.027310) | 0.077101 / 0.023109 (0.053991) | 0.363323 / 0.275898 (0.087425) | 0.399463 / 0.323480 (0.075983) | 0.005540 / 0.007986 (-0.002446) | 0.003480 / 0.004328 (-0.000849) | 0.065176 / 0.004250 (0.060926) | 0.060867 / 0.037052 (0.023815) | 0.365763 / 0.258489 (0.107273) | 0.407789 / 0.293841 (0.113949) | 0.032018 / 0.128546 (-0.096528) | 0.008550 / 0.075646 (-0.067096) | 0.071750 / 0.419271 (-0.347521) | 0.050625 / 0.043533 (0.007092) | 0.361434 / 0.255139 (0.106295) | 0.384799 / 0.283200 (0.101599) | 0.026104 / 0.141683 (-0.115579) | 1.496093 / 1.452155 (0.043938) | 1.592909 / 1.492716 (0.100193) |\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) | 0.185794 / 0.018006 (0.167787) | 0.453379 / 0.000490 (0.452890) | 0.004365 / 0.000200 (0.004165) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031666 / 0.037411 (-0.005746) | 0.088323 / 0.014526 (0.073798) | 0.104602 / 0.176557 (-0.071954) | 0.159827 / 0.737135 (-0.577308) | 0.103725 / 0.296338 (-0.192614) |\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) | 0.413509 / 0.215209 (0.198300) | 4.126071 / 2.077655 (2.048416) | 2.137088 / 1.504120 (0.632968) | 1.981034 / 1.541195 (0.439839) | 2.063660 / 1.468490 (0.595170) | 0.478798 / 4.584777 (-4.105979) | 3.642801 / 3.745712 (-0.102911) | 3.428994 / 5.269862 (-1.840867) | 2.031902 / 4.565676 (-2.533774) | 0.056244 / 0.424275 (-0.368032) | 0.007365 / 0.007607 (-0.000242) | 0.484371 / 0.226044 (0.258327) | 4.838537 / 2.268929 (2.569608) | 2.559497 / 55.444624 (-52.885127) | 2.251863 / 6.876477 (-4.624614) | 2.339227 / 2.142072 (0.197155) | 0.607228 / 4.805227 (-4.198000) | 0.133877 / 6.500664 (-6.366787) | 0.062049 / 0.075469 (-0.013420) |\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) | 1.350389 / 1.841788 (-0.491399) | 20.060359 / 8.074308 (11.986051) | 14.305675 / 10.191392 (4.114283) | 0.165642 / 0.680424 (-0.514782) | 0.018206 / 0.534201 (-0.515994) | 0.396907 / 0.579283 (-0.182376) | 0.431896 / 0.434364 (-0.002468) | 0.475778 / 0.540337 (-0.064559) | 0.644688 / 1.386936 (-0.742248) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f6fa96ae5de873a49ef28739e8f64edf8b18cae \"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) | 0.009048 / 0.011353 (-0.002305) | 0.005787 / 0.011008 (-0.005221) | 0.111617 / 0.038508 (0.073109) | 0.087603 / 0.023109 (0.064494) | 0.446481 / 0.275898 (0.170583) | 0.491726 / 0.323480 (0.168247) | 0.007052 / 0.007986 (-0.000934) | 0.004481 / 0.004328 (0.000152) | 0.084331 / 0.004250 (0.080081) | 0.072006 / 0.037052 (0.034953) | 0.454238 / 0.258489 (0.195749) | 0.496749 / 0.293841 (0.202908) | 0.049027 / 0.128546 (-0.079520) | 0.014005 / 0.075646 (-0.061641) | 0.372550 / 0.419271 (-0.046722) | 0.071414 / 0.043533 (0.027881) | 0.459432 / 0.255139 (0.204293) | 0.467332 / 0.283200 (0.184133) | 0.037539 / 0.141683 (-0.104144) | 1.869179 / 1.452155 (0.417024) | 1.983641 / 1.492716 (0.490925) |\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) | 0.265426 / 0.018006 (0.247419) | 0.672527 / 0.000490 (0.672037) | 0.001152 / 0.000200 (0.000953) | 0.000181 / 0.000054 (0.000127) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032967 / 0.037411 (-0.004445) | 0.103023 / 0.014526 (0.088497) | 0.115978 / 0.176557 (-0.060578) | 0.191698 / 0.737135 (-0.545438) | 0.117867 / 0.296338 (-0.178471) |\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) | 0.602208 / 0.215209 (0.386999) | 6.147784 / 2.077655 (4.070129) | 2.768933 / 1.504120 (1.264813) | 2.415619 / 1.541195 (0.874424) | 2.456159 / 1.468490 (0.987669) | 0.836270 / 4.584777 (-3.748507) | 5.447754 / 3.745712 (1.702042) | 7.751825 / 5.269862 (2.481963) | 4.591892 / 4.565676 (0.026215) | 0.108269 / 0.424275 (-0.316006) | 0.009626 / 0.007607 (0.002019) | 0.719260 / 0.226044 (0.493216) | 7.313442 / 2.268929 (5.044514) | 3.490739 / 55.444624 (-51.953885) | 2.743543 / 6.876477 (-4.132934) | 3.035071 / 2.142072 (0.892999) | 1.042791 / 4.805227 (-3.762436) | 0.217080 / 6.500664 (-6.283584) | 0.084286 / 0.075469 (0.008817) |\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) | 1.655427 / 1.841788 (-0.186361) | 25.386536 / 8.074308 (17.312228) | 21.740666 / 10.191392 (11.549274) | 0.246388 / 0.680424 (-0.434036) | 0.029723 / 0.534201 (-0.504478) | 0.491537 / 0.579283 (-0.087746) | 0.603495 / 0.434364 (0.169131) | 0.573938 / 0.540337 (0.033600) | 0.981875 / 1.386936 (-0.405061) |\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) | 0.009664 / 0.011353 (-0.001689) | 0.006446 / 0.011008 (-0.004562) | 0.085113 / 0.038508 (0.046605) | 0.094533 / 0.023109 (0.071424) | 0.498388 / 0.275898 (0.222490) | 0.540127 / 0.323480 (0.216647) | 0.007316 / 0.007986 (-0.000670) | 0.004252 / 0.004328 (-0.000077) | 0.086292 / 0.004250 (0.082041) | 0.067956 / 0.037052 (0.030903) | 0.507664 / 0.258489 (0.249175) | 0.554324 / 0.293841 (0.260483) | 0.050107 / 0.128546 (-0.078439) | 0.014277 / 0.075646 (-0.061370) | 0.098838 / 0.419271 (-0.320433) | 0.066053 / 0.043533 (0.022521) | 0.491090 / 0.255139 (0.235951) | 0.537432 / 0.283200 (0.254232) | 0.035937 / 0.141683 (-0.105746) | 1.820715 / 1.452155 (0.368561) | 1.996268 / 1.492716 (0.503552) |\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) | 0.300859 / 0.018006 (0.282852) | 0.610958 / 0.000490 (0.610468) | 0.000474 / 0.000200 (0.000274) | 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) | 0.036372 / 0.037411 (-0.001039) | 0.109115 / 0.014526 (0.094589) | 0.122802 / 0.176557 (-0.053755) | 0.187092 / 0.737135 (-0.550044) | 0.123432 / 0.296338 (-0.172906) |\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) | 0.646979 / 0.215209 (0.431770) | 6.577713 / 2.077655 (4.500058) | 3.004606 / 1.504120 (1.500486) | 2.661183 / 1.541195 (1.119989) | 2.726717 / 1.468490 (1.258227) | 0.889497 / 4.584777 (-3.695280) | 5.485055 / 3.745712 (1.739343) | 4.852043 / 5.269862 (-0.417819) | 3.177392 / 4.565676 (-1.388285) | 0.099796 / 0.424275 (-0.324479) | 0.009868 / 0.007607 (0.002261) | 0.819919 / 0.226044 (0.593874) | 7.911255 / 2.268929 (5.642326) | 3.839877 / 55.444624 (-51.604747) | 3.088663 / 6.876477 (-3.787813) | 3.371184 / 2.142072 (1.229112) | 1.072762 / 4.805227 (-3.732466) | 0.224536 / 6.500664 (-6.276128) | 0.083415 / 0.075469 (0.007946) |\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) | 1.754426 / 1.841788 (-0.087361) | 25.546690 / 8.074308 (17.472382) | 22.998252 / 10.191392 (12.806860) | 0.258019 / 0.680424 (-0.422405) | 0.030104 / 0.534201 (-0.504097) | 0.518406 / 0.579283 (-0.060877) | 0.605753 / 0.434364 (0.171389) | 0.599630 / 0.540337 (0.059292) | 0.819042 / 1.386936 (-0.567894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#350f4fd6caabbdfacb5fbf9193ab255c3d0daa4c \"CML watermark\")\n"
] | 2023-07-17T15:41:16Z
| 2023-07-19T16:59:55Z
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To make it easier to understand for users.
They can use "path" to specify a single path, <s>or "paths" to use a list of paths.</s>
Glob patterns are still supported though
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PR_kwDODunzps44-vhC
| 4,438
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Fix docstring of inspect_dataset
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-02T14:21:10Z
| 2022-06-02T16:40:55Z
| 2022-06-02T16:32:27Z
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As pointed out by @sgugger:
- huggingface/doc-builder/issues/235
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PR_kwDODunzps4tM2BV
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Fix error related to huggingface_hub timeout parameter
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| 2021-10-14T14:39:52Z
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The `huggingface_hub` package added the parameter `timeout` from version 0.0.19.
This PR bumps this minimal version.
Fix #3080.
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PR_kwDODunzps4-4NXX
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Add `fn_kwargs` param to `IterableDataset.map`
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thank you for adding this fix! \r\n\r\nWould it be possible to get `fn_kwargs` added to `IterableDatasetDict.map` as well? It looks like a very similar problem, and hopefully shouldn't be a huge change. \r\n",
"Hi @brianhill11! https://github.com/huggingface/datasets/pull/5810 adds this (opened a couple of days ago). It should be merged soon.",
"That's fantastic news, thanks @mariosasko ! I'll give it a shot once the changes are merged in. "
] | 2022-09-13T16:19:05Z
| 2023-05-05T16:53:43Z
| 2022-09-13T16:45:34Z
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Add the `fn_kwargs` parameter to `IterableDataset.map`.
("Resolves" https://discuss.huggingface.co/t/how-to-use-large-image-text-datasets-in-hugging-face-hub-without-downloading-for-free/22780/3)
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PR_kwDODunzps5ZNyBq
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Preserve split order in DataFilesDict
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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) | 0.007621 / 0.011353 (-0.003732) | 0.004534 / 0.011008 (-0.006475) | 0.099834 / 0.038508 (0.061326) | 0.083029 / 0.023109 (0.059920) | 0.387559 / 0.275898 (0.111661) | 0.422453 / 0.323480 (0.098973) | 0.006070 / 0.007986 (-0.001916) | 0.003725 / 0.004328 (-0.000604) | 0.075923 / 0.004250 (0.071672) | 0.060578 / 0.037052 (0.023525) | 0.403569 / 0.258489 (0.145079) | 0.444991 / 0.293841 (0.151150) | 0.035847 / 0.128546 (-0.092699) | 0.009872 / 0.075646 (-0.065774) | 0.335506 / 0.419271 (-0.083766) | 0.060509 / 0.043533 (0.016976) | 0.381034 / 0.255139 (0.125895) | 0.426938 / 0.283200 (0.143738) | 0.027662 / 0.141683 (-0.114021) | 1.729565 / 1.452155 (0.277410) | 1.842082 / 1.492716 (0.349366) |\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) | 0.230371 / 0.018006 (0.212365) | 0.518216 / 0.000490 (0.517726) | 0.003897 / 0.000200 (0.003697) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031942 / 0.037411 (-0.005470) | 0.096609 / 0.014526 (0.082083) | 0.112707 / 0.176557 (-0.063850) | 0.178849 / 0.737135 (-0.558286) | 0.112793 / 0.296338 (-0.183546) |\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) | 0.445896 / 0.215209 (0.230687) | 4.451173 / 2.077655 (2.373519) | 2.183380 / 1.504120 (0.679260) | 1.991583 / 1.541195 (0.450388) | 2.096219 / 1.468490 (0.627729) | 0.566692 / 4.584777 (-4.018085) | 4.078278 / 3.745712 (0.332566) | 3.787950 / 5.269862 (-1.481911) | 2.372651 / 4.565676 (-2.193025) | 0.065500 / 0.424275 (-0.358775) | 0.008918 / 0.007607 (0.001311) | 0.535589 / 0.226044 (0.309545) | 5.364130 / 2.268929 (3.095201) | 2.805381 / 55.444624 (-52.639244) | 2.350769 / 6.876477 (-4.525708) | 2.592887 / 2.142072 (0.450814) | 0.675475 / 4.805227 (-4.129752) | 0.153907 / 6.500664 (-6.346757) | 0.071138 / 0.075469 (-0.004331) |\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) | 1.498236 / 1.841788 (-0.343552) | 22.810460 / 8.074308 (14.736152) | 16.275035 / 10.191392 (6.083643) | 0.200242 / 0.680424 (-0.480182) | 0.021553 / 0.534201 (-0.512648) | 0.469437 / 0.579283 (-0.109846) | 0.477752 / 0.434364 (0.043388) | 0.537411 / 0.540337 (-0.002927) | 0.741730 / 1.386936 (-0.645206) |\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) | 0.008009 / 0.011353 (-0.003344) | 0.004626 / 0.011008 (-0.006382) | 0.074871 / 0.038508 (0.036363) | 0.085214 / 0.023109 (0.062105) | 0.478057 / 0.275898 (0.202159) | 0.522038 / 0.323480 (0.198558) | 0.007055 / 0.007986 (-0.000931) | 0.003813 / 0.004328 (-0.000515) | 0.076238 / 0.004250 (0.071988) | 0.065738 / 0.037052 (0.028686) | 0.484391 / 0.258489 (0.225902) | 0.524425 / 0.293841 (0.230584) | 0.038375 / 0.128546 (-0.090171) | 0.009964 / 0.075646 (-0.065682) | 0.084027 / 0.419271 (-0.335245) | 0.056979 / 0.043533 (0.013447) | 0.486910 / 0.255139 (0.231771) | 0.501185 / 0.283200 (0.217985) | 0.027000 / 0.141683 (-0.114683) | 1.767378 / 1.452155 (0.315224) | 1.870511 / 1.492716 (0.377795) |\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) | 0.267067 / 0.018006 (0.249061) | 0.501714 / 0.000490 (0.501224) | 0.012379 / 0.000200 (0.012179) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036706 / 0.037411 (-0.000706) | 0.110064 / 0.014526 (0.095538) | 0.124896 / 0.176557 (-0.051660) | 0.186730 / 0.737135 (-0.550405) | 0.123501 / 0.296338 (-0.172837) |\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) | 0.510793 / 0.215209 (0.295583) | 5.133056 / 2.077655 (3.055401) | 2.776456 / 1.504120 (1.272336) | 2.595557 / 1.541195 (1.054362) | 2.717922 / 1.468490 (1.249432) | 0.578333 / 4.584777 (-4.006444) | 4.169935 / 3.745712 (0.424223) | 3.800078 / 5.269862 (-1.469784) | 2.385866 / 4.565676 (-2.179810) | 0.068114 / 0.424275 (-0.356161) | 0.008771 / 0.007607 (0.001164) | 0.597894 / 0.226044 (0.371850) | 5.970293 / 2.268929 (3.701364) | 3.352715 / 55.444624 (-52.091909) | 2.972062 / 6.876477 (-3.904415) | 3.179232 / 2.142072 (1.037160) | 0.689838 / 4.805227 (-4.115389) | 0.154890 / 6.500664 (-6.345774) | 0.072321 / 0.075469 (-0.003148) |\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) | 1.613666 / 1.841788 (-0.228121) | 23.441538 / 8.074308 (15.367230) | 17.105417 / 10.191392 (6.914025) | 0.171449 / 0.680424 (-0.508975) | 0.023257 / 0.534201 (-0.510944) | 0.466724 / 0.579283 (-0.112559) | 0.470835 / 0.434364 (0.036471) | 0.561860 / 0.540337 (0.021523) | 0.759048 / 1.386936 (-0.627888) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f3b6eaf69d3352394d3bf3c4d6ed01dd2af5860 \"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) | 0.007557 / 0.011353 (-0.003796) | 0.004211 / 0.011008 (-0.006797) | 0.096243 / 0.038508 (0.057735) | 0.083603 / 0.023109 (0.060493) | 0.367114 / 0.275898 (0.091216) | 0.415182 / 0.323480 (0.091702) | 0.005796 / 0.007986 (-0.002189) | 0.003791 / 0.004328 (-0.000537) | 0.073505 / 0.004250 (0.069254) | 0.060335 / 0.037052 (0.023283) | 0.392182 / 0.258489 (0.133693) | 0.421315 / 0.293841 (0.127474) | 0.036128 / 0.128546 (-0.092419) | 0.009953 / 0.075646 (-0.065693) | 0.338965 / 0.419271 (-0.080307) | 0.061006 / 0.043533 (0.017473) | 0.372317 / 0.255139 (0.117178) | 0.414367 / 0.283200 (0.131167) | 0.026970 / 0.141683 (-0.114713) | 1.730381 / 1.452155 (0.278227) | 1.808340 / 1.492716 (0.315624) |\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) | 0.222622 / 0.018006 (0.204615) | 0.474064 / 0.000490 (0.473574) | 0.004817 / 0.000200 (0.004617) | 0.000089 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032528 / 0.037411 (-0.004883) | 0.097457 / 0.014526 (0.082931) | 0.112273 / 0.176557 (-0.064283) | 0.177953 / 0.737135 (-0.559182) | 0.112358 / 0.296338 (-0.183981) |\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) | 0.442601 / 0.215209 (0.227392) | 4.442065 / 2.077655 (2.364410) | 2.156813 / 1.504120 (0.652694) | 1.970289 / 1.541195 (0.429094) | 2.052878 / 1.468490 (0.584388) | 0.562661 / 4.584777 (-4.022116) | 4.255529 / 3.745712 (0.509817) | 3.767650 / 5.269862 (-1.502212) | 2.431078 / 4.565676 (-2.134598) | 0.065624 / 0.424275 (-0.358651) | 0.008738 / 0.007607 (0.001131) | 0.546839 / 0.226044 (0.320795) | 5.362863 / 2.268929 (3.093934) | 2.695924 / 55.444624 (-52.748701) | 2.334589 / 6.876477 (-4.541888) | 2.530757 / 2.142072 (0.388685) | 0.675991 / 4.805227 (-4.129236) | 0.153852 / 6.500664 (-6.346813) | 0.069189 / 0.075469 (-0.006280) |\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) | 1.522916 / 1.841788 (-0.318872) | 21.515907 / 8.074308 (13.441599) | 16.411708 / 10.191392 (6.220316) | 0.168245 / 0.680424 (-0.512179) | 0.021165 / 0.534201 (-0.513036) | 0.461838 / 0.579283 (-0.117446) | 0.488867 / 0.434364 (0.054503) | 0.536278 / 0.540337 (-0.004059) | 0.766690 / 1.386936 (-0.620246) |\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) | 0.007683 / 0.011353 (-0.003670) | 0.004401 / 0.011008 (-0.006608) | 0.075463 / 0.038508 (0.036955) | 0.081737 / 0.023109 (0.058628) | 0.466469 / 0.275898 (0.190571) | 0.514909 / 0.323480 (0.191429) | 0.006106 / 0.007986 (-0.001880) | 0.003936 / 0.004328 (-0.000393) | 0.076773 / 0.004250 (0.072523) | 0.061025 / 0.037052 (0.023973) | 0.473348 / 0.258489 (0.214858) | 0.525326 / 0.293841 (0.231485) | 0.038224 / 0.128546 (-0.090322) | 0.009559 / 0.075646 (-0.066087) | 0.080847 / 0.419271 (-0.338424) | 0.056738 / 0.043533 (0.013205) | 0.475116 / 0.255139 (0.219977) | 0.494689 / 0.283200 (0.211490) | 0.029364 / 0.141683 (-0.112319) | 1.796681 / 1.452155 (0.344527) | 1.850600 / 1.492716 (0.357884) |\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) | 0.327126 / 0.018006 (0.309119) | 0.469186 / 0.000490 (0.468696) | 0.050600 / 0.000200 (0.050400) | 0.000439 / 0.000054 (0.000385) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036710 / 0.037411 (-0.000701) | 0.108669 / 0.014526 (0.094143) | 0.119808 / 0.176557 (-0.056748) | 0.181501 / 0.737135 (-0.555634) | 0.121487 / 0.296338 (-0.174852) |\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) | 0.509076 / 0.215209 (0.293867) | 5.056970 / 2.077655 (2.979316) | 2.775958 / 1.504120 (1.271838) | 2.592548 / 1.541195 (1.051353) | 2.654381 / 1.468490 (1.185890) | 0.557407 / 4.584777 (-4.027370) | 4.418232 / 3.745712 (0.672519) | 3.698072 / 5.269862 (-1.571790) | 2.380607 / 4.565676 (-2.185069) | 0.066242 / 0.424275 (-0.358034) | 0.008350 / 0.007607 (0.000743) | 0.572354 / 0.226044 (0.346309) | 5.857637 / 2.268929 (3.588709) | 3.242512 / 55.444624 (-52.202112) | 2.891144 / 6.876477 (-3.985332) | 3.217987 / 2.142072 (1.075915) | 0.676049 / 4.805227 (-4.129178) | 0.155515 / 6.500664 (-6.345149) | 0.068616 / 0.075469 (-0.006853) |\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) | 1.670048 / 1.841788 (-0.171740) | 22.629573 / 8.074308 (14.555265) | 16.887676 / 10.191392 (6.696284) | 0.168571 / 0.680424 (-0.511853) | 0.023361 / 0.534201 (-0.510840) | 0.463358 / 0.579283 (-0.115925) | 0.463278 / 0.434364 (0.028914) | 0.602397 / 0.540337 (0.062060) | 0.793249 / 1.386936 (-0.593687) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eee318573aba6574a43d457aa0347348c1f3e4aa \"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) | 0.006693 / 0.011353 (-0.004660) | 0.004100 / 0.011008 (-0.006908) | 0.084166 / 0.038508 (0.045658) | 0.074469 / 0.023109 (0.051360) | 0.356092 / 0.275898 (0.080194) | 0.392389 / 0.323480 (0.068909) | 0.003996 / 0.007986 (-0.003990) | 0.004020 / 0.004328 (-0.000308) | 0.064997 / 0.004250 (0.060747) | 0.053897 / 0.037052 (0.016845) | 0.362942 / 0.258489 (0.104453) | 0.408694 / 0.293841 (0.114854) | 0.031656 / 0.128546 (-0.096890) | 0.008713 / 0.075646 (-0.066933) | 0.289306 / 0.419271 (-0.129966) | 0.053067 / 0.043533 (0.009534) | 0.358740 / 0.255139 (0.103601) | 0.393347 / 0.283200 (0.110147) | 0.025430 / 0.141683 (-0.116253) | 1.486114 / 1.452155 (0.033959) | 1.572698 / 1.492716 (0.079981) |\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) | 0.215423 / 0.018006 (0.197417) | 0.467694 / 0.000490 (0.467204) | 0.003965 / 0.000200 (0.003765) | 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) | 0.027936 / 0.037411 (-0.009475) | 0.084235 / 0.014526 (0.069709) | 0.136275 / 0.176557 (-0.040282) | 0.151154 / 0.737135 (-0.585982) | 0.185592 / 0.296338 (-0.110747) |\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) | 0.393784 / 0.215209 (0.178575) | 3.927878 / 2.077655 (1.850223) | 1.961216 / 1.504120 (0.457096) | 1.802264 / 1.541195 (0.261069) | 1.971186 / 1.468490 (0.502696) | 0.487981 / 4.584777 (-4.096796) | 3.649046 / 3.745712 (-0.096666) | 3.302471 / 5.269862 (-1.967391) | 2.058075 / 4.565676 (-2.507602) | 0.057072 / 0.424275 (-0.367203) | 0.007624 / 0.007607 (0.000017) | 0.470139 / 0.226044 (0.244095) | 4.697711 / 2.268929 (2.428783) | 2.494813 / 55.444624 (-52.949811) | 2.133084 / 6.876477 (-4.743393) | 2.329740 / 2.142072 (0.187667) | 0.585857 / 4.805227 (-4.219371) | 0.134442 / 6.500664 (-6.366223) | 0.060860 / 0.075469 (-0.014609) |\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) | 1.248504 / 1.841788 (-0.593283) | 19.448427 / 8.074308 (11.374119) | 14.446139 / 10.191392 (4.254747) | 0.168081 / 0.680424 (-0.512342) | 0.018028 / 0.534201 (-0.516173) | 0.395061 / 0.579283 (-0.184222) | 0.418777 / 0.434364 (-0.015587) | 0.454509 / 0.540337 (-0.085828) | 0.628488 / 1.386936 (-0.758448) |\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) | 0.006946 / 0.011353 (-0.004406) | 0.004096 / 0.011008 (-0.006912) | 0.065322 / 0.038508 (0.026813) | 0.074336 / 0.023109 (0.051227) | 0.405327 / 0.275898 (0.129429) | 0.436878 / 0.323480 (0.113398) | 0.006083 / 0.007986 (-0.001902) | 0.003345 / 0.004328 (-0.000984) | 0.065725 / 0.004250 (0.061474) | 0.056398 / 0.037052 (0.019345) | 0.406906 / 0.258489 (0.148417) | 0.443330 / 0.293841 (0.149489) | 0.033036 / 0.128546 (-0.095510) | 0.008503 / 0.075646 (-0.067144) | 0.071865 / 0.419271 (-0.347406) | 0.048956 / 0.043533 (0.005423) | 0.404579 / 0.255139 (0.149440) | 0.424904 / 0.283200 (0.141704) | 0.021786 / 0.141683 (-0.119897) | 1.491868 / 1.452155 (0.039713) | 1.565252 / 1.492716 (0.072536) |\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) | 0.231363 / 0.018006 (0.213357) | 0.454962 / 0.000490 (0.454472) | 0.004680 / 0.000200 (0.004480) | 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) | 0.032569 / 0.037411 (-0.004843) | 0.094928 / 0.014526 (0.080402) | 0.108096 / 0.176557 (-0.068461) | 0.158727 / 0.737135 (-0.578409) | 0.106951 / 0.296338 (-0.189387) |\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) | 0.431469 / 0.215209 (0.216260) | 4.283929 / 2.077655 (2.206274) | 2.283891 / 1.504120 (0.779771) | 2.118172 / 1.541195 (0.576977) | 2.192628 / 1.468490 (0.724138) | 0.492026 / 4.584777 (-4.092751) | 3.692126 / 3.745712 (-0.053587) | 3.269827 / 5.269862 (-2.000035) | 2.028948 / 4.565676 (-2.536728) | 0.057932 / 0.424275 (-0.366344) | 0.007301 / 0.007607 (-0.000306) | 0.508411 / 0.226044 (0.282367) | 5.072803 / 2.268929 (2.803875) | 2.756532 / 55.444624 (-52.688092) | 2.432192 / 6.876477 (-4.444285) | 2.654864 / 2.142072 (0.512791) | 0.589458 / 4.805227 (-4.215769) | 0.133924 / 6.500664 (-6.366740) | 0.060764 / 0.075469 (-0.014705) |\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) | 1.350737 / 1.841788 (-0.491051) | 20.265217 / 8.074308 (12.190909) | 14.969039 / 10.191392 (4.777647) | 0.164226 / 0.680424 (-0.516198) | 0.020090 / 0.534201 (-0.514111) | 0.397010 / 0.579283 (-0.182273) | 0.412927 / 0.434364 (-0.021437) | 0.473931 / 0.540337 (-0.066406) | 0.653462 / 1.386936 (-0.733474) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00cb5cc57337cdff338d7a54396bf25c5c5abd67 \"CML watermark\")\n"
] | 2023-08-31T09:00:26Z
| 2023-08-31T13:57:31Z
| 2023-08-31T13:48:42Z
|
MEMBER
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After investigation, I have found that this copy forces the splits to be sorted alphabetically: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/builder.py#L556
This PR removes the alphabetically sort of `DataFilesDict` keys.
- Note that for a `dict`, the order of keys is relevant when hashing:
```python
hash1 = Hasher.hash({'train': 'train.csv', 'test': 'test.csv'})
hash2 = Hasher.hash({'test': 'test.csv', 'train': 'train.csv'})
assert hash1 != hash2
```
- The `DataFilesDict` is a subclass of `dict`, thus the order should be relevant as well
```python
hash1 = Hasher.hash(DataFilesDict({'train': 'train.csv', 'test': 'test.csv'}))
hash2 = Hasher.hash(DataFilesDict({'test': 'test.csv', 'train': 'train.csv'}))
assert hash1 != hash2
```
Fix #6196.
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Add KDE4 Dataset
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"@lhoestq fixed :) "
] | 2020-12-09T15:32:58Z
| 2020-12-14T10:22:33Z
| 2020-12-14T10:22:32Z
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| 761
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Downloaded datasets are not usable offline
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[
"Yes currently you need an internet connection because the lib tries to check for the etag of the dataset script online to see if you don't have it locally already.\r\n\r\nIf we add a way to store the etag/hash locally after the first download, it would allow users to first download the dataset with an internet connection, and still have it working without an internet connection.\r\n\r\nI'll let you know when we add this feature.",
"Already fixed by:\r\n- #1726"
] | 2020-10-26T20:54:46Z
| 2022-02-15T10:32:28Z
| 2022-02-15T10:32:28Z
|
CONTRIBUTOR
| null | null | null |
I've been trying to use the IMDB dataset offline, but after downloading it and turning off the internet it still raises an error from the ```requests``` library trying to reach for the online dataset.
Is this the intended behavior ?
(Sorry, I wrote the the first version of this issue while still on nlp 0.3.0).
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Fix CONTRIBUTING once dataset scripts transferred to Hub
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-04T14:16:05Z
| 2022-10-06T06:14:43Z
| 2022-10-06T06:12:12Z
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This PR updates the `CONTRIBUTING.md` guide, once the all dataset scripts have been removed from the GitHub repo and transferred to the HF Hub:
- #4974
See diff here: https://github.com/huggingface/datasets/commit/e3291ecff9e54f09fcee3f313f051a03fdc3d94b
Additionally, this PR fixes the line separator that by some previous mistake was CRLF instead of LF.
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Support pickle protocol for dataset splits defined as ReadInstruction
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"@lhoestq But we don't perform conversion to a `NamedSplit` if `_split` is not a string which means it **will** be a `ReadInstruction` after reloading.",
"Yes right ! I read it wrong.\r\nPerfect then"
] | 2021-03-12T16:35:11Z
| 2021-03-16T14:25:38Z
| 2021-03-16T14:05:05Z
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Fixes #2022 (+ some style fixes)
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Filter expected warning log from transformers
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"I think the failing test has nothing to do with my PR..."
] | 2021-06-30T14:48:19Z
| 2021-07-02T04:08:17Z
| 2021-07-02T04:08:17Z
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Close #2569.
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|
Support custom feature types
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"Hi ! Interesting :) What kind of new types would you like to use ?\r\n\r\nNote that you can already implement your own decoding by using `set_transform` that can decode data on-the-fly when rows are accessed",
"An interesting proposal indeed. \r\n\r\nPandas and Polars have the \"extension API\", so doing something similar on our side could be useful, too. However, this requires defining a common interface for the existing feature types before discussing the API/workflow for defining/sharing custom feature types, and this could take some time.\r\n\r\nIt would also be nice if the datasets viewer could render these custom types.",
"Thank you for your replies! @lhoestq I have a use case involving whole-slide images in digital pathology. These are very large images (potentially gigapixel scale), so standard image tools are not suitable. Essentially, encoding/decoding can be done from/to [`OpenSlide`](https://openslide.org/api/python/) objects. Though there may be interest in this use case from the digital pathology community, it may not be sufficiently useful to suggest adding the feature type, but there will likely be many other use cases for a generic custom feature type.\r\n\r\nThank you for pointing out `set_transform`! I will make sure to keep this in mind in the future.\r\n\r\n@mariosasko An \"extension API\" sounds like a good idea, though I understand that this needs to be properly defined, and that you will need to discuss it internally. Support from the viewer would be awesome, too, though the generalization to arbitrary types sounds challenging.\r\n\r\nFor now, happy to know that you're considering the feature. Feel free to let me know if I can do anything to support the process."
] | 2023-04-17T15:46:41Z
| 2023-05-03T21:58:43Z
| null |
NONE
| null | null | null |
### Feature request
I think it would be nice to allow registering custom feature types with the 🤗 Datasets library. For example, allow to do something along the following lines:
```
from datasets.features import register_feature_type # this would be a new function
@register_feature_type
class CustomFeatureType:
def encode_example(self, value):
"""User-provided logic to encode an example of this feature."""
pass
def decode_example(self, value, token_per_repo_id=None):
"""User-provided logic to decode an example of this feature."""
pass
```
### Motivation
Users of 🤗 Datasets, such as myself, may want to use the library to load datasets with unsupported feature types (i.e., beyond `ClassLabel`, `Image`, or `Audio`). This would be useful for prototyping new feature types and for feature types that aren't used widely enough to warrant inclusion in 🤗 Datasets.
At the moment, this is only possible by monkey-patching 🤗 Datasets, which obfuscates the code and is prone to breaking with library updates. It also requires the user to write some custom code which could be easily avoided.
### Your contribution
I would be happy to contribute this feature. My proposed solution would involve changing the following call to `globals()` to an explicit feature type registry, which a user-facing `register_feature_type` decorator could update.
https://github.com/huggingface/datasets/blob/fd893098627230cc734f6009ad04cf885c979ac4/src/datasets/features/features.py#L1329
I would also provide an abstract base class for custom feature types which users could inherit. This would have at least an `encode_example` method and a `decode_example` method, similar to `Image` or `Audio`.
The existing `encode_nested_example` and `decode_nested_example` functions would also need to be updated to correctly call the corresponding functions for the new type.
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Loading Data from HDF files
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"I'm currently working on bringing [Ecoset](https://www.pnas.org/doi/10.1073/pnas.2011417118) to huggingface datasets and I would second this request...",
"I would also like this support or something similar. Geospatial datasets come in netcdf which is derived from hdf5, or zarr. I've gotten zarr stores to work with datasets and streaming, but it takes awhile to convert the data to zarr if it's not stored in that natively. ",
"@mariosasko , I would like to contribute on this \"good second issue\" . Is there anything in the works for this Issue or can I go ahead ? \r\n",
"Hi @VijayKalmath! As far as I know, nobody is working on it, so feel free to take over. Also, before you start, I suggest you comment `#self-assign` on this issue to assign it to yourself.",
"#self-assign",
"Hey @mariosasko can you assign this issue to me !!"
] | 2021-10-19T19:26:46Z
| 2023-10-09T06:57:55Z
| null |
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset.
**Describe the solution you'd like**
I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc).
**Describe alternatives you've considered**
Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible.
**Additional context**
HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.
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Distributed data parallel training for streaming datasets
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"Hi ! According to https://huggingface.co/docs/datasets/use_with_pytorch#stream-data you can use the pytorch DataLoader with `num_workers>0` to distribute the shards across your workers (it uses `torch.utils.data.get_worker_info()` to get the worker ID and select the right subsets of shards to use)\r\n\r\n<s> EDIT: here is a code example </s>\r\n```python\r\n# ds = ds.with_format(\"torch\")\r\n# dataloader = DataLoader(ds, num_workers=num_workers)\r\n```\r\n\r\nEDIT: `with_format(\"torch\")` is not required, now you can just do\r\n```python\r\ndataloader = DataLoader(ds, num_workers=num_workers)\r\n```",
"@cyk1337 does streaming datasets with multi-gpu works for you? I am testing on one node with multiple gpus, but this is freezing, https://github.com/huggingface/datasets/issues/5123 \r\nIn case you could make this work, could you share with me your data-loading codes?\r\nthank you",
"+1",
"This has been implemented in `datasets` 2.8:\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\n\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\ndocs: https://huggingface.co/docs/datasets/use_with_pytorch#distributed",
"i'm having hanging issues with this when using DDP and allocating the datasets with `split_dataset_by_node` 🤔\r\n\r\n--- \r\n### edit\r\nI don't want to pollute this thread, but for the sake of following up, I observed hanging close to the final iteration of the dataloader. I think this was happening on the final shard. First, I removed the final shard and things worked. Then (including all shards), I reordered the list of shards: `load_dataset('json', data_files=reordered, streaming=True)` and no hang. \r\n\r\nI won't open an issue yet bc I am not quite sure about this observation.",
"@wconnell would you mind opening a different bug issue and giving more details?\r\nhttps://github.com/huggingface/datasets/issues/new?assignees=&labels=&template=bug-report.yml\r\n\r\nThanks."
] | 2022-07-17T01:29:43Z
| 2023-04-26T18:21:09Z
| null |
NONE
| null | null | null |
### Feature request
Any documentations for the the `load_dataset(streaming=True)` for (multi-node multi-GPU) DDP training?
### Motivation
Given a bunch of data files, it is expected to split them onto different GPUs. Is there a guide or documentation?
### Your contribution
Does it requires manually split on data files for each worker in `DatasetBuilder._split_generator()`? What is`IterableDatasetShard` expected to do?
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Raise FileNotFoundError when passing data_files that don't exist
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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) | 0.009288 / 0.011353 (-0.002065) | 0.005950 / 0.011008 (-0.005058) | 0.122376 / 0.038508 (0.083868) | 0.093177 / 0.023109 (0.070068) | 0.448517 / 0.275898 (0.172619) | 0.474999 / 0.323480 (0.151520) | 0.005133 / 0.007986 (-0.002853) | 0.005123 / 0.004328 (0.000795) | 0.085479 / 0.004250 (0.081229) | 0.065613 / 0.037052 (0.028561) | 0.451179 / 0.258489 (0.192690) | 0.516876 / 0.293841 (0.223036) | 0.047536 / 0.128546 (-0.081010) | 0.013894 / 0.075646 (-0.061752) | 0.382149 / 0.419271 (-0.037122) | 0.067380 / 0.043533 (0.023848) | 0.419282 / 0.255139 (0.164143) | 0.482042 / 0.283200 (0.198842) | 0.041230 / 0.141683 (-0.100452) | 1.818127 / 1.452155 (0.365972) | 1.938123 / 1.492716 (0.445406) |\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) | 0.271824 / 0.018006 (0.253817) | 0.604933 / 0.000490 (0.604443) | 0.004953 / 0.000200 (0.004753) | 0.000173 / 0.000054 (0.000119) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036682 / 0.037411 (-0.000729) | 0.095604 / 0.014526 (0.081078) | 0.116862 / 0.176557 (-0.059695) | 0.191335 / 0.737135 (-0.545800) | 0.116620 / 0.296338 (-0.179718) |\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) | 0.620735 / 0.215209 (0.405526) | 6.157119 / 2.077655 (4.079465) | 2.848548 / 1.504120 (1.344428) | 2.493731 / 1.541195 (0.952536) | 2.505801 / 1.468490 (1.037311) | 0.837315 / 4.584777 (-3.747462) | 5.360653 / 3.745712 (1.614941) | 4.908863 / 5.269862 (-0.360999) | 3.184672 / 4.565676 (-1.381004) | 0.105687 / 0.424275 (-0.318588) | 0.011350 / 0.007607 (0.003743) | 0.745729 / 0.226044 (0.519684) | 7.431584 / 2.268929 (5.162655) | 3.644670 / 55.444624 (-51.799954) | 2.910159 / 6.876477 (-3.966317) | 3.257137 / 2.142072 (1.115065) | 1.041377 / 4.805227 (-3.763851) | 0.213289 / 6.500664 (-6.287375) | 0.089208 / 0.075469 (0.013739) |\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) | 1.727274 / 1.841788 (-0.114513) | 25.448436 / 8.074308 (17.374128) | 23.016108 / 10.191392 (12.824716) | 0.219454 / 0.680424 (-0.460970) | 0.028531 / 0.534201 (-0.505670) | 0.500231 / 0.579283 (-0.079052) | 0.614631 / 0.434364 (0.180267) | 0.557926 / 0.540337 (0.017588) | 0.786261 / 1.386936 (-0.600675) |\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) | 0.008608 / 0.011353 (-0.002745) | 0.006185 / 0.011008 (-0.004823) | 0.089258 / 0.038508 (0.050750) | 0.090109 / 0.023109 (0.067000) | 0.522200 / 0.275898 (0.246302) | 0.559218 / 0.323480 (0.235738) | 0.008983 / 0.007986 (0.000997) | 0.004488 / 0.004328 (0.000159) | 0.083658 / 0.004250 (0.079408) | 0.064962 / 0.037052 (0.027909) | 0.519477 / 0.258489 (0.260988) | 0.573842 / 0.293841 (0.280001) | 0.053984 / 0.128546 (-0.074562) | 0.014665 / 0.075646 (-0.060982) | 0.089438 / 0.419271 (-0.329834) | 0.065756 / 0.043533 (0.022223) | 0.525131 / 0.255139 (0.269992) | 0.568934 / 0.283200 (0.285734) | 0.037308 / 0.141683 (-0.104375) | 1.928790 / 1.452155 (0.476635) | 2.027926 / 1.492716 (0.535209) |\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) | 0.309595 / 0.018006 (0.291588) | 0.615675 / 0.000490 (0.615186) | 0.004869 / 0.000200 (0.004669) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033306 / 0.037411 (-0.004105) | 0.104429 / 0.014526 (0.089904) | 0.116989 / 0.176557 (-0.059568) | 0.183638 / 0.737135 (-0.553497) | 0.132624 / 0.296338 (-0.163714) |\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) | 0.644511 / 0.215209 (0.429302) | 6.425544 / 2.077655 (4.347889) | 3.079071 / 1.504120 (1.574951) | 2.720963 / 1.541195 (1.179769) | 2.835607 / 1.468490 (1.367117) | 0.863561 / 4.584777 (-3.721216) | 5.333462 / 3.745712 (1.587750) | 4.843183 / 5.269862 (-0.426678) | 3.106858 / 4.565676 (-1.458819) | 0.106790 / 0.424275 (-0.317485) | 0.008829 / 0.007607 (0.001222) | 0.759003 / 0.226044 (0.532958) | 7.771247 / 2.268929 (5.502318) | 3.896844 / 55.444624 (-51.547780) | 3.246671 / 6.876477 (-3.629806) | 3.486167 / 2.142072 (1.344094) | 1.071290 / 4.805227 (-3.733937) | 0.217972 / 6.500664 (-6.282692) | 0.089848 / 0.075469 (0.014379) |\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) | 1.816048 / 1.841788 (-0.025739) | 25.625084 / 8.074308 (17.550776) | 24.490882 / 10.191392 (14.299490) | 0.242356 / 0.680424 (-0.438067) | 0.027886 / 0.534201 (-0.506315) | 0.496997 / 0.579283 (-0.082286) | 0.613815 / 0.434364 (0.179451) | 0.607132 / 0.540337 (0.066795) | 0.833051 / 1.386936 (-0.553885) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0adfa9ada14c38fce5973b5e3f196a2c46dc9170 \"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) | 0.011580 / 0.011353 (0.000227) | 0.004199 / 0.011008 (-0.006809) | 0.084055 / 0.038508 (0.045547) | 0.096824 / 0.023109 (0.073715) | 0.308755 / 0.275898 (0.032857) | 0.341717 / 0.323480 (0.018237) | 0.006018 / 0.007986 (-0.001968) | 0.003597 / 0.004328 (-0.000731) | 0.064953 / 0.004250 (0.060702) | 0.059577 / 0.037052 (0.022525) | 0.316292 / 0.258489 (0.057803) | 0.358991 / 0.293841 (0.065150) | 0.033925 / 0.128546 (-0.094621) | 0.008828 / 0.075646 (-0.066818) | 0.288673 / 0.419271 (-0.130599) | 0.055494 / 0.043533 (0.011961) | 0.311181 / 0.255139 (0.056042) | 0.345220 / 0.283200 (0.062021) | 0.024033 / 0.141683 (-0.117649) | 1.504709 / 1.452155 (0.052554) | 1.587920 / 1.492716 (0.095204) |\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) | 0.301099 / 0.018006 (0.283093) | 0.594497 / 0.000490 (0.594007) | 0.006244 / 0.000200 (0.006044) | 0.000228 / 0.000054 (0.000174) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027663 / 0.037411 (-0.009748) | 0.081767 / 0.014526 (0.067241) | 0.097342 / 0.176557 (-0.079215) | 0.153200 / 0.737135 (-0.583935) | 0.097474 / 0.296338 (-0.198864) |\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) | 0.405929 / 0.215209 (0.190719) | 4.045398 / 2.077655 (1.967743) | 2.044669 / 1.504120 (0.540549) | 1.872889 / 1.541195 (0.331694) | 1.911901 / 1.468490 (0.443411) | 0.480939 / 4.584777 (-4.103838) | 3.652833 / 3.745712 (-0.092879) | 3.281659 / 5.269862 (-1.988202) | 2.038023 / 4.565676 (-2.527654) | 0.056501 / 0.424275 (-0.367775) | 0.007571 / 0.007607 (-0.000036) | 0.481053 / 0.226044 (0.255009) | 4.802048 / 2.268929 (2.533119) | 2.560479 / 55.444624 (-52.884145) | 2.164852 / 6.876477 (-4.711625) | 2.374595 / 2.142072 (0.232523) | 0.576309 / 4.805227 (-4.228918) | 0.134831 / 6.500664 (-6.365833) | 0.060649 / 0.075469 (-0.014820) |\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) | 1.254210 / 1.841788 (-0.587578) | 19.826143 / 8.074308 (11.751835) | 14.446391 / 10.191392 (4.254999) | 0.165707 / 0.680424 (-0.514717) | 0.018221 / 0.534201 (-0.515980) | 0.395996 / 0.579283 (-0.183287) | 0.424567 / 0.434364 (-0.009796) | 0.459836 / 0.540337 (-0.080501) | 0.635969 / 1.386936 (-0.750967) |\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) | 0.006696 / 0.011353 (-0.004657) | 0.004131 / 0.011008 (-0.006877) | 0.064587 / 0.038508 (0.026079) | 0.079189 / 0.023109 (0.056080) | 0.359977 / 0.275898 (0.084079) | 0.389331 / 0.323480 (0.065851) | 0.005502 / 0.007986 (-0.002483) | 0.003492 / 0.004328 (-0.000837) | 0.064967 / 0.004250 (0.060716) | 0.055953 / 0.037052 (0.018901) | 0.363997 / 0.258489 (0.105508) | 0.398405 / 0.293841 (0.104564) | 0.031292 / 0.128546 (-0.097254) | 0.008693 / 0.075646 (-0.066953) | 0.070451 / 0.419271 (-0.348820) | 0.048965 / 0.043533 (0.005432) | 0.358288 / 0.255139 (0.103149) | 0.379136 / 0.283200 (0.095936) | 0.024364 / 0.141683 (-0.117319) | 1.478998 / 1.452155 (0.026843) | 1.547282 / 1.492716 (0.054566) |\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) | 0.328188 / 0.018006 (0.310182) | 0.525968 / 0.000490 (0.525478) | 0.003782 / 0.000200 (0.003582) | 0.000089 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032528 / 0.037411 (-0.004883) | 0.087685 / 0.014526 (0.073159) | 0.100684 / 0.176557 (-0.075872) | 0.155944 / 0.737135 (-0.581192) | 0.101949 / 0.296338 (-0.194389) |\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) | 0.418591 / 0.215209 (0.203382) | 4.199235 / 2.077655 (2.121580) | 2.183880 / 1.504120 (0.679760) | 2.024502 / 1.541195 (0.483307) | 2.017435 / 1.468490 (0.548945) | 0.488881 / 4.584777 (-4.095896) | 3.635002 / 3.745712 (-0.110710) | 3.359992 / 5.269862 (-1.909870) | 2.089686 / 4.565676 (-2.475991) | 0.057813 / 0.424275 (-0.366462) | 0.007349 / 0.007607 (-0.000258) | 0.490719 / 0.226044 (0.264674) | 4.859950 / 2.268929 (2.591022) | 2.616711 / 55.444624 (-52.827914) | 2.238671 / 6.876477 (-4.637806) | 2.442262 / 2.142072 (0.300190) | 0.598368 / 4.805227 (-4.206859) | 0.135281 / 6.500664 (-6.365383) | 0.063072 / 0.075469 (-0.012397) |\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) | 1.356396 / 1.841788 (-0.485392) | 20.075123 / 8.074308 (12.000815) | 14.191317 / 10.191392 (3.999925) | 0.167691 / 0.680424 (-0.512732) | 0.018290 / 0.534201 (-0.515911) | 0.392881 / 0.579283 (-0.186402) | 0.413665 / 0.434364 (-0.020699) | 0.480766 / 0.540337 (-0.059571) | 0.655625 / 1.386936 (-0.731311) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ca9cc138754629be261522301e725c7d14152 \"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) | 0.007834 / 0.011353 (-0.003519) | 0.004744 / 0.011008 (-0.006264) | 0.102061 / 0.038508 (0.063553) | 0.089246 / 0.023109 (0.066137) | 0.399936 / 0.275898 (0.124038) | 0.436974 / 0.323480 (0.113494) | 0.004791 / 0.007986 (-0.003195) | 0.005976 / 0.004328 (0.001647) | 0.079336 / 0.004250 (0.075086) | 0.065947 / 0.037052 (0.028894) | 0.403747 / 0.258489 (0.145258) | 0.460249 / 0.293841 (0.166408) | 0.038065 / 0.128546 (-0.090482) | 0.010179 / 0.075646 (-0.065467) | 0.403620 / 0.419271 (-0.015652) | 0.066439 / 0.043533 (0.022906) | 0.412123 / 0.255139 (0.156984) | 0.452121 / 0.283200 (0.168921) | 0.033533 / 0.141683 (-0.108150) | 1.858650 / 1.452155 (0.406495) | 1.916248 / 1.492716 (0.423532) |\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) | 0.237180 / 0.018006 (0.219174) | 0.526844 / 0.000490 (0.526354) | 0.004220 / 0.000200 (0.004020) | 0.000123 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033860 / 0.037411 (-0.003552) | 0.105054 / 0.014526 (0.090528) | 0.116494 / 0.176557 (-0.060063) | 0.185990 / 0.737135 (-0.551145) | 0.119072 / 0.296338 (-0.177266) |\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) | 0.488549 / 0.215209 (0.273340) | 4.884950 / 2.077655 (2.807295) | 2.521819 / 1.504120 (1.017699) | 2.329382 / 1.541195 (0.788188) | 2.413710 / 1.468490 (0.945220) | 0.568325 / 4.584777 (-4.016452) | 4.243505 / 3.745712 (0.497793) | 3.785983 / 5.269862 (-1.483879) | 2.387146 / 4.565676 (-2.178531) | 0.067176 / 0.424275 (-0.357099) | 0.009145 / 0.007607 (0.001538) | 0.571482 / 0.226044 (0.345437) | 5.688822 / 2.268929 (3.419894) | 3.067346 / 55.444624 (-52.377278) | 2.688723 / 6.876477 (-4.187754) | 2.883785 / 2.142072 (0.741713) | 0.679326 / 4.805227 (-4.125901) | 0.156018 / 6.500664 (-6.344646) | 0.070947 / 0.075469 (-0.004522) |\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) | 1.556611 / 1.841788 (-0.285177) | 23.545074 / 8.074308 (15.470766) | 17.125108 / 10.191392 (6.933716) | 0.180180 / 0.680424 (-0.500244) | 0.021420 / 0.534201 (-0.512781) | 0.466888 / 0.579283 (-0.112395) | 0.485746 / 0.434364 (0.051383) | 0.606181 / 0.540337 (0.065843) | 0.776691 / 1.386936 (-0.610245) |\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) | 0.007820 / 0.011353 (-0.003533) | 0.004531 / 0.011008 (-0.006478) | 0.076142 / 0.038508 (0.037634) | 0.086367 / 0.023109 (0.063258) | 0.456150 / 0.275898 (0.180252) | 0.499712 / 0.323480 (0.176232) | 0.006545 / 0.007986 (-0.001441) | 0.003760 / 0.004328 (-0.000568) | 0.076400 / 0.004250 (0.072150) | 0.069689 / 0.037052 (0.032637) | 0.459732 / 0.258489 (0.201243) | 0.504217 / 0.293841 (0.210376) | 0.037838 / 0.128546 (-0.090709) | 0.009804 / 0.075646 (-0.065843) | 0.084654 / 0.419271 (-0.334617) | 0.060301 / 0.043533 (0.016768) | 0.452984 / 0.255139 (0.197845) | 0.479956 / 0.283200 (0.196757) | 0.029674 / 0.141683 (-0.112009) | 1.814059 / 1.452155 (0.361904) | 1.878886 / 1.492716 (0.386170) |\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) | 0.326174 / 0.018006 (0.308168) | 0.539722 / 0.000490 (0.539232) | 0.025637 / 0.000200 (0.025437) | 0.000209 / 0.000054 (0.000154) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036328 / 0.037411 (-0.001084) | 0.106369 / 0.014526 (0.091843) | 0.118598 / 0.176557 (-0.057958) | 0.182760 / 0.737135 (-0.554376) | 0.120013 / 0.296338 (-0.176326) |\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) | 0.507328 / 0.215209 (0.292119) | 5.092689 / 2.077655 (3.015034) | 2.962334 / 1.504120 (1.458214) | 2.507699 / 1.541195 (0.966504) | 2.612245 / 1.468490 (1.143755) | 0.568625 / 4.584777 (-4.016152) | 4.296484 / 3.745712 (0.550772) | 4.037788 / 5.269862 (-1.232073) | 2.579826 / 4.565676 (-1.985850) | 0.068558 / 0.424275 (-0.355717) | 0.008916 / 0.007607 (0.001309) | 0.601054 / 0.226044 (0.375010) | 6.016061 / 2.268929 (3.747133) | 3.311880 / 55.444624 (-52.132744) | 2.912926 / 6.876477 (-3.963551) | 3.101465 / 2.142072 (0.959393) | 0.686848 / 4.805227 (-4.118380) | 0.160243 / 6.500664 (-6.340421) | 0.074084 / 0.075469 (-0.001385) |\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) | 1.754343 / 1.841788 (-0.087444) | 24.215302 / 8.074308 (16.140994) | 17.211007 / 10.191392 (7.019615) | 0.188370 / 0.680424 (-0.492054) | 0.028157 / 0.534201 (-0.506044) | 0.490879 / 0.579283 (-0.088404) | 0.501508 / 0.434364 (0.067144) | 0.599719 / 0.540337 (0.059381) | 0.852438 / 1.386936 (-0.534498) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d84cd1d6f51ca75ec5f5c3db3f372f093758cac9 \"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) | 0.009736 / 0.011353 (-0.001617) | 0.004761 / 0.011008 (-0.006247) | 0.100069 / 0.038508 (0.061561) | 0.077944 / 0.023109 (0.054835) | 0.419944 / 0.275898 (0.144046) | 0.459803 / 0.323480 (0.136323) | 0.006296 / 0.007986 (-0.001689) | 0.005375 / 0.004328 (0.001047) | 0.089457 / 0.004250 (0.085207) | 0.060585 / 0.037052 (0.023532) | 0.437988 / 0.258489 (0.179499) | 0.482676 / 0.293841 (0.188835) | 0.049126 / 0.128546 (-0.079420) | 0.015043 / 0.075646 (-0.060603) | 0.342500 / 0.419271 (-0.076771) | 0.067088 / 0.043533 (0.023555) | 0.418364 / 0.255139 (0.163225) | 0.458259 / 0.283200 (0.175059) | 0.034091 / 0.141683 (-0.107592) | 1.721589 / 1.452155 (0.269434) | 1.823142 / 1.492716 (0.330426) |\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) | 0.212110 / 0.018006 (0.194103) | 0.530957 / 0.000490 (0.530467) | 0.003581 / 0.000200 (0.003382) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030202 / 0.037411 (-0.007210) | 0.100552 / 0.014526 (0.086026) | 0.108150 / 0.176557 (-0.068407) | 0.173203 / 0.737135 (-0.563932) | 0.108624 / 0.296338 (-0.187715) |\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) | 0.577340 / 0.215209 (0.362131) | 5.794197 / 2.077655 (3.716543) | 2.396285 / 1.504120 (0.892165) | 2.151972 / 1.541195 (0.610777) | 2.109485 / 1.468490 (0.640995) | 0.873906 / 4.584777 (-3.710871) | 5.083302 / 3.745712 (1.337589) | 4.600756 / 5.269862 (-0.669105) | 2.891731 / 4.565676 (-1.673945) | 0.096293 / 0.424275 (-0.327982) | 0.008651 / 0.007607 (0.001044) | 0.719095 / 0.226044 (0.493051) | 7.193225 / 2.268929 (4.924297) | 3.220145 / 55.444624 (-52.224479) | 2.496715 / 6.876477 (-4.379762) | 2.672972 / 2.142072 (0.530900) | 1.031656 / 4.805227 (-3.773571) | 0.207854 / 6.500664 (-6.292810) | 0.074507 / 0.075469 (-0.000962) |\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) | 1.552821 / 1.841788 (-0.288967) | 22.573015 / 8.074308 (14.498707) | 21.074321 / 10.191392 (10.882929) | 0.231911 / 0.680424 (-0.448513) | 0.027761 / 0.534201 (-0.506440) | 0.474644 / 0.579283 (-0.104639) | 0.563780 / 0.434364 (0.129416) | 0.527593 / 0.540337 (-0.012745) | 0.732299 / 1.386936 (-0.654637) |\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) | 0.008675 / 0.011353 (-0.002678) | 0.005268 / 0.011008 (-0.005741) | 0.079078 / 0.038508 (0.040570) | 0.073505 / 0.023109 (0.050395) | 0.453982 / 0.275898 (0.178083) | 0.487839 / 0.323480 (0.164359) | 0.005950 / 0.007986 (-0.002035) | 0.003848 / 0.004328 (-0.000481) | 0.076004 / 0.004250 (0.071754) | 0.058410 / 0.037052 (0.021358) | 0.460099 / 0.258489 (0.201610) | 0.514860 / 0.293841 (0.221019) | 0.048843 / 0.128546 (-0.079703) | 0.014275 / 0.075646 (-0.061371) | 0.090243 / 0.419271 (-0.329029) | 0.060092 / 0.043533 (0.016559) | 0.455669 / 0.255139 (0.200530) | 0.484738 / 0.283200 (0.201538) | 0.033012 / 0.141683 (-0.108671) | 1.738854 / 1.452155 (0.286699) | 1.852552 / 1.492716 (0.359835) |\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) | 0.245453 / 0.018006 (0.227447) | 0.519929 / 0.000490 (0.519439) | 0.007262 / 0.000200 (0.007062) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031446 / 0.037411 (-0.005965) | 0.094236 / 0.014526 (0.079710) | 0.114457 / 0.176557 (-0.062100) | 0.167448 / 0.737135 (-0.569687) | 0.108791 / 0.296338 (-0.187548) |\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) | 0.603331 / 0.215209 (0.388122) | 6.051556 / 2.077655 (3.973902) | 2.797110 / 1.504120 (1.292990) | 2.500517 / 1.541195 (0.959322) | 2.531421 / 1.468490 (1.062931) | 0.852075 / 4.584777 (-3.732702) | 5.034140 / 3.745712 (1.288427) | 4.576573 / 5.269862 (-0.693289) | 2.973541 / 4.565676 (-1.592135) | 0.101303 / 0.424275 (-0.322972) | 0.008467 / 0.007607 (0.000860) | 0.707143 / 0.226044 (0.481098) | 7.262803 / 2.268929 (4.993874) | 3.548841 / 55.444624 (-51.895783) | 2.895975 / 6.876477 (-3.980502) | 3.063521 / 2.142072 (0.921449) | 1.014961 / 4.805227 (-3.790266) | 0.208527 / 6.500664 (-6.292137) | 0.074939 / 0.075469 (-0.000530) |\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) | 1.670708 / 1.841788 (-0.171080) | 22.685227 / 8.074308 (14.610919) | 20.393017 / 10.191392 (10.201625) | 0.239303 / 0.680424 (-0.441121) | 0.027742 / 0.534201 (-0.506459) | 0.467230 / 0.579283 (-0.112053) | 0.564169 / 0.434364 (0.129805) | 0.554859 / 0.540337 (0.014522) | 0.767471 / 1.386936 (-0.619465) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72a57356a46ded67f4d7a02741141a96061246a8 \"CML watermark\")\n"
] | 2023-08-17T09:49:48Z
| 2023-08-18T13:45:58Z
| 2023-08-18T13:35:13Z
|
MEMBER
| null | 0
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e.g. when running `load_dataset("parquet", data_files="doesnt_exist.parquet")`
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https://api.github.com/repos/huggingface/datasets/issues/6155/timeline
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MDU6SXNzdWU3MDcwMzc3Mzg=
| 665
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runing dataset.map, it raises TypeError: can't pickle Tokenizer objects
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[
"Hi !\r\nIt works on my side with both the LongFormerTokenizer and the LongFormerTokenizerFast.\r\n\r\nWhich version of transformers/datasets are you using ?",
"transformers and datasets are both the latest",
"Then I guess you need to give us more informations on your setup (OS, python, GPU, etc) or a Google Colab reproducing the error for us to be able to debug this error.",
"And your version of `dill` if possible :)",
"I have the same issue with `transformers/BertJapaneseTokenizer`.\r\n\r\n\r\n\r\n```python\r\n# train_ds = Dataset(features: {\r\n# 'title': Value(dtype='string', id=None), \r\n# 'score': Value(dtype='float64', id=None)\r\n# }, num_rows: 99999)\r\n\r\nt = BertJapaneseTokenizer.from_pretrained('bert-base-japanese-whole-word-masking')\r\nencoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True)\r\n```\r\n\r\n<details><summary>Error Message</summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-35-2b7d66b291c1> in <module>\r\n 2 \r\n 3 encoded = train_ds.map(lambda examples:\r\n----> 4 {'tokens': t.encode(examples['title'])}, batched=True)\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n 1242 fn_kwargs=fn_kwargs,\r\n 1243 new_fingerprint=new_fingerprint,\r\n-> 1244 update_data=update_data,\r\n 1245 )\r\n 1246 else:\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)\r\n 151 \"output_all_columns\": self._output_all_columns,\r\n 152 }\r\n--> 153 out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n 154 if new_format[\"columns\"] is not None:\r\n 155 new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)\r\n 156 kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n 157 kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158 self._fingerprint, transform, kwargs_for_fingerprint\r\n 159 )\r\n 160 \r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n 103 for key in sorted(transform_args):\r\n 104 hasher.update(key)\r\n--> 105 hasher.update(transform_args[key])\r\n 106 return hasher.hexdigest()\r\n 107 \r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in update(self, value)\r\n 55 def update(self, value):\r\n 56 self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57 self.m.update(self.hash(value).encode(\"utf-8\"))\r\n 58 \r\n 59 def hexdigest(self):\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in hash(cls, value)\r\n 51 return cls.dispatch[type(value)](cls, value)\r\n 52 else:\r\n---> 53 return cls.hash_default(value)\r\n 54 \r\n 55 def update(self, value):\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in hash_default(cls, value)\r\n 44 @classmethod\r\n 45 def hash_default(cls, value):\r\n---> 46 return cls.hash_bytes(dumps(value))\r\n 47 \r\n 48 @classmethod\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/utils/py_utils.py in dumps(obj)\r\n 365 file = StringIO()\r\n 366 with _no_cache_fields(obj):\r\n--> 367 dump(obj, file)\r\n 368 return file.getvalue()\r\n 369 \r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/utils/py_utils.py in dump(obj, file)\r\n 337 def dump(obj, file):\r\n 338 \"\"\"pickle an object to a file\"\"\"\r\n--> 339 Pickler(file, recurse=True).dump(obj)\r\n 340 return\r\n 341 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in dump(self, obj)\r\n 444 raise PicklingError(msg)\r\n 445 else:\r\n--> 446 StockPickler.dump(self, obj)\r\n 447 stack.clear() # clear record of 'recursion-sensitive' pickled objects\r\n 448 return\r\n\r\n/usr/local/lib/python3.6/pickle.py in dump(self, obj)\r\n 407 if self.proto >= 4:\r\n 408 self.framer.start_framing()\r\n--> 409 self.save(obj)\r\n 410 self.write(STOP)\r\n 411 self.framer.end_framing()\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in save_function(pickler, obj)\r\n 1436 globs, obj.__name__,\r\n 1437 obj.__defaults__, obj.__closure__,\r\n-> 1438 obj.__dict__, fkwdefaults), obj=obj)\r\n 1439 else:\r\n 1440 _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 608 else:\r\n 609 save(func)\r\n--> 610 save(args)\r\n 611 write(REDUCE)\r\n 612 \r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/pickle.py in save_tuple(self, obj)\r\n 749 write(MARK)\r\n 750 for element in obj:\r\n--> 751 save(element)\r\n 752 \r\n 753 if id(obj) in memo:\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 931 # we only care about session the first pass thru\r\n 932 pickler._session = False\r\n--> 933 StockPickler.save_dict(pickler, obj)\r\n 934 log.info(\"# D2\")\r\n 935 return\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_dict(self, obj)\r\n 819 \r\n 820 self.memoize(obj)\r\n--> 821 self._batch_setitems(obj.items())\r\n 822 \r\n 823 dispatch[dict] = save_dict\r\n\r\n/usr/local/lib/python3.6/pickle.py in _batch_setitems(self, items)\r\n 850 k, v = tmp[0]\r\n 851 save(k)\r\n--> 852 save(v)\r\n 853 write(SETITEM)\r\n 854 # else tmp is empty, and we're done\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 519 \r\n 520 # Save the reduce() output and finally memoize the object\r\n--> 521 self.save_reduce(obj=obj, *rv)\r\n 522 \r\n 523 def persistent_id(self, obj):\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 632 \r\n 633 if state is not None:\r\n--> 634 save(state)\r\n 635 write(BUILD)\r\n 636 \r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 931 # we only care about session the first pass thru\r\n 932 pickler._session = False\r\n--> 933 StockPickler.save_dict(pickler, obj)\r\n 934 log.info(\"# D2\")\r\n 935 return\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_dict(self, obj)\r\n 819 \r\n 820 self.memoize(obj)\r\n--> 821 self._batch_setitems(obj.items())\r\n 822 \r\n 823 dispatch[dict] = save_dict\r\n\r\n/usr/local/lib/python3.6/pickle.py in _batch_setitems(self, items)\r\n 845 for k, v in tmp:\r\n 846 save(k)\r\n--> 847 save(v)\r\n 848 write(SETITEMS)\r\n 849 elif n:\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 519 \r\n 520 # Save the reduce() output and finally memoize the object\r\n--> 521 self.save_reduce(obj=obj, *rv)\r\n 522 \r\n 523 def persistent_id(self, obj):\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 632 \r\n 633 if state is not None:\r\n--> 634 save(state)\r\n 635 write(BUILD)\r\n 636 \r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 931 # we only care about session the first pass thru\r\n 932 pickler._session = False\r\n--> 933 StockPickler.save_dict(pickler, obj)\r\n 934 log.info(\"# D2\")\r\n 935 return\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_dict(self, obj)\r\n 819 \r\n 820 self.memoize(obj)\r\n--> 821 self._batch_setitems(obj.items())\r\n 822 \r\n 823 dispatch[dict] = save_dict\r\n\r\n/usr/local/lib/python3.6/pickle.py in _batch_setitems(self, items)\r\n 845 for k, v in tmp:\r\n 846 save(k)\r\n--> 847 save(v)\r\n 848 write(SETITEMS)\r\n 849 elif n:\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 494 reduce = getattr(obj, \"__reduce_ex__\", None)\r\n 495 if reduce is not None:\r\n--> 496 rv = reduce(self.proto)\r\n 497 else:\r\n 498 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tagger objects\r\n```\r\n\r\n</details>\r\n\r\ntrainsformers: 2.10.0\r\ndatasets: 1.0.2\r\ndill: 0.3.2\r\npython: 3.6.8\r\n\r\nOS: ubuntu 16.04 (Docker Image) on [Deep Learning VM](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning) (GCP)\r\nGPU: Tesla P100 (CUDA 10)\r\n",
"> I have the same issue with `transformers/BertJapaneseTokenizer`.\r\n\r\nIt looks like it this tokenizer is not supported unfortunately.\r\nThis is because `t.word_tokenizer.mecab` is a `fugashi.fugashi.GenericTagger` which is not compatible with pickle nor dill.\r\n\r\nWe need objects passes to `map` to be picklable for our caching system to work properly.\r\nHere it crashes because the caching system is not able to pickle the GenericTagger.\r\n\r\n\\> Maybe you can create an issue on [fugashi](https://github.com/polm/fugashi/issues) 's repo and ask to make `fugashi.fugashi.GenericTagger` compatible with pickle ?\r\n\r\nWhat you can do in the meantime is use a picklable wrapper of the tokenizer:\r\n\r\n\r\n```python\r\nfrom transformers import BertJapaneseTokenizer, MecabTokenizer\r\n\r\nclass PicklableTokenizer(BertJapaneseTokenizer):\r\n\r\n def __getstate__(self):\r\n state = dict(self.__dict__)\r\n state[\"do_lower_case\"] = self.word_tokenizer.do_lower_case\r\n state[\"never_split\"] = self.word_tokenizer.never_split \r\n del state[\"word_tokenizer\"]\r\n return state\r\n\r\n def __setstate__(self, state):\r\n do_lower_case = state.pop(\"do_lower_case\")\r\n never_split = state.pop(\"never_split\")\r\n self.__dict__ = state\r\n self.word_tokenizer = MecabTokenizer(\r\n do_lower_case=do_lower_case, never_split=never_split)\r\n )\r\n\r\nt = PicklableTokenizer.from_pretrained(\"cl-tohoku/bert-base-japanese-whole-word-masking\")\r\nencoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True) # it works\r\n```",
"We can also update the `BertJapaneseTokenizer` in `transformers` as you just shown @lhoestq to make it compatible with pickle. It will be faster than asking on fugashi 's repo and good for the other users of `transformers` as well.\r\n\r\nI'm currently working on `transformers` I'll include it in the https://github.com/huggingface/transformers/pull/7141 PR and the next release of `transformers`.",
"Thank you for the rapid and polite response!\r\n\r\n@lhoestq Thanks for the suggestion! I've passed the pickle phase, but another `ArrowInvalid` problem occored. I created another issue #687 .\r\n\r\n@thomwolf Wow, really fast work. I'm looking forward to the next release 🤗"
] | 2020-09-23T04:28:14Z
| 2020-10-08T09:32:16Z
| 2020-10-08T09:32:16Z
|
NONE
| null | null | null |
I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.
```
def convert_to_features(example):
# Tokenize contexts and questions (as pairs of inputs)
input_pairs = [example['question'], example['context']]
encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)
context_encodings = tokenizer.encode_plus(example['context'])
# Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.
# this will give us the position of answer span in the context text
start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])
start_positions_context = context_encodings.char_to_token(start_idx)
end_positions_context = context_encodings.char_to_token(end_idx-1)
# here we will compute the start and end position of the answer in the whole example
# as the example is encoded like this <s> question</s></s> context</s>
# and we know the postion of the answer in the context
# we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)
# this will give us the position of the answer span in whole example
sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)
start_positions = start_positions_context + sep_idx + 1
end_positions = end_positions_context + sep_idx + 1
if end_positions > 512:
start_positions, end_positions = 0, 0
encodings.update({'start_positions': start_positions,
'end_positions': end_positions,
'attention_mask': encodings['attention_mask']})
return encodings
```
Then I run `dataset.map(convert_to_features)`, it raise
```
In [59]: a.map(convert_to_features)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-59-c453b508761d> in <module>
----> 1 a.map(convert_to_features)
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1242 fn_kwargs=fn_kwargs,
1243 new_fingerprint=new_fingerprint,
-> 1244 update_data=update_data,
1245 )
1246 else:
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
151 "output_all_columns": self._output_all_columns,
152 }
--> 153 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
154 if new_format["columns"] is not None:
155 new_format["columns"] = list(set(new_format["columns"]) & set(out.column_names))
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
157 kwargs[fingerprint_name] = update_fingerprint(
--> 158 self._fingerprint, transform, kwargs_for_fingerprint
159 )
160
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)
103 for key in sorted(transform_args):
104 hasher.update(key)
--> 105 hasher.update(transform_args[key])
106 return hasher.hexdigest()
107
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in update(self, value)
55 def update(self, value):
56 self.m.update(f"=={type(value)}==".encode("utf8"))
---> 57 self.m.update(self.hash(value).encode("utf-8"))
58
59 def hexdigest(self):
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in hash(cls, value)
51 return cls.dispatch[type(value)](cls, value)
52 else:
---> 53 return cls.hash_default(value)
54
55 def update(self, value):
/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in hash_default(cls, value)
44 @classmethod
45 def hash_default(cls, value):
---> 46 return cls.hash_bytes(dumps(value))
47
48 @classmethod
/opt/conda/lib/python3.7/site-packages/datasets/utils/py_utils.py in dumps(obj)
365 file = StringIO()
366 with _no_cache_fields(obj):
--> 367 dump(obj, file)
368 return file.getvalue()
369
/opt/conda/lib/python3.7/site-packages/datasets/utils/py_utils.py in dump(obj, file)
337 def dump(obj, file):
338 """pickle an object to a file"""
--> 339 Pickler(file, recurse=True).dump(obj)
340 return
341
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in dump(self, obj)
444 raise PicklingError(msg)
445 else:
--> 446 StockPickler.dump(self, obj)
447 stack.clear() # clear record of 'recursion-sensitive' pickled objects
448 return
/opt/conda/lib/python3.7/pickle.py in dump(self, obj)
435 if self.proto >= 4:
436 self.framer.start_framing()
--> 437 self.save(obj)
438 self.write(STOP)
439 self.framer.end_framing()
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_function(pickler, obj)
1436 globs, obj.__name__,
1437 obj.__defaults__, obj.__closure__,
-> 1438 obj.__dict__, fkwdefaults), obj=obj)
1439 else:
1440 _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)
/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
636 else:
637 save(func)
--> 638 save(args)
639 write(REDUCE)
640
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/pickle.py in save_tuple(self, obj)
787 write(MARK)
788 for element in obj:
--> 789 save(element)
790
791 if id(obj) in memo:
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
931 # we only care about session the first pass thru
932 pickler._session = False
--> 933 StockPickler.save_dict(pickler, obj)
934 log.info("# D2")
935 return
/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
857
858 self.memoize(obj)
--> 859 self._batch_setitems(obj.items())
860
861 dispatch[dict] = save_dict
/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
883 for k, v in tmp:
884 save(k)
--> 885 save(v)
886 write(SETITEMS)
887 elif n:
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
931 # we only care about session the first pass thru
932 pickler._session = False
--> 933 StockPickler.save_dict(pickler, obj)
934 log.info("# D2")
935 return
/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
857
858 self.memoize(obj)
--> 859 self._batch_setitems(obj.items())
860
861 dispatch[dict] = save_dict
/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
883 for k, v in tmp:
884 save(k)
--> 885 save(v)
886 write(SETITEMS)
887 elif n:
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
931 # we only care about session the first pass thru
932 pickler._session = False
--> 933 StockPickler.save_dict(pickler, obj)
934 log.info("# D2")
935 return
/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
857
858 self.memoize(obj)
--> 859 self._batch_setitems(obj.items())
860
861 dispatch[dict] = save_dict
/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
883 for k, v in tmp:
884 save(k)
--> 885 save(v)
886 write(SETITEMS)
887 elif n:
/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
522 reduce = getattr(obj, "__reduce_ex__", None)
523 if reduce is not None:
--> 524 rv = reduce(self.proto)
525 else:
526 reduce = getattr(obj, "__reduce__", None)
TypeError: can't pickle Tokenizer objects
```
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Use python read for text dataset
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"One thing though, could we try to read the files in parallel?",
"We could but I'm not sure this would help a lot since the bottleneck is the drive IO if the files are big enough.\r\nIt could make sense for very small files.",
"Looks like windows is not a big fan of this approach\r\nI'm working on a fix",
"I remember issue https://github.com/huggingface/datasets/issues/546 where this was kinda requested (but maybe IO would bottleneck). What do you think?",
"I think it's worth testing multiprocessing. It could also be something we add to our speed benchmarks",
"> I remember issue #546 where this was kinda requested (but maybe IO would bottleneck). What do you think?\r\n\r\nIt still would be interesting I think, especially in scenarios where IO is less of an issue (SSDs particularly) and where there are many smaller files. Wrapping this function in a `pool.map` is perhaps an easy thing to try. ",
"Merging this one for now for the patch release"
] | 2020-10-05T09:47:55Z
| 2020-10-05T13:13:18Z
| 2020-10-05T13:13:17Z
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As mentioned in #622 the pandas reader used for text dataset doesn't work properly when there are \r characters in the text file.
Instead I switched to pure python using `open` and `read`.
From my benchmark on a 100MB text file, it's the same speed as the previous pandas reader.
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Offline loading
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"It's maybe a bit annoying to add but could we maybe have as well a version of the local data loading scripts in the package?\r\nThe `text`, `json`, `csv`. Thinking about people like in #1725 who are expecting to be able to work with local data without downloading anything.\r\n\r\nMaybe we can add them to package_data or something?",
"Yes I mentioned this in #824 as well. I'm looking into it",
"Alright now `csv`, `json`, `text` and `pandas` are \"packaged datasets\", i.e. they're part of the `datasets` package, which makes them available in offline mode without any change in terms of API:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"csv\", data_files=[\"path/to/data.csv\"])\r\n```\r\n\r\nInstead of loading the dataset script from the module cache, it's loaded from inside the `datasets` package.\r\n\r\nI updated the test to still be able to fetch the dummy data files for those datasets from `datasets/{text|csv|pandas|json}/dummy` in the repo.",
"Alright now all test pass :)\r\n(I don't thank you windows)",
"LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning?",
"> LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning?\r\n\r\nYep good idea. I added the date in the warning. For example `(last modified on Mon Nov 30 11:01:56 2020)`"
] | 2021-01-12T15:21:57Z
| 2022-02-15T10:32:10Z
| 2021-01-19T16:42:32Z
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As discussed in #824 it would be cool to make the library work in offline mode.
Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError.
This is because `prepare_module` fetches online for the latest version of the module.
To make it work in offline mode one suggestion was to reload the latest local version of the module.
I implemented that and I also raise a warning saying that the module that is loaded is the latest local version.
```python
logger.warning(
f"Using the latest cached version of the module from {cached_module_path} since it "
f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})."
)
```
I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing.
I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet.
Close #824, close #761.
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Add TweetQA dataset
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| 2020-12-10T15:10:30Z
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CONTRIBUTOR
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This PR adds the TweetQA dataset, the first dataset for QA on social media data by leveraging news media and crowdsourcing.
Paper: https://arxiv.org/abs/1907.06292
Repository: https://tweetqa.github.io/
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Use config pandas version in CSV dataset builder
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| 2022-02-15T16:55:45Z
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Fix #3724.
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`push_to_hub` is not robust to hub closing connection
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[
"Hi! We made some improvements to `push_to_hub` to make it more robust a couple of weeks ago but haven't published a release in the meantime, so it would help if you could install `datasets` from `main` (`pip install https://github.com/huggingface/datasets`) and let us know if this improved version of `push_to_hub` resolves the issue (in case the `ConnectionError` happens, re-running `push_to_hub` should be faster now).\r\n\r\nAlso, note that the previous implementation retries the upload, but sometimes this is not enough, so re-running the op is the only option.",
"The update helped push more data.\r\nHowever it still crashed a little later:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00206-of-00261.parquet' to the Hub.\r\n```",
"I think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n\r\nThe implementation in `main` pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n\r\nBelow is the another error log from another run with `main`. I've reverting back to the current release as it does the job for me.\r\n\r\n```\r\nUploading the dataset shards: 86%|████████▌ | 224/261 [21:46<03:35, 5.83s/it]s]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n p, glob_pattern_to_regex(PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n token = token if token is not None else HfFolder.get_token()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00224-of-00261.parquet' to the Hub.\r\n```",
"There's a new error from the hub now:\r\n```\r\nPushing dataset shards to the dataset hub: 49%|████▉ | 128/261 [11:38<12:05, 5.45s/it]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1641, in push_to_hub\r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5308, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 293, in _retry\r\n raise err\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, *args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3850, in upload_file\r\n commit_info = self.create_commit(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, *args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3237, in create_commit\r\n hf_raise_for_status(commit_resp, endpoint_name=\"commit\")\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main (Request ID: Root=1-654e48e6-598511b14413bb293fa67084;783522b4-66f9-4f8a-8a74-2accf7cabd17)\r\n\r\nYou have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n```\r\n\r\nAt least this is more explicit from the server side.",
"> think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n>\r\n>The implementation in main pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n>\r\n>Below is the another error log from another run with main. I've reverting back to the current release as it does the job for me.\r\n\r\nThe `preupload` step is instant for the already uploaded shards, so only the Parquet conversion is repeated without uploading the actual Parquet data (only to check the SHAs). The previous implementation manually checks the Parquet shard's fingerprint to resume uploading, so the current implementation is cleaner.\r\n\r\n> You have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n\r\nThis is the problem with the previous implementation. If the number of shards is large, it creates too many commits for the Hub in a short period.",
"But I agree that the `500 Server Error` returned by the Hub is annoying. Earlier today, I also got it on a small 5GB dataset (with 500 MB shards).\r\n\r\n@Wauplin @julien-c Is there something we can do about this?",
"@mariosasko can't do much if AWS raises a HTTP 500 unfortunately (we are simply pushing data to a S3 bucket).\r\nWhat we can do is to add a retry mechanism in the multi-part upload logic here: https://github.com/huggingface/huggingface_hub/blob/c972cba1fecb456a7b3325cdd1fdbcc425f21f94/src/huggingface_hub/lfs.py#L370 :confused: ",
"@Wauplin That code already retries the request using `http_backoff`, no?",
"> That code already retries the request using http_backoff, no?\r\n\r\nCurrently only on HTTP 503 by default. We should add 500 as well (and hope it is a transient error from AWS)",
"Opened a PR to retry in case S3 raises HTTP 500. Will also retry on any `ConnectionError` (connection reset by peer, connection lost,...). Hopefully this should make the upload process more robust to transient errors.",
"I still get the same error, using `push_to_hub`. Using `git lfs` and pushing the files solved it for me."
] | 2023-11-08T20:44:53Z
| 2023-12-01T17:51:34Z
| 2023-12-01T17:51:34Z
|
NONE
| null | null | null |
### Describe the bug
Like to #6172, `push_to_hub` will crash if Hub resets the connection and raise the following error:
```
Pushing dataset shards to the dataset hub: 32%|███▏ | 54/171 [06:38<14:23, 7.38s/it]
Traceback (most recent call last):
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen
httplib_response = self._make_request(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request
six.raise_from(e, None)
File "<string>", line 3, in raise_from
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request
httplib_response = conn.getresponse()
File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse
response.begin()
File "/usr/lib/python3.8/http/client.py", line 316, in begin
version, status, reason = self._read_status()
File "/usr/lib/python3.8/http/client.py", line 285, in _read_status
raise RemoteDisconnected("Remote end closed connection without"
http.client.RemoteDisconnected: Remote end closed connection without response
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 486, in send
resp = conn.urlopen(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 799, in urlopen
retries = retries.increment(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/util/retry.py", line 550, in increment
raise six.reraise(type(error), error, _stacktrace)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/packages/six.py", line 769, in reraise
raise value.with_traceback(tb)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen
httplib_response = self._make_request(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request
six.raise_from(e, None)
File "<string>", line 3, in raise_from
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request
httplib_response = conn.getresponse()
File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse
response.begin()
File "/usr/lib/python3.8/http/client.py", line 316, in begin
version, status, reason = self._read_status()
File "/usr/lib/python3.8/http/client.py", line 285, in _read_status
raise RemoteDisconnected("Remote end closed connection without"
urllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 383, in _wrapped_lfs_upload
lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 223, in lfs_upload
_upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action["href"])
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 319, in _upload_multi_part
else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 375, in _upload_parts_iteratively
part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 258, in http_backoff
response = session.request(method=method, url=url, **kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 63, in send
return super().send(request, *args, **kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 501, in send
raise ConnectionError(err, request=request)
requests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 2bab8c06-b701-4266-aead-fe2e0dc0e3ed)')
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "convert_to_hf.py", line 116, in <module>
main()
File "convert_to_hf.py", line 108, in main
audio_dataset.push_to_hub(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1641, in push_to_hub
repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 5308, in _push_parquet_shards_to_hub
_retry(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 290, in _retry
return func(*func_args, **func_kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner
return fn(self, *args, **kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file
commit_info = self.create_commit(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner
return fn(self, *args, **kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 2695, in create_commit
upload_lfs_files(
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 393, in upload_lfs_files
_wrapped_lfs_upload(filtered_actions[0])
File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 385, in _wrapped_lfs_upload
raise RuntimeError(f"Error while uploading '{operation.path_in_repo}' to the Hub.") from exc
RuntimeError: Error while uploading 'batch_19/train-00054-of-00171-932beb4082c034bf.parquet' to the Hub.
```
The function should retry if the operations fails, or at least offer a way to recover after such a failure.
Right now, calling the function again will start sending all the parquets files leading to duplicates in the repository, with no guarantee that it will actually be pushed.
Previously, it would crash with an error 400 #4677 .
### Steps to reproduce the bug
Any large dataset pushed the hub:
```py
audio_dataset.push_to_hub(
repo_id="org/dataset",
)
```
### Expected behavior
`push_to_hub` should have an option for max retries or resume.
### Environment info
- `datasets` version: 2.14.6
- Platform: Linux-5.15.0-1044-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.16.4
- PyArrow version: 13.0.0
- Pandas version: 2.0.3
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Print error message with filename when malformed CSV
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[] | 2021-02-05T11:07:59Z
| 2021-02-09T17:39:27Z
| 2021-02-09T17:39:27Z
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Print error message specifying filename when malformed CSV file.
Close #1821
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I_kwDODunzps50-5uy
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load_dataset save_to_disk load_from_disk error
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[
"solved.\r\nfsspec version problem",
"I'm using the latest datasets and fsspec , but still got this error!\r\n\r\ndatasets : Version: 2.13.0\r\n\r\nfsspec Version: 2023.10.0\r\n\r\n```\r\nFile \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/load.py\", line 1892, in load_from_disk\r\n return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1371, in load_from_disk\r\n dataset_dict[k] = Dataset.load_from_disk(\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1639, in load_from_disk\r\n fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/core.py\", line 610, in get_fs_token_paths\r\n chain = _un_chain(urlpath0, storage_options or {})\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/core.py\", line 325, in _un_chain\r\n cls = get_filesystem_class(protocol)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/registry.py\", line 232, in get_filesystem_class\r\n raise ValueError(f\"Protocol not known: {protocol}\")\r\n```",
"These two versions work.\r\n<img width=\"807\" alt=\"截圖 2023-11-22 下午5 55 28\" src=\"https://github.com/huggingface/datasets/assets/77866896/faa8333f-0519-4d69-b243-a8880cd7fc1f\">\r\n"
] | 2023-10-26T03:47:06Z
| 2023-11-22T09:57:17Z
| 2023-10-26T10:18:04Z
|
NONE
| null | null | null |
### Describe the bug
datasets version： 2.10.1
I `load_dataset `and `save_to_disk` sucessfully on windows10( **and I `load_from_disk(/LLM/data/wiki)` succcesfully on windows10**), and I copy the dataset `/LLM/data/wiki`
into a ubuntu system, but when I `load_from_disk(/LLM/data/wiki)` on ubuntu, something weird happens:
```
load_from_disk('/LLM/data/wiki')
File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1874, in load_from_disk
return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)
File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1309, in load_from_disk
dataset_dict[k] = Dataset.load_from_disk(
File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1543, in load_from_disk
fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options)
File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 610, in get_fs_token_paths
chain = _un_chain(urlpath0, storage_options or {})
File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 325, in _un_chain
cls = get_filesystem_class(protocol)
File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/registry.py", line 232, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: /LLM/data/wiki
```
It seems that something went wrong on the arrow file?
How can I solve this , since currently I can not save_to_disk on ubuntu system
### Steps to reproduce the bug
datasets version： 2.10.1
### Expected behavior
datasets version： 2.10.1
### Environment info
datasets version： 2.10.1
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Update stackexchange license
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The correct license of the stackexchange subset of the Pile is `cc-by-sa-4.0`, as can for example be seen here: https://stackoverflow.com/help/licensing
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How to load a dataset with load_from disk and save it again after doing transformations without changing the original?
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[
"Hi ! That looks like a bug, can you provide some code so that we can reproduce ?\r\nIt's not supposed to update the original dataset",
"Hi, I experimented with RAG. \r\n\r\nActually, you can run the [use_own_knowldge_dataset.py](https://github.com/shamanez/transformers/blob/rag-end-to-end-retrieval/examples/research_projects/rag/use_own_knowledge_dataset.py#L80). In the 80 you can save the dataset object to the disk with save_to_disk. Then in order to compute the embeddings in this use **load_from_disk**. \r\n\r\nThen finally save it. You can see the original dataset object (CSV after splitting also will be changed)\r\n\r\nOne more thing- when I save the dataset object with **save_to_disk** it name the arrow file with cache.... rather than using dataset. arrow. Can you add a variable that we can feed a name to save_to_disk function?",
"@lhoestq I also found that cache in tmp directory gets updated after transformations. This is really problematic when using datasets interactively. Let's say we use the shards function to a dataset loaded with csv, atm when we do transformations to shards and combine them it updates the original csv cache. ",
"I plan to update the save_to_disk method in #2025 so I can make sure the new save_to_disk doesn't corrupt your cache files.\r\nBut from your last message it looks like save_to_disk isn't the root cause right ?",
"ok, one more thing. When we use save_to_disk there are two files other than .arrow. dataset_info.json and state.json. Sometimes most of the fields in the dataset_infor.json are null, especially when saving dataset objects. Anyways I think load_from_disk uses the arrow files mentioned in state.json right? ",
"> Anyways I think load_from_disk uses the arrow files mentioned in state.json right?\r\n\r\nYes exactly",
"Perfect. For now, I am loading the dataset from CSV in my interactive process and will wait until you make the PR!"
] | 2021-03-05T05:25:50Z
| 2021-03-22T04:05:50Z
| 2021-03-22T04:05:50Z
|
NONE
| null | null | null |
I am using the latest datasets library. In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.
When I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it. How to prevent from this?
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Fix missing tags in dataset cards:
- asnq
- clue
- common_gen
- cosmos_qa
- guardian_authorship
- hindi_discourse
- py_ast
- x_stance
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
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Fix data URLs and metadata in DocRED dataset
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The host of `docred` dataset has updated the `dev` data file. This PR:
- Updates the dev URL
- Updates dataset metadata
This PR also fixes the URL of the `train_distant` split, which was wrong.
Fix #2882.
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`Dataset.class_encode` should support custom class names
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[
"Hi @Dref360, thanks a lot for your proposal.\r\n\r\nIt totally makes sense to have more flexibility when class encoding, I agree.\r\n\r\nYou could even further customize the class encoding by passing an instance of `ClassLabel` itself (instead of replicating `ClassLabel` instantiation arguments as `Dataset.class_encode_column` arguments).\r\n\r\nAnd the latter made me think of `Dataset.cast_column`...\r\n\r\nMaybe better to have some others' opinions @lhoestq @mariosasko ",
"Hi @Dref360! You can use [`Dataset.align_labels_with_mapping`](https://huggingface.co/docs/datasets/master/package_reference/main_classes.html#datasets.Dataset.align_labels_with_mapping) after `Dataset.class_encode_column` to assign a different mapping of labels to ids.\r\n\r\n@albertvillanova I'd like to avoid adding more complexity to the API where it's not (absolutely) needed, so I don't think introducing a new param in `Dataset.class_encode_column` is a good idea.\r\n\r\n",
"I wasn't aware that it existed thank you for the link.\n\nClosing then! "
] | 2022-02-19T21:21:45Z
| 2022-02-21T12:16:35Z
| 2022-02-21T12:16:35Z
|
CONTRIBUTOR
| null | null | null |
I can make a PR, just wanted approval before starting.
**Is your feature request related to a problem? Please describe.**
It is often the case that classes are not ordered in alphabetical order. Current `class_encode_column` sort the classes before indexing.
https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L1235
**Describe the solution you'd like**
I would like to add a **optional** parameter `class_names` to `class_encode_column` that would be used for the mapping instead of sorting the unique values.
**Describe alternatives you've considered**
One can use map instead. I find it harder to read.
```python
CLASS_NAMES = ['apple', 'orange', 'potato']
ds = ds.map(lambda item: CLASS_NAMES.index(item[label_column]))
# Proposition
ds = ds.class_encode_column(label_column, CLASS_NAMES)
```
**Additional context**
I can make the PR if this feature is accepted.
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Update version of xcopa dataset
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Note that there was a version update of the `xcopa` dataset: https://github.com/cambridgeltl/xcopa/releases
We updated our loading script, but we did not bump a new version number:
- #3254
This PR updates our loading script version from `1.0.0` to `1.1.0`.
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passing parameters to the method passed to Dataset.from_generator()
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"Hi! Yes, you can either use the `gen_kwargs` param in `Dataset.from_generator` (`ds = Dataset.from_generator(gen, gen_kwargs={\"param1\": val})`) or wrap the generator function with `functools.partial`\r\n(`ds = Dataset.from_generator(functools.partial(gen, param1=\"val\"))`) to pass custom parameters to it.\r\n"
] | 2022-09-26T15:20:06Z
| 2022-10-03T13:00:00Z
| 2022-10-03T13:00:00Z
|
NONE
| null | null | null |
Big thanks for providing dataset creation via a generator.
I want to ask whether there is any way that parameters can be passed to the method Dataset.from_generator() method, like as follows.
```
from datasets import Dataset
def gen(param1):
for idx in len(custom_dataset):
yield custom_dataset[idx] + param1
ds = Dataset.from_generator(gen(param1))
```
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Fix the error of _load_table_data function in msr_sqa dataset
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The _load_table_data function from the last version is wrong, it is wrong to use comma to split each row.
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Better access to all dataset information
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Moves all the dataset info down one level from `dataset.info.XXX` to `dataset.XXX`
This way it's easier to access `dataset.feature['label']` for instance
Also, add the original split instructions used to create the dataset in `dataset.split`
Ex:
```
from nlp import load_dataset
stsb = load_dataset('glue', name='stsb', split='train')
stsb.split
>>> NamedSplit('train')
```
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I_kwDODunzps5bjwPk
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|
Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage
|
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[
"Just for your information, Tensorflow confirmed this issue [here.](https://github.com/tensorflow/tensorflow/issues/59279)",
"Thanks for reporting, @HaoyuYang59.\r\n\r\nPlease note that these are different \"dataset\" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Dataset`.\r\n\r\nOur `datasets.Dataset.shuffle` method does not have a `reshuffle_each_iteration` argument. Therefore, I would say the statement in our docs is True because they refer to `datasets.Dataset.shuffle`, `datasets.Dataset.skip` and `datasets.Dataset.take`.\r\n\r\nI think this issue is restricted to TensorFlow dataset, and this would be addressed by them in the issue you opened in their repo: https://github.com/tensorflow/tensorflow/issues/59279",
"Also note that you are referring to an outdated documentation page: datasets 1.10.2 version\r\n\r\nCurrent datasets version is 2.8.0 and the corresponding documentation page is: https://huggingface.co/docs/datasets/stream#split-dataset",
"Hi @albertvillanova thanks for your reply and your explaination here. \r\n\r\nSorry for the confusion as I'm not actually a user of your repo and I just happen to find the thread by Google (and didn't read carefully).\r\n\r\nGreat to know that and you made everything very clear now.\r\n\r\nThanks for your time and sorry for the consusion.\r\n\r\nWishing you a wonderful time. \r\n\r\nRegards"
] | 2023-01-17T10:04:16Z
| 2023-01-19T09:56:03Z
| 2023-01-19T09:56:03Z
|
NONE
| null | null | null |
### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer. Therefore it is advised to shuffle the dataset before splitting using take or skip. See more details in the [Shuffling the dataset: shuffle](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#iterable-dataset-shuffling) section.`
>> \# You can also create splits from a shuffled dataset
>> train_dataset = shuffled_dataset.skip(1000)
>> eval_dataset = shuffled_dataset.take(1000)
Where the shuffled dataset comes from:
`shuffled_dataset = dataset.shuffle(buffer_size=10_000, seed=42)`
At least in Tensorflow 2.9/2.10/2.11, [docs](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) states the `reshuffle_each_iteration` argument is `True` by default. This means the dataset would be shuffled after each epoch, and as a result **the validation data would leak into training test**.
### Steps to reproduce the bug
N/A
### Expected behavior
The `reshuffle_each_iteration` argument should be set to `False`.
### Environment info
Tensorflow 2.9/2.10/2.11
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Repro windows crash
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5814). All of your documentation changes will be reflected on that endpoint."
] | 2023-05-02T23:30:18Z
| 2023-05-02T23:47:07Z
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Trying to load FEVER dataset results in NonMatchingChecksumError
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[
"Thanks for reporting @santhnm2. We are fixing it.\r\n\r\nData owners updated their URLs recently. We have to align with them, otherwise you do not download anything (that is why ignore_verifications does not work).",
"Hello! Is there any update on this? I am having the same issue 6 months later."
] | 2022-06-06T23:13:15Z
| 2022-12-15T13:36:40Z
| 2022-06-08T07:16:16Z
|
NONE
| null | null | null |
## Describe the bug
Trying to load the `fever` dataset fails with `datasets.utils.info_utils.NonMatchingChecksumError`.
I tried with `download_mode="force_redownload"` but that did not fix the error. I also tried with `ignore_verification=True` but then that raised a `json.decoder.JSONDecodeError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('fever', 'v1.0') # Fails with NonMatchingChecksumError
dataset = load_dataset('fever', 'v1.0', download_mode="force_redownload") # Fails with NonMatchingChecksumError
dataset = load_dataset('fever', 'v1.0', ignore_verification=True)` # Fails with JSONDecodeError
```
## Expected results
I expect this call to return with no error raised.
## Actual results
With `ignore_verification=False`:
```
*** datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://s3-eu-west-1.amazonaws.com/fever.public/train.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev_public.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_test.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_test.jsonl']
```
With `ignore_verification=True`:
```
*** json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.3.dev0
- Platform: Linux-4.15.0-50-generic-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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Fix tutorial (#5093)
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[
"Oops I merged without linking to the hacktoberfest issue - not sure if it counts in this case\r\n\r\nsorry about that..\r\n\r\nNext time you can just mention \"Close #XXXX\" in your issue to link it",
"It should :) (the `hacktoberfest` repo topic is all that matters)"
] | 2022-10-10T13:55:15Z
| 2022-10-10T17:50:52Z
| 2022-10-10T15:32:20Z
|
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Close #5093
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Helo Mayfrends
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| 2022-04-05T07:16:42Z
| 2022-04-05T07:16:42Z
|
NONE
| null | null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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OneDrive Integrations with HF Datasets
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"Hi! \r\n\r\nWe use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).\r\n\r\n[`gdrivefs`](https://github.com/fsspec/gdrivefs) makes it possible to use Google Drive as a storage service in Datasets, but this is not the case for OneDrive, since its[ Python SDK](https://github.com/OneDrive/onedrive-sdk-python) is not integrated with `fsspec`. Can you please request the integration with `fsspec` in their repo to address this limitation?",
"I'm closing this issue as implementing a fsspec-compliant OneDrive filesystem is not our responsibility."
] | 2023-01-19T23:12:08Z
| 2023-02-24T16:17:51Z
| 2023-02-24T16:17:51Z
|
NONE
| null | null | null |
### Feature request
First of all , I would like to thank all community who are developed DataSet storage and make it free available
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For example, if I have **50GB** on my **Onedrive** account and I want to move between drive and Hugging face repo or vis versa
### Motivation
make the dataset section more flexible with other possible storage
like the integration between Google Collab and Google drive the storage
### Your contribution
Can be done using Hugging face CLI
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Add CheXpert dataset for vision
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"Hey @SauravMaheshkar ! Just responded to your email.\r\n\r\n_For transparency, copying part of my response here:_\r\nI agree, it would be really great to have this and other BenchMD datasets easily accessible on the hub.\r\n\r\nI think the main limiting factor is that the ChexPert dataset is currently hosted on the Stanford AIMI Shared Datasets website, with a license that does not permit redistribution IIRC. Thus, I believe we would need to create a [dataset loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script) that would check authentication with the Stanford AIMI site before downloading and extracting the data. \r\n\r\nI've started a HF dataset repo [here](https://huggingface.co/datasets/katielink/CheXpert), in case you want to collaborate on writing up this loading script! I'm also happy to take a stab when I have some more time next week.",
"Hey @katielink I would love to try this out. Please guide me."
] | 2023-11-04T15:36:11Z
| 2023-12-11T17:55:52Z
| null |
NONE
| null | null | null |
### Feature request
### Name
**CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison**
### Paper
https://arxiv.org/abs/1901.07031
### Data
https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2
### Motivation
CheXpert is one of the fundamental models in medical image classification and can serve as a viable pre-training dataset for radiology classification or low-scale ablation / exploratory studies.
This could also serve as a good pre-training dataset for Kaggle competitions.
### Your contribution
Would love to make a PR and pre-process / get this into 🤗
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MDU6SXNzdWU2OTY4OTIzMDQ=
| 595
|
`Dataset`/`DatasetDict` has no attribute 'save_to_disk'
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"`pip install git+https://github.com/huggingface/nlp.git` should have done the job.\r\n\r\nDid you uninstall `nlp` before installing from github ?",
"> Did you uninstall `nlp` before installing from github ?\r\n\r\nI did not. I created a new environment and installed `nlp` directly from `github` and it worked!\r\n\r\nThanks.\r\n"
] | 2020-09-09T15:01:52Z
| 2020-09-09T16:20:19Z
| 2020-09-09T16:20:18Z
|
NONE
| null | null | null |
Hi,
As the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method. While the file [`arrow_dataset.py`](https://github.com/huggingface/nlp/blob/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1/src/nlp/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https://github.com/huggingface/nlp.git ` and still no luck. Do I need to install the library in another way?
|
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PR_kwDODunzps5ZB8Z9
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Don't alter input in Features.from_dict
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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) | 0.006166 / 0.011353 (-0.005187) | 0.003643 / 0.011008 (-0.007365) | 0.080966 / 0.038508 (0.042458) | 0.060538 / 0.023109 (0.037429) | 0.309205 / 0.275898 (0.033307) | 0.351007 / 0.323480 (0.027527) | 0.003592 / 0.007986 (-0.004393) | 0.002880 / 0.004328 (-0.001448) | 0.062957 / 0.004250 (0.058707) | 0.049015 / 0.037052 (0.011963) | 0.309436 / 0.258489 (0.050947) | 0.362695 / 0.293841 (0.068854) | 0.027818 / 0.128546 (-0.100728) | 0.008030 / 0.075646 (-0.067616) | 0.262678 / 0.419271 (-0.156594) | 0.046024 / 0.043533 (0.002491) | 0.316246 / 0.255139 (0.061107) | 0.337454 / 0.283200 (0.054254) | 0.022529 / 0.141683 (-0.119154) | 1.432492 / 1.452155 (-0.019662) | 1.499646 / 1.492716 (0.006929) |\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) | 0.190931 / 0.018006 (0.172925) | 0.428053 / 0.000490 (0.427564) | 0.002839 / 0.000200 (0.002639) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024042 / 0.037411 (-0.013370) | 0.073952 / 0.014526 (0.059426) | 0.905973 / 0.176557 (0.729417) | 0.177767 / 0.737135 (-0.559368) | 0.125779 / 0.296338 (-0.170559) |\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) | 0.398997 / 0.215209 (0.183788) | 3.959575 / 2.077655 (1.881920) | 1.907038 / 1.504120 (0.402918) | 1.732908 / 1.541195 (0.191713) | 1.757038 / 1.468490 (0.288548) | 0.495917 / 4.584777 (-4.088860) | 3.021437 / 3.745712 (-0.724275) | 2.793960 / 5.269862 (-2.475901) | 1.827753 / 4.565676 (-2.737923) | 0.057143 / 0.424275 (-0.367132) | 0.006583 / 0.007607 (-0.001024) | 0.469402 / 0.226044 (0.243357) | 4.685623 / 2.268929 (2.416695) | 2.325200 / 55.444624 (-53.119424) | 1.985559 / 6.876477 (-4.890918) | 2.151208 / 2.142072 (0.009136) | 0.589498 / 4.805227 (-4.215730) | 0.125433 / 6.500664 (-6.375231) | 0.060834 / 0.075469 (-0.014636) |\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) | 1.228217 / 1.841788 (-0.613571) | 18.076089 / 8.074308 (10.001780) | 13.814460 / 10.191392 (3.623068) | 0.144674 / 0.680424 (-0.535750) | 0.016749 / 0.534201 (-0.517452) | 0.332839 / 0.579283 (-0.246444) | 0.357211 / 0.434364 (-0.077153) | 0.380367 / 0.540337 (-0.159971) | 0.531177 / 1.386936 (-0.855759) |\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) | 0.006006 / 0.011353 (-0.005347) | 0.003552 / 0.011008 (-0.007456) | 0.061822 / 0.038508 (0.023313) | 0.057724 / 0.023109 (0.034615) | 0.462326 / 0.275898 (0.186428) | 0.492842 / 0.323480 (0.169362) | 0.004833 / 0.007986 (-0.003152) | 0.002847 / 0.004328 (-0.001481) | 0.062278 / 0.004250 (0.058028) | 0.046754 / 0.037052 (0.009702) | 0.464185 / 0.258489 (0.205696) | 0.496416 / 0.293841 (0.202576) | 0.028949 / 0.128546 (-0.099597) | 0.008038 / 0.075646 (-0.067608) | 0.067572 / 0.419271 (-0.351700) | 0.041176 / 0.043533 (-0.002356) | 0.460047 / 0.255139 (0.204908) | 0.482728 / 0.283200 (0.199528) | 0.020047 / 0.141683 (-0.121635) | 1.455958 / 1.452155 (0.003804) | 1.525730 / 1.492716 (0.033014) |\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) | 0.283643 / 0.018006 (0.265637) | 0.443046 / 0.000490 (0.442556) | 0.041019 / 0.000200 (0.040819) | 0.000340 / 0.000054 (0.000286) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026229 / 0.037411 (-0.011182) | 0.081498 / 0.014526 (0.066972) | 0.091412 / 0.176557 (-0.085145) | 0.146621 / 0.737135 (-0.590514) | 0.092113 / 0.296338 (-0.204225) |\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) | 0.463525 / 0.215209 (0.248315) | 4.629852 / 2.077655 (2.552198) | 2.564831 / 1.504120 (1.060711) | 2.386976 / 1.541195 (0.845781) | 2.457757 / 1.468490 (0.989266) | 0.507317 / 4.584777 (-4.077460) | 3.142418 / 3.745712 (-0.603294) | 2.851642 / 5.269862 (-2.418219) | 1.894444 / 4.565676 (-2.671233) | 0.058495 / 0.424275 (-0.365780) | 0.006453 / 0.007607 (-0.001154) | 0.545363 / 0.226044 (0.319319) | 5.448092 / 2.268929 (3.179164) | 2.996328 / 55.444624 (-52.448296) | 2.664666 / 6.876477 (-4.211811) | 2.832247 / 2.142072 (0.690174) | 0.597631 / 4.805227 (-4.207596) | 0.126101 / 6.500664 (-6.374563) | 0.062573 / 0.075469 (-0.012896) |\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) | 1.366502 / 1.841788 (-0.475286) | 18.872990 / 8.074308 (10.798682) | 14.892114 / 10.191392 (4.700722) | 0.146668 / 0.680424 (-0.533756) | 0.017876 / 0.534201 (-0.516325) | 0.338490 / 0.579283 (-0.240793) | 0.357471 / 0.434364 (-0.076893) | 0.398730 / 0.540337 (-0.141608) | 0.542464 / 1.386936 (-0.844472) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6ff3e846d86814fa6962326e9346a4f1f1e8a80 \"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) | 0.009132 / 0.011353 (-0.002221) | 0.005796 / 0.011008 (-0.005212) | 0.119495 / 0.038508 (0.080987) | 0.081708 / 0.023109 (0.058599) | 0.432940 / 0.275898 (0.157042) | 0.466793 / 0.323480 (0.143313) | 0.006464 / 0.007986 (-0.001521) | 0.004308 / 0.004328 (-0.000021) | 0.086344 / 0.004250 (0.082093) | 0.065987 / 0.037052 (0.028935) | 0.445213 / 0.258489 (0.186724) | 0.482405 / 0.293841 (0.188564) | 0.053553 / 0.128546 (-0.074993) | 0.015320 / 0.075646 (-0.060326) | 0.455669 / 0.419271 (0.036397) | 0.071619 / 0.043533 (0.028086) | 0.434843 / 0.255139 (0.179704) | 0.503224 / 0.283200 (0.220025) | 0.038280 / 0.141683 (-0.103403) | 1.901877 / 1.452155 (0.449722) | 2.040406 / 1.492716 (0.547690) |\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) | 0.268275 / 0.018006 (0.250269) | 0.622795 / 0.000490 (0.622305) | 0.004572 / 0.000200 (0.004372) | 0.000107 / 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) | 0.032514 / 0.037411 (-0.004898) | 0.100619 / 0.014526 (0.086093) | 0.118407 / 0.176557 (-0.058149) | 0.190311 / 0.737135 (-0.546824) | 0.117160 / 0.296338 (-0.179178) |\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) | 0.629836 / 0.215209 (0.414627) | 6.236124 / 2.077655 (4.158470) | 2.750775 / 1.504120 (1.246655) | 2.380111 / 1.541195 (0.838916) | 2.487279 / 1.468490 (1.018789) | 0.849568 / 4.584777 (-3.735209) | 5.571308 / 3.745712 (1.825596) | 4.934114 / 5.269862 (-0.335747) | 3.205478 / 4.565676 (-1.360198) | 0.104804 / 0.424275 (-0.319471) | 0.009856 / 0.007607 (0.002248) | 0.753352 / 0.226044 (0.527308) | 7.523482 / 2.268929 (5.254554) | 3.660088 / 55.444624 (-51.784537) | 2.726493 / 6.876477 (-4.149984) | 3.011344 / 2.142072 (0.869271) | 1.093410 / 4.805227 (-3.711817) | 0.229758 / 6.500664 (-6.270906) | 0.081516 / 0.075469 (0.006047) |\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) | 1.700199 / 1.841788 (-0.141588) | 25.238736 / 8.074308 (17.164428) | 23.188131 / 10.191392 (12.996739) | 0.257862 / 0.680424 (-0.422562) | 0.028885 / 0.534201 (-0.505316) | 0.510693 / 0.579283 (-0.068590) | 0.648474 / 0.434364 (0.214110) | 0.576314 / 0.540337 (0.035976) | 0.800606 / 1.386936 (-0.586330) |\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) | 0.009426 / 0.011353 (-0.001927) | 0.006205 / 0.011008 (-0.004803) | 0.083947 / 0.038508 (0.045438) | 0.089164 / 0.023109 (0.066055) | 0.540500 / 0.275898 (0.264602) | 0.578825 / 0.323480 (0.255345) | 0.006792 / 0.007986 (-0.001194) | 0.005125 / 0.004328 (0.000797) | 0.083284 / 0.004250 (0.079034) | 0.067539 / 0.037052 (0.030487) | 0.544330 / 0.258489 (0.285841) | 0.593836 / 0.293841 (0.299995) | 0.050647 / 0.128546 (-0.077899) | 0.014688 / 0.075646 (-0.060959) | 0.095977 / 0.419271 (-0.323295) | 0.062326 / 0.043533 (0.018793) | 0.536096 / 0.255139 (0.280957) | 0.578691 / 0.283200 (0.295492) | 0.035488 / 0.141683 (-0.106194) | 1.911145 / 1.452155 (0.458990) | 1.977647 / 1.492716 (0.484931) |\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) | 0.368365 / 0.018006 (0.350359) | 0.609836 / 0.000490 (0.609346) | 0.054720 / 0.000200 (0.054520) | 0.000465 / 0.000054 (0.000411) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036057 / 0.037411 (-0.001355) | 0.126434 / 0.014526 (0.111908) | 0.124740 / 0.176557 (-0.051817) | 0.198907 / 0.737135 (-0.538228) | 0.138201 / 0.296338 (-0.158137) |\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) | 0.684814 / 0.215209 (0.469605) | 6.738182 / 2.077655 (4.660527) | 3.231054 / 1.504120 (1.726934) | 2.889550 / 1.541195 (1.348355) | 2.933985 / 1.468490 (1.465495) | 0.867176 / 4.584777 (-3.717601) | 5.465475 / 3.745712 (1.719763) | 4.928370 / 5.269862 (-0.341492) | 3.126382 / 4.565676 (-1.439294) | 0.129673 / 0.424275 (-0.294603) | 0.009755 / 0.007607 (0.002148) | 0.797860 / 0.226044 (0.571816) | 8.003178 / 2.268929 (5.734250) | 4.081658 / 55.444624 (-51.362966) | 3.303837 / 6.876477 (-3.572640) | 3.574577 / 2.142072 (1.432505) | 1.064674 / 4.805227 (-3.740554) | 0.232894 / 6.500664 (-6.267770) | 0.082298 / 0.075469 (0.006829) |\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) | 1.858701 / 1.841788 (0.016913) | 25.839794 / 8.074308 (17.765485) | 24.291425 / 10.191392 (14.100033) | 0.250181 / 0.680424 (-0.430243) | 0.034479 / 0.534201 (-0.499722) | 0.540754 / 0.579283 (-0.038529) | 0.615996 / 0.434364 (0.181632) | 0.631499 / 0.540337 (0.091161) | 0.838719 / 1.386936 (-0.548217) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b6bb2f0e7a460d4ed04855eafe1184a7ce7c09c \"CML watermark\")\n"
] | 2023-08-29T12:29:47Z
| 2023-08-29T13:04:59Z
| 2023-08-29T12:52:48Z
|
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MDExOlB1bGxSZXF1ZXN0NjAxMjgzMDUx
| 2,118
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Remove os.environ.copy in Dataset.map
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"I thought deepcopy on `os.environ` is unsafe (see [this](https://stackoverflow.com/questions/13142972/using-copy-deepcopy-on-os-environ-in-python-appears-broken)), but I can't replicate the behavior described in the linked SO thread.\r\n\r\nClosing this one because #2119 has a much cleaner approach."
] | 2021-03-26T03:48:17Z
| 2021-03-26T12:03:23Z
| 2021-03-26T12:00:05Z
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CONTRIBUTOR
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Replace `os.environ.copy` with in-place modification
Fixes #2115
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PR_kwDODunzps5fmN1_
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Release: 2.15.0
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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) | 0.004331 / 0.011353 (-0.007022) | 0.002573 / 0.011008 (-0.008435) | 0.061002 / 0.038508 (0.022494) | 0.029259 / 0.023109 (0.006149) | 0.242983 / 0.275898 (-0.032915) | 0.267629 / 0.323480 (-0.055851) | 0.003906 / 0.007986 (-0.004080) | 0.002383 / 0.004328 (-0.001946) | 0.047574 / 0.004250 (0.043323) | 0.042153 / 0.037052 (0.005101) | 0.245821 / 0.258489 (-0.012668) | 0.276479 / 0.293841 (-0.017362) | 0.022498 / 0.128546 (-0.106049) | 0.006775 / 0.075646 (-0.068871) | 0.201795 / 0.419271 (-0.217477) | 0.052443 / 0.043533 (0.008910) | 0.248320 / 0.255139 (-0.006819) | 0.261964 / 0.283200 (-0.021235) | 0.016764 / 0.141683 (-0.124919) | 1.118702 / 1.452155 (-0.333453) | 1.203079 / 1.492716 (-0.289638) |\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) | 0.088808 / 0.018006 (0.070801) | 0.296526 / 0.000490 (0.296037) | 0.000203 / 0.000200 (0.000003) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018816 / 0.037411 (-0.018595) | 0.062295 / 0.014526 (0.047769) | 0.075228 / 0.176557 (-0.101329) | 0.119916 / 0.737135 (-0.617219) | 0.077206 / 0.296338 (-0.219132) |\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) | 0.276723 / 0.215209 (0.061514) | 2.711431 / 2.077655 (0.633776) | 1.425590 / 1.504120 (-0.078530) | 1.301383 / 1.541195 (-0.239812) | 1.316314 / 1.468490 (-0.152176) | 0.402709 / 4.584777 (-4.182068) | 2.347229 / 3.745712 (-1.398483) | 2.596937 / 5.269862 (-2.672925) | 1.560658 / 4.565676 (-3.005018) | 0.046162 / 0.424275 (-0.378113) | 0.004760 / 0.007607 (-0.002848) | 0.330522 / 0.226044 (0.104478) | 3.244072 / 2.268929 (0.975143) | 1.747603 / 55.444624 (-53.697021) | 1.475534 / 6.876477 (-5.400943) | 1.485135 / 2.142072 (-0.656938) | 0.476794 / 4.805227 (-4.328433) | 0.098496 / 6.500664 (-6.402168) | 0.040740 / 0.075469 (-0.034729) |\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) | 0.939020 / 1.841788 (-0.902768) | 11.235187 / 8.074308 (3.160878) | 10.194975 / 10.191392 (0.003583) | 0.126241 / 0.680424 (-0.554182) | 0.013990 / 0.534201 (-0.520211) | 0.269149 / 0.579283 (-0.310134) | 0.256950 / 0.434364 (-0.177414) | 0.301282 / 0.540337 (-0.239056) | 0.421490 / 1.386936 (-0.965446) |\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) | 0.004956 / 0.011353 (-0.006397) | 0.002478 / 0.011008 (-0.008530) | 0.047773 / 0.038508 (0.009265) | 0.050076 / 0.023109 (0.026967) | 0.261915 / 0.275898 (-0.013983) | 0.282553 / 0.323480 (-0.040927) | 0.003881 / 0.007986 (-0.004105) | 0.002329 / 0.004328 (-0.001999) | 0.048091 / 0.004250 (0.043841) | 0.038188 / 0.037052 (0.001135) | 0.265502 / 0.258489 (0.007013) | 0.292568 / 0.293841 (-0.001273) | 0.024172 / 0.128546 (-0.104374) | 0.006865 / 0.075646 (-0.068781) | 0.053199 / 0.419271 (-0.366072) | 0.032201 / 0.043533 (-0.011332) | 0.265774 / 0.255139 (0.010635) | 0.277954 / 0.283200 (-0.005245) | 0.017798 / 0.141683 (-0.123885) | 1.121503 / 1.452155 (-0.330652) | 1.176319 / 1.492716 (-0.316398) |\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) | 0.087027 / 0.018006 (0.069020) | 0.296182 / 0.000490 (0.295693) | 0.000216 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020990 / 0.037411 (-0.016421) | 0.069693 / 0.014526 (0.055168) | 0.081098 / 0.176557 (-0.095459) | 0.117760 / 0.737135 (-0.619375) | 0.081493 / 0.296338 (-0.214845) |\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) | 0.295078 / 0.215209 (0.079869) | 2.876602 / 2.077655 (0.798947) | 1.558011 / 1.504120 (0.053891) | 1.426715 / 1.541195 (-0.114480) | 1.443785 / 1.468490 (-0.024705) | 0.400826 / 4.584777 (-4.183951) | 2.378903 / 3.745712 (-1.366810) | 2.473128 / 5.269862 (-2.796734) | 1.500785 / 4.565676 (-3.064891) | 0.045438 / 0.424275 (-0.378837) | 0.004953 / 0.007607 (-0.002654) | 0.348182 / 0.226044 (0.122137) | 3.427751 / 2.268929 (1.158822) | 1.925173 / 55.444624 (-53.519451) | 1.633354 / 6.876477 (-5.243123) | 1.651573 / 2.142072 (-0.490499) | 0.473260 / 4.805227 (-4.331968) | 0.097613 / 6.500664 (-6.403051) | 0.040196 / 0.075469 (-0.035273) |\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) | 0.951780 / 1.841788 (-0.890008) | 11.709342 / 8.074308 (3.635034) | 10.571831 / 10.191392 (0.380439) | 0.134344 / 0.680424 (-0.546079) | 0.022116 / 0.534201 (-0.512084) | 0.269651 / 0.579283 (-0.309632) | 0.272310 / 0.434364 (-0.162054) | 0.306434 / 0.540337 (-0.233903) | 0.408320 / 1.386936 (-0.978616) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ea64b77079cf76675421917472c05d06ace63fc \"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) | 0.004402 / 0.011353 (-0.006951) | 0.002732 / 0.011008 (-0.008277) | 0.062799 / 0.038508 (0.024291) | 0.029155 / 0.023109 (0.006046) | 0.241925 / 0.275898 (-0.033973) | 0.275694 / 0.323480 (-0.047786) | 0.003989 / 0.007986 (-0.003997) | 0.002528 / 0.004328 (-0.001801) | 0.048410 / 0.004250 (0.044160) | 0.043729 / 0.037052 (0.006677) | 0.248843 / 0.258489 (-0.009646) | 0.282980 / 0.293841 (-0.010860) | 0.023828 / 0.128546 (-0.104718) | 0.006972 / 0.075646 (-0.068675) | 0.213222 / 0.419271 (-0.206049) | 0.054883 / 0.043533 (0.011350) | 0.251353 / 0.255139 (-0.003786) | 0.269818 / 0.283200 (-0.013381) | 0.016906 / 0.141683 (-0.124777) | 1.114109 / 1.452155 (-0.338045) | 1.162942 / 1.492716 (-0.329774) |\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) | 0.093724 / 0.018006 (0.075718) | 0.301989 / 0.000490 (0.301499) | 0.000213 / 0.000200 (0.000014) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018245 / 0.037411 (-0.019166) | 0.062237 / 0.014526 (0.047712) | 0.075644 / 0.176557 (-0.100913) | 0.119655 / 0.737135 (-0.617480) | 0.074525 / 0.296338 (-0.221814) |\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) | 0.274534 / 0.215209 (0.059324) | 2.683678 / 2.077655 (0.606024) | 1.453306 / 1.504120 (-0.050814) | 1.347630 / 1.541195 (-0.193564) | 1.352875 / 1.468490 (-0.115615) | 0.398425 / 4.584777 (-4.186352) | 2.375738 / 3.745712 (-1.369974) | 2.591573 / 5.269862 (-2.678289) | 1.555527 / 4.565676 (-3.010150) | 0.045656 / 0.424275 (-0.378619) | 0.004898 / 0.007607 (-0.002709) | 0.330591 / 0.226044 (0.104547) | 3.247638 / 2.268929 (0.978710) | 1.816676 / 55.444624 (-53.627948) | 1.531754 / 6.876477 (-5.344723) | 1.543196 / 2.142072 (-0.598877) | 0.472489 / 4.805227 (-4.332739) | 0.099311 / 6.500664 (-6.401353) | 0.042139 / 0.075469 (-0.033330) |\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) | 0.945472 / 1.841788 (-0.896316) | 11.476550 / 8.074308 (3.402242) | 10.281157 / 10.191392 (0.089765) | 0.141062 / 0.680424 (-0.539362) | 0.013634 / 0.534201 (-0.520567) | 0.268778 / 0.579283 (-0.310505) | 0.263542 / 0.434364 (-0.170822) | 0.307918 / 0.540337 (-0.232420) | 0.421231 / 1.386936 (-0.965705) |\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) | 0.005090 / 0.011353 (-0.006263) | 0.003135 / 0.011008 (-0.007873) | 0.048058 / 0.038508 (0.009550) | 0.052898 / 0.023109 (0.029789) | 0.273233 / 0.275898 (-0.002665) | 0.299516 / 0.323480 (-0.023964) | 0.004126 / 0.007986 (-0.003860) | 0.002331 / 0.004328 (-0.001997) | 0.047627 / 0.004250 (0.043376) | 0.039076 / 0.037052 (0.002023) | 0.276625 / 0.258489 (0.018136) | 0.308180 / 0.293841 (0.014340) | 0.024929 / 0.128546 (-0.103618) | 0.007396 / 0.075646 (-0.068251) | 0.053408 / 0.419271 (-0.365863) | 0.032896 / 0.043533 (-0.010637) | 0.275412 / 0.255139 (0.020273) | 0.292014 / 0.283200 (0.008814) | 0.018336 / 0.141683 (-0.123347) | 1.123565 / 1.452155 (-0.328589) | 1.175382 / 1.492716 (-0.317334) |\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) | 0.093799 / 0.018006 (0.075793) | 0.304219 / 0.000490 (0.303729) | 0.000231 / 0.000200 (0.000031) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021034 / 0.037411 (-0.016377) | 0.069961 / 0.014526 (0.055435) | 0.080311 / 0.176557 (-0.096246) | 0.118603 / 0.737135 (-0.618532) | 0.084003 / 0.296338 (-0.212335) |\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) | 0.305610 / 0.215209 (0.090401) | 2.962027 / 2.077655 (0.884372) | 1.598604 / 1.504120 (0.094484) | 1.476227 / 1.541195 (-0.064967) | 1.528960 / 1.468490 (0.060470) | 0.404545 / 4.584777 (-4.180232) | 2.423147 / 3.745712 (-1.322565) | 2.516632 / 5.269862 (-2.753229) | 1.529000 / 4.565676 (-3.036677) | 0.045780 / 0.424275 (-0.378495) | 0.004784 / 0.007607 (-0.002823) | 0.358836 / 0.226044 (0.132792) | 3.508782 / 2.268929 (1.239853) | 1.954513 / 55.444624 (-53.490111) | 1.672824 / 6.876477 (-5.203653) | 1.683482 / 2.142072 (-0.458590) | 0.479014 / 4.805227 (-4.326213) | 0.098325 / 6.500664 (-6.402340) | 0.040934 / 0.075469 (-0.034536) |\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) | 0.974770 / 1.841788 (-0.867017) | 11.956137 / 8.074308 (3.881829) | 10.956458 / 10.191392 (0.765066) | 0.141800 / 0.680424 (-0.538624) | 0.015439 / 0.534201 (-0.518762) | 0.271783 / 0.579283 (-0.307500) | 0.278058 / 0.434364 (-0.156306) | 0.305823 / 0.540337 (-0.234514) | 0.415677 / 1.386936 (-0.971259) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"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) | 0.004483 / 0.011353 (-0.006870) | 0.002560 / 0.011008 (-0.008448) | 0.061428 / 0.038508 (0.022920) | 0.029460 / 0.023109 (0.006351) | 0.238971 / 0.275898 (-0.036927) | 0.271768 / 0.323480 (-0.051712) | 0.003970 / 0.007986 (-0.004016) | 0.002408 / 0.004328 (-0.001921) | 0.047755 / 0.004250 (0.043505) | 0.043358 / 0.037052 (0.006306) | 0.245543 / 0.258489 (-0.012946) | 0.278230 / 0.293841 (-0.015611) | 0.023819 / 0.128546 (-0.104727) | 0.006856 / 0.075646 (-0.068790) | 0.204603 / 0.419271 (-0.214668) | 0.054521 / 0.043533 (0.010989) | 0.246277 / 0.255139 (-0.008862) | 0.271230 / 0.283200 (-0.011969) | 0.017283 / 0.141683 (-0.124400) | 1.088955 / 1.452155 (-0.363200) | 1.245141 / 1.492716 (-0.247575) |\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) | 0.091534 / 0.018006 (0.073528) | 0.299517 / 0.000490 (0.299027) | 0.000215 / 0.000200 (0.000015) | 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) | 0.018105 / 0.037411 (-0.019306) | 0.061860 / 0.014526 (0.047334) | 0.074494 / 0.176557 (-0.102063) | 0.120107 / 0.737135 (-0.617029) | 0.073406 / 0.296338 (-0.222932) |\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) | 0.278140 / 0.215209 (0.062931) | 2.746208 / 2.077655 (0.668553) | 1.476264 / 1.504120 (-0.027856) | 1.356498 / 1.541195 (-0.184697) | 1.362998 / 1.468490 (-0.105492) | 0.401884 / 4.584777 (-4.182893) | 2.409836 / 3.745712 (-1.335877) | 2.579087 / 5.269862 (-2.690775) | 1.545021 / 4.565676 (-3.020656) | 0.046001 / 0.424275 (-0.378274) | 0.004812 / 0.007607 (-0.002795) | 0.339767 / 0.226044 (0.113722) | 3.341599 / 2.268929 (1.072670) | 1.821498 / 55.444624 (-53.623127) | 1.559311 / 6.876477 (-5.317166) | 1.570368 / 2.142072 (-0.571704) | 0.472688 / 4.805227 (-4.332539) | 0.099549 / 6.500664 (-6.401115) | 0.041644 / 0.075469 (-0.033825) |\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) | 0.951988 / 1.841788 (-0.889799) | 11.371459 / 8.074308 (3.297150) | 10.229446 / 10.191392 (0.038054) | 0.128105 / 0.680424 (-0.552319) | 0.014418 / 0.534201 (-0.519783) | 0.268615 / 0.579283 (-0.310668) | 0.263956 / 0.434364 (-0.170407) | 0.302213 / 0.540337 (-0.238125) | 0.427224 / 1.386936 (-0.959712) |\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) | 0.005150 / 0.011353 (-0.006203) | 0.002557 / 0.011008 (-0.008451) | 0.048092 / 0.038508 (0.009584) | 0.050522 / 0.023109 (0.027413) | 0.272195 / 0.275898 (-0.003703) | 0.294191 / 0.323480 (-0.029289) | 0.004098 / 0.007986 (-0.003887) | 0.002350 / 0.004328 (-0.001978) | 0.048682 / 0.004250 (0.044432) | 0.038381 / 0.037052 (0.001328) | 0.275530 / 0.258489 (0.017041) | 0.303991 / 0.293841 (0.010150) | 0.024734 / 0.128546 (-0.103812) | 0.006926 / 0.075646 (-0.068720) | 0.053683 / 0.419271 (-0.365588) | 0.032675 / 0.043533 (-0.010858) | 0.272816 / 0.255139 (0.017677) | 0.291754 / 0.283200 (0.008554) | 0.018290 / 0.141683 (-0.123392) | 1.127696 / 1.452155 (-0.324459) | 1.187080 / 1.492716 (-0.305636) |\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) | 0.091224 / 0.018006 (0.073218) | 0.288838 / 0.000490 (0.288348) | 0.000226 / 0.000200 (0.000026) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021409 / 0.037411 (-0.016003) | 0.069846 / 0.014526 (0.055320) | 0.079962 / 0.176557 (-0.096594) | 0.118575 / 0.737135 (-0.618560) | 0.080223 / 0.296338 (-0.216115) |\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) | 0.290835 / 0.215209 (0.075626) | 2.831787 / 2.077655 (0.754133) | 1.587728 / 1.504120 (0.083608) | 1.461939 / 1.541195 (-0.079256) | 1.495257 / 1.468490 (0.026767) | 0.397653 / 4.584777 (-4.187124) | 2.399903 / 3.745712 (-1.345809) | 2.527615 / 5.269862 (-2.742247) | 1.501555 / 4.565676 (-3.064121) | 0.045742 / 0.424275 (-0.378533) | 0.004797 / 0.007607 (-0.002811) | 0.339139 / 0.226044 (0.113094) | 3.358340 / 2.268929 (1.089412) | 1.968955 / 55.444624 (-53.475670) | 1.663598 / 6.876477 (-5.212879) | 1.673995 / 2.142072 (-0.468078) | 0.463444 / 4.805227 (-4.341783) | 0.098008 / 6.500664 (-6.402656) | 0.040836 / 0.075469 (-0.034633) |\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) | 0.974033 / 1.841788 (-0.867755) | 11.863206 / 8.074308 (3.788897) | 10.892389 / 10.191392 (0.700997) | 0.128884 / 0.680424 (-0.551540) | 0.015319 / 0.534201 (-0.518882) | 0.268931 / 0.579283 (-0.310353) | 0.274148 / 0.434364 (-0.160216) | 0.305407 / 0.540337 (-0.234930) | 0.410574 / 1.386936 (-0.976362) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n"
] | 2023-11-16T07:37:20Z
| 2023-11-16T08:12:12Z
| 2023-11-16T07:43:05Z
|
MEMBER
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I_kwDODunzps4-vmq-
| 3,265
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Checksum error for kilt_task_wow
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[
"Using `dataset = load_dataset(\"kilt_tasks\", \"wow\", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution.",
"Hi @slyviacassell, thanks for reporting.\r\n\r\nYes, there is an issue with the checksum verification. I'm fixing it.\r\n\r\nAnd as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`. "
] | 2021-11-13T12:04:17Z
| 2021-11-16T11:23:53Z
| 2021-11-16T11:21:58Z
|
NONE
| null | null | null |
## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```
## Expected results
Download successful
## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
File "kilt_wow.py", line 30, in <module>
main()
File "kilt_wow.py", line 27, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "kilt_wow.py", line 21, in load_dataset
return datasets.load_dataset('kilt_tasks','wow')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
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| 755,310,318
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MDExOlB1bGxSZXF1ZXN0NTMxMDQ1NDcy
| 1,003
|
Add multi_x_science_sum
|
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[] | 2020-12-02T14:14:01Z
| 2020-12-02T17:39:05Z
| 2020-12-02T17:39:05Z
|
CONTRIBUTOR
| null | 0
|
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Add Multi-XScience Dataset.
github repo: https://github.com/yaolu/Multi-XScience
paper: [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://arxiv.org/abs/2010.14235)
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PR_kwDODunzps43fieR
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Fix missing lz4 dependency for tests
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-09T10:53:20Z
| 2022-05-09T11:21:22Z
| 2022-05-09T11:13:44Z
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Currently, `lz4` is not defined as a dependency for tests. Therefore, all tests marked with `@require_lz4` are skipped.
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DownloadManager Git LFS support
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"Hey ! Actually it works, just pass the right URL ;)\r\nThe URL must be the one with “/resolve/”\r\n\r\ne.g. https://huggingface.co/datasets/imagenet-1k/resolve/main/data/test_images.tar.gz\r\n\r\nYou can even pass a relative path to the dl_manager instead, like `dl_manager.download(\"data/test_images.tar.gz\")`",
"Amazing it works, thanks!"
] | 2022-10-20T15:29:29Z
| 2022-10-20T17:17:10Z
| 2022-10-20T17:17:10Z
|
CONTRIBUTOR
| null | null | null |
### Feature request
Maybe I'm mistaken but the `DownloadManager` does not support extracting git lfs files out of the box right?
Using `dl_manager.download()` or `dl_manager.download_and_extract()` still returns lfs files afaict.
Is there a good way to write a dataset loading script for a repo with lfs files?
### Motivation
/
### Your contribution
/
|
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Remove unused `load_from_cache_file` arg from `Dataset.shard()` docstring
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5493). All of your documentation changes will be reflected on that endpoint.",
"<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) | 0.008956 / 0.011353 (-0.002397) | 0.004590 / 0.011008 (-0.006418) | 0.101305 / 0.038508 (0.062797) | 0.030347 / 0.023109 (0.007237) | 0.302492 / 0.275898 (0.026594) | 0.335986 / 0.323480 (0.012506) | 0.007272 / 0.007986 (-0.000714) | 0.004303 / 0.004328 (-0.000025) | 0.078592 / 0.004250 (0.074341) | 0.035545 / 0.037052 (-0.001507) | 0.316052 / 0.258489 (0.057563) | 0.342523 / 0.293841 (0.048682) | 0.034128 / 0.128546 (-0.094419) | 0.011475 / 0.075646 (-0.064171) | 0.325272 / 0.419271 (-0.093999) | 0.041815 / 0.043533 (-0.001717) | 0.303093 / 0.255139 (0.047955) | 0.331987 / 0.283200 (0.048788) | 0.087264 / 0.141683 (-0.054419) | 1.476284 / 1.452155 (0.024129) | 1.562034 / 1.492716 (0.069318) |\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) | 0.206502 / 0.018006 (0.188496) | 0.409893 / 0.000490 (0.409404) | 0.002479 / 0.000200 (0.002279) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022891 / 0.037411 (-0.014520) | 0.100209 / 0.014526 (0.085683) | 0.105576 / 0.176557 (-0.070981) | 0.141035 / 0.737135 (-0.596100) | 0.109733 / 0.296338 (-0.186606) |\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) | 0.413791 / 0.215209 (0.198582) | 4.125890 / 2.077655 (2.048235) | 1.833023 / 1.504120 (0.328903) | 1.631325 / 1.541195 (0.090130) | 1.708406 / 1.468490 (0.239916) | 0.690100 / 4.584777 (-3.894677) | 3.379058 / 3.745712 (-0.366654) | 2.019044 / 5.269862 (-3.250818) | 1.323332 / 4.565676 (-3.242344) | 0.082709 / 0.424275 (-0.341566) | 0.012434 / 0.007607 (0.004827) | 0.527139 / 0.226044 (0.301095) | 5.271529 / 2.268929 (3.002601) | 2.297311 / 55.444624 (-53.147314) | 1.949021 / 6.876477 (-4.927456) | 2.001098 / 2.142072 (-0.140975) | 0.811591 / 4.805227 (-3.993636) | 0.149028 / 6.500664 (-6.351637) | 0.066233 / 0.075469 (-0.009236) |\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) | 1.254276 / 1.841788 (-0.587512) | 13.638485 / 8.074308 (5.564177) | 13.943274 / 10.191392 (3.751882) | 0.147426 / 0.680424 (-0.532997) | 0.028602 / 0.534201 (-0.505599) | 0.398080 / 0.579283 (-0.181203) | 0.402178 / 0.434364 (-0.032186) | 0.477045 / 0.540337 (-0.063292) | 0.567731 / 1.386936 (-0.819205) |\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) | 0.006936 / 0.011353 (-0.004417) | 0.004614 / 0.011008 (-0.006394) | 0.079779 / 0.038508 (0.041271) | 0.027941 / 0.023109 (0.004832) | 0.347224 / 0.275898 (0.071326) | 0.378183 / 0.323480 (0.054703) | 0.005249 / 0.007986 (-0.002737) | 0.004907 / 0.004328 (0.000579) | 0.078678 / 0.004250 (0.074428) | 0.041912 / 0.037052 (0.004860) | 0.347838 / 0.258489 (0.089349) | 0.386760 / 0.293841 (0.092919) | 0.032680 / 0.128546 (-0.095867) | 0.014321 / 0.075646 (-0.061325) | 0.087924 / 0.419271 (-0.331347) | 0.045060 / 0.043533 (0.001527) | 0.340986 / 0.255139 (0.085847) | 0.368689 / 0.283200 (0.085489) | 0.093274 / 0.141683 (-0.048409) | 1.474435 / 1.452155 (0.022281) | 1.569753 / 1.492716 (0.077037) |\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) | 0.206789 / 0.018006 (0.188783) | 0.416518 / 0.000490 (0.416028) | 0.000404 / 0.000200 (0.000204) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026207 / 0.037411 (-0.011205) | 0.101914 / 0.014526 (0.087388) | 0.108585 / 0.176557 (-0.067972) | 0.150438 / 0.737135 (-0.586697) | 0.110744 / 0.296338 (-0.185594) |\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) | 0.443571 / 0.215209 (0.228362) | 4.433139 / 2.077655 (2.355485) | 2.109525 / 1.504120 (0.605405) | 1.901484 / 1.541195 (0.360290) | 1.968812 / 1.468490 (0.500322) | 0.704334 / 4.584777 (-3.880443) | 3.392028 / 3.745712 (-0.353684) | 3.072693 / 5.269862 (-2.197168) | 1.552227 / 4.565676 (-3.013449) | 0.083741 / 0.424275 (-0.340534) | 0.012627 / 0.007607 (0.005020) | 0.544706 / 0.226044 (0.318662) | 5.462743 / 2.268929 (3.193815) | 2.551265 / 55.444624 (-52.893360) | 2.208075 / 6.876477 (-4.668401) | 2.259092 / 2.142072 (0.117020) | 0.810687 / 4.805227 (-3.994540) | 0.152347 / 6.500664 (-6.348317) | 0.068346 / 0.075469 (-0.007123) |\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) | 1.269716 / 1.841788 (-0.572072) | 14.215698 / 8.074308 (6.141390) | 13.691773 / 10.191392 (3.500381) | 0.152620 / 0.680424 (-0.527804) | 0.017219 / 0.534201 (-0.516982) | 0.382533 / 0.579283 (-0.196750) | 0.388994 / 0.434364 (-0.045370) | 0.479400 / 0.540337 (-0.060938) | 0.572699 / 1.386936 (-0.814237) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2d90f14cd6e756abeb27045940a6756104cc2d6 \"CML watermark\")\n"
] | 2023-02-01T18:57:48Z
| 2023-02-08T15:10:46Z
| 2023-02-08T15:03:50Z
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Dataset configuration
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"Originally we also though about adding it to the YAML part of the README.md:\r\n\r\n```yaml\r\nbuilder_config:\r\n data_dir: data\r\n data_files:\r\n - split: train\r\n pattern: \"train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*\"\r\n```\r\n\r\nHaving it in the README.md could make it easier to modify it in the UI on HF, and for validation on commit",
"From internal discussions we agreed to go with the YAML approach, since it's the one that seems more appropriate to be modified by a human on the Hub or locally (while JSON e.g. for models are usually created programmatically).",
"Current format:\r\n```yaml\r\nbuilder_config:\r\n data_files:\r\n - split: train\r\n pattern: data/train-*\r\n```"
] | 2023-04-01T13:08:05Z
| 2023-04-04T14:54:37Z
| null |
MEMBER
| null | null | null |
Following discussions from https://github.com/huggingface/datasets/pull/5331
We could have something like `config.json` to define the configuration of a dataset.
```json
{
"data_dir": "data"
"data_files": {
"train": "train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"
}
}
```
we could also support a list for several configs with a 'config_name' field.
The alternative was to use YAML in the README.md.
I think it could also support a `dataset_type` field to specify which dataset builder class to use, and the other parameters would be the builder's parameters. Some parameters exist for all builders like `data_files` and `data_dir`, but some parameters are builder specific like `sep` for csv.
This format would be used in `push_to_hub` to be able to push multiple configs.
cc @huggingface/datasets
EDIT: actually we're going for the YAML approach in README.md
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AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
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[
"Hi @arymbe, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your problem.\r\n\r\nCould you please write the complete stack trace? That way we will be able to see which package originates the exception.",
"Hello, thank you for your fast replied. this is the complete error that I got\r\n\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError Traceback (most recent call last)\r\n\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError Traceback (most recent call last)\r\n\r\nInput In [27], in <module>\r\n----> 1 from datasets import load_dataset\r\n\r\nvenv/lib/python3.8/site-packages/datasets/__init__.py:39, in <module>\r\n 37 from .arrow_dataset import Dataset, concatenate_datasets\r\n 38 from .arrow_reader import ReadInstruction\r\n---> 39 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n 40 from .combine import interleave_datasets\r\n 41 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\nvenv/lib/python3.8/site-packages/datasets/builder.py:40, in <module>\r\n 32 from .arrow_reader import (\r\n 33 HF_GCP_BASE_URL,\r\n 34 ArrowReader,\r\n (...)\r\n 37 ReadInstruction,\r\n 38 )\r\n 39 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 40 from .data_files import DataFilesDict, sanitize_patterns\r\n 41 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n 42 from .features import Features\r\n\r\nvenv/lib/python3.8/site-packages/datasets/data_files.py:297, in <module>\r\n 292 except FileNotFoundError:\r\n 293 raise FileNotFoundError(f\"The directory at {base_path} doesn't contain any data file\") from None\r\n 296 def _resolve_single_pattern_in_dataset_repository(\r\n--> 297 dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n 298 pattern: str,\r\n 299 allowed_extensions: Optional[list] = None,\r\n 300 ) -> List[PurePath]:\r\n 301 data_files_ignore = FILES_TO_IGNORE\r\n 302 fs = HfFileSystem(repo_info=dataset_info)\r\n\r\nAttributeError: module 'huggingface_hub' has no attribute 'hf_api'",
"This is weird... It is long ago that the package `huggingface_hub` has a submodule called `hf_api`.\r\n\r\nMaybe you have a problem with your installed `huggingface_hub`...\r\n\r\nCould you please try to update it?\r\n```shell\r\npip install -U huggingface_hub\r\n```",
"Yap, I've updated several times. Then, I've tried numeral combination of datasets and huggingface_hub versions. However, I think your point is right that there is a problem with my huggingface_hub installation. I'll try another way to find the solution. I'll update it later when I get the solution. Thank you :)",
"I'm sorry I can't reproduce your problem.\r\n\r\nMaybe you could try to create a new Python virtual environment and install all dependencies there from scratch. You can use either:\r\n- Python venv: https://docs.python.org/3/library/venv.html\r\n- or conda venv (if you are using conda): https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html",
"Facing the same issue.\r\n\r\nResponse from `pip show datasets`\r\n```\r\nName: datasets\r\nVersion: 1.15.1\r\nSummary: HuggingFace community-driven open-source library of datasets\r\nHome-page: https://github.com/huggingface/datasets\r\nAuthor: HuggingFace Inc.\r\nAuthor-email: thomas@huggingface.co\r\nLicense: Apache 2.0\r\nLocation: /usr/local/lib/python3.8/dist-packages\r\nRequires: aiohttp, dill, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, requests, tqdm, xxhash\r\nRequired-by: lm-eval\r\n```\r\n\r\nResponse from `pip show huggingface_hub`\r\n\r\n```\r\nName: huggingface-hub\r\nVersion: 0.8.1\r\nSummary: Client library to download and publish models, datasets and other repos on the huggingface.co hub\r\nHome-page: https://github.com/huggingface/huggingface_hub\r\nAuthor: Hugging Face, Inc.\r\nAuthor-email: julien@huggingface.co\r\nLicense: Apache\r\nLocation: /usr/local/lib/python3.8/dist-packages\r\nRequires: filelock, packaging, pyyaml, requests, tqdm, typing-extensions\r\nRequired-by: datasets\r\n```\r\n\r\nresponse from `datasets-cli env`\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/datasets-cli\", line 5, in <module>\r\n from datasets.commands.datasets_cli import main\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/__init__.py\", line 37, in <module>\r\n from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 44, in <module>\r\n from .data_files import DataFilesDict, _sanitize_patterns\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/data_files.py\", line 120, in <module>\r\n dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n File \"/usr/local/lib/python3.8/dist-packages/huggingface_hub/__init__.py\", line 105, in __getattr__\r\n raise AttributeError(f\"No {package_name} attribute {name}\")\r\nAttributeError: No huggingface_hub attribute hf_api\r\n```",
"A workaround: \r\nI changed lines around Line 125 in `__init__.py` of `huggingface_hub` to something like\r\n```\r\n__getattr__, __dir__, __all__ = _attach(\r\n __name__,\r\n submodules=['hf_api'],\r\n```\r\nand it works ( which gives `datasets` direct access to `huggingface_hub.hf_api` ).",
"I was getting the same issue. After trying a few versions, following combination worked for me.\r\ndataset==2.3.2\r\nhuggingface_hub==0.7.0\r\n\r\nIn another environment, I just installed latest repos from pip through `pip install -U transformers datasets tokenizers evaluate`, resulting in following versions. This also worked. Hope it helps someone. \r\n\r\ndatasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.20.1",
"For layoutlm_v3 finetune\r\ndatasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.12.5",
"(For layoutlmv3 fine-tuning) In my case, modifying `requirements.txt` as below worked.\r\n\r\n- python = 3.7\r\n\r\n```\r\ndatasets==2.3.2\r\nevaluate==0.1.2\r\nhuggingface-hub==0.8.1\r\nresponse==0.5.0\r\ntokenizers==0.10.1\r\ntransformers==4.12.5\r\nseqeval==1.2.2\r\ndeepspeed==0.5.7\r\ntensorboard==2.7.0\r\nseqeval==1.2.2\r\nsentencepiece\r\ntimm==0.4.12\r\nPillow\r\neinops\r\ntextdistance\r\nshapely\r\n```",
"> For layoutlm_v3 finetune datasets-2.3.2 evaluate-0.1.2 huggingface-hub-0.8.1 responses-0.18.0 tokenizers-0.12.1 transformers-4.12.5\r\n\r\nGOOD!! Thanks!"
] | 2022-04-07T05:52:36Z
| 2022-07-28T16:44:04Z
| 2022-04-19T15:36:35Z
|
NONE
| null | null | null |
## Describe the bug
Could you help me please. I got this following error.
AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
## Steps to reproduce the bug
when I imported the datasets
# Sample code to reproduce the bug
from datasets import list_datasets, load_dataset, list_metrics, load_metric
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-12.3-x86_64-i386-64bit
- Python version: 3.8.9
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- Huggingface-hub: 0.5.0
- Transformers: 4.18.0
Thank you in advance.
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5537 speedup load
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[
"Awesome ! cc @mariosasko :)",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5704). All of your documentation changes will be reflected on that endpoint.",
"Hi, thanks for working on this!\r\n\r\nYour solution only works if the `root` is `\"\"`, e.g., this would yield an incorrect result:\r\n```python\r\ndset = load_dataset(\"user/hf-dataset-repo\", data_dir=\"path/to/data_dir\")\r\n```\r\n\r\nAlso, the `HfFileSystem` implementation in `datasets` will be replaced with the more powerful [one](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/hf_file_system.py) from `huggingface_hub` soon (I plan to open a PR that makes `find` much faster in the coming days). \r\n\r\nSo I don't think we want to merge this PR in the current state, but thanks again for the effort.\r\n\r\n (I'll comment on the original issue to propose a cleaner solution)",
"Ooof. Sorry, I should have checked that more thoroughly then! I would say we could just add that check and only use my approach if the root is \"\", which would still be faster in many cases, but it sounds like you have a better solution on the way. Thanks for the feedback Mario."
] | 2023-04-04T08:58:14Z
| 2023-04-07T16:10:55Z
| null |
NONE
| null | 0
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I reimplemented fsspec.spec.glob() in `hffilesystem.py` as `_glob`, used it in `_resolve_single_pattern_in_dataset_repository` only, and saw a 20% speedup in times to load the config, on average.
That's not much when usually this step takes only 2-3 seconds for most datasets, but in this particular case, `bigcode/the-stack-dedup` , the loading time to get the config (not download the entire 6tb dataset, of course), went from ~170 secs to ~20 secs.
What makes this work is this code in `_glob`:
```
if self.dir_cache is not None:
allpaths = self.dir_cache
else:
allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs)
```
I also had to `import glob.has_magic( )` for `_glob()` (confusing, I know).
I hope there is no issue with copying most of the code from `fsspec.spec.glob`, as it is a BSD 3-Clause License,
and I left a comment about this in the docstring of` _glob()`, that we may want to delete.
As mentioned, I evaluated the speedup across a random selection of about 1000 datasets (not all 27k+), and verified that old_config.eq(new_method_config) with the build in method, but deleted this test and related code changes on the subsequent commit. It's in the commit history if anyone wants to see it. (Note this does not include the outlier of `bigcode/the-stack-dedup`
| | old_time | new _time | diff | pct_diff |
| -- | -- | -- | -- | -- |
| mean | 3.340 | 2.642 | 0.698 | 18.404 |
| min | 2.024 | 1.976 | -0.840 | -37.634 |
| max | 66.582 | 41.517 | 30.927 | 85.538 |
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| 2020-12-02T16:49:02Z
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[] | 2020-12-08T15:39:35Z
| 2020-12-10T10:12:00Z
| 2020-12-10T10:12:00Z
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# Summary
Providing a GitHub release URL to `DownloadManager.download()` currently throws a `ConnectionError: Couldn't reach [DOWNLOAD_URL]`. This PR fixes this problem by adding an exception for GitHub releases in `datasets.utils.file_utils.get_from_cache()`.
# Reproduce
```
import datasets
url = 'http://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz'
result = datasets.utils.file_utils.get_from_cache(url)
# Returns: ConnectionError: Couldn't reach http://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz
```
# Cause
GitHub releases returns a HTTP status 403 (FOUND), indicating that the request is being redirected (to AWS S3, in this case). `get_from_cache()` checks whether the status is 200 (OK) or if it is part of two exceptions (Google Drive or Firebase), otherwise the mentioned error is thrown.
# Solution
Just like the exceptions for Google Drive and Firebase, add a condition for GitHub releases URLs that return the HTTP status 403. If this is the case, continue normally.
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Fix named split sorting and remove unnecessary casting
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"_The documentation is not available anymore as the PR was closed or merged._",
"hahaha what a timing, I added my comment right after you merged x)\r\n\r\nyou can ignore my (nit), it's fine",
"Sorry, just too sync... :sweat_smile: "
] | 2022-07-19T09:48:28Z
| 2022-07-22T09:39:45Z
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This PR:
- makes `NamedSplit` sortable: so that `sorted()` can be called on them
- removes unnecessary `sorted()` on `dict.keys()`: `dict_keys` view is already like a `set`
- removes unnecessary casting of `NamedSplit` to `str`
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Bug in skip_rows argument of load_dataset function ?
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"Hi,\r\n\r\ntry `skiprows` instead. This part is not properly documented in the docs it seems.\r\n\r\n@lhoestq I'll fix this as part of a bigger PR that fixes typos in the docs."
] | 2021-02-27T23:32:54Z
| 2021-03-09T10:21:32Z
| 2021-03-09T10:21:32Z
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NONE
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Hello everyone,
I'm quite new to Git so sorry in advance if I'm breaking some ground rules of issues posting... :/
I tried to use the load_dataset function, from Huggingface datasets library, on a csv file using the skip_rows argument described on Huggingface page to skip the first row containing column names
`test_dataset = load_dataset('csv', data_files=['test_wLabel.tsv'], delimiter='\t', column_names=["id", "sentence", "label"], skip_rows=1)`
But I got the following error message
`__init__() got an unexpected keyword argument 'skip_rows'`
Have I used the wrong argument ? Am I missing something or is this a bug ?
Thank you very much for your time,
Best regards,
Arthur
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Make creating/editing dataset cards easier, by editing on site and dumping info from test command.
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.**
Creating and editing dataset cards should be but not that easy
- If other else know Some information I don't know (bias of dataset, dataset curation, supported dataset, ...), he/she should know the description on hf.co comes from README.md under github huggingface/datasets/datasets/the dataset, and willing to make a pr to add or fix information.
- Many information is also saved in `dataset_info.json` (citaion, description), but still need to write it down to README.md again.
- Contributor need to pip install and start a local server just for tagging the dataset's size. And contributor may be creating the dataset on lab's server, which can't open a browser.
- if any one proposes a new tag, it doesn't show in the list that another creator see. (a stackoverflow way may be ideal)
- dataset card generator web app doesn't generate the necessary subsecion `Contributions` for us.
**Describe the solution you'd like**
- Everyone (or at least the author/contributor) can edit the description, information, tags of the dataset, on hf.co website. Just like wikipedia+stackoverflow
- We can infer the actual data size, citation, data instance, ... from `dataset_info.json` and `dataset.arrow` via `dataset-cli test`
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bookcorpusopen no longer works
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[
"Hi ! Thanks for reporting :) I think #3280 should fix this",
"I tried with the latest changes from #3280 on google colab and it worked fine :)\r\nWe'll do a new release soon, in the meantime you can use the updated version with:\r\n```python\r\nload_dataset(\"bookcorpusopen\", revision=\"master\")\r\n```",
"Fixed by #3280."
] | 2021-10-26T16:06:15Z
| 2021-11-17T15:53:46Z
| 2021-11-17T15:53:46Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
When using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...).
This did not happen with 1.4.1.
I tried also `rm -rf ~/.cache/huggingface` but did not help.
Changing python version between 3.7, 3.8 and 3.9 did not help too.
## Steps to reproduce the bug
```python
import datasets
d = datasets.load_dataset('bookcorpusopen')
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1
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Add CIFAR-100 Dataset
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"Hi @lhoestq,\r\nI have updated with the changes from the review.",
"Thanks for approving :)"
] | 2021-02-02T15:22:59Z
| 2021-02-08T11:10:18Z
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Adding CIFAR-100 Dataset.
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"@lhoestq Thanks for the review. I totally understand what you are saying. Normally, I would definitely agree with you, but in this particular case, the quality of v1 is poor, and the dataset itself is small (at the time we created v1 it was the only RO NER dataset, and its size was limited by the available resources). \r\n\r\nThis is why we worked to build a larger one, with much better inter-annotator agreement. Fact is, models trained on v1 will be of very low quality and I would not recommend to anybody to use/do that. That's why I'd strongly suggest we replace v1 with v2, and kindof make v1 vanish :) \r\n\r\nWhat do you think? If you insist on having v1 accessible, I'll add the required code. Thanks!\r\n\r\n",
"Ok I see ! I think it's fine then, no need to re-add V1"
] | 2021-10-30T10:50:03Z
| 2021-11-02T16:02:23Z
| 2021-11-02T16:02:22Z
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Hi, as we've recently finished with the new RONEC (Romanian Named Entity Corpus), we'd like to update the dataset here as well. It's actually essential as links to V1 are no longer valid.
In reality we'd like to replace completely v1, as v2 is a full re-annotation of v1 with additional data (up to 2x size vs v1).
I've run the make style and all the dummy and real data test, and they passed.
I hope it's okay to merge the new RONEC v2 in the datasets.
Thanks!
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Fix main_classes docs index
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hmm it's still not good \r\n\r\n\r\nany idea what could cause this ?",
"Ok fixed :)"
] | 2022-03-15T16:33:46Z
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Currently the `main_classes` documentation has a wrong index. I believe this comes from issues in the examples of the Translation feature types
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1.18.x
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Adding PersiNLU reading-comprehension
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"@lhoestq I think I have addressed all your comments. ",
"Thanks! @lhoestq Let me know if you want me to address anything to get this merged. ",
"It's all good thanks ;)\r\nmerging"
] | 2021-03-11T04:41:13Z
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| 2021-03-15T09:39:57Z
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`metric.compute` throws `ArrowInvalid` error
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[
"Hmm might be related to what we are solving in #564",
"Could you try to update to `datasets>=1.0.0` (we changed the name of the library) and try again ?\r\nIf is was related to the distributed setup settings it must be fixed.\r\nIf it was related to empty metric inputs it's going to be fixed in #654 ",
"Closing this one as it was fixed in #654 \r\nFeel free to re-open if you have other questions"
] | 2020-09-03T04:56:57Z
| 2020-10-05T16:33:53Z
| 2020-10-05T16:33:53Z
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NONE
| null | null | null |
I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0`
```
File "/home/beltagy/trainer.py", line 92, in validation_step
rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL'])
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 224, in compute
self.finalize(timeout=timeout)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 213, in finalize
self.data = Dataset(**reader.read_files(node_files))
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 217, in read_files
dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 162, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 276, in _get_dataset_from_filename
f = pa.ipc.open_stream(mmap)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 173, in open_stream
return RecordBatchStreamReader(source)
File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 64, in __init__
self._open(source)
File "pyarrow/ipc.pxi", line 469, in pyarrow.lib._RecordBatchStreamReader._open
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0
```
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Support multiprocessing for download/extract nestedly
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### Feature request
Current multiprocessing for download/extract is not done nestedly. For example, when processing SlimPajama, there is only 3 processes (for train/test/val), while there are many files inside these 3 folders
```
Downloading data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #1: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #2: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #1: 0%| | 0/1 [00:00<?, ?obj/s][A
Extracting data files #2: 0%| | 0/1 [00:00<?, ?obj/s][A[A
```
### Motivation
speedup dataset loading
### Your contribution
I can help test the feature
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[
"I ping @krandiash to keep him up to date.",
"TODO:\r\n- [x] Add a section in the docs about this.\r\n- ~Add a warning if someone tries to specify `cache_file_name=` in `map`, `filter` etc. on a dataset that is in memory, since the computation is not going to be cached in this case.~",
"@lhoestq I have a question, regarding:\r\n> Also maybe we should add a warning if someone tries to specify cache_file_name= in map, filter etc. on a dataset that is in memory, since the computation is not going to be cached in this case.\r\n\r\n- It might be the case that the user has an in-memory dataset and might want to use `map` and cache it, by passing `cache_file_name=`\r\n- This is indeed allowed by the library and works as expected: the dataset is cached.\r\n\r\nWhy adding a warning?",
"Yes right, I meant if `load_from_cache_file` is set to True and `cache_file_name ` is None. my bad :p"
] | 2021-04-07T13:00:18Z
| 2021-04-20T14:20:12Z
| 2021-04-20T10:04:04Z
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Set a default value for `in_memory` depending on the size of the dataset to be loaded.
Close #2179.
TODO:
- [x] Add a section in the docs about this.
- ~Add a warning if someone tries to specify `cache_file_name=` in `map`, `filter` etc. on a dataset that is in memory, since the computation is not going to be cached in this case.~
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Implement to_csv for Dataset
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[
"@lhoestq I stumbled upon an interesting failure when adding tests for CSV serialization of `ArrayXD` features (see the failing unit tests in the CI)\r\n\r\nIt's due to the fact that booleans cannot be converted from arrow format to numpy without copy: https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy",
"Good catch ! I must be able to fix that one by allowing copies for this kind of arrays.\r\nThis is the kind of surprise you get sometimes when playing with arrow x)",
"Raising this error for booleans was introduced in https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22 without much explanations unfortunately.\r\nSo \"no copy\" only works for primitive types - except booleans.\r\nThis is confirmed in the source code at https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/array.pxi#L621\r\n\r\nI'm opening a PR to allow copies for booleans...",
"I just merged the fix for boolean ArrayXD, feel free to merge from master to see if it fixes the ci :)",
"@lhoestq unfirtunately, arrays of strings (or any other non-primitive type) require a copy too\r\n\r\nA list of primitive types can be found here: https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/types.pxi#L821\r\n\r\npyarrow provides a `is_primitive` function to check whether a type is primitive , I used it to set `zero_copy_only`\r\n\r\nAlso, `PandasArrayExtensionArray.isna` was using `numpy.isnan` which fails for arrays of strings. I replaced it with `pandas.isna`. Let me know what you think! :) "
] | 2021-02-16T11:27:29Z
| 2021-02-19T09:41:59Z
| 2021-02-19T09:41:59Z
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| null | 0
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cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory
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`ModuleNotFoundError: No module named 'fsspec.exceptions'`
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"Thanks for reporting, @VictorSanh.\r\n\r\nI'm fixing it."
] | 2021-10-15T19:34:38Z
| 2021-10-18T07:51:54Z
| 2021-10-18T07:51:54Z
|
MEMBER
| null | null | null |
## Describe the bug
I keep runnig into a fsspec ModuleNotFound error
## Steps to reproduce the bug
```python
>>> from datasets import get_dataset_infos
2021-10-15 15:25:37.863206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-10-15 15:25:37.863252: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 56, in <module>
from .utils.streaming_download_manager import StreamingDownloadManager
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 11, in <module>
from fsspec.exceptions import FSTimeoutError
ModuleNotFoundError: No module named 'fsspec.exceptions'
```
Yet, I do have `fsspec`:
```bash
hf@victor-scale:~/dev/promptsource$ pip show fsspec
Name: fsspec
Version: 2021.5.0
Summary: File-system specification
Home-page: http://github.com/intake/filesystem_spec
Author: None
Author-email: None
License: BSD
Location: /home/hf/dev/promptsource/.venv/lib/python3.7/site-packages
Requires:
Required-by: datasets
```
With the same version of fsspec and `datasets==1.9.0`, I don't see this problem....
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I can't even run `datasets-cli env` actually.., but here's my env:
- `datasets` version: 1.13.3
- Platform: Ubuntu 18.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6473). 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) | 0.005270 / 0.011353 (-0.006083) | 0.003471 / 0.011008 (-0.007537) | 0.061942 / 0.038508 (0.023434) | 0.052671 / 0.023109 (0.029562) | 0.250541 / 0.275898 (-0.025357) | 0.270677 / 0.323480 (-0.052803) | 0.002933 / 0.007986 (-0.005053) | 0.003264 / 0.004328 (-0.001064) | 0.048055 / 0.004250 (0.043804) | 0.037459 / 0.037052 (0.000407) | 0.254926 / 0.258489 (-0.003563) | 0.292547 / 0.293841 (-0.001294) | 0.027959 / 0.128546 (-0.100587) | 0.010762 / 0.075646 (-0.064884) | 0.204961 / 0.419271 (-0.214310) | 0.035488 / 0.043533 (-0.008045) | 0.254102 / 0.255139 (-0.001037) | 0.273654 / 0.283200 (-0.009546) | 0.018126 / 0.141683 (-0.123556) | 1.082330 / 1.452155 (-0.369825) | 1.147179 / 1.492716 (-0.345538) |\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) | 0.093223 / 0.018006 (0.075217) | 0.301912 / 0.000490 (0.301422) | 0.000219 / 0.000200 (0.000019) | 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) | 0.018407 / 0.037411 (-0.019004) | 0.060412 / 0.014526 (0.045886) | 0.074063 / 0.176557 (-0.102494) | 0.118743 / 0.737135 (-0.618392) | 0.076484 / 0.296338 (-0.219854) |\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) | 0.289929 / 0.215209 (0.074720) | 2.825096 / 2.077655 (0.747442) | 1.511444 / 1.504120 (0.007324) | 1.394812 / 1.541195 (-0.146383) | 1.419751 / 1.468490 (-0.048739) | 0.569995 / 4.584777 (-4.014782) | 2.402586 / 3.745712 (-1.343126) | 2.826223 / 5.269862 (-2.443639) | 1.751554 / 4.565676 (-2.814123) | 0.064266 / 0.424275 (-0.360009) | 0.005047 / 0.007607 (-0.002561) | 0.341513 / 0.226044 (0.115469) | 3.372106 / 2.268929 (1.103177) | 1.872693 / 55.444624 (-53.571931) | 1.588200 / 6.876477 (-5.288276) | 1.630800 / 2.142072 (-0.511272) | 0.654266 / 4.805227 (-4.150961) | 0.124292 / 6.500664 (-6.376372) | 0.042876 / 0.075469 (-0.032593) |\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) | 0.948406 / 1.841788 (-0.893382) | 11.652947 / 8.074308 (3.578639) | 10.218195 / 10.191392 (0.026803) | 0.128447 / 0.680424 (-0.551976) | 0.014092 / 0.534201 (-0.520109) | 0.287631 / 0.579283 (-0.291652) | 0.264843 / 0.434364 (-0.169521) | 0.329997 / 0.540337 (-0.210340) | 0.439597 / 1.386936 (-0.947339) |\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) | 0.005418 / 0.011353 (-0.005935) | 0.003589 / 0.011008 (-0.007419) | 0.050074 / 0.038508 (0.011566) | 0.052566 / 0.023109 (0.029456) | 0.293447 / 0.275898 (0.017549) | 0.320518 / 0.323480 (-0.002962) | 0.004094 / 0.007986 (-0.003892) | 0.002690 / 0.004328 (-0.001639) | 0.048200 / 0.004250 (0.043949) | 0.040692 / 0.037052 (0.003640) | 0.297086 / 0.258489 (0.038597) | 0.323827 / 0.293841 (0.029986) | 0.029511 / 0.128546 (-0.099035) | 0.011079 / 0.075646 (-0.064568) | 0.058562 / 0.419271 (-0.360709) | 0.032897 / 0.043533 (-0.010636) | 0.297244 / 0.255139 (0.042105) | 0.316812 / 0.283200 (0.033612) | 0.018468 / 0.141683 (-0.123215) | 1.140948 / 1.452155 (-0.311207) | 1.195453 / 1.492716 (-0.297263) |\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) | 0.092677 / 0.018006 (0.074671) | 0.300775 / 0.000490 (0.300285) | 0.000225 / 0.000200 (0.000025) | 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) | 0.021617 / 0.037411 (-0.015794) | 0.077135 / 0.014526 (0.062610) | 0.079848 / 0.176557 (-0.096709) | 0.118475 / 0.737135 (-0.618661) | 0.081174 / 0.296338 (-0.215164) |\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) | 0.294424 / 0.215209 (0.079215) | 2.863989 / 2.077655 (0.786334) | 1.590604 / 1.504120 (0.086484) | 1.474345 / 1.541195 (-0.066849) | 1.482120 / 1.468490 (0.013630) | 0.567829 / 4.584777 (-4.016948) | 2.493782 / 3.745712 (-1.251930) | 2.823460 / 5.269862 (-2.446402) | 1.732677 / 4.565676 (-2.833000) | 0.065518 / 0.424275 (-0.358757) | 0.004923 / 0.007607 (-0.002684) | 0.349313 / 0.226044 (0.123268) | 3.428618 / 2.268929 (1.159689) | 1.970641 / 55.444624 (-53.473983) | 1.655884 / 6.876477 (-5.220593) | 1.657151 / 2.142072 (-0.484921) | 0.661208 / 4.805227 (-4.144019) | 0.119129 / 6.500664 (-6.381535) | 0.040770 / 0.075469 (-0.034699) |\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) | 0.964865 / 1.841788 (-0.876923) | 12.050218 / 8.074308 (3.975910) | 10.458749 / 10.191392 (0.267357) | 0.141856 / 0.680424 (-0.538568) | 0.015091 / 0.534201 (-0.519109) | 0.288897 / 0.579283 (-0.290387) | 0.275343 / 0.434364 (-0.159021) | 0.328363 / 0.540337 (-0.211975) | 0.579243 / 1.386936 (-0.807693) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7721021e284859ea0952444bae6300a0d00794f \"CML watermark\")\n"
] | 2023-12-05T15:36:23Z
| 2023-12-05T18:14:50Z
| 2023-12-05T18:08:41Z
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Fix #6472.
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MDExOlB1bGxSZXF1ZXN0NDY3OTkwNjc2
| 506
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fix dataset.map for function without outputs
|
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| 2020-08-17T11:24:39Z
| 2020-08-17T11:24:38Z
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As noticed in #505 , giving a function that doesn't return anything in `.map` raises an error because of an unreferenced variable.
I fixed that and added tests.
Thanks @avloss for reporting
|
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PR_kwDODunzps5AIDWj
| 5,066
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Support streaming gzip.open
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] | 2022-10-04T11:20:05Z
| 2022-10-06T15:13:51Z
| 2022-10-06T15:11:29Z
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This PR implements support for streaming out-of-the-box dataset scripts containing `gzip.open`.
This has been a recurring issue. See, e.g.:
- #5060
- #3191
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I_kwDODunzps5HXoUc
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dataset metadata for reproducibility
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"+1 on this idea. This could be powerful for helping better track datasets used for model training and help with automatic model card creation. \r\n\r\nOne possible way of doing this would be to store some/most/all the arguments passed to `load_dataset` if a hub id is passed. i.e. store the Hub ID, configuration, etc. \r\n\r\ncc @tomaarsen"
] | 2022-04-08T14:17:28Z
| 2023-09-29T09:23:56Z
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NONE
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When pulling a dataset from the hub, it would be useful to have some metadata about the specific dataset and version that is used. The metadata could then be passed to the `Trainer` which could then be saved to a model card. This is useful for people who run many experiments on different versions (commits/branches) of the same dataset.
The dataset could have a list of “source datasets” metadata and ignore what happens to them before arriving in the Trainer (i.e. ignore mapping, filtering, etc.).
Here is a basic representation (made by @lhoestq )
```python
>>> from datasets import load_dataset
>>>
>>> my_dataset = load_dataset(...)["train"]
>>> my_dataset = my_dataset.map(...)
>>>
>>> my_dataset.sources
[HFHubDataset(repo_id=..., revision=..., arguments={...})]
```
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Support xPath for Windows pathnames
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This PR implements a string representation of `xPath`, which is valid for local paths (also windows) and remote URLs.
Additionally, some `os.path` methods are fixed for remote URLs on Windows machines.
Now, on Windows machines:
```python
In [2]: str(xPath("C:\\dir\\file.txt"))
Out[2]: 'C:\\dir\\file.txt'
In [3]: str(xPath("http://domain.com/file.txt"))
Out[3]: 'http://domain.com/file.txt'
```
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Fix weird spacing in ManualDownloadError message
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[] | 2021-12-27T11:20:36Z
| 2021-12-28T09:03:26Z
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CONTRIBUTOR
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`textwrap.dedent` works based on the spaces at the beginning. Before this change, there wasn't any space.
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MDExOlB1bGxSZXF1ZXN0NDQ0Njc4NDEw
| 343
|
Fix nested tensorflow format
|
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In #339 and #337 we are thinking about adding a way to export datasets to tfrecords.
However I noticed that it was not possible to do `dset.set_format("tensorflow")` on datasets with nested features like `squad`. I fixed that using a nested map operations to convert features to `tf.ragged.constant`.
I also added tests on the `set_format` function.
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|
Don't use take on dataset table in pyarrow 1.0.x
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[
"I tried lower batch sizes and it didn't accelerate filter (quite the opposite actually).\r\nThe slow-down also appears for pyarrow 0.17.1 for some reason, not sure it comes from these changes",
"I just checked the benchmarks of other PRs and some of them had 300s (!!) for filter. This needs some investigation..",
"Merging this one since it's not the cause of the the slow down",
"@lhoestq What might be the reason for the slowdown? When I was training large batchsize, the slowdown was obvious, and my original assumption was to increase write_batch_size"
] | 2020-09-18T17:31:34Z
| 2023-09-19T07:59:19Z
| 2020-09-19T16:46:31Z
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Fix #615
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I_kwDODunzps5Cz8kI
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push_to_hub but preview not working
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"Hi @thomas-happify, please note that the preview may take some time before rendering the data.\r\n\r\nI've seen it is already working.\r\n\r\nI close this issue. Please feel free to reopen it if the problem arises again."
] | 2022-02-01T16:23:57Z
| 2022-02-09T08:00:37Z
| 2022-02-09T08:00:37Z
|
NONE
| null | null | null |
## Dataset viewer issue for '*happifyhealth/twitter_pnn*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/happifyhealth/twitter_pnn)*
I used
```
dataset.push_to_hub("happifyhealth/twitter_pnn")
```
but the preview is not working.
Am I the one who added this dataset ? Yes
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| 772
|
Fix metric with cache dir
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The cache_dir provided by the user was concatenated twice and therefore causing FileNotFound errors.
The tests didn't cover the case of providing `cache_dir=` for metrics because of a stupid issue (it was not using the right parameter).
I remove the double concatenation and I fixed the tests
Fix #728
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load_dataset processing failed with OS error after downloading a dataset
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[
"Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?",
"> Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!"
] | 2021-07-14T12:23:53Z
| 2021-07-15T09:34:02Z
| 2021-07-15T09:34:02Z
|
NONE
| null | null | null |
## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
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New version release request
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[
"We will publish it soon (we usually do it in intervals of 1-2 months, so probably next week)",
"Thanks!"
] | 2023-12-04T07:08:26Z
| 2023-12-04T15:42:22Z
| 2023-12-04T15:42:22Z
|
CONTRIBUTOR
| null | null | null |
### Feature request
Hi!
I am using `datasets` in library `xtuner` and am highly interested in the features introduced since v2.15.0.
To avoid installation from source in our pypi wheels, we are eagerly waiting for the new release. So, Does your team have a new release plan for v2.15.1 and could you please share it with us?
Thanks very much!
### Motivation
.
### Your contribution
.
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MDExOlB1bGxSZXF1ZXN0NTMxNzYyNzg4
| 1,053
|
Fix dataset URL and file names, and add column name in "Social Bias Frames" dataset
|
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"Thanks a lot, looks good!"
] | 2020-12-03T13:03:05Z
| 2020-12-03T13:42:26Z
| 2020-12-03T13:42:26Z
|
CONTRIBUTOR
| null | 0
|
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# Why I did
When I use "social_bias_frames" datasets in this library, I got 404 Errors.
So, I fixed this error and another some problems that I faced to use the dataset.
# What I did
* Modify this dataset URL
* Modify this dataset file names
* Add a "dataSource" column
Thank you!
|
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I_kwDODunzps5Bdysm
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Cannot load ‘bookcorpusopen’
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[
"The host of this copy of the dataset (https://the-eye.eu) is down and has been down for a good amount of time ([potentially months](https://www.reddit.com/r/Roms/comments/q82s15/theeye_downdied/))\r\n\r\nFinding this dataset is a little esoteric, as the original authors took down the official BookCorpus dataset some time ago.\r\n\r\nThere are community-created versions of BookCorpus, such as the files hosted in the link below.\r\nhttps://battle.shawwn.com/sdb/bookcorpus/\r\n\r\nAnd more discussion here:\r\nhttps://github.com/soskek/bookcorpus\r\n\r\nDo we want to remove this dataset entirely? There's a fair argument for this, given that the official BookCorpus dataset was taken down by the authors. If not, perhaps can open a PR with the link to the community-created tar above and updated dataset description.",
"Hi! The `bookcorpusopen` dataset is not working for the same reason as explained in this comment: https://github.com/huggingface/datasets/issues/3504#issuecomment-1004564980",
"Hi @HUIYINXUE, it should work now that the data owners created a mirror server with all data, and we updated the URL in our library."
] | 2022-01-10T20:17:18Z
| 2022-02-14T09:19:27Z
| 2022-02-14T09:18:47Z
|
NONE
| null | null | null |
## Describe the bug
Cannot load 'bookcorpusopen'
## Steps to reproduce the bug
```python
dataset = load_dataset('bookcorpusopen')
```
or
```python
dataset = load_dataset('bookcorpusopen',script_version='master')
```
## Actual results
ConnectionError: Couldn't reach https://the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz
## Environment info
- `datasets` version: 1.9.0
- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64
- Python version: 3.6.13
- PyArrow version: 6.0.1
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adding DataCommons fact checking
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[] | 2020-12-06T19:56:12Z
| 2020-12-16T16:22:48Z
| 2020-12-16T16:22:48Z
|
MEMBER
| null | 0
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Adding the data from: https://datacommons.org/factcheck/
Had to cheat a bit with the dummy data as the test doesn't recognize `.txt.gz`: had to rename uncompressed files with the `.gz` extension manually without actually compressing
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PR_kwDODunzps4x5f1I
| 3,657
|
Extend dataset builder for streaming in `get_dataset_split_names`
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[
"I'm impatient to see if it has an impact on the number of valid datasets for the dataset viewer. For the record, today:\r\n\r\n<img width=\"660\" alt=\"Capture d’écran 2022-02-01 à 14 32 19\" src=\"https://user-images.githubusercontent.com/1676121/151977579-b5a239d9-6662-4aeb-bfd1-eef6b8249991.png\">\r\n",
"This is now available in `datasets` 1.18.3 :)",
"I'm on it https://github.com/huggingface/datasets-preview-backend/issues/130\r\n",
"The result:\r\n<img width=\"671\" alt=\"Capture d’écran 2022-02-03 à 23 45 55\" src=\"https://user-images.githubusercontent.com/1676121/152442169-bfdac643-9a00-4901-bfa7-1d60a1679d4b.png\">\r\n\r\nNot very different. Maybe it fixed issues in the community datasets... But I'm not 100% the two states are comparable (datasets have been created, or updated, meanwhile)"
] | 2022-02-01T12:21:24Z
| 2022-02-03T22:49:06Z
| 2022-02-02T11:22:01Z
|
CONTRIBUTOR
| null | 0
|
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Currently, `get_dataset_split_names` doesn't extend a builder module to support streaming, even though it uses `StreamingDownloadManager` to download data. This PR fixes that.
To test the change, run the following:
```bash
pip install git+https://github.com/huggingface/datasets.git@fix-get_dataset_split_names-streaming
python -c "from datasets import get_dataset_split_names; print(get_dataset_split_names('facebook/multilingual_librispeech', 'german', download_mode='force_redownload', revision='137923f945552c6afdd8b60e4a7b43e3088972c1'))"
```
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