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https://api.github.com/repos/huggingface/datasets/issues/7036
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PR_kwDODunzps507bZk
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Fix doc generation when NamedSplit is used as parameter default value
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7036). 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) | 0.005582 / 0.011353 (-0.005771) | 0.003968 / 0.011008 (-0.007041) | 0.063672 / 0.038508 (0.025164) | 0.032360 / 0.023109 (0.009251) | 0.241351 / 0.275898 (-0.034547) | 0.264926 / 0.323480 (-0.058554) | 0.003186 / 0.007986 (-0.004800) | 0.003423 / 0.004328 (-0.000906) | 0.049600 / 0.004250 (0.045350) | 0.045558 / 0.037052 (0.008506) | 0.253326 / 0.258489 (-0.005163) | 0.289474 / 0.293841 (-0.004367) | 0.030285 / 0.128546 (-0.098261) | 0.012424 / 0.075646 (-0.063222) | 0.203914 / 0.419271 (-0.215358) | 0.036569 / 0.043533 (-0.006964) | 0.245252 / 0.255139 (-0.009887) | 0.261971 / 0.283200 (-0.021228) | 0.018276 / 0.141683 (-0.123406) | 1.120386 / 1.452155 (-0.331769) | 1.181736 / 1.492716 (-0.310980) |\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) | 0.095427 / 0.018006 (0.077421) | 0.300666 / 0.000490 (0.300176) | 0.000205 / 0.000200 (0.000005) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019255 / 0.037411 (-0.018156) | 0.062645 / 0.014526 (0.048119) | 0.074822 / 0.176557 (-0.101734) | 0.121222 / 0.737135 (-0.615913) | 0.076136 / 0.296338 (-0.220202) |\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) | 0.279756 / 0.215209 (0.064547) | 2.769680 / 2.077655 (0.692025) | 1.466156 / 1.504120 (-0.037964) | 1.348337 / 1.541195 (-0.192857) | 1.348311 / 1.468490 (-0.120179) | 0.710414 / 4.584777 (-3.874363) | 2.379192 / 3.745712 (-1.366520) | 2.990227 / 5.269862 (-2.279635) | 1.909749 / 4.565676 (-2.655928) | 0.079677 / 0.424275 (-0.344598) | 0.005116 / 0.007607 (-0.002491) | 0.335442 / 0.226044 (0.109398) | 3.308757 / 2.268929 (1.039828) | 1.831681 / 55.444624 (-53.612944) | 1.528642 / 6.876477 (-5.347835) | 1.554577 / 2.142072 (-0.587496) | 0.777722 / 4.805227 (-4.027505) | 0.132164 / 6.500664 (-6.368501) | 0.042277 / 0.075469 (-0.033193) |\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) | 0.964461 / 1.841788 (-0.877327) | 11.436569 / 8.074308 (3.362261) | 9.801367 / 10.191392 (-0.390025) | 0.130214 / 0.680424 (-0.550210) | 0.015288 / 0.534201 (-0.518913) | 0.303992 / 0.579283 (-0.275292) | 0.258128 / 0.434364 (-0.176236) | 0.347259 / 0.540337 (-0.193078) | 0.438156 / 1.386936 (-0.948780) |\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) | 0.006019 / 0.011353 (-0.005334) | 0.003872 / 0.011008 (-0.007136) | 0.050763 / 0.038508 (0.012255) | 0.033993 / 0.023109 (0.010884) | 0.271789 / 0.275898 (-0.004109) | 0.298849 / 0.323480 (-0.024631) | 0.004486 / 0.007986 (-0.003500) | 0.002789 / 0.004328 (-0.001540) | 0.049926 / 0.004250 (0.045676) | 0.040470 / 0.037052 (0.003418) | 0.287533 / 0.258489 (0.029044) | 0.320066 / 0.293841 (0.026225) | 0.033039 / 0.128546 (-0.095508) | 0.011842 / 0.075646 (-0.063804) | 0.061016 / 0.419271 (-0.358256) | 0.034807 / 0.043533 (-0.008726) | 0.272079 / 0.255139 (0.016940) | 0.291603 / 0.283200 (0.008403) | 0.018676 / 0.141683 (-0.123007) | 1.171214 / 1.452155 (-0.280940) | 1.210691 / 1.492716 (-0.282025) |\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) | 0.093045 / 0.018006 (0.075038) | 0.301045 / 0.000490 (0.300556) | 0.000213 / 0.000200 (0.000013) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022616 / 0.037411 (-0.014795) | 0.077271 / 0.014526 (0.062746) | 0.088959 / 0.176557 (-0.087598) | 0.129961 / 0.737135 (-0.607174) | 0.090495 / 0.296338 (-0.205843) |\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) | 0.301864 / 0.215209 (0.086655) | 2.947486 / 2.077655 (0.869831) | 1.587123 / 1.504120 (0.083003) | 1.453799 / 1.541195 (-0.087396) | 1.474296 / 1.468490 (0.005806) | 0.718609 / 4.584777 (-3.866168) | 0.948426 / 3.745712 (-2.797286) | 2.877275 / 5.269862 (-2.392586) | 1.930940 / 4.565676 (-2.634736) | 0.079207 / 0.424275 (-0.345068) | 0.005379 / 0.007607 (-0.002228) | 0.357969 / 0.226044 (0.131925) | 3.576455 / 2.268929 (1.307527) | 1.985058 / 55.444624 (-53.459566) | 1.663730 / 6.876477 (-5.212747) | 1.812752 / 2.142072 (-0.329320) | 0.800200 / 4.805227 (-4.005027) | 0.135124 / 6.500664 (-6.365540) | 0.041211 / 0.075469 (-0.034258) |\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) | 1.032394 / 1.841788 (-0.809394) | 12.082436 / 8.074308 (4.008128) | 10.198703 / 10.191392 (0.007311) | 0.143578 / 0.680424 (-0.536846) | 0.015576 / 0.534201 (-0.518625) | 0.301450 / 0.579283 (-0.277833) | 0.126596 / 0.434364 (-0.307768) | 0.339437 / 0.540337 (-0.200900) | 0.445454 / 1.386936 (-0.941482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#347f1664a31c1c0fcb6a1a0914ebfb99c134e116 \"CML watermark\")\n"
] | 2024-07-10T07:58:46Z
| 2024-07-26T07:58:00Z
| 2024-07-26T07:51:52Z
|
MEMBER
| null | null | null |
Fix doc generation when `NamedSplit` is used as parameter default value.
Fix #7035.
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PR_kwDODunzps6S99KB
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correct use with polars example
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I_kwDODunzps5jCQhr
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load_dataset fails with FileNotFound error on Windows
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"Hi! Can you please paste the entire error stack trace, not only the last few lines?",
"`----> 1 dataset = datasets.load_dataset(\"glue\", \"ax\")\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1767, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1762 verification_mode = VerificationMode(\r\n 1763 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS\r\n 1764 )\r\n 1766 # Create a dataset builder\r\n-> 1767 builder_instance = load_dataset_builder(\r\n 1768 path=path,\r\n 1769 name=name,\r\n 1770 data_dir=data_dir,\r\n 1771 data_files=data_files,\r\n 1772 cache_dir=cache_dir,\r\n 1773 features=features,\r\n 1774 download_config=download_config,\r\n 1775 download_mode=download_mode,\r\n 1776 revision=revision,\r\n 1777 use_auth_token=use_auth_token,\r\n 1778 storage_options=storage_options,\r\n 1779 **config_kwargs,\r\n 1780 )\r\n 1782 # Return iterable dataset in case of streaming\r\n 1783 if streaming:\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1498, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, storage_options, **config_kwargs)\r\n 1496 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 1497 download_config.use_auth_token = use_auth_token\r\n-> 1498 dataset_module = dataset_module_factory(\r\n 1499 path,\r\n 1500 revision=revision,\r\n 1501 download_config=download_config,\r\n 1502 download_mode=download_mode,\r\n 1503 data_dir=data_dir,\r\n 1504 data_files=data_files,\r\n 1505 )\r\n 1507 # Get dataset builder class from the processing script\r\n 1508 builder_cls = import_main_class(dataset_module.module_path)\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1211, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n 1209 raise e1 from None\r\n 1210 if isinstance(e1, FileNotFoundError):\r\n-> 1211 raise FileNotFoundError(\r\n 1212 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n 1213 f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n 1214 ) from None\r\n 1215 raise e1 from None\r\n 1216 else:`",
"Okay, this is the issue:\r\n```\r\nFileNotFoundError: [WinError 3] The system cannot find the path specified: \r\n'C:\\\\Users\\\\...\\\\.cache\\\\huggingface'\r\n``` \r\n\r\nI don't remember seeing this error before.\r\n\r\nI guess it could happen in a multi-process environment if one of the processes deletes the `datasets` cache as the other one is loading a dataset (with `load_dataset`), so make sure that's not the case. Also, you can disable the Windows max path length limit (if enabled), but this is most likely not the problem.",
"Closing due to inactivity."
] | 2023-04-10T23:21:12Z
| 2023-07-21T14:08:20Z
| 2023-07-21T14:08:19Z
|
NONE
| null | null |
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### Describe the bug
Although I can import and run the datasets library in a Colab environment, I cannot successfully load any data on my own machine (Windows 10) despite following the install steps:
(1) create conda environment
(2) activate environment
(3) install with: ``conda` install -c huggingface -c conda-forge datasets`
Then
```
from datasets import load_dataset
# this or any other example from the website fails with the FileNotFoundError
glue = load_dataset("glue", "ax")
```
**Below I have pasted the error omitting the full path**:
```
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at C:\Users\...\glue\glue.py or any data file in the same directory. Couldn't find 'glue' on the Hugging Face Hub either: FileNotFoundError: [WinError 3] The system cannot find the path specified:
'C:\\Users\\...\\.cache\\huggingface'
```
### Steps to reproduce the bug
On Windows 10
1) create a minimal conda environment (with just Python)
(2) activate environment
(3) install datasets with: ``conda` install -c huggingface -c conda-forge datasets`
(4) import load_dataset and follow example usage from any dataset card.
### Expected behavior
The expected behavior is to load the file into the Python session running on my machine without error.
### Environment info
```
# Name Version Build Channel
aiohttp 3.8.4 py311ha68e1ae_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
arrow-cpp 11.0.0 h57928b3_13_cpu conda-forge
async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge
attrs 22.2.0 pyh71513ae_0 conda-forge
aws-c-auth 0.6.26 h1262f0c_1 conda-forge
aws-c-cal 0.5.21 h7cda486_2 conda-forge
aws-c-common 0.8.14 hcfcfb64_0 conda-forge
aws-c-compression 0.2.16 h8a79959_5 conda-forge
aws-c-event-stream 0.2.20 h5f78564_4 conda-forge
aws-c-http 0.7.6 h2545be9_0 conda-forge
aws-c-io 0.13.19 h0d2781e_3 conda-forge
aws-c-mqtt 0.8.6 hd211e0c_12 conda-forge
aws-c-s3 0.2.7 h8113e7b_1 conda-forge
aws-c-sdkutils 0.1.8 h8a79959_0 conda-forge
aws-checksums 0.1.14 h8a79959_5 conda-forge
aws-crt-cpp 0.19.8 he6d3b81_12 conda-forge
aws-sdk-cpp 1.10.57 h64004b3_8 conda-forge
brotlipy 0.7.0 py311ha68e1ae_1005 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
c-ares 1.19.0 h2bbff1b_0
ca-certificates 2023.01.10 haa95532_0
certifi 2022.12.7 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py311h7d9ee11_3 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
cryptography 40.0.1 py311h28e9c30_0 conda-forge
dataclasses 0.8 pyhc8e2a94_3 conda-forge
datasets 2.11.0 py_0 huggingface
dill 0.3.6 pyhd8ed1ab_1 conda-forge
filelock 3.11.0 pyhd8ed1ab_0 conda-forge
frozenlist 1.3.3 py311ha68e1ae_0 conda-forge
fsspec 2023.4.0 pyh1a96a4e_0 conda-forge
gflags 2.2.2 ha925a31_1004 conda-forge
glog 0.6.0 h4797de2_0 conda-forge
huggingface_hub 0.13.4 py_0 huggingface
idna 3.4 pyhd8ed1ab_0 conda-forge
importlib-metadata 6.3.0 pyha770c72_0 conda-forge
importlib_metadata 6.3.0 hd8ed1ab_0 conda-forge
intel-openmp 2023.0.0 h57928b3_25922 conda-forge
krb5 1.20.1 heb0366b_0 conda-forge
libabseil 20230125.0 cxx17_h63175ca_1 conda-forge
libarrow 11.0.0 h04c43f8_13_cpu conda-forge
libblas 3.9.0 16_win64_mkl conda-forge
libbrotlicommon 1.0.9 hcfcfb64_8 conda-forge
libbrotlidec 1.0.9 hcfcfb64_8 conda-forge
libbrotlienc 1.0.9 hcfcfb64_8 conda-forge
libcblas 3.9.0 16_win64_mkl conda-forge
libcrc32c 1.1.2 h0e60522_0 conda-forge
libcurl 7.88.1 h68f0423_1 conda-forge
libexpat 2.5.0 h63175ca_1 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libgoogle-cloud 2.8.0 hf2ff781_1 conda-forge
libgrpc 1.52.1 h32da247_1 conda-forge
libhwloc 2.9.0 h51c2c0f_0 conda-forge
libiconv 1.17 h8ffe710_0 conda-forge
liblapack 3.9.0 16_win64_mkl conda-forge
libprotobuf 3.21.12 h12be248_0 conda-forge
libsqlite 3.40.0 hcfcfb64_0 conda-forge
libssh2 1.10.0 h9a1e1f7_3 conda-forge
libthrift 0.18.1 h9ce19ad_0 conda-forge
libutf8proc 2.8.0 h82a8f57_0 conda-forge
libxml2 2.10.3 hc3477c8_6 conda-forge
libzlib 1.2.13 hcfcfb64_4 conda-forge
lz4-c 1.9.4 hcfcfb64_0 conda-forge
mkl 2022.1.0 h6a75c08_874 conda-forge
multidict 6.0.4 py311ha68e1ae_0 conda-forge
multiprocess 0.70.14 py311ha68e1ae_3 conda-forge
numpy 1.24.2 py311h0b4df5a_0 conda-forge
openssl 3.1.0 hcfcfb64_0 conda-forge
orc 1.8.3 hada7b9e_0 conda-forge
packaging 23.0 pyhd8ed1ab_0 conda-forge
pandas 2.0.0 py311hf63dbb6_0 conda-forge
parquet-cpp 1.5.1 2 conda-forge
pip 23.0.1 pyhd8ed1ab_0 conda-forge
pthreads-win32 2.9.1 hfa6e2cd_3 conda-forge
pyarrow 11.0.0 py311h6a6099b_13_cpu conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pyopenssl 23.1.1 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyh0701188_6 conda-forge
python 3.11.3 h2628c8c_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge
python-xxhash 3.2.0 py311ha68e1ae_0 conda-forge
python_abi 3.11 3_cp311 conda-forge
pytz 2023.3 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py311ha68e1ae_5 conda-forge
re2 2023.02.02 h63175ca_0 conda-forge
requests 2.28.2 pyhd8ed1ab_1 conda-forge
setuptools 67.6.1 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 hfb803bf_0 conda-forge
tbb 2021.8.0 h91493d7_0 conda-forge
tk 8.6.12 h8ffe710_0 conda-forge
tqdm 4.65.0 pyhd8ed1ab_1 conda-forge
typing-extensions 4.5.0 hd8ed1ab_0 conda-forge
typing_extensions 4.5.0 pyha770c72_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
ucrt 10.0.22621.0 h57928b3_0 conda-forge
urllib3 1.26.15 pyhd8ed1ab_0 conda-forge
vc 14.3 hb6edc58_10 conda-forge
vs2015_runtime 14.34.31931 h4c5c07a_10 conda-forge
wheel 0.40.0 pyhd8ed1ab_0 conda-forge
win_inet_pton 1.1.0 pyhd8ed1ab_6 conda-forge
xxhash 0.8.1 hcfcfb64_0 conda-forge
xz 5.2.10 h8cc25b3_1
yaml 0.2.5 h8ffe710_2 conda-forge
yarl 1.8.2 py311ha68e1ae_0 conda-forge
zipp 3.15.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hcfcfb64_4 conda-forge
zstd 1.5.4 hd43e919_0
```
|
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PR_kwDODunzps4_-PZw
| 5,052
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added from_generator method to IterableDataset class.
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"I added a test and moved the `streaming` param from `read` to `__init_`. Then, I also decided to update the `read` method of the rest of the packaged modules to account for this param. \r\n\r\n@hamid-vakilzadeh Are you OK with these changes? ",
"@mariosasko these all look great! Thanks for the updates."
] | 2022-09-30T22:14:05Z
| 2022-10-05T12:51:48Z
| 2022-10-05T12:10:48Z
|
CONTRIBUTOR
| null | null | null |
Hello,
This resolves issues #4988.
I added a method `from_generator` to class `IterableDataset`.
I modified the `read` method of input stream generator to also return Iterable_dataset.
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I_kwDODunzps5i3jWK
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Errror when saving to disk a dataset of images
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[
"Looks like as long as the number of shards makes a batch lower than 1000 images it works. In my training set I have 40K images. If I use `num_shards=40` (batch of 1000 images) I get the error, but if I update it to `num_shards=50` (batch of 800 images) it works.\r\n\r\nI will be happy to share my dataset privately if it can help to better debug.",
"Hi! I didn't manage to reproduce this behavior, so sharing the dataset with us would help a lot. \r\n\r\n> My dataset is around 50K images, is this error might be due to a bad image?\r\n\r\nThis shouldn't be the case as we save raw data to disk without decoding it.",
"OK, thanks! The dataset is currently hosted on a gcs bucket. How would you like to proceed for sharing the link? ",
"You could follow [this](https://cloud.google.com/storage/docs/collaboration#browser) procedure or upload the dataset to Google Drive (50K images is not that much unless high-res) and send me an email with the link.",
"Thanks @mariosasko. I just sent you the GDrive link.",
"Thanks @jplu! I managed to reproduce the `TypeError` - it stems from [this](https://github.com/huggingface/datasets/blob/e3f4f124a1b118a5bfff5bae76b25a68aedbebbc/src/datasets/features/image.py#L258-L264) line, which can return a `ChunkedArray` (its mask is also chunked then, which Arrow does not allow) when the embedded data is too big to fit in a standard `Array`.\r\n\r\nI'm working on a fix.",
"@yairl-dn You should be able to bypass this issue by reducing `datasets.config.DEFAULT_MAX_BATCH_SIZE` (1000 by default)\r\n\r\nIn Datasets 3.0, the Image storage format will be simplified, so this should be easier to fix then.",
"The same error occurs with my save_to_disk() of Audio() items. I still get it with:\r\n```python\r\nimport datasets\r\ndatasets.config.DEFAULT_MAX_BATCH_SIZE=35\r\nfrom datasets import Features, Array2D, Value, Dataset, Sequence, Audio\r\n```\r\n\r\n```\r\nSaving the dataset (41/47 shards): 88%|██████████████████████████████████████████▉ | 297/339 [01:21<00:11, 3.65 examples/s]\r\nTraceback (most recent call last):\r\nFile \"/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py\", line 155, in <module>\r\ncreate_dataset(args)\r\nFile \"/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py\", line 137, in create_dataset\r\nhf_dataset.save_to_disk(args.outds)\r\nFile \"/home/j/src/py/datasets/src/datasets/arrow_dataset.py\", line 1532, in save_to_disk\r\nfor job_id, done, content in Dataset._save_to_disk_single(**kwargs):\r\nFile \"/home/j/src/py/datasets/src/datasets/arrow_dataset.py\", line 1563, in _save_to_disk_single\r\nwriter.write_table(pa_table)\r\nFile \"/home/j/src/py/datasets/src/datasets/arrow_writer.py\", line 574, in write_table\r\npa_table = embed_table_storage(pa_table)\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 2307, in embed_table_storage\r\narrays = [\r\n^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 2308, in <listcomp>\r\nembed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 1831, in wrapper\r\nreturn pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 1831, in <listcomp>\r\nreturn pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 2177, in embed_array_storage\r\nreturn feature.embed_storage(array)\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/features/audio.py\", line 276, in embed_storage\r\nstorage = pa.StructArray.from_arrays([bytes_array, path_array], [\"bytes\", \"path\"], mask=bytes_array.is_null())\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"pyarrow/array.pxi\", line 2850, in pyarrow.lib.StructArray.from_arrays\r\nFile \"pyarrow/array.pxi\", line 3290, in pyarrow.lib.c_mask_inverted_from_obj\r\nTypeError: Mask must be a pyarrow.Array of type boolean\r\n```",
"Similar to @jaggzh, setting `datasets.config.DEFAULT_MAX_BATCH_SIZE` did not help in my case (same error here but for different dataset: https://github.com/Stanford-AIMI/RRG24/issues/2).\r\n\r\nThis is also reproducible with this open dataset: https://huggingface.co/datasets/nlphuji/winogavil/discussions/1\r\n\r\nHere's some code to do so:\r\n```python\r\nimport datasets\r\n\r\ndatasets.config.DEFAULT_MAX_BATCH_SIZE = 1\r\n\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"nlphuji/winogavil\")\r\n\r\nds.save_to_disk(\"temp\")\r\n```\r\n\r\nI've done some more debugging with `datasets==2.18.0` (which incorporates PR #6283 as suggested by @lhoestq in the above dataset discussion), and it seems like the culprit might now be these lines: https://github.com/huggingface/datasets/blob/ca8409a8bec4508255b9c3e808d0751eb1005260/src/datasets/table.py#L2111-L2115\r\n\r\nFrom what I understand (and apologies I'm new to pyarrow), for an Image or Audio feature, these lines recursively call `embed_array_storage` for a list of either feature, ending up in the feature's `embed_storage` function. For all values in the list, `embed_storage` reads the bytes if they're not already loaded. The issue is the list being passed to the first recursive call is `array.values` which are the underlying values of `array` regardless of `array`'s slicing (as influenced by parameters such as `datasets.config.DEFAULT_MAX_BATCH_SIZE`). This results in the same overflowing list of bytes that result in the ChunkedArray being returned in `embed_storage`. Even if the array weren't to overflow and this code ran without throwing an exception, it still seems incorrect to load all values if you ultimately only want some subset with `ListArray.from_arrays(offsets, values)`; it seems some wasted effort if those values thrown out will get loaded again in the next batch and vice versa for the current batch of values during later batches.\r\n\r\nMaybe there's a fix where you could pass a mask to `embed_storage` such that it only loads the values you ultimately want for the current batch? Curious to see if you agree with this diagnosis of the problem and if you think this fix is viable @mariosasko?",
"Would be nice if they have something similar to Dagshub's S3 sync; it worked like a charm for my bigger datasets.",
"I guess also the proposed masking solution simply enables `datasets.config.DEFAULT_MAX_BATCH_SIZE` by reducing the number of elements loaded, it does not address the underlying problem of trying to load all the images as bytes into a pyarrow array.\r\n\r\nI'm happy to turn this into an actual PR but here's what I've implemented locally at `tables.py:embed_array_storage` to fix the above test case (`nlphuji/winogavil`) and my own use case:\r\n```python\r\n elif pa.types.is_list(array.type):\r\n # feature must be either [subfeature] or Sequence(subfeature)\r\n # Merge offsets with the null bitmap to avoid the \"Null bitmap with offsets slice not supported\" ArrowNotImplementedError\r\n array_offsets = _combine_list_array_offsets_with_mask(array)\r\n\r\n # mask underlying struct array so array_values.to_pylist()\r\n # fills None (see feature.embed_storage)\r\n idxs = np.arange(len(array.values))\r\n idxs = pa.ListArray.from_arrays(array_offsets, idxs).flatten()\r\n mask = np.ones(len(array.values)).astype(bool)\r\n mask[idxs] = False\r\n mask = pa.array(mask)\r\n # indexing 0 might be problematic but not sure\r\n # how else to get arbitrary keys from a struct array\r\n array_keys = array.values[0].keys()\r\n # is array.values always a struct array?\r\n array_values = pa.StructArray.from_arrays(\r\n arrays=[array.values.field(k) for k in array_keys],\r\n names=array_keys,\r\n mask=mask,\r\n )\r\n if isinstance(feature, list):\r\n return pa.ListArray.from_arrays(array_offsets, _e(array_values, feature[0]))\r\n if isinstance(feature, Sequence) and feature.length == -1:\r\n return pa.ListArray.from_arrays(array_offsets, _e(array_values, feature.feature))\r\n```\r\n\r\nAgain though I'm new to pyarrow so this might not be the cleanest implementation, also I'm really not sure if there are other cases where this solution doesn't work. Would love to get some feedback from the hf folks!",
"I have the same issue, with an audio dataset where file sizes vary significantly (~0.2-200 mb). Reducing `datasets.config.DEFAULT_MAX_BATCH_SIZE` doesn't help.",
"Still the problem is occured.\r\nHuggingface is sucks 🤮🤮🤮🤮",
"Came across this issue myself, with the same symptoms and reasons as everyone else; `pa.array` is returning a `ChunkedArray` in `features.audio.Audio.embed_storage` for my audio which varies between ~1MB and ~10MB in size.\r\n\r\nI would rather remove a troublesome file from my dataset than have to switch off this library, but it would be difficult to identify which file(s) caused the issue, and it may just shift the issue down to another shard or another file anyway. So, I took the path of least resistance and simply **dropped** anything beyond the first chunk when this issue occurred, and added a warning to indicate what was dropped.\r\n\r\nIn the end I lost **one** file out of 105,024 samples and was able to complete the 1,479 shard dataset after only the one issue on shard 228.\r\n\r\nWhile this is certainly not an ideal solution, it does represent a much better user experience, and was acceptable for my use case. I'm going to test the Image portion and then open a pull request to propose this \"lossy\" behavior become the way these edge cases are handled (maybe behind an environment flag?) until someone like @mariosasko or others can formulate a more holistic solution.\r\n\r\nMy work-in-progress \"fix\": https://github.com/huggingface/datasets/compare/main...painebenjamin:datasets:main (https://github.com/painebenjamin/datasets)",
"Another option could be to use `pa.large_binary` instead of `pa.binary` in certain cases ?",
"For my large audio dataset, what seems to work for me is to locally change `pa_type` to `pa.large_binary` in both\r\n\r\nhttps://github.com/huggingface/datasets/blob/01f91bae037c98f2e05456287bab21470adb8f07/src/datasets/features/audio.py#L71\r\n\r\nand\r\n\r\nhttps://github.com/huggingface/datasets/blob/01f91bae037c98f2e05456287bab21470adb8f07/src/datasets/features/audio.py#L270\r\n\r\nprior to uploading the dataset. Before downloading it, I just remove both changes to make sure any user with latest `datasets` can use it.\r\n\r\nAs a side note, the other proposed workarounds did not work for me.",
"Hey @fdschmidt93 I am not sure to follow. Can users downloading your dataset from the hub read it if you created the files with large_binary? It sounds like it will not be casted properly for them? ",
"Yes, that should work. In full detail -\r\n\r\nI have two separate conda environments. \r\n\r\n1. The one I prepare the data with for which I apply the above changes.\r\n2. Another one I actually run my experiments with that uses latest available `datasets` from pip\r\n\r\nThis seems to work just fine. More concretely, I'm downloading my own data uploaded with environment 1 from a private HF datasets repo in environment 2 (i.e., `load_dataset(...)`) and run the experiments.\r\n\r\nE: The private HF repo I was referring to now is public: https://huggingface.co/datasets/WueNLP/belebele-fleurs",
"Interesting ... so it's not even relevant at reading time, only writting ... Thanks I'll try this out.",
"I'm experiencing the same issue with the image dataset. Has this problem been resolved? Neither reducing the number of images per batch nor decreasing the DEFAULT_MAX_BATCH_SIZE has solved the issue.\n\nWhat I discovered is that in [line 272](https://github.com/huggingface/datasets/blob/3a4e74a9ace62ecd5c9cde7dcb6bcabd65cc7857/src/datasets/features/image.py#L272), when the storage length becomes large (probably over 1000), it returns a chunked array, which causes an error. \n\n Therefore, I tried to explicitly flatten it using `.combine_chunks()`, but encountered errors like `pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays`. \n```python\n bytes_array = pa.array(\n [\n (path_to_bytes(x[\"path\"]) if x[\"bytes\"] is None else x[\"bytes\"]) if x is not None else None\n for x in storage.to_pylist()\n ],\n type=pa.binary(),\n ).combine_chunks() # <- HERE\n```\n\nThis makes me feel there are limitations in processing images with arrow.\n\nWhen constructing an image dataset, I think storing image paths as strings might be the most straightforward bypass rather than using `Image()`. I'm curious if there would be any performance disadvantages to this approach.\n"
] | 2023-04-07T11:59:17Z
| 2025-02-09T09:35:25Z
| null |
CONTRIBUTOR
| null | null |
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### Describe the bug
Hello!
I have an issue when I try to save on disk my dataset of images. The error I get is:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1442, in save_to_disk
for job_id, done, content in Dataset._save_to_disk_single(**kwargs):
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1473, in _save_to_disk_single
writer.write_table(pa_table)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_writer.py", line 570, in write_table
pa_table = embed_table_storage(pa_table)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2268, in embed_table_storage
arrays = [
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2269, in <listcomp>
embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 1817, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 1817, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2142, in embed_array_storage
return feature.embed_storage(array)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/features/image.py", line 269, in embed_storage
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
File "pyarrow/array.pxi", line 2766, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/array.pxi", line 2961, in pyarrow.lib.c_mask_inverted_from_obj
TypeError: Mask must be a pyarrow.Array of type boolean
```
My dataset is around 50K images, is this error might be due to a bad image?
Thanks for the help.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="/path/to/dataset")
dataset["train"].save_to_disk("./myds", num_shards=40)
```
### Expected behavior
Having my dataset properly saved to disk.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.10
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
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PR_kwDODunzps5Imhk2
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|
Remove deprecated `shard_size` arg from `.push_to_hub()`
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008272 / 0.011353 (-0.003081) | 0.004494 / 0.011008 (-0.006515) | 0.100764 / 0.038508 (0.062256) | 0.028741 / 0.023109 (0.005632) | 0.309020 / 0.275898 (0.033122) | 0.354184 / 0.323480 (0.030704) | 0.007455 / 0.007986 (-0.000531) | 0.003377 / 0.004328 (-0.000951) | 0.078472 / 0.004250 (0.074222) | 0.034719 / 0.037052 (-0.002333) | 0.312787 / 0.258489 (0.054298) | 0.342878 / 0.293841 (0.049037) | 0.033326 / 0.128546 (-0.095221) | 0.011519 / 0.075646 (-0.064127) | 0.323556 / 0.419271 (-0.095716) | 0.039929 / 0.043533 (-0.003604) | 0.304627 / 0.255139 (0.049488) | 0.322876 / 0.283200 (0.039677) | 0.086410 / 0.141683 (-0.055273) | 1.502607 / 1.452155 (0.050453) | 1.577953 / 1.492716 (0.085237) |\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) | 0.192861 / 0.018006 (0.174855) | 0.406008 / 0.000490 (0.405519) | 0.001075 / 0.000200 (0.000875) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023351 / 0.037411 (-0.014060) | 0.096086 / 0.014526 (0.081561) | 0.104641 / 0.176557 (-0.071915) | 0.141940 / 0.737135 (-0.595195) | 0.109266 / 0.296338 (-0.187073) |\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) | 0.416496 / 0.215209 (0.201287) | 4.161581 / 2.077655 (2.083926) | 1.815357 / 1.504120 (0.311238) | 1.609536 / 1.541195 (0.068341) | 1.654105 / 1.468490 (0.185615) | 0.693947 / 4.584777 (-3.890830) | 3.349029 / 3.745712 (-0.396683) | 1.883968 / 5.269862 (-3.385893) | 1.287988 / 4.565676 (-3.277688) | 0.081765 / 0.424275 (-0.342511) | 0.012373 / 0.007607 (0.004766) | 0.517186 / 0.226044 (0.291142) | 5.200892 / 2.268929 (2.931964) | 2.247414 / 55.444624 (-53.197211) | 1.910601 / 6.876477 (-4.965876) | 1.965407 / 2.142072 (-0.176666) | 0.814386 / 4.805227 (-3.990841) | 0.149295 / 6.500664 (-6.351369) | 0.064667 / 0.075469 (-0.010802) |\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) | 1.247258 / 1.841788 (-0.594530) | 13.837355 / 8.074308 (5.763047) | 13.850454 / 10.191392 (3.659062) | 0.136078 / 0.680424 (-0.544346) | 0.028322 / 0.534201 (-0.505878) | 0.391394 / 0.579283 (-0.187889) | 0.407494 / 0.434364 (-0.026870) | 0.473784 / 0.540337 (-0.066554) | 0.562953 / 1.386936 (-0.823983) |\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) | 0.006559 / 0.011353 (-0.004794) | 0.004546 / 0.011008 (-0.006462) | 0.099527 / 0.038508 (0.061019) | 0.027428 / 0.023109 (0.004319) | 0.344276 / 0.275898 (0.068377) | 0.377897 / 0.323480 (0.054417) | 0.004913 / 0.007986 (-0.003072) | 0.003338 / 0.004328 (-0.000990) | 0.077589 / 0.004250 (0.073339) | 0.038819 / 0.037052 (0.001766) | 0.343165 / 0.258489 (0.084676) | 0.386228 / 0.293841 (0.092387) | 0.031753 / 0.128546 (-0.096794) | 0.011756 / 0.075646 (-0.063890) | 0.322537 / 0.419271 (-0.096735) | 0.049865 / 0.043533 (0.006332) | 0.340493 / 0.255139 (0.085354) | 0.372179 / 0.283200 (0.088980) | 0.099669 / 0.141683 (-0.042013) | 1.487841 / 1.452155 (0.035686) | 1.527400 / 1.492716 (0.034683) |\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) | 0.180782 / 0.018006 (0.162776) | 0.393494 / 0.000490 (0.393004) | 0.003004 / 0.000200 (0.002804) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024997 / 0.037411 (-0.012415) | 0.098232 / 0.014526 (0.083707) | 0.107869 / 0.176557 (-0.068688) | 0.141042 / 0.737135 (-0.596093) | 0.109551 / 0.296338 (-0.186787) |\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) | 0.477115 / 0.215209 (0.261906) | 4.783928 / 2.077655 (2.706273) | 2.435725 / 1.504120 (0.931605) | 2.233111 / 1.541195 (0.691916) | 2.341097 / 1.468490 (0.872607) | 0.694304 / 4.584777 (-3.890473) | 3.345687 / 3.745712 (-0.400025) | 1.886932 / 5.269862 (-3.382929) | 1.155585 / 4.565676 (-3.410092) | 0.082867 / 0.424275 (-0.341408) | 0.012420 / 0.007607 (0.004813) | 0.576575 / 0.226044 (0.350530) | 5.777691 / 2.268929 (3.508762) | 2.882219 / 55.444624 (-52.562405) | 2.543613 / 6.876477 (-4.332864) | 2.578939 / 2.142072 (0.436866) | 0.803143 / 4.805227 (-4.002084) | 0.151929 / 6.500664 (-6.348735) | 0.067777 / 0.075469 (-0.007693) |\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) | 1.282711 / 1.841788 (-0.559077) | 13.942771 / 8.074308 (5.868463) | 13.376206 / 10.191392 (3.184814) | 0.152916 / 0.680424 (-0.527508) | 0.016619 / 0.534201 (-0.517582) | 0.375141 / 0.579283 (-0.204142) | 0.381660 / 0.434364 (-0.052704) | 0.465090 / 0.540337 (-0.075247) | 0.555068 / 1.386936 (-0.831868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10a6a638e0feb955f7b607b4433ee715c30acccf \"CML watermark\")\n"
] | 2023-01-26T15:40:56Z
| 2023-01-26T17:37:51Z
| 2023-01-26T17:30:59Z
|
CONTRIBUTOR
| null | null | null |
The docstrings say that it was supposed to be deprecated since version 2.4.0, can we remove it?
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PR_kwDODunzps6O-I6K
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Adds EXR format to store depth images in float32
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"Hi ! I'mn wondering if this shouldn't this be an `Image()` type and decoded as a `PIL.Image` ?\r\n\r\nThis would make it easier to integrate with the rest of the HF ecosystem, and you could still get a numpy array using `ds = ds.with_format(\"numpy\")` which sets all the images to be formatted as numpy arrays",
"@lhoestq do you mean to add the decoder, and exr extension to the image format? Yes, that probably would be better ",
"yes exactly"
] | 2025-03-17T17:42:40Z
| 2025-04-02T12:33:39Z
| null |
NONE
| null | null | null |
This PR adds the EXR feature to store depth images (or can be normals, etc) in float32.
It relies on [openexr_numpy](https://github.com/martinResearch/openexr_numpy/tree/main) to manipulate EXR images.
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I_kwDODunzps5iTiqb
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raise AttributeError(f"No {package_name} attribute {name}") AttributeError: No huggingface_hub attribute hf_api
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"Hi @wccccp, thanks for reporting. \r\nThat's weird since `huggingface_hub` _has_ a module called `hf_api` and you are using a recent version of it. \r\n\r\nWhich version of `datasets` are you using? And is it a bug that you experienced only recently? (cc @lhoestq can it be somehow related to the recent release of `datasets`?)\r\n\r\n~@wccccp what I can suggest you is to uninstall and reinstall completely huggingface_hub and datasets? My first guess is that there is a discrepancy somewhere in your setup 😕~",
"@wccccp Actually I have also been able to reproduce the error so it's not an issue with your setup.\r\n\r\n@huggingface/datasets I found this issue quite weird. Is this a module that is not used very often?\r\nThe problematic line is [this one](https://github.com/huggingface/datasets/blame/c33e8ce68b5000988bf6b2e4bca27ffaa469acea/src/datasets/data_files.py#L476) where `huggingface_hub.hf_api.DatasetInfo` is used. `huggingface_hub` is imported [here](https://github.com/huggingface/datasets/blame/c33e8ce68b5000988bf6b2e4bca27ffaa469acea/src/datasets/data_files.py#L6) as `import huggingface_hub`. However since modules are lazy-loaded in `hfh` you need to explicitly import them (i.e. `import huggingface_hub.hf_api`).\r\n\r\nWhat's weird is that nothing has changed for months. Datasets code seems that it didn't change for 2 years when I git-blame this part. And lazy-loading was introduced 1 year ago in `huggingface_hub`. Could it be that `data_files.py` is a file almost never used?\r\n",
"For context, I tried to run `import huggingface_hub; huggingface_hub.hf_api.DatasetInfo` in the terminal with different versions of `hfh` and I need to go back to `huggingface_hub==0.7.0` to make it work (latest is 0.13.3).",
"Before the error happens at line 120 in `data_files.py`, `datasets.filesystems.hffilesystem` is imported at the top of `data_files.py` and this file does `from huggingface_hub.hf_api import DatasetInfo` - so `huggingface_hub.hf_api` is imported. Not sure how the error could happen, what version of `datasets` are you using @wccccp ?",
"Closing due to inactivity."
] | 2023-03-31T08:22:22Z
| 2023-07-21T14:21:57Z
| 2023-07-21T14:21:57Z
|
NONE
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### Describe the bug
rta.sh
Traceback (most recent call last):
File "run.py", line 7, in <module>
import datasets
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/builder.py", line 44, in <module>
from .data_files import DataFilesDict, _sanitize_patterns
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/data_files.py", line 120, in <module>
dataset_info: huggingface_hub.hf_api.DatasetInfo,
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/huggingface_hub/__init__.py", line 290, in __getattr__
raise AttributeError(f"No {package_name} attribute {name}")
AttributeError: No huggingface_hub attribute hf_api
### Reproduction
_No response_
### Logs
```shell
Traceback (most recent call last):
File "run.py", line 7, in <module>
import datasets
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/builder.py", line 44, in <module>
from .data_files import DataFilesDict, _sanitize_patterns
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/data_files.py", line 120, in <module>
dataset_info: huggingface_hub.hf_api.DatasetInfo,
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/huggingface_hub/__init__.py", line 290, in __getattr__
raise AttributeError(f"No {package_name} attribute {name}")
AttributeError: No huggingface_hub attribute hf_api
```
### System info
```shell
- huggingface_hub version: 0.13.2
- Platform: Linux-5.4.0-144-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /home/appuser/.cache/huggingface/token
- Has saved token ?: False
- Configured git credential helpers:
- FastAI: N/A
- Tensorflow: N/A
- Torch: 1.7.1
- Jinja2: N/A
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.3.0
- hf_transfer: N/A
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /home/appuser/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /home/appuser/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/appuser/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
```
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`write_examples_on_file` and `write_batch` are broken in `ArrowWriter`
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[
"Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.",
"> Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.\r\n\r\nI feel that'd be good, but it'd be great to release a hotfix ASAP (a revert is a fast thing to do) so people can continue using this library and then focus on still applying the improvement.",
"Fixed by #6664 "
] | 2024-02-15T01:43:27Z
| 2024-02-16T09:25:00Z
| 2024-02-16T09:25:00Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
`write_examples_on_file` and `write_batch` are broken in `ArrowWriter` since #6636. The order between the columns and the schema is not preserved anymore. So these functions don't work anymore unless the order happens to align well.
### Steps to reproduce the bug
Try to do `write_batch` with anything that has many columns, and it's likely to break.
### Expected behavior
I expect these functions to work, instead of it trying to cast a column to its incorrect type.
### Environment info
- `datasets` version: 2.17.0
- Platform: Linux-5.15.0-1040-aws-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.19.4
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2023.10.0
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I_kwDODunzps5MTW5e
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Avoid splitting by` .py` for the file.
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[
"Hi @espoirMur, thanks for reporting.\r\n\r\nYou are right: that code line could be improved and made more generically valid.\r\n\r\nOn the other hand, I would suggest using `os.path.splitext` instead.\r\n\r\nAre you willing to open a PR? :)",
"I will have a look.. \r\n\r\nThis weekend .. ",
"@albertvillanova , Can you have a look at #4590. \r\n\r\nThanks ",
"#self-assign"
] | 2022-06-22T13:26:55Z
| 2022-07-07T13:17:44Z
| 2022-07-07T13:17:44Z
|
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https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272
Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.
Step to reproduce.
- If you have a home folder which ends with `.py`
- load a module with a local folder
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed
A possible workaround would be to use pathlib at the mentioned line
` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .
Let me what are your thought on this and I can try to fix it by A PR.
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I_kwDODunzps5rjjYD
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`map` function doesn't fully utilize `input_columns`.
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[] | 2023-07-14T08:49:28Z
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| 2023-07-14T09:16:04Z
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NONE
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### Describe the bug
I wanted to select only some columns of data.
And I thought that's why the argument `input_columns` exists.
What I expected is like this:
If there are ["a", "b", "c", "d"] columns, and if I set `input_columns=["a", "d"]`, the data will have only ["a", "d"] columns.
But it doesn't select columns.
It preserves existing columns.
The main cause is `update` function of `dictionary` type `transformed_batch`.
https://github.com/huggingface/datasets/blob/682d21e94ab1e64c11b583de39dc4c93f0101c5a/src/datasets/iterable_dataset.py#L687-L691
`transformed_batch` gets all the columns by `transformed_batch = dict(batch)`.
Even `function_args` selects `input_columns`, `update` preserves columns other than `input_columns`.
I think it should take a new dictionary with columns in `input_columns` like this:
```
# transformed_batch = dict(batch)
# transformed_batch.update(self.function(*function_args, **self.fn_kwargs)
# This is what I think correct.
transformed_batch = self.function(*function_args, **self.fn_kwargs)
```
Let me know how to use `input_columns`.
### Steps to reproduce the bug
Described all above.
### Expected behavior
Described all above.
### Environment info
datasets: 2.12
python: 3.8
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I_kwDODunzps6yWEwt
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Incompatibile dill version (0.3.9) in datasets 2.18.0 - 3.5.0
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[
"Hi ! We can bump `dill` to 0.3.9 if we make sure it's deterministic and doesn't break the caching mechanism in `datasets`.\n\nWould you be interested in opening a PR ? Then we can run the CI to see if it works",
"Hi!. Yeah I can do it. Should I make any changes besides dill versions?",
"There are probably some usage of internal functions from `dill` that we'll need to update in `datasets`\n\nIf you run `pytest tests/test_fingerprint.py` you should already have a good idea of what works and what doesn't.\nBut feel free to open a PR anyway, this way we can run the full CI and see the results\n",
"Hi, sorry for no response from my side. I will try to do it today.",
"Created pull request: [LINK](https://github.com/huggingface/datasets/pull/7535)\nTried to run tests by using command you have send and got few errors:\n\n"
] | 2025-04-14T07:22:44Z
| 2025-04-24T19:49:31Z
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NONE
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### Describe the bug
Datasets 2.18.0 - 3.5.0 has a dependency on dill < 0.3.9. This causes errors with dill >= 0.3.9.
Could you please take a look into it and make it compatible?
### Steps to reproduce the bug
1. Install setuptools >= 2.18.0
2. Install dill >=0.3.9
3. Run pip check
4. Output:
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
datasets 2.18.0 requires dill<0.3.9,>=0.3.0, but you have dill 0.3.9 which is incompatible.
### Expected behavior
Pip install both libraries without any errors
### Environment info
Datasets version: 2.18 - 3.5
Python: 3.11
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PR_kwDODunzps51MLbh
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Mark tests that require librosa
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7044). 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) | 0.005797 / 0.011353 (-0.005556) | 0.004017 / 0.011008 (-0.006991) | 0.063829 / 0.038508 (0.025321) | 0.031329 / 0.023109 (0.008220) | 0.249388 / 0.275898 (-0.026510) | 0.273129 / 0.323480 (-0.050351) | 0.004250 / 0.007986 (-0.003736) | 0.002821 / 0.004328 (-0.001507) | 0.049250 / 0.004250 (0.044999) | 0.046175 / 0.037052 (0.009123) | 0.252040 / 0.258489 (-0.006449) | 0.296537 / 0.293841 (0.002696) | 0.030579 / 0.128546 (-0.097967) | 0.012436 / 0.075646 (-0.063210) | 0.205829 / 0.419271 (-0.213443) | 0.036979 / 0.043533 (-0.006554) | 0.251354 / 0.255139 (-0.003785) | 0.272262 / 0.283200 (-0.010938) | 0.019047 / 0.141683 (-0.122636) | 1.112410 / 1.452155 (-0.339745) | 1.137445 / 1.492716 (-0.355271) |\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) | 0.097270 / 0.018006 (0.079264) | 0.309329 / 0.000490 (0.308839) | 0.000221 / 0.000200 (0.000021) | 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) | 0.019021 / 0.037411 (-0.018390) | 0.066801 / 0.014526 (0.052276) | 0.075280 / 0.176557 (-0.101276) | 0.122499 / 0.737135 (-0.614637) | 0.077424 / 0.296338 (-0.218914) |\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) | 0.279469 / 0.215209 (0.064259) | 2.787511 / 2.077655 (0.709856) | 1.411389 / 1.504120 (-0.092731) | 1.285796 / 1.541195 (-0.255399) | 1.354252 / 1.468490 (-0.114238) | 0.735341 / 4.584777 (-3.849436) | 2.418557 / 3.745712 (-1.327155) | 2.983406 / 5.269862 (-2.286455) | 2.005853 / 4.565676 (-2.559823) | 0.080440 / 0.424275 (-0.343835) | 0.005242 / 0.007607 (-0.002365) | 0.343557 / 0.226044 (0.117513) | 3.358984 / 2.268929 (1.090055) | 1.816709 / 55.444624 (-53.627915) | 1.500225 / 6.876477 (-5.376252) | 1.715405 / 2.142072 (-0.426667) | 0.829054 / 4.805227 (-3.976174) | 0.138352 / 6.500664 (-6.362312) | 0.043709 / 0.075469 (-0.031760) |\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) | 0.969135 / 1.841788 (-0.872652) | 12.510750 / 8.074308 (4.436442) | 10.140368 / 10.191392 (-0.051024) | 0.133117 / 0.680424 (-0.547307) | 0.015775 / 0.534201 (-0.518426) | 0.302203 / 0.579283 (-0.277080) | 0.268214 / 0.434364 (-0.166150) | 0.347041 / 0.540337 (-0.193296) | 0.456095 / 1.386936 (-0.930841) |\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) | 0.006255 / 0.011353 (-0.005098) | 0.004453 / 0.011008 (-0.006555) | 0.052298 / 0.038508 (0.013790) | 0.034808 / 0.023109 (0.011699) | 0.274723 / 0.275898 (-0.001175) | 0.297199 / 0.323480 (-0.026281) | 0.004499 / 0.007986 (-0.003486) | 0.003086 / 0.004328 (-0.001242) | 0.051315 / 0.004250 (0.047065) | 0.042764 / 0.037052 (0.005712) | 0.285636 / 0.258489 (0.027147) | 0.321819 / 0.293841 (0.027978) | 0.033350 / 0.128546 (-0.095196) | 0.013457 / 0.075646 (-0.062189) | 0.063930 / 0.419271 (-0.355342) | 0.034537 / 0.043533 (-0.008996) | 0.272630 / 0.255139 (0.017491) | 0.289245 / 0.283200 (0.006045) | 0.018910 / 0.141683 (-0.122773) | 1.153064 / 1.452155 (-0.299091) | 1.207065 / 1.492716 (-0.285651) |\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) | 0.093008 / 0.018006 (0.075002) | 0.301313 / 0.000490 (0.300823) | 0.000214 / 0.000200 (0.000014) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023168 / 0.037411 (-0.014244) | 0.080837 / 0.014526 (0.066312) | 0.089667 / 0.176557 (-0.086889) | 0.135849 / 0.737135 (-0.601286) | 0.092082 / 0.296338 (-0.204257) |\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) | 0.298933 / 0.215209 (0.083723) | 2.847736 / 2.077655 (0.770082) | 1.550268 / 1.504120 (0.046148) | 1.425675 / 1.541195 (-0.115520) | 1.469251 / 1.468490 (0.000761) | 0.720446 / 4.584777 (-3.864331) | 0.976149 / 3.745712 (-2.769563) | 3.081804 / 5.269862 (-2.188057) | 1.982797 / 4.565676 (-2.582880) | 0.078598 / 0.424275 (-0.345677) | 0.005229 / 0.007607 (-0.002379) | 0.345475 / 0.226044 (0.119430) | 3.421312 / 2.268929 (1.152384) | 1.929034 / 55.444624 (-53.515590) | 1.631523 / 6.876477 (-5.244953) | 1.671996 / 2.142072 (-0.470077) | 0.776916 / 4.805227 (-4.028311) | 0.133966 / 6.500664 (-6.366699) | 0.042183 / 0.075469 (-0.033286) |\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) | 0.993023 / 1.841788 (-0.848764) | 12.981642 / 8.074308 (4.907334) | 10.610457 / 10.191392 (0.419065) | 0.146748 / 0.680424 (-0.533676) | 0.016556 / 0.534201 (-0.517645) | 0.303613 / 0.579283 (-0.275670) | 0.132671 / 0.434364 (-0.301693) | 0.344786 / 0.540337 (-0.195552) | 0.443049 / 1.386936 (-0.943887) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8419c40a085d67eb5832cecebf3ef8213112857d \"CML watermark\")\n"
] | 2024-07-12T08:06:59Z
| 2024-07-12T09:06:32Z
| 2024-07-12T09:00:09Z
|
MEMBER
| null | null | null |
Mark tests that require `librosa`.
Note that `librosa` is an optional dependency (installed with `audio` option) and we should be able to test environments without that library installed. This is the case if we want to test Numpy 2.0, which is currently incompatible with `librosa` due to its dependency on `soxr`:
- https://github.com/dofuuz/python-soxr/issues/28
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load_dataset() function's cache_dir does not seems to work
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[
"Can you share the error message?\r\n\r\nAlso, it would help if you could check whether `huggingface_hub`'s download behaves the same:\r\n```python\r\nfrom huggingface_hub import snapshot_download\r\nsnapshot_download(\"trec\", repo_type=\"dataset\", cache_dir='/path/to/my/dir)\r\n```\r\n\r\nIn the next major release, we aim to switch to `huggingface_hub` for file download/caching, but we could align the `cache_dir`'s `umask` behavior earlier than this if their solution works for your use case.",
"@mariosasko, Hey , is there any update on this? ",
"Yes, I am still facing the same issue.\r\n\r\n```python\r\nfrom huggingface_hub import snapshot_download\r\nsnapshot_download(\"trec\", repo_type=\"dataset\", cache_dir='/path/to/my/dir)\r\n```\r\n\r\nBut this is working for me.\r\n\r\nThanks.",
"Could you clarify a bit? What if I wanted only 100 samples from train split, how would i change the above? Thanks",
"I have exactly same issue"
] | 2023-09-24T15:34:06Z
| 2024-10-08T15:45:19Z
| 2024-10-08T15:45:18Z
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### Describe the bug
datasets version: 2.14.5
when trying to run the following command
trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir')
I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine.
It seems the cache_dir parameter cannot change the dataset saving directory from the default
what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work
### Steps to reproduce the bug
datasets version: 2.14.5
when trying to run the following command
trec = load_dataset('trec', split='train[:1000]', cache_dir='/path/to/my/dir')
I keep getting error saying the command does not have permission to the default cache directory on my macbook pro machine.
It seems the cache_dir parameter cannot change the dataset saving directory from the default
what ever explained in the https://huggingface.co/docs/datasets/cache does not seem to work
### Expected behavior
the dataset should be saved to the cache_dir points to
### Environment info
datasets version: 2.14.5
macos X: Ventura 13.4.1 (c)
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PyArrow 'Memory mapping file failed: Cannot allocate memory' bug
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"Thanks for a very clean explanation. This happened to me too, and I don't have sudo access to update the value. I wonder if there might be another workaround.",
"One option is to just have more data in each file - /proc/sys/vm/max_map_count limits the maximum number of concurrently open files, but I don't know if the size of a single file is restricted in any way. E.g. 5000 files with 1GB each is 5TB of data. https://huggingface.co/docs/datasets/v2.18.0/en/package_reference/main_classes#datasets.concatenate_datasets can come in handy."
] | 2024-04-07T19:25:39Z
| 2024-09-06T19:09:37Z
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### Describe the bug
Hello,
I've been struggling with a problem using Huggingface datasets caused by PyArrow memory allocation. I finally managed to solve it, and thought to document it since similar issues have been raised here before (https://github.com/huggingface/datasets/issues/5710, https://github.com/huggingface/datasets/issues/6176).
In my case, I was trying to load ~70k dataset files from disk using `datasets.load_from_disk(data_path)` (meaning 70k repeated calls to load_from_disk). This triggered an (uninformative) exception around 64k loaded files:
```
File "pyarrow/io.pxi", line 1053, in pyarrow.lib.memory_map
File "pyarrow/io.pxi", line 1000, in pyarrow.lib.MemoryMappedFile._open
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
OSError: Memory mapping file failed: Cannot allocate memory
```
Despite system RAM usage being very low. After a lot of digging around, I discovered that my Ubuntu machine had a limit on the maximum number of memory mapped files in `/proc/sys/vm/max_map_count` set to 65530, which was causing my data loader to crash. Increasing the limit in the file (`echo <new_mmap_size> | sudo tee /proc/sys/vm/max_map_count`) made the issue go away.
While this isn't a bug as such in either Datasets or PyArrow, this behavior can be very confusing to users. Maybe this should be mentioned in documentation? I suspect the other issues raised here about memory mapping OOM errors could actually be consequence of system configuration.
Br,
Lauri
### Steps to reproduce the bug
```
import numpy as np
import pyarrow as pa
import tqdm
# Write some data to disk
arr = pa.array(np.arange(100))
schema = pa.schema([
pa.field('nums', arr.type)
])
with pa.OSFile('arraydata.arrow', 'wb') as sink:
with pa.ipc.new_file(sink, schema=schema) as writer:
batch = pa.record_batch([arr], schema=schema)
writer.write(batch)
# Number of times to open the memory map
nums = 70000
# Read the data back
arrays = [pa.memory_map('arraydata.arrow', 'r') for _ in tqdm.tqdm(range(nums))]
```
### Expected behavior
No errors.
### Environment info
datasets: 2.18.0
pyarrow: 15.0.0
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Fix language tags resource file
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4882). All of your documentation changes will be reflected on that endpoint."
] | 2022-08-24T06:06:01Z
| 2022-08-24T13:58:33Z
| 2022-08-24T13:58:30Z
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MEMBER
| null | null | null |
This PR fixes/updates/adds ALL language tags from IANA (as of 2022-08-08).
This PR also removes all BCP47 suffixes (the languages file only contains language subtags, i.e. ISO 639 1 or 2 codes; no script/region/variant suffixes). See:
- #4753
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PR_kwDODunzps5mlmmo
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Change default compression argument for JsonDatasetWriter
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6659). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Can someone check this out?",
"<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) | 0.005008 / 0.011353 (-0.006345) | 0.003267 / 0.011008 (-0.007741) | 0.064140 / 0.038508 (0.025632) | 0.027419 / 0.023109 (0.004309) | 0.246692 / 0.275898 (-0.029206) | 0.271303 / 0.323480 (-0.052177) | 0.004127 / 0.007986 (-0.003859) | 0.002698 / 0.004328 (-0.001631) | 0.050415 / 0.004250 (0.046165) | 0.040323 / 0.037052 (0.003271) | 0.265738 / 0.258489 (0.007249) | 0.291556 / 0.293841 (-0.002285) | 0.027924 / 0.128546 (-0.100622) | 0.010206 / 0.075646 (-0.065441) | 0.207106 / 0.419271 (-0.212165) | 0.036087 / 0.043533 (-0.007446) | 0.250412 / 0.255139 (-0.004727) | 0.269014 / 0.283200 (-0.014186) | 0.018102 / 0.141683 (-0.123581) | 1.135137 / 1.452155 (-0.317018) | 1.177718 / 1.492716 (-0.314998) |\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) | 0.095557 / 0.018006 (0.077550) | 0.306235 / 0.000490 (0.305745) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 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) | 0.018217 / 0.037411 (-0.019194) | 0.060993 / 0.014526 (0.046467) | 0.072748 / 0.176557 (-0.103808) | 0.119357 / 0.737135 (-0.617778) | 0.073719 / 0.296338 (-0.222619) |\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) | 0.295924 / 0.215209 (0.080715) | 2.901071 / 2.077655 (0.823417) | 1.497316 / 1.504120 (-0.006804) | 1.371232 / 1.541195 (-0.169962) | 1.395643 / 1.468490 (-0.072847) | 0.577548 / 4.584777 (-4.007229) | 2.383813 / 3.745712 (-1.361899) | 2.764451 / 5.269862 (-2.505411) | 1.733074 / 4.565676 (-2.832602) | 0.063730 / 0.424275 (-0.360545) | 0.004933 / 0.007607 (-0.002674) | 0.347135 / 0.226044 (0.121090) | 3.390814 / 2.268929 (1.121885) | 1.849454 / 55.444624 (-53.595170) | 1.561801 / 6.876477 (-5.314675) | 1.587818 / 2.142072 (-0.554254) | 0.652061 / 4.805227 (-4.153166) | 0.117195 / 6.500664 (-6.383469) | 0.041922 / 0.075469 (-0.033548) |\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) | 0.949050 / 1.841788 (-0.892738) | 11.353664 / 8.074308 (3.279355) | 9.261581 / 10.191392 (-0.929811) | 0.140374 / 0.680424 (-0.540050) | 0.014254 / 0.534201 (-0.519946) | 0.288124 / 0.579283 (-0.291159) | 0.262888 / 0.434364 (-0.171476) | 0.330774 / 0.540337 (-0.209564) | 0.444777 / 1.386936 (-0.942159) |\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) | 0.005162 / 0.011353 (-0.006191) | 0.003418 / 0.011008 (-0.007591) | 0.049764 / 0.038508 (0.011256) | 0.029336 / 0.023109 (0.006226) | 0.278570 / 0.275898 (0.002672) | 0.300676 / 0.323480 (-0.022804) | 0.004292 / 0.007986 (-0.003694) | 0.002745 / 0.004328 (-0.001584) | 0.049194 / 0.004250 (0.044943) | 0.044036 / 0.037052 (0.006984) | 0.299258 / 0.258489 (0.040769) | 0.324451 / 0.293841 (0.030610) | 0.029777 / 0.128546 (-0.098769) | 0.010426 / 0.075646 (-0.065221) | 0.057267 / 0.419271 (-0.362004) | 0.051276 / 0.043533 (0.007743) | 0.278012 / 0.255139 (0.022873) | 0.297099 / 0.283200 (0.013899) | 0.018340 / 0.141683 (-0.123343) | 1.179255 / 1.452155 (-0.272899) | 1.231536 / 1.492716 (-0.261180) |\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) | 0.092546 / 0.018006 (0.074540) | 0.299959 / 0.000490 (0.299469) | 0.000220 / 0.000200 (0.000020) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021657 / 0.037411 (-0.015755) | 0.075440 / 0.014526 (0.060914) | 0.086246 / 0.176557 (-0.090310) | 0.126511 / 0.737135 (-0.610624) | 0.091303 / 0.296338 (-0.205036) |\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) | 0.294775 / 0.215209 (0.079566) | 2.868973 / 2.077655 (0.791319) | 1.666971 / 1.504120 (0.162851) | 1.545680 / 1.541195 (0.004486) | 1.559983 / 1.468490 (0.091493) | 0.572191 / 4.584777 (-4.012586) | 2.429317 / 3.745712 (-1.316395) | 2.673334 / 5.269862 (-2.596527) | 1.758114 / 4.565676 (-2.807563) | 0.063766 / 0.424275 (-0.360509) | 0.005070 / 0.007607 (-0.002537) | 0.345488 / 0.226044 (0.119443) | 3.464525 / 2.268929 (1.195596) | 1.975717 / 55.444624 (-53.468908) | 1.686671 / 6.876477 (-5.189806) | 1.825434 / 2.142072 (-0.316638) | 0.655853 / 4.805227 (-4.149374) | 0.116372 / 6.500664 (-6.384292) | 0.040647 / 0.075469 (-0.034822) |\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) | 1.014080 / 1.841788 (-0.827707) | 12.038496 / 8.074308 (3.964188) | 10.354536 / 10.191392 (0.163144) | 0.130285 / 0.680424 (-0.550139) | 0.015514 / 0.534201 (-0.518687) | 0.284743 / 0.579283 (-0.294540) | 0.280275 / 0.434364 (-0.154088) | 0.321175 / 0.540337 (-0.219162) | 0.425840 / 1.386936 (-0.961096) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bb6c6d46a171c4fa1b65167cb81998e2f863892 \"CML watermark\")\n"
] | 2024-02-11T23:49:07Z
| 2024-03-01T17:51:50Z
| 2024-03-01T17:44:55Z
|
CONTRIBUTOR
| null | null | null |
Change default compression type from `None` to "infer", to align with pandas' defaults.
Documentation asks the user to supply `to_json_kwargs` with arguments suitable for pandas' `to_json` method. At the same time, while pandas' by default uses ["infer"](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) for compression, datasets enforce `None` as default. This, likely, confuses user, as they expect the same behaviour, i.e they expect that if they name their output file as "dataset.jsonl.zst" then the compression would be inferred as "zstd" and file will be compressed before writing.
Moreover, while it is probably outside of the scope of this pull request, `compression` argument needs to be capable of taking `dict` as input (along with `str`), as it does in pandas, in order to allow user to specify compression parameters. Current implementation will likely fail with `NotImplementedError`, as it expects either `None` or `str` specifying compression algo.
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Unable to load dataset that was saved with `save_to_disk`
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"`save_to_disk` uses a special serialization that can only be read using `load_from_disk`.\r\n\r\nContrary to `load_dataset`, `load_from_disk` directly loads Arrow files and uses the dataset directory as cache.\r\n\r\nOn the other hand `load_dataset` does a conversion step to get Arrow files from the raw data files (could be in JSON, CSV, Parquet etc.) and caches them in the `datasets` cache directory (default is `~/.cache/huggingface/datasets`). We haven't implemented any logic in `load_dataset` to support datasets saved with `save_to_disk` because they don't use the same cache.\r\n\r\nEDIT: note that you can save your dataset in Parquet format locally using `.to.parquet()` (make sure to shard in multiple files your dataset if it's multiple GBs - you can use `.shard()` + `.to_parquet()` to do that) and you'll be able to reload it using `load_dataset`",
"@lhoestq, so is it correctly understood that if I run `to_parquet()` and then `save_to_disk()`, I can load it with `load_dataset`? If yes, then it would resolve this issue (and should probably be documented somewhere 😄)",
"Here is an example:\r\n```python\r\nds.to_parquet(\"my/local/dir/data.parquet\")\r\n\r\n# later\r\nds = load_dataset(\"my/local/dir\")\r\n```\r\n\r\nand for bigger datasets:\r\n```python\r\nnum_shards = 1024 # set number of files to save (e.g. try to have files smaller than 5GB)\r\nfor shard_idx in num_shards:\r\n shard = ds.shard(index=shard_idx, num_shards=num_shards)\r\n shard.to_parquet(f\"my/local/dir/{shard_idx:05d}.parquet\") # 00000.parquet to 01023.parquet\r\n\r\n# later\r\nds = load_dataset(\"my/local/dir\")\r\n```\r\n\r\n\r\nI hope this helps :)",
"Thanks for helping out! Does this approach work with `s3fs`? e.g. something like this:\r\n\r\n```python\r\nimport s3fs\r\ns3 = s3fs.S3FileSystem(anon=True)\r\nwith s3.open('mybucket/new-file.parquet', 'w') as f:\r\n ds.to_parquet(f)\r\n```\r\n\r\nThis is instead of `save_to_disk` to save to an S3 bucket.\r\n\r\nOtherwise, I am not sure how to make this work when saving the dataset to an S3 bucket. Would `dataset.set_format(\"arrow\")` work as a replacement?",
"`load_dataset` does't support S3 buckets unfortunately :/",
"> `load_dataset` does't support S3 buckets unfortunately :/\r\n\r\nI am aware but I have some code that downloads it to disk before using that method. The most important part is to store it in a format that load_dataset is compatible with. ",
"Feel free to use Parquet then :)",
"I ended up with this. Not ideal to save to local disk, but it works and loads via `load_datasets` after downloading from S3 with another method.\r\n\r\n```python\r\nwith tempfile.TemporaryDirectory() as dir:\r\n dataset_nbytes = ds._estimate_nbytes()\r\n max_shard_size_local = convert_file_size_to_int(max_shard_size)\r\n num_shards = int(dataset_nbytes / max_shard_size_local) + 1\r\n\r\n for shard_idx in range(num_shards):\r\n shard = ds.shard(index=shard_idx, num_shards=num_shards)\r\n shard.to_parquet(f\"{dir}/{shard_idx:05d}.parquet\")\r\n \r\n fs.upload(\r\n lpath=dir,\r\n rpath=s3_path,\r\n recursive=True,\r\n )\r\n```"
] | 2024-03-01T11:59:56Z
| 2024-03-04T13:46:20Z
| 2024-03-04T13:46:20Z
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### Describe the bug
I get the following error message: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.
### Steps to reproduce the bug
1. Save a dataset with `save_to_disk`
2. Try to load it with `load_datasets`
### Expected behavior
I am able to load the dataset again with `load_datasets` which most packages uses over `load_from_disk`. I want to have a workaround that allows me to create the same indexing that `push_to_hub` creates for you before using `save_to_disk` - how can that be achieved?
### Environment info
datasets 2.17.1, python 3.10
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"(Sorry, meant to open this against my own fork. I'm attempting to debug this issue (https://github.com/astral-sh/uv/issues/1921#issuecomment-2058056192) reported by `huggingface/datasets` on the uv repo.)"
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[docs] Update return statement of index search
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007697 / 0.011353 (-0.003656) | 0.004233 / 0.011008 (-0.006776) | 0.087890 / 0.038508 (0.049382) | 0.065305 / 0.023109 (0.042196) | 0.366919 / 0.275898 (0.091020) | 0.399656 / 0.323480 (0.076176) | 0.006753 / 0.007986 (-0.001232) | 0.003428 / 0.004328 (-0.000900) | 0.070180 / 0.004250 (0.065930) | 0.054164 / 0.037052 (0.017112) | 0.377130 / 0.258489 (0.118641) | 0.403456 / 0.293841 (0.109615) | 0.042639 / 0.128546 (-0.085907) | 0.012396 / 0.075646 (-0.063250) | 0.314235 / 0.419271 (-0.105036) | 0.061976 / 0.043533 (0.018443) | 0.376959 / 0.255139 (0.121820) | 0.433313 / 0.283200 (0.150113) | 0.031253 / 0.141683 (-0.110430) | 1.555749 / 1.452155 (0.103594) | 1.643905 / 1.492716 (0.151189) |\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) | 0.208630 / 0.018006 (0.190624) | 0.519532 / 0.000490 (0.519042) | 0.003719 / 0.000200 (0.003519) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027403 / 0.037411 (-0.010008) | 0.080990 / 0.014526 (0.066464) | 0.090424 / 0.176557 (-0.086133) | 0.153922 / 0.737135 (-0.583213) | 0.098156 / 0.296338 (-0.198183) |\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) | 0.519453 / 0.215209 (0.304244) | 5.100089 / 2.077655 (3.022434) | 2.212165 / 1.504120 (0.708045) | 1.894405 / 1.541195 (0.353210) | 1.922914 / 1.468490 (0.454424) | 0.762443 / 4.584777 (-3.822334) | 4.669214 / 3.745712 (0.923502) | 5.016066 / 5.269862 (-0.253796) | 3.128821 / 4.565676 (-1.436856) | 0.091541 / 0.424275 (-0.332734) | 0.007582 / 0.007607 (-0.000026) | 0.652753 / 0.226044 (0.426709) | 6.601375 / 2.268929 (4.332446) | 3.076948 / 55.444624 (-52.367677) | 2.250544 / 6.876477 (-4.625933) | 2.404059 / 2.142072 (0.261987) | 0.994917 / 4.805227 (-3.810311) | 0.200318 / 6.500664 (-6.300346) | 0.069354 / 0.075469 (-0.006115) |\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) | 1.482559 / 1.841788 (-0.359229) | 20.722092 / 8.074308 (12.647784) | 17.703217 / 10.191392 (7.511825) | 0.215370 / 0.680424 (-0.465053) | 0.028208 / 0.534201 (-0.505993) | 0.425992 / 0.579283 (-0.153291) | 0.492785 / 0.434364 (0.058421) | 0.474154 / 0.540337 (-0.066183) | 0.644599 / 1.386936 (-0.742337) |\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) | 0.008372 / 0.011353 (-0.002981) | 0.004543 / 0.011008 (-0.006465) | 0.070564 / 0.038508 (0.032056) | 0.066855 / 0.023109 (0.043746) | 0.386724 / 0.275898 (0.110826) | 0.432184 / 0.323480 (0.108704) | 0.005250 / 0.007986 (-0.002736) | 0.003630 / 0.004328 (-0.000698) | 0.069310 / 0.004250 (0.065060) | 0.055759 / 0.037052 (0.018707) | 0.375789 / 0.258489 (0.117299) | 0.417335 / 0.293841 (0.123494) | 0.043424 / 0.128546 (-0.085122) | 0.013106 / 0.075646 (-0.062541) | 0.087836 / 0.419271 (-0.331436) | 0.057770 / 0.043533 (0.014237) | 0.396694 / 0.255139 (0.141555) | 0.439350 / 0.283200 (0.156150) | 0.031660 / 0.141683 (-0.110023) | 1.571339 / 1.452155 (0.119185) | 1.667169 / 1.492716 (0.174452) |\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) | 0.180534 / 0.018006 (0.162528) | 0.540027 / 0.000490 (0.539537) | 0.003573 / 0.000200 (0.003373) | 0.000141 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031380 / 0.037411 (-0.006032) | 0.083762 / 0.014526 (0.069236) | 0.098166 / 0.176557 (-0.078390) | 0.160761 / 0.737135 (-0.576374) | 0.097683 / 0.296338 (-0.198656) |\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) | 0.568074 / 0.215209 (0.352865) | 5.660544 / 2.077655 (3.582889) | 2.416698 / 1.504120 (0.912578) | 2.177096 / 1.541195 (0.635901) | 2.206178 / 1.468490 (0.737688) | 0.844864 / 4.584777 (-3.739912) | 4.793636 / 3.745712 (1.047923) | 7.062387 / 5.269862 (1.792525) | 4.201228 / 4.565676 (-0.364449) | 0.091997 / 0.424275 (-0.332279) | 0.007881 / 0.007607 (0.000274) | 0.679466 / 0.226044 (0.453422) | 6.580268 / 2.268929 (4.311340) | 3.229907 / 55.444624 (-52.214717) | 2.524877 / 6.876477 (-4.351600) | 2.463796 / 2.142072 (0.321723) | 0.975627 / 4.805227 (-3.829600) | 0.186670 / 6.500664 (-6.313994) | 0.065307 / 0.075469 (-0.010163) |\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) | 1.501447 / 1.841788 (-0.340340) | 21.231037 / 8.074308 (13.156729) | 17.591671 / 10.191392 (7.400279) | 0.212745 / 0.680424 (-0.467679) | 0.026100 / 0.534201 (-0.508101) | 0.428391 / 0.579283 (-0.150892) | 0.535268 / 0.434364 (0.100904) | 0.506733 / 0.540337 (-0.033604) | 0.660832 / 1.386936 (-0.726104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#962537d7ee9191438ef47a4185d0ba626b2ee949 \"CML watermark\")\n"
] | 2023-07-11T21:33:32Z
| 2023-07-12T17:13:02Z
| 2023-07-12T17:03:00Z
|
MEMBER
| null | null | null |
Clarifies in the return statement of the docstring that the retrieval score is `IndexFlatL2` by default (see [PR](https://github.com/huggingface/transformers/issues/24739) and internal Slack [convo](https://huggingface.slack.com/archives/C01229B19EX/p1689105179711689)), and fixes the formatting because multiple return values are not supported.
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I_kwDODunzps584HhS
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Wrong path for cache directory in offline mode
|
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[
"+1",
"same error in 2.16.1",
"@kongjiellx any luck with the issue?",
"I opened https://github.com/huggingface/datasets/pull/6632 to fix this issue. Once it's merged we'll do a new release of `datasets`",
"Thanks @lhoestq !"
] | 2024-01-23T01:47:19Z
| 2024-02-06T17:21:25Z
| 2024-02-06T17:21:25Z
|
NONE
| null | null |
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### Describe the bug
Dear huggingfacers,
I'm trying to use a subset of the-stack dataset. When I run the command the first time
```
dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )
```
It downloads the files and caches them normally.
Nevertheless, since my compute nodes are not online (`HF_DATASETS_OFFLINE=1`) . Whenever I try to run the command again, the library is passing the wrong cache path:
`Cache directory for the-stack doesn't exist at /Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data%2Ffortran-data_dir=data%2Ffortran`
when the right path is:
`'/Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data\%2Ffortran`
Not sure why those redundancies are included in the path. If I try adding the correct path through the the cache_dir argument it throws an error:
ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'bigcode/the-stack': Offline mode is enabled.
Your help with this issue is greatly appreciated. Thanks a lot for the great work.
### Steps to reproduce the bug
1:
`dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )`
2:
`HF_DATASETS_OFFLINE=1`
3:
`dataset = load_dataset(
path='bigcode/the-stack',
data_dir='data/fortran',
split='train' )`
### Expected behavior
being able to use the cached data
### Environment info
several different systems
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PR_kwDODunzps46ZOpZ
| 4,574
|
Support streaming mlsum dataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"After unpinning `s3fs` and pinning `fsspec[http]>=2021.11.1`, the CI installs\r\n- `fsspec-2022.1.0`\r\n- `s3fs-0.5.1`\r\n\r\nand raises the following error:\r\n```\r\n ImportError while loading conftest '/home/runner/work/datasets/datasets/tests/conftest.py'.\r\ntests/conftest.py:13: in <module>\r\n import datasets\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/__init__.py:37: in <module>\r\n from .arrow_dataset import Dataset\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/arrow_dataset.py:62: in <module>\r\n from .arrow_reader import ArrowReader\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/arrow_reader.py:29: in <module>\r\n from .download.download_config import DownloadConfig\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/download/__init__.py:10: in <module>\r\n from .streaming_download_manager import StreamingDownloadManager\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/download/streaming_download_manager.py:20: in <module>\r\n from ..filesystems import COMPRESSION_FILESYSTEMS\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/filesystems/__init__.py:13: in <module>\r\n from .s3filesystem import S3FileSystem # noqa: F401\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/datasets/filesystems/s3filesystem.py:1: in <module>\r\n import s3fs\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/s3fs/__init__.py:1: in <module>\r\n from .core import S3FileSystem, S3File\r\n/opt/hostedtoolcache/Python/3.6.15/x64/lib/python3.6/site-packages/s3fs/core.py:12: in <module>\r\n from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper, maybe_sync\r\nE ImportError: cannot import name 'maybe_sync'\r\n```\r\n\r\nThe installed `s3fs` version is too old. What about pinning a min version?",
"Maybe you can try setting the same minimum version as fsspec ? `s3fs>=2021.11.1`",
"Yes, I have checked that they both require to have the same version. \r\n\r\nThe issue then was coming from aiobotocore, boto3, botocore. I have changed them from strict to min version requirements.\r\n> s3fs 2021.11.1 depends on aiobotocore~=2.0.1",
"I have updated all min versions so that they are compatible one with each other. I'm pushing again...",
"Thanks !",
"Nice!"
] | 2022-06-27T07:37:03Z
| 2022-07-21T13:37:30Z
| 2022-07-21T12:40:00Z
|
MEMBER
| null | null | null |
Support streaming mlsum dataset.
This PR:
- pins `fsspec` min version with fixed BlockSizeError: `fsspec[http]>=2021.11.1`
- https://github.com/fsspec/filesystem_spec/pull/830
- unpins `s3fs==2021.08.1` to align it with `fsspec` requirement: `s3fs>=2021.11.1`
> s3fs 2021.8.1 requires fsspec==2021.08.1
- see discussion: https://github.com/huggingface/datasets/pull/2858/files#r700027326
- updates the following requirements to be compatible with the previous ones and one with each other:
- `aiobotocore==1.4.2` to `aiobotocore>=2.0.1` (required by s3fs>=2021.11.1)
- `boto3==1.17.106` to `boto3>=1.19.8` (to be compatible with aiobotocore>=2.0.1)
- `botocore==1.20.106` to `botocore>=1.22.8` (to be compatible with aiobotocore and boto3)
Fix #4572.
|
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PyArrow Dataset error when calling `load_dataset`
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[
"Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nTo achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.\r\n\r\nDo you have a minimum reproducible example that we can share with the Arrow team for further debugging ?",
"> If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nYup, I did not encounter this bug when I was testing my script with a slice of <1000 samples for my dataset.\r\n\r\n> Do you have a minimum reproducible example...\r\n\r\nNot sure if I can get more minimal than the script I shared above. Are you asking for a sample json file?\r\nJust generate a random manifest list, I can add that to the above script if that's what you mean?\r\n",
"Actually this is probably linked to this open issue: https://issues.apache.org/jira/browse/ARROW-5030.\r\n\r\nsetting `max_shard_size=\"2GB\"` should do the job (or `max_shard_size=\"1GB\"` if you want to be on the safe side, especially given that there can be some variance in the shard sizes if the dataset is not evenly distributed)"
] | 2022-07-20T01:16:03Z
| 2022-07-22T14:11:47Z
| null |
NONE
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## Describe the bug
I am fine tuning a wav2vec2 model following the script here using my own dataset: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
Loading my Audio dataset from the hub which was originally generated from disk results in the following PyArrow error:
```sh
File "/home/ubuntu/w2v2/run_speech_recognition_ctc.py", line 227, in main
raw_datasets = load_dataset(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/load.py", line 1679, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/builder.py", line 1268, in _prepare_split
for key, table in logging.tqdm(
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/ubuntu/.virtualenvs/meval/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1309, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
## Steps to reproduce the bug
I created a dataset from a JSON lines manifest of `audio_filepath`, `text`, and `duration`.
When creating the dataset, I do something like this:
```python
import json
from datasets import Dataset, Audio
# manifest_lines is a list of dicts w/ "audio_filepath", "duration", and "text
for line in manifest_lines:
line = line.strip()
if line:
line_dict = json.loads(line)
manifest_dict["audio"].append(f"{root_path}/{line_dict['audio_filepath']}")
manifest_dict["duration"].append(line_dict["duration"])
manifest_dict["transcription"].append(line_dict["text"])
# Create a HF dataset
dataset = Dataset.from_dict(manifest_dict).cast_column(
"audio", Audio(sampling_rate=16_000),
)
# From the docs for saving to disk
# https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.save_to_disk
def read_audio_file(example):
with open(example["audio"]["path"], "rb") as f:
return {"audio": {"bytes": f.read()}}
dataset = dataset.map(read_audio_file, num_proc=70)
dataset.save_to_disk(f"/audio-data/hf/{artifact_name}")
dataset.push_to_hub(f"{org-name}/{artifact_name}", max_shard_size="5GB", private=True)
```
Then when I call `load_dataset()` in my training script, with the same dataset I generated above, and download from the huggingface hub I get the above stack trace.
I am able to load the dataset fine if I use `load_from_disk()`.
## Expected results
`load_dataset()` should behave just like `load_from_disk()` and not cause any errors.
## Actual results
See above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I am using the `huggingface/transformers-pytorch-gpu:latest` image
- `datasets` version: 2.3.0
- Platform: Docker/Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 8.0.0
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[Errno 13] Permission denied: on `.incomplete` file
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[
"It must be an issue with umask being used by multiple threads indeed. Maybe we can try to make a thread safe function to apply the umask (using filelock for example)"
] | 2025-04-24T20:52:45Z
| 2025-04-26T12:40:25Z
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### Describe the bug
When downloading a dataset, we frequently hit the below Permission Denied error. This looks to happen (at least) across datasets in from HF, S3, and GCS.
It looks like the `temp_file` being passed [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L412) can sometimes be created with `000` permissions leading to the permission denied error (the user running the code is still the owner of the file). Deleting that particular file and re-running the code with 0 changes will usually succeed.
Is there some race condition happening with the [umask](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L416), which is process global, and the [file creation](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L404)?
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv/lib/python3.12/site-packages/datasets/load.py:2084: in load_dataset
builder_instance.download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:925: in download_and_prepare
self._download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:1649: in _download_and_prepare
super()._download_and_prepare(
.venv/lib/python3.12/site-packages/datasets/builder.py:979: in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
.venv/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:120: in _split_generators
downloaded_files = dl_manager.download(files)
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:159: in download
downloaded_path_or_paths = map_nested(
.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py:514: in map_nested
_single_map_nested((function, obj, batched, batch_size, types, None, True, None))
.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py:382: in _single_map_nested
return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:206: in _download_batched
return thread_map(
.venv/lib/python3.12/site-packages/tqdm/contrib/concurrent.py:69: in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
.venv/lib/python3.12/site-packages/tqdm/contrib/concurrent.py:51: in _executor_map
return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))
.venv/lib/python3.12/site-packages/tqdm/std.py:1181: in __iter__
for obj in iterable:
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:619: in result_iterator
yield _result_or_cancel(fs.pop())
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:317: in _result_or_cancel
return fut.result(timeout)
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:449: in result
return self.__get_result()
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:401: in __get_result
raise self._exception
../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/thread.py:59: in run
result = self.fn(*self.args, **self.kwargs)
.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:229: in _download_single
out = cached_path(url_or_filename, download_config=download_config)
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:206: in cached_path
output_path = get_from_cache(
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:412: in get_from_cache
fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)
.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:331: in fsspec_get
fs.get_file(path, temp_file.name, callback=callback)
.venv/lib/python3.12/site-packages/fsspec/asyn.py:118: in wrapper
return sync(self.loop, func, *args, **kwargs)
.venv/lib/python3.12/site-packages/fsspec/asyn.py:103: in sync
raise return_result
.venv/lib/python3.12/site-packages/fsspec/asyn.py:56: in _runner
result[0] = await coro
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <s3fs.core.S3FileSystem object at 0x7f27c18b2e70>
rpath = '<my-bucket>/<my-prefix>/img_1.jpg'
lpath = '/home/runner/_work/_temp/hf_cache/downloads/6c97983efa4e24e534557724655df8247a0bd04326cdfc4a95b638c11e78222d.incomplete'
callback = <datasets.utils.file_utils.TqdmCallback object at 0x7f27c00cdbe0>
version_id = None, kwargs = {}
_open_file = <function S3FileSystem._get_file.<locals>._open_file at 0x7f27628d1120>
body = <StreamingBody at 0x7f276344fa80 for ClientResponse at 0x7f27c015fce0>
content_length = 521923, failed_reads = 0, bytes_read = 0
async def _get_file(
self, rpath, lpath, callback=_DEFAULT_CALLBACK, version_id=None, **kwargs
):
if os.path.isdir(lpath):
return
bucket, key, vers = self.split_path(rpath)
async def _open_file(range: int):
kw = self.req_kw.copy()
if range:
kw["Range"] = f"bytes={range}-"
resp = await self._call_s3(
"get_object",
Bucket=bucket,
Key=key,
**version_id_kw(version_id or vers),
**kw,
)
return resp["Body"], resp.get("ContentLength", None)
body, content_length = await _open_file(range=0)
callback.set_size(content_length)
failed_reads = 0
bytes_read = 0
try:
> with open(lpath, "wb") as f0:
E PermissionError: [Errno 13] Permission denied: '/home/runner/_work/_temp/hf_cache/downloads/6c97983efa4e24e534557724655df8247a0bd04326cdfc4a95b638c11e78222d.incomplete'
.venv/lib/python3.12/site-packages/s3fs/core.py:1355: PermissionError
```
### Steps to reproduce the bug
I believe this is a race condition and cannot reliably re-produce it, but it happens fairly frequently in our GitHub Actions tests and can also be re-produced (with lesser frequency) on cloud VMs.
### Expected behavior
The dataset loads properly with no permission denied error.
### Environment info
- `datasets` version: 3.5.0
- Platform: Linux-5.10.0-34-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.12.10
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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PR_kwDODunzps5EGK3y
| 5,329
|
Clarify imagefolder is for small datasets
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"I think it's also reasonable to add the same note to the AudioFolder decription",
"Thank you ! I think \"regular\" is more appropriate than \"small\". It can easily scale to a few thousands of images - just not millions x)",
"Replaced \"small\" with \"several thousand\" since what is considered \"regular\" and even \"small\" can be kind of vague!"
] | 2022-12-01T21:47:29Z
| 2022-12-06T17:20:04Z
| 2022-12-06T17:16:53Z
|
MEMBER
| null | null | null |
Based on feedback from [here](https://github.com/huggingface/datasets/issues/5317#issuecomment-1334108824), this PR adds a note to the `imagefolder` loading and creating docs that `imagefolder` is designed for small scale image datasets.
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I_kwDODunzps50sEBe
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|
support squad structure datasets using a YAML parameter
|
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### Feature request
Since the squad structure is widely used, I think it could be beneficial to support it using a YAML parameter.
could you implement automatic data loading of squad-like data using squad JSON format, to read it from JSON files and view it in the correct squad structure.
The dataset structure should be like this:
https://huggingface.co/datasets/squad
Columns:id,title,context,question,answers
### Motivation
Dataset repo requires arbitrary Python code execution
### Your contribution
The dataset structure should be like this:
https://huggingface.co/datasets/squad
Columns:id,title,context,question,answers
train and dev sets in squad structure JSON files
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|
Fix NonMatchingSplitsSizesError/ExpectedMoreSplits when passing data_dir/data_files in no-code Hub datasets
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6925). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Do you think this is worth making a patch release for?\r\nCC: @huggingface/datasets",
"I will add some regression tests before merging.\r\n\r\nAnd I will make a patch release afterwards.",
"<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) | 0.004959 / 0.011353 (-0.006394) | 0.003654 / 0.011008 (-0.007354) | 0.064087 / 0.038508 (0.025579) | 0.031942 / 0.023109 (0.008833) | 0.236830 / 0.275898 (-0.039068) | 0.265359 / 0.323480 (-0.058121) | 0.003108 / 0.007986 (-0.004878) | 0.002824 / 0.004328 (-0.001504) | 0.049102 / 0.004250 (0.044852) | 0.046070 / 0.037052 (0.009017) | 0.248830 / 0.258489 (-0.009659) | 0.283900 / 0.293841 (-0.009941) | 0.027799 / 0.128546 (-0.100747) | 0.010572 / 0.075646 (-0.065074) | 0.223595 / 0.419271 (-0.195677) | 0.036951 / 0.043533 (-0.006582) | 0.238813 / 0.255139 (-0.016326) | 0.253841 / 0.283200 (-0.029359) | 0.018471 / 0.141683 (-0.123212) | 1.131969 / 1.452155 (-0.320186) | 1.173763 / 1.492716 (-0.318954) |\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) | 0.095504 / 0.018006 (0.077498) | 0.301469 / 0.000490 (0.300979) | 0.000212 / 0.000200 (0.000012) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019194 / 0.037411 (-0.018217) | 0.062313 / 0.014526 (0.047787) | 0.075852 / 0.176557 (-0.100704) | 0.121996 / 0.737135 (-0.615140) | 0.076416 / 0.296338 (-0.219923) |\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) | 0.292465 / 0.215209 (0.077256) | 2.910234 / 2.077655 (0.832579) | 1.479672 / 1.504120 (-0.024448) | 1.332281 / 1.541195 (-0.208913) | 1.354095 / 1.468490 (-0.114395) | 0.573438 / 4.584777 (-4.011339) | 2.382406 / 3.745712 (-1.363307) | 2.708289 / 5.269862 (-2.561572) | 1.739665 / 4.565676 (-2.826011) | 0.063514 / 0.424275 (-0.360761) | 0.005008 / 0.007607 (-0.002599) | 0.350070 / 0.226044 (0.124025) | 3.475837 / 2.268929 (1.206909) | 1.804639 / 55.444624 (-53.639985) | 1.520472 / 6.876477 (-5.356005) | 1.658061 / 2.142072 (-0.484011) | 0.648495 / 4.805227 (-4.156732) | 0.118394 / 6.500664 (-6.382270) | 0.042557 / 0.075469 (-0.032912) |\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) | 0.960772 / 1.841788 (-0.881016) | 11.451629 / 8.074308 (3.377321) | 9.613331 / 10.191392 (-0.578061) | 0.130259 / 0.680424 (-0.550164) | 0.015828 / 0.534201 (-0.518373) | 0.287581 / 0.579283 (-0.291702) | 0.266517 / 0.434364 (-0.167847) | 0.327334 / 0.540337 (-0.213003) | 0.427881 / 1.386936 (-0.959055) |\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) | 0.005364 / 0.011353 (-0.005989) | 0.003723 / 0.011008 (-0.007285) | 0.049990 / 0.038508 (0.011482) | 0.032023 / 0.023109 (0.008913) | 0.258609 / 0.275898 (-0.017289) | 0.281250 / 0.323480 (-0.042230) | 0.004222 / 0.007986 (-0.003764) | 0.002799 / 0.004328 (-0.001529) | 0.049546 / 0.004250 (0.045296) | 0.040298 / 0.037052 (0.003246) | 0.273552 / 0.258489 (0.015063) | 0.304042 / 0.293841 (0.010201) | 0.030116 / 0.128546 (-0.098430) | 0.010792 / 0.075646 (-0.064855) | 0.058427 / 0.419271 (-0.360845) | 0.033415 / 0.043533 (-0.010118) | 0.258794 / 0.255139 (0.003655) | 0.275304 / 0.283200 (-0.007896) | 0.017944 / 0.141683 (-0.123739) | 1.109291 / 1.452155 (-0.342864) | 1.156627 / 1.492716 (-0.336090) |\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) | 0.096700 / 0.018006 (0.078693) | 0.301108 / 0.000490 (0.300618) | 0.000208 / 0.000200 (0.000008) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022632 / 0.037411 (-0.014779) | 0.075813 / 0.014526 (0.061287) | 0.090302 / 0.176557 (-0.086254) | 0.130375 / 0.737135 (-0.606760) | 0.089710 / 0.296338 (-0.206629) |\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) | 0.297091 / 0.215209 (0.081882) | 2.910379 / 2.077655 (0.832725) | 1.570460 / 1.504120 (0.066340) | 1.441619 / 1.541195 (-0.099576) | 1.442417 / 1.468490 (-0.026073) | 0.570034 / 4.584777 (-4.014743) | 0.952613 / 3.745712 (-2.793099) | 2.659274 / 5.269862 (-2.610588) | 1.751013 / 4.565676 (-2.814663) | 0.064639 / 0.424275 (-0.359636) | 0.005145 / 0.007607 (-0.002462) | 0.347478 / 0.226044 (0.121434) | 3.443862 / 2.268929 (1.174933) | 1.897246 / 55.444624 (-53.547379) | 1.609267 / 6.876477 (-5.267210) | 1.755116 / 2.142072 (-0.386956) | 0.658982 / 4.805227 (-4.146245) | 0.117000 / 6.500664 (-6.383664) | 0.041453 / 0.075469 (-0.034016) |\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) | 1.005843 / 1.841788 (-0.835944) | 12.101306 / 8.074308 (4.026998) | 10.370706 / 10.191392 (0.179314) | 0.139374 / 0.680424 (-0.541050) | 0.015605 / 0.534201 (-0.518596) | 0.286978 / 0.579283 (-0.292305) | 0.122951 / 0.434364 (-0.311413) | 0.331729 / 0.540337 (-0.208609) | 0.422088 / 1.386936 (-0.964848) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#157585f964b1c7f675860af0d21712555b34aabc \"CML watermark\")\n",
"I'm hitting this error now, using Spaces. Here's what an attempt to just get the 'validation' split is doing:\r\n\r\ncode:\r\n\r\n```\r\n\r\n | import os\r\n | from huggingface_hub import HfApi\r\n | from datasets import Dataset, load_dataset, DownloadConfig\r\n | \r\n | \r\n | GATED_IMAGENET = os.environ.get(\"GATED_IMAGENET\")\r\n | api = HfApi(token=GATED_IMAGENET)\r\n |\r\n | ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val*'}, split='validation', trust_remote_code=True)\r\n\r\n\r\n```\r\n\r\nlog:\r\n\r\n```\r\nGenerating validation split: 0%| | 0/50000 [00:00<?, ? examples/s]\r\nGenerating validation split: 12%|█▏ | 6172/50000 [00:01<00:07, 5804.90 examples/s]\r\nGenerating validation split: 25%|██▌ | 12716/50000 [00:02<00:06, 6167.77 examples/s]\r\nGenerating validation split: 38%|███▊ | 19060/50000 [00:03<00:04, 6218.99 examples/s]\r\nGenerating validation split: 51%|█████ | 25603/50000 [00:04<00:03, 6126.35 examples/s]\r\nGenerating validation split: 64%|██████▍ | 32145/50000 [00:05<00:02, 6166.95 examples/s]\r\nGenerating validation split: 77%|███████▋ | 38716/50000 [00:06<00:01, 6272.66 examples/s]\r\nGenerating validation split: 90%|█████████ | 45158/50000 [00:07<00:00, 6307.44 examples/s]\r\nGenerating validation split: 100%|██████████| 50000/50000 [00:08<00:00, 6212.19 examples/s]\r\nTraceback (most recent call last):\r\n File \"/home/user/app/app.py\", line 12, in <module>\r\n ds = load_dataset('datacomp/imagenet-1k-random0.0', token=GATED_IMAGENET, data_files={'validation': 'data/val*'}, split='validation', trust_remote_code=True)\r\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 2154, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.10/site-packages/datasets/builder.py\", line 924, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.10/site-packages/datasets/builder.py\", line 1018, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/usr/local/lib/python3.10/site-packages/datasets/utils/info_utils.py\", line 68, in verify_splits\r\n raise ExpectedMoreSplitsError(str(set(expected_splits) - set(recorded_splits)))\r\ndatasets.exceptions.ExpectedMoreSplitsError: {'train', 'test'}\r\n```",
"Hi Meg ! Thanks for reporting, I'll see how I can fix this. In the meantime feel free to pass `verification_mode=\"no_checks\"` to `load_dataset`"
] | 2024-05-28T13:33:38Z
| 2024-11-07T20:41:58Z
| 2024-05-31T17:10:37Z
|
MEMBER
| null | null | null |
Fix `NonMatchingSplitsSizesError` or `ExpectedMoreSplits` error for no-code Hub datasets if the user passes:
- `data_dir`
- `data_files`
The proposed solution is to avoid using exported dataset info (from Parquet exports) in these cases.
Additionally, also if the user passes `revision` other than "main" (so that no network requests are made).
This PR fixes a bug introduced by:
- #6714
Fix #6918, fix #6939.
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CI is broken with AssertionError: 3 failed, 12 errors
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[] | 2023-09-04T06:47:05Z
| 2023-09-04T07:30:01Z
| 2023-09-04T07:30:01Z
|
MEMBER
| null | null |
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Our CI is broken: 3 failed, 12 errors
See: https://github.com/huggingface/datasets/actions/runs/6069947111/job/16465138041
```
=========================== short test summary info ============================
FAILED tests/test_load.py::ModuleFactoryTest::test_LocalDatasetModuleFactoryWithoutScript_with_data_dir - AssertionError: assert ({NamedSplit('train'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'], NamedSplit('test'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt']} is not None and 2 == 1)
+ where 2 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'])
FAILED tests/test_load.py::test_load_dataset_arrow[False] - AssertionError: assert 20 == 10
+ where 20 = Dataset({\n features: ['col_1'],\n num_rows: 20\n}).num_rows
FAILED tests/test_load.py::test_load_dataset_arrow[True] - assert 20 == 10
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv'])
= 3 failed, 2383 passed, 26 skipped, 9 warnings, 12 errors in 280.79s (0:04:40) =
```
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| 2,466,510,783
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I_kwDODunzps6TA_e_
| 7,101
|
`load_dataset` from Hub with `name` to specify `config` using incorrect builder type when multiple data formats are present
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[] |
open
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[] | null |
[
"Having looked into this further it seems the core of the issue is with two different formats in the same repo.\r\n\r\nWhen the `parquet` config is first, the `WebDataset`s are loaded as `parquet`, if the `WebDataset` configs are first, the `parquet` is loaded as `WebDataset`.\r\n\r\nA workaround in my case would be to just turn the `parquet` into a `WebDataset`, although I'd still need the Dataset Viewer config limit increasing. In other cases using the same format may not be possible.\r\n\r\nRelevant code: \r\n- [HubDatasetModuleFactoryWithoutScript](https://github.com/huggingface/datasets/blob/5f42139a2c5583a55d34a2f60d537f5fba285c28/src/datasets/load.py#L964)\r\n- [get_data_patterns](https://github.com/huggingface/datasets/blob/5f42139a2c5583a55d34a2f60d537f5fba285c28/src/datasets/data_files.py#L415)"
] | 2024-08-14T18:12:25Z
| 2024-08-18T10:33:38Z
| null |
NONE
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Following [documentation](https://huggingface.co/docs/datasets/repository_structure#define-your-splits-and-subsets-in-yaml) I had defined different configs for [`Dataception`](https://huggingface.co/datasets/bigdata-pw/Dataception), a dataset of datasets:
```yaml
configs:
- config_name: dataception
data_files:
- path: dataception.parquet
split: train
default: true
- config_name: dataset_5423
data_files:
- path: datasets/5423.tar
split: train
...
- config_name: dataset_721736
data_files:
- path: datasets/721736.tar
split: train
```
The intent was for metadata to be browsable via Dataset Viewer, in addition to each individual dataset, and to allow datasets to be loaded by specifying the config/name to `load_dataset`.
While testing `load_dataset` I encountered the following error:
```python
>>> dataset = load_dataset("bigdata-pw/Dataception", "dataset_7691")
Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 467k/467k [00:00<00:00, 1.99MB/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.0M/71.0M [00:02<00:00, 26.8MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "datasets\load.py", line 2145, in load_dataset
builder_instance.download_and_prepare(
File "datasets\builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "datasets\builder.py", line 1100, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "datasets\packaged_modules\parquet\parquet.py", line 58, in _split_generators
self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
^^^^^^^^^^^^^^^^^
File "pyarrow\parquet\core.py", line 2325, in read_schema
file = ParquetFile(
^^^^^^^^^^^^
File "pyarrow\parquet\core.py", line 318, in __init__
self.reader.open(
File "pyarrow\_parquet.pyx", line 1470, in pyarrow._parquet.ParquetReader.open
File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
The correct file is downloaded, however the incorrect builder type is detected; `parquet` due to other content of the repository. It would appear that the config needs to be taken into account.
Note that I have removed the additional configs from the repository because of this issue and there is a limit of 3000 configs anyway so the Dataset Viewer doesn't work as I intended. I'll add them back in if it assists with testing.
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| 1,294,547,836
|
I_kwDODunzps5NKTt8
| 4,636
|
Add info in docs about behavior of download_config.num_proc
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[] | 2022-07-05T17:01:00Z
| 2022-07-28T10:40:32Z
| 2022-07-28T10:40:32Z
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.**
I went to override `download_config.num_proc` and was confused about what was happening under the hood. It would be nice to have the behavior documented a bit better so folks know what's happening when they use it.
**Describe the solution you'd like**
- Add note about how the default number of workers is 16. Related code:
https://github.com/huggingface/datasets/blob/7bcac0a6a0fc367cc068f184fa132b8de8dfa11d/src/datasets/download/download_manager.py#L299-L302
- Add note that if the number of workers is higher than the number of files to download, it won't use multiprocessing.
**Describe alternatives you've considered**
maybe it would also be nice to set `num_proc` = `num_files` when `num_proc` > `num_files`.
**Additional context**
...
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I_kwDODunzps5dY4X8
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to_tf_dataset fails with datetime UTC columns even if not included in columns argument
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[
"Hi! This is indeed a bug in our zero-copy logic.\r\n\r\nTo fix it, instead of the line:\r\nhttps://github.com/huggingface/datasets/blob/7cfac43b980ab9e4a69c2328f085770996323005/src/datasets/features/features.py#L702\r\n\r\nwe should have:\r\n```python\r\nreturn pa.types.is_primitive(pa_type) and not (pa.types.is_boolean(pa_type) or pa.types.is_temporal(pa_type))\r\n```",
"@mariosasko submitted a small PR [here](https://github.com/huggingface/datasets/pull/5504)"
] | 2023-02-01T20:47:33Z
| 2023-02-08T14:33:19Z
| 2023-02-08T14:33:19Z
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CONTRIBUTOR
| null | null |
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### Describe the bug
There appears to be some eager behavior in `to_tf_dataset` that runs against every column in a dataset even if they aren't included in the columns argument. This is problematic with datetime UTC columns due to them not working with zero copy. If I don't have UTC information in my datetime column, then everything works as expected.
### Steps to reproduce the bug
```python
import numpy as np
import pandas as pd
from datasets import Dataset
df = pd.DataFrame(np.random.rand(2, 1), columns=["x"])
# df["dt"] = pd.to_datetime(["2023-01-01", "2023-01-01"]) # works fine
df["dt"] = pd.to_datetime(["2023-01-01 00:00:00.00000+00:00", "2023-01-01 00:00:00.00000+00:00"])
df.to_parquet("test.pq")
ds = Dataset.from_parquet("test.pq")
tf_ds = ds.to_tf_dataset(columns=["x"], batch_size=2, shuffle=True)
```
```
ArrowInvalid Traceback (most recent call last)
Cell In[1], line 12
8 df.to_parquet("test.pq")
11 ds = Dataset.from_parquet("test.pq")
---> 12 tf_ds = ds.to_tf_dataset(columns=["r"], batch_size=2, shuffle=True)
File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:411, in TensorflowDatasetMixin.to_tf_dataset(self, batch_size, columns, shuffle, collate_fn, drop_remainder, collate_fn_args, label_cols, prefetch, num_workers)
407 dataset = self
409 # TODO(Matt, QL): deprecate the retention of label_ids and label
--> 411 output_signature, columns_to_np_types = dataset._get_output_signature(
412 dataset,
413 collate_fn=collate_fn,
414 collate_fn_args=collate_fn_args,
415 cols_to_retain=cols_to_retain,
416 batch_size=batch_size if drop_remainder else None,
417 )
419 if "labels" in output_signature:
420 if ("label_ids" in columns or "label" in columns) and "labels" not in columns:
File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:254, in TensorflowDatasetMixin._get_output_signature(dataset, collate_fn, collate_fn_args, cols_to_retain, batch_size, num_test_batches)
252 for _ in range(num_test_batches):
253 indices = sample(range(len(dataset)), test_batch_size)
--> 254 test_batch = dataset[indices]
255 if cols_to_retain is not None:
256 test_batch = {key: value for key, value in test_batch.items() if key in cols_to_retain}
File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2590, in Dataset.__getitem__(self, key)
2588 def __getitem__(self, key): # noqa: F811
2589 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2590 return self._getitem(
2591 key,
2592 )
File ~/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2575, in Dataset._getitem(self, key, **kwargs)
2573 formatter = get_formatter(format_type, features=self.features, **format_kwargs)
2574 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2575 formatted_output = format_table(
2576 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2577 )
2578 return formatted_output
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:634, in format_table(table, key, formatter, format_columns, output_all_columns)
632 python_formatter = PythonFormatter(features=None)
633 if format_columns is None:
--> 634 return formatter(pa_table, query_type=query_type)
635 elif query_type == "column":
636 if key in format_columns:
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:410, in Formatter.__call__(self, pa_table, query_type)
408 return self.format_column(pa_table)
409 elif query_type == "batch":
--> 410 return self.format_batch(pa_table)
File ~/venv/lib/python3.8/site-packages/datasets/formatting/np_formatter.py:78, in NumpyFormatter.format_batch(self, pa_table)
77 def format_batch(self, pa_table: pa.Table) -> Mapping:
---> 78 batch = self.numpy_arrow_extractor().extract_batch(pa_table)
79 batch = self.python_features_decoder.decode_batch(batch)
80 batch = self.recursive_tensorize(batch)
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:164, in NumpyArrowExtractor.extract_batch(self, pa_table)
163 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 164 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:164, in <dictcomp>(.0)
163 def extract_batch(self, pa_table: pa.Table) -> dict:
--> 164 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names}
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:185, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array)
181 else:
182 zero_copy_only = _is_zero_copy_only(pa_array.type) and all(
183 not _is_array_with_nulls(chunk) for chunk in pa_array.chunks
184 )
--> 185 array: List = [
186 row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only)
187 ]
188 else:
189 if isinstance(pa_array.type, _ArrayXDExtensionType):
190 # don't call to_pylist() to preserve dtype of the fixed-size array
File ~/venv/lib/python3.8/site-packages/datasets/formatting/formatting.py:186, in <listcomp>(.0)
181 else:
182 zero_copy_only = _is_zero_copy_only(pa_array.type) and all(
183 not _is_array_with_nulls(chunk) for chunk in pa_array.chunks
184 )
185 array: List = [
--> 186 row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only)
187 ]
188 else:
189 if isinstance(pa_array.type, _ArrayXDExtensionType):
190 # don't call to_pylist() to preserve dtype of the fixed-size array
File ~/venv/lib/python3.8/site-packages/pyarrow/array.pxi:1475, in pyarrow.lib.Array.to_numpy()
File ~/venv/lib/python3.8/site-packages/pyarrow/error.pxi:100, in pyarrow.lib.check_status()
ArrowInvalid: Needed to copy 1 chunks with 0 nulls, but zero_copy_only was True
```
### Expected behavior
I think there are two potential issues/fixes
1. Proper handling of datetime UTC columns (perhaps there is something incorrect with zero copy handling here)
2. Not eagerly running against every column in a dataset when the columns argument of `to_tf_dataset` specifies a subset of columns (although I'm not sure if this is unavoidable)
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-13.2-x86_64-i386-64bit
- Python version: 3.8.12
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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Perform hidden file check on relative data file path
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"I'm aware of this behavior, which is tricky to solve due to fsspec's hidden file handling (see https://github.com/huggingface/datasets/issues/4115#issuecomment-1108819538). I've tested some regex patterns to address this, and they seem to work (will push them on Monday; btw they don't break any of fsspec's tests, so maybe we can contribute this as an enhancement to them). Also, perhaps we should include the files starting with `__` in the results again (we hadn't had issues with this pattern before). WDYT?",
"I see. Feel free to merge this one if it's good for you btw :)\r\n\r\n> Also, perhaps we should include the files starting with __ in the results again (we hadn't had issues with this pattern before)\r\n\r\nThe point was mainly to ignore `__pycache__` directories for example. Also also for consistency with the iter_files/iter_archive which are already ignoring them",
"Very elegant solution! Feel free to merge if the CI is green after adding the tests.",
"CI failure is unrelated to this PR"
] | 2022-06-23T14:49:11Z
| 2022-06-30T14:49:20Z
| 2022-06-30T14:38:18Z
|
COLLABORATOR
| null | null | null |
Fix #4549
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-20T10:04:37Z
| 2022-07-20T17:02:33Z
| 2022-07-20T16:49:36Z
|
NONE
| null | null | null |
`href="/docs/datasets/quickstart#audio"` implicitly goes to `href="/docs/datasets/{$LATEST_STABLE_VERSION}/quickstart#audio"`. Therefore, https://huggingface.co/docs/datasets/quickstart#audio #audio hashlink does not work since the new docs were not added to v2.3.2 (LATEST_STABLE_VERSION)
to preserve the version, it should be just `href="#audio"`, which will implicilty go to curren_page + #audio element
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I_kwDODunzps5Ymwj4
| 5,345
|
Wrong dtype for array in audio features
|
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"After some more investigation, this is due to [this line of code](https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L279). The function `sf.read(file)` should be updated to `sf.read(file, dtype=\"float32\")`\r\n\r\nIndeed, the default value in soundfile is `float64` ([see here](https://pysoundfile.readthedocs.io/en/latest/#soundfile.read)). \r\n",
"@qmeeus I agree, decoding of different audio formats should return the same dtypes indeed!\r\n\r\nBut note that here you are concatenating datasets with different sampling rates: 48000 for CommonVoice and 16000 for Voxpopuli. So you should cast them to the same sampling rate value before interleaving, for example:\r\n```\r\ncv = cv.cast_column(\"audio\", Audio(sampling_rate=16000))\r\n```\r\notherwise you would get the same error because features of the same column (\"audio\") are not the same.\r\n\r\nAlso, the error you get is unexpected. Could you please confirm that you use the latest main version of the `datasets`? We had an issue that could lead to an error like this after using `rename_column` method, but it was fixed in https://github.com/huggingface/datasets/pull/5287 ",
"Hi Polina,\r\nSorry for the late answer\r\nIt is possible that the issue was due to a bug that is now fixed. I installed an editable version of datasets from github, but I don't recall whether I had updated it at the time of the issue. My research led me to other directions so I did not follow through on the interleave datasets.\r\n"
] | 2022-12-09T11:05:11Z
| 2023-02-10T14:39:28Z
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NONE
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### Describe the bug
When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datasets cannot be merged.
### Steps to reproduce the bug
For example, for `facebook/voxpopuli` and `mozilla-foundation/common_voice_11_0`:
```
from datasets import load_dataset, interleave_datasets
covost = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="train", streaming=True)
voxpopuli = datasets.load_dataset("facebook/voxpopuli", "nl", split="train", streaming=True)
sample_cv, = covost.take(1)
sample_vp, = voxpopuli.take(1)
assert sample_cv["audio"]["array"].dtype == sample_vp["audio"]["array"].dtype
# Fails
dataset = interleave_datasets([covost, voxpopuli])
# ValueError: The features can't be aligned because the key audio of features {'audio_id': Value(dtype='string', id=None), 'language': Value(dtype='int64', id=None), 'audio': {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)}, 'normalized_text': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'speaker_id': Value(dtype='string', id=None), 'is_gold_transcript': Value(dtype='bool', id=None), 'accent': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None)} has unexpected type - {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)} (expected either Audio(sampling_rate=16000, mono=True, decode=True, id=None) or Value("null").
```
### Expected behavior
The audio should be loaded to arrays with a unique dtype (I guess `float32`)
### Environment info
```
- `datasets` version: 2.7.1.dev0
- Platform: Linux-4.18.0-425.3.1.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.15
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
```
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Make tiktoken tokenizers hashable
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011635 / 0.011353 (0.000282) | 0.005446 / 0.011008 (-0.005562) | 0.111044 / 0.038508 (0.072536) | 0.034243 / 0.023109 (0.011134) | 0.357560 / 0.275898 (0.081662) | 0.403940 / 0.323480 (0.080460) | 0.008532 / 0.007986 (0.000546) | 0.004327 / 0.004328 (-0.000002) | 0.084659 / 0.004250 (0.080408) | 0.040914 / 0.037052 (0.003861) | 0.367142 / 0.258489 (0.108653) | 0.381651 / 0.293841 (0.087810) | 0.053865 / 0.128546 (-0.074681) | 0.019060 / 0.075646 (-0.056587) | 0.371994 / 0.419271 (-0.047277) | 0.058417 / 0.043533 (0.014884) | 0.357740 / 0.255139 (0.102601) | 0.367423 / 0.283200 (0.084224) | 0.104336 / 0.141683 (-0.037347) | 1.632128 / 1.452155 (0.179974) | 1.676216 / 1.492716 (0.183499) |\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) | 0.199649 / 0.018006 (0.181642) | 0.490945 / 0.000490 (0.490455) | 0.001598 / 0.000200 (0.001398) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024541 / 0.037411 (-0.012871) | 0.104713 / 0.014526 (0.090187) | 0.119438 / 0.176557 (-0.057118) | 0.160854 / 0.737135 (-0.576281) | 0.127323 / 0.296338 (-0.169016) |\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) | 0.586483 / 0.215209 (0.371274) | 5.771689 / 2.077655 (3.694034) | 2.378962 / 1.504120 (0.874842) | 1.998787 / 1.541195 (0.457592) | 1.993016 / 1.468490 (0.524526) | 1.199169 / 4.584777 (-3.385608) | 5.281648 / 3.745712 (1.535936) | 5.589235 / 5.269862 (0.319373) | 2.715162 / 4.565676 (-1.850514) | 0.153312 / 0.424275 (-0.270963) | 0.014302 / 0.007607 (0.006695) | 0.761185 / 0.226044 (0.535140) | 7.602517 / 2.268929 (5.333589) | 3.095271 / 55.444624 (-52.349354) | 2.407394 / 6.876477 (-4.469083) | 2.519074 / 2.142072 (0.377002) | 1.459270 / 4.805227 (-3.345957) | 0.259578 / 6.500664 (-6.241086) | 0.077356 / 0.075469 (0.001887) |\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) | 1.502123 / 1.841788 (-0.339665) | 16.254010 / 8.074308 (8.179702) | 19.971713 / 10.191392 (9.780321) | 0.221491 / 0.680424 (-0.458933) | 0.043959 / 0.534201 (-0.490242) | 0.512566 / 0.579283 (-0.066717) | 0.594724 / 0.434364 (0.160360) | 0.573855 / 0.540337 (0.033518) | 0.680503 / 1.386936 (-0.706433) |\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) | 0.008543 / 0.011353 (-0.002810) | 0.005828 / 0.011008 (-0.005180) | 0.083696 / 0.038508 (0.045188) | 0.036186 / 0.023109 (0.013077) | 0.379777 / 0.275898 (0.103879) | 0.437361 / 0.323480 (0.113881) | 0.006788 / 0.007986 (-0.001197) | 0.005110 / 0.004328 (0.000782) | 0.106075 / 0.004250 (0.101824) | 0.048770 / 0.037052 (0.011718) | 0.390770 / 0.258489 (0.132281) | 0.420813 / 0.293841 (0.126972) | 0.050622 / 0.128546 (-0.077924) | 0.019939 / 0.075646 (-0.055707) | 0.106890 / 0.419271 (-0.312382) | 0.070800 / 0.043533 (0.027267) | 0.406094 / 0.255139 (0.150955) | 0.419796 / 0.283200 (0.136597) | 0.107237 / 0.141683 (-0.034446) | 1.687894 / 1.452155 (0.235739) | 1.735680 / 1.492716 (0.242963) |\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) | 0.216403 / 0.018006 (0.198397) | 0.495002 / 0.000490 (0.494512) | 0.004841 / 0.000200 (0.004641) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043774 / 0.037411 (0.006363) | 0.119144 / 0.014526 (0.104618) | 0.143694 / 0.176557 (-0.032862) | 0.195548 / 0.737135 (-0.541587) | 0.151426 / 0.296338 (-0.144912) |\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) | 0.617694 / 0.215209 (0.402485) | 6.216237 / 2.077655 (4.138582) | 2.578341 / 1.504120 (1.074221) | 2.184868 / 1.541195 (0.643673) | 2.244954 / 1.468490 (0.776464) | 1.236072 / 4.584777 (-3.348705) | 5.257919 / 3.745712 (1.512207) | 4.634682 / 5.269862 (-0.635180) | 2.722579 / 4.565676 (-1.843097) | 0.131433 / 0.424275 (-0.292843) | 0.012928 / 0.007607 (0.005321) | 0.768315 / 0.226044 (0.542270) | 7.625277 / 2.268929 (5.356349) | 3.146364 / 55.444624 (-52.298260) | 2.577886 / 6.876477 (-4.298590) | 2.572626 / 2.142072 (0.430554) | 1.468160 / 4.805227 (-3.337067) | 0.252524 / 6.500664 (-6.248140) | 0.083264 / 0.075469 (0.007794) |\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) | 1.452614 / 1.841788 (-0.389174) | 15.906162 / 8.074308 (7.831853) | 17.803630 / 10.191392 (7.612238) | 0.210769 / 0.680424 (-0.469655) | 0.024672 / 0.534201 (-0.509529) | 0.486486 / 0.579283 (-0.092797) | 0.545256 / 0.434364 (0.110892) | 0.598736 / 0.540337 (0.058399) | 0.689083 / 1.386936 (-0.697853) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#189a870b4f0964d77b43c2f4e79c4ca7b799f690 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008806 / 0.011353 (-0.002547) | 0.004947 / 0.011008 (-0.006061) | 0.098559 / 0.038508 (0.060051) | 0.034293 / 0.023109 (0.011183) | 0.311924 / 0.275898 (0.036026) | 0.377501 / 0.323480 (0.054021) | 0.007916 / 0.007986 (-0.000069) | 0.004131 / 0.004328 (-0.000197) | 0.074934 / 0.004250 (0.070684) | 0.043396 / 0.037052 (0.006344) | 0.344788 / 0.258489 (0.086299) | 0.369943 / 0.293841 (0.076102) | 0.036846 / 0.128546 (-0.091700) | 0.011803 / 0.075646 (-0.063843) | 0.331306 / 0.419271 (-0.087965) | 0.047015 / 0.043533 (0.003483) | 0.305890 / 0.255139 (0.050751) | 0.332658 / 0.283200 (0.049459) | 0.101134 / 0.141683 (-0.040549) | 1.485615 / 1.452155 (0.033461) | 1.510230 / 1.492716 (0.017514) |\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) | 0.274272 / 0.018006 (0.256266) | 0.514739 / 0.000490 (0.514250) | 0.003433 / 0.000200 (0.003234) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027054 / 0.037411 (-0.010357) | 0.106416 / 0.014526 (0.091890) | 0.118761 / 0.176557 (-0.057796) | 0.156115 / 0.737135 (-0.581021) | 0.123801 / 0.296338 (-0.172537) |\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) | 0.403121 / 0.215209 (0.187912) | 4.008806 / 2.077655 (1.931151) | 1.891253 / 1.504120 (0.387133) | 1.698523 / 1.541195 (0.157328) | 1.778533 / 1.468490 (0.310043) | 0.688207 / 4.584777 (-3.896570) | 3.674350 / 3.745712 (-0.071362) | 1.848438 / 5.269862 (-3.421423) | 1.202380 / 4.565676 (-3.363297) | 0.073490 / 0.424275 (-0.350785) | 0.010655 / 0.007607 (0.003048) | 0.446939 / 0.226044 (0.220894) | 4.478489 / 2.268929 (2.209560) | 1.992281 / 55.444624 (-53.452343) | 1.684077 / 6.876477 (-5.192400) | 1.715435 / 2.142072 (-0.426638) | 0.731454 / 4.805227 (-4.073773) | 0.143679 / 6.500664 (-6.356985) | 0.053415 / 0.075469 (-0.022054) |\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) | 1.060583 / 1.841788 (-0.781205) | 13.730462 / 8.074308 (5.656153) | 13.038976 / 10.191392 (2.847583) | 0.144168 / 0.680424 (-0.536256) | 0.025788 / 0.534201 (-0.508413) | 0.393332 / 0.579283 (-0.185951) | 0.409495 / 0.434364 (-0.024869) | 0.523745 / 0.540337 (-0.016592) | 0.601595 / 1.386936 (-0.785341) |\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) | 0.006369 / 0.011353 (-0.004983) | 0.005019 / 0.011008 (-0.005990) | 0.065226 / 0.038508 (0.026718) | 0.029634 / 0.023109 (0.006524) | 0.302871 / 0.275898 (0.026972) | 0.331055 / 0.323480 (0.007575) | 0.005470 / 0.007986 (-0.002516) | 0.005372 / 0.004328 (0.001043) | 0.064930 / 0.004250 (0.060680) | 0.046979 / 0.037052 (0.009927) | 0.305633 / 0.258489 (0.047144) | 0.345305 / 0.293841 (0.051464) | 0.032951 / 0.128546 (-0.095596) | 0.011447 / 0.075646 (-0.064199) | 0.077054 / 0.419271 (-0.342218) | 0.045744 / 0.043533 (0.002211) | 0.303446 / 0.255139 (0.048307) | 0.319837 / 0.283200 (0.036637) | 0.098631 / 0.141683 (-0.043052) | 1.266593 / 1.452155 (-0.185562) | 1.355388 / 1.492716 (-0.137328) |\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) | 0.291301 / 0.018006 (0.273295) | 0.537848 / 0.000490 (0.537359) | 0.006697 / 0.000200 (0.006497) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027677 / 0.037411 (-0.009734) | 0.099633 / 0.014526 (0.085107) | 0.110626 / 0.176557 (-0.065931) | 0.144724 / 0.737135 (-0.592412) | 0.114955 / 0.296338 (-0.181383) |\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) | 0.375344 / 0.215209 (0.160135) | 3.717490 / 2.077655 (1.639835) | 1.845886 / 1.504120 (0.341766) | 1.713274 / 1.541195 (0.172079) | 1.761286 / 1.468490 (0.292796) | 0.627924 / 4.584777 (-3.956853) | 3.628154 / 3.745712 (-0.117558) | 3.261851 / 5.269862 (-2.008011) | 1.701008 / 4.565676 (-2.864669) | 0.076703 / 0.424275 (-0.347572) | 0.010839 / 0.007607 (0.003231) | 0.459193 / 0.226044 (0.233148) | 4.589066 / 2.268929 (2.320137) | 2.193972 / 55.444624 (-53.250653) | 1.892115 / 6.876477 (-4.984362) | 1.892453 / 2.142072 (-0.249619) | 0.745727 / 4.805227 (-4.059500) | 0.150232 / 6.500664 (-6.350432) | 0.057245 / 0.075469 (-0.018224) |\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) | 1.114657 / 1.841788 (-0.727131) | 13.595215 / 8.074308 (5.520907) | 12.267177 / 10.191392 (2.075785) | 0.151362 / 0.680424 (-0.529061) | 0.015609 / 0.534201 (-0.518591) | 0.379151 / 0.579283 (-0.200132) | 0.386125 / 0.434364 (-0.048238) | 0.470037 / 0.540337 (-0.070301) | 0.562340 / 1.386936 (-0.824596) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#526578cd473a266fa86643d15905181bf346ecac \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009847 / 0.011353 (-0.001505) | 0.005609 / 0.011008 (-0.005399) | 0.101951 / 0.038508 (0.063443) | 0.038082 / 0.023109 (0.014972) | 0.299933 / 0.275898 (0.024035) | 0.377081 / 0.323480 (0.053601) | 0.008900 / 0.007986 (0.000915) | 0.004608 / 0.004328 (0.000279) | 0.077723 / 0.004250 (0.073473) | 0.048592 / 0.037052 (0.011540) | 0.310789 / 0.258489 (0.052300) | 0.345627 / 0.293841 (0.051787) | 0.038716 / 0.128546 (-0.089830) | 0.012653 / 0.075646 (-0.062993) | 0.336885 / 0.419271 (-0.082387) | 0.048715 / 0.043533 (0.005182) | 0.295336 / 0.255139 (0.040197) | 0.316735 / 0.283200 (0.033536) | 0.115142 / 0.141683 (-0.026541) | 1.480332 / 1.452155 (0.028177) | 1.604972 / 1.492716 (0.112256) |\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) | 0.299516 / 0.018006 (0.281510) | 0.525892 / 0.000490 (0.525402) | 0.002246 / 0.000200 (0.002046) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031547 / 0.037411 (-0.005864) | 0.120611 / 0.014526 (0.106085) | 0.124516 / 0.176557 (-0.052041) | 0.166036 / 0.737135 (-0.571100) | 0.131689 / 0.296338 (-0.164650) |\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) | 0.400728 / 0.215209 (0.185519) | 4.007027 / 2.077655 (1.929372) | 1.793922 / 1.504120 (0.289803) | 1.596709 / 1.541195 (0.055514) | 1.752130 / 1.468490 (0.283640) | 0.717464 / 4.584777 (-3.867313) | 3.798844 / 3.745712 (0.053132) | 3.685088 / 5.269862 (-1.584774) | 1.914041 / 4.565676 (-2.651636) | 0.086181 / 0.424275 (-0.338094) | 0.012753 / 0.007607 (0.005146) | 0.507984 / 0.226044 (0.281940) | 5.086255 / 2.268929 (2.817326) | 2.280650 / 55.444624 (-53.163974) | 1.929294 / 6.876477 (-4.947183) | 2.057884 / 2.142072 (-0.084188) | 0.852863 / 4.805227 (-3.952364) | 0.165497 / 6.500664 (-6.335168) | 0.063356 / 0.075469 (-0.012113) |\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) | 1.212593 / 1.841788 (-0.629194) | 16.270507 / 8.074308 (8.196199) | 15.708406 / 10.191392 (5.517014) | 0.162346 / 0.680424 (-0.518078) | 0.029702 / 0.534201 (-0.504499) | 0.447685 / 0.579283 (-0.131598) | 0.449361 / 0.434364 (0.014997) | 0.530536 / 0.540337 (-0.009801) | 0.613439 / 1.386936 (-0.773497) |\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) | 0.007741 / 0.011353 (-0.003612) | 0.005752 / 0.011008 (-0.005256) | 0.076600 / 0.038508 (0.038092) | 0.034841 / 0.023109 (0.011732) | 0.345106 / 0.275898 (0.069208) | 0.385685 / 0.323480 (0.062205) | 0.006466 / 0.007986 (-0.001519) | 0.005806 / 0.004328 (0.001478) | 0.075110 / 0.004250 (0.070860) | 0.052936 / 0.037052 (0.015883) | 0.343576 / 0.258489 (0.085087) | 0.408749 / 0.293841 (0.114908) | 0.037345 / 0.128546 (-0.091201) | 0.012807 / 0.075646 (-0.062839) | 0.087732 / 0.419271 (-0.331540) | 0.050218 / 0.043533 (0.006685) | 0.338963 / 0.255139 (0.083824) | 0.361629 / 0.283200 (0.078429) | 0.107488 / 0.141683 (-0.034195) | 1.465284 / 1.452155 (0.013130) | 1.562218 / 1.492716 (0.069502) |\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) | 0.322496 / 0.018006 (0.304489) | 0.522782 / 0.000490 (0.522292) | 0.006680 / 0.000200 (0.006480) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031801 / 0.037411 (-0.005611) | 0.116839 / 0.014526 (0.102313) | 0.127552 / 0.176557 (-0.049005) | 0.167670 / 0.737135 (-0.569465) | 0.134170 / 0.296338 (-0.162168) |\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) | 0.425449 / 0.215209 (0.210240) | 4.229367 / 2.077655 (2.151713) | 2.014663 / 1.504120 (0.510543) | 1.812981 / 1.541195 (0.271787) | 1.964039 / 1.468490 (0.495549) | 0.703454 / 4.584777 (-3.881323) | 3.786985 / 3.745712 (0.041273) | 2.262377 / 5.269862 (-3.007485) | 1.404868 / 4.565676 (-3.160808) | 0.086234 / 0.424275 (-0.338041) | 0.012616 / 0.007607 (0.005009) | 0.525784 / 0.226044 (0.299739) | 5.268295 / 2.268929 (2.999366) | 2.496674 / 55.444624 (-52.947950) | 2.177773 / 6.876477 (-4.698704) | 2.313677 / 2.142072 (0.171605) | 0.846202 / 4.805227 (-3.959026) | 0.170152 / 6.500664 (-6.330513) | 0.066772 / 0.075469 (-0.008698) |\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) | 1.254719 / 1.841788 (-0.587069) | 16.017627 / 8.074308 (7.943319) | 14.560583 / 10.191392 (4.369191) | 0.168275 / 0.680424 (-0.512149) | 0.017935 / 0.534201 (-0.516266) | 0.430806 / 0.579283 (-0.148477) | 0.428737 / 0.434364 (-0.005626) | 0.532001 / 0.540337 (-0.008336) | 0.633680 / 1.386936 (-0.753256) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2c75dff81c3f060cc4731be3416fd962cc6383e \"CML watermark\")\n"
] | 2023-02-20T16:50:09Z
| 2023-02-21T13:20:42Z
| 2023-02-21T13:13:05Z
|
COLLABORATOR
| null | null | null |
Fix for https://discord.com/channels/879548962464493619/1075729627546406912/1075729627546406912
|
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The speechocean762 dataset
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"CircleCL reported two errors, but I didn't find the reason. The error message:\r\n```\r\n_________________ ERROR collecting tests/test_dataset_cards.py _________________\r\ntests/test_dataset_cards.py:53: in <module>\r\n @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\ntests/test_dataset_cards.py:35: in get_changed_datasets\r\n diff_output = check_output([\"git\", \"diff\", \"--name-only\", \"origin/master...HEAD\"], cwd=repo_path)\r\n../.pyenv/versions/3.6.15/lib/python3.6/subprocess.py:356: in check_output\r\n **kwargs).stdout\r\n../.pyenv/versions/3.6.15/lib/python3.6/subprocess.py:438: in run\r\n output=stdout, stderr=stderr)\r\nE subprocess.CalledProcessError: Command '['git', 'diff', '--name-only', 'origin/master...HEAD']' returned non-zero exit status 128.\r\n\r\n=========================== short test summary info ============================\r\nERROR tests/test_dataset_cards.py - subprocess.CalledProcessError: Command '[...\r\nERROR tests/test_dataset_cards.py - subprocess.CalledProcessError: Command '[...\r\n= 4011 passed, 2357 skipped, 2 xfailed, 1 xpassed, 116 warnings, 2 errors in 284.32s (0:04:44) =\r\n\r\nExited with code exit status 1\r\n```\r\nI'm not sure if it was caused by this PR ...\r\n\r\nI ran `tests/test_dataset_cards.py` in my local environment, and it passed:\r\n```\r\n(venv)$ pytest tests/test_dataset_cards.py\r\n============================== test session starts ==============================\r\nplatform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0\r\nrootdir: /home/zhangjunbo/src/datasets\r\nplugins: forked-1.4.0, datadir-1.3.1, xdist-2.5.0\r\ncollected 1531 items\r\n\r\ntests/test_dataset_cards.py ..... [100%]\r\n======================= 766 passed, 765 skipped in 2.55s ========================\r\n```\r\n",
"@sanchit-gandhi could you also maybe take a quick look? :-)",
"Thanks for your contribution, @jimbozhang. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help.",
"> Thanks for your contribution, @jimbozhang. Are you still interested in adding this dataset?\r\n> \r\n> We are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n> \r\n> We would suggest you create this dataset there. Please, feel free to tell us if you need some help.\r\n\r\nYes, I just planned to finish this dataset these days, and this suggestion is just in time! Thanks a lot!\r\nI will create this dataset to Hugging Face Hub soon, maybe this week."
] | 2022-07-06T06:17:30Z
| 2022-10-03T09:34:36Z
| 2022-10-03T09:34:36Z
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NONE
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[speechocean762](https://www.openslr.org/101/) is a non-native English corpus for pronunciation scoring tasks. It is free for both commercial and non-commercial use.
I believe it will be easier to use if it could be available on Hugging Face.
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Arrow map type in parquet files unsupported
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"I'm attaching a minimal reproducible example:\r\n```python\r\nfrom datasets import load_dataset\r\nimport pyarrow as pa\r\nimport pyarrow.parquet as pq\r\n\r\ntable_with_map = pa.Table.from_pydict(\r\n {\"a\": [1, 2], \"b\": [[(\"a\", 2)], [(\"b\", 4)]]},\r\n schema=pa.schema({\"a\": pa.int32(), \"b\": pa.map_(pa.string(), pa.int32())})\r\n)\r\npq.write_table(table_with_map, \"parquet_with_map.parquet\")\r\ndset = load_dataset(\"parquet\", data_files=\"parquet_with_map.parquet\", split=\"train\") # error unless streaming=True\r\n``` \r\n\r\nFor a dataset generated with the packaged loaders (CSV, JSON, Parquet), `streaming=True` sets the dataset's features to `None` (unless explicitly provided in `load_dataset`), hence no error will be thrown as long as the features stay \"unresolved\" (resolving the features with `_resolve_features` will lead to an error).",
"I've also been wondering about datasets support for Arrow Map datatypes. I had a situation where I had a pandas series of dict[str, float] with hundreds of different possible key values (ie. not bounded), and this got converted to a sequence of structs where every single struct had the entire set of keys.\r\n\r\nI worked around it, by explicitly creating a sequence of [str, float], but given that pyarrow has an explicit Map datatype, it would be good to be able to explicitly cast/force this data type combination.",
"(feel free to ignore) polars will not support this type: https://github.com/pola-rs/polars/issues/3942#issuecomment-1202331210\r\n\r\n> Polars will not add the map dtype. It's benefit do not outweigh the extra complexity. Maybe we can investigate conversion of maps to struct. But I will have to explore that.",
"Looks like they chose to convert every instance with https://github.com/pola-rs/polars/pull/4226"
] | 2023-03-06T12:03:24Z
| 2024-03-15T18:56:12Z
| null |
CONTRIBUTOR
| null | null |
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### Describe the bug
When I try to load parquet files that were processed with Spark, I get the following issue:
`ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.`
Strangely, loading the dataset with `streaming=True` solves the issue.
### Steps to reproduce the bug
The dataset is private, but this can be reproduced with any dataset that has Arrow maps.
### Expected behavior
Loading the dataset no matter whether streaming is True or not.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31
- Python version: 3.10.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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Fix push_to_hub by not calling create_branch if PR branch
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7069). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"cc @Wauplin maybe it's a `huggingface_hub` bug ?\r\n\r\nEDIT: ah actually the issue is opened at https://github.com/huggingface/huggingface_hub/issues/2419",
"I think we need to make this fix anyway, ~~unless we pin the lower version of huggingface-hub (once they release the patch)~~.\r\n- Calling create_branch with a PR ref raises an error",
"Comment by @Wauplin: https://github.com/huggingface/huggingface_hub/pull/2426#issuecomment-2257657543\r\n> I think this should be something to fix in datasets directly. Having a 400 Bad request when trying to create the branch refs/pr/1 seems normal to me since it's not a branch.",
"does this mean we should use `create_pull_request()` in that case ?",
"> does this mean we should use create_pull_request() in that case ?\r\n\r\nIf user wants to push some data to a new PR, they can already pass `create_pr=True` which will automatically do the job for you (without using `revision`). If user is passing `revision=\"refs/pr/1\"` explicitly, you should assume the PR already exists.",
"ah yes we do pass create_pr in `preupload_lfs_files()` ! sounds good then",
"<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) | 0.005806 / 0.011353 (-0.005547) | 0.004082 / 0.011008 (-0.006927) | 0.064277 / 0.038508 (0.025769) | 0.032289 / 0.023109 (0.009180) | 0.242066 / 0.275898 (-0.033832) | 0.272574 / 0.323480 (-0.050906) | 0.003281 / 0.007986 (-0.004705) | 0.002957 / 0.004328 (-0.001371) | 0.049930 / 0.004250 (0.045679) | 0.047306 / 0.037052 (0.010253) | 0.252216 / 0.258489 (-0.006273) | 0.286678 / 0.293841 (-0.007163) | 0.030182 / 0.128546 (-0.098364) | 0.012967 / 0.075646 (-0.062680) | 0.204949 / 0.419271 (-0.214323) | 0.036999 / 0.043533 (-0.006534) | 0.243577 / 0.255139 (-0.011562) | 0.265044 / 0.283200 (-0.018156) | 0.021149 / 0.141683 (-0.120534) | 1.112293 / 1.452155 (-0.339862) | 1.186483 / 1.492716 (-0.306233) |\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) | 0.093239 / 0.018006 (0.075233) | 0.286372 / 0.000490 (0.285883) | 0.000224 / 0.000200 (0.000024) | 0.000062 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019042 / 0.037411 (-0.018369) | 0.063690 / 0.014526 (0.049164) | 0.075034 / 0.176557 (-0.101523) | 0.123053 / 0.737135 (-0.614083) | 0.076843 / 0.296338 (-0.219495) |\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) | 0.276554 / 0.215209 (0.061345) | 2.749338 / 2.077655 (0.671683) | 1.442764 / 1.504120 (-0.061356) | 1.327860 / 1.541195 (-0.213335) | 1.369885 / 1.468490 (-0.098606) | 0.722645 / 4.584777 (-3.862132) | 2.430707 / 3.745712 (-1.315005) | 3.105293 / 5.269862 (-2.164568) | 1.961617 / 4.565676 (-2.604060) | 0.077728 / 0.424275 (-0.346547) | 0.005189 / 0.007607 (-0.002418) | 0.335511 / 0.226044 (0.109467) | 3.315618 / 2.268929 (1.046690) | 1.858254 / 55.444624 (-53.586371) | 1.552173 / 6.876477 (-5.324304) | 1.627086 / 2.142072 (-0.514987) | 0.790871 / 4.805227 (-4.014356) | 0.136958 / 6.500664 (-6.363706) | 0.043207 / 0.075469 (-0.032262) |\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) | 0.969314 / 1.841788 (-0.872473) | 12.145318 / 8.074308 (4.071010) | 9.834839 / 10.191392 (-0.356553) | 0.141896 / 0.680424 (-0.538528) | 0.014304 / 0.534201 (-0.519897) | 0.306091 / 0.579283 (-0.273192) | 0.260994 / 0.434364 (-0.173369) | 0.348096 / 0.540337 (-0.192242) | 0.441458 / 1.386936 (-0.945478) |\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) | 0.005989 / 0.011353 (-0.005363) | 0.003907 / 0.011008 (-0.007102) | 0.050819 / 0.038508 (0.012310) | 0.033178 / 0.023109 (0.010069) | 0.279059 / 0.275898 (0.003161) | 0.300161 / 0.323480 (-0.023319) | 0.004383 / 0.007986 (-0.003603) | 0.002834 / 0.004328 (-0.001495) | 0.048779 / 0.004250 (0.044528) | 0.040502 / 0.037052 (0.003450) | 0.291786 / 0.258489 (0.033297) | 0.323827 / 0.293841 (0.029986) | 0.032175 / 0.128546 (-0.096371) | 0.012157 / 0.075646 (-0.063489) | 0.060796 / 0.419271 (-0.358476) | 0.033924 / 0.043533 (-0.009609) | 0.278047 / 0.255139 (0.022908) | 0.297878 / 0.283200 (0.014678) | 0.019137 / 0.141683 (-0.122546) | 1.138996 / 1.452155 (-0.313158) | 1.172731 / 1.492716 (-0.319985) |\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) | 0.110148 / 0.018006 (0.092142) | 0.307232 / 0.000490 (0.306742) | 0.000209 / 0.000200 (0.000009) | 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) | 0.023082 / 0.037411 (-0.014330) | 0.076590 / 0.014526 (0.062065) | 0.088444 / 0.176557 (-0.088113) | 0.129293 / 0.737135 (-0.607842) | 0.090470 / 0.296338 (-0.205868) |\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) | 0.305016 / 0.215209 (0.089807) | 2.931671 / 2.077655 (0.854016) | 1.586055 / 1.504120 (0.081935) | 1.463517 / 1.541195 (-0.077678) | 1.479654 / 1.468490 (0.011164) | 0.726194 / 4.584777 (-3.858583) | 0.970512 / 3.745712 (-2.775200) | 2.850496 / 5.269862 (-2.419365) | 1.920112 / 4.565676 (-2.645564) | 0.079921 / 0.424275 (-0.344354) | 0.005367 / 0.007607 (-0.002240) | 0.347022 / 0.226044 (0.120978) | 3.472425 / 2.268929 (1.203497) | 1.965400 / 55.444624 (-53.479225) | 1.669116 / 6.876477 (-5.207361) | 1.859504 / 2.142072 (-0.282568) | 0.802703 / 4.805227 (-4.002525) | 0.134776 / 6.500664 (-6.365888) | 0.041800 / 0.075469 (-0.033669) |\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) | 1.039665 / 1.841788 (-0.802122) | 12.024071 / 8.074308 (3.949763) | 10.338743 / 10.191392 (0.147351) | 0.139495 / 0.680424 (-0.540929) | 0.015249 / 0.534201 (-0.518952) | 0.298580 / 0.579283 (-0.280703) | 0.124625 / 0.434364 (-0.309739) | 0.341868 / 0.540337 (-0.198470) | 0.431396 / 1.386936 (-0.955540) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#65b9499348fa4c6e5bfa977ee9b5e8574bf64eea \"CML watermark\")\n"
] | 2024-07-25T07:50:04Z
| 2024-07-31T07:10:07Z
| 2024-07-30T10:51:01Z
|
MEMBER
| null | null | null |
Fix push_to_hub by not calling create_branch if PR branch (e.g. `refs/pr/1`).
Note that currently create_branch raises a 400 Bad Request error if the user passes a PR branch (e.g. `refs/pr/1`).
EDIT:
~~Fix push_to_hub by not calling create_branch if branch exists.~~
Note that currently create_branch raises a 403 Forbidden error even if all these conditions are met:
- exist_ok is passed
- the branch already exists
- the user does not have WRITE permission
Fix #7067.
Related issue:
- https://github.com/huggingface/huggingface_hub/issues/2419
|
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I_kwDODunzps57mk4s
| 6,576
|
document page 404 not found after redirection
|
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[
"Thanks for reporting! I've opened a PR with a fix."
] | 2024-01-10T06:48:14Z
| 2024-01-17T14:01:31Z
| 2024-01-17T14:01:31Z
|
NONE
| null | null |
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### Describe the bug
The redirected page encountered 404 not found.
### Steps to reproduce the bug
1. In this tutorial: https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt
original md: https://github.com/huggingface/course/blob/2c733c2246b8b7e0e6f19a9e5d15bb12df43b2a3/chapters/en/chapter5/4.mdx#L49
```
By default, 🤗 Datasets will decompress the files needed to load a dataset. If you want to preserve hard drive space, you can pass `DownloadConfig(delete_extracted=True)` to the `download_config` argument of `load_dataset()`. See the [documentation](https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig) for more details.
```
The documentation points to `https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig`
it shows `The documentation page PACKAGE_REFERENCE/BUILDER_CLASSES.HTML doesn’t exist in v2.16.1, but exists on the main version. Click [here](https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html) to redirect to the main version of the documentation.`
But the redirected website `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html` is 404 not found.
### Expected behavior
I Guess the redirected webisite should be
`https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes` (without `.html`)
or `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes#datasets.DownloadConfig`.
### Environment info
Datasets main
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| 1,448,052,660
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PR_kwDODunzps5C1pjc
| 5,235
|
Pin `typer` version in tests to <0.5 to fix Windows CI
|
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[] | 2022-11-14T13:17:02Z
| 2022-11-14T15:43:01Z
| 2022-11-14T13:41:12Z
|
CONTRIBUTOR
| null | null | null |
Otherwise `click` fails on Windows:
```
Traceback (most recent call last):
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\__main__.py", line 4, in <module>
setup_cli()
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\cli\_util.py", line 71, in setup_cli
command(prog_name=COMMAND)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\click\core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 785, in main
**extra,
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 190, in _main
args = click.utils._expand_args(args)
AttributeError: module 'click.utils' has no attribute '_expand_args'
```
See https://github.com/tiangolo/typer/issues/427
|
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I_kwDODunzps6uKSnC
| 7,455
|
Problems with local dataset after upgrade from 3.3.2 to 3.4.0
|
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[
"Hi ! I just released 3.4.1 with a fix, let me know if it's working now !"
] | 2025-03-15T09:22:50Z
| 2025-03-17T16:20:43Z
| null |
NONE
| null | null |
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### Describe the bug
I was not able to open a local saved dataset anymore that was created using an older datasets version after the upgrade yesterday from datasets 3.3.2 to 3.4.0
The traceback is
```
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 67, in _generate_tables
batches = pa.ipc.open_stream(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 190, in open_stream
return RecordBatchStreamReader(source, options=options,
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 52, in __init__
self._open(source, options=options, memory_pool=memory_pool)
File "pyarrow/ipc.pxi", line 1006, in pyarrow.lib._RecordBatchStreamReader._open
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Expected to read 538970747 metadata bytes, but only read 2126
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 1855, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 69, in _generate_tables
reader = pa.ipc.open_file(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 234, in open_file
return RecordBatchFileReader(
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 110, in __init__
self._open(source, footer_offset=footer_offset,
File "pyarrow/ipc.pxi", line 1090, in pyarrow.lib._RecordBatchFileReader._open
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Not an Arrow file
```
### Steps to reproduce the bug
Load a dataset from a local folder with
```
dataset = load_dataset(
args.train_data_dir,
cache_dir=args.cache_dir,
)
```
as it is done for example in the training script for SD3 controlnet.
This is the minimal script to test it:
```
from datasets import load_dataset
def main():
dataset = load_dataset(
"local_dataset",
)
print(dataset)
print("Sample data:", dataset["train"][0])
if __name__ == "__main__":
main()
````
### Expected behavior
Work in 3.4.0 like in 3.3.2
### Environment info
- `datasets` version: 3.4.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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I_kwDODunzps6b6uO6
| 7,253
|
Unable to upload a large dataset zip either from command line or UI
|
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### Describe the bug
Unable to upload a large dataset zip from command line or UI. UI simply says error. I am trying to a upload a tar.gz file of 17GB.
<img width="550" alt="image" src="https://github.com/user-attachments/assets/f9d29024-06c8-49c4-a109-0492cff79d34">
<img width="755" alt="image" src="https://github.com/user-attachments/assets/a8d4acda-7f02-4279-9c2d-b2e0282b4faa">
### Steps to reproduce the bug
Upload a large file
### Expected behavior
The file should upload without any issue.
### Environment info
None
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I_kwDODunzps5nIkfM
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|
Missing elements in `map` a batched dataset
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[
"Hi ! in your code batching is **only used within** `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\r\n```"
] | 2023-05-29T08:09:19Z
| 2023-07-26T15:48:15Z
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### Describe the bug
As outlined [here](https://discuss.huggingface.co/t/length-error-using-map-with-datasets/40969/3?u=sachin), the following collate function drops 5 out of possible 6 elements in the batch (it is 6 because out of the eight, two are bad links in laion). A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
The weirdest part is when inspecting the sizes of the tensors as shown below, both `tokenized_captions["input_ids"]` and `image_features` show the correct shapes. Simply the output only has one element (with the batch dimension squeezed out).
```python
class CollateFn:
def get_image(self, url):
try:
response = requests.get(url)
return Image.open(io.BytesIO(response.content)).convert("RGB")
except PIL.UnidentifiedImageError:
logger.info(f"Reading error: Could not transform f{url}")
return None
except requests.exceptions.ConnectionError:
logger.info(f"Connection error: Could not transform f{url}")
return None
def __call__(self, batch):
images = [self.get_image(url) for url in batch["url"]]
captions = [caption for caption, image in zip(batch["caption"], images) if image is not None]
images = [image for image in images if image is not None]
tokenized_captions = tokenizer(
captions,
padding="max_length",
truncation=True,
max_length=tokenizer.model_max_length,
return_tensors="pt",
)
image_features = torch.stack([torch.Tensor(feature_extractor(image)["pixel_values"][0]) for image in images])
# import pdb; pdb.set_trace()
return {"input_ids": tokenized_captions["input_ids"], "images": image_features}
collate_fn = CollateFn()
laion_ds = datasets.load_dataset("laion/laion400m", split="train", streaming=True)
laion_ds_batched = laion_ds.map(collate_fn, batched=True, batch_size=8, remove_columns=next(iter(laion_ds)).keys())
```
### Steps to reproduce the bug
A reproducible [kaggle kernel ](https://www.kaggle.com/sachin/laion-hf-dataset/edit) can be found here.
### Expected behavior
Would expect `next(iter(laion_ds_batched))` to produce two tensors of shape `(batch_size, 77)` and `batch_size, image_shape`.
### Environment info
datasets==2.12.0
python==3.10
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005974 / 0.011353 (-0.005378) | 0.003707 / 0.011008 (-0.007301) | 0.079908 / 0.038508 (0.041399) | 0.036891 / 0.023109 (0.013781) | 0.390355 / 0.275898 (0.114457) | 0.424439 / 0.323480 (0.100960) | 0.004936 / 0.007986 (-0.003050) | 0.002886 / 0.004328 (-0.001442) | 0.062793 / 0.004250 (0.058542) | 0.054192 / 0.037052 (0.017139) | 0.394697 / 0.258489 (0.136208) | 0.437775 / 0.293841 (0.143934) | 0.027596 / 0.128546 (-0.100950) | 0.008006 / 0.075646 (-0.067640) | 0.262515 / 0.419271 (-0.156757) | 0.071014 / 0.043533 (0.027481) | 0.392964 / 0.255139 (0.137825) | 0.417449 / 0.283200 (0.134249) | 0.021819 / 0.141683 (-0.119864) | 1.458083 / 1.452155 (0.005929) | 1.489042 / 1.492716 (-0.003674) |\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) | 0.230303 / 0.018006 (0.212297) | 0.439361 / 0.000490 (0.438871) | 0.010615 / 0.000200 (0.010415) | 0.000303 / 0.000054 (0.000249) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026600 / 0.037411 (-0.010811) | 0.078605 / 0.014526 (0.064079) | 0.088552 / 0.176557 (-0.088005) | 0.149429 / 0.737135 (-0.587706) | 0.087921 / 0.296338 (-0.208417) |\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) | 0.422063 / 0.215209 (0.206854) | 4.201333 / 2.077655 (2.123678) | 1.982284 / 1.504120 (0.478164) | 1.779625 / 1.541195 (0.238431) | 1.872454 / 1.468490 (0.403964) | 0.502713 / 4.584777 (-4.082063) | 3.103372 / 3.745712 (-0.642340) | 3.030516 / 5.269862 (-2.239346) | 1.909123 / 4.565676 (-2.656554) | 0.057134 / 0.424275 (-0.367141) | 0.006405 / 0.007607 (-0.001202) | 0.494452 / 0.226044 (0.268408) | 4.839345 / 2.268929 (2.570417) | 2.424721 / 55.444624 (-53.019904) | 2.028618 / 6.876477 (-4.847859) | 2.082528 / 2.142072 (-0.059545) | 0.587396 / 4.805227 (-4.217831) | 0.125013 / 6.500664 (-6.375651) | 0.061369 / 0.075469 (-0.014100) |\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) | 1.235799 / 1.841788 (-0.605989) | 17.919977 / 8.074308 (9.845669) | 13.868524 / 10.191392 (3.677132) | 0.146058 / 0.680424 (-0.534366) | 0.016826 / 0.534201 (-0.517375) | 0.337512 / 0.579283 (-0.241771) | 0.390263 / 0.434364 (-0.044101) | 0.385336 / 0.540337 (-0.155001) | 0.566004 / 1.386936 (-0.820932) |\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) | 0.006537 / 0.011353 (-0.004816) | 0.003787 / 0.011008 (-0.007221) | 0.062568 / 0.038508 (0.024060) | 0.066672 / 0.023109 (0.043563) | 0.420447 / 0.275898 (0.144549) | 0.457260 / 0.323480 (0.133780) | 0.005005 / 0.007986 (-0.002981) | 0.003037 / 0.004328 (-0.001291) | 0.062095 / 0.004250 (0.057844) | 0.049619 / 0.037052 (0.012567) | 0.429935 / 0.258489 (0.171446) | 0.471566 / 0.293841 (0.177725) | 0.029688 / 0.128546 (-0.098859) | 0.008028 / 0.075646 (-0.067619) | 0.067915 / 0.419271 (-0.351356) | 0.042066 / 0.043533 (-0.001467) | 0.419275 / 0.255139 (0.164136) | 0.444819 / 0.283200 (0.161619) | 0.020100 / 0.141683 (-0.121583) | 1.439057 / 1.452155 (-0.013098) | 1.495657 / 1.492716 (0.002940) |\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) | 0.211148 / 0.018006 (0.193142) | 0.423777 / 0.000490 (0.423288) | 0.005892 / 0.000200 (0.005693) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026469 / 0.037411 (-0.010942) | 0.081438 / 0.014526 (0.066912) | 0.092007 / 0.176557 (-0.084550) | 0.143433 / 0.737135 (-0.593703) | 0.093039 / 0.296338 (-0.203300) |\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) | 0.410468 / 0.215209 (0.195259) | 4.083783 / 2.077655 (2.006128) | 2.234501 / 1.504120 (0.730381) | 2.122323 / 1.541195 (0.581128) | 2.255036 / 1.468490 (0.786546) | 0.497712 / 4.584777 (-4.087065) | 3.231187 / 3.745712 (-0.514525) | 3.005399 / 5.269862 (-2.264463) | 1.909516 / 4.565676 (-2.656161) | 0.057529 / 0.424275 (-0.366746) | 0.006475 / 0.007607 (-0.001132) | 0.477282 / 0.226044 (0.251238) | 4.799566 / 2.268929 (2.530637) | 2.497070 / 55.444624 (-52.947554) | 2.206359 / 6.876477 (-4.670118) | 2.281614 / 2.142072 (0.139541) | 0.581710 / 4.805227 (-4.223518) | 0.121572 / 6.500664 (-6.379092) | 0.058774 / 0.075469 (-0.016695) |\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) | 1.301880 / 1.841788 (-0.539908) | 18.287330 / 8.074308 (10.213021) | 14.939642 / 10.191392 (4.748250) | 0.153941 / 0.680424 (-0.526483) | 0.018345 / 0.534201 (-0.515856) | 0.335986 / 0.579283 (-0.243297) | 0.384264 / 0.434364 (-0.050099) | 0.393115 / 0.540337 (-0.147223) | 0.573343 / 1.386936 (-0.813594) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d54b6459f4ed0b2519ddec605dd71956d2d1d3e4 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004805 / 0.011353 (-0.006548) | 0.003261 / 0.011008 (-0.007747) | 0.061585 / 0.038508 (0.023077) | 0.030236 / 0.023109 (0.007127) | 0.234767 / 0.275898 (-0.041131) | 0.260478 / 0.323480 (-0.063002) | 0.004121 / 0.007986 (-0.003865) | 0.002525 / 0.004328 (-0.001803) | 0.048213 / 0.004250 (0.043962) | 0.045229 / 0.037052 (0.008176) | 0.245143 / 0.258489 (-0.013346) | 0.271818 / 0.293841 (-0.022023) | 0.023594 / 0.128546 (-0.104952) | 0.007335 / 0.075646 (-0.068311) | 0.206246 / 0.419271 (-0.213026) | 0.060783 / 0.043533 (0.017250) | 0.238588 / 0.255139 (-0.016551) | 0.274985 / 0.283200 (-0.008214) | 0.018342 / 0.141683 (-0.123341) | 1.135445 / 1.452155 (-0.316710) | 1.184836 / 1.492716 (-0.307881) |\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) | 0.095603 / 0.018006 (0.077597) | 0.290340 / 0.000490 (0.289850) | 0.000219 / 0.000200 (0.000019) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018804 / 0.037411 (-0.018607) | 0.062525 / 0.014526 (0.047999) | 0.074797 / 0.176557 (-0.101760) | 0.120360 / 0.737135 (-0.616775) | 0.076182 / 0.296338 (-0.220156) |\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) | 0.274981 / 0.215209 (0.059772) | 2.684931 / 2.077655 (0.607276) | 1.453845 / 1.504120 (-0.050275) | 1.348361 / 1.541195 (-0.192834) | 1.402820 / 1.468490 (-0.065670) | 0.396311 / 4.584777 (-4.188466) | 2.396314 / 3.745712 (-1.349398) | 2.744379 / 5.269862 (-2.525482) | 1.615268 / 4.565676 (-2.950409) | 0.045920 / 0.424275 (-0.378355) | 0.004844 / 0.007607 (-0.002763) | 0.331132 / 0.226044 (0.105087) | 3.325484 / 2.268929 (1.056556) | 1.845734 / 55.444624 (-53.598890) | 1.537268 / 6.876477 (-5.339209) | 1.565155 / 2.142072 (-0.576918) | 0.480032 / 4.805227 (-4.325195) | 0.099917 / 6.500664 (-6.400747) | 0.042276 / 0.075469 (-0.033193) |\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) | 0.973128 / 1.841788 (-0.868660) | 12.643790 / 8.074308 (4.569482) | 10.319586 / 10.191392 (0.128194) | 0.131733 / 0.680424 (-0.548691) | 0.014849 / 0.534201 (-0.519352) | 0.270960 / 0.579283 (-0.308323) | 0.265409 / 0.434364 (-0.168955) | 0.309073 / 0.540337 (-0.231264) | 0.466204 / 1.386936 (-0.920732) |\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) | 0.005067 / 0.011353 (-0.006286) | 0.003344 / 0.011008 (-0.007665) | 0.047917 / 0.038508 (0.009409) | 0.059556 / 0.023109 (0.036447) | 0.275777 / 0.275898 (-0.000121) | 0.299703 / 0.323480 (-0.023777) | 0.004185 / 0.007986 (-0.003801) | 0.002602 / 0.004328 (-0.001726) | 0.048723 / 0.004250 (0.044472) | 0.040686 / 0.037052 (0.003634) | 0.281078 / 0.258489 (0.022589) | 0.314725 / 0.293841 (0.020885) | 0.024645 / 0.128546 (-0.103901) | 0.007465 / 0.075646 (-0.068182) | 0.053827 / 0.419271 (-0.365445) | 0.033395 / 0.043533 (-0.010138) | 0.273675 / 0.255139 (0.018536) | 0.291261 / 0.283200 (0.008062) | 0.019733 / 0.141683 (-0.121950) | 1.134084 / 1.452155 (-0.318071) | 1.189186 / 1.492716 (-0.303531) |\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) | 0.114960 / 0.018006 (0.096954) | 0.308800 / 0.000490 (0.308311) | 0.000237 / 0.000200 (0.000037) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021633 / 0.037411 (-0.015778) | 0.073192 / 0.014526 (0.058666) | 0.081598 / 0.176557 (-0.094959) | 0.123085 / 0.737135 (-0.614050) | 0.088677 / 0.296338 (-0.207661) |\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) | 0.300865 / 0.215209 (0.085656) | 2.956847 / 2.077655 (0.879192) | 1.613890 / 1.504120 (0.109770) | 1.494074 / 1.541195 (-0.047121) | 1.550345 / 1.468490 (0.081855) | 0.408880 / 4.584777 (-4.175897) | 2.422848 / 3.745712 (-1.322865) | 2.690623 / 5.269862 (-2.579239) | 1.546922 / 4.565676 (-3.018755) | 0.047192 / 0.424275 (-0.377083) | 0.004882 / 0.007607 (-0.002725) | 0.360625 / 0.226044 (0.134580) | 3.512678 / 2.268929 (1.243749) | 1.978633 / 55.444624 (-53.465992) | 1.686927 / 6.876477 (-5.189549) | 1.748387 / 2.142072 (-0.393685) | 0.480780 / 4.805227 (-4.324447) | 0.099163 / 6.500664 (-6.401501) | 0.041194 / 0.075469 (-0.034275) |\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) | 0.989087 / 1.841788 (-0.852700) | 12.341951 / 8.074308 (4.267643) | 11.109329 / 10.191392 (0.917936) | 0.143329 / 0.680424 (-0.537095) | 0.015565 / 0.534201 (-0.518636) | 0.269532 / 0.579283 (-0.309751) | 0.274899 / 0.434364 (-0.159465) | 0.309308 / 0.540337 (-0.231030) | 0.439651 / 1.386936 (-0.947285) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a3f006a1a88c894ea10610d66dfddd73ad1490 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007880 / 0.011353 (-0.003473) | 0.004386 / 0.011008 (-0.006622) | 0.099067 / 0.038508 (0.060559) | 0.048036 / 0.023109 (0.024927) | 0.368349 / 0.275898 (0.092451) | 0.400052 / 0.323480 (0.076572) | 0.004493 / 0.007986 (-0.003493) | 0.003732 / 0.004328 (-0.000597) | 0.076153 / 0.004250 (0.071902) | 0.071024 / 0.037052 (0.033972) | 0.379771 / 0.258489 (0.121282) | 0.425005 / 0.293841 (0.131164) | 0.036092 / 0.128546 (-0.092454) | 0.009825 / 0.075646 (-0.065822) | 0.340217 / 0.419271 (-0.079055) | 0.089571 / 0.043533 (0.046038) | 0.371426 / 0.255139 (0.116287) | 0.397864 / 0.283200 (0.114664) | 0.029440 / 0.141683 (-0.112243) | 1.778100 / 1.452155 (0.325945) | 1.857202 / 1.492716 (0.364486) |\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) | 0.254022 / 0.018006 (0.236015) | 0.549844 / 0.000490 (0.549354) | 0.012824 / 0.000200 (0.012624) | 0.000378 / 0.000054 (0.000324) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032334 / 0.037411 (-0.005077) | 0.096101 / 0.014526 (0.081576) | 0.117825 / 0.176557 (-0.058731) | 0.179277 / 0.737135 (-0.557858) | 0.112614 / 0.296338 (-0.183724) |\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) | 0.455051 / 0.215209 (0.239842) | 4.537086 / 2.077655 (2.459431) | 2.198662 / 1.504120 (0.694542) | 1.982772 / 1.541195 (0.441578) | 2.058673 / 1.468490 (0.590182) | 0.569268 / 4.584777 (-4.015509) | 4.095000 / 3.745712 (0.349288) | 3.891680 / 5.269862 (-1.378182) | 2.345129 / 4.565676 (-2.220548) | 0.066974 / 0.424275 (-0.357301) | 0.008557 / 0.007607 (0.000950) | 0.545290 / 0.226044 (0.319245) | 5.453377 / 2.268929 (3.184448) | 2.858688 / 55.444624 (-52.585936) | 2.502367 / 6.876477 (-4.374109) | 2.515658 / 2.142072 (0.373586) | 0.681423 / 4.805227 (-4.123804) | 0.155975 / 6.500664 (-6.344689) | 0.070872 / 0.075469 (-0.004597) |\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) | 1.474674 / 1.841788 (-0.367114) | 21.653619 / 8.074308 (13.579311) | 16.277111 / 10.191392 (6.085719) | 0.166445 / 0.680424 (-0.513979) | 0.021676 / 0.534201 (-0.512525) | 0.466949 / 0.579283 (-0.112334) | 0.500953 / 0.434364 (0.066589) | 0.540413 / 0.540337 (0.000076) | 0.792989 / 1.386936 (-0.593947) |\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) | 0.007633 / 0.011353 (-0.003720) | 0.004468 / 0.011008 (-0.006540) | 0.075573 / 0.038508 (0.037065) | 0.081174 / 0.023109 (0.058064) | 0.440741 / 0.275898 (0.164843) | 0.489493 / 0.323480 (0.166013) | 0.006180 / 0.007986 (-0.001805) | 0.003693 / 0.004328 (-0.000636) | 0.074692 / 0.004250 (0.070441) | 0.061732 / 0.037052 (0.024680) | 0.460391 / 0.258489 (0.201902) | 0.505575 / 0.293841 (0.211734) | 0.037692 / 0.128546 (-0.090854) | 0.009870 / 0.075646 (-0.065776) | 0.083830 / 0.419271 (-0.335442) | 0.056255 / 0.043533 (0.012723) | 0.439330 / 0.255139 (0.184191) | 0.475598 / 0.283200 (0.192399) | 0.026626 / 0.141683 (-0.115056) | 1.794410 / 1.452155 (0.342255) | 1.882510 / 1.492716 (0.389794) |\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) | 0.236194 / 0.018006 (0.218187) | 0.486109 / 0.000490 (0.485619) | 0.006652 / 0.000200 (0.006453) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037277 / 0.037411 (-0.000134) | 0.108904 / 0.014526 (0.094378) | 0.122699 / 0.176557 (-0.053857) | 0.182388 / 0.737135 (-0.554747) | 0.122826 / 0.296338 (-0.173512) |\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) | 0.485989 / 0.215209 (0.270780) | 4.913263 / 2.077655 (2.835609) | 2.571618 / 1.504120 (1.067498) | 2.401248 / 1.541195 (0.860054) | 2.501117 / 1.468490 (1.032627) | 0.570989 / 4.584777 (-4.013788) | 4.107420 / 3.745712 (0.361708) | 3.814977 / 5.269862 (-1.454885) | 2.282539 / 4.565676 (-2.283138) | 0.067765 / 0.424275 (-0.356511) | 0.008561 / 0.007607 (0.000954) | 0.584515 / 0.226044 (0.358471) | 5.817821 / 2.268929 (3.548893) | 3.211202 / 55.444624 (-52.233422) | 2.764480 / 6.876477 (-4.111996) | 2.807301 / 2.142072 (0.665229) | 0.676882 / 4.805227 (-4.128346) | 0.150124 / 6.500664 (-6.350540) | 0.067205 / 0.075469 (-0.008265) |\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) | 1.594945 / 1.841788 (-0.246843) | 22.533511 / 8.074308 (14.459203) | 17.099693 / 10.191392 (6.908301) | 0.195954 / 0.680424 (-0.484470) | 0.023968 / 0.534201 (-0.510233) | 0.471337 / 0.579283 (-0.107946) | 0.491017 / 0.434364 (0.056653) | 0.561342 / 0.540337 (0.021004) | 0.797116 / 1.386936 (-0.589820) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98871b9ba46e89e75e9d0dddc49f4241373c575d \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006235 / 0.011353 (-0.005118) | 0.003688 / 0.011008 (-0.007321) | 0.080801 / 0.038508 (0.042293) | 0.036243 / 0.023109 (0.013134) | 0.312173 / 0.275898 (0.036275) | 0.346239 / 0.323480 (0.022759) | 0.003429 / 0.007986 (-0.004556) | 0.003806 / 0.004328 (-0.000523) | 0.063236 / 0.004250 (0.058986) | 0.053229 / 0.037052 (0.016177) | 0.315184 / 0.258489 (0.056695) | 0.360124 / 0.293841 (0.066283) | 0.027447 / 0.128546 (-0.101099) | 0.008029 / 0.075646 (-0.067618) | 0.262766 / 0.419271 (-0.156505) | 0.068421 / 0.043533 (0.024888) | 0.309028 / 0.255139 (0.053889) | 0.345859 / 0.283200 (0.062659) | 0.021388 / 0.141683 (-0.120295) | 1.452807 / 1.452155 (0.000652) | 1.502803 / 1.492716 (0.010087) |\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) | 0.211297 / 0.018006 (0.193291) | 0.423364 / 0.000490 (0.422874) | 0.004574 / 0.000200 (0.004374) | 0.000272 / 0.000054 (0.000218) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023805 / 0.037411 (-0.013606) | 0.072309 / 0.014526 (0.057783) | 0.083274 / 0.176557 (-0.093283) | 0.143594 / 0.737135 (-0.593541) | 0.083777 / 0.296338 (-0.212561) |\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) | 0.415691 / 0.215209 (0.200482) | 4.128621 / 2.077655 (2.050967) | 1.931128 / 1.504120 (0.427008) | 1.737486 / 1.541195 (0.196292) | 1.806314 / 1.468490 (0.337823) | 0.501405 / 4.584777 (-4.083372) | 3.082042 / 3.745712 (-0.663670) | 2.980224 / 5.269862 (-2.289637) | 1.879780 / 4.565676 (-2.685897) | 0.057546 / 0.424275 (-0.366729) | 0.006422 / 0.007607 (-0.001186) | 0.479813 / 0.226044 (0.253768) | 4.854497 / 2.268929 (2.585568) | 2.529674 / 55.444624 (-52.914950) | 2.283041 / 6.876477 (-4.593436) | 2.377173 / 2.142072 (0.235101) | 0.589654 / 4.805227 (-4.215573) | 0.126190 / 6.500664 (-6.374474) | 0.062391 / 0.075469 (-0.013079) |\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) | 1.232023 / 1.841788 (-0.609764) | 17.576621 / 8.074308 (9.502313) | 13.437075 / 10.191392 (3.245683) | 0.143367 / 0.680424 (-0.537057) | 0.016638 / 0.534201 (-0.517563) | 0.332806 / 0.579283 (-0.246477) | 0.356029 / 0.434364 (-0.078335) | 0.385610 / 0.540337 (-0.154727) | 0.563268 / 1.386936 (-0.823668) |\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) | 0.006293 / 0.011353 (-0.005060) | 0.003692 / 0.011008 (-0.007317) | 0.062075 / 0.038508 (0.023567) | 0.062104 / 0.023109 (0.038995) | 0.407478 / 0.275898 (0.131580) | 0.434982 / 0.323480 (0.111502) | 0.004889 / 0.007986 (-0.003097) | 0.002915 / 0.004328 (-0.001413) | 0.061426 / 0.004250 (0.057176) | 0.048027 / 0.037052 (0.010974) | 0.410504 / 0.258489 (0.152015) | 0.435383 / 0.293841 (0.141542) | 0.029419 / 0.128546 (-0.099127) | 0.008275 / 0.075646 (-0.067371) | 0.067796 / 0.419271 (-0.351476) | 0.041696 / 0.043533 (-0.001837) | 0.398882 / 0.255139 (0.143743) | 0.419480 / 0.283200 (0.136281) | 0.021519 / 0.141683 (-0.120164) | 1.436961 / 1.452155 (-0.015194) | 1.507961 / 1.492716 (0.015245) |\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) | 0.223190 / 0.018006 (0.205184) | 0.416281 / 0.000490 (0.415791) | 0.003370 / 0.000200 (0.003170) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025923 / 0.037411 (-0.011488) | 0.079989 / 0.014526 (0.065463) | 0.091289 / 0.176557 (-0.085268) | 0.141212 / 0.737135 (-0.595923) | 0.091717 / 0.296338 (-0.204622) |\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) | 0.434640 / 0.215209 (0.219431) | 4.326154 / 2.077655 (2.248500) | 2.364845 / 1.504120 (0.860725) | 2.194040 / 1.541195 (0.652846) | 2.276665 / 1.468490 (0.808175) | 0.501879 / 4.584777 (-4.082898) | 3.073307 / 3.745712 (-0.672405) | 2.893823 / 5.269862 (-2.376039) | 1.820594 / 4.565676 (-2.745083) | 0.057595 / 0.424275 (-0.366680) | 0.006516 / 0.007607 (-0.001091) | 0.513633 / 0.226044 (0.287589) | 5.104799 / 2.268929 (2.835870) | 2.845025 / 55.444624 (-52.599599) | 2.513852 / 6.876477 (-4.362624) | 2.561044 / 2.142072 (0.418972) | 0.582711 / 4.805227 (-4.222516) | 0.120631 / 6.500664 (-6.380034) | 0.056738 / 0.075469 (-0.018731) |\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) | 1.303370 / 1.841788 (-0.538418) | 18.023568 / 8.074308 (9.949259) | 14.637973 / 10.191392 (4.446581) | 0.145182 / 0.680424 (-0.535241) | 0.018061 / 0.534201 (-0.516140) | 0.333219 / 0.579283 (-0.246065) | 0.373184 / 0.434364 (-0.061180) | 0.388176 / 0.540337 (-0.152161) | 0.564752 / 1.386936 (-0.822184) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aecdc94580d105d4b70c94e8e238ce097f97af90 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007230 / 0.011353 (-0.004122) | 0.003727 / 0.011008 (-0.007281) | 0.078893 / 0.038508 (0.040385) | 0.042600 / 0.023109 (0.019491) | 0.301905 / 0.275898 (0.026007) | 0.328478 / 0.323480 (0.004998) | 0.003960 / 0.007986 (-0.004026) | 0.004530 / 0.004328 (0.000201) | 0.059446 / 0.004250 (0.055196) | 0.061241 / 0.037052 (0.024189) | 0.301878 / 0.258489 (0.043389) | 0.340935 / 0.293841 (0.047095) | 0.030559 / 0.128546 (-0.097988) | 0.008016 / 0.075646 (-0.067630) | 0.305174 / 0.419271 (-0.114097) | 0.080374 / 0.043533 (0.036842) | 0.307162 / 0.255139 (0.052023) | 0.342459 / 0.283200 (0.059259) | 0.025881 / 0.141683 (-0.115801) | 1.443311 / 1.452155 (-0.008844) | 1.631060 / 1.492716 (0.138344) |\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) | 0.242676 / 0.018006 (0.224670) | 0.463941 / 0.000490 (0.463451) | 0.007762 / 0.000200 (0.007562) | 0.000582 / 0.000054 (0.000527) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027334 / 0.037411 (-0.010077) | 0.078910 / 0.014526 (0.064384) | 0.091399 / 0.176557 (-0.085157) | 0.143318 / 0.737135 (-0.593818) | 0.089761 / 0.296338 (-0.206577) |\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) | 0.463002 / 0.215209 (0.247793) | 4.627235 / 2.077655 (2.549580) | 2.256699 / 1.504120 (0.752579) | 2.057615 / 1.541195 (0.516421) | 2.126424 / 1.468490 (0.657934) | 0.571969 / 4.584777 (-4.012808) | 4.130260 / 3.745712 (0.384548) | 3.833521 / 5.269862 (-1.436341) | 2.320141 / 4.565676 (-2.245535) | 0.067587 / 0.424275 (-0.356688) | 0.008452 / 0.007607 (0.000845) | 0.546478 / 0.226044 (0.320433) | 5.070678 / 2.268929 (2.801750) | 2.325387 / 55.444624 (-53.119237) | 2.044041 / 6.876477 (-4.832435) | 2.019714 / 2.142072 (-0.122358) | 0.563589 / 4.805227 (-4.241639) | 0.135269 / 6.500664 (-6.365395) | 0.058208 / 0.075469 (-0.017261) |\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) | 1.283156 / 1.841788 (-0.558631) | 18.617776 / 8.074308 (10.543468) | 13.360700 / 10.191392 (3.169308) | 0.160001 / 0.680424 (-0.520423) | 0.021538 / 0.534201 (-0.512663) | 0.384169 / 0.579283 (-0.195114) | 0.407517 / 0.434364 (-0.026847) | 0.427295 / 0.540337 (-0.113042) | 0.655288 / 1.386936 (-0.731648) |\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) | 0.006854 / 0.011353 (-0.004499) | 0.003442 / 0.011008 (-0.007566) | 0.060622 / 0.038508 (0.022114) | 0.074649 / 0.023109 (0.051540) | 0.341733 / 0.275898 (0.065835) | 0.360096 / 0.323480 (0.036616) | 0.006235 / 0.007986 (-0.001751) | 0.003447 / 0.004328 (-0.000882) | 0.057301 / 0.004250 (0.053051) | 0.059022 / 0.037052 (0.021970) | 0.369523 / 0.258489 (0.111034) | 0.386280 / 0.293841 (0.092439) | 0.034319 / 0.128546 (-0.094228) | 0.008291 / 0.075646 (-0.067355) | 0.070403 / 0.419271 (-0.348868) | 0.050433 / 0.043533 (0.006901) | 0.347262 / 0.255139 (0.092123) | 0.380543 / 0.283200 (0.097343) | 0.024492 / 0.141683 (-0.117191) | 1.446721 / 1.452155 (-0.005433) | 1.541614 / 1.492716 (0.048898) |\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) | 0.226148 / 0.018006 (0.208142) | 0.442150 / 0.000490 (0.441660) | 0.004997 / 0.000200 (0.004797) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032866 / 0.037411 (-0.004546) | 0.088097 / 0.014526 (0.073571) | 0.102178 / 0.176557 (-0.074379) | 0.151129 / 0.737135 (-0.586006) | 0.103953 / 0.296338 (-0.192386) |\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) | 0.376701 / 0.215209 (0.161492) | 3.886997 / 2.077655 (1.809342) | 2.027143 / 1.504120 (0.523023) | 1.808647 / 1.541195 (0.267453) | 1.867664 / 1.468490 (0.399173) | 0.459487 / 4.584777 (-4.125290) | 3.640801 / 3.745712 (-0.104911) | 3.242512 / 5.269862 (-2.027350) | 1.889174 / 4.565676 (-2.676503) | 0.052415 / 0.424275 (-0.371860) | 0.007479 / 0.007607 (-0.000128) | 0.457706 / 0.226044 (0.231662) | 4.815041 / 2.268929 (2.546112) | 2.542470 / 55.444624 (-52.902154) | 2.137084 / 6.876477 (-4.739392) | 2.122867 / 2.142072 (-0.019205) | 0.553756 / 4.805227 (-4.251471) | 0.118902 / 6.500664 (-6.381763) | 0.058149 / 0.075469 (-0.017320) |\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) | 1.272615 / 1.841788 (-0.569173) | 19.455709 / 8.074308 (11.381401) | 14.111693 / 10.191392 (3.920301) | 0.165741 / 0.680424 (-0.514683) | 0.023680 / 0.534201 (-0.510521) | 0.431458 / 0.579283 (-0.147825) | 0.433612 / 0.434364 (-0.000752) | 0.465615 / 0.540337 (-0.074722) | 0.678177 / 1.386936 (-0.708759) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#998623fa51991320740b945d0853ee20807304d7 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004870 / 0.011353 (-0.006483) | 0.002834 / 0.011008 (-0.008175) | 0.061359 / 0.038508 (0.022851) | 0.031286 / 0.023109 (0.008177) | 0.236701 / 0.275898 (-0.039197) | 0.258139 / 0.323480 (-0.065341) | 0.002943 / 0.007986 (-0.005043) | 0.002989 / 0.004328 (-0.001339) | 0.048046 / 0.004250 (0.043796) | 0.044927 / 0.037052 (0.007874) | 0.241339 / 0.258489 (-0.017151) | 0.273912 / 0.293841 (-0.019929) | 0.023427 / 0.128546 (-0.105119) | 0.007251 / 0.075646 (-0.068395) | 0.202730 / 0.419271 (-0.216542) | 0.056223 / 0.043533 (0.012691) | 0.239908 / 0.255139 (-0.015231) | 0.254723 / 0.283200 (-0.028476) | 0.018223 / 0.141683 (-0.123460) | 1.119691 / 1.452155 (-0.332464) | 1.163802 / 1.492716 (-0.328915) |\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) | 0.091303 / 0.018006 (0.073297) | 0.302097 / 0.000490 (0.301607) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 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) | 0.018201 / 0.037411 (-0.019210) | 0.062092 / 0.014526 (0.047566) | 0.074806 / 0.176557 (-0.101751) | 0.119625 / 0.737135 (-0.617510) | 0.074680 / 0.296338 (-0.221659) |\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) | 0.281140 / 0.215209 (0.065931) | 2.752094 / 2.077655 (0.674439) | 1.436813 / 1.504120 (-0.067307) | 1.312947 / 1.541195 (-0.228247) | 1.331022 / 1.468490 (-0.137468) | 0.396579 / 4.584777 (-4.188198) | 2.406181 / 3.745712 (-1.339531) | 2.597180 / 5.269862 (-2.672682) | 1.565879 / 4.565676 (-2.999798) | 0.046330 / 0.424275 (-0.377945) | 0.004776 / 0.007607 (-0.002831) | 0.339681 / 0.226044 (0.113637) | 3.279533 / 2.268929 (1.010605) | 1.793352 / 55.444624 (-53.651272) | 1.493910 / 6.876477 (-5.382567) | 1.514494 / 2.142072 (-0.627579) | 0.467955 / 4.805227 (-4.337272) | 0.097764 / 6.500664 (-6.402900) | 0.041659 / 0.075469 (-0.033810) |\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) | 0.943204 / 1.841788 (-0.898583) | 11.350848 / 8.074308 (3.276540) | 10.169944 / 10.191392 (-0.021448) | 0.130882 / 0.680424 (-0.549542) | 0.013804 / 0.534201 (-0.520397) | 0.269107 / 0.579283 (-0.310177) | 0.261685 / 0.434364 (-0.172679) | 0.305610 / 0.540337 (-0.234727) | 0.430586 / 1.386936 (-0.956350) |\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) | 0.004835 / 0.011353 (-0.006518) | 0.002530 / 0.011008 (-0.008479) | 0.047383 / 0.038508 (0.008875) | 0.052559 / 0.023109 (0.029450) | 0.265015 / 0.275898 (-0.010883) | 0.286955 / 0.323480 (-0.036525) | 0.003931 / 0.007986 (-0.004054) | 0.002038 / 0.004328 (-0.002290) | 0.047458 / 0.004250 (0.043207) | 0.038257 / 0.037052 (0.001205) | 0.270569 / 0.258489 (0.012080) | 0.298968 / 0.293841 (0.005127) | 0.024615 / 0.128546 (-0.103932) | 0.006969 / 0.075646 (-0.068677) | 0.052361 / 0.419271 (-0.366911) | 0.032701 / 0.043533 (-0.010832) | 0.269126 / 0.255139 (0.013987) | 0.285934 / 0.283200 (0.002735) | 0.018121 / 0.141683 (-0.123562) | 1.129796 / 1.452155 (-0.322359) | 1.272831 / 1.492716 (-0.219885) |\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) | 0.092058 / 0.018006 (0.074051) | 0.303544 / 0.000490 (0.303054) | 0.000232 / 0.000200 (0.000032) | 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) | 0.020983 / 0.037411 (-0.016428) | 0.069798 / 0.014526 (0.055272) | 0.081410 / 0.176557 (-0.095146) | 0.120403 / 0.737135 (-0.616732) | 0.082813 / 0.296338 (-0.213525) |\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) | 0.295943 / 0.215209 (0.080734) | 2.895761 / 2.077655 (0.818106) | 1.583534 / 1.504120 (0.079414) | 1.458397 / 1.541195 (-0.082798) | 1.492113 / 1.468490 (0.023623) | 0.402364 / 4.584777 (-4.182413) | 2.469777 / 3.745712 (-1.275935) | 2.565262 / 5.269862 (-2.704599) | 1.525914 / 4.565676 (-3.039763) | 0.047168 / 0.424275 (-0.377107) | 0.004800 / 0.007607 (-0.002808) | 0.348356 / 0.226044 (0.122311) | 3.463184 / 2.268929 (1.194255) | 1.930240 / 55.444624 (-53.514385) | 1.644312 / 6.876477 (-5.232165) | 1.625477 / 2.142072 (-0.516596) | 0.480781 / 4.805227 (-4.324446) | 0.098431 / 6.500664 (-6.402233) | 0.041071 / 0.075469 (-0.034398) |\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) | 0.973633 / 1.841788 (-0.868154) | 11.952261 / 8.074308 (3.877953) | 11.038222 / 10.191392 (0.846830) | 0.142755 / 0.680424 (-0.537669) | 0.015389 / 0.534201 (-0.518812) | 0.274144 / 0.579283 (-0.305139) | 0.282319 / 0.434364 (-0.152045) | 0.314330 / 0.540337 (-0.226007) | 0.435315 / 1.386936 (-0.951621) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05200c0a4f8f02c3890ab79a10b44ab0bcf11629 \"CML watermark\")\n",
"The red CI job is unrelated to this PR. It appeared 5 days ago. See:\r\n- https://github.com/huggingface/datasets/pull/6390#pullrequestreview-1721070927\r\n- https://github.com/huggingface/datasets/issues/6406",
"Let's do a new release once this is merged ? cc @mariosasko as well let us know if the fix sounds good to you",
"@lhoestq Yes, this sounds good to me!",
"<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) | 0.004932 / 0.011353 (-0.006421) | 0.002956 / 0.011008 (-0.008052) | 0.061999 / 0.038508 (0.023491) | 0.030174 / 0.023109 (0.007065) | 0.241483 / 0.275898 (-0.034415) | 0.261578 / 0.323480 (-0.061902) | 0.002881 / 0.007986 (-0.005105) | 0.002451 / 0.004328 (-0.001878) | 0.048176 / 0.004250 (0.043925) | 0.045028 / 0.037052 (0.007976) | 0.244304 / 0.258489 (-0.014185) | 0.275834 / 0.293841 (-0.018007) | 0.023312 / 0.128546 (-0.105234) | 0.007361 / 0.075646 (-0.068286) | 0.204433 / 0.419271 (-0.214838) | 0.054561 / 0.043533 (0.011028) | 0.236902 / 0.255139 (-0.018237) | 0.269358 / 0.283200 (-0.013842) | 0.017736 / 0.141683 (-0.123947) | 1.112444 / 1.452155 (-0.339711) | 1.170260 / 1.492716 (-0.322456) |\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) | 0.093081 / 0.018006 (0.075074) | 0.311470 / 0.000490 (0.310981) | 0.000212 / 0.000200 (0.000013) | 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) | 0.018654 / 0.037411 (-0.018757) | 0.063239 / 0.014526 (0.048714) | 0.073759 / 0.176557 (-0.102798) | 0.120279 / 0.737135 (-0.616857) | 0.076214 / 0.296338 (-0.220124) |\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) | 0.287219 / 0.215209 (0.072010) | 2.765378 / 2.077655 (0.687723) | 1.459733 / 1.504120 (-0.044387) | 1.325999 / 1.541195 (-0.215196) | 1.349957 / 1.468490 (-0.118533) | 0.413093 / 4.584777 (-4.171684) | 2.394758 / 3.745712 (-1.350954) | 2.633916 / 5.269862 (-2.635945) | 1.621629 / 4.565676 (-2.944047) | 0.046839 / 0.424275 (-0.377436) | 0.004786 / 0.007607 (-0.002822) | 0.336261 / 0.226044 (0.110217) | 3.348196 / 2.268929 (1.079267) | 1.853050 / 55.444624 (-53.591574) | 1.543926 / 6.876477 (-5.332551) | 1.573675 / 2.142072 (-0.568398) | 0.484088 / 4.805227 (-4.321139) | 0.100820 / 6.500664 (-6.399845) | 0.042194 / 0.075469 (-0.033275) |\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) | 0.945186 / 1.841788 (-0.896601) | 11.859855 / 8.074308 (3.785547) | 10.459883 / 10.191392 (0.268491) | 0.142024 / 0.680424 (-0.538400) | 0.013882 / 0.534201 (-0.520319) | 0.269584 / 0.579283 (-0.309699) | 0.264353 / 0.434364 (-0.170011) | 0.307988 / 0.540337 (-0.232349) | 0.423655 / 1.386936 (-0.963281) |\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) | 0.004891 / 0.011353 (-0.006461) | 0.003087 / 0.011008 (-0.007921) | 0.048206 / 0.038508 (0.009697) | 0.058570 / 0.023109 (0.035461) | 0.268552 / 0.275898 (-0.007346) | 0.287839 / 0.323480 (-0.035641) | 0.004044 / 0.007986 (-0.003942) | 0.002388 / 0.004328 (-0.001940) | 0.048186 / 0.004250 (0.043935) | 0.038719 / 0.037052 (0.001667) | 0.271940 / 0.258489 (0.013451) | 0.299716 / 0.293841 (0.005875) | 0.027166 / 0.128546 (-0.101380) | 0.007388 / 0.075646 (-0.068258) | 0.053885 / 0.419271 (-0.365387) | 0.032804 / 0.043533 (-0.010729) | 0.271664 / 0.255139 (0.016525) | 0.284613 / 0.283200 (0.001414) | 0.018488 / 0.141683 (-0.123195) | 1.125854 / 1.452155 (-0.326301) | 1.195896 / 1.492716 (-0.296820) |\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) | 0.092438 / 0.018006 (0.074431) | 0.315265 / 0.000490 (0.314775) | 0.000228 / 0.000200 (0.000028) | 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) | 0.021373 / 0.037411 (-0.016038) | 0.070611 / 0.014526 (0.056085) | 0.080391 / 0.176557 (-0.096165) | 0.118749 / 0.737135 (-0.618386) | 0.082340 / 0.296338 (-0.213999) |\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) | 0.295583 / 0.215209 (0.080374) | 2.882152 / 2.077655 (0.804497) | 1.565088 / 1.504120 (0.060968) | 1.451954 / 1.541195 (-0.089241) | 1.505783 / 1.468490 (0.037293) | 0.404699 / 4.584777 (-4.180078) | 2.451703 / 3.745712 (-1.294009) | 2.596301 / 5.269862 (-2.673560) | 1.547014 / 4.565676 (-3.018662) | 0.047750 / 0.424275 (-0.376525) | 0.004850 / 0.007607 (-0.002757) | 0.346893 / 0.226044 (0.120849) | 3.383355 / 2.268929 (1.114426) | 1.943933 / 55.444624 (-53.500692) | 1.657513 / 6.876477 (-5.218964) | 1.687166 / 2.142072 (-0.454906) | 0.478543 / 4.805227 (-4.326685) | 0.097804 / 6.500664 (-6.402860) | 0.041392 / 0.075469 (-0.034078) |\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) | 0.983894 / 1.841788 (-0.857893) | 12.446443 / 8.074308 (4.372135) | 10.973461 / 10.191392 (0.782069) | 0.131630 / 0.680424 (-0.548794) | 0.017196 / 0.534201 (-0.517005) | 0.270873 / 0.579283 (-0.308411) | 0.284379 / 0.434364 (-0.149985) | 0.306103 / 0.540337 (-0.234234) | 0.413762 / 1.386936 (-0.973174) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#980ad4c6e6e33f0129db8745e84de8c298741aa2 \"CML watermark\")\n",
"Note I had to add `pa.ExtensionType.__reduce__` because this is used by `copy.deepcopy` when using `.with_format`. See error below.\r\n\r\nThis method was added in pyarrow-13.0.0: https://github.com/apache/arrow/pull/36170\r\n- We need to re-implement it as long we support lower pyarrow versions\r\n\r\nErrors: https://github.com/huggingface/datasets/actions/runs/6861278161/job/18656665772\r\n```\r\n ____________________________ test_dataset_map[True] ____________________________\r\n[gw1] linux -- Python 3.8.18 /opt/hostedtoolcache/Python/3.8.18/x64/bin/python\r\n\r\n> ???\r\nE KeyError: 'extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>'\r\n\r\npyarrow/types.pxi:3155: KeyError\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nwith_none = True\r\n\r\n @pytest.mark.parametrize(\"with_none\", [False, True])\r\n def test_dataset_map(with_none):\r\n ds = datasets.Dataset.from_dict({\"path\": [\"path1\", \"path2\"]})\r\n \r\n def process_data(batch):\r\n batch = {\r\n \"image\": [\r\n np.array(\r\n [\r\n [[1, 2, 3], [4, 5, 6], [7, 8, 9]],\r\n [[10, 20, 30], [40, 50, 60], [70, 80, 90]],\r\n [[100, 200, 300], [400, 500, 600], [700, 800, 900]],\r\n ]\r\n )\r\n for _ in batch[\"path\"]\r\n ]\r\n }\r\n if with_none:\r\n batch[\"image\"][0] = None\r\n return batch\r\n \r\n features = datasets.Features({\"image\": Array3D(dtype=\"int32\", shape=(3, 3, 3))})\r\n processed_ds = ds.map(process_data, batched=True, remove_columns=ds.column_names, features=features)\r\n assert processed_ds.shape == (2, 1)\r\n> with processed_ds.with_format(\"numpy\") as pds:\r\n\r\ntests/features/test_array_xd.py:459: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:2669: in with_format\r\n dataset = copy.deepcopy(self)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, *rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:270: in _reconstruct\r\n state = deepcopy(state, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:230: in _deepcopy_dict\r\n y[deepcopy(key, memo)] = deepcopy(value, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:153: in deepcopy\r\n y = copier(memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:188: in __deepcopy__\r\n return _deepcopy(self, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:86: in _deepcopy\r\n setattr(result, k, copy.deepcopy(v, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, *rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:205: in _deepcopy_list\r\n append(deepcopy(a, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, *rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, *rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\n> ???\r\nE ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n\r\npyarrow/types.pxi:3157: ValueError\r\n```\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_class_encode_column_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_dummy_dataset_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_csv_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_sql_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[True] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[False] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[mix] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::ArrayXDDynamicTest::test_map_dataset - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[False] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[True] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n===== 15 failed,\r\n```",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007338 / 0.011353 (-0.004015) | 0.004308 / 0.011008 (-0.006700) | 0.088788 / 0.038508 (0.050280) | 0.039369 / 0.023109 (0.016260) | 0.334527 / 0.275898 (0.058629) | 0.373748 / 0.323480 (0.050268) | 0.005550 / 0.007986 (-0.002435) | 0.003606 / 0.004328 (-0.000723) | 0.072238 / 0.004250 (0.067988) | 0.061271 / 0.037052 (0.024218) | 0.336333 / 0.258489 (0.077844) | 0.398256 / 0.293841 (0.104415) | 0.041941 / 0.128546 (-0.086605) | 0.013372 / 0.075646 (-0.062274) | 0.336221 / 0.419271 (-0.083050) | 0.083013 / 0.043533 (0.039480) | 0.334743 / 0.255139 (0.079604) | 0.362572 / 0.283200 (0.079373) | 0.031161 / 0.141683 (-0.110521) | 1.563441 / 1.452155 (0.111287) | 1.704059 / 1.492716 (0.211343) |\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) | 0.252978 / 0.018006 (0.234972) | 0.506348 / 0.000490 (0.505859) | 0.011679 / 0.000200 (0.011479) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026257 / 0.037411 (-0.011154) | 0.085936 / 0.014526 (0.071410) | 0.098542 / 0.176557 (-0.078015) | 0.154507 / 0.737135 (-0.582628) | 0.111493 / 0.296338 (-0.184845) |\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) | 0.575941 / 0.215209 (0.360732) | 5.590230 / 2.077655 (3.512576) | 2.463330 / 1.504120 (0.959211) | 2.125760 / 1.541195 (0.584565) | 2.095933 / 1.468490 (0.627443) | 0.844768 / 4.584777 (-3.740009) | 4.768995 / 3.745712 (1.023282) | 4.670484 / 5.269862 (-0.599377) | 2.630386 / 4.565676 (-1.935290) | 0.085996 / 0.424275 (-0.338279) | 0.007900 / 0.007607 (0.000293) | 0.685463 / 0.226044 (0.459419) | 6.699310 / 2.268929 (4.430381) | 3.132542 / 55.444624 (-52.312083) | 2.527963 / 6.876477 (-4.348513) | 2.381835 / 2.142072 (0.239763) | 0.909668 / 4.805227 (-3.895559) | 0.209979 / 6.500664 (-6.290685) | 0.079222 / 0.075469 (0.003753) |\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) | 1.444895 / 1.841788 (-0.396892) | 20.388140 / 8.074308 (12.313832) | 19.354148 / 10.191392 (9.162756) | 0.222433 / 0.680424 (-0.457991) | 0.029710 / 0.534201 (-0.504491) | 0.427153 / 0.579283 (-0.152130) | 0.537500 / 0.434364 (0.103136) | 0.506917 / 0.540337 (-0.033421) | 0.726088 / 1.386936 (-0.660848) |\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) | 0.007652 / 0.011353 (-0.003701) | 0.004320 / 0.011008 (-0.006688) | 0.072721 / 0.038508 (0.034212) | 0.068204 / 0.023109 (0.045095) | 0.392087 / 0.275898 (0.116189) | 0.431638 / 0.323480 (0.108158) | 0.005419 / 0.007986 (-0.002566) | 0.004305 / 0.004328 (-0.000023) | 0.069042 / 0.004250 (0.064791) | 0.051555 / 0.037052 (0.014503) | 0.412141 / 0.258489 (0.153651) | 0.438802 / 0.293841 (0.144961) | 0.043631 / 0.128546 (-0.084915) | 0.014169 / 0.075646 (-0.061478) | 0.079571 / 0.419271 (-0.339701) | 0.056707 / 0.043533 (0.013174) | 0.413698 / 0.255139 (0.158559) | 0.414127 / 0.283200 (0.130928) | 0.031380 / 0.141683 (-0.110303) | 1.677157 / 1.452155 (0.225003) | 1.755155 / 1.492716 (0.262439) |\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) | 0.257236 / 0.018006 (0.239230) | 0.521347 / 0.000490 (0.520858) | 0.006282 / 0.000200 (0.006082) | 0.000139 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028433 / 0.037411 (-0.008978) | 0.087698 / 0.014526 (0.073172) | 0.108840 / 0.176557 (-0.067716) | 0.157432 / 0.737135 (-0.579704) | 0.103144 / 0.296338 (-0.193195) |\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) | 0.598745 / 0.215209 (0.383536) | 5.981460 / 2.077655 (3.903805) | 2.556931 / 1.504120 (1.052811) | 2.179915 / 1.541195 (0.638720) | 2.240841 / 1.468490 (0.772351) | 0.811501 / 4.584777 (-3.773276) | 4.718282 / 3.745712 (0.972570) | 4.365738 / 5.269862 (-0.904124) | 2.669798 / 4.565676 (-1.895878) | 0.099135 / 0.424275 (-0.325140) | 0.007369 / 0.007607 (-0.000238) | 0.669491 / 0.226044 (0.443447) | 6.700389 / 2.268929 (4.431461) | 3.155328 / 55.444624 (-52.289296) | 2.563375 / 6.876477 (-4.313102) | 2.545191 / 2.142072 (0.403119) | 0.961359 / 4.805227 (-3.843868) | 0.189391 / 6.500664 (-6.311273) | 0.061597 / 0.075469 (-0.013873) |\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) | 1.564008 / 1.841788 (-0.277780) | 21.401307 / 8.074308 (13.326999) | 20.693441 / 10.191392 (10.502049) | 0.229340 / 0.680424 (-0.451084) | 0.033637 / 0.534201 (-0.500564) | 0.429394 / 0.579283 (-0.149889) | 0.557202 / 0.434364 (0.122838) | 0.510284 / 0.540337 (-0.030054) | 0.725661 / 1.386936 (-0.661276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45abe297c178b829afcee853f9958b0c5a6469aa \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004820 / 0.011353 (-0.006533) | 0.003152 / 0.011008 (-0.007856) | 0.061842 / 0.038508 (0.023334) | 0.030127 / 0.023109 (0.007018) | 0.257409 / 0.275898 (-0.018489) | 0.269382 / 0.323480 (-0.054097) | 0.004288 / 0.007986 (-0.003698) | 0.002500 / 0.004328 (-0.001829) | 0.048520 / 0.004250 (0.044270) | 0.046815 / 0.037052 (0.009763) | 0.245858 / 0.258489 (-0.012631) | 0.289636 / 0.293841 (-0.004205) | 0.023983 / 0.128546 (-0.104563) | 0.007336 / 0.075646 (-0.068310) | 0.202347 / 0.419271 (-0.216924) | 0.057737 / 0.043533 (0.014204) | 0.245922 / 0.255139 (-0.009217) | 0.268788 / 0.283200 (-0.014412) | 0.017819 / 0.141683 (-0.123864) | 1.149889 / 1.452155 (-0.302265) | 1.227192 / 1.492716 (-0.265524) |\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) | 0.092234 / 0.018006 (0.074228) | 0.310259 / 0.000490 (0.309769) | 0.000223 / 0.000200 (0.000023) | 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) | 0.019059 / 0.037411 (-0.018352) | 0.064904 / 0.014526 (0.050378) | 0.073531 / 0.176557 (-0.103026) | 0.120879 / 0.737135 (-0.616257) | 0.075410 / 0.296338 (-0.220929) |\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) | 0.275364 / 0.215209 (0.060155) | 2.724379 / 2.077655 (0.646725) | 1.447617 / 1.504120 (-0.056503) | 1.366794 / 1.541195 (-0.174401) | 1.345849 / 1.468490 (-0.122641) | 0.411205 / 4.584777 (-4.173572) | 2.412712 / 3.745712 (-1.333000) | 2.612469 / 5.269862 (-2.657393) | 1.552113 / 4.565676 (-3.013564) | 0.045783 / 0.424275 (-0.378492) | 0.004782 / 0.007607 (-0.002825) | 0.339218 / 0.226044 (0.113174) | 3.359540 / 2.268929 (1.090612) | 1.821369 / 55.444624 (-53.623256) | 1.540742 / 6.876477 (-5.335734) | 1.531845 / 2.142072 (-0.610227) | 0.462009 / 4.805227 (-4.343218) | 0.097794 / 6.500664 (-6.402870) | 0.041222 / 0.075469 (-0.034247) |\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) | 0.938319 / 1.841788 (-0.903469) | 11.712003 / 8.074308 (3.637695) | 10.325317 / 10.191392 (0.133925) | 0.126812 / 0.680424 (-0.553612) | 0.013734 / 0.534201 (-0.520467) | 0.279509 / 0.579283 (-0.299774) | 0.269265 / 0.434364 (-0.165099) | 0.322033 / 0.540337 (-0.218304) | 0.441610 / 1.386936 (-0.945326) |\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) | 0.004882 / 0.011353 (-0.006471) | 0.002984 / 0.011008 (-0.008024) | 0.048318 / 0.038508 (0.009810) | 0.054642 / 0.023109 (0.031533) | 0.268599 / 0.275898 (-0.007299) | 0.292916 / 0.323480 (-0.030564) | 0.004108 / 0.007986 (-0.003878) | 0.002500 / 0.004328 (-0.001829) | 0.048452 / 0.004250 (0.044202) | 0.038835 / 0.037052 (0.001782) | 0.275410 / 0.258489 (0.016921) | 0.307284 / 0.293841 (0.013443) | 0.024720 / 0.128546 (-0.103826) | 0.007274 / 0.075646 (-0.068372) | 0.054419 / 0.419271 (-0.364853) | 0.032815 / 0.043533 (-0.010718) | 0.273660 / 0.255139 (0.018521) | 0.289183 / 0.283200 (0.005984) | 0.017746 / 0.141683 (-0.123937) | 1.153876 / 1.452155 (-0.298278) | 1.212778 / 1.492716 (-0.279938) |\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) | 0.095286 / 0.018006 (0.077280) | 0.305185 / 0.000490 (0.304696) | 0.000230 / 0.000200 (0.000030) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021556 / 0.037411 (-0.015855) | 0.071029 / 0.014526 (0.056503) | 0.081914 / 0.176557 (-0.094643) | 0.120553 / 0.737135 (-0.616582) | 0.086696 / 0.296338 (-0.209642) |\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) | 0.289750 / 0.215209 (0.074541) | 2.794247 / 2.077655 (0.716592) | 1.577105 / 1.504120 (0.072985) | 1.457706 / 1.541195 (-0.083489) | 1.500481 / 1.468490 (0.031991) | 0.403834 / 4.584777 (-4.180943) | 2.466810 / 3.745712 (-1.278902) | 2.701008 / 5.269862 (-2.568854) | 1.634821 / 4.565676 (-2.930856) | 0.046954 / 0.424275 (-0.377322) | 0.004811 / 0.007607 (-0.002796) | 0.347622 / 0.226044 (0.121578) | 3.407125 / 2.268929 (1.138197) | 1.987121 / 55.444624 (-53.457504) | 1.689978 / 6.876477 (-5.186499) | 1.731801 / 2.142072 (-0.410271) | 0.478926 / 4.805227 (-4.326301) | 0.100730 / 6.500664 (-6.399934) | 0.043078 / 0.075469 (-0.032391) |\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) | 0.963575 / 1.841788 (-0.878212) | 12.675331 / 8.074308 (4.601023) | 11.167584 / 10.191392 (0.976192) | 0.131199 / 0.680424 (-0.549225) | 0.016030 / 0.534201 (-0.518171) | 0.277783 / 0.579283 (-0.301500) | 0.278693 / 0.434364 (-0.155671) | 0.315141 / 0.540337 (-0.225196) | 0.429104 / 1.386936 (-0.957832) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#825c1d25835b64fc3533a63d60bd237f4465f15e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004807 / 0.011353 (-0.006546) | 0.002925 / 0.011008 (-0.008083) | 0.062560 / 0.038508 (0.024052) | 0.029926 / 0.023109 (0.006817) | 0.264708 / 0.275898 (-0.011190) | 0.273464 / 0.323480 (-0.050016) | 0.003197 / 0.007986 (-0.004788) | 0.002544 / 0.004328 (-0.001784) | 0.048230 / 0.004250 (0.043980) | 0.046552 / 0.037052 (0.009500) | 0.249553 / 0.258489 (-0.008936) | 0.282078 / 0.293841 (-0.011762) | 0.023201 / 0.128546 (-0.105346) | 0.007306 / 0.075646 (-0.068340) | 0.241361 / 0.419271 (-0.177910) | 0.058286 / 0.043533 (0.014753) | 0.245854 / 0.255139 (-0.009285) | 0.266053 / 0.283200 (-0.017146) | 0.020294 / 0.141683 (-0.121388) | 1.102215 / 1.452155 (-0.349939) | 1.170733 / 1.492716 (-0.321984) |\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) | 0.094647 / 0.018006 (0.076641) | 0.303819 / 0.000490 (0.303329) | 0.000250 / 0.000200 (0.000050) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019036 / 0.037411 (-0.018375) | 0.064729 / 0.014526 (0.050203) | 0.074143 / 0.176557 (-0.102414) | 0.120082 / 0.737135 (-0.617054) | 0.076835 / 0.296338 (-0.219503) |\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) | 0.283786 / 0.215209 (0.068577) | 2.751446 / 2.077655 (0.673791) | 1.473789 / 1.504120 (-0.030331) | 1.336968 / 1.541195 (-0.204226) | 1.384148 / 1.468490 (-0.084342) | 0.397452 / 4.584777 (-4.187325) | 2.388042 / 3.745712 (-1.357670) | 2.661291 / 5.269862 (-2.608571) | 1.595454 / 4.565676 (-2.970223) | 0.045919 / 0.424275 (-0.378356) | 0.004879 / 0.007607 (-0.002728) | 0.337862 / 0.226044 (0.111818) | 3.355665 / 2.268929 (1.086737) | 1.875261 / 55.444624 (-53.569363) | 1.540874 / 6.876477 (-5.335603) | 1.653632 / 2.142072 (-0.488440) | 0.473090 / 4.805227 (-4.332138) | 0.100151 / 6.500664 (-6.400513) | 0.042357 / 0.075469 (-0.033112) |\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) | 0.959550 / 1.841788 (-0.882238) | 12.307145 / 8.074308 (4.232837) | 10.719321 / 10.191392 (0.527929) | 0.128376 / 0.680424 (-0.552048) | 0.014406 / 0.534201 (-0.519795) | 0.295208 / 0.579283 (-0.284075) | 0.268891 / 0.434364 (-0.165473) | 0.305446 / 0.540337 (-0.234892) | 0.429591 / 1.386936 (-0.957345) |\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) | 0.005189 / 0.011353 (-0.006164) | 0.003082 / 0.011008 (-0.007926) | 0.048956 / 0.038508 (0.010448) | 0.063403 / 0.023109 (0.040294) | 0.272858 / 0.275898 (-0.003040) | 0.295207 / 0.323480 (-0.028273) | 0.004253 / 0.007986 (-0.003733) | 0.002552 / 0.004328 (-0.001776) | 0.048042 / 0.004250 (0.043792) | 0.040429 / 0.037052 (0.003377) | 0.269614 / 0.258489 (0.011125) | 0.307205 / 0.293841 (0.013364) | 0.027912 / 0.128546 (-0.100634) | 0.007621 / 0.075646 (-0.068026) | 0.054020 / 0.419271 (-0.365251) | 0.036958 / 0.043533 (-0.006574) | 0.272457 / 0.255139 (0.017318) | 0.287966 / 0.283200 (0.004766) | 0.019542 / 0.141683 (-0.122141) | 1.116742 / 1.452155 (-0.335413) | 1.194739 / 1.492716 (-0.297977) |\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) | 0.093532 / 0.018006 (0.075526) | 0.303262 / 0.000490 (0.302773) | 0.000217 / 0.000200 (0.000017) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021984 / 0.037411 (-0.015428) | 0.075024 / 0.014526 (0.060498) | 0.080959 / 0.176557 (-0.095598) | 0.121780 / 0.737135 (-0.615356) | 0.082817 / 0.296338 (-0.213522) |\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) | 0.292766 / 0.215209 (0.077557) | 2.857457 / 2.077655 (0.779802) | 1.621860 / 1.504120 (0.117740) | 1.473783 / 1.541195 (-0.067412) | 1.535211 / 1.468490 (0.066721) | 0.402212 / 4.584777 (-4.182565) | 2.467143 / 3.745712 (-1.278569) | 2.618162 / 5.269862 (-2.651700) | 1.568682 / 4.565676 (-2.996994) | 0.047123 / 0.424275 (-0.377152) | 0.004780 / 0.007607 (-0.002827) | 0.346959 / 0.226044 (0.120914) | 3.395196 / 2.268929 (1.126268) | 1.957835 / 55.444624 (-53.486789) | 1.674287 / 6.876477 (-5.202190) | 1.715879 / 2.142072 (-0.426193) | 0.479481 / 4.805227 (-4.325746) | 0.100043 / 6.500664 (-6.400621) | 0.041289 / 0.075469 (-0.034180) |\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) | 0.965418 / 1.841788 (-0.876370) | 12.703830 / 8.074308 (4.629522) | 11.301401 / 10.191392 (1.110009) | 0.131429 / 0.680424 (-0.548995) | 0.016597 / 0.534201 (-0.517604) | 0.273290 / 0.579283 (-0.305993) | 0.285400 / 0.434364 (-0.148964) | 0.307327 / 0.540337 (-0.233011) | 0.434186 / 1.386936 (-0.952750) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c096bd288d07ed86f340ae090e5d4d9c5351f76f \"CML watermark\")\n"
] | 2023-11-13T09:15:39Z
| 2023-11-14T10:29:48Z
| 2023-11-14T10:23:29Z
|
MEMBER
| null | null | null |
Support `pyarrow` 14.0.1 and fix vulnerability [CVE-2023-47248](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm).
Fix #6396.
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I_kwDODunzps5dQBJ5
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Incorrect filepath for dill module
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[
"Hi! The correct path is still `dill._dill.XXXX` in the latest release. What do you get when you run `python -c \"import dill; print(dill.__version__)\"` in your environment?",
"`0.3.6` I feel like that's bad news, because it's probably not the issue.\r\n\r\nMy mistake, about the wrong path guess. I think I didn't notice that the first `dill` in the path isn't supposed to be included in the path specification in python.\r\n<img width=\"146\" alt=\"Screen Shot 2023-01-31 at 12 58 32 PM\" src=\"https://user-images.githubusercontent.com/35349273/215844209-74af6a8f-9bff-4c75-9495-44c658c8e9f7.png\">\r\n",
"Hi, @avivbrokman, this issue you report appeared only with old versions of dill. See:\r\n- #288\r\n\r\nAre you sure you are in the right Python environment?\r\n- Please note that Jupyter (where I guess you get the error) may have multiple execution backends (IPython kernels) that might be different from the Python environment your are using to get the dill version\r\n - Have you run `import dill; print(dill.__version__)` in the same Jupyter/IPython that you were using when you got the error while executing `import datasets`?",
"I'm using spyder, and I am still getting `0.3.6` for `dill`, so unfortunately #288 isn't applicable, I think. However, I found something odd that I believe is a clue: \r\n\r\n```\r\nimport inspect\r\nimport dill\r\n\r\ninspect.getfile(dill)\r\n>>> '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill/__init__.py'\r\n```\r\n\r\nI checked out the directory, and there is no `dill` subdirectory within '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill`, as there should be. Rather, `_dill.py` is in '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill` itself. \r\n\r\n If I run `pip install dill` or `pip install --upgrade dill`, I get the message `Requirement already satisfied: dill in ./opt/anaconda3/lib/python3.9/site-packages (0.3.6)`. If I run `conda upgrade dill`, I get the message `Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.` a couple of times, followed by\r\n\r\n```\r\nSolving environment: failed\r\nSolving environment: / \r\nFound conflicts! Looking for incompatible packages.\r\n```\r\n\r\nAnd then terminal proceeds to list conflicts between different packages I have.\r\n\r\nThis is all very strange to me because I recently uninstalled and reinstalled `anaconda`.\r\n",
"As I said above, I guess this is not a problem with `datasets`. I think you have different Python environments: one with the new dill version (the one you get while using pip) and other with the old dill version (the one where you get the AttributeError).\r\n\r\nYou should update `dill` in the Python environment you are using within spyder.\r\n\r\nPlease note that the `_dill` module is present in the `dill` package since their 2.8.0 version."
] | 2023-01-31T15:01:08Z
| 2023-02-24T16:18:36Z
| 2023-02-24T16:18:36Z
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### Describe the bug
I installed the `datasets` package and when I try to `import` it, I get the following error:
```
Traceback (most recent call last):
File "/var/folders/jt/zw5g74ln6tqfdzsl8tx378j00000gn/T/ipykernel_3805/3458380017.py", line 1, in <module>
import datasets
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/__init__.py", line 43, in <module>
from .arrow_dataset import Dataset
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 66, in <module>
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 27, in <module>
from .features import Features, Image, Value
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/__init__.py", line 17, in <module>
from .audio import Audio
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/features/audio.py", line 12, in <module>
from ..download.streaming_download_manager import xopen
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/__init__.py", line 9, in <module>
from .download_manager import DownloadManager, DownloadMode
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/download/download_manager.py", line 36, in <module>
from ..utils.py_utils import NestedDataStructure, map_nested, size_str
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 602, in <module>
class Pickler(dill.Pickler):
File "/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 605, in Pickler
dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())
AttributeError: module 'dill' has no attribute '_dill'
```
Looking at the github source code for dill, it appears that `datasets` has a bug or is not compatible with the latest `dill`. Specifically, rather than `dill._dill.XXXX` it should be `dill.dill._dill.XXXX`. But given the popularity of `datasets` I feel confused about me being the first person to have this issue, so it makes me wonder if I'm misdiagnosing the issue.
### Steps to reproduce the bug
Install `dill` and `datasets` packages and then `import datasets`
### Expected behavior
I expect `datasets` to import.
### Environment info
- `datasets` version: 2.9.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.13
- PyArrow version: 11.0.0
- Pandas version: 1.4.4
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I_kwDODunzps5lxysX
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CSV datasets should only read the CSV data files in the repo
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[] | 2023-05-12T12:29:53Z
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When a no-script dataset has many CSV files and a JPG file, the library infers to use the Csv builder, but tries to read as CSV all files in the repo, also the JPG file.
I think the Csv builder should filter out non-CSV files when reading.
An analogue solution should be implemented for other packaged builders.
Related to:
- https://huggingface.co/datasets/abidlabs/img2text/discussions/1
- https://github.com/gradio-app/gradio/pull/3973#issuecomment-1545409061
CC: @abidlabs @severo
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NIH exporter file not found
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"related: https://github.com/huggingface/datasets/issues/3504",
"another file not found:\r\n```\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 417, in _info\r\n await _file_info(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 837, in _file_info\r\n r.raise_for_status()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/aiohttp/client_reqrep.py\", line 1005, in raise_for_status\r\n raise ClientResponseError(\r\naiohttp.client_exceptions.ClientResponseError: 404, message='Not Found', url=URL('https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 86, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 526, in <module>\r\n experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 475, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights\r\n column_names = next(iter(dataset)).keys()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 1353, in __iter__\r\n for key, example in ex_iterable:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 207, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py\", line 257, in _generate_examples\r\n for path, file in files[subset]:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 840, in __iter__\r\n yield from self.generator(*self.args, **self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 891, in _iter_from_urlpath\r\n with xopen(urlpath, \"rb\", download_config=download_config) as f:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 496, in xopen\r\n file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 134, in open\r\n return self.__enter__()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 102, in __enter__\r\n f = self.fs.open(self.path, mode=mode)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py\", line 1241, in open\r\n f = self._open(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 356, in _open\r\n size = size or self.info(path, **kwargs)[\"size\"]\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 121, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 106, in sync\r\n raise return_result\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 61, in _runner\r\n result[0] = await coro\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 430, in _info\r\n raise FileNotFoundError(url) from exc\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```",
"```\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```\r\nmost relevant line I think.",
"link to tweet: https://twitter.com/BrandoHablando/status/1690081313519489024?s=20 about issue",
"so: https://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th",
"this seems to work but it's rather annoying.\r\n\r\nSummary of how to make it work:\r\n1. get urls to parquet files into a list\r\n2. load list to load_dataset via `load_dataset('parquet', data_files=urls)` (note api names to hf are really confusing sometimes)\r\n3. then it should work, print a batch of text.\r\n\r\npresudo code\r\n```python\r\nurls_hacker_news = [\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00000-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00001-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00002-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00003-of-00004.parquet\"\r\n]\r\n\r\n...\r\n\r\n\r\n # streaming = False\r\n from diversity.pile_subset_urls import urls_hacker_news\r\n path, name, data_files = 'parquet', 'hacker_news', urls_hacker_news\r\n # not changing\r\n batch_size = 512\r\n today = datetime.datetime.now().strftime('%Y-m%m-d%d-t%Hh_%Mm_%Ss')\r\n run_name = f'{path} div_coeff_{num_batches=} ({today=} ({name=}) {data_mixture_name=} {probabilities=})'\r\n print(f'{run_name=}')\r\n\r\n # - Init wandb\r\n debug: bool = mode == 'dryrun'\r\n run = wandb.init(mode=mode, project=\"beyond-scale\", name=run_name, save_code=True)\r\n wandb.config.update({\"num_batches\": num_batches, \"path\": path, \"name\": name, \"today\": today, 'probabilities': probabilities, 'batch_size': batch_size, 'debug': debug, 'data_mixture_name': data_mixture_name, 'streaming': streaming, 'data_files': data_files})\r\n # run.notify_on_failure() # https://community.wandb.ai/t/how-do-i-set-the-wandb-alert-programatically-for-my-current-run/4891\r\n print(f'{debug=}')\r\n print(f'{wandb.config=}')\r\n\r\n # -- Get probe network\r\n from datasets import load_dataset\r\n import torch\r\n from transformers import GPT2Tokenizer, GPT2LMHeadModel\r\n\r\n tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n if tokenizer.pad_token_id is None:\r\n tokenizer.pad_token = tokenizer.eos_token\r\n probe_network = GPT2LMHeadModel.from_pretrained(\"gpt2\")\r\n device = torch.device(f\"cuda:{0}\" if torch.cuda.is_available() else \"cpu\")\r\n probe_network = probe_network.to(device)\r\n\r\n # -- Get data set\r\n def my_load_dataset(path, name):\r\n print(f'{path=} {name=} {streaming=}')\r\n if path == 'json' or path == 'bin' or path == 'csv':\r\n print(f'{data_files_prefix+name=}')\r\n return load_dataset(path, data_files=data_files_prefix+name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n elif path == 'parquet':\r\n print(f'{data_files=}')\r\n return load_dataset(path, data_files=data_files, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n else:\r\n return load_dataset(path, name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n # - get data set for real now\r\n if isinstance(path, str):\r\n dataset = my_load_dataset(path, name)\r\n else:\r\n print('-- interleaving datasets')\r\n datasets = [my_load_dataset(path, name).with_format(\"torch\") for path, name in zip(path, name)]\r\n [print(f'{dataset.description=}') for dataset in datasets]\r\n dataset = interleave_datasets(datasets, probabilities)\r\n print(f'{dataset=}')\r\n batch = dataset.take(batch_size)\r\n print(f'{next(iter(batch))=}')\r\n column_names = next(iter(batch)).keys()\r\n print(f'{column_names=}')\r\n\r\n # - Prepare functions to tokenize batch\r\n def preprocess(examples):\r\n return tokenizer(examples[\"text\"], padding=\"max_length\", max_length=128, truncation=True, return_tensors=\"pt\")\r\n remove_columns = column_names # remove all keys that are not tensors to avoid bugs in collate function in task2vec's pytorch data loader\r\n def map(batch):\r\n return batch.map(preprocess, batched=True, remove_columns=remove_columns)\r\n tokenized_batch = map(batch)\r\n print(f'{next(iter(tokenized_batch))=}')\r\n```\r\n\r\nhttps://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th/76902681#76902681\r\n\r\nhttps://discuss.huggingface.co/t/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-the-files-are-not-available/50555/5?u=severo"
] | 2023-08-11T19:05:25Z
| 2023-08-14T23:28:38Z
| null |
NONE
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### Describe the bug
can't use or download the nih exporter pile data.
```
15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()
16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights
17 column_names = next(iter(dataset)).keys()
18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__
19 for key, example in ex_iterable:
20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__
21 yield from self.generate_examples_fn(**self.kwargs)
22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples
23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f:
24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper
25 return function(*args, download_config=download_config, **kwargs)
26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open
29 return self.__enter__()
30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__
31 f = self.fs.open(self.path, mode=mode)
32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open
33 f = self._open(
34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open
35 size = size or self.info(path, **kwargs)["size"]
36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper
37 return sync(self.loop, func, *args, **kwargs)
38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync
39 raise return_result
40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner
41 result[0] = await coro
42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info
43 raise FileNotFoundError(url) from exc
44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst
```
### Steps to reproduce the bug
run this:
```
from datasets import load_dataset
path, name = 'EleutherAI/pile', 'nih_exporter'
# -- Get data set
dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch")
batch = dataset.take(512)
print(f'{batch=}')
```
### Expected behavior
print the batch
### Environment info
```
(beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.14.4
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
```
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PR_kwDODunzps5xyVt6
| 6,959
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Better error handling in `dataset_module_factory`
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6959). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Test should be fixed by https://github.com/huggingface/datasets/pull/6959/commits/ef8f7cee79ffb070d9b5190f21128fc523b3d3ee (tested locally). Let's see what CI says :crossed_fingers: ",
"<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) | 0.005678 / 0.011353 (-0.005675) | 0.004119 / 0.011008 (-0.006889) | 0.063901 / 0.038508 (0.025393) | 0.032071 / 0.023109 (0.008961) | 0.243182 / 0.275898 (-0.032716) | 0.280709 / 0.323480 (-0.042770) | 0.004195 / 0.007986 (-0.003791) | 0.002810 / 0.004328 (-0.001518) | 0.048722 / 0.004250 (0.044472) | 0.049381 / 0.037052 (0.012328) | 0.257816 / 0.258489 (-0.000673) | 0.288460 / 0.293841 (-0.005381) | 0.028518 / 0.128546 (-0.100029) | 0.010775 / 0.075646 (-0.064871) | 0.203149 / 0.419271 (-0.216122) | 0.038792 / 0.043533 (-0.004741) | 0.248502 / 0.255139 (-0.006637) | 0.268251 / 0.283200 (-0.014949) | 0.019536 / 0.141683 (-0.122147) | 1.133935 / 1.452155 (-0.318220) | 1.182855 / 1.492716 (-0.309862) |\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) | 0.097531 / 0.018006 (0.079525) | 0.303612 / 0.000490 (0.303122) | 0.000222 / 0.000200 (0.000022) | 0.000044 / 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) | 0.019670 / 0.037411 (-0.017741) | 0.063439 / 0.014526 (0.048913) | 0.075119 / 0.176557 (-0.101438) | 0.122419 / 0.737135 (-0.614717) | 0.076965 / 0.296338 (-0.219374) |\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) | 0.286780 / 0.215209 (0.071571) | 2.811860 / 2.077655 (0.734206) | 1.485165 / 1.504120 (-0.018954) | 1.373296 / 1.541195 (-0.167898) | 1.412700 / 1.468490 (-0.055790) | 0.566442 / 4.584777 (-4.018335) | 2.382616 / 3.745712 (-1.363096) | 2.677214 / 5.269862 (-2.592647) | 1.760073 / 4.565676 (-2.805603) | 0.062673 / 0.424275 (-0.361602) | 0.005050 / 0.007607 (-0.002557) | 0.341701 / 0.226044 (0.115657) | 3.321182 / 2.268929 (1.052253) | 1.811715 / 55.444624 (-53.632909) | 1.554986 / 6.876477 (-5.321491) | 1.727448 / 2.142072 (-0.414624) | 0.642193 / 4.805227 (-4.163034) | 0.117878 / 6.500664 (-6.382786) | 0.042814 / 0.075469 (-0.032655) |\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) | 0.985894 / 1.841788 (-0.855894) | 12.195975 / 8.074308 (4.121667) | 9.890180 / 10.191392 (-0.301212) | 0.142638 / 0.680424 (-0.537786) | 0.015207 / 0.534201 (-0.518994) | 0.283140 / 0.579283 (-0.296143) | 0.266016 / 0.434364 (-0.168348) | 0.325518 / 0.540337 (-0.214820) | 0.418994 / 1.386936 (-0.967942) |\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) | 0.005978 / 0.011353 (-0.005374) | 0.003915 / 0.011008 (-0.007093) | 0.051592 / 0.038508 (0.013084) | 0.033338 / 0.023109 (0.010229) | 0.267925 / 0.275898 (-0.007973) | 0.296011 / 0.323480 (-0.027469) | 0.004503 / 0.007986 (-0.003483) | 0.002854 / 0.004328 (-0.001475) | 0.049958 / 0.004250 (0.045707) | 0.041708 / 0.037052 (0.004656) | 0.287185 / 0.258489 (0.028696) | 0.322715 / 0.293841 (0.028874) | 0.030088 / 0.128546 (-0.098458) | 0.010709 / 0.075646 (-0.064938) | 0.059736 / 0.419271 (-0.359536) | 0.034294 / 0.043533 (-0.009239) | 0.264316 / 0.255139 (0.009177) | 0.285471 / 0.283200 (0.002272) | 0.019197 / 0.141683 (-0.122486) | 1.135571 / 1.452155 (-0.316583) | 1.190019 / 1.492716 (-0.302698) |\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) | 0.099251 / 0.018006 (0.081245) | 0.305357 / 0.000490 (0.304867) | 0.000215 / 0.000200 (0.000015) | 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) | 0.023206 / 0.037411 (-0.014205) | 0.077835 / 0.014526 (0.063310) | 0.090242 / 0.176557 (-0.086315) | 0.131208 / 0.737135 (-0.605928) | 0.091726 / 0.296338 (-0.204612) |\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) | 0.292487 / 0.215209 (0.077278) | 2.837044 / 2.077655 (0.759389) | 1.553155 / 1.504120 (0.049035) | 1.433645 / 1.541195 (-0.107550) | 1.476702 / 1.468490 (0.008212) | 0.561926 / 4.584777 (-4.022851) | 0.954630 / 3.745712 (-2.791082) | 2.752286 / 5.269862 (-2.517575) | 1.782746 / 4.565676 (-2.782931) | 0.062984 / 0.424275 (-0.361291) | 0.005056 / 0.007607 (-0.002551) | 0.341700 / 0.226044 (0.115656) | 3.343726 / 2.268929 (1.074798) | 1.953390 / 55.444624 (-53.491234) | 1.616989 / 6.876477 (-5.259488) | 1.785104 / 2.142072 (-0.356969) | 0.643465 / 4.805227 (-4.161763) | 0.115905 / 6.500664 (-6.384759) | 0.041678 / 0.075469 (-0.033791) |\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) | 1.000237 / 1.841788 (-0.841550) | 12.633517 / 8.074308 (4.559208) | 10.553485 / 10.191392 (0.362092) | 0.143188 / 0.680424 (-0.537236) | 0.016020 / 0.534201 (-0.518181) | 0.286739 / 0.579283 (-0.292544) | 0.128488 / 0.434364 (-0.305876) | 0.321932 / 0.540337 (-0.218405) | 0.418635 / 1.386936 (-0.968301) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9510252f03fded02b8cc87ca6dfa3195d17594ba \"CML watermark\")\n"
] | 2024-06-07T11:24:15Z
| 2024-06-10T07:33:53Z
| 2024-06-10T07:27:43Z
|
CONTRIBUTOR
| null | null | null |
cc @cakiki who reported it on [slack](https://huggingface.slack.com/archives/C039P47V1L5/p1717754405578539) (private link)
This PR updates how errors are handled in `dataset_module_factory` when the `dataset_info` cannot be accessed:
1. Use multiple `except ... as e` instead of using `isinstance(e, ...)`
2. Always raise `DatasetNotFoundError` with `from e` so that the initial error is explicitly logged in the stacktrace.
3. Differentiate `RepoNotFoundError` / `GatedRepoError` / `RevisionNotFoundError` cases
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PR_kwDODunzps6QtBsD
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docs: update cache.mdx to include HF_DATASETS_CACHE documentation
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"Already included HF_DATASETS_CACHE"
] | 2025-03-30T20:35:03Z
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| 2025-03-30T20:36:40Z
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I_kwDODunzps6pWbMQ
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Dynamic adjusting dataloader sampling weight
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"You mean based on a condition that has to be checked on-the-fly during training ? Otherwise if you know in advance after how many samples you need to change the sampling you can simply concatenate the two mixes",
"Yes, like during training, if one data sample's prediction is consistently wrong, its sampling weight gets higher and higher, and if one data sample's prediction is already correct, then we rarely sample it",
"it's not possible to use `interleave_datasets()` and modify the probabilities while iterating on the dataset at the moment, so you'd have to implement your own torch `Sampler` or your own`IterableDataset` to implement this logic"
] | 2025-02-10T03:18:47Z
| 2025-03-07T14:06:54Z
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Hi,
Thanks for your wonderful work! I'm wondering is there a way to dynamically adjust the sampling weight of each data in the dataset during training? Looking forward to your reply, thanks again.
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I_kwDODunzps5h21KW
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Filter datasets by language code
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[
"The dataset still can be found, if instead of using the search form you just enter the language code in the url, like https://huggingface.co/datasets?language=language:myv. \r\n\r\nBut of course having a more complete list of languages in the search form (or just a fallback to the language codes, if they are missing from the code=>language mapping) would be much more convenient!",
"Hi! I've opened a PR to make these languages searchable on the Hub.",
"Thanks @mariosasko!\r\nDo you think it is possible to turn this into a more scalable pipeline? Such as:\r\n1. Looping through all the datasets on the hub and collecting the set of all their language codes;\r\n2. Selecting the codes not covered yet in `Language.ts`\r\n3. Looking up their codes at https://iso639-3.sil.org/code_tables/639/data\r\n4. Adding all the newly found language codes to `Language.ts`",
"@avidale This has been discussed in https://github.com/huggingface/datasets/issues/4881, so also feel free to share your opinion there."
] | 2023-03-27T09:42:28Z
| 2023-03-30T08:08:15Z
| 2023-03-30T08:08:15Z
|
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Hi! I use the language search field on https://huggingface.co/datasets
However, some of the datasets tagged by ISO language code are not accessible by this search form.
For example, [myv_ru_2022](https://huggingface.co/datasets/slone/myv_ru_2022) is has `myv` language tag but it is not included in Languages search form.
I've also noticed the same problem with `mhr` (see https://huggingface.co/datasets/AigizK/mari-russian-parallel-corpora)
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PR_kwDODunzps5JCSAZ
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Remove unused `load_from_cache_file` arg from `Dataset.shard()` docstring
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5493). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008956 / 0.011353 (-0.002397) | 0.004590 / 0.011008 (-0.006418) | 0.101305 / 0.038508 (0.062797) | 0.030347 / 0.023109 (0.007237) | 0.302492 / 0.275898 (0.026594) | 0.335986 / 0.323480 (0.012506) | 0.007272 / 0.007986 (-0.000714) | 0.004303 / 0.004328 (-0.000025) | 0.078592 / 0.004250 (0.074341) | 0.035545 / 0.037052 (-0.001507) | 0.316052 / 0.258489 (0.057563) | 0.342523 / 0.293841 (0.048682) | 0.034128 / 0.128546 (-0.094419) | 0.011475 / 0.075646 (-0.064171) | 0.325272 / 0.419271 (-0.093999) | 0.041815 / 0.043533 (-0.001717) | 0.303093 / 0.255139 (0.047955) | 0.331987 / 0.283200 (0.048788) | 0.087264 / 0.141683 (-0.054419) | 1.476284 / 1.452155 (0.024129) | 1.562034 / 1.492716 (0.069318) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206502 / 0.018006 (0.188496) | 0.409893 / 0.000490 (0.409404) | 0.002479 / 0.000200 (0.002279) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022891 / 0.037411 (-0.014520) | 0.100209 / 0.014526 (0.085683) | 0.105576 / 0.176557 (-0.070981) | 0.141035 / 0.737135 (-0.596100) | 0.109733 / 0.296338 (-0.186606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413791 / 0.215209 (0.198582) | 4.125890 / 2.077655 (2.048235) | 1.833023 / 1.504120 (0.328903) | 1.631325 / 1.541195 (0.090130) | 1.708406 / 1.468490 (0.239916) | 0.690100 / 4.584777 (-3.894677) | 3.379058 / 3.745712 (-0.366654) | 2.019044 / 5.269862 (-3.250818) | 1.323332 / 4.565676 (-3.242344) | 0.082709 / 0.424275 (-0.341566) | 0.012434 / 0.007607 (0.004827) | 0.527139 / 0.226044 (0.301095) | 5.271529 / 2.268929 (3.002601) | 2.297311 / 55.444624 (-53.147314) | 1.949021 / 6.876477 (-4.927456) | 2.001098 / 2.142072 (-0.140975) | 0.811591 / 4.805227 (-3.993636) | 0.149028 / 6.500664 (-6.351637) | 0.066233 / 0.075469 (-0.009236) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254276 / 1.841788 (-0.587512) | 13.638485 / 8.074308 (5.564177) | 13.943274 / 10.191392 (3.751882) | 0.147426 / 0.680424 (-0.532997) | 0.028602 / 0.534201 (-0.505599) | 0.398080 / 0.579283 (-0.181203) | 0.402178 / 0.434364 (-0.032186) | 0.477045 / 0.540337 (-0.063292) | 0.567731 / 1.386936 (-0.819205) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006936 / 0.011353 (-0.004417) | 0.004614 / 0.011008 (-0.006394) | 0.079779 / 0.038508 (0.041271) | 0.027941 / 0.023109 (0.004832) | 0.347224 / 0.275898 (0.071326) | 0.378183 / 0.323480 (0.054703) | 0.005249 / 0.007986 (-0.002737) | 0.004907 / 0.004328 (0.000579) | 0.078678 / 0.004250 (0.074428) | 0.041912 / 0.037052 (0.004860) | 0.347838 / 0.258489 (0.089349) | 0.386760 / 0.293841 (0.092919) | 0.032680 / 0.128546 (-0.095867) | 0.014321 / 0.075646 (-0.061325) | 0.087924 / 0.419271 (-0.331347) | 0.045060 / 0.043533 (0.001527) | 0.340986 / 0.255139 (0.085847) | 0.368689 / 0.283200 (0.085489) | 0.093274 / 0.141683 (-0.048409) | 1.474435 / 1.452155 (0.022281) | 1.569753 / 1.492716 (0.077037) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206789 / 0.018006 (0.188783) | 0.416518 / 0.000490 (0.416028) | 0.000404 / 0.000200 (0.000204) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026207 / 0.037411 (-0.011205) | 0.101914 / 0.014526 (0.087388) | 0.108585 / 0.176557 (-0.067972) | 0.150438 / 0.737135 (-0.586697) | 0.110744 / 0.296338 (-0.185594) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443571 / 0.215209 (0.228362) | 4.433139 / 2.077655 (2.355485) | 2.109525 / 1.504120 (0.605405) | 1.901484 / 1.541195 (0.360290) | 1.968812 / 1.468490 (0.500322) | 0.704334 / 4.584777 (-3.880443) | 3.392028 / 3.745712 (-0.353684) | 3.072693 / 5.269862 (-2.197168) | 1.552227 / 4.565676 (-3.013449) | 0.083741 / 0.424275 (-0.340534) | 0.012627 / 0.007607 (0.005020) | 0.544706 / 0.226044 (0.318662) | 5.462743 / 2.268929 (3.193815) | 2.551265 / 55.444624 (-52.893360) | 2.208075 / 6.876477 (-4.668401) | 2.259092 / 2.142072 (0.117020) | 0.810687 / 4.805227 (-3.994540) | 0.152347 / 6.500664 (-6.348317) | 0.068346 / 0.075469 (-0.007123) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269716 / 1.841788 (-0.572072) | 14.215698 / 8.074308 (6.141390) | 13.691773 / 10.191392 (3.500381) | 0.152620 / 0.680424 (-0.527804) | 0.017219 / 0.534201 (-0.516982) | 0.382533 / 0.579283 (-0.196750) | 0.388994 / 0.434364 (-0.045370) | 0.479400 / 0.540337 (-0.060938) | 0.572699 / 1.386936 (-0.814237) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2d90f14cd6e756abeb27045940a6756104cc2d6 \"CML watermark\")\n"
] | 2023-02-01T18:57:48Z
| 2023-02-08T15:10:46Z
| 2023-02-08T15:03:50Z
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CONTRIBUTOR
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Image & Audio formatting for numpy/torch/tf/jax
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"_The documentation is not available anymore as the PR was closed or merged._",
"I just added a consolidation step so that numpy arrays or tensors of images are stacked together if the shapes match, instead of having lists of tensors\r\n\r\nFeel free to review @mariosasko :)",
"I added a few lines in the docs and reverted the ragged numpy array change :)\r\n\r\nready for another review @mariosasko !"
] | 2022-10-05T13:07:03Z
| 2022-10-10T13:24:10Z
| 2022-10-10T13:21:32Z
|
MEMBER
| null | null | null |
Added support for image and audio formatting for numpy, torch, tf and jax.
For images, the dtype used is the one of the image (the one returned by PIL.Image), e.g. uint8
I also added support for string, binary and None types. In particular for torch and jax, strings are kept unchanged (previously it was returning an error because you can't create a tensor of strings)
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[
"Hello @FelixLabelle,\r\n\r\nAs you can see in the Community tab of the corresponding dataset, it is a known issue: https://huggingface.co/datasets/EleutherAI/pile/discussions/15\r\n\r\nThe data has been taken down due to reported copyright infringement.\r\n\r\nFeel free to continue the discussion there."
] | 2023-12-21T21:25:09Z
| 2023-12-22T09:17:05Z
| 2023-12-22T09:17:05Z
|
NONE
| null | null |
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### Describe the bug
The pile does not stream, a "File not Found error" is returned. It looks like the Pile's files have been moved.
### Steps to reproduce the bug
To reproduce run the following code:
```
from datasets import load_dataset
dataset = load_dataset('EleutherAI/pile', 'en', split='train', streaming=True)
next(iter(dataset))
```
I get the following error:
`FileNotFoundError: https://the-eye.eu/public/AI/pile/train/00.jsonl.zst`
### Expected behavior
Return the data in a stream.
### Environment info
- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.5
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.3
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I_kwDODunzps5XPdHR
| 5,293
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Support streaming datasets with pathlib.Path.with_suffix
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[] | 2022-11-24T17:52:08Z
| 2022-11-29T07:06:33Z
| 2022-11-29T07:06:33Z
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MEMBER
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Extend support for streaming datasets that use `pathlib.Path.with_suffix`.
This feature will be useful e.g. for datasets containing text files and annotated files with the same name but different extension.
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Running add_column() seems to corrupt existing sequence-type column info
|
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[
"Nevermind, I was incorrect."
] | 2022-09-17T17:42:05Z
| 2022-09-19T12:54:54Z
| 2022-09-19T12:54:54Z
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I have a dataset that contains a column ("foo") that is a sequence type of length 4. So when I run .to_pandas() on it, the resulting dataframe correctly contains 4 columns - foo_0, foo_1, foo_2, foo_3. So the 1st row of the dataframe might look like:
ds = load_dataset(...)
df = ds.to_pandas()
df:
foo_0 | foo_1 | foo_2 | foo_3
0.0 | 1.0 | 2.0 | 3.0
If I run .add_column("new_col", data) on the dataset, and then .to_pandas() on the resulting new dataset, the resulting dataframe contains only 2 columns - foo, new_col. The values in column foo are lists of length 4, the 4 elements that should have been split into separate columns. Dataframe 1st row would be:
ds = load_dataset(...)
new_ds = ds.add_column("new_col", data)
df = new_ds.to_pandas()
df:
foo | new_col
[0.0, 1.0, 2.0, 3.0] | new_val
I've explored the 2 datasets in a debugger and haven't noticed any changes to any attributes related to the foo column, but I can't determine why the dataframes are so different.
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Add column type guessing from map return function
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[
"Thanks for working on this. However, having thought about this issue a bit more, supporting this doesn't seem like a good idea - it's better to be explicit than implicit, according to the Zen of Python 🙂. Also, I don't think many users would use this, so this raises the question of whether this is something we want to maintain.\r\n\r\ncc @lhoestq for the 2nd opinion",
"@mariosasko I was going to quote the Zen of Python in the other direction :) To me, this actually is much more explicit than the current behavior of guessing pyarrow types based on the raw dictionary return values. Explicit typehinting is increasingly the de facto way to deal with this dynamic type serialization - plus it feels like a clearer fit to me than separating out the mapper function from the feature column definition in the call to the actual `.map()`. Another benefit is providing typehinting support for clients that use mypy or other static typecheckers to detect return mismatches.\r\n\r\nBut will leave it to you and @lhoestq to see if it's something you'd like in core versus a support package.",
"I meant that explicitly specifying the target features (the `features` param) is cleaner (easier to track) than relying on type hints.",
"Passing features= to `map()` is richer and more explicit. Also I don't think users would guess that such API exist.\r\n\r\nOther libraries like dask also infer the type from the output or requires the typing to be specified using the `meta` argument",
"Point about discoverability is a fair one, would certainly need some docs around it. All good! Will close this out and keep in our extension utilities."
] | 2023-07-25T00:34:17Z
| 2023-07-26T15:13:45Z
| 2023-07-26T15:13:44Z
|
NONE
| null | null | null |
As discussed [here](https://github.com/huggingface/datasets/issues/5965), there are some cases where datasets is unable to automatically promote columns during mapping. The fix is to explicitly provide a `features` definition so pyarrow can configure itself with the right column types from the outset.
This PR provides an alternative approach, which is functionally equivalent to specifying features but a bit cleaner within a larger mapping pipeline. It allows clients to typehint the return variable coming from the mapper function - if we find one of these type annotations specified, and no explicit features have been passed in, we'll try to convert it into a Features map. If the map function runs and casting is unable to succeed, it will raise a DatasetTransformationNotAllowedError that indicates the typehint may be to blame. It works for batched and non-batched mapping functions.
Currently supported column types:
- builtins primitives: string, int, float, bool
- dictionaries, lists (nested and one-deep)
- Optional types and None-Unions (synonymous with optional types)
It's used like:
```python
class DatasetTyped(TypedDict):
texts: list[str]
def dataset_typed_map(batch) -> DatasetTyped:
return {"texts": [text.split() for text in batch["raw_text"]]}
dataset = {"raw_text": ["", "This is a test", "This is another test"]}
with Dataset.from_dict(dataset) as dset:
new_dataset = dset.map(
dataset_typed_map,
batched=True,
batch_size=1,
num_proc=1,
)
```
Open questions:
- Should logging indicate we have automatically guessed these types? Or proceed quietly until we hit an error (as is the current implementation).
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PR_kwDODunzps47jA6Z
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|
Fix Authentification Error while streaming
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[
"Hi, thanks for working on this, but the fix for this has already been merged in https://github.com/huggingface/datasets/pull/4608."
] | 2022-07-18T08:03:41Z
| 2022-07-20T13:10:44Z
| 2022-07-20T13:10:43Z
|
NONE
| null | null | null |
I fixed a few errors when it occurs while streaming the private dataset on the Huggingface Hub.
```
from datasets import load_dataset
dataset = load_dataset(<repo_id>, use_auth_token=<private_token>, streaming=True)
for d in dataset['train']:
print(d)
break # this is for checking
```
This code is an example for streaming private datasets.
when the version of the datasets is 2.2.2, it works well but datasets>2.2.2 occurs error like this,
```
/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)
1007 status=self.status,
1008 message=self.reason,
→ 1009 headers=self.headers,
1010 )
1011
ClientResponseError: 401, message='Unauthorized', url=URL('https://huggingface.co/datasets/.../train-00000-of-00001-168b451062c67c34.parquet')
```
(this is an example on the dataset has `parquet` extenstion)
It seems that the `xisfile `module in `download/streaming_download_manager.py` couldn't recognize the file on "https://huggingface.co/~".
so I add three lines.
With this change, there is no error anymore(but this code is ad-hoc).
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I_kwDODunzps5tA7OD
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|
HF Datasets data access is extremely slow even when in memory
|
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[
"Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462"
] | 2023-07-31T11:12:19Z
| 2023-08-01T11:22:43Z
| null |
CONTRIBUTOR
| null | null |
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### Describe the bug
Doing a simple `some_dataset[:10]` can take more than a minute.
Profiling it:
<img width="1280" alt="image" src="https://github.com/huggingface/datasets/assets/36224762/e641fb95-ff02-4072-9016-5416a65f75ab">
`some_dataset` is completely in memory with no disk cache.
This is proving fatal to my usage of HF Datasets. Is there a way I can forgo the arrow format and store the dataset as PyTorch tensors so that `_tensorize` is not needed? And is `_consolidate` supposed to take this long?
It's faster to produce the dataset from scratch than to access it from HF Datasets!
### Steps to reproduce the bug
I have uploaded the dataset that causes this problem [here](https://huggingface.co/datasets/NightMachinery/hf_datasets_bug1).
```python
#!/usr/bin/env python3
import sys
import time
import torch
from datasets import load_dataset
def main(dataset_name):
# Start the timer
start_time = time.time()
# Load the dataset from Hugging Face Hub
dataset = load_dataset(dataset_name)
# Set the dataset format as torch
dataset.set_format(type="torch")
# Perform an identity map
dataset = dataset.map(lambda example: example, batched=True, batch_size=20)
# End the timer
end_time = time.time()
# Print the time taken
print(f"Time taken: {end_time - start_time:.2f} seconds")
if __name__ == "__main__":
dataset_name = "NightMachinery/hf_datasets_bug1"
print(f"dataset_name: {dataset_name}")
main(dataset_name)
```
### Expected behavior
_
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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Release: 3.3.1
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7409). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-02-17T14:52:12Z
| 2025-02-17T14:54:32Z
| 2025-02-17T14:53:13Z
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MEMBER
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Fix max lock length on unix
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6482). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I'm getting `AttributeError: module 'os' has no attribute 'statvfs'` on windows - reverting",
"<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) | 0.005294 / 0.011353 (-0.006059) | 0.003562 / 0.011008 (-0.007446) | 0.062030 / 0.038508 (0.023522) | 0.053335 / 0.023109 (0.030226) | 0.233303 / 0.275898 (-0.042595) | 0.252029 / 0.323480 (-0.071451) | 0.002835 / 0.007986 (-0.005151) | 0.002732 / 0.004328 (-0.001597) | 0.047973 / 0.004250 (0.043723) | 0.038380 / 0.037052 (0.001328) | 0.235028 / 0.258489 (-0.023461) | 0.265555 / 0.293841 (-0.028286) | 0.027136 / 0.128546 (-0.101410) | 0.010806 / 0.075646 (-0.064840) | 0.205040 / 0.419271 (-0.214231) | 0.035063 / 0.043533 (-0.008470) | 0.236351 / 0.255139 (-0.018788) | 0.254556 / 0.283200 (-0.028643) | 0.019528 / 0.141683 (-0.122155) | 1.099012 / 1.452155 (-0.353142) | 1.156250 / 1.492716 (-0.336466) |\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) | 0.093952 / 0.018006 (0.075946) | 0.304181 / 0.000490 (0.303692) | 0.000227 / 0.000200 (0.000027) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018568 / 0.037411 (-0.018844) | 0.060323 / 0.014526 (0.045798) | 0.073010 / 0.176557 (-0.103546) | 0.121723 / 0.737135 (-0.615412) | 0.075668 / 0.296338 (-0.220670) |\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) | 0.288429 / 0.215209 (0.073220) | 2.797834 / 2.077655 (0.720180) | 1.480957 / 1.504120 (-0.023163) | 1.360872 / 1.541195 (-0.180323) | 1.406828 / 1.468490 (-0.061663) | 0.587596 / 4.584777 (-3.997181) | 2.533997 / 3.745712 (-1.211715) | 2.906697 / 5.269862 (-2.363164) | 1.801753 / 4.565676 (-2.763923) | 0.064360 / 0.424275 (-0.359915) | 0.005016 / 0.007607 (-0.002591) | 0.347334 / 0.226044 (0.121290) | 3.426344 / 2.268929 (1.157416) | 1.856014 / 55.444624 (-53.588610) | 1.581774 / 6.876477 (-5.294703) | 1.640036 / 2.142072 (-0.502037) | 0.656096 / 4.805227 (-4.149131) | 0.120212 / 6.500664 (-6.380452) | 0.044003 / 0.075469 (-0.031466) |\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) | 0.943933 / 1.841788 (-0.897855) | 11.846572 / 8.074308 (3.772263) | 10.330705 / 10.191392 (0.139313) | 0.129767 / 0.680424 (-0.550657) | 0.013508 / 0.534201 (-0.520693) | 0.289672 / 0.579283 (-0.289611) | 0.266427 / 0.434364 (-0.167937) | 0.342766 / 0.540337 (-0.197571) | 0.452068 / 1.386936 (-0.934868) |\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) | 0.005308 / 0.011353 (-0.006045) | 0.003712 / 0.011008 (-0.007296) | 0.048848 / 0.038508 (0.010340) | 0.055156 / 0.023109 (0.032047) | 0.271942 / 0.275898 (-0.003956) | 0.293166 / 0.323480 (-0.030314) | 0.004056 / 0.007986 (-0.003930) | 0.002722 / 0.004328 (-0.001606) | 0.048418 / 0.004250 (0.044167) | 0.039320 / 0.037052 (0.002268) | 0.277184 / 0.258489 (0.018695) | 0.312398 / 0.293841 (0.018557) | 0.029392 / 0.128546 (-0.099155) | 0.011314 / 0.075646 (-0.064332) | 0.057883 / 0.419271 (-0.361389) | 0.032603 / 0.043533 (-0.010930) | 0.273025 / 0.255139 (0.017886) | 0.289265 / 0.283200 (0.006065) | 0.017553 / 0.141683 (-0.124129) | 1.127725 / 1.452155 (-0.324430) | 1.202293 / 1.492716 (-0.290423) |\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) | 0.097179 / 0.018006 (0.079173) | 0.309712 / 0.000490 (0.309222) | 0.000269 / 0.000200 (0.000069) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024742 / 0.037411 (-0.012670) | 0.070097 / 0.014526 (0.055571) | 0.082273 / 0.176557 (-0.094283) | 0.121696 / 0.737135 (-0.615439) | 0.082983 / 0.296338 (-0.213355) |\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) | 0.292688 / 0.215209 (0.077479) | 2.853436 / 2.077655 (0.775781) | 1.588999 / 1.504120 (0.084879) | 1.454547 / 1.541195 (-0.086648) | 1.476342 / 1.468490 (0.007852) | 0.559464 / 4.584777 (-4.025313) | 2.564597 / 3.745712 (-1.181115) | 2.900460 / 5.269862 (-2.369402) | 1.782156 / 4.565676 (-2.783520) | 0.061768 / 0.424275 (-0.362507) | 0.005042 / 0.007607 (-0.002565) | 0.345168 / 0.226044 (0.119124) | 3.412273 / 2.268929 (1.143344) | 1.953154 / 55.444624 (-53.491470) | 1.667347 / 6.876477 (-5.209130) | 1.685138 / 2.142072 (-0.456934) | 0.643270 / 4.805227 (-4.161958) | 0.115955 / 6.500664 (-6.384709) | 0.041090 / 0.075469 (-0.034379) |\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) | 0.976324 / 1.841788 (-0.865464) | 12.252294 / 8.074308 (4.177986) | 10.598062 / 10.191392 (0.406670) | 0.129779 / 0.680424 (-0.550644) | 0.015697 / 0.534201 (-0.518504) | 0.287241 / 0.579283 (-0.292042) | 0.287331 / 0.434364 (-0.147033) | 0.331710 / 0.540337 (-0.208628) | 0.574571 / 1.386936 (-0.812365) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#702344140461b7a111139860c944d3dd0a2689e3 \"CML watermark\")\n"
] | 2023-12-08T13:39:30Z
| 2023-12-12T11:53:32Z
| 2023-12-12T11:47:27Z
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MEMBER
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reported in https://github.com/huggingface/datasets/pull/6482
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I_kwDODunzps6IL1RW
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Connection Error When Using By-pass Proxies
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"Changing the supplier of the proxy will solve this problem, or you can visit and follow the instructions in https://hf-mirror.com "
] | 2024-05-08T06:40:14Z
| 2024-05-17T06:38:30Z
| null |
NONE
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### Describe the bug
I'm currently using Clash for Windows as my proxy tunnel, after exporting HTTP_PROXY and HTTPS_PROXY to the port that clash provides🤔, it runs into a connection error saying "Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f969d391870>: Failed to establish a new connection: [Errno 111] Connection refused'))")))"
I have already read the documentation provided on the hugginface, but I think I didn't see the detailed instruction on how to set up proxies for this library.
### Steps to reproduce the bug
1. Turn on any proxy software like Clash / ShadosocksR etc.
2. export system varibles to the port provided by your proxy software in wsl (It's ok for other applications to use proxy expect dataset-library)
3. load any dataset from hugginface online
### Expected behavior
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
Cell In[33], [line 3](vscode-notebook-cell:?execution_count=33&line=3)
[1](vscode-notebook-cell:?execution_count=33&line=1) from datasets import load_metric
----> [3](vscode-notebook-cell:?execution_count=33&line=3) metric = load_metric("seqeval")
File ~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:46, in deprecated.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
[44](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:44) warnings.warn(warning_msg, category=FutureWarning, stacklevel=2)
[45](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:45) _emitted_deprecation_warnings.add(func_hash)
---> [46](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:46) return deprecated_function(*args, **kwargs)
File ~/.local/lib/python3.10/site-packages/datasets/load.py:2104, in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, trust_remote_code, **metric_init_kwargs)
[2101](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2101) warnings.filterwarnings("ignore", message=".*https://huggingface.co/docs/evaluate$", category=FutureWarning)
[2103](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2103) download_mode = DownloadMode(download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS)
-> [2104](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2104) metric_module = metric_module_factory(
[2105](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2105) path,
[2106](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2106) revision=revision,
[2107](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2107) download_config=download_config,
[2108](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2108) download_mode=download_mode,
[2109](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2109) trust_remote_code=trust_remote_code,
[2110](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2110) ).module_path
[2111](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2111) metric_cls = import_main_class(metric_module, dataset=False)
[2112](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2112) metric = metric_cls(
[2113](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2113) config_name=config_name,
[2114](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2114) process_id=process_id,
...
--> [633](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:633) raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
[634](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:634) elif response is not None:
[635](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:635) raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))")))
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0
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Dataset Viewer issue for multi_news
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[
"Thanks for reporting, @lewtun.\r\n\r\nI forced the refreshing of the preview and it worked OK for train and validation splits.\r\n\r\nI guess the error has to do with the data files being hosted at Google Drive: this gives errors when requested automatically using scripts.\r\nWe should host them to fix the error. Let's see if the license allows that.",
"I guess we can host the data: https://github.com/Alex-Fabbri/Multi-News/blob/master/LICENSE.txt"
] | 2022-06-27T20:25:25Z
| 2022-06-28T14:08:48Z
| 2022-06-28T14:08:48Z
|
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### Link
https://huggingface.co/datasets/multi_news
### Description
Not sure what the index error is referring to here:
```
Status code: 400
Exception: IndexError
Message: list index out of range
```
### Owner
No
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I_kwDODunzps5Y7Eo0
| 5,353
|
Support remote file systems for `Audio`
|
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[
"Just seen https://github.com/huggingface/datasets/issues/5281"
] | 2022-12-12T13:22:13Z
| 2022-12-12T13:37:14Z
| 2022-12-12T13:37:14Z
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### Feature request
Hi there!
It would be super cool if `Audio()`, and potentially other features, could read files from a remote file system.
### Motivation
Large amounts of data is often stored in buckets. `load_from_disk` is able to retrieve data from cloud storage but to my knowledge actually copies the datasets across first, so if you're working off a system with smaller disk specs (like a VM), you can run out of space very quickly.
### Your contribution
Something like this (for Google Cloud Platform in this instance):
```python
from datasets import Dataset, Audio
import gcsfs
fs = gcsfs.GCSFileSystem()
list_of_audio_fp = {'audio': ['1', '2', '3']}
ds = Dataset.from_dict(list_of_audio_fp)
ds = ds.cast_column("audio", Audio(sampling_rate=16000, fs=fs))
```
Under the hood:
```python
import librosa
from io import BytesIO
def load_audio(fp, sampling_rate=None, fs=None):
if fs is not None:
with fs.open(fp, 'rb') as f:
arr, sr = librosa.load(BytesIO(f), sr=sampling_rate)
else:
# Perform existing io operations
```
Written from memory so some things could be wrong.
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| 6,389
|
Index 339 out of range for dataset of size 339 <-- save_to_file()
|
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[
"Hi! Can you make the above reproducer self-contained by adding code that generates the data?",
"I managed a workaround eventually but I don't know what it was (I made a lot of changes to seq2seq). I'll try to include generating code in the future. (If I close, I don't know if you see it. Feel free to close; I'll re-open if I encounter it again (if I can))."
] | 2023-11-08T12:52:09Z
| 2023-11-24T09:14:13Z
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### Describe the bug
When saving out some Audio() data.
The data is audio recordings with associated 'sentences'.
(They use the audio 'bytes' approach because they're clips within audio files).
Code is below the traceback (I can't upload the voice audio/text (it's not even me)).
```
Traceback (most recent call last):
File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 156, in <module>
create_dataset(args)
File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 138, in create_dataset
hf_dataset.save_to_disk(args.outds, max_shard_size='50MB')
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1531, in save_to_disk
for kwargs in kwargs_per_job:
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1508, in <genexpr>
"shard": self.shard(num_shards=num_shards, index=shard_idx, contiguous=True),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 4609, in shard
return self.select(
^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3797, in select
return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3857, in _select_contiguous
_check_valid_indices_value(start, len(self))
File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 648, in _check_valid_indices_value
raise IndexError(f"Index {index} out of range for dataset of size {size}.")
IndexError: Index 339 out of range for dataset of size 339.
```
### Steps to reproduce the bug
(I had to set the default max batch size down due to a different bug... or maybe it's related: https://github.com/huggingface/datasets/issues/5717)
```python3
#!/usr/bin/env python3
import argparse
import os
from pathlib import Path
import soundfile as sf
import datasets
datasets.config.DEFAULT_MAX_BATCH_SIZE=35
from datasets import Features, Array2D, Value, Dataset, Sequence, Audio
import numpy as np
import librosa
import sys
import soundfile as sf
import io
import logging
logging.basicConfig(level=logging.DEBUG, filename='debug.log', filemode='w',
format='%(name)s - %(levelname)s - %(message)s')
# Define the arguments for the command-line interface
def parse_args():
parser = argparse.ArgumentParser(description="Create a Huggingface dataset from labeled audio files.")
parser.add_argument("--indir_labeled", action="append", help="Directory containing labeled audio files.", required=True)
parser.add_argument("--outds", help="Path to save the dataset file.", required=True)
parser.add_argument("--max_clips", type=int, help="Max count of audio samples to add to the dataset.", default=None)
parser.add_argument("-r", "--sr", type=int, help="Sample rate for the audio files.", default=16000)
parser.add_argument("--no-resample", action="store_true", help="Disable resampling of the audio files.")
parser.add_argument("--max_clip_secs", type=float, help="Max length of audio clips in seconds.", default=3.0)
parser.add_argument("-v", "--verbose", action='count', default=1, help="Increase verbosity")
return parser.parse_args()
# Convert the NumPy arrays to audio bytes in WAV format
def numpy_to_bytes(audio_array, sampling_rate=16000):
with io.BytesIO() as bytes_io:
sf.write(bytes_io, audio_array, samplerate=sampling_rate,
format='wav', subtype='FLOAT') # float32
return bytes_io.getvalue()
# Function to find audio and label files in a directory
def find_audio_label_pairs(indir_labeled):
audio_label_pairs = []
for root, _, files in os.walk(indir_labeled):
for file in files:
if file.endswith(('.mp3', '.wav', '.aac', '.flac')):
audio_path = Path(root) / file
if args.verbose>1:
print(f'File: {audio_path}')
label_path = audio_path.with_suffix('.labels.txt')
if label_path.exists():
if args.verbose>0:
print(f' Pair: {audio_path}')
audio_label_pairs.append((audio_path, label_path))
return audio_label_pairs
def process_audio_label_pair(audio_path, label_path, sampling_rate, no_resample, max_clip_secs):
# Read the label file
with open(label_path, 'r') as label_file:
labels = label_file.readlines()
# Load the full audio file
full_audio, current_sr = sf.read(audio_path)
if not no_resample and current_sr != sampling_rate:
# You can use librosa.resample here if librosa is available
full_audio = librosa.resample(full_audio, orig_sr=current_sr, target_sr=sampling_rate)
audio_segments = []
sentences = []
# Process each label
for label in labels:
start_secs, end_secs, label_text = label.strip().split('\t')
start_sample = int(float(start_secs) * sampling_rate)
end_sample = int(float(end_secs) * sampling_rate)
# Extract segment and truncate or pad to max_clip_secs
audio_segment = full_audio[start_sample:end_sample]
max_samples = int(max_clip_secs * sampling_rate)
if len(audio_segment) > max_samples: # Truncate
audio_segment = audio_segment[:max_samples]
elif len(audio_segment) < max_samples: # Pad
padding = np.zeros(max_samples - len(audio_segment), dtype=audio_segment.dtype)
audio_segment = np.concatenate((audio_segment, padding))
audio_segment = numpy_to_bytes(audio_segment)
audio_data = {
'path': str(audio_path),
'bytes': audio_segment,
}
audio_segments.append(audio_data)
sentences.append(label_text)
return audio_segments, sentences
# Main function to create the dataset
def create_dataset(args):
audio_label_pairs = []
for indir in args.indir_labeled:
audio_label_pairs.extend(find_audio_label_pairs(indir))
# Initialize our dataset data
dataset_data = {
'path': [], # This will be a list of strings
'audio': [], # This will be a list of dictionaries
'sentence': [], # This will be a list of strings
}
# Process each audio-label pair and add the data to the dataset
for audio_path, label_path in audio_label_pairs[:args.max_clips]:
audio_segments, sentences = process_audio_label_pair(audio_path, label_path, args.sr, args.no_resample, args.max_clip_secs)
if audio_segments and sentences:
for audio_data, sentence in zip(audio_segments, sentences):
if args.verbose>1:
print(f'Appending {audio_data["path"]}')
dataset_data['path'].append(audio_data['path'])
dataset_data['audio'].append({
'path': audio_data['path'],
'bytes': audio_data['bytes'],
})
dataset_data['sentence'].append(sentence)
features = Features({
'path': Value('string'), # Path is redundant in common voice set also
'audio': Audio(sampling_rate=16000),
'sentence': Value('string'),
})
hf_dataset = Dataset.from_dict(dataset_data, features=features)
for key in dataset_data:
for i, item in enumerate(dataset_data[key]):
if item is None or (isinstance(item, bytes) and len(item) == 0):
logging.error(f"Invalid {key} at index {i}: {item}")
import ipdb; ipdb.set_trace(context=16); pass
hf_dataset.save_to_disk(args.outds, max_shard_size='50MB')
# try:
# hf_dataset.save_to_disk(args.outds)
# except TypeError as e:
# # If there's a TypeError, log the exception and the dataset data that might have caused it
# logging.exception("An error occurred while saving the dataset.")
# import ipdb; ipdb.set_trace(context=16); pass
# for key in dataset_data:
# logging.debug(f"{key} length: {len(dataset_data[key])}")
# if key == 'audio':
# # Log the first 100 bytes of the audio data to avoid huge log files
# for i, audio in enumerate(dataset_data[key]):
# logging.debug(f"Audio {i}: {audio['bytes'][:100]}")
# raise
# Run the script
if __name__ == "__main__":
args = parse_args()
create_dataset(args)
```
### Expected behavior
It shouldn't fail.
### Environment info
- `datasets` version: 2.14.7.dev0
- Platform: Linux-6.1.0-13-amd64-x86_64-with-glibc2.36
- Python version: 3.11.2
- `huggingface_hub` version: 0.17.3
- PyArrow version: 13.0.0
- Pandas version: 2.1.2
- `fsspec` version: 2023.9.2
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Logs are delayed when doing .map when `docker logs`
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"Hi! `tqdm` doesn't work well in non-interactive environments, so there isn't much we can do about this. It's best to [disable it](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/utilities#datasets.disable_progress_bars) in such environments and instead use logging to track progress."
] | 2024-01-15T17:05:21Z
| 2024-02-12T17:35:21Z
| 2024-02-12T17:35:21Z
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NONE
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### Describe the bug
When I run my SD training in a Docker image and then listen to logs like `docker logs train -f`, the progress bar is delayed.
It's updating every few percent.
When you have a large dataset that has to be mapped (like 1+ million samples), it's crucial to see the updates in real-time, not every couple hours to make sure nothing got frozen or broken
### Steps to reproduce the bug
1. Run any huge dataset processing as a Docker image
2. `docker logs image_name` to it
### Expected behavior
...
### Environment info
...
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009437 / 0.011353 (-0.001916) | 0.004999 / 0.011008 (-0.006010) | 0.098839 / 0.038508 (0.060331) | 0.035496 / 0.023109 (0.012386) | 0.300726 / 0.275898 (0.024828) | 0.359793 / 0.323480 (0.036313) | 0.007694 / 0.007986 (-0.000292) | 0.003980 / 0.004328 (-0.000348) | 0.075240 / 0.004250 (0.070989) | 0.041149 / 0.037052 (0.004097) | 0.313185 / 0.258489 (0.054696) | 0.344111 / 0.293841 (0.050270) | 0.037775 / 0.128546 (-0.090772) | 0.011901 / 0.075646 (-0.063745) | 0.332631 / 0.419271 (-0.086641) | 0.047194 / 0.043533 (0.003661) | 0.306902 / 0.255139 (0.051763) | 0.321725 / 0.283200 (0.038525) | 0.101031 / 0.141683 (-0.040652) | 1.458778 / 1.452155 (0.006623) | 1.530196 / 1.492716 (0.037480) |\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) | 0.203241 / 0.018006 (0.185235) | 0.447147 / 0.000490 (0.446657) | 0.004159 / 0.000200 (0.003959) | 0.000131 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025845 / 0.037411 (-0.011566) | 0.106966 / 0.014526 (0.092440) | 0.115876 / 0.176557 (-0.060681) | 0.179052 / 0.737135 (-0.558084) | 0.123012 / 0.296338 (-0.173327) |\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) | 0.408766 / 0.215209 (0.193557) | 4.080400 / 2.077655 (2.002745) | 1.893747 / 1.504120 (0.389627) | 1.709389 / 1.541195 (0.168194) | 1.768071 / 1.468490 (0.299581) | 0.689717 / 4.584777 (-3.895059) | 3.760897 / 3.745712 (0.015185) | 2.017050 / 5.269862 (-3.252811) | 1.333027 / 4.565676 (-3.232650) | 0.083559 / 0.424275 (-0.340716) | 0.011951 / 0.007607 (0.004344) | 0.512313 / 0.226044 (0.286268) | 5.162696 / 2.268929 (2.893767) | 2.418559 / 55.444624 (-53.026065) | 2.110178 / 6.876477 (-4.766299) | 2.113635 / 2.142072 (-0.028437) | 0.835171 / 4.805227 (-3.970056) | 0.164222 / 6.500664 (-6.336442) | 0.061955 / 0.075469 (-0.013515) |\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) | 1.198336 / 1.841788 (-0.643452) | 14.531468 / 8.074308 (6.457160) | 13.882133 / 10.191392 (3.690741) | 0.154524 / 0.680424 (-0.525900) | 0.028782 / 0.534201 (-0.505419) | 0.441808 / 0.579283 (-0.137475) | 0.433096 / 0.434364 (-0.001268) | 0.518229 / 0.540337 (-0.022108) | 0.603201 / 1.386936 (-0.783735) |\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) | 0.007385 / 0.011353 (-0.003967) | 0.005193 / 0.011008 (-0.005815) | 0.075517 / 0.038508 (0.037009) | 0.033192 / 0.023109 (0.010083) | 0.332299 / 0.275898 (0.056401) | 0.363043 / 0.323480 (0.039563) | 0.006368 / 0.007986 (-0.001617) | 0.004003 / 0.004328 (-0.000326) | 0.073710 / 0.004250 (0.069460) | 0.046916 / 0.037052 (0.009863) | 0.336307 / 0.258489 (0.077818) | 0.384910 / 0.293841 (0.091069) | 0.038132 / 0.128546 (-0.090414) | 0.012283 / 0.075646 (-0.063364) | 0.088036 / 0.419271 (-0.331235) | 0.049699 / 0.043533 (0.006166) | 0.333953 / 0.255139 (0.078814) | 0.352961 / 0.283200 (0.069762) | 0.101905 / 0.141683 (-0.039778) | 1.470480 / 1.452155 (0.018325) | 1.498212 / 1.492716 (0.005496) |\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) | 0.275067 / 0.018006 (0.257061) | 0.452589 / 0.000490 (0.452099) | 0.047067 / 0.000200 (0.046867) | 0.000983 / 0.000054 (0.000929) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028649 / 0.037411 (-0.008762) | 0.108385 / 0.014526 (0.093859) | 0.121213 / 0.176557 (-0.055343) | 0.192236 / 0.737135 (-0.544899) | 0.124620 / 0.296338 (-0.171719) |\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) | 0.428742 / 0.215209 (0.213533) | 4.264893 / 2.077655 (2.187238) | 2.061650 / 1.504120 (0.557530) | 1.873267 / 1.541195 (0.332072) | 1.961012 / 1.468490 (0.492522) | 0.708904 / 4.584777 (-3.875873) | 3.821289 / 3.745712 (0.075577) | 3.287231 / 5.269862 (-1.982631) | 1.903539 / 4.565676 (-2.662137) | 0.086474 / 0.424275 (-0.337801) | 0.012101 / 0.007607 (0.004494) | 0.531411 / 0.226044 (0.305367) | 5.216785 / 2.268929 (2.947857) | 2.575209 / 55.444624 (-52.869416) | 2.264902 / 6.876477 (-4.611574) | 2.291225 / 2.142072 (0.149153) | 0.853486 / 4.805227 (-3.951741) | 0.168550 / 6.500664 (-6.332114) | 0.064158 / 0.075469 (-0.011311) |\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) | 1.295830 / 1.841788 (-0.545958) | 14.419524 / 8.074308 (6.345216) | 13.397985 / 10.191392 (3.206593) | 0.181367 / 0.680424 (-0.499057) | 0.017666 / 0.534201 (-0.516535) | 0.420645 / 0.579283 (-0.158638) | 0.421025 / 0.434364 (-0.013339) | 0.527369 / 0.540337 (-0.012969) | 0.627175 / 1.386936 (-0.759761) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#589b49dfaffa729bc9997a38d4cedafb107ea2e4 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008717 / 0.011353 (-0.002635) | 0.004573 / 0.011008 (-0.006435) | 0.103660 / 0.038508 (0.065151) | 0.035274 / 0.023109 (0.012165) | 0.298563 / 0.275898 (0.022665) | 0.384397 / 0.323480 (0.060917) | 0.006932 / 0.007986 (-0.001053) | 0.003422 / 0.004328 (-0.000907) | 0.080193 / 0.004250 (0.075943) | 0.039767 / 0.037052 (0.002714) | 0.310296 / 0.258489 (0.051807) | 0.351361 / 0.293841 (0.057520) | 0.033532 / 0.128546 (-0.095014) | 0.011543 / 0.075646 (-0.064104) | 0.374816 / 0.419271 (-0.044456) | 0.046046 / 0.043533 (0.002513) | 0.306918 / 0.255139 (0.051779) | 0.382242 / 0.283200 (0.099042) | 0.098945 / 0.141683 (-0.042738) | 1.456929 / 1.452155 (0.004775) | 1.535763 / 1.492716 (0.043046) |\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) | 0.011759 / 0.018006 (-0.006247) | 0.405345 / 0.000490 (0.404855) | 0.002667 / 0.000200 (0.002467) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023924 / 0.037411 (-0.013487) | 0.095537 / 0.014526 (0.081011) | 0.106959 / 0.176557 (-0.069598) | 0.170782 / 0.737135 (-0.566353) | 0.109169 / 0.296338 (-0.187170) |\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) | 0.437521 / 0.215209 (0.222312) | 4.383556 / 2.077655 (2.305902) | 2.092055 / 1.504120 (0.587935) | 1.889316 / 1.541195 (0.348121) | 1.937436 / 1.468490 (0.468946) | 0.700175 / 4.584777 (-3.884602) | 3.358107 / 3.745712 (-0.387605) | 3.243226 / 5.269862 (-2.026636) | 1.620497 / 4.565676 (-2.945180) | 0.083063 / 0.424275 (-0.341212) | 0.012970 / 0.007607 (0.005363) | 0.544226 / 0.226044 (0.318181) | 5.483315 / 2.268929 (3.214386) | 2.555183 / 55.444624 (-52.889441) | 2.204230 / 6.876477 (-4.672247) | 2.230551 / 2.142072 (0.088478) | 0.816121 / 4.805227 (-3.989106) | 0.151356 / 6.500664 (-6.349308) | 0.068564 / 0.075469 (-0.006905) |\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) | 1.208420 / 1.841788 (-0.633367) | 13.652597 / 8.074308 (5.578289) | 14.096318 / 10.191392 (3.904926) | 0.154473 / 0.680424 (-0.525951) | 0.028436 / 0.534201 (-0.505765) | 0.399949 / 0.579283 (-0.179334) | 0.398961 / 0.434364 (-0.035403) | 0.488703 / 0.540337 (-0.051634) | 0.572640 / 1.386936 (-0.814296) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006373 / 0.011353 (-0.004979) | 0.004368 / 0.011008 (-0.006640) | 0.076410 / 0.038508 (0.037902) | 0.027055 / 0.023109 (0.003945) | 0.336969 / 0.275898 (0.061071) | 0.374533 / 0.323480 (0.051053) | 0.004781 / 0.007986 (-0.003204) | 0.003317 / 0.004328 (-0.001011) | 0.076099 / 0.004250 (0.071849) | 0.038414 / 0.037052 (0.001361) | 0.339578 / 0.258489 (0.081089) | 0.384138 / 0.293841 (0.090297) | 0.031581 / 0.128546 (-0.096965) | 0.011666 / 0.075646 (-0.063981) | 0.085690 / 0.419271 (-0.333582) | 0.042277 / 0.043533 (-0.001256) | 0.337931 / 0.255139 (0.082792) | 0.365827 / 0.283200 (0.082628) | 0.088713 / 0.141683 (-0.052970) | 1.519789 / 1.452155 (0.067635) | 1.583097 / 1.492716 (0.090381) |\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) | 0.223472 / 0.018006 (0.205466) | 0.392474 / 0.000490 (0.391984) | 0.002739 / 0.000200 (0.002539) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024373 / 0.037411 (-0.013038) | 0.099822 / 0.014526 (0.085296) | 0.106128 / 0.176557 (-0.070428) | 0.174688 / 0.737135 (-0.562447) | 0.112660 / 0.296338 (-0.183678) |\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) | 0.436317 / 0.215209 (0.221108) | 4.358277 / 2.077655 (2.280622) | 2.089746 / 1.504120 (0.585626) | 1.881040 / 1.541195 (0.339845) | 1.923653 / 1.468490 (0.455163) | 0.698176 / 4.584777 (-3.886601) | 3.346460 / 3.745712 (-0.399252) | 3.301429 / 5.269862 (-1.968433) | 1.391042 / 4.565676 (-3.174634) | 0.083025 / 0.424275 (-0.341250) | 0.012459 / 0.007607 (0.004851) | 0.533011 / 0.226044 (0.306967) | 5.334984 / 2.268929 (3.066056) | 2.534105 / 55.444624 (-52.910520) | 2.206295 / 6.876477 (-4.670181) | 2.231752 / 2.142072 (0.089680) | 0.798650 / 4.805227 (-4.006577) | 0.150070 / 6.500664 (-6.350594) | 0.066898 / 0.075469 (-0.008571) |\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) | 1.310527 / 1.841788 (-0.531261) | 13.920492 / 8.074308 (5.846184) | 13.359382 / 10.191392 (3.167990) | 0.154561 / 0.680424 (-0.525863) | 0.016387 / 0.534201 (-0.517814) | 0.379892 / 0.579283 (-0.199391) | 0.376746 / 0.434364 (-0.057618) | 0.462606 / 0.540337 (-0.077732) | 0.550895 / 1.386936 (-0.836041) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cac733fdaef84cfee92856bd259ce024ec157c91 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009373 / 0.011353 (-0.001980) | 0.005212 / 0.011008 (-0.005797) | 0.099287 / 0.038508 (0.060779) | 0.035175 / 0.023109 (0.012066) | 0.307012 / 0.275898 (0.031114) | 0.335105 / 0.323480 (0.011625) | 0.008006 / 0.007986 (0.000020) | 0.004017 / 0.004328 (-0.000311) | 0.075519 / 0.004250 (0.071269) | 0.040276 / 0.037052 (0.003223) | 0.302615 / 0.258489 (0.044126) | 0.361742 / 0.293841 (0.067901) | 0.038773 / 0.128546 (-0.089773) | 0.011892 / 0.075646 (-0.063754) | 0.334199 / 0.419271 (-0.085073) | 0.048035 / 0.043533 (0.004503) | 0.301361 / 0.255139 (0.046222) | 0.321996 / 0.283200 (0.038796) | 0.101818 / 0.141683 (-0.039865) | 1.442601 / 1.452155 (-0.009554) | 1.530669 / 1.492716 (0.037953) |\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) | 0.201470 / 0.018006 (0.183464) | 0.496305 / 0.000490 (0.495815) | 0.003794 / 0.000200 (0.003594) | 0.000149 / 0.000054 (0.000094) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028401 / 0.037411 (-0.009010) | 0.107924 / 0.014526 (0.093398) | 0.121716 / 0.176557 (-0.054840) | 0.187407 / 0.737135 (-0.549728) | 0.124755 / 0.296338 (-0.171583) |\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) | 0.395667 / 0.215209 (0.180457) | 3.939079 / 2.077655 (1.861424) | 1.776308 / 1.504120 (0.272188) | 1.583487 / 1.541195 (0.042292) | 1.682957 / 1.468490 (0.214467) | 0.677322 / 4.584777 (-3.907455) | 3.796987 / 3.745712 (0.051275) | 3.406199 / 5.269862 (-1.863663) | 1.905467 / 4.565676 (-2.660210) | 0.083189 / 0.424275 (-0.341086) | 0.012156 / 0.007607 (0.004549) | 0.507078 / 0.226044 (0.281033) | 5.031293 / 2.268929 (2.762365) | 2.228403 / 55.444624 (-53.216221) | 1.885760 / 6.876477 (-4.990717) | 1.962340 / 2.142072 (-0.179732) | 0.824979 / 4.805227 (-3.980248) | 0.162107 / 6.500664 (-6.338557) | 0.062324 / 0.075469 (-0.013145) |\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) | 1.205104 / 1.841788 (-0.636683) | 15.368896 / 8.074308 (7.294588) | 14.757540 / 10.191392 (4.566148) | 0.177544 / 0.680424 (-0.502880) | 0.029097 / 0.534201 (-0.505104) | 0.445252 / 0.579283 (-0.134031) | 0.456521 / 0.434364 (0.022157) | 0.544166 / 0.540337 (0.003829) | 0.640675 / 1.386936 (-0.746261) |\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) | 0.007438 / 0.011353 (-0.003914) | 0.005236 / 0.011008 (-0.005772) | 0.075379 / 0.038508 (0.036871) | 0.033274 / 0.023109 (0.010165) | 0.344584 / 0.275898 (0.068686) | 0.372161 / 0.323480 (0.048681) | 0.005914 / 0.007986 (-0.002071) | 0.004176 / 0.004328 (-0.000152) | 0.073311 / 0.004250 (0.069061) | 0.050845 / 0.037052 (0.013793) | 0.338978 / 0.258489 (0.080489) | 0.391563 / 0.293841 (0.097722) | 0.037559 / 0.128546 (-0.090987) | 0.012455 / 0.075646 (-0.063192) | 0.086224 / 0.419271 (-0.333047) | 0.052956 / 0.043533 (0.009423) | 0.338529 / 0.255139 (0.083390) | 0.356752 / 0.283200 (0.073553) | 0.105864 / 0.141683 (-0.035819) | 1.467727 / 1.452155 (0.015572) | 1.588727 / 1.492716 (0.096010) |\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) | 0.215959 / 0.018006 (0.197953) | 0.440619 / 0.000490 (0.440129) | 0.000397 / 0.000200 (0.000197) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028855 / 0.037411 (-0.008556) | 0.114239 / 0.014526 (0.099713) | 0.121726 / 0.176557 (-0.054830) | 0.190377 / 0.737135 (-0.546759) | 0.127858 / 0.296338 (-0.168480) |\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) | 0.415399 / 0.215209 (0.200190) | 4.159012 / 2.077655 (2.081357) | 1.987593 / 1.504120 (0.483474) | 1.794785 / 1.541195 (0.253591) | 1.924819 / 1.468490 (0.456329) | 0.696082 / 4.584777 (-3.888694) | 3.820461 / 3.745712 (0.074749) | 2.139236 / 5.269862 (-3.130626) | 1.348593 / 4.565676 (-3.217084) | 0.086536 / 0.424275 (-0.337739) | 0.012510 / 0.007607 (0.004902) | 0.518804 / 0.226044 (0.292760) | 5.188659 / 2.268929 (2.919730) | 2.501303 / 55.444624 (-52.943322) | 2.138831 / 6.876477 (-4.737646) | 2.220451 / 2.142072 (0.078378) | 0.836277 / 4.805227 (-3.968950) | 0.170940 / 6.500664 (-6.329724) | 0.067326 / 0.075469 (-0.008143) |\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) | 1.307848 / 1.841788 (-0.533940) | 15.995785 / 8.074308 (7.921477) | 13.646285 / 10.191392 (3.454893) | 0.181120 / 0.680424 (-0.499304) | 0.017500 / 0.534201 (-0.516701) | 0.426697 / 0.579283 (-0.152586) | 0.436702 / 0.434364 (0.002338) | 0.518060 / 0.540337 (-0.022278) | 0.632577 / 1.386936 (-0.754359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cac733fdaef84cfee92856bd259ce024ec157c91 \"CML watermark\")\n"
] | 2023-02-22T12:48:52Z
| 2023-02-22T13:05:55Z
| 2023-02-22T12:56:48Z
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Getting statistics about filtered examples
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"You can actually track a running sum in map() or filter() :)\n\n```python\nnum_filtered = 0\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered += 1\n return condition\n\nds = ds.filter(f)\nprint(num_filtered)\n```\n\nand if you want to use multiprocessing, make sure to use a variable that is shared across processes\n\n\n```python\nfrom multiprocess import Manager\n\nmanager = Manager()\nnum_filtered = manager.Value('i', 0)\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered.value += 1\n return condition\n\nds = ds.filter(f, num_proc=4)\nprint(num_filtered.value)\n```\n\nPS: `datasets` uses `multiprocess` instead of the `multiprocessing` package to support lambda functions in map() and filter()",
"Oh that's great to know!\n\nI guess this value would not be exactly synced with the batch in cases of pre-fetch and shuffle buffers and so on, but that's probably fine. Thanks!"
] | 2025-02-10T20:48:29Z
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@lhoestq wondering if the team has thought about this and if there are any recommendations?
Currently when processing datasets some examples are bound to get filtered out, whether it's due to bad format, or length is too long, or any other custom filters that might be getting applied. Let's just focus on the filter by length for now, since that would be something that gets applied dynamically for each training run. Say we want to show a graph in W&B with the running total of the number of filtered examples so far.
What would be a good way to go about hooking this up? Because the map/filter operations happen before the DataLoader batches are created, at training time if we're just grabbing batches from the DataLoader then we won't know how many things have been filtered already. But there's not really a good way to include a 'num_filtered' key into the dataset itself either because dataset map/filter process examples independently and don't have a way to track a running sum.
The only approach I can kind of think of is having a 'is_filtered' key in the dataset, and then creating a custom batcher/collator that reads that and tracks the metric?
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2023-02-10T19:35:50Z
| 2023-02-10T19:51:45Z
| 2023-02-10T19:49:12Z
|
MEMBER
| null | null | null |
Hi to whoever is reading this! 🤗
## What's in this PR?
~~Basically, I've removed the 🤗`datasets` installation as `python -m pip install ".[quality]" in the `check_code_quality` job in `.github/workflows/ci.yaml`, as we don't need to install the whole package to run the CI, unless that's done on purpose e.g. to check that the Python package installation succeeds before running the tests over the matrix of os?~~
~~So I just wanted to check whether the time was reduced doing this (which I assume it will), plus whether this is something that can be improved, or just discarded in case you're also using that step to make sure that the package can be installed.~~
## What's missing?
~~I was just wondering whether you consider replacing `isort` and `flake8` with `ruff` (if possible), since it's way faster, more information at [`ruff`](https://github.com/charliermarsh/ruff). Before creating this PR the average time of the `check_code_quality` job was around 40s.~~
## Edit
Sorry for the inconvenience this may have caused, didn't realise that the config is defined in `setup.cfg` and `pyproject.toml`, so running those without installing the Python package leads to failure, my bad 😞
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decode mp3 with librosa if torchaudio is > 0.12 as a temporary workaround
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks ! Should we still support torchaudio>0.12 if it works ? And if it doesn't we can explain that downgrading is the right solution, or alternatively use librosa",
"@lhoestq \r\n\r\n> Should we still support torchaudio>0.12 if it works ? And if it doesn't we can explain that downgrading is the right solution, or alternatively use librosa\r\n\r\nI'm not sure here, because from the one hand, if `torchaudio` works - it works 60 times faster then `librosa`.\r\nBut from the other hand, we will get inconsistent behavior (=different results of decoding) for users of `torchaudio>=0.12`. \r\nI'd better go for using `librosa` only to avoid inconsistency then. wdyt?",
"It seems a bit too constraining to not allow users who have a working torchaudio 0.12 setup to not use it. \r\n\r\nIf the issue is about avoiding silent errors if the decoding changes, maybe we can log which back-end is used ? It can even be a warning with performance suggestions (\"you're using librosa but torchaudio 0.xx is recommended\").\r\n\r\nNote that users can still have a requirements.txt or whatever in their projects if they really want full reproducibility (and it's the bare minimum imo)\r\n\r\nThere are multiple possible back-ends so it's maybe not reasonable to only allow one back-end, especially since each back-end has installation constrains and there's no \"best\" back-end.",
"Woohoo all green ! Feel free to merge if it's all good for you :)"
] | 2022-08-31T18:57:59Z
| 2022-11-02T11:54:33Z
| 2022-09-20T13:12:52Z
|
CONTRIBUTOR
| null | null | null |
`torchaudio>0.12` fails with decoding mp3 files if `ffmpeg<4`. currently we ask users to downgrade torchaudio, but sometimes it's not possible as torchaudio version is binded to torch version. as a temporary workaround we can decode mp3 with librosa (though it 60 times slower, at least it works)
another option would be to ask users to install the required version of `ffmpeg`, but is non-trivial on colab: it's not in apt packages in ubuntu 18 and `conda` is not preinstalled (with `conda` it would be easily installable)
- [x] decode with torchaudio anyway if the version of ffmpeg is correct? it's 60 times faster
- [x] tests
- [x] DO NOT FORGET to get back all the tests
see https://github.com/huggingface/datasets/issues/4776 and https://github.com/huggingface/datasets/issues/3663#issuecomment-1225797165 (there is a Colab notebook to reproduce the error)
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"Thanks for reporting @conceptofmind.\r\n\r\nCould you please give details about your environment? \r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```",
"Hi @albertvillanova ,\r\n\r\nHere is the environment information:\r\n```\r\n- `datasets` version: 2.3.2\r\n- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.12\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.2\r\n```\r\nThanks,\r\n\r\nEnrico",
"I think this issue is solved here https://discuss.huggingface.co/t/minhash-deduplication/19992/12?u=loubnabnl, this only happens for very large datasets we will update it in CodeParrot code",
"Hi @loubnabnl,\r\n\r\nYes, the issue is solved in the discussion thread.\r\n\r\nI will close this issue.\r\n\r\nThank you again for all of your help.\r\n\r\nEnrico",
"Thanks @loubnabnl for pointing out the solution to this issue."
] | 2022-08-02T18:36:05Z
| 2022-08-22T09:46:28Z
| 2022-08-20T02:11:53Z
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NONE
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## Describe the bug
Following the example in CodeParrot, I receive an array size limitation error when deduplicating larger datasets.
## Steps to reproduce the bug
```python
dataset_name = "the_pile"
ds = load_dataset(dataset_name, split="train")
ds = ds.map(preprocess, num_proc=num_workers)
uniques = set(ds.unique("hash"))
```
Gists for minimum reproducible example:
https://gist.github.com/conceptofmind/c5804428ea1bd89767815f9cd5f02d9a
https://gist.github.com/conceptofmind/feafb07e236f28d79c2d4b28ffbdb6e2
## Expected results
Chunking and writing out a deduplicated dataset.
## Actual results
```
return dataset._data.column(column).unique().to_pylist()
File "pyarrow/table.pxi", line 394, in pyarrow.lib.ChunkedArray.unique
File "pyarrow/_compute.pyx", line 531, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 330, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 124, in pyarrow.lib.check_status
pyarrow.lib.ArrowCapacityError: array cannot contain more than 2147483646 bytes, have 2147483648
```
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Improve inconsistency of `Dataset.map` interface for `load_from_cache_file`
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[
"Hi, thanks for noticing this! We can't just remove the cache control as this allows us to control where the arrow files generated by the ops are written (cached on disk if enabled or a temporary directory if disabled). The right way to address this inconsistency would be by having `load_from_cache_file=None` by default everywhere.",
"Hi! Yes, this seems more plausible. I can implement that. One last thing is the type annotation `load_from_cache_file: bool = None`. Which I then would change to `load_from_cache_file: Optional[bool] = None`.",
"PR #5515 ",
"Yes, `Optional[bool]` is the correct type annotation and thanks for the PR."
] | 2023-02-08T16:40:44Z
| 2023-02-14T14:26:44Z
| 2023-02-14T14:26:44Z
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CONTRIBUTOR
| null | null |
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### Feature request
1. Replace the `load_from_cache_file` default value to `True`.
2. Remove or alter checks from `is_caching_enabled` logic.
### Motivation
I stumbled over an inconsistency in the `Dataset.map` interface. The documentation (and source) states for the parameter `load_from_cache_file`:
```
load_from_cache_file (`bool`, defaults to `True` if caching is enabled):
If a cache file storing the current computation from `function`
can be identified, use it instead of recomputing.
```
1. `load_from_cache_file` default value is `None`, while being annotated as `bool`
2. It is inconsistent with other method signatures like `filter`, that have the default value `True`
3. The logic is inconsistent, as the `map` method checks if caching is enabled through `is_caching_enabled`. This logic is not used for other similar methods.
### Your contribution
I am not fully aware of the logic behind caching checks. If this is just a inconsistency that historically grew, I would suggest to remove the `is_caching_enabled` logic as the "default" logic. Maybe someone can give insights, if environment variables have a higher priority than local variables or vice versa.
If this is clarified, I could adjust the source according to the "Feature request" section of this issue.
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"_The documentation is not available anymore as the PR was closed or merged._",
"Super, thanks a lot for adding this support, Albert!"
] | 2022-10-05T06:28:39Z
| 2022-10-06T14:43:12Z
| 2022-10-06T14:40:26Z
|
MEMBER
| null | null | null |
This PR supports defining a default config name, even if no predefined allowed config names are set.
Fix #5070.
CC: @stas00
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I_kwDODunzps5lo3i1
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load model error.
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[
"Please report this in the `transformers` repo, as it's not related to `datasets`"
] | 2023-05-11T07:12:38Z
| 2023-05-12T13:44:07Z
| 2023-05-12T13:44:06Z
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### Describe the bug
I had trained one model use deepspeed, when I load the final load I get the follow error:
OSError: Can't load tokenizer for '/XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/home/fm001/hzl/Project/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor' is the correct path to a directory containing all relevant files for a BloomTokenizerFast tokenizer.
my load code is : python chat.py --path /XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor/
### Steps to reproduce the bug
。。。
### Expected behavior
。。。
### Environment info
。。。
|
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PR_kwDODunzps5NxIOc
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Fix xnumpy_load for .npz files
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006498 / 0.011353 (-0.004855) | 0.004406 / 0.011008 (-0.006602) | 0.097136 / 0.038508 (0.058628) | 0.027711 / 0.023109 (0.004601) | 0.303092 / 0.275898 (0.027194) | 0.336804 / 0.323480 (0.013324) | 0.004838 / 0.007986 (-0.003148) | 0.004533 / 0.004328 (0.000204) | 0.075062 / 0.004250 (0.070812) | 0.035105 / 0.037052 (-0.001947) | 0.310245 / 0.258489 (0.051756) | 0.347086 / 0.293841 (0.053245) | 0.030867 / 0.128546 (-0.097679) | 0.011436 / 0.075646 (-0.064211) | 0.320728 / 0.419271 (-0.098544) | 0.042303 / 0.043533 (-0.001230) | 0.308177 / 0.255139 (0.053038) | 0.333673 / 0.283200 (0.050473) | 0.084736 / 0.141683 (-0.056947) | 1.477391 / 1.452155 (0.025237) | 1.530399 / 1.492716 (0.037682) |\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) | 0.212698 / 0.018006 (0.194692) | 0.409098 / 0.000490 (0.408608) | 0.004202 / 0.000200 (0.004002) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022725 / 0.037411 (-0.014686) | 0.095866 / 0.014526 (0.081340) | 0.104153 / 0.176557 (-0.072404) | 0.162964 / 0.737135 (-0.574171) | 0.106505 / 0.296338 (-0.189834) |\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) | 0.431336 / 0.215209 (0.216127) | 4.283290 / 2.077655 (2.205635) | 1.982418 / 1.504120 (0.478298) | 1.762104 / 1.541195 (0.220909) | 1.807528 / 1.468490 (0.339038) | 0.695507 / 4.584777 (-3.889270) | 3.376299 / 3.745712 (-0.369413) | 1.856642 / 5.269862 (-3.413219) | 1.154258 / 4.565676 (-3.411419) | 0.082749 / 0.424275 (-0.341526) | 0.012289 / 0.007607 (0.004682) | 0.525842 / 0.226044 (0.299798) | 5.285764 / 2.268929 (3.016835) | 2.389926 / 55.444624 (-53.054698) | 2.021830 / 6.876477 (-4.854646) | 2.107460 / 2.142072 (-0.034612) | 0.808118 / 4.805227 (-3.997109) | 0.150791 / 6.500664 (-6.349873) | 0.065825 / 0.075469 (-0.009644) |\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) | 1.206939 / 1.841788 (-0.634849) | 13.795902 / 8.074308 (5.721594) | 14.107950 / 10.191392 (3.916558) | 0.144300 / 0.680424 (-0.536124) | 0.016478 / 0.534201 (-0.517723) | 0.379395 / 0.579283 (-0.199888) | 0.388437 / 0.434364 (-0.045927) | 0.451443 / 0.540337 (-0.088894) | 0.523142 / 1.386936 (-0.863794) |\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) | 0.006503 / 0.011353 (-0.004850) | 0.004578 / 0.011008 (-0.006430) | 0.076278 / 0.038508 (0.037770) | 0.028052 / 0.023109 (0.004943) | 0.337873 / 0.275898 (0.061975) | 0.371368 / 0.323480 (0.047888) | 0.005086 / 0.007986 (-0.002899) | 0.003354 / 0.004328 (-0.000975) | 0.076876 / 0.004250 (0.072625) | 0.039146 / 0.037052 (0.002093) | 0.340299 / 0.258489 (0.081810) | 0.381209 / 0.293841 (0.087368) | 0.031771 / 0.128546 (-0.096775) | 0.011670 / 0.075646 (-0.063976) | 0.085156 / 0.419271 (-0.334116) | 0.041990 / 0.043533 (-0.001543) | 0.338644 / 0.255139 (0.083505) | 0.362461 / 0.283200 (0.079262) | 0.089772 / 0.141683 (-0.051911) | 1.480341 / 1.452155 (0.028187) | 1.562815 / 1.492716 (0.070099) |\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) | 0.205700 / 0.018006 (0.187694) | 0.402206 / 0.000490 (0.401716) | 0.001212 / 0.000200 (0.001012) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025172 / 0.037411 (-0.012240) | 0.100959 / 0.014526 (0.086433) | 0.108464 / 0.176557 (-0.068093) | 0.161321 / 0.737135 (-0.575814) | 0.114245 / 0.296338 (-0.182093) |\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) | 0.437425 / 0.215209 (0.222216) | 4.362212 / 2.077655 (2.284557) | 2.068815 / 1.504120 (0.564695) | 1.864089 / 1.541195 (0.322894) | 1.909038 / 1.468490 (0.440548) | 0.696097 / 4.584777 (-3.888680) | 3.358628 / 3.745712 (-0.387084) | 2.999085 / 5.269862 (-2.270777) | 1.533917 / 4.565676 (-3.031760) | 0.083010 / 0.424275 (-0.341266) | 0.012372 / 0.007607 (0.004765) | 0.539926 / 0.226044 (0.313882) | 5.438326 / 2.268929 (3.169397) | 2.498581 / 55.444624 (-52.946043) | 2.153359 / 6.876477 (-4.723117) | 2.177891 / 2.142072 (0.035819) | 0.803169 / 4.805227 (-4.002059) | 0.151079 / 6.500664 (-6.349585) | 0.065981 / 0.075469 (-0.009489) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336682 / 1.841788 (-0.505106) | 14.133055 / 8.074308 (6.058747) | 14.033972 / 10.191392 (3.842580) | 0.152109 / 0.680424 (-0.528315) | 0.016475 / 0.534201 (-0.517726) | 0.387808 / 0.579283 (-0.191475) | 0.378347 / 0.434364 (-0.056017) | 0.484732 / 0.540337 (-0.055606) | 0.569907 / 1.386936 (-0.817029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1c4ec00511868bd881e84a6f7e0333648d833b8e \"CML watermark\")\n"
] | 2023-04-06T13:01:45Z
| 2023-04-07T09:23:54Z
| 2023-04-07T09:16:57Z
|
MEMBER
| null | null | null |
PR:
- #5626
implemented support for streaming `.npy` files by using `numpy.load`.
However, it introduced a bug when used with `.npz` files, within a context manager:
```
ValueError: seek of closed file
```
or in streaming mode:
```
ValueError: I/O operation on closed file.
```
This PR fixes the bug and tests for both `.npy` and `.npz` files.
Fix #5711.
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I_kwDODunzps55kx7n
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|
Image Data loaded Twice
|
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### Describe the bug
When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images
### Steps to reproduce the bug
from datasets import Dataset, load_dataset
dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False)
# print(dataset["train"][0]["image"] == dataset["train"][1]["image"])
print(dataset)
print(dataset["train"]["image"])
print(len(dataset["train"]["image"]))
### Expected behavior
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 8
})
})
[<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>]
8
### Environment info
- `datasets` version: 2.14.5
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.9.17
- Huggingface_hub version: 0.19.4
- PyArrow version: 13.0.0
- Pandas version: 2.0.3
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PR_kwDODunzps46pm9A
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|
Fix xisfile, xgetsize, xisdir, xlistdir in private repo
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"_The documentation is not available anymore as the PR was closed or merged._",
"Added tests for xisfile, xgetsize, xlistdir and xglob for private repos, and also tests for xwalk that was untested"
] | 2022-06-30T15:23:21Z
| 2022-07-06T12:45:59Z
| 2022-07-06T12:34:19Z
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MEMBER
| null | null | null |
`xisfile` is working in a private repository when passing a chained URL to a file inside an archive, e.g. `zip://a.txt::https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`. However it's not working when passing a simple file `https://huggingface/datasets/username/dataset_name/resolve/main/data.zip`.
This is because the authentication headers are not passed correctly in this case.
This is causing dataset streaming to fail in private parquet repositories, as noted in https://github.com/huggingface/datasets/issues/4605
I fixed `xisfile` and the other functions that behave the same way: xgetsize, xisdir and xlistdir
TODO:
- [x] tests
fix https://github.com/huggingface/datasets/issues/4605
|
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I_kwDODunzps5XoC9h
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Bug in sample_by="paragraph"
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[
"Thanks for reporting, @adampauls.\r\n\r\nWe are having a look at it. "
] | 2022-11-30T19:24:13Z
| 2022-12-01T15:19:02Z
| 2022-12-01T15:19:02Z
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### Describe the bug
I think [this line](https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/text/text.py#L96) is wrong and should be `batch = f.read(self.config.chunksize)`. Otherwise it will never terminate because even when `f` is finished reading, `batch` will still be truthy from the last iteration.
### Steps to reproduce the bug
```
> cat test.txt
a b c
d e f
````
```python
>>> import datasets
>>> datasets.load_dataset("text", data_files={"train":"test.txt"}, sample_by="paragraph")
```
This will go on forever.
### Expected behavior
Terminates very quickly.
### Environment info
`version = "2.6.1"` but I think the bug is still there on main.
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Support JSON lines with empty struct
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] | 2024-09-23T11:16:12Z
| 2024-09-23T11:30:08Z
| 2024-09-23T11:30:06Z
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MEMBER
| null | null | null |
Support JSON lines with empty struct.
Fix #7161.
Related to:
- #7160
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Require Pillow >= 9.4.0 to avoid AttributeError when loading image dataset
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6883). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Do you think this is worth making a patch release for?\r\nCC: @huggingface/datasets ",
"<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) | 0.005764 / 0.011353 (-0.005589) | 0.004182 / 0.011008 (-0.006826) | 0.064520 / 0.038508 (0.026012) | 0.034260 / 0.023109 (0.011151) | 0.245677 / 0.275898 (-0.030221) | 0.277889 / 0.323480 (-0.045591) | 0.004569 / 0.007986 (-0.003417) | 0.002905 / 0.004328 (-0.001423) | 0.049346 / 0.004250 (0.045095) | 0.050529 / 0.037052 (0.013476) | 0.264718 / 0.258489 (0.006229) | 0.295705 / 0.293841 (0.001864) | 0.028144 / 0.128546 (-0.100402) | 0.011048 / 0.075646 (-0.064598) | 0.206290 / 0.419271 (-0.212982) | 0.035886 / 0.043533 (-0.007647) | 0.245038 / 0.255139 (-0.010101) | 0.269835 / 0.283200 (-0.013365) | 0.018927 / 0.141683 (-0.122756) | 1.136536 / 1.452155 (-0.315619) | 1.183256 / 1.492716 (-0.309460) |\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) | 0.115372 / 0.018006 (0.097366) | 0.315471 / 0.000490 (0.314982) | 0.000238 / 0.000200 (0.000038) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021201 / 0.037411 (-0.016210) | 0.070374 / 0.014526 (0.055848) | 0.077557 / 0.176557 (-0.099000) | 0.124713 / 0.737135 (-0.612423) | 0.078850 / 0.296338 (-0.217489) |\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) | 0.278674 / 0.215209 (0.063465) | 2.739597 / 2.077655 (0.661942) | 1.438214 / 1.504120 (-0.065906) | 1.326373 / 1.541195 (-0.214822) | 1.370961 / 1.468490 (-0.097529) | 0.569160 / 4.584777 (-4.015617) | 2.411890 / 3.745712 (-1.333822) | 2.954073 / 5.269862 (-2.315788) | 1.816883 / 4.565676 (-2.748794) | 0.063123 / 0.424275 (-0.361152) | 0.005531 / 0.007607 (-0.002076) | 0.328184 / 0.226044 (0.102140) | 3.263083 / 2.268929 (0.994155) | 1.809159 / 55.444624 (-53.635465) | 1.535257 / 6.876477 (-5.341220) | 1.583428 / 2.142072 (-0.558644) | 0.642950 / 4.805227 (-4.162277) | 0.122240 / 6.500664 (-6.378424) | 0.044596 / 0.075469 (-0.030873) |\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) | 0.999993 / 1.841788 (-0.841795) | 12.941508 / 8.074308 (4.867200) | 10.417519 / 10.191392 (0.226127) | 0.134345 / 0.680424 (-0.546079) | 0.014651 / 0.534201 (-0.519550) | 0.288660 / 0.579283 (-0.290623) | 0.274550 / 0.434364 (-0.159814) | 0.327785 / 0.540337 (-0.212553) | 0.422954 / 1.386936 (-0.963982) |\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) | 0.006051 / 0.011353 (-0.005302) | 0.003926 / 0.011008 (-0.007082) | 0.051480 / 0.038508 (0.012972) | 0.036102 / 0.023109 (0.012992) | 0.273358 / 0.275898 (-0.002540) | 0.293261 / 0.323480 (-0.030219) | 0.004562 / 0.007986 (-0.003424) | 0.002918 / 0.004328 (-0.001410) | 0.050386 / 0.004250 (0.046135) | 0.048427 / 0.037052 (0.011375) | 0.280178 / 0.258489 (0.021689) | 0.314599 / 0.293841 (0.020758) | 0.030876 / 0.128546 (-0.097670) | 0.010571 / 0.075646 (-0.065076) | 0.058555 / 0.419271 (-0.360717) | 0.034974 / 0.043533 (-0.008559) | 0.266604 / 0.255139 (0.011465) | 0.284712 / 0.283200 (0.001512) | 0.020296 / 0.141683 (-0.121387) | 1.116760 / 1.452155 (-0.335395) | 1.157794 / 1.492716 (-0.334922) |\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) | 0.103777 / 0.018006 (0.085771) | 0.314267 / 0.000490 (0.313778) | 0.000226 / 0.000200 (0.000026) | 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) | 0.023837 / 0.037411 (-0.013574) | 0.082145 / 0.014526 (0.067619) | 0.090434 / 0.176557 (-0.086123) | 0.132096 / 0.737135 (-0.605040) | 0.092426 / 0.296338 (-0.203913) |\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) | 0.299554 / 0.215209 (0.084345) | 2.932382 / 2.077655 (0.854727) | 1.549994 / 1.504120 (0.045874) | 1.454944 / 1.541195 (-0.086251) | 1.474987 / 1.468490 (0.006497) | 0.586149 / 4.584777 (-3.998628) | 0.972118 / 3.745712 (-2.773594) | 2.991719 / 5.269862 (-2.278142) | 1.876365 / 4.565676 (-2.689311) | 0.065178 / 0.424275 (-0.359098) | 0.005114 / 0.007607 (-0.002493) | 0.353704 / 0.226044 (0.127660) | 3.500940 / 2.268929 (1.232012) | 1.965581 / 55.444624 (-53.479043) | 1.662594 / 6.876477 (-5.213883) | 1.702761 / 2.142072 (-0.439311) | 0.663879 / 4.805227 (-4.141348) | 0.120036 / 6.500664 (-6.380628) | 0.043195 / 0.075469 (-0.032274) |\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) | 0.997690 / 1.841788 (-0.844098) | 13.448914 / 8.074308 (5.374606) | 10.132469 / 10.191392 (-0.058923) | 0.148493 / 0.680424 (-0.531930) | 0.016670 / 0.534201 (-0.517531) | 0.289708 / 0.579283 (-0.289575) | 0.132938 / 0.434364 (-0.301425) | 0.411425 / 0.540337 (-0.128913) | 0.430748 / 1.386936 (-0.956188) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70e38090f070d323d452b5e746686f31b1086bd8 \"CML watermark\")\n",
"maybe not super important since it was not reported by users, this can be included in the next release",
"I observed the same AttributeError with Pillow == 10.3.0, while 9.4.0 works for me.",
"What's the error you're getting @Eric2i ?\r\n\r\nOn my side on 10.3.0 I could run this without errors:\r\n\r\n```python\r\nimport PIL.Image\r\nPIL.Image.ExifTags.Base.Orientation is not None # True\r\n```",
"Sorry, false alarm. I double-checked that 10.3.0 is also good on my side. Thanks for your sample codes.",
"I just faced the same bug after installing recent versions of Huggingface and datasets in a new environment. I solved it by uninstalling the recent version of Pillow and sticking to 9.4.0.\r\n`pip uninstall Pillow`\r\n`pip install Pillow==9.4.0` \r\n",
"> I just faced the same bug after installing recent versions of Huggingface and datasets in a new environment. I solved it by uninstalling the recent version of Pillow and sticking to 9.4.0. `pip uninstall Pillow` `pip install Pillow==9.4.0`\r\n\r\nThanks! That error was annoying and this fixed it for me.",
"Just to say I also bumped into this and this issue was very helpful for finding the right pillow version. Thanks."
] | 2024-05-08T06:43:29Z
| 2024-08-28T13:13:57Z
| 2024-05-16T14:34:02Z
|
MEMBER
| null | null | null |
Require Pillow >= 9.4.0 to avoid AttributeError when loading image dataset.
The `PIL.Image.ExifTags` that we use in our code was implemented in Pillow-9.4.0: https://github.com/python-pillow/Pillow/commit/24a5405a9f7ea22f28f9c98b3e407292ea5ee1d3
The bug #6881 was introduced in datasets-2.19.0 by this PR:
- #6739
Fix #6881.
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I_kwDODunzps5SM4aC
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Get a specific Exception when the dataset has no data
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[] | 2022-09-20T09:31:59Z
| 2022-09-21T12:21:25Z
| 2022-09-21T12:21:25Z
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COLLABORATOR
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In the dataset viewer on the Hub (https://huggingface.co/datasets/glue/viewer), we would like (https://github.com/huggingface/moon-landing/issues/3882) to show a specific message when the repository lacks any data files.
In that case, instead of showing a complex traceback, we want to show a call to action to help the user upload data.
To do that, it would be very helpful to know for sure that the repository is missing any (supported) data files.
It could be done by raising a custom exception, for example, `NoDataError`.
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[DOC] Mistaken docs on set_format
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"Thanks for reporting!"
] | 2023-02-27T08:03:09Z
| 2023-02-28T19:19:17Z
| 2023-02-28T19:19:17Z
|
CONTRIBUTOR
| null | null |
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### Describe the bug
https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format
<img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png">
While actually running it will result in:
<img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png">
### Steps to reproduce the bug
_
### Expected behavior
_
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
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PR_kwDODunzps6Rd4Zp
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Added cache dirs to load and file_utils
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"hi ! the `hf_hub_download` cache_dir is a different cache directory than the one for `datasets`.\r\n\r\n`hf_hub_download` uses the `huggingface_hub` cache which is located in by default in `~/.cache/huggingface/hub`, while `datasets` uses a different cache for Arrow files and map() results `~/.cache/huggingface/datasets`",
"Is there a way to change the default cache directory for both of these on calling load_dataset? Currently, cache_dir makes dealing with where I want files to go a bit confusing as the documentation doesn't mention it only relocates.../datasets and not .../hub.",
"You can set `HF_HOME` which is the common parent directory for those two caches. Or individually `HF_DATASETS_CACHE` and `HF_HUB_CACHE`",
"Got it. Can this be added to the documentation for load_dataset and related functions to avoid confusion with cache_dir?"
] | 2025-04-04T22:36:04Z
| 2025-04-15T13:19:15Z
| null |
NONE
| null | null | null |
When adding "cache_dir" to datasets.load_dataset, the cache_dir gets lost in the function calls, changing the cache dir to the default path. This fixes a few of these instances.
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Intermittent JSON parse error when streaming the Pile
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"Maybe #2838 can help. In this PR we allow to skip bad chunks of JSON data to not crash the training\r\n\r\nDid you have warning messages before the error ?\r\n\r\nsomething like this maybe ?\r\n```\r\n03/24/2022 02:19:46 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 5sec [1/20]\r\n03/24/2022 02:20:01 - WARNING - datasets.utils.streaming_download_manager - Got disconnected from remote data host. Retrying in 5sec [2/20]\r\n03/24/2022 02:20:09 - ERROR - datasets.packaged_modules.json.json - Failed to read file 'gzip://file-000000000007.json::https://huggingface.co/datasets/lvwerra/codeparrot-clean-train/resolve/1d740acb9d09cf7a3307553323e2c677a6535407/file-000000000007.json.gz' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0\r\n```",
"Ah, thanks! I did get errors like that. Sad that PR wasn't merged in! \r\n\r\nI'm currently just downloading 200GB of the Pile locally to avoid streaming (I have space and it's faster anyway), but that's really useful! I can probably apply the dumb patch of just commenting out the bits that raise the JSON Parse Error lol, based on your code - if I continue the loop should it be fine?",
"Yup you can get some inspiration from this PR. It simply ignores the bad chunks (a chunk is ~a few MBs of data).\r\nWe'll try to merge this PR soon"
] | 2022-10-02T11:56:46Z
| 2022-10-04T17:59:03Z
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NONE
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## Describe the bug
I have an intermittent error when streaming the Pile, where I get a JSON parse error which causes my program to crash.
This is intermittent - when I rerun the program with the same random seed it does not crash in the same way. The exact point this happens also varied - it happened to me 11B tokens and 4 days into a training run, and now just happened 2 minutes into one, but I can't reliably reproduce it.
I'm using a remote machine with 8 A6000 GPUs via runpod.io
## Expected results
I have a DataLoader which can iterate through the whole Pile
## Actual results
Stack trace:
```
Failed to read file 'zstd://12.jsonl::https://the-eye.eu/public/AI/pile/train/12.jsonl.zst' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0
```
I'm currently using HuggingFace accelerate, which also gave me the following stack trace, but I've also experienced this problem intermittently when using DataParallel, so I don't think it's to do with parallelisation
```
Traceback (most recent call last):
File "ddp_script.py", line 1258, in <module>
main()
File "ddp_script.py", line 1143, in main
for c, batch in tqdm.tqdm(enumerate(data_iter)):
File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 503, in __iter__
next_batch, next_batch_info, next_skip = self._fetch_batches(main_iterator)
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 454, in _fetch_batches
broadcast_object_list(batch_info)
File "/opt/conda/lib/python3.7/site-packages/accelerate/utils/operations.py", line 333, in broadcast_object_list
torch.distributed.broadcast_object_list(object_list, src=from_process)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1900, in broadcast_object_list
object_list[i] = _tensor_to_object(obj_view, obj_size)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1571, in _tensor_to_object
return _unpickler(io.BytesIO(buf)).load()
_pickle.UnpicklingError: invalid load key, '@'.
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset(
cfg["dataset_name"], streaming=True, split="train")
dataset = dataset.remove_columns("meta")
dataset = dataset.map(tokenize_and_concatenate, batched=True)
dataset = dataset.with_format(type="torch")
train_data_loader = DataLoader(
dataset, batch_size=cfg["batch_size"], num_workers=3)
for batch in train_data_loader:
continue
```
`tokenize_and_concatenate` is a custom tokenization function I defined on the GPT-NeoX tokenizer to tokenize the text, separated by endoftext tokens, and reshape to have length batch_size, I don't think this is related to tokenization:
```
import numpy as np
import einops
import torch
def tokenize_and_concatenate(examples):
texts = examples["text"]
full_text = tokenizer.eos_token.join(texts)
div = 20
length = len(full_text) // div
text_list = [full_text[i * length: (i + 1) * length]
for i in range(div)]
tokens = tokenizer(text_list, return_tensors="np", padding=True)[
"input_ids"
].flatten()
tokens = tokens[tokens != tokenizer.pad_token_id]
n = len(tokens)
curr_batch_size = n // (seq_len - 1)
tokens = tokens[: (seq_len - 1) * curr_batch_size]
tokens = einops.rearrange(
tokens,
"(batch_size seq) -> batch_size seq",
batch_size=curr_batch_size,
seq=seq_len - 1,
)
prefix = np.ones((curr_batch_size, 1), dtype=np.int64) * \
tokenizer.bos_token_id
return {
"text": np.concatenate([prefix, tokens], axis=1)
}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-105-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
ZStandard data:
Version: 0.18.0
Summary: Zstandard bindings for Python
Home-page: https://github.com/indygreg/python-zstandard
Author: Gregory Szorc
Author-email: gregory.szorc@gmail.com
License: BSD
Location: /opt/conda/lib/python3.7/site-packages
Requires:
Required-by:
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Fix ArrayXD YAML conversion
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6168). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009350 / 0.011353 (-0.002003) | 0.005658 / 0.011008 (-0.005350) | 0.123173 / 0.038508 (0.084664) | 0.096354 / 0.023109 (0.073244) | 0.464398 / 0.275898 (0.188500) | 0.544455 / 0.323480 (0.220975) | 0.007337 / 0.007986 (-0.000648) | 0.004424 / 0.004328 (0.000096) | 0.089715 / 0.004250 (0.085465) | 0.072462 / 0.037052 (0.035410) | 0.460601 / 0.258489 (0.202112) | 0.544384 / 0.293841 (0.250543) | 0.052994 / 0.128546 (-0.075552) | 0.014459 / 0.075646 (-0.061187) | 0.464368 / 0.419271 (0.045096) | 0.072889 / 0.043533 (0.029356) | 0.471387 / 0.255139 (0.216248) | 0.560982 / 0.283200 (0.277783) | 0.041398 / 0.141683 (-0.100285) | 1.964688 / 1.452155 (0.512533) | 2.240727 / 1.492716 (0.748011) |\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) | 0.308524 / 0.018006 (0.290518) | 0.669306 / 0.000490 (0.668816) | 0.006644 / 0.000200 (0.006444) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037395 / 0.037411 (-0.000016) | 0.111303 / 0.014526 (0.096777) | 0.158988 / 0.176557 (-0.017569) | 0.236155 / 0.737135 (-0.500980) | 0.134775 / 0.296338 (-0.161564) |\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) | 0.648830 / 0.215209 (0.433621) | 6.614794 / 2.077655 (4.537139) | 2.867526 / 1.504120 (1.363407) | 2.472967 / 1.541195 (0.931772) | 2.488419 / 1.468490 (1.019929) | 0.915785 / 4.584777 (-3.668992) | 6.010754 / 3.745712 (2.265042) | 5.468873 / 5.269862 (0.199011) | 3.446535 / 4.565676 (-1.119141) | 0.118592 / 0.424275 (-0.305684) | 0.012005 / 0.007607 (0.004398) | 0.808467 / 0.226044 (0.582423) | 8.152122 / 2.268929 (5.883193) | 3.751282 / 55.444624 (-51.693342) | 3.009569 / 6.876477 (-3.866908) | 3.282613 / 2.142072 (1.140540) | 1.152727 / 4.805227 (-3.652500) | 0.240224 / 6.500664 (-6.260440) | 0.097871 / 0.075469 (0.022402) |\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) | 1.824944 / 1.841788 (-0.016843) | 27.840842 / 8.074308 (19.766533) | 24.368669 / 10.191392 (14.177277) | 0.260621 / 0.680424 (-0.419803) | 0.033730 / 0.534201 (-0.500471) | 0.552494 / 0.579283 (-0.026789) | 0.666921 / 0.434364 (0.232557) | 0.648812 / 0.540337 (0.108475) | 0.912602 / 1.386936 (-0.474334) |\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) | 0.011688 / 0.011353 (0.000335) | 0.005794 / 0.011008 (-0.005215) | 0.093466 / 0.038508 (0.054958) | 0.102583 / 0.023109 (0.079474) | 0.593572 / 0.275898 (0.317674) | 0.614351 / 0.323480 (0.290871) | 0.007006 / 0.007986 (-0.000980) | 0.005557 / 0.004328 (0.001229) | 0.087779 / 0.004250 (0.083529) | 0.072639 / 0.037052 (0.035586) | 0.577464 / 0.258489 (0.318975) | 0.628240 / 0.293841 (0.334399) | 0.053876 / 0.128546 (-0.074670) | 0.015383 / 0.075646 (-0.060263) | 0.110633 / 0.419271 (-0.308639) | 0.067467 / 0.043533 (0.023934) | 0.613457 / 0.255139 (0.358318) | 0.604939 / 0.283200 (0.321739) | 0.041738 / 0.141683 (-0.099945) | 1.967167 / 1.452155 (0.515012) | 2.121009 / 1.492716 (0.628293) |\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) | 0.449937 / 0.018006 (0.431930) | 0.694410 / 0.000490 (0.693921) | 0.064051 / 0.000200 (0.063851) | 0.000810 / 0.000054 (0.000756) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.045138 / 0.037411 (0.007727) | 0.116831 / 0.014526 (0.102306) | 0.131906 / 0.176557 (-0.044651) | 0.202421 / 0.737135 (-0.534714) | 0.132568 / 0.296338 (-0.163770) |\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) | 0.698046 / 0.215209 (0.482837) | 7.112591 / 2.077655 (5.034936) | 3.332679 / 1.504120 (1.828559) | 2.946384 / 1.541195 (1.405189) | 3.074484 / 1.468490 (1.605994) | 0.970917 / 4.584777 (-3.613859) | 6.143506 / 3.745712 (2.397794) | 5.572496 / 5.269862 (0.302634) | 3.602673 / 4.565676 (-0.963004) | 0.115068 / 0.424275 (-0.309207) | 0.009971 / 0.007607 (0.002364) | 0.891090 / 0.226044 (0.665046) | 8.761788 / 2.268929 (6.492859) | 4.362685 / 55.444624 (-51.081939) | 3.612893 / 6.876477 (-3.263583) | 3.797948 / 2.142072 (1.655876) | 1.202890 / 4.805227 (-3.602337) | 0.238120 / 6.500664 (-6.262544) | 0.095612 / 0.075469 (0.020143) |\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) | 1.958880 / 1.841788 (0.117092) | 28.216454 / 8.074308 (20.142146) | 25.361424 / 10.191392 (15.170032) | 0.308203 / 0.680424 (-0.372221) | 0.032903 / 0.534201 (-0.501298) | 0.539714 / 0.579283 (-0.039569) | 0.688278 / 0.434364 (0.253914) | 0.644818 / 0.540337 (0.104481) | 0.905694 / 1.386936 (-0.481242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5289345e5b23548fee680a0bbc047c0b9a5ee8c \"CML watermark\")\n",
"Maybe convert all the tuples by default instead of hardcoding a logic specific to ArrayXD ?",
"@mariosasko Have you been able to fix this issue ? we're having quite a rough time updating our dataset lately",
"@lhoestq Does it look good now?",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005519 / 0.011353 (-0.005834) | 0.003482 / 0.011008 (-0.007527) | 0.064904 / 0.038508 (0.026396) | 0.052399 / 0.023109 (0.029289) | 0.247238 / 0.275898 (-0.028660) | 0.273426 / 0.323480 (-0.050054) | 0.003102 / 0.007986 (-0.004884) | 0.003420 / 0.004328 (-0.000908) | 0.048029 / 0.004250 (0.043779) | 0.039378 / 0.037052 (0.002326) | 0.253809 / 0.258489 (-0.004680) | 0.287483 / 0.293841 (-0.006358) | 0.028096 / 0.128546 (-0.100450) | 0.010806 / 0.075646 (-0.064841) | 0.207799 / 0.419271 (-0.211472) | 0.035861 / 0.043533 (-0.007672) | 0.251912 / 0.255139 (-0.003227) | 0.278877 / 0.283200 (-0.004323) | 0.019498 / 0.141683 (-0.122185) | 1.104916 / 1.452155 (-0.347238) | 1.157376 / 1.492716 (-0.335340) |\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) | 0.093051 / 0.018006 (0.075045) | 0.303331 / 0.000490 (0.302841) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 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) | 0.018052 / 0.037411 (-0.019359) | 0.060597 / 0.014526 (0.046071) | 0.074033 / 0.176557 (-0.102524) | 0.120966 / 0.737135 (-0.616169) | 0.075012 / 0.296338 (-0.221326) |\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) | 0.283922 / 0.215209 (0.068713) | 2.805445 / 2.077655 (0.727791) | 1.498292 / 1.504120 (-0.005828) | 1.371686 / 1.541195 (-0.169509) | 1.402074 / 1.468490 (-0.066416) | 0.567231 / 4.584777 (-4.017546) | 2.393291 / 3.745712 (-1.352422) | 2.800329 / 5.269862 (-2.469533) | 1.789197 / 4.565676 (-2.776479) | 0.063620 / 0.424275 (-0.360655) | 0.005008 / 0.007607 (-0.002600) | 0.338929 / 0.226044 (0.112884) | 3.292122 / 2.268929 (1.023193) | 1.813313 / 55.444624 (-53.631311) | 1.557122 / 6.876477 (-5.319354) | 1.576395 / 2.142072 (-0.565677) | 0.666714 / 4.805227 (-4.138513) | 0.118253 / 6.500664 (-6.382411) | 0.042633 / 0.075469 (-0.032836) |\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) | 0.950678 / 1.841788 (-0.891110) | 11.589806 / 8.074308 (3.515498) | 10.436701 / 10.191392 (0.245309) | 0.141048 / 0.680424 (-0.539376) | 0.014766 / 0.534201 (-0.519435) | 0.298359 / 0.579283 (-0.280924) | 0.268850 / 0.434364 (-0.165514) | 0.340242 / 0.540337 (-0.200095) | 0.451447 / 1.386936 (-0.935489) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005405 / 0.011353 (-0.005948) | 0.003545 / 0.011008 (-0.007464) | 0.048959 / 0.038508 (0.010451) | 0.056565 / 0.023109 (0.033455) | 0.274289 / 0.275898 (-0.001609) | 0.296565 / 0.323480 (-0.026915) | 0.004790 / 0.007986 (-0.003196) | 0.002772 / 0.004328 (-0.001557) | 0.048605 / 0.004250 (0.044354) | 0.040676 / 0.037052 (0.003624) | 0.279949 / 0.258489 (0.021460) | 0.312816 / 0.293841 (0.018976) | 0.029605 / 0.128546 (-0.098941) | 0.010799 / 0.075646 (-0.064848) | 0.056941 / 0.419271 (-0.362331) | 0.034518 / 0.043533 (-0.009014) | 0.277193 / 0.255139 (0.022054) | 0.292334 / 0.283200 (0.009134) | 0.018836 / 0.141683 (-0.122847) | 1.145228 / 1.452155 (-0.306927) | 1.198958 / 1.492716 (-0.293758) |\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) | 0.093618 / 0.018006 (0.075612) | 0.303687 / 0.000490 (0.303197) | 0.000234 / 0.000200 (0.000034) | 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) | 0.021347 / 0.037411 (-0.016065) | 0.067811 / 0.014526 (0.053286) | 0.080631 / 0.176557 (-0.095926) | 0.119289 / 0.737135 (-0.617846) | 0.082085 / 0.296338 (-0.214254) |\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) | 0.293374 / 0.215209 (0.078165) | 2.864516 / 2.077655 (0.786861) | 1.611042 / 1.504120 (0.106922) | 1.466124 / 1.541195 (-0.075071) | 1.480509 / 1.468490 (0.012019) | 0.569463 / 4.584777 (-4.015314) | 2.448181 / 3.745712 (-1.297531) | 2.841732 / 5.269862 (-2.428130) | 1.754458 / 4.565676 (-2.811219) | 0.063771 / 0.424275 (-0.360505) | 0.004976 / 0.007607 (-0.002631) | 0.346094 / 0.226044 (0.120050) | 3.440090 / 2.268929 (1.171162) | 1.961862 / 55.444624 (-53.482763) | 1.675780 / 6.876477 (-5.200697) | 1.679676 / 2.142072 (-0.462396) | 0.641063 / 4.805227 (-4.164164) | 0.116268 / 6.500664 (-6.384396) | 0.041767 / 0.075469 (-0.033702) |\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) | 0.980286 / 1.841788 (-0.861502) | 12.055227 / 8.074308 (3.980919) | 10.685417 / 10.191392 (0.494025) | 0.140842 / 0.680424 (-0.539582) | 0.015413 / 0.534201 (-0.518788) | 0.286939 / 0.579283 (-0.292344) | 0.278796 / 0.434364 (-0.155568) | 0.326740 / 0.540337 (-0.213597) | 0.574516 / 1.386936 (-0.812421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53b8c2d9d3714f4a56d74637c0840a90cbfb7aa0 \"CML watermark\")\n"
] | 2023-08-22T17:02:54Z
| 2023-12-12T15:06:59Z
| 2023-12-12T15:00:43Z
|
COLLABORATOR
| null | null | null |
Replace the `shape` tuple with a list in the `ArrayXD` YAML conversion.
Fix #6112
|
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The following code:
```python
import datasets
train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)
train_dataset.save_to_disk("s3://datasets/", fs=s3)
```
produces following traceback:
```shell
File "C:\Users\Hong Knop\AppData\Local\Programs\Python\Python310\lib\site-packages\botocore\auth.py", line 374, in scope
return '/'.join(scope)
```
I invoke print(scope) in <auth.py> (line 373) and find this:
```python
[('4VA08VLL3VTKQJKCAI8M',), '20220803', 'us-east-1', 's3', 'aws4_request']
```
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PR_kwDODunzps5hONZc
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Deprecate Beam API and download from HF GCS bucket
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6474). 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) | 0.005280 / 0.011353 (-0.006073) | 0.003770 / 0.011008 (-0.007238) | 0.064320 / 0.038508 (0.025812) | 0.031250 / 0.023109 (0.008141) | 0.245113 / 0.275898 (-0.030785) | 0.268646 / 0.323480 (-0.054834) | 0.003147 / 0.007986 (-0.004839) | 0.002736 / 0.004328 (-0.001592) | 0.050802 / 0.004250 (0.046551) | 0.044539 / 0.037052 (0.007487) | 0.261702 / 0.258489 (0.003213) | 0.304696 / 0.293841 (0.010855) | 0.028601 / 0.128546 (-0.099945) | 0.010731 / 0.075646 (-0.064915) | 0.208183 / 0.419271 (-0.211089) | 0.036597 / 0.043533 (-0.006936) | 0.245107 / 0.255139 (-0.010032) | 0.265936 / 0.283200 (-0.017264) | 0.019430 / 0.141683 (-0.122253) | 1.184697 / 1.452155 (-0.267458) | 1.206073 / 1.492716 (-0.286643) |\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) | 0.110242 / 0.018006 (0.092236) | 0.304185 / 0.000490 (0.303695) | 0.000220 / 0.000200 (0.000020) | 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) | 0.018839 / 0.037411 (-0.018573) | 0.062840 / 0.014526 (0.048314) | 0.075849 / 0.176557 (-0.100708) | 0.122983 / 0.737135 (-0.614153) | 0.075352 / 0.296338 (-0.220987) |\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) | 0.283617 / 0.215209 (0.068408) | 2.801903 / 2.077655 (0.724248) | 1.447678 / 1.504120 (-0.056442) | 1.327561 / 1.541195 (-0.213633) | 1.348714 / 1.468490 (-0.119776) | 0.574283 / 4.584777 (-4.010493) | 2.449396 / 3.745712 (-1.296316) | 2.908659 / 5.269862 (-2.361203) | 1.813935 / 4.565676 (-2.751742) | 0.063141 / 0.424275 (-0.361134) | 0.005024 / 0.007607 (-0.002583) | 0.338854 / 0.226044 (0.112810) | 3.346680 / 2.268929 (1.077752) | 1.833792 / 55.444624 (-53.610832) | 1.553680 / 6.876477 (-5.322796) | 1.590403 / 2.142072 (-0.551670) | 0.657442 / 4.805227 (-4.147785) | 0.119961 / 6.500664 (-6.380703) | 0.042602 / 0.075469 (-0.032867) |\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) | 0.977510 / 1.841788 (-0.864278) | 12.105448 / 8.074308 (4.031140) | 9.635282 / 10.191392 (-0.556110) | 0.132453 / 0.680424 (-0.547971) | 0.014225 / 0.534201 (-0.519976) | 0.292847 / 0.579283 (-0.286436) | 0.271498 / 0.434364 (-0.162866) | 0.329785 / 0.540337 (-0.210552) | 0.435058 / 1.386936 (-0.951879) |\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) | 0.005656 / 0.011353 (-0.005697) | 0.003903 / 0.011008 (-0.007106) | 0.050670 / 0.038508 (0.012162) | 0.032405 / 0.023109 (0.009296) | 0.271443 / 0.275898 (-0.004455) | 0.298989 / 0.323480 (-0.024491) | 0.004397 / 0.007986 (-0.003589) | 0.003027 / 0.004328 (-0.001301) | 0.050227 / 0.004250 (0.045977) | 0.047798 / 0.037052 (0.010745) | 0.287610 / 0.258489 (0.029120) | 0.314084 / 0.293841 (0.020243) | 0.030406 / 0.128546 (-0.098140) | 0.011159 / 0.075646 (-0.064487) | 0.059510 / 0.419271 (-0.359762) | 0.056842 / 0.043533 (0.013309) | 0.274653 / 0.255139 (0.019514) | 0.290175 / 0.283200 (0.006976) | 0.019822 / 0.141683 (-0.121861) | 1.168894 / 1.452155 (-0.283261) | 1.205867 / 1.492716 (-0.286850) |\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) | 0.094907 / 0.018006 (0.076901) | 0.304920 / 0.000490 (0.304431) | 0.000218 / 0.000200 (0.000018) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023015 / 0.037411 (-0.014396) | 0.076868 / 0.014526 (0.062342) | 0.089099 / 0.176557 (-0.087458) | 0.128786 / 0.737135 (-0.608350) | 0.090836 / 0.296338 (-0.205503) |\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) | 0.284552 / 0.215209 (0.069343) | 2.800870 / 2.077655 (0.723215) | 1.577167 / 1.504120 (0.073047) | 1.448788 / 1.541195 (-0.092406) | 1.475493 / 1.468490 (0.007003) | 0.566639 / 4.584777 (-4.018138) | 2.503671 / 3.745712 (-1.242041) | 2.796769 / 5.269862 (-2.473092) | 1.829344 / 4.565676 (-2.736332) | 0.064200 / 0.424275 (-0.360075) | 0.005066 / 0.007607 (-0.002541) | 0.335390 / 0.226044 (0.109346) | 3.421265 / 2.268929 (1.152336) | 1.942245 / 55.444624 (-53.502379) | 1.641401 / 6.876477 (-5.235076) | 1.824574 / 2.142072 (-0.317499) | 0.661379 / 4.805227 (-4.143849) | 0.117207 / 6.500664 (-6.383457) | 0.041630 / 0.075469 (-0.033839) |\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) | 1.009636 / 1.841788 (-0.832152) | 12.998379 / 8.074308 (4.924071) | 10.609013 / 10.191392 (0.417621) | 0.142794 / 0.680424 (-0.537630) | 0.015648 / 0.534201 (-0.518553) | 0.292825 / 0.579283 (-0.286458) | 0.280790 / 0.434364 (-0.153574) | 0.329016 / 0.540337 (-0.211322) | 0.442440 / 1.386936 (-0.944496) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f90b65d6ccaceaa532b851a1010ba3dcc77b8b42 \"CML watermark\")\n"
] | 2023-12-05T19:51:33Z
| 2024-03-12T14:56:25Z
| 2024-03-12T14:50:12Z
|
COLLABORATOR
| null | null | null |
Deprecate the Beam API and download from the HF GCS bucked.
TODO:
- [x] Convert the Beam-based [`wikipedia`](https://huggingface.co/datasets/wikipedia) to an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wikipedia/discussions/19))
- [x] Make [`natural_questions`](https://huggingface.co/datasets/natural_questions) a no-code dataset ([Hub PR](https://huggingface.co/datasets/natural_questions/discussions/7))
- [x] Make [`wiki40b`](https://huggingface.co/datasets/wiki40b) a no-code dataset ([Hub PR](https://huggingface.co/datasets/wiki40b/discussions/5))
- [x] Make [`wiki_dpr`](https://huggingface.co/datasets/wiki_dpr) an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wiki_dpr/discussions/14))
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I_kwDODunzps5cZGli
| 5,442
|
OneDrive Integrations with HF Datasets
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[
"Hi! \r\n\r\nWe use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).\r\n\r\n[`gdrivefs`](https://github.com/fsspec/gdrivefs) makes it possible to use Google Drive as a storage service in Datasets, but this is not the case for OneDrive, since its[ Python SDK](https://github.com/OneDrive/onedrive-sdk-python) is not integrated with `fsspec`. Can you please request the integration with `fsspec` in their repo to address this limitation?",
"I'm closing this issue as implementing a fsspec-compliant OneDrive filesystem is not our responsibility."
] | 2023-01-19T23:12:08Z
| 2023-02-24T16:17:51Z
| 2023-02-24T16:17:51Z
|
NONE
| null | null |
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### Feature request
First of all , I would like to thank all community who are developed DataSet storage and make it free available
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For example, if I have **50GB** on my **Onedrive** account and I want to move between drive and Hugging face repo or vis versa
### Motivation
make the dataset section more flexible with other possible storage
like the integration between Google Collab and Google drive the storage
### Your contribution
Can be done using Hugging face CLI
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set dev version
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5376). All of your documentation changes will be reflected on that endpoint."
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Split order is not preserved
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I have noticed that in some cases the split order is not preserved.
For example, consider a no-script dataset with configs:
```yaml
configs:
- config_name: default
data_files:
- split: train
path: train.csv
- split: test
path: test.csv
```
- Note the defined split order is [train, test]
Once the dataset is loaded, the split order is not preserved:
```python
In [16]: ds
Out[16]:
DatasetDict({
test: Dataset({
features: ['text', 'label'],
num_rows: 1
})
train: Dataset({
features: ['text', 'label'],
num_rows: 2
})
})
```
- Note the obtained split order is [test, train]
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Support cloud storage in load_dataset
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"Or for example an archive on GitHub releases! Before I added support for JXL (locally only, PR still pending) I was considering hosting my files on GitHub instead...",
"+1 to this. I would like to use 'audiofolder' with a data_dir that's on S3, for example. I don't want to upload my dataset to the Hub, but I would find all the fingerprinting/caching features useful.",
"Adding to the conversation, Dask also uses `fsspec` for this feature.\r\n\r\n[Dask: How to connect to remote data](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html)\r\n\r\nHappy to help on this feature :D ",
"+1 to this feature request since I think it also tackles my use-case. I am collaborating with a team, working with a loading script which takes some time to generate the dataset artifacts. It would be very handy to use this as a cloud cache to avoid duplicating the effort. \r\n\r\nCurrently we could use `builder.download_and_prepare(path_to_cloud_storage, storage_options, ...)` to cache the artifacts to cloud storage, but then `builder.as_dataset()` yields `NotImplementedError: Loading a dataset cached in SomeCloudFileSystem is not supported`",
"Makes sense ! If you want to load locally a dataset that you download_and_prepared on a cloud storage, you would use `load_dataset(path_to_cloud_storage)` indeed. It would download the data from the cloud storage, cache them locally, and return a `Dataset`.",
"It seems currently the `cached_path` function handles all URLs by `get_from_cache` that only supports `ftp` and `http(s)` here:\r\nhttps://github.com/huggingface/datasets/blob/b5672a956d5de864e6f5550e493527d962d6ae55/src/datasets/utils/file_utils.py#L181\r\n\r\nI guess one can add another condition that handles `s3://` or `gs://` URLs via `fsspec` here.",
"I could use this functionality, so I put together a PR using @kyamagu's suggestion to use `fsspec` in `datasets.utils.file_utils`\r\n\r\nhttps://github.com/huggingface/datasets/pull/5580",
"Thanks @dwyatte for adding support for fsspec urls\r\n\r\nLet me just reopen this since the original issue is not resolved",
"I'm not yet understanding how to use https://github.com/huggingface/datasets/pull/5580 in order to use `load_dataset(data_files=\"s3://...\")`. Any help/example would be much appreciated :) thanks! ",
"It's still not officially supported x) But you can try to update `request_etag` in `file_utils.py` to use `fsspec_head` instead of `http_head`. It is responsible of getting the ETags of the remote files for caching. This change may do the trick for S3 urls",
"Thank you for your guys help on this and merging in #5580. I manually pulled the changes to my local datasets package (datasets.utils.file_utils.py) since it only seemed to be this file that was changed in the PR and I'm getting the error: \r\nInvalidSchema: No connection adapters were found for 's3://bucket/folder/'. I'm calling load_dataset using the S3 URI. When I use the S3 URL I get HTTPError: 403 Client Error. \r\nAm I not supposed to use the S3 URI? How do I pull in the changes from this merge? I'm running datasets 2.10.1. ",
"The current implementation depends on gcsfs/s3fs being able to authenticate through some other means e.g., environmental variables. For AWS, it looks like you can set `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN`\r\n\r\nNote that while testing this just now, I did note a discrepancy between gcsfs and s3fs that we might want to address where gcsfs passes the timeout from `storage_options` [here](https://github.com/huggingface/datasets/blob/3e6269979fc80ae8939294d26298897f0db5b84d/src/datasets/utils/file_utils.py#L333) down into the `aiohttp.ClientSession.request`, but s3fs does not handle this (tries to pass to the `aiobotocore.session.AioSession` constructor raising `TypeError: __init__() got an unexpected keyword argument 'requests_timeout'`).\r\n\r\nIt seems like some work trying to unify kwargs across different fsspec implementations, so if the plan is to pass down `storage_options`, I wonder if we should just let users control the timeout (and other kwargs) using that and if not specified, use the default?",
"> Note that while testing this just now, I did note a discrepancy between gcsfs and s3fs that we might want to address where gcsfs passes the timeout from storage_options [here](https://github.com/huggingface/datasets/blob/3e6269979fc80ae8939294d26298897f0db5b84d/src/datasets/utils/file_utils.py#L333) down into the aiohttp.ClientSession.request, but s3fs does not handle this (tries to pass to the aiobotocore.session.AioSession constructor raising TypeError: __init__() got an unexpected keyword argument 'requests_timeout').\r\n\r\n> It seems like some work trying to unify kwargs across different fsspec implementations, so if the plan is to pass down storage_options, I wonder if we should just let users control the timeout (and other kwargs) and if not specified, use the default?\r\n\r\n@lhoestq here's a small PR for this: https://github.com/huggingface/datasets/pull/5673\r\n\r\n",
"@lhoestq sorry for being a little dense here but I am very keen to use fsspec / adlfs for for a larger image dataset I have for object detection. I have to keep it on Azure storage and would also like to avoid a full download or zipping (so use `load_dataset(..., streaming=True)`. So this development is godsend :) only... I am unable to make it work.\r\n\r\nWould you expect the setup to work for:\r\n- azure blob storage\r\n- image files (not the standard formats `json`, `parquet`....\r\n\r\n? I appreciate that you mostly focus on s3 but it seems that, similar to the [remaining cloud storage functionality](https://huggingface.co/docs/datasets/filesystems#cloud-storage), it should also work for Azure blob storage.\r\n\r\n\r\nI would imagine that something like (Streaming true or false):\r\n```python\r\nd = load_dataset(\"new_dataset.py\", storage_options=storage_options, split=\"train\")\r\n```\r\nwould work with \r\n```python\r\n# new_dataset.py\r\n....\r\n_URL=\"abfs://container/image_folder``` \r\n\r\narchive_path = dl_manager.download(_URL)\r\nsplit_metadata_paths = dl_manager.download(_METADATA_URLS)\r\nreturn [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={\r\n \"annotation_file_path\": split_metadata_paths[\"train\"],\r\n \"files\": dl_manager.iter_files(archive_path)\r\n},\r\n ),\r\n...\r\n```\r\nbut I get \r\n```\r\nTraceback (most recent call last):\r\n... ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/load.py\", line 1797, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/builder.py\", line 890, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/builder.py\", line 1649, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/builder.py\", line 963, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"~/.cache/huggingface/modules/datasets_modules/datasets/new_dataset/dd26a081eab90074f41fa2c821b458424fde393cc73d3d8241aca956d1fb3aa0/new_dataset_script.py\", line 56, in _split_generators\r\n archive_path = dl_manager.download(_URL)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/download/download_manager.py\", line 427, in download\r\n downloaded_path_or_paths = map_nested(\r\n ^^^^^^^^^^^\r\n File \"~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/utils/py_utils.py\", line 435, in map_nested\r\n return function(data_struct)\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/download/download_manager.py\", line 453, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"~/miniconda3/envs/hf/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 206, in cached_path\r\n raise ValueError(f\"unable to parse {url_or_filename} as a URL or as a local path\")\r\nValueError: unable to parse abfs://container/image_folder as a URL or as a local path\r\n\r\n```\r\n",
"What version of `datasets` are you using ?",
"@lhoestq \r\nhello, i still have problem with loading json from S3:\r\n\r\n\r\nstorage_options = {\r\n \"key\": xxxx,\r\n \"secret\": xxx,\r\n \"endpoint_url\": xxxx\r\n}\r\npath = 's3://xxx/xxxxxxx.json'\r\ndataset = load_dataset(\"json\", data_files=path, storage_options=storage_options)\r\n\r\nand it throws an error:\r\nTypeError: AioSession.__init__() got an unexpected keyword argument 'hf'\r\nand I use the lastest 2.14.4_dev0 version",
"Hi @lhoestq, thanks for getting back to me :) you have been busy over the summer I see... I was on `2.12.0`. I have updated to `2.14.4`. \r\n\r\nNow `d = load_dataset(\"new_dataset.py\", storage_options=storage_options, split=\"train\", streaming=True)` works for Azure blob storage (with a local data loader script) when I explicitly list all blobs (I am struggling to make `fs.ls(<path>)` work in the script to make the list available to the download manager).\r\n\r\n Any chance that it could work out-of-the-box by supplying just the image folder, not the full list of image filenames? It seems that `dl_manager.download(_URL)` always wants one or more (possibly archived) files. In my situation, where I don't want to archive or download, it would be great to just supply the folder (seems reasonably doable with fsspec).\r\n\r\nLet me know if there is anything I can do to help.\r\n\r\nThanks,",
"> Any chance that it could work out-of-the-box by supplying just the image folder, not the full list of image filenames? It seems that dl_manager.download(_URL) always wants one or more (possibly archived) files. In my situation, where I don't want to archive or download, it would be great to just supply the folder (seems reasonably doable with fsspec).\r\n\r\n@mayorblock This is not supported right now, you have to use archives or implement a way to get the list by yourself\r\n\r\n> TypeError: AioSession.init() got an unexpected keyword argument 'hf'\r\n\r\n@hjq133 Can you update `fsspec` and try again ?\r\n\r\n```python\r\npip install -U fsspec\r\n```",
"thanks for your suggestion，it works now ! ",
"I'm seeing same problem as @hjq133 with following versions:\r\n\r\n```\r\ndatasets==2.15.0\r\n(venv) ➜ finetuning-llama2 git:(main) ✗ pip freeze | grep s3fs \r\ns3fs==2023.10.0\r\n(venv) ➜ finetuning-llama2 git:(main) ✗ pip freeze | grep fsspec\r\nfsspec==2023.10.0\r\n```",
"> @lhoestq hello, i still have problem with loading json from S3:\r\n> \r\n> storage_options = { \"key\": xxxx, \"secret\": xxx, \"endpoint_url\": xxxx } path = 's3://xxx/xxxxxxx.json' dataset = load_dataset(\"json\", data_files=path, storage_options=storage_options)\r\n> \r\n> and it throws an error: TypeError: AioSession.**init**() got an unexpected keyword argument 'hf' and I use the lastest 2.14.4_dev0 version\r\n\r\nI am trying to do the same thing, but the loading is just hanging, without any error. \r\n@lhoestq is there any documentation how to load from private s3 buckets?\r\n",
"Hi ! S3 support is still experimental. It seems like there is an extra `hf` field passed to the `s3fs` storage_options that causes this error. I just check the source code of `_prepare_single_hop_path_and_storage_options` and I think you can try passing explicitly your own `storage_options={\"s3\": {...}}`. Also note that it's generally better to load datasets from HF (we run extensive tests and benchmarks for speed and robustness)",
"That worked! Thanks\r\nIt seems thought that `data_dir=...` doesn't work on s3, only `data_files`.\r\n",
"@lhoestq Would this work either with an Azure Blob Storage Container or its respective Azure Machine Learning Datastore? If yes, what would that look like in code? I've tried a couple of combinations but no success so far, on the latest version of `datasets`. I need to migrate a dataset to the Azure cloud, `load_dataset(\"path_to_data\")` worked perfectly while the files were local only. Thank you!\r\n\r\n@mayorblock would you mind sharing how you got it to work? What did you pass as `storage_options`? Would it maybe work without a custom data loader script?",
"This ticket would be of so much help.",
"@lhoestq I've been using this feature for the last year on GCS without problem, but I think we need to fix an issue with S3 and then document the supported calling patterns to reduce confusion\r\n\r\nIt looks like `datasets` uses a default `DownloadConfig` which is where some potentially unintended storage options are getting passed to fsspec\r\n\r\n```\r\nDownloadConfig(\r\n\tcache_dir=None, \r\n\tforce_download=False, \r\n\tresume_download=False, \r\n\tlocal_files_only=False, \r\n\tproxies=None, \r\n\tuser_agent=None, \r\n\textract_compressed_file=False, \r\n\tforce_extract=False, \r\n\tdelete_extracted=False, \r\n\tuse_etag=True, \r\n\tnum_proc=None, \r\n\tmax_retries=1, \r\n\ttoken=None, \r\n\tignore_url_params=False, \r\n\tstorage_options={'hf': {'token': None, 'endpoint': 'https://huggingface.co'}}, \r\n\tdownload_desc=None\r\n)\r\n```\r\n(specifically the `storage_options={'hf': {'token': None, 'endpoint': 'https://huggingface.co'}}` part)\r\n\r\n`gcsfs` is robust to the extra key in storage options for whatever reason, but `s3fs` is not (haven't dug into why). I'm unable to test `adlfs` but it looks like people here got it working\r\n\r\nIs this an issue that needs fixed with s3fs? Or can we avoid passing these default storage options in some cases?\r\n\r\nUpdate: I think probably https://github.com/huggingface/datasets/pull/6127 is where these default storage options were introduced",
"Hmm not sure, maybe it has to do with `_prepare_single_hop_path_and_storage_options` returning the \"hf\" storage options when it shouldn't",
"Also running into this issue downloading a parquet dataset from S3 (Upload worked fine using [current main branch](https://github.com/huggingface/datasets/commit/90b896193a0a315789022580eb4c80305d168d4d)).\r\n`dataset = Dataset.from_parquet('s3://path-to-file')`\r\nraises \r\n`TypeError: AioSession.__init__() got an unexpected keyword argument 'hf'`\r\nFound that the issue is introduced in [#6028](https://github.com/huggingface/datasets/commit/14f6edd9222e577dccb962ed5338b79b73502fa5#diff-8a868b8c1127c5cbe7870bbeec30bd2cbec6696a378e65b66782a6935e3f412e)\r\n\r\nWhen commenting out the __post_init__ part to set 'hf', I am able to download the dataset.",
"Can someone paste a complete example for getting this to work? Running this:\r\n\r\n```python\r\nfrom datasets import load_from_disk, load_dataset\r\nfrom os import environ\r\nfrom s3fs import S3FileSystem\r\n\r\nstorage_options = {\r\n \"key\": \"AzureDiamond\",\r\n \"secret\": \"hunter2\",\r\n \"endpoint_url\": \"https://fly.storage.tigris.dev\"\r\n}\r\n\r\nmodel_name = \"Qwen/Qwen2.5-32B\"\r\ndataset_name = \"mlabonne/FineTome-100k\"\r\n\r\ndataset = load_dataset(\r\n f\"s3://datnybakfu/model-ready/{model_name}/{dataset_name}\",\r\n storage_options=storage_options,\r\n filesystem=S3FileSystem(),\r\n streaming=True,\r\n)\r\n```\r\n\r\nI get the following error trace:\r\n\r\n<details>\r\n<summary>Stacktrace (folded)</summary\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\n\r\n[<ipython-input-19-0ae7d5f39c46>](https://localhost:8080/#) in <cell line: 29>()\r\n 27 dataset_name = \"mlabonne/FineTome-100k\"\r\n 28 \r\n---> 29 dataset = load_dataset(\r\n 30 f\"s3://datnybakfu/model-ready/{model_name}/{dataset_name}\",\r\n 31 storage_options=storage_options,\r\n\r\n2 frames\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\r\n 2130 \r\n 2131 # Create a dataset builder\r\n-> 2132 builder_instance = load_dataset_builder(\r\n 2133 path=path,\r\n 2134 name=name,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)\r\n 1851 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 1852 download_config.storage_options.update(storage_options)\r\n-> 1853 dataset_module = dataset_module_factory(\r\n 1854 path,\r\n 1855 revision=revision,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\r\n 1733 )\r\n 1734 else:\r\n-> 1735 raise FileNotFoundError(f\"Couldn't find any data file at {relative_to_absolute_path(path)}.\")\r\n 1736 \r\n 1737 \r\n\r\nFileNotFoundError: Couldn't find any data file at /content/s3:/datnybakfu/model-ready/Qwen/Qwen2.5-32B/mlabonne/FineTome-100k.\r\n```\r\n\r\n</details>",
"Hi, using `s3://` as first argument in load_dataset directly is not supported. You can pass a local path or a HF repository (in the form `username/dataset_name`)\r\n\r\ncc @Wauplin if you have a script to move data from S3 to HF ? I know there is this code adapted from https://github.com/fsspec/filesystem_spec/issues/909 that works well (it creates one commit per file)\r\n\r\n```python\r\nfrom multiprocessing.pool import ThreadPool\r\n\r\nimport fsspec\r\nfrom tqdm import tqdm\r\n\r\ns3_storage_options = {} # add S3 credentials here if needed, e.g. {\"key\": aws_access_key_id, \"secret\": aws_secret_access_key}\r\na = fsspec.get_mapper(\"s3://bucket/dataset_folder\", **s3_storage_options)\r\nb = fsspec.get_mapper(\"hf://datasets/username/dataset_name\")\r\n\r\ndef f(k):\r\n b[k]=a[k]\r\n\r\nwith ThreadPool(32) as p:\r\n keys = [key for key in a.keys() if key and not key.endswith(\"/\")] # ignore root and directories\r\n for _ in tqdm(p.imap_unordered(f, keys), total=len(keys)):\r\n pass\r\n```"
] | 2022-11-22T14:00:10Z
| 2024-11-15T15:03:41Z
| null |
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Would be nice to be able to do
```python
data_files=["s3://..."] # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been requested several times already. Some users want to use their data from private cloud storage to train models
related:
https://github.com/huggingface/datasets/issues/3490
https://github.com/huggingface/datasets/issues/5244
[forum](https://discuss.huggingface.co/t/how-to-use-s3-path-with-load-dataset-with-streaming-true/25739/2)
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I_kwDODunzps6zWGXH
| 7,531
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Deepspeed reward training hangs at end of training with Dataset.from_list
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There seems to be a weird interaction between Deepspeed, the Dataset.from_list method and trl's RewardTrainer. On a multi-GPU setup (10 A100s), training always hangs at the very end of training until it times out. The training itself works fine until the end of training and running the same script with Deepspeed on a single GPU works without hangig. The issue persisted across a wide range of Deepspeed configs and training arguments. The issue went away when storing the exact same dataset as a JSON and using `dataset = load_dataset("json", ...)`. Here is my training script:
```python
import pickle
import os
import random
import warnings
import torch
from datasets import load_dataset, Dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from trl import RewardConfig, RewardTrainer, ModelConfig
####################################### Reward model #################################################
# Explicitly set arguments
model_name_or_path = "Qwen/Qwen2.5-1.5B"
output_dir = "Qwen2-0.5B-Reward-LoRA"
per_device_train_batch_size = 2
num_train_epochs = 5
gradient_checkpointing = True
learning_rate = 1.0e-4
logging_steps = 25
eval_strategy = "steps"
eval_steps = 50
max_length = 2048
torch_dtype = "auto"
trust_remote_code = False
model_args = ModelConfig(
model_name_or_path=model_name_or_path,
model_revision=None,
trust_remote_code=trust_remote_code,
torch_dtype=torch_dtype,
lora_task_type="SEQ_CLS", # Make sure task type is seq_cls
)
training_args = RewardConfig(
output_dir=output_dir,
per_device_train_batch_size=per_device_train_batch_size,
num_train_epochs=num_train_epochs,
gradient_checkpointing=gradient_checkpointing,
learning_rate=learning_rate,
logging_steps=logging_steps,
eval_strategy=eval_strategy,
eval_steps=eval_steps,
max_length=max_length,
gradient_checkpointing_kwargs=dict(use_reentrant=False),
center_rewards_coefficient = 0.01,
fp16=False,
bf16=True,
save_strategy="no",
dataloader_num_workers=0,
# deepspeed="./configs/deepspeed_config.json",
)
################
# Model & Tokenizer
################
model_kwargs = dict(
revision=model_args.model_revision,
use_cache=False if training_args.gradient_checkpointing else True,
torch_dtype=model_args.torch_dtype,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, use_fast=True
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path, num_labels=1, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
# Align padding tokens between tokenizer and model
model.config.pad_token_id = tokenizer.pad_token_id
# If post-training a base model, use ChatML as the default template
if tokenizer.chat_template is None:
model, tokenizer = setup_chat_format(model, tokenizer)
if model_args.use_peft and model_args.lora_task_type != "SEQ_CLS":
warnings.warn(
"You are using a `task_type` that is different than `SEQ_CLS` for PEFT. This will lead to silent bugs"
" Make sure to pass --lora_task_type SEQ_CLS when using this script with PEFT.",
UserWarning,
)
##############
# Load dataset
##############
with open('./prefs.pkl', 'rb') as fh:
loaded_data = pickle.load(fh)
random.shuffle(loaded_data)
dataset = []
for a_wins, a, b in loaded_data:
if a_wins == 0:
a, b = b, a
dataset.append({'chosen': a, 'rejected': b})
dataset = Dataset.from_list(dataset)
# Split the dataset into training and evaluation sets
train_eval_split = dataset.train_test_split(test_size=0.15, shuffle=True, seed=42)
# Access the training and evaluation datasets
train_dataset = train_eval_split['train']
eval_dataset = train_eval_split['test']
##########
# Training
##########
trainer = RewardTrainer(
model=model,
processing_class=tokenizer,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
trainer.train()
```
Replacing `dataset = Dataset.from_list(dataset)` with
```python
with open('./prefs.json', 'w') as fh:
json.dump(dataset, fh)
dataset = load_dataset("json", data_files="./prefs.json", split='train')
```
resolves the issue.
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PR_kwDODunzps5Icjwe
| 5,459
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Disable aiohttp requoting of redirection URL
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Comment by @lhoestq:\r\n> Do you think we need this in `datasets` if it's fixed on the moon landing side ? In the aiohttp doc they consider those symbols as \"non-safe\" ",
"The lib `requests` does not perform that requote on redirect URLs.",
"Indeed, the `requests` library does perform a requoting, but this does not unquote `%27`:\r\n```python\r\nIn [1]: from requests.utils import requote_uri\r\n\r\nIn [2]: url = \"https://netloc/path?param=param%27%27value\"\r\n\r\nIn [3]: url\r\nOut[3]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [4]: requote_uri(url)\r\nOut[4]: 'https://netloc/path?param=param%27%27value'\r\n```\r\n\r\nHowever, the `aiohttp` library uses `yarl.ULR` and this does unquote `%27`:\r\n```python\r\nIn [5]: from yarl import URL\r\n\r\nIn [6]: url\r\nOut[6]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [7]: str(URL(url))\r\nOut[7]: \"https://netloc/path?param=param''value\"\r\n```\r\n\r\nIf we pass `requote_redirect_url=False` to `aiohttp`, then it passes `encoded=True` to `yarl.ULR`: https://github.com/aio-libs/aiohttp/blob/4635161ee8e7ad321cca46e01ce5bfeb1ad8bf26/aiohttp/client.py#L578-L580\r\n```python\r\nparsed_url = URL(\r\n r_url, encoded=not self._requote_redirect_url\r\n)\r\n```\r\nwhich does not unquote `%27`:\r\n```python\r\nIn [8]: url\r\nOut[8]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [9]: str(URL(url, encoded=True))\r\nOut[9]: 'https://netloc/path?param=param%27%27value'\r\n```",
"See the issues we opened in the respective libraries:\r\n- aiohttp\r\n - aio-libs/yarl#1077\r\n- requests\r\n - psf/requests#6341",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012399 / 0.011353 (0.001047) | 0.006388 / 0.011008 (-0.004620) | 0.134173 / 0.038508 (0.095665) | 0.037059 / 0.023109 (0.013949) | 0.420697 / 0.275898 (0.144799) | 0.473981 / 0.323480 (0.150502) | 0.009857 / 0.007986 (0.001871) | 0.004791 / 0.004328 (0.000463) | 0.106886 / 0.004250 (0.102636) | 0.044871 / 0.037052 (0.007818) | 0.429843 / 0.258489 (0.171354) | 0.461569 / 0.293841 (0.167728) | 0.057285 / 0.128546 (-0.071261) | 0.018809 / 0.075646 (-0.056837) | 0.432613 / 0.419271 (0.013342) | 0.058086 / 0.043533 (0.014553) | 0.413064 / 0.255139 (0.157925) | 0.444407 / 0.283200 (0.161207) | 0.119102 / 0.141683 (-0.022581) | 1.875954 / 1.452155 (0.423799) | 1.916392 / 1.492716 (0.423676) |\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) | 0.267489 / 0.018006 (0.249483) | 0.567554 / 0.000490 (0.567064) | 0.005901 / 0.000200 (0.005701) | 0.000134 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031248 / 0.037411 (-0.006164) | 0.123014 / 0.014526 (0.108489) | 0.140001 / 0.176557 (-0.036556) | 0.191476 / 0.737135 (-0.545659) | 0.141687 / 0.296338 (-0.154652) |\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) | 0.637481 / 0.215209 (0.422272) | 6.255969 / 2.077655 (4.178314) | 2.559811 / 1.504120 (1.055691) | 2.118154 / 1.541195 (0.576960) | 2.079487 / 1.468490 (0.610997) | 1.201079 / 4.584777 (-3.383698) | 5.592625 / 3.745712 (1.846913) | 5.143344 / 5.269862 (-0.126517) | 2.764716 / 4.565676 (-1.800960) | 0.142539 / 0.424275 (-0.281736) | 0.015541 / 0.007607 (0.007934) | 0.771407 / 0.226044 (0.545363) | 7.631657 / 2.268929 (5.362728) | 3.279684 / 55.444624 (-52.164940) | 2.587566 / 6.876477 (-4.288911) | 2.624622 / 2.142072 (0.482549) | 1.427878 / 4.805227 (-3.377350) | 0.257759 / 6.500664 (-6.242906) | 0.078616 / 0.075469 (0.003147) |\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) | 1.609305 / 1.841788 (-0.232483) | 18.258792 / 8.074308 (10.184484) | 20.345242 / 10.191392 (10.153850) | 0.267366 / 0.680424 (-0.413058) | 0.047035 / 0.534201 (-0.487166) | 0.568881 / 0.579283 (-0.010402) | 0.662763 / 0.434364 (0.228399) | 0.668927 / 0.540337 (0.128590) | 0.755766 / 1.386936 (-0.631170) |\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) | 0.010017 / 0.011353 (-0.001336) | 0.006816 / 0.011008 (-0.004192) | 0.105038 / 0.038508 (0.066529) | 0.038689 / 0.023109 (0.015580) | 0.482113 / 0.275898 (0.206215) | 0.540072 / 0.323480 (0.216592) | 0.007738 / 0.007986 (-0.000248) | 0.005134 / 0.004328 (0.000806) | 0.102203 / 0.004250 (0.097953) | 0.054080 / 0.037052 (0.017028) | 0.501057 / 0.258489 (0.242568) | 0.567186 / 0.293841 (0.273345) | 0.060330 / 0.128546 (-0.068217) | 0.020059 / 0.075646 (-0.055587) | 0.123102 / 0.419271 (-0.296170) | 0.063426 / 0.043533 (0.019893) | 0.494171 / 0.255139 (0.239032) | 0.538238 / 0.283200 (0.255039) | 0.119613 / 0.141683 (-0.022069) | 1.853728 / 1.452155 (0.401574) | 1.984621 / 1.492716 (0.491904) |\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) | 0.282511 / 0.018006 (0.264505) | 0.563190 / 0.000490 (0.562700) | 0.000465 / 0.000200 (0.000265) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029267 / 0.037411 (-0.008144) | 0.135618 / 0.014526 (0.121093) | 0.146286 / 0.176557 (-0.030271) | 0.188570 / 0.737135 (-0.548565) | 0.155839 / 0.296338 (-0.140499) |\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) | 0.671660 / 0.215209 (0.456451) | 6.718775 / 2.077655 (4.641120) | 3.004601 / 1.504120 (1.500481) | 2.640504 / 1.541195 (1.099309) | 2.666788 / 1.468490 (1.198298) | 1.242655 / 4.584777 (-3.342122) | 5.780119 / 3.745712 (2.034407) | 3.247935 / 5.269862 (-2.021927) | 2.114007 / 4.565676 (-2.451669) | 0.147546 / 0.424275 (-0.276729) | 0.014408 / 0.007607 (0.006801) | 0.824407 / 0.226044 (0.598362) | 8.278185 / 2.268929 (6.009257) | 3.733463 / 55.444624 (-51.711161) | 2.976732 / 6.876477 (-3.899745) | 3.132758 / 2.142072 (0.990686) | 1.446095 / 4.805227 (-3.359132) | 0.258628 / 6.500664 (-6.242036) | 0.085513 / 0.075469 (0.010043) |\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) | 1.702681 / 1.841788 (-0.139106) | 18.725123 / 8.074308 (10.650815) | 19.622808 / 10.191392 (9.431416) | 0.215845 / 0.680424 (-0.464579) | 0.029246 / 0.534201 (-0.504955) | 0.554819 / 0.579283 (-0.024464) | 0.630926 / 0.434364 (0.196562) | 0.637663 / 0.540337 (0.097325) | 0.837948 / 1.386936 (-0.548988) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a4f96ef0a4ec4b25f0872f160fa1eb9d2e711c \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008540 / 0.011353 (-0.002813) | 0.004538 / 0.011008 (-0.006470) | 0.101507 / 0.038508 (0.062999) | 0.029751 / 0.023109 (0.006641) | 0.292608 / 0.275898 (0.016710) | 0.354734 / 0.323480 (0.031254) | 0.007430 / 0.007986 (-0.000556) | 0.003365 / 0.004328 (-0.000964) | 0.078703 / 0.004250 (0.074452) | 0.034858 / 0.037052 (-0.002194) | 0.303518 / 0.258489 (0.045029) | 0.336523 / 0.293841 (0.042682) | 0.033741 / 0.128546 (-0.094805) | 0.011460 / 0.075646 (-0.064186) | 0.319551 / 0.419271 (-0.099721) | 0.041102 / 0.043533 (-0.002431) | 0.295914 / 0.255139 (0.040775) | 0.322142 / 0.283200 (0.038943) | 0.084694 / 0.141683 (-0.056989) | 1.481308 / 1.452155 (0.029153) | 1.530271 / 1.492716 (0.037554) |\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) | 0.180516 / 0.018006 (0.162510) | 0.405741 / 0.000490 (0.405251) | 0.002806 / 0.000200 (0.002606) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023359 / 0.037411 (-0.014052) | 0.096950 / 0.014526 (0.082424) | 0.103991 / 0.176557 (-0.072566) | 0.143700 / 0.737135 (-0.593435) | 0.106764 / 0.296338 (-0.189575) |\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) | 0.416966 / 0.215209 (0.201757) | 4.145601 / 2.077655 (2.067946) | 1.838258 / 1.504120 (0.334139) | 1.629396 / 1.541195 (0.088201) | 1.649707 / 1.468490 (0.181217) | 0.689624 / 4.584777 (-3.895153) | 3.414584 / 3.745712 (-0.331129) | 1.874295 / 5.269862 (-3.395566) | 1.251930 / 4.565676 (-3.313746) | 0.081782 / 0.424275 (-0.342493) | 0.012868 / 0.007607 (0.005261) | 0.523904 / 0.226044 (0.297859) | 5.251032 / 2.268929 (2.982104) | 2.301549 / 55.444624 (-53.143075) | 1.942110 / 6.876477 (-4.934367) | 2.023014 / 2.142072 (-0.119058) | 0.816492 / 4.805227 (-3.988736) | 0.150107 / 6.500664 (-6.350558) | 0.065118 / 0.075469 (-0.010351) |\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) | 1.226433 / 1.841788 (-0.615355) | 13.852569 / 8.074308 (5.778261) | 13.862779 / 10.191392 (3.671387) | 0.146361 / 0.680424 (-0.534062) | 0.028652 / 0.534201 (-0.505549) | 0.398251 / 0.579283 (-0.181032) | 0.403590 / 0.434364 (-0.030774) | 0.492184 / 0.540337 (-0.048154) | 0.581040 / 1.386936 (-0.805896) |\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) | 0.006859 / 0.011353 (-0.004494) | 0.004632 / 0.011008 (-0.006376) | 0.076653 / 0.038508 (0.038145) | 0.027865 / 0.023109 (0.004755) | 0.354472 / 0.275898 (0.078573) | 0.385462 / 0.323480 (0.061982) | 0.005125 / 0.007986 (-0.002861) | 0.003420 / 0.004328 (-0.000909) | 0.076018 / 0.004250 (0.071768) | 0.040197 / 0.037052 (0.003144) | 0.353675 / 0.258489 (0.095186) | 0.394911 / 0.293841 (0.101070) | 0.032909 / 0.128546 (-0.095637) | 0.011713 / 0.075646 (-0.063933) | 0.085921 / 0.419271 (-0.333350) | 0.044462 / 0.043533 (0.000929) | 0.349997 / 0.255139 (0.094858) | 0.375207 / 0.283200 (0.092008) | 0.091288 / 0.141683 (-0.050394) | 1.536515 / 1.452155 (0.084361) | 1.581878 / 1.492716 (0.089162) |\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) | 0.273284 / 0.018006 (0.255277) | 0.424457 / 0.000490 (0.423967) | 0.044659 / 0.000200 (0.044459) | 0.000247 / 0.000054 (0.000192) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025473 / 0.037411 (-0.011938) | 0.100014 / 0.014526 (0.085488) | 0.108551 / 0.176557 (-0.068006) | 0.147913 / 0.737135 (-0.589223) | 0.112729 / 0.296338 (-0.183610) |\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) | 0.448162 / 0.215209 (0.232953) | 4.472701 / 2.077655 (2.395046) | 2.078384 / 1.504120 (0.574264) | 1.861292 / 1.541195 (0.320097) | 1.920482 / 1.468490 (0.451991) | 0.706968 / 4.584777 (-3.877809) | 3.433109 / 3.745712 (-0.312603) | 1.898684 / 5.269862 (-3.371178) | 1.174375 / 4.565676 (-3.391302) | 0.083666 / 0.424275 (-0.340609) | 0.012388 / 0.007607 (0.004781) | 0.546011 / 0.226044 (0.319966) | 5.487514 / 2.268929 (3.218585) | 2.534124 / 55.444624 (-52.910500) | 2.168441 / 6.876477 (-4.708036) | 2.203458 / 2.142072 (0.061386) | 0.813333 / 4.805227 (-3.991894) | 0.153169 / 6.500664 (-6.347495) | 0.067151 / 0.075469 (-0.008318) |\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) | 1.277815 / 1.841788 (-0.563972) | 13.920545 / 8.074308 (5.846237) | 13.473801 / 10.191392 (3.282409) | 0.129035 / 0.680424 (-0.551389) | 0.016737 / 0.534201 (-0.517464) | 0.388413 / 0.579283 (-0.190870) | 0.388785 / 0.434364 (-0.045579) | 0.481735 / 0.540337 (-0.058602) | 0.576390 / 1.386936 (-0.810546) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a4f96ef0a4ec4b25f0872f160fa1eb9d2e711c \"CML watermark\")\n"
] | 2023-01-24T17:18:59Z
| 2024-09-01T18:08:31Z
| 2023-01-31T08:37:54Z
|
MEMBER
| null | null | null |
The library `aiohttp` performs a requoting of redirection URLs that unquotes the single quotation mark character: `%27` => `'`
This is a problem for our Hugging Face Hub, which requires exact URL from location header.
Specifically, in the query component of the URL (`https://netloc/path?query`), the value for `response-content-disposition` contains `%27`:
```
response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B
```
and after the requoting, the `%27` characters get unquoted to `'`:
```
response-content-disposition=attachment%3B+filename*%3DUTF-8''sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B
```
This PR disables the `aiohttp` requoting of redirection URLs.
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PR_kwDODunzps5YexBL
| 6,165
|
Fix multiprocessing with spawn in iterable datasets
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq \r\nA test is failing, but I don't think it is due to my changes",
"Good catch ! Could you add a test to make sure transformed IterableDataset objects are still picklable ?\r\n\r\nSomething like `test_pickle_after_many_transforms` in in `test_iterable_dataset.py` that does a bunch or rename, map, take on a dataset and checks that the dataset can be pickled at the end and the reloaded dataset returns the same elements",
"@lhoestq \r\nI added the test and fixed one last method",
"<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) | 0.006537 / 0.011353 (-0.004816) | 0.003960 / 0.011008 (-0.007048) | 0.085135 / 0.038508 (0.046627) | 0.079271 / 0.023109 (0.056162) | 0.383743 / 0.275898 (0.107845) | 0.414622 / 0.323480 (0.091143) | 0.004202 / 0.007986 (-0.003784) | 0.003537 / 0.004328 (-0.000791) | 0.065758 / 0.004250 (0.061508) | 0.054225 / 0.037052 (0.017173) | 0.395715 / 0.258489 (0.137226) | 0.438985 / 0.293841 (0.145144) | 0.030590 / 0.128546 (-0.097956) | 0.008754 / 0.075646 (-0.066892) | 0.288415 / 0.419271 (-0.130857) | 0.051863 / 0.043533 (0.008330) | 0.382501 / 0.255139 (0.127363) | 0.414428 / 0.283200 (0.131228) | 0.024084 / 0.141683 (-0.117599) | 1.478726 / 1.452155 (0.026572) | 1.544763 / 1.492716 (0.052047) |\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) | 0.285143 / 0.018006 (0.267136) | 0.603859 / 0.000490 (0.603369) | 0.004330 / 0.000200 (0.004131) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027856 / 0.037411 (-0.009555) | 0.081963 / 0.014526 (0.067437) | 0.104106 / 0.176557 (-0.072451) | 0.151378 / 0.737135 (-0.585757) | 0.096476 / 0.296338 (-0.199862) |\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) | 0.402938 / 0.215209 (0.187729) | 4.042312 / 2.077655 (1.964657) | 2.068421 / 1.504120 (0.564301) | 1.877870 / 1.541195 (0.336675) | 1.947643 / 1.468490 (0.479153) | 0.482031 / 4.584777 (-4.102746) | 3.554747 / 3.745712 (-0.190965) | 3.307811 / 5.269862 (-1.962050) | 2.082886 / 4.565676 (-2.482791) | 0.056853 / 0.424275 (-0.367422) | 0.007535 / 0.007607 (-0.000072) | 0.483694 / 0.226044 (0.257649) | 4.827906 / 2.268929 (2.558978) | 2.567572 / 55.444624 (-52.877052) | 2.167206 / 6.876477 (-4.709271) | 2.414442 / 2.142072 (0.272369) | 0.579472 / 4.805227 (-4.225755) | 0.132976 / 6.500664 (-6.367688) | 0.059315 / 0.075469 (-0.016154) |\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) | 1.260086 / 1.841788 (-0.581702) | 19.438297 / 8.074308 (11.363989) | 14.188161 / 10.191392 (3.996769) | 0.168534 / 0.680424 (-0.511890) | 0.018070 / 0.534201 (-0.516131) | 0.394241 / 0.579283 (-0.185043) | 0.411057 / 0.434364 (-0.023307) | 0.461123 / 0.540337 (-0.079215) | 0.626844 / 1.386936 (-0.760092) |\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) | 0.006896 / 0.011353 (-0.004457) | 0.004207 / 0.011008 (-0.006801) | 0.064981 / 0.038508 (0.026473) | 0.080261 / 0.023109 (0.057152) | 0.399403 / 0.275898 (0.123505) | 0.433099 / 0.323480 (0.109619) | 0.005697 / 0.007986 (-0.002288) | 0.003601 / 0.004328 (-0.000728) | 0.065924 / 0.004250 (0.061673) | 0.058868 / 0.037052 (0.021815) | 0.403705 / 0.258489 (0.145216) | 0.439218 / 0.293841 (0.145377) | 0.032789 / 0.128546 (-0.095757) | 0.008675 / 0.075646 (-0.066971) | 0.071217 / 0.419271 (-0.348055) | 0.048487 / 0.043533 (0.004954) | 0.399878 / 0.255139 (0.144739) | 0.412816 / 0.283200 (0.129616) | 0.023905 / 0.141683 (-0.117778) | 1.541402 / 1.452155 (0.089247) | 1.588080 / 1.492716 (0.095364) |\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) | 0.322863 / 0.018006 (0.304856) | 0.530291 / 0.000490 (0.529802) | 0.004862 / 0.000200 (0.004662) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032697 / 0.037411 (-0.004715) | 0.092416 / 0.014526 (0.077891) | 0.107355 / 0.176557 (-0.069201) | 0.160217 / 0.737135 (-0.576918) | 0.109286 / 0.296338 (-0.187052) |\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) | 0.437375 / 0.215209 (0.222166) | 4.362644 / 2.077655 (2.284990) | 2.335404 / 1.504120 (0.831284) | 2.173215 / 1.541195 (0.632020) | 2.254061 / 1.468490 (0.785571) | 0.493906 / 4.584777 (-4.090871) | 3.609025 / 3.745712 (-0.136687) | 3.352380 / 5.269862 (-1.917481) | 2.074185 / 4.565676 (-2.491492) | 0.057863 / 0.424275 (-0.366412) | 0.007297 / 0.007607 (-0.000310) | 0.512464 / 0.226044 (0.286420) | 5.135921 / 2.268929 (2.866993) | 2.788889 / 55.444624 (-52.655736) | 2.479097 / 6.876477 (-4.397379) | 2.717848 / 2.142072 (0.575776) | 0.590442 / 4.805227 (-4.214785) | 0.133721 / 6.500664 (-6.366943) | 0.061491 / 0.075469 (-0.013978) |\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) | 1.429564 / 1.841788 (-0.412224) | 20.628733 / 8.074308 (12.554425) | 15.299571 / 10.191392 (5.108179) | 0.171032 / 0.680424 (-0.509392) | 0.019995 / 0.534201 (-0.514206) | 0.401283 / 0.579283 (-0.178000) | 0.416504 / 0.434364 (-0.017860) | 0.471219 / 0.540337 (-0.069118) | 0.641299 / 1.386936 (-0.745637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5503e7beb5a31926aec03c6c9b24813f9f83cd7b \"CML watermark\")\n"
] | 2023-08-22T10:07:23Z
| 2023-08-29T13:27:14Z
| 2023-08-29T13:18:11Z
|
CONTRIBUTOR
| null | null | null |
The "Spawn" method is preferred when multiprocessing on macOS or Windows systems, instead of the "Fork" method on linux systems.
This causes some methods of Iterable Datasets to break when using a dataloader with more than 0 workers.
I fixed the issue by replacing lambda and local methods which are not pickle-able.
See the example below:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
if __name__ == "__main__":
dataset = load_dataset("lhoestq/demo1", split="train")
dataset = dataset.to_iterable_dataset(num_shards=3)
dataset = dataset.remove_columns(["package_name"])
dataset = dataset.rename_columns({
"review": "review1"
})
dataset = dataset.rename_column("date", "date1")
for sample in DataLoader(dataset, batch_size=None, num_workers=3):
print(sample)
```
To notice the fix on a linux system, adding these lines should do the trick:
```python
import multiprocessing
multiprocessing.set_start_method('spawn')
```
I also removed what looks like code duplication between rename_colums and rename_column
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PR_kwDODunzps5oo36i
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|
Fix sliced ConcatenationTable pickling with mixed schemas vertically
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6715). 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) | 0.005294 / 0.011353 (-0.006059) | 0.003598 / 0.011008 (-0.007411) | 0.062798 / 0.038508 (0.024290) | 0.027479 / 0.023109 (0.004370) | 0.247146 / 0.275898 (-0.028752) | 0.272103 / 0.323480 (-0.051377) | 0.002979 / 0.007986 (-0.005007) | 0.002701 / 0.004328 (-0.001628) | 0.049384 / 0.004250 (0.045134) | 0.041562 / 0.037052 (0.004510) | 0.269924 / 0.258489 (0.011435) | 0.290749 / 0.293841 (-0.003092) | 0.028285 / 0.128546 (-0.100261) | 0.010464 / 0.075646 (-0.065183) | 0.207000 / 0.419271 (-0.212272) | 0.036186 / 0.043533 (-0.007347) | 0.254524 / 0.255139 (-0.000615) | 0.274843 / 0.283200 (-0.008356) | 0.020044 / 0.141683 (-0.121638) | 1.119223 / 1.452155 (-0.332931) | 1.156557 / 1.492716 (-0.336159) |\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) | 0.092014 / 0.018006 (0.074008) | 0.297349 / 0.000490 (0.296859) | 0.000205 / 0.000200 (0.000005) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018617 / 0.037411 (-0.018794) | 0.061879 / 0.014526 (0.047354) | 0.072877 / 0.176557 (-0.103680) | 0.121850 / 0.737135 (-0.615286) | 0.074686 / 0.296338 (-0.221653) |\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) | 0.281204 / 0.215209 (0.065995) | 2.728688 / 2.077655 (0.651033) | 1.469659 / 1.504120 (-0.034461) | 1.355306 / 1.541195 (-0.185889) | 1.350598 / 1.468490 (-0.117892) | 0.563669 / 4.584777 (-4.021108) | 2.377177 / 3.745712 (-1.368535) | 2.767402 / 5.269862 (-2.502460) | 1.720188 / 4.565676 (-2.845489) | 0.062594 / 0.424275 (-0.361681) | 0.005004 / 0.007607 (-0.002603) | 0.333017 / 0.226044 (0.106972) | 3.354543 / 2.268929 (1.085615) | 1.840031 / 55.444624 (-53.604593) | 1.545548 / 6.876477 (-5.330929) | 1.569858 / 2.142072 (-0.572214) | 0.642680 / 4.805227 (-4.162547) | 0.117463 / 6.500664 (-6.383201) | 0.042472 / 0.075469 (-0.032997) |\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) | 0.977436 / 1.841788 (-0.864351) | 11.285982 / 8.074308 (3.211673) | 9.441848 / 10.191392 (-0.749544) | 0.140773 / 0.680424 (-0.539650) | 0.013783 / 0.534201 (-0.520418) | 0.292304 / 0.579283 (-0.286979) | 0.275011 / 0.434364 (-0.159353) | 0.339094 / 0.540337 (-0.201244) | 0.447593 / 1.386936 (-0.939343) |\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) | 0.005258 / 0.011353 (-0.006095) | 0.003539 / 0.011008 (-0.007469) | 0.049920 / 0.038508 (0.011412) | 0.029789 / 0.023109 (0.006680) | 0.277187 / 0.275898 (0.001288) | 0.296817 / 0.323480 (-0.026663) | 0.004133 / 0.007986 (-0.003852) | 0.002679 / 0.004328 (-0.001649) | 0.048999 / 0.004250 (0.044749) | 0.044087 / 0.037052 (0.007034) | 0.290359 / 0.258489 (0.031870) | 0.319572 / 0.293841 (0.025731) | 0.030248 / 0.128546 (-0.098298) | 0.010453 / 0.075646 (-0.065194) | 0.058734 / 0.419271 (-0.360537) | 0.051216 / 0.043533 (0.007683) | 0.278667 / 0.255139 (0.023528) | 0.298792 / 0.283200 (0.015592) | 0.019131 / 0.141683 (-0.122552) | 1.131814 / 1.452155 (-0.320340) | 1.167208 / 1.492716 (-0.325508) |\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) | 0.088316 / 0.018006 (0.070309) | 0.297143 / 0.000490 (0.296653) | 0.000207 / 0.000200 (0.000007) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022457 / 0.037411 (-0.014954) | 0.075251 / 0.014526 (0.060726) | 0.086747 / 0.176557 (-0.089809) | 0.124975 / 0.737135 (-0.612161) | 0.087320 / 0.296338 (-0.209019) |\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) | 0.292339 / 0.215209 (0.077130) | 2.860196 / 2.077655 (0.782541) | 1.599058 / 1.504120 (0.094938) | 1.476104 / 1.541195 (-0.065091) | 1.509109 / 1.468490 (0.040619) | 0.564056 / 4.584777 (-4.020721) | 2.388870 / 3.745712 (-1.356842) | 2.582356 / 5.269862 (-2.687506) | 1.726033 / 4.565676 (-2.839644) | 0.061788 / 0.424275 (-0.362487) | 0.005021 / 0.007607 (-0.002586) | 0.345644 / 0.226044 (0.119600) | 3.384000 / 2.268929 (1.115071) | 1.946591 / 55.444624 (-53.498033) | 1.693485 / 6.876477 (-5.182992) | 1.790300 / 2.142072 (-0.351773) | 0.654637 / 4.805227 (-4.150590) | 0.116271 / 6.500664 (-6.384393) | 0.040710 / 0.075469 (-0.034759) |\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) | 1.007367 / 1.841788 (-0.834421) | 11.868065 / 8.074308 (3.793757) | 10.146212 / 10.191392 (-0.045180) | 0.128902 / 0.680424 (-0.551522) | 0.015259 / 0.534201 (-0.518942) | 0.288087 / 0.579283 (-0.291196) | 0.281516 / 0.434364 (-0.152848) | 0.325755 / 0.540337 (-0.214583) | 0.424814 / 1.386936 (-0.962122) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8247202a7ed1c3164c88f8f183513c5f003aa2af \"CML watermark\")\n"
] | 2024-03-04T21:02:07Z
| 2024-03-05T11:23:05Z
| 2024-03-05T11:17:04Z
|
MEMBER
| null | null | null |
A sliced + pickled ConcatenationTable could end up with a different schema than the original schema, if the slice only contains blocks with only a subset of the columns.
This can lead to issues when saving datasets from a concatenation of datasets with mixed schemas
Reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595
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PR_kwDODunzps5dBPnY
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Add return_file_name in load_dataset
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6310). All of your documentation changes will be reflected on that endpoint.",
"> Thanks for the change !\r\n> \r\n> Since `return` in python often refers to what is actually returned by the function (here `load_dataset`), I think we can use another word for the parameter. Maybe name it `with_file_names`?\r\n> \r\n> cc @mariosasko in case you have an opinion\r\n\r\nI changed the argument name to your suggestion, I agree that it should be less confusing :)",
"> Thanks! I've left some comments.\r\n> \r\n> @lhoestq WDYT about returning a data file's name (the last part) instead of the full path? This way we could have the same values in the streaming and the non-streaming mode. (In the non-streaming mode, we would also have to iterate over remote files to not output the files' hash (from the HF cache))\r\n\r\nConcerning the last part of the file name, do you have suggestions on how to do that? Because it can happen that the files are located in different folders with the same name so I am wondering what would be the way to go.",
"Hi @lhoestq @mariosasko, I just pushed a version rebased on main I've seen some activity on the issue associated to the PR so I wonder if you are interested in keeping up the process of merging this PR. I still have the same issues as indicated in the previous comment.",
"The original issue https://github.com/huggingface/datasets/issues/5806 is about returning the file names in order to be able to resume data loading from a checkpoint.\r\n\r\nData loading checkpointing has been implemented recently actually, using `torchdata` `StatefulDataloader`\r\n\r\nSee the discussion at https://github.com/huggingface/datasets/issues/5454\r\n\r\nI'm not sure if it's worth continuing this PR then ?",
"> The original issue #5806 is about returning the file names in order to be able to resume data loading from a checkpoint.\r\n> \r\n> Data loading checkpointing has been implemented recently actually, using `torchdata` `StatefulDataloader`\r\n> \r\n> See the discussion at #5454\r\n> \r\n> I'm not sure if it's worth continuing this PR then ?\r\n\r\nThanks for your answer, I'll close the PR if now the feature already exists :).",
"I need this for downstream filter. People always put important information in the file name."
] | 2023-10-17T13:36:57Z
| 2024-08-09T11:51:55Z
| 2024-07-31T13:56:50Z
|
NONE
| null | null | null |
Proposition to fix #5806.
Added an optional parameter `return_file_name` in the dataset builder config. When set to `True`, the function will include the file name corresponding to the sample in the returned output.
There is a difference between arrow-based and folder-based datasets to return the file name:
- for arrow-based: a column is concatenated after the table is cast.
- for folder-based: `dataset.info.features` has the entry `file_name` and the original file name is passed to the `sample_metadata` dictionary.
The difference in behavior might be a concern, also I do not know whether the `file_name` should return the original file path or the downloaded one for folder-based datasets.
I added some tests for the datasets that already had a test file.
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I_kwDODunzps6iwtA2
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Introduce support for PDFs
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[
"#self-assign",
"Awesome ! Let me know if you have any question or if I can help :)\r\n\r\ncc @AndreaFrancis as well for viz",
"Other candidates libraries for the Pdf type: PyMuPDF pypdf and pdfplumber\r\n\r\nEDIT: Pymupdf looks like a good choice when it comes to maturity + performance + versatility BUT the license is maybe an issue, and pypdf, pypdfium2 or pdfplumber are good options imo",
"Related to https://github.com/huggingface/datasets/issues/7058",
"PyMuPDF is AGPL licensed, so we can't use it. I will move forward with [pdfplumber](https://github.com/jsvine/pdfplumber?tab=readme-ov-file#python-library).",
"Hi both! I have made a pull request with a first basic implementation of the Pdf feature. I followed closely what I saw on the Video and Image features. It is my first time contributing so any comments are very welcomed. I think it would be useful to outline together what additional things we can implement (e.g. enabling parsing of the pdf). Thanks :) "
] | 2024-12-10T16:59:48Z
| 2024-12-12T18:38:13Z
| null |
CONTRIBUTOR
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### Feature request
The idea (discussed in the Discord server with @lhoestq ) is to have a Pdf type like Image/Audio/Video. For example [Video](https://github.com/huggingface/datasets/blob/main/src/datasets/features/video.py) was recently added and contains how to decode a video file encoded in a dictionary like {"path": ..., "bytes": ...} as a VideoReader using decord. We want to do the same with pdf and get a [pypdfium2.PdfDocument](https://pypdfium2.readthedocs.io/en/stable/_modules/pypdfium2/_helpers/document.html#PdfDocument).
### Motivation
In many cases PDFs contain very valuable information beyond text (e.g. images, figures). Support for PDFs would help create datasets where all the information is preserved.
### Your contribution
I can start the implementation of the Pdf type :)
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Fix CI
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6780). 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) | 0.005074 / 0.011353 (-0.006279) | 0.003395 / 0.011008 (-0.007614) | 0.062358 / 0.038508 (0.023849) | 0.031041 / 0.023109 (0.007932) | 0.244039 / 0.275898 (-0.031859) | 0.266361 / 0.323480 (-0.057119) | 0.003201 / 0.007986 (-0.004785) | 0.002609 / 0.004328 (-0.001719) | 0.049269 / 0.004250 (0.045018) | 0.045713 / 0.037052 (0.008661) | 0.264075 / 0.258489 (0.005586) | 0.295428 / 0.293841 (0.001587) | 0.027882 / 0.128546 (-0.100664) | 0.010424 / 0.075646 (-0.065222) | 0.208417 / 0.419271 (-0.210854) | 0.035728 / 0.043533 (-0.007805) | 0.246803 / 0.255139 (-0.008336) | 0.267169 / 0.283200 (-0.016031) | 0.019797 / 0.141683 (-0.121885) | 1.163299 / 1.452155 (-0.288856) | 1.196118 / 1.492716 (-0.296599) |\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) | 0.106091 / 0.018006 (0.088085) | 0.303970 / 0.000490 (0.303480) | 0.000219 / 0.000200 (0.000019) | 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) | 0.017955 / 0.037411 (-0.019456) | 0.060539 / 0.014526 (0.046013) | 0.072884 / 0.176557 (-0.103673) | 0.119205 / 0.737135 (-0.617931) | 0.074072 / 0.296338 (-0.222266) |\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) | 0.272676 / 0.215209 (0.057467) | 2.715169 / 2.077655 (0.637514) | 1.419090 / 1.504120 (-0.085030) | 1.303903 / 1.541195 (-0.237292) | 1.311903 / 1.468490 (-0.156587) | 0.562005 / 4.584777 (-4.022772) | 2.432817 / 3.745712 (-1.312896) | 2.770599 / 5.269862 (-2.499263) | 1.723043 / 4.565676 (-2.842633) | 0.064341 / 0.424275 (-0.359934) | 0.004923 / 0.007607 (-0.002684) | 0.330507 / 0.226044 (0.104463) | 3.240829 / 2.268929 (0.971901) | 1.787638 / 55.444624 (-53.656986) | 1.522971 / 6.876477 (-5.353506) | 1.529496 / 2.142072 (-0.612576) | 0.645768 / 4.805227 (-4.159459) | 0.116405 / 6.500664 (-6.384259) | 0.041524 / 0.075469 (-0.033945) |\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) | 0.968515 / 1.841788 (-0.873272) | 11.628911 / 8.074308 (3.554603) | 9.495023 / 10.191392 (-0.696369) | 0.142219 / 0.680424 (-0.538204) | 0.013859 / 0.534201 (-0.520342) | 0.285727 / 0.579283 (-0.293556) | 0.276842 / 0.434364 (-0.157522) | 0.321247 / 0.540337 (-0.219090) | 0.409958 / 1.386936 (-0.976978) |\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) | 0.005102 / 0.011353 (-0.006251) | 0.003213 / 0.011008 (-0.007796) | 0.049250 / 0.038508 (0.010742) | 0.030649 / 0.023109 (0.007540) | 0.276629 / 0.275898 (0.000731) | 0.297315 / 0.323480 (-0.026165) | 0.004198 / 0.007986 (-0.003787) | 0.002744 / 0.004328 (-0.001585) | 0.047899 / 0.004250 (0.043649) | 0.040596 / 0.037052 (0.003544) | 0.287248 / 0.258489 (0.028759) | 0.313573 / 0.293841 (0.019732) | 0.029067 / 0.128546 (-0.099480) | 0.010122 / 0.075646 (-0.065524) | 0.058869 / 0.419271 (-0.360402) | 0.033012 / 0.043533 (-0.010521) | 0.272995 / 0.255139 (0.017856) | 0.297102 / 0.283200 (0.013903) | 0.018209 / 0.141683 (-0.123474) | 1.157785 / 1.452155 (-0.294369) | 1.184999 / 1.492716 (-0.307717) |\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) | 0.094228 / 0.018006 (0.076221) | 0.302055 / 0.000490 (0.301565) | 0.000221 / 0.000200 (0.000021) | 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) | 0.022020 / 0.037411 (-0.015391) | 0.074970 / 0.014526 (0.060444) | 0.087682 / 0.176557 (-0.088875) | 0.126506 / 0.737135 (-0.610629) | 0.092046 / 0.296338 (-0.204293) |\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) | 0.295634 / 0.215209 (0.080425) | 2.891554 / 2.077655 (0.813899) | 1.579963 / 1.504120 (0.075843) | 1.462924 / 1.541195 (-0.078271) | 1.463806 / 1.468490 (-0.004684) | 0.558371 / 4.584777 (-4.026406) | 2.513500 / 3.745712 (-1.232212) | 2.754146 / 5.269862 (-2.515716) | 1.762317 / 4.565676 (-2.803360) | 0.063965 / 0.424275 (-0.360310) | 0.005538 / 0.007607 (-0.002069) | 0.348114 / 0.226044 (0.122070) | 3.484558 / 2.268929 (1.215630) | 1.940002 / 55.444624 (-53.504623) | 1.658469 / 6.876477 (-5.218008) | 1.645777 / 2.142072 (-0.496295) | 0.639367 / 4.805227 (-4.165861) | 0.115605 / 6.500664 (-6.385059) | 0.040647 / 0.075469 (-0.034822) |\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) | 1.036002 / 1.841788 (-0.805786) | 12.286895 / 8.074308 (4.212587) | 10.146719 / 10.191392 (-0.044673) | 0.140867 / 0.680424 (-0.539557) | 0.015517 / 0.534201 (-0.518684) | 0.290126 / 0.579283 (-0.289157) | 0.298702 / 0.434364 (-0.135662) | 0.325518 / 0.540337 (-0.214819) | 0.412597 / 1.386936 (-0.974339) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3ddb1ef00334a6f973679a51e783905fbc9ef0b \"CML watermark\")\n"
] | 2024-04-04T17:45:04Z
| 2024-04-04T18:46:04Z
| 2024-04-04T18:23:34Z
|
COLLABORATOR
| null | null | null |
Updates the `wmt_t2t` test to pin the `revision` to the version with a loading script (cc @albertvillanova).
Additionally, it replaces the occurrences of the `lhoestq/test` repo id with `hf-internal-testing/dataset_with_script` and re-enables logging checks in the `Dataset.from_sql` tests.
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The dataset viewer should be available soon. Please retry later.
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"Waiting is all you need. 10 hours later, it works."
] | 2024-10-30T16:32:00Z
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| 2024-10-31T03:48:10Z
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### Describe the bug
After waiting for 2 hours, it still presents ``The dataset viewer should be available soon. Please retry later.''
### Steps to reproduce the bug
dataset link: https://huggingface.co/datasets/BryanW/HI_EDIT
### Expected behavior
Present the dataset viewer.
### Environment info
NA
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Iterating over Image feature columns is extremely slow
|
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[
"Hi ! Could it be because the `Image()` type in dataset does `image = Image.open(image_path)` and also `image.load()` which actually loads the image data in memory ? This is needed to avoid too many open files issues, see https://github.com/huggingface/datasets/issues/3985",
"Yes, that seems to be it. For my purposes, I've cast the column to `Image(decode=False)`, and only load the images when necessary, which is much much faster"
] | 2025-04-10T19:00:54Z
| 2025-04-15T17:57:08Z
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We are trying to load datasets where the image column stores `PIL.PngImagePlugin.PngImageFile` images. However, iterating over these datasets is extremely slow.
What I have found:
1. It is the presence of the image column that causes the slowdown. Removing the column from the dataset results in blazingly fast (as expected) times
2. It is ~2x faster to iterate when the column contains a single image as opposed to a list of images i.e., the feature is a Sequence of Image objects. We often need multiple images per sample, so we need to work with a list of images
3. It is ~17x faster to store paths to PNG files and load them using `PIL.Image.open`, as opposed to iterating over a `Dataset` with an Image column, and ~30x faster compared to `Sequence` of `Image`s. See a simple script below with an openly available dataset.
It would be great to understand the standard practices for storing and loading multimodal datasets (image + text).
https://huggingface.co/docs/datasets/en/image_load seems a bit underdeveloped? (e.g., `dataset.decode` only works with `IterableDataset`, but it's not clear from the doc)
Thanks!
```python
from datasets import load_dataset, load_from_disk
from PIL import Image
from pathlib import Path
ds = load_dataset("getomni-ai/ocr-benchmark")
for idx, sample in enumerate(ds["test"]):
image = sample["image"]
image.save(f"/tmp/ds_files/images/image_{idx}.png")
ds.save_to_disk("/tmp/ds_columns")
# Remove the 'image' column
ds["test"] = ds["test"].remove_columns(["image"])
# Create image paths for each sample
image_paths = [f"images/image_{idx}.png" for idx in range(len(ds["test"]))]
# Add the 'image_path' column to the dataset
ds["test"] = ds["test"].add_column("image_path", image_paths)
# Save the updated dataset
ds.save_to_disk("/tmp/ds_files")
files_path = Path("/tmp/ds_files")
column_path = Path("/tmp/ds_columns")
# load and benchmark
ds_file = load_from_disk(files_path)
ds_column = load_from_disk(column_path)
import time
images_files = []
start = time.time()
for idx in range(len(ds_file["test"])):
image_path = files_path / ds_file["test"][idx]["image_path"]
image = Image.open(image_path)
images_files.append(image)
end = time.time()
print(f"Time taken to load images from files: {end - start} seconds")
# Time taken to load images from files: 1.2364635467529297 seconds
images_column = []
start = time.time()
for idx in range(len(ds_column["test"])):
images_column.append(ds_column["test"][idx]["image"])
end = time.time()
print(f"Time taken to load images from columns: {end - start} seconds")
# Time taken to load images from columns: 20.49347186088562 seconds
```
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