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PR_kwDODunzps4-coaB
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Remove main branch rename notice
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"_The documentation is not available anymore as the PR was closed or merged._"
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We added a notice in README.md to show that we renamed the master branch to main, but we can remove it now (it's been 2 months)
I also unpinned the github issue about the branch renaming
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|
Add OCNLI, a new CLUE dataset
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"Thanks :) merging it"
] | 2020-10-19T11:06:33Z
| 2020-10-22T16:19:49Z
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OCNLI stands for Original Chinese Natural Language Inference. It is a corpus for
Chinese Natural Language Inference, collected following closely the procedures of MNLI,
but with enhanced strategies aiming for more challenging inference pairs. We want to
emphasize we did not use human/machine translation in creating the dataset, and thus
our Chinese texts are original and not translated.
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MDExOlB1bGxSZXF1ZXN0NTMyODQ4MTQx
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Add Species-800
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[
"thanks @lhoestq ! I probably need to do the same change in the `SplitGenerator`s (lines 107, 110 and 113). I'll open a new PR for that",
"Yes indeed ! Good catch 👍 \r\nFeel free to open a PR and ping me",
"Hi , theres a issue pulling species_800 dataset , throws google drive error \r\n\r\nerror: \r\n\r\n```\r\nraise ConnectionError(f\"Couldn't reach {url} ({repr(head_error)})\")\r\nConnectionError: Couldn't reach https://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/ (ReadTimeout(ReadTimeoutError(\"HTTPSConnectionPool(host='drive.google.com', port=443): Read timed out. (read timeout=10)\")))\r\n```\r\ncode: \r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"species_800\")\r\n```",
"Hi @obonyojimmy! I am running the same commands and they work for me. Did you check your internet connection?"
] | 2020-12-04T23:44:51Z
| 2022-01-13T03:09:20Z
| 2020-12-05T16:35:01Z
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Fix backward compatibility for dataset_infos.json
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"_The documentation is not available anymore as the PR was closed or merged._"
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| 2022-10-03T13:43:55Z
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While working on https://github.com/huggingface/datasets/pull/5018 I noticed a small bug introduced in #4926 regarding backward compatibility for dataset_infos.json
Indeed, when a dataset repo had both dataset_infos.json and README.md, the JSON file was ignored. This is unexpected: in practice it should be ignored only if the README.md has a dataset_info field, which has precedence over the data in the JSON file.
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UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)
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"Hi @andreybond, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to able to reproduce your issue:\r\n```python\r\nIn [4]: from datasets import load_dataset\r\n ...: datasets = load_dataset(\"nielsr/XFUN\", \"xfun.ja\")\r\n\r\nIn [5]: datasets\r\nOut[5]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'],\r\n num_rows: 194\r\n })\r\n validation: Dataset({\r\n features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'],\r\n num_rows: 71\r\n })\r\n})\r\n```\r\n\r\nThe only reason I can imagine this issue may arise is if your default encoding is not \"UTF-8\" (and it is ASCII instead). This is usually the case on Windows machines; but you say your environment is a Linux machine. Maybe you change your machine default encoding?\r\n\r\nCould you please check this?\r\n```python\r\nIn [6]: import sys\r\n\r\nIn [7]: sys.getdefaultencoding()\r\nOut[7]: 'utf-8'\r\n```",
"I opened a PR in the original dataset loading script:\r\n- microsoft/unilm#677\r\n\r\nand fixed the corresponding dataset script on the Hub:\r\n- https://huggingface.co/datasets/nielsr/XFUN/commit/73ba5e026621e05fb756ae0f267eb49971f70ebd",
"import sys\r\nsys.getdefaultencoding()\r\n\r\nreturned: 'utf-8'\r\n\r\n---------------------\r\n\r\nI've just cloned master branch - your fix works! Thank you!"
] | 2022-04-05T14:42:38Z
| 2022-04-06T06:37:44Z
| 2022-04-06T06:35:54Z
|
NONE
| null | null | null |
## Describe the bug
Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset("nielsr/XFUN", "xfun.ja")
```
## Expected results
Dataset should be downloaded without exceptions
## Actual results
Stack trace (for the second-time execution):
Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477...
Downloading data files: 100%
2/2 [00:00<00:00, 88.48it/s]
Extracting data files: 100%
2/2 [00:00<00:00, 79.60it/s]
UnicodeDecodeErrorTraceback (most recent call last)
<ipython-input-31-79c26bd1109c> in <module>
1 from datasets import load_dataset
2
----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja")
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 )
605
--> 606 # By default, return all splits
607 if split is None:
608 split = {s: s for s in self.info.splits}
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
692 Args:
693 split: `datasets.Split` which subset of the data to read.
--> 694
695 Returns:
696 `Dataset`
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys)
/usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self)
252 if not self.disable:
253 self.display(check_delay=False)
--> 254
255 def __iter__(self):
256 try:
/usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self)
1183 for obj in iterable:
1184 yield obj
-> 1185 return
1186
1187 mininterval = self.mininterval
~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths)
140 logger.info("Generating examples from = %s", filepath)
141 with open(filepath[0], "r") as f:
--> 142 data = json.load(f)
143
144 for doc in data["documents"]:
/usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
294
295 """
--> 296 return loads(fp.read(),
297 cls=cls, object_hook=object_hook,
298 parse_float=parse_float, parse_int=parse_int,
/usr/lib/python3.6/encodings/ascii.py in decode(self, input, final)
24 class IncrementalDecoder(codecs.IncrementalDecoder):
25 def decode(self, input, final=False):
---> 26 return codecs.ascii_decode(input, self.errors)[0]
27
28 class StreamWriter(Codec,codecs.StreamWriter):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0 (but reproduced with many previous versions)
- Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu
- Python version: 3.6.9
- PyArrow version: 6.0.1
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I_kwDODunzps51-dky
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Get an error when i try to concatenate the squad dataset with my own dataset
|
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"The `answers.text` field in the JSON dataset needs to be a list of strings, not a string.\r\n\r\nSo, here is the fixed code:\r\n```python\r\nfrom huggingface_hub import notebook_login\r\nfrom datasets import load_dataset\r\n\r\n\r\n\r\nnotebook_login(\"mymailadresse\", \"mypassword\")\r\nsquad = load_dataset(\"squad\", split=\"train[:5000]\")\r\nsquad = squad.train_test_split(test_size=0.2)\r\ndataset1 = squad[\"train\"]\r\n\r\n\r\n\r\n\r\nimport json\r\n\r\nmybase = [\r\n {\r\n \"id\": \"1\",\r\n \"context\": \"She lives in Nantes\",\r\n \"question\": \"Where does she live?\",\r\n \"answers\": {\r\n \"text\": [\"Nantes\"],\r\n \"answer_start\": [13],\r\n }\r\n }\r\n]\r\n\r\n\r\n\r\n\r\n# Save the data to a JSON file\r\njson_file_path = r\"data\"\r\nwith open(json_file_path, \"w\", encoding= \"utf-8\") as json_file:\r\n json.dump(mybase, json_file, indent=4)\r\n\r\n\r\n\r\n\r\n# Load the JSON file as a dataset\r\ncustom_dataset = load_dataset(\"json\", data_files=json_file_path, features=dataset1.features)\r\n\r\n\r\n# Access the train split\r\ntrain_dataset = custom_dataset[\"train\"]\r\n\r\n\r\nfrom datasets import concatenate_datasets\r\n\r\n\r\n# Concatenate the datasets\r\nconcatenated_dataset = concatenate_datasets([train_dataset, dataset1])\r\n```",
"Thank you @mariosasko for your help ! It works !"
] | 2023-11-06T14:29:22Z
| 2023-11-06T16:50:45Z
| 2023-11-06T16:50:45Z
|
NONE
| null | null | null |
### Describe the bug
Hello,
I'm new here and I need to concatenate the squad dataset with my own dataset i created. I find the following error when i try to do it: Traceback (most recent call last):
Cell In[9], line 1
concatenated_dataset = concatenate_datasets([train_dataset, dataset1])
File ~\anaconda3\Lib\site-packages\datasets\combine.py:213 in concatenate_datasets
return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)
File ~\anaconda3\Lib\site-packages\datasets\arrow_dataset.py:6002 in _concatenate_map_style_datasets
_check_if_features_can_be_aligned([dset.features for dset in dsets])
File ~\anaconda3\Lib\site-packages\datasets\features\features.py:2122 in _check_if_features_can_be_aligned
raise ValueError(
ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Value(dtype='string', id=None)} or Value("null").
### Steps to reproduce the bug
```python
from huggingface_hub import notebook_login
from datasets import load_dataset
notebook_login("mymailadresse", "mypassword")
squad = load_dataset("squad", split="train[:5000]")
squad = squad.train_test_split(test_size=0.2)
dataset1 = squad["train"]
import json
mybase = [
{
"id": "1",
"context": "She lives in Nantes",
"question": "Where does she live?",
"answers": {
"text": "Nantes",
"answer_start": [13],
}
}
]
# Save the data to a JSON file
json_file_path = r"C:\Users\mypath\thefile.json"
with open(json_file_path, "w", encoding= "utf-8") as json_file:
json.dump(mybase, json_file, indent=4)
# Load the JSON file as a dataset
custom_dataset = load_dataset("json", data_files=json_file_path)
# Access the train split
train_dataset = custom_dataset["train"]
from datasets import concatenate_datasets
# Concatenate the datasets
concatenated_dataset = concatenate_datasets([train_dataset, dataset1])
```
### Expected behavior
I would expect the two datasets to be concatenated without error. The len(dataset1) is equal to 4000 and the len(train_dataset) is equal to 1 so I would exepect concatenated_dataset to be created and having lenght 4001.
### Environment info
Python 3.11.4 and using windows
Thank you for your help
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| 745
|
Fix emotion description
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"Hello, I probably have a silly question but the labels of the emotion dataset are in the form of numbers and not string, so I can not use the function classification_report because it mixes numbers and string (prediction). How can I access the label in the form of a string and not a number? \r\nThank you in advance."
] | 2020-10-20T13:28:39Z
| 2021-04-22T14:47:31Z
| 2020-10-21T08:38:27Z
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Fixes the description of the emotion dataset to reflect the class names observed in the data, not the ones described in the paper.
I also took the liberty to make use of `ClassLabel` for the emotion labels.
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PR_kwDODunzps4zD1D9
| 3,752
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Update metadata JSON for cats_vs_dogs dataset
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| 2022-02-18T14:56:12Z
| 2022-02-18T14:56:11Z
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Note that the number of examples in the train split was already fixed in the dataset card.
Fix #3750.
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PR_kwDODunzps5fiwDC
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[docs] troubleshooting guide
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6424). 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) | 0.005323 / 0.011353 (-0.006030) | 0.003560 / 0.011008 (-0.007448) | 0.062572 / 0.038508 (0.024064) | 0.049549 / 0.023109 (0.026440) | 0.236522 / 0.275898 (-0.039376) | 0.260601 / 0.323480 (-0.062879) | 0.002887 / 0.007986 (-0.005099) | 0.003225 / 0.004328 (-0.001103) | 0.048210 / 0.004250 (0.043960) | 0.038783 / 0.037052 (0.001731) | 0.242506 / 0.258489 (-0.015983) | 0.273906 / 0.293841 (-0.019935) | 0.027202 / 0.128546 (-0.101344) | 0.010577 / 0.075646 (-0.065069) | 0.211669 / 0.419271 (-0.207603) | 0.035727 / 0.043533 (-0.007806) | 0.242303 / 0.255139 (-0.012836) | 0.260468 / 0.283200 (-0.022732) | 0.020109 / 0.141683 (-0.121573) | 1.089603 / 1.452155 (-0.362552) | 1.149899 / 1.492716 (-0.342817) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088768 / 0.018006 (0.070761) | 0.300300 / 0.000490 (0.299810) | 0.000212 / 0.000200 (0.000013) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018758 / 0.037411 (-0.018653) | 0.060097 / 0.014526 (0.045571) | 0.074060 / 0.176557 (-0.102496) | 0.119977 / 0.737135 (-0.617158) | 0.075298 / 0.296338 (-0.221040) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278640 / 0.215209 (0.063431) | 2.715574 / 2.077655 (0.637919) | 1.466644 / 1.504120 (-0.037476) | 1.344470 / 1.541195 (-0.196725) | 1.386984 / 1.468490 (-0.081506) | 0.575796 / 4.584777 (-4.008981) | 2.392324 / 3.745712 (-1.353388) | 2.826284 / 5.269862 (-2.443578) | 1.758997 / 4.565676 (-2.806679) | 0.062474 / 0.424275 (-0.361801) | 0.004930 / 0.007607 (-0.002678) | 0.332595 / 0.226044 (0.106551) | 3.240076 / 2.268929 (0.971147) | 1.785283 / 55.444624 (-53.659341) | 1.527594 / 6.876477 (-5.348882) | 1.562840 / 2.142072 (-0.579233) | 0.655474 / 4.805227 (-4.149754) | 0.116682 / 6.500664 (-6.383983) | 0.042664 / 0.075469 (-0.032805) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.936306 / 1.841788 (-0.905481) | 11.561239 / 8.074308 (3.486931) | 10.341918 / 10.191392 (0.150526) | 0.140602 / 0.680424 (-0.539822) | 0.013857 / 0.534201 (-0.520344) | 0.294241 / 0.579283 (-0.285042) | 0.268359 / 0.434364 (-0.166005) | 0.326344 / 0.540337 (-0.213993) | 0.430936 / 1.386936 (-0.956000) |\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) | 0.005197 / 0.011353 (-0.006156) | 0.003543 / 0.011008 (-0.007465) | 0.049051 / 0.038508 (0.010542) | 0.052742 / 0.023109 (0.029633) | 0.277032 / 0.275898 (0.001134) | 0.300799 / 0.323480 (-0.022681) | 0.003922 / 0.007986 (-0.004064) | 0.002573 / 0.004328 (-0.001755) | 0.047270 / 0.004250 (0.043019) | 0.039782 / 0.037052 (0.002730) | 0.282780 / 0.258489 (0.024291) | 0.308858 / 0.293841 (0.015017) | 0.028641 / 0.128546 (-0.099905) | 0.010516 / 0.075646 (-0.065131) | 0.056367 / 0.419271 (-0.362904) | 0.032346 / 0.043533 (-0.011186) | 0.277591 / 0.255139 (0.022452) | 0.298539 / 0.283200 (0.015339) | 0.018168 / 0.141683 (-0.123515) | 1.104331 / 1.452155 (-0.347823) | 1.187691 / 1.492716 (-0.305025) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089511 / 0.018006 (0.071505) | 0.301309 / 0.000490 (0.300820) | 0.000213 / 0.000200 (0.000013) | 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) | 0.021466 / 0.037411 (-0.015945) | 0.069917 / 0.014526 (0.055391) | 0.081105 / 0.176557 (-0.095452) | 0.119619 / 0.737135 (-0.617516) | 0.083928 / 0.296338 (-0.212410) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296471 / 0.215209 (0.081262) | 2.912139 / 2.077655 (0.834484) | 1.588861 / 1.504120 (0.084741) | 1.452148 / 1.541195 (-0.089047) | 1.475388 / 1.468490 (0.006898) | 0.555779 / 4.584777 (-4.028998) | 2.425599 / 3.745712 (-1.320113) | 2.792848 / 5.269862 (-2.477013) | 1.718757 / 4.565676 (-2.846919) | 0.077687 / 0.424275 (-0.346588) | 0.007522 / 0.007607 (-0.000085) | 0.348254 / 0.226044 (0.122210) | 3.439315 / 2.268929 (1.170386) | 1.925907 / 55.444624 (-53.518717) | 1.646163 / 6.876477 (-5.230314) | 1.662148 / 2.142072 (-0.479924) | 0.637277 / 4.805227 (-4.167950) | 0.116159 / 6.500664 (-6.384505) | 0.041518 / 0.075469 (-0.033952) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.966358 / 1.841788 (-0.875430) | 12.125201 / 8.074308 (4.050892) | 10.629939 / 10.191392 (0.438547) | 0.132439 / 0.680424 (-0.547984) | 0.015622 / 0.534201 (-0.518579) | 0.288824 / 0.579283 (-0.290459) | 0.277634 / 0.434364 (-0.156730) | 0.327200 / 0.540337 (-0.213138) | 0.549679 / 1.386936 (-0.837257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0850f663f5498e0f296461e99a345dfd65e3358f \"CML watermark\")\n"
] | 2023-11-15T17:28:14Z
| 2023-11-30T17:29:55Z
| 2023-11-30T17:23:46Z
|
CONTRIBUTOR
| null | 0
|
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Hi all! This is a PR adding a troubleshooting guide for Datasets docs.
I went through the library's GitHub Issues and Forum questions and identified a few issues that are common enough that I think it would be valuable to include them in the troubleshooting guide. These are:
- creating a dataset from a folder and not following the required format
- authentication issues when using `push_to_hub`
- `Too Many Requests` with `push_to_hub`
- Pickling issues when using Dataset.from_generator()
There's also a section on asking for help. Please let me know if there are other common issues or advice that we can include here.
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Fix dependency conflict within CI build documentation
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2023-11-14T09:52:51Z
| 2023-11-14T10:05:59Z
| 2023-11-14T10:05:35Z
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MEMBER
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Manually fix dependency conflict on `typing-extensions` version originated by `apache-beam` + `pydantic` (now a dependency of `huggingface-hub`).
This is a temporary hot fix of our CI build documentation until we stop using `apache-beam`.
Fix #6406.
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MDU6SXNzdWU5NTEwOTI2NjA=
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QASC: incomplete training set
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"Hi @danyaljj, thanks for reporting.\r\n\r\nUnfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:\r\n```ipython\r\nIn [10]: ds[\"train\"]\r\nOut[10]:\r\nDataset({\r\n features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_question'],\r\n num_rows: 8134\r\n})\r\n\r\nIn [11]: ds[\"train\"].shape\r\nOut[11]: (8134, 8)\r\n```\r\nand the content of the last 5 examples is:\r\n```ipython\r\nIn [12]: for i in range(8129, 8134):\r\n ...: print(json.dumps(ds[\"train\"][i]))\r\n ...:\r\n{\"id\": \"3KAKFY4PGU1LGXM77JAK2700NGCI3X\", \"question\": \"Chitin can be used for protection by whom?\", \"choices\": {\"text\": [\"Fungi\", \"People\", \"Man\", \"Fish\", \"trees\", \"Dogs\", \"animal\", \"Birds\"], \"label\": [\"A\", \"B\",\r\n \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"D\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish scales are also composed of chitin.\", \"combinedfact\": \"Chitin can be used for prote\r\nction by fish.\", \"formatted_question\": \"Chitin can be used for protection by whom? (A) Fungi (B) People (C) Man (D) Fish (E) trees (F) Dogs (G) animal (H) Birds\"}\r\n{\"id\": \"336YQZE83VDAQVZ26HW59X51JZ9M5M\", \"question\": \"Which type of animal uses plates for protection?\", \"choices\": {\"text\": [\"squids\", \"reptiles\", \"sea urchins\", \"fish\", \"amphibians\", \"Frogs\", \"mammals\", \"salm\r\non\"], \"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"B\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Reptiles have scales or plates.\", \"combinedfact\": \"Reptiles use\r\n their plates for protection.\", \"formatted_question\": \"Which type of animal uses plates for protection? (A) squids (B) reptiles (C) sea urchins (D) fish (E) amphibians (F) Frogs (G) mammals (H) salmon\"}\r\n{\"id\": \"3WZ36BJEV3FGS66VGOOUYX0LN8GTBU\", \"question\": \"What are used for protection by fish?\", \"choices\": {\"text\": [\"scales\", \"fins\", \"streams.\", \"coral\", \"gills\", \"Collagen\", \"mussels\", \"whiskers\"], \"label\": [\"\r\nA\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"A\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish are backboned aquatic animals.\", \"combinedfact\": \"scales are used for prote\r\nction by fish \", \"formatted_question\": \"What are used for protection by fish? (A) scales (B) fins (C) streams. (D) coral (E) gills (F) Collagen (G) mussels (H) whiskers\"}\r\n{\"id\": \"3Z2R0DQ0JHDKFAO2706OYIXGNA4E28\", \"question\": \"What are pangolins covered in?\", \"choices\": {\"text\": [\"tunicates\", \"Echinoids\", \"shells\", \"exoskeleton\", \"blastoids\", \"barrel-shaped\", \"protection\", \"white\"\r\n], \"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"G\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Pangolins have an elongate and tapering body covered above with ov\r\nerlapping scales.\", \"combinedfact\": \"Pangolins are covered in overlapping protection.\", \"formatted_question\": \"What are pangolins covered in? (A) tunicates (B) Echinoids (C) shells (D) exoskeleton (E) blastoids\r\n (F) barrel-shaped (G) protection (H) white\"}\r\n{\"id\": \"3PMBY0YE272GIWPNWIF8IH5RBHVC9S\", \"question\": \"What are covered with protection?\", \"choices\": {\"text\": [\"apples\", \"trees\", \"coral\", \"clams\", \"roses\", \"wings\", \"hats\", \"fish\"], \"label\": [\"A\", \"B\", \"C\", \"D\r\n\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"H\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish are covered with scales.\", \"combinedfact\": \"Fish are covered with protection\", \"formatted_q\r\nuestion\": \"What are covered with protection? (A) apples (B) trees (C) coral (D) clams (E) roses (F) wings (G) hats (H) fish\"}\r\n```\r\n\r\nCould you please load again your dataset and print its shape, like this:\r\n```python\r\nds = load_dataset(\"qasc\", split=\"train)\r\nprint(ds.shape)\r\n```\r\nand confirm which is your output?",
"Hmm .... it must have been a mistake on my side. Sorry for the hassle! "
] | 2021-07-22T21:59:44Z
| 2021-07-23T13:30:07Z
| 2021-07-23T13:30:07Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
The training instances are not loaded properly.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("qasc", script_version='1.10.2')
def load_instances(split):
instances = dataset[split]
print(f"split: {split} - size: {len(instances)}")
for x in instances:
print(json.dumps(x))
load_instances('test')
load_instances('validation')
load_instances('train')
```
## results
For test and validation, we can see the examples in the output (which is good!):
```
split: test - size: 920
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"}
...
```
However, only a few instances are loaded for the training split, which is not correct.
## Environment info
- `datasets` version: '1.10.2'
- Platform: MaxOS
- Python version:3.7
- PyArrow version: 3.0.0
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PR_kwDODunzps41l7yd
| 4,089
|
Create metric card for Frugal Score
|
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-04-04T14:53:49Z
| 2022-04-05T14:14:46Z
| 2022-04-05T14:06:50Z
|
NONE
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Proposing metric card for Frugal Score.
@albertvillanova or @lhoestq -- there are certain aspects that I'm not 100% sure on (such as how exactly the distillation between BertScore and FrugalScore is done) -- so if you find that something isn't clear, please let me know!
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[Docs] Add description of `select_columns` to guide
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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) | 0.007755 / 0.011353 (-0.003598) | 0.004618 / 0.011008 (-0.006391) | 0.098132 / 0.038508 (0.059624) | 0.086759 / 0.023109 (0.063650) | 0.374668 / 0.275898 (0.098770) | 0.417131 / 0.323480 (0.093651) | 0.004604 / 0.007986 (-0.003382) | 0.005461 / 0.004328 (0.001132) | 0.077249 / 0.004250 (0.072999) | 0.063247 / 0.037052 (0.026195) | 0.391801 / 0.258489 (0.133312) | 0.432139 / 0.293841 (0.138298) | 0.036755 / 0.128546 (-0.091791) | 0.010011 / 0.075646 (-0.065636) | 0.346175 / 0.419271 (-0.073097) | 0.061503 / 0.043533 (0.017971) | 0.374063 / 0.255139 (0.118924) | 0.435873 / 0.283200 (0.152673) | 0.029476 / 0.141683 (-0.112207) | 1.786945 / 1.452155 (0.334790) | 1.857190 / 1.492716 (0.364474) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253939 / 0.018006 (0.235933) | 0.506847 / 0.000490 (0.506358) | 0.007278 / 0.000200 (0.007079) | 0.000451 / 0.000054 (0.000397) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032938 / 0.037411 (-0.004474) | 0.097493 / 0.014526 (0.082967) | 0.112090 / 0.176557 (-0.064467) | 0.177986 / 0.737135 (-0.559149) | 0.112060 / 0.296338 (-0.184278) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.481858 / 0.215209 (0.266649) | 4.814894 / 2.077655 (2.737239) | 2.496428 / 1.504120 (0.992308) | 2.309965 / 1.541195 (0.768770) | 2.393819 / 1.468490 (0.925329) | 0.564670 / 4.584777 (-4.020107) | 4.151222 / 3.745712 (0.405510) | 3.676115 / 5.269862 (-1.593747) | 2.346165 / 4.565676 (-2.219512) | 0.066344 / 0.424275 (-0.357931) | 0.009006 / 0.007607 (0.001399) | 0.567699 / 0.226044 (0.341654) | 5.686799 / 2.268929 (3.417871) | 3.031044 / 55.444624 (-52.413580) | 2.606259 / 6.876477 (-4.270217) | 2.864876 / 2.142072 (0.722804) | 0.681730 / 4.805227 (-4.123498) | 0.155405 / 6.500664 (-6.345259) | 0.071492 / 0.075469 (-0.003977) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.514446 / 1.841788 (-0.327341) | 22.624912 / 8.074308 (14.550604) | 16.754145 / 10.191392 (6.562753) | 0.193113 / 0.680424 (-0.487311) | 0.021808 / 0.534201 (-0.512393) | 0.468241 / 0.579283 (-0.111042) | 0.499647 / 0.434364 (0.065283) | 0.539571 / 0.540337 (-0.000766) | 0.771268 / 1.386936 (-0.615668) |\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) | 0.007562 / 0.011353 (-0.003791) | 0.004548 / 0.011008 (-0.006460) | 0.075998 / 0.038508 (0.037490) | 0.081648 / 0.023109 (0.058539) | 0.462876 / 0.275898 (0.186978) | 0.499366 / 0.323480 (0.175886) | 0.005839 / 0.007986 (-0.002147) | 0.003753 / 0.004328 (-0.000576) | 0.075918 / 0.004250 (0.071668) | 0.063233 / 0.037052 (0.026181) | 0.459024 / 0.258489 (0.200535) | 0.506388 / 0.293841 (0.212547) | 0.036179 / 0.128546 (-0.092367) | 0.009961 / 0.075646 (-0.065685) | 0.082061 / 0.419271 (-0.337211) | 0.056469 / 0.043533 (0.012936) | 0.459567 / 0.255139 (0.204428) | 0.482578 / 0.283200 (0.199378) | 0.026363 / 0.141683 (-0.115320) | 1.742247 / 1.452155 (0.290092) | 1.807166 / 1.492716 (0.314450) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.330526 / 0.018006 (0.312520) | 0.511674 / 0.000490 (0.511184) | 0.040969 / 0.000200 (0.040769) | 0.000176 / 0.000054 (0.000121) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035492 / 0.037411 (-0.001920) | 0.104338 / 0.014526 (0.089813) | 0.116973 / 0.176557 (-0.059583) | 0.180218 / 0.737135 (-0.556917) | 0.118801 / 0.296338 (-0.177538) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492196 / 0.215209 (0.276987) | 4.910271 / 2.077655 (2.832616) | 2.542562 / 1.504120 (1.038442) | 2.333516 / 1.541195 (0.792321) | 2.439682 / 1.468490 (0.971192) | 0.571966 / 4.584777 (-4.012811) | 4.089801 / 3.745712 (0.344089) | 3.732129 / 5.269862 (-1.537733) | 2.375887 / 4.565676 (-2.189789) | 0.067376 / 0.424275 (-0.356900) | 0.008350 / 0.007607 (0.000743) | 0.583942 / 0.226044 (0.357897) | 5.840002 / 2.268929 (3.571074) | 3.062520 / 55.444624 (-52.382104) | 2.722512 / 6.876477 (-4.153965) | 2.938307 / 2.142072 (0.796234) | 0.689459 / 4.805227 (-4.115769) | 0.155632 / 6.500664 (-6.345032) | 0.072387 / 0.075469 (-0.003082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595587 / 1.841788 (-0.246201) | 23.035478 / 8.074308 (14.961170) | 16.457675 / 10.191392 (6.266283) | 0.170819 / 0.680424 (-0.509605) | 0.022042 / 0.534201 (-0.512159) | 0.466824 / 0.579283 (-0.112459) | 0.486350 / 0.434364 (0.051986) | 0.574330 / 0.540337 (0.033993) | 0.764913 / 1.386936 (-0.622023) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63 \"CML watermark\")\n"
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| 2023-08-16T10:13:02Z
| 2023-08-16T10:02:52Z
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Closes #6116
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Dataset infos in yaml
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"_The documentation is not available anymore as the PR was closed or merged._",
"Alright this is ready for review :)\r\nI mostly would like your opinion on the YAML structure and what we can do in the docs (IMO we can add the docs about those fields in the Hub docs). Other than that let me know if the changes in info.py and features.py look good to you",
"LGTM and looking forward to having this merged!! ❤️ ",
"We plan to do a release today, we'll merge this after the release :)\r\n\r\nEDIT: actually tomorrow",
"Created https://github.com/huggingface/datasets/pull/5018 where I added the YAML `dataset_info` of every single dataset in this repo\r\n\r\nsee other dataset cards: [imagenet-1k](https://github.com/huggingface/datasets/blob/040102f100964a33fd334e2695f1c493fa6b92db/datasets/imagenet-1k/README.md), [glue](https://github.com/huggingface/datasets/blob/040102f100964a33fd334e2695f1c493fa6b92db/datasets/glue/README.md), [flores](https://github.com/huggingface/datasets/blob/040102f100964a33fd334e2695f1c493fa6b92db/datasets/flores/README.md), [gem](https://github.com/huggingface/datasets/blob/040102f100964a33fd334e2695f1c493fa6b92db/datasets/gem/README.md)",
"Took your comments into account and updated `push_to_hub` to push the dataset_info to the README.md instead of json :) Let me know if it sounds good to you now !"
] | 2022-09-02T16:10:05Z
| 2022-10-03T09:13:07Z
| 2022-10-03T09:11:12Z
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To simplify the addition of new datasets, we'd like to have the dataset infos in the YAML and deprecate the dataset_infos.json file. YAML is readable and easy to edit, and the YAML metadata of the readme already contain dataset metadata so we would have everything in one place.
To be more specific, I moved these fields from DatasetInfo to the YAML:
- config_name (if there are several configs)
- download_size
- dataset_size
- features
- splits
Here is what I ended up with for `squad`:
```yaml
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 79346360
num_examples: 87599
- name: validation
num_bytes: 10473040
num_examples: 10570
config_name: plain_text
download_size: 35142551
dataset_size: 89819400
```
and it can be a list if there are several configs
I already did the change for `conll2000` and `crime_and_punish` as an example.
## Implementation details
### Load/Read
This is done via `DatasetInfosDict.write_to_directory/from_directory`
I had to implement a custom the YAML export logic for `SplitDict`, `Version` and `Features`.
The first two are trivial, but the logic for `Features` is more complicated, because I added a simplification step (or the YAML would be too long and less readable): it's just a formatting step to remove unnecessary nesting of YAML data.
### Other changes
I had to update the DatasetModule factories to also download the README.md alongside the dataset scripts/data files, and not just the dataset_infos.json
## YAML validation
I removed the old validation code that was in metadata.py, now we can just use the Hub YAML validation
## Datasets-cli
The `datasets-cli test --save_infos` command now creates a README.md file with the dataset_infos in it, instead of a datasets_infos.json file
## Backward compatibility
`dataset_infos.json` files are still supported and loaded if they exist to have full backward compatibility.
Though I removed the unnecessary keys when the value is the default (like all the `id: null` from the Value feature types) to make them easier to read.
## TODO
- [x] add comments
- [x] tests
- [x] document the new YAML fields
- [x] try to reload the new dataset_infos.json file content with an old version of `datasets`
## EDITS
- removed "config_name" when there's only one config
- removed "version" for now (?), because it's not useful in general
- renamed the yaml field "dataset_info" instead of "dataset_infos", since it only has one by default (and because "infos" is not english)
Fix https://github.com/huggingface/datasets/issues/4876
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[arrow_dataset.py] add new filter function
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"I agree that a `.filter` method would be VERY useful and appreciated. I'm not a big fan of using `flatten_nested` as it completely breaks down the structure of the example and it may create bugs. Right now I think it may not work for nested structures. Maybe there's a simpler way that we've not figured out yet.",
"Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:\r\n```python\r\nfor i in range(num_examples):\r\n example = map_nested(lambda x: x[i], batch)\r\n # ... then test to keep it or not\r\n```",
"> Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:\r\n> \r\n> ```python\r\n> for i in range(num_examples):\r\n> example = map_nested(lambda x: x[i], batch)\r\n> # ... then test to keep it or not\r\n> ```\r\n\r\nAwesome I'll check it out :-) ",
"> Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:\r\n> \r\n> ```python\r\n> for i in range(num_examples):\r\n> example = map_nested(lambda x: x[i], batch)\r\n> # ... then test to keep it or not\r\n> ```\r\n\r\nAwesome this function is definitely much nicer!",
"Actually I just realized that `map_nested` might not work either as it applies the function at the very last list of the structure. However we can imagine that a single example has also a list in its structure:\r\n```python\r\none_example = {\r\n \"title\": \"blabla\",\r\n \"paragraphs\": [\r\n \"p1\", \"p2\", ...\r\n ]\r\n}\r\n```",
"We'll probably have to take into account the `dset._data.schema` to extract the examples from the batch.",
"> Actually I just realized that `map_nested` might not work either as it applies the function at the very last list of the structure. However we can imagine that a single example has also a list in its structure:\r\n> \r\n> ```python\r\n> one_example = {\r\n> \"title\": \"blabla\",\r\n> \"paragraphs\": [\r\n> \"p1\", \"p2\", ...\r\n> ]\r\n> }\r\n> ```\r\n\r\nThey both work. I'm using it on trivia_qa which is pretty nested. If you use the option `dict_only=True` I think it's fine.",
"> We'll probably have to take into account the `dset._data.schema` to extract the examples from the batch.\r\n\r\nWhy? ",
"Actually it's fine. I guess this is going to be yet another thing to be unit-tested just to make sure ^^",
"Yes, I will need to add tests and documentation! \r\n@thomwolf - would a function like this be ok? It abstracts `.map()` a bit which might be hard to understand. ",
"I tried on some datasets with nested structure and it works fine ! Great work :D \r\n",
"Awesome :-), I will add documentation and some simple unittests",
"Ok merging!"
] | 2020-05-28T16:21:40Z
| 2020-05-29T11:43:29Z
| 2020-05-29T11:32:20Z
|
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The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
```
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| 2,186
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GEM: new challenge sets
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"cc @sebastiangehrmann"
] | 2021-04-07T21:39:07Z
| 2021-04-07T21:56:35Z
| 2021-04-07T21:56:35Z
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This PR updates the GEM dataset to:
- remove extraneous fields in WikiAuto after https://github.com/huggingface/datasets/pull/2171 fixed the source
- add context and services to Schema Guided Dialog
- Add new or update challenge sets for MLSUM ES and DE, XSUM, and SGD
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Don't use tqdm 4.50.0
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tqdm 4.50.0 introduced permission errors on windows
see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/235/workflows/cfb6a39f-68eb-4802-8b17-2cd5e8ea7369/jobs/1111) for the error details.
For now I just added `<4.50.0` in the setup.py
Hopefully we can find what's wrong with this version soon
|
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PR_kwDODunzps5K8YYz
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Flatten dataset on the fly in `save_to_disk`
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009866 / 0.011353 (-0.001487) | 0.005334 / 0.011008 (-0.005675) | 0.101771 / 0.038508 (0.063263) | 0.037722 / 0.023109 (0.014613) | 0.301026 / 0.275898 (0.025128) | 0.336618 / 0.323480 (0.013138) | 0.008679 / 0.007986 (0.000693) | 0.005640 / 0.004328 (0.001312) | 0.077076 / 0.004250 (0.072825) | 0.045068 / 0.037052 (0.008016) | 0.302570 / 0.258489 (0.044081) | 0.359093 / 0.293841 (0.065252) | 0.038865 / 0.128546 (-0.089681) | 0.012318 / 0.075646 (-0.063328) | 0.334819 / 0.419271 (-0.084452) | 0.047980 / 0.043533 (0.004447) | 0.296999 / 0.255139 (0.041860) | 0.318855 / 0.283200 (0.035656) | 0.110633 / 0.141683 (-0.031050) | 1.464326 / 1.452155 (0.012172) | 1.537386 / 1.492716 (0.044670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282906 / 0.018006 (0.264900) | 0.498418 / 0.000490 (0.497928) | 0.001507 / 0.000200 (0.001307) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029948 / 0.037411 (-0.007463) | 0.114385 / 0.014526 (0.099859) | 0.125783 / 0.176557 (-0.050774) | 0.193458 / 0.737135 (-0.543678) | 0.129725 / 0.296338 (-0.166614) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403822 / 0.215209 (0.188613) | 4.034180 / 2.077655 (1.956525) | 1.768206 / 1.504120 (0.264086) | 1.579267 / 1.541195 (0.038072) | 1.725077 / 1.468490 (0.256587) | 0.698743 / 4.584777 (-3.886034) | 3.723481 / 3.745712 (-0.022231) | 2.302374 / 5.269862 (-2.967488) | 1.497954 / 4.565676 (-3.067723) | 0.087360 / 0.424275 (-0.336915) | 0.012453 / 0.007607 (0.004846) | 0.523374 / 0.226044 (0.297329) | 5.244962 / 2.268929 (2.976033) | 2.272874 / 55.444624 (-53.171750) | 1.935570 / 6.876477 (-4.940907) | 2.043151 / 2.142072 (-0.098921) | 0.866298 / 4.805227 (-3.938929) | 0.169376 / 6.500664 (-6.331288) | 0.064578 / 0.075469 (-0.010892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.217372 / 1.841788 (-0.624416) | 15.896050 / 8.074308 (7.821742) | 15.165190 / 10.191392 (4.973798) | 0.171168 / 0.680424 (-0.509256) | 0.029770 / 0.534201 (-0.504431) | 0.449030 / 0.579283 (-0.130253) | 0.454704 / 0.434364 (0.020340) | 0.550689 / 0.540337 (0.010351) | 0.651182 / 1.386936 (-0.735754) |\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) | 0.008072 / 0.011353 (-0.003281) | 0.005533 / 0.011008 (-0.005475) | 0.076343 / 0.038508 (0.037835) | 0.037997 / 0.023109 (0.014888) | 0.350465 / 0.275898 (0.074567) | 0.391168 / 0.323480 (0.067688) | 0.006475 / 0.007986 (-0.001511) | 0.004299 / 0.004328 (-0.000029) | 0.074867 / 0.004250 (0.070617) | 0.055256 / 0.037052 (0.018204) | 0.363919 / 0.258489 (0.105430) | 0.396521 / 0.293841 (0.102680) | 0.037746 / 0.128546 (-0.090801) | 0.012556 / 0.075646 (-0.063091) | 0.087974 / 0.419271 (-0.331297) | 0.050850 / 0.043533 (0.007317) | 0.345857 / 0.255139 (0.090718) | 0.361019 / 0.283200 (0.077820) | 0.111007 / 0.141683 (-0.030676) | 1.444014 / 1.452155 (-0.008140) | 1.533154 / 1.492716 (0.040438) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.332114 / 0.018006 (0.314108) | 0.517232 / 0.000490 (0.516742) | 0.004459 / 0.000200 (0.004259) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033147 / 0.037411 (-0.004264) | 0.119983 / 0.014526 (0.105457) | 0.125970 / 0.176557 (-0.050586) | 0.196375 / 0.737135 (-0.540760) | 0.133849 / 0.296338 (-0.162489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429477 / 0.215209 (0.214267) | 4.263750 / 2.077655 (2.186096) | 2.079409 / 1.504120 (0.575289) | 1.899831 / 1.541195 (0.358636) | 2.048472 / 1.468490 (0.579982) | 0.720945 / 4.584777 (-3.863832) | 3.813195 / 3.745712 (0.067483) | 2.250353 / 5.269862 (-3.019508) | 1.401496 / 4.565676 (-3.164181) | 0.090052 / 0.424275 (-0.334223) | 0.012552 / 0.007607 (0.004945) | 0.536839 / 0.226044 (0.310794) | 5.361089 / 2.268929 (3.092161) | 2.559710 / 55.444624 (-52.884914) | 2.226963 / 6.876477 (-4.649513) | 2.341898 / 2.142072 (0.199825) | 0.872115 / 4.805227 (-3.933112) | 0.173776 / 6.500664 (-6.326888) | 0.068567 / 0.075469 (-0.006902) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294583 / 1.841788 (-0.547205) | 16.624099 / 8.074308 (8.549791) | 13.698509 / 10.191392 (3.507117) | 0.161917 / 0.680424 (-0.518506) | 0.017744 / 0.534201 (-0.516457) | 0.428547 / 0.579283 (-0.150736) | 0.424687 / 0.434364 (-0.009677) | 0.525812 / 0.540337 (-0.014525) | 0.629075 / 1.386936 (-0.757861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33e4d6af919db17bf9a1eac544a0501b5972393b \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008667 / 0.011353 (-0.002686) | 0.004921 / 0.011008 (-0.006087) | 0.098352 / 0.038508 (0.059844) | 0.033983 / 0.023109 (0.010873) | 0.291640 / 0.275898 (0.015742) | 0.323388 / 0.323480 (-0.000092) | 0.007943 / 0.007986 (-0.000043) | 0.003922 / 0.004328 (-0.000407) | 0.075861 / 0.004250 (0.071610) | 0.042606 / 0.037052 (0.005554) | 0.298571 / 0.258489 (0.040081) | 0.345496 / 0.293841 (0.051655) | 0.037443 / 0.128546 (-0.091103) | 0.012114 / 0.075646 (-0.063532) | 0.333269 / 0.419271 (-0.086003) | 0.047762 / 0.043533 (0.004229) | 0.295452 / 0.255139 (0.040313) | 0.319641 / 0.283200 (0.036441) | 0.101083 / 0.141683 (-0.040600) | 1.432179 / 1.452155 (-0.019976) | 1.523976 / 1.492716 (0.031260) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241327 / 0.018006 (0.223321) | 0.538315 / 0.000490 (0.537825) | 0.003479 / 0.000200 (0.003279) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025857 / 0.037411 (-0.011554) | 0.104833 / 0.014526 (0.090307) | 0.116826 / 0.176557 (-0.059730) | 0.183460 / 0.737135 (-0.553675) | 0.119595 / 0.296338 (-0.176743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397533 / 0.215209 (0.182324) | 3.968664 / 2.077655 (1.891010) | 1.774025 / 1.504120 (0.269905) | 1.577424 / 1.541195 (0.036229) | 1.623049 / 1.468490 (0.154559) | 0.701008 / 4.584777 (-3.883769) | 3.753278 / 3.745712 (0.007565) | 2.078313 / 5.269862 (-3.191549) | 1.335639 / 4.565676 (-3.230037) | 0.085216 / 0.424275 (-0.339059) | 0.012087 / 0.007607 (0.004480) | 0.513219 / 0.226044 (0.287174) | 5.097693 / 2.268929 (2.828765) | 2.275030 / 55.444624 (-53.169594) | 1.928037 / 6.876477 (-4.948439) | 1.941216 / 2.142072 (-0.200856) | 0.856720 / 4.805227 (-3.948507) | 0.166723 / 6.500664 (-6.333941) | 0.062263 / 0.075469 (-0.013206) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196054 / 1.841788 (-0.645734) | 14.190526 / 8.074308 (6.116218) | 14.053768 / 10.191392 (3.862376) | 0.179982 / 0.680424 (-0.500442) | 0.029024 / 0.534201 (-0.505177) | 0.440391 / 0.579283 (-0.138892) | 0.445627 / 0.434364 (0.011264) | 0.543098 / 0.540337 (0.002761) | 0.640577 / 1.386936 (-0.746359) |\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) | 0.007008 / 0.011353 (-0.004345) | 0.005015 / 0.011008 (-0.005993) | 0.073783 / 0.038508 (0.035274) | 0.032401 / 0.023109 (0.009292) | 0.343382 / 0.275898 (0.067484) | 0.358317 / 0.323480 (0.034837) | 0.005548 / 0.007986 (-0.002437) | 0.005188 / 0.004328 (0.000859) | 0.072867 / 0.004250 (0.068617) | 0.048555 / 0.037052 (0.011502) | 0.334516 / 0.258489 (0.076027) | 0.390263 / 0.293841 (0.096422) | 0.036343 / 0.128546 (-0.092203) | 0.012243 / 0.075646 (-0.063404) | 0.087067 / 0.419271 (-0.332205) | 0.049025 / 0.043533 (0.005492) | 0.333977 / 0.255139 (0.078838) | 0.354427 / 0.283200 (0.071227) | 0.104771 / 0.141683 (-0.036912) | 1.434588 / 1.452155 (-0.017567) | 1.519788 / 1.492716 (0.027072) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264002 / 0.018006 (0.245996) | 0.547902 / 0.000490 (0.547412) | 0.000461 / 0.000200 (0.000261) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028916 / 0.037411 (-0.008496) | 0.110267 / 0.014526 (0.095741) | 0.119190 / 0.176557 (-0.057367) | 0.188599 / 0.737135 (-0.548537) | 0.126948 / 0.296338 (-0.169391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422777 / 0.215209 (0.207568) | 4.209813 / 2.077655 (2.132158) | 2.001360 / 1.504120 (0.497240) | 1.802651 / 1.541195 (0.261456) | 1.860357 / 1.468490 (0.391867) | 0.695006 / 4.584777 (-3.889771) | 3.741917 / 3.745712 (-0.003795) | 3.313071 / 5.269862 (-1.956791) | 1.726366 / 4.565676 (-2.839311) | 0.086185 / 0.424275 (-0.338090) | 0.012256 / 0.007607 (0.004649) | 0.536874 / 0.226044 (0.310830) | 5.253008 / 2.268929 (2.984079) | 2.457189 / 55.444624 (-52.987436) | 2.112199 / 6.876477 (-4.764278) | 2.117867 / 2.142072 (-0.024205) | 0.831914 / 4.805227 (-3.973314) | 0.168238 / 6.500664 (-6.332426) | 0.065075 / 0.075469 (-0.010394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280795 / 1.841788 (-0.560993) | 14.606608 / 8.074308 (6.532299) | 13.317597 / 10.191392 (3.126205) | 0.166590 / 0.680424 (-0.513834) | 0.017520 / 0.534201 (-0.516681) | 0.420978 / 0.579283 (-0.158305) | 0.415708 / 0.434364 (-0.018656) | 0.523619 / 0.540337 (-0.016718) | 0.625299 / 1.386936 (-0.761637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2a83a8ea4b3a87a925ef44b787e87b59bf68225 \"CML watermark\")\n"
] | 2023-02-28T15:37:46Z
| 2023-02-28T17:28:35Z
| 2023-02-28T17:21:17Z
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Flatten a dataset on the fly in `save_to_disk` instead of doing it with `flatten_indices` to avoid creating an additional cache file.
(this is one of the sub-tasks in https://github.com/huggingface/datasets/issues/5507)
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| 139
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Add GermEval 2014 NER dataset
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"Had really fun playing around with this new library :heart: ",
"That's awesome - thanks @stefan-it :-) \r\n\r\nCould you maybe rebase to master and check if all dummy data tests are fine. I should have included the local tests directly in the test suite so that all PRs are fully checked: #140 - sorry :D ",
"@patrickvonplaten Rebased it 😅\r\n\r\nHow can it test 🤔 I used:\r\n\r\n```bash\r\nRUN_SLOW=1 RUN_LOCAL=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_local_germeval_14\r\n# and\r\nRUN_SLOW=1 RUN_LOCAL=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_dataset_all_configs_local_germeval_14\r\n```\r\n\r\nand the tests still pass :)",
"Perfect, if these tests pass that's great - I'll merge the PR then :-) Was it very difficult to create the dummy data structure? "
] | 2020-05-15T23:42:09Z
| 2020-05-16T13:56:37Z
| 2020-05-16T13:56:22Z
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CONTRIBUTOR
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Hi,
this PR adds the GermEval 2014 NER dataset 😃
> The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation [1] with the following properties:
> - The data was sampled from German Wikipedia and News Corpora as a collection of citations.
> - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens.
> - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].
Dataset will be downloaded from the [official GermEval 2014 website](https://sites.google.com/site/germeval2014ner/data).
## Dataset format
Here's an example of the dataset format from the original dataset:
```tsv
# http://de.wikipedia.org/wiki/Manfred_Korfmann [2009-10-17]
1 Aufgrund O O
2 seiner O O
3 Initiative O O
4 fand O O
5 2001/2002 O O
6 in O O
7 Stuttgart B-LOC O
8 , O O
9 Braunschweig B-LOC O
10 und O O
11 Bonn B-LOC O
12 eine O O
13 große O O
14 und O O
15 publizistisch O O
16 vielbeachtete O O
17 Troia-Ausstellung B-LOCpart O
18 statt O O
19 , O O
20 „ O O
21 Troia B-OTH B-LOC
22 - I-OTH O
23 Traum I-OTH O
24 und I-OTH O
25 Wirklichkeit I-OTH O
26 “ O O
27 . O O
```
The sentence is encoded as one token per line (tab separated columns.
The first column contains either a `#`, which signals the source the sentence is cited from and the date it was retrieved, or the token number within the sentence.
The second column contains the token.
Column three and four contain the named entity (in IOB2 scheme).
Outer spans are encoded in the third column, embedded/nested spans in the fourth column.
## Features
I decided to keep most information from the dataset. That means the so called "source" information (where the sentences come from + date information) is also returned for each sentence in the feature vector.
For each sentence in the dataset, one feature vector (`nlp.Features` definition) will be returned:
| Feature | Example | Description
| ---- | ---- | -----------------
| `id` | `0` | Number (id) of current sentence
| `source` | `http://de.wikipedia.org/wiki/Manfred_Korfmann [2009-10-17]` | URL and retrieval date as string
| `tokens` | `["Schwartau", "sagte", ":"]` | List of tokens (strings) for a sentence
| `labels` | `["B-PER", "O", "O"]` | List of labels (outer span)
| `nested-labels` | `["O", "O", "O"]` | List of labels for nested span
## Example
The following command downloads the dataset from the official GermEval 2014 page and pre-processed it:
```bash
python nlp-cli test datasets/germeval_14 --all_configs
```
It then outputs the number for training, development and testset. The training set consists of 24,000 sentences, the development set of 2,200 and the test of 5,100 sentences.
Now it can be imported and used with `nlp`:
```python
import nlp
germeval = nlp.load_dataset("./datasets/germeval_14/germeval_14.py")
assert len(germeval["train"]) == 24000
# Show first sentence of training set:
germeval["train"][0]
```
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Fix code examples indentation
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3895). All of your documentation changes will be reflected on that endpoint.",
"Still not rendered properly: https://moon-ci-docs.huggingface.co/docs/datasets/pr_3895/en/package_reference/main_classes#datasets.Dataset.align_labels_with_mapping",
"My last commit should have fixed it, I don't know why the dev doc build is not showing my last changes",
"Let me merge this and we can see on `master` how it renders, until the dev doc build is fixed"
] | 2022-03-11T16:29:04Z
| 2022-03-11T17:34:30Z
| 2022-03-11T17:34:29Z
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Some code examples are currently not rendered correctly. I think this is because they are over-indented
cc @mariosasko
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[CI] fixing seqeval install in ci by pinning setuptools-scm
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-23T09:24:37Z
| 2022-06-23T10:24:16Z
| 2022-06-23T10:13:44Z
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The latest setuptools-scm version supported on 3.6 is 6.4.2. However for some reason circleci has version 7, which doesn't work.
I fixed this by pinning the version of setuptools-scm in the circleci job
Fix https://github.com/huggingface/datasets/issues/4544
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I_kwDODunzps5TsDuW
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Classes label error when loading symbolic links using imagefolder
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"It can be solved temporarily by remove `resolve` in \r\nhttps://github.com/huggingface/datasets/blob/bef23be3d9543b1ca2da87ab2f05070201044ddc/src/datasets/data_files.py#L278",
"Hi, thanks for reporting and suggesting a fix! We still need to account for `.`/`..` in the file path, so a more robust fix would be `Path(os.path.abspath(filepath))`.",
"> Hi, thanks for reporting and suggesting a fix! We still need to account for `.`/`..` in the file path, so a more robust fix would be `Path(os.path.abspath(filepath))`.\r\n\r\nThanks for your reply!"
] | 2022-10-11T06:10:58Z
| 2022-11-14T14:40:20Z
| 2022-11-14T14:40:20Z
|
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
Like this: #4015
When there are **symbolic links** to pictures in the data folder, the parent folder name of the **real file** will be used as the class name instead of the parent folder of the symbolic link itself. Can you give an option to decide whether to enable symbolic link tracking?
This is inconsistent with the `torchvision.datasets.ImageFolder` behavior.
For example:
It use `others` in green circle as class label but not `abnormal`, I wish `load_dataset` not use the real file parent as label.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context about the feature request here.
|
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"Checksums didn't match for dataset source files" error while loading openwebtext dataset
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"Hi try, to provide more information please.\r\n\r\nExample code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages version).",
"> Hi try, to provide more information please.\r\n> \r\n> Example code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages version).\r\n\r\nI have update the description, sorry for the incomplete issue by mistake.",
"Hi, I have manually downloaded the compressed dataset `openwebtext.tar.xz' and use the following command to preprocess the examples:\r\n```\r\n>>> dataset = load_dataset('/home/admin/workspace/datasets/datasets-master/datasets-master/datasets/openwebtext', data_dir='/home/admin/workspace/datasets')\r\nUsing custom data configuration default\r\nDownloading and preparing dataset openwebtext/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/admin/.cache/huggingface/datasets/openwebtext/default/0.0.0/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nDataset openwebtext downloaded and prepared to /home/admin/.cache/huggingface/datasets/openwebtext/default/0.0.0/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02. Subsequent calls will reuse this data.\r\n>>> len(dataset['train'])\r\n74571\r\n>>>\r\n```\r\nThe size of the pre-processed example file is only 354MB, however the processed bookcorpus dataset is 4.6g. Are there any problems?",
"NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n\r\ni got this issue when i try to work on my own datasets kindly tell me, from where i can get checksums of train and dev file in my github repo",
"Hi, I got the similar issue for xnli dataset while working on colab with python3.7. \r\n\r\n`nlp.load_dataset(path = 'xnli')`\r\n\r\nThe above command resulted in following issue : \r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip']\r\n```\r\n\r\nAny idea how to fix this ?",
"Did anyone figure out how to fix this error?",
"Fixed by:\r\n- #2857",
"Says fixed but I'm still getting it. \r\n\r\ncommand:\r\n\r\n dataset = load_dataset(\"ted_talks_iwslt\", language_pair=(\"en\", \"es\"), year=\"2014\",download_mode=\"force_redownload\")\r\n\r\ngot:\r\n\r\nUsing custom data configuration en_es_2014-35a2d3350a0f9823\r\nDownloading and preparing dataset ted_talks_iwslt/en_es_2014 (download: 2.15 KiB, generated: Unknown size, post-processed: Unknown size, total: 2.15 KiB) to /home/ken/.cache/huggingface/datasets/ted_talks_iwslt/en_es_2014-35a2d3350a0f9823/1.1.0/43935b3fe470c753a023642e1f54b068c590847f9928bd3f2ec99f15702ad6a6...\r\nDownloading:\r\n2.21k/? [00:00<00:00, 141kB/s]\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://drive.google.com/u/0/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download']"
] | 2020-10-12T11:45:10Z
| 2022-02-17T17:53:54Z
| 2022-02-15T10:38:57Z
|
NONE
| null | null | null |
Hi,
I have encountered this problem during loading the openwebtext dataset:
```
>>> dataset = load_dataset('openwebtext')
Downloading and preparing dataset openwebtext/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to /home/admin/.cache/huggingface/datasets/openwebtext/plain_text/1.0.0/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/admin/workspace/anaconda3/envs/torch1.6-py3.7/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/home/admin/workspace/anaconda3/envs/torch1.6-py3.7/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/admin/workspace/anaconda3/envs/torch1.6-py3.7/lib/python3.7/site-packages/datasets/builder.py", line 536, in _download_and_prepare
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
File "/home/admin/workspace/anaconda3/envs/torch1.6-py3.7/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 39, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://zenodo.org/record/3834942/files/openwebtext.tar.xz']
```
I think this problem is caused because the released dataset has changed. Or I should download the dataset manually?
Sorry for release the unfinised issue by mistake.
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Use a new low-memory approach for tf dataset index shuffling
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5863). 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) | 0.007764 / 0.011353 (-0.003588) | 0.005397 / 0.011008 (-0.005611) | 0.097995 / 0.038508 (0.059487) | 0.036360 / 0.023109 (0.013251) | 0.312148 / 0.275898 (0.036250) | 0.349427 / 0.323480 (0.025947) | 0.006635 / 0.007986 (-0.001350) | 0.004373 / 0.004328 (0.000044) | 0.074350 / 0.004250 (0.070099) | 0.054667 / 0.037052 (0.017614) | 0.301621 / 0.258489 (0.043132) | 0.364233 / 0.293841 (0.070392) | 0.035356 / 0.128546 (-0.093191) | 0.012512 / 0.075646 (-0.063134) | 0.333399 / 0.419271 (-0.085873) | 0.051363 / 0.043533 (0.007830) | 0.302372 / 0.255139 (0.047233) | 0.326542 / 0.283200 (0.043343) | 0.118610 / 0.141683 (-0.023073) | 1.438485 / 1.452155 (-0.013669) | 1.539131 / 1.492716 (0.046415) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010920 / 0.018006 (-0.007086) | 0.561263 / 0.000490 (0.560773) | 0.003972 / 0.000200 (0.003772) | 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) | 0.030333 / 0.037411 (-0.007078) | 0.113608 / 0.014526 (0.099083) | 0.125802 / 0.176557 (-0.050755) | 0.183885 / 0.737135 (-0.553250) | 0.130242 / 0.296338 (-0.166097) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404147 / 0.215209 (0.188938) | 4.021990 / 2.077655 (1.944335) | 1.821450 / 1.504120 (0.317330) | 1.619032 / 1.541195 (0.077837) | 1.791267 / 1.468490 (0.322777) | 0.706683 / 4.584777 (-3.878094) | 3.819056 / 3.745712 (0.073344) | 3.485714 / 5.269862 (-1.784147) | 1.938968 / 4.565676 (-2.626709) | 0.086501 / 0.424275 (-0.337774) | 0.012300 / 0.007607 (0.004693) | 0.503600 / 0.226044 (0.277555) | 5.042123 / 2.268929 (2.773195) | 2.269712 / 55.444624 (-53.174912) | 1.944912 / 6.876477 (-4.931565) | 2.155196 / 2.142072 (0.013123) | 0.853434 / 4.805227 (-3.951793) | 0.175554 / 6.500664 (-6.325110) | 0.072005 / 0.075469 (-0.003464) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.203765 / 1.841788 (-0.638022) | 15.836634 / 8.074308 (7.762326) | 15.707348 / 10.191392 (5.515956) | 0.164828 / 0.680424 (-0.515596) | 0.018115 / 0.534201 (-0.516086) | 0.434591 / 0.579283 (-0.144692) | 0.437858 / 0.434364 (0.003495) | 0.524672 / 0.540337 (-0.015665) | 0.610535 / 1.386936 (-0.776401) |\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) | 0.007558 / 0.011353 (-0.003795) | 0.005258 / 0.011008 (-0.005750) | 0.075263 / 0.038508 (0.036755) | 0.033915 / 0.023109 (0.010805) | 0.371368 / 0.275898 (0.095470) | 0.399239 / 0.323480 (0.075760) | 0.006547 / 0.007986 (-0.001439) | 0.004675 / 0.004328 (0.000347) | 0.074230 / 0.004250 (0.069980) | 0.054653 / 0.037052 (0.017601) | 0.376655 / 0.258489 (0.118166) | 0.438437 / 0.293841 (0.144596) | 0.035838 / 0.128546 (-0.092709) | 0.012641 / 0.075646 (-0.063005) | 0.087279 / 0.419271 (-0.331993) | 0.046311 / 0.043533 (0.002778) | 0.356649 / 0.255139 (0.101510) | 0.377876 / 0.283200 (0.094677) | 0.108097 / 0.141683 (-0.033586) | 1.478461 / 1.452155 (0.026306) | 1.560375 / 1.492716 (0.067658) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316384 / 0.018006 (0.298378) | 0.539382 / 0.000490 (0.538892) | 0.002029 / 0.000200 (0.001829) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029950 / 0.037411 (-0.007462) | 0.111371 / 0.014526 (0.096846) | 0.125254 / 0.176557 (-0.051303) | 0.173064 / 0.737135 (-0.564071) | 0.130446 / 0.296338 (-0.165893) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424882 / 0.215209 (0.209673) | 4.241575 / 2.077655 (2.163920) | 2.096216 / 1.504120 (0.592096) | 1.916017 / 1.541195 (0.374823) | 2.016318 / 1.468490 (0.547828) | 0.701197 / 4.584777 (-3.883580) | 3.762365 / 3.745712 (0.016652) | 3.307805 / 5.269862 (-1.962057) | 1.841752 / 4.565676 (-2.723925) | 0.086003 / 0.424275 (-0.338272) | 0.012247 / 0.007607 (0.004640) | 0.532926 / 0.226044 (0.306882) | 5.370509 / 2.268929 (3.101580) | 2.587853 / 55.444624 (-52.856772) | 2.264541 / 6.876477 (-4.611936) | 2.374833 / 2.142072 (0.232760) | 0.827751 / 4.805227 (-3.977476) | 0.169454 / 6.500664 (-6.331210) | 0.066340 / 0.075469 (-0.009129) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.319128 / 1.841788 (-0.522660) | 16.702085 / 8.074308 (8.627777) | 13.559957 / 10.191392 (3.368565) | 0.146659 / 0.680424 (-0.533765) | 0.017384 / 0.534201 (-0.516817) | 0.421126 / 0.579283 (-0.158157) | 0.422067 / 0.434364 (-0.012297) | 0.490615 / 0.540337 (-0.049723) | 0.587151 / 1.386936 (-0.799785) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79f4b6de25128999f5fc0a7bde9aa71c461f518f \"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) | 0.006604 / 0.011353 (-0.004749) | 0.004508 / 0.011008 (-0.006500) | 0.098652 / 0.038508 (0.060144) | 0.028172 / 0.023109 (0.005063) | 0.366997 / 0.275898 (0.091099) | 0.403691 / 0.323480 (0.080211) | 0.005127 / 0.007986 (-0.002859) | 0.003340 / 0.004328 (-0.000989) | 0.075408 / 0.004250 (0.071157) | 0.038049 / 0.037052 (0.000996) | 0.367914 / 0.258489 (0.109425) | 0.410958 / 0.293841 (0.117118) | 0.030454 / 0.128546 (-0.098093) | 0.011422 / 0.075646 (-0.064224) | 0.325048 / 0.419271 (-0.094223) | 0.042959 / 0.043533 (-0.000574) | 0.374536 / 0.255139 (0.119397) | 0.394738 / 0.283200 (0.111538) | 0.090481 / 0.141683 (-0.051201) | 1.504858 / 1.452155 (0.052703) | 1.569072 / 1.492716 (0.076356) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010062 / 0.018006 (-0.007945) | 0.408619 / 0.000490 (0.408130) | 0.002307 / 0.000200 (0.002107) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022898 / 0.037411 (-0.014514) | 0.096975 / 0.014526 (0.082449) | 0.103032 / 0.176557 (-0.073524) | 0.164877 / 0.737135 (-0.572259) | 0.107324 / 0.296338 (-0.189014) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446652 / 0.215209 (0.231442) | 4.466939 / 2.077655 (2.389285) | 2.204590 / 1.504120 (0.700471) | 2.004048 / 1.541195 (0.462853) | 2.053035 / 1.468490 (0.584545) | 0.696617 / 4.584777 (-3.888160) | 3.391173 / 3.745712 (-0.354539) | 1.863306 / 5.269862 (-3.406556) | 1.160637 / 4.565676 (-3.405039) | 0.083115 / 0.424275 (-0.341160) | 0.012470 / 0.007607 (0.004862) | 0.547207 / 0.226044 (0.321163) | 5.500667 / 2.268929 (3.231739) | 2.656615 / 55.444624 (-52.788009) | 2.313281 / 6.876477 (-4.563195) | 2.395632 / 2.142072 (0.253559) | 0.815361 / 4.805227 (-3.989867) | 0.152112 / 6.500664 (-6.348552) | 0.067485 / 0.075469 (-0.007984) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206975 / 1.841788 (-0.634813) | 13.684136 / 8.074308 (5.609828) | 13.919129 / 10.191392 (3.727737) | 0.140767 / 0.680424 (-0.539657) | 0.016445 / 0.534201 (-0.517756) | 0.379136 / 0.579283 (-0.200147) | 0.385395 / 0.434364 (-0.048969) | 0.445781 / 0.540337 (-0.094556) | 0.522056 / 1.386936 (-0.864880) |\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) | 0.006370 / 0.011353 (-0.004983) | 0.004514 / 0.011008 (-0.006495) | 0.075671 / 0.038508 (0.037163) | 0.026723 / 0.023109 (0.003614) | 0.359819 / 0.275898 (0.083921) | 0.387935 / 0.323480 (0.064456) | 0.004888 / 0.007986 (-0.003098) | 0.004619 / 0.004328 (0.000290) | 0.075546 / 0.004250 (0.071295) | 0.039024 / 0.037052 (0.001971) | 0.361173 / 0.258489 (0.102684) | 0.411425 / 0.293841 (0.117584) | 0.030842 / 0.128546 (-0.097705) | 0.011555 / 0.075646 (-0.064091) | 0.084697 / 0.419271 (-0.334574) | 0.039281 / 0.043533 (-0.004252) | 0.370082 / 0.255139 (0.114943) | 0.382113 / 0.283200 (0.098913) | 0.091237 / 0.141683 (-0.050445) | 1.534185 / 1.452155 (0.082030) | 1.576488 / 1.492716 (0.083772) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226568 / 0.018006 (0.208562) | 0.401566 / 0.000490 (0.401076) | 0.002915 / 0.000200 (0.002715) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025357 / 0.037411 (-0.012054) | 0.099747 / 0.014526 (0.085221) | 0.106443 / 0.176557 (-0.070113) | 0.157147 / 0.737135 (-0.579989) | 0.110759 / 0.296338 (-0.185580) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444648 / 0.215209 (0.229439) | 4.437930 / 2.077655 (2.360275) | 2.154033 / 1.504120 (0.649913) | 1.958351 / 1.541195 (0.417157) | 1.991031 / 1.468490 (0.522541) | 0.691440 / 4.584777 (-3.893337) | 3.369087 / 3.745712 (-0.376625) | 1.847103 / 5.269862 (-3.422758) | 1.152509 / 4.565676 (-3.413168) | 0.082519 / 0.424275 (-0.341756) | 0.012609 / 0.007607 (0.005001) | 0.547267 / 0.226044 (0.321222) | 5.501335 / 2.268929 (3.232407) | 2.621079 / 55.444624 (-52.823545) | 2.281332 / 6.876477 (-4.595145) | 2.300427 / 2.142072 (0.158354) | 0.803611 / 4.805227 (-4.001616) | 0.151784 / 6.500664 (-6.348880) | 0.067801 / 0.075469 (-0.007669) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.343201 / 1.841788 (-0.498587) | 13.901033 / 8.074308 (5.826725) | 13.114738 / 10.191392 (2.923346) | 0.149358 / 0.680424 (-0.531066) | 0.016596 / 0.534201 (-0.517605) | 0.377310 / 0.579283 (-0.201973) | 0.387045 / 0.434364 (-0.047319) | 0.441272 / 0.540337 (-0.099065) | 0.525783 / 1.386936 (-0.861153) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c127e5575ab4e22648976ad268d76264ef5d04f8 \"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) | 0.008147 / 0.011353 (-0.003205) | 0.005531 / 0.011008 (-0.005477) | 0.099796 / 0.038508 (0.061288) | 0.041574 / 0.023109 (0.018465) | 0.315752 / 0.275898 (0.039854) | 0.369846 / 0.323480 (0.046366) | 0.006489 / 0.007986 (-0.001497) | 0.004339 / 0.004328 (0.000010) | 0.074769 / 0.004250 (0.070519) | 0.051313 / 0.037052 (0.014261) | 0.313463 / 0.258489 (0.054974) | 0.369918 / 0.293841 (0.076077) | 0.035893 / 0.128546 (-0.092653) | 0.012487 / 0.075646 (-0.063159) | 0.336464 / 0.419271 (-0.082807) | 0.052870 / 0.043533 (0.009337) | 0.310795 / 0.255139 (0.055656) | 0.333146 / 0.283200 (0.049946) | 0.112813 / 0.141683 (-0.028870) | 1.488192 / 1.452155 (0.036038) | 1.563438 / 1.492716 (0.070721) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.015015 / 0.018006 (-0.002991) | 0.531783 / 0.000490 (0.531294) | 0.005039 / 0.000200 (0.004839) | 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) | 0.030205 / 0.037411 (-0.007207) | 0.115997 / 0.014526 (0.101471) | 0.122958 / 0.176557 (-0.053599) | 0.186956 / 0.737135 (-0.550180) | 0.130268 / 0.296338 (-0.166071) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402648 / 0.215209 (0.187439) | 3.996121 / 2.077655 (1.918466) | 1.811715 / 1.504120 (0.307595) | 1.640805 / 1.541195 (0.099610) | 1.810478 / 1.468490 (0.341988) | 0.699996 / 4.584777 (-3.884781) | 3.834020 / 3.745712 (0.088308) | 3.688364 / 5.269862 (-1.581498) | 1.973828 / 4.565676 (-2.591849) | 0.087085 / 0.424275 (-0.337190) | 0.012501 / 0.007607 (0.004894) | 0.498934 / 0.226044 (0.272889) | 4.977608 / 2.268929 (2.708680) | 2.258678 / 55.444624 (-53.185947) | 1.934251 / 6.876477 (-4.942226) | 2.177409 / 2.142072 (0.035337) | 0.873470 / 4.805227 (-3.931757) | 0.173132 / 6.500664 (-6.327532) | 0.069144 / 0.075469 (-0.006325) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.181554 / 1.841788 (-0.660234) | 15.694468 / 8.074308 (7.620160) | 15.026954 / 10.191392 (4.835562) | 0.167092 / 0.680424 (-0.513332) | 0.017921 / 0.534201 (-0.516280) | 0.425649 / 0.579283 (-0.153634) | 0.423225 / 0.434364 (-0.011139) | 0.522132 / 0.540337 (-0.018205) | 0.612806 / 1.386936 (-0.774130) |\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) | 0.007896 / 0.011353 (-0.003457) | 0.005581 / 0.011008 (-0.005427) | 0.076338 / 0.038508 (0.037830) | 0.037064 / 0.023109 (0.013954) | 0.399706 / 0.275898 (0.123808) | 0.431698 / 0.323480 (0.108218) | 0.006846 / 0.007986 (-0.001140) | 0.006010 / 0.004328 (0.001682) | 0.075771 / 0.004250 (0.071520) | 0.058214 / 0.037052 (0.021161) | 0.395753 / 0.258489 (0.137264) | 0.459925 / 0.293841 (0.166084) | 0.036349 / 0.128546 (-0.092197) | 0.012720 / 0.075646 (-0.062926) | 0.087248 / 0.419271 (-0.332024) | 0.049405 / 0.043533 (0.005872) | 0.387576 / 0.255139 (0.132437) | 0.409861 / 0.283200 (0.126661) | 0.111639 / 0.141683 (-0.030043) | 1.482840 / 1.452155 (0.030685) | 1.574465 / 1.492716 (0.081749) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.320628 / 0.018006 (0.302622) | 0.556338 / 0.000490 (0.555848) | 0.000445 / 0.000200 (0.000245) | 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) | 0.032905 / 0.037411 (-0.004507) | 0.121253 / 0.014526 (0.106727) | 0.127241 / 0.176557 (-0.049316) | 0.178090 / 0.737135 (-0.559045) | 0.143285 / 0.296338 (-0.153054) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437852 / 0.215209 (0.222643) | 4.369770 / 2.077655 (2.292115) | 2.219932 / 1.504120 (0.715812) | 2.032520 / 1.541195 (0.491325) | 2.154300 / 1.468490 (0.685810) | 0.678942 / 4.584777 (-3.905835) | 3.768148 / 3.745712 (0.022436) | 2.152738 / 5.269862 (-3.117124) | 1.341480 / 4.565676 (-3.224197) | 0.084326 / 0.424275 (-0.339949) | 0.012288 / 0.007607 (0.004681) | 0.547677 / 0.226044 (0.321633) | 5.496777 / 2.268929 (3.227848) | 2.702267 / 55.444624 (-52.742357) | 2.388580 / 6.876477 (-4.487897) | 2.471673 / 2.142072 (0.329601) | 0.833645 / 4.805227 (-3.971582) | 0.167113 / 6.500664 (-6.333551) | 0.067658 / 0.075469 (-0.007811) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282050 / 1.841788 (-0.559737) | 16.413677 / 8.074308 (8.339369) | 14.080910 / 10.191392 (3.889518) | 0.171782 / 0.680424 (-0.508642) | 0.018186 / 0.534201 (-0.516015) | 0.425244 / 0.579283 (-0.154039) | 0.430260 / 0.434364 (-0.004104) | 0.500838 / 0.540337 (-0.039499) | 0.591900 / 1.386936 (-0.795036) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5fc5c538de84da400118e3712077acc580ce85c4 \"CML watermark\")\n",
"The approach we take here is to no longer materialize the entire index array or shuffle buffer. Instead, we do the following:\r\n\r\n1) Generate a dataset with `tf.data.Dataset.range`. This dataset is not materialized - it's basically a range iterator.\r\n2) When we begin iterating over a dataset, generate a random seed. This value is constant for each pass over the dataset, and is regenerated if we start a new iteration or epoch over the dataset.\r\n3) Map the range dataset and the random seed with `tf.random.index_shuffle`. This converts indices into the equivalent values in a permuted array. In other words `tf.random.index_shuffle(indices, maxval=50_000_000)` is equivalent to `np.random.permutation(50_000_000)[indices]`, but without ever materializing the `np.random.permutation(50_000_000)` array.\r\n\r\nUsing this approach gives us a complete iteration over the dataset that does not skip any samples, compiles in TF and also never materializes the complete index array, which should avoid the memory usage issues. I'm testing that now!",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008395 / 0.011353 (-0.002958) | 0.005893 / 0.011008 (-0.005115) | 0.117081 / 0.038508 (0.078573) | 0.040987 / 0.023109 (0.017878) | 0.394234 / 0.275898 (0.118336) | 0.447036 / 0.323480 (0.123556) | 0.006703 / 0.007986 (-0.001283) | 0.006085 / 0.004328 (0.001757) | 0.086479 / 0.004250 (0.082228) | 0.050192 / 0.037052 (0.013140) | 0.400958 / 0.258489 (0.142469) | 0.455551 / 0.293841 (0.161710) | 0.041481 / 0.128546 (-0.087065) | 0.014135 / 0.075646 (-0.061511) | 0.399929 / 0.419271 (-0.019343) | 0.060824 / 0.043533 (0.017291) | 0.395946 / 0.255139 (0.140807) | 0.428811 / 0.283200 (0.145611) | 0.120057 / 0.141683 (-0.021626) | 1.703244 / 1.452155 (0.251090) | 1.841153 / 1.492716 (0.348436) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.021826 / 0.018006 (0.003820) | 0.494279 / 0.000490 (0.493789) | 0.011258 / 0.000200 (0.011058) | 0.000382 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031651 / 0.037411 (-0.005760) | 0.132871 / 0.014526 (0.118345) | 0.137388 / 0.176557 (-0.039169) | 0.205808 / 0.737135 (-0.531327) | 0.147585 / 0.296338 (-0.148753) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474483 / 0.215209 (0.259274) | 4.726568 / 2.077655 (2.648914) | 2.136172 / 1.504120 (0.632052) | 1.918364 / 1.541195 (0.377169) | 2.068794 / 1.468490 (0.600304) | 0.836481 / 4.584777 (-3.748296) | 4.550583 / 3.745712 (0.804871) | 2.456287 / 5.269862 (-2.813574) | 1.563127 / 4.565676 (-3.002550) | 0.102541 / 0.424275 (-0.321734) | 0.014492 / 0.007607 (0.006885) | 0.598572 / 0.226044 (0.372528) | 5.953321 / 2.268929 (3.684392) | 2.695210 / 55.444624 (-52.749414) | 2.294317 / 6.876477 (-4.582160) | 2.456585 / 2.142072 (0.314513) | 1.019907 / 4.805227 (-3.785320) | 0.201225 / 6.500664 (-6.299439) | 0.077113 / 0.075469 (0.001644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.497662 / 1.841788 (-0.344126) | 18.216941 / 8.074308 (10.142633) | 17.016638 / 10.191392 (6.825246) | 0.193271 / 0.680424 (-0.487153) | 0.020440 / 0.534201 (-0.513761) | 0.509361 / 0.579283 (-0.069922) | 0.513389 / 0.434364 (0.079025) | 0.622266 / 0.540337 (0.081928) | 0.741733 / 1.386936 (-0.645203) |\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) | 0.008641 / 0.011353 (-0.002712) | 0.005792 / 0.011008 (-0.005216) | 0.086020 / 0.038508 (0.047512) | 0.040005 / 0.023109 (0.016896) | 0.435120 / 0.275898 (0.159222) | 0.480269 / 0.323480 (0.156789) | 0.006669 / 0.007986 (-0.001317) | 0.006039 / 0.004328 (0.001711) | 0.083468 / 0.004250 (0.079218) | 0.057700 / 0.037052 (0.020648) | 0.416418 / 0.258489 (0.157929) | 0.508286 / 0.293841 (0.214445) | 0.041198 / 0.128546 (-0.087349) | 0.014346 / 0.075646 (-0.061301) | 0.100553 / 0.419271 (-0.318718) | 0.054201 / 0.043533 (0.010668) | 0.438232 / 0.255139 (0.183093) | 0.454707 / 0.283200 (0.171508) | 0.118332 / 0.141683 (-0.023351) | 1.657607 / 1.452155 (0.205452) | 1.825510 / 1.492716 (0.332794) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236156 / 0.018006 (0.218150) | 0.487612 / 0.000490 (0.487123) | 0.005747 / 0.000200 (0.005547) | 0.000111 / 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) | 0.035127 / 0.037411 (-0.002284) | 0.132013 / 0.014526 (0.117487) | 0.142316 / 0.176557 (-0.034241) | 0.198627 / 0.737135 (-0.538508) | 0.145454 / 0.296338 (-0.150885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513041 / 0.215209 (0.297832) | 5.066197 / 2.077655 (2.988542) | 2.508779 / 1.504120 (1.004659) | 2.273901 / 1.541195 (0.732706) | 2.364958 / 1.468490 (0.896468) | 0.811367 / 4.584777 (-3.773410) | 4.504744 / 3.745712 (0.759032) | 2.499811 / 5.269862 (-2.770050) | 1.583349 / 4.565676 (-2.982328) | 0.101701 / 0.424275 (-0.322574) | 0.014379 / 0.007607 (0.006772) | 0.669506 / 0.226044 (0.443462) | 6.556702 / 2.268929 (4.287774) | 3.123457 / 55.444624 (-52.321167) | 2.731997 / 6.876477 (-4.144480) | 2.862866 / 2.142072 (0.720794) | 0.992956 / 4.805227 (-3.812271) | 0.200473 / 6.500664 (-6.300191) | 0.078780 / 0.075469 (0.003311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540718 / 1.841788 (-0.301070) | 18.749344 / 8.074308 (10.675036) | 15.648983 / 10.191392 (5.457591) | 0.174089 / 0.680424 (-0.506335) | 0.020441 / 0.534201 (-0.513760) | 0.503742 / 0.579283 (-0.075541) | 0.500648 / 0.434364 (0.066284) | 0.598558 / 0.540337 (0.058221) | 0.712093 / 1.386936 (-0.674843) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#621554280f964b5fe87ece1a46b794406d943b1e \"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) | 0.009940 / 0.011353 (-0.001412) | 0.006193 / 0.011008 (-0.004815) | 0.125874 / 0.038508 (0.087366) | 0.038664 / 0.023109 (0.015555) | 0.380013 / 0.275898 (0.104115) | 0.430152 / 0.323480 (0.106672) | 0.006961 / 0.007986 (-0.001025) | 0.004749 / 0.004328 (0.000420) | 0.099743 / 0.004250 (0.095492) | 0.052349 / 0.037052 (0.015297) | 0.433354 / 0.258489 (0.174865) | 0.436273 / 0.293841 (0.142433) | 0.053929 / 0.128546 (-0.074617) | 0.019369 / 0.075646 (-0.056278) | 0.421783 / 0.419271 (0.002511) | 0.062746 / 0.043533 (0.019213) | 0.377225 / 0.255139 (0.122086) | 0.413708 / 0.283200 (0.130508) | 0.111371 / 0.141683 (-0.030312) | 1.819166 / 1.452155 (0.367011) | 1.974527 / 1.492716 (0.481810) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090664 / 0.018006 (0.072658) | 0.566166 / 0.000490 (0.565676) | 0.079305 / 0.000200 (0.079105) | 0.000755 / 0.000054 (0.000700) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029720 / 0.037411 (-0.007691) | 0.126030 / 0.014526 (0.111504) | 0.146020 / 0.176557 (-0.030537) | 0.210354 / 0.737135 (-0.526781) | 0.149428 / 0.296338 (-0.146910) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.624371 / 0.215209 (0.409162) | 6.332839 / 2.077655 (4.255184) | 2.547784 / 1.504120 (1.043664) | 2.150508 / 1.541195 (0.609313) | 2.240816 / 1.468490 (0.772326) | 1.271131 / 4.584777 (-3.313646) | 5.642726 / 3.745712 (1.897014) | 3.212988 / 5.269862 (-2.056874) | 2.258123 / 4.565676 (-2.307553) | 0.149477 / 0.424275 (-0.274798) | 0.014603 / 0.007607 (0.006996) | 0.782155 / 0.226044 (0.556111) | 7.855191 / 2.268929 (5.586262) | 3.308638 / 55.444624 (-52.135986) | 2.548142 / 6.876477 (-4.328335) | 2.627374 / 2.142072 (0.485301) | 1.515170 / 4.805227 (-3.290058) | 0.262479 / 6.500664 (-6.238185) | 0.082181 / 0.075469 (0.006712) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.573169 / 1.841788 (-0.268618) | 18.105719 / 8.074308 (10.031411) | 22.015179 / 10.191392 (11.823787) | 0.254678 / 0.680424 (-0.425746) | 0.027098 / 0.534201 (-0.507103) | 0.578045 / 0.579283 (-0.001238) | 0.647130 / 0.434364 (0.212766) | 0.650522 / 0.540337 (0.110185) | 0.797713 / 1.386936 (-0.589223) |\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) | 0.010376 / 0.011353 (-0.000977) | 0.005990 / 0.011008 (-0.005018) | 0.097144 / 0.038508 (0.058635) | 0.038205 / 0.023109 (0.015096) | 0.468347 / 0.275898 (0.192449) | 0.497646 / 0.323480 (0.174166) | 0.006916 / 0.007986 (-0.001069) | 0.004760 / 0.004328 (0.000431) | 0.109838 / 0.004250 (0.105587) | 0.048321 / 0.037052 (0.011269) | 0.437458 / 0.258489 (0.178969) | 0.534864 / 0.293841 (0.241023) | 0.053655 / 0.128546 (-0.074892) | 0.021915 / 0.075646 (-0.053732) | 0.121047 / 0.419271 (-0.298224) | 0.059694 / 0.043533 (0.016162) | 0.466937 / 0.255139 (0.211798) | 0.482030 / 0.283200 (0.198831) | 0.117458 / 0.141683 (-0.024225) | 1.835551 / 1.452155 (0.383396) | 1.965748 / 1.492716 (0.473031) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234885 / 0.018006 (0.216879) | 0.529925 / 0.000490 (0.529436) | 0.000484 / 0.000200 (0.000284) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030959 / 0.037411 (-0.006453) | 0.128905 / 0.014526 (0.114379) | 0.136913 / 0.176557 (-0.039643) | 0.195133 / 0.737135 (-0.542002) | 0.147929 / 0.296338 (-0.148410) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.715661 / 0.215209 (0.500451) | 6.994125 / 2.077655 (4.916470) | 3.033178 / 1.504120 (1.529058) | 2.663709 / 1.541195 (1.122515) | 2.707558 / 1.468490 (1.239068) | 1.316195 / 4.584777 (-3.268582) | 5.688264 / 3.745712 (1.942552) | 3.260897 / 5.269862 (-2.008964) | 2.134985 / 4.565676 (-2.430691) | 0.153945 / 0.424275 (-0.270330) | 0.014727 / 0.007607 (0.007119) | 0.911339 / 0.226044 (0.685294) | 8.902640 / 2.268929 (6.633711) | 3.806606 / 55.444624 (-51.638018) | 3.052238 / 6.876477 (-3.824238) | 3.046945 / 2.142072 (0.904873) | 1.559837 / 4.805227 (-3.245390) | 0.272276 / 6.500664 (-6.228388) | 0.087728 / 0.075469 (0.012259) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.712691 / 1.841788 (-0.129097) | 18.127575 / 8.074308 (10.053267) | 19.734063 / 10.191392 (9.542671) | 0.235006 / 0.680424 (-0.445418) | 0.027581 / 0.534201 (-0.506620) | 0.551080 / 0.579283 (-0.028203) | 0.608564 / 0.434364 (0.174200) | 0.636578 / 0.540337 (0.096241) | 0.732374 / 1.386936 (-0.654562) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#36911ca06d9c4e37ce36da6228cb3af8b40c2add \"CML watermark\")\n",
"Looks good in testing - this should be ready for review! cc @lhoestq @massquantity",
"Looks good to me, though i doubt that very few people will upgrade to TF >= 2.9 unless their memory is full:)",
"Is it more efficient than using numpy to shuffle as in multiprocessing ? Why not use the same strategy ?",
"Good question, honestly! The NumPy strategy works fine, but requires us to handle multiple processes instead of doing everything in `tf.data`. We could just scrap this entire code path and always use the multiprocessing NumPy approach, but I think single-threaded throughput would be lower if we did that. If you prefer it for code simplicity, though, I can do that.\r\n\r\nIn the longer term, I'm hoping that `tf.data` gets native support for our data structures and we can transition the whole pipeline to pure `tf.data`, but that still hasn't happened 🫠",
"And @massquantity TF 2.13 is going to release in a couple of days, so I hope most users are at least on TF 2.9 by now!",
"Unless there is a big gap in performance I think code simplicity would be appreciated ^^",
"<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) | 0.008638 / 0.011353 (-0.002715) | 0.006013 / 0.011008 (-0.004995) | 0.116456 / 0.038508 (0.077948) | 0.040419 / 0.023109 (0.017310) | 0.418374 / 0.275898 (0.142476) | 0.447693 / 0.323480 (0.124213) | 0.007002 / 0.007986 (-0.000984) | 0.006175 / 0.004328 (0.001847) | 0.087801 / 0.004250 (0.083550) | 0.051980 / 0.037052 (0.014928) | 0.393275 / 0.258489 (0.134786) | 0.449601 / 0.293841 (0.155760) | 0.041670 / 0.128546 (-0.086876) | 0.014396 / 0.075646 (-0.061251) | 0.399175 / 0.419271 (-0.020096) | 0.060635 / 0.043533 (0.017102) | 0.391449 / 0.255139 (0.136310) | 0.420713 / 0.283200 (0.137513) | 0.121369 / 0.141683 (-0.020314) | 1.692630 / 1.452155 (0.240475) | 1.815526 / 1.492716 (0.322810) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244321 / 0.018006 (0.226315) | 0.487947 / 0.000490 (0.487458) | 0.004563 / 0.000200 (0.004363) | 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) | 0.033425 / 0.037411 (-0.003987) | 0.134458 / 0.014526 (0.119932) | 0.138810 / 0.176557 (-0.037746) | 0.208871 / 0.737135 (-0.528264) | 0.147964 / 0.296338 (-0.148374) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483347 / 0.215209 (0.268138) | 4.799550 / 2.077655 (2.721895) | 2.174149 / 1.504120 (0.670029) | 1.943276 / 1.541195 (0.402081) | 2.010884 / 1.468490 (0.542394) | 0.832030 / 4.584777 (-3.752747) | 4.716713 / 3.745712 (0.971001) | 4.615810 / 5.269862 (-0.654052) | 2.379600 / 4.565676 (-2.186077) | 0.103560 / 0.424275 (-0.320715) | 0.014683 / 0.007607 (0.007076) | 0.598558 / 0.226044 (0.372514) | 5.999126 / 2.268929 (3.730197) | 2.677819 / 55.444624 (-52.766805) | 2.320838 / 6.876477 (-4.555639) | 2.503684 / 2.142072 (0.361611) | 1.016459 / 4.805227 (-3.788769) | 0.201672 / 6.500664 (-6.298992) | 0.079310 / 0.075469 (0.003841) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.446374 / 1.841788 (-0.395413) | 19.219310 / 8.074308 (11.145002) | 17.294665 / 10.191392 (7.103273) | 0.246115 / 0.680424 (-0.434309) | 0.021406 / 0.534201 (-0.512795) | 0.524084 / 0.579283 (-0.055200) | 0.511254 / 0.434364 (0.076890) | 0.621304 / 0.540337 (0.080966) | 0.727088 / 1.386936 (-0.659848) |\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) | 0.008907 / 0.011353 (-0.002446) | 0.006165 / 0.011008 (-0.004843) | 0.090786 / 0.038508 (0.052278) | 0.040893 / 0.023109 (0.017784) | 0.451252 / 0.275898 (0.175354) | 0.477811 / 0.323480 (0.154331) | 0.007418 / 0.007986 (-0.000568) | 0.005789 / 0.004328 (0.001461) | 0.087422 / 0.004250 (0.083171) | 0.061800 / 0.037052 (0.024748) | 0.459085 / 0.258489 (0.200596) | 0.488897 / 0.293841 (0.195056) | 0.048157 / 0.128546 (-0.080389) | 0.014676 / 0.075646 (-0.060970) | 0.104372 / 0.419271 (-0.314900) | 0.058066 / 0.043533 (0.014534) | 0.446131 / 0.255139 (0.190992) | 0.460428 / 0.283200 (0.177228) | 0.128492 / 0.141683 (-0.013191) | 1.811419 / 1.452155 (0.359265) | 1.894781 / 1.492716 (0.402064) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220527 / 0.018006 (0.202520) | 0.487663 / 0.000490 (0.487173) | 0.003864 / 0.000200 (0.003664) | 0.000162 / 0.000054 (0.000107) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036354 / 0.037411 (-0.001057) | 0.140469 / 0.014526 (0.125944) | 0.149990 / 0.176557 (-0.026566) | 0.212369 / 0.737135 (-0.524766) | 0.154000 / 0.296338 (-0.142338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.514172 / 0.215209 (0.298963) | 5.129247 / 2.077655 (3.051593) | 2.536773 / 1.504120 (1.032653) | 2.317253 / 1.541195 (0.776058) | 2.424066 / 1.468490 (0.955576) | 0.836160 / 4.584777 (-3.748617) | 4.906235 / 3.745712 (1.160523) | 4.431395 / 5.269862 (-0.838467) | 2.332845 / 4.565676 (-2.232831) | 0.102867 / 0.424275 (-0.321409) | 0.014851 / 0.007607 (0.007244) | 0.644104 / 0.226044 (0.418060) | 6.415847 / 2.268929 (4.146918) | 3.186984 / 55.444624 (-52.257641) | 2.774125 / 6.876477 (-4.102352) | 2.848045 / 2.142072 (0.705972) | 1.018757 / 4.805227 (-3.786470) | 0.212333 / 6.500664 (-6.288331) | 0.079405 / 0.075469 (0.003936) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748375 / 1.841788 (-0.093412) | 19.733829 / 8.074308 (11.659521) | 15.766665 / 10.191392 (5.575273) | 0.192087 / 0.680424 (-0.488337) | 0.027641 / 0.534201 (-0.506560) | 0.504101 / 0.579283 (-0.075182) | 0.493815 / 0.434364 (0.059451) | 0.583247 / 0.540337 (0.042910) | 0.697432 / 1.386936 (-0.689504) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#95c177e02ca20bf7bb3ed8f185d2d6f05a5e5f30 \"CML watermark\")\n",
"Hi @lhoestq, I tried moving everything to the NumPy path but ran into issues - the `SharedMemory` constructs it depends on were only added in Python 3.8. As a result, if we move everything to that path then `to_tf_dataset` does not work on older Python versions.\r\n\r\nFor now, how do you feel about reverting and using my original solution, which has fallbacks for all versions of Python and TensorFlow? Once our minimum versions pass Python 3.8 or TF 2.9 we can remove the older code paths.",
"Gentle ping on this question @lhoestq!",
"Ah yes indeed. Feel free to revert and add comments to explain why you needed to have a different approach for single process",
"<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) | 0.008395 / 0.011353 (-0.002958) | 0.005773 / 0.011008 (-0.005235) | 0.115702 / 0.038508 (0.077194) | 0.039897 / 0.023109 (0.016788) | 0.483140 / 0.275898 (0.207242) | 0.531288 / 0.323480 (0.207808) | 0.006739 / 0.007986 (-0.001246) | 0.004419 / 0.004328 (0.000090) | 0.086374 / 0.004250 (0.082124) | 0.056498 / 0.037052 (0.019446) | 0.491589 / 0.258489 (0.233100) | 0.556366 / 0.293841 (0.262525) | 0.041366 / 0.128546 (-0.087181) | 0.014373 / 0.075646 (-0.061274) | 0.395504 / 0.419271 (-0.023767) | 0.094382 / 0.043533 (0.050849) | 0.483000 / 0.255139 (0.227861) | 0.522693 / 0.283200 (0.239494) | 0.138804 / 0.141683 (-0.002879) | 1.719563 / 1.452155 (0.267409) | 1.853470 / 1.492716 (0.360753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235616 / 0.018006 (0.217610) | 0.483267 / 0.000490 (0.482777) | 0.008663 / 0.000200 (0.008463) | 0.000401 / 0.000054 (0.000347) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033124 / 0.037411 (-0.004287) | 0.128821 / 0.014526 (0.114295) | 0.138910 / 0.176557 (-0.037647) | 0.213570 / 0.737135 (-0.523566) | 0.146646 / 0.296338 (-0.149693) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479998 / 0.215209 (0.264789) | 4.772325 / 2.077655 (2.694670) | 2.228424 / 1.504120 (0.724304) | 2.000915 / 1.541195 (0.459721) | 2.105799 / 1.468490 (0.637309) | 0.824235 / 4.584777 (-3.760542) | 4.511902 / 3.745712 (0.766189) | 4.723073 / 5.269862 (-0.546789) | 2.333442 / 4.565676 (-2.232235) | 0.101161 / 0.424275 (-0.323114) | 0.014403 / 0.007607 (0.006796) | 0.596395 / 0.226044 (0.370351) | 5.961046 / 2.268929 (3.692117) | 2.746679 / 55.444624 (-52.697946) | 2.352085 / 6.876477 (-4.524392) | 2.609812 / 2.142072 (0.467740) | 0.996950 / 4.805227 (-3.808277) | 0.197923 / 6.500664 (-6.302741) | 0.075546 / 0.075469 (0.000077) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.529896 / 1.841788 (-0.311892) | 18.183887 / 8.074308 (10.109578) | 16.352332 / 10.191392 (6.160940) | 0.213504 / 0.680424 (-0.466920) | 0.020388 / 0.534201 (-0.513813) | 0.497832 / 0.579283 (-0.081451) | 0.495477 / 0.434364 (0.061113) | 0.585984 / 0.540337 (0.045647) | 0.688726 / 1.386936 (-0.698210) |\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) | 0.008422 / 0.011353 (-0.002931) | 0.005876 / 0.011008 (-0.005132) | 0.089310 / 0.038508 (0.050802) | 0.039769 / 0.023109 (0.016660) | 0.425279 / 0.275898 (0.149381) | 0.470818 / 0.323480 (0.147338) | 0.006519 / 0.007986 (-0.001467) | 0.006276 / 0.004328 (0.001948) | 0.085753 / 0.004250 (0.081503) | 0.053867 / 0.037052 (0.016815) | 0.429193 / 0.258489 (0.170704) | 0.480278 / 0.293841 (0.186437) | 0.040657 / 0.128546 (-0.087889) | 0.014055 / 0.075646 (-0.061591) | 0.101422 / 0.419271 (-0.317849) | 0.053803 / 0.043533 (0.010271) | 0.428348 / 0.255139 (0.173209) | 0.452193 / 0.283200 (0.168994) | 0.124914 / 0.141683 (-0.016769) | 1.750122 / 1.452155 (0.297968) | 1.850875 / 1.492716 (0.358159) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249958 / 0.018006 (0.231952) | 0.485183 / 0.000490 (0.484694) | 0.000472 / 0.000200 (0.000272) | 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) | 0.034563 / 0.037411 (-0.002848) | 0.135565 / 0.014526 (0.121039) | 0.143271 / 0.176557 (-0.033285) | 0.199080 / 0.737135 (-0.538056) | 0.149336 / 0.296338 (-0.147003) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.526170 / 0.215209 (0.310961) | 5.270960 / 2.077655 (3.193305) | 2.664585 / 1.504120 (1.160465) | 2.440027 / 1.541195 (0.898832) | 2.612764 / 1.468490 (1.144274) | 0.828965 / 4.584777 (-3.755812) | 4.769983 / 3.745712 (1.024271) | 2.441962 / 5.269862 (-2.827900) | 1.549032 / 4.565676 (-3.016644) | 0.100851 / 0.424275 (-0.323424) | 0.014425 / 0.007607 (0.006818) | 0.640908 / 0.226044 (0.414864) | 6.399041 / 2.268929 (4.130113) | 3.242424 / 55.444624 (-52.202200) | 2.836317 / 6.876477 (-4.040160) | 2.933010 / 2.142072 (0.790938) | 1.002277 / 4.805227 (-3.802950) | 0.201247 / 6.500664 (-6.299417) | 0.078777 / 0.075469 (0.003308) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.620415 / 1.841788 (-0.221373) | 19.153631 / 8.074308 (11.079323) | 16.744068 / 10.191392 (6.552676) | 0.167327 / 0.680424 (-0.513097) | 0.020186 / 0.534201 (-0.514015) | 0.503683 / 0.579283 (-0.075600) | 0.500051 / 0.434364 (0.065687) | 0.587188 / 0.540337 (0.046850) | 0.699975 / 1.386936 (-0.686961) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#291d7ffa695edb4b4e818c783b16d3466246cd56 \"CML watermark\")\n",
"This is probably ready, but likely conflicts with #5883. I'll wait for that PR to be merged and then rebase and merge this one.",
"<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) | 0.008387 / 0.011353 (-0.002965) | 0.005824 / 0.011008 (-0.005184) | 0.117721 / 0.038508 (0.079213) | 0.040420 / 0.023109 (0.017311) | 0.404961 / 0.275898 (0.129063) | 0.426695 / 0.323480 (0.103215) | 0.006634 / 0.007986 (-0.001352) | 0.006033 / 0.004328 (0.001705) | 0.088652 / 0.004250 (0.084402) | 0.048075 / 0.037052 (0.011022) | 0.400683 / 0.258489 (0.142194) | 0.432489 / 0.293841 (0.138648) | 0.042065 / 0.128546 (-0.086482) | 0.014071 / 0.075646 (-0.061575) | 0.399398 / 0.419271 (-0.019873) | 0.066034 / 0.043533 (0.022501) | 0.400056 / 0.255139 (0.144918) | 0.421130 / 0.283200 (0.137930) | 0.119721 / 0.141683 (-0.021962) | 1.752166 / 1.452155 (0.300011) | 1.820161 / 1.492716 (0.327444) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244264 / 0.018006 (0.226258) | 0.480882 / 0.000490 (0.480392) | 0.005604 / 0.000200 (0.005404) | 0.000175 / 0.000054 (0.000121) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032397 / 0.037411 (-0.005015) | 0.131632 / 0.014526 (0.117106) | 0.139765 / 0.176557 (-0.036792) | 0.213135 / 0.737135 (-0.524000) | 0.147891 / 0.296338 (-0.148447) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474534 / 0.215209 (0.259325) | 4.730424 / 2.077655 (2.652770) | 2.163706 / 1.504120 (0.659586) | 1.936051 / 1.541195 (0.394857) | 2.012185 / 1.468490 (0.543695) | 0.826583 / 4.584777 (-3.758194) | 4.921494 / 3.745712 (1.175782) | 2.431401 / 5.269862 (-2.838460) | 1.566020 / 4.565676 (-2.999656) | 0.101255 / 0.424275 (-0.323020) | 0.014553 / 0.007607 (0.006946) | 0.608301 / 0.226044 (0.382256) | 6.089801 / 2.268929 (3.820873) | 2.691986 / 55.444624 (-52.752638) | 2.296498 / 6.876477 (-4.579979) | 2.455388 / 2.142072 (0.313315) | 0.984342 / 4.805227 (-3.820885) | 0.200447 / 6.500664 (-6.300217) | 0.077602 / 0.075469 (0.002133) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.445067 / 1.841788 (-0.396721) | 18.588670 / 8.074308 (10.514362) | 16.950216 / 10.191392 (6.758824) | 0.169688 / 0.680424 (-0.510736) | 0.020544 / 0.534201 (-0.513657) | 0.508506 / 0.579283 (-0.070777) | 0.516218 / 0.434364 (0.081854) | 0.646072 / 0.540337 (0.105734) | 0.763227 / 1.386936 (-0.623709) |\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) | 0.008816 / 0.011353 (-0.002537) | 0.006016 / 0.011008 (-0.004992) | 0.090946 / 0.038508 (0.052438) | 0.040189 / 0.023109 (0.017080) | 0.446723 / 0.275898 (0.170825) | 0.494633 / 0.323480 (0.171153) | 0.007206 / 0.007986 (-0.000779) | 0.004508 / 0.004328 (0.000180) | 0.088477 / 0.004250 (0.084226) | 0.055587 / 0.037052 (0.018535) | 0.445349 / 0.258489 (0.186860) | 0.504940 / 0.293841 (0.211099) | 0.041976 / 0.128546 (-0.086570) | 0.014296 / 0.075646 (-0.061351) | 0.102835 / 0.419271 (-0.316436) | 0.054786 / 0.043533 (0.011253) | 0.444789 / 0.255139 (0.189651) | 0.472306 / 0.283200 (0.189106) | 0.123365 / 0.141683 (-0.018318) | 1.725803 / 1.452155 (0.273648) | 1.832216 / 1.492716 (0.339500) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252680 / 0.018006 (0.234674) | 0.476719 / 0.000490 (0.476229) | 0.000461 / 0.000200 (0.000261) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035961 / 0.037411 (-0.001450) | 0.135399 / 0.014526 (0.120873) | 0.147549 / 0.176557 (-0.029007) | 0.207468 / 0.737135 (-0.529667) | 0.151591 / 0.296338 (-0.144747) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528143 / 0.215209 (0.312934) | 5.270766 / 2.077655 (3.193111) | 2.675644 / 1.504120 (1.171524) | 2.472855 / 1.541195 (0.931660) | 2.636020 / 1.468490 (1.167530) | 0.841325 / 4.584777 (-3.743452) | 4.702290 / 3.745712 (0.956578) | 2.523537 / 5.269862 (-2.746325) | 1.595617 / 4.565676 (-2.970059) | 0.102095 / 0.424275 (-0.322180) | 0.014568 / 0.007607 (0.006961) | 0.652090 / 0.226044 (0.426046) | 6.503086 / 2.268929 (4.234158) | 3.277025 / 55.444624 (-52.167599) | 2.931264 / 6.876477 (-3.945213) | 3.021667 / 2.142072 (0.879594) | 1.002560 / 4.805227 (-3.802668) | 0.202621 / 6.500664 (-6.298043) | 0.080583 / 0.075469 (0.005114) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.639281 / 1.841788 (-0.202507) | 18.911529 / 8.074308 (10.837220) | 17.082795 / 10.191392 (6.891403) | 0.179456 / 0.680424 (-0.500968) | 0.021740 / 0.534201 (-0.512460) | 0.526426 / 0.579283 (-0.052857) | 0.535083 / 0.434364 (0.100719) | 0.583304 / 0.540337 (0.042967) | 0.696733 / 1.386936 (-0.690203) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#757f19283f22eeb3e9aedefd82abc0aa2235f797 \"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) | 0.006823 / 0.011353 (-0.004530) | 0.004847 / 0.011008 (-0.006161) | 0.096038 / 0.038508 (0.057530) | 0.033037 / 0.023109 (0.009928) | 0.298379 / 0.275898 (0.022481) | 0.333319 / 0.323480 (0.009839) | 0.005343 / 0.007986 (-0.002643) | 0.003863 / 0.004328 (-0.000465) | 0.072928 / 0.004250 (0.068678) | 0.040898 / 0.037052 (0.003846) | 0.303116 / 0.258489 (0.044627) | 0.334021 / 0.293841 (0.040181) | 0.034780 / 0.128546 (-0.093767) | 0.011978 / 0.075646 (-0.063668) | 0.331642 / 0.419271 (-0.087629) | 0.052729 / 0.043533 (0.009196) | 0.298586 / 0.255139 (0.043447) | 0.319296 / 0.283200 (0.036097) | 0.097711 / 0.141683 (-0.043972) | 1.416899 / 1.452155 (-0.035256) | 1.546008 / 1.492716 (0.053292) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234303 / 0.018006 (0.216296) | 0.492767 / 0.000490 (0.492278) | 0.004935 / 0.000200 (0.004736) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030617 / 0.037411 (-0.006795) | 0.121203 / 0.014526 (0.106677) | 0.126677 / 0.176557 (-0.049879) | 0.186379 / 0.737135 (-0.550756) | 0.129849 / 0.296338 (-0.166490) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416324 / 0.215209 (0.201115) | 4.135563 / 2.077655 (2.057908) | 1.976182 / 1.504120 (0.472062) | 1.807611 / 1.541195 (0.266416) | 1.886282 / 1.468490 (0.417792) | 0.713006 / 4.584777 (-3.871771) | 3.899205 / 3.745712 (0.153493) | 2.283427 / 5.269862 (-2.986435) | 1.543088 / 4.565676 (-3.022589) | 0.086189 / 0.424275 (-0.338087) | 0.012908 / 0.007607 (0.005301) | 0.516156 / 0.226044 (0.290112) | 5.144199 / 2.268929 (2.875271) | 2.460142 / 55.444624 (-52.984482) | 2.209054 / 6.876477 (-4.667423) | 2.325277 / 2.142072 (0.183204) | 0.849890 / 4.805227 (-3.955337) | 0.173687 / 6.500664 (-6.326977) | 0.070178 / 0.075469 (-0.005291) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.241790 / 1.841788 (-0.599997) | 16.047257 / 8.074308 (7.972949) | 15.774146 / 10.191392 (5.582754) | 0.145871 / 0.680424 (-0.534553) | 0.018106 / 0.534201 (-0.516095) | 0.433642 / 0.579283 (-0.145641) | 0.425311 / 0.434364 (-0.009053) | 0.533963 / 0.540337 (-0.006375) | 0.638786 / 1.386936 (-0.748151) |\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) | 0.007242 / 0.011353 (-0.004111) | 0.005599 / 0.011008 (-0.005410) | 0.073443 / 0.038508 (0.034935) | 0.033764 / 0.023109 (0.010655) | 0.365990 / 0.275898 (0.090092) | 0.392943 / 0.323480 (0.069463) | 0.005987 / 0.007986 (-0.001999) | 0.004312 / 0.004328 (-0.000016) | 0.072831 / 0.004250 (0.068580) | 0.048854 / 0.037052 (0.011802) | 0.362477 / 0.258489 (0.103988) | 0.399993 / 0.293841 (0.106152) | 0.035602 / 0.128546 (-0.092944) | 0.012445 / 0.075646 (-0.063202) | 0.085768 / 0.419271 (-0.333504) | 0.048544 / 0.043533 (0.005011) | 0.362246 / 0.255139 (0.107107) | 0.388753 / 0.283200 (0.105554) | 0.109829 / 0.141683 (-0.031854) | 1.546881 / 1.452155 (0.094726) | 1.619454 / 1.492716 (0.126737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.189926 / 0.018006 (0.171920) | 0.447936 / 0.000490 (0.447446) | 0.002354 / 0.000200 (0.002155) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031740 / 0.037411 (-0.005671) | 0.122595 / 0.014526 (0.108069) | 0.128389 / 0.176557 (-0.048168) | 0.180570 / 0.737135 (-0.556566) | 0.132939 / 0.296338 (-0.163399) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425073 / 0.215209 (0.209863) | 4.238964 / 2.077655 (2.161309) | 2.095116 / 1.504120 (0.590996) | 1.913925 / 1.541195 (0.372730) | 2.024669 / 1.468490 (0.556179) | 0.699172 / 4.584777 (-3.885605) | 3.845807 / 3.745712 (0.100094) | 2.167502 / 5.269862 (-3.102360) | 1.375267 / 4.565676 (-3.190410) | 0.086739 / 0.424275 (-0.337536) | 0.012198 / 0.007607 (0.004591) | 0.525975 / 0.226044 (0.299931) | 5.249449 / 2.268929 (2.980521) | 2.550565 / 55.444624 (-52.894060) | 2.257557 / 6.876477 (-4.618920) | 2.298936 / 2.142072 (0.156863) | 0.850295 / 4.805227 (-3.954932) | 0.170506 / 6.500664 (-6.330158) | 0.065659 / 0.075469 (-0.009810) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330556 / 1.841788 (-0.511231) | 16.920203 / 8.074308 (8.845894) | 15.966739 / 10.191392 (5.775347) | 0.164000 / 0.680424 (-0.516424) | 0.018211 / 0.534201 (-0.515990) | 0.436253 / 0.579283 (-0.143030) | 0.449666 / 0.434364 (0.015302) | 0.522287 / 0.540337 (-0.018050) | 0.615944 / 1.386936 (-0.770992) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#824f96c11a02b3817d6b1bf4dfed0abab27777f0 \"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) | 0.007273 / 0.011353 (-0.004080) | 0.005198 / 0.011008 (-0.005810) | 0.114362 / 0.038508 (0.075854) | 0.031113 / 0.023109 (0.008003) | 0.378568 / 0.275898 (0.102670) | 0.441695 / 0.323480 (0.118215) | 0.006037 / 0.007986 (-0.001949) | 0.005102 / 0.004328 (0.000774) | 0.098682 / 0.004250 (0.094432) | 0.042797 / 0.037052 (0.005745) | 0.360028 / 0.258489 (0.101539) | 0.435757 / 0.293841 (0.141916) | 0.041438 / 0.128546 (-0.087109) | 0.013728 / 0.075646 (-0.061918) | 0.376154 / 0.419271 (-0.043117) | 0.075324 / 0.043533 (0.031791) | 0.357221 / 0.255139 (0.102082) | 0.416378 / 0.283200 (0.133178) | 0.110707 / 0.141683 (-0.030975) | 1.603215 / 1.452155 (0.151061) | 1.736843 / 1.492716 (0.244127) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249479 / 0.018006 (0.231473) | 0.513205 / 0.000490 (0.512715) | 0.003856 / 0.000200 (0.003656) | 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) | 0.027750 / 0.037411 (-0.009661) | 0.105437 / 0.014526 (0.090911) | 0.115903 / 0.176557 (-0.060653) | 0.179662 / 0.737135 (-0.557474) | 0.116305 / 0.296338 (-0.180033) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.551681 / 0.215209 (0.336472) | 5.544590 / 2.077655 (3.466935) | 2.193933 / 1.504120 (0.689813) | 1.898395 / 1.541195 (0.357201) | 1.877288 / 1.468490 (0.408798) | 0.858097 / 4.584777 (-3.726680) | 4.920982 / 3.745712 (1.175270) | 2.478220 / 5.269862 (-2.791641) | 1.779608 / 4.565676 (-2.786069) | 0.101321 / 0.424275 (-0.322954) | 0.012627 / 0.007607 (0.005020) | 0.674865 / 0.226044 (0.448820) | 6.808224 / 2.268929 (4.539295) | 2.822466 / 55.444624 (-52.622159) | 2.170379 / 6.876477 (-4.706098) | 2.224278 / 2.142072 (0.082205) | 1.032763 / 4.805227 (-3.772464) | 0.198851 / 6.500664 (-6.301813) | 0.069249 / 0.075469 (-0.006220) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.425987 / 1.841788 (-0.415801) | 16.212942 / 8.074308 (8.138634) | 18.945770 / 10.191392 (8.754378) | 0.192901 / 0.680424 (-0.487522) | 0.025343 / 0.534201 (-0.508858) | 0.465441 / 0.579283 (-0.113842) | 0.540966 / 0.434364 (0.106602) | 0.576736 / 0.540337 (0.036399) | 0.675717 / 1.386936 (-0.711219) |\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) | 0.007426 / 0.011353 (-0.003927) | 0.005023 / 0.011008 (-0.005985) | 0.085083 / 0.038508 (0.046575) | 0.030559 / 0.023109 (0.007449) | 0.398461 / 0.275898 (0.122563) | 0.418998 / 0.323480 (0.095518) | 0.006697 / 0.007986 (-0.001288) | 0.004665 / 0.004328 (0.000337) | 0.087724 / 0.004250 (0.083473) | 0.045799 / 0.037052 (0.008747) | 0.395165 / 0.258489 (0.136676) | 0.430172 / 0.293841 (0.136331) | 0.040486 / 0.128546 (-0.088060) | 0.014237 / 0.075646 (-0.061409) | 0.099429 / 0.419271 (-0.319843) | 0.056006 / 0.043533 (0.012473) | 0.389046 / 0.255139 (0.133907) | 0.419559 / 0.283200 (0.136359) | 0.108550 / 0.141683 (-0.033132) | 1.614052 / 1.452155 (0.161897) | 1.677785 / 1.492716 (0.185069) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202178 / 0.018006 (0.184172) | 0.486365 / 0.000490 (0.485875) | 0.003844 / 0.000200 (0.003644) | 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) | 0.027963 / 0.037411 (-0.009449) | 0.110399 / 0.014526 (0.095873) | 0.122266 / 0.176557 (-0.054291) | 0.178551 / 0.737135 (-0.558585) | 0.129259 / 0.296338 (-0.167080) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604178 / 0.215209 (0.388969) | 6.135943 / 2.077655 (4.058288) | 2.547576 / 1.504120 (1.043456) | 2.262470 / 1.541195 (0.721276) | 2.275402 / 1.468490 (0.806912) | 0.878804 / 4.584777 (-3.705972) | 5.152200 / 3.745712 (1.406488) | 2.553715 / 5.269862 (-2.716147) | 1.580959 / 4.565676 (-2.984717) | 0.107895 / 0.424275 (-0.316380) | 0.012751 / 0.007607 (0.005143) | 0.770678 / 0.226044 (0.544633) | 7.744303 / 2.268929 (5.475374) | 3.342037 / 55.444624 (-52.102588) | 2.756848 / 6.876477 (-4.119629) | 2.739357 / 2.142072 (0.597285) | 1.086330 / 4.805227 (-3.718897) | 0.230983 / 6.500664 (-6.269681) | 0.073771 / 0.075469 (-0.001698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.493441 / 1.841788 (-0.348347) | 16.621611 / 8.074308 (8.547303) | 19.081000 / 10.191392 (8.889608) | 0.215623 / 0.680424 (-0.464801) | 0.025660 / 0.534201 (-0.508541) | 0.446490 / 0.579283 (-0.132793) | 0.560078 / 0.434364 (0.125714) | 0.527231 / 0.540337 (-0.013106) | 0.636551 / 1.386936 (-0.750385) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b899ea45c0a7e724ceb5f43c3a8b9fdb081fa67a \"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) | 0.008266 / 0.011353 (-0.003087) | 0.005082 / 0.011008 (-0.005927) | 0.119858 / 0.038508 (0.081350) | 0.032907 / 0.023109 (0.009798) | 0.362816 / 0.275898 (0.086918) | 0.403684 / 0.323480 (0.080204) | 0.006296 / 0.007986 (-0.001690) | 0.006220 / 0.004328 (0.001891) | 0.095609 / 0.004250 (0.091359) | 0.048734 / 0.037052 (0.011682) | 0.385724 / 0.258489 (0.127235) | 0.424315 / 0.293841 (0.130475) | 0.042344 / 0.128546 (-0.086202) | 0.016147 / 0.075646 (-0.059500) | 0.409661 / 0.419271 (-0.009610) | 0.057900 / 0.043533 (0.014367) | 0.387013 / 0.255139 (0.131874) | 0.388901 / 0.283200 (0.105702) | 0.103920 / 0.141683 (-0.037762) | 1.732730 / 1.452155 (0.280575) | 1.863912 / 1.492716 (0.371196) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237406 / 0.018006 (0.219400) | 0.514398 / 0.000490 (0.513909) | 0.005941 / 0.000200 (0.005741) | 0.000109 / 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) | 0.027524 / 0.037411 (-0.009888) | 0.116498 / 0.014526 (0.101972) | 0.129034 / 0.176557 (-0.047522) | 0.218272 / 0.737135 (-0.518864) | 0.148389 / 0.296338 (-0.147950) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604555 / 0.215209 (0.389346) | 5.921576 / 2.077655 (3.843921) | 2.410483 / 1.504120 (0.906363) | 2.220286 / 1.541195 (0.679092) | 2.138880 / 1.468490 (0.670390) | 0.934962 / 4.584777 (-3.649815) | 5.808855 / 3.745712 (2.063143) | 4.881554 / 5.269862 (-0.388308) | 2.536408 / 4.565676 (-2.029268) | 0.124260 / 0.424275 (-0.300015) | 0.017798 / 0.007607 (0.010190) | 0.778991 / 0.226044 (0.552947) | 7.899262 / 2.268929 (5.630333) | 3.208667 / 55.444624 (-52.235957) | 2.631182 / 6.876477 (-4.245295) | 2.676199 / 2.142072 (0.534127) | 1.165516 / 4.805227 (-3.639711) | 0.228751 / 6.500664 (-6.271913) | 0.081378 / 0.075469 (0.005909) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.522156 / 1.841788 (-0.319632) | 17.975381 / 8.074308 (9.901073) | 18.918882 / 10.191392 (8.727490) | 0.223984 / 0.680424 (-0.456440) | 0.025171 / 0.534201 (-0.509030) | 0.467894 / 0.579283 (-0.111389) | 0.559501 / 0.434364 (0.125137) | 0.550392 / 0.540337 (0.010055) | 0.696923 / 1.386936 (-0.690013) |\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) | 0.008577 / 0.011353 (-0.002775) | 0.006735 / 0.011008 (-0.004273) | 0.095108 / 0.038508 (0.056600) | 0.035059 / 0.023109 (0.011950) | 0.448576 / 0.275898 (0.172677) | 0.492049 / 0.323480 (0.168569) | 0.006600 / 0.007986 (-0.001385) | 0.004760 / 0.004328 (0.000431) | 0.094670 / 0.004250 (0.090419) | 0.052543 / 0.037052 (0.015491) | 0.458927 / 0.258489 (0.200438) | 0.511522 / 0.293841 (0.217681) | 0.046046 / 0.128546 (-0.082500) | 0.015227 / 0.075646 (-0.060419) | 0.114585 / 0.419271 (-0.304686) | 0.057569 / 0.043533 (0.014036) | 0.441989 / 0.255139 (0.186850) | 0.487001 / 0.283200 (0.203801) | 0.115688 / 0.141683 (-0.025995) | 1.777366 / 1.452155 (0.325211) | 1.906216 / 1.492716 (0.413499) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224880 / 0.018006 (0.206874) | 0.504153 / 0.000490 (0.503664) | 0.001143 / 0.000200 (0.000943) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033618 / 0.037411 (-0.003793) | 0.127396 / 0.014526 (0.112870) | 0.135648 / 0.176557 (-0.040909) | 0.193140 / 0.737135 (-0.543995) | 0.142129 / 0.296338 (-0.154209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.692845 / 0.215209 (0.477636) | 6.804897 / 2.077655 (4.727242) | 2.851041 / 1.504120 (1.346921) | 2.480698 / 1.541195 (0.939504) | 2.488619 / 1.468490 (1.020129) | 0.970439 / 4.584777 (-3.614338) | 5.466059 / 3.745712 (1.720347) | 2.790261 / 5.269862 (-2.479601) | 1.727638 / 4.565676 (-2.838039) | 0.116345 / 0.424275 (-0.307930) | 0.014348 / 0.007607 (0.006740) | 0.845510 / 0.226044 (0.619465) | 8.397198 / 2.268929 (6.128270) | 3.591998 / 55.444624 (-51.852626) | 2.858339 / 6.876477 (-4.018137) | 2.905075 / 2.142072 (0.763003) | 1.193569 / 4.805227 (-3.611658) | 0.243091 / 6.500664 (-6.257573) | 0.082198 / 0.075469 (0.006729) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.610327 / 1.841788 (-0.231461) | 17.191414 / 8.074308 (9.117106) | 20.176518 / 10.191392 (9.985126) | 0.246574 / 0.680424 (-0.433850) | 0.024343 / 0.534201 (-0.509858) | 0.482091 / 0.579283 (-0.097192) | 0.585241 / 0.434364 (0.150877) | 0.558833 / 0.540337 (0.018496) | 0.654811 / 1.386936 (-0.732125) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#81761dbfa738354a9c50309313dfe90bea26d872 \"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) | 0.006353 / 0.011353 (-0.004999) | 0.004393 / 0.011008 (-0.006616) | 0.098751 / 0.038508 (0.060242) | 0.029090 / 0.023109 (0.005981) | 0.304169 / 0.275898 (0.028271) | 0.339879 / 0.323480 (0.016399) | 0.005577 / 0.007986 (-0.002408) | 0.003516 / 0.004328 (-0.000813) | 0.077347 / 0.004250 (0.073097) | 0.041935 / 0.037052 (0.004882) | 0.305865 / 0.258489 (0.047376) | 0.357063 / 0.293841 (0.063222) | 0.025245 / 0.128546 (-0.103301) | 0.008753 / 0.075646 (-0.066893) | 0.316734 / 0.419271 (-0.102538) | 0.043464 / 0.043533 (-0.000069) | 0.300944 / 0.255139 (0.045805) | 0.330091 / 0.283200 (0.046891) | 0.088593 / 0.141683 (-0.053090) | 1.588958 / 1.452155 (0.136803) | 1.641376 / 1.492716 (0.148660) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220290 / 0.018006 (0.202284) | 0.445430 / 0.000490 (0.444940) | 0.004800 / 0.000200 (0.004600) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023828 / 0.037411 (-0.013583) | 0.103446 / 0.014526 (0.088920) | 0.110668 / 0.176557 (-0.065889) | 0.169604 / 0.737135 (-0.567531) | 0.114818 / 0.296338 (-0.181520) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416951 / 0.215209 (0.201742) | 4.138917 / 2.077655 (2.061263) | 1.891265 / 1.504120 (0.387145) | 1.687068 / 1.541195 (0.145873) | 1.726618 / 1.468490 (0.258128) | 0.546977 / 4.584777 (-4.037800) | 3.536153 / 3.745712 (-0.209560) | 1.795206 / 5.269862 (-3.474656) | 1.019845 / 4.565676 (-3.545831) | 0.067040 / 0.424275 (-0.357235) | 0.012038 / 0.007607 (0.004431) | 0.520583 / 0.226044 (0.294539) | 5.211520 / 2.268929 (2.942591) | 2.336136 / 55.444624 (-53.108488) | 2.011262 / 6.876477 (-4.865215) | 2.137311 / 2.142072 (-0.004762) | 0.654779 / 4.805227 (-4.150448) | 0.134555 / 6.500664 (-6.366109) | 0.066427 / 0.075469 (-0.009042) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240187 / 1.841788 (-0.601600) | 14.104063 / 8.074308 (6.029755) | 13.369572 / 10.191392 (3.178180) | 0.147891 / 0.680424 (-0.532533) | 0.016993 / 0.534201 (-0.517208) | 0.364863 / 0.579283 (-0.214420) | 0.398684 / 0.434364 (-0.035680) | 0.430524 / 0.540337 (-0.109813) | 0.520920 / 1.386936 (-0.866016) |\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) | 0.006845 / 0.011353 (-0.004508) | 0.004420 / 0.011008 (-0.006588) | 0.078334 / 0.038508 (0.039825) | 0.030566 / 0.023109 (0.007457) | 0.409568 / 0.275898 (0.133670) | 0.458389 / 0.323480 (0.134910) | 0.005739 / 0.007986 (-0.002247) | 0.005222 / 0.004328 (0.000893) | 0.076066 / 0.004250 (0.071816) | 0.049239 / 0.037052 (0.012187) | 0.409841 / 0.258489 (0.151352) | 0.472250 / 0.293841 (0.178409) | 0.025463 / 0.128546 (-0.103084) | 0.008738 / 0.075646 (-0.066909) | 0.083114 / 0.419271 (-0.336157) | 0.041233 / 0.043533 (-0.002300) | 0.407158 / 0.255139 (0.152019) | 0.438724 / 0.283200 (0.155524) | 0.097974 / 0.141683 (-0.043709) | 1.536514 / 1.452155 (0.084360) | 1.636704 / 1.492716 (0.143987) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240589 / 0.018006 (0.222583) | 0.440328 / 0.000490 (0.439838) | 0.000937 / 0.000200 (0.000737) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027559 / 0.037411 (-0.009853) | 0.109930 / 0.014526 (0.095405) | 0.113366 / 0.176557 (-0.063190) | 0.166849 / 0.737135 (-0.570286) | 0.118872 / 0.296338 (-0.177467) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474120 / 0.215209 (0.258911) | 4.739222 / 2.077655 (2.661567) | 2.484386 / 1.504120 (0.980266) | 2.281937 / 1.541195 (0.740742) | 2.362974 / 1.468490 (0.894484) | 0.549897 / 4.584777 (-4.034879) | 3.425540 / 3.745712 (-0.320172) | 1.765810 / 5.269862 (-3.504051) | 1.008277 / 4.565676 (-3.557400) | 0.067288 / 0.424275 (-0.356987) | 0.011954 / 0.007607 (0.004347) | 0.577216 / 0.226044 (0.351172) | 5.790659 / 2.268929 (3.521731) | 2.946732 / 55.444624 (-52.497892) | 2.608835 / 6.876477 (-4.267641) | 2.642987 / 2.142072 (0.500915) | 0.652798 / 4.805227 (-4.152429) | 0.135909 / 6.500664 (-6.364755) | 0.068480 / 0.075469 (-0.006989) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353550 / 1.841788 (-0.488237) | 14.732084 / 8.074308 (6.657775) | 14.439174 / 10.191392 (4.247782) | 0.131445 / 0.680424 (-0.548979) | 0.016608 / 0.534201 (-0.517593) | 0.368103 / 0.579283 (-0.211180) | 0.393918 / 0.434364 (-0.040446) | 0.423562 / 0.540337 (-0.116776) | 0.515041 / 1.386936 (-0.871895) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8907bdb23f78545303eb3bb0561e33ec6787f96c \"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) | 0.006414 / 0.011353 (-0.004938) | 0.004704 / 0.011008 (-0.006305) | 0.096012 / 0.038508 (0.057504) | 0.032910 / 0.023109 (0.009800) | 0.290676 / 0.275898 (0.014778) | 0.319646 / 0.323480 (-0.003834) | 0.005806 / 0.007986 (-0.002180) | 0.004008 / 0.004328 (-0.000320) | 0.073982 / 0.004250 (0.069731) | 0.048985 / 0.037052 (0.011933) | 0.299498 / 0.258489 (0.041009) | 0.338118 / 0.293841 (0.044277) | 0.027680 / 0.128546 (-0.100866) | 0.009051 / 0.075646 (-0.066595) | 0.325051 / 0.419271 (-0.094221) | 0.051011 / 0.043533 (0.007478) | 0.292249 / 0.255139 (0.037110) | 0.315733 / 0.283200 (0.032533) | 0.100327 / 0.141683 (-0.041356) | 1.481862 / 1.452155 (0.029707) | 1.544884 / 1.492716 (0.052168) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289610 / 0.018006 (0.271603) | 0.510164 / 0.000490 (0.509675) | 0.004726 / 0.000200 (0.004526) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027617 / 0.037411 (-0.009794) | 0.107593 / 0.014526 (0.093068) | 0.122783 / 0.176557 (-0.053774) | 0.181086 / 0.737135 (-0.556049) | 0.128030 / 0.296338 (-0.168308) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403571 / 0.215209 (0.188362) | 4.002881 / 2.077655 (1.925227) | 1.805550 / 1.504120 (0.301430) | 1.619165 / 1.541195 (0.077971) | 1.606536 / 1.468490 (0.138046) | 0.518917 / 4.584777 (-4.065860) | 3.731498 / 3.745712 (-0.014214) | 3.206645 / 5.269862 (-2.063217) | 1.641615 / 4.565676 (-2.924062) | 0.065100 / 0.424275 (-0.359175) | 0.011396 / 0.007607 (0.003789) | 0.500597 / 0.226044 (0.274553) | 4.992293 / 2.268929 (2.723364) | 2.278726 / 55.444624 (-53.165898) | 1.960823 / 6.876477 (-4.915654) | 2.038684 / 2.142072 (-0.103388) | 0.640910 / 4.805227 (-4.164318) | 0.140597 / 6.500664 (-6.360067) | 0.062114 / 0.075469 (-0.013355) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.167366 / 1.841788 (-0.674422) | 14.748193 / 8.074308 (6.673884) | 13.592381 / 10.191392 (3.400989) | 0.165341 / 0.680424 (-0.515083) | 0.017360 / 0.534201 (-0.516841) | 0.393448 / 0.579283 (-0.185836) | 0.422951 / 0.434364 (-0.011413) | 0.460491 / 0.540337 (-0.079847) | 0.558238 / 1.386936 (-0.828698) |\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) | 0.006373 / 0.011353 (-0.004980) | 0.004587 / 0.011008 (-0.006421) | 0.076421 / 0.038508 (0.037913) | 0.032162 / 0.023109 (0.009052) | 0.385531 / 0.275898 (0.109633) | 0.410424 / 0.323480 (0.086944) | 0.006154 / 0.007986 (-0.001832) | 0.005533 / 0.004328 (0.001205) | 0.077035 / 0.004250 (0.072784) | 0.051571 / 0.037052 (0.014519) | 0.393283 / 0.258489 (0.134794) | 0.433756 / 0.293841 (0.139915) | 0.028381 / 0.128546 (-0.100165) | 0.009034 / 0.075646 (-0.066613) | 0.083836 / 0.419271 (-0.335435) | 0.048246 / 0.043533 (0.004713) | 0.385437 / 0.255139 (0.130298) | 0.394187 / 0.283200 (0.110987) | 0.105453 / 0.141683 (-0.036230) | 1.459173 / 1.452155 (0.007018) | 1.575083 / 1.492716 (0.082367) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.320324 / 0.018006 (0.302318) | 0.502945 / 0.000490 (0.502455) | 0.004470 / 0.000200 (0.004270) | 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) | 0.028118 / 0.037411 (-0.009293) | 0.111430 / 0.014526 (0.096904) | 0.123141 / 0.176557 (-0.053415) | 0.175215 / 0.737135 (-0.561920) | 0.126429 / 0.296338 (-0.169909) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433407 / 0.215209 (0.218198) | 4.329945 / 2.077655 (2.252291) | 2.096822 / 1.504120 (0.592702) | 1.908173 / 1.541195 (0.366978) | 1.967167 / 1.468490 (0.498676) | 0.529207 / 4.584777 (-4.055570) | 3.798424 / 3.745712 (0.052712) | 3.050716 / 5.269862 (-2.219146) | 1.445009 / 4.565676 (-3.120668) | 0.066467 / 0.424275 (-0.357809) | 0.011698 / 0.007607 (0.004090) | 0.528660 / 0.226044 (0.302615) | 5.282069 / 2.268929 (3.013141) | 2.535501 / 55.444624 (-52.909124) | 2.202856 / 6.876477 (-4.673621) | 2.293225 / 2.142072 (0.151153) | 0.640216 / 4.805227 (-4.165011) | 0.140884 / 6.500664 (-6.359780) | 0.064231 / 0.075469 (-0.011238) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.292129 / 1.841788 (-0.549659) | 15.371370 / 8.074308 (7.297062) | 15.114854 / 10.191392 (4.923462) | 0.176870 / 0.680424 (-0.503554) | 0.017380 / 0.534201 (-0.516821) | 0.398156 / 0.579283 (-0.181127) | 0.442277 / 0.434364 (0.007913) | 0.467093 / 0.540337 (-0.073244) | 0.561599 / 1.386936 (-0.825337) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#323747a5ff7d9b204ea3c4989d658af7102f7bbd \"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) | 0.009360 / 0.011353 (-0.001993) | 0.006297 / 0.011008 (-0.004712) | 0.133131 / 0.038508 (0.094623) | 0.040261 / 0.023109 (0.017152) | 0.419101 / 0.275898 (0.143203) | 0.453087 / 0.323480 (0.129607) | 0.007718 / 0.007986 (-0.000268) | 0.005698 / 0.004328 (0.001369) | 0.102261 / 0.004250 (0.098010) | 0.055147 / 0.037052 (0.018095) | 0.428355 / 0.258489 (0.169866) | 0.505241 / 0.293841 (0.211400) | 0.046745 / 0.128546 (-0.081802) | 0.015559 / 0.075646 (-0.060088) | 0.441775 / 0.419271 (0.022503) | 0.070165 / 0.043533 (0.026632) | 0.421957 / 0.255139 (0.166818) | 0.445156 / 0.283200 (0.161957) | 0.126321 / 0.141683 (-0.015362) | 1.900486 / 1.452155 (0.448331) | 2.088630 / 1.492716 (0.595913) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260244 / 0.018006 (0.242237) | 0.606317 / 0.000490 (0.605828) | 0.006827 / 0.000200 (0.006627) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031958 / 0.037411 (-0.005453) | 0.139362 / 0.014526 (0.124836) | 0.148748 / 0.176557 (-0.027809) | 0.226269 / 0.737135 (-0.510866) | 0.161145 / 0.296338 (-0.135194) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.666287 / 0.215209 (0.451078) | 6.588707 / 2.077655 (4.511053) | 2.736155 / 1.504120 (1.232035) | 2.329601 / 1.541195 (0.788406) | 2.324991 / 1.468490 (0.856501) | 0.943608 / 4.584777 (-3.641169) | 6.051653 / 3.745712 (2.305941) | 2.929150 / 5.269862 (-2.340711) | 1.804461 / 4.565676 (-2.761216) | 0.113302 / 0.424275 (-0.310973) | 0.015245 / 0.007607 (0.007638) | 0.827029 / 0.226044 (0.600984) | 8.211536 / 2.268929 (5.942608) | 3.445231 / 55.444624 (-51.999393) | 2.756728 / 6.876477 (-4.119748) | 2.904039 / 2.142072 (0.761966) | 1.162339 / 4.805227 (-3.642888) | 0.231168 / 6.500664 (-6.269496) | 0.089038 / 0.075469 (0.013569) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.640619 / 1.841788 (-0.201169) | 20.034157 / 8.074308 (11.959849) | 22.346006 / 10.191392 (12.154614) | 0.255300 / 0.680424 (-0.425124) | 0.031452 / 0.534201 (-0.502749) | 0.563290 / 0.579283 (-0.015993) | 0.653556 / 0.434364 (0.219192) | 0.687663 / 0.540337 (0.147326) | 0.816432 / 1.386936 (-0.570504) |\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) | 0.010340 / 0.011353 (-0.001013) | 0.006245 / 0.011008 (-0.004764) | 0.128012 / 0.038508 (0.089504) | 0.041799 / 0.023109 (0.018690) | 0.533340 / 0.275898 (0.257442) | 0.592243 / 0.323480 (0.268763) | 0.009256 / 0.007986 (0.001271) | 0.005310 / 0.004328 (0.000982) | 0.110973 / 0.004250 (0.106722) | 0.065465 / 0.037052 (0.028412) | 0.533845 / 0.258489 (0.275356) | 0.602190 / 0.293841 (0.308349) | 0.060245 / 0.128546 (-0.068301) | 0.016954 / 0.075646 (-0.058693) | 0.119727 / 0.419271 (-0.299545) | 0.064628 / 0.043533 (0.021095) | 0.558229 / 0.255139 (0.303090) | 0.563696 / 0.283200 (0.280496) | 0.137225 / 0.141683 (-0.004458) | 2.038605 / 1.452155 (0.586451) | 2.158655 / 1.492716 (0.665939) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.327067 / 0.018006 (0.309061) | 0.628812 / 0.000490 (0.628323) | 0.010259 / 0.000200 (0.010059) | 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) | 0.037023 / 0.037411 (-0.000388) | 0.142462 / 0.014526 (0.127936) | 0.158165 / 0.176557 (-0.018392) | 0.220808 / 0.737135 (-0.516328) | 0.163608 / 0.296338 (-0.132731) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.776119 / 0.215209 (0.560910) | 7.813044 / 2.077655 (5.735389) | 3.610901 / 1.504120 (2.106781) | 3.195144 / 1.541195 (1.653950) | 3.218245 / 1.468490 (1.749755) | 1.092732 / 4.584777 (-3.492045) | 5.965526 / 3.745712 (2.219813) | 2.914683 / 5.269862 (-2.355179) | 1.848397 / 4.565676 (-2.717280) | 0.114436 / 0.424275 (-0.309839) | 0.014794 / 0.007607 (0.007187) | 0.887141 / 0.226044 (0.661096) | 9.009743 / 2.268929 (6.740815) | 4.180143 / 55.444624 (-51.264481) | 3.452194 / 6.876477 (-3.424283) | 3.493520 / 2.142072 (1.351448) | 1.233327 / 4.805227 (-3.571900) | 0.235390 / 6.500664 (-6.265274) | 0.099544 / 0.075469 (0.024075) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.853482 / 1.841788 (0.011694) | 20.071177 / 8.074308 (11.996869) | 24.507618 / 10.191392 (14.316226) | 0.260164 / 0.680424 (-0.420260) | 0.028433 / 0.534201 (-0.505768) | 0.549181 / 0.579283 (-0.030102) | 0.650069 / 0.434364 (0.215705) | 0.629541 / 0.540337 (0.089203) | 0.808932 / 1.386936 (-0.578004) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f39ba76af62c8037de3f464e87cbb095f8729062 \"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) | 0.009537 / 0.011353 (-0.001816) | 0.006036 / 0.011008 (-0.004972) | 0.141210 / 0.038508 (0.102701) | 0.037493 / 0.023109 (0.014384) | 0.404285 / 0.275898 (0.128386) | 0.458906 / 0.323480 (0.135427) | 0.007224 / 0.007986 (-0.000761) | 0.005148 / 0.004328 (0.000819) | 0.103889 / 0.004250 (0.099639) | 0.048877 / 0.037052 (0.011824) | 0.413220 / 0.258489 (0.154731) | 0.458153 / 0.293841 (0.164312) | 0.046008 / 0.128546 (-0.082538) | 0.015116 / 0.075646 (-0.060531) | 0.439836 / 0.419271 (0.020565) | 0.067527 / 0.043533 (0.023994) | 0.435794 / 0.255139 (0.180656) | 0.451687 / 0.283200 (0.168487) | 0.121274 / 0.141683 (-0.020409) | 1.950199 / 1.452155 (0.498044) | 2.035589 / 1.492716 (0.542873) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247056 / 0.018006 (0.229050) | 0.550348 / 0.000490 (0.549858) | 0.005504 / 0.000200 (0.005305) | 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) | 0.032171 / 0.037411 (-0.005240) | 0.135983 / 0.014526 (0.121457) | 0.149587 / 0.176557 (-0.026970) | 0.233414 / 0.737135 (-0.503722) | 0.152598 / 0.296338 (-0.143740) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634813 / 0.215209 (0.419604) | 6.453619 / 2.077655 (4.375964) | 2.582070 / 1.504120 (1.077951) | 2.214292 / 1.541195 (0.673097) | 2.220012 / 1.468490 (0.751522) | 0.987374 / 4.584777 (-3.597403) | 5.543760 / 3.745712 (1.798047) | 2.808865 / 5.269862 (-2.460996) | 1.714713 / 4.565676 (-2.850963) | 0.111016 / 0.424275 (-0.313259) | 0.014688 / 0.007607 (0.007081) | 0.842542 / 0.226044 (0.616498) | 8.414336 / 2.268929 (6.145407) | 3.501021 / 55.444624 (-51.943604) | 2.665335 / 6.876477 (-4.211142) | 2.843706 / 2.142072 (0.701633) | 1.196398 / 4.805227 (-3.608829) | 0.245508 / 6.500664 (-6.255156) | 0.086970 / 0.075469 (0.011501) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.590244 / 1.841788 (-0.251544) | 18.694141 / 8.074308 (10.619833) | 21.752463 / 10.191392 (11.561071) | 0.264511 / 0.680424 (-0.415913) | 0.028713 / 0.534201 (-0.505488) | 0.531102 / 0.579283 (-0.048181) | 0.626302 / 0.434364 (0.191938) | 0.624541 / 0.540337 (0.084203) | 0.745745 / 1.386936 (-0.641191) |\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) | 0.010097 / 0.011353 (-0.001256) | 0.005558 / 0.011008 (-0.005451) | 0.111326 / 0.038508 (0.072818) | 0.036465 / 0.023109 (0.013356) | 0.472116 / 0.275898 (0.196218) | 0.524479 / 0.323480 (0.200999) | 0.007466 / 0.007986 (-0.000520) | 0.005440 / 0.004328 (0.001112) | 0.103482 / 0.004250 (0.099231) | 0.053217 / 0.037052 (0.016165) | 0.476685 / 0.258489 (0.218196) | 0.554011 / 0.293841 (0.260170) | 0.047157 / 0.128546 (-0.081390) | 0.015895 / 0.075646 (-0.059751) | 0.115997 / 0.419271 (-0.303274) | 0.062290 / 0.043533 (0.018758) | 0.474166 / 0.255139 (0.219027) | 0.498854 / 0.283200 (0.215655) | 0.121798 / 0.141683 (-0.019885) | 1.956583 / 1.452155 (0.504428) | 2.069620 / 1.492716 (0.576904) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278637 / 0.018006 (0.260631) | 0.555295 / 0.000490 (0.554805) | 0.007401 / 0.000200 (0.007201) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033576 / 0.037411 (-0.003835) | 0.136479 / 0.014526 (0.121954) | 0.153960 / 0.176557 (-0.022597) | 0.203422 / 0.737135 (-0.533713) | 0.154159 / 0.296338 (-0.142180) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.672561 / 0.215209 (0.457352) | 6.956675 / 2.077655 (4.879020) | 3.063636 / 1.504120 (1.559516) | 2.668256 / 1.541195 (1.127061) | 2.794793 / 1.468490 (1.326303) | 0.964242 / 4.584777 (-3.620535) | 5.785992 / 3.745712 (2.040279) | 2.850079 / 5.269862 (-2.419782) | 1.782491 / 4.565676 (-2.783186) | 0.114859 / 0.424275 (-0.309416) | 0.015229 / 0.007607 (0.007622) | 0.858406 / 0.226044 (0.632362) | 8.646296 / 2.268929 (6.377367) | 3.842133 / 55.444624 (-51.602492) | 3.180017 / 6.876477 (-3.696460) | 3.241315 / 2.142072 (1.099243) | 1.248988 / 4.805227 (-3.556239) | 0.235075 / 6.500664 (-6.265589) | 0.087192 / 0.075469 (0.011723) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.783877 / 1.841788 (-0.057910) | 19.477223 / 8.074308 (11.402914) | 22.926734 / 10.191392 (12.735342) | 0.246970 / 0.680424 (-0.433454) | 0.026386 / 0.534201 (-0.507815) | 0.517599 / 0.579283 (-0.061684) | 0.626504 / 0.434364 (0.192140) | 0.606943 / 0.540337 (0.066606) | 0.739115 / 1.386936 (-0.647821) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e8f051a41454f8625091338e6b53119a5eb9b2a0 \"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) | 0.008085 / 0.011353 (-0.003268) | 0.005568 / 0.011008 (-0.005440) | 0.119674 / 0.038508 (0.081166) | 0.040452 / 0.023109 (0.017343) | 0.360288 / 0.275898 (0.084390) | 0.409448 / 0.323480 (0.085968) | 0.007281 / 0.007986 (-0.000705) | 0.004931 / 0.004328 (0.000602) | 0.089956 / 0.004250 (0.085706) | 0.056088 / 0.037052 (0.019036) | 0.384708 / 0.258489 (0.126219) | 0.423506 / 0.293841 (0.129665) | 0.033280 / 0.128546 (-0.095266) | 0.010696 / 0.075646 (-0.064951) | 0.394851 / 0.419271 (-0.024421) | 0.058412 / 0.043533 (0.014879) | 0.361514 / 0.255139 (0.106375) | 0.399121 / 0.283200 (0.115921) | 0.117927 / 0.141683 (-0.023756) | 1.791499 / 1.452155 (0.339344) | 1.889000 / 1.492716 (0.396284) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253324 / 0.018006 (0.235318) | 0.536151 / 0.000490 (0.535661) | 0.010450 / 0.000200 (0.010250) | 0.000171 / 0.000054 (0.000117) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034646 / 0.037411 (-0.002765) | 0.145999 / 0.014526 (0.131473) | 0.153793 / 0.176557 (-0.022763) | 0.232871 / 0.737135 (-0.504265) | 0.161151 / 0.296338 (-0.135188) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471407 / 0.215209 (0.256197) | 4.715702 / 2.077655 (2.638047) | 2.228939 / 1.504120 (0.724819) | 2.008511 / 1.541195 (0.467317) | 2.135182 / 1.468490 (0.666692) | 0.620720 / 4.584777 (-3.964057) | 4.960731 / 3.745712 (1.215019) | 2.222469 / 5.269862 (-3.047393) | 1.284467 / 4.565676 (-3.281209) | 0.077931 / 0.424275 (-0.346344) | 0.013935 / 0.007607 (0.006328) | 0.593164 / 0.226044 (0.367120) | 5.940829 / 2.268929 (3.671900) | 2.664277 / 55.444624 (-52.780347) | 2.290655 / 6.876477 (-4.585822) | 2.496664 / 2.142072 (0.354592) | 0.759166 / 4.805227 (-4.046061) | 0.168011 / 6.500664 (-6.332653) | 0.077993 / 0.075469 (0.002524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.440663 / 1.841788 (-0.401125) | 19.105377 / 8.074308 (11.031069) | 16.068118 / 10.191392 (5.876726) | 0.193024 / 0.680424 (-0.487400) | 0.022348 / 0.534201 (-0.511853) | 0.517454 / 0.579283 (-0.061829) | 0.528072 / 0.434364 (0.093708) | 0.565293 / 0.540337 (0.024955) | 0.676578 / 1.386936 (-0.710358) |\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) | 0.008089 / 0.011353 (-0.003264) | 0.005287 / 0.011008 (-0.005721) | 0.087964 / 0.038508 (0.049456) | 0.041548 / 0.023109 (0.018439) | 0.437733 / 0.275898 (0.161835) | 0.487878 / 0.323480 (0.164398) | 0.006898 / 0.007986 (-0.001087) | 0.004649 / 0.004328 (0.000320) | 0.086982 / 0.004250 (0.082732) | 0.056874 / 0.037052 (0.019822) | 0.437397 / 0.258489 (0.178908) | 0.490636 / 0.293841 (0.196795) | 0.033550 / 0.128546 (-0.094997) | 0.010430 / 0.075646 (-0.065216) | 0.096076 / 0.419271 (-0.323196) | 0.054028 / 0.043533 (0.010495) | 0.450262 / 0.255139 (0.195123) | 0.465566 / 0.283200 (0.182366) | 0.119987 / 0.141683 (-0.021696) | 1.764428 / 1.452155 (0.312273) | 1.841547 / 1.492716 (0.348831) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271427 / 0.018006 (0.253420) | 0.506386 / 0.000490 (0.505896) | 0.001213 / 0.000200 (0.001013) | 0.000125 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036159 / 0.037411 (-0.001253) | 0.140578 / 0.014526 (0.126053) | 0.147517 / 0.176557 (-0.029040) | 0.206215 / 0.737135 (-0.530921) | 0.152560 / 0.296338 (-0.143779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522833 / 0.215209 (0.307624) | 5.215732 / 2.077655 (3.138077) | 2.553406 / 1.504120 (1.049286) | 2.344815 / 1.541195 (0.803620) | 2.422377 / 1.468490 (0.953886) | 0.631197 / 4.584777 (-3.953580) | 4.906216 / 3.745712 (1.160504) | 2.212923 / 5.269862 (-3.056938) | 1.352937 / 4.565676 (-3.212740) | 0.079141 / 0.424275 (-0.345135) | 0.013691 / 0.007607 (0.006084) | 0.634939 / 0.226044 (0.408895) | 6.578770 / 2.268929 (4.309842) | 3.080339 / 55.444624 (-52.364286) | 2.710243 / 6.876477 (-4.166234) | 2.740476 / 2.142072 (0.598404) | 0.783610 / 4.805227 (-4.021617) | 0.171589 / 6.500664 (-6.329075) | 0.077311 / 0.075469 (0.001842) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584847 / 1.841788 (-0.256941) | 19.510132 / 8.074308 (11.435824) | 18.074572 / 10.191392 (7.883180) | 0.173494 / 0.680424 (-0.506930) | 0.021149 / 0.534201 (-0.513052) | 0.469026 / 0.579283 (-0.110258) | 0.518463 / 0.434364 (0.084099) | 0.550363 / 0.540337 (0.010026) | 0.667087 / 1.386936 (-0.719849) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5dfcd876c25cc0ffbd6b5b518b017419390a8ada \"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) | 0.007144 / 0.011353 (-0.004209) | 0.004783 / 0.011008 (-0.006225) | 0.103991 / 0.038508 (0.065483) | 0.039098 / 0.023109 (0.015989) | 0.319851 / 0.275898 (0.043952) | 0.356104 / 0.323480 (0.032625) | 0.007077 / 0.007986 (-0.000909) | 0.004188 / 0.004328 (-0.000141) | 0.078360 / 0.004250 (0.074109) | 0.050951 / 0.037052 (0.013899) | 0.321791 / 0.258489 (0.063302) | 0.356123 / 0.293841 (0.062283) | 0.028967 / 0.128546 (-0.099579) | 0.009091 / 0.075646 (-0.066555) | 0.355265 / 0.419271 (-0.064007) | 0.052521 / 0.043533 (0.008988) | 0.317333 / 0.255139 (0.062194) | 0.340747 / 0.283200 (0.057547) | 0.104354 / 0.141683 (-0.037329) | 1.522791 / 1.452155 (0.070636) | 1.579835 / 1.492716 (0.087118) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260539 / 0.018006 (0.242532) | 0.454230 / 0.000490 (0.453740) | 0.036588 / 0.000200 (0.036388) | 0.000289 / 0.000054 (0.000235) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028375 / 0.037411 (-0.009036) | 0.118939 / 0.014526 (0.104413) | 0.126553 / 0.176557 (-0.050004) | 0.184596 / 0.737135 (-0.552539) | 0.130583 / 0.296338 (-0.165755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417353 / 0.215209 (0.202144) | 4.171595 / 2.077655 (2.093940) | 1.855096 / 1.504120 (0.350976) | 1.673941 / 1.541195 (0.132747) | 1.761370 / 1.468490 (0.292880) | 0.544081 / 4.584777 (-4.040696) | 3.851877 / 3.745712 (0.106165) | 1.896661 / 5.269862 (-3.373200) | 1.093303 / 4.565676 (-3.472373) | 0.067967 / 0.424275 (-0.356308) | 0.012313 / 0.007607 (0.004706) | 0.532316 / 0.226044 (0.306272) | 5.336016 / 2.268929 (3.067087) | 2.344780 / 55.444624 (-53.099845) | 1.993909 / 6.876477 (-4.882568) | 2.167324 / 2.142072 (0.025251) | 0.670334 / 4.805227 (-4.134893) | 0.147705 / 6.500664 (-6.352959) | 0.067634 / 0.075469 (-0.007835) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251005 / 1.841788 (-0.590783) | 15.405531 / 8.074308 (7.331223) | 14.197019 / 10.191392 (4.005627) | 0.144230 / 0.680424 (-0.536193) | 0.018352 / 0.534201 (-0.515849) | 0.427536 / 0.579283 (-0.151748) | 0.433135 / 0.434364 (-0.001229) | 0.502624 / 0.540337 (-0.037713) | 0.612312 / 1.386936 (-0.774624) |\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) | 0.007011 / 0.011353 (-0.004342) | 0.004857 / 0.011008 (-0.006151) | 0.077797 / 0.038508 (0.039289) | 0.035411 / 0.023109 (0.012302) | 0.368234 / 0.275898 (0.092336) | 0.408359 / 0.323480 (0.084879) | 0.005883 / 0.007986 (-0.002102) | 0.004311 / 0.004328 (-0.000017) | 0.077216 / 0.004250 (0.072966) | 0.052062 / 0.037052 (0.015010) | 0.368502 / 0.258489 (0.110013) | 0.428681 / 0.293841 (0.134840) | 0.028889 / 0.128546 (-0.099657) | 0.009146 / 0.075646 (-0.066501) | 0.085515 / 0.419271 (-0.333756) | 0.050216 / 0.043533 (0.006683) | 0.359562 / 0.255139 (0.104423) | 0.378335 / 0.283200 (0.095135) | 0.106351 / 0.141683 (-0.035332) | 1.538943 / 1.452155 (0.086788) | 1.663572 / 1.492716 (0.170855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216917 / 0.018006 (0.198911) | 0.444130 / 0.000490 (0.443641) | 0.002640 / 0.000200 (0.002440) | 0.000093 / 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) | 0.032509 / 0.037411 (-0.004902) | 0.123955 / 0.014526 (0.109430) | 0.133236 / 0.176557 (-0.043321) | 0.187408 / 0.737135 (-0.549727) | 0.136696 / 0.296338 (-0.159643) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443714 / 0.215209 (0.228505) | 4.416973 / 2.077655 (2.339318) | 2.145279 / 1.504120 (0.641159) | 1.946669 / 1.541195 (0.405474) | 2.044105 / 1.468490 (0.575614) | 0.534463 / 4.584777 (-4.050314) | 3.824926 / 3.745712 (0.079214) | 3.151796 / 5.269862 (-2.118066) | 1.497513 / 4.565676 (-3.068164) | 0.066799 / 0.424275 (-0.357476) | 0.012408 / 0.007607 (0.004801) | 0.544182 / 0.226044 (0.318138) | 5.419403 / 2.268929 (3.150474) | 2.605191 / 55.444624 (-52.839433) | 2.285354 / 6.876477 (-4.591123) | 2.359520 / 2.142072 (0.217448) | 0.655489 / 4.805227 (-4.149738) | 0.143496 / 6.500664 (-6.357168) | 0.066782 / 0.075469 (-0.008687) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329370 / 1.841788 (-0.512418) | 16.058019 / 8.074308 (7.983711) | 15.119769 / 10.191392 (4.928377) | 0.147967 / 0.680424 (-0.532457) | 0.018360 / 0.534201 (-0.515841) | 0.436847 / 0.579283 (-0.142436) | 0.435136 / 0.434364 (0.000773) | 0.507176 / 0.540337 (-0.033161) | 0.610627 / 1.386936 (-0.776309) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4cc3ee6d8945052283076854eb77575d52b7432 \"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) | 0.006425 / 0.011353 (-0.004927) | 0.003710 / 0.011008 (-0.007298) | 0.102072 / 0.038508 (0.063564) | 0.033974 / 0.023109 (0.010865) | 0.273146 / 0.275898 (-0.002752) | 0.313254 / 0.323480 (-0.010226) | 0.004889 / 0.007986 (-0.003096) | 0.004803 / 0.004328 (0.000475) | 0.067359 / 0.004250 (0.063109) | 0.040281 / 0.037052 (0.003228) | 0.302106 / 0.258489 (0.043617) | 0.318039 / 0.293841 (0.024198) | 0.028839 / 0.128546 (-0.099707) | 0.008726 / 0.075646 (-0.066921) | 0.322532 / 0.419271 (-0.096739) | 0.048845 / 0.043533 (0.005312) | 0.299836 / 0.255139 (0.044697) | 0.300983 / 0.283200 (0.017784) | 0.103384 / 0.141683 (-0.038299) | 1.417245 / 1.452155 (-0.034910) | 1.538819 / 1.492716 (0.046102) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219798 / 0.018006 (0.201792) | 0.442297 / 0.000490 (0.441807) | 0.013792 / 0.000200 (0.013592) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024996 / 0.037411 (-0.012416) | 0.098558 / 0.014526 (0.084032) | 0.116423 / 0.176557 (-0.060133) | 0.163481 / 0.737135 (-0.573654) | 0.115031 / 0.296338 (-0.181308) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.392411 / 0.215209 (0.177202) | 4.025992 / 2.077655 (1.948337) | 1.850809 / 1.504120 (0.346690) | 1.668330 / 1.541195 (0.127136) | 1.627041 / 1.468490 (0.158551) | 0.510721 / 4.584777 (-4.074055) | 3.841318 / 3.745712 (0.095606) | 3.416979 / 5.269862 (-1.852883) | 1.640796 / 4.565676 (-2.924880) | 0.061968 / 0.424275 (-0.362307) | 0.010281 / 0.007607 (0.002674) | 0.485592 / 0.226044 (0.259548) | 4.872205 / 2.268929 (2.603277) | 2.146753 / 55.444624 (-53.297871) | 1.832087 / 6.876477 (-5.044390) | 1.920928 / 2.142072 (-0.221144) | 0.606363 / 4.805227 (-4.198864) | 0.134351 / 6.500664 (-6.366313) | 0.057583 / 0.075469 (-0.017886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.153048 / 1.841788 (-0.688739) | 14.165743 / 8.074308 (6.091435) | 12.237798 / 10.191392 (2.046406) | 0.159815 / 0.680424 (-0.520608) | 0.018226 / 0.534201 (-0.515975) | 0.372390 / 0.579283 (-0.206893) | 0.396552 / 0.434364 (-0.037811) | 0.439445 / 0.540337 (-0.100892) | 0.521924 / 1.386936 (-0.865012) |\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) | 0.006162 / 0.011353 (-0.005191) | 0.004006 / 0.011008 (-0.007002) | 0.067226 / 0.038508 (0.028718) | 0.030285 / 0.023109 (0.007176) | 0.361220 / 0.275898 (0.085322) | 0.386783 / 0.323480 (0.063303) | 0.005202 / 0.007986 (-0.002784) | 0.003453 / 0.004328 (-0.000876) | 0.068299 / 0.004250 (0.064048) | 0.041433 / 0.037052 (0.004381) | 0.360222 / 0.258489 (0.101733) | 0.399327 / 0.293841 (0.105486) | 0.026066 / 0.128546 (-0.102480) | 0.008025 / 0.075646 (-0.067621) | 0.079588 / 0.419271 (-0.339683) | 0.042616 / 0.043533 (-0.000917) | 0.347639 / 0.255139 (0.092500) | 0.386092 / 0.283200 (0.102893) | 0.100869 / 0.141683 (-0.040814) | 1.386901 / 1.452155 (-0.065254) | 1.471523 / 1.492716 (-0.021193) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217020 / 0.018006 (0.199014) | 0.431033 / 0.000490 (0.430543) | 0.002902 / 0.000200 (0.002702) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027396 / 0.037411 (-0.010015) | 0.114154 / 0.014526 (0.099629) | 0.117918 / 0.176557 (-0.058638) | 0.173342 / 0.737135 (-0.563794) | 0.125812 / 0.296338 (-0.170526) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424843 / 0.215209 (0.209634) | 4.324828 / 2.077655 (2.247174) | 2.188263 / 1.504120 (0.684143) | 1.912288 / 1.541195 (0.371094) | 2.011621 / 1.468490 (0.543131) | 0.560944 / 4.584777 (-4.023833) | 3.975047 / 3.745712 (0.229335) | 3.130242 / 5.269862 (-2.139619) | 1.667902 / 4.565676 (-2.897775) | 0.062245 / 0.424275 (-0.362030) | 0.011300 / 0.007607 (0.003692) | 0.498571 / 0.226044 (0.272527) | 5.024887 / 2.268929 (2.755958) | 2.482967 / 55.444624 (-52.961657) | 2.216125 / 6.876477 (-4.660352) | 2.175856 / 2.142072 (0.033783) | 0.615207 / 4.805227 (-4.190021) | 0.133808 / 6.500664 (-6.366856) | 0.058681 / 0.075469 (-0.016788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.370150 / 1.841788 (-0.471637) | 14.580907 / 8.074308 (6.506599) | 14.209955 / 10.191392 (4.018563) | 0.139738 / 0.680424 (-0.540686) | 0.018722 / 0.534201 (-0.515479) | 0.375755 / 0.579283 (-0.203528) | 0.428335 / 0.434364 (-0.006029) | 0.438957 / 0.540337 (-0.101380) | 0.541130 / 1.386936 (-0.845806) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c14806a42a20f44a60f3663642bae1de199ab1ec \"CML watermark\")\n"
] | 2023-05-15T15:28:34Z
| 2023-06-08T16:40:18Z
| 2023-06-08T16:32:51Z
|
MEMBER
| null | 0
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This PR tries out a new approach to generating the index tensor in `to_tf_dataset`, which should reduce memory usage for very large datasets. I'll need to do some testing before merging it!
Fixes #5855
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MDExOlB1bGxSZXF1ZXN0NTMzNzY4OTgy
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Add Google Sentence Compression dataset
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[] | 2020-12-07T15:47:43Z
| 2020-12-08T17:01:59Z
| 2020-12-08T17:01:59Z
|
CONTRIBUTOR
| null | 0
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For more information: https://www.aclweb.org/anthology/D13-1155.pdf
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I_kwDODunzps5GS7Ef
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Yelp not working
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"I don't think it's an issue with the dataset-viewer. Maybe @lhoestq or @albertvillanova could confirm.\r\n\r\n```python\r\n>>> from datasets import load_dataset, DownloadMode\r\n>>> import itertools\r\n>>> # without streaming\r\n>>> dataset = load_dataset(\"yelp_review_full\", name=\"yelp_review_full\", split=\"train\", download_mode=DownloadMode.FORCE_REDOWNLOAD)\r\n\r\nDownloading builder script: 4.39kB [00:00, 5.97MB/s]\r\nDownloading metadata: 2.13kB [00:00, 3.14MB/s]\r\nDownloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /home/slesage/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43...\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.10k/1.10k [00:00<00:00, 1.39MB/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets/src/datasets/load.py\", line 1687, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 1104, in _download_and_prepare\r\n super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 676, in _download_and_prepare\r\n verify_checksums(\r\n File \"/home/slesage/hf/datasets/src/datasets/utils/info_utils.py\", line 40, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0']\r\n\r\n>>> # with streaming\r\n>>> dataset = load_dataset(\"yelp_review_full\", name=\"yelp_review_full\", split=\"train\", download_mode=DownloadMode.FORCE_REDOWNLOAD, streaming=True)\r\n\r\nDownloading builder script: 4.39kB [00:00, 5.53MB/s]\r\nDownloading metadata: 2.13kB [00:00, 3.14MB/s]\r\nTraceback (most recent call last):\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py\", line 375, in _info\r\n await _file_info(\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py\", line 736, in _file_info\r\n r.raise_for_status()\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/aiohttp/client_reqrep.py\", line 1000, in raise_for_status\r\n raise ClientResponseError(\r\naiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://doc-0g-bs-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/gklhpdq1arj8v15qrg7ces34a8c3413d/1648144575000/07511006523564980941/*/0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0?e=download')\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 \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets/src/datasets/load.py\", line 1677, in load_dataset\r\n return builder_instance.as_streaming_dataset(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 906, in as_streaming_dataset\r\n splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/yelp_review_full/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43/yelp_review_full.py\", line 102, in _split_generators\r\n data_dir = dl_manager.download_and_extract(my_urls)\r\n File \"/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py\", line 800, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py\", line 778, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/home/slesage/hf/datasets/src/datasets/utils/py_utils.py\", line 306, in map_nested\r\n return function(data_struct)\r\n File \"/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py\", line 783, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py\", line 372, in _get_extraction_protocol\r\n with fsspec.open(urlpath, **kwargs) as f:\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/core.py\", line 102, in __enter__\r\n f = self.fs.open(self.path, mode=mode)\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/spec.py\", line 978, in open\r\n f = self._open(\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py\", line 335, in _open\r\n size = size or self.info(path, **kwargs)[\"size\"]\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py\", line 88, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py\", line 69, in sync\r\n raise result[0]\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py\", line 25, in _runner\r\n result[0] = await coro\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py\", line 388, in _info\r\n raise FileNotFoundError(url) from exc\r\nFileNotFoundError: https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0&confirm=t\r\n```\r\n\r\nAnd this is before even trying to access the rows with\r\n\r\n```python\r\n>>> rows = list(itertools.islice(dataset, 100))\r\n>>> rows = list(dataset.take(100))\r\n```",
"Yet another issue related to google drive not being nice. Most likely your IP has been banned from using their API programmatically. Do you know if we are allowed to host and redistribute the data ourselves ?",
"Hi,\r\n\r\nFacing the same issue while loading the dataset: \r\n\r\n`Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files`\r\n\r\nThanks",
"> Facing the same issue while loading the dataset:\r\n> \r\n> Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files\r\n\r\nThanks for reporting. I think this is the same issue. Feel free to try again later, once Google Drive stopped blocking you. You can retry by passing `download_mode=\"force_redownload\"` to `load_dataset`",
"I noticed that FastAI hosts the Yelp dataset at https://s3.amazonaws.com/fast-ai-nlp/yelp_review_full_csv.tgz (from their catalog [here](https://course.fast.ai/datasets))\r\n\r\nLet's update the yelp dataset script to download from there instead of Google Drive",
"I updated the link to not use Google Drive anymore, we will do a release early next week with the updated download url of the dataset :)"
] | 2022-03-24T11:14:00Z
| 2022-03-25T14:59:57Z
| 2022-03-25T14:56:10Z
|
MEMBER
| null | null | null |
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
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Keys should be unique error on code_search_net
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[
"Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in the code of `code_search_net`, how can I debug this actually?",
"Thanks for reporting. There was indeed an issue with the keys. The key was the addition of the file id and row id, which resulted in collisions. I just opened a PR to fix this at https://github.com/huggingface/datasets/pull/2555\r\n\r\nTo help users debug this kind of errors we could try to show a message like this\r\n```python\r\nDuplicateKeysError: both 42th and 1337th examples have the same keys `48`.\r\nPlease fix the dataset script at <path/to/the/dataset/script>\r\n```\r\n\r\nThis way users who what to look for if they want to debug this issue. I opened an issue to track this: https://github.com/huggingface/datasets/issues/2556",
"and are we sure there are not a lot of datasets which are now broken with this change?",
"Thanks to the dummy data, we know for sure that most of them work as expected.\r\n`code_search_net` wasn't caught because the dummy data only have one dummy data file while the dataset script can actually load several of them using `os.listdir`. Let me take a look at all the other datasets that use `os.listdir` to see if the keys are alright",
"I found one issue on `fever` (PR here: https://github.com/huggingface/datasets/pull/2557)\r\nAll the other ones seem fine :)",
"Hi! Got same error when loading other dataset:\r\n```python3\r\nload_dataset('wikicorpus', 'raw_en')\r\n```\r\n\r\ntb:\r\n```pytb\r\n---------------------------------------------------------------------------\r\nDuplicatedKeysError Traceback (most recent call last)\r\n/opt/conda/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator)\r\n 1109 example = self.info.features.encode_example(record)\r\n-> 1110 writer.write(example, key)\r\n 1111 finally:\r\n\r\n/opt/conda/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n 341 if self._check_duplicates:\r\n--> 342 self.check_duplicate_keys()\r\n 343 # Re-intializing to empty list for next batch\r\n\r\n/opt/conda/lib/python3.8/site-packages/datasets/arrow_writer.py in check_duplicate_keys(self)\r\n 352 if hash in tmp_record:\r\n--> 353 raise DuplicatedKeysError(key)\r\n 354 else:\r\n\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 519\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\nVersion: datasets==1.11.0",
"Fixed by #2555.",
"The wikicorpus issue has been fixed by https://github.com/huggingface/datasets/pull/2844\r\n\r\nWe'll do a new release of `datasets` soon :)"
] | 2021-06-28T09:15:20Z
| 2021-09-06T14:08:30Z
| 2021-09-02T08:25:29Z
|
MEMBER
| null | null | null |
## Describe the bug
Loading `code_search_net` seems not possible at the moment.
## Steps to reproduce the bug
```python
>>> load_dataset('code_search_net')
Downloading: 8.50kB [00:00, 3.09MB/s]
Downloading: 19.1kB [00:00, 10.1MB/s]
No config specified, defaulting to: code_search_net/all
Downloading and preparing dataset code_search_net/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to /Users/thomwolf/.cache/huggingface/datasets/code_search_net/all/1.0.0/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...
Traceback (most recent call last):
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/builder.py", line 1067, in _prepare_split
writer.write(example, key)
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 343, in write
self.check_duplicate_keys()
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 354, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```
## Environment info
- `datasets` version: 1.8.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 2.0.0
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bug in SNLI dataset
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[
"Hi ! The labels -1 correspond to the examples without gold labels in the original snli dataset.\r\nFeel free to remove these examples if you don't need them by using\r\n```python\r\ndata = data.filter(lambda x: x[\"label\"] != -1)\r\n```"
] | 2021-02-28T19:36:20Z
| 2022-10-05T13:13:46Z
| 2022-10-05T13:13:46Z
|
NONE
| null | null | null |
Hi
There is label of -1 in train set of SNLI dataset, please find the code below:
```
import numpy as np
import datasets
data = datasets.load_dataset("snli")["train"]
labels = []
for d in data:
labels.append(d["label"])
print(np.unique(labels))
```
and results:
`[-1 0 1 2]`
version of datasets used:
`datasets 1.2.1 <pip>
`
thanks for your help. @lhoestq
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`ImageFolder` performs poorly with large datasets
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[
"Hi ! ImageFolder is made for small scale datasets indeed. For large scale image datasets you better group your images in TAR archives or Arrow/Parquet files. This is true not just for ImageFolder loading performance, but also because having millions of files is not ideal for your filesystem or when moving the data around.\r\n\r\nOption 1. use TAR archives\r\n\r\nI'd suggest you to take a look at how we load [Imagenet](https://huggingface.co/datasets/imagenet-1k/tree/main) for example. The dataset is sharded in multiple TAR archives and there is a [script](https://huggingface.co/datasets/imagenet-1k/blob/main/imagenet-1k.py) that iterates over the archives to load the images.\r\n\r\nOption 2. use Arrow/Parquet\r\n\r\nYou can load your images as an Arrow Dataset with\r\n```python\r\nfrom datasets import Dataset, Image, load_from_disk, load_dataset\r\n\r\nds = Dataset.from_dict({\"image\": list(glob.glob(\"path/to/dir/**/*.jpg\"))})\r\n\r\ndef add_metadata(example):\r\n ...\r\n\r\nds = ds.map(add_metadata, num_proc=16) # num_proc for multiprocessing\r\nds = ds.cast_column(\"image\", Image())\r\n\r\n# save as Arrow locally\r\nds.save_to_disk(\"output_dir\")\r\nreloaded = load_from_disk(\"output_dir\")\r\n\r\n# OR save as Parquet on the HF Hub\r\nds.push_to_hub(\"username/dataset_name\")\r\nreloaded = load_dataset(\"username/dataset_name\")\r\n# reloaded = load_dataset(\"username/dataset_name\", num_proc=16) # to use multiprocessing\r\n```\r\n\r\nPS: maybe we can actually have something similar to ImageFolder but for image archives at one point ?",
"@lhoestq Thanks!\r\n\r\nPerhaps it'd be worth adding a note on the documentation that `ImageFolder` is not intended for large datasets? This limitation is not intuitively obvious to someone who has not used it before, I think.",
"Thanks for the feedback @salieri! I opened #5329 to make it clear `ImageFolder` is not intended for large datasets. Please feel free to comment if you have any other feedback! 🙂 "
] | 2022-12-01T00:04:21Z
| 2022-12-01T21:49:26Z
| null |
NONE
| null | null | null |
### Describe the bug
While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.
## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metadata.jsonl` file
## Performance Degradation Point 1
Degradation occurs because [`get_data_files_patterns`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L231-L243) runs the exact same scan for many different types of patterns, and there doesn't seem to be a way to easily limit this. It's controlled by the definition of [`ALL_DEFAULT_PATTERNS`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L82-L85).
One scan with 3M+ files takes about 10-15 minutes to complete on my setup, so having those extra scans really slows things down – from 10 minutes to 60+. Most of the scans return no matches, but they still take a significant amount of time to complete – hence the poor performance.
As a side effect, when this scan is run on 3M+ image files, Python also consumes up to 12 GB of RAM, which is not ideal.
## Performance Degradation Point 2
The second performance bottleneck is in [`PackagedDatasetModuleFactory.get_module`](https://github.com/huggingface/datasets/blob/d7dfbc83d68e87ba002c5eb2555f7a932e59038a/src/datasets/load.py#L707-L711), which calls `DataFilesDict.from_local_or_remote`.
It runs for a long time (60min+), consuming significant amounts of RAM – even more than the point 1 above. Based on `iostat -d 2`, it performs **zero** disk operations, which to me suggests that there is a code based bottleneck there that could be sorted out.
### Steps to reproduce the bug
```python
from datasets import load_dataset
import os
import huggingface_hub
dataset = load_dataset(
'imagefolder',
data_dir='/some/path',
# just to spell it out:
split=None,
drop_labels=True,
keep_in_memory=False
)
dataset.push_to_hub('account/dataset', private=True)
```
### Expected behavior
While it's certainly possible to write a custom loader to replace `ImageFolder` with, it'd be great if the off-the-shelf `ImageFolder` would by default have a setup that can scale to large datasets.
Or perhaps there could be a dedicated loader just for large datasets that trades off flexibility for performance? As in, maybe you have to define explicitly how you want it to work rather than it trying to guess your data structure like `_get_data_files_patterns()` does?
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-4.14.296-222.539.amzn2.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.7.10
- PyArrow version: 10.0.1
- Pandas version: 1.3.5
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IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
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"Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nimport torch\r\n\r\ndataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='train')\r\ndef t(batch):\r\n return {\"test\": torch.tensor([1] * len(batch[next(iter(batch))]))}\r\n \r\ndataset.set_transform(t)\r\nd_0 = dataset[0]\r\n```\r\n\r\nStill, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.",
"I can take this",
"Fixed in #5553 ",
"> Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> import torch\r\n> \r\n> dataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='train')\r\n> def t(batch):\r\n> return {\"test\": torch.tensor([1] * len(batch[next(iter(batch))]))}\r\n> \r\n> dataset.set_transform(t)\r\n> d_0 = dataset[0]\r\n> ```\r\n> \r\n> Still, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.\r\n\r\nok, will change it according to suggestion. Thanks for the reply!"
] | 2023-02-16T16:08:51Z
| 2023-02-22T10:30:30Z
| 2023-02-21T13:03:57Z
|
NONE
| null | null | null |
### Describe the bug
When dataset contains a 0-dim tensor, formatting.py raises a following error and fails.
```bash
Traceback (most recent call last):
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row
return _unnest(formatted_batch)
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest
return {key: array[0] for key, array in py_dict.items()}
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp>
return {key: array[0] for key, array in py_dict.items()}
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
```
### Steps to reproduce the bug
Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g.
```python
from datasets import load_dataset
import torch
dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
return {"test": torch.tensor(1)}
dataset.set_transform(t)
d_0 = dataset[0]
```
### Expected behavior
Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor.
### Environment info
`datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February)
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Fix language tags in dataset cards
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"_The documentation is not available anymore as the PR was closed or merged._",
"The non-passing tests are caused by other missing information in the dataset cards."
] | 2022-08-11T13:47:14Z
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Fix language tags in all dataset cards, so that they are validated (aligned with our `languages.json` resource).
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Rename QuestionAnswering template to QuestionAnsweringExtractive
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[
"> I like having \"extractive\" in the name to make things explicit. However this creates an inconsistency with transformers.\r\n> \r\n> See\r\n> https://huggingface.co/transformers/task_summary.html#extractive-question-answering\r\n> \r\n> But this is minor IMO and I'm ok with this renaming\r\n\r\nyes i chose this convention because it allows us to match the `QuestionAnsweringXxx` naming and i think it's better to have `task_name-subtask_name` should auto-complete ever become part of the Hub :)"
] | 2021-05-31T10:04:42Z
| 2021-05-31T15:57:26Z
| 2021-05-31T15:57:24Z
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Following the discussion with @thomwolf in #2255, this PR renames the QA template to distinguish extractive vs abstractive QA. The abstractive template will be added in a future PR.
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Set dev version
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5247). All of your documentation changes will be reflected on that endpoint."
] | 2022-11-16T10:17:31Z
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TypeError when using save_to_disk in a dataset loaded with ReadInstruction split
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[
"Hi !\r\nI think this has been fixed recently on `master`.\r\nCan you try again by installing `datasets` from `master` ?\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```",
"Hi!\r\n\r\nUsing that version of the code solves the issue. Thanks!"
] | 2021-03-29T18:23:54Z
| 2021-03-30T09:12:53Z
| 2021-03-30T09:12:53Z
|
NONE
| null | null | null |
Hi,
Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`.
Here is the minimal reproducible example:
```python
from datasets import load_dataset
from datasets import ReadInstruction
data_1 = load_dataset(
"wikiann",
"en",
split="validation",
)
data_1.save_to_disk("temporary_path_1")
print("Save with regular split works.")
data_2 = load_dataset(
"wikiann",
"en",
split=ReadInstruction("validation", to=50, unit="%"),
)
data_2.save_to_disk("temporary_path_2")
```
and the corresponding output:
```
Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)
Save with regular split works.
Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)
Traceback (most recent call last):
File "bug.py", line 20, in <module>
data_2.save_to_disk("temporary_path_2")
File "/xxxxx/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 645, in save_to_disk
json.dump(state, state_file, indent=2, sort_keys=True)
File "/usr/lib/python3.7/json/__init__.py", line 179, in dump
for chunk in iterable:
File "/usr/lib/python3.7/json/encoder.py", line 431, in _iterencode
yield from _iterencode_dict(o, _current_indent_level)
File "/usr/lib/python3.7/json/encoder.py", line 405, in _iterencode_dict
yield from chunks
File "/usr/lib/python3.7/json/encoder.py", line 438, in _iterencode
o = _default(o)
File "/usr/lib/python3.7/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type ReadInstruction is not JSON serializable
```
Let me know if there is some misuse from my end.
Thanks in advance.
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I_kwDODunzps55Zj5h
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load_dataset imageflder for aws s3 path
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[] | 2023-12-12T00:08:43Z
| 2023-12-12T00:09:27Z
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NONE
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### Feature request
I would like to load a dataset from S3 using the imagefolder option
something like
`dataset = datasets.load_dataset('imagefolder', data_dir='s3://.../lsun/train/bedroom', fs=S3FileSystem(), streaming=True) `
### Motivation
no need of data_files
### Your contribution
no experience with this
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Unsuitable project description in PyPI
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[] | 2021-10-18T12:45:00Z
| 2021-10-18T12:59:56Z
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MEMBER
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Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/
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Raise an error if no config name for datasets like glue
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Some datasets like glue (see #130) and scientific_papers (see #197) have many configs.
For example for glue there are cola, sst2, mrpc etc.
Currently if a user does `load_dataset('glue')`, then Cola is loaded by default and it can be confusing. Instead, we should raise an error to let the user know that he has to pick one of the available configs (as proposed in #152). For example for glue, the message looks like:
```
ValueError: Config name is missing.
Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']
Example of usage:
`load_dataset('glue', 'cola')`
```
The error is raised if the config name is missing and if there are >=2 possible configs.
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Properly delete metrics when a process is killed
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Tests are flaky when using metrics in distributed setup.
There is because of one test that make sure that using two possibly incompatible metric computation (same exp id) either works or raises the right error.
However if the error is raised, all the processes of the metric are killed, and the open files (arrow + lock files) are not closed correctly. This causes PermissionError on Windows when deleting the temporary directory.
To fix that I added a `finally` clause in the function passed to multiprocess to properly close the files when the process exits.
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[FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage
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[
"You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n# dummy example\r\nds_new = ds.map(lambda x: {\"new_col\": x[\"col\"] + 2}, remove_columns=ds.column_names)\r\nds_combined = concatenate_datasets([ds, ds_new], axis=1)\r\n```\r\n\r\nDoing this automatically is hard to implement efficiently unless we know ahead of time which existing columns will be modified by a `map` transform. We have this info when `input_columns` are specified, so I think this is the only case we can optimize."
] | 2023-07-10T06:42:20Z
| 2023-07-10T15:37:52Z
| null |
CONTRIBUTOR
| null | null | null |
### Feature request
Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns.
### Motivation
This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented.
### Your contribution
_
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UnicodeDecodeError when downloading GLUE-MNLI
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[
"It should be good now, thanks for noticing and fixing it ! I would say that it was because you are on windows but not 100% sure",
"On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts would always set the encoding='utf-8' in calls to open explicitly. \r\nIn the meantime: since Python 3.7 Windows users can set the default encoding for everything including open() to Unicode by setting this environment variable: set PYTHONUTF8=1 (details can be found in [PEP 540](https://www.python.org/dev/peps/pep-0540/))\r\n\r\nFor me this fixed the problem described by the OP."
] | 2020-06-05T16:30:01Z
| 2020-06-09T16:06:47Z
| 2020-06-08T08:45:03Z
|
CONTRIBUTOR
| null | null | null |
When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:
1130 yield obj
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\glue.py in _generate_examples(self, data_file, split, mrpc_files)
529
--> 530 for n, row in enumerate(reader):
531 if is_cola_non_test:
~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
110 self.fieldnames
--> 111 row = next(self.reader)
112 self.line_num = self.reader.line_num
~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
22 def decode(self, input, final=False):
---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]
24
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>
```
Anyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay.
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fix Dataset.map when num_procs > num rows
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"Hi ! Thanks for fixing this :)\r\n\r\nLooks like you have tons of changes due to code formatting.\r\nWe're using `black` for this, with a custom line length. To run our code formatting, you just need to run\r\n```\r\nmake style\r\n```\r\n\r\nThen for the windows error in the CI, I'm looking into it. It's probably just a file that isn't properly closed",
"CI is all green now ! Thanks :)\r\n\r\nThere are still many code formatting changes in your PR - probably due to the first commit you did.\r\nTo avoid conflicts with future PRs it would be nice to only have the changes related to the `num_proc` warning, and not have all those code formatting changes,\r\n\r\nCould you try remove those code formatting changes ?\r\n\r\nIf it's easier for you, you can make a new branch from `master` if needed",
"Thanks, @lhoestq! Apologies for the half-baked commits yesterday! I wasn’t able to step back in to resolve those CI issues until this morning.\r\n\r\nAlso, I’m surprised that `make style` isn’t resolving the formatting changes. I’m a bit stumped on that, so I’m going to re-apply on a new branch and open a PR as you suggested."
] | 2021-06-29T02:24:11Z
| 2021-06-29T15:00:18Z
| 2021-06-29T14:53:31Z
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closes #2470
## Testing notes
To run updated tests:
```sh
pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s
```
With Python code (to view warning):
```python
from datasets import Dataset
dataset = Dataset.from_dict({"x": ["sample"]})
print(len(dataset))
dataset.map(lambda x: x, num_proc=10)
```
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Added more information in the README about contributors and encouraged reading the thesis for more infos
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"The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)."
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## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension)
Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue).
KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC.
Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC.
- [x] All tests passed
- [x] Added a metric card (referred the metric card of SQuAD 2.0)
- [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed
### References
- [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf)
- [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue)
- #2416
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Allow download only some files from the Wikipedia dataset
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"Hi @jvanz, thank you for your proposal.\r\n\r\nIn fact, we are aware that it is very common the problem you mention. Because of that, we are currently working in implementing a new version of wikipedia on the Hub, with all data preprocessed (no need to use Apache Beam), from where you will be able to use `data_files` to load only a specific subset of the data files.\r\n\r\nSee:\r\n- #3401 "
] | 2022-02-22T13:46:41Z
| 2022-02-22T14:50:02Z
| null |
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
The Wikipedia dataset can be really big. This is a problem if you want to use it locally in a laptop with the Apache Beam `DirectRunner`. Even if your laptop have a considerable amount of memory (e.g. 32gb).
**Describe the solution you'd like**
I would like to use the `data_files` argument in the `load_dataset` function to define which file in the wikipedia dataset I would like to download. Thus, I can work with the dataset in a smaller machine using the Apache Beam `DirectRunner`.
**Describe alternatives you've considered**
I've tried to use the `simple` Wikipedia dataset. But it's in English and I would like to use Portuguese texts in my model.
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Add Tashkeela dataset
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"Sorry @lhoestq for the trouble, sometime I forget to change the names :/",
"> Sorry @lhoestq for the trouble, sometime I forget to change the names :/\r\n\r\nhaha it's ok ;)"
] | 2020-12-04T14:26:18Z
| 2020-12-04T15:47:01Z
| 2020-12-04T15:46:51Z
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Arabic Vocalized Words Dataset.
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[] | 2023-01-20T10:26:31Z
| 2023-01-20T13:26:05Z
| 2023-01-20T13:26:05Z
|
MEMBER
| null | null | null |
Once we find out the root cause of:
- #5445
we should revert the temporary pin on fsspec introduced by:
- #5447
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PR_kwDODunzps4yXXgH
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|
Force unique keys in newsqa dataset
|
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[] | 2022-02-10T10:09:19Z
| 2022-02-14T08:37:20Z
| 2022-02-14T08:37:19Z
|
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| null | 0
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Currently, it may raise `DuplicatedKeysError`.
Fix #3630.
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Running `datase.map` with `num_proc > 1` uses a lot of memory
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[
"Thanks for reporting ! We are working on this and we'll do a patch release very soon.",
"We did a patch release to fix this issue.\r\nIt should be fixed in the new version 1.6.1\r\n\r\nThanks again for reporting and for the details :)"
] | 2021-04-24T09:56:20Z
| 2021-04-26T17:12:15Z
| 2021-04-26T17:12:15Z
|
NONE
| null | null | null |
## Describe the bug
Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False)
def _prepare_sample(batch):
return {"input_ids": list(), "attention_mask": list()}
for split_name, dataset_split in list(dstc8_datset.items()):
print(f"Processing {split_name}")
encoded_dataset_split = dataset_split.map(
function=_prepare_sample,
batched=True,
num_proc=4,
remove_columns=dataset_split.column_names,
batch_size=10,
writer_batch_size=10,
keep_in_memory=False,
)
print(encoded_dataset_split)
path = f"./data/encoded_{split_name}"
encoded_dataset_split.save_to_disk(path)
```
## Expected results
Memory usage should stay within reasonable boundaries.
## Actual results
This is htop-output from running the provided script.
## Versions
```
- Datasets: 1.6.0
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)
[GCC 7.3.0]
- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10
```
Running on WSL2
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IsADirectoryError when trying to download C4
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[
"I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.\r\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.\r\nValid files are paths to local files or URLs to remote files.",
"Fixed once processed data is used instead:\r\n- #2575"
] | 2021-01-08T07:31:30Z
| 2022-08-04T11:56:10Z
| 2022-08-04T11:55:04Z
|
NONE
| null | null | null |
**TLDR**:
I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.
How can the problem be fixed?
**VERBOSE**:
I use Python version 3.7 and have the following dependencies listed in my project:
```
datasets==1.2.0
apache-beam==2.26.0
```
When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:
```
from datasets import load_dataset
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
```
I get the following stacktrace:
```
/Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...
Traceback (most recent call last):
File "/Users/fredriko/source/misc/main.py", line 3, in <module>
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare
pipeline=pipeline,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators
file_paths = dl_manager.download_and_extract(files_to_download)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums
self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/'
Process finished with exit code 1
```
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| 771
|
Using `Dataset.map` with `n_proc>1` print multiple progress bars
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[
"Yes it allows to monitor the speed of each process. Currently each process takes care of one shard of the dataset.\r\n\r\nAt one point we can consider using streaming batches to a pool of processes instead of sharding the dataset in `num_proc` parts. At that point it will be easy to use only one progress bar",
"Hi @lhoestq, I am facing a similar issue, it is annoying when lots of progress bars are printed. Is there a way to turn off this behavior? ",
"You can disable the progress bars with\r\n```python\r\nimport datasets\r\n\r\ndatasets.disable_progress_bar()\r\n```"
] | 2020-10-28T14:13:27Z
| 2023-02-13T20:16:39Z
| 2023-02-13T20:16:39Z
|
CONTRIBUTOR
| null | null | null |
When using `Dataset.map` with `n_proc > 1`, only one of the processes should print a progress bar (to make the output readable). Right now, `n_proc` progress bars are printed.
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Mistakes in MLQA features names
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"Indeed you're right ! Thanks for reporting that\r\n\r\nCould you open a PR to fix the features names ?"
] | 2020-09-11T20:46:23Z
| 2020-09-16T06:59:19Z
| 2020-09-16T06:59:19Z
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CONTRIBUTOR
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I think the following features in MLQA shouldn't be named the way they are:
1. `questions` (should be `question`)
2. `ids` (should be `id`)
3. `start` (should be `answer_start`)
The reasons I'm suggesting these features be renamed are:
* To make them consistent with other QA datasets like SQuAD, XQuAD, TyDiQA etc. and hence make it easier to concatenate multiple QA datasets.
* The features names are not the same as the ones provided in the original MLQA datasets (it uses the names I suggested).
I know these columns can be renamed using using `Dataset.rename_column_`, `questions` and `ids` can be easily renamed but `start` on the other hand is annoying to rename since it's nested inside the feature `answers`.
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Add optional progress bar for .save_to_disk(..) and .load_from_disk(..) when working with remote filesystems
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"Hi! I like this idea. For consistency with `load_dataset`, we can use `fsspec`'s `TqdmCallback` in `.load_from_disk` to monitor the number of bytes downloaded, and in `.save_to_disk`, we can track the number of saved shards for consistency with `push_to_hub` (after we implement https://github.com/huggingface/datasets/issues/4196)."
] | 2022-05-14T11:30:42Z
| 2022-12-14T18:22:59Z
| 2022-12-14T18:22:59Z
|
NONE
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**Is your feature request related to a problem? Please describe.**
When working with large datasets stored on remote filesystems(such as s3), the process of uploading a dataset could take really long time. For instance: I was uploading a re-processed version of wmt17 en-ru to my s3 bucket and it took like 35 minutes(and that's given that I have a fiber optic connection). The only output during that process was a progress bar for flattening indices and then ~35 minutes of complete silence.
**Describe the solution you'd like**
I want to be able to enable a progress bar when calling .save_to_disk(..) and .load_from_disk(..), it would track either amount of bytes sent/received or amount of records written/loaded, and will give some ETA. Basically just tqdm.
**Describe alternatives you've considered**
- Save dataset to tmp folder at the disk and then upload it using custom wrapper over botocore, which will work with progress bar, like [this](https://alexwlchan.net/2021/04/s3-progress-bars/).
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Big_Patent dataset broken
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[
"Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive.\r\n\r\nSee related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com\r\n\r\nTo quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551):\r\n\r\n> PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.\r\n\r\n",
"We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.",
"The data owners have agreed on hosting their data on the Hub."
] | 2022-04-25T15:31:45Z
| 2022-05-26T06:29:43Z
| 2022-05-02T18:21:15Z
|
NONE
| null | null | null |
## Dataset viewer issue for '*big_patent*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)*
*Unable to view because it says FileNotFound, also cannot download it through the python API*
Am I the one who added this dataset ? No
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Add LiveQA
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| 2020-12-14T09:40:28Z
| 2020-12-14T09:40:28Z
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This PR adds LiveQA, the Chinese real-time/timeline-based QA task by [Liu et al., 2020](https://arxiv.org/pdf/2010.00526.pdf).
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Fixes base_url of limit dataset
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"OK, apparently it is a lot more complicated than simply changing the URL? Going to make an issue."
] | 2021-02-08T12:26:35Z
| 2021-02-08T12:42:50Z
| 2021-02-08T12:42:50Z
|
NONE
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`test.json` is not available in the master branch of the repository anymore. Linking to a specific commit.
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Adds XED dataset
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[
"Hi @lhoestq @yjernite, requesting you to review this for any changes needed. Thanks! :)"
] | 2020-12-12T09:47:00Z
| 2020-12-14T21:20:59Z
| 2020-12-14T21:20:59Z
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Release: 2.13.2
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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) | 0.007801 / 0.011353 (-0.003552) | 0.004831 / 0.011008 (-0.006177) | 0.123101 / 0.038508 (0.084593) | 0.053246 / 0.023109 (0.030137) | 0.381787 / 0.275898 (0.105889) | 0.461822 / 0.323480 (0.138342) | 0.004655 / 0.007986 (-0.003331) | 0.004818 / 0.004328 (0.000490) | 0.090865 / 0.004250 (0.086614) | 0.070626 / 0.037052 (0.033574) | 0.409122 / 0.258489 (0.150633) | 0.449627 / 0.293841 (0.155787) | 0.037477 / 0.128546 (-0.091069) | 0.010677 / 0.075646 (-0.064970) | 0.419970 / 0.419271 (0.000699) | 0.064626 / 0.043533 (0.021093) | 0.379536 / 0.255139 (0.124397) | 0.405790 / 0.283200 (0.122590) | 0.027290 / 0.141683 (-0.114393) | 1.884973 / 1.452155 (0.432819) | 1.960547 / 1.492716 (0.467831) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259393 / 0.018006 (0.241386) | 0.502130 / 0.000490 (0.501640) | 0.013053 / 0.000200 (0.012853) | 0.000336 / 0.000054 (0.000281) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033459 / 0.037411 (-0.003953) | 0.135888 / 0.014526 (0.121362) | 0.145354 / 0.176557 (-0.031203) | 0.213289 / 0.737135 (-0.523847) | 0.151239 / 0.296338 (-0.145100) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.510817 / 0.215209 (0.295608) | 5.077888 / 2.077655 (3.000234) | 2.502991 / 1.504120 (0.998871) | 2.275566 / 1.541195 (0.734371) | 2.353025 / 1.468490 (0.884535) | 0.659062 / 4.584777 (-3.925715) | 4.411399 / 3.745712 (0.665686) | 2.227395 / 5.269862 (-3.042467) | 1.306771 / 4.565676 (-3.258905) | 0.081121 / 0.424275 (-0.343154) | 0.014252 / 0.007607 (0.006645) | 0.635040 / 0.226044 (0.408996) | 6.357500 / 2.268929 (4.088572) | 3.056647 / 55.444624 (-52.387977) | 2.671997 / 6.876477 (-4.204480) | 2.847955 / 2.142072 (0.705883) | 0.808163 / 4.805227 (-3.997064) | 0.177176 / 6.500664 (-6.323488) | 0.079984 / 0.075469 (0.004515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.490471 / 1.841788 (-0.351317) | 17.927433 / 8.074308 (9.853124) | 17.744967 / 10.191392 (7.553575) | 0.171034 / 0.680424 (-0.509390) | 0.021432 / 0.534201 (-0.512769) | 0.515745 / 0.579283 (-0.063538) | 0.504746 / 0.434364 (0.070382) | 0.630862 / 0.540337 (0.090524) | 0.755275 / 1.386936 (-0.631662) |\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) | 0.008227 / 0.011353 (-0.003126) | 0.004864 / 0.011008 (-0.006144) | 0.092801 / 0.038508 (0.054293) | 0.054996 / 0.023109 (0.031887) | 0.500348 / 0.275898 (0.224450) | 0.565028 / 0.323480 (0.241548) | 0.004792 / 0.007986 (-0.003194) | 0.005052 / 0.004328 (0.000723) | 0.090640 / 0.004250 (0.086390) | 0.074427 / 0.037052 (0.037374) | 0.499908 / 0.258489 (0.241419) | 0.566260 / 0.293841 (0.272419) | 0.040011 / 0.128546 (-0.088536) | 0.010438 / 0.075646 (-0.065208) | 0.099385 / 0.419271 (-0.319887) | 0.060485 / 0.043533 (0.016952) | 0.480603 / 0.255139 (0.225464) | 0.508807 / 0.283200 (0.225607) | 0.025976 / 0.141683 (-0.115707) | 1.870860 / 1.452155 (0.418705) | 1.943460 / 1.492716 (0.450744) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227753 / 0.018006 (0.209747) | 0.501859 / 0.000490 (0.501369) | 0.008211 / 0.000200 (0.008011) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038329 / 0.037411 (0.000918) | 0.148214 / 0.014526 (0.133688) | 0.162704 / 0.176557 (-0.013852) | 0.218543 / 0.737135 (-0.518592) | 0.162992 / 0.296338 (-0.133347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.553195 / 0.215209 (0.337986) | 5.568080 / 2.077655 (3.490425) | 2.936616 / 1.504120 (1.432496) | 2.712624 / 1.541195 (1.171429) | 2.713245 / 1.468490 (1.244755) | 0.648593 / 4.584777 (-3.936184) | 4.641361 / 3.745712 (0.895648) | 2.207064 / 5.269862 (-3.062798) | 1.315325 / 4.565676 (-3.250351) | 0.080285 / 0.424275 (-0.343990) | 0.014143 / 0.007607 (0.006536) | 0.672467 / 0.226044 (0.446423) | 6.730262 / 2.268929 (4.461333) | 3.344468 / 55.444624 (-52.100157) | 2.927837 / 6.876477 (-3.948640) | 3.124735 / 2.142072 (0.982662) | 0.795894 / 4.805227 (-4.009333) | 0.170985 / 6.500664 (-6.329679) | 0.077406 / 0.075469 (0.001937) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.598059 / 1.841788 (-0.243729) | 18.531854 / 8.074308 (10.457546) | 18.394895 / 10.191392 (8.203503) | 0.195702 / 0.680424 (-0.484722) | 0.023633 / 0.534201 (-0.510568) | 0.518110 / 0.579283 (-0.061173) | 0.517773 / 0.434364 (0.083409) | 0.617902 / 0.540337 (0.077565) | 0.736459 / 1.386936 (-0.650477) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d4bb03237b74c0009043d50c5b4e4339cb98b2b \"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) | 0.006943 / 0.011353 (-0.004410) | 0.004524 / 0.011008 (-0.006485) | 0.121603 / 0.038508 (0.083095) | 0.047462 / 0.023109 (0.024353) | 0.362393 / 0.275898 (0.086495) | 0.440577 / 0.323480 (0.117098) | 0.004153 / 0.007986 (-0.003832) | 0.003778 / 0.004328 (-0.000550) | 0.090402 / 0.004250 (0.086152) | 0.066268 / 0.037052 (0.029216) | 0.380721 / 0.258489 (0.122232) | 0.442959 / 0.293841 (0.149118) | 0.035228 / 0.128546 (-0.093318) | 0.010217 / 0.075646 (-0.065429) | 0.408587 / 0.419271 (-0.010684) | 0.062609 / 0.043533 (0.019076) | 0.372682 / 0.255139 (0.117543) | 0.389270 / 0.283200 (0.106070) | 0.026699 / 0.141683 (-0.114984) | 1.760476 / 1.452155 (0.308321) | 1.795081 / 1.492716 (0.302365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229912 / 0.018006 (0.211906) | 0.476837 / 0.000490 (0.476348) | 0.008178 / 0.000200 (0.007978) | 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) | 0.031116 / 0.037411 (-0.006296) | 0.126767 / 0.014526 (0.112241) | 0.134242 / 0.176557 (-0.042315) | 0.202120 / 0.737135 (-0.535016) | 0.142777 / 0.296338 (-0.153561) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470690 / 0.215209 (0.255481) | 4.723198 / 2.077655 (2.645543) | 2.163870 / 1.504120 (0.659750) | 1.914177 / 1.541195 (0.372982) | 2.034529 / 1.468490 (0.566038) | 0.620472 / 4.584777 (-3.964305) | 4.391008 / 3.745712 (0.645296) | 2.100966 / 5.269862 (-3.168896) | 1.225945 / 4.565676 (-3.339732) | 0.076279 / 0.424275 (-0.347996) | 0.013551 / 0.007607 (0.005944) | 0.600989 / 0.226044 (0.374945) | 5.946715 / 2.268929 (3.677787) | 2.665117 / 55.444624 (-52.779508) | 2.320004 / 6.876477 (-4.556473) | 2.413131 / 2.142072 (0.271059) | 0.771908 / 4.805227 (-4.033320) | 0.165438 / 6.500664 (-6.335226) | 0.074512 / 0.075469 (-0.000957) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.432728 / 1.841788 (-0.409060) | 17.398133 / 8.074308 (9.323824) | 16.819152 / 10.191392 (6.627760) | 0.191849 / 0.680424 (-0.488575) | 0.021557 / 0.534201 (-0.512644) | 0.514380 / 0.579283 (-0.064903) | 0.501453 / 0.434364 (0.067089) | 0.634091 / 0.540337 (0.093753) | 0.756786 / 1.386936 (-0.630150) |\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) | 0.007946 / 0.011353 (-0.003407) | 0.004751 / 0.011008 (-0.006257) | 0.090190 / 0.038508 (0.051682) | 0.052841 / 0.023109 (0.029732) | 0.480150 / 0.275898 (0.204252) | 0.537509 / 0.323480 (0.214029) | 0.004833 / 0.007986 (-0.003153) | 0.004796 / 0.004328 (0.000467) | 0.090616 / 0.004250 (0.086366) | 0.074325 / 0.037052 (0.037273) | 0.483776 / 0.258489 (0.225287) | 0.552094 / 0.293841 (0.258254) | 0.039240 / 0.128546 (-0.089307) | 0.010416 / 0.075646 (-0.065230) | 0.100275 / 0.419271 (-0.318996) | 0.058086 / 0.043533 (0.014553) | 0.468989 / 0.255139 (0.213850) | 0.485502 / 0.283200 (0.202302) | 0.027514 / 0.141683 (-0.114169) | 1.849625 / 1.452155 (0.397470) | 1.919515 / 1.492716 (0.426798) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248061 / 0.018006 (0.230055) | 0.475630 / 0.000490 (0.475141) | 0.006248 / 0.000200 (0.006048) | 0.000105 / 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) | 0.037746 / 0.037411 (0.000335) | 0.141638 / 0.014526 (0.127112) | 0.149530 / 0.176557 (-0.027026) | 0.209255 / 0.737135 (-0.527880) | 0.156447 / 0.296338 (-0.139892) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.544640 / 0.215209 (0.329431) | 5.493152 / 2.077655 (3.415497) | 2.869733 / 1.504120 (1.365613) | 2.624216 / 1.541195 (1.083022) | 2.710818 / 1.468490 (1.242328) | 0.640626 / 4.584777 (-3.944151) | 4.516130 / 3.745712 (0.770418) | 2.128097 / 5.269862 (-3.141765) | 1.278990 / 4.565676 (-3.286686) | 0.077114 / 0.424275 (-0.347161) | 0.013280 / 0.007607 (0.005673) | 0.655552 / 0.226044 (0.429507) | 6.526875 / 2.268929 (4.257947) | 3.347072 / 55.444624 (-52.097553) | 2.992435 / 6.876477 (-3.884041) | 3.124351 / 2.142072 (0.982278) | 0.778523 / 4.805227 (-4.026704) | 0.161873 / 6.500664 (-6.338791) | 0.072897 / 0.075469 (-0.002572) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.587058 / 1.841788 (-0.254730) | 18.170612 / 8.074308 (10.096304) | 17.220483 / 10.191392 (7.029091) | 0.207863 / 0.680424 (-0.472561) | 0.023746 / 0.534201 (-0.510455) | 0.512607 / 0.579283 (-0.066676) | 0.513258 / 0.434364 (0.078894) | 0.597880 / 0.540337 (0.057543) | 0.714974 / 1.386936 (-0.671962) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"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) | 0.006224 / 0.011353 (-0.005128) | 0.003857 / 0.011008 (-0.007151) | 0.099786 / 0.038508 (0.061278) | 0.037919 / 0.023109 (0.014810) | 0.315294 / 0.275898 (0.039396) | 0.390178 / 0.323480 (0.066698) | 0.005358 / 0.007986 (-0.002628) | 0.002989 / 0.004328 (-0.001340) | 0.077834 / 0.004250 (0.073583) | 0.053315 / 0.037052 (0.016263) | 0.325155 / 0.258489 (0.066666) | 0.374712 / 0.293841 (0.080871) | 0.029176 / 0.128546 (-0.099370) | 0.008658 / 0.075646 (-0.066988) | 0.314245 / 0.419271 (-0.105027) | 0.046684 / 0.043533 (0.003151) | 0.316473 / 0.255139 (0.061334) | 0.346119 / 0.283200 (0.062919) | 0.022452 / 0.141683 (-0.119230) | 1.540497 / 1.452155 (0.088343) | 1.594888 / 1.492716 (0.102172) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204349 / 0.018006 (0.186343) | 0.426842 / 0.000490 (0.426353) | 0.003060 / 0.000200 (0.002860) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023611 / 0.037411 (-0.013801) | 0.100247 / 0.014526 (0.085721) | 0.107824 / 0.176557 (-0.068733) | 0.166845 / 0.737135 (-0.570291) | 0.112782 / 0.296338 (-0.183556) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423053 / 0.215209 (0.207844) | 4.235553 / 2.077655 (2.157899) | 1.936589 / 1.504120 (0.432469) | 1.738519 / 1.541195 (0.197325) | 1.787905 / 1.468490 (0.319415) | 0.573362 / 4.584777 (-4.011414) | 3.395272 / 3.745712 (-0.350440) | 1.765977 / 5.269862 (-3.503884) | 1.049596 / 4.565676 (-3.516081) | 0.068868 / 0.424275 (-0.355407) | 0.011028 / 0.007607 (0.003421) | 0.532835 / 0.226044 (0.306791) | 5.314890 / 2.268929 (3.045962) | 2.368733 / 55.444624 (-53.075891) | 2.033959 / 6.876477 (-4.842518) | 2.130481 / 2.142072 (-0.011591) | 0.689360 / 4.805227 (-4.115867) | 0.140271 / 6.500664 (-6.360393) | 0.068198 / 0.075469 (-0.007271) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237212 / 1.841788 (-0.604576) | 14.182215 / 8.074308 (6.107907) | 14.972608 / 10.191392 (4.781216) | 0.133977 / 0.680424 (-0.546447) | 0.016759 / 0.534201 (-0.517442) | 0.361552 / 0.579283 (-0.217731) | 0.394932 / 0.434364 (-0.039432) | 0.442601 / 0.540337 (-0.097736) | 0.535709 / 1.386936 (-0.851227) |\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) | 0.006327 / 0.011353 (-0.005026) | 0.003780 / 0.011008 (-0.007228) | 0.078358 / 0.038508 (0.039850) | 0.037271 / 0.023109 (0.014162) | 0.456766 / 0.275898 (0.180868) | 0.515721 / 0.323480 (0.192241) | 0.004770 / 0.007986 (-0.003216) | 0.002942 / 0.004328 (-0.001387) | 0.077383 / 0.004250 (0.073132) | 0.051773 / 0.037052 (0.014721) | 0.460722 / 0.258489 (0.202233) | 0.519997 / 0.293841 (0.226157) | 0.030461 / 0.128546 (-0.098085) | 0.008622 / 0.075646 (-0.067024) | 0.083271 / 0.419271 (-0.336000) | 0.042242 / 0.043533 (-0.001291) | 0.447691 / 0.255139 (0.192552) | 0.481965 / 0.283200 (0.198765) | 0.019510 / 0.141683 (-0.122173) | 1.536718 / 1.452155 (0.084563) | 1.588433 / 1.492716 (0.095717) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215880 / 0.018006 (0.197874) | 0.426102 / 0.000490 (0.425612) | 0.003976 / 0.000200 (0.003776) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026168 / 0.037411 (-0.011243) | 0.105786 / 0.014526 (0.091260) | 0.113772 / 0.176557 (-0.062785) | 0.166576 / 0.737135 (-0.570559) | 0.117560 / 0.296338 (-0.178779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.490485 / 0.215209 (0.275276) | 4.890105 / 2.077655 (2.812450) | 2.515099 / 1.504120 (1.010979) | 2.306591 / 1.541195 (0.765396) | 2.383634 / 1.468490 (0.915144) | 0.573780 / 4.584777 (-4.010997) | 3.474394 / 3.745712 (-0.271318) | 1.746795 / 5.269862 (-3.523067) | 1.044678 / 4.565676 (-3.520998) | 0.069176 / 0.424275 (-0.355099) | 0.011045 / 0.007607 (0.003438) | 0.597234 / 0.226044 (0.371189) | 5.979614 / 2.268929 (3.710685) | 3.024203 / 55.444624 (-52.420422) | 2.687502 / 6.876477 (-4.188975) | 2.781637 / 2.142072 (0.639565) | 0.690482 / 4.805227 (-4.114745) | 0.150138 / 6.500664 (-6.350526) | 0.077076 / 0.075469 (0.001607) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307501 / 1.841788 (-0.534287) | 14.366780 / 8.074308 (6.292471) | 14.966981 / 10.191392 (4.775589) | 0.153829 / 0.680424 (-0.526594) | 0.018047 / 0.534201 (-0.516154) | 0.361391 / 0.579283 (-0.217892) | 0.398345 / 0.434364 (-0.036019) | 0.424574 / 0.540337 (-0.115764) | 0.517165 / 1.386936 (-0.869771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"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) | 0.006944 / 0.011353 (-0.004409) | 0.004504 / 0.011008 (-0.006504) | 0.105224 / 0.038508 (0.066716) | 0.047830 / 0.023109 (0.024721) | 0.339723 / 0.275898 (0.063825) | 0.419249 / 0.323480 (0.095769) | 0.005510 / 0.007986 (-0.002476) | 0.003574 / 0.004328 (-0.000754) | 0.079879 / 0.004250 (0.075628) | 0.066610 / 0.037052 (0.029557) | 0.353818 / 0.258489 (0.095329) | 0.397992 / 0.293841 (0.104151) | 0.031551 / 0.128546 (-0.096995) | 0.009037 / 0.075646 (-0.066610) | 0.355310 / 0.419271 (-0.063961) | 0.054931 / 0.043533 (0.011398) | 0.335153 / 0.255139 (0.080014) | 0.357460 / 0.283200 (0.074260) | 0.026031 / 0.141683 (-0.115652) | 1.546705 / 1.452155 (0.094550) | 1.627324 / 1.492716 (0.134608) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.276708 / 0.018006 (0.258701) | 0.589402 / 0.000490 (0.588912) | 0.009560 / 0.000200 (0.009360) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031041 / 0.037411 (-0.006370) | 0.117219 / 0.014526 (0.102693) | 0.125200 / 0.176557 (-0.051356) | 0.181528 / 0.737135 (-0.555607) | 0.131898 / 0.296338 (-0.164440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409965 / 0.215209 (0.194756) | 4.102700 / 2.077655 (2.025045) | 1.887578 / 1.504120 (0.383458) | 1.696490 / 1.541195 (0.155295) | 1.821352 / 1.468490 (0.352862) | 0.545422 / 4.584777 (-4.039355) | 3.933784 / 3.745712 (0.188071) | 1.934254 / 5.269862 (-3.335607) | 1.114935 / 4.565676 (-3.450742) | 0.067615 / 0.424275 (-0.356660) | 0.012004 / 0.007607 (0.004397) | 0.522048 / 0.226044 (0.296004) | 5.209224 / 2.268929 (2.940296) | 2.369911 / 55.444624 (-53.074714) | 2.032960 / 6.876477 (-4.843517) | 2.228874 / 2.142072 (0.086802) | 0.673172 / 4.805227 (-4.132055) | 0.147017 / 6.500664 (-6.353647) | 0.067020 / 0.075469 (-0.008449) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281490 / 1.841788 (-0.560298) | 16.129701 / 8.074308 (8.055393) | 15.474730 / 10.191392 (5.283338) | 0.143934 / 0.680424 (-0.536490) | 0.018311 / 0.534201 (-0.515890) | 0.435940 / 0.579283 (-0.143343) | 0.446846 / 0.434364 (0.012482) | 0.543943 / 0.540337 (0.003605) | 0.648041 / 1.386936 (-0.738895) |\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) | 0.007380 / 0.011353 (-0.003973) | 0.004510 / 0.011008 (-0.006499) | 0.080741 / 0.038508 (0.042233) | 0.050907 / 0.023109 (0.027797) | 0.425548 / 0.275898 (0.149650) | 0.487959 / 0.323480 (0.164479) | 0.005887 / 0.007986 (-0.002099) | 0.003689 / 0.004328 (-0.000639) | 0.079588 / 0.004250 (0.075338) | 0.071841 / 0.037052 (0.034788) | 0.425172 / 0.258489 (0.166683) | 0.471185 / 0.293841 (0.177344) | 0.035768 / 0.128546 (-0.092779) | 0.009229 / 0.075646 (-0.066418) | 0.086021 / 0.419271 (-0.333250) | 0.052424 / 0.043533 (0.008891) | 0.413634 / 0.255139 (0.158495) | 0.422310 / 0.283200 (0.139111) | 0.026019 / 0.141683 (-0.115664) | 1.616861 / 1.452155 (0.164707) | 1.653660 / 1.492716 (0.160943) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.280096 / 0.018006 (0.262090) | 0.587853 / 0.000490 (0.587363) | 0.006560 / 0.000200 (0.006360) | 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) | 0.033747 / 0.037411 (-0.003665) | 0.125089 / 0.014526 (0.110564) | 0.137995 / 0.176557 (-0.038561) | 0.188192 / 0.737135 (-0.548943) | 0.141438 / 0.296338 (-0.154900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471524 / 0.215209 (0.256315) | 4.713988 / 2.077655 (2.636334) | 2.414785 / 1.504120 (0.910665) | 2.226815 / 1.541195 (0.685620) | 2.259222 / 1.468490 (0.790732) | 0.551663 / 4.584777 (-4.033114) | 4.031399 / 3.745712 (0.285686) | 1.966917 / 5.269862 (-3.302945) | 1.154487 / 4.565676 (-3.411190) | 0.068500 / 0.424275 (-0.355775) | 0.012127 / 0.007607 (0.004520) | 0.579342 / 0.226044 (0.353298) | 5.757415 / 2.268929 (3.488486) | 2.820012 / 55.444624 (-52.624613) | 2.521783 / 6.876477 (-4.354694) | 2.699994 / 2.142072 (0.557921) | 0.686152 / 4.805227 (-4.119075) | 0.148521 / 6.500664 (-6.352143) | 0.068478 / 0.075469 (-0.006991) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336260 / 1.841788 (-0.505528) | 17.016935 / 8.074308 (8.942627) | 16.406951 / 10.191392 (6.215559) | 0.166907 / 0.680424 (-0.513517) | 0.020166 / 0.534201 (-0.514035) | 0.437690 / 0.579283 (-0.141593) | 0.480337 / 0.434364 (0.045973) | 0.518065 / 0.540337 (-0.022272) | 0.625904 / 1.386936 (-0.761032) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"CML watermark\")\n"
] | 2023-09-06T08:15:32Z
| 2023-09-06T08:52:18Z
| 2023-09-06T08:22:43Z
|
MEMBER
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MDExOlB1bGxSZXF1ZXN0NjU5MTQ5Njg0
| 2,438
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Fix NQ features loading: reorder fields of features to match nested fields order in arrow data
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[] | 2021-06-01T16:09:30Z
| 2021-06-04T09:02:31Z
| 2021-06-04T09:02:31Z
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As mentioned in #2401, there is an issue when loading the features of `natural_questions` since the order of the nested fields in the features don't match. The order is important since it matters for the underlying arrow schema.
To fix that I re-order the features based on the arrow schema:
```python
inferred_features = Features.from_arrow_schema(arrow_table.schema)
self.info.features = self.info.features.reorder_fields_as(inferred_features)
assert self.info.features.type == inferred_features.type
```
The re-ordering is a recursive function. It takes into account that the `Sequence` feature type is a struct of list and not a list of struct.
Now it's possible to load `natural_questions` again :)
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PR_kwDODunzps43oqXD
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Catch pull error when mirroring
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-11T09:38:35Z
| 2022-05-11T12:54:07Z
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Catch pull errors when mirroring so that the script continues to update the other datasets.
The error will still be printed at the end of the job. In this case the job also fails, and asks to manually update the datasets that failed.
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DatasetDict save load Failing test in 1.6 not in 1.5
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"Thanks for reporting ! We're looking into it",
"I'm not able to reproduce this, do you think you can provide a code that creates a DatasetDict that has this issue when saving and reloading ?",
"Hi, I just ran into a similar error. Here is the minimal code to reproduce:\r\n```python\r\nfrom datasets import load_dataset, DatasetDict\r\nds = load_dataset('super_glue', 'multirc')\r\n\r\nds.save_to_disk('tempds')\r\n\r\nds = DatasetDict.load_from_disk('tempds')\r\n\r\n```\r\n\r\n```bash\r\nReusing dataset super_glue (/home/idahl/.cache/huggingface/datasets/super_glue/multirc/1.0.2/2fb163bca9085c1deb906aff20f00c242227ff704a4e8c9cfdfe820be3abfc83)\r\nTraceback (most recent call last):\r\n File \"/home/idahl/eval-util-expl/multirc/tmp.py\", line 7, in <module>\r\n ds = DatasetDict.load_from_disk('tempds')\r\n File \"/home/idahl/miniconda3/envs/eval-util-expl/lib/python3.9/site-packages/datasets/dataset_dict.py\", line 710, in load_from_disk\r\n dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n File \"/home/idahl/miniconda3/envs/eval-util-expl/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 687, in load_from_disk\r\n return Dataset(\r\n File \"/home/idahl/miniconda3/envs/eval-util-expl/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 274, in __init__\r\n raise ValueError(\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'answer': Value(dtype='string', id=None), 'idx': {'answer': Value(dtype='int32', id=None), 'paragraph': Value(dtype='int32', id=None), 'question': Value(dtype='int32', id=None)}, 'label': ClassLabel(num_classes=2, names=['False', 'True'], names_file=None, id=None), 'paragraph': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<answer: string, idx: struct<answer: int32, paragraph: int32, question: int32>, label: int64, paragraph: string, question: string>\r\n\r\nbut expected something like\r\n{'answer': Value(dtype='string', id=None), 'idx': {'paragraph': Value(dtype='int32', id=None), 'question': Value(dtype='int32', id=None), 'answer': Value(dtype='int32', id=None)}, 'label': Value(dtype='int64', id=None), 'paragraph': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<answer: string, idx: struct<paragraph: int32, question: int32, answer: int32>, label: int64, paragraph: string, question: string>\r\n\r\n```\r\n\r\nThe non-matching part seems to be\r\n`'label': ClassLabel(num_classes=2, names=['False', 'True'], names_file=None, id=None),`\r\nvs \r\n`'label': Value(dtype='int64', id=None),`\r\n\r\nAnd the order in the `<struct...` being different, which might cause the [features.type != inferred_features.type](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L274) condition to become true and raise this ValueError.\r\n\r\n\r\nI am using datasets version 1.6.2.\r\n\r\nEdit: can confirm, this works without error in version 1.5.0",
"My current workaround is to remove the idx feature:\r\n\r\n```\r\n\r\nfrom datasets import load_dataset, DatasetDict, Value\r\nds = load_dataset('super_glue', 'multirc')\r\nds = ds.remove_columns('idx')\r\n\r\nds.save_to_disk('tempds')\r\n\r\nds = DatasetDict.load_from_disk('tempds')\r\n\r\n```\r\n\r\nworks.",
"It looks like this issue comes from the order of the fields in the 'idx' struct that is different for some reason.\r\nI'm looking into it. Note that as a workaround you can also flatten the nested features with `ds = ds.flatten()`",
"I just pushed a fix on `master`. We'll do a new release soon !\r\n\r\nThanks for reporting"
] | 2021-04-27T00:03:25Z
| 2021-05-28T15:27:34Z
| null |
NONE
| null | null | null |
## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a dataset dict from jsonl
path = '/test/foo'
ds_dict.save_to_disk(path)
ds_from_disk = DatasetDict.load_from_disk(path). ## <-- this is where I see the error on 1.6
```
## Expected results
Upgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.
## Actual results
```
# Infer features if None
inferred_features = Features.from_arrow_schema(arrow_table.schema)
if self.info.features is None:
self.info.features = inferred_features
# Infer fingerprint if None
if self._fingerprint is None:
self._fingerprint = generate_fingerprint(self)
# Sanity checks
assert self.features is not None, "Features can't be None in a Dataset object"
assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object"
if self.info.features.type != inferred_features.type:
> raise ValueError(
"External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format(
self.info.features, self.info.features.type, inferred_features, inferred_features.type
)
)
E ValueError: External features info don't match the dataset:
E Got
E {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}
E with type
E struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>
E
E but expected something like
E {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}
E with type
E struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>
../../../../../.virtualenvs/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:274: ValueError
```
## Versions
- Datasets: 1.6.1
- Python: 3.8.5 (default, Jan 26 2021, 10:01:04)
[Clang 12.0.0 (clang-1200.0.32.2)]
- Platform: macOS-10.15.7-x86_64-i386-64bit
```
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How concatenate `Audio` elements using batch mapping
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[
"You can try something like this ?\r\n```python\r\ndef mapper_function(batch):\r\n return {\"concatenated_audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset = dataset.map(\r\n mapper_function,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n```",
"Thanks for the snippet!\r\n\r\nOne more question. I wonder why those two mappers are working so different that one taking 4 sec while other taking over 1 min :\r\n\r\n```python\r\n%%time\r\ndef mapper_function1(batch):\r\n # list_audio\r\n return {\r\n \"audio\": [\r\n {\r\n \"array\": np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]]),\r\n \"sampling_rate\": 16_000,\r\n }\r\n ]\r\n }\r\n\r\ndataset.map(\r\n mapper_function1,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n\r\n# 100%\r\n# 135/135 [01:13<00:00, 1.93ba/s]\r\n# CPU times: user 1min 10s, sys: 3.21 s, total: 1min 13s\r\n# Wall time: 1min 13s\r\n# Dataset({\r\n# features: ['audio'],\r\n# num_rows: 135\r\n# })\r\n\r\n# --------------------------------\r\n%%time\r\ndef mapper_function2(batch):\r\n # list_audio\r\n return {\"audio\": [np.concatenate([audio[\"array\"] for audio in batch[\"audio\"]])]}\r\n\r\ndataset.map(\r\n mapper_function2,\r\n batched=True,\r\n batch_size=3,\r\n remove_columns=list(dataset.features),\r\n)\r\n\r\n# 100%\r\n# 135/135 [00:03<00:00, 40.69ba/s]\r\n# CPU times: user 1.88 s, sys: 1.48 s, total: 3.36 s\r\n# Wall time: 4.8 s\r\n# Dataset({\r\n# features: ['audio'],\r\n# num_rows: 135\r\n# })\r\n```\r\n",
"In the first one you get a dataset with an Audio type, and in the second one you get a dataset with a sequence of floats type.\r\n\r\nThe Audio type encodes the data as WAV to save disk space, so it takes more time to create.\r\nThe Audio type is automatically inferred because you modify the column \"audio\" which was already an Audio type. If you name it to something else, type inference will use a type struct with array and sampling rate fields."
] | 2022-12-14T18:13:55Z
| 2023-07-21T14:30:51Z
| 2023-07-21T14:30:51Z
|
NONE
| null | null | null |
### Describe the bug
I am trying to do concatenate audios in a dataset e.g. `google/fleurs`.
```python
print(dataset)
# Dataset({
# features: ['path', 'audio'],
# num_rows: 24
# })
def mapper_function(batch):
# to merge every 3 audio
# np.concatnate(audios[i: i+3]) for i in range(i, len(batch), 3)
dataset = dataset.map(mapper_function, batch=True, batch_size=24)
print(dataset)
# Expected output:
# Dataset({
# features: ['path', 'audio'],
# num_rows: 8
# })
```
I tried to construct `result={}` dictionary inside the mapper function, I just found it will not work because it needs `byte` also needed :((
I'd appreciate if your share any use cases similar to my problem or any solutions really. Thanks!
cc: @lhoestq
### Steps to reproduce the bug
1. load audio dataset
2. try to merge every k audios and return as one
### Expected behavior
Merged dataset with a fewer rows. If we merge every 3 rows, then `n // 3` number of examples.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
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Remove unused head_hf_s3 function
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| 2021-05-07T09:31:42Z
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Currently, the function `head_hf_s3` is not used:
- neither its returned result is used
- nor it raises any exception, as exceptions are catched and returned (not raised)
This PR removes it.
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Dictionary Feature
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"Hi @jordiae,\r\n\r\nInstead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n```python\r\n\"list_of_dict_feature\": [\r\n {\r\n \"key1_in_dict\": datasets.Value(\"string\"),\r\n \"key2_in_dict\": datasets.Value(\"int32\"),\r\n ...\r\n }\r\n],\r\n```\r\n\r\nFeel free to re-open this issue if that does not work for your use case.",
"> Hi @jordiae,\r\n> \r\n> Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:\r\n> \r\n> ```python\r\n> \"list_of_dict_feature\": [\r\n> {\r\n> \"key1_in_dict\": datasets.Value(\"string\"),\r\n> \"key2_in_dict\": datasets.Value(\"int32\"),\r\n> ...\r\n> }\r\n> ],\r\n> ```\r\n> \r\n> Feel free to re-open this issue if that does not work for your use case.\r\n\r\nThank you"
] | 2022-04-26T12:50:18Z
| 2022-04-29T14:52:19Z
| 2022-04-28T17:04:58Z
|
NONE
| null | null | null |
Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something?
Thank you in advance.
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[Circle ci] Adds circle ci config
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@thomwolf can you take a look and set up circle ci on:
https://app.circleci.com/projects/project-dashboard/github/huggingface
I think for `nlp` only admins can set it up, which I guess is you :-)
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MENYO-20k repo has moved, updating URL
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Dataset repo moved to https://github.com/uds-lsv/menyo-20k_MT, now editing URL to match.
https://github.com/uds-lsv/menyo-20k_MT/blob/master/data/train.tsv is the file we're looking for
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Map cache with tokenizer
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[
"https://github.com/huggingface/datasets/issues/5147 may be a solution, by passing in the tokenizer in a fn_kwargs and ignoring it in the fingerprint calculations",
"I have a similar issue. I was using a Jupyter Notebook and every time I call the map function it performs tokenization from scratch again although the cache files of last run still exists. \r\n\r\nI ran with 20 processes and now in the cache folder there are two groups of cached results of tokenized dataset:\r\n\r\n```\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:46 2023 cache-1982fea76aa54a13_00001_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 13:02:08 2023 cache-1982fea76aa54a13_00004_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:40 2023 cache-1982fea76aa54a13_00005_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:50:59 2023 cache-1982fea76aa54a13_00006_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:37 2023 cache-1982fea76aa54a13_00007_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:31 2023 cache-1982fea76aa54a13_00008_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:59:47 2023 cache-1982fea76aa54a13_00010_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:59:44 2023 cache-1982fea76aa54a13_00011_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:55:24 2023 cache-1982fea76aa54a13_00012_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:56:21 2023 cache-1982fea76aa54a13_00013_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:24 2023 cache-1982fea76aa54a13_00014_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 13:00:48 2023 cache-1982fea76aa54a13_00015_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:56 2023 cache-1982fea76aa54a13_00017_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:54 2023 cache-1982fea76aa54a13_00018_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:27 2023 cache-1982fea76aa54a13_00019_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:15:40 2023 cache-454431f643cdc5e8_00000_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:46 2023 cache-454431f643cdc5e8_00001_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:53 2023 cache-454431f643cdc5e8_00002_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:13:10 2023 cache-454431f643cdc5e8_00003_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:13:04 2023 cache-454431f643cdc5e8_00004_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:42 2023 cache-454431f643cdc5e8_00005_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:01:29 2023 cache-454431f643cdc5e8_00006_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:41 2023 cache-454431f643cdc5e8_00007_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:04 2023 cache-454431f643cdc5e8_00008_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:41 2023 cache-454431f643cdc5e8_00009_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:06 2023 cache-454431f643cdc5e8_00010_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:17:16 2023 cache-454431f643cdc5e8_00011_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:15:13 2023 cache-454431f643cdc5e8_00012_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:16:01 2023 cache-454431f643cdc5e8_00013_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:35 2023 cache-454431f643cdc5e8_00014_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:20 2023 cache-454431f643cdc5e8_00015_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:48 2023 cache-454431f643cdc5e8_00016_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 18:59:32 2023 cache-454431f643cdc5e8_00017_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:58 2023 cache-454431f643cdc5e8_00018_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:15:25 2023 cache-454431f643cdc5e8_00019_of_00020.arrow\r\n```\r\n\r\ncan we specify the cache file for map so that it won't redo everything again?",
"@Luosuu [map](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.map) has cache_file_name parameter\r\n\r\nIn my case, I do want the cache to detect when the map function changes, so I can't pass a constant cache file name.",
"Implementing a proper hashing function for the (fast) tokenizers is currently impossible for the reasons mentioned in the referenced issues. So the only alternative to the `cache_file_name` (or `new_fingerprint`) parameter is a custom serializer (e.g., that deserializes the tokenizer from a local save path) defined using `copyreg` or a class that wraps the tokenizer object and has `__reduce__`(`__setstate__`/`__getstate__`) "
] | 2023-08-25T12:55:18Z
| 2023-08-31T15:17:24Z
| null |
NONE
| null | null | null |
Similar issue to https://github.com/huggingface/datasets/issues/5985, but across different sessions rather than two calls in the same session.
Unlike that issue, explicitly calling tokenizer(my_args) before the map() doesn't help, because the tokenizer was created with a different hash to begin with...
setup
```
from transformers import AutoTokenizer
AutoTokenizer.from_pretrained('bert-base-uncased').save_pretrained("tok")
```
this prints different value each time
```
from transformers import AutoTokenizer
from datasets.utils.py_utils import dumps # Huggingface datasets
print(hash(dumps(AutoTokenizer.from_pretrained("tok"))))
```
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Fix streaming xnli
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6503). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005003 / 0.011353 (-0.006350) | 0.003020 / 0.011008 (-0.007988) | 0.061370 / 0.038508 (0.022862) | 0.050996 / 0.023109 (0.027887) | 0.243434 / 0.275898 (-0.032464) | 0.266317 / 0.323480 (-0.057163) | 0.003888 / 0.007986 (-0.004098) | 0.002607 / 0.004328 (-0.001721) | 0.047541 / 0.004250 (0.043290) | 0.037933 / 0.037052 (0.000881) | 0.259695 / 0.258489 (0.001206) | 0.279374 / 0.293841 (-0.014467) | 0.027258 / 0.128546 (-0.101288) | 0.010184 / 0.075646 (-0.065462) | 0.207412 / 0.419271 (-0.211860) | 0.034978 / 0.043533 (-0.008554) | 0.247871 / 0.255139 (-0.007267) | 0.265273 / 0.283200 (-0.017927) | 0.017886 / 0.141683 (-0.123796) | 1.090451 / 1.452155 (-0.361704) | 1.152034 / 1.492716 (-0.340682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094383 / 0.018006 (0.076377) | 0.301151 / 0.000490 (0.300661) | 0.000211 / 0.000200 (0.000011) | 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) | 0.018927 / 0.037411 (-0.018484) | 0.062152 / 0.014526 (0.047626) | 0.072177 / 0.176557 (-0.104380) | 0.119792 / 0.737135 (-0.617343) | 0.073333 / 0.296338 (-0.223005) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282671 / 0.215209 (0.067462) | 2.721148 / 2.077655 (0.643494) | 1.472689 / 1.504120 (-0.031431) | 1.355226 / 1.541195 (-0.185969) | 1.375935 / 1.468490 (-0.092556) | 0.562600 / 4.584777 (-4.022177) | 2.364046 / 3.745712 (-1.381666) | 2.714984 / 5.269862 (-2.554878) | 1.738413 / 4.565676 (-2.827263) | 0.062564 / 0.424275 (-0.361711) | 0.004964 / 0.007607 (-0.002643) | 0.341300 / 0.226044 (0.115255) | 3.345187 / 2.268929 (1.076259) | 1.857822 / 55.444624 (-53.586803) | 1.581002 / 6.876477 (-5.295475) | 1.585919 / 2.142072 (-0.556153) | 0.640105 / 4.805227 (-4.165122) | 0.117880 / 6.500664 (-6.382784) | 0.042032 / 0.075469 (-0.033437) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962701 / 1.841788 (-0.879086) | 11.309251 / 8.074308 (3.234943) | 10.462520 / 10.191392 (0.271128) | 0.127399 / 0.680424 (-0.553025) | 0.014549 / 0.534201 (-0.519652) | 0.297017 / 0.579283 (-0.282266) | 0.266152 / 0.434364 (-0.168212) | 0.349252 / 0.540337 (-0.191085) | 0.457015 / 1.386936 (-0.929921) |\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) | 0.005341 / 0.011353 (-0.006012) | 0.003108 / 0.011008 (-0.007900) | 0.048862 / 0.038508 (0.010353) | 0.053354 / 0.023109 (0.030245) | 0.274499 / 0.275898 (-0.001399) | 0.296698 / 0.323480 (-0.026782) | 0.003974 / 0.007986 (-0.004012) | 0.002631 / 0.004328 (-0.001697) | 0.048013 / 0.004250 (0.043762) | 0.040416 / 0.037052 (0.003363) | 0.276581 / 0.258489 (0.018092) | 0.301296 / 0.293841 (0.007455) | 0.029049 / 0.128546 (-0.099497) | 0.010253 / 0.075646 (-0.065393) | 0.057157 / 0.419271 (-0.362114) | 0.031830 / 0.043533 (-0.011703) | 0.274341 / 0.255139 (0.019202) | 0.292583 / 0.283200 (0.009383) | 0.018449 / 0.141683 (-0.123234) | 1.145099 / 1.452155 (-0.307055) | 1.192958 / 1.492716 (-0.299758) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091596 / 0.018006 (0.073590) | 0.300917 / 0.000490 (0.300427) | 0.000225 / 0.000200 (0.000025) | 0.000054 / 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) | 0.021657 / 0.037411 (-0.015754) | 0.068464 / 0.014526 (0.053938) | 0.079869 / 0.176557 (-0.096687) | 0.117523 / 0.737135 (-0.619613) | 0.081257 / 0.296338 (-0.215082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294876 / 0.215209 (0.079667) | 2.879372 / 2.077655 (0.801718) | 1.619887 / 1.504120 (0.115767) | 1.482154 / 1.541195 (-0.059041) | 1.494656 / 1.468490 (0.026166) | 0.558914 / 4.584777 (-4.025862) | 2.420948 / 3.745712 (-1.324765) | 2.728992 / 5.269862 (-2.540869) | 1.722135 / 4.565676 (-2.843542) | 0.062182 / 0.424275 (-0.362093) | 0.004933 / 0.007607 (-0.002674) | 0.342759 / 0.226044 (0.116715) | 3.424083 / 2.268929 (1.155154) | 1.950673 / 55.444624 (-53.493951) | 1.683126 / 6.876477 (-5.193351) | 1.673135 / 2.142072 (-0.468937) | 0.633711 / 4.805227 (-4.171516) | 0.114898 / 6.500664 (-6.385766) | 0.040332 / 0.075469 (-0.035137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975102 / 1.841788 (-0.866685) | 11.975731 / 8.074308 (3.901423) | 10.961103 / 10.191392 (0.769711) | 0.131152 / 0.680424 (-0.549272) | 0.016268 / 0.534201 (-0.517933) | 0.285031 / 0.579283 (-0.294252) | 0.279556 / 0.434364 (-0.154808) | 0.324183 / 0.540337 (-0.216154) | 0.571404 / 1.386936 (-0.815532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f67312956fc15572b6a0ca0dfcc0ceb90fbb794 \"CML watermark\")\n"
] | 2023-12-15T14:40:57Z
| 2023-12-15T14:51:06Z
| 2023-12-15T14:44:47Z
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This code was failing
```python
In [1]: from datasets import load_dataset
In [2]:
...: ds = load_dataset("xnli", "all_languages", split="test", streaming=True)
...:
...: sample_data = next(iter(ds))["premise"] # pick up one data
...: input_text = list(sample_data.values())
```
```
File ~/hf/datasets/src/datasets/features/translation.py:104, in TranslationVariableLanguages.encode_example(self, translation_dict)
102 return translation_dict
103 elif self.languages and set(translation_dict) - lang_set:
--> 104 raise ValueError(
105 f'Some languages in example ({", ".join(sorted(set(translation_dict) - lang_set))}) are not in valid set ({", ".join(lang_set)}).'
106 )
108 # Convert dictionary into tuples, splitting out cases where there are
109 # multiple translations for a single language.
110 translation_tuples = []
ValueError: Some languages in example (language, translation) are not in valid set (ur, fr, hi, sw, vi, el, de, th, en, tr, zh, ar, bg, ru, es).
```
because in streaming mode we expect features encode methods to be no-ops if the example is already encoded.
I fixed `TranslationVariableLanguages` to account for that
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"It actually seems to happen all the time in above configuration:\r\n* the function `filter_by_duration` correctly loads cached processed dataset\r\n* the function `prepare_dataset` is always reexecuted\r\n\r\nI end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug or limitation here.",
"Hi ! The hash used for caching `map` results is the fingerprint of the resulting dataset. It is computed using three things:\r\n- the old fingerprint of the dataset\r\n- the hash of the function\r\n- the hash of the other parameters passed to `map`\r\n\r\nYou can compute the hash of your function (or any python object) with\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nmy_func = lambda x: x + 1\r\nprint(Hasher.hash(my_func))\r\n```\r\n\r\nIf `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.",
"> If `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.\r\n\r\nYes I think that was the issue.\r\n\r\nFor the hash of the function:\r\n* does it consider just the name or the actual code of the function\r\n* does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)",
"> does it consider just the name or the actual code of the function\r\n\r\nIt looks at the name and the actual code and all variables such as recursively. It uses `dill` to do so, which is based on `pickle`.\r\nBasically the hash is computed using the pickle bytes of your function (computed using `dill` to support most python objects).\r\n\r\n> does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)\r\n\r\nYes it does thanks to recursive pickling.",
"Thanks for these explanations. I'm closing the issue."
] | 2021-05-27T15:40:06Z
| 2021-06-02T13:15:47Z
| 2021-06-02T13:15:47Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def filter_by_duration(batch):
return (
batch["duration"] <= 10
and batch["duration"] >= 1
and len(batch["target_text"]) > 5
)
def prepare_dataset(batch):
batch["input_values"] = processor(
batch["speech"], sampling_rate=batch["sampling_rate"][0]
).input_values
with processor.as_target_processor():
batch["labels"] = processor(batch["target_text"]).input_ids
return batch
train_dataset = train_dataset.filter(
filter_by_duration,
remove_columns=["duration"],
num_proc=data_args.preprocessing_num_workers,
)
# PROBLEM HERE -> below function is reexecuted and cache is not loaded
train_dataset = train_dataset.map(
prepare_dataset,
remove_columns=train_dataset.column_names,
batch_size=training_args.per_device_train_batch_size,
batched=True,
num_proc=data_args.preprocessing_num_workers,
)
# Later in script
set_caching_enabled(False)
# apply map on trained model to eval/test sets
```
## Expected results
The cached dataset should always be reloaded.
## Actual results
The function is reexecuted.
I have access to cached files `cache-xxxxx.arrow`.
Is there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.5
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
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I_kwDODunzps5vAVyH
| 6,169
|
Configurations in yaml not working
|
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[
"Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.",
"Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?",
"Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed).\r\n\r\nI've tried uninstalling/reinstnalling to no avail",
"hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?"
] | 2023-08-23T00:13:22Z
| 2023-08-23T15:35:31Z
| null |
NONE
| null | null | null |
### Dataset configurations cannot be created in YAML/README
Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118
I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test):
```
configs:
- config_name: main_data
data_files: "main_data.csv"
- config_name: additional_data
data_files: "additional_data.csv"
```
Yet, I'm unable to load different configurations:
```
from datasets import get_dataset_config_names
get_dataset_config_names('tsor13/test', use_auth_token=True)
```
returns a single split, `['tsor13--test']`
Does anyone have any insights?
@polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas?
### Steps to reproduce the bug
from datasets import get_dataset_config_names
get_dataset_config_names('tsor13/test')
### Expected behavior
I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.4-arm64-arm-64bit
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.1
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Align `column_names` type check with type hint in `sort`
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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) | 0.006038 / 0.011353 (-0.005315) | 0.003797 / 0.011008 (-0.007211) | 0.097686 / 0.038508 (0.059178) | 0.035235 / 0.023109 (0.012126) | 0.317294 / 0.275898 (0.041396) | 0.377682 / 0.323480 (0.054202) | 0.003485 / 0.007986 (-0.004501) | 0.003603 / 0.004328 (-0.000725) | 0.077268 / 0.004250 (0.073017) | 0.054649 / 0.037052 (0.017597) | 0.322293 / 0.258489 (0.063804) | 0.372277 / 0.293841 (0.078436) | 0.027927 / 0.128546 (-0.100619) | 0.008495 / 0.075646 (-0.067151) | 0.313078 / 0.419271 (-0.106193) | 0.046974 / 0.043533 (0.003441) | 0.313848 / 0.255139 (0.058709) | 0.338454 / 0.283200 (0.055255) | 0.020462 / 0.141683 (-0.121221) | 1.473027 / 1.452155 (0.020873) | 1.539468 / 1.492716 (0.046752) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221429 / 0.018006 (0.203423) | 0.412044 / 0.000490 (0.411555) | 0.005866 / 0.000200 (0.005666) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022870 / 0.037411 (-0.014541) | 0.099129 / 0.014526 (0.084603) | 0.103463 / 0.176557 (-0.073094) | 0.164969 / 0.737135 (-0.572166) | 0.110000 / 0.296338 (-0.186339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431311 / 0.215209 (0.216102) | 4.293562 / 2.077655 (2.215907) | 1.961209 / 1.504120 (0.457089) | 1.733680 / 1.541195 (0.192485) | 1.793171 / 1.468490 (0.324681) | 0.568566 / 4.584777 (-4.016211) | 3.401794 / 3.745712 (-0.343918) | 1.827949 / 5.269862 (-3.441913) | 1.055963 / 4.565676 (-3.509714) | 0.068459 / 0.424275 (-0.355816) | 0.011586 / 0.007607 (0.003979) | 0.533936 / 0.226044 (0.307891) | 5.347637 / 2.268929 (3.078708) | 2.378056 / 55.444624 (-53.066569) | 2.032159 / 6.876477 (-4.844318) | 2.159064 / 2.142072 (0.016991) | 0.674528 / 4.805227 (-4.130699) | 0.136859 / 6.500664 (-6.363805) | 0.066629 / 0.075469 (-0.008840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.218084 / 1.841788 (-0.623704) | 14.141710 / 8.074308 (6.067402) | 13.588415 / 10.191392 (3.397023) | 0.155104 / 0.680424 (-0.525320) | 0.017160 / 0.534201 (-0.517041) | 0.375558 / 0.579283 (-0.203725) | 0.386293 / 0.434364 (-0.048071) | 0.459476 / 0.540337 (-0.080862) | 0.548561 / 1.386936 (-0.838375) |\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) | 0.005878 / 0.011353 (-0.005475) | 0.003750 / 0.011008 (-0.007259) | 0.077720 / 0.038508 (0.039212) | 0.034955 / 0.023109 (0.011846) | 0.357480 / 0.275898 (0.081582) | 0.418210 / 0.323480 (0.094730) | 0.004566 / 0.007986 (-0.003419) | 0.002918 / 0.004328 (-0.001410) | 0.076517 / 0.004250 (0.072266) | 0.050202 / 0.037052 (0.013150) | 0.368166 / 0.258489 (0.109677) | 0.415681 / 0.293841 (0.121840) | 0.029496 / 0.128546 (-0.099050) | 0.008547 / 0.075646 (-0.067099) | 0.083037 / 0.419271 (-0.336234) | 0.045001 / 0.043533 (0.001468) | 0.356503 / 0.255139 (0.101364) | 0.383747 / 0.283200 (0.100547) | 0.025071 / 0.141683 (-0.116612) | 1.541985 / 1.452155 (0.089830) | 1.594710 / 1.492716 (0.101994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204491 / 0.018006 (0.186484) | 0.408686 / 0.000490 (0.408196) | 0.002505 / 0.000200 (0.002305) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024446 / 0.037411 (-0.012965) | 0.101432 / 0.014526 (0.086906) | 0.108105 / 0.176557 (-0.068452) | 0.161195 / 0.737135 (-0.575940) | 0.112671 / 0.296338 (-0.183667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459697 / 0.215209 (0.244488) | 4.570071 / 2.077655 (2.492416) | 2.211547 / 1.504120 (0.707427) | 1.996651 / 1.541195 (0.455457) | 2.015621 / 1.468490 (0.547131) | 0.567423 / 4.584777 (-4.017354) | 3.408027 / 3.745712 (-0.337685) | 2.913824 / 5.269862 (-2.356038) | 1.423223 / 4.565676 (-3.142453) | 0.068740 / 0.424275 (-0.355535) | 0.010997 / 0.007607 (0.003390) | 0.567340 / 0.226044 (0.341296) | 5.666280 / 2.268929 (3.397351) | 2.804934 / 55.444624 (-52.639690) | 2.430761 / 6.876477 (-4.445716) | 2.451820 / 2.142072 (0.309748) | 0.681926 / 4.805227 (-4.123301) | 0.137761 / 6.500664 (-6.362903) | 0.067173 / 0.075469 (-0.008296) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329853 / 1.841788 (-0.511934) | 14.436232 / 8.074308 (6.361924) | 14.398645 / 10.191392 (4.207253) | 0.147421 / 0.680424 (-0.533002) | 0.016743 / 0.534201 (-0.517458) | 0.364964 / 0.579283 (-0.214319) | 0.387072 / 0.434364 (-0.047292) | 0.423892 / 0.540337 (-0.116445) | 0.521304 / 1.386936 (-0.865632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a62b6ce65f718e9ff4189da86d160ae4bb197fc2 \"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) | 0.006463 / 0.011353 (-0.004889) | 0.003923 / 0.011008 (-0.007086) | 0.102096 / 0.038508 (0.063588) | 0.040230 / 0.023109 (0.017121) | 0.384688 / 0.275898 (0.108789) | 0.445574 / 0.323480 (0.122094) | 0.003590 / 0.007986 (-0.004395) | 0.004023 / 0.004328 (-0.000306) | 0.080125 / 0.004250 (0.075875) | 0.057406 / 0.037052 (0.020354) | 0.395049 / 0.258489 (0.136560) | 0.438065 / 0.293841 (0.144224) | 0.028963 / 0.128546 (-0.099583) | 0.008693 / 0.075646 (-0.066954) | 0.317158 / 0.419271 (-0.102114) | 0.047930 / 0.043533 (0.004397) | 0.382442 / 0.255139 (0.127303) | 0.410665 / 0.283200 (0.127466) | 0.020127 / 0.141683 (-0.121555) | 1.558554 / 1.452155 (0.106400) | 1.590959 / 1.492716 (0.098242) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208826 / 0.018006 (0.190820) | 0.432037 / 0.000490 (0.431547) | 0.006509 / 0.000200 (0.006309) | 0.000285 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023460 / 0.037411 (-0.013951) | 0.099070 / 0.014526 (0.084545) | 0.105771 / 0.176557 (-0.070785) | 0.166683 / 0.737135 (-0.570452) | 0.108755 / 0.296338 (-0.187583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424324 / 0.215209 (0.209115) | 4.225696 / 2.077655 (2.148042) | 1.910955 / 1.504120 (0.406835) | 1.704493 / 1.541195 (0.163298) | 1.782784 / 1.468490 (0.314293) | 0.562927 / 4.584777 (-4.021850) | 3.380163 / 3.745712 (-0.365550) | 1.779641 / 5.269862 (-3.490221) | 1.029134 / 4.565676 (-3.536543) | 0.068325 / 0.424275 (-0.355950) | 0.011528 / 0.007607 (0.003921) | 0.530141 / 0.226044 (0.304097) | 5.323443 / 2.268929 (3.054514) | 2.346956 / 55.444624 (-53.097668) | 2.013335 / 6.876477 (-4.863142) | 2.118531 / 2.142072 (-0.023541) | 0.675206 / 4.805227 (-4.130021) | 0.135473 / 6.500664 (-6.365191) | 0.064804 / 0.075469 (-0.010665) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240179 / 1.841788 (-0.601608) | 14.692449 / 8.074308 (6.618141) | 13.672223 / 10.191392 (3.480831) | 0.147748 / 0.680424 (-0.532676) | 0.017119 / 0.534201 (-0.517082) | 0.369481 / 0.579283 (-0.209802) | 0.390133 / 0.434364 (-0.044231) | 0.458768 / 0.540337 (-0.081569) | 0.548989 / 1.386936 (-0.837947) |\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) | 0.006319 / 0.011353 (-0.005034) | 0.003975 / 0.011008 (-0.007033) | 0.077886 / 0.038508 (0.039378) | 0.038322 / 0.023109 (0.015213) | 0.379851 / 0.275898 (0.103953) | 0.456749 / 0.323480 (0.133269) | 0.005320 / 0.007986 (-0.002665) | 0.003135 / 0.004328 (-0.001194) | 0.078272 / 0.004250 (0.074022) | 0.059919 / 0.037052 (0.022866) | 0.430062 / 0.258489 (0.171573) | 0.477432 / 0.293841 (0.183591) | 0.029713 / 0.128546 (-0.098833) | 0.008704 / 0.075646 (-0.066942) | 0.082488 / 0.419271 (-0.336784) | 0.044667 / 0.043533 (0.001134) | 0.354910 / 0.255139 (0.099771) | 0.434637 / 0.283200 (0.151438) | 0.026402 / 0.141683 (-0.115281) | 1.528825 / 1.452155 (0.076671) | 1.548209 / 1.492716 (0.055493) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237988 / 0.018006 (0.219982) | 0.420402 / 0.000490 (0.419913) | 0.003098 / 0.000200 (0.002898) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026253 / 0.037411 (-0.011159) | 0.106137 / 0.014526 (0.091611) | 0.110273 / 0.176557 (-0.066284) | 0.165316 / 0.737135 (-0.571819) | 0.115720 / 0.296338 (-0.180619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454244 / 0.215209 (0.239035) | 4.526018 / 2.077655 (2.448364) | 2.395985 / 1.504120 (0.891865) | 2.234822 / 1.541195 (0.693627) | 2.370235 / 1.468490 (0.901745) | 0.567607 / 4.584777 (-4.017169) | 3.650156 / 3.745712 (-0.095556) | 3.360094 / 5.269862 (-1.909768) | 1.415252 / 4.565676 (-3.150424) | 0.068012 / 0.424275 (-0.356263) | 0.011135 / 0.007607 (0.003528) | 0.561967 / 0.226044 (0.335923) | 5.621819 / 2.268929 (3.352890) | 2.676912 / 55.444624 (-52.767712) | 2.338306 / 6.876477 (-4.538171) | 2.430888 / 2.142072 (0.288815) | 0.684576 / 4.805227 (-4.120651) | 0.138923 / 6.500664 (-6.361741) | 0.069933 / 0.075469 (-0.005536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313383 / 1.841788 (-0.528405) | 15.125088 / 8.074308 (7.050780) | 14.801501 / 10.191392 (4.610109) | 0.134235 / 0.680424 (-0.546189) | 0.017058 / 0.534201 (-0.517143) | 0.365166 / 0.579283 (-0.214117) | 0.395415 / 0.434364 (-0.038949) | 0.419355 / 0.540337 (-0.120983) | 0.513411 / 1.386936 (-0.873525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b9649b3cfb49342e44873ce7e29e0c75eaf3efa \"CML watermark\")\n"
] | 2023-06-30T13:15:50Z
| 2023-06-30T14:18:32Z
| 2023-06-30T14:11:24Z
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CONTRIBUTOR
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Fix #5998
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How to reuse functionality of a (generic) dataset?
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"Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https://github.com/huggingface/nlp/tree/master/datasets/csv\r\n- json: https://github.com/huggingface/nlp/tree/master/datasets/json\r\n- text: https://github.com/huggingface/nlp/tree/master/datasets/text\r\n\r\nYou can find more details about this way to load datasets here in the documentation: https://huggingface.co/nlp/loading_datasets.html#from-local-files\r\n\r\nMaybe your brat loading script could be shared in a similar fashion?",
"> Maybe your brat loading script could be shared in a similar fashion?\r\n\r\n@thomwolf that was also my first idea and I think I will tackle that in the next days. I separated the code and created a real abstract class `AbstractBrat` to allow to inherit from that (I've just seen that the dataset_loader loads the first non abstract class), now `Brat` is very similar in its functionality to https://github.com/huggingface/nlp/tree/master/datasets/text but inherits from `AbstractBrat`.\r\n\r\nHowever, it is still not clear to me how to add a specific dataset (as explained in https://huggingface.co/nlp/add_dataset.html) to your repo that uses this format/abstract class, i.e. re-using the `features` entry of the `DatasetInfo` object and `_generate_examples()`. Again, by doing so, the only remaining entries/functions to define would be `_DESCRIPTION`, `_CITATION`, `homepage` and `_URL` (which is all copy-paste stuff) and `_split_generators()`.\r\n \r\nIn a lack of better ideas, I tried sth like below, but of course it does not work outside `nlp` (`AbstractBrat` is currently defined in [datasets/brat.py](https://github.com/ArneBinder/nlp/blob/5e81fb8710546ee7be3353a7f02a3045e9a8351e/datasets/brat/brat.py)):\r\n```python\r\nfrom __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\n\r\nimport nlp\r\n\r\nfrom datasets.brat.brat import AbstractBrat\r\n\r\n_CITATION = \"\"\"\r\n@inproceedings{lauscher2018b,\r\n title = {An argument-annotated corpus of scientific publications},\r\n booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},\r\n publisher = {Association for Computational Linguistics},\r\n author = {Lauscher, Anne and Glava\\v{s}, Goran and Ponzetto, Simone Paolo},\r\n address = {Brussels, Belgium},\r\n year = {2018},\r\n pages = {40–46}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThis dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \r\nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \r\npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \r\nscientific writing.\r\n\"\"\"\r\n\r\n_URL = \"http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip\"\r\n\r\n\r\nclass Sciarg(AbstractBrat):\r\n\r\n VERSION = nlp.Version(\"1.0.0\")\r\n\r\n def _info(self):\r\n\r\n brat_features = super()._info().features\r\n return nlp.DatasetInfo(\r\n # This is the description that will appear on the datasets page.\r\n description=_DESCRIPTION,\r\n # nlp.features.FeatureConnectors\r\n features=brat_features,\r\n # If there's a common (input, target) tuple from the features,\r\n # specify them here. They'll be used if as_supervised=True in\r\n # builder.as_dataset.\r\n #supervised_keys=None,\r\n # Homepage of the dataset for documentation\r\n homepage=\"https://github.com/anlausch/ArguminSci\",\r\n citation=_CITATION,\r\n )\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n # TODO: Downloads the data and defines the splits\r\n # dl_manager is a nlp.download.DownloadManager that can be used to\r\n # download and extract URLs\r\n dl_dir = dl_manager.download_and_extract(_URL)\r\n data_dir = os.path.join(dl_dir, \"compiled_corpus\")\r\n print(f'data_dir: {data_dir}')\r\n return [\r\n nlp.SplitGenerator(\r\n name=nlp.Split.TRAIN,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n \"directory\": data_dir,\r\n },\r\n ),\r\n ]\r\n``` \r\n\r\nNevertheless, many thanks for tackling the dataset accessibility problem with this great library!",
"As temporary fix I've created [ArneBinder/nlp-formats](https://github.com/ArneBinder/nlp-formats) (contributions welcome).",
"Hi! You can either copy&paste the builder script and import the builder from there or use `datasets.load_dataset_builder` inside the script and call the methods of the returned builder object."
] | 2020-07-24T17:27:37Z
| 2022-10-04T17:59:34Z
| 2022-10-04T17:59:33Z
|
NONE
| null | null | null |
I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https://brat.nlplab.org/standoff.html), [dataset code](https://github.com/ArneBinder/nlp/blob/brat/datasets/brat/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format?
In my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library.
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Add QED Amara Dataset
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Infinitely attempting lock
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[
"Hi ! Thanks for reporting. We're using `py-filelock` as our locking mechanism.\r\n\r\nCan you try deleting the .lock file mentioned in the logs and try again ? Make sure that no other process is generating the `cnn_dailymail` dataset.\r\n\r\nIf it doesn't work, could you try to set up a lock using the latest version of `py-filelock` and see if it works ?\r\n\r\n```\r\npip install filelock\r\n```\r\nhere is a code example from the `py-filelock` documentation that you can try:\r\n```python\r\nfrom filelock import Timeout, FileLock\r\n\r\nlock = FileLock(\"high_ground.txt.lock\")\r\nwith lock:\r\n with open(\"high_ground.txt\", \"a\") as f:\r\n f.write(\"You were the chosen one.\")\r\n```"
] | 2022-03-21T18:11:57Z
| 2022-05-06T16:12:18Z
| 2022-05-06T16:12:18Z
|
NONE
| null | null | null |
I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this?
|
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move under the facebook org?
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[
"Related to https://github.com/huggingface/datasets/issues/4562#issuecomment-1166911751\r\n\r\nI'll assign @albertvillanova ",
"I'm just wondering why we don't have this dataset under:\r\n- the `facebook` namespace\r\n- or the canonical dataset `flores`: why does this only have 2 languages?",
"fwiw: the dataset viewer is working. Renaming the issue"
] | 2022-06-26T11:19:09Z
| 2023-09-25T12:05:18Z
| null |
MEMBER
| null | null | null |
### Link
https://huggingface.co/datasets/gsarti/flores_101
### Description
It seems like streaming isn't supported for this dataset:
```
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset/flores101_dataset.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
### Owner
No
|
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Inject ASR template for lj_speech dataset
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[] | 2021-07-13T06:04:54Z
| 2021-07-13T09:05:09Z
| 2021-07-13T09:05:09Z
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Related to: #2565, #2633.
cc: @lewtun
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Fix metric compute (original_instructions missing)
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[
"Awesome! This is working now:\r\n\r\n```python\r\nimport nlp \r\nseqeval = nlp.load_metric(\"seqeval\") \r\ny_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] \r\ny_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] \r\n\r\nresults = seqeval.compute(y_true, y_pred)\r\n```\r\n\r\nI heavily need this fix for an upcoming `nlp` integration PR for Transformers (token classification example) 😅",
"Haha nice ! We'll ship this fix with the next release that will probably come out on thursday :)"
] | 2020-06-16T08:52:01Z
| 2020-06-18T07:41:45Z
| 2020-06-18T07:41:44Z
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When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset.
However metrics load data the same way but don't need instructions (we use one single file).
In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object.
This should fix #269
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[] | null |
[
"Nice thanks :) \r\nNote that in `datasets` the tags are simply the version without the `v`. For example `1.2.1`.",
"Do you push tags only for versions?",
"Yes I've always used tags only for versions"
] | 2021-01-14T15:11:25Z
| 2021-01-15T10:08:20Z
| 2021-01-15T10:08:19Z
|
MEMBER
| null | 0
|
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"diff_url": "https://github.com/huggingface/datasets/pull/1738.diff",
"html_url": "https://github.com/huggingface/datasets/pull/1738",
"merged_at": "2021-01-15T10:08:18Z",
"patch_url": "https://github.com/huggingface/datasets/pull/1738.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/1738"
}
|
Will push a new version on anaconda cloud every time a tag starting with `v` is pushed (like `v1.2.2`).
Will appear here: https://anaconda.org/huggingface/datasets
Depends on `conda-forge` for now, so the following is required for installation:
```
conda install -c huggingface -c conda-forge datasets
```
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I_kwDODunzps52zQvo
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Bug: LayoutLMv3 finetuning on FUNSD Notebook; Arrow Error
|
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[
"Very strange: `datasets-cli env`\r\n> \r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.9.0\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.13\r\n> - PyArrow version: 8.0.0\r\n> - Pandas version: 1.3.5\r\n\r\nAfter updating datasets and pyarrow on base environment, although I am using a different one called layoutLM\r\n\r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.14.6\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.18\r\n> - Huggingface_hub version: 0.17.3\r\n> - PyArrow version: 14.0.1\r\n> - Pandas version: 2.1.3",
"Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.",
"> Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.\r\n\r\nThanks for the info and the latest release, it seems this has also solved my issue. First run after the update worked and I am training right now :D\r\nWill close the Issu"
] | 2023-11-14T16:53:20Z
| 2023-11-16T20:23:41Z
| 2023-11-16T20:23:41Z
|
NONE
| null | null | null |
### Describe the bug
Arrow issues when running the example Notebook laptop locally on Mac with M1. Works on Google Collab.
**Notebook**: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv3/Fine_tune_LayoutLMv3_on_FUNSD_(HuggingFace_Trainer).ipynb
**Error**: `ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.`
**Caused by**:
```
# we need to define custom features for `set_format` (used later on) to work properly
features = Features({
'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)),
'input_ids': Sequence(feature=Value(dtype='int64')),
'attention_mask': Sequence(Value(dtype='int64')),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
'labels': Sequence(feature=Value(dtype='int64')),
})
```
### Steps to reproduce the bug
Run the notebook provided, locally. If possible also on M1.
### Expected behavior
The cell where features are mapped to Array2D and Array3D should work without any issues.
### Environment info
Tried with Python 3.9 and 3.10 conda envs. Running Mac M1.
`pip show datasets`
> Name: datasets
Version: 2.14.6
Summary: HuggingFace community-driven open-source library of datasets
`pip list`
> Package Version
> ------------------------- ------------
> accelerate 0.24.1
> aiohttp 3.8.6
> aiosignal 1.3.1
> anyio 3.5.0
> appnope 0.1.2
> argon2-cffi 21.3.0
> argon2-cffi-bindings 21.2.0
> asttokens 2.0.5
> async-timeout 4.0.3
> attrs 23.1.0
> backcall 0.2.0
> beautifulsoup4 4.12.2
> bleach 4.1.0
> certifi 2023.7.22
> cffi 1.15.1
> charset-normalizer 3.3.2
> comm 0.1.2
> datasets 2.14.6
> debugpy 1.6.7
> decorator 5.1.1
> defusedxml 0.7.1
> dill 0.3.7
> entrypoints 0.4
> exceptiongroup 1.0.4
> executing 0.8.3
> fastjsonschema 2.16.2
> filelock 3.13.1
> frozenlist 1.4.0
> fsspec 2023.10.0
> huggingface-hub 0.17.3
> idna 3.4
> importlib-metadata 6.0.0
> IProgress 0.4
> ipykernel 6.25.0
> ipython 8.15.0
> ipython-genutils 0.2.0
> jedi 0.18.1
> Jinja2 3.1.2
> joblib 1.3.2
> jsonschema 4.19.2
> jsonschema-specifications 2023.7.1
> jupyter_client 7.4.9
> jupyter_core 5.5.0
> jupyter-server 1.23.4
> jupyterlab-pygments 0.1.2
> MarkupSafe 2.1.1
> matplotlib-inline 0.1.6
> mistune 2.0.4
> mpmath 1.3.0
> multidict 6.0.4
> multiprocess 0.70.15
> nbclassic 1.0.0
> nbclient 0.8.0
> nbconvert 7.10.0
> nbformat 5.9.2
> nest-asyncio 1.5.6
> networkx 3.2.1
> notebook 6.5.4
> notebook_shim 0.2.3
> numpy 1.26.1
> packaging 23.1
> pandas 2.1.3
> pandocfilters 1.5.0
> parso 0.8.3
> pexpect 4.8.0
> pickleshare 0.7.5
> Pillow 10.1.0
> pip 23.3
> platformdirs 3.10.0
> prometheus-client 0.14.1
> prompt-toolkit 3.0.36
> psutil 5.9.0
> ptyprocess 0.7.0
> pure-eval 0.2.2
> pyarrow 14.0.1
> pycparser 2.21
> Pygments 2.15.1
> python-dateutil 2.8.2
> pytz 2023.3.post1
> PyYAML 6.0.1
> pyzmq 23.2.0
> referencing 0.30.2
> regex 2023.10.3
> requests 2.31.0
> rpds-py 0.10.6
> safetensors 0.4.0
> scikit-learn 1.3.2
> scipy 1.11.3
> Send2Trash 1.8.2
> seqeval 1.2.2
> setuptools 68.0.0
> six 1.16.0
> sniffio 1.2.0
> soupsieve 2.5
> stack-data 0.2.0
> sympy 1.12
> terminado 0.17.1
> threadpoolctl 3.2.0
> tinycss2 1.2.1
> tokenizers 0.14.1
> torch 2.1.0
> tornado 6.3.3
> tqdm 4.66.1
> traitlets 5.7.1
> transformers 4.36.0.dev0
> typing_extensions 4.7.1
> tzdata 2023.3
> urllib3 2.0.7
> wcwidth 0.2.5
> webencodings 0.5.1
> websocket-client 0.58.0
> wheel 0.41.2
> xxhash 3.4.1
> yarl 1.9.2
> zipp 3.11.0
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MDU6SXNzdWU4NzM5NDEyNjY=
| 2,301
|
Unable to setup dev env on Windows
|
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[
"Hi @gchhablani, \r\n\r\nThere are some 3rd-party dependencies that require to build code in C. In this case, it is the library `python-Levenshtein`.\r\n\r\nOn Windows, in order to be able to build C code, you need to install at least `Microsoft C++ Build Tools` version 14. You can find more info here: https://visualstudio.microsoft.com/visual-cpp-build-tools/",
"Hi @albertvillanova \r\n\r\nSorry for such a trivial issue ;-; \r\n\r\nThanks a lot."
] | 2021-05-02T13:20:42Z
| 2021-05-03T15:18:01Z
| 2021-05-03T15:17:34Z
|
CONTRIBUTOR
| null | null | null |
Hi
I tried installing the `".[dev]"` version on Windows 10 after cloning.
Here is the error I'm facing:
```bat
(env) C:\testing\datasets>pip install -e ".[dev]"
Obtaining file:///C:/testing/datasets
Requirement already satisfied: numpy>=1.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.5)
Collecting pyarrow>=0.17.1
Using cached pyarrow-4.0.0-cp37-cp37m-win_amd64.whl (13.3 MB)
Requirement already satisfied: dill in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.3.1.1)
Collecting pandas
Using cached pandas-1.2.4-cp37-cp37m-win_amd64.whl (9.1 MB)
Requirement already satisfied: requests>=2.19.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.25.1)
Requirement already satisfied: tqdm<4.50.0,>=4.27 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.49.0)
Requirement already satisfied: xxhash in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.0.2)
Collecting multiprocess
Using cached multiprocess-0.70.11.1-py37-none-any.whl (108 kB)
Requirement already satisfied: fsspec in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2021.4.0)
Collecting huggingface_hub<0.1.0
Using cached huggingface_hub-0.0.8-py3-none-any.whl (34 kB)
Requirement already satisfied: importlib_metadata in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.0.1)
Requirement already satisfied: absl-py in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.12.0)
Requirement already satisfied: pytest in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (6.2.3)
Collecting pytest-xdist
Using cached pytest_xdist-2.2.1-py3-none-any.whl (37 kB)
Collecting apache-beam>=2.24.0
Using cached apache_beam-2.29.0-cp37-cp37m-win_amd64.whl (3.7 MB)
Collecting elasticsearch
Using cached elasticsearch-7.12.1-py2.py3-none-any.whl (339 kB)
Requirement already satisfied: boto3==1.16.43 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.16.43)
Requirement already satisfied: botocore==1.19.43 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.43)
Collecting moto[s3]==1.3.16
Using cached moto-1.3.16-py2.py3-none-any.whl (879 kB)
Collecting rarfile>=4.0
Using cached rarfile-4.0-py3-none-any.whl (28 kB)
Collecting tensorflow>=2.3
Using cached tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB)
Requirement already satisfied: torch in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.8.1)
Requirement already satisfied: transformers in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.5.1)
Collecting bs4
Using cached bs4-0.0.1-py3-none-any.whl
Collecting conllu
Using cached conllu-4.4-py2.py3-none-any.whl (15 kB)
Collecting langdetect
Using cached langdetect-1.0.8-py3-none-any.whl
Collecting lxml
Using cached lxml-4.6.3-cp37-cp37m-win_amd64.whl (3.5 MB)
Collecting mwparserfromhell
Using cached mwparserfromhell-0.6-cp37-cp37m-win_amd64.whl (101 kB)
Collecting nltk
Using cached nltk-3.6.2-py3-none-any.whl (1.5 MB)
Collecting openpyxl
Using cached openpyxl-3.0.7-py2.py3-none-any.whl (243 kB)
Collecting py7zr
Using cached py7zr-0.15.2-py3-none-any.whl (66 kB)
Collecting tldextract
Using cached tldextract-3.1.0-py2.py3-none-any.whl (87 kB)
Collecting zstandard
Using cached zstandard-0.15.2-cp37-cp37m-win_amd64.whl (582 kB)
Collecting bert_score>=0.3.6
Using cached bert_score-0.3.9-py3-none-any.whl (59 kB)
Collecting rouge_score
Using cached rouge_score-0.0.4-py2.py3-none-any.whl (22 kB)
Collecting sacrebleu
Using cached sacrebleu-1.5.1-py3-none-any.whl (54 kB)
Requirement already satisfied: scipy in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.6.3)
Collecting seqeval
Using cached seqeval-1.2.2-py3-none-any.whl
Collecting sklearn
Using cached sklearn-0.0-py2.py3-none-any.whl
Collecting jiwer
Using cached jiwer-2.2.0-py3-none-any.whl (13 kB)
Requirement already satisfied: toml>=0.10.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.10.2)
Requirement already satisfied: requests_file>=1.5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.5.1)
Requirement already satisfied: texttable>=1.6.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.6.3)
Requirement already satisfied: s3fs>=0.4.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.4.2)
Requirement already satisfied: Werkzeug>=1.0.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.0.1)
Collecting black
Using cached black-21.4b2-py3-none-any.whl (130 kB)
Collecting isort
Using cached isort-5.8.0-py3-none-any.whl (103 kB)
Collecting flake8==3.7.9
Using cached flake8-3.7.9-py2.py3-none-any.whl (69 kB)
Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from boto3==1.16.43->datasets==1.5.0.dev0) (0.10.0)
Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from boto3==1.16.43->datasets==1.5.0.dev0) (0.3.7)
Requirement already satisfied: urllib3<1.27,>=1.25.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from botocore==1.19.43->datasets==1.5.0.dev0) (1.26.4)
Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from botocore==1.19.43->datasets==1.5.0.dev0) (2.8.1)
Collecting entrypoints<0.4.0,>=0.3.0
Using cached entrypoints-0.3-py2.py3-none-any.whl (11 kB)
Collecting pyflakes<2.2.0,>=2.1.0
Using cached pyflakes-2.1.1-py2.py3-none-any.whl (59 kB)
Collecting pycodestyle<2.6.0,>=2.5.0
Using cached pycodestyle-2.5.0-py2.py3-none-any.whl (51 kB)
Collecting mccabe<0.7.0,>=0.6.0
Using cached mccabe-0.6.1-py2.py3-none-any.whl (8.6 kB)
Requirement already satisfied: jsondiff>=1.1.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.3.0)
Requirement already satisfied: pytz in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2021.1)
Requirement already satisfied: mock in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (4.0.3)
Requirement already satisfied: MarkupSafe<2.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.1.1)
Requirement already satisfied: python-jose[cryptography]<4.0.0,>=3.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.2.0)
Requirement already satisfied: aws-xray-sdk!=0.96,>=0.93 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.8.0)
Requirement already satisfied: cryptography>=2.3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.4.7)
Requirement already satisfied: more-itertools in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (8.7.0)
Requirement already satisfied: PyYAML>=5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (5.4.1)
Requirement already satisfied: boto>=2.36.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.49.0)
Requirement already satisfied: idna<3,>=2.5 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.10)
Requirement already satisfied: sshpubkeys>=3.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.3.1)
Requirement already satisfied: responses>=0.9.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.13.3)
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Building wheels for collected packages: python-Levenshtein
Building wheel for python-Levenshtein (setup.py) ... error
ERROR: Command errored out with exit status 1:
command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\VKC~1\AppData\Local\Temp\pip-wheel-8jh7fm18'
cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\
Complete output (27 lines):
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.7
creating build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein
running egg_info
writing python_Levenshtein.egg-info\PKG-INFO
writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt
writing entry points to python_Levenshtein.egg-info\entry_points.txt
writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt
writing requirements to python_Levenshtein.egg-info\requires.txt
writing top-level names to python_Levenshtein.egg-info\top_level.txt
reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '*pyc' found anywhere in distribution
warning: no previously-included files matching '*so' found anywhere in distribution
warning: no previously-included files matching '.project' found anywhere in distribution
warning: no previously-included files matching '.pydevproject' found anywhere in distribution
writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein
running build_ext
building 'Levenshtein._levenshtein' extension
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
----------------------------------------
ERROR: Failed building wheel for python-Levenshtein
Running setup.py clean for python-Levenshtein
Failed to build python-Levenshtein
Installing collected packages: python-Levenshtein, pytest-forked, pyppmd, pymongo, pyflakes, pydot, pycryptodome, pycodestyle, pyarrow, portalocker, pathspec, pandas, opt-einsum, oauth2client, nltk, mypy-extensions, multivolumefile, multiprocess, moto, mccabe, matplotlib, keras-preprocessing, huggingface-hub, hdfs, h5py, google-pasta, gast, flatbuffers, fastavro, execnet, et-xmlfile, entrypoints, crcmod, beautifulsoup4, bcj-cffi, avro-python3, astunparse, appdirs, zstandard, tldextract, tensorflow, sklearn, seqeval, sacrebleu, rouge-score, rarfile, pytest-xdist, py7zr, openpyxl, mwparserfromhell, lxml, langdetect, jiwer, isort, flake8, elasticsearch, datasets, conllu, bs4, black, bert-score, apache-beam
Running setup.py install for python-Levenshtein ... error
ERROR: Command errored out with exit status 1:
command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein'
cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\
Complete output (27 lines):
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.7
creating build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein
running egg_info
writing python_Levenshtein.egg-info\PKG-INFO
writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt
writing entry points to python_Levenshtein.egg-info\entry_points.txt
writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt
writing requirements to python_Levenshtein.egg-info\requires.txt
writing top-level names to python_Levenshtein.egg-info\top_level.txt
reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '*pyc' found anywhere in distribution
warning: no previously-included files matching '*so' found anywhere in distribution
warning: no previously-included files matching '.project' found anywhere in distribution
warning: no previously-included files matching '.pydevproject' found anywhere in distribution
writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein
running build_ext
building 'Levenshtein._levenshtein' extension
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
----------------------------------------
ERROR: Command errored out with exit status 1: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein' Check the logs for full command output.
```
Here are conda and python versions:
```bat
(env) C:\testing\datasets>conda --version
conda 4.9.2
(env) C:\testing\datasets>python --version
Python 3.7.10
```
Please help me out. Thanks.
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Add PASS dataset
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[] | 2021-10-07T16:43:43Z
| 2022-01-20T16:50:47Z
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## Adding a Dataset
- **Name:** PASS
- **Description:** An ImageNet replacement for self-supervised pretraining without humans
- **Data:** https://www.robots.ox.ac.uk/~vgg/research/pass/ https://github.com/yukimasano/PASS
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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[Breaking] Rename formated to formatted
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`formated` is not correct but `formatted` is
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PR_kwDODunzps4yXP44
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Fix ClassLabel to/from dict when passed names_file
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[] | 2022-02-10T09:47:10Z
| 2022-02-11T23:02:32Z
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Currently, `names_file` is a field of the data class `ClassLabel`, thus appearing when transforming it to dict (when saving infos). Afterwards, when trying to read it from infos, it conflicts with the other field `names`.
This PR, removes `names_file` as a field of the data class `ClassLabel`.
- it is only used at instantiation to generate the `labels` field
Fix #3631.
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Use hfh hf_hub_url function
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5196). All of your documentation changes will be reflected on that endpoint.",
"@lhoestq I think we should first agree if `datasets` can introduce the breaking change of ignoring `config.HUB_DATASETS_URL`: some users may have override this.\r\n\r\nIf so, I then would suggest to initiate a deprecation cycle.",
"After a discussion with the rest of the datasets team, we agreed we can introduce the breaking change of ignoring `config.HUB_DATASETS_URL`: this will have minimal impact, only for **private Hubs**. We will address eventual possible impacts in the future.\r\n\r\nAdditionally, we also ignore `config.HUB_DEFAULT_VERSION`.\r\n\r\nSee explanation in this PR description: https://github.com/huggingface/datasets/pull/5196#issue-1434401646",
"I'm trying to upgrade datasets to 2.7.0 in https://github.com/huggingface/datasets-server, and the tests fail due to this change. I think it's a breaking change (that was not listed in https://github.com/huggingface/datasets/releases/tag/2.7.0) since code that previously worked (by setting `datasets.config.HUB_DATASETS_URL = CI_HUB_DATASETS_URL` for example) does not work anymore.\r\n\r\nI'm not sure what is the correct way to set up the tests; besides setting the env var \"HF_ENDPOINT\" before launching the tests (which, I think, is not a good way to do: the tests should not depend on the environment).",
"OK, I re-read this thread, and https://github.com/huggingface/datasets/pull/5196#issuecomment-1307430175 explicitely states that `config.HUB_DATASETS_URL` (as well as `config.HUB_DEFAULT_VERSION`) is now ignored. I was expecting the breaking changes to be listed in the release notes: https://github.com/huggingface/datasets/releases/tag/2.7.0.",
"> I'm not sure what is the correct way to set up the tests; besides setting the env var \"HF_ENDPOINT\" before launching the tests (which, I think, is not a good way to do: the tests should not depend on the environment).\r\n\r\nI think the current workaround of settings an env variable before launching the tests is \"not so bad\" when considering the fact that env variables are evaluated at import time in `huggingface_hub` (and most probable `datasets` as well). I think that when refactoring this in huggingface_hub (https://github.com/huggingface/huggingface_hub/issues/1172) I'll opt for instantiating a `Settings` object (or `Constants`) that contains all the settings variables. This way it will not be possible to import attributes individually + tests would be easier. As I see it, it would be similar to [what `Pydantic` does](https://pydantic-docs.helpmanual.io/usage/settings/) even though we most probably don't want Pydantic as a root dependency just for that. ",
"You can use fixtures in your tests:\r\n```python\r\nCI_HUB_ENDPOINT = \"https://hub-ci.huggingface.co\"\r\nCI_HUB_DATASETS_URL = CI_HUB_ENDPOINT + \"/datasets/{repo_id}/resolve/{revision}/{path}\"\r\nCI_HFH_HUGGINGFACE_CO_URL_TEMPLATE = CI_HUB_ENDPOINT + \"/{repo_id}/resolve/{revision}/{filename}\"\r\n\r\n@pytest.fixture\r\ndef ci_hfh_hf_hub_url(monkeypatch):\r\n monkeypatch.setattr(\r\n \"huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE\", CI_HFH_HUGGINGFACE_CO_URL_TEMPLATE\r\n )\r\n\r\n@pytest.fixture\r\ndef ci_hub_config(monkeypatch):\r\n monkeypatch.setattr(\"datasets.config.HF_ENDPOINT\", CI_HUB_ENDPOINT)\r\n monkeypatch.setattr(\"datasets.config.HUB_DATASETS_URL\", CI_HUB_DATASETS_URL)\r\n```\r\n\r\nand use `@pytest.fixture(autouse=True)` if you want to always use the CI endpoints.\r\n\r\nAnd when `huggingface-hub` and `datasets` change the way we can set the endpoint, we'll just need to update the fixtures.\r\nI think ultimately you'll only have to change the `huggingface-hub` endpoint settings\r\n",
"OK.\r\n\r\nIn fact, in datasets-server we set `config.HUB_DATASETS_URL` (https://github.com/huggingface/datasets-server/blob/35a30dbcd687b26db1f02502ea8305f70c064473/workers/splits/src/splits/config.py#L26) at config time, before starting the workers. It's not an issue with how to launch the tests, but with the app in itself.\r\n\r\nI understand that for now, the only way to fix this is to setup `HF_ENDPOINT` in the env when launching the app (currently, we set the endpoint with `COMMON_HF_ENDPOINT`, a custom env var I set to be sure not to have side-effects)",
"> You can use fixtures in your tests:\r\n\r\nThanks, used in https://github.com/huggingface/datasets-server/pull/644."
] | 2022-11-03T10:08:09Z
| 2022-12-06T11:38:17Z
| 2022-11-09T07:15:12Z
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Small refactoring to use `hf_hub_url` function from `huggingface_hub`.
This PR also creates the `hub` module that will contain all `huggingface_hub` functionalities relevant to `datasets`.
This is a necessary stage before implementing the use of the `hfh` caching system (which uses its `hf_hub_url` under the hood).
EDIT:
~~Finally, we use our `config.HUB_DATASETS_URL` when using `hfh.hf_hub_url`~~
There is a breaking change: the `hfh` `hf_hub_url` function uses
- `hfh` `HUGGINGFACE_CO_URL_TEMPLATE` URL template, different from the `datasets` `config.HUB_DATASETS_URL`
- also, `hfh` `DEFAULT_REVISION`, instead of `datasets` `config.HUB_DEFAULT_VERSION`
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I_kwDODunzps5FRNGO
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Load audio dataset error
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[
"Hi @lemoner20, thanks for reporting.\r\n\r\nI'm sorry but I cannot reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset, load_metric, Audio\r\n ...: raw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\n ...: print(raw_datasets[0][\"audio\"])\r\nDownloading builder script: 30.2kB [00:00, 13.0MB/s] \r\nDownloading metadata: 38.0kB [00:00, 16.6MB/s] \r\nDownloading and preparing dataset superb/ks (download: 1.45 GiB, generated: 9.64 MiB, post-processed: Unknown size, total: 1.46 GiB) to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.49G/1.49G [00:37<00:00, 39.3MB/s]\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.3M/71.3M [00:01<00:00, 36.1MB/s]\r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:41<00:00, 20.67s/it]\r\nExtracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:28<00:00, 14.24s/it]\r\nDataset superb downloaded and prepared to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9. Subsequent calls will reuse this data.\r\n{'path': '.../.cache/huggingface/datasets/downloads/extracted/8571921d3088b48f58f75b2e514815033e1ffbd06aa63fd4603691ac9f1c119f/_background_noise_/doing_the_dishes.wav', 'array': array([ 0. , 0. , 0. , ..., -0.00592041,\r\n -0.00405884, -0.00253296], dtype=float32), 'sampling_rate': 16000}\r\n``` \r\n\r\nWhich version of `datasets` are you using? Could you please fill in the environment info requested in the bug report template? You can run the command `datasets-cli env` and copy-and-paste its output below\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:",
"@albertvillanova Thanks for your reply. The environment info below\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.91-007.ali4000.alios7.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.12\r\n- PyArrow version: 6.0.1",
"Thanks @lemoner20,\r\n\r\nI cannot reproduce your issue in datasets version 1.18.3 either.\r\n\r\nMaybe redownloading the data file may work if you had already cached this dataset previously. Could you please try passing \"force_redownload\"?\r\n```python\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\", download_mode=\"force_redownload\")",
"Thanks, @albertvillanova,\r\n\r\nI install the python package of **librosa=0.9.1** again, it works now!\r\n\r\n\r\n",
"Cool!",
"@albertvillanova, you can actually reproduce the error if you reach the cell `common_voice_train[0][\"path\"]` of this [notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=_0kRndSvqaKk). Error gets solved after updating the versions of the libraries used in there.",
"@jvel07, thanks for reporting and finding a solution.\r\n\r\nMaybe we could tell @patrickvonplaten about the version pinning issue in his notebook.",
"Should I update the version of datasets @albertvillanova ? "
] | 2022-03-08T02:16:04Z
| 2022-09-27T12:13:55Z
| 2022-03-08T11:20:06Z
|
NONE
| null | null | null |
## Load audio dataset error
Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb,
```
from datasets import load_dataset, load_metric, Audio
raw_datasets = load_dataset("superb", "ks", split="train")
print(raw_datasets[0]["audio"])
```
following errors occur
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-169-3f8253239fa0> in <module>
----> 1 raw_datasets[0]["audio"]
/usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key)
1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
1925 return self._getitem(
-> 1926 key,
1927 )
1928
/usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)
1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
1910 formatted_output = format_table(
-> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1912 )
1913 return formatted_output
/usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
/usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
/usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table)
310 row = self.python_arrow_extractor().extract_row(pa_table)
311 if self.decoded:
--> 312 row = self.python_features_decoder.decode_row(row)
313 return row
314
/usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row)
219
220 def decode_row(self, row: dict) -> dict:
--> 221 return self.features.decode_example(row) if self.features else row
222
223 def decode_column(self, column: list, column_name: str) -> list:
/usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example)
1320 else value
1321 for column_name, (feature, value) in utils.zip_dict(
-> 1322 {key: value for key, value in self.items() if key in example}, example
1323 )
1324 }
/usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0)
1319 if self._column_requires_decoding[column_name]
1320 else value
-> 1321 for column_name, (feature, value) in utils.zip_dict(
1322 {key: value for key, value in self.items() if key in example}, example
1323 )
/usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj)
1053 # Object with special decoding:
1054 elif isinstance(schema, (Audio, Image)):
-> 1055 return schema.decode_example(obj) if obj is not None else None
1056 return obj
1057
/usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value)
100 array, sampling_rate = self._decode_non_mp3_file_like(file)
101 else:
--> 102 array, sampling_rate = self._decode_non_mp3_path_like(path)
103 return {"path": path, "array": array, "sampling_rate": sampling_rate}
104
/usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path)
143
144 with xopen(path, "rb") as f:
--> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
146 return array, sampling_rate
147
/usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)
110
111 y = []
--> 112 with audioread.audio_open(os.path.realpath(path)) as input_file:
113 sr_native = input_file.samplerate
114 n_channels = input_file.channels
/usr/lib/python3.6/posixpath.py in realpath(filename)
392 """Return the canonical path of the specified filename, eliminating any
393 symbolic links encountered in the path."""
--> 394 filename = os.fspath(filename)
395 path, ok = _joinrealpath(filename[:0], filename, {})
396 return abspath(path)
TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader
```
## Expected results
```
>>> raw_datasets[0]["audio"]
{'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347,
0.01623535, 0.01724243]),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav',
'sampling_rate': 16000}
```
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Spelling mistake
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[
"Thanks!"
] | 2020-07-16T04:37:58Z
| 2020-07-16T06:49:48Z
| 2020-07-16T06:49:37Z
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CONTRIBUTOR
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In "Formatting the dataset" part, "The two toehr modifications..." should be "The two other modifications..." ,the word "other" wrong spelled as "toehr".
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| 855
|
Fix kor nli csv reader
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[] | 2020-11-16T09:53:41Z
| 2020-11-16T13:59:14Z
| 2020-11-16T13:59:12Z
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The kor_nli dataset had an issue with the csv reader that was not able to parse the lines correctly. Some lines were merged together for some reason.
I fixed that by iterating through the lines directly instead of using a csv reader.
I also changed the feature names to match the other NLI datasets (i.e. use "premise", "hypothesis", "label" features)
Fix #821
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Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data?
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[
"Thanks for reporting @beyondguo.\r\n\r\nIndeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip\r\nAnd that URL only contains the English version.",
"The German data requires payment\r\n\r\nThe [original task page](https://www.clips.uantwerpen.be/conll2003/ner/) states \"The German data is a collection of articles from the Frankfurter Rundschau. The named entities have been annotated by people of the University of Antwerp. Only the annotations are available here. In order to build these data sets you need access to the ECI Multilingual Text Corpus. It can be ordered from the Linguistic Data Consortium (2003 non-member price: US$ 35.00).\"\r\n\r\nInflation since 2003 has also affected LDC's prices, and today the dataset [LDC94T5](https://catalog.ldc.upenn.edu/LDC94T5) is available under license for $75 a copy. The [license](https://catalog.ldc.upenn.edu/license/eci-slash-mci-user-agreement.pdf) includes a non-distribution condition, which is probably why the data has not turned up openly.\r\n\r\nThe ACL hold copyright of this data; I'll mail them and anyone I can find at ECI to see if they'll open this up now. After all, it worked with Microsoft 3DMM, why not here too, after 28 years? :)\r\n",
"Closing this issue as we are not allowed to share publicly the German subset."
] | 2022-04-27T00:53:52Z
| 2023-07-25T15:10:15Z
| 2023-07-25T15:10:15Z
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NONE
| null | null | null |
But on huggingface datasets:
Where is the German data?
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[wip testing docs]
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5195). All of your documentation changes will be reflected on that endpoint."
] | 2022-11-03T08:37:34Z
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PR_kwDODunzps40LGoV
| 3,873
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Create SQuAD metric README.md
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3873). All of your documentation changes will be reflected on that endpoint.",
"Oh one last thing I almost forgot, I think I would add a section \"Examples\" with examples of inputs and outputs and in particular: an example giving maximal values, an examples giving minimal values and maybe a standard examples from SQuAD. What do you think?"
] | 2022-03-09T13:47:08Z
| 2022-03-10T16:45:57Z
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Proposal for a metrics card structure (with an example based on the SQuAD metric).
@thomwolf @lhoestq @douwekiela @lewtun -- feel free to comment on structure or content (it's an initial draft, so I realize there's stuff missing!).
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MDU6SXNzdWU3MDI0NDA0ODQ=
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Load large text file for LM pre-training resulting in OOM
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"Not sure what could cause that on the `datasets` side. Could this be a `Trainer` issue ? cc @julien-c @sgugger ?",
"There was a memory leak issue fixed recently in master. You should install from source and see if it fixes your problem.",
"@lhoestq @sgugger Thanks for your comments. I have install from source code as you told, but the problem is still there.\r\nTo reproduce the issue, just replace [these lines](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py#L241-L258) with: \r\n(load_dataset and DataCollatorForDatasetsLanguageModeling as [above mentioned](https://github.com/huggingface/datasets/issues/633#issue-702440484))\r\n```python\r\n dataset = load_dataset(\"bookcorpus\")\r\n dataset = dataset.train_test_split(test_size=0.1)\r\n train_dataset = dataset['train']\r\n eval_dataset = dataset['test'] if training_args.do_eval else None\r\n\r\n data_collator = DataCollatorForDatasetsLanguageModeling(\r\n tokenizer=tokenizer,\r\n mlm=data_args.mlm,\r\n mlm_probability=data_args.mlm_probability,\r\n block_size=data_args.block_size\r\n )\r\n```\r\nand run by:\r\n```bash\r\npython run_language_modeling.py\r\n--output_dir=output \\\r\n--model_type=bert \\\r\n--model_name_or_path=bert-base-uncased \\\r\n--do_train \\\r\n--do_eval \\\r\n--mlm \r\n```",
"Same here. Pre-training on wikitext-103 to do some test. At the end of the training it takes 32GB of RAM + ~30GB of SWAP. I installed dataset==1.1.0, not built from source. I will try uninstalling and building from source when it finish.",
"This seems to be on the `transformers` library side.\r\n\r\nIf you have more informations (pip env) or even better, a colab reproducing the error we can investigate.",
"It seems like it's solved with freshed versions of transformers. I have tried to replicate the error doing a fresh pip install transformers & datasets on colab and the error doesn't continue. On colab it keeps stable on 5GB! (Y)\r\n\r\nEdit: **Thanks for your great work**. Have a good day.",
"@gaceladri witch version transformers and datasets are you using now? I want to try again. Thanks.",
"transformers==3.3.1\r\ndatasets==1.1.0\r\ntokenizers==0.8.1rc2\r\n",
"doing some modifications to mobilebert\r\nhttps://colab.research.google.com/drive/1ba09ZOpyHGAOQLcsxiQAHRXl10qnMU5o?usp=sharing ",
"It does not happen to me anymore. Can we close? @leethu2012 ",
"It's happening to me again. After 4 hours of pre-training, my ram memory gets full and the kernel dies. I am using the last transformers version as today. 4.4.0 and the last version of datasets 1.2.1, both installed from master. The memory consumption keeps increasing.",
"It looks like it is something from pytorch/python itself :face_with_head_bandage: https://github.com/pytorch/pytorch/issues/13246 ",
"Thanks for the investigation @gaceladri \r\n\r\nApparently this happens when `num_workers>0` and has to do with objects being copied-on-write.\r\nDid you try setting num_workers to 0 @gaceladri ?\r\nIf the issue doesn't happen with `num_workers=0` then this would confirm that it's indeed related to this python/pytorch issue.\r\n\r\nSince a `Dataset` object is a wrapper of a pyarrow Table, we should investigate if the data being copied comes from the Table itself or from metadata in the `Dataset` object. If it comes from the metadata in the `Dataset` object, we should be able to implement a workaround. But if it comes from the Table, we'll need to see with the pyarrow team what we can do... ",
"@lhoestq I have tried and it keeps increasing also with `dataloader_num_workers=0`",
"Hmmm so this might come from another issue...\r\nSince it doesn't seem to be related to multiprocessing it should be easier to investigate though.\r\nDo you have some ideas @gaceladri ?",
"@lhoestq I looked quickly to a previously spoted bug in my env wandb /sdk/interface/interface.py, because sometimes when I load the dataset I got a multiprocessing error at line 510 in wandb...interface.py\r\n\r\nThis bug is reported here https://github.com/huggingface/datasets/issues/847\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/transformers/trainer.py in train(self, model_path, trial)\r\n 877 print(len(epoch_iterator))\r\n 878 \r\n--> 879 for step, inputs in enumerate(epoch_iterator):\r\n 880 \r\n 881 start_step = time.time()\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)\r\n 433 if self._sampler_iter is None:\r\n 434 self._reset()\r\n--> 435 data = self._next_data()\r\n 436 self._num_yielded += 1\r\n 437 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)\r\n 1083 else:\r\n 1084 del self._task_info[idx]\r\n-> 1085 return self._process_data(data)\r\n 1086 \r\n 1087 def _try_put_index(self):\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)\r\n 1109 self._try_put_index()\r\n 1110 if isinstance(data, ExceptionWrapper):\r\n-> 1111 data.reraise()\r\n 1112 return data\r\n 1113 \r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/_utils.py in reraise(self)\r\n 426 # have message field\r\n 427 raise self.exc_type(message=msg)\r\n--> 428 raise self.exc_type(msg)\r\n 429 \r\n 430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1083, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1070, in _getitem\r\n format_kwargs=format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 886, in _convert_outputs\r\n v = map_nested(command, v, **map_nested_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/utils/py_utils.py\", line 216, in map_nested\r\n return function(data_struct)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 847, in command\r\n return torch.tensor(x, **format_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 101, in _showwarnmsg\r\n _showwarnmsg_impl(msg)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 30, in _showwarnmsg_impl\r\n file.write(text)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/wandb_run.py\", line 729, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 186, in publish_output\r\n self._publish_output(o)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 191, in _publish_output\r\n self._publish(rec)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 510, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nMy workaround was to just comment those lines without looking to much into consecuences:\r\n\r\n```\r\ndef _publish(self, record: pb.Record, local: bool = None) -> None:\r\n #if self._process and not self._process.is_alive():\r\n # raise Exception(\"The wandb backend process has shutdown\")\r\n```\r\n\r\nIt worked so far... I need to try running without wandb and see if it could be causing something wrong with multiprocessing. I am going to try to launch the training setting wandb to false and I will let you know again.",
"@lhoestq But despite this, I got lost into the [class Dataset()](https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset) reading the pyarrow files.\r\n\r\nEdit: but you should be rigth, that it does not have to be related to multiprocessing since it keeps happening when `num_workers=0` ",
"Or maybe wandb uses multiprocessing ? One process for wandb logging and one for actual training ? If this is the case then even setting `num_workers=0` would cause the process to be forked for wandb and therefore cause the memory issue.",
"@lhoestq could be, but if we set wandb to false this should not happen. I am going to try.",
"@lhoestq It keeps happening. I have uninstalled wandb from my env, setted `%env WANDB_DISABLED=true` on my notebook, and commented this func:\r\n\r\n```\r\ndef get_available_reporting_integrations():\r\n integrations = []\r\n if is_azureml_available():\r\n integrations.append(\"azure_ml\")\r\n if is_comet_available():\r\n integrations.append(\"comet_ml\")\r\n if is_mlflow_available():\r\n integrations.append(\"mlflow\")\r\n if is_tensorboard_available():\r\n integrations.append(\"tensorboard\")\r\n # if is_wandb_available():\r\n # integrations.append(\"wandb\")\r\n return integrations\r\n```\r\nAs a fast test and it keeps increasing the ram memory. Wandb could not be the blameworthy here.",
"Thanks for checking @gaceladri . Let's investigate the single process setting then.\r\nIf you have some sort of colab notebook with a minimal code example that shows this behavior feel free to share it @gaceladri so that we can play around with it to find what causes this. Otherwise I'll probably try to reproduce on my side at one point",
"@lhoestq sure. Here you have https://colab.research.google.com/drive/1ba09ZOpyHGAOQLcsxiQAHRXl10qnMU5o?usp=sharing let me know if the link works and it reproduces the issue. To me, it reproduces the issue, since if you start the training the ram memory keeps increasing.\r\n\r\nLet me know. Thanks!",
"Could the bug be comming from tokenizers?\r\n\r\nI got this warning at the terminal from my jupyter notebook: \r\n```\r\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\r\nTo disable this warning, you can either:\r\n\t- Avoid using `tokenizers` before the fork if possible\r\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\n```",
"I've never experienced memory issues with tokenizers so I don't know\r\nCc @n1t0 are you aware of any issue that would cause memory to keep increasing when the tokenizer is used in the Data Collator for language modeling ?",
"@lhoestq Thanks for pointing to n1t0, just to clarify. That warning was doing fine-tuning, without collator:\r\n```\r\n\r\nfrom datasets import load_dataset, load_metric\r\nimport numpy as np\r\n\r\nGLUE_TASKS = [\r\n \"cola\",\r\n \"mnli\",\r\n \"mnli-mm\",\r\n \"mrpc\",\r\n \"qnli\",\r\n \"qqp\",\r\n \"rte\",\r\n \"sst2\",\r\n \"stsb\",\r\n \"wnli\",\r\n]\r\ntask = \"mnli\"\r\nactual_task = \"mnli\" if task == \"mnli-mm\" else task\r\ndataset = load_dataset(\"glue\", actual_task)\r\nmetric = load_metric(\"glue\", actual_task)\r\nbatch_size = 16\r\nattention_type = \"linear\"\r\n\r\nfrom transformers.models.mobilebert_mod import (\r\n MobileBertForSequenceClassification,\r\n MobileBertTokenizerFast,\r\n)\r\nfrom transformers.models.mobilebert_mod.configuration_mobilebert import (\r\n MobileBertConfigMod,\r\n)\r\nfrom transformers import TrainingArguments, Trainer\r\n\r\nnum_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\r\ntokenizer = MobileBertTokenizerFast.from_pretrained(\r\n \"/media/ad/00b5422b-9d54-4449-8b5d-08eab5cdac8c/training_trfm/big_linear_layerdrop_shared/checkpoint-23000/\",\r\n max_len=512,\r\n)\r\nmodel = MobileBertForSequenceClassification.from_pretrained(\r\n \"/media/ad/00b5422b-9d54-4449-8b5d-08eab5cdac8c/training_trfm/big_linear_layerdrop_shared/checkpoint-23000/\",\r\n num_labels=num_labels,\r\n)\r\nprint(model.num_parameters())\r\n\r\ntask_to_keys = {\r\n \"cola\": (\"sentence\", None),\r\n \"mnli\": (\"premise\", \"hypothesis\"),\r\n \"mnli-mm\": (\"premise\", \"hypothesis\"),\r\n \"mrpc\": (\"sentence1\", \"sentence2\"),\r\n \"qnli\": (\"question\", \"sentence\"),\r\n \"qqp\": (\"question1\", \"question2\"),\r\n \"rte\": (\"sentence1\", \"sentence2\"),\r\n \"sst2\": (\"sentence\", None),\r\n \"stsb\": (\"sentence1\", \"sentence2\"),\r\n \"wnli\": (\"sentence1\", \"sentence2\"),\r\n}\r\n\r\nsentence1_key, sentence2_key = task_to_keys[task]\r\nif sentence2_key is None:\r\n print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\r\nelse:\r\n print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\r\n print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")\r\n\r\n\r\ndef preprocess_function(examples):\r\n if sentence2_key is None:\r\n return tokenizer(examples[sentence1_key], truncation=True)\r\n return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\r\n\r\n\r\nencoded_dataset = dataset.map(preprocess_function, batched=True)\r\nmetric_name = (\r\n \"pearson\"\r\n if task == \"stsb\"\r\n else \"matthews_correlation\"\r\n if task == \"cola\"\r\n else \"accuracy\"\r\n)\r\n\r\nargs = TrainingArguments(\r\n f\"test-glue/{task}_{attention_type}\",\r\n evaluation_strategy=\"steps\",\r\n learning_rate=1e-5,\r\n per_device_train_batch_size=batch_size,\r\n per_device_eval_batch_size=batch_size,\r\n logging_steps=200,\r\n num_train_epochs=5,\r\n gradient_accumulation_steps=1,\r\n warmup_steps=10000,\r\n fp16=True,\r\n dataloader_num_workers=10,\r\n weight_decay=0.1,\r\n load_best_model_at_end=True,\r\n metric_for_best_model=metric_name,\r\n)\r\n\r\n\r\ndef compute_metrics(eval_pred):\r\n predictions, labels = eval_pred\r\n if task != \"stsb\":\r\n predictions = np.argmax(predictions, axis=1)\r\n else:\r\n predictions = predictions[:, 0]\r\n return metric.compute(predictions=predictions, references=labels)\r\n\r\n\r\nvalidation_key = (\r\n \"validation_mismatched\"\r\n if task == \"mnli-mm\"\r\n else \"validation_matched\"\r\n if task == \"mnli\"\r\n else \"validation\"\r\n)\r\n\r\ntrainer = Trainer(\r\n model,\r\n args,\r\n train_dataset=encoded_dataset[\"train\"],\r\n eval_dataset=encoded_dataset[validation_key],\r\n tokenizer=tokenizer,\r\n compute_metrics=compute_metrics,\r\n)\r\n\r\ntrainer.train()\r\n```\r\n\r\nNow, I have come back to pre-training. The changes that I think I have done are: not formatting the dataset to torch: ~~`big_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])`~~ so maybe some column is dropped and not freezed in memory and now I have not setted any validation dataset in the trainer. \r\n\r\nMy validation dataset before:\r\n```\r\nbook_corpus_eval = load_dataset(\r\n \"bookcorpus\",\r\n \"plain_text\",\r\n cache_dir=\"/home/ad/Desktop/bookcorpus\",\r\n split=\"train[98:99%]\",\r\n)\r\nbook_corpus_eval = book_corpus_eval.map(encode, batched=True)\r\nbook_corpus_eval.set_format(\r\n type=\"torch\", columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"]\r\n)\r\n**book_corpus_eval = book_corpus_eval.select([i for i in range(1500)])**\r\n```\r\nMaybe _selecting_ or indexing the dataset before feeding it to the trainer, do something strange.\r\n\r\nMy trainer now:\r\n```\r\n\r\nbig_dataset = load_from_disk(\"/home/ad/Desktop/35percent_data.arrow/\")\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./big_linear_layerdrop_shared_silu_secondtry\",\r\n overwrite_output_dir=True,\r\n per_device_train_batch_size=60,\r\n per_device_eval_batch_size=60,\r\n save_steps=500,\r\n save_total_limit=10,\r\n logging_first_step=True,\r\n logging_steps=100,\r\n# evaluation_strategy='steps',\r\n# eval_steps=250,\r\n gradient_accumulation_steps=8,\r\n fp16=True,\r\n dataloader_num_workers=10,\r\n warmup_steps=15000,\r\n learning_rate=6e-4,\r\n adam_epsilon=1e-6,\r\n adam_beta2=0.98,\r\n weight_decay=0.01,\r\n max_grad_norm=1.0,\r\n max_steps=500000, \r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=big_dataset,\r\n# eval_dataset=book_corpus_eval,\r\n tokenizer=tokenizer)\r\n\r\nimport wandb\r\nwandb.login()\r\n\r\ntrainer.train()\r\n```\r\n\r\nAnd surprisingly, the ram now keeps going up and down. The training is up now for 12h without collapse the ram. I don't know what could cause the leakage. :mag: \r\n\r\nEdit: I didn't see the swap memory, that keeps increasing. So the problem persist. ",
"Thanks for sharing your results.\r\nSo you still had the issue for fine-tuning ?\r\nAnd the issue still appears with a bare-bone dataset from an arrow file...",
"Yes, on both cases. Fine-tuning a pre-trained model and pre-training from scratch with a local arrow file already pre-processed."
] | 2020-09-16T04:33:15Z
| 2021-02-16T12:02:01Z
| null |
NONE
| null | null | null |
I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:
```python
from datasets import load_dataset
@dataclass
class DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):
"""
Data collator used for language modeling based on DataCollatorForLazyLanguageModeling
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for masked language modeling
"""
block_size: int = 512
def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:
examples = [example['text'] for example in examples]
batch, attention_mask = self._tensorize_batch(examples)
if self.mlm:
inputs, labels = self.mask_tokens(batch)
return {"input_ids": inputs, "labels": labels}
else:
labels = batch.clone().detach()
if self.tokenizer.pad_token_id is not None:
labels[labels == self.tokenizer.pad_token_id] = -100
return {"input_ids": batch, "labels": labels}
def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:
if self.tokenizer._pad_token is None:
raise ValueError(
"You are attempting to pad samples but the tokenizer you are using"
f" ({self.tokenizer.__class__.__name__}) does not have one."
)
tensor_examples = self.tokenizer.batch_encode_plus(
[ex for ex in examples if ex],
max_length=self.block_size,
return_tensors="pt",
pad_to_max_length=True,
return_attention_mask=True,
truncation=True,
)
input_ids, attention_mask = tensor_examples["input_ids"], tensor_examples["attention_mask"]
return input_ids, attention_mask
dataset = load_dataset('text', data_files='train.txt',cache_dir="./", , split='train')
data_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True,
mlm_probability=0.15, block_size=tokenizer.max_len)
trainer = Trainer(model=model, args=args, data_collator=data_collator,
train_dataset=train_dataset, prediction_loss_only=True, )
trainer.train(model_path=model_path)
```
This train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words.
During training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.
Could you please give me any suggestions on why this happened and how to fix it?
Thanks.
|
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MDExOlB1bGxSZXF1ZXN0NTIwNTE0MDY3
| 850
|
Create ClassLabel for labelling tasks datasets
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[
"@lhoestq Better?"
] | 2020-11-13T11:07:22Z
| 2020-11-16T10:32:05Z
| 2020-11-16T10:31:58Z
|
CONTRIBUTOR
| null | 0
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This PR adds a specific `ClassLabel` for the datasets that are about a labelling task such as POS, NER or Chunking.
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I_kwDODunzps52Aop-
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Formatting overhead
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[
"Ah I think the `line-profiler` log is off-by-one and it is in fact the `extract_batch` method that's taking forever. Will investigate further.",
"I tracked it down to a quirk of my setup. Apologies."
] | 2023-11-06T19:06:38Z
| 2023-11-06T23:56:12Z
| 2023-11-06T23:56:12Z
|
NONE
| null | null | null |
### Describe the bug
Hi! I very recently noticed that my training time is dominated by batch formatting. Using Lightning's profilers, I located the bottleneck within `datasets.formatting.formatting` and then narrowed it down with `line-profiler`. It turns out that almost all of the overhead is due to creating new instances of `self.python_arrow_extractor`. I admit I'm confused why that could be the case - as far as I can tell there's no complex `__init__` logic to execute.
### Steps to reproduce the bug
1. Set up a dataset `ds` with potentially several (4+) columns (not sure if this is necessary, but it did at one point of the investigation make overhead worse)
2. Process it using a custom transform, `ds = ds.with_transform(transform_func)`
3. Decorate this function https://github.com/huggingface/datasets/blob/main/src/datasets/formatting/formatting.py#L512 with `@profile` from https://pypi.org/project/line-profiler/
4. Profile with `$ kernprof -l script_to_profile.py`
### Expected behavior
Batch formatting should have acceptable overhead.
### Environment info
```
datasets=2.14.6
pyarrow=14.0.0
```
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Add Text2log Dataset
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"The CI fails are unrelated to your PR and fixed on master, I think we can merge now !"
] | 2022-01-14T10:45:01Z
| 2022-01-20T17:09:44Z
| 2022-01-20T17:09:44Z
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Adding the text2log dataset used for training FOL sentence translating models
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Fix python formatting for complex types in `format_table`
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[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008047 / 0.011353 (-0.003305) | 0.004649 / 0.011008 (-0.006359) | 0.100275 / 0.038508 (0.061767) | 0.089551 / 0.023109 (0.066442) | 0.369831 / 0.275898 (0.093933) | 0.431023 / 0.323480 (0.107544) | 0.004721 / 0.007986 (-0.003265) | 0.004904 / 0.004328 (0.000575) | 0.076345 / 0.004250 (0.072095) | 0.066902 / 0.037052 (0.029849) | 0.377208 / 0.258489 (0.118718) | 0.430989 / 0.293841 (0.137148) | 0.036260 / 0.128546 (-0.092287) | 0.010158 / 0.075646 (-0.065488) | 0.344923 / 0.419271 (-0.074349) | 0.062504 / 0.043533 (0.018971) | 0.373038 / 0.255139 (0.117899) | 0.399918 / 0.283200 (0.116718) | 0.028257 / 0.141683 (-0.113425) | 1.782546 / 1.452155 (0.330391) | 1.920010 / 1.492716 (0.427293) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277670 / 0.018006 (0.259664) | 0.500543 / 0.000490 (0.500053) | 0.018256 / 0.000200 (0.018056) | 0.000343 / 0.000054 (0.000289) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033337 / 0.037411 (-0.004074) | 0.100542 / 0.014526 (0.086017) | 0.114903 / 0.176557 (-0.061654) | 0.181267 / 0.737135 (-0.555868) | 0.115019 / 0.296338 (-0.181320) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457333 / 0.215209 (0.242124) | 4.542082 / 2.077655 (2.464427) | 2.231817 / 1.504120 (0.727697) | 2.028523 / 1.541195 (0.487328) | 2.110715 / 1.468490 (0.642225) | 0.583162 / 4.584777 (-4.001615) | 4.179413 / 3.745712 (0.433701) | 4.145620 / 5.269862 (-1.124241) | 2.452458 / 4.565676 (-2.113218) | 0.068229 / 0.424275 (-0.356046) | 0.009027 / 0.007607 (0.001420) | 0.549002 / 0.226044 (0.322957) | 5.485707 / 2.268929 (3.216779) | 2.789467 / 55.444624 (-52.655157) | 2.397499 / 6.876477 (-4.478977) | 2.492083 / 2.142072 (0.350010) | 0.692445 / 4.805227 (-4.112782) | 0.160527 / 6.500664 (-6.340137) | 0.071597 / 0.075469 (-0.003872) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.486043 / 1.841788 (-0.355744) | 22.377207 / 8.074308 (14.302899) | 16.443719 / 10.191392 (6.252327) | 0.170740 / 0.680424 (-0.509684) | 0.021511 / 0.534201 (-0.512690) | 0.470798 / 0.579283 (-0.108485) | 0.511851 / 0.434364 (0.077487) | 0.551154 / 0.540337 (0.010817) | 0.768420 / 1.386936 (-0.618516) |\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) | 0.008049 / 0.011353 (-0.003303) | 0.004676 / 0.011008 (-0.006332) | 0.076360 / 0.038508 (0.037852) | 0.093648 / 0.023109 (0.070539) | 0.480597 / 0.275898 (0.204699) | 0.524674 / 0.323480 (0.201194) | 0.006242 / 0.007986 (-0.001744) | 0.003827 / 0.004328 (-0.000501) | 0.077039 / 0.004250 (0.072788) | 0.067992 / 0.037052 (0.030940) | 0.480287 / 0.258489 (0.221798) | 0.528546 / 0.293841 (0.234706) | 0.038347 / 0.128546 (-0.090199) | 0.010036 / 0.075646 (-0.065611) | 0.084386 / 0.419271 (-0.334885) | 0.057211 / 0.043533 (0.013678) | 0.475993 / 0.255139 (0.220854) | 0.504881 / 0.283200 (0.221682) | 0.026658 / 0.141683 (-0.115025) | 1.777095 / 1.452155 (0.324940) | 1.896446 / 1.492716 (0.403730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242450 / 0.018006 (0.224443) | 0.488864 / 0.000490 (0.488374) | 0.007329 / 0.000200 (0.007129) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039093 / 0.037411 (0.001682) | 0.114724 / 0.014526 (0.100198) | 0.124965 / 0.176557 (-0.051591) | 0.188165 / 0.737135 (-0.548971) | 0.125336 / 0.296338 (-0.171002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515718 / 0.215209 (0.300509) | 5.150865 / 2.077655 (3.073210) | 2.767866 / 1.504120 (1.263746) | 2.571003 / 1.541195 (1.029808) | 2.656224 / 1.468490 (1.187734) | 0.583771 / 4.584777 (-4.001006) | 4.268713 / 3.745712 (0.523001) | 3.938699 / 5.269862 (-1.331163) | 2.413569 / 4.565676 (-2.152108) | 0.068848 / 0.424275 (-0.355427) | 0.008758 / 0.007607 (0.001151) | 0.610831 / 0.226044 (0.384786) | 6.099965 / 2.268929 (3.831037) | 3.337530 / 55.444624 (-52.107095) | 2.910962 / 6.876477 (-3.965514) | 3.149813 / 2.142072 (1.007740) | 0.700576 / 4.805227 (-4.104651) | 0.157569 / 6.500664 (-6.343095) | 0.072237 / 0.075469 (-0.003232) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655840 / 1.841788 (-0.185947) | 23.639061 / 8.074308 (15.564753) | 17.301593 / 10.191392 (7.110201) | 0.201717 / 0.680424 (-0.478707) | 0.023836 / 0.534201 (-0.510365) | 0.470941 / 0.579283 (-0.108342) | 0.498157 / 0.434364 (0.063794) | 0.581195 / 0.540337 (0.040857) | 0.788304 / 1.386936 (-0.598632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f657900acfd8ea1afaf47267e552a7ad2c6ef28b \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004823 / 0.011353 (-0.006530) | 0.002976 / 0.011008 (-0.008032) | 0.062070 / 0.038508 (0.023562) | 0.051623 / 0.023109 (0.028513) | 0.242249 / 0.275898 (-0.033649) | 0.271223 / 0.323480 (-0.052257) | 0.003906 / 0.007986 (-0.004079) | 0.002709 / 0.004328 (-0.001620) | 0.047874 / 0.004250 (0.043624) | 0.038123 / 0.037052 (0.001071) | 0.253737 / 0.258489 (-0.004752) | 0.281942 / 0.293841 (-0.011899) | 0.023750 / 0.128546 (-0.104797) | 0.007227 / 0.075646 (-0.068420) | 0.203137 / 0.419271 (-0.216134) | 0.036254 / 0.043533 (-0.007278) | 0.243923 / 0.255139 (-0.011216) | 0.263908 / 0.283200 (-0.019291) | 0.017795 / 0.141683 (-0.123888) | 1.105680 / 1.452155 (-0.346475) | 1.166804 / 1.492716 (-0.325912) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097388 / 0.018006 (0.079381) | 0.305481 / 0.000490 (0.304991) | 0.000210 / 0.000200 (0.000010) | 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) | 0.020096 / 0.037411 (-0.017315) | 0.063990 / 0.014526 (0.049464) | 0.073694 / 0.176557 (-0.102863) | 0.122909 / 0.737135 (-0.614227) | 0.076199 / 0.296338 (-0.220140) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285612 / 0.215209 (0.070403) | 2.770524 / 2.077655 (0.692869) | 1.451624 / 1.504120 (-0.052496) | 1.329223 / 1.541195 (-0.211972) | 1.369980 / 1.468490 (-0.098510) | 0.398269 / 4.584777 (-4.186507) | 2.418740 / 3.745712 (-1.326972) | 2.796384 / 5.269862 (-2.473478) | 1.686490 / 4.565676 (-2.879186) | 0.046417 / 0.424275 (-0.377858) | 0.005414 / 0.007607 (-0.002193) | 0.345505 / 0.226044 (0.119460) | 3.391857 / 2.268929 (1.122929) | 1.856696 / 55.444624 (-53.587929) | 1.538061 / 6.876477 (-5.338416) | 1.631489 / 2.142072 (-0.510584) | 0.479188 / 4.805227 (-4.326039) | 0.101549 / 6.500664 (-6.399116) | 0.042150 / 0.075469 (-0.033319) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957961 / 1.841788 (-0.883827) | 12.349371 / 8.074308 (4.275063) | 10.778214 / 10.191392 (0.586822) | 0.141265 / 0.680424 (-0.539158) | 0.014559 / 0.534201 (-0.519642) | 0.272071 / 0.579283 (-0.307212) | 0.262493 / 0.434364 (-0.171871) | 0.310351 / 0.540337 (-0.229986) | 0.399220 / 1.386936 (-0.987716) |\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) | 0.005127 / 0.011353 (-0.006226) | 0.002926 / 0.011008 (-0.008082) | 0.048320 / 0.038508 (0.009812) | 0.063082 / 0.023109 (0.039973) | 0.269846 / 0.275898 (-0.006052) | 0.294470 / 0.323480 (-0.029010) | 0.004201 / 0.007986 (-0.003784) | 0.002434 / 0.004328 (-0.001894) | 0.048020 / 0.004250 (0.043770) | 0.043909 / 0.037052 (0.006856) | 0.271328 / 0.258489 (0.012839) | 0.298820 / 0.293841 (0.004979) | 0.024565 / 0.128546 (-0.103981) | 0.007752 / 0.075646 (-0.067894) | 0.054171 / 0.419271 (-0.365101) | 0.033147 / 0.043533 (-0.010386) | 0.266628 / 0.255139 (0.011489) | 0.288651 / 0.283200 (0.005452) | 0.018910 / 0.141683 (-0.122773) | 1.153679 / 1.452155 (-0.298476) | 1.214979 / 1.492716 (-0.277737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097064 / 0.018006 (0.079057) | 0.307504 / 0.000490 (0.307014) | 0.000230 / 0.000200 (0.000030) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021848 / 0.037411 (-0.015563) | 0.071159 / 0.014526 (0.056633) | 0.081310 / 0.176557 (-0.095247) | 0.120175 / 0.737135 (-0.616961) | 0.082619 / 0.296338 (-0.213720) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296606 / 0.215209 (0.081397) | 2.908495 / 2.077655 (0.830840) | 1.606522 / 1.504120 (0.102402) | 1.528599 / 1.541195 (-0.012596) | 1.508332 / 1.468490 (0.039842) | 0.396336 / 4.584777 (-4.188441) | 2.449163 / 3.745712 (-1.296549) | 2.533372 / 5.269862 (-2.736490) | 1.623061 / 4.565676 (-2.942615) | 0.046723 / 0.424275 (-0.377552) | 0.005120 / 0.007607 (-0.002487) | 0.345763 / 0.226044 (0.119718) | 3.427382 / 2.268929 (1.158454) | 1.962806 / 55.444624 (-53.481819) | 1.678548 / 6.876477 (-5.197929) | 1.865773 / 2.142072 (-0.276300) | 0.477932 / 4.805227 (-4.327295) | 0.100994 / 6.500664 (-6.399670) | 0.042212 / 0.075469 (-0.033258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.992766 / 1.841788 (-0.849022) | 12.764885 / 8.074308 (4.690577) | 10.892094 / 10.191392 (0.700702) | 0.143211 / 0.680424 (-0.537213) | 0.016347 / 0.534201 (-0.517853) | 0.270181 / 0.579283 (-0.309102) | 0.278658 / 0.434364 (-0.155706) | 0.307134 / 0.540337 (-0.233203) | 0.396792 / 1.386936 (-0.990144) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6d2f2a5e0fea3827eccfd1717d8021c15fc4292a \"CML watermark\")\n",
"Thanks for the fix ! It was probably my mistake (forgot to re-apply the features)"
] | 2023-10-31T19:48:08Z
| 2023-11-02T14:42:28Z
| 2023-11-02T14:21:16Z
|
CONTRIBUTOR
| null | 0
|
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Fix #6366
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https://api.github.com/repos/huggingface/datasets/issues/6368/timeline
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https://api.github.com/repos/huggingface/datasets/issues/3370
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https://github.com/huggingface/datasets/pull/3370
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PR_kwDODunzps4vUVA3
| 3,370
|
Document a training loop for streaming dataset
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| 2021-12-03T13:34:35Z
| 2021-12-03T13:34:34Z
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I added some docs about streaming dataset. In particular I added two subsections:
- one on how to use `map` for preprocessing
- one on how to use a streaming dataset in a pytorch training loop
cc @patrickvonplaten @stevhliu if you have some comments
cc @Rocketknight1 later we can add the one for TF and I might need your help ^^'
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Export to_iterable_dataset to document
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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) | 0.006076 / 0.011353 (-0.005277) | 0.003730 / 0.011008 (-0.007279) | 0.080778 / 0.038508 (0.042270) | 0.062970 / 0.023109 (0.039860) | 0.395864 / 0.275898 (0.119966) | 0.430024 / 0.323480 (0.106544) | 0.004823 / 0.007986 (-0.003162) | 0.002949 / 0.004328 (-0.001379) | 0.062423 / 0.004250 (0.058172) | 0.047343 / 0.037052 (0.010291) | 0.403153 / 0.258489 (0.144664) | 0.443666 / 0.293841 (0.149825) | 0.027798 / 0.128546 (-0.100748) | 0.008056 / 0.075646 (-0.067590) | 0.262260 / 0.419271 (-0.157011) | 0.045958 / 0.043533 (0.002425) | 0.391349 / 0.255139 (0.136210) | 0.421831 / 0.283200 (0.138632) | 0.021837 / 0.141683 (-0.119846) | 1.485509 / 1.452155 (0.033355) | 1.542940 / 1.492716 (0.050224) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196831 / 0.018006 (0.178825) | 0.435774 / 0.000490 (0.435285) | 0.003647 / 0.000200 (0.003447) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023756 / 0.037411 (-0.013655) | 0.075737 / 0.014526 (0.061211) | 0.303703 / 0.176557 (0.127146) | 0.164862 / 0.737135 (-0.572273) | 0.198483 / 0.296338 (-0.097855) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405220 / 0.215209 (0.190011) | 4.065983 / 2.077655 (1.988328) | 2.043001 / 1.504120 (0.538881) | 1.853318 / 1.541195 (0.312123) | 1.977452 / 1.468490 (0.508962) | 0.500897 / 4.584777 (-4.083880) | 3.065756 / 3.745712 (-0.679956) | 2.924096 / 5.269862 (-2.345765) | 1.876194 / 4.565676 (-2.689482) | 0.057774 / 0.424275 (-0.366501) | 0.006809 / 0.007607 (-0.000798) | 0.470979 / 0.226044 (0.244934) | 4.719546 / 2.268929 (2.450618) | 2.449651 / 55.444624 (-52.994973) | 2.211817 / 6.876477 (-4.664660) | 2.398760 / 2.142072 (0.256687) | 0.590608 / 4.805227 (-4.214619) | 0.125836 / 6.500664 (-6.374829) | 0.060759 / 0.075469 (-0.014710) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.243609 / 1.841788 (-0.598179) | 18.836193 / 8.074308 (10.761885) | 13.835053 / 10.191392 (3.643661) | 0.129708 / 0.680424 (-0.550716) | 0.016708 / 0.534201 (-0.517493) | 0.337219 / 0.579283 (-0.242065) | 0.359045 / 0.434364 (-0.075319) | 0.383329 / 0.540337 (-0.157009) | 0.539629 / 1.386936 (-0.847307) |\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) | 0.006073 / 0.011353 (-0.005280) | 0.003713 / 0.011008 (-0.007295) | 0.062642 / 0.038508 (0.024134) | 0.062618 / 0.023109 (0.039508) | 0.362029 / 0.275898 (0.086130) | 0.401924 / 0.323480 (0.078445) | 0.004689 / 0.007986 (-0.003297) | 0.002945 / 0.004328 (-0.001384) | 0.062720 / 0.004250 (0.058470) | 0.048901 / 0.037052 (0.011848) | 0.363780 / 0.258489 (0.105291) | 0.405111 / 0.293841 (0.111270) | 0.027738 / 0.128546 (-0.100808) | 0.008046 / 0.075646 (-0.067600) | 0.067752 / 0.419271 (-0.351519) | 0.041955 / 0.043533 (-0.001577) | 0.361615 / 0.255139 (0.106476) | 0.388762 / 0.283200 (0.105562) | 0.021302 / 0.141683 (-0.120380) | 1.473527 / 1.452155 (0.021372) | 1.529753 / 1.492716 (0.037037) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300446 / 0.018006 (0.282440) | 0.425844 / 0.000490 (0.425354) | 0.054507 / 0.000200 (0.054307) | 0.000282 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025478 / 0.037411 (-0.011933) | 0.078298 / 0.014526 (0.063772) | 0.087647 / 0.176557 (-0.088909) | 0.138978 / 0.737135 (-0.598157) | 0.088396 / 0.296338 (-0.207942) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421345 / 0.215209 (0.206136) | 4.209188 / 2.077655 (2.131533) | 2.260731 / 1.504120 (0.756611) | 2.072329 / 1.541195 (0.531134) | 2.086778 / 1.468490 (0.618288) | 0.495425 / 4.584777 (-4.089352) | 2.987519 / 3.745712 (-0.758194) | 2.895106 / 5.269862 (-2.374756) | 1.874637 / 4.565676 (-2.691039) | 0.057080 / 0.424275 (-0.367195) | 0.006402 / 0.007607 (-0.001205) | 0.498233 / 0.226044 (0.272188) | 4.974385 / 2.268929 (2.705457) | 2.671755 / 55.444624 (-52.772870) | 2.356120 / 6.876477 (-4.520357) | 2.531374 / 2.142072 (0.389301) | 0.581955 / 4.805227 (-4.223272) | 0.125491 / 6.500664 (-6.375173) | 0.062267 / 0.075469 (-0.013202) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307233 / 1.841788 (-0.534555) | 18.929740 / 8.074308 (10.855431) | 14.029693 / 10.191392 (3.838301) | 0.161992 / 0.680424 (-0.518431) | 0.017127 / 0.534201 (-0.517074) | 0.336644 / 0.579283 (-0.242639) | 0.336550 / 0.434364 (-0.097814) | 0.400554 / 0.540337 (-0.139783) | 0.560725 / 1.386936 (-0.826211) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb8c5de5145c7e7eee65391cb7f4d92f0d565d62 \"CML watermark\")\n"
] | 2023-08-12T07:00:14Z
| 2023-08-15T17:04:01Z
| 2023-08-15T16:55:24Z
|
CONTRIBUTOR
| null | 0
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Fix the export of a missing method of `Dataset`
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PR_kwDODunzps40ewN2
| 3,927
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Update main readme
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"_The documentation is not available anymore as the PR was closed or merged._",
"What do you think @albertvillanova ?"
] | 2022-03-15T18:09:59Z
| 2022-03-29T10:13:47Z
| 2022-03-29T10:08:20Z
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The main readme was still focused on text datasets - I extended it by mentioning that we also support image and audio datasets
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Missing elements in `map` a batched dataset
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"Hi ! in your code batching is **only used within** `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\r\n```"
] | 2023-05-29T08:09:19Z
| 2023-07-26T15:48:15Z
| 2023-07-26T15:48:15Z
|
NONE
| null | null | null |
### Describe the bug
As outlined [here](https://discuss.huggingface.co/t/length-error-using-map-with-datasets/40969/3?u=sachin), the following collate function drops 5 out of possible 6 elements in the batch (it is 6 because out of the eight, two are bad links in laion). A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
The weirdest part is when inspecting the sizes of the tensors as shown below, both `tokenized_captions["input_ids"]` and `image_features` show the correct shapes. Simply the output only has one element (with the batch dimension squeezed out).
```python
class CollateFn:
def get_image(self, url):
try:
response = requests.get(url)
return Image.open(io.BytesIO(response.content)).convert("RGB")
except PIL.UnidentifiedImageError:
logger.info(f"Reading error: Could not transform f{url}")
return None
except requests.exceptions.ConnectionError:
logger.info(f"Connection error: Could not transform f{url}")
return None
def __call__(self, batch):
images = [self.get_image(url) for url in batch["url"]]
captions = [caption for caption, image in zip(batch["caption"], images) if image is not None]
images = [image for image in images if image is not None]
tokenized_captions = tokenizer(
captions,
padding="max_length",
truncation=True,
max_length=tokenizer.model_max_length,
return_tensors="pt",
)
image_features = torch.stack([torch.Tensor(feature_extractor(image)["pixel_values"][0]) for image in images])
# import pdb; pdb.set_trace()
return {"input_ids": tokenized_captions["input_ids"], "images": image_features}
collate_fn = CollateFn()
laion_ds = datasets.load_dataset("laion/laion400m", split="train", streaming=True)
laion_ds_batched = laion_ds.map(collate_fn, batched=True, batch_size=8, remove_columns=next(iter(laion_ds)).keys())
```
### Steps to reproduce the bug
A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
### Expected behavior
Would expect `next(iter(laion_ds_batched))` to produce two tensors of shape `(batch_size, 77)` and `batch_size, image_shape`.
### Environment info
datasets==2.12.0
python==3.10
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