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https://api.github.com/repos/huggingface/datasets/issues/1884 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1884/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1884/comments | https://api.github.com/repos/huggingface/datasets/issues/1884/events | https://github.com/huggingface/datasets/pull/1884 | 808,755,894 | MDExOlB1bGxSZXF1ZXN0NTczNzQwNzI5 | 1,884 | dtype fix when using numpy arrays | [] | closed | false | null | 0 | 2021-02-15T18:55:25Z | 2021-07-30T11:01:18Z | 2021-07-30T11:01:18Z | null | As discussed in #625 this fix lets the user preserve the dtype of numpy array to pyarrow array which was getting lost due to conversion of numpy array -> list -> pyarrow array | {
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https://api.github.com/repos/huggingface/datasets/issues/5634 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5634/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5634/comments | https://api.github.com/repos/huggingface/datasets/issues/5634/events | https://github.com/huggingface/datasets/issues/5634 | 1,622,424,174 | I_kwDODunzps5gtDpu | 5,634 | Not all progress bars are showing up when they should for downloading dataset | [] | open | false | null | 2 | 2023-03-13T23:04:18Z | 2023-03-21T01:59:59Z | null | null | ### Describe the bug
During downloading the rotten tomatoes dataset, not all progress bars are displayed properly. This might be related to [this ticket](https://github.com/huggingface/datasets/issues/5117) as it raised the same concern but its not clear if the fix solves this issue too.
ipywidgets
<img width="1243" alt="image" src="https://user-images.githubusercontent.com/110427462/224851138-13fee5b7-ab51-4883-b96f-1b9808782e3b.png">
tqdm
<img width="1251" alt="Screen Shot 2023-03-13 at 3 58 59 PM" src="https://user-images.githubusercontent.com/110427462/224851180-5feb7825-9250-4b1e-ad0c-f3172ac1eb78.png">
### Steps to reproduce the bug
1. Run this line
```
from datasets import load_dataset
rotten_tomatoes = load_dataset("rotten_tomatoes", split="train")
```
### Expected behavior
all progress bars for builder script, metadata, readme, training, validation, and test set
### Environment info
requirements.txt
```
aiofiles==22.1.0
aiohttp==3.8.4
aiosignal==1.3.1
aiosqlite==0.18.0
anyio==3.6.2
appnope==0.1.3
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens==2.2.1
async-generator==1.10
async-timeout==4.0.2
attrs==22.2.0
Babel==2.12.1
backcall==0.2.0
beautifulsoup4==4.11.2
bleach==6.0.0
brotlipy @ file:///Users/runner/miniforge3/conda-bld/brotlipy_1666764961872/work
certifi==2022.12.7
cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1671179414629/work
cfgv==3.3.1
charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work
comm==0.1.2
conda==22.9.0
conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work
conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work
coverage==7.2.1
cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1669592251328/work
datasets==2.1.0
debugpy==1.6.6
decorator==5.1.1
defusedxml==0.7.1
dill==0.3.6
distlib==0.3.6
distro==1.4.0
entrypoints==0.4
exceptiongroup==1.1.0
executing==1.2.0
fastjsonschema==2.16.3
filelock==3.9.0
flaky==3.7.0
fqdn==1.5.1
frozenlist==1.3.3
fsspec==2023.3.0
huggingface-hub==0.10.1
identify==2.5.18
idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work
iniconfig==2.0.0
ipykernel==6.12.1
ipyparallel==8.4.1
ipython==7.32.0
ipython-genutils==0.2.0
ipywidgets==8.0.4
isoduration==20.11.0
jedi==0.18.2
Jinja2==3.1.2
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jsonschema==4.17.3
jupyter-events==0.6.3
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jupyter_client==8.0.3
jupyter_core==5.2.0
jupyter_server==2.4.0
jupyter_server_fileid==0.8.0
jupyter_server_terminals==0.4.4
jupyter_server_ydoc==0.6.1
jupyterlab==3.6.1
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.5
jupyterlab_server==2.20.0
libmambapy @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/libmambapy
mamba @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/mamba
MarkupSafe==2.1.2
matplotlib-inline==0.1.6
mistune==2.0.5
multidict==6.0.4
multiprocess==0.70.14
nbclassic==0.5.3
nbclient==0.7.2
nbconvert==7.2.9
nbformat==5.7.3
nest-asyncio==1.5.6
nodeenv==1.7.0
notebook==6.5.3
notebook_shim==0.2.2
numpy==1.24.2
outcome==1.2.0
packaging==23.0
pandas==1.5.3
pandocfilters==1.5.0
parso==0.8.3
pexpect==4.8.0
pickleshare==0.7.5
platformdirs==3.0.0
plotly==5.13.1
pluggy==1.0.0
pre-commit==3.1.0
prometheus-client==0.16.0
prompt-toolkit==3.0.38
psutil==5.9.4
ptyprocess==0.7.0
pure-eval==0.2.2
pyarrow==11.0.0
pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1666836580084/work
pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work
Pygments==2.14.0
pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1665350324128/work
pyrsistent==0.19.3
PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work
pytest==7.2.1
pytest-asyncio==0.20.3
pytest-cov==4.0.0
pytest-timeout==2.1.0
python-dateutil==2.8.2
python-json-logger==2.0.7
pytz==2022.7.1
PyYAML==6.0
pyzmq==25.0.0
requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1661872987712/work
responses==0.18.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1666819760545/work
Send2Trash==1.8.0
simplegeneric==0.8.1
six==1.16.0
sniffio==1.3.0
sortedcontainers==2.4.0
soupsieve==2.4
stack-data==0.6.2
tenacity==8.2.2
terminado==0.17.1
tinycss2==1.2.1
tomli==2.0.1
toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work
tornado==6.2
tqdm==4.64.1
traitlets==5.8.1
trio==0.22.0
typing_extensions==4.5.0
uri-template==1.2.0
urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1669259737463/work
virtualenv==20.19.0
wcwidth==0.2.6
webcolors==1.12
webencodings==0.5.1
websocket-client==1.5.1
widgetsnbextension==4.0.5
xxhash==3.2.0
y-py==0.5.9
yarl==1.8.2
ypy-websocket==0.8.2
zstandard==0.19.0
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"Hi! \r\n\r\nBy default, tqdm has `leave=True` to \"keep all traces of the progress bar upon the termination of iteration\". However, we use `leave=False` in some places (as of recently), which removes the bar once the iteration is over.\r\n\r\nI feel like our TQDM bars are noisy, so I think we should always set `leave=False` and also use the `delay` parameter to display progress bars only for tasks that take time (e.g., more than 3s). What do you think about this? Do you find these bars useful (after the dataset generation is over)?\r\n",
"Hi sorry for the late update. I think the problem still exists despite the `leave` flag\r\n\r\n<img width=\"1105\" alt=\"image\" src=\"https://user-images.githubusercontent.com/110427462/226501615-5b02fb02-fd5f-4eda-b1f7-a7ed6570892d.png\">\r\n\r\n\r\n```\r\nPackage Version\r\n------------------------ ---------\r\naiofiles 22.1.0\r\naiohttp 3.8.4\r\naiosignal 1.3.1\r\naiosqlite 0.18.0\r\nanyio 3.6.2\r\nappnope 0.1.3\r\nargon2-cffi 21.3.0\r\nargon2-cffi-bindings 21.2.0\r\narrow 1.2.3\r\nasttokens 2.2.1\r\nasync-generator 1.10\r\nasync-timeout 4.0.2\r\nattrs 22.2.0\r\nBabel 2.12.1\r\nbackcall 0.2.0\r\nbeautifulsoup4 4.11.2\r\nbleach 6.0.0\r\nbrotlipy 0.7.0\r\ncertifi 2022.12.7\r\ncffi 1.15.1\r\ncfgv 3.3.1\r\ncharset-normalizer 2.1.1\r\ncomm 0.1.2\r\nconda 22.9.0\r\nconda-package-handling 2.0.2\r\nconda_package_streaming 0.7.0\r\ncoverage 7.2.1\r\ncryptography 38.0.4\r\ndatasets 2.8.0\r\ndebugpy 1.6.6\r\ndecorator 5.1.1\r\ndefusedxml 0.7.1\r\ndill 0.3.6\r\ndistlib 0.3.6\r\ndistro 1.4.0\r\nentrypoints 0.4\r\nexceptiongroup 1.1.0\r\nexecuting 1.2.0\r\nfastjsonschema 2.16.3\r\nfilelock 3.9.0\r\nflaky 3.7.0\r\nfqdn 1.5.1\r\nfrozenlist 1.3.3\r\nfsspec 2023.3.0\r\nhuggingface-hub 0.10.1\r\nidentify 2.5.18\r\nidna 3.4\r\niniconfig 2.0.0\r\nipykernel 6.12.1\r\nipyparallel 8.4.1\r\nipython 7.32.0\r\nipython-genutils 0.2.0\r\nipywidgets 8.0.4\r\nisoduration 20.11.0\r\njedi 0.18.2\r\nJinja2 3.1.2\r\njson5 0.9.11\r\njsonpointer 2.3\r\njsonschema 4.17.3\r\njupyter_client 8.0.3\r\njupyter_core 5.2.0\r\njupyter-events 0.6.3\r\njupyter_server 2.4.0\r\njupyter_server_fileid 0.8.0\r\njupyter_server_terminals 0.4.4\r\njupyter_server_ydoc 0.6.1\r\njupyter-ydoc 0.2.2\r\njupyterlab 3.6.1\r\njupyterlab-pygments 0.2.2\r\njupyterlab_server 2.20.0\r\njupyterlab-widgets 3.0.5\r\nlibmambapy 1.1.0\r\nmamba 1.1.0\r\nMarkupSafe 2.1.2\r\nmatplotlib-inline 0.1.6\r\nmistune 2.0.5\r\nmultidict 6.0.4\r\nmultiprocess 0.70.14\r\nnbclassic 0.5.3\r\nnbclient 0.7.2\r\nnbconvert 7.2.9\r\nnbformat 5.7.3\r\nnest-asyncio 1.5.6\r\nnodeenv 1.7.0\r\nnotebook 6.5.3\r\nnotebook_shim 0.2.2\r\nnumpy 1.24.2\r\noutcome 1.2.0\r\npackaging 23.0\r\npandas 1.5.3\r\npandocfilters 1.5.0\r\nparso 0.8.3\r\npexpect 4.8.0\r\npickleshare 0.7.5\r\npip 22.3.1\r\nplatformdirs 3.0.0\r\nplotly 5.13.1\r\npluggy 1.0.0\r\npre-commit 3.1.0\r\nprometheus-client 0.16.0\r\nprompt-toolkit 3.0.38\r\npsutil 5.9.4\r\nptyprocess 0.7.0\r\npure-eval 0.2.2\r\npyarrow 11.0.0\r\npycosat 0.6.4\r\npycparser 2.21\r\nPygments 2.14.0\r\npyOpenSSL 22.1.0\r\npyrsistent 0.19.3\r\nPySocks 1.7.1\r\npytest 7.2.1\r\npytest-asyncio 0.20.3\r\npytest-cov 4.0.0\r\npytest-timeout 2.1.0\r\npython-dateutil 2.8.2\r\npython-json-logger 2.0.7\r\npytz 2022.7.1\r\nPyYAML 6.0\r\npyzmq 25.0.0\r\nrequests 2.28.1\r\nresponses 0.18.0\r\nrfc3339-validator 0.1.4\r\nrfc3986-validator 0.1.1\r\nruamel-yaml-conda 0.15.80\r\nSend2Trash 1.8.0\r\nsetuptools 65.6.3\r\nsimplegeneric 0.8.1\r\nsix 1.16.0\r\nsniffio 1.3.0\r\nsortedcontainers 2.4.0\r\nsoupsieve 2.4\r\nstack-data 0.6.2\r\ntenacity 8.2.2\r\nterminado 0.17.1\r\ntinycss2 1.2.1\r\ntomli 2.0.1\r\ntoolz 0.12.0\r\ntornado 6.2\r\ntqdm 4.65.0\r\ntraitlets 5.8.1\r\ntrio 0.22.0\r\ntyping_extensions 4.5.0\r\nuri-template 1.2.0\r\nurllib3 1.26.13\r\nvirtualenv 20.19.0\r\nwcwidth 0.2.6\r\nwebcolors 1.12\r\nwebencodings 0.5.1\r\nwebsocket-client 1.5.1\r\nwheel 0.38.4\r\nwidgetsnbextension 4.0.5\r\nxxhash 3.2.0\r\ny-py 0.5.9\r\nyarl 1.8.2\r\nypy-websocket 0.8.2\r\nzstandard 0.19.0\r\n```\r\n\r\nAny idea why this is happening? I debugged this to know the tqdm.pbar value is not being updated properly and its not the kernel not sending the comm messages to the IProgress bar"
] |
https://api.github.com/repos/huggingface/datasets/issues/5389 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5389/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5389/comments | https://api.github.com/repos/huggingface/datasets/issues/5389/events | https://github.com/huggingface/datasets/pull/5389 | 1,509,348,626 | PR_kwDODunzps5GHsOo | 5,389 | Fix link in `load_dataset` docstring | [] | closed | false | null | 6 | 2022-12-23T13:26:31Z | 2023-01-25T19:00:43Z | 2023-01-24T16:33:38Z | null | Fix https://github.com/huggingface/datasets/issues/5387, fix https://github.com/huggingface/datasets/issues/4566 | {
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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) | 0.008935 / 0.011353 (-0.002417) | 0.004582 / 0.011008 (-0.006426) | 0.100950 / 0.038508 (0.062442) | 0.030305 / 0.023109 (0.007196) | 0.299759 / 0.275898 (0.023861) | 0.378577 / 0.323480 (0.055097) | 0.007834 / 0.007986 (-0.000152) | 0.003399 / 0.004328 (-0.000930) | 0.078568 / 0.004250 (0.074318) | 0.037990 / 0.037052 (0.000938) | 0.313025 / 0.258489 (0.054536) | 0.359543 / 0.293841 (0.065702) | 0.033631 / 0.128546 (-0.094916) | 0.011681 / 0.075646 (-0.063966) | 0.324542 / 0.419271 (-0.094729) | 0.041014 / 0.043533 (-0.002519) | 0.302884 / 0.255139 (0.047745) | 0.337059 / 0.283200 (0.053859) | 0.089403 / 0.141683 (-0.052280) | 1.491262 / 1.452155 (0.039108) | 1.521626 / 1.492716 (0.028910) |\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) | 0.172627 / 0.018006 (0.154621) | 0.419406 / 0.000490 (0.418917) | 0.001974 / 0.000200 (0.001775) | 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) | 0.023598 / 0.037411 (-0.013814) | 0.098127 / 0.014526 (0.083601) | 0.105611 / 0.176557 (-0.070946) | 0.142612 / 0.737135 (-0.594523) | 0.121687 / 0.296338 (-0.174651) |\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) | 0.418512 / 0.215209 (0.203303) | 4.173099 / 2.077655 (2.095444) | 1.865900 / 1.504120 (0.361780) | 1.664053 / 1.541195 (0.122858) | 1.726289 / 1.468490 (0.257799) | 0.693214 / 4.584777 (-3.891563) | 3.499982 / 3.745712 (-0.245730) | 1.894278 / 5.269862 (-3.375583) | 1.178214 / 4.565676 (-3.387463) | 0.082391 / 0.424275 (-0.341884) | 0.012486 / 0.007607 (0.004878) | 0.532190 / 0.226044 (0.306145) | 5.286612 / 2.268929 (3.017684) | 2.316680 / 55.444624 (-53.127944) | 1.964020 / 6.876477 (-4.912457) | 2.016457 / 2.142072 (-0.125616) | 0.812290 / 4.805227 (-3.992937) | 0.149102 / 6.500664 (-6.351562) | 0.064215 / 0.075469 (-0.011254) |\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) | 1.281919 / 1.841788 (-0.559869) | 14.107509 / 8.074308 (6.033201) | 13.892369 / 10.191392 (3.700977) | 0.146164 / 0.680424 (-0.534260) | 0.028740 / 0.534201 (-0.505460) | 0.395218 / 0.579283 (-0.184066) | 0.406321 / 0.434364 (-0.028043) | 0.460880 / 0.540337 (-0.079458) | 0.545975 / 1.386936 (-0.840961) |\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) | 0.006797 / 0.011353 (-0.004556) | 0.004522 / 0.011008 (-0.006486) | 0.098440 / 0.038508 (0.059932) | 0.027722 / 0.023109 (0.004613) | 0.423995 / 0.275898 (0.148097) | 0.456164 / 0.323480 (0.132684) | 0.005156 / 0.007986 (-0.002830) | 0.003439 / 0.004328 (-0.000889) | 0.075307 / 0.004250 (0.071057) | 0.039599 / 0.037052 (0.002547) | 0.423671 / 0.258489 (0.165181) | 0.463841 / 0.293841 (0.170001) | 0.032473 / 0.128546 (-0.096073) | 0.011674 / 0.075646 (-0.063972) | 0.320548 / 0.419271 (-0.098723) | 0.041618 / 0.043533 (-0.001915) | 0.426133 / 0.255139 (0.170994) | 0.443018 / 0.283200 (0.159819) | 0.091103 / 0.141683 (-0.050579) | 1.468758 / 1.452155 (0.016604) | 1.532695 / 1.492716 (0.039978) |\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) | 0.255314 / 0.018006 (0.237308) | 0.422982 / 0.000490 (0.422492) | 0.015405 / 0.000200 (0.015205) | 0.000103 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025260 / 0.037411 (-0.012152) | 0.102062 / 0.014526 (0.087537) | 0.108161 / 0.176557 (-0.068395) | 0.144205 / 0.737135 (-0.592930) | 0.111686 / 0.296338 (-0.184653) |\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) | 0.482633 / 0.215209 (0.267424) | 4.824777 / 2.077655 (2.747123) | 2.488626 / 1.504120 (0.984506) | 2.285410 / 1.541195 (0.744215) | 2.336793 / 1.468490 (0.868303) | 0.701894 / 4.584777 (-3.882883) | 3.506908 / 3.745712 (-0.238804) | 3.399789 / 5.269862 (-1.870072) | 1.536359 / 4.565676 (-3.029317) | 0.083621 / 0.424275 (-0.340655) | 0.012702 / 0.007607 (0.005094) | 0.581259 / 0.226044 (0.355215) | 5.829640 / 2.268929 (3.560711) | 2.932201 / 55.444624 (-52.512424) | 2.577175 / 6.876477 (-4.299301) | 2.621782 / 2.142072 (0.479710) | 0.812074 / 4.805227 (-3.993153) | 0.152840 / 6.500664 (-6.347824) | 0.067982 / 0.075469 (-0.007487) |\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) | 1.274915 / 1.841788 (-0.566873) | 14.345800 / 8.074308 (6.271492) | 14.242475 / 10.191392 (4.051083) | 0.143636 / 0.680424 (-0.536788) | 0.016824 / 0.534201 (-0.517377) | 0.376449 / 0.579283 (-0.202834) | 0.394219 / 0.434364 (-0.040145) | 0.435368 / 0.540337 (-0.104969) | 0.518393 / 1.386936 (-0.868544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#187e4faa978fef267a055f6988564f922e51eaa4 \"CML watermark\")\n",
"I also fixed the rest of the links that point to the markdown files. \r\n\r\nPS: the CI failures are unrelated ",
"<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) | 0.008641 / 0.011353 (-0.002712) | 0.004560 / 0.011008 (-0.006448) | 0.100559 / 0.038508 (0.062051) | 0.029744 / 0.023109 (0.006635) | 0.300580 / 0.275898 (0.024682) | 0.359100 / 0.323480 (0.035620) | 0.007016 / 0.007986 (-0.000970) | 0.003393 / 0.004328 (-0.000936) | 0.078649 / 0.004250 (0.074399) | 0.038138 / 0.037052 (0.001086) | 0.307730 / 0.258489 (0.049241) | 0.347678 / 0.293841 (0.053837) | 0.033630 / 0.128546 (-0.094917) | 0.011452 / 0.075646 (-0.064194) | 0.320903 / 0.419271 (-0.098369) | 0.042659 / 0.043533 (-0.000874) | 0.298886 / 0.255139 (0.043747) | 0.324371 / 0.283200 (0.041171) | 0.092582 / 0.141683 (-0.049101) | 1.490017 / 1.452155 (0.037863) | 1.512825 / 1.492716 (0.020109) |\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) | 0.178965 / 0.018006 (0.160958) | 0.420001 / 0.000490 (0.419512) | 0.002686 / 0.000200 (0.002486) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023568 / 0.037411 (-0.013843) | 0.097027 / 0.014526 (0.082502) | 0.104721 / 0.176557 (-0.071836) | 0.148757 / 0.737135 (-0.588378) | 0.110849 / 0.296338 (-0.185489) |\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) | 0.415034 / 0.215209 (0.199825) | 4.155249 / 2.077655 (2.077594) | 1.837027 / 1.504120 (0.332907) | 1.627754 / 1.541195 (0.086559) | 1.687958 / 1.468490 (0.219468) | 0.699542 / 4.584777 (-3.885235) | 3.376707 / 3.745712 (-0.369005) | 2.900778 / 5.269862 (-2.369083) | 1.556168 / 4.565676 (-3.009508) | 0.082438 / 0.424275 (-0.341837) | 0.012339 / 0.007607 (0.004732) | 0.524952 / 0.226044 (0.298907) | 5.269852 / 2.268929 (3.000924) | 2.278770 / 55.444624 (-53.165854) | 1.917987 / 6.876477 (-4.958490) | 1.955000 / 2.142072 (-0.187072) | 0.821169 / 4.805227 (-3.984058) | 0.149019 / 6.500664 (-6.351645) | 0.064604 / 0.075469 (-0.010865) |\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) | 1.199768 / 1.841788 (-0.642020) | 13.760897 / 8.074308 (5.686589) | 13.911550 / 10.191392 (3.720158) | 0.161727 / 0.680424 (-0.518697) | 0.028615 / 0.534201 (-0.505586) | 0.393917 / 0.579283 (-0.185366) | 0.392524 / 0.434364 (-0.041840) | 0.451763 / 0.540337 (-0.088574) | 0.536880 / 1.386936 (-0.850056) |\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) | 0.006407 / 0.011353 (-0.004946) | 0.004420 / 0.011008 (-0.006588) | 0.097244 / 0.038508 (0.058736) | 0.027114 / 0.023109 (0.004005) | 0.412512 / 0.275898 (0.136614) | 0.448189 / 0.323480 (0.124709) | 0.005831 / 0.007986 (-0.002155) | 0.005423 / 0.004328 (0.001095) | 0.076051 / 0.004250 (0.071801) | 0.038828 / 0.037052 (0.001776) | 0.414586 / 0.258489 (0.156097) | 0.457196 / 0.293841 (0.163355) | 0.031615 / 0.128546 (-0.096931) | 0.011542 / 0.075646 (-0.064104) | 0.316967 / 0.419271 (-0.102304) | 0.041278 / 0.043533 (-0.002254) | 0.411371 / 0.255139 (0.156232) | 0.436376 / 0.283200 (0.153177) | 0.090212 / 0.141683 (-0.051471) | 1.461831 / 1.452155 (0.009677) | 1.606515 / 1.492716 (0.113799) |\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) | 0.221453 / 0.018006 (0.203447) | 0.404140 / 0.000490 (0.403650) | 0.000422 / 0.000200 (0.000222) | 0.000060 / 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) | 0.024588 / 0.037411 (-0.012824) | 0.098604 / 0.014526 (0.084078) | 0.113682 / 0.176557 (-0.062874) | 0.141141 / 0.737135 (-0.595994) | 0.110069 / 0.296338 (-0.186270) |\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) | 0.477267 / 0.215209 (0.262058) | 4.775086 / 2.077655 (2.697431) | 2.445449 / 1.504120 (0.941329) | 2.242220 / 1.541195 (0.701025) | 2.303542 / 1.468490 (0.835051) | 0.693448 / 4.584777 (-3.891329) | 3.413319 / 3.745712 (-0.332393) | 3.052734 / 5.269862 (-2.217127) | 1.434075 / 4.565676 (-3.131602) | 0.082429 / 0.424275 (-0.341846) | 0.012594 / 0.007607 (0.004987) | 0.584259 / 0.226044 (0.358214) | 5.865098 / 2.268929 (3.596169) | 2.926301 / 55.444624 (-52.518324) | 2.572555 / 6.876477 (-4.303921) | 2.608584 / 2.142072 (0.466512) | 0.805029 / 4.805227 (-4.000198) | 0.151247 / 6.500664 (-6.349417) | 0.067142 / 0.075469 (-0.008327) |\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) | 1.285454 / 1.841788 (-0.556334) | 14.296425 / 8.074308 (6.222117) | 14.147278 / 10.191392 (3.955886) | 0.151698 / 0.680424 (-0.528726) | 0.016876 / 0.534201 (-0.517325) | 0.383302 / 0.579283 (-0.195981) | 0.388461 / 0.434364 (-0.045902) | 0.438286 / 0.540337 (-0.102051) | 0.525249 / 1.386936 (-0.861687) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"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) | 0.008677 / 0.011353 (-0.002676) | 0.004863 / 0.011008 (-0.006145) | 0.096606 / 0.038508 (0.058098) | 0.034004 / 0.023109 (0.010895) | 0.296362 / 0.275898 (0.020464) | 0.323445 / 0.323480 (-0.000035) | 0.007341 / 0.007986 (-0.000644) | 0.005518 / 0.004328 (0.001189) | 0.073584 / 0.004250 (0.069334) | 0.041471 / 0.037052 (0.004419) | 0.302183 / 0.258489 (0.043694) | 0.339369 / 0.293841 (0.045528) | 0.037375 / 0.128546 (-0.091171) | 0.011827 / 0.075646 (-0.063819) | 0.330723 / 0.419271 (-0.088549) | 0.048751 / 0.043533 (0.005218) | 0.298370 / 0.255139 (0.043231) | 0.317781 / 0.283200 (0.034582) | 0.097488 / 0.141683 (-0.044195) | 1.456242 / 1.452155 (0.004088) | 1.530149 / 1.492716 (0.037433) |\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) | 0.207053 / 0.018006 (0.189046) | 0.438165 / 0.000490 (0.437675) | 0.001161 / 0.000200 (0.000961) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025353 / 0.037411 (-0.012059) | 0.105536 / 0.014526 (0.091010) | 0.116122 / 0.176557 (-0.060434) | 0.151605 / 0.737135 (-0.585530) | 0.121777 / 0.296338 (-0.174561) |\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) | 0.402780 / 0.215209 (0.187571) | 4.017882 / 2.077655 (1.940227) | 1.813111 / 1.504120 (0.308991) | 1.620000 / 1.541195 (0.078805) | 1.649186 / 1.468490 (0.180696) | 0.687523 / 4.584777 (-3.897254) | 3.712595 / 3.745712 (-0.033117) | 2.038535 / 5.269862 (-3.231326) | 1.414794 / 4.565676 (-3.150882) | 0.083357 / 0.424275 (-0.340918) | 0.012032 / 0.007607 (0.004425) | 0.502899 / 0.226044 (0.276854) | 5.038914 / 2.268929 (2.769985) | 2.250476 / 55.444624 (-53.194148) | 1.919954 / 6.876477 (-4.956523) | 1.930928 / 2.142072 (-0.211144) | 0.826634 / 4.805227 (-3.978593) | 0.161599 / 6.500664 (-6.339066) | 0.061356 / 0.075469 (-0.014113) |\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) | 1.228998 / 1.841788 (-0.612790) | 14.587914 / 8.074308 (6.513606) | 14.237514 / 10.191392 (4.046122) | 0.190913 / 0.680424 (-0.489510) | 0.029104 / 0.534201 (-0.505097) | 0.436160 / 0.579283 (-0.143123) | 0.431464 / 0.434364 (-0.002900) | 0.511670 / 0.540337 (-0.028668) | 0.609046 / 1.386936 (-0.777890) |\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) | 0.006980 / 0.011353 (-0.004373) | 0.005260 / 0.011008 (-0.005748) | 0.095288 / 0.038508 (0.056780) | 0.032465 / 0.023109 (0.009356) | 0.410799 / 0.275898 (0.134901) | 0.423814 / 0.323480 (0.100334) | 0.005533 / 0.007986 (-0.002452) | 0.005764 / 0.004328 (0.001436) | 0.070713 / 0.004250 (0.066462) | 0.048193 / 0.037052 (0.011141) | 0.405742 / 0.258489 (0.147253) | 0.458773 / 0.293841 (0.164932) | 0.036415 / 0.128546 (-0.092131) | 0.012192 / 0.075646 (-0.063454) | 0.330655 / 0.419271 (-0.088617) | 0.055945 / 0.043533 (0.012412) | 0.407497 / 0.255139 (0.152358) | 0.421496 / 0.283200 (0.138296) | 0.106285 / 0.141683 (-0.035398) | 1.459837 / 1.452155 (0.007683) | 1.573147 / 1.492716 (0.080431) |\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) | 0.205776 / 0.018006 (0.187770) | 0.441523 / 0.000490 (0.441033) | 0.003073 / 0.000200 (0.002873) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029207 / 0.037411 (-0.008205) | 0.110295 / 0.014526 (0.095770) | 0.130233 / 0.176557 (-0.046324) | 0.157489 / 0.737135 (-0.579647) | 0.125374 / 0.296338 (-0.170965) |\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) | 0.440942 / 0.215209 (0.225733) | 4.389647 / 2.077655 (2.311992) | 2.234883 / 1.504120 (0.730763) | 2.029510 / 1.541195 (0.488315) | 2.082503 / 1.468490 (0.614013) | 0.698046 / 4.584777 (-3.886731) | 3.769127 / 3.745712 (0.023415) | 2.058511 / 5.269862 (-3.211351) | 1.324302 / 4.565676 (-3.241375) | 0.085695 / 0.424275 (-0.338580) | 0.012122 / 0.007607 (0.004515) | 0.552406 / 0.226044 (0.326362) | 5.527073 / 2.268929 (3.258145) | 2.711354 / 55.444624 (-52.733270) | 2.328848 / 6.876477 (-4.547629) | 2.340750 / 2.142072 (0.198678) | 0.846300 / 4.805227 (-3.958927) | 0.167465 / 6.500664 (-6.333199) | 0.063419 / 0.075469 (-0.012050) |\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) | 1.262452 / 1.841788 (-0.579336) | 15.043537 / 8.074308 (6.969229) | 14.212563 / 10.191392 (4.021171) | 0.170229 / 0.680424 (-0.510194) | 0.017696 / 0.534201 (-0.516505) | 0.423194 / 0.579283 (-0.156089) | 0.430908 / 0.434364 (-0.003456) | 0.491733 / 0.540337 (-0.048604) | 0.599267 / 1.386936 (-0.787669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n",
"Program enthusiastic "
] |
https://api.github.com/repos/huggingface/datasets/issues/4078 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4078/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4078/comments | https://api.github.com/repos/huggingface/datasets/issues/4078/events | https://github.com/huggingface/datasets/pull/4078 | 1,189,513,572 | PR_kwDODunzps41eWnl | 4,078 | Fix GithubMetricModuleFactory instantiation with None download_config | [] | closed | false | null | 1 | 2022-04-01T09:26:58Z | 2022-04-01T14:44:51Z | 2022-04-01T14:39:27Z | null | Recent PR:
- #4063
introduced a potential bug if `GithubMetricModuleFactory` is instantiated with None `download_config`.
This PR add instantiation tests and fix that potential issue.
CC: @lhoestq | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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https://api.github.com/repos/huggingface/datasets/issues/5588 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5588/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5588/comments | https://api.github.com/repos/huggingface/datasets/issues/5588/events | https://github.com/huggingface/datasets/pull/5588 | 1,603,304,766 | PR_kwDODunzps5K8YYz | 5,588 | Flatten dataset on the fly in `save_to_disk` | [] | closed | false | null | 3 | 2023-02-28T15:37:46Z | 2023-02-28T17:28:35Z | 2023-02-28T17:21:17Z | null | Flatten a dataset on the fly in `save_to_disk` instead of doing it with `flatten_indices` to avoid creating an additional cache file.
(this is one of the sub-tasks in https://github.com/huggingface/datasets/issues/5507) | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009866 / 0.011353 (-0.001487) | 0.005334 / 0.011008 (-0.005675) | 0.101771 / 0.038508 (0.063263) | 0.037722 / 0.023109 (0.014613) | 0.301026 / 0.275898 (0.025128) | 0.336618 / 0.323480 (0.013138) | 0.008679 / 0.007986 (0.000693) | 0.005640 / 0.004328 (0.001312) | 0.077076 / 0.004250 (0.072825) | 0.045068 / 0.037052 (0.008016) | 0.302570 / 0.258489 (0.044081) | 0.359093 / 0.293841 (0.065252) | 0.038865 / 0.128546 (-0.089681) | 0.012318 / 0.075646 (-0.063328) | 0.334819 / 0.419271 (-0.084452) | 0.047980 / 0.043533 (0.004447) | 0.296999 / 0.255139 (0.041860) | 0.318855 / 0.283200 (0.035656) | 0.110633 / 0.141683 (-0.031050) | 1.464326 / 1.452155 (0.012172) | 1.537386 / 1.492716 (0.044670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282906 / 0.018006 (0.264900) | 0.498418 / 0.000490 (0.497928) | 0.001507 / 0.000200 (0.001307) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029948 / 0.037411 (-0.007463) | 0.114385 / 0.014526 (0.099859) | 0.125783 / 0.176557 (-0.050774) | 0.193458 / 0.737135 (-0.543678) | 0.129725 / 0.296338 (-0.166614) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403822 / 0.215209 (0.188613) | 4.034180 / 2.077655 (1.956525) | 1.768206 / 1.504120 (0.264086) | 1.579267 / 1.541195 (0.038072) | 1.725077 / 1.468490 (0.256587) | 0.698743 / 4.584777 (-3.886034) | 3.723481 / 3.745712 (-0.022231) | 2.302374 / 5.269862 (-2.967488) | 1.497954 / 4.565676 (-3.067723) | 0.087360 / 0.424275 (-0.336915) | 0.012453 / 0.007607 (0.004846) | 0.523374 / 0.226044 (0.297329) | 5.244962 / 2.268929 (2.976033) | 2.272874 / 55.444624 (-53.171750) | 1.935570 / 6.876477 (-4.940907) | 2.043151 / 2.142072 (-0.098921) | 0.866298 / 4.805227 (-3.938929) | 0.169376 / 6.500664 (-6.331288) | 0.064578 / 0.075469 (-0.010892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.217372 / 1.841788 (-0.624416) | 15.896050 / 8.074308 (7.821742) | 15.165190 / 10.191392 (4.973798) | 0.171168 / 0.680424 (-0.509256) | 0.029770 / 0.534201 (-0.504431) | 0.449030 / 0.579283 (-0.130253) | 0.454704 / 0.434364 (0.020340) | 0.550689 / 0.540337 (0.010351) | 0.651182 / 1.386936 (-0.735754) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008072 / 0.011353 (-0.003281) | 0.005533 / 0.011008 (-0.005475) | 0.076343 / 0.038508 (0.037835) | 0.037997 / 0.023109 (0.014888) | 0.350465 / 0.275898 (0.074567) | 0.391168 / 0.323480 (0.067688) | 0.006475 / 0.007986 (-0.001511) | 0.004299 / 0.004328 (-0.000029) | 0.074867 / 0.004250 (0.070617) | 0.055256 / 0.037052 (0.018204) | 0.363919 / 0.258489 (0.105430) | 0.396521 / 0.293841 (0.102680) | 0.037746 / 0.128546 (-0.090801) | 0.012556 / 0.075646 (-0.063091) | 0.087974 / 0.419271 (-0.331297) | 0.050850 / 0.043533 (0.007317) | 0.345857 / 0.255139 (0.090718) | 0.361019 / 0.283200 (0.077820) | 0.111007 / 0.141683 (-0.030676) | 1.444014 / 1.452155 (-0.008140) | 1.533154 / 1.492716 (0.040438) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.332114 / 0.018006 (0.314108) | 0.517232 / 0.000490 (0.516742) | 0.004459 / 0.000200 (0.004259) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033147 / 0.037411 (-0.004264) | 0.119983 / 0.014526 (0.105457) | 0.125970 / 0.176557 (-0.050586) | 0.196375 / 0.737135 (-0.540760) | 0.133849 / 0.296338 (-0.162489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429477 / 0.215209 (0.214267) | 4.263750 / 2.077655 (2.186096) | 2.079409 / 1.504120 (0.575289) | 1.899831 / 1.541195 (0.358636) | 2.048472 / 1.468490 (0.579982) | 0.720945 / 4.584777 (-3.863832) | 3.813195 / 3.745712 (0.067483) | 2.250353 / 5.269862 (-3.019508) | 1.401496 / 4.565676 (-3.164181) | 0.090052 / 0.424275 (-0.334223) | 0.012552 / 0.007607 (0.004945) | 0.536839 / 0.226044 (0.310794) | 5.361089 / 2.268929 (3.092161) | 2.559710 / 55.444624 (-52.884914) | 2.226963 / 6.876477 (-4.649513) | 2.341898 / 2.142072 (0.199825) | 0.872115 / 4.805227 (-3.933112) | 0.173776 / 6.500664 (-6.326888) | 0.068567 / 0.075469 (-0.006902) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294583 / 1.841788 (-0.547205) | 16.624099 / 8.074308 (8.549791) | 13.698509 / 10.191392 (3.507117) | 0.161917 / 0.680424 (-0.518506) | 0.017744 / 0.534201 (-0.516457) | 0.428547 / 0.579283 (-0.150736) | 0.424687 / 0.434364 (-0.009677) | 0.525812 / 0.540337 (-0.014525) | 0.629075 / 1.386936 (-0.757861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33e4d6af919db17bf9a1eac544a0501b5972393b \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008667 / 0.011353 (-0.002686) | 0.004921 / 0.011008 (-0.006087) | 0.098352 / 0.038508 (0.059844) | 0.033983 / 0.023109 (0.010873) | 0.291640 / 0.275898 (0.015742) | 0.323388 / 0.323480 (-0.000092) | 0.007943 / 0.007986 (-0.000043) | 0.003922 / 0.004328 (-0.000407) | 0.075861 / 0.004250 (0.071610) | 0.042606 / 0.037052 (0.005554) | 0.298571 / 0.258489 (0.040081) | 0.345496 / 0.293841 (0.051655) | 0.037443 / 0.128546 (-0.091103) | 0.012114 / 0.075646 (-0.063532) | 0.333269 / 0.419271 (-0.086003) | 0.047762 / 0.043533 (0.004229) | 0.295452 / 0.255139 (0.040313) | 0.319641 / 0.283200 (0.036441) | 0.101083 / 0.141683 (-0.040600) | 1.432179 / 1.452155 (-0.019976) | 1.523976 / 1.492716 (0.031260) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241327 / 0.018006 (0.223321) | 0.538315 / 0.000490 (0.537825) | 0.003479 / 0.000200 (0.003279) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025857 / 0.037411 (-0.011554) | 0.104833 / 0.014526 (0.090307) | 0.116826 / 0.176557 (-0.059730) | 0.183460 / 0.737135 (-0.553675) | 0.119595 / 0.296338 (-0.176743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397533 / 0.215209 (0.182324) | 3.968664 / 2.077655 (1.891010) | 1.774025 / 1.504120 (0.269905) | 1.577424 / 1.541195 (0.036229) | 1.623049 / 1.468490 (0.154559) | 0.701008 / 4.584777 (-3.883769) | 3.753278 / 3.745712 (0.007565) | 2.078313 / 5.269862 (-3.191549) | 1.335639 / 4.565676 (-3.230037) | 0.085216 / 0.424275 (-0.339059) | 0.012087 / 0.007607 (0.004480) | 0.513219 / 0.226044 (0.287174) | 5.097693 / 2.268929 (2.828765) | 2.275030 / 55.444624 (-53.169594) | 1.928037 / 6.876477 (-4.948439) | 1.941216 / 2.142072 (-0.200856) | 0.856720 / 4.805227 (-3.948507) | 0.166723 / 6.500664 (-6.333941) | 0.062263 / 0.075469 (-0.013206) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196054 / 1.841788 (-0.645734) | 14.190526 / 8.074308 (6.116218) | 14.053768 / 10.191392 (3.862376) | 0.179982 / 0.680424 (-0.500442) | 0.029024 / 0.534201 (-0.505177) | 0.440391 / 0.579283 (-0.138892) | 0.445627 / 0.434364 (0.011264) | 0.543098 / 0.540337 (0.002761) | 0.640577 / 1.386936 (-0.746359) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007008 / 0.011353 (-0.004345) | 0.005015 / 0.011008 (-0.005993) | 0.073783 / 0.038508 (0.035274) | 0.032401 / 0.023109 (0.009292) | 0.343382 / 0.275898 (0.067484) | 0.358317 / 0.323480 (0.034837) | 0.005548 / 0.007986 (-0.002437) | 0.005188 / 0.004328 (0.000859) | 0.072867 / 0.004250 (0.068617) | 0.048555 / 0.037052 (0.011502) | 0.334516 / 0.258489 (0.076027) | 0.390263 / 0.293841 (0.096422) | 0.036343 / 0.128546 (-0.092203) | 0.012243 / 0.075646 (-0.063404) | 0.087067 / 0.419271 (-0.332205) | 0.049025 / 0.043533 (0.005492) | 0.333977 / 0.255139 (0.078838) | 0.354427 / 0.283200 (0.071227) | 0.104771 / 0.141683 (-0.036912) | 1.434588 / 1.452155 (-0.017567) | 1.519788 / 1.492716 (0.027072) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264002 / 0.018006 (0.245996) | 0.547902 / 0.000490 (0.547412) | 0.000461 / 0.000200 (0.000261) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028916 / 0.037411 (-0.008496) | 0.110267 / 0.014526 (0.095741) | 0.119190 / 0.176557 (-0.057367) | 0.188599 / 0.737135 (-0.548537) | 0.126948 / 0.296338 (-0.169391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422777 / 0.215209 (0.207568) | 4.209813 / 2.077655 (2.132158) | 2.001360 / 1.504120 (0.497240) | 1.802651 / 1.541195 (0.261456) | 1.860357 / 1.468490 (0.391867) | 0.695006 / 4.584777 (-3.889771) | 3.741917 / 3.745712 (-0.003795) | 3.313071 / 5.269862 (-1.956791) | 1.726366 / 4.565676 (-2.839311) | 0.086185 / 0.424275 (-0.338090) | 0.012256 / 0.007607 (0.004649) | 0.536874 / 0.226044 (0.310830) | 5.253008 / 2.268929 (2.984079) | 2.457189 / 55.444624 (-52.987436) | 2.112199 / 6.876477 (-4.764278) | 2.117867 / 2.142072 (-0.024205) | 0.831914 / 4.805227 (-3.973314) | 0.168238 / 6.500664 (-6.332426) | 0.065075 / 0.075469 (-0.010394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280795 / 1.841788 (-0.560993) | 14.606608 / 8.074308 (6.532299) | 13.317597 / 10.191392 (3.126205) | 0.166590 / 0.680424 (-0.513834) | 0.017520 / 0.534201 (-0.516681) | 0.420978 / 0.579283 (-0.158305) | 0.415708 / 0.434364 (-0.018656) | 0.523619 / 0.540337 (-0.016718) | 0.625299 / 1.386936 (-0.761637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2a83a8ea4b3a87a925ef44b787e87b59bf68225 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/146 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/146/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/146/comments | https://api.github.com/repos/huggingface/datasets/issues/146/events | https://github.com/huggingface/datasets/pull/146 | 619,564,653 | MDExOlB1bGxSZXF1ZXN0NDE5MDI5MjUx | 146 | Add BERTScore to metrics | [] | closed | false | null | 0 | 2020-05-16T22:09:39Z | 2020-05-17T22:22:10Z | 2020-05-17T22:22:09Z | null | This PR adds [BERTScore](https://arxiv.org/abs/1904.09675) to metrics.
Here is an example of how to use it.
```sh
import nlp
bertscore = nlp.load_metric('metrics/bertscore') # or simply nlp.load_metric('bertscore') after this is added to huggingface's s3 bucket
predictions = ['example', 'fruit']
references = [['this is an example.', 'this is one example.'], ['apple']]
results = bertscore.compute(predictions, references, lang='en')
print(results)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/871 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/871/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/871/comments | https://api.github.com/repos/huggingface/datasets/issues/871/events | https://github.com/huggingface/datasets/issues/871 | 747,470,136 | MDU6SXNzdWU3NDc0NzAxMzY= | 871 | terminate called after throwing an instance of 'google::protobuf::FatalException' | [] | closed | false | null | 2 | 2020-11-20T12:56:24Z | 2020-12-12T21:16:32Z | 2020-12-12T21:16:32Z | null | Hi
I am using the dataset "iwslt2017-en-nl", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks
100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [02:47<00:00, 2.18s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0):
terminate called after throwing an instance of 'google::protobuf::FatalException'
what(): CHECK failed: (index) >= (0):
run_t5_base_eval.sh: line 19: 5795 Aborted | {
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"Loading the iwslt2017-en-nl config of iwslt2017 works fine on my side. \r\nMaybe you can open an issue on transformers as well ? And also add more details about your environment (OS, python version, version of transformers and datasets etc.)",
"closing now, figured out this is because the max length of decoder was set smaller than the input_dimensions. thanks "
] |
https://api.github.com/repos/huggingface/datasets/issues/1590 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1590/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1590/comments | https://api.github.com/repos/huggingface/datasets/issues/1590/events | https://github.com/huggingface/datasets/issues/1590 | 769,242,858 | MDU6SXNzdWU3NjkyNDI4NTg= | 1,590 | Add helper to resolve namespace collision | [] | closed | false | null | 5 | 2020-12-16T20:17:24Z | 2022-06-01T15:32:04Z | 2022-06-01T15:32:04Z | null | Many projects use a module called `datasets`, however this is incompatible with huggingface datasets. It would be great if there if there was some helper or similar function to resolve such a common conflict. | {
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"Do you have an example?",
"I was thinking about using something like [importlib](https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly) to over-ride the collision. \r\n\r\n**Reason requested**: I use the [following template](https://github.com/jramapuram/ml_base/) repo where I house all my datasets as a submodule.",
"Alternatively huggingface could consider some submodule type structure like:\r\n\r\n`import huggingface.datasets`\r\n`import huggingface.transformers`\r\n\r\n`datasets` is a very common module in ML and should be an end-user decision and not scope all of python ¯\\_(ツ)_/¯ \r\n",
"That's a interesting option indeed. We'll think about it.",
"It also wasn't initially obvious to me that the samples which contain `import datasets` were in fact importing a huggingface library (in fact all the huggingface imports are very generic - transformers, tokenizers, datasets...)"
] |
https://api.github.com/repos/huggingface/datasets/issues/557 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/557/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/557/comments | https://api.github.com/repos/huggingface/datasets/issues/557/events | https://github.com/huggingface/datasets/pull/557 | 690,220,135 | MDExOlB1bGxSZXF1ZXN0NDc3MTQ1NjAx | 557 | Fix a few typos | [] | closed | false | null | 0 | 2020-09-01T15:03:24Z | 2020-09-02T07:39:08Z | 2020-09-02T07:39:07Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5101 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5101/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5101/comments | https://api.github.com/repos/huggingface/datasets/issues/5101/events | https://github.com/huggingface/datasets/pull/5101 | 1,404,513,085 | PR_kwDODunzps5AkHJc | 5,101 | Free the "hf" filesystem protocol for `hffs` | [] | closed | false | null | 1 | 2022-10-11T11:57:21Z | 2022-10-12T15:32:59Z | 2022-10-12T15:30:38Z | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/2109 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2109/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2109/comments | https://api.github.com/repos/huggingface/datasets/issues/2109/events | https://github.com/huggingface/datasets/pull/2109 | 840,746,598 | MDExOlB1bGxSZXF1ZXN0NjAwNTg1MzM5 | 2,109 | Add more issue templates and customize issue template chooser | [] | closed | false | null | 2 | 2021-03-25T09:41:53Z | 2021-04-19T06:20:11Z | 2021-04-19T06:20:11Z | null | When opening an issue, it is not evident for the users how to choose a blank issue template. There is a link at the bottom of all the other issue templates (`Don’t see your issue here? Open a blank issue.`), but this is not very visible for users. This is the reason why many users finally chose the `add-dataset` template instead (this is more visible) for issues that indeed are not requesting the addition of a new dataset.
~~With this PR, the default blank issue template would be as visible as the other templates (as the `add-dataset` template), thus making easier for the users to choose it.~~
With this PR:
- more issue templates, besides `add-dataset`, are added: `bug-report` and `feature-request`
- the issue template chooser is customized, so that it now includes a link to `Discussions` for questions | {
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"If you agree, I could also add a link to [Discussions](https://github.com/huggingface/datasets/discussions) in order to reinforce the use of Discussion to make Questions (instead of Issues).\r\n\r\nI could also add some other templates: Bug, Feature Request,...",
"@theo-m we wrote our same comments at the same time... 😉 "
] |
https://api.github.com/repos/huggingface/datasets/issues/1224 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1224/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1224/comments | https://api.github.com/repos/huggingface/datasets/issues/1224/events | https://github.com/huggingface/datasets/pull/1224 | 758,022,998 | MDExOlB1bGxSZXF1ZXN0NTMzMjY2Njg1 | 1,224 | adding conceptnet5 | [] | closed | false | null | 11 | 2020-12-06T21:06:53Z | 2020-12-09T16:38:16Z | 2020-12-09T14:37:17Z | null | Adding the conceptnet5 and omcs txt files used to create the conceptnet5 dataset. Conceptne5 is a common sense dataset. More info can be found here: https://github.com/commonsense/conceptnet5/wiki | {
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"Thank you. I'll make those changes. but I'm having problems trying to push my changes to my fork\r\n",
"Hi, I've removed the TODO, and added a README.md. How do I push these changes?\r\n",
"Also, what docstring are you recommending?\r\n",
"> Hi, I've removed the TODO, and added a README.md. How do I push these changes?\r\n\r\nyou can just commit and push your changes to the same branch as your first commit.",
"@ghomasHudson I've tried it but still getting code quality error. I've removed all blank lines, etc. required by flake8. Don't know what else to do",
"> @ghomasHudson I've tried it but still getting code quality error. I've removed all blank lines, etc. required by flake8. Don't know what else to do\r\n\r\nDid you run `make style` before committing? When I run it, it fixes some things (e.g. Splitting line 96 which is currently too long).",
"I think @yjernite is looking into this. I did \"make style\" but nothing happens",
"looks like your PR includes changes about many other files than the ones related to conceptnet5\r\n\r\ncould you create another branch and another PR please ?",
"@lhoestq I'm not sure what I did wrong. What did I push that wasn't conceptnet5? How do I see this?\r\n\r\n did this\r\n\r\nmake style\r\nflake8 datasets\r\ngit add datasets/<your_dataset_name>\r\ngit commit\r\ngit fetch upstream\r\ngit rebase upstream/master\r\ngit pull\r\ngit push -u origin conceptnet5",
"Thanks for rebasing and force push :) ",
"Yeah! Thank you @lhoestq, @ghomasHudson and @yjernite !"
] |
https://api.github.com/repos/huggingface/datasets/issues/153 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/153/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/153/comments | https://api.github.com/repos/huggingface/datasets/issues/153/events | https://github.com/huggingface/datasets/issues/153 | 619,972,246 | MDU6SXNzdWU2MTk5NzIyNDY= | 153 | Meta-datasets (GLUE/XTREME/...) – Special care to attributions and citations | [
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] | open | false | null | 4 | 2020-05-18T07:24:22Z | 2020-05-18T21:18:16Z | null | null | Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation/specific homepage/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.
Let's take GLUE as an example:
The configuration has the citation for each dataset included (e.g. [here](https://github.com/huggingface/nlp/blob/master/datasets/glue/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded. | {
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"As @yoavgo suggested, there should be the possibility to call a function like nlp.bib that outputs all bibtex ref from the datasets and models actually used and eventually nlp.bib.forreadme that would output the same info + versions numbers so they can be included in a readme.md file.",
"Actually, double checking with @mariamabarham, we already have this feature I think.\r\n\r\nIt's like this currently:\r\n```python\r\n>>> from nlp import load_dataset\r\n>>> \r\n>>> dataset = load_dataset('glue', 'cola', split='train')\r\n>>> print(dataset.info.citation)\r\n@article{warstadt2018neural,\r\n title={Neural Network Acceptability Judgments},\r\n author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},\r\n journal={arXiv preprint arXiv:1805.12471},\r\n year={2018}\r\n}\r\n@inproceedings{wang2019glue,\r\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\r\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\r\n note={In the Proceedings of ICLR.},\r\n year={2019}\r\n}\r\n\r\nNote that each GLUE dataset has its own citation. Please see the source to see\r\nthe correct citation for each contained dataset.\r\n```\r\n\r\nWhat do you think @dseddah?",
"Looks good but why would there be a difference between the ref in the source and the one to be printed? ",
"Yes, I think we should remove this warning @mariamabarham.\r\n\r\nIt's probably a relic of tfds which didn't have the same way to access citations. "
] |
https://api.github.com/repos/huggingface/datasets/issues/3852 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3852/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3852/comments | https://api.github.com/repos/huggingface/datasets/issues/3852/events | https://github.com/huggingface/datasets/pull/3852 | 1,162,252,337 | PR_kwDODunzps40Fb26 | 3,852 | Redundant add dataset information and dead link. | [] | closed | false | null | 1 | 2022-03-08T05:57:05Z | 2022-03-08T16:54:36Z | 2022-03-08T16:54:36Z | null | > Alternatively, you can follow the steps to [add a dataset](https://huggingface.co/docs/datasets/add_dataset.html) and [share a dataset](https://huggingface.co/docs/datasets/share_dataset.html) in the documentation.
The "add a dataset link" gives 404 Error, and the share_dataset link has changed. I feel this information is redundant/deprecated now since we have a more detailed guide for "How to add a dataset?". | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3852). All of your documentation changes will be reflected on that endpoint."
] |
https://api.github.com/repos/huggingface/datasets/issues/5763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5763/comments | https://api.github.com/repos/huggingface/datasets/issues/5763/events | https://github.com/huggingface/datasets/pull/5763 | 1,670,476,302 | PR_kwDODunzps5OcMI7 | 5,763 | fix typo: "mow" -> "now" | [] | closed | false | null | 2 | 2023-04-17T06:03:44Z | 2023-04-17T15:01:53Z | 2023-04-17T14:54:46Z | null | I noticed a typo as I was reading the datasets documentation. This PR contains a trivial fix changing "mow" to "now." | {
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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) | 0.006804 / 0.011353 (-0.004549) | 0.004984 / 0.011008 (-0.006024) | 0.096781 / 0.038508 (0.058273) | 0.033049 / 0.023109 (0.009939) | 0.297681 / 0.275898 (0.021783) | 0.329553 / 0.323480 (0.006073) | 0.005697 / 0.007986 (-0.002289) | 0.004019 / 0.004328 (-0.000310) | 0.072691 / 0.004250 (0.068441) | 0.046921 / 0.037052 (0.009868) | 0.311467 / 0.258489 (0.052978) | 0.337616 / 0.293841 (0.043775) | 0.042400 / 0.128546 (-0.086146) | 0.011919 / 0.075646 (-0.063727) | 0.331390 / 0.419271 (-0.087881) | 0.051004 / 0.043533 (0.007471) | 0.295317 / 0.255139 (0.040178) | 0.316570 / 0.283200 (0.033371) | 0.099283 / 0.141683 (-0.042400) | 1.430583 / 1.452155 (-0.021572) | 1.493550 / 1.492716 (0.000834) |\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) | 0.213634 / 0.018006 (0.195628) | 0.432557 / 0.000490 (0.432067) | 0.001586 / 0.000200 (0.001386) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025249 / 0.037411 (-0.012162) | 0.105433 / 0.014526 (0.090908) | 0.113474 / 0.176557 (-0.063082) | 0.168799 / 0.737135 (-0.568336) | 0.119363 / 0.296338 (-0.176975) |\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) | 0.412450 / 0.215209 (0.197241) | 4.117432 / 2.077655 (2.039777) | 1.935176 / 1.504120 (0.431056) | 1.745674 / 1.541195 (0.204479) | 1.853872 / 1.468490 (0.385382) | 0.703429 / 4.584777 (-3.881348) | 3.756981 / 3.745712 (0.011269) | 3.730607 / 5.269862 (-1.539255) | 1.839052 / 4.565676 (-2.726624) | 0.087574 / 0.424275 (-0.336701) | 0.012293 / 0.007607 (0.004686) | 0.517234 / 0.226044 (0.291190) | 5.189759 / 2.268929 (2.920831) | 2.418739 / 55.444624 (-53.025885) | 2.081424 / 6.876477 (-4.795053) | 2.204464 / 2.142072 (0.062392) | 0.842768 / 4.805227 (-3.962459) | 0.169014 / 6.500664 (-6.331650) | 0.063711 / 0.075469 (-0.011758) |\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) | 1.180636 / 1.841788 (-0.661152) | 14.816088 / 8.074308 (6.741779) | 14.290085 / 10.191392 (4.098693) | 0.165267 / 0.680424 (-0.515156) | 0.017290 / 0.534201 (-0.516911) | 0.419678 / 0.579283 (-0.159605) | 0.418164 / 0.434364 (-0.016200) | 0.492210 / 0.540337 (-0.048127) | 0.588528 / 1.386936 (-0.798408) |\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) | 0.007144 / 0.011353 (-0.004209) | 0.005223 / 0.011008 (-0.005785) | 0.073583 / 0.038508 (0.035075) | 0.033534 / 0.023109 (0.010425) | 0.339020 / 0.275898 (0.063122) | 0.366546 / 0.323480 (0.043066) | 0.006245 / 0.007986 (-0.001741) | 0.004081 / 0.004328 (-0.000247) | 0.073089 / 0.004250 (0.068839) | 0.047024 / 0.037052 (0.009971) | 0.342540 / 0.258489 (0.084051) | 0.379743 / 0.293841 (0.085902) | 0.037551 / 0.128546 (-0.090995) | 0.012246 / 0.075646 (-0.063400) | 0.084796 / 0.419271 (-0.334476) | 0.052256 / 0.043533 (0.008723) | 0.342675 / 0.255139 (0.087536) | 0.367157 / 0.283200 (0.083957) | 0.102939 / 0.141683 (-0.038744) | 1.409039 / 1.452155 (-0.043115) | 1.526137 / 1.492716 (0.033420) |\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) | 0.208143 / 0.018006 (0.190136) | 0.437940 / 0.000490 (0.437450) | 0.000424 / 0.000200 (0.000224) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028321 / 0.037411 (-0.009091) | 0.110417 / 0.014526 (0.095891) | 0.119449 / 0.176557 (-0.057107) | 0.168081 / 0.737135 (-0.569054) | 0.126658 / 0.296338 (-0.169681) |\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) | 0.429302 / 0.215209 (0.214093) | 4.270547 / 2.077655 (2.192892) | 2.061323 / 1.504120 (0.557203) | 1.857877 / 1.541195 (0.316682) | 1.873317 / 1.468490 (0.404827) | 0.688750 / 4.584777 (-3.896027) | 3.767951 / 3.745712 (0.022239) | 2.011436 / 5.269862 (-3.258426) | 1.299965 / 4.565676 (-3.265712) | 0.084799 / 0.424275 (-0.339476) | 0.012082 / 0.007607 (0.004475) | 0.521981 / 0.226044 (0.295937) | 5.265333 / 2.268929 (2.996405) | 2.494326 / 55.444624 (-52.950298) | 2.144672 / 6.876477 (-4.731804) | 2.365624 / 2.142072 (0.223551) | 0.839868 / 4.805227 (-3.965359) | 0.166614 / 6.500664 (-6.334050) | 0.063804 / 0.075469 (-0.011665) |\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) | 1.264623 / 1.841788 (-0.577164) | 14.946515 / 8.074308 (6.872207) | 14.450115 / 10.191392 (4.258723) | 0.163878 / 0.680424 (-0.516546) | 0.017501 / 0.534201 (-0.516700) | 0.420992 / 0.579283 (-0.158291) | 0.423005 / 0.434364 (-0.011359) | 0.489505 / 0.540337 (-0.050832) | 0.594631 / 1.386936 (-0.792305) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd893098627230cc734f6009ad04cf885c979ac4 \"CML watermark\")\n"
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https://api.github.com/repos/huggingface/datasets/issues/2863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2863/comments | https://api.github.com/repos/huggingface/datasets/issues/2863/events | https://github.com/huggingface/datasets/pull/2863 | 986,156,755 | MDExOlB1bGxSZXF1ZXN0NzI1MzkwMTkx | 2,863 | Update dataset URL | [] | closed | false | null | 1 | 2021-09-02T05:22:18Z | 2021-09-02T08:10:50Z | 2021-09-02T08:10:50Z | null | null | {
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"Superseded by PR #2864.\r\n\r\n@mrm8488 next time you would like to work on an issue, you can first self-assign it to you (by writing `#self-assign` in a comment on the issue). That way, other people can see you are already working on it and there are not multiple people working on the same issue. 😉 "
] |
https://api.github.com/repos/huggingface/datasets/issues/5735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5735/comments | https://api.github.com/repos/huggingface/datasets/issues/5735/events | https://github.com/huggingface/datasets/pull/5735 | 1,662,150,903 | PR_kwDODunzps5OAY3A | 5,735 | Implement sharding on merged iterable datasets | [] | closed | false | null | 11 | 2023-04-11T10:02:25Z | 2023-04-27T16:39:04Z | 2023-04-27T16:32:09Z | null | This PR allows sharding of merged iterable datasets.
Merged iterable datasets with for instance the `interleave_datasets` command are comprised of multiple sub-iterable, one for each dataset that has been merged.
With this PR, sharding a merged iterable will result in multiple merged datasets each comprised of sharded sub-iterable, ensuring that there is no duplication of data.
As a result it is now possible to set any amount of workers in the dataloader as long as it is lower or equal to the lowest amount of shards amongst the datasets. Before it had to be set to 0.
I previously talked about this issue on the forum [here](https://discuss.huggingface.co/t/interleaving-iterable-dataset-with-num-workers-0/35801) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi ! What if one of the sub-iterables only has one shard ? In that case I don't think we'd end up with a correctly interleaved dataset, since only rank 0 would yield examples from this sub-iterable",
"Hi ! \r\nI just tested this out with the code below and it seems to be ok. Both datasets are alternating and we get all the examples with no duplicates.\r\n\r\nOn thing to keep in mind is that the max amount of workers is equal to the lowest amount of shard amongst the datasets to be merged (1 in this example).\r\n\r\n ```python\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, interleave_datasets\r\n\r\n\r\ndef process_dataset_train(batch):\r\n return {\"input\": f'train: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef process_dataset_test(batch):\r\n return {\"input\": f'test: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef identity_collator(x):\r\n return x\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ds = load_dataset(\"lhoestq/demo1\")\r\n ds[\"train\"] = ds[\"train\"].map(process_dataset_train, remove_columns=ds[\"train\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].map(process_dataset_test, remove_columns=ds[\"test\"].column_names)\r\n\r\n ds1 = ds[\"train\"].to_iterable_dataset(num_shards=5)\r\n ds2 = ds[\"test\"].to_iterable_dataset(num_shards=1)\r\n\r\n ds_merged = interleave_datasets([ds1, ds2], stopping_strategy=\"all_exhausted\")\r\n\r\n dataloader = DataLoader(ds_merged, collate_fn=identity_collator, num_workers=1, batch_size=1)\r\n\r\n for i, element in enumerate(dataloader):\r\n print(i, element)\r\n\r\n```\r\n\r\n```\r\n0 [{'input': 'train: Great app! The new v'}]\r\n1 [{'input': 'test: Works with RTL and N'}]\r\n2 [{'input': \"train: Great It's not fully\"}]\r\n3 [{'input': 'test: Works with RTL SDR W'}]\r\n4 [{'input': 'train: Works on a Nexus 6p '}]\r\n5 [{'input': 'test: Awsome App! Easy to '}]\r\n6 [{'input': 'train: The bandwidth seemed'}]\r\n7 [{'input': \"test: I'll forgo the refun\"}]\r\n8 [{'input': 'train: Works well with my H'}]\r\n9 [{'input': 'test: looks like a great p'}]\r\n```",
"<s> Could you try with `num_workers>1` ? </s>\r\n\r\nedit: Oh I see\r\n\r\n> On thing to keep in mind is that the max amount of workers is equal to the lowest amount of shard amongst the datasets to be merged (1 in this example).",
"Great ! It's ok to have the max amount of workers is equal to the lowest amount of shard :)\r\n\r\nSo in the case of `num_workers>min(n_shards_per_dataset)` maybe some workers should turn off, and a warning can probably be shown. This is already the case if you use a single dataset with a single shard and `num_workers>1`.\r\n\r\n\r\nRight now it seems to raise an error:\r\n\r\n```python\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 979, in __iter__\r\n yield from self._iter_pytorch(ex_iterable)\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 912, in _iter_pytorch\r\n for key, example in ex_iterable.shard_data_sources(worker_info.id, worker_info.num_workers):\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 259, in shard_data_sources\r\n [iterable.shard_data_sources(worker_id, num_workers) for iterable in self.ex_iterables],\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 259, in <listcomp>\r\n [iterable.shard_data_sources(worker_id, num_workers) for iterable in self.ex_iterables],\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 125, in shard_data_sources\r\n requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices])\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/utils/sharding.py\", line 76, in _merge_gen_kwargs\r\n for key in gen_kwargs_list[0]\r\nIndexError: list index out of range\r\n```",
"Good point. I have fixed the n_shards property of merged iterable datasets so that this warning is raised properly",
"Hey @lhoestq, what do you think of the last modifications ? ",
"Hello! No problem :)\r\n\r\n- About HorizontallyConcatenatedMultiSourcesExamplesIterable, I've haven't been able to create a bug with sharding. So either I missed something or it's working somehow:\r\n\r\n```python\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, interleave_datasets, concatenate_datasets\r\n\r\n\r\ndef process_dataset_train(batch):\r\n return {\"input\": f'train: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef process_dataset_test(batch):\r\n return {\"input\": f'test: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef identity_collator(x):\r\n return x\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ds = load_dataset(\"lhoestq/demo1\")\r\n ds[\"train\"] = ds[\"train\"].map(process_dataset_train, remove_columns=ds[\"train\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].map(process_dataset_test, remove_columns=ds[\"test\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].rename_columns({\"input\": \"input2\"})\r\n\r\n ds1 = ds[\"train\"].to_iterable_dataset(num_shards=5)\r\n ds2 = ds[\"test\"].to_iterable_dataset(num_shards=3)\r\n\r\n ds_merged = concatenate_datasets([ds1, ds2], axis=1)\r\n\r\n #n_shards is always 1 for HorizontallyConcatenatedMultiSourcesExamplesIterable\r\n dataloader = DataLoader(ds_merged, collate_fn=identity_collator, num_workers=1, batch_size=1)\r\n\r\n for i, element in enumerate(dataloader):\r\n print(i, element)\r\n```\r\n\r\n```\r\n0 [{'input': 'train: Great app! The new v', 'input2': 'test: Works with RTL and N'}]\r\n1 [{'input': \"train: Great It's not fully\", 'input2': 'test: Works with RTL SDR W'}]\r\n2 [{'input': 'train: Works on a Nexus 6p ', 'input2': 'test: Awsome App! Easy to '}]\r\n3 [{'input': 'train: The bandwidth seemed', 'input2': \"test: I'll forgo the refun\"}]\r\n4 [{'input': 'train: Works well with my H', 'input2': 'test: looks like a great p'}]\r\n```\r\n\r\n- I've added a test but I'm not completely happy with it. My issue is that multiprocessing makes interleaving not completely deterministic as samples are yielded whenever ready by each process, if I'm correct.\r\nAs a result I opted to check for the amount of samples yielded and make that they are all unique, which should be equivalent.\r\nBut now my issue is that the \"first_exhausted\" method breaks the loop when one of the datasets of one of the shards is empty which means that all shards stop yielding and we could be missing up to n_workers samples. I don't know if this is the behaviour expected, but I had to modify the test to accomodate this.\r\n\r\nWhat are your thoughts about this ?",
"Ah indeed it works because it's set to be only 1 shard - my bad :)",
"> But now my issue is that the \"first_exhausted\" method breaks the loop when one of the datasets of one of the shards is empty which means that all shards stop yielding and we could be missing up to n_workers samples. I don't know if this is the behaviour expected, but I had to modify the test to accomodate this.\r\n\r\nThis looks reasonable, maybe this can be documented in the `interleave_datasets` docstring ?\r\n```\r\nNote for iterable datasets:\r\n\r\nIn a distributed setup or in PyTorch DataLoader workers, the stopping strategy is applied per process.\r\nTherefore the \"first_exhausted\" strategy on an sharded iterable dataset can generate less samples in total (up to 1 missing sample per subdataset per worker).\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) | 0.006441 / 0.011353 (-0.004912) | 0.004551 / 0.011008 (-0.006457) | 0.099144 / 0.038508 (0.060636) | 0.028163 / 0.023109 (0.005054) | 0.386342 / 0.275898 (0.110444) | 0.398347 / 0.323480 (0.074867) | 0.004836 / 0.007986 (-0.003150) | 0.004724 / 0.004328 (0.000395) | 0.076277 / 0.004250 (0.072027) | 0.036305 / 0.037052 (-0.000747) | 0.377179 / 0.258489 (0.118690) | 0.410694 / 0.293841 (0.116853) | 0.030196 / 0.128546 (-0.098351) | 0.011436 / 0.075646 (-0.064211) | 0.325911 / 0.419271 (-0.093360) | 0.043709 / 0.043533 (0.000177) | 0.375801 / 0.255139 (0.120662) | 0.396511 / 0.283200 (0.113311) | 0.088346 / 0.141683 (-0.053337) | 1.483427 / 1.452155 (0.031272) | 1.553708 / 1.492716 (0.060992) |\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) | 0.190974 / 0.018006 (0.172968) | 0.451309 / 0.000490 (0.450819) | 0.004045 / 0.000200 (0.003845) | 0.000077 / 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) | 0.023814 / 0.037411 (-0.013597) | 0.096922 / 0.014526 (0.082396) | 0.101506 / 0.176557 (-0.075050) | 0.164694 / 0.737135 (-0.572441) | 0.106899 / 0.296338 (-0.189439) |\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) | 0.432164 / 0.215209 (0.216954) | 4.308076 / 2.077655 (2.230421) | 2.092434 / 1.504120 (0.588314) | 1.937405 / 1.541195 (0.396210) | 1.988030 / 1.468490 (0.519540) | 0.695476 / 4.584777 (-3.889301) | 3.436413 / 3.745712 (-0.309299) | 2.892954 / 5.269862 (-2.376908) | 1.519906 / 4.565676 (-3.045771) | 0.082579 / 0.424275 (-0.341696) | 0.012233 / 0.007607 (0.004626) | 0.531329 / 0.226044 (0.305284) | 5.365272 / 2.268929 (3.096344) | 2.391452 / 55.444624 (-53.053172) | 2.051116 / 6.876477 (-4.825361) | 2.140663 / 2.142072 (-0.001410) | 0.807262 / 4.805227 (-3.997966) | 0.151290 / 6.500664 (-6.349374) | 0.066137 / 0.075469 (-0.009333) |\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) | 1.193106 / 1.841788 (-0.648682) | 13.577240 / 8.074308 (5.502932) | 14.280126 / 10.191392 (4.088734) | 0.142538 / 0.680424 (-0.537886) | 0.016641 / 0.534201 (-0.517560) | 0.386318 / 0.579283 (-0.192965) | 0.385991 / 0.434364 (-0.048373) | 0.440712 / 0.540337 (-0.099625) | 0.524189 / 1.386936 (-0.862747) |\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) | 0.006628 / 0.011353 (-0.004725) | 0.004664 / 0.011008 (-0.006344) | 0.077254 / 0.038508 (0.038746) | 0.028369 / 0.023109 (0.005259) | 0.343076 / 0.275898 (0.067178) | 0.376491 / 0.323480 (0.053011) | 0.005298 / 0.007986 (-0.002687) | 0.004853 / 0.004328 (0.000524) | 0.075927 / 0.004250 (0.071677) | 0.039951 / 0.037052 (0.002899) | 0.346225 / 0.258489 (0.087736) | 0.382367 / 0.293841 (0.088526) | 0.031133 / 0.128546 (-0.097413) | 0.011666 / 0.075646 (-0.063981) | 0.086383 / 0.419271 (-0.332889) | 0.042885 / 0.043533 (-0.000647) | 0.343885 / 0.255139 (0.088746) | 0.366840 / 0.283200 (0.083640) | 0.095942 / 0.141683 (-0.045741) | 1.528972 / 1.452155 (0.076817) | 1.586392 / 1.492716 (0.093676) |\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) | 0.223952 / 0.018006 (0.205946) | 0.410767 / 0.000490 (0.410277) | 0.001014 / 0.000200 (0.000814) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024210 / 0.037411 (-0.013201) | 0.100308 / 0.014526 (0.085782) | 0.106899 / 0.176557 (-0.069658) | 0.156514 / 0.737135 (-0.580621) | 0.109548 / 0.296338 (-0.186790) |\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) | 0.434763 / 0.215209 (0.219554) | 4.348485 / 2.077655 (2.270831) | 2.064255 / 1.504120 (0.560135) | 1.864394 / 1.541195 (0.323199) | 1.899732 / 1.468490 (0.431242) | 0.694147 / 4.584777 (-3.890630) | 3.357898 / 3.745712 (-0.387815) | 2.909155 / 5.269862 (-2.360707) | 1.424790 / 4.565676 (-3.140886) | 0.082597 / 0.424275 (-0.341678) | 0.012442 / 0.007607 (0.004835) | 0.538758 / 0.226044 (0.312713) | 5.390288 / 2.268929 (3.121359) | 2.532016 / 55.444624 (-52.912609) | 2.185724 / 6.876477 (-4.690753) | 2.274176 / 2.142072 (0.132104) | 0.804785 / 4.805227 (-4.000442) | 0.152649 / 6.500664 (-6.348015) | 0.067707 / 0.075469 (-0.007762) |\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) | 1.285219 / 1.841788 (-0.556568) | 13.958098 / 8.074308 (5.883790) | 14.043653 / 10.191392 (3.852261) | 0.144526 / 0.680424 (-0.535898) | 0.016813 / 0.534201 (-0.517388) | 0.390286 / 0.579283 (-0.188997) | 0.389184 / 0.434364 (-0.045180) | 0.470810 / 0.540337 (-0.069527) | 0.562391 / 1.386936 (-0.824545) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bb172c9772858c188f85ffc9a51f8cb1da292a0 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/3380 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3380/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3380/comments | https://api.github.com/repos/huggingface/datasets/issues/3380/events | https://github.com/huggingface/datasets/issues/3380 | 1,071,166,270 | I_kwDODunzps4_2LM- | 3,380 | [Quick poll] Give your opinion on the future of the Hugging Face Open Source ecosystem! | [] | closed | false | null | 0 | 2021-12-04T09:18:33Z | 2022-01-11T12:29:53Z | 2022-01-11T12:29:53Z | null | Thanks to all of you, `datasets` will pass 11.5k stars :star2: this week!
If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts:
[**hf.co/oss-survey**](https://hf.co/oss-survey)
(please reply in the above feedback form rather than to this thread)
Thank you all on behalf of the HuggingFace team! 🤗 | {
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https://api.github.com/repos/huggingface/datasets/issues/3081 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3081/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3081/comments | https://api.github.com/repos/huggingface/datasets/issues/3081/events | https://github.com/huggingface/datasets/pull/3081 | 1,026,383,749 | PR_kwDODunzps4tM1Gy | 3,081 | [Audio datasets] Adapting all audio datasets | [] | closed | false | null | 4 | 2021-10-14T13:13:45Z | 2021-10-15T12:52:03Z | 2021-10-15T12:22:33Z | null | This PR adds the new `Audio(...)` features - see: https://github.com/huggingface/datasets/pull/2324 to the most important audio datasets:
- Librispeech
- Timit
- Common Voice
- AMI
- ... (others I'm forgetting now)
The PR is curently blocked because the following leads to a problem:
```python
from datasets import load_dataset
# load first time works
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
# load from cache breaks
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
```
As soon as it's unblocked, I'll adapt the other audio datasets as well. | {
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"@lhoestq - are there other important speech datasets that I'm forgetting here? \r\n\r\nThink PR is good to go otherwise",
"@lhoestq @albertvillanova - how can we make an exception for the AMI README so that the test doesn't fail? The dataset card definitely should have a data preprocessing section",
"Hi @patrickvonplaten ,\r\n\r\nthe data preprocessing section is not defined as a valid section in the readme validation file. After this line:\r\nhttps://github.com/huggingface/datasets/blob/568db594d51110da9e23d224abded2a976b3c8c7/src/datasets/utils/resources/readme_structure.yaml#L20\r\nfeel free to insert (correctly indented of course):\r\n```python\r\n- name: \"Dataset Preprocessing\"\r\n allow_empty: true\r\n allow_empty_text: true\r\n subsections: null\r\n```\r\nand then the tests should pass.",
"Thanks a lot @albertvillanova - I've added the feature to all audio datasets and corrected the task of `covost2`"
] |
https://api.github.com/repos/huggingface/datasets/issues/5163 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5163/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5163/comments | https://api.github.com/repos/huggingface/datasets/issues/5163/events | https://github.com/huggingface/datasets/pull/5163 | 1,422,540,337 | PR_kwDODunzps5BgQxp | 5,163 | Reduce default max `writer_batch_size` | [] | closed | false | null | 1 | 2022-10-25T14:14:52Z | 2022-10-27T12:19:27Z | 2022-10-27T12:16:47Z | null | Reduce the default writer_batch_size from 10k to 1k examples. Additionally, align the default values of `batch_size` and `writer_batch_size` in `Dataset.cast` with the values from the corresponding docstring. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/5974 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5974/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5974/comments | https://api.github.com/repos/huggingface/datasets/issues/5974/events | https://github.com/huggingface/datasets/pull/5974 | 1,767,981,231 | PR_kwDODunzps5TkXCb | 5,974 | Deprecate `errors` param in favor of `encoding_errors` in text builder | [] | closed | false | null | 3 | 2023-06-21T16:31:38Z | 2023-06-26T10:34:43Z | 2023-06-26T10:27:40Z | null | For consistency with the JSON builder and Pandas | {
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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) | 0.006518 / 0.011353 (-0.004835) | 0.004121 / 0.011008 (-0.006887) | 0.103350 / 0.038508 (0.064842) | 0.045030 / 0.023109 (0.021920) | 0.351670 / 0.275898 (0.075772) | 0.408110 / 0.323480 (0.084630) | 0.003883 / 0.007986 (-0.004102) | 0.003352 / 0.004328 (-0.000977) | 0.078786 / 0.004250 (0.074535) | 0.063977 / 0.037052 (0.026925) | 0.369759 / 0.258489 (0.111270) | 0.415103 / 0.293841 (0.121262) | 0.033069 / 0.128546 (-0.095477) | 0.008863 / 0.075646 (-0.066783) | 0.353660 / 0.419271 (-0.065611) | 0.055714 / 0.043533 (0.012181) | 0.350458 / 0.255139 (0.095319) | 0.369505 / 0.283200 (0.086305) | 0.022822 / 0.141683 (-0.118861) | 1.537588 / 1.452155 (0.085433) | 1.590569 / 1.492716 (0.097853) |\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) | 0.206826 / 0.018006 (0.188819) | 0.471625 / 0.000490 (0.471135) | 0.005188 / 0.000200 (0.004988) | 0.000316 / 0.000054 (0.000261) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028148 / 0.037411 (-0.009263) | 0.111941 / 0.014526 (0.097415) | 0.122106 / 0.176557 (-0.054451) | 0.181127 / 0.737135 (-0.556009) | 0.127534 / 0.296338 (-0.168805) |\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) | 0.409520 / 0.215209 (0.194311) | 4.098455 / 2.077655 (2.020800) | 1.852447 / 1.504120 (0.348327) | 1.657036 / 1.541195 (0.115842) | 1.709624 / 1.468490 (0.241134) | 0.542806 / 4.584777 (-4.041970) | 3.809352 / 3.745712 (0.063640) | 1.855412 / 5.269862 (-3.414449) | 1.109180 / 4.565676 (-3.456497) | 0.066801 / 0.424275 (-0.357474) | 0.011832 / 0.007607 (0.004225) | 0.518338 / 0.226044 (0.292293) | 5.190108 / 2.268929 (2.921179) | 2.320602 / 55.444624 (-53.124023) | 1.991416 / 6.876477 (-4.885060) | 2.106989 / 2.142072 (-0.035084) | 0.668914 / 4.805227 (-4.136313) | 0.145325 / 6.500664 (-6.355340) | 0.065145 / 0.075469 (-0.010324) |\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) | 1.254706 / 1.841788 (-0.587082) | 14.707264 / 8.074308 (6.632956) | 14.615423 / 10.191392 (4.424031) | 0.170764 / 0.680424 (-0.509659) | 0.017905 / 0.534201 (-0.516296) | 0.435606 / 0.579283 (-0.143677) | 0.434648 / 0.434364 (0.000284) | 0.520813 / 0.540337 (-0.019524) | 0.633902 / 1.386936 (-0.753034) |\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) | 0.007212 / 0.011353 (-0.004141) | 0.004301 / 0.011008 (-0.006707) | 0.080767 / 0.038508 (0.042258) | 0.051949 / 0.023109 (0.028840) | 0.398473 / 0.275898 (0.122575) | 0.465038 / 0.323480 (0.141558) | 0.005580 / 0.007986 (-0.002406) | 0.003556 / 0.004328 (-0.000773) | 0.080682 / 0.004250 (0.076431) | 0.059517 / 0.037052 (0.022464) | 0.421171 / 0.258489 (0.162682) | 0.459752 / 0.293841 (0.165911) | 0.032960 / 0.128546 (-0.095586) | 0.009107 / 0.075646 (-0.066539) | 0.086382 / 0.419271 (-0.332889) | 0.056053 / 0.043533 (0.012520) | 0.393357 / 0.255139 (0.138218) | 0.412972 / 0.283200 (0.129772) | 0.031115 / 0.141683 (-0.110568) | 1.576961 / 1.452155 (0.124806) | 1.627249 / 1.492716 (0.134533) |\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) | 0.227618 / 0.018006 (0.209612) | 0.444640 / 0.000490 (0.444150) | 0.004376 / 0.000200 (0.004176) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030826 / 0.037411 (-0.006586) | 0.117587 / 0.014526 (0.103062) | 0.127467 / 0.176557 (-0.049089) | 0.184440 / 0.737135 (-0.552695) | 0.133664 / 0.296338 (-0.162675) |\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) | 0.443183 / 0.215209 (0.227974) | 4.408312 / 2.077655 (2.330658) | 2.132487 / 1.504120 (0.628367) | 1.923632 / 1.541195 (0.382438) | 1.967882 / 1.468490 (0.499392) | 0.552954 / 4.584777 (-4.031823) | 3.777701 / 3.745712 (0.031989) | 1.857686 / 5.269862 (-3.412176) | 1.104847 / 4.565676 (-3.460829) | 0.068350 / 0.424275 (-0.355925) | 0.012437 / 0.007607 (0.004830) | 0.559258 / 0.226044 (0.333214) | 5.593258 / 2.268929 (3.324330) | 2.648059 / 55.444624 (-52.796565) | 2.277428 / 6.876477 (-4.599049) | 2.351685 / 2.142072 (0.209612) | 0.678750 / 4.805227 (-4.126477) | 0.145550 / 6.500664 (-6.355114) | 0.066556 / 0.075469 (-0.008913) |\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) | 1.327128 / 1.841788 (-0.514659) | 15.649079 / 8.074308 (7.574771) | 14.478659 / 10.191392 (4.287267) | 0.147633 / 0.680424 (-0.532791) | 0.018502 / 0.534201 (-0.515699) | 0.438556 / 0.579283 (-0.140727) | 0.433381 / 0.434364 (-0.000983) | 0.514367 / 0.540337 (-0.025970) | 0.618347 / 1.386936 (-0.768589) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16aa1c886c5b499641a4bb3d8ce4a4f7de8244b7 \"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) | 0.006078 / 0.011353 (-0.005275) | 0.003914 / 0.011008 (-0.007095) | 0.102039 / 0.038508 (0.063531) | 0.037660 / 0.023109 (0.014551) | 0.348963 / 0.275898 (0.073065) | 0.407284 / 0.323480 (0.083804) | 0.004661 / 0.007986 (-0.003324) | 0.003253 / 0.004328 (-0.001076) | 0.078276 / 0.004250 (0.074025) | 0.054144 / 0.037052 (0.017091) | 0.376715 / 0.258489 (0.118225) | 0.418499 / 0.293841 (0.124658) | 0.027627 / 0.128546 (-0.100919) | 0.008494 / 0.075646 (-0.067152) | 0.316894 / 0.419271 (-0.102377) | 0.046560 / 0.043533 (0.003027) | 0.339835 / 0.255139 (0.084696) | 0.374628 / 0.283200 (0.091428) | 0.020729 / 0.141683 (-0.120954) | 1.502769 / 1.452155 (0.050615) | 1.548756 / 1.492716 (0.056040) |\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) | 0.229192 / 0.018006 (0.211186) | 0.426245 / 0.000490 (0.425756) | 0.005190 / 0.000200 (0.004990) | 0.000081 / 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) | 0.024271 / 0.037411 (-0.013140) | 0.098869 / 0.014526 (0.084343) | 0.105079 / 0.176557 (-0.071477) | 0.164707 / 0.737135 (-0.572428) | 0.110337 / 0.296338 (-0.186002) |\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) | 0.426593 / 0.215209 (0.211383) | 4.293977 / 2.077655 (2.216323) | 1.928502 / 1.504120 (0.424382) | 1.728623 / 1.541195 (0.187428) | 1.792084 / 1.468490 (0.323594) | 0.568737 / 4.584777 (-4.016040) | 3.438534 / 3.745712 (-0.307178) | 1.797798 / 5.269862 (-3.472063) | 1.054078 / 4.565676 (-3.511598) | 0.068711 / 0.424275 (-0.355564) | 0.011250 / 0.007607 (0.003643) | 0.529299 / 0.226044 (0.303255) | 5.283965 / 2.268929 (3.015037) | 2.358274 / 55.444624 (-53.086350) | 2.012818 / 6.876477 (-4.863659) | 2.109923 / 2.142072 (-0.032149) | 0.679556 / 4.805227 (-4.125671) | 0.138346 / 6.500664 (-6.362318) | 0.066349 / 0.075469 (-0.009120) |\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) | 1.193994 / 1.841788 (-0.647794) | 14.073158 / 8.074308 (5.998850) | 13.488525 / 10.191392 (3.297133) | 0.144536 / 0.680424 (-0.535888) | 0.016748 / 0.534201 (-0.517453) | 0.362703 / 0.579283 (-0.216580) | 0.389511 / 0.434364 (-0.044853) | 0.427296 / 0.540337 (-0.113041) | 0.513227 / 1.386936 (-0.873709) |\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) | 0.006215 / 0.011353 (-0.005138) | 0.003834 / 0.011008 (-0.007174) | 0.078001 / 0.038508 (0.039493) | 0.036537 / 0.023109 (0.013428) | 0.369724 / 0.275898 (0.093826) | 0.426761 / 0.323480 (0.103281) | 0.003602 / 0.007986 (-0.004383) | 0.003001 / 0.004328 (-0.001327) | 0.075989 / 0.004250 (0.071739) | 0.048618 / 0.037052 (0.011566) | 0.374296 / 0.258489 (0.115807) | 0.430330 / 0.293841 (0.136489) | 0.028299 / 0.128546 (-0.100247) | 0.008537 / 0.075646 (-0.067109) | 0.083275 / 0.419271 (-0.335997) | 0.043136 / 0.043533 (-0.000397) | 0.359072 / 0.255139 (0.103933) | 0.387391 / 0.283200 (0.104192) | 0.021202 / 0.141683 (-0.120481) | 1.520832 / 1.452155 (0.068677) | 1.567030 / 1.492716 (0.074313) |\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) | 0.230944 / 0.018006 (0.212938) | 0.422159 / 0.000490 (0.421669) | 0.003447 / 0.000200 (0.003247) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025442 / 0.037411 (-0.011969) | 0.103944 / 0.014526 (0.089418) | 0.110577 / 0.176557 (-0.065979) | 0.161393 / 0.737135 (-0.575743) | 0.113482 / 0.296338 (-0.182857) |\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) | 0.485765 / 0.215209 (0.270556) | 4.845737 / 2.077655 (2.768083) | 2.556732 / 1.504120 (1.052612) | 2.348638 / 1.541195 (0.807443) | 2.379289 / 1.468490 (0.910799) | 0.561261 / 4.584777 (-4.023516) | 3.482468 / 3.745712 (-0.263244) | 3.061319 / 5.269862 (-2.208543) | 1.483938 / 4.565676 (-3.081738) | 0.067584 / 0.424275 (-0.356691) | 0.011333 / 0.007607 (0.003726) | 0.594342 / 0.226044 (0.368297) | 5.935477 / 2.268929 (3.666548) | 3.025029 / 55.444624 (-52.419595) | 2.687032 / 6.876477 (-4.189445) | 2.752470 / 2.142072 (0.610398) | 0.674470 / 4.805227 (-4.130757) | 0.136777 / 6.500664 (-6.363887) | 0.068335 / 0.075469 (-0.007134) |\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) | 1.336456 / 1.841788 (-0.505332) | 14.376007 / 8.074308 (6.301699) | 14.171375 / 10.191392 (3.979983) | 0.159620 / 0.680424 (-0.520804) | 0.016685 / 0.534201 (-0.517516) | 0.364344 / 0.579283 (-0.214939) | 0.395358 / 0.434364 (-0.039006) | 0.424876 / 0.540337 (-0.115461) | 0.513267 / 1.386936 (-0.873669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ed837325cb539a5deb99129e5ad181d0269e050 \"CML watermark\")\n"
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https://api.github.com/repos/huggingface/datasets/issues/4172 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4172/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4172/comments | https://api.github.com/repos/huggingface/datasets/issues/4172/events | https://github.com/huggingface/datasets/pull/4172 | 1,204,433,160 | PR_kwDODunzps42O7LW | 4,172 | Update assin2 dataset_infos.json | [] | closed | false | null | 1 | 2022-04-14T11:53:06Z | 2022-04-15T14:47:42Z | 2022-04-15T14:41:22Z | null | Following comments in https://github.com/huggingface/datasets/issues/4003 we found that it was outdated and casing an error when loading the dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/4735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4735/comments | https://api.github.com/repos/huggingface/datasets/issues/4735/events | https://github.com/huggingface/datasets/pull/4735 | 1,314,501,641 | PR_kwDODunzps477CuP | 4,735 | Pin rouge_score test dependency | [] | closed | false | null | 1 | 2022-07-22T07:18:21Z | 2022-07-22T07:58:14Z | 2022-07-22T07:45:18Z | null | Temporarily pin `rouge_score` (to avoid latest version 0.7.0) until the issue is fixed.
Fix #4734 | {
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https://api.github.com/repos/huggingface/datasets/issues/121 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/121/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/121/comments | https://api.github.com/repos/huggingface/datasets/issues/121/events | https://github.com/huggingface/datasets/pull/121 | 618,790,040 | MDExOlB1bGxSZXF1ZXN0NDE4NDQ4MTkx | 121 | make style | [] | closed | false | null | 0 | 2020-05-15T08:23:36Z | 2020-05-15T08:25:39Z | 2020-05-15T08:25:38Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/1458 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1458/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1458/comments | https://api.github.com/repos/huggingface/datasets/issues/1458/events | https://github.com/huggingface/datasets/pull/1458 | 761,235,962 | MDExOlB1bGxSZXF1ZXN0NTM1OTMyMTA1 | 1,458 | Add id_nergrit_corpus | [] | closed | false | null | 1 | 2020-12-10T13:20:34Z | 2020-12-17T10:45:15Z | 2020-12-17T10:45:15Z | null | Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis.
Recently my PR for id_nergrit_ner has been accepted and merged to the main branch. The id_nergrit_ner has only one dataset (NER), and this new PR renamed the dataset from id_nergrit_ner to id_nergrit_corpus and added 2 other remaining datasets (Statement Extraction, and Sentiment Analysis.) | {
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https://api.github.com/repos/huggingface/datasets/issues/3458 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3458/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3458/comments | https://api.github.com/repos/huggingface/datasets/issues/3458/events | https://github.com/huggingface/datasets/pull/3458 | 1,084,926,025 | PR_kwDODunzps4wFiRb | 3,458 | Fix duplicated tag in wikicorpus dataset card | [] | closed | false | null | 1 | 2021-12-20T15:34:16Z | 2021-12-20T16:03:25Z | 2021-12-20T16:03:24Z | null | null | {
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"CI is failing just because of empty sections - merging"
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https://api.github.com/repos/huggingface/datasets/issues/5662 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5662/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5662/comments | https://api.github.com/repos/huggingface/datasets/issues/5662/events | https://github.com/huggingface/datasets/pull/5662 | 1,637,140,813 | PR_kwDODunzps5MtvsM | 5,662 | Fix unnecessary dict comprehension | [] | closed | false | null | 3 | 2023-03-23T09:18:58Z | 2023-03-23T09:46:59Z | 2023-03-23T09:37:49Z | null | After ruff-0.0.258 release, the C416 rule was updated with unnecessary dict comprehensions. See:
- https://github.com/charliermarsh/ruff/releases/tag/v0.0.258
- https://github.com/charliermarsh/ruff/pull/3605
This PR fixes one unnecessary dict comprehension in our code: no need to unpack and re-pack the tuple values.
Fix #5661 | {
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"I am merging because the CI error is unrelated.",
"<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) | 0.009448 / 0.011353 (-0.001905) | 0.006156 / 0.011008 (-0.004852) | 0.123656 / 0.038508 (0.085147) | 0.034998 / 0.023109 (0.011889) | 0.374722 / 0.275898 (0.098824) | 0.418912 / 0.323480 (0.095432) | 0.007348 / 0.007986 (-0.000637) | 0.004779 / 0.004328 (0.000450) | 0.097541 / 0.004250 (0.093291) | 0.052523 / 0.037052 (0.015471) | 0.380118 / 0.258489 (0.121628) | 0.429448 / 0.293841 (0.135607) | 0.055156 / 0.128546 (-0.073390) | 0.019884 / 0.075646 (-0.055763) | 0.429613 / 0.419271 (0.010341) | 0.067554 / 0.043533 (0.024021) | 0.373940 / 0.255139 (0.118801) | 0.408115 / 0.283200 (0.124916) | 0.111353 / 0.141683 (-0.030329) | 1.821013 / 1.452155 (0.368858) | 1.972882 / 1.492716 (0.480165) |\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) | 0.236686 / 0.018006 (0.218679) | 0.516519 / 0.000490 (0.516029) | 0.009582 / 0.000200 (0.009383) | 0.000404 / 0.000054 (0.000349) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029425 / 0.037411 (-0.007986) | 0.123972 / 0.014526 (0.109446) | 0.133768 / 0.176557 (-0.042789) | 0.207562 / 0.737135 (-0.529573) | 0.142841 / 0.296338 (-0.153497) |\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) | 0.618531 / 0.215209 (0.403322) | 6.216854 / 2.077655 (4.139199) | 2.480138 / 1.504120 (0.976018) | 2.139884 / 1.541195 (0.598689) | 2.122992 / 1.468490 (0.654502) | 1.233824 / 4.584777 (-3.350953) | 5.426142 / 3.745712 (1.680430) | 4.891039 / 5.269862 (-0.378822) | 2.767033 / 4.565676 (-1.798643) | 0.142224 / 0.424275 (-0.282051) | 0.015754 / 0.007607 (0.008147) | 0.772210 / 0.226044 (0.546166) | 7.620484 / 2.268929 (5.351556) | 3.141617 / 55.444624 (-52.303007) | 2.471406 / 6.876477 (-4.405070) | 2.648008 / 2.142072 (0.505935) | 1.429281 / 4.805227 (-3.375946) | 0.255981 / 6.500664 (-6.244683) | 0.077710 / 0.075469 (0.002241) |\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) | 1.547714 / 1.841788 (-0.294073) | 17.859985 / 8.074308 (9.785677) | 21.791878 / 10.191392 (11.600486) | 0.238569 / 0.680424 (-0.441854) | 0.027520 / 0.534201 (-0.506681) | 0.553960 / 0.579283 (-0.025324) | 0.616165 / 0.434364 (0.181801) | 0.622492 / 0.540337 (0.082154) | 0.716345 / 1.386936 (-0.670591) |\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) | 0.009624 / 0.011353 (-0.001729) | 0.006091 / 0.011008 (-0.004917) | 0.096623 / 0.038508 (0.058115) | 0.034903 / 0.023109 (0.011793) | 0.421009 / 0.275898 (0.145111) | 0.459236 / 0.323480 (0.135756) | 0.007778 / 0.007986 (-0.000207) | 0.004726 / 0.004328 (0.000398) | 0.099603 / 0.004250 (0.095353) | 0.051426 / 0.037052 (0.014373) | 0.420461 / 0.258489 (0.161972) | 0.469747 / 0.293841 (0.175906) | 0.053769 / 0.128546 (-0.074777) | 0.020636 / 0.075646 (-0.055011) | 0.115785 / 0.419271 (-0.303486) | 0.062692 / 0.043533 (0.019160) | 0.419388 / 0.255139 (0.164249) | 0.448675 / 0.283200 (0.165475) | 0.112099 / 0.141683 (-0.029584) | 1.787982 / 1.452155 (0.335827) | 1.884581 / 1.492716 (0.391864) |\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) | 0.208837 / 0.018006 (0.190831) | 0.515593 / 0.000490 (0.515103) | 0.000447 / 0.000200 (0.000247) | 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) | 0.031025 / 0.037411 (-0.006386) | 0.125179 / 0.014526 (0.110653) | 0.137050 / 0.176557 (-0.039506) | 0.203582 / 0.737135 (-0.533553) | 0.139209 / 0.296338 (-0.157130) |\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) | 0.601507 / 0.215209 (0.386298) | 6.034778 / 2.077655 (3.957123) | 2.550277 / 1.504120 (1.046157) | 2.242277 / 1.541195 (0.701082) | 2.306378 / 1.468490 (0.837888) | 1.251219 / 4.584777 (-3.333558) | 5.448698 / 3.745712 (1.702986) | 3.044666 / 5.269862 (-2.225196) | 2.000684 / 4.565676 (-2.564992) | 0.148385 / 0.424275 (-0.275890) | 0.015175 / 0.007607 (0.007567) | 0.800839 / 0.226044 (0.574795) | 8.062099 / 2.268929 (5.793171) | 3.400980 / 55.444624 (-52.043644) | 2.639583 / 6.876477 (-4.236894) | 2.660691 / 2.142072 (0.518618) | 1.467715 / 4.805227 (-3.337512) | 0.266429 / 6.500664 (-6.234235) | 0.076981 / 0.075469 (0.001512) |\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) | 1.621128 / 1.841788 (-0.220659) | 17.949989 / 8.074308 (9.875680) | 20.946426 / 10.191392 (10.755034) | 0.259357 / 0.680424 (-0.421067) | 0.026094 / 0.534201 (-0.508107) | 0.527840 / 0.579283 (-0.051443) | 0.629027 / 0.434364 (0.194663) | 0.603931 / 0.540337 (0.063594) | 0.711370 / 1.386936 (-0.675566) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2ccf01db81bb7b70f3ea97b185e345c2b1df0274 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/1338 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1338/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1338/comments | https://api.github.com/repos/huggingface/datasets/issues/1338/events | https://github.com/huggingface/datasets/pull/1338 | 759,725,770 | MDExOlB1bGxSZXF1ZXN0NTM0Njc5ODcz | 1,338 | Add GigaFren Dataset | [] | closed | false | null | 1 | 2020-12-08T19:42:04Z | 2020-12-14T10:03:47Z | 2020-12-14T10:03:46Z | null | {
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"@lhoestq fixed"
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|
https://api.github.com/repos/huggingface/datasets/issues/1661 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1661/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1661/comments | https://api.github.com/repos/huggingface/datasets/issues/1661/events | https://github.com/huggingface/datasets/pull/1661 | 775,840,801 | MDExOlB1bGxSZXF1ZXN0NTQ2NDQzNjYx | 1,661 | updated dataset cards | [] | closed | false | null | 0 | 2020-12-29T11:20:40Z | 2020-12-30T17:15:16Z | 2020-12-30T17:15:16Z | null | added dataset instance in the card. | {
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https://api.github.com/repos/huggingface/datasets/issues/4072 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4072/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4072/comments | https://api.github.com/repos/huggingface/datasets/issues/4072/events | https://github.com/huggingface/datasets/pull/4072 | 1,188,266,410 | PR_kwDODunzps41aIUG | 4,072 | Add installation instructions to image_process doc | [] | closed | false | null | 1 | 2022-03-31T15:29:37Z | 2022-03-31T17:05:46Z | 2022-03-31T17:00:19Z | null | This PR adds the installation instructions for the Image feature to the image process doc. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/5434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5434/comments | https://api.github.com/repos/huggingface/datasets/issues/5434/events | https://github.com/huggingface/datasets/issues/5434 | 1,536,090,042 | I_kwDODunzps5bjt-6 | 5,434 | sample_dataset module not found | [] | closed | false | null | 3 | 2023-01-17T09:57:54Z | 2023-01-19T13:52:12Z | 2023-01-19T07:55:11Z | null | null | {
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"Hi! Can you describe what the actual error is?",
"working on the setfit example script\r\n\r\n from setfit import SetFitModel, SetFitTrainer, sample_dataset\r\n\r\nImportError: cannot import name 'sample_dataset' from 'setfit' (C:\\Python\\Python38\\lib\\site-packages\\setfit\\__init__.py)\r\n\r\n apart from that, I also had to hack these loads to import thses modules:\r\n from datasets.load import load_dataset \r\n from datasets.arrow_dataset import Dataset\r\n from datasets.dataset_dict import DatasetDict",
"Hi! This issue is related to the [SetFit](https://github.com/huggingface/setfit) project, so can you please open it there?"
] |
https://api.github.com/repos/huggingface/datasets/issues/5703 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5703/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5703/comments | https://api.github.com/repos/huggingface/datasets/issues/5703/events | https://github.com/huggingface/datasets/pull/5703 | 1,653,158,955 | PR_kwDODunzps5NjCCV | 5,703 | [WIP][Test, Please ignore] Investigate performance impact of using multiprocessing only | [] | closed | false | null | 4 | 2023-04-04T04:37:49Z | 2023-04-20T03:17:37Z | 2023-04-20T03:17:32Z | null | null | {
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"`multiprocess` uses `dill` instead of `pickle` for pickling shared objects and, as such, can pickle more types than `multiprocessing`. And I don't think this is something we want to change :).",
"That makes sense to me, and I don't think you should merge this change. I was only curious about the performance impact. I saw the benchmarks that was produced in other PRs, and wanted to get a better understanding of it. I created this PR to see if it got automatically added here.\r\n\r\nIs there a way I can generate those benchmarks myself?",
"You can find some speed comparisons between dill and pickle on SO if you google \"dill vs pickle speed\".\r\n\r\nAnd for the benchmarks, you can generate them locally with DVC running this code from the repo root: https://github.com/huggingface/datasets/blob/0803a006db1c395ac715662cc6079651f77c11ea/.github/workflows/benchmarks.yaml#L23-L47.",
"Thanks for the help @mariosasko!"
] |
https://api.github.com/repos/huggingface/datasets/issues/5669 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5669/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5669/comments | https://api.github.com/repos/huggingface/datasets/issues/5669/events | https://github.com/huggingface/datasets/issues/5669 | 1,638,070,046 | I_kwDODunzps5hovce | 5,669 | Almost identical datasets, huge performance difference | [] | open | false | null | 7 | 2023-03-23T18:20:20Z | 2023-04-09T18:56:23Z | null | null | ### Describe the bug
I am struggling to understand (huge) performance difference between two datasets that are almost identical.
### Steps to reproduce the bug
# Fast (normal) dataset speed:
```python
import cv2
from datasets import load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("beans", split="train")
for x in DataLoader(dataset.with_format("torch"), batch_size=16, shuffle=True, num_workers=8):
pass
```
The above pass over the dataset takes about 1.5 seconds on my computer.
However, if I re-create (almost) the same dataset, the sweep takes HUGE amount of time: 15 minutes. Steps to reproduce:
```python
def transform(example):
example["image2"] = cv2.imread(example["image_file_path"])
return example
dataset2 = dataset.map(transform, remove_columns=["image"])
for x in DataLoader(dataset2.with_format("torch"), batch_size=16, shuffle=True, num_workers=8):
pass
```
### Expected behavior
Same timings
### Environment info
python==3.10.9
datasets==2.10.1 | {
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"Do I miss something here?",
"Hi! \r\n\r\nThe first dataset stores images as bytes (the \"image\" column type is `datasets.Image()`) and decodes them as `PIL.Image` objects and the second dataset stores them as variable-length lists (the \"image\" column type is `datasets.Sequence(...)`)), so I guess going from `arrow bytes -> NumPy -> decoding as PIL.Image -> PyTorch` is faster than going from `arrow list -> NumPy -> PyTorch`. \r\n\r\nTo store image bytes in the second example, you can do the following:\r\n\r\n```python\r\ndef transform(example):\r\n example[\"image2\"] = cv2.imread(example[\"image_file_path\"])\r\n return example\r\n\r\nfeatures = dataset.features.copy()\r\ndel features[\"image\"]\r\nfeatures[\"image2\"] = datasets.Image()\r\ndataset2 = dataset.map(transform, remove_columns=[\"image\"], features=features)\r\n\r\nfor x in DataLoader(dataset2.with_format(\"torch\"), batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n```",
"Thanks, @mariosasko. I could not understand why a (decoded) sequence should be MUCH slower than an encoded image (that must be decoded every time). At any rate, I tried you suggestion. It made the `map` step to run extremely slow (consumes all the 16GB of memory and starts swapping)\r\n\r\nI tried also the easiest (as I see it) scenario, where images are kept as bytes, but it made things even worse: not only it was extremely slow, but also crashes\r\n\r\n```python\r\n\r\ndef transform(example):\r\n example[\"image2\"] = cv2.imread(example[\"image_file_path\"]).tobytes()\r\n return example\r\n\r\ndataset2 = dataset.map(transform, remove_columns=[\"image\"])\r\n\r\nfor x in DataLoader(dataset2.with_format(\"torch\"), batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n\r\n\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nOutput exceeds the size limit. Open the full output data in a text editor\r\n---------------------------------------------------------------------------\r\nRuntimeError Traceback (most recent call last)\r\nFile ~/virtenvs/py310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1133, in _MultiProcessingDataLoaderIter._try_get_data(self, timeout)\r\n 1132 try:\r\n-> 1133 data = self._data_queue.get(timeout=timeout)\r\n 1134 return (True, data)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/queues.py:113, in Queue.get(self, block, timeout)\r\n 112 timeout = deadline - time.monotonic()\r\n--> 113 if not self._poll(timeout):\r\n 114 raise Empty\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:257, in _ConnectionBase.poll(self, timeout)\r\n 256 self._check_readable()\r\n--> 257 return self._poll(timeout)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:424, in Connection._poll(self, timeout)\r\n 423 def _poll(self, timeout):\r\n--> 424 r = wait([self], timeout)\r\n 425 return bool(r)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:931, in wait(object_list, timeout)\r\n 930 while True:\r\n--> 931 ready = selector.select(timeout)\r\n 932 if ready:\r\n...\r\n-> 1146 raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e\r\n 1147 if isinstance(e, queue.Empty):\r\n 1148 return (False, None)\r\n\r\nRuntimeError: DataLoader worker (pid(s) 195393) exited unexpectedly\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\n```\r\n",
"Correction: the `beans` dataset stores the image file paths, not the bytes.\r\n\r\nFor your use case, I think it makes more sense to use `with_tranform` than `map` and lazily decode images with `cv2.imread` when indexing an example/batch:\r\n```python\r\nimport cv2\r\n\r\ndef transform(batch):\r\n batch[\"image2\"] = np.stack([cv2.imread(image_file_path) for image_file_path in batch[\"image_file_path\"]])\r\n return batch\r\n\r\ndataset = dataset.with_transform(transform)\r\n```\r\n",
"This is incorrect.\n\nDid you try to run it? dataset[0] returns a tensor of numbers. dataset2[0]\nreturns the same tensor, but after a few long seconds. Looping over a\nthousand of images cannot take 15 minutes.\n\nOn Fri, 24 Mar 2023 at 19:28 Mario Šaško ***@***.***> wrote:\n\n> Correction: the beans dataset stores the image file paths, not the bytes.\n>\n> For your use case, I think it makes more sense to use with_tranform than\n> map and lazily decode images with cv2.imread when accessing an\n> example/batch:\n>\n> import cv2\n> def transform(batch):\n> batch[\"image2\"] = np.stack([cv2.imread(image_file_path) for image_file_path in batch[\"image_file_path\"]])\n> return batch\n> dataset = dataset.with_transform(transform)\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5669#issuecomment-1483084347>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AASS73SHRWXIQX6SCYCJ7ITW5XDUDANCNFSM6AAAAAAWFSHWEM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n",
"I updated the transform with the NumPy -> PyTorch conversion.\r\n\r\nI'm sharing the entire code:\r\n```python\r\nimport cv2\r\nimport numpy as np\r\nimport datasets\r\nimport torch\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\ndataset = load_dataset(\"beans\", split=\"train\")\r\n\r\ndef transform(batch):\r\n # # Pillow decodes as RGB\r\n # batch[\"image\"] = torch.stack([torch.from_numpy(cv2.cvtColor(cv2.imread(image_file_path), cv2.COLOR_BGR2RGB)) for image_file_path in batch[\"image_file_path\"]])\r\n batch[\"image\"] = torch.stack([torch.from_numpy(cv2.imread(image_file_path)) for image_file_path in batch[\"image_file_path\"]])\r\n batch[\"labels\"] = torch.tensor(batch[\"labels\"])\r\n return batch\r\n\r\ndataset2 = dataset.cast_column(\"image\", datasets.Image(decode=False)).with_transform(transform)\r\n\r\nfor x in DataLoader(dataset2, batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n```\r\n\r\nThis code is ≈ 10% faster on my machine than the default decoding with Pillow and `.with_format(\"torch\")`.",
"Thanks, @mariosasko \r\nMy question remain unanswered though. Why is the `map`ed dataset so slow? My understanding is that a dataset of numpy arrays should be must faster than a dataset that has to decode images into numpy arrays every time one accesses an item. "
] |
https://api.github.com/repos/huggingface/datasets/issues/1665 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1665/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1665/comments | https://api.github.com/repos/huggingface/datasets/issues/1665/events | https://github.com/huggingface/datasets/pull/1665 | 776,431,087 | MDExOlB1bGxSZXF1ZXN0NTQ2OTI1NTgw | 1,665 | Add language to dataset card for Counter dataset. | [] | closed | false | null | 0 | 2020-12-30T12:23:20Z | 2020-12-30T17:20:20Z | 2020-12-30T17:20:20Z | null | Add language. | {
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https://api.github.com/repos/huggingface/datasets/issues/2465 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2465/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2465/comments | https://api.github.com/repos/huggingface/datasets/issues/2465/events | https://github.com/huggingface/datasets/pull/2465 | 915,525,071 | MDExOlB1bGxSZXF1ZXN0NjY1MzMxMDMz | 2,465 | adding masahaner dataset | [] | closed | false | null | 3 | 2021-06-08T21:20:25Z | 2021-06-14T14:59:05Z | 2021-06-14T14:59:05Z | null | Adding Masakhane dataset https://github.com/masakhane-io/masakhane-ner
@lhoestq , can you please review | {
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"Thank you for the review. ",
"Thanks a lot for the corrections and comments. \r\n\r\nI have resolved point 2. The make style still throws some errors, please see below\r\n\r\nblack --line-length 119 --target-version py36 tests src benchmarks datasets/**/*.py metrics\r\n/bin/sh: 1: black: not found\r\nMakefile:13: recipe for target 'style' failed\r\nmake: *** [style] Error 127\r\n\r\nCan you help to resolve this?",
"Thank you very much @lhoestq for the help. "
] |
https://api.github.com/repos/huggingface/datasets/issues/5461 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5461/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5461/comments | https://api.github.com/repos/huggingface/datasets/issues/5461/events | https://github.com/huggingface/datasets/issues/5461 | 1,555,532,719 | I_kwDODunzps5ct4uv | 5,461 | Discrepancy in `nyu_depth_v2` dataset | [] | open | false | null | 37 | 2023-01-24T19:15:46Z | 2023-02-06T20:52:00Z | null | null | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-side comparison,
I tried to find the origin of this issue but sadly as I mentioned in tensorflow/datasets/issues/4674, the download link from `fast-depth` doesn't work anymore hence couldn't verify if the error originated there or during porting data from there to HF.
Hi @sayakpaul, as you worked on huggingface/datasets/issues/5255, if you still have access to that data could you please share the data or perhaps checkout this issue?
### Steps to reproduce the bug
This [notebook](https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing#scrollTo=UEW7QSh0jf0i) from @sayakpaul could be used to generate depth maps and actual ground truths could be checked from this [dataset](https://www.kaggle.com/datasets/awsaf49/nyuv2-bts-dataset) from BTS repo.
> Note: BTS dataset has only 36K data compared to the train-test 50K. They sampled the data as adjacent frames look quite the same
### Expected behavior
Expected depth maps should be smooth rather than discrete/clipped.
### Environment info
- `datasets` version: 2.8.1.dev0
- 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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"Ccing @dwofk (the author of `fast-depth`). \r\n\r\nThanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed. \r\n\r\nIf you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :) ",
"Good catch ! Ideally it would be nice to have the datasets in the raw form, this way users can choose whatever processing they want to apply",
"> Ccing @dwofk (the author of `fast-depth`).\r\n> \r\n> Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed.\r\n> \r\n> If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :)\r\n\r\n@sayakpaul I would love to create a PR on this. As this will be my first PR here, some guidance would be helpful.\r\n\r\nNeed a bit of advice on the dataset, there are three publicly available datasets. Which one should I consider for PR?\r\n1. [BTS](https://github.com/cleinc/bts): Containst train/test: 36K/654 data, dtype = `uint16` hence more precise\r\n2. [DenseDepth](https://github.com/ialhashim/DenseDepth) It contains train/test: 50K/654 data, dtype = `uint8` hence less precise\r\n3. [Official](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html#raw_parts): Size is big 400GB+, requires **MatLab** code for fixing **projection** and **sync**, DataType: `pgm` and `dump` hence can't be used directly.\r\n\r\ncc: @lhoestq\r\n\r\n",
"I think BTS. Repositories like https://github.com/vinvino02/GLPDepth usually use BTS. Also, just for clarity, the PR will be to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. Once we have worked it out, we can update the following things:\r\n\r\n* https://github.com/huggingface/blog/pull/718\r\n* https://huggingface.co/docs/datasets/main/en/depth_estimation\r\n\r\nDon't worry about it if it seems overwhelming. We will work it out together :) \r\n\r\n@lhoestq what do you think? ",
"@sayakpaul If I get this right I have to,\r\n1. Create a PR on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2\r\n2. Create a PR on https://github.com/huggingface/blog\r\n3. Create a PR on https://github.com/huggingface/datasets to update https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx",
"The last two are low-hanging fruits. Don't worry about them. ",
"Yup opening a PR to use BTS on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 sounds good :) Thanks for the help !",
"Finally, I have found the origin of the **discretized depth map**. When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_map from `h5py`, I found depth_map from `h5py` came with `float32`. But when the data is processed in HF with `datasets.Image()` it was directly converted to `uint8` from `float32` hence the **discretized** depth map.\r\nhttps://github.com/huggingface/datasets/blob/c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead/src/datasets/features/image.py#L91-L93\r\n\r\n## Solutions:\r\n\r\n#### 1. Array2D\r\nUse `Array2D` feature with `float32` for depth_map \r\n\r\n* Code:\r\n```py\r\nFeatures({'depth_map': Array2D(shape=(480, 640), dtype='float32')})\r\n```\r\n* Pros:\r\nNo precision loss.\r\n\r\n* Cons:\r\nAs depth_map is saved as Array I think it can't be visuzlied in [hf.co/dataset](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) page like segmentation mask.\r\n\r\n#### 2. Uint16\r\nUse `uint16` as dtype for Image in `_h5_loader` for saving depth maps and accept `uint16` dtype in `datasets.Image()` feature.\r\n\r\n* Code\r\n```py\r\ndepth = np.array(h5f[\"depth\"])\r\ndepth /= 10.0 # [0, max_depth] -> [0, 1]\r\ndepth *= (2**16 -1) # transform from [0, 1] -> [0, 2^16 - 1]\r\ndepth = depth.astype('uint16')\r\n```\r\n* Pros:\r\n * We can visualize depth map in hf.co/datasets page like segmentation mask.\r\n * No need for post-processing.\r\n\r\n* Cons:\r\n * We need to make two change\r\n * Modify `_h5_loader` in https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 to convert depth_map from `float32` to `uint16`.\r\n * Make sure `datasets.Image()` converts `np.ndarray` to `uint16` checking max value\r\n * Precision loss due to `float32` to `uint16`\r\n * Post-processing required for depth_map to transform from `[0, 2^16 - 1]` to `[0, max_depth]` before feeding them to model.",
"Thanks so much for digging into this. \r\n\r\nSince the second solution entails changes to core datatypes in `datasets`, I think it's better to go with the first solution. \r\n\r\n@lhoestq WDYT?",
"@sayakpaul Yes, Solution 1 requires minimal change and provides no precision loss. But I think support for `uint16` image would be a great addition as many datasets come with `uint16` image. For example [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) dataset, here the image itself comes with `uint16` dtype rather than mask. So, saving `uint16` image with `uint8` will result in precision loss.\r\n\r\nPerhaps we can adapt solution 1 for this issue and Add support for `uint16` image separately?",
"Using Array2D makes it not practical to use to train a model - in `transformers` we expect an image type.\r\n\r\nThere is a pull request to support more precision than uint8 in Image() here: https://github.com/huggingface/datasets/pull/5365/files\r\n\r\nwe can probably merge it today and do a release right away",
"Fantastic, @lhoestq! \r\n\r\n@awsaf49 then let's wait for the PR to get merged and then take the next steps? ",
"Sure",
"The PR adds support for uint16 which is ok for BTS if I understand correctly, would it be ok for you ?",
"If the main issue with the current version of NYU we have on the Hub is related to the precision loss stemming from `Image()`, I'd prefer if `Image()` supported float32 as well. ",
"I also prefer `float32` as it offers more precision. But I'm not sure if we'll be able to visualize image with `float32` precision.",
"We could have a separate loading for the float32 one using Array2D, but I feel like it's less convenient to use due to the amount of disk space and because it's not an Image() type. That's why I think uint16 is a better solution for users",
"A bit confused here, If https://github.com/huggingface/datasets/pull/5365 gets merged won't this issue will be resolved automatically?",
"Yes in theory :)",
"actually float32 also seems to work in this PR (it just doesn't work for multi-channel)",
"In that case, a new PR isn't necessary, right?",
"Yep. I just tested from the PR and it works:\r\n```python\r\n>>> train_dataset = load_dataset(\"sayakpaul/nyu_depth_v2\", split=\"train\", streaming=True) \r\nDownloading readme: 100%|██████████████████| 8.71k/8.71k [00:00<00:00, 3.60MB/s]\r\n>>> next(iter(train_dataset))\r\n{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x480 at 0x1382ED7F0>,\r\n 'depth_map': <PIL.TiffImagePlugin.TiffImageFile image mode=F size=640x480 at 0x1382EDF28>}\r\n>>> x = next(iter(train_dataset))\r\n>>> np.asarray(x[\"depth_map\"]) \r\narray([[0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n ...,\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ]], dtype=float32)\r\n```",
"Great! the case is closed! This issue has been solved and I have to say, it was quite the thrill ride. I felt like Sherlock Holmes, solving a mystery and finding the bug🕵️♂️. But in all seriousness, it was a pleasure working on this issue and I'm glad we could get to the bottom of it.\r\n\r\nOn another note, should I consider closing the issue? I think we still need to make updates on https://github.com/huggingface/blog and https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx",
"Haha thanks Mr Holmes :p\r\n\r\nmaybe let's close this issue when we're done updating the blog post and the documentation",
"@awsaf49 thank you for your hard work! \r\n\r\nI am a little unsure why the other links need to be updated, though. They all rely on datasets internally. ",
"I think depth_map still shows discretized version. It would be nice to have corrected one.\r\n<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/depth_est_target_viz.png\" width = 300>",
"Also, I think we need to make some changes in the code to visualize depth_map as it is `float32` . `plot.imshow()` supports either [0, 1] + float32 or [0. 255] + uint8",
"Oh yes! Do you want to start with the fixes? Please feel free to say no but I wanted to make sure your contributions are reflected properly in our doc and the blog :)",
"Yes I think that would be nice :)",
"I'll make the changes tomorrow. I hope it's okay..."
] |
https://api.github.com/repos/huggingface/datasets/issues/5099 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5099/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5099/comments | https://api.github.com/repos/huggingface/datasets/issues/5099/events | https://github.com/huggingface/datasets/issues/5099 | 1,404,370,191 | I_kwDODunzps5TtP0P | 5,099 | datasets doesn't support # in data paths | [
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] | closed | false | null | 9 | 2022-10-11T10:05:32Z | 2022-10-13T13:14:20Z | 2022-10-13T13:14:20Z | null | ## Describe the bug
dataset files with `#` symbol their paths aren't read correctly.
## Steps to reproduce the bug
The data in folder `c#`of this [dataset](https://huggingface.co/datasets/loubnabnl/bigcode_csharp) can't be loaded. While the folder `c_sharp` with the same data is loaded properly
```python
ds = load_dataset('loubnabnl/bigcode_csharp', split="train", data_files=["data/c#/*"])
```
```
FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/27a3166cff4bb18e11919cafa6f169c0f57483de/data/c#/data_0003.jsonl
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-12.2.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
cc @lhoestq | {
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} | https://api.github.com/repos/huggingface/datasets/issues/5099/timeline | null | completed | null | null | false | [
"`datasets` doesn't seem to urlencode the directory names here\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/utils/file_utils.py#L109-L111\r\n\r\nfor example we should have\r\n```python\r\nfrom datasets.utils.file_utils import hf_hub_url\r\n\r\nurl = hf_hub_url(\"loubnabnl/bigcode_csharp\", \"data/c#/data_0003.jsonl\")\r\nprint(url)\r\n# Currently returns\r\n# https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/main/data/c#/data_0003.jsonl\r\n# while it should be \r\n# https://huggingface.co/datasets/loubnabnl/bigcode_csharp/resolve/main/data/c%23/data_0003.jsonl\r\n```",
"I'll work on this :)",
"@loubnabnl The dataset you linked in the description of the bug does not work and returns a 404. Where can I find the dataset to reproduce the bug?",
"I think you can create a dataset repository on the Hub with a dummy file containing a `#`",
"Ah sorry it was private I just made it public, I can also help with this if needed",
"@lhoestq Should I url encode also repo_id and revision parameters? I'm not sure what are the valid characters there.\r\n\r\nPersonally, I would be cautious and only url encode the path parameter.",
"These are possible solutions (assuming `from urllib.parse import quote`):\r\n\r\n1) url encode only the path parameter:\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HUB_DATASETS_URL.format(repo_id=repo_id, path=quote(path), revision=revision)\r\n```\r\n2) url encode all parameters:\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HUB_DATASETS_URL.format(repo_id=quote(repo_id), path=quote(path), revision=quote(revision))\r\n```\r\n3) url encode the whole url:\r\n```\r\n# src/datasets/config.py\r\nHUB_DATASETS_PATH = \"/datasets/{repo_id}/resolve/{revision}/{path}\"\r\nHUB_DATASETS_URL = HF_ENDPOINT + HUB_DATASETS_PATH\r\n```\r\n```\r\n# src/datasets/utils/file_utils.py\r\ndef hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:\r\n revision = revision or config.HUB_DEFAULT_VERSION\r\n return config.HF_ENDPOINT + quote(config.HUB_DATASETS_PATH.format(repo_id=repo_id, path=path, revision=revision))\r\n```",
"repo_id can only contain alphanumeric characters and _- so it doesn't need to be encoded.\r\n\r\nHowever I agree it's a good idea to also apply `quote` to the revision as well as in 2. !",
"Should be fixed by https://github.com/huggingface/datasets/issues/5099 - we'll do a release later today"
] |
https://api.github.com/repos/huggingface/datasets/issues/1976 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1976/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1976/comments | https://api.github.com/repos/huggingface/datasets/issues/1976/events | https://github.com/huggingface/datasets/pull/1976 | 820,228,538 | MDExOlB1bGxSZXF1ZXN0NTgzMjA3NDI4 | 1,976 | Add datasets full offline mode with HF_DATASETS_OFFLINE | [] | closed | false | null | 0 | 2021-03-02T17:26:59Z | 2021-03-03T15:45:31Z | 2021-03-03T15:45:30Z | null | Add the HF_DATASETS_OFFLINE environment variable for users who want to use `datasets` offline without having to wait for the network timeouts/retries to happen. This was requested in https://github.com/huggingface/datasets/issues/1939
cc @stas00 | {
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https://api.github.com/repos/huggingface/datasets/issues/488 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/488/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/488/comments | https://api.github.com/repos/huggingface/datasets/issues/488/events | https://github.com/huggingface/datasets/issues/488 | 676,299,993 | MDU6SXNzdWU2NzYyOTk5OTM= | 488 | issues with downloading datasets for wmt16 and wmt19 | [] | closed | false | null | 3 | 2020-08-10T17:32:51Z | 2022-10-04T17:46:59Z | 2022-10-04T17:46:58Z | null | I have encountered multiple issues while trying to:
```
import nlp
dataset = nlp.load_dataset('wmt16', 'ru-en')
metric = nlp.load_metric('wmt16')
```
1. I had to do `pip install -e ".[dev]" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and now it worked. So it must have been some outdated dependencies that `pip install -e ".[dev]" ` fixed.
2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for.
I tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below)
3. my machine has crushed and when I retried I got:
```
Traceback (most recent call last):
File "./download.py", line 9, in <module>
dataset = nlp.load_dataset('wmt16', 'ru-en')
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 449, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/stas/anaconda3/envs/main/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/home/stas/anaconda3/envs/main/lib/python3.7/os.py", line 221, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/stas/.cache/huggingface/datasets/wmt16/ru-en/1.0.0/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete'
```
it can't handle resumes. but neither allows a new start. Had to delete it manually.
4. and finally when it downloaded the dataset, it then failed to fetch the metrics:
```
Traceback (most recent call last):
File "./download.py", line 15, in <module>
metric = nlp.load_metric('wmt16')
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 442, in load_metric
module_path, hash = prepare_module(path, download_config=download_config, dataset=False)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 258, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 198, in cached_path
local_files_only=download_config.local_files_only,
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 356, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/metrics/wmt16/wmt16.py
```
5. If I run the same code with `wmt19`, it fails too:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz
``` | {
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"I found `UNv1.0.en-ru.tar.gz` here: https://conferences.unite.un.org/uncorpus/en/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02\r\ncat UNv1.0.en-ru.tar.gz.0* > UNv1.0.en-ru.tar.gz\r\n```\r\nit has other languages as well, in case https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/ is gone",
"Further, `nlp.load_dataset('wmt19', 'ru-en')` has only the `train` and `val` datasets. `test` is missing.\r\n\r\nFixed locally for summarization needs, by running:\r\n```\r\npip install sacrebleu\r\nsacrebleu -t wmt19 -l ru-en --echo src > test.source\r\nsacrebleu -t wmt19 -l ru-en --echo ref > test.target\r\n```\r\nh/t @sshleifer ",
"Fixed in https://github.com/huggingface/datasets/pull/1912"
] |
https://api.github.com/repos/huggingface/datasets/issues/1760 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1760/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1760/comments | https://api.github.com/repos/huggingface/datasets/issues/1760/events | https://github.com/huggingface/datasets/pull/1760 | 791,110,857 | MDExOlB1bGxSZXF1ZXN0NTU5MjE3MjY0 | 1,760 | More tags | [] | closed | false | null | 2 | 2021-01-21T13:50:10Z | 2021-01-22T09:40:01Z | 2021-01-22T09:40:00Z | null | Since the hub v2 is going to be released soon I figured it would be great to add the missing tags at least for some of the datasets of reference listed [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#write-the-loadingprocessing-code) | {
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"Conll has `multilingual` but is only tagged as `en`",
"good catch, that was a bad copy paste x)"
] |
https://api.github.com/repos/huggingface/datasets/issues/1634 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1634/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1634/comments | https://api.github.com/repos/huggingface/datasets/issues/1634/events | https://github.com/huggingface/datasets/issues/1634 | 774,487,934 | MDU6SXNzdWU3NzQ0ODc5MzQ= | 1,634 | Inspecting datasets per category | [] | closed | false | null | 4 | 2020-12-24T15:26:34Z | 2022-10-04T14:57:33Z | 2022-10-04T14:57:33Z | null | Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq | {
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"That's interesting, can you tell me what you think would be useful to access to inspect a dataset?\r\n\r\nYou can filter them in the hub with the search by the way: https://huggingface.co/datasets have you seen it?",
"Hi @thomwolf \r\nthank you, I was not aware of this, I was looking into the data viewer linked into readme page. \r\n\r\nThis is exactly what I was looking for, but this does not work currently, please see the attached \r\nI am selecting to see all nli datasets in english and it retrieves none. thanks\r\n\r\n\r\n\r\n\r\n\r\n",
"I see 4 results for NLI in English but indeed some are not tagged yet and missing (GLUE), we will focus on that in January (cc @yjernite): https://huggingface.co/datasets?filter=task_ids:natural-language-inference,languages:en",
"Hi! You can use `huggingface_hub`'s `list_datasets` for that now:\r\n```python\r\nimport huggingface_hub # pip install huggingface_hub\r\nhuggingface_hub.list_datasets(filter=\"task_categories:question-answering\")\r\n# or\r\nhuggingface_hub.list_datasets(filter=(\"task_categories:natural-language-inference\", \"languages:\"en\"))\r\n```"
] |
https://api.github.com/repos/huggingface/datasets/issues/2411 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2411/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2411/comments | https://api.github.com/repos/huggingface/datasets/issues/2411/events | https://github.com/huggingface/datasets/pull/2411 | 903,671,778 | MDExOlB1bGxSZXF1ZXN0NjU0OTAzNjg2 | 2,411 | Add DOI badge to README | [] | closed | false | null | 0 | 2021-05-27T12:36:47Z | 2021-05-27T13:42:54Z | 2021-05-27T13:42:54Z | null | Once published the latest release, the DOI badge has been automatically generated by Zenodo. | {
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https://api.github.com/repos/huggingface/datasets/issues/4737 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4737/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4737/comments | https://api.github.com/repos/huggingface/datasets/issues/4737/events | https://github.com/huggingface/datasets/issues/4737 | 1,315,011,004 | I_kwDODunzps5OYXm8 | 4,737 | Download error on scene_parse_150 | [
{
"color": "d73a4a",
"default": true,
"description": "Something isn't working",
"id": 1935892857,
"name": "bug",
"node_id": "MDU6TGFiZWwxOTM1ODkyODU3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/bug"
}
] | closed | false | null | 2 | 2022-07-22T13:28:28Z | 2022-09-01T15:37:11Z | 2022-09-01T15:37:11Z | null | ```
from datasets import load_dataset
dataset = load_dataset("scene_parse_150", "scene_parsing")
FileNotFoundError: Couldn't find file at http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
```
| {
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"Hi! The server with the data seems to be down. I've reported this issue (https://github.com/CSAILVision/sceneparsing/issues/34) in the dataset repo. ",
"The URL seems to work now, and therefore the script as well."
] |
https://api.github.com/repos/huggingface/datasets/issues/5065 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5065/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5065/comments | https://api.github.com/repos/huggingface/datasets/issues/5065/events | https://github.com/huggingface/datasets/pull/5065 | 1,396,003,362 | PR_kwDODunzps5AHxlQ | 5,065 | Ci py3.10 | [] | closed | false | null | 2 | 2022-10-04T10:13:51Z | 2022-11-29T15:28:05Z | 2022-11-29T15:25:26Z | null | Added a CI job for python 3.10
Some dependencies don't work on 3.10 like apache beam, so I remove them from the extras in this case.
I also removed some s3 fixtures that we don't use anymore (and that don't work on 3.10 anyway) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Does it sound good to you @albertvillanova ?"
] |
https://api.github.com/repos/huggingface/datasets/issues/2074 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2074/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2074/comments | https://api.github.com/repos/huggingface/datasets/issues/2074/events | https://github.com/huggingface/datasets/pull/2074 | 834,268,463 | MDExOlB1bGxSZXF1ZXN0NTk1MTIzMjYw | 2,074 | Fix size categories in YAML Tags | [] | closed | false | null | 9 | 2021-03-18T00:02:36Z | 2021-03-23T17:11:10Z | 2021-03-23T17:11:10Z | null | This PR fixes several `size_categories` in YAML tags and makes them consistent. Additionally, I have added a few more categories after `1M`, up to `1T`. I would like to add that to the streamlit app also.
This PR also adds a couple of infos that I found missing.
The code for generating this:
```python
for dataset in sorted(os.listdir('./datasets/')):
if '.' not in dataset and dataset not in ['c4', 'csv', 'downloads', 'cc100', 'ccaligned_multilingual', 'celeb_a', 'chr_en', 'emea', 'glue']:
infos = {}
stats = {}
st = ''
with open(f'datasets/{dataset}/README.md') as f:
d = f.read()
start_dash = d.find('---') + 3
end_dash = d[start_dash:].find('---') + 3
rest_text = d[end_dash + 3:]
try:
full_yaml = OmegaConf.create(d[start_dash:end_dash])
readme = OmegaConf.to_container(full_yaml['size_categories'], resolve=True)
except Exception as e:
print(e)
continue
try:
with open(f'datasets/{dataset}/dataset_infos.json') as f:
data = json.load(f)
except Exception as e:
print(e)
continue # Skip those without infos.
done_set = set([])
num_keys = len(data.keys())
for keys in data:
# dataset = load_dataset('opus100', f'{dirs}')
total = 0
for split in data[keys]['splits']:
total = total + data[keys]['splits'][split]['num_examples']
if total < 1000:
st += "- n<1K" + '\n'
infos[keys] = ["n<1K"]
elif total >= 1000 and total < 10000:
infos[keys] = ["1K<n<10K"]
elif total >= 10000 and total < 100000:
infos[keys] = ["10K<n<100K"]
elif total >= 100000 and total < 1000000:
infos[keys] = ["100K<n<1M"]
elif total >= 1000000 and total < 10000000:
infos[keys] = ["1M<n<10M"]
elif total >= 10000000 and total < 100000000:
infos[keys] = ["10M<n<100M"]
elif total >= 100000000 and total < 1000000000:
infos[keys] = ["100M<n<1B"]
elif total >= 1000000000 and total < 10000000000:
infos[keys] = ["1B<n<10B"]
elif total >= 10000000000 and total < 100000000000:
infos[keys] = ["10B<n<100B"]
elif total >= 100000000000 and total < 1000000000000:
infos[keys] = ["100B<n<1T"]
else:
infos[keys] = ["n>1T"]
done_set = done_set.union(infos[keys])
if (isinstance(readme, list) and list(infos.values())[0] != readme) or (isinstance(readme, dict) and readme != infos):
print('-' * 30)
print(done_set)
print(f"Changing Full YAML for {dataset}")
print(OmegaConf.to_yaml(full_yaml))
if len(done_set) == 1:
full_yaml['size_categories'] = list(done_set)
else:
full_yaml['size_categories'] = dict([(k, v) for k, v in sorted(infos.items(), key=lambda x: x[0])])
full_yaml_string = OmegaConf.to_yaml(full_yaml)
print('-' * 30)
print(full_yaml_string)
inp = input('Do you wish to continue?(Y/N)')
if inp == 'Y':
with open(f'./datasets/{dataset}/README.md', 'w') as f:
f.write('---\n')
f.write(full_yaml_string)
f.write('---')
f.write(rest_text)
else:
break
```
Note that the lower-bound is inclusive. I'm unsure if this is how it is done in the tagging app.
EDIT:
It would be great if there was a way to make the task categories consistent too. For this, the streamlit app can look into all the datasets and check for existing categories and show them in the list. This may add some consistency.
EDIT:
I understand this will not work for cases where only the infos for some of the configs are present, for example: `ccaligned_multingual` has only 5 out of several configs present, and infos has only information about them. Hence, I have skipped a few datasets in the code, if there are more such datasets, then I'll ignore them too. | {
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"> It would be great if there was a way to make the task categories consistent too. For this, the streamlit app can look into all the datasets and check for existing categories and show them in the list. This may add some consistency.\r\n\r\nWe can also update the task lists here: https://github.com/huggingface/datasets-tagging/blob/main/task_set.json",
"Hi @lhoestq,\r\n\r\nThanks for approving.\r\nHow do I add the new categories to the tagging app? What I have added is till `1T` and not `1M`.\r\n\r\nI'll also check the task list :)\r\n\r\nThanks,\r\nGunjan",
"I think you can change it here: https://github.com/huggingface/datasets-tagging/blob/main/tagging_app.py#L412-L423",
"Hi @lhoestq,\r\n\r\nI have made a PR for size categories on `datasets-tagging`\r\n\r\nFor tags, I have thought of adding more tags and categories, based on what I know about the existing datasets, any list will not be exhaustive because the contributors can be very specific or very general. Hence, there could be a continuous process of evaluating existing tags and adding more and more.\r\n\r\n```json\r\n{\r\n \"image-classification\": {\r\n \"description\": \"image classification tasks\",\r\n \"options\": [\r\n \"multi-class-classification\",\r\n \"multi-label-classification\",\r\n \"other\"\r\n ]\r\n },\r\n \"conditional-text-generation\": {\r\n \"description\": \"data-to-text and text transduction tasks such as translation or summarization\",\r\n \"options\": [\r\n \"machine-translation\",\r\n \"sentence-splitting-fusion\",\r\n \"extractive-and-abstractive-summarization\",\r\n \"abstractive-summarization\",\r\n \"extractive-summarization\",\r\n \"multi-document-summarization\",\r\n \"table-to-text\",\r\n \"text-simplification\",\r\n \"explanation-generation\",\r\n \"stuctured-to-text\",\r\n \"other\"\r\n ]\r\n },\r\n \"conditional-speech-generation\": {\r\n \"description\": \"speech generation tasks\",\r\n \"options\": [\r\n \"text-to-speech\",\r\n \"speech-translation\",\r\n \"other\"\r\n ]\r\n },\r\n\r\n \"conditional-structure-generation\":{\r\n \"description\": \"text or speech to structured data\",\r\n \"options\":[\r\n \"knowlege-graph-mining\",\r\n \"code-generation\",\r\n ]\r\n },\r\n \"question-answering\": {\r\n \"description\": \"question answering tasks\",\r\n \"options\": [\r\n \"open-domain-qa\",\r\n \"closed-domain-qa\",\r\n \"multiple-choice-qa\",\r\n \"extractive-qa\",\r\n \"abstractive-qa\",\r\n \"conversational-qa\",\r\n \"multi-document-qa\",\r\n \"other\"\r\n ]\r\n },\r\n \"speech-classification\": {\r\n \"description\": \"speech to label tasks\",\r\n \"options\": [\r\n \"other\"\r\n ]\r\n },\r\n \"sequence-modeling\": {\r\n \"description\": \"such as language, speech or dialogue modeling\",\r\n \"options\": [\r\n \"dialogue-modeling\",\r\n \"language-modeling\",\r\n \"speech-modeling\",\r\n \"multi-turn\",\r\n \"slot-filling\",\r\n \"other\"\r\n ]\r\n },\r\n \"speech-recognition\": {\r\n \"description\": \"speech to text tasks\",\r\n \"options\": [\r\n \"automatic-speech-recognition\",\r\n \"other\"\r\n ]\r\n },\r\n \"structure-prediction\": {\r\n \"description\": \"predicting structural properties of the text, such as syntax\",\r\n \"options\": [\r\n \"coreference-resolution\",\r\n \"named-entity-recognition\",\r\n \"part-of-speech-tagging\",\r\n \"parsing\",\r\n \"sentence-segmentation\",\r\n \"single-span-prediction\",\r\n \"multi-span-prediction\",\r\n \"clause-or-phrase-segmentation\",\r\n \"dependency-parsing\",\r\n \"constituency-parsing\",\r\n \"other\"\r\n ]\r\n },\r\n\r\n \"text-classification\": {\r\n \"description\": \"predicting a class index or boolean value\",\r\n \"options\": [\r\n \"acceptability-classification\",\r\n \"entity-linking-classification\",\r\n \"relation-extraction\",\r\n \"common-sense-reasoning\",\r\n \"fact-checking\",\r\n \"intent-classification\",\r\n \"multi-class-classification\",\r\n \"multi-label-classification\",\r\n \"natural-language-inference\",\r\n \"semantic-similarity-classification\",\r\n \"sentiment-classification\",\r\n \"topic-classification\",\r\n \"emotion-classification\",\r\n \"token-classification\",\r\n \"word-sense-disambiguation\",\r\n \"offense-classification\",\r\n \"hate-speech-classification\",\r\n \"language-classification\",\r\n \"bias-classification\",\r\n \"other\"\r\n ]\r\n },\r\n \"text-retrieval\": {\r\n \"description\": \"information or text retrieval tasks\",\r\n \"options\": [\r\n \"document-retrieval\",\r\n \"utterance-retrieval\",\r\n \"entity-linking-retrieval\",\r\n \"fact-checking-retrieval\",\r\n \"other\"\r\n ]\r\n },\r\n \"text-scoring\": {\r\n \"description\": \"text scoring tasks, predicting a real valued score for some text\",\r\n \"options\": [\r\n \"semantic-similarity-scoring\",\r\n \"sentiment-scoring\",\r\n \"other\"\r\n ]\r\n },\r\n \"other\": {\r\n \"description\": \"raw data or other task families\",\r\n \"options\": [\r\n \"data-mining\",\r\n \"raw-text\",\r\n \"raw-speech\",\r\n \"raw-image\",\r\n \"other\"\r\n ]\r\n }\r\n}\r\n```\r\nI'll sort this when adding it to the .json. Also, I'll change categories according to this if this seems okay to you and commit it to this PR.\r\n\r\nI'll also fix spelling others, and some categories which are partially correct, for e.g. `other-machine-translation` to the correct tag.\r\nLastly, with the options also we can add a description to make it easier for the users to understand what we mean by each option. Example, for \"emotion-classification\", we can explain what kinds of data we are talking about, or what we mean by \"single-span-prediction\", etc.",
"Good idea thank you ! Can you open a PR on datasets-tagging for the tasks as well ?\r\nAlso you can update the dataset card with the new tasks categories in another PR if you don't mind",
"Hi @lhoestq,\r\n\r\nThanks, what all do I need to add to merge this PR?",
"We can merge this one once the PR on dataset sizes is merged on `datasets-tagging` ;)",
"Hi @lhoestq,\r\n\r\nOne problem with this approach is that for datasets like `ccaligned_multilingual`, the infos won't be complete because we don't have all configs. In that case, people might face trouble finding the datatset using the tag. Although, they probably won't be checking the size tag for a dataset like that.\r\n\r\nWhat do you think?\r\n\r\nCC @theo-m ",
"For datasets like `ccaligned_multilingual` it's important to have all the tags for users to search and find it. Currently is has the full list of tags (without the config names). So you can actually find the dataset, but you don't know what tag correspond to what configuration. "
] |
https://api.github.com/repos/huggingface/datasets/issues/5197 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5197/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5197/comments | https://api.github.com/repos/huggingface/datasets/issues/5197/events | https://github.com/huggingface/datasets/pull/5197 | 1,434,676,150 | PR_kwDODunzps5CI0Ac | 5,197 | [zstd] Use max window log size | [] | open | false | null | 2 | 2022-11-03T13:35:58Z | 2022-11-03T13:45:19Z | null | null | ZstdDecompressor has a parameter `max_window_size` to limit max memory usage when decompressing zstd files. The default `max_window_size` is not enough when files are compressed by `zstd --ultra` flags.
Change `max_window_size` to the zstd's max window size. NOTE, the `zstd.WINDOWLOG_MAX` is the log_2 value of the max window size. | {
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"@albertvillanova Please take a review.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5197). All of your documentation changes will be reflected on that endpoint."
] |
https://api.github.com/repos/huggingface/datasets/issues/5297 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5297/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5297/comments | https://api.github.com/repos/huggingface/datasets/issues/5297/events | https://github.com/huggingface/datasets/pull/5297 | 1,464,554,491 | PR_kwDODunzps5DtZjg | 5,297 | Fix xjoin for Windows pathnames | [] | closed | false | null | 1 | 2022-11-25T13:30:17Z | 2022-11-29T08:07:39Z | 2022-11-29T08:05:12Z | null | This PR fixes a bug in `xjoin` function with Windows pathnames.
Fix #5296. | {
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https://api.github.com/repos/huggingface/datasets/issues/1873 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1873/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1873/comments | https://api.github.com/repos/huggingface/datasets/issues/1873/events | https://github.com/huggingface/datasets/pull/1873 | 807,750,745 | MDExOlB1bGxSZXF1ZXN0NTcyOTM4MTYy | 1,873 | add iapp_wiki_qa_squad | [] | closed | false | null | 0 | 2021-02-13T13:34:27Z | 2021-02-16T14:21:58Z | 2021-02-16T14:21:58Z | null | `iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles.
It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset)
to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in
5761/742/739 questions from 1529/191/192 articles. | {
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https://api.github.com/repos/huggingface/datasets/issues/2034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2034/comments | https://api.github.com/repos/huggingface/datasets/issues/2034/events | https://github.com/huggingface/datasets/pull/2034 | 829,381,388 | MDExOlB1bGxSZXF1ZXN0NTkxMDU2MTEw | 2,034 | Fix typo | [] | closed | false | null | 0 | 2021-03-11T17:46:13Z | 2021-03-11T18:06:25Z | 2021-03-11T18:06:25Z | null | Change `ENV_XDG_CACHE_HOME ` to `XDG_CACHE_HOME ` | {
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https://api.github.com/repos/huggingface/datasets/issues/5225 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5225/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5225/comments | https://api.github.com/repos/huggingface/datasets/issues/5225/events | https://github.com/huggingface/datasets/issues/5225 | 1,444,305,183 | I_kwDODunzps5WFlkf | 5,225 | Add video feature | [
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] | open | false | null | 7 | 2022-11-10T17:36:11Z | 2022-12-02T15:13:15Z | null | null | ### Feature request
Add a `Video` feature to the library so folks can include videos in their datasets.
### Motivation
Being able to load Video data would be quite helpful. However, there are some challenges when it comes to videos:
1. Videos, unlike images, can end up being extremely large files
2. Often times when training video models, you need to do some very specific sampling. Videos might end up needing to be broken down into X number of clips used for training/inference
3. Videos have an additional audio stream, which must be accounted for
4. The feature needs to be able to encode/decode videos (with right video settings) from bytes.
### Your contribution
I did work on this a while back in [this (now closed) PR](https://github.com/huggingface/datasets/pull/4532). It used a library I made called [encoded_video](https://github.com/nateraw/encoded-video), which is basically the utils from [pytorchvideo](https://github.com/facebookresearch/pytorchvideo), but without the `torch` dep. It included the ability to read/write from bytes, as we need to do here. We don't want to be using a sketchy library that I made as a dependency in this repo, though.
Would love to use this issue as a place to:
- brainstorm ideas on how to do this right
- list ways/examples to work around it for now
CC @sayakpaul @mariosasko @fcakyon | {
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"@NielsRogge @rwightman may have additional requirements regarding this feature.\r\n\r\nWhen adding a new (decodable) type, the hardest part is choosing the right decoding library. What I mean by \"right\" here is that it has all the features we need and is easy to install (with GPU support?).\r\n\r\nSome candidates/options:\r\n* [`decord`](https://github.com/dmlc/decord): no longer [maintained](https://github.com/dmlc/decord/issues/214), not trivial to install with GPU support\r\n* [`pyAV`](https://github.com/PyAV-Org/PyAV): used for CPU decoding in `torchvision`, GPU decoding not supported if I'm not mistaken, otherwise the best candidate probably\r\n* [`video_reader`](https://github.com/pytorch/vision/blob/de350bc01ad2193ea2888f0ce8a6a346d3cba5a9/torchvision/csrc/io/video_reader/video_reader.cpp): used for GPU decoding in `torchvision`, depends on `torch'\r\n* OpenCV: uses `ffmpeg` for video decoding under the hood\r\n* ...\r\n\r\nAnd the last resort is building our own library, which is the most flexible solution but also requires the most work.\r\n\r\nPS: I'm adding a link to an article that compares various video decoding libraries: https://towardsdatascience.com/lightning-fast-video-reading-in-python-c1438771c4e6",
"@mariosasko is GPU decoding a hard requirement here? Do we really need it? (I don't know)\r\n\r\nSomething to consider with `decord` is that it doesn't (AFAIK) support writing videos, so you'd still need something else for that. also I've noticed [issues](https://github.com/dmlc/decord/issues/242) with decord's ability to decode stereo audio streams along side the video (which you don't run into with PyAV).\r\n\r\n---\r\n\r\nI think PyAV should be able to do the job just fine to start. If we write the video io utilities as their own functions, we can hot swap them later if we find/write a different solution that's faster/better.",
"Video is still a bit of a mess, but I'd say pyAV is likely the best approach (or supporting all three via pytorchvideo, but that adds a middle man dependency).\r\n\r\nBeing able to decode on the GPU, into memory that could be passed off to a Tensor in whatever framework is being used would be the dream, I don't think there is any interop of that nature working right now. Number of decoder instances per GPU is limited so it's not clear if balancing load btw GPU decoders and CPUs would be needed in say large scale video training.\r\n\r\nAny of these solutions is less than ideal due to the nature of video, having a simple Python interface video / start -> end results in lots of extra memory (you need to decode whole range of the clips into a buffer before using anything). Any scalable video system would be streaming on the fly (issuing frames via callbacks as soon as the stream is far enough along to have re-ordered the frames and synced audio+video+other metadata (sensors, CC, etc).\r\n\r\n",
"For standalone usage, decoding on GPU could be ideal but isn't async processing of inputs on CPUs while letting the accelerator busy for training the de-facto? Of course, I am aware of other advanced mechanisms such as CPU offloading, but I think my point is conveyed. ",
"Here's a minimal implementation of the helper functions we'd need from PyAV, a lot of which I borrowed from `pytorchvideo`, stripping out the `torch` specific stuff:\r\n\r\n[](https://colab.research.google.com/gist/nateraw/c327cb6ff6b074e6ddc8068d19c0367d/pyav-io.ipynb)\r\n \r\nIt's not too much code...@mariosasko we could probably just maintain these helper fns within the `datasets` library, right? ",
"Also wanted to note I added a PR for video classification in `transformers` here, which uses `decord`. It's still open...should we make a decision now to align the libraries we are using between `datasets` and `transformers`? (CC @Narsil )\r\n\r\nhttps://github.com/huggingface/transformers/pull/20151",
"Fully agree on at least trying to unite things.\r\n\r\nMaking clear function boundaries to help us change dependency if needed seems like a good idea since there doesn't seem to be a clear winner.\r\n\r\nI also happen to like directly calling ffmpeg. For some reason it was a lot faster than pyav. "
] |
https://api.github.com/repos/huggingface/datasets/issues/1090 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1090/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1090/comments | https://api.github.com/repos/huggingface/datasets/issues/1090/events | https://github.com/huggingface/datasets/pull/1090 | 756,825,941 | MDExOlB1bGxSZXF1ZXN0NTMyMzA1OTk1 | 1,090 | add thaisum | [] | closed | false | null | 0 | 2020-12-04T05:54:48Z | 2020-12-04T11:16:06Z | 2020-12-04T11:16:06Z | null | ThaiSum, a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs written by journalists. We evaluate the performance of various existing summarization models on ThaiSum dataset and analyse the characteristic of the dataset to present its difficulties. | {
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https://api.github.com/repos/huggingface/datasets/issues/1725 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1725/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1725/comments | https://api.github.com/repos/huggingface/datasets/issues/1725/events | https://github.com/huggingface/datasets/issues/1725 | 784,182,273 | MDU6SXNzdWU3ODQxODIyNzM= | 1,725 | load the local dataset | [] | closed | false | null | 7 | 2021-01-12T12:12:55Z | 2022-06-01T16:00:59Z | 2022-06-01T16:00:59Z | null | your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot! | {
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"You should rephrase your question or give more examples and details on what you want to do.\r\n\r\nit’s not possible to understand it and help you with only this information.",
"sorry for that.\r\ni want to know how could i load the train set and the test set from the local ,which api or function should i use .\r\n",
"Did you try to follow the instructions in the documentation?\r\nHere: https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files",
"thanks a lot \r\ni find that the problem is i dont use vpn...\r\nso i have to keep my net work even if i want to load the local data ?",
"We will solve this soon (cf #1724)",
"thanks a lot",
"Hi! `json` is a packaged dataset now, which means its script comes with the library and doesn't require an internet connection."
] |
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006038 / 0.011353 (-0.005315) | 0.003797 / 0.011008 (-0.007211) | 0.097686 / 0.038508 (0.059178) | 0.035235 / 0.023109 (0.012126) | 0.317294 / 0.275898 (0.041396) | 0.377682 / 0.323480 (0.054202) | 0.003485 / 0.007986 (-0.004501) | 0.003603 / 0.004328 (-0.000725) | 0.077268 / 0.004250 (0.073017) | 0.054649 / 0.037052 (0.017597) | 0.322293 / 0.258489 (0.063804) | 0.372277 / 0.293841 (0.078436) | 0.027927 / 0.128546 (-0.100619) | 0.008495 / 0.075646 (-0.067151) | 0.313078 / 0.419271 (-0.106193) | 0.046974 / 0.043533 (0.003441) | 0.313848 / 0.255139 (0.058709) | 0.338454 / 0.283200 (0.055255) | 0.020462 / 0.141683 (-0.121221) | 1.473027 / 1.452155 (0.020873) | 1.539468 / 1.492716 (0.046752) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221429 / 0.018006 (0.203423) | 0.412044 / 0.000490 (0.411555) | 0.005866 / 0.000200 (0.005666) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022870 / 0.037411 (-0.014541) | 0.099129 / 0.014526 (0.084603) | 0.103463 / 0.176557 (-0.073094) | 0.164969 / 0.737135 (-0.572166) | 0.110000 / 0.296338 (-0.186339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431311 / 0.215209 (0.216102) | 4.293562 / 2.077655 (2.215907) | 1.961209 / 1.504120 (0.457089) | 1.733680 / 1.541195 (0.192485) | 1.793171 / 1.468490 (0.324681) | 0.568566 / 4.584777 (-4.016211) | 3.401794 / 3.745712 (-0.343918) | 1.827949 / 5.269862 (-3.441913) | 1.055963 / 4.565676 (-3.509714) | 0.068459 / 0.424275 (-0.355816) | 0.011586 / 0.007607 (0.003979) | 0.533936 / 0.226044 (0.307891) | 5.347637 / 2.268929 (3.078708) | 2.378056 / 55.444624 (-53.066569) | 2.032159 / 6.876477 (-4.844318) | 2.159064 / 2.142072 (0.016991) | 0.674528 / 4.805227 (-4.130699) | 0.136859 / 6.500664 (-6.363805) | 0.066629 / 0.075469 (-0.008840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.218084 / 1.841788 (-0.623704) | 14.141710 / 8.074308 (6.067402) | 13.588415 / 10.191392 (3.397023) | 0.155104 / 0.680424 (-0.525320) | 0.017160 / 0.534201 (-0.517041) | 0.375558 / 0.579283 (-0.203725) | 0.386293 / 0.434364 (-0.048071) | 0.459476 / 0.540337 (-0.080862) | 0.548561 / 1.386936 (-0.838375) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005878 / 0.011353 (-0.005475) | 0.003750 / 0.011008 (-0.007259) | 0.077720 / 0.038508 (0.039212) | 0.034955 / 0.023109 (0.011846) | 0.357480 / 0.275898 (0.081582) | 0.418210 / 0.323480 (0.094730) | 0.004566 / 0.007986 (-0.003419) | 0.002918 / 0.004328 (-0.001410) | 0.076517 / 0.004250 (0.072266) | 0.050202 / 0.037052 (0.013150) | 0.368166 / 0.258489 (0.109677) | 0.415681 / 0.293841 (0.121840) | 0.029496 / 0.128546 (-0.099050) | 0.008547 / 0.075646 (-0.067099) | 0.083037 / 0.419271 (-0.336234) | 0.045001 / 0.043533 (0.001468) | 0.356503 / 0.255139 (0.101364) | 0.383747 / 0.283200 (0.100547) | 0.025071 / 0.141683 (-0.116612) | 1.541985 / 1.452155 (0.089830) | 1.594710 / 1.492716 (0.101994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204491 / 0.018006 (0.186484) | 0.408686 / 0.000490 (0.408196) | 0.002505 / 0.000200 (0.002305) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024446 / 0.037411 (-0.012965) | 0.101432 / 0.014526 (0.086906) | 0.108105 / 0.176557 (-0.068452) | 0.161195 / 0.737135 (-0.575940) | 0.112671 / 0.296338 (-0.183667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459697 / 0.215209 (0.244488) | 4.570071 / 2.077655 (2.492416) | 2.211547 / 1.504120 (0.707427) | 1.996651 / 1.541195 (0.455457) | 2.015621 / 1.468490 (0.547131) | 0.567423 / 4.584777 (-4.017354) | 3.408027 / 3.745712 (-0.337685) | 2.913824 / 5.269862 (-2.356038) | 1.423223 / 4.565676 (-3.142453) | 0.068740 / 0.424275 (-0.355535) | 0.010997 / 0.007607 (0.003390) | 0.567340 / 0.226044 (0.341296) | 5.666280 / 2.268929 (3.397351) | 2.804934 / 55.444624 (-52.639690) | 2.430761 / 6.876477 (-4.445716) | 2.451820 / 2.142072 (0.309748) | 0.681926 / 4.805227 (-4.123301) | 0.137761 / 6.500664 (-6.362903) | 0.067173 / 0.075469 (-0.008296) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329853 / 1.841788 (-0.511934) | 14.436232 / 8.074308 (6.361924) | 14.398645 / 10.191392 (4.207253) | 0.147421 / 0.680424 (-0.533002) | 0.016743 / 0.534201 (-0.517458) | 0.364964 / 0.579283 (-0.214319) | 0.387072 / 0.434364 (-0.047292) | 0.423892 / 0.540337 (-0.116445) | 0.521304 / 1.386936 (-0.865632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a62b6ce65f718e9ff4189da86d160ae4bb197fc2 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006463 / 0.011353 (-0.004889) | 0.003923 / 0.011008 (-0.007086) | 0.102096 / 0.038508 (0.063588) | 0.040230 / 0.023109 (0.017121) | 0.384688 / 0.275898 (0.108789) | 0.445574 / 0.323480 (0.122094) | 0.003590 / 0.007986 (-0.004395) | 0.004023 / 0.004328 (-0.000306) | 0.080125 / 0.004250 (0.075875) | 0.057406 / 0.037052 (0.020354) | 0.395049 / 0.258489 (0.136560) | 0.438065 / 0.293841 (0.144224) | 0.028963 / 0.128546 (-0.099583) | 0.008693 / 0.075646 (-0.066954) | 0.317158 / 0.419271 (-0.102114) | 0.047930 / 0.043533 (0.004397) | 0.382442 / 0.255139 (0.127303) | 0.410665 / 0.283200 (0.127466) | 0.020127 / 0.141683 (-0.121555) | 1.558554 / 1.452155 (0.106400) | 1.590959 / 1.492716 (0.098242) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208826 / 0.018006 (0.190820) | 0.432037 / 0.000490 (0.431547) | 0.006509 / 0.000200 (0.006309) | 0.000285 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023460 / 0.037411 (-0.013951) | 0.099070 / 0.014526 (0.084545) | 0.105771 / 0.176557 (-0.070785) | 0.166683 / 0.737135 (-0.570452) | 0.108755 / 0.296338 (-0.187583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424324 / 0.215209 (0.209115) | 4.225696 / 2.077655 (2.148042) | 1.910955 / 1.504120 (0.406835) | 1.704493 / 1.541195 (0.163298) | 1.782784 / 1.468490 (0.314293) | 0.562927 / 4.584777 (-4.021850) | 3.380163 / 3.745712 (-0.365550) | 1.779641 / 5.269862 (-3.490221) | 1.029134 / 4.565676 (-3.536543) | 0.068325 / 0.424275 (-0.355950) | 0.011528 / 0.007607 (0.003921) | 0.530141 / 0.226044 (0.304097) | 5.323443 / 2.268929 (3.054514) | 2.346956 / 55.444624 (-53.097668) | 2.013335 / 6.876477 (-4.863142) | 2.118531 / 2.142072 (-0.023541) | 0.675206 / 4.805227 (-4.130021) | 0.135473 / 6.500664 (-6.365191) | 0.064804 / 0.075469 (-0.010665) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240179 / 1.841788 (-0.601608) | 14.692449 / 8.074308 (6.618141) | 13.672223 / 10.191392 (3.480831) | 0.147748 / 0.680424 (-0.532676) | 0.017119 / 0.534201 (-0.517082) | 0.369481 / 0.579283 (-0.209802) | 0.390133 / 0.434364 (-0.044231) | 0.458768 / 0.540337 (-0.081569) | 0.548989 / 1.386936 (-0.837947) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006319 / 0.011353 (-0.005034) | 0.003975 / 0.011008 (-0.007033) | 0.077886 / 0.038508 (0.039378) | 0.038322 / 0.023109 (0.015213) | 0.379851 / 0.275898 (0.103953) | 0.456749 / 0.323480 (0.133269) | 0.005320 / 0.007986 (-0.002665) | 0.003135 / 0.004328 (-0.001194) | 0.078272 / 0.004250 (0.074022) | 0.059919 / 0.037052 (0.022866) | 0.430062 / 0.258489 (0.171573) | 0.477432 / 0.293841 (0.183591) | 0.029713 / 0.128546 (-0.098833) | 0.008704 / 0.075646 (-0.066942) | 0.082488 / 0.419271 (-0.336784) | 0.044667 / 0.043533 (0.001134) | 0.354910 / 0.255139 (0.099771) | 0.434637 / 0.283200 (0.151438) | 0.026402 / 0.141683 (-0.115281) | 1.528825 / 1.452155 (0.076671) | 1.548209 / 1.492716 (0.055493) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237988 / 0.018006 (0.219982) | 0.420402 / 0.000490 (0.419913) | 0.003098 / 0.000200 (0.002898) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026253 / 0.037411 (-0.011159) | 0.106137 / 0.014526 (0.091611) | 0.110273 / 0.176557 (-0.066284) | 0.165316 / 0.737135 (-0.571819) | 0.115720 / 0.296338 (-0.180619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454244 / 0.215209 (0.239035) | 4.526018 / 2.077655 (2.448364) | 2.395985 / 1.504120 (0.891865) | 2.234822 / 1.541195 (0.693627) | 2.370235 / 1.468490 (0.901745) | 0.567607 / 4.584777 (-4.017169) | 3.650156 / 3.745712 (-0.095556) | 3.360094 / 5.269862 (-1.909768) | 1.415252 / 4.565676 (-3.150424) | 0.068012 / 0.424275 (-0.356263) | 0.011135 / 0.007607 (0.003528) | 0.561967 / 0.226044 (0.335923) | 5.621819 / 2.268929 (3.352890) | 2.676912 / 55.444624 (-52.767712) | 2.338306 / 6.876477 (-4.538171) | 2.430888 / 2.142072 (0.288815) | 0.684576 / 4.805227 (-4.120651) | 0.138923 / 6.500664 (-6.361741) | 0.069933 / 0.075469 (-0.005536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313383 / 1.841788 (-0.528405) | 15.125088 / 8.074308 (7.050780) | 14.801501 / 10.191392 (4.610109) | 0.134235 / 0.680424 (-0.546189) | 0.017058 / 0.534201 (-0.517143) | 0.365166 / 0.579283 (-0.214117) | 0.395415 / 0.434364 (-0.038949) | 0.419355 / 0.540337 (-0.120983) | 0.513411 / 1.386936 (-0.873525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b9649b3cfb49342e44873ce7e29e0c75eaf3efa \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/5343 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5343/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5343/comments | https://api.github.com/repos/huggingface/datasets/issues/5343/events | https://github.com/huggingface/datasets/issues/5343 | 1,485,297,823 | I_kwDODunzps5Yh9if | 5,343 | T5 for Q&A produces truncated sentence | [] | closed | false | null | 0 | 2022-12-08T19:48:46Z | 2022-12-08T19:57:17Z | 2022-12-08T19:57:17Z | null | Dear all, I am fine-tuning T5 for Q&A task using the MedQuAD ([GitHub - abachaa/MedQuAD: Medical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites](https://github.com/abachaa/MedQuAD)) dataset. In the dataset, there are many long answers with thousands of words. I have used pytorch_lightning to train the T5-large model. I have two questions.
For example, I set both the max_length, max_input_length, max_output_length to 128.
How to deal with those long answers? I just left them as is and the T5Tokenizer can automatically handle. I would assume the tokenizer just truncates an answer at the position of 128th word (or 127th). Is it possible that I manually split an answer into different parts, each part has 128 words; and then all these sub-answers serve as a separate answer to the same question?
Another question is that I get incomplete (truncated) answers when using the fine-tuned model in inference, even though the predicted answer is shorter than 128 words. I found a message posted 2 years ago saying that one should add at the end of texts when fine-tuning T5. I followed that but then got a warning message that duplicated were found. I am assuming that this is because the tokenizer truncates an answer text, thus is missing in the truncated answer, such that the end token is not produced in predicted answer. However, I am not sure. Can anybody point out how to address this issue?
Any suggestions are highly appreciated.
Below is some code snippet.
`
import pytorch_lightning as pl
from torch.utils.data import DataLoader
import torch
import numpy as np
import time
from pathlib import Path
from transformers import (
Adafactor,
T5ForConditionalGeneration,
T5Tokenizer,
get_linear_schedule_with_warmup
)
from torch.utils.data import RandomSampler
from question_answering.utils import *
class T5FineTuner(pl.LightningModule):
def __init__(self, hyparams):
super(T5FineTuner, self).__init__()
self.hyparams = hyparams
self.model = T5ForConditionalGeneration.from_pretrained(hyparams.model_name_or_path)
self.tokenizer = T5Tokenizer.from_pretrained(hyparams.tokenizer_name_or_path)
if self.hyparams.freeze_embeds:
self.freeze_embeds()
if self.hyparams.freeze_encoder:
self.freeze_params(self.model.get_encoder())
# assert_all_frozen()
self.step_count = 0
self.output_dir = Path(self.hyparams.output_dir)
n_observations_per_split = {
'train': self.hyparams.n_train,
'validation': self.hyparams.n_val,
'test': self.hyparams.n_test
}
self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
self.em_score_list = []
self.subset_score_list = []
data_folder = r'C:\Datasets\MedQuAD-master'
self.train_data, self.val_data, self.test_data = load_medqa_data(data_folder)
def freeze_params(self, model):
for param in model.parameters():
param.requires_grad = False
def freeze_embeds(self):
try:
self.freeze_params(self.model.model.shared)
for d in [self.model.model.encoder, self.model.model.decoder]:
self.freeze_params(d.embed_positions)
self.freeze_params(d.embed_tokens)
except AttributeError:
self.freeze_params(self.model.shared)
for d in [self.model.encoder, self.model.decoder]:
self.freeze_params(d.embed_tokens)
def lmap(self, f, x):
return list(map(f, x))
def is_logger(self):
return self.trainer.proc_rank <= 0
def forward(self, input_ids, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, labels=None):
return self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
labels=labels
)
def _step(self, batch):
labels = batch['target_ids']
labels[labels[:, :] == self.tokenizer.pad_token_id] = -100
outputs = self(
input_ids = batch['source_ids'],
attention_mask=batch['source_mask'],
labels=labels,
decoder_attention_mask=batch['target_mask']
)
loss = outputs[0]
return loss
def ids_to_clean_text(self, generated_ids):
gen_text = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
return self.lmap(str.strip, gen_text)
def _generative_step(self, batch):
t0 = time.time()
generated_ids = self.model.generate(
batch["source_ids"],
attention_mask=batch["source_mask"],
use_cache=True,
decoder_attention_mask=batch['target_mask'],
max_length=128,
num_beams=2,
early_stopping=True
)
preds = self.ids_to_clean_text(generated_ids)
targets = self.ids_to_clean_text(batch["target_ids"])
gen_time = (time.time() - t0) / batch["source_ids"].shape[0]
loss = self._step(batch)
base_metrics = {'val_loss': loss}
summ_len = np.mean(self.lmap(len, generated_ids))
base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=targets)
em_score, subset_match_score = calculate_scores(preds, targets)
self.em_score_list.append(em_score)
self.subset_score_list.append(subset_match_score)
em_score = torch.tensor(em_score, dtype=torch.float32)
subset_match_score = torch.tensor(subset_match_score, dtype=torch.float32)
base_metrics.update(em_score=em_score, subset_match_score=subset_match_score)
# rouge_results = self.rouge_metric.compute()
# rouge_dict = self.parse_score(rouge_results)
return base_metrics
def training_step(self, batch, batch_idx):
loss = self._step(batch)
tensorboard_logs = {'train_loss': loss}
return {'loss': loss, 'log': tensorboard_logs}
def training_epoch_end(self, outputs):
avg_train_loss = torch.stack([x['loss'] for x in outputs]).mean()
tensorboard_logs = {'avg_train_loss': avg_train_loss}
# return {'avg_train_loss': avg_train_loss, 'log': tensorboard_logs, 'progress_bar': tensorboard_logs}
def validation_step(self, batch, batch_idx):
return self._generative_step(batch)
def validation_epoch_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
tensorboard_logs = {'val_loss': avg_loss}
if len(self.em_score_list) <= 2:
average_em_score = sum(self.em_score_list) / len(self.em_score_list)
average_subset_match_score = sum(self.subset_score_list) / len(self.subset_score_list)
else:
latest_em_score = self.em_score_list[:-2]
latest_subset_score = self.subset_score_list[:-2]
average_em_score = sum(latest_em_score) / len(latest_em_score)
average_subset_match_score = sum(latest_subset_score) / len(latest_subset_score)
average_em_score = torch.tensor(average_em_score, dtype=torch.float32)
average_subset_match_score = torch.tensor(average_subset_match_score, dtype=torch.float32)
tensorboard_logs.update(em_score=average_em_score, subset_match_score=average_subset_match_score)
self.target_gen = []
self.prediction_gen = []
return {
'avg_val_loss': avg_loss,
'em_score': average_em_score,
'subset_match_socre': average_subset_match_score,
'log': tensorboard_logs,
'progress_bar': tensorboard_logs
}
def configure_optimizers(self):
model = self.model
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": self.hyparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = Adafactor(optimizer_grouped_parameters, lr=self.hyparams.learning_rate, scale_parameter=False,
relative_step=False)
self.opt = optimizer
return [optimizer]
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure=None,
on_tpu=False, using_native_amp=False, using_lbfgs=False):
optimizer.step(closure=optimizer_closure)
optimizer.zero_grad()
self.lr_scheduler.step()
def get_tqdm_dict(self):
tqdm_dict = {"loss": "{:.3f}".format(self.trainer.avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]}
return tqdm_dict
def train_dataloader(self):
n_samples = self.n_obs['train']
train_dataset = get_dataset(tokenizer=self.tokenizer, data=self.train_data, num_samples=n_samples,
args=self.hyparams)
sampler = RandomSampler(train_dataset)
dataloader = DataLoader(train_dataset, sampler=sampler, batch_size=self.hyparams.train_batch_size,
drop_last=True, num_workers=4)
# t_total = (
# (len(dataloader.dataset) // (self.hyparams.train_batch_size * max(1, self.hyparams.n_gpu)))
# // self.hyparams.gradient_accumulation_steps
# * float(self.hyparams.num_train_epochs)
# )
t_total = 100000
scheduler = get_linear_schedule_with_warmup(
self.opt, num_warmup_steps=self.hyparams.warmup_steps, num_training_steps=t_total
)
self.lr_scheduler = scheduler
return dataloader
def val_dataloader(self):
n_samples = self.n_obs['validation']
validation_dataset = get_dataset(tokenizer=self.tokenizer, data=self.val_data, num_samples=n_samples,
args=self.hyparams)
sampler = RandomSampler(validation_dataset)
return DataLoader(validation_dataset, shuffle=False, batch_size=self.hyparams.eval_batch_size, sampler=sampler, num_workers=4)
def test_dataloader(self):
n_samples = self.n_obs['test']
test_dataset = get_dataset(tokenizer=self.tokenizer, data=self.test_data, num_samples=n_samples, args=self.hyparams)
return DataLoader(test_dataset, batch_size=self.hyparams.eval_batch_size, num_workers=4)
def on_save_checkpoint(self, checkpoint):
save_path = self.output_dir.joinpath("best_tfmr")
self.model.config.save_step = self.step_count
self.model.save_pretrained(save_path)
self.tokenizer.save_pretrained(save_path)
import os
import argparse
import pytorch_lightning as pl
from question_answering.t5_closed_book import T5FineTuner
if __name__ == '__main__':
args_dict = dict(
output_dir="", # path to save the checkpoints
model_name_or_path='t5-large',
tokenizer_name_or_path='t5-large',
max_input_length=128,
max_output_length=128,
freeze_encoder=False,
freeze_embeds=False,
learning_rate=1e-5,
weight_decay=0.0,
adam_epsilon=1e-8,
warmup_steps=0,
train_batch_size=4,
eval_batch_size=4,
num_train_epochs=2,
gradient_accumulation_steps=10,
n_gpu=1,
resume_from_checkpoint=None,
val_check_interval=0.5,
n_val=4000,
n_train=-1,
n_test=-1,
early_stop_callback=False,
fp_16=False,
opt_level='O1',
max_grad_norm=1.0,
seed=101,
)
args_dict.update({'output_dir': 't5_large_MedQuAD_256', 'num_train_epochs': 100,
'train_batch_size': 16, 'eval_batch_size': 16, 'learning_rate': 1e-3})
args = argparse.Namespace(**args_dict)
checkpoint_callback = pl.callbacks.ModelCheckpoint(dirpath=args.output_dir, monitor="em_score", mode="max", save_top_k=1)
## If resuming from checkpoint, add an arg resume_from_checkpoint
train_params = dict(
accumulate_grad_batches=args.gradient_accumulation_steps,
gpus=args.n_gpu,
max_epochs=args.num_train_epochs,
# early_stop_callback=False,
precision=16 if args.fp_16 else 32,
# amp_level=args.opt_level,
# resume_from_checkpoint=args.resume_from_checkpoint,
gradient_clip_val=args.max_grad_norm,
checkpoint_callback=checkpoint_callback,
val_check_interval=args.val_check_interval,
# accelerator='dp'
# logger=wandb_logger,
# callbacks=[LoggingCallback()],
)
model = T5FineTuner(args)
trainer = pl.Trainer(**train_params)
trainer.fit(model)
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https://api.github.com/repos/huggingface/datasets/issues/1339 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1339/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1339/comments | https://api.github.com/repos/huggingface/datasets/issues/1339/events | https://github.com/huggingface/datasets/pull/1339 | 759,744,088 | MDExOlB1bGxSZXF1ZXN0NTM0Njk0NDI4 | 1,339 | hate_speech_18 initial commit | [] | closed | false | null | 2 | 2020-12-08T20:10:08Z | 2020-12-12T16:17:32Z | 2020-12-12T16:17:32Z | null | {
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"> Nice thanks !\r\n> \r\n> Can you rename the dataset folder and the dataset script name `hate_speech18` instead of `hate_speech_18` to follow the snake case convention we're using ?\r\n> \r\n> Also it looks like the dummy_data.zip file is quite big (almost 4MB).\r\n> Can you try to reduce its size ?\r\n> \r\n> To do so feel free to take a look inside it and remove all the unnecessary files or chunks of texts. The idea is to only keep a few examples\r\n\r\nDone, thanks! ",
"Re-opened in https://github.com/huggingface/datasets/pull/1486"
] |
|
https://api.github.com/repos/huggingface/datasets/issues/6055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6055/comments | https://api.github.com/repos/huggingface/datasets/issues/6055/events | https://github.com/huggingface/datasets/issues/6055 | 1,813,524,145 | I_kwDODunzps5sGC6x | 6,055 | Fix host URL in The Pile datasets | [] | open | false | null | 0 | 2023-07-20T09:08:52Z | 2023-07-20T09:09:37Z | null | null | ### Describe the bug
In #3627 and #5543, you tried to fix the host URL in The Pile datasets. But both URLs are not working now:
`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`
And
`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`
### Steps to reproduce the bug
```
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://mystic.the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
Result:
`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`
And
```
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
Result:
`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`
### Expected behavior
Downloading as normal.
### Environment info
Environment info
`datasets` version: 2.9.0
Platform: Windows
Python version: 3.9.13
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https://api.github.com/repos/huggingface/datasets/issues/2804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2804/comments | https://api.github.com/repos/huggingface/datasets/issues/2804/events | https://github.com/huggingface/datasets/pull/2804 | 971,353,437 | MDExOlB1bGxSZXF1ZXN0NzEzMTA2NTMw | 2,804 | Add Food-101 | [] | closed | false | null | 0 | 2021-08-16T04:26:15Z | 2021-08-20T14:31:33Z | 2021-08-19T12:48:06Z | null | Adds image classification dataset [Food-101](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/). | {
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https://api.github.com/repos/huggingface/datasets/issues/248 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/248/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/248/comments | https://api.github.com/repos/huggingface/datasets/issues/248/events | https://github.com/huggingface/datasets/pull/248 | 633,390,427 | MDExOlB1bGxSZXF1ZXN0NDMwMDQ0MzU0 | 248 | add Toronto BooksCorpus | [] | closed | false | null | 11 | 2020-06-07T12:54:56Z | 2020-06-12T08:45:03Z | 2020-06-12T08:45:02Z | null | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | {
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"Thanks for adding this one !\r\n\r\nAbout the three points you mentioned:\r\n1. I think the `toronto_books_corpus` branch can be removed @mariamabarham ? \r\n2. You can use the download manager to download from google drive. For you case you can just do something like \r\n```python\r\nURL = \"https://drive.google.com/uc?export=download&id=16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z\"\r\n...\r\narch_path = dl_manager.download_and_extract(URL)\r\n```\r\nAlso this is is an unofficial host of the dataset, we should probably host it ourselves if we can.\r\n3. Not sure about the copyright here, but I maybe @thomwolf has better insights about it. ",
"Yes it can be removed",
"I just downloaded the file and put it on gs. The public url is\r\nhttps://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2\r\n\r\nCould you try to change the url to this one and heck that everything is ok ?",
"In `books.py`\r\n```\r\nURL = \"https://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2\"\r\n```\r\n```\r\nPython 3.7.6 (default, Jan 8 2020, 19:59:22) \r\n[GCC 7.3.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from nlp import load_dataset\r\n>>> book = load_dataset(\"nlp/datasets/bookscorpus/books.py\", cache_dir='~/tmp')\r\nDownloading and preparing dataset bookscorpus/plain_text (download: 1.10 GiB, generated: 4.52 GiB, total: 5.62 GiB) to /home/yisiang/tmp/bookscorpus/plain_text/1.0.0...\r\nDownloading: 100%|███████████████████████████████████████████████████████████| 1.18G/1.18G [00:39<00:00, 30.0MB/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/yisiang/nlp/src/nlp/load.py\", line 520, in load_dataset\r\n save_infos=save_infos,\r\n File \"/home/yisiang/nlp/src/nlp/builder.py\", line 420, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home/yisiang/nlp/src/nlp/builder.py\", line 460, in _download_and_prepare\r\n verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())\r\n File \"/home/yisiang/nlp/src/nlp/utils/info_utils.py\", line 31, in verify_checksums\r\n raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\nnlp.utils.info_utils.ExpectedMoreDownloadedFiles: {'16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z'}\r\n>>>\r\n```\r\n\r\nBTW, I notice the path `huggingface-nlp/datasets/toronto_books_corpus`, does it mean I have to change folder name \"bookscorpus\" to \"toronto_books_corpus\"",
"> In `books.py`\r\n> \r\n> ```\r\n> URL = \"https://storage.googleapis.com/huggingface-nlp/datasets/toronto_books_corpus/bookcorpus.tar.bz2\"\r\n> ```\r\n> \r\n> ```\r\n> Python 3.7.6 (default, Jan 8 2020, 19:59:22) \r\n> [GCC 7.3.0] :: Anaconda, Inc. on linux\r\n> Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n> >>> from nlp import load_dataset\r\n> >>> book = load_dataset(\"nlp/datasets/bookscorpus/books.py\", cache_dir='~/tmp')\r\n> Downloading and preparing dataset bookscorpus/plain_text (download: 1.10 GiB, generated: 4.52 GiB, total: 5.62 GiB) to /home/yisiang/tmp/bookscorpus/plain_text/1.0.0...\r\n> Downloading: 100%|███████████████████████████████████████████████████████████| 1.18G/1.18G [00:39<00:00, 30.0MB/s]\r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 1, in <module>\r\n> File \"/home/yisiang/nlp/src/nlp/load.py\", line 520, in load_dataset\r\n> save_infos=save_infos,\r\n> File \"/home/yisiang/nlp/src/nlp/builder.py\", line 420, in download_and_prepare\r\n> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n> File \"/home/yisiang/nlp/src/nlp/builder.py\", line 460, in _download_and_prepare\r\n> verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())\r\n> File \"/home/yisiang/nlp/src/nlp/utils/info_utils.py\", line 31, in verify_checksums\r\n> raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n> nlp.utils.info_utils.ExpectedMoreDownloadedFiles: {'16KCjV9z_FHm8LgZw05RSuk4EsAWPOP_z'}\r\n> >>>\r\n> ```\r\n> \r\n> BTW, I notice the path `huggingface-nlp/datasets/toronto_books_corpus`, does it mean I have to change folder name \"bookscorpus\" to \"toronto_books_corpus\"\r\n\r\nLet me change the url to match \"bookscorpus\", so that you don't have to change anything. Good catch.\r\n\r\nAbout the error you're getting: you just have to remove the `dataset_infos.json` and regenerate it",
"The new url is https://storage.googleapis.com/huggingface-nlp/datasets/bookscorpus/bookcorpus.tar.bz2",
"Hi, I found I made a mistake. I found the ELECTRA paper refer it as \"BooksCorpus\", but actually it is caleld \"BookCorpus\", according to the original paper. Sorry, I should have checked the original paper .\r\n\r\nCan you do me a favor and change the url path to ` https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2` ?",
"Yep I'm doing it right now. Could you please rename all the references to `bookscorpus` and `BooksCorpus` to `book_corpus` and `BookCorpus` (with the right casing) ?",
"Thank you @lhoestq ,\r\nJust to confirm it fits your naming convention\r\n* make the file path `book_corpus/book_corpus.py` ?\r\n* make `class Bookscorpus(nlp.GeneratorBasedBuilder)` -> `BookCorpus` (which make cache folder name `book_corpus` and user use `load_dataset('book_corpus')`) ?\r\n(Cuz I found \"HellaSwag\" dataset is named \"nlp/datasets/hellaswag\" and `class Hellaswag` )",
"Oh yea you're right about the Hellaswag example. We should keep the \"_\" symbol to replace spaces. As there are no space in BookCorpus, what we should do here is use:\r\n- class name: 'Bookcorpus'\r\n- script name: `bookcorpus/bookcorpus.py`\r\n- use url https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2\r\nAnd therefore the dataset name will be `bookcorpus`\r\n\r\nDon't forget to regenerate the `dataset_infos.json` and we'll be good :D ",
"Awesome thanks :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/656 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/656/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/656/comments | https://api.github.com/repos/huggingface/datasets/issues/656/events | https://github.com/huggingface/datasets/pull/656 | 705,736,319 | MDExOlB1bGxSZXF1ZXN0NDkwNDEwODAz | 656 | Use multiprocess from pathos for multiprocessing | [] | closed | false | null | 4 | 2020-09-21T16:12:19Z | 2020-09-28T14:45:40Z | 2020-09-28T14:45:39Z | null | [Multiprocess](https://github.com/uqfoundation/multiprocess) (from the [pathos](https://github.com/uqfoundation/pathos) project) allows to use lambda functions in multiprocessed map.
It was suggested to use it by @kandorm.
We're already using dill which is its only dependency. | {
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"We can just install multiprocess actually, I'll change that",
"Just an FYI: I remember that I wanted to try pathos a couple of years back and I ran into issues considering cross-platform; the code would just break on Windows. If I can verify this PR by running CPU tests on Windows, let me know!",
"That's good to know thanks\r\nI guess we can just wait for #644 to be merged first. I'm working on fixing the tests for windows",
"Looks like all the CI jobs on windows passed !\r\nI also tested locally on my windows and it works great :) \r\n\r\nI think this is ready to merge, let me know if you have any remarks @thomwolf @BramVanroy "
] |
https://api.github.com/repos/huggingface/datasets/issues/1432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1432/comments | https://api.github.com/repos/huggingface/datasets/issues/1432/events | https://github.com/huggingface/datasets/pull/1432 | 760,808,449 | MDExOlB1bGxSZXF1ZXN0NTM1NTc3ODk3 | 1,432 | Adding journalists questions dataset | [] | closed | false | null | 2 | 2020-12-10T01:44:47Z | 2020-12-14T13:51:05Z | 2020-12-14T13:51:04Z | null | This is my first dataset to be added to HF. | {
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"@lhoestq Thanks a lot for checking! I hope I addressed all your comments. ",
"merging since the CI is fixed on master"
] |
https://api.github.com/repos/huggingface/datasets/issues/603 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/603/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/603/comments | https://api.github.com/repos/huggingface/datasets/issues/603/events | https://github.com/huggingface/datasets/pull/603 | 697,758,750 | MDExOlB1bGxSZXF1ZXN0NDgzNjY2ODk5 | 603 | Set scripts version to master | [] | closed | false | null | 0 | 2020-09-10T10:47:44Z | 2020-09-10T11:02:05Z | 2020-09-10T11:02:04Z | null | By default the scripts version is master, so that if the library is installed with
```
pip install git+http://github.com/huggingface/nlp.git
```
or
```
git clone http://github.com/huggingface/nlp.git
pip install -e ./nlp
```
will use the latest scripts, and not the ones from the previous version. | {
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https://api.github.com/repos/huggingface/datasets/issues/2956 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2956/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2956/comments | https://api.github.com/repos/huggingface/datasets/issues/2956/events | https://github.com/huggingface/datasets/issues/2956 | 1,004,306,367 | I_kwDODunzps473H-_ | 2,956 | Cache problem in the `load_dataset` method for local compressed file(s) | [
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] | open | false | null | 0 | 2021-09-22T13:34:32Z | 2021-09-22T13:34:32Z | null | null | ## Describe the bug
Cache problem in the `load_dataset` method: when modifying a compressed file in a local folder `load_dataset` doesn't detect the change and load the previous version.
## Steps to reproduce the bug
To test it directly, I have prepared a [Google Colaboratory notebook](https://colab.research.google.com/drive/11Em_Amoc-aPGhSBIkSHU2AvEh24nVayy?usp=sharing) that shows this behavior.
For this example, I have created a toy dataset at: https://huggingface.co/datasets/SaulLu/toy_struc_dataset
This dataset is composed of two versions:
- v1 on commit `a6beb46` which has a single example `{'id': 1, 'value': {'tag': 'a', 'value': 1}}` in file `train.jsonl.gz`
- v2 on commit `e7935f4` (`main` head) which has a single example `{'attr': 1, 'id': 1, 'value': 'a'}` in file `train.jsonl.gz`
With a terminal, we can start to get the v1 version of the dataset
```bash
git lfs install
git clone https://huggingface.co/datasets/SaulLu/toy_struc_dataset
cd toy_struc_dataset
git checkout a6beb46
```
Then we can load it with python and look at the content:
```python
from datasets import load_dataset
path = "/content/toy_struc_dataset"
dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"})
print(dataset["train"][0])
```
Output
```
{'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1
```
With a terminal, we can now start to get the v1 version of the dataset
```bash
git checkout main
```
Then we can load it with python and look at the content:
```python
from datasets import load_dataset
path = "/content/toy_struc_dataset"
dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"})
print(dataset["train"][0])
```
Output
```
{'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 (not v2)
```
## Expected results
The last output should have been
```
{"id":1, "value": "a", "attr": 1} # This is the example in v2
```
## Ideas
As discussed offline with Quentin, if the cache hash was ever sensitive to changes in a compressed file we would probably not have the problem anymore.
This situation leads me to suggest 2 other features:
- to also have an `load_from_cache_file` argument in the "load_dataset" method
- to reorganize the cache so that we can delete the caches related to a dataset (cf issue #ToBeFilledSoon)
And thanks again for this great library :hugs:
## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/5784 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5784/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5784/comments | https://api.github.com/repos/huggingface/datasets/issues/5784/events | https://github.com/huggingface/datasets/pull/5784 | 1,680,950,726 | PR_kwDODunzps5O_G9S | 5,784 | Raise subprocesses traceback when interrupting | [] | closed | false | null | 4 | 2023-04-24T10:34:03Z | 2023-04-26T16:04:42Z | 2023-04-26T15:54:44Z | null | When a subprocess hangs in `filter` or `map`, one should be able to get the subprocess' traceback when interrupting the main process. Right now it shows nothing.
To do so I `.get()` the subprocesses async results even the main process is stopped with e.g. `KeyboardInterrupt`. I added a timeout in case the subprocess is hanging or crashed. | {
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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) | 0.008959 / 0.011353 (-0.002394) | 0.005804 / 0.011008 (-0.005204) | 0.112663 / 0.038508 (0.074155) | 0.043406 / 0.023109 (0.020297) | 0.348582 / 0.275898 (0.072684) | 0.382332 / 0.323480 (0.058852) | 0.007469 / 0.007986 (-0.000517) | 0.006211 / 0.004328 (0.001883) | 0.086576 / 0.004250 (0.082326) | 0.059223 / 0.037052 (0.022170) | 0.361051 / 0.258489 (0.102562) | 0.411359 / 0.293841 (0.117518) | 0.043640 / 0.128546 (-0.084906) | 0.014239 / 0.075646 (-0.061408) | 0.389729 / 0.419271 (-0.029542) | 0.072319 / 0.043533 (0.028786) | 0.351025 / 0.255139 (0.095886) | 0.371893 / 0.283200 (0.088693) | 0.125994 / 0.141683 (-0.015688) | 1.675249 / 1.452155 (0.223094) | 1.808740 / 1.492716 (0.316024) |\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) | 0.255172 / 0.018006 (0.237166) | 0.536003 / 0.000490 (0.535514) | 0.000365 / 0.000200 (0.000165) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031989 / 0.037411 (-0.005423) | 0.126854 / 0.014526 (0.112328) | 0.142458 / 0.176557 (-0.034098) | 0.207821 / 0.737135 (-0.529314) | 0.145610 / 0.296338 (-0.150728) |\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) | 0.468924 / 0.215209 (0.253715) | 4.696677 / 2.077655 (2.619023) | 2.183133 / 1.504120 (0.679013) | 1.994219 / 1.541195 (0.453024) | 2.101375 / 1.468490 (0.632885) | 0.827168 / 4.584777 (-3.757609) | 4.710167 / 3.745712 (0.964455) | 2.377062 / 5.269862 (-2.892800) | 1.712245 / 4.565676 (-2.853431) | 0.100620 / 0.424275 (-0.323655) | 0.014302 / 0.007607 (0.006695) | 0.590813 / 0.226044 (0.364769) | 5.871991 / 2.268929 (3.603063) | 2.722229 / 55.444624 (-52.722395) | 2.323585 / 6.876477 (-4.552892) | 2.503289 / 2.142072 (0.361217) | 0.983644 / 4.805227 (-3.821583) | 0.193942 / 6.500664 (-6.306722) | 0.076493 / 0.075469 (0.001024) |\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) | 1.463107 / 1.841788 (-0.378681) | 17.876918 / 8.074308 (9.802610) | 16.755740 / 10.191392 (6.564348) | 0.167556 / 0.680424 (-0.512868) | 0.020514 / 0.534201 (-0.513687) | 0.508385 / 0.579283 (-0.070898) | 0.505873 / 0.434364 (0.071509) | 0.603630 / 0.540337 (0.063293) | 0.708856 / 1.386936 (-0.678080) |\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) | 0.008504 / 0.011353 (-0.002849) | 0.005894 / 0.011008 (-0.005114) | 0.085523 / 0.038508 (0.047015) | 0.038780 / 0.023109 (0.015671) | 0.402869 / 0.275898 (0.126971) | 0.423819 / 0.323480 (0.100339) | 0.006427 / 0.007986 (-0.001559) | 0.004598 / 0.004328 (0.000269) | 0.079807 / 0.004250 (0.075556) | 0.050852 / 0.037052 (0.013799) | 0.403232 / 0.258489 (0.144743) | 0.452489 / 0.293841 (0.158648) | 0.041501 / 0.128546 (-0.087045) | 0.014996 / 0.075646 (-0.060650) | 0.101548 / 0.419271 (-0.317724) | 0.056993 / 0.043533 (0.013461) | 0.403153 / 0.255139 (0.148014) | 0.424587 / 0.283200 (0.141388) | 0.114507 / 0.141683 (-0.027176) | 1.707098 / 1.452155 (0.254943) | 1.799008 / 1.492716 (0.306291) |\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) | 0.288003 / 0.018006 (0.269996) | 0.496526 / 0.000490 (0.496036) | 0.010923 / 0.000200 (0.010723) | 0.000159 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033948 / 0.037411 (-0.003463) | 0.142343 / 0.014526 (0.127817) | 0.143862 / 0.176557 (-0.032695) | 0.202655 / 0.737135 (-0.534480) | 0.151177 / 0.296338 (-0.145162) |\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) | 0.508003 / 0.215209 (0.292794) | 5.320394 / 2.077655 (3.242740) | 2.409854 / 1.504120 (0.905734) | 2.190656 / 1.541195 (0.649462) | 2.272171 / 1.468490 (0.803681) | 0.809492 / 4.584777 (-3.775285) | 4.554412 / 3.745712 (0.808699) | 4.413643 / 5.269862 (-0.856218) | 2.374034 / 4.565676 (-2.191642) | 0.099458 / 0.424275 (-0.324817) | 0.014553 / 0.007607 (0.006946) | 0.613916 / 0.226044 (0.387871) | 6.121430 / 2.268929 (3.852502) | 2.945661 / 55.444624 (-52.498964) | 2.595247 / 6.876477 (-4.281230) | 2.734047 / 2.142072 (0.591975) | 0.952217 / 4.805227 (-3.853010) | 0.196933 / 6.500664 (-6.303731) | 0.073391 / 0.075469 (-0.002078) |\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) | 1.475666 / 1.841788 (-0.366122) | 18.564281 / 8.074308 (10.489973) | 16.865259 / 10.191392 (6.673867) | 0.166494 / 0.680424 (-0.513930) | 0.020655 / 0.534201 (-0.513546) | 0.495120 / 0.579283 (-0.084163) | 0.502602 / 0.434364 (0.068238) | 0.622448 / 0.540337 (0.082110) | 0.721036 / 1.386936 (-0.665900) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#40c204c777793d64e8bb8ce357e9c07b3b303e41 \"CML watermark\")\n",
"Whoops mario you're off this week sorry. I'm taking the liberty to merge this one",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009079 / 0.011353 (-0.002274) | 0.005960 / 0.011008 (-0.005049) | 0.116530 / 0.038508 (0.078022) | 0.046649 / 0.023109 (0.023540) | 0.391906 / 0.275898 (0.116008) | 0.438892 / 0.323480 (0.115412) | 0.007134 / 0.007986 (-0.000851) | 0.004997 / 0.004328 (0.000668) | 0.085947 / 0.004250 (0.081697) | 0.059814 / 0.037052 (0.022762) | 0.396423 / 0.258489 (0.137934) | 0.455941 / 0.293841 (0.162100) | 0.042535 / 0.128546 (-0.086011) | 0.014667 / 0.075646 (-0.060980) | 0.402023 / 0.419271 (-0.017249) | 0.060381 / 0.043533 (0.016848) | 0.393829 / 0.255139 (0.138690) | 0.426557 / 0.283200 (0.143358) | 0.131519 / 0.141683 (-0.010163) | 1.758098 / 1.452155 (0.305943) | 1.848194 / 1.492716 (0.355478) |\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) | 0.236405 / 0.018006 (0.218399) | 0.611442 / 0.000490 (0.610952) | 0.005143 / 0.000200 (0.004943) | 0.000146 / 0.000054 (0.000092) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034317 / 0.037411 (-0.003094) | 0.182485 / 0.014526 (0.167959) | 0.183149 / 0.176557 (0.006592) | 0.293592 / 0.737135 (-0.443543) | 0.197137 / 0.296338 (-0.099202) |\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) | 0.475690 / 0.215209 (0.260481) | 4.757344 / 2.077655 (2.679690) | 2.184079 / 1.504120 (0.679959) | 1.956599 / 1.541195 (0.415404) | 2.043041 / 1.468490 (0.574551) | 0.817602 / 4.584777 (-3.767175) | 6.432267 / 3.745712 (2.686555) | 5.999402 / 5.269862 (0.729541) | 3.095970 / 4.565676 (-1.469706) | 0.181589 / 0.424275 (-0.242686) | 0.023286 / 0.007607 (0.015679) | 1.090318 / 0.226044 (0.864274) | 7.919330 / 2.268929 (5.650401) | 2.702821 / 55.444624 (-52.741804) | 2.375442 / 6.876477 (-4.501034) | 2.543075 / 2.142072 (0.401003) | 1.011763 / 4.805227 (-3.793464) | 0.203676 / 6.500664 (-6.296988) | 0.080075 / 0.075469 (0.004606) |\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) | 1.875420 / 1.841788 (0.033632) | 23.059278 / 8.074308 (14.984970) | 19.250807 / 10.191392 (9.059415) | 0.323678 / 0.680424 (-0.356746) | 0.028682 / 0.534201 (-0.505519) | 0.698231 / 0.579283 (0.118948) | 0.668129 / 0.434364 (0.233765) | 0.831218 / 0.540337 (0.290880) | 0.941191 / 1.386936 (-0.445745) |\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) | 0.013122 / 0.011353 (0.001769) | 0.006123 / 0.011008 (-0.004886) | 0.090493 / 0.038508 (0.051985) | 0.070660 / 0.023109 (0.047551) | 0.413486 / 0.275898 (0.137588) | 0.450364 / 0.323480 (0.126884) | 0.010288 / 0.007986 (0.002302) | 0.006590 / 0.004328 (0.002261) | 0.087174 / 0.004250 (0.082923) | 0.077304 / 0.037052 (0.040252) | 0.428480 / 0.258489 (0.169991) | 0.459872 / 0.293841 (0.166032) | 0.060477 / 0.128546 (-0.068069) | 0.014859 / 0.075646 (-0.060788) | 0.103915 / 0.419271 (-0.315356) | 0.087466 / 0.043533 (0.043933) | 0.418644 / 0.255139 (0.163505) | 0.433409 / 0.283200 (0.150209) | 0.166716 / 0.141683 (0.025033) | 1.712068 / 1.452155 (0.259914) | 1.827869 / 1.492716 (0.335153) |\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) | 0.372491 / 0.018006 (0.354484) | 0.493426 / 0.000490 (0.492937) | 0.005497 / 0.000200 (0.005297) | 0.000129 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036531 / 0.037411 (-0.000880) | 0.142152 / 0.014526 (0.127626) | 0.148183 / 0.176557 (-0.028373) | 0.212918 / 0.737135 (-0.524217) | 0.154092 / 0.296338 (-0.142246) |\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) | 0.551733 / 0.215209 (0.336524) | 5.421498 / 2.077655 (3.343843) | 2.418848 / 1.504120 (0.914728) | 2.213185 / 1.541195 (0.671991) | 2.294881 / 1.468490 (0.826391) | 0.827031 / 4.584777 (-3.757746) | 6.365622 / 3.745712 (2.619910) | 4.927996 / 5.269862 (-0.341866) | 2.756133 / 4.565676 (-1.809544) | 0.101474 / 0.424275 (-0.322801) | 0.014523 / 0.007607 (0.006916) | 0.619082 / 0.226044 (0.393037) | 6.200132 / 2.268929 (3.931204) | 3.015590 / 55.444624 (-52.429034) | 2.711181 / 6.876477 (-4.165296) | 2.857157 / 2.142072 (0.715084) | 0.993329 / 4.805227 (-3.811898) | 0.203364 / 6.500664 (-6.297301) | 0.079167 / 0.075469 (0.003698) |\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) | 1.709881 / 1.841788 (-0.131907) | 24.867536 / 8.074308 (16.793228) | 21.755361 / 10.191392 (11.563969) | 0.295837 / 0.680424 (-0.384586) | 0.031934 / 0.534201 (-0.502267) | 0.709994 / 0.579283 (0.130711) | 0.779656 / 0.434364 (0.345293) | 0.780669 / 0.540337 (0.240331) | 0.712808 / 1.386936 (-0.674128) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf4a1951bdca7175adac9c8b85550e89dcceb6fa \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/4146 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4146/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4146/comments | https://api.github.com/repos/huggingface/datasets/issues/4146/events | https://github.com/huggingface/datasets/issues/4146 | 1,200,215,789 | I_kwDODunzps5Hidbt | 4,146 | SAMSum dataset viewer not working | [
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] | closed | false | null | 3 | 2022-04-11T16:22:57Z | 2022-04-29T16:26:09Z | 2022-04-29T16:26:09Z | null | ## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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"https://huggingface.co/datasets/samsum\r\n\r\n```\r\nStatus code: 400\r\nException: ValueError\r\nMessage: Cannot seek streaming HTTP file\r\n```",
"Currently, only the datasets that can be streamed support the dataset viewer. Maybe @lhoestq @albertvillanova or @mariosasko could give more details about why the dataset cannot be streamed.",
"It looks like the host (https://arxiv.org) doesn't allow HTTP Range requests, which is what we use to stream data.\r\n\r\nThis can be fix if we host the data ourselves, which is ok since the dataset is under CC BY-NC-ND 4.0"
] |
https://api.github.com/repos/huggingface/datasets/issues/3726 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3726/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3726/comments | https://api.github.com/repos/huggingface/datasets/issues/3726/events | https://github.com/huggingface/datasets/pull/3726 | 1,138,870,362 | PR_kwDODunzps4y3iSv | 3,726 | Use config pandas version in CSV dataset builder | [] | closed | false | null | 0 | 2022-02-15T15:47:49Z | 2022-02-15T16:55:45Z | 2022-02-15T16:55:44Z | null | Fix #3724. | {
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https://api.github.com/repos/huggingface/datasets/issues/3396 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3396/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3396/comments | https://api.github.com/repos/huggingface/datasets/issues/3396/events | https://github.com/huggingface/datasets/issues/3396 | 1,073,467,183 | I_kwDODunzps4_-88v | 3,396 | Install Audio dependencies to support audio decoding | [
{
"color": "E5583E",
"default": false,
"description": "Related to the dataset viewer on huggingface.co",
"id": 3470211881,
"name": "dataset-viewer",
"node_id": "LA_kwDODunzps7O1zsp",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer"
},
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"name": "audio_column",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/audio_column"
}
] | closed | false | null | 5 | 2021-12-07T15:11:36Z | 2022-04-25T16:12:22Z | 2022-04-25T16:12:01Z | null | ## Dataset viewer issue for '*openslr*', '*projecte-aina/parlament_parla*'
**Link:** *https://huggingface.co/datasets/openslr*
**Link:** *https://huggingface.co/datasets/projecte-aina/parlament_parla*
Error:
```
Status code: 400
Exception: ImportError
Message: To support decoding audio files, please install 'librosa'.
```
Am I the one who added this dataset ? Yes-No
- openslr: No
- projecte-aina/parlament_parla: Yes
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} | https://api.github.com/repos/huggingface/datasets/issues/3396/timeline | null | completed | null | null | false | [
"https://huggingface.co/datasets/projecte-aina/parlament_parla -> works (but we still have to show an audio player)\r\n\r\nhttps://huggingface.co/datasets/openslr -> another issue: `Message: [Errno 2] No such file or directory: '/home/hf/datasets-preview-backend/zip:/asr_javanese/data/00/00004fe6aa.flac'`",
"Done",
"https://huggingface.co/datasets/projecte-aina/parlament_parla/viewer/clean/train works\r\n\r\n<img width=\"1535\" alt=\"Capture d’écran 2022-04-12 à 13 58 47\" src=\"https://user-images.githubusercontent.com/1676121/162957855-cb3d9e2e-4b61-488c-99ca-8065cd8fe377.png\">\r\n",
"But https://huggingface.co/datasets/openslr/viewer does not work\r\n\r\n<img width=\"678\" alt=\"Capture d’écran 2022-04-12 à 13 59 46\" src=\"https://user-images.githubusercontent.com/1676121/162958013-e31ef2ae-f886-47b7-9f27-664ed3d4b5a1.png\">\r\n\r\nSame issue as #4126:\r\n\r\n```\r\nStatus code: 400\r\nException: TypeError\r\nMessage: __init__() got an unexpected keyword argument 'audio_column'\r\n```",
"Fixed:\r\n<img width=\"1561\" alt=\"Capture d’écran 2022-04-25 à 18 11 51\" src=\"https://user-images.githubusercontent.com/1676121/165129813-018ece9e-8b20-4544-844d-4e88148e738f.png\">\r\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/1846 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1846/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1846/comments | https://api.github.com/repos/huggingface/datasets/issues/1846/events | https://github.com/huggingface/datasets/pull/1846 | 803,806,380 | MDExOlB1bGxSZXF1ZXN0NTY5NjczMzcy | 1,846 | Make DownloadManager downloaded/extracted paths accessible | [] | closed | false | null | 3 | 2021-02-08T18:14:42Z | 2021-02-25T14:10:18Z | 2021-02-25T14:10:18Z | null | Make accessible the file paths downloaded/extracted by DownloadManager.
Close #1831.
The approach:
- I set these paths as DownloadManager attributes: these are DownloadManager's concerns
- To access to these from DatasetBuilder, I set the DownloadManager instance as DatasetBuilder attribute: object composition | {
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"First I was thinking of the dict, which makes sense for .download, mapping URL to downloaded path. However does this make sense for .extract, mapping the downloaded path to the extracted path? I ask this because the user did not chose the downloaded path, so this is completely unknown for them...",
"There could be several situations:\r\n- download a file with no extraction\r\n- download a file and extract it\r\n- download a file, extract it and then inside the output folder extract some more files\r\n- extract a local file (for datasets with data that are manually downloaded for example)\r\n- extract a local file, and then inside the output folder extract some more files\r\n\r\nSo I think it's ok to have `downloaded_paths` as a dict url -> downloaded_path and `extracted_paths` as a dict local_path -> extracted_path.",
"OK. I am refactoring this. I have opened #1879, as an intermediate step..."
] |
https://api.github.com/repos/huggingface/datasets/issues/2783 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2783/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2783/comments | https://api.github.com/repos/huggingface/datasets/issues/2783/events | https://github.com/huggingface/datasets/pull/2783 | 965,461,382 | MDExOlB1bGxSZXF1ZXN0NzA3NzcxOTM3 | 2,783 | Add KS task to SUPERB | [] | closed | false | null | 5 | 2021-08-10T22:14:07Z | 2021-08-12T16:45:01Z | 2021-08-11T20:19:17Z | null | Add the KS (keyword spotting) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051).
- [s3prl instructions](https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/README.md#ks-keyword-spotting)
- [s3prl implementation](https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/speech_commands/dataset.py)
- [TFDS implementation](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/audio/speech_commands.py)
Some notable quirks:
- The dataset is originally single-archive (train+val+test all in one), but the test set has a "canonical" distribution in a separate archive, which is also used here (see `_split_ks_files()`).
- The `_background_noise_`/`_silence_` audio files are much longer than others, so they require some sort of slicing for downstream training. I decided to leave the implementation of that up to the users, since TFDS and s3prl take different approaches (either slicing wavs deterministically, or subsampling randomly at runtime)
Related to #2619. | {
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"thanks a lot for implementing this @anton-l !!\r\n\r\ni won't have time to review this while i'm away, so happy for @albertvillanova and @patrickvonplaten to decide when to merge :)",
"@albertvillanova thanks! Everything should be ready now :)",
"> The _background_noise_/_silence_ audio files are much longer than others, so they require some sort of slicing for downstream training. I decided to leave the implementation of that up to the users, since TFDS and s3prl take different approaches (either slicing wavs deterministically, or subsampling randomly at runtime)\r\n\r\n@anton-l I was thinking that maybe we could give some hints in the dataset card (in a Usage section); something similar as for diarization: https://github.com/huggingface/datasets/blob/master/datasets/superb/README.md#example-of-usage\r\nNote that for diarization it is not yet finished: we have to test it and then provide an end-to-end example: https://github.com/huggingface/datasets/pull/2661/files#r680224909 ",
"@albertvillanova yeah, I'm not sure how to best implement it in pure `datasets` yet. It's something like this, where `sample_noise()` needs to be called from a pytorch batch collator or other framework-specific variant:\r\n\r\n```python\r\ndef map_to_array(example):\r\n import soundfile as sf\r\n\r\n speech_array, sample_rate = sf.read(example[\"file\"])\r\n example[\"speech\"] = speech_array\r\n example[\"sample_rate\"] = sample_rate\r\n return example\r\n\r\n\r\ndef sample_noise(example):\r\n # Use a version of this function in a stateless way to extract random 1 sec slices of background noise\r\n # on each epoch\r\n from random import randint\r\n\r\n # _silence_ audios are longer than 1 sec\r\n if example[\"label\"] == \"_silence_\":\r\n random_offset = randint(0, len(example[\"speech\"]) - example[\"sample_rate\"] - 1)\r\n example[\"speech\"] = example[\"speech\"][random_offset : random_offset + example[\"sample_rate\"]]\r\n\r\n return example\r\n```",
"I see... Yes, not trivial indeed. Maybe for the moment you could add those functions above to the README (as it is the case for now in diarization)? What do you think?"
] |
https://api.github.com/repos/huggingface/datasets/issues/2817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2817/comments | https://api.github.com/repos/huggingface/datasets/issues/2817/events | https://github.com/huggingface/datasets/pull/2817 | 974,486,051 | MDExOlB1bGxSZXF1ZXN0NzE1NzgzMDQ3 | 2,817 | Rename The Pile subsets | [] | closed | false | null | 2 | 2021-08-19T09:56:22Z | 2021-08-23T16:24:10Z | 2021-08-23T16:24:09Z | null | After discussing with @yjernite we think it's better to have the subsets of The Pile explicitly have "the_pile" in their names.
I'm doing the changes for the subsets that @richarddwang added:
- [x] books3 -> the_pile_books3 https://github.com/huggingface/datasets/pull/2801
- [x] stack_exchange -> the_pile_stack_exchange https://github.com/huggingface/datasets/pull/2803
- [x] openwebtext2 -> the_pile_openwebtext2 https://github.com/huggingface/datasets/pull/2802
For consistency we should also rename `bookcorpusopen` to `the_pile_bookcorpus` IMO, but let me know what you think.
(we can just add a deprecation message to `bookcorpusopen` for now and add `the_pile_bookcorpus`) | {
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"Sounds good. Should we also have a “the_pile” dataset with the subsets as configuration?",
"I think the main `the_pile` datasets will be the one that is the mix of all the subsets: https://the-eye.eu/public/AI/pile/\r\n\r\nWe can also add configurations for each subset, and even allow users to specify the subsets they want:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"the_pile\", subsets=[\"openwebtext2\", \"books3\", \"hn\"])\r\n```\r\n\r\nWe're alrady doing something similar for mC4, where users can specify the list of languages they want to load."
] |
https://api.github.com/repos/huggingface/datasets/issues/4082 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4082/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4082/comments | https://api.github.com/repos/huggingface/datasets/issues/4082/events | https://github.com/huggingface/datasets/pull/4082 | 1,189,965,845 | PR_kwDODunzps41f3fb | 4,082 | Add chrF(++) Metric Card | [] | closed | false | null | 1 | 2022-04-01T15:32:12Z | 2022-04-12T20:43:55Z | 2022-04-12T20:38:06Z | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/2774 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2774/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2774/comments | https://api.github.com/repos/huggingface/datasets/issues/2774/events | https://github.com/huggingface/datasets/pull/2774 | 963,932,199 | MDExOlB1bGxSZXF1ZXN0NzA2NDY2MDc0 | 2,774 | Prevent .map from using multiprocessing when loading from cache | [] | closed | false | null | 6 | 2021-08-09T12:11:38Z | 2021-09-09T10:20:28Z | 2021-09-09T10:20:28Z | null | ## Context
On our setup, we use different setup to train vs proprocessing datasets. Usually we are able to obtain a high number of cpus to preprocess, which allows us to use `num_proc` however we can't use as many during training phase. Currently if we use `num_proc={whatever the preprocessing value was}` we load from cache, but we get:
```
Traceback (most recent call last):
File "lib/python3.8/site-packages/multiprocess/pool.py", line 131, in worker
put((job, i, result))
File "lib/python3.8/site-packages/multiprocess/queues.py", line 371, in put
self._writer.send_bytes(obj)
File "lib/python3.8/site-packages/multiprocess/connection.py", line 203, in send_bytes
self._send_bytes(m[offset:offset + size])
File "lib/python3.8/site-packages/multiprocess/connection.py", line 414, in _send_bytes
self._send(header + buf)
File "lib/python3.8/site-packages/multiprocess/connection.py", line 371, in _send
n = write(self._handle, buf)
BrokenPipeError: [Errno 32] Broken pipe
```
Our current guess, is that we're spawning too many processes compared to the number of cpus available, and it's running OOM. Also we're loading this in DDP setting which means that for each gpu, I need to spawn a high number of processes to match the preprocessing fingerprint.
Instead what we suggest:
- Allow loading shard sequentially, sharing the same fingerprint as the multiprocessed one, in order to leverage multiprocessing when we actually generate the cache, and remove it when loading from cache.
## Current issues
~I'm having a hard time making fingerprints match. For some reason, the multiprocessing and the sequential version generate two different hash.~
**EDIT**: Turns out multiprocessing and sequential have different `transform` value for fingerprinting (check `fingerprint_transform`) when running `_map_single`:
- sequential : `datasets.arrow_dataset.Dataset._map_single`
- multiprocessing: `datasets.arrow_dataset._map_single`
This discrepancy is caused by multiprocessing pickling the transformer function, it doesn't seem to keep the `Dataset` hierarchy. I'm still unclear on why `func.__qual_name__` isn't handled correctly in multiprocessing. But replacing `__qualname__` by `__name__` fixes the issue.
## What was done
~We try to prevent the usage of multiprocessing when loading a dataset. Instead we load all cached shards sequentially.~
I couldn't find a nice way to obtain the cached_file_name and check they all exist before deciding to use the multiprocessing flow or not. Instead I expose an optional boolean `sequential` in `map` method.
## TODO
- [x] Check that the multiprocessed version and the sequential version output the same output
- [x] Check that sequential can load multiprocessed
- [x] Check that multiprocessed can load sequential
## Test
```python
from datasets import load_dataset
from multiprocessing import Pool
import random
def process(batch, rng):
length = len(batch["text"])
return {**batch, "processed_text": [f"PROCESSED {rng.random()}" for _ in range(length)]}
dataset = load_dataset("stas/openwebtext-10k", split="train")
print(dataset.column_names)
print(type(dataset))
rng = random.Random(42)
dataset1 = dataset.map(process, batched=True, batch_size=50, num_proc=4, fn_kwargs={"rng": rng})
# This one should be loaded from cache
rng = random.Random(42)
dataset2 = dataset.map(process, batched=True, batch_size=50, num_proc=4, fn_kwargs={"rng": rng}, sequential=True)
# Just to check that the random generator was correct
print(dataset1[-1]["processed_text"])
print(dataset2[-1]["processed_text"])
```
## Other solutions
I chose to load everything sequentially, but we can probably find a way to load shards in parallel using another number of workers (essentially this would be an argument not used for fingerprinting, allowing to allow `m` shards using `n` processes, which would be very useful when same dataset have to be loaded on two different setup, and we still want to leverage cache).
Also we can use a env variable similarly to `TOKENIZERS_PARALLELISM` as this seems generally setup related (though this changes slightly if we use multiprocessing).
cc @lhoestq (since I had asked you previously on `num_proc` being used for fingerprinting). Don't know if this is acceptable. | {
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"I'm guessing tests are failling, because this was pushed before https://github.com/huggingface/datasets/pull/2779 was merged? cc @albertvillanova ",
"Hi @thomasw21, yes you are right: those failing tests were fixed with #2779.\r\n\r\nWould you mind to merge current upstream master branch and push again?\r\n```\r\ngit checkout sequential_map_when_cached\r\ngit fetch upstream master\r\ngit merge upstream/master\r\ngit push -u origin sequential_map_when_cached\r\n```",
"Thanks for working on this ! I'm sure we can figure something out ;)\r\n\r\nCurrently `map` starts a process to apply the map function on each shard. If the shard has already been processed, then the process that has been spawned loads the processed shard from the cache and returns it.\r\n\r\nI think we should be able to simply not start a process if a shard is already processed and cached.\r\nThis way:\r\n- you won't need to specify `sequential=True`\r\n- it won't create new processes if the dataset is already processed and cached\r\n- it will properly reload each processed shard that is cached\r\n\r\nTo know if we have to start a new process for a shard you can use the function `update_fingerprint` from fingerprint.py to know the expected fingerprint of the processed shard.\r\nThen, if the shard has already been processed, there will be a cache file named `cached-<new_fingerprint>.arrow` and you can load it with\r\n```\r\nDataset.from_file(path_to_cache_file, info=self.info, split=self.split)\r\n```\r\n\r\nLet me know if that makes sense !",
"Yes that makes total sense, I tried to initially do that, except the way fingerprint is handled doesn't allow to easily manipulate such a field. Typically the fingerprinting is handled in `@fingerprint_transform` which has a bunch of params that aren't quite easy to extract. Those params are used to manipulate args, kwargs in fancy ways in order to finally obtain a dictionary used for fingerprint. I could duplicate everything, but this look like a very risky thing to do. I'll take a look if I can make something work with `inspect` if I can make a very simple wrapper.\r\n\r\nA much more simpler solution I think is adding an optional `shard: Optional[int] = None` parameter. If None, we use the number of proc as the number of shards, otherwise we pass down the expected number of shards and use either sequential/multiprocessing (with arbitrary number of workers) to load the shards? This would allow the weird case where one wants a large number of shards with a limited amount of processes. Not the smartest thing to do, but it's not an absurd behaviour. Would this be acceptable?",
"@lhoestq friendly ping as I feel it's up for review.",
"The CI error is unrelated to the changes of this PR - it looks like an SSL issue with conda"
] |
https://api.github.com/repos/huggingface/datasets/issues/5452 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5452/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5452/comments | https://api.github.com/repos/huggingface/datasets/issues/5452/events | https://github.com/huggingface/datasets/pull/5452 | 1,552,655,939 | PR_kwDODunzps5ITcA3 | 5,452 | Swap log messages for symbolic/hard links in tar extractor | [] | closed | false | null | 2 | 2023-01-23T07:53:38Z | 2023-01-23T09:40:55Z | 2023-01-23T08:31:17Z | null | The log messages do not match their if-condition. This PR swaps them.
Found while investigating:
- #5441
CC: @lhoestq | {
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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) | 0.011848 / 0.011353 (0.000495) | 0.006988 / 0.011008 (-0.004020) | 0.138078 / 0.038508 (0.099570) | 0.040310 / 0.023109 (0.017201) | 0.411857 / 0.275898 (0.135959) | 0.509496 / 0.323480 (0.186016) | 0.010695 / 0.007986 (0.002709) | 0.005275 / 0.004328 (0.000946) | 0.107157 / 0.004250 (0.102907) | 0.050987 / 0.037052 (0.013935) | 0.432387 / 0.258489 (0.173898) | 0.495136 / 0.293841 (0.201295) | 0.055273 / 0.128546 (-0.073273) | 0.019573 / 0.075646 (-0.056074) | 0.460356 / 0.419271 (0.041084) | 0.060916 / 0.043533 (0.017383) | 0.426140 / 0.255139 (0.171002) | 0.430461 / 0.283200 (0.147261) | 0.124569 / 0.141683 (-0.017114) | 1.989404 / 1.452155 (0.537250) | 1.942052 / 1.492716 (0.449335) |\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) | 0.287233 / 0.018006 (0.269227) | 0.606056 / 0.000490 (0.605566) | 0.004435 / 0.000200 (0.004235) | 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) | 0.032353 / 0.037411 (-0.005058) | 0.124237 / 0.014526 (0.109711) | 0.143280 / 0.176557 (-0.033276) | 0.182081 / 0.737135 (-0.555055) | 0.148085 / 0.296338 (-0.148253) |\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) | 0.613550 / 0.215209 (0.398341) | 6.172421 / 2.077655 (4.094766) | 2.466018 / 1.504120 (0.961898) | 2.166433 / 1.541195 (0.625238) | 2.192511 / 1.468490 (0.724021) | 1.248777 / 4.584777 (-3.336000) | 5.746150 / 3.745712 (2.000438) | 3.097184 / 5.269862 (-2.172678) | 2.078176 / 4.565676 (-2.487501) | 0.144351 / 0.424275 (-0.279924) | 0.014830 / 0.007607 (0.007223) | 0.761699 / 0.226044 (0.535655) | 7.713201 / 2.268929 (5.444272) | 3.359647 / 55.444624 (-52.084977) | 2.652595 / 6.876477 (-4.223882) | 2.721952 / 2.142072 (0.579880) | 1.493036 / 4.805227 (-3.312192) | 0.252336 / 6.500664 (-6.248328) | 0.082906 / 0.075469 (0.007436) |\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) | 1.643887 / 1.841788 (-0.197901) | 18.762775 / 8.074308 (10.688466) | 22.003583 / 10.191392 (11.812191) | 0.256361 / 0.680424 (-0.424062) | 0.048048 / 0.534201 (-0.486153) | 0.601971 / 0.579283 (0.022688) | 0.712801 / 0.434364 (0.278438) | 0.684473 / 0.540337 (0.144136) | 0.802566 / 1.386936 (-0.584370) |\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) | 0.010410 / 0.011353 (-0.000943) | 0.006719 / 0.011008 (-0.004289) | 0.132862 / 0.038508 (0.094354) | 0.036973 / 0.023109 (0.013863) | 0.470925 / 0.275898 (0.195027) | 0.502864 / 0.323480 (0.179384) | 0.007447 / 0.007986 (-0.000539) | 0.005629 / 0.004328 (0.001301) | 0.091985 / 0.004250 (0.087734) | 0.057537 / 0.037052 (0.020485) | 0.458362 / 0.258489 (0.199873) | 0.518324 / 0.293841 (0.224483) | 0.056540 / 0.128546 (-0.072007) | 0.021266 / 0.075646 (-0.054380) | 0.448289 / 0.419271 (0.029018) | 0.064211 / 0.043533 (0.020678) | 0.492596 / 0.255139 (0.237457) | 0.495030 / 0.283200 (0.211830) | 0.121858 / 0.141683 (-0.019825) | 1.823821 / 1.452155 (0.371667) | 2.012165 / 1.492716 (0.519449) |\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) | 0.296252 / 0.018006 (0.278245) | 0.601688 / 0.000490 (0.601198) | 0.006369 / 0.000200 (0.006169) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035821 / 0.037411 (-0.001590) | 0.132722 / 0.014526 (0.118196) | 0.141819 / 0.176557 (-0.034738) | 0.205115 / 0.737135 (-0.532020) | 0.148917 / 0.296338 (-0.147422) |\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) | 0.678207 / 0.215209 (0.462998) | 6.969918 / 2.077655 (4.892263) | 3.077831 / 1.504120 (1.573711) | 2.689296 / 1.541195 (1.148102) | 2.706462 / 1.468490 (1.237972) | 1.249125 / 4.584777 (-3.335652) | 5.793917 / 3.745712 (2.048205) | 3.137565 / 5.269862 (-2.132297) | 2.056880 / 4.565676 (-2.508796) | 0.151918 / 0.424275 (-0.272357) | 0.015029 / 0.007607 (0.007422) | 0.833975 / 0.226044 (0.607930) | 8.575649 / 2.268929 (6.306720) | 3.812115 / 55.444624 (-51.632509) | 3.124219 / 6.876477 (-3.752258) | 3.178645 / 2.142072 (1.036572) | 1.488260 / 4.805227 (-3.316967) | 0.268239 / 6.500664 (-6.232425) | 0.089463 / 0.075469 (0.013993) |\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) | 1.645461 / 1.841788 (-0.196327) | 19.074412 / 8.074308 (11.000104) | 21.626726 / 10.191392 (11.435334) | 0.210525 / 0.680424 (-0.469899) | 0.032166 / 0.534201 (-0.502035) | 0.555572 / 0.579283 (-0.023711) | 0.654667 / 0.434364 (0.220303) | 0.632471 / 0.540337 (0.092133) | 0.756510 / 1.386936 (-0.630426) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6681c36bbaae9b8b1daa3dbbd4a96b35aaae271b \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/3735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3735/comments | https://api.github.com/repos/huggingface/datasets/issues/3735/events | https://github.com/huggingface/datasets/issues/3735 | 1,140,087,891 | I_kwDODunzps5D9FxT | 3,735 | Performance of `datasets` at scale | [] | open | false | null | 5 | 2022-02-16T14:23:32Z | 2022-03-15T09:15:29Z | null | null | # Performance of `datasets` at 1TB scale
## What is this?
During the processing of a large dataset I monitored the performance of the `datasets` library to see if there are any bottlenecks. The insights of this analysis could guide the decision making to improve the performance of the library.
## Dataset
The dataset is a 1.1TB extract from GitHub with 120M code files and is stored as 5000 `.json.gz` files. The goal of the preprocessing is to remove duplicates and filter files based on their stats. While the calculating of the hashes for deduplication and stats for filtering can be parallelized the filtering itself is run with a single process. After processing the files are pushed to the hub.
## Machine
The experiment was run on a `m1` machine on GCP with 96 CPU cores and 1.3TB RAM.
## Performance breakdown
- Loading the data **3.5h** (_30sec_ from cache)
- **1h57min** single core loading (not sure what is going on here, corresponds to second progress bar)
- **1h10min** multi core json reading
- **20min** remaining time before and after the two main processes mentioned above
- Process the data **2h** (_20min_ from cache)
- **20min** Getting reading for processing
- **40min** Hashing and files stats (96 workers)
- **58min** Deduplication filtering (single worker)
- Save parquet files **5h**
- Saving 1000 parquet files (16 workers)
- Push to hub **37min**
- **34min** git add
- **3min** git push (several hours with `Repository.git_push()`)
## Conclusion
It appears that loading and saving the data is the main bottleneck at that scale (**8.5h**) whereas processing (**2h**) and pushing the data to the hub (**0.5h**) is relatively fast. To optimize the performance at this scale it would make sense to consider such an end-to-end example and target the bottlenecks which seem to be loading from and saving to disk. The processing itself seems to run relatively fast.
## Notes
- map operation on a 1TB dataset with 96 workers requires >1TB RAM
- map operation does not maintain 100% CPU utilization with 96 workers
- sometimes when the script crashes all the data files have a corresponding `*.lock` file in the data folder (or multiple e.g. `*.lock.lock` when it happened a several times). This causes the cache **not** to be triggered (which is significant at that scale) - i guess because there are new data files
- parallelizing `to_parquet` decreased the saving time from 17h to 5h, however adding more workers at this point had almost no effect. not sure if this is:
a) a bug in my parallelization logic,
b) i/o limit to load data form disk to memory or
c) i/o limit to write from memory to disk.
- Using `Repository.git_push()` was much slower than using command line `git-lfs` - 10-20MB/s vs. 300MB/s! The `Dataset.push_to_hub()` function is even slower as it only uploads one file at a time with only a few MB/s, whereas `Repository.git_push()` pushes files in parallel (each at a similar speed).
cc @lhoestq @julien-c @LysandreJik @SBrandeis
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"> using command line git-lfs - [...] 300MB/s!\r\n\r\nwhich server location did you upload from?",
"From GCP region `us-central1-a`.",
"The most surprising part to me is the saving time. Wondering if it could be due to compression (`ParquetWriter` uses SNAPPY compression by default; it can be turned off with `to_parquet(..., compression=None)`). ",
"+1 to what @mariosasko mentioned. Also, @lvwerra did you parallelize `to_parquet` using similar approach in #2747? (we used multiprocessing at the shard level). I'm working on a similar PR to add multi_proc in `to_parquet` which might give you further speed up. \r\nStas benchmarked his approach and mine in this [gist](https://gist.github.com/stas00/dc1597a1e245c5915cfeefa0eee6902c) for `lama` dataset when we were working on adding multi_proc support for `to_json`.",
"@mariosasko I did not turn it off but I can try the next time - I have to run the pipeline again, anyway. \r\n\r\n@bhavitvyamalik Yes, I also sharded the dataset and used multiprocessing to save each shard. I'll have a closer look at your approach, too."
] |
https://api.github.com/repos/huggingface/datasets/issues/1779 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1779/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1779/comments | https://api.github.com/repos/huggingface/datasets/issues/1779/events | https://github.com/huggingface/datasets/pull/1779 | 793,539,703 | MDExOlB1bGxSZXF1ZXN0NTYxMjEwNjI5 | 1,779 | Ignore definition line number of functions for caching | [] | closed | false | null | 0 | 2021-01-25T16:42:29Z | 2021-01-26T10:20:20Z | 2021-01-26T10:20:19Z | null | As noticed in #1718 , when a function used for processing with `map` is moved inside its python file, then the change of line number causes the caching mechanism to consider it as a different function. Therefore in this case, it recomputes everything.
This is because we were not ignoring the line number definition for such functions (even though we're doing it for lambda functions).
For example this code currently prints False:
```python
from datasets.fingerprint import Hasher
# define once
def foo(x):
return x
h = Hasher.hash(foo)
# define a second time elsewhere
def foo(x):
return x
print(h == Hasher.hash(foo))
```
I changed this by ignoring the line number for all functions. | {
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https://api.github.com/repos/huggingface/datasets/issues/5114 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5114/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5114/comments | https://api.github.com/repos/huggingface/datasets/issues/5114/events | https://github.com/huggingface/datasets/issues/5114 | 1,409,236,738 | I_kwDODunzps5T_z8C | 5,114 | load_from_disk with remote filesystem fails due to a wrong temporary local folder path | [
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] | open | false | null | 2 | 2022-10-14T11:54:53Z | 2022-11-19T07:13:10Z | null | null | ## Describe the bug
The function load_from_disk fails when using a remote filesystem because of a wrong temporary path generation in the load_from_disk method of arrow_dataset.py:
```python
if is_remote_filesystem(fs):
src_dataset_path = extract_path_from_uri(dataset_path)
dataset_path = Dataset._build_local_temp_path(src_dataset_path)
fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)
```
If _dataset_path_ is `gs://speech/mydataset/train`, then _src_dataset_path_ will be `speech/mydataset/train` and _dataset_path_ will be something like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train`
Then, after downloading the **folder** _src_dataset_path_, you will get a path like `/var/folders/9s/gf0b/T/tmp6t/speech/mydataset/train/train/state.json` (notice we have train twice)
Instead of downloading the remote folder we should be downloading all the files in the folder for the path to be right:
```python
fs.download(os.path.join(src_dataset_path,*), dataset_path.as_posix(), recursive=True)
```
## Steps to reproduce the bug
```python
fs = gcsfs.GCSFileSystem(**storage_options)
dataset = load_from_disk("common_voice_processed") # loading local dataset previously saved locally, works fine
dataset.save_to_disk(output_dir, fs=fs) #works fine
dataset = load_from_disk(output_dir, fs=fs) # crashes
```
## Expected results
The dataset is loaded
## Actual results
FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/9s/gf0b9jz15d517yrf7m3nvlxr0000gn/T/tmp6t5e221_/speech/datasets/tests/common_voice_processed/train/state.json'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets-2.6.1.dev0
- Platform: mac os monterey 12.5.1
- Python version: 3.8.13
- PyArrow version:pyarrow==9.0.0
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"Hi Hubert! Could you please probably create a publicly available `gs://` dataset link? I think this would be easier for others to directly start to debug.",
"What seems to work is to change the line to:\r\n```\r\nfs.download(src_dataset_path, dataset_path.parent.as_posix(), recursive=True)\r\n```"
] |
https://api.github.com/repos/huggingface/datasets/issues/1866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1866/comments | https://api.github.com/repos/huggingface/datasets/issues/1866/events | https://github.com/huggingface/datasets/pull/1866 | 807,017,816 | MDExOlB1bGxSZXF1ZXN0NTcyMzM3NDQ1 | 1,866 | Add dataset for Financial PhraseBank | [] | closed | false | null | 1 | 2021-02-12T07:30:56Z | 2021-02-17T14:22:36Z | 2021-02-17T14:22:36Z | null | {
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"Thanks for the feedback. All accepted and metadata regenerated."
] |
|
https://api.github.com/repos/huggingface/datasets/issues/2406 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2406/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2406/comments | https://api.github.com/repos/huggingface/datasets/issues/2406/events | https://github.com/huggingface/datasets/issues/2406 | 902,643,844 | MDU6SXNzdWU5MDI2NDM4NDQ= | 2,406 | Add guide on using task templates to documentation | [
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"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
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] | closed | false | null | 0 | 2021-05-26T16:28:26Z | 2022-10-05T17:07:00Z | 2022-10-05T17:07:00Z | null | Once we have a stable API on the text classification and question answering task templates, add a guide on how to use them in the documentation.
| {
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} | https://api.github.com/repos/huggingface/datasets/issues/2406/timeline | null | completed | null | null | false | [] |
https://api.github.com/repos/huggingface/datasets/issues/2021 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2021/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2021/comments | https://api.github.com/repos/huggingface/datasets/issues/2021/events | https://github.com/huggingface/datasets/issues/2021 | 826,988,016 | MDU6SXNzdWU4MjY5ODgwMTY= | 2,021 | Interactively doing save_to_disk and load_from_disk corrupts the datasets object? | [] | closed | false | null | 1 | 2021-03-10T02:48:34Z | 2021-03-13T10:07:41Z | 2021-03-13T10:07:41Z | null | dataset_info.json file saved after using save_to_disk gets corrupted as follows.
Is there a way to disable the cache that will save to /tmp/huggiface/datastes ?
I have a feeling there is a serious issue with cashing. | {
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} | https://api.github.com/repos/huggingface/datasets/issues/2021/timeline | null | completed | null | null | false | [
"Hi,\r\n\r\nCan you give us a minimal reproducible example? This [part](https://huggingface.co/docs/datasets/master/processing.html#controling-the-cache-behavior) of the docs explains how to control caching."
] |
https://api.github.com/repos/huggingface/datasets/issues/468 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/468/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/468/comments | https://api.github.com/repos/huggingface/datasets/issues/468/events | https://github.com/huggingface/datasets/issues/468 | 671,622,441 | MDU6SXNzdWU2NzE2MjI0NDE= | 468 | UnicodeDecodeError while loading PAN-X task of XTREME dataset | [] | closed | false | null | 5 | 2020-08-02T14:05:10Z | 2020-08-20T08:16:08Z | 2020-08-20T08:16:08Z | null | Hi 🤗 team!
## Description of the problem
I'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset:
```
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-5-1d61f439b843> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
528 ignore_verifications = ignore_verifications or save_infos
529 # Download/copy dataset processing script
--> 530 module_path, hash = prepare_module(path, download_config=download_config, dataset=True)
531
532 # Get dataset builder class from the processing script
/usr/local/lib/python3.6/dist-packages/nlp/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)
265
266 # Download external imports if needed
--> 267 imports = get_imports(local_path)
268 local_imports = []
269 library_imports = []
/usr/local/lib/python3.6/dist-packages/nlp/load.py in get_imports(file_path)
156 lines = []
157 with open(file_path, mode="r") as f:
--> 158 lines.extend(f.readlines())
159
160 logger.info("Checking %s for additional imports.", file_path)
/usr/lib/python3.6/encodings/ascii.py in decode(self, input, final)
24 class IncrementalDecoder(codecs.IncrementalDecoder):
25 def decode(self, input, final=False):
---> 26 return codecs.ascii_decode(input, self.errors)[0]
27
28 class StreamWriter(Codec,codecs.StreamWriter):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)
```
## Steps to reproduce
Install from nlp's master branch
```python
pip install git+https://github.com/huggingface/nlp.git
```
then run
```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
```
## OS / platform details
- `nlp` version: latest from master
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: True
- Using distributed or parallel set-up in script?: False
## Proposed solution
Either change [line 762](https://github.com/huggingface/nlp/blob/7ada00b1d62f94eee22a7df38c6b01e3f27194b7/datasets/xtreme/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:
```
# old
with open(filepath) as f
# new
with open(filepath, encoding='utf-8') as f
```
or raise a warning that suggests setting the locale explicitly, e.g.
```python
import locale
locale.setlocale(locale.LC_ALL, 'C.UTF-8')
```
I have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix! | {
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} | https://api.github.com/repos/huggingface/datasets/issues/468/timeline | null | completed | null | null | false | [
"Indeed. Solution 1 is the simplest.\r\n\r\nThis is actually a recurring problem.\r\nI think we should scan all the datasets with regexpr to fix the use of `open()` without encodings.\r\nAnd probably add a test in the CI to forbid using this in the future.",
"I'm happy to tackle the broader problem - will open a PR when it's ready!",
"That would be awesome!",
"I've created a simple function that seems to do the trick:\r\n\r\n```python\r\ndef apply_encoding_on_file_open(filepath: str):\r\n \"\"\"Apply UTF-8 encoding for all instances where a non-binary file is opened.\"\"\"\r\n \r\n with open(filepath, 'r', encoding='utf-8') as input_file:\r\n regexp = re.compile(r\"\"\"\r\n (?!.*\\b(?:encoding|rb|wb|wb+|ab|ab+)\\b)\r\n (open)\r\n \\((.*)\\)\r\n \"\"\")\r\n input_text = input_file.read()\r\n match = regexp.search(input_text)\r\n \r\n if match:\r\n print('Found match!', match.group())\r\n # append utf-8 encoding to matching groups in-place\r\n output = regexp.sub(lambda m: m.group()[:-1]+', encoding=\"utf-8\")', input_text)\r\n with open(filepath, 'w', encoding='utf-8') as output_file:\r\n output_file.write(output)\r\n else:\r\n print(\"No match found!\")\r\n```\r\n\r\nThe regexp does a negative lookahead to avoid matching on cases where the encoding is already specified or when binary files are involved.\r\n\r\nFrom an implementation perspective:\r\n\r\n* Would it make sense to include this function in `nlp-cli` so that we can run something like\r\n```\r\nnlp-cli fix_encoding path/to/folder\r\n```\r\nand the command recursively fixes all files in the target?\r\n* What is the desired behaviour in the CI test? Here we could either have a simple script that we run as a `job` in the CI and raises an error if a missing encoding is detected. Alternatively we could incorporate this behaviour into the CLI and run that in the CI.\r\n\r\nPlease let me know what you prefer among the alternatives.\r\n",
"I realised I was overthinking the problem, so decided to just run the regexp over the codebase and make the PR. In other words, we can ignore my comments about using the CLI 😸 "
] |
https://api.github.com/repos/huggingface/datasets/issues/1137 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1137/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1137/comments | https://api.github.com/repos/huggingface/datasets/issues/1137/events | https://github.com/huggingface/datasets/pull/1137 | 757,358,145 | MDExOlB1bGxSZXF1ZXN0NTMyNzQ4NDAx | 1,137 | add wmt mlqe 2020 shared task | [] | closed | false | null | 1 | 2020-12-04T19:45:34Z | 2020-12-06T19:59:44Z | 2020-12-06T19:53:46Z | null | First commit for Shared task 1 (wmt_mlqw_task1) of WMT20 MLQE (quality estimation of machine translation)
Note that I copied the tags in the README for only one (of the 7 configurations): `en-de`.
There is one configuration for each pair of languages. | {
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"re-created in #1218 because this was too messy"
] |
https://api.github.com/repos/huggingface/datasets/issues/748 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/748/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/748/comments | https://api.github.com/repos/huggingface/datasets/issues/748/events | https://github.com/huggingface/datasets/pull/748 | 726,196,589 | MDExOlB1bGxSZXF1ZXN0NTA3MzAyNjE3 | 748 | New version of CompGuessWhat?! with refined annotations | [] | closed | false | null | 1 | 2020-10-21T06:55:41Z | 2020-10-21T08:52:42Z | 2020-10-21T08:46:19Z | null | This pull request introduces a few fixes to the annotations for VisualGenome in the CompGuessWhat?! original split. | {
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"No worries. Always happy to help and thanks for your support in fixing the issue :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/1720 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1720/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1720/comments | https://api.github.com/repos/huggingface/datasets/issues/1720/events | https://github.com/huggingface/datasets/pull/1720 | 783,721,833 | MDExOlB1bGxSZXF1ZXN0NTUzMDM0MzYx | 1,720 | Adding the NorNE dataset for NER | [] | closed | false | null | 13 | 2021-01-11T21:34:13Z | 2021-03-31T14:23:49Z | 2021-03-31T14:13:17Z | null | NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. | {
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"Quick question, @lhoestq. In this specific dataset, two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. However, I have not found an easy way to implement that. Using splits or configs does not seem appropriate.\r\n",
"About the `GPE_LOC` and `GPE_ORG`. The original NorNE paper in which they published the dataset, does an evaluation on three different NER tag sets, one considering `GPE_LOC` and `GPE_ORG` as they are, another changing them to be just `GPE`, and another one by changing it to become `LOC` and `ORG`. The called these sets, `norne-full`, `norne-7`, and `norne-9`. What I would like is to provide a way for the user of this dataset to get `norne-7` and `norne-9` without having to duplicate the code.",
"Ok I see !\r\nI guess you can have three configurations `norne-full`, `norne-7` and `norne-9`.\r\nEach config can have different feature types. You can simply check for the `self.config.name` in the `_info(self)` method and pick the right ClassLabel names accordingly. And then in `_generate_examples` as well you can check for `self.config.name` to know how to process the labels to yield either GPE_LOC/GPE_ORG, GPE or LOC/ORG",
"But I'm already using the configurations for the different language\nvarieties. So you propose having something like `bokmaal`, `bokmaal-7`,\netc? Would there be a different way? If not, I'd be fine the corpus as it\nis until we come up with a solution. Thanks in any case.\n\n--\nSent using a cell-phone, so sorry for the typos and wrong auto-corrections.\n\nOn Tue, Jan 19, 2021, 4:56 PM Quentin Lhoest <[email protected]>\nwrote:\n\n> Ok I see !\n> I guess you can have three configurations norne-full, norne-7 and norne-9.\n> Each config can have different feature types. You can simply check for the\n> self.config.name in the _info(self) method and pick the right ClassLabel\n> names accordingly. And then in _generate_examples as well you can check\n> for self.config.name to know how to process the labels to yield either\n> GPE_LOC/GPE_ORG, GPE or LOC/ORG\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/1720#issuecomment-762936612>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AABKLYOWNDBD76WZPJHFCWLS2WTTHANCNFSM4V6GSUQA>\n> .\n>\n",
"The first option about having configurations like `bokmaal-7`, `bokmaal-9` etc. would definitely work.\r\n\r\nA second option would be to add a parameter `ner_tags_set` to `NorneConfig` and then one could load them with\r\n```python\r\nbokmaal_full = load_dataset(\"norne\", \"bokmaal\", ner_tags_set=\"norne-full\")\r\n```\r\nfor example.\r\n\r\nWhat do you think ?",
"Hi @versae have you had a chance to consider one of the two options for the config ?\r\nI think both are ok but I have a small preference for the first one since it's simpler to implement.\r\n\r\nFeel free to ping me if you have questions or if I can help :) ",
"Hi @lhoestq. Agree, option 1 seems easier to implement. Just haven't had bandwidth to get to it yet. Hopefully starting next week I'll be able to update the PR.",
"Hi @versae ! Did you manage to add the configurations ? Let me know if we can help you on this",
"Hi @lhoestq, I do actually have to code ready, just need to generate the dummy data for it. ",
"One thing I don't know how to do is to make `_info(self)` return the different NER tags in its `DatasetInfo` object depending on the specific config.",
"OK, I think it's ready now.",
"Closing this one and opening a new one with a cleaner commit log.",
"All set now in #2154."
] |
https://api.github.com/repos/huggingface/datasets/issues/1857 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1857/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1857/comments | https://api.github.com/repos/huggingface/datasets/issues/1857/events | https://github.com/huggingface/datasets/issues/1857 | 805,391,107 | MDU6SXNzdWU4MDUzOTExMDc= | 1,857 | Unable to upload "community provided" dataset - 400 Client Error | [] | closed | false | null | 1 | 2021-02-10T10:39:01Z | 2021-08-03T05:06:13Z | 2021-08-03T05:06:13Z | null | Hi,
i'm trying to a upload a dataset as described [here](https://huggingface.co/docs/datasets/v1.2.0/share_dataset.html#sharing-a-community-provided-dataset). This is what happens:
```
$ datasets-cli login
$ datasets-cli upload_dataset my_dataset
About to upload file /path/to/my_dataset/dataset_infos.json to S3 under filename my_dataset/dataset_infos.json and namespace username
About to upload file /path/to/my_dataset/my_dataset.py to S3 under filename my_dataset/my_dataset.py and namespace username
Proceed? [Y/n] Y
Uploading... This might take a while if files are large
400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/presign
huggingface.co migrated to a new model hosting system.
You need to upgrade to transformers v3.5+ to upload new models.
More info at https://discuss.hugginface.co or https://twitter.com/julien_c. Thank you!
```
I'm using the latest releases of datasets and transformers. | {
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} | https://api.github.com/repos/huggingface/datasets/issues/1857/timeline | null | completed | null | null | false | [
"Hi ! We're in the process of switching the community datasets to git repos, exactly like what we're doing for models.\r\nYou can find an example here:\r\nhttps://huggingface.co/datasets/lhoestq/custom_squad/tree/main\r\n\r\nWe'll update the CLI in the coming days and do a new release :)\r\n\r\nAlso cc @julien-c maybe we can make improve the error message ?"
] |
https://api.github.com/repos/huggingface/datasets/issues/3282 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3282/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3282/comments | https://api.github.com/repos/huggingface/datasets/issues/3282/events | https://github.com/huggingface/datasets/issues/3282 | 1,055,054,898 | I_kwDODunzps4-4twy | 3,282 | ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py | [
{
"color": "E5583E",
"default": false,
"description": "Related to the dataset viewer on huggingface.co",
"id": 3470211881,
"name": "dataset-viewer",
"node_id": "LA_kwDODunzps7O1zsp",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer"
}
] | closed | false | null | 7 | 2021-11-16T16:05:19Z | 2022-04-12T11:57:43Z | 2022-04-12T11:57:43Z | null | ## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*
*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*
```
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```
Am I the one who added this dataset ? No
Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library. | {
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} | https://api.github.com/repos/huggingface/datasets/issues/3282/timeline | null | completed | null | null | false | [
"Hi ! Thanks for reporting :)\r\nI think this is because the dataset is behind an access page. We can fix the dataset viewer\r\n\r\nIf you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.",
"Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.\r\n\r\nRegards!",
"Hi, \r\n\r\nUsing `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.\r\n\r\nShould I leave the issue open until you fix the Dataset viewer issue?",
"Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks",
"The error I get when trying to load OSCAR 21.09 is this\r\n```\r\nConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n```\r\n\r\nThe URL I get in the browser is this\r\n```\r\nhttps://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n```\r\n\r\nMaybe URL is the issue? (resolve vs blob)",
"> The error I get when trying to load OSCAR 21.09 is this\r\n> \r\n> ```\r\n> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> The URL I get in the browser is this\r\n> \r\n> ```\r\n> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> Maybe URL is the issue? (resolve vs blob)\r\n\r\nYou need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:\r\n`\r\nload_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`",
"Fixed.\r\n\r\n<img width=\"1542\" alt=\"Capture d’écran 2022-04-12 à 13 57 24\" src=\"https://user-images.githubusercontent.com/1676121/162957585-af96d19c-f86c-47fe-80c4-2b071083cee4.png\">\r\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/3173 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3173/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3173/comments | https://api.github.com/repos/huggingface/datasets/issues/3173/events | https://github.com/huggingface/datasets/pull/3173 | 1,038,404,300 | PR_kwDODunzps4typcA | 3,173 | Fix issue with filelock filename being too long on encrypted filesystems | [] | closed | false | null | 0 | 2021-10-28T11:28:57Z | 2021-10-29T09:42:24Z | 2021-10-29T09:42:24Z | null | Infer max filename length in filelock on Unix-like systems. Should fix problems on encrypted filesystems such as eCryptfs.
Fix #2924
cc: @lmmx | {
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https://api.github.com/repos/huggingface/datasets/issues/666 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/666/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/666/comments | https://api.github.com/repos/huggingface/datasets/issues/666/events | https://github.com/huggingface/datasets/issues/666 | 707,608,578 | MDU6SXNzdWU3MDc2MDg1Nzg= | 666 | Does both 'bookcorpus' and 'wikipedia' belong to the same datasets which Google used for pretraining BERT? | [] | closed | false | null | 1 | 2020-09-23T19:02:25Z | 2020-10-27T15:19:25Z | 2020-10-27T15:19:25Z | null | {
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"No they are other similar copies but they are not provided by the official Bert models authors."
] |
|
https://api.github.com/repos/huggingface/datasets/issues/3824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3824/comments | https://api.github.com/repos/huggingface/datasets/issues/3824/events | https://github.com/huggingface/datasets/pull/3824 | 1,159,574,186 | PR_kwDODunzps4z85SO | 3,824 | Allow not specifying feature cols other than `predictions`/`references` in `Metric.compute` | [] | closed | false | null | 1 | 2022-03-04T12:04:40Z | 2022-03-04T18:04:22Z | 2022-03-04T18:04:21Z | null | Fix #3818 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3824). All of your documentation changes will be reflected on that endpoint."
] |
https://api.github.com/repos/huggingface/datasets/issues/2666 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2666/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2666/comments | https://api.github.com/repos/huggingface/datasets/issues/2666/events | https://github.com/huggingface/datasets/pull/2666 | 946,825,140 | MDExOlB1bGxSZXF1ZXN0NjkxOTMzMDM1 | 2,666 | Adds CodeClippy dataset [WIP] | [
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] | closed | false | null | 2 | 2021-07-17T13:32:04Z | 2023-07-26T23:06:01Z | 2022-10-03T09:37:35Z | null | CodeClippy is an opensource code dataset scrapped from github during flax-jax-community-week
https://the-eye.eu/public/AI/training_data/code_clippy_data/ | {
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"Thanks for your contribution, @arampacha. 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.",
"Sorry to resurrect a dead issue, but any chance the dataset will make it to HuggingFace? I would love to use it to finetune Llama 2 and HF makes this a breeze. Also happy to submit a PR prepping it for HF if that is needed. "
] |
https://api.github.com/repos/huggingface/datasets/issues/1585 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1585/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1585/comments | https://api.github.com/repos/huggingface/datasets/issues/1585/events | https://github.com/huggingface/datasets/issues/1585 | 768,831,171 | MDU6SXNzdWU3Njg4MzExNzE= | 1,585 | FileNotFoundError for `amazon_polarity` | [] | closed | false | null | 1 | 2020-12-16T12:51:05Z | 2020-12-16T16:02:56Z | 2020-12-16T16:02:56Z | null | Version: `datasets==v1.1.3`
### Reproduction
```python
from datasets import load_dataset
data = load_dataset("amazon_polarity")
```
crashes with
```bash
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/amazon_polarity/amazon_polarity.py
```
and
```bash
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/amazon_polarity/amazon_polarity.py
```
and
```bash
FileNotFoundError: Couldn't find file locally at amazon_polarity/amazon_polarity.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/amazon_polarity/amazon_polarity.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/amazon_polarity/amazon_polarity.py
``` | {
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"Hi @phtephanx , the `amazon_polarity` dataset has not been released yet. It will be available in the coming soon v2of `datasets` :) \r\n\r\nYou can still access it now if you want, but you will need to install datasets via the master branch:\r\n`pip install git+https://github.com/huggingface/datasets.git@master`"
] |
https://api.github.com/repos/huggingface/datasets/issues/4457 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4457/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4457/comments | https://api.github.com/repos/huggingface/datasets/issues/4457/events | https://github.com/huggingface/datasets/pull/4457 | 1,263,531,911 | PR_kwDODunzps45QZCU | 4,457 | First draft of the docs for TF + Datasets | [
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] | closed | false | null | 4 | 2022-06-07T16:06:48Z | 2022-06-14T16:08:41Z | 2022-06-14T15:59:08Z | null | I might cc a few of the other TF people to take a look when this is closer to being finished, but it's still a draft for now. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Some links are still missing I think :)",
"This is probably quite close to being ready, so cc some TF people @gante @amyeroberts @merveenoyan just so they see it! No need for a full review, but if you have any comments or suggestions feel free.",
"Thanks ! We plan to make a new release later today for `to_tf_dataset` FYI, so I think we can merge it soon and include this documentation in the new release"
] |
https://api.github.com/repos/huggingface/datasets/issues/106 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/106/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/106/comments | https://api.github.com/repos/huggingface/datasets/issues/106/events | https://github.com/huggingface/datasets/pull/106 | 618,361,418 | MDExOlB1bGxSZXF1ZXN0NDE4MTAzMjM3 | 106 | Add data dir test command | [] | closed | false | null | 1 | 2020-05-14T16:18:39Z | 2020-05-14T16:49:11Z | 2020-05-14T16:49:10Z | null | {
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"Nice - I think we can merge this. I will update the checksums for `wikihow` then as well"
] |
|
https://api.github.com/repos/huggingface/datasets/issues/2322 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2322/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2322/comments | https://api.github.com/repos/huggingface/datasets/issues/2322/events | https://github.com/huggingface/datasets/issues/2322 | 876,383,853 | MDU6SXNzdWU4NzYzODM4NTM= | 2,322 | Calls to map are not cached. | [
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] | closed | false | null | 6 | 2021-05-05T12:11:27Z | 2021-06-08T19:10:02Z | 2021-06-08T19:08:21Z | null | ## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):
print("executed", i[:10])
return samples
# first call
x = sst.map(foo, batched=True, with_indices=True, num_proc=2)
print('\n'*3, "#" * 30, '\n'*3)
# second call
y = sst.map(foo, batched=True, with_indices=True, num_proc=2)
# print version
import sys
import platform
print(f"""
- Datasets: {datasets.__version__}
- Python: {sys.version}
- Platform: {platform.platform()}
""")
```
## Actual results
This code prints the following output for me:
```bash
No config specified, defaulting to: sst/default
Reusing dataset sst (/home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)
#0: 0%| | 0/5 [00:00<?, ?ba/s]
#1: 0%| | 0/5 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]
executed [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]
executed [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]
executed [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]
executed [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]
executed [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]
#0: 100%|██████████| 5/5 [00:00<00:00, 59.85ba/s]
executed [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]
#1: 100%|██████████| 5/5 [00:00<00:00, 60.85ba/s]
#0: 0%| | 0/1 [00:00<?, ?ba/s]
#1: 0%| | 0/1 [00:00<?, ?ba/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
#0: 100%|██████████| 1/1 [00:00<00:00, 69.32ba/s]
executed [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]
#1: 100%|██████████| 1/1 [00:00<00:00, 70.93ba/s]
#0: 0%| | 0/2 [00:00<?, ?ba/s]
#1: 0%| | 0/2 [00:00<?, ?ba/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
#0: 100%|██████████| 2/2 [00:00<00:00, 63.25ba/s]
executed [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]
executed [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]
#1: 100%|██████████| 2/2 [00:00<00:00, 57.69ba/s]
##############################
#0: 0%| | 0/5 [00:00<?, ?ba/s]
#1: 0%| | 0/5 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]
executed [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]
executed [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]
executed [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]
executed [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]
#0: 100%|██████████| 5/5 [00:00<00:00, 58.10ba/s]
executed [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]
executed [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]
#1: 100%|██████████| 5/5 [00:00<00:00, 57.19ba/s]
#0: 0%| | 0/1 [00:00<?, ?ba/s]
#1: 0%| | 0/1 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
#0: 100%|██████████| 1/1 [00:00<00:00, 60.10ba/s]
executed [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]
#1: 100%|██████████| 1/1 [00:00<00:00, 53.82ba/s]
#0: 0%| | 0/2 [00:00<?, ?ba/s]
#1: 0%| | 0/2 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]
#0: 100%|██████████| 2/2 [00:00<00:00, 72.76ba/s]
executed [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]
#1: 100%|██████████| 2/2 [00:00<00:00, 71.55ba/s]
- Datasets: 1.6.1
- Python: 3.8.3 (default, May 19 2020, 18:47:26)
[GCC 7.3.0]
- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10
```
## Expected results
Caching should work.
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"I tried upgrading to `datasets==1.6.2` and downgrading to `1.6.0`. Both versions produce the same output.\r\n\r\nDowngrading to `1.5.0` works and produces the following output for me:\r\n\r\n```bash\r\nDownloading: 9.20kB [00:00, 3.94MB/s] \r\nDownloading: 5.99kB [00:00, 3.29MB/s] \r\nNo config specified, defaulting to: sst/default\r\nDownloading and preparing dataset sst/default (download: 6.83 MiB, generated: 3.73 MiB, post-processed: Unknown size, total: 10.56 MiB) to /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b...\r\n Dataset sst downloaded and prepared to /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b. Subsequent calls will reuse this data.\r\nexecuted [0, 1]\r\n#0: 0%| | 0/5 [00:00<?, ?ba/s]\r\n#1: 0%| | 0/5 [00:00<?, ?ba/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|██████████| 5/5 [00:00<00:00, 94.83ba/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|██████████| 5/5 [00:00<00:00, 92.75ba/s]\r\nexecuted [0, 1]\r\n#0: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#1: 0%| | 0/1 [00:00<?, ?ba/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#0: 100%|██████████| 1/1 [00:00<00:00, 118.81ba/s]\r\n#1: 100%|██████████| 1/1 [00:00<00:00, 123.06ba/s]\r\nexecuted [0, 1]\r\n#0: 0%| | 0/2 [00:00<?, ?ba/s]\r\n#1: 0%| | 0/2 [00:00<?, ?ba/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|██████████| 2/2 [00:00<00:00, 119.42ba/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|██████████| 2/2 [00:00<00:00, 123.33ba/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\nexecuted [0, 1]\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-6079777aa097c8f8.arrow\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-2dc05c46f68eda6e.arrow\r\nexecuted [0, 1]\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-1ca347e7430b98f1.arrow\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-c0f1a73ce3ba40cd.arrow\r\nexecuted [0, 1]\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-832a1407bf1ac5b7.arrow\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-036316a259b773c4.arrow\r\n- Datasets: 1.5.0\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```",
"Hi,\r\n\r\nset `keep_in_memory` to False when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them):\r\n\r\nhttps://github.com/huggingface/datasets/blob/241a0b4a3a868778ee91e767ad406f9da7610df2/src/datasets/arrow_dataset.py#L1718\r\n\r\n@albertvillanova It seems like this behavior was overlooked in #2182.\r\n\r\n",
"Hi @villmow, thanks for reporting. \r\n\r\nAs @mariosasko has pointed out, we did not consider this case when introducing the feature of automatic in-memory for small datasets. This needs to be fixed.",
"Hi ! Currently a dataset that is in memory doesn't know doesn't know in which directory it has to read/write cache files.\r\nOn the other hand, a dataset that loaded from the disk (via memory mapping) uses the directory from which the dataset is located to read/write cache files.\r\n\r\nBecause of that, currently in-memory datasets simply don't use caching.\r\n\r\nMaybe a Dataset object could have a `cache_dir` that is set to the directory where the arrow files are created during `load_dataset` ?",
"Fixed once reverted the default in-memory feature:\r\nClosed by #2460 (to close issue #2458).",
"Please @villmow, feel free to update to `Datasets` latest version (1.8)."
] |
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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) | 0.007700 / 0.011353 (-0.003653) | 0.004680 / 0.011008 (-0.006328) | 0.098812 / 0.038508 (0.060304) | 0.085062 / 0.023109 (0.061952) | 0.371472 / 0.275898 (0.095574) | 0.412552 / 0.323480 (0.089072) | 0.004700 / 0.007986 (-0.003285) | 0.003765 / 0.004328 (-0.000564) | 0.074267 / 0.004250 (0.070017) | 0.063003 / 0.037052 (0.025951) | 0.391842 / 0.258489 (0.133353) | 0.436955 / 0.293841 (0.143114) | 0.035291 / 0.128546 (-0.093255) | 0.009309 / 0.075646 (-0.066338) | 0.313097 / 0.419271 (-0.106174) | 0.060098 / 0.043533 (0.016565) | 0.350726 / 0.255139 (0.095587) | 0.402692 / 0.283200 (0.119493) | 0.029321 / 0.141683 (-0.112361) | 1.671806 / 1.452155 (0.219651) | 1.743760 / 1.492716 (0.251044) |\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) | 0.242281 / 0.018006 (0.224275) | 0.505054 / 0.000490 (0.504564) | 0.006595 / 0.000200 (0.006395) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032174 / 0.037411 (-0.005238) | 0.094483 / 0.014526 (0.079957) | 0.108527 / 0.176557 (-0.068030) | 0.178983 / 0.737135 (-0.558152) | 0.113766 / 0.296338 (-0.182572) |\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) | 0.419764 / 0.215209 (0.204555) | 4.282650 / 2.077655 (2.204995) | 2.075325 / 1.504120 (0.571205) | 1.897668 / 1.541195 (0.356473) | 2.027109 / 1.468490 (0.558619) | 0.519983 / 4.584777 (-4.064794) | 4.134603 / 3.745712 (0.388891) | 6.586711 / 5.269862 (1.316849) | 3.811726 / 4.565676 (-0.753951) | 0.058628 / 0.424275 (-0.365647) | 0.007586 / 0.007607 (-0.000021) | 0.502180 / 0.226044 (0.276136) | 5.101588 / 2.268929 (2.832660) | 2.534295 / 55.444624 (-52.910330) | 2.220170 / 6.876477 (-4.656307) | 2.441110 / 2.142072 (0.299038) | 0.644775 / 4.805227 (-4.160452) | 0.144716 / 6.500664 (-6.355948) | 0.067018 / 0.075469 (-0.008451) |\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) | 1.431279 / 1.841788 (-0.410508) | 21.947814 / 8.074308 (13.873506) | 15.548236 / 10.191392 (5.356844) | 0.174774 / 0.680424 (-0.505650) | 0.021182 / 0.534201 (-0.513019) | 0.441320 / 0.579283 (-0.137963) | 0.476685 / 0.434364 (0.042321) | 0.506277 / 0.540337 (-0.034060) | 0.809943 / 1.386936 (-0.576993) |\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) | 0.007172 / 0.011353 (-0.004181) | 0.004358 / 0.011008 (-0.006650) | 0.068604 / 0.038508 (0.030096) | 0.083956 / 0.023109 (0.060847) | 0.402579 / 0.275898 (0.126681) | 0.444714 / 0.323480 (0.121235) | 0.005940 / 0.007986 (-0.002046) | 0.003607 / 0.004328 (-0.000722) | 0.073134 / 0.004250 (0.068883) | 0.061722 / 0.037052 (0.024669) | 0.410957 / 0.258489 (0.152468) | 0.458819 / 0.293841 (0.164978) | 0.033710 / 0.128546 (-0.094836) | 0.010230 / 0.075646 (-0.065417) | 0.084678 / 0.419271 (-0.334593) | 0.058203 / 0.043533 (0.014670) | 0.444972 / 0.255139 (0.189833) | 0.470962 / 0.283200 (0.187763) | 0.029222 / 0.141683 (-0.112461) | 1.671460 / 1.452155 (0.219306) | 1.759471 / 1.492716 (0.266754) |\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) | 0.238894 / 0.018006 (0.220888) | 0.493605 / 0.000490 (0.493115) | 0.001979 / 0.000200 (0.001780) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036498 / 0.037411 (-0.000913) | 0.095245 / 0.014526 (0.080719) | 0.112147 / 0.176557 (-0.064409) | 0.171128 / 0.737135 (-0.566007) | 0.115295 / 0.296338 (-0.181044) |\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) | 0.461067 / 0.215209 (0.245858) | 4.723932 / 2.077655 (2.646277) | 2.432697 / 1.504120 (0.928578) | 2.237302 / 1.541195 (0.696107) | 2.351320 / 1.468490 (0.882830) | 0.509963 / 4.584777 (-4.074813) | 4.194817 / 3.745712 (0.449105) | 6.689529 / 5.269862 (1.419667) | 3.351198 / 4.565676 (-1.214478) | 0.064563 / 0.424275 (-0.359712) | 0.008605 / 0.007607 (0.000998) | 0.575590 / 0.226044 (0.349546) | 5.644179 / 2.268929 (3.375250) | 3.021375 / 55.444624 (-52.423249) | 2.595305 / 6.876477 (-4.281172) | 2.839228 / 2.142072 (0.697156) | 0.657148 / 4.805227 (-4.148079) | 0.144831 / 6.500664 (-6.355834) | 0.067882 / 0.075469 (-0.007587) |\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) | 1.595580 / 1.841788 (-0.246208) | 22.431609 / 8.074308 (14.357301) | 15.700845 / 10.191392 (5.509453) | 0.164675 / 0.680424 (-0.515749) | 0.021322 / 0.534201 (-0.512879) | 0.455270 / 0.579283 (-0.124013) | 0.451547 / 0.434364 (0.017183) | 0.520955 / 0.540337 (-0.019383) | 0.687803 / 1.386936 (-0.699133) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d19574e9f44bd3b59a3e47ca7c4ea66305a8e6b \"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) | 0.008171 / 0.011353 (-0.003182) | 0.005563 / 0.011008 (-0.005445) | 0.102265 / 0.038508 (0.063757) | 0.074755 / 0.023109 (0.051646) | 0.431317 / 0.275898 (0.155419) | 0.472179 / 0.323480 (0.148699) | 0.006153 / 0.007986 (-0.001833) | 0.003832 / 0.004328 (-0.000496) | 0.078480 / 0.004250 (0.074230) | 0.056250 / 0.037052 (0.019197) | 0.432938 / 0.258489 (0.174449) | 0.480983 / 0.293841 (0.187142) | 0.048861 / 0.128546 (-0.079685) | 0.016252 / 0.075646 (-0.059394) | 0.343508 / 0.419271 (-0.075763) | 0.065057 / 0.043533 (0.021524) | 0.468418 / 0.255139 (0.213279) | 0.463692 / 0.283200 (0.180492) | 0.032912 / 0.141683 (-0.108771) | 1.795194 / 1.452155 (0.343039) | 1.833047 / 1.492716 (0.340331) |\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) | 0.197980 / 0.018006 (0.179974) | 0.500662 / 0.000490 (0.500172) | 0.007380 / 0.000200 (0.007181) | 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) | 0.028323 / 0.037411 (-0.009089) | 0.089817 / 0.014526 (0.075291) | 0.102923 / 0.176557 (-0.073633) | 0.173851 / 0.737135 (-0.563284) | 0.104006 / 0.296338 (-0.192333) |\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) | 0.580277 / 0.215209 (0.365068) | 5.878739 / 2.077655 (3.801085) | 2.404673 / 1.504120 (0.900553) | 2.071765 / 1.541195 (0.530571) | 2.106024 / 1.468490 (0.637534) | 0.855217 / 4.584777 (-3.729560) | 4.918602 / 3.745712 (1.172890) | 5.354984 / 5.269862 (0.085122) | 3.141288 / 4.565676 (-1.424389) | 0.099553 / 0.424275 (-0.324723) | 0.008152 / 0.007607 (0.000545) | 0.709857 / 0.226044 (0.483813) | 7.144602 / 2.268929 (4.875673) | 3.137637 / 55.444624 (-52.306987) | 2.379851 / 6.876477 (-4.496626) | 2.346426 / 2.142072 (0.204353) | 1.033416 / 4.805227 (-3.771811) | 0.213120 / 6.500664 (-6.287544) | 0.076037 / 0.075469 (0.000568) |\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) | 1.597742 / 1.841788 (-0.244046) | 21.745366 / 8.074308 (13.671058) | 20.830698 / 10.191392 (10.639306) | 0.238727 / 0.680424 (-0.441697) | 0.027923 / 0.534201 (-0.506278) | 0.466073 / 0.579283 (-0.113210) | 0.548647 / 0.434364 (0.114283) | 0.549245 / 0.540337 (0.008908) | 0.977148 / 1.386936 (-0.409788) |\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) | 0.008252 / 0.011353 (-0.003101) | 0.004653 / 0.011008 (-0.006356) | 0.084012 / 0.038508 (0.045504) | 0.077418 / 0.023109 (0.054309) | 0.440748 / 0.275898 (0.164850) | 0.464279 / 0.323480 (0.140799) | 0.005762 / 0.007986 (-0.002224) | 0.004909 / 0.004328 (0.000581) | 0.086441 / 0.004250 (0.082190) | 0.057883 / 0.037052 (0.020831) | 0.466655 / 0.258489 (0.208166) | 0.479751 / 0.293841 (0.185910) | 0.047166 / 0.128546 (-0.081380) | 0.014480 / 0.075646 (-0.061166) | 0.092599 / 0.419271 (-0.326672) | 0.062454 / 0.043533 (0.018921) | 0.449753 / 0.255139 (0.194614) | 0.461876 / 0.283200 (0.178676) | 0.034828 / 0.141683 (-0.106855) | 1.752249 / 1.452155 (0.300095) | 1.865449 / 1.492716 (0.372732) |\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) | 0.245028 / 0.018006 (0.227022) | 0.509564 / 0.000490 (0.509074) | 0.003930 / 0.000200 (0.003730) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034746 / 0.037411 (-0.002665) | 0.096563 / 0.014526 (0.082037) | 0.107581 / 0.176557 (-0.068975) | 0.184952 / 0.737135 (-0.552184) | 0.108747 / 0.296338 (-0.187591) |\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) | 0.613091 / 0.215209 (0.397882) | 5.994985 / 2.077655 (3.917330) | 2.711276 / 1.504120 (1.207156) | 2.415862 / 1.541195 (0.874668) | 2.391055 / 1.468490 (0.922565) | 0.868723 / 4.584777 (-3.716054) | 4.953992 / 3.745712 (1.208280) | 4.606542 / 5.269862 (-0.663319) | 2.942162 / 4.565676 (-1.623515) | 0.102737 / 0.424275 (-0.321538) | 0.008634 / 0.007607 (0.001027) | 0.722122 / 0.226044 (0.496078) | 7.245097 / 2.268929 (4.976168) | 3.428232 / 55.444624 (-52.016393) | 2.709539 / 6.876477 (-4.166938) | 2.857956 / 2.142072 (0.715884) | 1.045594 / 4.805227 (-3.759634) | 0.213344 / 6.500664 (-6.287320) | 0.073601 / 0.075469 (-0.001868) |\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) | 1.651954 / 1.841788 (-0.189834) | 22.458646 / 8.074308 (14.384338) | 19.583203 / 10.191392 (9.391811) | 0.246932 / 0.680424 (-0.433492) | 0.025730 / 0.534201 (-0.508471) | 0.473475 / 0.579283 (-0.105808) | 0.521411 / 0.434364 (0.087047) | 0.562038 / 0.540337 (0.021700) | 0.767673 / 1.386936 (-0.619263) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3869d99628329c696f6975377f65e625dd8ef3e0 \"CML watermark\")\n",
"The CI error is unrelated.",
"<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) | 0.006649 / 0.011353 (-0.004703) | 0.003963 / 0.011008 (-0.007045) | 0.084564 / 0.038508 (0.046056) | 0.075668 / 0.023109 (0.052559) | 0.314233 / 0.275898 (0.038335) | 0.343320 / 0.323480 (0.019841) | 0.005405 / 0.007986 (-0.002581) | 0.003356 / 0.004328 (-0.000973) | 0.065094 / 0.004250 (0.060844) | 0.058774 / 0.037052 (0.021722) | 0.320772 / 0.258489 (0.062283) | 0.353546 / 0.293841 (0.059705) | 0.030921 / 0.128546 (-0.097625) | 0.008463 / 0.075646 (-0.067184) | 0.287490 / 0.419271 (-0.131781) | 0.053188 / 0.043533 (0.009656) | 0.324023 / 0.255139 (0.068884) | 0.337828 / 0.283200 (0.054628) | 0.024764 / 0.141683 (-0.116918) | 1.458028 / 1.452155 (0.005873) | 1.521615 / 1.492716 (0.028899) |\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) | 0.209360 / 0.018006 (0.191353) | 0.461331 / 0.000490 (0.460841) | 0.000386 / 0.000200 (0.000186) | 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) | 0.028405 / 0.037411 (-0.009006) | 0.081074 / 0.014526 (0.066548) | 0.094868 / 0.176557 (-0.081689) | 0.151050 / 0.737135 (-0.586085) | 0.095854 / 0.296338 (-0.200484) |\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) | 0.393957 / 0.215209 (0.178748) | 3.938649 / 2.077655 (1.860994) | 1.938190 / 1.504120 (0.434070) | 1.766458 / 1.541195 (0.225263) | 1.818028 / 1.468490 (0.349538) | 0.483926 / 4.584777 (-4.100851) | 3.641957 / 3.745712 (-0.103755) | 4.883845 / 5.269862 (-0.386016) | 2.960300 / 4.565676 (-1.605377) | 0.057227 / 0.424275 (-0.367048) | 0.007285 / 0.007607 (-0.000322) | 0.475928 / 0.226044 (0.249884) | 4.756757 / 2.268929 (2.487828) | 2.502659 / 55.444624 (-52.941966) | 2.178067 / 6.876477 (-4.698410) | 2.378298 / 2.142072 (0.236226) | 0.578639 / 4.805227 (-4.226588) | 0.132512 / 6.500664 (-6.368152) | 0.059656 / 0.075469 (-0.015813) |\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) | 1.272673 / 1.841788 (-0.569115) | 19.266884 / 8.074308 (11.192576) | 14.272930 / 10.191392 (4.081538) | 0.165897 / 0.680424 (-0.514527) | 0.018436 / 0.534201 (-0.515765) | 0.395177 / 0.579283 (-0.184107) | 0.420134 / 0.434364 (-0.014229) | 0.460781 / 0.540337 (-0.079557) | 0.645376 / 1.386936 (-0.741560) |\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) | 0.006504 / 0.011353 (-0.004849) | 0.003942 / 0.011008 (-0.007066) | 0.064936 / 0.038508 (0.026428) | 0.075015 / 0.023109 (0.051905) | 0.396871 / 0.275898 (0.120973) | 0.423448 / 0.323480 (0.099968) | 0.005239 / 0.007986 (-0.002747) | 0.003265 / 0.004328 (-0.001063) | 0.064910 / 0.004250 (0.060660) | 0.055006 / 0.037052 (0.017953) | 0.392818 / 0.258489 (0.134329) | 0.429735 / 0.293841 (0.135894) | 0.031847 / 0.128546 (-0.096699) | 0.008626 / 0.075646 (-0.067021) | 0.071591 / 0.419271 (-0.347681) | 0.049006 / 0.043533 (0.005473) | 0.384913 / 0.255139 (0.129774) | 0.408969 / 0.283200 (0.125769) | 0.023573 / 0.141683 (-0.118110) | 1.490271 / 1.452155 (0.038117) | 1.564620 / 1.492716 (0.071904) |\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) | 0.225917 / 0.018006 (0.207911) | 0.450369 / 0.000490 (0.449880) | 0.000375 / 0.000200 (0.000175) | 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) | 0.031196 / 0.037411 (-0.006215) | 0.090486 / 0.014526 (0.075960) | 0.102326 / 0.176557 (-0.074231) | 0.157483 / 0.737135 (-0.579653) | 0.103670 / 0.296338 (-0.192668) |\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) | 0.417577 / 0.215209 (0.202368) | 4.170798 / 2.077655 (2.093143) | 2.123689 / 1.504120 (0.619569) | 1.948231 / 1.541195 (0.407037) | 2.040277 / 1.468490 (0.571787) | 0.497919 / 4.584777 (-4.086858) | 3.633270 / 3.745712 (-0.112442) | 4.851698 / 5.269862 (-0.418164) | 2.691992 / 4.565676 (-1.873684) | 0.058641 / 0.424275 (-0.365634) | 0.007719 / 0.007607 (0.000112) | 0.500652 / 0.226044 (0.274607) | 4.988657 / 2.268929 (2.719728) | 2.604488 / 55.444624 (-52.840136) | 2.329829 / 6.876477 (-4.546648) | 2.468239 / 2.142072 (0.326167) | 0.598724 / 4.805227 (-4.206503) | 0.135959 / 6.500664 (-6.364706) | 0.061088 / 0.075469 (-0.014381) |\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) | 1.352107 / 1.841788 (-0.489681) | 19.973976 / 8.074308 (11.899668) | 14.292812 / 10.191392 (4.101420) | 0.163855 / 0.680424 (-0.516568) | 0.018402 / 0.534201 (-0.515799) | 0.393128 / 0.579283 (-0.186155) | 0.407379 / 0.434364 (-0.026985) | 0.462324 / 0.540337 (-0.078013) | 0.607501 / 1.386936 (-0.779435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae126ac974cad3050f90106e5909232140786811 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/605 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/605/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/605/comments | https://api.github.com/repos/huggingface/datasets/issues/605/events | https://github.com/huggingface/datasets/pull/605 | 697,887,401 | MDExOlB1bGxSZXF1ZXN0NDgzNzg1Mjc1 | 605 | [Datasets] Transmit format to children | [] | closed | false | null | 1 | 2020-09-10T12:30:18Z | 2020-09-10T16:15:21Z | 2020-09-10T16:15:21Z | null | Transmit format to children obtained when processing a dataset.
Added a test.
When concatenating datasets, if the formats are disparate, the concatenated dataset has a format reset to defaults. | {
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"Closing as #607 was merged"
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https://api.github.com/repos/huggingface/datasets/issues/4165 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4165/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4165/comments | https://api.github.com/repos/huggingface/datasets/issues/4165/events | https://github.com/huggingface/datasets/pull/4165 | 1,203,730,187 | PR_kwDODunzps42MubF | 4,165 | Fix google bleu typos, examples | [] | closed | false | null | 1 | 2022-04-13T19:59:54Z | 2022-05-03T12:23:52Z | 2022-05-03T12:16:44Z | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/2081 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2081/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2081/comments | https://api.github.com/repos/huggingface/datasets/issues/2081/events | https://github.com/huggingface/datasets/pull/2081 | 835,112,968 | MDExOlB1bGxSZXF1ZXN0NTk1ODE3OTM4 | 2,081 | Fix docstrings issues | [
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https://api.github.com/repos/huggingface/datasets/issues/2996 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2996/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2996/comments | https://api.github.com/repos/huggingface/datasets/issues/2996/events | https://github.com/huggingface/datasets/pull/2996 | 1,013,266,373 | PR_kwDODunzps4sjrP6 | 2,996 | Remove all query parameters when extracting protocol | [] | closed | false | null | 4 | 2021-10-01T12:05:34Z | 2021-10-04T08:48:13Z | 2021-10-04T08:48:13Z | null | Fix `_get_extraction_protocol` to remove all query parameters, like `?raw=true`, `?dl=1`,... | {
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"Beware of cases like: `http://ufal.ms.mff.cuni.cz/umc/005-en-ur/download.php?f=umc005-corpus.zip` or `gzip://bg-cs.xml::https://opus.nlpl.eu/download.php?f=Europarl/v8/xml/bg-cs.xml.gz`. I see these URLs in the errors (https://observablehq.com/@huggingface/quality-assessment-of-datasets-loading?collection=@huggingface/datasets), but not in the `Extraction protocol for file at xxx is not implemented yet` error, so I'm not sure if they would break now or not.\r\n\r\nMaybe: first try to find an extension, and if none, try to remove the `?...` part and retry to find the extension.\r\n\r\nBy the way, here is the list of URLs for errors of this type, with a '?' in the URL:\r\n\r\n```\r\nhttps://dl.orangedox.com/WyaCpL?dl=1\r\nhttps://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download\r\nhttps://drive.google.com/u/0/uc?id=1-CaP3xHgZxOGjQ3pXC5tr9YnIajmel-t&export=download\r\nhttps://drive.google.com/u/0/uc?id=11EBGHMAswT5JDO60xh7gnZfYjpMQs7h7&export=download\r\nhttps://drive.google.com/u/0/uc?id=13JCCr-IjZK7uhbLXeufptr_AxvsKinVl&export=download\r\nhttps://drive.google.com/u/0/uc?id=13ZyFc2qepAYSg9WIFaeJ9y402gblsl2e&export=download\r\nhttps://drive.google.com/u/0/uc?id=15auwrFAlq52JJ61u7eSfnhT9rZtI5sjk&export=download\r\nhttps://drive.google.com/u/0/uc?id=16OgJ_OrfzUF_i3ftLjFn9kpcyoi7UJeO&export=download\r\nhttps://drive.google.com/u/0/uc?id=1BFYF05rx-DK9Eb5hgoIgd6EcB8zOI-zu&export=download\r\nhttps://drive.google.com/u/0/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download\r\nhttps://drive.google.com/u/0/uc?id=1H7FphKVVCYoH49sUXl79CuztEfJLaKoF&export=download\r\nhttps://drive.google.com/u/0/uc?id=1NAeuWLgYBzLwU5jCdkrtj4_PRUocuvlb&export=download\r\nhttps://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\r\nhttps://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/\r\nhttps://drive.google.com/u/0/uc?id=1R1jR4DcH2UEaM1ZwDSRHdfTGvkCNu6NW&export=download\r\nhttps://drive.google.com/u/0/uc?id=1hDHeoFIfQzJec1NgZNXh3CTNbchiIvuG&export=download\r\nhttps://drive.google.com/u/0/uc?id=1wxwqnWGRzwvc_-ugRoFX8BPgpO3Q7sch&export=download\r\nhttps://drive.google.com/u/0/uc?id=1ydsOTvBZXKqcRvXawOuePrJ99slOEbkk&export=download\r\nhttps://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ\r\nhttps://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k\r\nhttps://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0\r\nhttps://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU\r\nhttps://drive.google.com/uc?export=download&id=1-w-0uqaC6hnRn1F_3XqJEvi09zlcTIhX\r\nhttps://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff\r\nhttps://drive.google.com/uc?export=download&id=17FGi8KI9N9SuGe7elM8qU8_3fx4sfgTr\r\nhttps://drive.google.com/uc?export=download&id=1AHUm1-_V9GCtGuDcc8XrMUCJE8B-HHoL\r\nhttps://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U\r\nhttps://drive.google.com/uc?export=download&id=1Ev4RqWcPsLI9rgOGAKh-_dFKqcEZ1u-G\r\nhttps://drive.google.com/uc?export=download&id=1GTHUJxxmjLmG2lnF9dwRgIDRFZaOY3-F\r\nhttps://drive.google.com/uc?export=download&id=1GcUN6mytEcOMBBOvjJOQzBmEkc-LdgQg\r\nhttps://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa\r\nhttps://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P\r\nhttps://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil\r\nhttps://drive.google.com/uc?export=download&id=1M1M5yIOyjKWGprc3LUeVVwxgKXxgpqxm\r\nhttps://drive.google.com/uc?export=download&id=1Nug7-Sri50mkJL4GrWw6C2ZIbfeU-6Am\r\nhttps://drive.google.com/uc?export=download&id=1PGa8j1_IqxiGTc3SU6NMB38sAzxCPS34\r\nhttps://drive.google.com/uc?export=download&id=1QsV8C5EPJrQl37mwva_5-IJOrCaOi2tH\r\nhttps://drive.google.com/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ\r\nhttps://drive.google.com/uc?export=download&id=1TuWH7uwu6V90QWmZn25qhou1rm97Egmn\r\nhttps://drive.google.com/uc?export=download&id=1U7WdBpd9kJ85S7BbBhWUSiy9NnXrKdO6\r\nhttps://drive.google.com/uc?export=download&id=1USoQ8lJgN8kAWnUnRrupMGrPMLlDVqlV\r\nhttps://drive.google.com/uc?export=download&id=1Uit4Og1pk-br_0UJIO5sdhApyhTuHzqo\r\nhttps://drive.google.com/uc?export=download&id=1Z2ty5hU0tIGRZRDlFQZLO7b5vijRfvo0\r\nhttps://drive.google.com/uc?export=download&id=1ZyFGufe4puX3vjGPbp4xg9Hca3Gwq22g\r\nhttps://drive.google.com/uc?export=download&id=1ZzlIQvw1KNBG97QQCfdatvVrrbeLaM1u\r\nhttps://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0\r\nhttps://drive.google.com/uc?export=download&id=1__EjA6oZsgXQpggPm-h54jZu3kP6Y6zu\r\nhttps://drive.google.com/uc?export=download&id=1aHPVfC5TrlnUjehtagVZoDfq4VccgaNT\r\nhttps://drive.google.com/uc?export=download&id=1cqu_YAgvlyVSzzjcUyP1Cz7q0k8Pw7vN\r\nhttps://drive.google.com/uc?export=download&id=1dUIqVwvoZAtbX_-z5axCoe97XNcFo1No\r\nhttps://drive.google.com/uc?export=download&id=1eTtRs5cUlBP5dXsx-FTAlmXuB6JQi2qj\r\nhttps://drive.google.com/uc?export=download&id=1fUR3MqJ8jTMka6owA0S-Fe6aHmiophc_\r\nhttps://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF\r\nhttps://drive.google.com/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx\r\nhttps://drive.google.com/uc?export=download&id=1meSNZHxd_0TZLKCRCYGN-Ke3IA5c1qOE\r\nhttps://drive.google.com/uc?export=download&id=1okwGJiOZmTpNRNgJLCnjFF4Q0H1z4l6_\r\nhttps://drive.google.com/uc?export=download&id=1phryJg4FjCFkn0mSCqIOP2-FscAeKGV0\r\nhttps://drive.google.com/uc?export=download&id=1s8NSFT4Kz0caKZ4VybPNzt88F8ZanprY\r\nhttps://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\r\nhttps://drive.google.com/uc?export=download&id=1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA\r\nhttps://drive.google.com/uc?id=12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X\r\nhttps://drive.google.com/uc?id=1PGH5H_oW7wUvMw_5xaXvbEN7DFll-wDX\r\nhttps://github.com/MaazAmjad/Datasets-for-Urdu-news/blob/master/Urdu%20Fake%20News%20Dataset.zip?raw=true\r\nhttps://github.com/TevenLeScao/glucose/blob/master/GLUCOSE_training_data.zip?raw=true\r\nhttps://github.com/TevenLeScao/what-time-is-it/blob/master/gutenberg_time_phrases.zip?raw=true\r\nhttps://github.com/aviaefrat/cryptonite/blob/main/data/cryptonite-official-split.zip?raw=true\r\nhttps://github.com/facebookresearch/Imppres/blob/master/dataset/IMPPRES.zip?raw=true\r\nhttps://github.com/ljos/navnkjenner/blob/master/data/bokmaal/no_bokmaal-ud-train.bioes?raw=true\r\nhttps://github.com/ljos/navnkjenner/blob/master/data/nynorsk/no_nynorsk-ud-train.bioes?raw=true\r\nhttps://github.com/ljos/navnkjenner/blob/master/data/samnorsk/no_samnorsk-ud-train.bioes?raw=true\r\nhttps://github.com/mirfan899/Urdu/blob/master/sentiment/imdb_urdu_reviews.csv.tar.gz?raw=true\r\nhttps://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew/blob/master/data/morph_train.tsv?raw=true\r\nhttps://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew/blob/master/data/token_train.tsv?raw=true\r\nhttps://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11858/00-097C-0000-0023-625F-0/hindencorp05.plaintext.gz?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/299/nchlt_afrikaans_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/312/nchlt_isixhosa_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/319/nchlt_isizulu_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/328/nchlt_sepedi_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/334/nchlt_sesotho_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/341/nchlt_setswana_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://repo.sadilar.org/bitstream/handle/20.500.12185/346/nchlt_siswati_named_entity_annotated_corpus.zip?sequence=3&isAllowed=y\r\nhttps://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1\r\nhttps://zenodo.org/record/1043504/files/corpus-webis-tldr-17.zip?download=1\r\nhttps://zenodo.org/record/1489920/files/articles-training-byarticle-20181122.zip?download=1\r\nhttps://zenodo.org/record/1489920/files/articles-training-bypublisher-20181122.zip?download=1\r\nhttps://zenodo.org/record/2787612/files/SICK.zip?download=1\r\nhttps://zenodo.org/record/3553423/files/Swahili%20data.zip?download=1\r\nhttps://zenodo.org/record/3707949/files/tapaco_v1.0.zip?download=1\r\nhttps://zenodo.org/record/4300294/files/train.csv?download=1\r\n```\r\n\r\n",
"Hi @severo, I just saw your comment. Thank you.\r\n\r\nFinally I just swapped the 2 parsings: first I extract extension and then I remove query parameters. 😉 ",
"OK :) Maybe we should add some unit tests to ensure we improve the detection without regressions (it's Friday afternoon, I trust the unit tests more than my analysis of the code)",
"Great! For the tests, I think we should also add some URLs in the form: `http://ufal.ms.mff.cuni.cz/umc/005-en-ur/download.php?f=umc005-corpus.zip` to be sure they are still correctly detected."
] |
https://api.github.com/repos/huggingface/datasets/issues/4698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4698/comments | https://api.github.com/repos/huggingface/datasets/issues/4698/events | https://github.com/huggingface/datasets/pull/4698 | 1,307,539,585 | PR_kwDODunzps47i9gN | 4,698 | Enable streaming dataset to use the "all" split | [] | open | false | null | 9 | 2022-07-18T07:47:39Z | 2023-01-19T10:11:38Z | null | null | Fixes #4637 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4698). All of your documentation changes will be reflected on that endpoint.",
"@albertvillanova \r\nAdding the validation split causes these two `assert_called_once` assertions to fail with `AssertionError: Expected 'ArrowWriter' to have been called once. Called 2 times`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/main/tests/test_builder.py#L548-L562\r\n\r\nIt might be better to create a new dummy generator for the streaming tests, WDYT? Alternatively we could test for `self.call_count` equalling 2.",
"@cakiki have you read my comment in the issue page?\r\nhttps://github.com/huggingface/datasets/issues/4637#issuecomment-1175984812",
"Streaming with `split=all` seems to be working, will fix the failing test next",
"Not sure if marking the PR as \"ready for review\" actually notified you, so tagging @albertvillanova just in case :smiley_cat: ",
"cc @lhoestq ",
"Hi @cakiki, still interested in working on this? :) ",
"@albertvillanova So sorry; I have no idea how this slipped through the cracks. Yes, I'd still like to work on this. Is it okay if I DM you on slack?",
"Sure!! And nevermind!"
] |
https://api.github.com/repos/huggingface/datasets/issues/1140 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1140/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1140/comments | https://api.github.com/repos/huggingface/datasets/issues/1140/events | https://github.com/huggingface/datasets/pull/1140 | 757,399,142 | MDExOlB1bGxSZXF1ZXN0NTMyNzgyODc0 | 1,140 | Add Urdu Sentiment Corpus (USC). | [] | closed | false | null | 2 | 2020-12-04T20:55:27Z | 2020-12-07T03:27:23Z | 2020-12-07T03:27:23Z | null | Added Urdu Sentiment Corpus. More details about the dataset over <a href="https://github.com/MuhammadYaseenKhan/Urdu-Sentiment-Corpus">here</a>. | {
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"@lhoestq have made the suggested changes in the README file.",
"@lhoestq Created a new PR #1231 with only the relevant files.\r\nclosing this one :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/5231 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5231/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5231/comments | https://api.github.com/repos/huggingface/datasets/issues/5231/events | https://github.com/huggingface/datasets/issues/5231 | 1,445,883,267 | I_kwDODunzps5WLm2D | 5,231 | Using `set_format(type='torch', columns=columns)` makes Array2D/3D columns stop formatting correctly | [] | closed | false | null | 1 | 2022-11-11T18:54:36Z | 2022-11-11T20:42:29Z | 2022-11-11T18:59:50Z | null | I have a Dataset with two Features defined as follows:
```
'image': Array3D(dtype="int64", shape=(3, 224, 224)),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
```
On said dataset, if I `dataset.set_format(type='torch')` and then use the dataset in a dataloader, these columns are correctly cast to Tensors of (batch_size, 3, 224, 244) for example.
However, if I `dataset.set_format(type='torch', columns=['image', 'bbox'])` these columns are cast to Lists of tensors and miss the batch size completely (the 3 dimension is the list length).
I'm currently digging through datasets formatting code to try and find out why, but was curious if someone knew an immediate solution for this. | {
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"In case others find this, the problem was not with set_format, but my usages of `to_pandas()` and `from_pandas()` which I was using during dataset splitting; somewhere in the chain of converting to and from pandas the `Array2D/Array3D` types get converted to series of `Sequence()` types"
] |
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