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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type timestamp[s] to null
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1962, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type timestamp[s] to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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https://api.github.com/repos/huggingface/datasets/issues/6999 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6999/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6999/comments | https://api.github.com/repos/huggingface/datasets/issues/6999/events | https://github.com/huggingface/datasets/pull/6999 | 2,372,124,589 | PR_kwDODunzps5zd-ak | 6,999 | Remove tasks | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6999). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6998). 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) | 0.005396 / 0.011353 (-0.005957) | 0.003974 / 0.011008 (-0.007034) | 0.063490 / 0.038508 (0.024982) | 0.030299 / 0.023109 (0.007189) | 0.244489 / 0.275898 (-0.031409) | 0.274116 / 0.323480 (-0.049364) | 0.003187 / 0.007986 (-0.004798) | 0.003433 / 0.004328 (-0.000896) | 0.049313 / 0.004250 (0.045062) | 0.043677 / 0.037052 (0.006624) | 0.260198 / 0.258489 (0.001709) | 0.283558 / 0.293841 (-0.010283) | 0.029728 / 0.128546 (-0.098819) | 0.011950 / 0.075646 (-0.063696) | 0.204371 / 0.419271 (-0.214901) | 0.035712 / 0.043533 (-0.007821) | 0.252715 / 0.255139 (-0.002424) | 0.268906 / 0.283200 (-0.014293) | 0.021153 / 0.141683 (-0.120529) | 1.125599 / 1.452155 (-0.326556) | 1.163122 / 1.492716 (-0.329594) |\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) | 0.095089 / 0.018006 (0.077083) | 0.298576 / 0.000490 (0.298086) | 0.000214 / 0.000200 (0.000014) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018567 / 0.037411 (-0.018844) | 0.062337 / 0.014526 (0.047811) | 0.074231 / 0.176557 (-0.102326) | 0.120960 / 0.737135 (-0.616175) | 0.076124 / 0.296338 (-0.220215) |\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) | 0.286936 / 0.215209 (0.071727) | 2.816656 / 2.077655 (0.739001) | 1.486772 / 1.504120 (-0.017348) | 1.373289 / 1.541195 (-0.167905) | 1.392739 / 1.468490 (-0.075752) | 0.708091 / 4.584777 (-3.876686) | 2.410034 / 3.745712 (-1.335678) | 2.912701 / 5.269862 (-2.357161) | 1.850924 / 4.565676 (-2.714752) | 0.078896 / 0.424275 (-0.345380) | 0.005116 / 0.007607 (-0.002491) | 0.332275 / 0.226044 (0.106231) | 3.306562 / 2.268929 (1.037633) | 1.853051 / 55.444624 (-53.591573) | 1.556210 / 6.876477 (-5.320267) | 1.558892 / 2.142072 (-0.583181) | 0.789917 / 4.805227 (-4.015310) | 0.133683 / 6.500664 (-6.366981) | 0.042566 / 0.075469 (-0.032904) |\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) | 0.957050 / 1.841788 (-0.884738) | 11.401462 / 8.074308 (3.327154) | 9.782988 / 10.191392 (-0.408404) | 0.142127 / 0.680424 (-0.538296) | 0.014730 / 0.534201 (-0.519471) | 0.302647 / 0.579283 (-0.276636) | 0.264654 / 0.434364 (-0.169710) | 0.341340 / 0.540337 (-0.198998) | 0.425808 / 1.386936 (-0.961128) |\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) | 0.005679 / 0.011353 (-0.005674) | 0.003513 / 0.011008 (-0.007495) | 0.050135 / 0.038508 (0.011627) | 0.031614 / 0.023109 (0.008505) | 0.260064 / 0.275898 (-0.015834) | 0.285816 / 0.323480 (-0.037664) | 0.004428 / 0.007986 (-0.003558) | 0.002816 / 0.004328 (-0.001512) | 0.048441 / 0.004250 (0.044191) | 0.039622 / 0.037052 (0.002570) | 0.274940 / 0.258489 (0.016451) | 0.311837 / 0.293841 (0.017996) | 0.031439 / 0.128546 (-0.097107) | 0.012056 / 0.075646 (-0.063590) | 0.060109 / 0.419271 (-0.359163) | 0.033123 / 0.043533 (-0.010409) | 0.261563 / 0.255139 (0.006424) | 0.282640 / 0.283200 (-0.000560) | 0.017168 / 0.141683 (-0.124515) | 1.127859 / 1.452155 (-0.324295) | 1.182414 / 1.492716 (-0.310303) |\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) | 0.095517 / 0.018006 (0.077510) | 0.300578 / 0.000490 (0.300088) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022192 / 0.037411 (-0.015220) | 0.076617 / 0.014526 (0.062091) | 0.087405 / 0.176557 (-0.089151) | 0.127011 / 0.737135 (-0.610124) | 0.088706 / 0.296338 (-0.207632) |\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) | 0.294260 / 0.215209 (0.079051) | 2.872879 / 2.077655 (0.795224) | 1.531374 / 1.504120 (0.027254) | 1.399232 / 1.541195 (-0.141962) | 1.400708 / 1.468490 (-0.067782) | 0.714003 / 4.584777 (-3.870773) | 0.943144 / 3.745712 (-2.802568) | 2.833396 / 5.269862 (-2.436466) | 1.890570 / 4.565676 (-2.675106) | 0.077664 / 0.424275 (-0.346611) | 0.005651 / 0.007607 (-0.001956) | 0.349476 / 0.226044 (0.123431) | 3.405768 / 2.268929 (1.136840) | 1.869739 / 55.444624 (-53.574885) | 1.575293 / 6.876477 (-5.301184) | 1.692981 / 2.142072 (-0.449092) | 0.795363 / 4.805227 (-4.009865) | 0.131532 / 6.500664 (-6.369132) | 0.041183 / 0.075469 (-0.034286) |\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) | 1.000821 / 1.841788 (-0.840967) | 11.987795 / 8.074308 (3.913487) | 10.147652 / 10.191392 (-0.043740) | 0.141314 / 0.680424 (-0.539110) | 0.015506 / 0.534201 (-0.518695) | 0.305090 / 0.579283 (-0.274193) | 0.123403 / 0.434364 (-0.310960) | 0.346507 / 0.540337 (-0.193831) | 0.471318 / 1.386936 (-0.915618) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#186b560eb2393c7d1913f4b3e76e9e04a081e09b \"CML watermark\")\n"
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Fix #6997. | {
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```
FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other']
Right contains one more item: 'other'
Full diff:
[
'clean',
- 'other',
]
FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None
```
Note that repository was recently converted to Parquet: https://huggingface.co./datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6996). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,719,298,480,000 | 1,719,306,481,000 | null | MEMBER | null | 1 | {
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"What is the version of your installed `huggingface-hub`:\r\n```python\r\nimport huggingface_hub\r\nprint(huggingface_hub.__version__)\r\n```\r\n\r\nIt seems you have a very old version of `huggingface-hub`, where `CommitInfo` was not still implemented. You need to update it:\r\n```\r\npip install -U huggingface-hub\r\n```\r\n\r\nNote that `CommitInfo` was implemented in huggingface-hub 0.10.0 and datasets requires \"huggingface-hub>=0.21.2\"",
"The version of my huggingface-hub is 0.23.4.",
"The error message says there is no CommitInfo in your installed huggingface-hub library:\r\n```\r\nImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\\Anaconda3\\envs\\CS224S\\Lib\\site-packages\\huggingface_hub_init_.py)\r\n```\r\n\r\nAnd this is implemented since version 0.10.0:\r\n- https://github.com/huggingface/huggingface_hub/pull/1066"
] | 1,719,248,842,000 | 1,719,299,845,000 | 1,719,295,897,000 | NONE | null | null | null | ### Describe the bug
I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'.
### Steps to reproduce the bug
1. pip install git+https://github.com/huggingface/datasets
2. from datasets import load_dataset
### Expected behavior
ImportError Traceback (most recent call last)
Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1)
----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
[3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train")
[4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test")
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7
1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
[2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) #
[3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License");
(...)
[12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and
[13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License.
[15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0"
---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset
[18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction
[19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63
[61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc
[62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs
---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import (
[64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo,
[65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd,
...
[70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) )
[71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile
[72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Environment info
Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub
$ datasets-cli env
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module>
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module>
from .arrow_dataset import Dataset
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module>
from huggingface_hub import (
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
(CS224S) | {
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"Sure~",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6994). 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) | 0.005538 / 0.011353 (-0.005815) | 0.003997 / 0.011008 (-0.007011) | 0.063444 / 0.038508 (0.024935) | 0.032552 / 0.023109 (0.009442) | 0.266574 / 0.275898 (-0.009324) | 0.282841 / 0.323480 (-0.040639) | 0.004279 / 0.007986 (-0.003706) | 0.002788 / 0.004328 (-0.001540) | 0.049226 / 0.004250 (0.044976) | 0.044688 / 0.037052 (0.007636) | 0.275464 / 0.258489 (0.016975) | 0.305278 / 0.293841 (0.011437) | 0.030097 / 0.128546 (-0.098450) | 0.012237 / 0.075646 (-0.063410) | 0.205526 / 0.419271 (-0.213745) | 0.036145 / 0.043533 (-0.007388) | 0.267395 / 0.255139 (0.012256) | 0.289149 / 0.283200 (0.005949) | 0.019044 / 0.141683 (-0.122639) | 1.162294 / 1.452155 (-0.289861) | 1.183642 / 1.492716 (-0.309074) |\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) | 0.139125 / 0.018006 (0.121119) | 0.301743 / 0.000490 (0.301253) | 0.000260 / 0.000200 (0.000061) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019494 / 0.037411 (-0.017917) | 0.063078 / 0.014526 (0.048552) | 0.076989 / 0.176557 (-0.099567) | 0.121363 / 0.737135 (-0.615773) | 0.080040 / 0.296338 (-0.216298) |\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) | 0.284401 / 0.215209 (0.069192) | 2.805397 / 2.077655 (0.727742) | 1.555609 / 1.504120 (0.051489) | 1.405662 / 1.541195 (-0.135533) | 1.459492 / 1.468490 (-0.008999) | 0.718376 / 4.584777 (-3.866401) | 2.395918 / 3.745712 (-1.349794) | 2.976753 / 5.269862 (-2.293108) | 1.883938 / 4.565676 (-2.681738) | 0.078867 / 0.424275 (-0.345408) | 0.005207 / 0.007607 (-0.002400) | 0.335178 / 0.226044 (0.109133) | 3.313414 / 2.268929 (1.044485) | 1.856929 / 55.444624 (-53.587696) | 1.565319 / 6.876477 (-5.311158) | 1.592723 / 2.142072 (-0.549350) | 0.793621 / 4.805227 (-4.011606) | 0.134208 / 6.500664 (-6.366456) | 0.042853 / 0.075469 (-0.032616) |\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) | 0.981553 / 1.841788 (-0.860235) | 11.810438 / 8.074308 (3.736130) | 9.529874 / 10.191392 (-0.661518) | 0.142216 / 0.680424 (-0.538207) | 0.014303 / 0.534201 (-0.519898) | 0.304600 / 0.579283 (-0.274684) | 0.261869 / 0.434364 (-0.172495) | 0.347301 / 0.540337 (-0.193036) | 0.437395 / 1.386936 (-0.949541) |\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) | 0.005881 / 0.011353 (-0.005472) | 0.004039 / 0.011008 (-0.006969) | 0.050241 / 0.038508 (0.011733) | 0.032670 / 0.023109 (0.009561) | 0.264940 / 0.275898 (-0.010959) | 0.287105 / 0.323480 (-0.036374) | 0.004844 / 0.007986 (-0.003142) | 0.002867 / 0.004328 (-0.001462) | 0.048083 / 0.004250 (0.043833) | 0.040965 / 0.037052 (0.003913) | 0.274390 / 0.258489 (0.015901) | 0.312107 / 0.293841 (0.018266) | 0.031714 / 0.128546 (-0.096832) | 0.012603 / 0.075646 (-0.063043) | 0.060698 / 0.419271 (-0.358573) | 0.033130 / 0.043533 (-0.010402) | 0.264444 / 0.255139 (0.009305) | 0.282797 / 0.283200 (-0.000403) | 0.027872 / 0.141683 (-0.113811) | 1.139026 / 1.452155 (-0.313129) | 1.181431 / 1.492716 (-0.311285) |\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) | 0.097314 / 0.018006 (0.079308) | 0.301326 / 0.000490 (0.300836) | 0.000215 / 0.000200 (0.000015) | 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) | 0.023394 / 0.037411 (-0.014018) | 0.076270 / 0.014526 (0.061744) | 0.089065 / 0.176557 (-0.087491) | 0.129996 / 0.737135 (-0.607139) | 0.089642 / 0.296338 (-0.206697) |\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) | 0.295390 / 0.215209 (0.080181) | 2.877849 / 2.077655 (0.800194) | 1.537129 / 1.504120 (0.033009) | 1.409441 / 1.541195 (-0.131754) | 1.432468 / 1.468490 (-0.036023) | 0.718054 / 4.584777 (-3.866722) | 0.930872 / 3.745712 (-2.814841) | 2.841028 / 5.269862 (-2.428834) | 1.921990 / 4.565676 (-2.643686) | 0.077638 / 0.424275 (-0.346637) | 0.005494 / 0.007607 (-0.002113) | 0.336331 / 0.226044 (0.110287) | 3.330490 / 2.268929 (1.061561) | 1.887994 / 55.444624 (-53.556630) | 1.593332 / 6.876477 (-5.283144) | 1.726956 / 2.142072 (-0.415116) | 0.783612 / 4.805227 (-4.021615) | 0.129926 / 6.500664 (-6.370738) | 0.040792 / 0.075469 (-0.034677) |\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) | 0.980274 / 1.841788 (-0.861514) | 12.193871 / 8.074308 (4.119563) | 10.348934 / 10.191392 (0.157542) | 0.141584 / 0.680424 (-0.538840) | 0.015737 / 0.534201 (-0.518464) | 0.300725 / 0.579283 (-0.278558) | 0.127190 / 0.434364 (-0.307174) | 0.341142 / 0.540337 (-0.199196) | 0.459523 / 1.386936 (-0.927413) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#637246baf96f07b19b193ed101f34b65cb35cffb \"CML watermark\")\n"
] | 1,719,241,667,000 | 1,719,241,667,000 | null | NONE | null | 0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6993). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,719,240,328,000 | 1,719,240,470,000 | null | MEMBER | null | 0 | {
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"Hi ! can you try updating `datasets` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U datasets huggingface_hub\r\n```"
] | 1,719,072,728,000 | 1,719,072,728,000 | null | NONE | null | null | null | ### Describe the bug
I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine.
### Steps to reproduce the bug
use load_dataset with streaming = True in AIMOS
### Expected behavior
does not hang indefinitely and loads batches to start training run
### Environment info
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"ah yes good catch ! feel free to open a PR with your suggested fix"
] | 1,718,979,926,000 | 1,719,221,137,000 | null | NONE | null | null | null | ### Describe the bug
I'm trying to split `IterableDataset` by `split_dataset_by_node`.
But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`.
### Steps to reproduce the bug
Here is the minimal code for reproduction:
```py
>>> from datasets import load_dataset
>>> from datasets.distributed import split_dataset_by_node
>>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True)
>>> dataset = split_dataset_by_node(dataset, 1, 32)
>>> dataset._distributed
DistributedConfig(rank=1, world_size=32)
>>> dataset = split_dataset_by_node(dataset, 1, 15)
>>> dataset._distributed
DistributedConfig(rank=481, world_size=480)
```
As you can see, the second rank 481 > 480, which is problematic.
### Expected behavior
I think this error comes from this line @lhoestq
https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944
We may need to obtain the rank first. Then the above code gives
```py
>>> dataset._distributed
DistributedConfig(rank=16, world_size=480)
```
### Environment info
datasets==2.20.0 | {
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} | [] | open | false | null | [] | null | [] | 1,718,935,762,000 | 1,718,935,975,000 | null | NONE | null | null | null | ### Describe the bug
- When reading dataset, a cache will be generated to the ~/. cache/huggingface/datasets directory
- When using .map and .filter operations, runtime cache will be generated to the /tmp/hf_datasets-* directory
- The default is to use the path of tempfile.tempdir
- If I modify this path to the NFS disk, an error will be reported, but the program will continue to run
- https://github.com/huggingface/datasets/blob/main/src/datasets/config.py#L257
```
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs000000038330a012000030b4'
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs0000000400064d4a000030e5'
```
### Steps to reproduce the bug
```
import os
import time
import tempfile
from datasets import load_dataset
def add_column(sample):
# print(type(sample))
# time.sleep(0.1)
sample['__ds__stats__'] = {'data': 123}
return sample
def filt_column(sample):
# print(type(sample))
if len(sample['content']) > 10:
return True
else:
return False
if __name__ == '__main__':
input_dir = '/mnt/temp/CN/small' # some json dataset
dataset = load_dataset('json', data_dir=input_dir)
temp_dir = '/media/release/release/temp/temp' # a nfs folder
os.makedirs(temp_dir, exist_ok=True)
# change huggingface-datasets runtime cache in nfs（default in /tmp）
tempfile.tempdir = temp_dir
aa = dataset.map(add_column, num_proc=64)
aa = aa.filter(filt_column, num_proc=64)
print(aa)
```
### Expected behavior
no error occur
### Environment info
datasets==2.18.0
ubuntu 20.04 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6988). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"`Dataset` objects are not made to be subclassed, so I don't think going in that direction is a good idea. In particular there is absolutely no test to make sure it works well, and nothing in the internal has been made to anticipate this use case.\r\n\r\nI'd suggest to use a separate function to push changes to the Dataset card, and call it after `push_to_hub()`. This way people can also use a similar logic with other tools that `datasets`. You can also use composition instead of subclassing.",
"Would you consider an alternative where a Dataset instance carries a dataset card template which can be updated?\n\nI don't want to burden my users with having to call another method after `push_to_hub` themselves. If you're not a fan of the template approach above either, then I'll likely subclass `push_to_hub` to once again download the just-uploaded-but-empty dataset card, update it, and reupload it. It'll just be a bit more requests than necessary, but not a big deal overall.\n\n- Tom Aarsen ",
"Actually I find the idea of overriding `_create_dataset_card` better than implementing a templating logic. My main concern is that if we go in that direction we better make sure that subclasses of `Dataset` are working well. \r\n\r\nWell if it's been working fine on your side why not, but make sure you test correctly features that could not work because of subclassing (e.g. I'm pretty sure `map()` won't return your subclass of `Dataset`). Or at least the ones that matter for your lib.\r\n\r\nIf it sounds good to you I'm fine with merging your addition to let you override the dataset card.",
"> e.g. I'm pretty sure map() won't return your subclass of Dataset\r\n\r\nI understand that there's limitations such as this one. The subclass doesn't have to be robust - I'd just like some simple automatic dataset card generation options directly after generating the dataset. This can be removed if the user does additional steps before pushing the model, e.g. mapping, filtering, saving to disk and uploading the loaded dataset, etc.\r\n\r\n> If it sounds good to you I'm fine with merging your addition to let you override the dataset card.\r\n\r\nThat would be quite useful for me! I appreciate it.\r\n\r\nI'm not very sure what the test failures are caused by, I believe the only change in behaviour is that\r\n```python\r\n DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)\r\n MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)\r\n```\r\nare not called when `dataset_card` was already defined. Unless these have side-effects other than updating `dataset_card_data`, it shouldn't be any different than `main`.\r\n\r\n- Tom Aarsen",
"Let's try to have this PR merged then !\r\n\r\nIMO your current implementation can be improved since you path both the dataset card data and the dataset card itself, which is redundant. Also I anticipate the failures in the CI to come from your default implementation which doesn't correspond to what it was doing before\r\n\r\n> Unless these have side-effects other than updating dataset_card_data, it shouldn't be any different than main.\r\n\r\nIndeed the dataset_card_data is the value from attribute of the dataset_card from a few lines before your changes, so yes it modifies the dataset_card object too."
] | 1,718,880,477,000 | 1,718,985,898,000 | null | MEMBER | null | 0 | {
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} | Hello!
## Pull Request overview
* Move dataset card creation to method for easier overriding
## Details
It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co./datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either:
1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time.
2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card.
So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case:
````python
import json
from typing import Any, Dict, Optional
from datasets import Dataset, load_dataset
from datasets.info import DatasetInfosDict, DatasetInfo
from datasets.utils.metadata import MetadataConfigs
from huggingface_hub import DatasetCardData, DatasetCard
TEMPLATE = r"""---
{dataset_card_data}
---
# Dataset Card for {source_dataset_name} with mined hard negatives
This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co./datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.
## Mining Parameters
The negative samples have been mined using the following parameters:
- `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples
- `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples
- `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer
- `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives
- `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair
## Dataset Format
- Columns: {column_one}, {column_two}, negative
- Column types: str, str, str
- Example:
```python
{example}
```
"""
class HNMDataset(Dataset):
@classmethod
def from_dict(cls, *args, mining_kwargs: Dict[str, Any], **kwargs) -> "HNMDataset":
dataset = super().from_dict(*args, **kwargs)
dataset.mining_kwargs = mining_kwargs
return dataset
def _create_dataset_card(
self,
dataset_card_data: DatasetCardData,
dataset_card: Optional[DatasetCard],
config_name: str,
info_to_dump: DatasetInfo,
metadata_config_to_dump: MetadataConfigs,
) -> DatasetCard:
if dataset_card:
return dataset_card
DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)
MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)
dataset_card_data.tags = ["sentence-transformers"]
dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name
# Very messy, just as an example:
dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0]
content = TEMPLATE.format(**{
"dataset_card_data": str(dataset_card_data),
"source_dataset_name": dataset_name,
"source_dataset_id": dataset_id,
"range_min": self.mining_kwargs["range_min"],
"range_max": self.mining_kwargs["range_max"],
"margin": self.mining_kwargs["margin"],
"sampling_strategy": self.mining_kwargs["sampling_strategy"],
"num_negatives": self.mining_kwargs["num_negatives"],
"column_one": self.column_names[0],
"column_two": self.column_names[1],
"example": json.dumps(self[0], indent=4),
})
return DatasetCard(content)
source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]")
dataset = HNMDataset.from_dict({
"query": source_dataset["question"],
"answer": source_dataset["answer"],
# "negative": ... <- In my case, this column would be 'mined' automatically with these parameters
}, mining_kwargs={
"range_min": 10,
"range_max": 20,
"max_score": 0.9,
"margin": 0.1,
"sampling_strategy": "random",
"num_negatives": 3,
"source_dataset": source_dataset,
})
dataset.push_to_hub("tomaarsen/mining_demo", private=True)
````
In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co./datasets/tomaarsen/mining_demo
This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card.
## Note
I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :)
cc @albertvillanova @lhoestq
cc @LysandreJik
- Tom Aarsen | {
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https://api.github.com/repos/huggingface/datasets/issues/6987 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6987/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6987/comments | https://api.github.com/repos/huggingface/datasets/issues/6987/events | https://github.com/huggingface/datasets/pull/6987 | 2,363,728,190 | PR_kwDODunzps5zCRH6 | 6,987 | Remove beam | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6987). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,718,868,434,000 | 1,718,870,181,000 | null | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6986 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6986/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6986/comments | https://api.github.com/repos/huggingface/datasets/issues/6986/events | https://github.com/huggingface/datasets/pull/6986 | 2,362,584,179 | PR_kwDODunzps5y-Zi0 | 6,986 | Add large_list type support in string_to_arrow | {
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"@albertvillanova @KennethEnevoldsen"
] | 1,718,808,865,000 | 1,718,863,078,000 | null | NONE | null | 0 | {
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} | add large_list type support in string_to_arrow() and _arrow_to_datasets_dtype() in features.py
Fix #6984
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"Please note that the error is raised just at import:\r\n```python\r\nimport pyarrow.parquet as pq\r\n```\r\n\r\nTherefore it must be caused by some problem with your pyarrow installation. I would recommend you uninstall and install pyarrow again.\r\n\r\nI also see that it seems you use conda to install pyarrow. Please note that pyarrow offers 3 different packages in conda-forge: https://arrow.apache.org/docs/python/install.html#using-conda\r\n```\r\nconda install -c conda-forge pyarrow\r\n```\r\n> While the pyarrow [conda-forge](https://conda-forge.org/) package is the right choice for most users, both a minimal and maximal variant of the package exist, either of which may be better for your use case. See [Differences between conda-forge packages](https://arrow.apache.org/docs/python/install.html#python-conda-differences).\r\n\r\nPlease, make sure you install the right one: I guess it is either `pyarrow` (or `pyarrow-all`).",
"I have same issue, please downgrade pyarrow==15.0.2, it seem datasets library need to be fix",
"It is not a problem with the `datasets` library: we support latest version of `pyarrow` and our Continuous Integration tests are using pyarrow 16.1.0 without any problem.\r\n\r\nThe error reported here is raised when importing pyarrow.parquet:\r\n```\r\n---> 29 import pyarrow.parquet as pq\r\n```\r\n```\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20\r\n 1 # Licensed to the Apache Software Foundation (ASF) under one\r\n 2 # or more contributor license agreements. See the NOTICE file\r\n 3 # distributed with this work for additional information\r\n (...)\r\n 17 \r\n 18 # flake8: noqa\r\n---> 20 from .core import *\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33\r\n 30 import pyarrow as pa\r\n 32 try:\r\n---> 33 import pyarrow._parquet as _parquet\r\n 34 except ImportError as exc:\r\n 35 raise ImportError(\r\n 36 \"The pyarrow installation is not built with support \"\r\n 37 f\"for the Parquet file format ({str(exc)})\"\r\n 38 ) from None\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()\r\n\r\nAttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'\r\n```\r\n\r\nThis can only be explained if pyarrow was not properly installed. \r\n\r\nIf the user just installed `pyarrow-core` from conda-forge, then its parquet subpackage is not installed and cannot be imported. You can check pyarrow docs:\r\n- Differences between conda-forge packages: https://arrow.apache.org/docs/python/install.html#python-conda-differences\r\n> The `pyarrow-core` package includes the following functionality:\r\n> ...\r\n> The `pyarrow` package adds the following:\r\n> ...\r\n> Parquet (i.e., `pyarrow.parquet`)"
] | 1,718,803,348,000 | 1,719,314,918,000 | 1,719,294,051,000 | NONE | null | null | null | ### Describe the bug
I have been struggling with this for two days, any help would be appreciated. Python 3.10
```
from setfit import SetFitModel
from huggingface_hub import login
access_token_read = "cccxxxccc"
# Authenticate with the Hugging Face Hub
login(token=access_token_read)
# Load the models from the Hugging Face Hub
trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance")
trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust")
trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent")
trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic")
```
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 from setfit import SetFitModel
2 from huggingface_hub import login
4 access_token_read = "ccsddsds"
File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7
4 import os
5 import warnings
----> 7 from .data import get_templated_dataset, sample_dataset
8 from .model_card import SetFitModelCardData
9 from .modeling import SetFitHead, SetFitModel
File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5
3 import pandas as pd
4 import torch
----> 5 from datasets import Dataset, DatasetDict, load_dataset
6 from torch.utils.data import Dataset as TorchDataset
8 from . import logging
File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18
1 # ruff: noqa
2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
3 #
(...)
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
16 __version__ = "2.19.0"
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76
73 from tqdm.contrib.concurrent import thread_map
75 from . import config
---> 76 from .arrow_reader import ArrowReader
77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
78 from .data_files import sanitize_patterns
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29
26 from typing import TYPE_CHECKING, List, Optional, Union
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
32 from .download.download_config import DownloadConfig
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20
1 # Licensed to the Apache Software Foundation (ASF) under one
2 # or more contributor license agreements. See the NOTICE file
3 # distributed with this work for additional information
(...)
17
18 # flake8: noqa
---> 20 from .core import *
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33
30 import pyarrow as pa
32 try:
---> 33 import pyarrow._parquet as _parquet
34 except ImportError as exc:
35 raise ImportError(
36 "The pyarrow installation is not built with support "
37 f"for the Parquet file format ({str(exc)})"
38 ) from None
File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()
AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
```
setfit: 1.0.3
transformers: 4.41.2
lingua-language-detector: 2.0.2
polars: 0.20.31
lightning: None
google-cloud-bigquery: 3.24.0
shapely: 2.0.4
pyarrow: 16.0.0
### Steps to reproduce the bug
I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have.
### Expected behavior
Just ron normally.
### Environment info
3.10 | {
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"Hi ! Thanks for reporting :)\r\n\r\nWe don't support `large_list` yet, though it should be added to `Sequence` IMO (maybe with a parameter `large=True` ?)"
] | 1,718,797,128,000 | 1,718,797,128,000 | null | NONE | null | null | null | ### Feature request
This returns error.
```python
from datasets import Dataset
dsdf = Dataset.from_dict({"x": [[1, 2], [3, 4, 5]], "y": ["a", "b"]})
Dataset.from_polars(dsdf.to_polars())
```
ValueError: Arrow type large_list<item: int64> does not have a datasets dtype equivalent.
### Motivation
When datasets contain Sequence data type, it will be converted to Arrow type large_list. However, the reverse (from large_list to Sequence) does not work.
### Your contribution
No | {
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https://api.github.com/repos/huggingface/datasets/issues/6983 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6983/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6983/comments | https://api.github.com/repos/huggingface/datasets/issues/6983/events | https://github.com/huggingface/datasets/pull/6983 | 2,361,806,201 | PR_kwDODunzps5y7tK7 | 6,983 | Remove metrics | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6983). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 1,718,788,135,000 | 1,718,864,842,000 | null | MEMBER | null | 0 | {
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} | Remove all metrics, as part of the 3.0 release.
Note they are deprecated since 2.5.0 version. | {
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https://api.github.com/repos/huggingface/datasets/issues/6982 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6982/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6982/comments | https://api.github.com/repos/huggingface/datasets/issues/6982/events | https://github.com/huggingface/datasets/issues/6982 | 2,361,661,469 | I_kwDODunzps6MxBgd | 6,982 | cannot split dataset when using load_dataset | {
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"it seems the bug will happened in all windows system, I tried it in windows8.1, 10, 11 and all of them failed. But it won't happened in the Linux(Ubuntu and Centos7) and Mac (both my virtual and physical machine). I still don't know what the problem is. May be related to the path? I cannot run the split file in my windows server which created in Linux (even I replace the path in the arrow document)....work for it for a week but still cannot fix it .....upset"
] | 1,718,784,436,000 | 1,718,866,533,000 | null | NONE | null | null | null | ### Describe the bug
when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document,
This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generating train split and validation split but bug happen again in test split.
### Steps to reproduce the bug
from datasets import load_dataset, load_metric, Audio
common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True)
### Expected behavior
```
{
"name": "ValueError",
"message": "Instruction \"train\" corresponds to no data!",
"stack": "---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 3
1 from datasets import load_dataset, load_metric, Audio
----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True)
4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2622 # Build dataset for splits
2623 keep_in_memory = (
2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2625 )
-> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2627 # Rename and cast features to match task schema
2628 if task is not None:
2629 # To avoid issuing the same warning twice
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1265 # Create a dataset for each of the given splits
-> 1266 datasets = map_nested(
1267 partial(
1268 self._build_single_dataset,
1269 run_post_process=run_post_process,
1270 verification_mode=verification_mode,
1271 in_memory=in_memory,
1272 ),
1273 split,
1274 map_tuple=True,
1275 disable_tqdm=True,
1276 )
1277 if isinstance(datasets, dict):
1278 datasets = DatasetDict(datasets)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)
482 if batched:
483 data_struct = [data_struct]
--> 484 mapped = function(data_struct)
485 if batched:
486 mapped = mapped[0]
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory)
1293 split = Split(split)
1295 # Build base dataset
-> 1296 ds = self._as_dataset(
1297 split=split,
1298 in_memory=in_memory,
1299 )
1300 if run_post_process:
1301 for resource_file_name in self._post_processing_resources(split).values():
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory)
1368 if self._check_legacy_cache():
1369 dataset_name = self.name
-> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read(
1371 name=dataset_name,
1372 instructions=split,
1373 split_infos=self.info.splits.values(),
1374 in_memory=in_memory,
1375 )
1376 fingerprint = self._get_dataset_fingerprint(split)
1377 return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory)
254 msg = f'Instruction \"{instructions}\" corresponds to no data!'
255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!'
--> 256 raise ValueError(msg)
257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
ValueError: Instruction \"train\" corresponds to no data!"
}
```
### Environment info
Environment:
python 3.9
windows 11 pro
VScode+jupyter | {
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https://api.github.com/repos/huggingface/datasets/issues/6981 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6981/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6981/comments | https://api.github.com/repos/huggingface/datasets/issues/6981/events | https://github.com/huggingface/datasets/pull/6981 | 2,361,520,022 | PR_kwDODunzps5y6tnN | 6,981 | Update docs on trust_remote_code defaults to False | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6981). 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) | 0.005578 / 0.011353 (-0.005775) | 0.003946 / 0.011008 (-0.007062) | 0.063317 / 0.038508 (0.024808) | 0.031878 / 0.023109 (0.008769) | 0.312571 / 0.275898 (0.036673) | 0.281415 / 0.323480 (-0.042065) | 0.004139 / 0.007986 (-0.003846) | 0.002730 / 0.004328 (-0.001598) | 0.049539 / 0.004250 (0.045289) | 0.045056 / 0.037052 (0.008003) | 0.263820 / 0.258489 (0.005330) | 0.297817 / 0.293841 (0.003976) | 0.029490 / 0.128546 (-0.099056) | 0.012467 / 0.075646 (-0.063179) | 0.204607 / 0.419271 (-0.214664) | 0.036305 / 0.043533 (-0.007228) | 0.244102 / 0.255139 (-0.011037) | 0.267855 / 0.283200 (-0.015345) | 0.019794 / 0.141683 (-0.121889) | 1.130784 / 1.452155 (-0.321371) | 1.172507 / 1.492716 (-0.320209) |\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) | 0.092430 / 0.018006 (0.074424) | 0.296460 / 0.000490 (0.295970) | 0.000210 / 0.000200 (0.000010) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019467 / 0.037411 (-0.017944) | 0.062850 / 0.014526 (0.048324) | 0.074067 / 0.176557 (-0.102490) | 0.123280 / 0.737135 (-0.613856) | 0.077036 / 0.296338 (-0.219302) |\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) | 0.282687 / 0.215209 (0.067478) | 2.786715 / 2.077655 (0.709060) | 1.492028 / 1.504120 (-0.012092) | 1.373603 / 1.541195 (-0.167592) | 1.405004 / 1.468490 (-0.063486) | 0.714408 / 4.584777 (-3.870369) | 2.376785 / 3.745712 (-1.368927) | 2.916150 / 5.269862 (-2.353712) | 1.921184 / 4.565676 (-2.644493) | 0.078354 / 0.424275 (-0.345921) | 0.005236 / 0.007607 (-0.002371) | 0.334647 / 0.226044 (0.108603) | 3.262069 / 2.268929 (0.993140) | 1.858300 / 55.444624 (-53.586324) | 1.572968 / 6.876477 (-5.303509) | 1.659145 / 2.142072 (-0.482927) | 0.779546 / 4.805227 (-4.025681) | 0.132623 / 6.500664 (-6.368041) | 0.042423 / 0.075469 (-0.033046) |\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) | 0.985516 / 1.841788 (-0.856271) | 12.001321 / 8.074308 (3.927013) | 9.927011 / 10.191392 (-0.264381) | 0.142645 / 0.680424 (-0.537779) | 0.013808 / 0.534201 (-0.520393) | 0.303422 / 0.579283 (-0.275861) | 0.262666 / 0.434364 (-0.171698) | 0.339369 / 0.540337 (-0.200969) | 0.431028 / 1.386936 (-0.955908) |\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) | 0.005848 / 0.011353 (-0.005505) | 0.003971 / 0.011008 (-0.007037) | 0.050746 / 0.038508 (0.012238) | 0.031554 / 0.023109 (0.008445) | 0.277678 / 0.275898 (0.001780) | 0.300776 / 0.323480 (-0.022704) | 0.004428 / 0.007986 (-0.003558) | 0.002773 / 0.004328 (-0.001555) | 0.049882 / 0.004250 (0.045632) | 0.039833 / 0.037052 (0.002780) | 0.289143 / 0.258489 (0.030654) | 0.321425 / 0.293841 (0.027584) | 0.031701 / 0.128546 (-0.096845) | 0.012687 / 0.075646 (-0.062960) | 0.060650 / 0.419271 (-0.358621) | 0.033318 / 0.043533 (-0.010215) | 0.277019 / 0.255139 (0.021880) | 0.292345 / 0.283200 (0.009145) | 0.018520 / 0.141683 (-0.123163) | 1.143933 / 1.452155 (-0.308222) | 1.183913 / 1.492716 (-0.308803) |\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) | 0.094467 / 0.018006 (0.076461) | 0.298822 / 0.000490 (0.298332) | 0.000201 / 0.000200 (0.000001) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022811 / 0.037411 (-0.014601) | 0.078084 / 0.014526 (0.063558) | 0.089079 / 0.176557 (-0.087477) | 0.130229 / 0.737135 (-0.606906) | 0.090851 / 0.296338 (-0.205487) |\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) | 0.294981 / 0.215209 (0.079772) | 2.908294 / 2.077655 (0.830639) | 1.591281 / 1.504120 (0.087161) | 1.446032 / 1.541195 (-0.095162) | 1.469441 / 1.468490 (0.000951) | 0.726477 / 4.584777 (-3.858300) | 0.983086 / 3.745712 (-2.762626) | 2.892715 / 5.269862 (-2.377147) | 1.974092 / 4.565676 (-2.591584) | 0.079500 / 0.424275 (-0.344775) | 0.005497 / 0.007607 (-0.002110) | 0.342220 / 0.226044 (0.116176) | 3.414508 / 2.268929 (1.145579) | 1.941550 / 55.444624 (-53.503074) | 1.645268 / 6.876477 (-5.231209) | 1.805909 / 2.142072 (-0.336163) | 0.814483 / 4.805227 (-3.990744) | 0.135867 / 6.500664 (-6.364797) | 0.041718 / 0.075469 (-0.033751) |\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) | 0.999751 / 1.841788 (-0.842036) | 12.488263 / 8.074308 (4.413954) | 10.867040 / 10.191392 (0.675648) | 0.143999 / 0.680424 (-0.536425) | 0.015496 / 0.534201 (-0.518705) | 0.302170 / 0.579283 (-0.277113) | 0.123753 / 0.434364 (-0.310611) | 0.340424 / 0.540337 (-0.199913) | 0.458339 / 1.386936 (-0.928597) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6ccf944e42c1a84de81bf326accab9999b86c90 \"CML watermark\")\n"
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} | Update docs on trust_remote_code defaults to False.
The docs needed to be updated due to this PR:
- #6954 | {
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https://api.github.com/repos/huggingface/datasets/issues/6980 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6980/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6980/comments | https://api.github.com/repos/huggingface/datasets/issues/6980/events | https://github.com/huggingface/datasets/issues/6980 | 2,360,909,930 | I_kwDODunzps6MuKBq | 6,980 | Support NumPy 2.0 | {
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] | open | false | null | [] | null | [] | 1,718,753,422,000 | 1,719,048,017,000 | null | NONE | null | null | null | ### Feature request
Support NumPy 2.0.
### Motivation
NumPy introduces the Array API, which bridges the gap between machine learning libraries. Many clients of HuggingFace are eager to start using the Array API.
Besides that, NumPy 2 provides a cleaner interface than NumPy 1.
### Tasks
NumPy 2.0 was released for testing so that libraries could ensure compatibility [since mid-March](https://github.com/numpy/numpy/issues/24300#issuecomment-1986815755). What needs to be done for HuggingFace to support Numpy 2?
- [x] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976
- [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975): https://github.com/huggingface/datasets/pull/6991 | {
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https://api.github.com/repos/huggingface/datasets/issues/6979 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6979/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6979/comments | https://api.github.com/repos/huggingface/datasets/issues/6979/events | https://github.com/huggingface/datasets/issues/6979 | 2,360,175,363 | I_kwDODunzps6MrWsD | 6,979 | How can I load partial parquet files only? | {
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"Hello,\r\n\r\nHave you tried loading the dataset in streaming mode? [Documentation](https://huggingface.co./docs/datasets/v2.20.0/stream)\r\n\r\nThis way you wouldn't have to load it all. Also, let's be nice to Parquet, it's a really nice technology and we don't need to be mean :)",
"I have downloaded part of it, just want to know how to load part of it, stream mode is not work for me since my network (in china) not stable, I don't want do it all again and again.\r\n\r\nJust curious, doesn't there a way to load part of it?",
"Could you convert the IterableDataset to a Dataset after taking the first 100 rows with `.take`? This way, you would have a local copy of the first 100 rows on your system and thus won't need to download. Would that work?\r\n\r\nHere is a [SO question](https://stackoverflow.com/questions/76227219/can-i-convert-an-iterabledataset-to-dataset) detailing how to do the conversion.",
"I mean, the parquet is like:\r\n\r\n00000-0143554\r\n00001-0143554\r\n00002-0143554\r\n...\r\n00100-0143554\r\n...\r\n09100-0143554\r\n\r\nI just downloaded the first 9900 part of it. \r\n\r\nI can not load with load_dataset, it throw an error says my file is not same as parquet all amount.\r\n\r\nHow could I load the only I have? \r\n\r\n( I really don't want downlaod them all, cause, I don't need all, and pulus, its huge.... )\r\n\r\nAs I said, I have donwloaded about 9999... It's not about stream... I just wnat to konw how to load offline... part....",
"Hi, @lucasjinreal.\r\n\r\nI am not sure of understanding your issue. What is the error message and stack trace you get? What version of `datasets` are you using? Could you provide a reproducible example?\r\n\r\nWithout knowing all those details, I would naively say that you can load whatever number of Parquet files by using the \"parquet\" loader: https://huggingface.co./docs/datasets/loading#parquet\r\n```python\r\nds = load_dataset(\"parquet\", data_files=\"data/train-001*-of-00314.parquet\", split=\"train\")\r\n```",
"@albertvillanova Not sure you have tested with this or not, but I have tried,\r\n\r\nthe only error I got is it still laodding all parquet with a progress bar maxium to the whole number 014354, and it loads my 0 - 000999 part, then throws an error.\r\n\r\nSays Numinfo is not same.\r\n\r\nI am so confused,",
"Yes, my code snippet works.\n\nCould you copy-paste your code and the output? Otherwise we are not able to know what the issue is.",
"@albertvillanova Hi, thanks for the tracing of the issue.\r\n\r\nThis is the output:\r\n\r\n```\r\nython get_llava_recap_cc3m.py\r\nGenerating train split: 3%|███▋ | 101910/3199866 [00:16<08:30, 6065.67 examples/s]\r\nTraceback (most recent call last):\r\n File \"get_llava_recap_cc3m.py\", line 31, in <module>\r\n dataset = load_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000*-of-00314.parquet\")\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/load.py\", line 2582, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1118, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/info_utils.py\", line 101, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=156885281898.75, num_examples=3199866, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=4994080770, num_examples=101910, shard_lengths=[10191, 10291, 10291, 10291, 10291, 10191, 10191, 10291, 10291, 9591], dataset_name='llava-recap-cc3m')}]\r\n```\r\n\r\nthis is my code:\r\n\r\n```\r\ndataset = load_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000*-of-00314.parquet\")\r\n```\r\n\r\nMy situation and requirements:\r\n\r\n00314 is all, but I downlaode about 150, half of it, as you can see, i used `0000*-of-00314.` which should be at most 99 file being loaded.\r\n\r\nBut it just fail.\r\n\r\nCan u understand my issue now?\r\n\r\nIf so, then **do not** suggest me with stream, Just want to know, is there a way to load part if it...... **and please don't say you can not replicate my issue when you have downloaded them all**, my english is not good, but I think all situations and all prerequists I have addressed already.\r\n\r\n",
"I see you did not use the \"parquet\" loader as I suggested in my code snippet above: https://github.com/huggingface/datasets/issues/6979#issuecomment-2182031415\r\nPlease try passing \"parquet\" instead of \"llava-recap-cc3m/\" to `load_dataset`, and the complete path to data files in `data_files`:\r\n```python\r\nload_dataset(\"parquet\", data_files=\"llava-recap-cc3m/data/train-001*-of-00314.parquet\")\r\n```",
"Let me explain that you get the error because of this content within the `dataset_info` YAML tag in the `llava-recap-cc3m/README.md`:\r\n```\r\n - name: train\r\n num_bytes: 156885281898.75\r\n num_examples: 3199866\r\n```\r\n\r\nBy default, if there is that content in the README file, `load_dataset` performs a basic check to verify it the generated number of examples matches the expected one and raises a `NonMatchingSplitsSizesError` if that is not the case. \r\n\r\nYou can avoid this basic check by passing `verification_mode=\"no_checks\"`:\r\n```python\r\nload_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000*-of-00314.parquet\", verification_mode=\"no_checks\")\r\n```",
"And please, next time you have an issue, please fill the Bug template issue with all the necessary information: https://github.com/huggingface/datasets/issues/new?assignees=&labels=&projects=&template=bug-report.yml\r\n\r\nOtherwise it is very difficult for us to understand the underlying problem and to propose a pertinent solution.",
"thank u albert!\r\n\r\nIt solved my issue!"
] | 1,718,725,456,000 | 1,718,989,772,000 | 1,718,976,770,000 | NONE | null | null | null | I have a HUGE dataset about 14TB, I unable to download all parquet all. I just take about 100 from it.
dataset = load_dataset("xx/", data_files="data/train-001*-of-00314.parquet")
How can I just using 000 - 100 from a 00314 from all partially?
I search whole net didn't found a solution, **this is stupid if they didn't support it, and I swear I wont using stupid parquet any more**
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6978). 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) | 0.005144 / 0.011353 (-0.006209) | 0.003500 / 0.011008 (-0.007509) | 0.063670 / 0.038508 (0.025162) | 0.031793 / 0.023109 (0.008683) | 0.239611 / 0.275898 (-0.036287) | 0.276681 / 0.323480 (-0.046799) | 0.004148 / 0.007986 (-0.003838) | 0.002713 / 0.004328 (-0.001615) | 0.048832 / 0.004250 (0.044582) | 0.043066 / 0.037052 (0.006014) | 0.256835 / 0.258489 (-0.001655) | 0.292224 / 0.293841 (-0.001617) | 0.027530 / 0.128546 (-0.101017) | 0.010509 / 0.075646 (-0.065137) | 0.203370 / 0.419271 (-0.215901) | 0.035643 / 0.043533 (-0.007890) | 0.252161 / 0.255139 (-0.002978) | 0.271883 / 0.283200 (-0.011316) | 0.018658 / 0.141683 (-0.123024) | 1.081676 / 1.452155 (-0.370479) | 1.142146 / 1.492716 (-0.350571) |\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) | 0.093484 / 0.018006 (0.075477) | 0.298607 / 0.000490 (0.298117) | 0.000220 / 0.000200 (0.000020) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019021 / 0.037411 (-0.018390) | 0.062471 / 0.014526 (0.047946) | 0.075393 / 0.176557 (-0.101163) | 0.121040 / 0.737135 (-0.616095) | 0.077613 / 0.296338 (-0.218726) |\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) | 0.294857 / 0.215209 (0.079648) | 2.931143 / 2.077655 (0.853489) | 1.510866 / 1.504120 (0.006746) | 1.379574 / 1.541195 (-0.161621) | 1.352358 / 1.468490 (-0.116133) | 0.561670 / 4.584777 (-4.023107) | 2.378434 / 3.745712 (-1.367278) | 2.713203 / 5.269862 (-2.556658) | 1.706416 / 4.565676 (-2.859260) | 0.062355 / 0.424275 (-0.361920) | 0.004971 / 0.007607 (-0.002636) | 0.336498 / 0.226044 (0.110453) | 3.316464 / 2.268929 (1.047535) | 1.833035 / 55.444624 (-53.611589) | 1.532808 / 6.876477 (-5.343668) | 1.537323 / 2.142072 (-0.604749) | 0.639430 / 4.805227 (-4.165798) | 0.115808 / 6.500664 (-6.384856) | 0.043545 / 0.075469 (-0.031924) |\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) | 0.974428 / 1.841788 (-0.867360) | 11.368914 / 8.074308 (3.294606) | 9.754488 / 10.191392 (-0.436904) | 0.146277 / 0.680424 (-0.534146) | 0.013917 / 0.534201 (-0.520284) | 0.286809 / 0.579283 (-0.292474) | 0.267144 / 0.434364 (-0.167219) | 0.326161 / 0.540337 (-0.214177) | 0.418059 / 1.386936 (-0.968877) |\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) | 0.003460 / 0.011008 (-0.007548) | 0.050135 / 0.038508 (0.011627) | 0.032014 / 0.023109 (0.008905) | 0.259835 / 0.275898 (-0.016063) | 0.286275 / 0.323480 (-0.037205) | 0.004350 / 0.007986 (-0.003636) | 0.002800 / 0.004328 (-0.001529) | 0.049358 / 0.004250 (0.045107) | 0.040182 / 0.037052 (0.003130) | 0.278352 / 0.258489 (0.019863) | 0.307869 / 0.293841 (0.014028) | 0.029151 / 0.128546 (-0.099395) | 0.010091 / 0.075646 (-0.065555) | 0.058814 / 0.419271 (-0.360458) | 0.033150 / 0.043533 (-0.010383) | 0.263594 / 0.255139 (0.008455) | 0.284065 / 0.283200 (0.000866) | 0.017968 / 0.141683 (-0.123714) | 1.145605 / 1.452155 (-0.306550) | 1.196884 / 1.492716 (-0.295832) |\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) | 0.094045 / 0.018006 (0.076039) | 0.299031 / 0.000490 (0.298541) | 0.000210 / 0.000200 (0.000011) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022510 / 0.037411 (-0.014901) | 0.077478 / 0.014526 (0.062953) | 0.087746 / 0.176557 (-0.088811) | 0.129311 / 0.737135 (-0.607825) | 0.089921 / 0.296338 (-0.206418) |\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) | 0.290279 / 0.215209 (0.075070) | 2.880725 / 2.077655 (0.803070) | 1.541262 / 1.504120 (0.037142) | 1.424475 / 1.541195 (-0.116719) | 1.436397 / 1.468490 (-0.032093) | 0.578237 / 4.584777 (-4.006540) | 0.965249 / 3.745712 (-2.780463) | 2.682534 / 5.269862 (-2.587327) | 1.732859 / 4.565676 (-2.832817) | 0.065523 / 0.424275 (-0.358752) | 0.005466 / 0.007607 (-0.002141) | 0.343985 / 0.226044 (0.117940) | 3.397463 / 2.268929 (1.128534) | 1.929370 / 55.444624 (-53.515255) | 1.605135 / 6.876477 (-5.271342) | 1.753926 / 2.142072 (-0.388146) | 0.659929 / 4.805227 (-4.145298) | 0.118093 / 6.500664 (-6.382571) | 0.041252 / 0.075469 (-0.034217) |\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) | 1.009177 / 1.841788 (-0.832610) | 11.959624 / 8.074308 (3.885316) | 10.484672 / 10.191392 (0.293280) | 0.142085 / 0.680424 (-0.538339) | 0.015955 / 0.534201 (-0.518245) | 0.283649 / 0.579283 (-0.295634) | 0.125681 / 0.434364 (-0.308683) | 0.320490 / 0.540337 (-0.219847) | 0.440353 / 1.386936 (-0.946583) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e47a746bcda4b97db2467542b76d3215b3569ff0 \"CML watermark\")\n",
"Maybe a patch release will be needed with this fix."
] | 1,718,706,394,000 | 1,718,778,204,000 | 1,718,776,218,000 | MEMBER | null | 0 | {
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} | A regression was introduced for pandas < 2.0.0 in PR:
- #6914
As described in pandas docs, the `dtype_backend` parameter was first added in pandas 2.0.0: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html
This PR fixes the regression by passing (or not) the `dtype_backend` parameter depending on pandas version.
Maybe, in a future 3.0 `datasets` release, we could just require pandas > 2.0.
Reported by:
- #6977 | {
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https://api.github.com/repos/huggingface/datasets/issues/6977 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6977/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6977/comments | https://api.github.com/repos/huggingface/datasets/issues/6977/events | https://github.com/huggingface/datasets/issues/6977 | 2,359,295,045 | I_kwDODunzps6Mn_xF | 6,977 | load json file error with v2.20.0 | {
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"Thanks for reporting, @xiaoyaolangzhi.\r\n\r\nIndeed, we are currently requiring `pandas` >= 2.0.0.\r\n\r\nYou will need to update pandas in your local environment:\r\n```\r\npip install -U pandas\r\n``` ",
"Thank you very much."
] | 1,718,700,061,000 | 1,718,705,170,000 | 1,718,705,169,000 | NONE | null | null | null | ### Describe the bug
```
load_dataset(path="json", data_files="./test.json")
```
```
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 132, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1997, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 155, in _generate_tables
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/t1.py", line 11, in <module>
load_dataset(path=data_path, data_files="./t2.json")
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2616, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 2040, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
```
```
import pandas as pd
with open("./test.json", "r") as f:
df = pd.read_json(f, dtype_backend="pyarrow")
```
```
Traceback (most recent call last):
File "/app/t3.py", line 3, in <module>
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
```
datasets 2.20.0
pandas 1.5.3
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/6976 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6976/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6976/comments | https://api.github.com/repos/huggingface/datasets/issues/6976/events | https://github.com/huggingface/datasets/pull/6976 | 2,357,107,203 | PR_kwDODunzps5yrmNP | 6,976 | Ensure compatibility with numpy 2.0.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6976). 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) | 0.005361 / 0.011353 (-0.005992) | 0.003983 / 0.011008 (-0.007025) | 0.062865 / 0.038508 (0.024357) | 0.029880 / 0.023109 (0.006771) | 0.261465 / 0.275898 (-0.014433) | 0.269791 / 0.323480 (-0.053689) | 0.004198 / 0.007986 (-0.003788) | 0.002942 / 0.004328 (-0.001387) | 0.049002 / 0.004250 (0.044751) | 0.043232 / 0.037052 (0.006180) | 0.328774 / 0.258489 (0.070285) | 0.297308 / 0.293841 (0.003467) | 0.030552 / 0.128546 (-0.097994) | 0.012632 / 0.075646 (-0.063015) | 0.204156 / 0.419271 (-0.215116) | 0.036014 / 0.043533 (-0.007519) | 0.241224 / 0.255139 (-0.013915) | 0.268358 / 0.283200 (-0.014842) | 0.019227 / 0.141683 (-0.122456) | 1.114515 / 1.452155 (-0.337639) | 1.147029 / 1.492716 (-0.345688) |\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) | 0.094925 / 0.018006 (0.076919) | 0.301548 / 0.000490 (0.301059) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018875 / 0.037411 (-0.018536) | 0.062824 / 0.014526 (0.048298) | 0.075657 / 0.176557 (-0.100900) | 0.121926 / 0.737135 (-0.615209) | 0.077102 / 0.296338 (-0.219236) |\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) | 0.286018 / 0.215209 (0.070808) | 2.832222 / 2.077655 (0.754567) | 1.462629 / 1.504120 (-0.041491) | 1.354746 / 1.541195 (-0.186449) | 1.339504 / 1.468490 (-0.128986) | 0.718381 / 4.584777 (-3.866396) | 2.401456 / 3.745712 (-1.344256) | 3.013518 / 5.269862 (-2.256343) | 1.944892 / 4.565676 (-2.620784) | 0.078793 / 0.424275 (-0.345482) | 0.005219 / 0.007607 (-0.002388) | 0.349551 / 0.226044 (0.123507) | 3.417844 / 2.268929 (1.148916) | 1.830669 / 55.444624 (-53.613956) | 1.502134 / 6.876477 (-5.374343) | 1.529242 / 2.142072 (-0.612830) | 0.793732 / 4.805227 (-4.011495) | 0.133571 / 6.500664 (-6.367093) | 0.042588 / 0.075469 (-0.032881) |\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) | 0.988167 / 1.841788 (-0.853620) | 11.926728 / 8.074308 (3.852420) | 9.806971 / 10.191392 (-0.384421) | 0.173951 / 0.680424 (-0.506473) | 0.015308 / 0.534201 (-0.518893) | 0.310768 / 0.579283 (-0.268515) | 0.268261 / 0.434364 (-0.166103) | 0.342962 / 0.540337 (-0.197375) | 0.431255 / 1.386936 (-0.955681) |\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) | 0.005680 / 0.011353 (-0.005673) | 0.004231 / 0.011008 (-0.006778) | 0.051009 / 0.038508 (0.012501) | 0.031431 / 0.023109 (0.008322) | 0.268582 / 0.275898 (-0.007316) | 0.287942 / 0.323480 (-0.035538) | 0.004442 / 0.007986 (-0.003543) | 0.002818 / 0.004328 (-0.001511) | 0.050241 / 0.004250 (0.045991) | 0.039933 / 0.037052 (0.002881) | 0.285814 / 0.258489 (0.027325) | 0.316082 / 0.293841 (0.022241) | 0.032416 / 0.128546 (-0.096130) | 0.012398 / 0.075646 (-0.063248) | 0.060779 / 0.419271 (-0.358493) | 0.033706 / 0.043533 (-0.009827) | 0.273915 / 0.255139 (0.018776) | 0.289752 / 0.283200 (0.006553) | 0.017859 / 0.141683 (-0.123824) | 1.150224 / 1.452155 (-0.301930) | 1.197467 / 1.492716 (-0.295250) |\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) | 0.093810 / 0.018006 (0.075803) | 0.302529 / 0.000490 (0.302039) | 0.000221 / 0.000200 (0.000021) | 0.000047 / 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) | 0.022903 / 0.037411 (-0.014508) | 0.077445 / 0.014526 (0.062919) | 0.089335 / 0.176557 (-0.087222) | 0.130848 / 0.737135 (-0.606287) | 0.091106 / 0.296338 (-0.205232) |\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) | 0.294194 / 0.215209 (0.078985) | 2.886983 / 2.077655 (0.809328) | 1.557768 / 1.504120 (0.053648) | 1.424467 / 1.541195 (-0.116727) | 1.440625 / 1.468490 (-0.027865) | 0.724793 / 4.584777 (-3.859984) | 0.985216 / 3.745712 (-2.760496) | 2.856826 / 5.269862 (-2.413036) | 1.911638 / 4.565676 (-2.654039) | 0.080350 / 0.424275 (-0.343925) | 0.005616 / 0.007607 (-0.001991) | 0.348713 / 0.226044 (0.122668) | 3.414764 / 2.268929 (1.145835) | 1.925056 / 55.444624 (-53.519568) | 1.635752 / 6.876477 (-5.240725) | 1.761117 / 2.142072 (-0.380955) | 0.808309 / 4.805227 (-3.996918) | 0.136893 / 6.500664 (-6.363771) | 0.042116 / 0.075469 (-0.033354) |\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) | 1.004740 / 1.841788 (-0.837048) | 12.495859 / 8.074308 (4.421550) | 10.681233 / 10.191392 (0.489841) | 0.133320 / 0.680424 (-0.547104) | 0.015943 / 0.534201 (-0.518258) | 0.304869 / 0.579283 (-0.274414) | 0.128616 / 0.434364 (-0.305748) | 0.345930 / 0.540337 (-0.194407) | 0.457434 / 1.386936 (-0.929502) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84d9dea52098c9403efb43d5b542dd6d45000bec \"CML watermark\")\n"
] | 1,718,623,762,000 | 1,718,807,432,000 | 1,718,805,874,000 | CONTRIBUTOR | null | 0 | {
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"merged_at": "2024-06-19T14:04:34",
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/6976"
} | Following the conversion guide, copy=False is no longer required and will result in an error: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword.
The following fix should resolve the issue.
error found during testing on the MTEB repository e.g. [here](https://github.com/embeddings-benchmark/mteb/pull/938) | {
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End of preview.