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https://api.github.com/repos/huggingface/datasets/issues/2771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2771/comments | https://api.github.com/repos/huggingface/datasets/issues/2771/events | https://github.com/huggingface/datasets/pull/2771 | 963,257,036 | MDExOlB1bGxSZXF1ZXN0NzA1OTExMDMw | 2,771 | [WIP][Common Voice 7] Add common voice 7.0 | [] | closed | false | null | 2 | 2021-08-07T16:01:10Z | 2021-12-06T23:24:02Z | 2021-12-06T23:24:02Z | null | This PR allows to load the new common voice dataset manually as explained when doing:
```python
from datasets import load_dataset
ds = load_dataset("./datasets/datasets/common_voice_7", "ab")
```
=>
```
Please follow the manual download instructions:
You need to manually the dataset from `https://commonvoice.mozilla.org/en/datasets`.
Make sure you choose the version `Common Voice Corpus 7.0`.
Choose a language of your choice and find the corresponding language-id, *e.g.*, `Abkhaz` with language-id `ab`. The following language-ids are available:
['ab', 'ar', 'as', 'az', 'ba', 'bas', 'be', 'bg', 'br', 'ca', 'cnh', 'cs', 'cv', 'cy', 'de', 'dv', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy-NL', 'ga-IE', 'gl', 'gn', 'ha', 'hi', 'hsb', 'hu', 'hy-AM', 'ia', 'id', 'it', 'ja', 'ka', 'kab', 'kk', 'kmr', 'ky', 'lg', 'lt', 'lv', 'mn', 'mt', 'nl', 'or', 'pa-IN', 'pl', 'pt', 'rm-sursilv', 'rm-vallader', 'ro', 'ru', 'rw', 'sah', 'sk', 'sl', 'sr', 'sv-SE', 'ta', 'th', 'tr', 'tt', 'ug', 'uk', 'ur', 'uz', 'vi', 'vot', 'zh-CN', 'zh-HK', 'zh-TW']
Next, you will have to enter your email address to download the dataset in the `tar.gz` format. Save the file under <path-to-file>.
The file should then be extracted with: ``tar -xvzf <path-to-file>`` which will extract a folder called ``cv-corpus-7.0-2021-07-21``.
The dataset can then be loaded with `datasets.load_dataset("common_voice", <language-id>, data_dir="<path-to-'cv-corpus-7.0-2021-07-21'-folder>", ignore_verifications=True).
```
Having followed those instructions one can then download the data as follows:
```python
from datasets import load_dataset
ds = load_dataset("./datasets/datasets/common_voice_7", "ab", data_dir="./cv-corpus-7.0-2021-07-21/", ignore_verifications=True)
```
## TODO
- [ ] Discuss naming. Is the name ok here "common_voice_7"? The dataset script differs only really in one point from `common_voice.py` in that all the metadata is different (more hours etc...) and that it has to use manual data dir for now
- [ ] Ideally we should get a bundled download link. For `common_voice.py` there is a bundled download link: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/{}.tar.gz` that allows one to directly download the data. However such a link is missing for Common Voice 7. I guess we should try to contact common voice about it and ask whether we could host the data or help otherwise somehow. See: https://github.com/common-voice/common-voice-bundler/issues/15 cc @yjernite
- [ ] I did not compute the dataset.json and it would mean that I'd have to download 76 datasets totalling around 1TB manually before running the checksum command. This just takes too much time. For now the user will have to add a `ignore_verifications=True` to download the data. This step would also be much easier if we could get a bundled link
- [ ] Add dummy data | {
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"Hi ! I think the name `common_voice_7` is fine :)\r\nMoreover if the dataset_infos.json is missing I'm pretty sure you don't need to specify `ignore_verifications=True`",
"Hi, how about to add a new parameter \"version\" in the function load_dataset, something like: \r\n`load_dataset(\"common_voice\", \"lg\", version=\"7.0\") `\r\nThis is to avoid creating a new common_voice_? dataset (with almost the same code) every time \r\nMozilla updates their Common Voice dataset.\r\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/5603 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5603/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5603/comments | https://api.github.com/repos/huggingface/datasets/issues/5603/events | https://github.com/huggingface/datasets/pull/5603 | 1,607,143,509 | PR_kwDODunzps5LJZzG | 5,603 | Don't compute checksums if not necessary in `datasets-cli test` | [] | closed | false | null | 3 | 2023-03-02T16:42:39Z | 2023-03-03T15:45:32Z | 2023-03-03T15:38:28Z | null | we only need them if there exists a `dataset_infos.json` | {
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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) | 0.008550 / 0.011353 (-0.002803) | 0.004476 / 0.011008 (-0.006532) | 0.100902 / 0.038508 (0.062394) | 0.029684 / 0.023109 (0.006575) | 0.308081 / 0.275898 (0.032183) | 0.363435 / 0.323480 (0.039955) | 0.006987 / 0.007986 (-0.000999) | 0.003401 / 0.004328 (-0.000927) | 0.078218 / 0.004250 (0.073967) | 0.036657 / 0.037052 (-0.000395) | 0.319670 / 0.258489 (0.061181) | 0.349952 / 0.293841 (0.056111) | 0.033416 / 0.128546 (-0.095130) | 0.011511 / 0.075646 (-0.064135) | 0.323888 / 0.419271 (-0.095384) | 0.042429 / 0.043533 (-0.001104) | 0.307310 / 0.255139 (0.052171) | 0.329459 / 0.283200 (0.046259) | 0.085209 / 0.141683 (-0.056474) | 1.475893 / 1.452155 (0.023739) | 1.502782 / 1.492716 (0.010065) |\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) | 0.200137 / 0.018006 (0.182131) | 0.411269 / 0.000490 (0.410780) | 0.000415 / 0.000200 (0.000215) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022626 / 0.037411 (-0.014785) | 0.097045 / 0.014526 (0.082519) | 0.102955 / 0.176557 (-0.073602) | 0.148411 / 0.737135 (-0.588725) | 0.107238 / 0.296338 (-0.189100) |\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) | 0.421683 / 0.215209 (0.206474) | 4.203031 / 2.077655 (2.125376) | 1.908232 / 1.504120 (0.404112) | 1.698867 / 1.541195 (0.157672) | 1.743561 / 1.468490 (0.275071) | 0.693199 / 4.584777 (-3.891578) | 3.361022 / 3.745712 (-0.384690) | 2.989610 / 5.269862 (-2.280251) | 1.533036 / 4.565676 (-3.032641) | 0.082675 / 0.424275 (-0.341601) | 0.012419 / 0.007607 (0.004812) | 0.531543 / 0.226044 (0.305499) | 5.330595 / 2.268929 (3.061666) | 2.347519 / 55.444624 (-53.097105) | 1.975672 / 6.876477 (-4.900804) | 2.039541 / 2.142072 (-0.102532) | 0.810281 / 4.805227 (-3.994946) | 0.148917 / 6.500664 (-6.351747) | 0.065441 / 0.075469 (-0.010028) |\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) | 1.266213 / 1.841788 (-0.575574) | 13.628106 / 8.074308 (5.553798) | 13.852191 / 10.191392 (3.660799) | 0.149004 / 0.680424 (-0.531420) | 0.028549 / 0.534201 (-0.505652) | 0.399824 / 0.579283 (-0.179459) | 0.401231 / 0.434364 (-0.033133) | 0.473251 / 0.540337 (-0.067086) | 0.561094 / 1.386936 (-0.825842) |\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) | 0.006669 / 0.011353 (-0.004684) | 0.004477 / 0.011008 (-0.006532) | 0.077514 / 0.038508 (0.039006) | 0.027489 / 0.023109 (0.004380) | 0.341935 / 0.275898 (0.066037) | 0.377392 / 0.323480 (0.053912) | 0.004947 / 0.007986 (-0.003039) | 0.004600 / 0.004328 (0.000271) | 0.075938 / 0.004250 (0.071687) | 0.039586 / 0.037052 (0.002534) | 0.344966 / 0.258489 (0.086477) | 0.392181 / 0.293841 (0.098340) | 0.031838 / 0.128546 (-0.096708) | 0.011572 / 0.075646 (-0.064075) | 0.085811 / 0.419271 (-0.333461) | 0.042250 / 0.043533 (-0.001283) | 0.345605 / 0.255139 (0.090466) | 0.367814 / 0.283200 (0.084615) | 0.090683 / 0.141683 (-0.051000) | 1.483168 / 1.452155 (0.031014) | 1.559724 / 1.492716 (0.067008) |\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) | 0.235655 / 0.018006 (0.217649) | 0.399016 / 0.000490 (0.398527) | 0.003096 / 0.000200 (0.002896) | 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) | 0.024454 / 0.037411 (-0.012957) | 0.100710 / 0.014526 (0.086185) | 0.107950 / 0.176557 (-0.068606) | 0.161560 / 0.737135 (-0.575576) | 0.111840 / 0.296338 (-0.184498) |\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) | 0.441362 / 0.215209 (0.226153) | 4.428105 / 2.077655 (2.350450) | 2.074501 / 1.504120 (0.570381) | 1.866672 / 1.541195 (0.325477) | 1.928266 / 1.468490 (0.459776) | 0.703561 / 4.584777 (-3.881216) | 3.396537 / 3.745712 (-0.349175) | 3.047369 / 5.269862 (-2.222492) | 1.595133 / 4.565676 (-2.970543) | 0.084028 / 0.424275 (-0.340247) | 0.012349 / 0.007607 (0.004741) | 0.539354 / 0.226044 (0.313310) | 5.401535 / 2.268929 (3.132606) | 2.499874 / 55.444624 (-52.944750) | 2.161406 / 6.876477 (-4.715071) | 2.197385 / 2.142072 (0.055313) | 0.810864 / 4.805227 (-3.994363) | 0.152277 / 6.500664 (-6.348387) | 0.067266 / 0.075469 (-0.008203) |\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) | 1.280900 / 1.841788 (-0.560887) | 13.815731 / 8.074308 (5.741423) | 13.007438 / 10.191392 (2.816046) | 0.129711 / 0.680424 (-0.550713) | 0.016852 / 0.534201 (-0.517349) | 0.380775 / 0.579283 (-0.198508) | 0.384143 / 0.434364 (-0.050221) | 0.459954 / 0.540337 (-0.080383) | 0.549335 / 1.386936 (-0.837601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8805d67bd81ce48f481d5c1e56b84e6ebcaa2b2b \"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) | 0.009570 / 0.011353 (-0.001783) | 0.005219 / 0.011008 (-0.005789) | 0.098472 / 0.038508 (0.059964) | 0.035429 / 0.023109 (0.012320) | 0.303086 / 0.275898 (0.027188) | 0.365926 / 0.323480 (0.042446) | 0.008797 / 0.007986 (0.000811) | 0.004220 / 0.004328 (-0.000108) | 0.076670 / 0.004250 (0.072419) | 0.045596 / 0.037052 (0.008543) | 0.309476 / 0.258489 (0.050987) | 0.343958 / 0.293841 (0.050117) | 0.038741 / 0.128546 (-0.089805) | 0.011990 / 0.075646 (-0.063657) | 0.332326 / 0.419271 (-0.086945) | 0.048897 / 0.043533 (0.005364) | 0.296002 / 0.255139 (0.040863) | 0.322048 / 0.283200 (0.038849) | 0.104403 / 0.141683 (-0.037280) | 1.461777 / 1.452155 (0.009622) | 1.516362 / 1.492716 (0.023645) |\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) | 0.201565 / 0.018006 (0.183559) | 0.435781 / 0.000490 (0.435291) | 0.004215 / 0.000200 (0.004015) | 0.000282 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027272 / 0.037411 (-0.010139) | 0.106157 / 0.014526 (0.091631) | 0.116948 / 0.176557 (-0.059609) | 0.160404 / 0.737135 (-0.576731) | 0.122518 / 0.296338 (-0.173820) |\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) | 0.397721 / 0.215209 (0.182512) | 3.966433 / 2.077655 (1.888778) | 1.755410 / 1.504120 (0.251290) | 1.566480 / 1.541195 (0.025285) | 1.623684 / 1.468490 (0.155194) | 0.696820 / 4.584777 (-3.887957) | 3.750437 / 3.745712 (0.004725) | 2.105875 / 5.269862 (-3.163986) | 1.442026 / 4.565676 (-3.123650) | 0.085026 / 0.424275 (-0.339249) | 0.012239 / 0.007607 (0.004632) | 0.502613 / 0.226044 (0.276569) | 5.049016 / 2.268929 (2.780087) | 2.314499 / 55.444624 (-53.130126) | 1.967943 / 6.876477 (-4.908534) | 2.033507 / 2.142072 (-0.108565) | 0.861908 / 4.805227 (-3.943319) | 0.167784 / 6.500664 (-6.332880) | 0.063022 / 0.075469 (-0.012447) |\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) | 1.210434 / 1.841788 (-0.631353) | 14.979319 / 8.074308 (6.905011) | 14.095263 / 10.191392 (3.903871) | 0.174203 / 0.680424 (-0.506221) | 0.028547 / 0.534201 (-0.505654) | 0.442509 / 0.579283 (-0.136774) | 0.445811 / 0.434364 (0.011447) | 0.531313 / 0.540337 (-0.009024) | 0.636541 / 1.386936 (-0.750395) |\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) | 0.007341 / 0.011353 (-0.004012) | 0.005197 / 0.011008 (-0.005811) | 0.075413 / 0.038508 (0.036905) | 0.033261 / 0.023109 (0.010152) | 0.339596 / 0.275898 (0.063698) | 0.376051 / 0.323480 (0.052571) | 0.005827 / 0.007986 (-0.002159) | 0.005473 / 0.004328 (0.001144) | 0.074851 / 0.004250 (0.070600) | 0.049059 / 0.037052 (0.012007) | 0.357182 / 0.258489 (0.098693) | 0.384589 / 0.293841 (0.090748) | 0.037122 / 0.128546 (-0.091424) | 0.012298 / 0.075646 (-0.063348) | 0.088191 / 0.419271 (-0.331081) | 0.052002 / 0.043533 (0.008469) | 0.343216 / 0.255139 (0.088077) | 0.364534 / 0.283200 (0.081334) | 0.105462 / 0.141683 (-0.036221) | 1.486717 / 1.452155 (0.034562) | 1.584725 / 1.492716 (0.092009) |\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) | 0.199210 / 0.018006 (0.181203) | 0.439069 / 0.000490 (0.438580) | 0.000436 / 0.000200 (0.000236) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029931 / 0.037411 (-0.007480) | 0.109564 / 0.014526 (0.095038) | 0.122284 / 0.176557 (-0.054273) | 0.170819 / 0.737135 (-0.566317) | 0.125886 / 0.296338 (-0.170452) |\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) | 0.422724 / 0.215209 (0.207515) | 4.210304 / 2.077655 (2.132650) | 2.001481 / 1.504120 (0.497361) | 1.810818 / 1.541195 (0.269623) | 1.901367 / 1.468490 (0.432877) | 0.686004 / 4.584777 (-3.898773) | 3.768850 / 3.745712 (0.023138) | 2.079501 / 5.269862 (-3.190360) | 1.326970 / 4.565676 (-3.238706) | 0.085991 / 0.424275 (-0.338284) | 0.012298 / 0.007607 (0.004690) | 0.526878 / 0.226044 (0.300833) | 5.267241 / 2.268929 (2.998312) | 2.451781 / 55.444624 (-52.992843) | 2.109143 / 6.876477 (-4.767333) | 2.185426 / 2.142072 (0.043353) | 0.830165 / 4.805227 (-3.975063) | 0.166167 / 6.500664 (-6.334497) | 0.064077 / 0.075469 (-0.011392) |\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) | 1.270430 / 1.841788 (-0.571358) | 14.844852 / 8.074308 (6.770544) | 13.196672 / 10.191392 (3.005280) | 0.162853 / 0.680424 (-0.517571) | 0.017727 / 0.534201 (-0.516474) | 0.424803 / 0.579283 (-0.154480) | 0.439970 / 0.434364 (0.005606) | 0.530691 / 0.540337 (-0.009647) | 0.630474 / 1.386936 (-0.756462) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24fb01b720ef4203d4ae6225f43cba912b1f6d55 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/5111 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5111/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5111/comments | https://api.github.com/repos/huggingface/datasets/issues/5111/events | https://github.com/huggingface/datasets/issues/5111 | 1,408,143,170 | I_kwDODunzps5T7o9C | 5,111 | map and filter not working properly in multiprocessing with the new release 2.6.0 | [
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] | closed | false | null | 14 | 2022-10-13T17:00:55Z | 2022-10-17T08:26:59Z | 2022-10-14T14:59:59Z | null | ## Describe the bug
When mapping is used on a dataset with more than one process, there is a weird behavior when trying to use `filter` , it's like only the samples from one worker are retrieved, one needs to specify the same `num_proc` in filter for it to work properly. This doesn't happen with `datasets` version 2.5.2
In the code below the data is filtered differently when we increase `num_proc` used in `map` although the datsets before and after mapping have identical elements.
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
def preprocess(example):
return example
ds = load_dataset("codeparrot/codeparrot-clean-valid", split="train").select([i for i in range(10)])
ds1 = ds.map(preprocess, num_proc=2)
ds2 = ds.map(preprocess)
# the datasets elements are the same
for i in range(len(ds1)):
assert ds1[i]==ds2[i]
print(f'Target column before filtering {ds1["autogenerated"]}')
print(f'Target column before filtering {ds2["autogenerated"]}')
print(f"datasets version {datasets.__version__}")
ds_filtered_1 = ds1.filter(lambda x: not x["autogenerated"])
ds_filtered_2 = ds2.filter(lambda x: not x["autogenerated"])
# all elements in Target column are false so they should all be kept, but for ds2 only the first 5=num_samples/num_proc are kept
print(ds_filtered_1)
print(ds_filtered_2)
```
```
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Target column before filtering [False, False, False, False, False, False, False, False, False, False]
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5
})
Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 10
})
```
## Expected results
Increasing `num_proc` in mapping shouldn't alter filtering. With the previous version 2.5.2 this doesn't happen
## Actual results
Filtering doesn't work properly when we increase `num_proc` in mapping but not when calling `filter`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.6.0
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | {
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} | https://api.github.com/repos/huggingface/datasets/issues/5111/timeline | null | completed | null | null | false | [
"Same bug exists with `num_proc=1` on colab. `3.7.14 (default, Sep 8 2022, 00:06:44) [GCC 7.5.0]` ",
"Thanks for reporting, @loubnabnl and for the additional information, @PartiallyTyped.\r\n\r\nHowever, I'm not able to reproduce this issue, neither locally nor on Colab:\r\n```\r\nDataset({\r\n features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],\r\n num_rows: 10\r\n})\r\nDataset({\r\n features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],\r\n num_rows: 10\r\n})\r\n```\r\nCC: @huggingface/datasets can anybody reproduce this?",
"This is the minimum reproducible example. I ran this on the premium instances of colab.\r\n\r\n```\r\n# !pip install datasets\r\nimport datasets\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"copenlu/answerable_tydiqa\").filter(\"english\".__eq__, input_columns=\"language\")\r\nassert all(map(\"english\".__eq__, ds[\"train\"][\"language\"]))\r\n```\r\n\r\nIn my case, the number of samples is correct, however, the samples selected when indexing are wrong.\r\n\r\n```python\r\nDatasetDict({\r\n validation: Dataset({\r\n features: ['question_text', 'document_title', 'language', 'annotations', 'document_plaintext', 'document_url'],\r\n num_rows: 990\r\n })\r\n train: Dataset({\r\n features: ['question_text', 'document_title', 'language', 'annotations', 'document_plaintext', 'document_url'],\r\n num_rows: 7389\r\n })\r\n})\r\n```\r\n\r\nThe number of rows is indeed correct, and i have checked it with a version that works.",
"I can reproduce the issue on my mac too \r\n```\r\n- `datasets` version: 2.6.0\r\n- Platform: macOS-12.2.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 9.0.0\r\n- Pandas version: 1.4.3\r\n```\r\nBut not on Colab with python 3.7, maybe related to python version? (didn't manage to install python 3.9)\r\n```\r\n- `datasets` version: 2.6.0\r\n- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.14\r\n- PyArrow version: 9.0.0\r\n- Pandas version: 1.3.5\r\n```",
"I have the same issue, here's a simple notebook to reproduce: https://colab.research.google.com/drive/1Lvo9fg5DSpGUUgXW5JAutZ0bFsR-WV--?usp=sharing\r\n\r\n\r\n\r\n",
"I think there are 2 different issues here:\r\n- the one reported by @loubnabnl is related to multiprocessing in map and then filter; we should reproduce it first: I have tried with Python version 3.9.7 and I can't reproduce it either; maybe it is related to the version of PyArrow? To be checked.\r\n- the issue reported by @PartiallyTyped is related just to \"filter\" (without multiprocessing) and I can reproduce it.",
"Could you create another issue for the @PartiallyTyped one please ?\r\n\r\nRegarding the OP issue, I also tried on colab or locally on py3.7 or py3.10 but didn't reproduce",
"I have created another issue for the one reported by @PartiallyTyped: \r\n- #5112 ",
"I managed to reproduce your issue @loubnabnl on colab by upgrading pyarrow to 9.0.0 instead of 6.0.1",
"I managed to have a _super_ minimal reproducible example:\r\n```python\r\n\r\nfrom datasets import Dataset, concatenate_datasets\r\n\r\nds = concatenate_datasets([Dataset.from_dict({\"a\": [i]}) for i in range(10)])\r\nds2 = ds.map(lambda _: {}, batched=True)\r\nassert list(ds2) == list(ds)\r\n```\r\n(filter uses a batched `map` under the hood)",
"> the one reported by @loubnabnl is related to multiprocessing in map and then filter; we should reproduce it first: I have tried with Python version 3.9.7 and I can't reproduce it either; maybe it is related to the version of PyArrow? To be checked.\r\n\r\nSo finally it was related to PyArrow version! :+1: ",
"Doing a patch release asap :)",
"Did the patch release yesterday, lmk if you still have issues",
"It works now, thanks!\r\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/2344 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2344/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2344/comments | https://api.github.com/repos/huggingface/datasets/issues/2344/events | https://github.com/huggingface/datasets/issues/2344 | 885,331,505 | MDU6SXNzdWU4ODUzMzE1MDU= | 2,344 | Is there a way to join multiple datasets in one? | [
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] | open | false | null | 2 | 2021-05-10T23:16:10Z | 2022-10-05T17:27:05Z | null | null | **Is your feature request related to a problem? Please describe.**
I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2?
**Describe the solution you'd like**
Id like to join them with a merge or join method, just like pandas dataframes.
**Additional context**
If you want to extend an existing dataset with more data, for example for training a language model, you need that functionality. I've not found it in the documentation. | {
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} | https://api.github.com/repos/huggingface/datasets/issues/2344/timeline | null | null | null | null | false | [
"Hi ! We don't have `join`/`merge` on a certain column as in pandas.\r\nMaybe you can just use the [concatenate_datasets](https://huggingface.co./docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets) function.\r\n",
"Hi! You can use `datasets_sql` for that now. As of recently, PyArrow also supports querying tables via Substrait, so I think we can start adding these methods to the API soon."
] |
https://api.github.com/repos/huggingface/datasets/issues/3848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3848/comments | https://api.github.com/repos/huggingface/datasets/issues/3848/events | https://github.com/huggingface/datasets/issues/3848 | 1,162,076,902 | I_kwDODunzps5FQ-Lm | 3,848 | NonMatchingChecksumError when checksum is None | [
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] | closed | false | null | 7 | 2022-03-08T00:24:12Z | 2022-03-15T14:37:26Z | 2022-03-15T12:28:23Z | null | I ran into the following error when adding a new dataset:
```bash
expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}}
recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}
verification_name = 'dataset source files'
def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):
if expected_checksums is None:
logger.info("Unable to verify checksums.")
return
if len(set(expected_checksums) - set(recorded_checksums)) > 0:
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
if len(set(recorded_checksums) - set(expected_checksums)) > 0:
raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
for_verification_name = " for " + verification_name if verification_name is not None else ""
if len(bad_urls) > 0:
error_msg = "Checksums didn't match" + for_verification_name + ":\n"
> raise NonMatchingChecksumError(error_msg + str(bad_urls))
E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
E ['https://adversarialglue.github.io/dataset/dev.zip']
src/datasets/utils/info_utils.py:40: NonMatchingChecksumError
```
## Expected results
The dataset downloads correctly, and there is no error.
## Actual results
Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. | {
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} | https://api.github.com/repos/huggingface/datasets/issues/3848/timeline | null | completed | null | null | false | [
"Hi @jxmorris12, thanks for reporting.\r\n\r\nThe objective of `verify_checksums` is to check that both checksums are equal. Therefore if one is None and the other is non-None, they are not equal, and the function accordingly raises a NonMatchingChecksumError. That behavior is expected.\r\n\r\nThe question is: how did you generate the expected checksum? Normally, it should not be None. To properly generate it (it is contained in the `dataset_infos.json` file), you should have runned: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nOn the other hand, you should take into account that the generation of this file is NOT mandatory for personal/community datasets (we only require it for \"canonical\" datasets, i.e., datasets added to our library GitHub repository: https://github.com/huggingface/datasets/tree/master/datasets). Therefore, other option would be just to delete the `dataset_infos.json` file. If that file is not present, the function `verify_checksums` is not executed.\r\n\r\nFinally, you can circumvent the `verify_checksums` function by passing `ignore_verifications=True` to `load_dataset`:\r\n```python\r\nload_dataset(..., ignore_verifications=True)\r\n``` ",
"Thanks @albertvillanova!\r\n\r\nThat's fine. I did run that command when I was adding a new dataset. Maybe because the command crashed in the middle, the checksum wasn't stored properly. I don't know where the bug is happening. But either (i) `verify_checksums` should properly handle this edge case, where the passed checksum is None or (ii) the `datasets-cli test` shouldn't generate a corrupted dataset_infos.json file.\r\n\r\nJust a more high-level thing, I was trying to follow the instructions for adding a dataset in the CONTRIBUTING.md, so if running that command isn't even necessary, that should probably be mentioned in the document, right? But that's somewhat of a moot point, since something isn't working quite right internally if I was able to get into this corrupted state in the first place, just by following those instructions.",
"Hi @jxmorris12,\r\n\r\nDefinitely, your `dataset_infos.json` was corrupted (and wrongly contains expected None checksum). \r\n\r\nWhile we further investigate how this can happen and fix it, feel free to delete your `dataset_infos.json` file and recreate it with:\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nAlso note that `verify_checksum` is working as expected: if it receives a None and and a non-None checksums as input pair, it must raise an exception: they are not equal. That is not a bug.",
"At a higher level, also note that we are preparing the release of `datasets` version 2.0, and some docs are being updated...\r\n\r\nIn order to add a dataset, I think the most updated instructions are in our official documentation pages: https://huggingface.co./docs/datasets/share",
"Thanks for the info. Maybe you can update the contributing.md if it's not up-to-date.",
"Hi @jxmorris12, we have discovered the bug why `None` checksums wrongly appeared when generating the `dataset_infos.json` file:\r\n- #3892\r\n\r\nThe fix will be accessible once this PR merged. And we are planning to do our 2.0 release today.\r\n\r\nWe are also working on updating all our docs for our release today.",
"Thanks @albertvillanova - congrats on the release!"
] |
https://api.github.com/repos/huggingface/datasets/issues/3716 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3716/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3716/comments | https://api.github.com/repos/huggingface/datasets/issues/3716/events | https://github.com/huggingface/datasets/issues/3716 | 1,136,831,092 | I_kwDODunzps5Dwqp0 | 3,716 | `FaissIndex` to support multiple GPU and `custom_index` | [
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"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
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] | closed | false | null | 2 | 2022-02-14T06:21:43Z | 2022-03-07T16:28:56Z | 2022-03-07T16:28:56Z | null | **Is your feature request related to a problem? Please describe.**
Currently, because `device` is of the type `int | None`, to leverage `faiss-gpu`'s multi-gpu support, you need to create a `custom_index`. However, if using a `custom_index` created by e.g. `faiss.index_cpu_to_all_gpus`, then `FaissIndex.save` does not work properly because it checks the device id (which is an int, so no multiple GPUs).
**Describe the solution you'd like**
I would like `FaissIndex` to support multiple GPUs, by passing in a list to `add_faiss_index`.
**Describe alternatives you've considered**
Alternatively, I would like it to at least provide a warning cause it wasn't the behavior that I expected.
**Additional context**
Relavent source code here:
https://github.com/huggingface/datasets/blob/6ed6ac9448311930557810383d2cfd4fe6aae269/src/datasets/search.py#L340-L349
Device management needs changing to support multiple GPUs, probably by `isinstance` calls.
I can provide a PR if you like :)
Thanks for reading!
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"Hi @rentruewang, thansk for reporting and for your PR!!! We should definitely support this. ",
"@albertvillanova Great! :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/5958 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5958/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5958/comments | https://api.github.com/repos/huggingface/datasets/issues/5958/events | https://github.com/huggingface/datasets/pull/5958 | 1,757,265,971 | PR_kwDODunzps5TA3__ | 5,958 | set dev version | [] | closed | false | null | 3 | 2023-06-14T16:26:34Z | 2023-06-14T16:34:55Z | 2023-06-14T16:26:51Z | null | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5958). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006232 / 0.011353 (-0.005121) | 0.003788 / 0.011008 (-0.007220) | 0.100014 / 0.038508 (0.061506) | 0.036488 / 0.023109 (0.013379) | 0.306255 / 0.275898 (0.030357) | 0.363337 / 0.323480 (0.039857) | 0.004765 / 0.007986 (-0.003221) | 0.002935 / 0.004328 (-0.001394) | 0.078897 / 0.004250 (0.074647) | 0.052221 / 0.037052 (0.015169) | 0.315169 / 0.258489 (0.056680) | 0.353050 / 0.293841 (0.059209) | 0.029059 / 0.128546 (-0.099488) | 0.008599 / 0.075646 (-0.067047) | 0.318770 / 0.419271 (-0.100502) | 0.046631 / 0.043533 (0.003098) | 0.303728 / 0.255139 (0.048589) | 0.332379 / 0.283200 (0.049180) | 0.021164 / 0.141683 (-0.120519) | 1.576963 / 1.452155 (0.124808) | 1.629575 / 1.492716 (0.136859) |\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) | 0.204246 / 0.018006 (0.186240) | 0.426600 / 0.000490 (0.426110) | 0.004336 / 0.000200 (0.004136) | 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) | 0.024039 / 0.037411 (-0.013372) | 0.098240 / 0.014526 (0.083715) | 0.108889 / 0.176557 (-0.067668) | 0.170827 / 0.737135 (-0.566308) | 0.111288 / 0.296338 (-0.185051) |\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) | 0.418103 / 0.215209 (0.202894) | 4.190759 / 2.077655 (2.113104) | 1.875978 / 1.504120 (0.371858) | 1.679198 / 1.541195 (0.138003) | 1.737965 / 1.468490 (0.269474) | 0.556660 / 4.584777 (-4.028117) | 3.413800 / 3.745712 (-0.331912) | 3.004999 / 5.269862 (-2.264862) | 1.464030 / 4.565676 (-3.101647) | 0.067338 / 0.424275 (-0.356937) | 0.011486 / 0.007607 (0.003879) | 0.522589 / 0.226044 (0.296544) | 5.214653 / 2.268929 (2.945724) | 2.316903 / 55.444624 (-53.127722) | 1.991941 / 6.876477 (-4.884536) | 2.110601 / 2.142072 (-0.031471) | 0.665400 / 4.805227 (-4.139828) | 0.135755 / 6.500664 (-6.364910) | 0.065980 / 0.075469 (-0.009489) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197269 / 1.841788 (-0.644519) | 14.085205 / 8.074308 (6.010897) | 14.083360 / 10.191392 (3.891968) | 0.148054 / 0.680424 (-0.532369) | 0.016548 / 0.534201 (-0.517653) | 0.371538 / 0.579283 (-0.207745) | 0.391068 / 0.434364 (-0.043296) | 0.430589 / 0.540337 (-0.109748) | 0.529319 / 1.386936 (-0.857617) |\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) | 0.006214 / 0.011353 (-0.005138) | 0.003846 / 0.011008 (-0.007162) | 0.078559 / 0.038508 (0.040051) | 0.037855 / 0.023109 (0.014745) | 0.437479 / 0.275898 (0.161581) | 0.497588 / 0.323480 (0.174108) | 0.003491 / 0.007986 (-0.004494) | 0.003900 / 0.004328 (-0.000428) | 0.078443 / 0.004250 (0.074193) | 0.048019 / 0.037052 (0.010967) | 0.452076 / 0.258489 (0.193587) | 0.494597 / 0.293841 (0.200756) | 0.028127 / 0.128546 (-0.100419) | 0.008549 / 0.075646 (-0.067098) | 0.082977 / 0.419271 (-0.336295) | 0.043133 / 0.043533 (-0.000400) | 0.441342 / 0.255139 (0.186203) | 0.464339 / 0.283200 (0.181139) | 0.020110 / 0.141683 (-0.121573) | 1.485181 / 1.452155 (0.033026) | 1.532019 / 1.492716 (0.039302) |\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) | 0.228014 / 0.018006 (0.210007) | 0.416887 / 0.000490 (0.416397) | 0.001133 / 0.000200 (0.000933) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026452 / 0.037411 (-0.010960) | 0.104328 / 0.014526 (0.089802) | 0.110045 / 0.176557 (-0.066511) | 0.164725 / 0.737135 (-0.572410) | 0.116348 / 0.296338 (-0.179990) |\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) | 0.483502 / 0.215209 (0.268293) | 4.829814 / 2.077655 (2.752159) | 2.505271 / 1.504120 (1.001151) | 2.305819 / 1.541195 (0.764624) | 2.348633 / 1.468490 (0.880143) | 0.562316 / 4.584777 (-4.022461) | 3.426425 / 3.745712 (-0.319287) | 1.737934 / 5.269862 (-3.531927) | 1.042616 / 4.565676 (-3.523061) | 0.068088 / 0.424275 (-0.356187) | 0.011735 / 0.007607 (0.004128) | 0.586339 / 0.226044 (0.360295) | 5.861283 / 2.268929 (3.592354) | 2.953956 / 55.444624 (-52.490668) | 2.626611 / 6.876477 (-4.249865) | 2.687978 / 2.142072 (0.545906) | 0.672748 / 4.805227 (-4.132479) | 0.137231 / 6.500664 (-6.363433) | 0.068149 / 0.075469 (-0.007320) |\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) | 1.323139 / 1.841788 (-0.518649) | 14.503102 / 8.074308 (6.428794) | 14.092102 / 10.191392 (3.900710) | 0.165395 / 0.680424 (-0.515028) | 0.016898 / 0.534201 (-0.517303) | 0.366905 / 0.579283 (-0.212378) | 0.396671 / 0.434364 (-0.037692) | 0.421831 / 0.540337 (-0.118506) | 0.514075 / 1.386936 (-0.872861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d4238c132dd44b9a6e1dfe7101228bdeb538d57 \"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) | 0.007778 / 0.011353 (-0.003575) | 0.004624 / 0.011008 (-0.006384) | 0.123426 / 0.038508 (0.084918) | 0.052209 / 0.023109 (0.029100) | 0.341084 / 0.275898 (0.065186) | 0.421905 / 0.323480 (0.098425) | 0.005768 / 0.007986 (-0.002217) | 0.003647 / 0.004328 (-0.000682) | 0.085569 / 0.004250 (0.081319) | 0.070473 / 0.037052 (0.033421) | 0.356626 / 0.258489 (0.098136) | 0.407413 / 0.293841 (0.113572) | 0.038800 / 0.128546 (-0.089746) | 0.010289 / 0.075646 (-0.065357) | 0.462707 / 0.419271 (0.043436) | 0.060390 / 0.043533 (0.016858) | 0.349805 / 0.255139 (0.094666) | 0.355288 / 0.283200 (0.072088) | 0.025364 / 0.141683 (-0.116318) | 1.745720 / 1.452155 (0.293565) | 1.852764 / 1.492716 (0.360048) |\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) | 0.290582 / 0.018006 (0.272576) | 0.480044 / 0.000490 (0.479554) | 0.007658 / 0.000200 (0.007458) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031529 / 0.037411 (-0.005882) | 0.130441 / 0.014526 (0.115915) | 0.147653 / 0.176557 (-0.028904) | 0.215935 / 0.737135 (-0.521200) | 0.149871 / 0.296338 (-0.146467) |\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) | 0.461662 / 0.215209 (0.246453) | 4.570353 / 2.077655 (2.492698) | 2.104416 / 1.504120 (0.600297) | 1.936974 / 1.541195 (0.395779) | 2.139167 / 1.468490 (0.670677) | 0.645100 / 4.584777 (-3.939677) | 4.361536 / 3.745712 (0.615824) | 2.155960 / 5.269862 (-3.113902) | 1.207854 / 4.565676 (-3.357822) | 0.080162 / 0.424275 (-0.344113) | 0.014265 / 0.007607 (0.006658) | 0.606294 / 0.226044 (0.380250) | 5.928093 / 2.268929 (3.659165) | 2.701811 / 55.444624 (-52.742813) | 2.344490 / 6.876477 (-4.531987) | 2.435997 / 2.142072 (0.293925) | 0.761020 / 4.805227 (-4.044207) | 0.165860 / 6.500664 (-6.334804) | 0.075666 / 0.075469 (0.000197) |\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) | 1.427318 / 1.841788 (-0.414469) | 17.327468 / 8.074308 (9.253160) | 15.323065 / 10.191392 (5.131673) | 0.178518 / 0.680424 (-0.501905) | 0.020888 / 0.534201 (-0.513313) | 0.497891 / 0.579283 (-0.081393) | 0.487717 / 0.434364 (0.053353) | 0.581430 / 0.540337 (0.041093) | 0.703430 / 1.386936 (-0.683506) |\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) | 0.007954 / 0.011353 (-0.003399) | 0.004442 / 0.011008 (-0.006566) | 0.090950 / 0.038508 (0.052442) | 0.054282 / 0.023109 (0.031173) | 0.424474 / 0.275898 (0.148576) | 0.531770 / 0.323480 (0.208290) | 0.004492 / 0.007986 (-0.003493) | 0.004745 / 0.004328 (0.000416) | 0.088213 / 0.004250 (0.083962) | 0.063967 / 0.037052 (0.026914) | 0.454256 / 0.258489 (0.195767) | 0.502870 / 0.293841 (0.209029) | 0.038203 / 0.128546 (-0.090343) | 0.010327 / 0.075646 (-0.065319) | 0.097809 / 0.419271 (-0.321463) | 0.062136 / 0.043533 (0.018604) | 0.426148 / 0.255139 (0.171009) | 0.467812 / 0.283200 (0.184612) | 0.029148 / 0.141683 (-0.112535) | 1.762307 / 1.452155 (0.310152) | 1.814238 / 1.492716 (0.321521) |\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) | 0.195676 / 0.018006 (0.177670) | 0.475382 / 0.000490 (0.474892) | 0.003070 / 0.000200 (0.002870) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033945 / 0.037411 (-0.003466) | 0.134666 / 0.014526 (0.120140) | 0.147585 / 0.176557 (-0.028971) | 0.209472 / 0.737135 (-0.527664) | 0.154471 / 0.296338 (-0.141867) |\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) | 0.518132 / 0.215209 (0.302923) | 5.103423 / 2.077655 (3.025768) | 2.565207 / 1.504120 (1.061087) | 2.389454 / 1.541195 (0.848259) | 2.391706 / 1.468490 (0.923216) | 0.606463 / 4.584777 (-3.978314) | 4.392227 / 3.745712 (0.646515) | 2.067121 / 5.269862 (-3.202741) | 1.217551 / 4.565676 (-3.348125) | 0.074304 / 0.424275 (-0.349971) | 0.013418 / 0.007607 (0.005811) | 0.623327 / 0.226044 (0.397282) | 6.340233 / 2.268929 (4.071304) | 3.153948 / 55.444624 (-52.290677) | 2.824548 / 6.876477 (-4.051929) | 2.938402 / 2.142072 (0.796329) | 0.774305 / 4.805227 (-4.030922) | 0.170681 / 6.500664 (-6.329983) | 0.075895 / 0.075469 (0.000426) |\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) | 1.473491 / 1.841788 (-0.368296) | 17.372294 / 8.074308 (9.297986) | 15.550201 / 10.191392 (5.358809) | 0.191402 / 0.680424 (-0.489022) | 0.021401 / 0.534201 (-0.512800) | 0.484377 / 0.579283 (-0.094906) | 0.488844 / 0.434364 (0.054480) | 0.563336 / 0.540337 (0.022999) | 0.694210 / 1.386936 (-0.692726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96da7f51d81e52d7b587685f820b5e55f71e07d \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/4845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4845/comments | https://api.github.com/repos/huggingface/datasets/issues/4845/events | https://github.com/huggingface/datasets/pull/4845 | 1,337,928,283 | PR_kwDODunzps49IOjf | 4,845 | Mark CI tests as xfail if Hub HTTP error | [] | closed | false | null | 1 | 2022-08-13T10:45:11Z | 2022-08-23T04:57:12Z | 2022-08-23T04:42:26Z | null | In order to make testing more robust (and avoid merges to master with red tests), we could mark tests as xfailed (instead of failed) when the Hub raises some temporary HTTP errors.
This PR:
- marks tests as xfailed only if the Hub raises a 500 error for:
- test_upstream_hub
- makes pytest report the xfailed/xpassed tests.
More tests could also be marked if needed.
Examples of CI failures due to temporary Hub HTTP errors:
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files
- https://github.com/huggingface/datasets/runs/7806855399?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-16603108028233/commit/main (Request ID: aZeAQ5yLktoGHQYBcJ3zo)`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_no_token
- https://github.com/huggingface/datasets/runs/7840022996?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://s3.us-east-1.amazonaws.com/lfs-staging.huggingface.co/repos/81/e3/81e3b831fa9bf23190ec041f26ef7ff6d6b71c1a937b8ec1ef1f1f05b508c089/caae596caa179cf45e7c9ac0c6d9a9cb0fe2d305291bfbb2d8b648ae26ed38b6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20220815%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220815T144713Z&X-Amz-Expires=900&X-Amz-Signature=5ddddfe8ef2b0601e80ab41c78a4d77d921942b0d8160bcab40ff894095e6823&X-Amz-SignedHeaders=host&x-id=PutObject`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_private
- https://github.com/huggingface/datasets/runs/7835921082?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hub-ci.huggingface.co/api/repos/create (Request ID: gL_1I7i2dii9leBhlZen-) - Internal Error - We're working hard to fix that as soon as possible!`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features_image_list
- https://github.com/huggingface/datasets/runs/7835920900?check_suite_focus=true
- This is not 500, but 404:
`requests.exceptions.HTTPError: 404 Client Error: Not Found for url: [https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16605586458339.git/info/lfs/objects](https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16605586458339.git/info/lfs/objects/batch)`
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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/241 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/241/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/241/comments | https://api.github.com/repos/huggingface/datasets/issues/241/events | https://github.com/huggingface/datasets/pull/241 | 631,703,079 | MDExOlB1bGxSZXF1ZXN0NDI4NTQwMDM0 | 241 | Fix empty cache dir | [] | closed | false | null | 2 | 2020-06-05T15:45:22Z | 2020-06-08T08:35:33Z | 2020-06-08T08:35:31Z | null | If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239
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"Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think",
"> Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think\r\n\r\nNo it shouldn't force to redownload"
] |
https://api.github.com/repos/huggingface/datasets/issues/681 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/681/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/681/comments | https://api.github.com/repos/huggingface/datasets/issues/681/events | https://github.com/huggingface/datasets/pull/681 | 710,075,721 | MDExOlB1bGxSZXF1ZXN0NDkzOTkwMjEz | 681 | Adding missing @property (+2 small flake8 fixes). | [] | closed | false | null | 0 | 2020-09-28T08:53:53Z | 2020-09-28T10:26:13Z | 2020-09-28T10:26:09Z | null | Fixes #678 | {
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https://api.github.com/repos/huggingface/datasets/issues/412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/412/comments | https://api.github.com/repos/huggingface/datasets/issues/412/events | https://github.com/huggingface/datasets/issues/412 | 660,047,139 | MDU6SXNzdWU2NjAwNDcxMzk= | 412 | Unable to load XTREME dataset from disk | [] | closed | false | null | 3 | 2020-07-18T09:55:00Z | 2020-07-21T08:15:44Z | 2020-07-21T08:15:44Z | null | Hi 🤗 team!
## Description of the problem
Following the [docs](https://huggingface.co./nlp/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https://github.com/google-research/xtreme) benchmark.
I have manually downloaded the `AmazonPhotos.zip` file from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.
As far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:
```
# path where load_dataset is looking for fr.tar.gz
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/
# path where it actually exists
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/panx_dataset/
```
## Steps to reproduce the problem
1. Manually download the XTREME benchmark from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)
2. Run the following code snippet
```python
from nlp import load_dataset
# AmazonPhotos.zip is in the root of the folder
dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
```
3. Here is the stack trace
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-26786bb5fa93> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
464 split_dict = SplitDict(dataset_name=self.name)
465 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 466 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
467 # Checksums verification
468 if verify_infos:
/usr/local/lib/python3.6/dist-packages/nlp/datasets/xtreme/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746/xtreme.py in _split_generators(self, dl_manager)
725 panx_dl_dir = dl_manager.extract(panx_path)
726 lang = self.config.name.split(".")[1]
--> 727 lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + ".tar.gz"))
728 return [
729 nlp.SplitGenerator(
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in extract(self, path_or_paths)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)
170 return tuple(mapped)
171 # Singleton
--> 172 return function(data_struct)
173
174
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in <lambda>(path)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
203 elif urlparse(url_or_filename).scheme == "":
204 # File, but it doesn't exist.
--> 205 raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename))
206 else:
207 # Something unknown
FileNotFoundError: Local file /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/fr.tar.gz doesn't exist
```
## OS and hardware
```
- `nlp` version: 0.3.0
- 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?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
``` | {
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} | https://api.github.com/repos/huggingface/datasets/issues/412/timeline | null | completed | null | null | false | [
"Hi @lewtun, you have to provide the full path to the downloaded file for example `/home/lewtum/..`",
"I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !",
"Thanks for the rapid fix @lhoestq!"
] |
https://api.github.com/repos/huggingface/datasets/issues/4528 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4528/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4528/comments | https://api.github.com/repos/huggingface/datasets/issues/4528/events | https://github.com/huggingface/datasets/issues/4528 | 1,276,679,155 | I_kwDODunzps5MGJPz | 4,528 | Memory leak when iterating a Dataset | [
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"color": "d73a4a",
"default": true,
"description": "Something isn't working",
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] | closed | false | null | 5 | 2022-06-20T10:03:14Z | 2022-09-12T08:51:39Z | 2022-09-12T08:51:39Z | null | e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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"Is someone assigned to this issue?",
"The same issue is being debugged here: https://github.com/huggingface/datasets/issues/4883\r\n",
"Here is a modified repro example that makes it easier to see the leak:\r\n\r\n```\r\n$ cat ds2.py\r\nimport gc, sys\r\nimport time\r\nfrom datasets import load_dataset\r\nimport os, psutil\r\n\r\nprocess = psutil.Process(os.getpid())\r\n\r\nprint(process.memory_info().rss/2**20)\r\n\r\ncorpus = load_dataset(\"BeIR/msmarco\", 'corpus', keep_in_memory=False, streaming=False)['corpus']\r\ncorpus = corpus.select(range(200000))\r\n\r\nprint(process.memory_info().rss/2**20)\r\n\r\nbatch = None\r\n\r\nmem_before_start = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n\r\nstep = 20000\r\nfor i in range(0, 10*step, step):\r\n mem_before = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n batch = corpus[i:i+step]\r\n import objgraph\r\n #objgraph.show_refs([batch])\r\n #objgraph.show_refs([corpus])\r\n #sys.exit()\r\n gc.collect()\r\n\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n print(f\"{i:6d} {mem_after - mem_before:12.4f} {mem_after - mem_before_start:12.4f}\")\r\n\r\n```\r\n\r\nLet's run:\r\n\r\n```\r\n$ python ds2.py\r\n 0 36.5391 36.5391\r\n 20000 10.4609 47.0000\r\n 40000 5.9766 52.9766\r\n 60000 7.8906 60.8672\r\n 80000 6.0586 66.9258\r\n100000 8.4453 75.3711\r\n120000 6.7422 82.1133\r\n140000 8.5664 90.6797\r\n160000 5.7344 96.4141\r\n180000 8.3398 104.7539\r\n```\r\n\r\nYou can see the last column of total RSS memory keeps on growing in MBs. The mid column is by how much it was grown during a single iteration of the repro script (20000 items)",
"@NouamaneTazi, please check my analysis here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242599722 so if you agree with my research this Issue can be closed as well.\r\n\r\nI also made a suggestion at how to proceed to hunt for a real leak here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242600626\r\n\r\nyou may find this one to be useful as well https://github.com/huggingface/datasets/issues/4883#issuecomment-1242597966",
"Amazing job! Thanks for taking time to debug this 🤗\r\n\r\nFor my side, I tried to do some more research as well, but to no avail. https://github.com/huggingface/datasets/issues/4883#issuecomment-1243415957"
] |
https://api.github.com/repos/huggingface/datasets/issues/2442 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2442/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2442/comments | https://api.github.com/repos/huggingface/datasets/issues/2442/events | https://github.com/huggingface/datasets/pull/2442 | 909,677,029 | MDExOlB1bGxSZXF1ZXN0NjYwMjE1ODY1 | 2,442 | add english language tags for ~100 datasets | [] | closed | false | null | 1 | 2021-06-02T16:24:56Z | 2021-06-04T09:51:40Z | 2021-06-04T09:51:39Z | null | As discussed on Slack, I have manually checked for ~100 datasets that they have at least one subset in English. This information was missing so adding into the READMEs.
Note that I didn't check all the subsets so it's possible that some of the datasets have subsets in other languages than English... | {
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"Fixing the tags of all the datasets is out of scope for this PR so I'm merging even though the CI fails because of the missing tags"
] |
https://api.github.com/repos/huggingface/datasets/issues/2505 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2505/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2505/comments | https://api.github.com/repos/huggingface/datasets/issues/2505/events | https://github.com/huggingface/datasets/pull/2505 | 921,234,797 | MDExOlB1bGxSZXF1ZXN0NjcwMjY2NjQy | 2,505 | Make numpy arrow extractor faster | [] | closed | false | null | 5 | 2021-06-15T10:11:32Z | 2021-06-28T09:53:39Z | 2021-06-28T09:53:38Z | null | I changed the NumpyArrowExtractor to call directly to_numpy and see if it can lead to speed-ups as discussed in https://github.com/huggingface/datasets/issues/2498
This could make the numpy/torch/tf/jax formatting faster | {
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"Looks like we have a nice speed up in some benchmarks. For example:\r\n- `read_formatted numpy 5000`: 4.584777 sec -> 0.487113 sec\r\n- `read_formatted torch 5000`: 4.565676 sec -> 1.289514 sec",
"Can we convert this draft to PR @lhoestq ?",
"Ready for review ! cc @vblagoje",
"@lhoestq I tried the branch and it works for me. Although performance trace now shows a speedup, the overall pre-training speed up is minimal. But that's on my plate to explore further. ",
"Thanks for investigating @vblagoje \r\n\r\n@albertvillanova , do you have any comments on this PR ? Otherwise I think we can merge it"
] |
https://api.github.com/repos/huggingface/datasets/issues/4374 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4374/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4374/comments | https://api.github.com/repos/huggingface/datasets/issues/4374/events | https://github.com/huggingface/datasets/issues/4374 | 1,241,860,535 | I_kwDODunzps5KBUm3 | 4,374 | extremely slow processing when using a custom dataset | [
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"url": "https://api.github.com/repos/huggingface/datasets/labels/bug"
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{
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"default": true,
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"name": "question",
"node_id": "MDU6TGFiZWwxOTM1ODkyOTEy",
"url": "https://api.github.com/repos/huggingface/datasets/labels/question"
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] | closed | false | null | 2 | 2022-05-19T14:18:05Z | 2023-07-25T15:07:17Z | 2023-07-25T15:07:16Z | null | ## processing a custom dataset loaded as .txt file is extremely slow, compared to a dataset of similar volume from the hub
I have a large .txt file of 22 GB which i load into HF dataset
`lang_dataset = datasets.load_dataset("text", data_files="hi.txt")`
further i use a pre-processing function to clean the dataset
`lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)`
the following processing takes astronomical time to process, while hoging all the ram.
similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data.
`lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)`
the hours predicted to preprocess are as follows:
huggingface hub dataset: 6.5 hrs
custom loaded dataset: 7000 hrs
note: both the datasets are almost actually same, just provided by different sources with has +/- some samples, only one is hosted on the HF hub and the other is downloaded in a text format.
## Steps to reproduce the bug
```
import datasets
import psutil
import sys
import glob
from fastcore.utils import listify
import re
import gc
def remove_non_indic_sentences(example):
tmp_ls = []
eng_regex = r'[. a-zA-Z0-9ÖÄÅöäå _.,!"\'\/$]*'
for e in listify(example['text']):
matches = re.findall(eng_regex, e)
for match in (str(match).strip() for match in matches if match not in [""," ", " ", ",", " ,", ", ", " , "]):
if len(list(match.split(" "))) > 2:
e = re.sub(match," ",e,count=1)
tmp_ls.append(e)
gc.collect()
example['clean_text'] = tmp_ls
return example
lang_dataset = datasets.load_dataset("text", data_files="hi.txt")
lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)
## same thing work much faster when loading similar dataset from hub
lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", split="train", use_auth_token=True)
lang_dataset["train"] = lang_dataset["train"].map(
remove_non_indic_sentences, num_proc=12, batched=True, remove_columns=lang_dataset['train'].column_names), batch_size=64)
```
## Actual results
similar dataset of same size that's available in the huggingface hub works completely fine. which runs the same processing function and has the same amount of data.
`lang_dataset = datasets.load_dataset("oscar-corpus/OSCAR-2109", "hi", use_auth_token=True)
**the hours predicted to preprocess are as follows:**
huggingface hub dataset: 6.5 hrs
custom loaded dataset: 7000 hrs
**i even tried the following:**
- sharding the large 22gb text files into smaller files and loading
- saving the file to disk and then loading
- using lesser num_proc
- using smaller batch size
- processing without batches ie : without `batched=True`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2.dev0
- Platform: Ubuntu 20.04 LTS
- Python version: 3.9.7
- PyArrow version:8.0.0
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} | https://api.github.com/repos/huggingface/datasets/issues/4374/timeline | null | completed | null | null | false | [
"Hi !\r\n\r\nMy guess is that some examples in your dataset are bigger than your RAM, and therefore loading them in RAM to pass them to `remove_non_indic_sentences` takes forever because it might use SWAP memory.\r\n\r\nMaybe several examples in your dataset are grouped together, can you check `len(lang_dataset[\"train\"])` and `lang_dataset[\"train\"].data.nbytes` of both datasets please ? It can also be helpful to check the distribution of lengths of each examples in your dataset.",
"Closing due to inactivity"
] |
https://api.github.com/repos/huggingface/datasets/issues/1280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1280/comments | https://api.github.com/repos/huggingface/datasets/issues/1280/events | https://github.com/huggingface/datasets/pull/1280 | 759,151,028 | MDExOlB1bGxSZXF1ZXN0NTM0MTk2MDc0 | 1,280 | disaster response messages dataset | [] | closed | false | null | 2 | 2020-12-08T07:27:16Z | 2020-12-09T16:21:57Z | 2020-12-09T16:21:57Z | null | {
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} | true | [
"I have added the Readme.md as well, the PR is ready for review. \r\n\r\nThank you ",
"Hi @lhoestq I have updated the code and files. Please if you could check once.\r\n\r\nThank you"
] |
|
https://api.github.com/repos/huggingface/datasets/issues/1758 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1758/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1758/comments | https://api.github.com/repos/huggingface/datasets/issues/1758/events | https://github.com/huggingface/datasets/issues/1758 | 790,626,116 | MDU6SXNzdWU3OTA2MjYxMTY= | 1,758 | dataset.search() (elastic) cannot reliably retrieve search results | [] | closed | false | null | 2 | 2021-01-21T02:26:37Z | 2021-01-22T00:25:50Z | 2021-01-22T00:25:50Z | null | I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.
The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.
I am indexing data that looks like the following from the HF SQuAD 2.0 data set:
```
['57318658e6313a140071d02b',
'56f7165e3d8e2e1400e3733a',
'570e2f6e0b85d914000d7d21',
'5727e58aff5b5019007d97d0',
'5a3b5a503ff257001ab8441f',
'57262fab271a42140099d725']
```
To reproduce the issue, try:
```
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast, BertForQuestionAnswering
from elasticsearch import Elasticsearch
import numpy as np
import collections
from tqdm.auto import tqdm
import torch
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
max_length = 384 # The maximum length of a feature (question and context)
doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.
pad_on_right = tokenizer.padding_side == "right"
squad_v2 = True
# from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv-
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# We keep the example_id that gave us this feature and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(list(o) if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
# build base examples, features set of training data
shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list()
examples = load_dataset("squad_v2").shuffle(seed=1)['train']
features = load_dataset("squad_v2").shuffle(seed=1)['train'].map(
prepare_validation_features,
batched=True,
remove_columns=['answers', 'context', 'id', 'question', 'title'])
# reorder features by the training process
features = features.select(indices=shuffled_idx)
# get the example ids to match with the "example" data; get unique entries
id_list = list(dict.fromkeys(features['example_id']))
# now search for their index positions in the examples data set; load elastic search
es = Elasticsearch([{'host': 'localhost'}]).ping()
# add an index to the id column for the examples
examples.add_elasticsearch_index(column='id')
# retrieve the example index
example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]
example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]
example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]
example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]
len(example_idx_k1) # should be 130319
len(example_idx_k2) # should be 130319
#trial 1 lengths:
# k=1: 130314
# k=3: 130319
# trial 2:
# just run k=3 first: 130310
# try k=1 after k=3: 130319
```
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} | https://api.github.com/repos/huggingface/datasets/issues/1758/timeline | null | completed | null | null | false | [
"Hi !\r\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\r\nMaybe this is because the index is not updated yet on the ElasticSearch side ?",
"Thanks for the feedback! I added a 30 second \"sleep\" and that seemed to work well!"
] |
https://api.github.com/repos/huggingface/datasets/issues/922 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/922/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/922/comments | https://api.github.com/repos/huggingface/datasets/issues/922/events | https://github.com/huggingface/datasets/pull/922 | 753,559,130 | MDExOlB1bGxSZXF1ZXN0NTI5NjEzOTA4 | 922 | Add XOR QA Dataset | [] | closed | false | null | 4 | 2020-11-30T15:10:54Z | 2020-12-02T03:12:21Z | 2020-12-02T03:12:21Z | null | Added XOR Question Answering Dataset. The link to the dataset can be found [here](https://nlp.cs.washington.edu/xorqa/)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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"Hi @sumanthd17 \r\n\r\nLooks like a good start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)",
"I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite ",
"> I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite\r\n\r\nThe best way is to run the tagging app locally and provide it the location to the `dataset_infos.json` after you've run the CLI:\r\nhttps://github.com/huggingface/datasets-tagging\r\n",
"This is a really good data card!!\r\n\r\nSmall changes to make it even better:\r\n- Tags: the dataset has both \"original\" data and data that is \"extended\" from a source dataset: TydiQA - you should choose both options in the tagging apps\r\n- The language and annotation creator tags are off: the language here is the questions: I understand it's a mix of crowd-sourced and expert-generated? Is there any machine translation involved? The annotations are the span selections: is that crowd-sourced?\r\n- Personal and sensitive information: there should be a statement there, even if only to say that none could be found or that it only mentions public figures"
] |
https://api.github.com/repos/huggingface/datasets/issues/1252 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1252/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1252/comments | https://api.github.com/repos/huggingface/datasets/issues/1252/events | https://github.com/huggingface/datasets/pull/1252 | 758,511,388 | MDExOlB1bGxSZXF1ZXN0NTMzNjczMDcx | 1,252 | Add Naver sentiment movie corpus | [] | closed | false | null | 0 | 2020-12-07T13:33:45Z | 2020-12-08T14:32:33Z | 2020-12-08T14:21:37Z | null | Supersedes #1168
> This PR adds the [Naver sentiment movie corpus](https://github.com/e9t/nsmc), a dataset containing Korean movie reviews from Naver, the most commonly used search engine in Korea. This dataset is often used to benchmark models on Korean NLP tasks, as seen in [this paper](https://www.aclweb.org/anthology/2020.lrec-1.199.pdf). | {
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https://api.github.com/repos/huggingface/datasets/issues/2560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2560/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2560/comments | https://api.github.com/repos/huggingface/datasets/issues/2560/events | https://github.com/huggingface/datasets/pull/2560 | 932,143,634 | MDExOlB1bGxSZXF1ZXN0Njc5NTMyODk4 | 2,560 | fix Dataset.map when num_procs > num rows | [] | closed | false | null | 3 | 2021-06-29T02:24:11Z | 2021-06-29T15:00:18Z | 2021-06-29T14:53:31Z | null | closes #2470
## Testing notes
To run updated tests:
```sh
pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s
```
With Python code (to view warning):
```python
from datasets import Dataset
dataset = Dataset.from_dict({"x": ["sample"]})
print(len(dataset))
dataset.map(lambda x: x, num_proc=10)
``` | {
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"Hi ! Thanks for fixing this :)\r\n\r\nLooks like you have tons of changes due to code formatting.\r\nWe're using `black` for this, with a custom line length. To run our code formatting, you just need to run\r\n```\r\nmake style\r\n```\r\n\r\nThen for the windows error in the CI, I'm looking into it. It's probably just a file that isn't properly closed",
"CI is all green now ! Thanks :)\r\n\r\nThere are still many code formatting changes in your PR - probably due to the first commit you did.\r\nTo avoid conflicts with future PRs it would be nice to only have the changes related to the `num_proc` warning, and not have all those code formatting changes,\r\n\r\nCould you try remove those code formatting changes ?\r\n\r\nIf it's easier for you, you can make a new branch from `master` if needed",
"Thanks, @lhoestq! Apologies for the half-baked commits yesterday! I wasn’t able to step back in to resolve those CI issues until this morning.\r\n\r\nAlso, I’m surprised that `make style` isn’t resolving the formatting changes. I’m a bit stumped on that, so I’m going to re-apply on a new branch and open a PR as you suggested."
] |
https://api.github.com/repos/huggingface/datasets/issues/622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/622/comments | https://api.github.com/repos/huggingface/datasets/issues/622/events | https://github.com/huggingface/datasets/issues/622 | 700,225,826 | MDU6SXNzdWU3MDAyMjU4MjY= | 622 | load_dataset for text files not working | [
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"id": 2067388877,
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] | closed | false | null | 41 | 2020-09-12T12:49:28Z | 2020-10-28T11:07:31Z | 2020-10-28T11:07:30Z | null | Trying the following snippet, I get different problems on Linux and Windows.
```python
dataset = load_dataset("text", data_files="data.txt")
# or
dataset = load_dataset("text", data_files=["data.txt"])
```
(ps [This example](https://huggingface.co./docs/datasets/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)
The problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.
Linux stack trace:
```
PyTorch version 1.6.0+cu101 available.
Checking /home/bram/.cache/huggingface/datasets/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.json
Using custom data configuration default
Generating dataset text (/home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)
Downloading and preparing dataset text/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...
Dataset not on Hf google storage. Downloading and preparing it from source
Downloading took 0.0 min
Checksum Computation took 0.0 min
Unable to verify checksums.
Generating split train
Traceback (most recent call last):
File "/home/bram/Python/projects/dutch-simplification/utils.py", line 45, in prepare_data
dataset = load_dataset("text", data_files=dataset_f)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/load.py", line 608, in load_dataset
builder_instance.download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 468, in download_and_prepare
self._download_and_prepare(
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 546, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 888, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/tqdm/std.py", line 1130, in __iter__
for obj in iterable:
File "/home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py", line 100, in _generate_tables
pa_table = pac.read_csv(
File "pyarrow/_csv.pyx", line 714, in pyarrow._csv.read_csv
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2
```
Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:
```
Checking C:\Users\bramv\.cache\huggingface\datasets\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.json
Using custom data configuration default
```
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"Can you give us more information on your os and pip environments (pip list)?",
"@thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage - Version\r\n---------------------\r\ncertifi 2020.6.20\r\nchardet 3.0.4\r\nclick 7.1.2\r\ndatasets 1.0.1\r\ndill 0.3.2\r\nfasttext 0.9.2\r\nfilelock 3.0.12\r\nfuture 0.18.2\r\nidna 2.10\r\njoblib 0.16.0\r\nnltk 3.5\r\nnumpy 1.19.1\r\npackaging 20.4\r\npandas 1.1.2\r\npip 20.0.2\r\nprotobuf 3.13.0\r\npyarrow 1.0.1\r\npybind11 2.5.0\r\npyparsing 2.4.7\r\npython-dateutil 2.8.1\r\npytz 2020.1\r\nregex 2020.7.14\r\nrequests 2.24.0\r\nsacremoses 0.0.43\r\nscikit-learn 0.23.2\r\nscipy 1.5.2\r\nsentence-transformers 0.3.6\r\nsentencepiece 0.1.91\r\nsetuptools 46.1.3\r\nsix 1.15.0\r\nstanza 1.1.1\r\nthreadpoolctl 2.1.0\r\ntokenizers 0.8.1rc2\r\ntorch 1.6.0+cu101\r\ntqdm 4.48.2\r\ntransformers 3.1.0\r\nurllib3 1.25.10\r\nwheel 0.34.2\r\nxxhash 2.0.0\r\n\r\nWindows 10 - Python 3.8\r\n================\r\nPackage - Version\r\n----------------------------\r\ncertifi 2020.6.20\r\nchardet 3.0.4\r\nclick 7.1.2\r\ndatasets 1.0.1\r\ndill 0.3.2\r\nfasttext 0.9.2\r\nfilelock 3.0.12\r\nfuture 0.18.2\r\nidna 2.10\r\njoblib 0.16.0\r\nnlp 0.4.0\r\nnltk 3.5\r\nnumpy 1.19.1\r\npackaging 20.4\r\npandas 1.1.1\r\npip 20.0.2\r\nprotobuf 3.13.0\r\npyarrow 1.0.1\r\npybind11 2.5.0\r\npyparsing 2.4.7\r\npython-dateutil 2.8.1\r\npytz 2020.1\r\nregex 2020.7.14\r\nrequests 2.24.0\r\nsacremoses 0.0.43\r\nscikit-learn 0.23.2\r\nscipy 1.5.2\r\nsentence-transformers 0.3.5.1\r\nsentencepiece 0.1.91\r\nsetuptools 46.1.3\r\nsix 1.15.0\r\nstanza 1.1.1\r\nthreadpoolctl 2.1.0\r\ntokenizers 0.8.1rc1\r\ntorch 1.6.0+cu101\r\ntqdm 4.48.2\r\ntransformers 3.0.2\r\nurllib3 1.25.10\r\nwheel 0.34.2\r\nxxhash 2.0.0",
"Downgrading to 3.7 does not help. Here is a dummy text file:\r\n\r\n```text\r\nVerzekering weigert vaker te betalen\r\nBedrijven van verzekeringen erkennen steeds minder arbeidsongevallen .\r\nIn 2012 weigerden de bedrijven te betalen voor 21.055 ongevallen op het werk .\r\nDat is 11,8 % van alle ongevallen op het werk .\r\nNog nooit weigerden verzekeraars zoveel zaken .\r\nIn 2012 hadden 135.118 mensen een ongeval op het werk .\r\nDat zijn elke werkdag 530 mensen .\r\nBij die ongevallen stierven 67 mensen .\r\nBijna 12.000 hebben een handicap na het ongeval .\r\nGeen echt arbeidsongeval Bedrijven moeten een verzekering hebben voor hun werknemers .\r\n```\r\n\r\nA temporary work around for the \"text\" type, is\r\n\r\n```python\r\ndataset = Dataset.from_dict({\"text\": Path(dataset_f).read_text().splitlines()})\r\n```",
"\r\n\r\neven i am facing the same issue.",
"@banunitte Please do not post screenshots in the future but copy-paste your code and the errors. That allows others to copy-and-paste your code and test it. You may also want to provide the Python version that you are using.",
"I have the exact same problem in Windows 10, Python 3.8.\r\n",
"I have the same problem on Linux of the script crashing with a CSV error. This may be caused by 'CRLF', when changed 'CRLF' to 'LF', the problem solved.",
"I pushed a fix for `pyarrow.lib.ArrowInvalid: CSV parse error`. Let me know if you still have this issue.\r\n\r\nNot sure about the windows one yet",
"To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n```python\r\ndataset = load_dataset('text', script_version='master', data_files=XXX)\r\n```\r\nWe do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).",
"\r\nwin10, py3.6\r\n\r\n\r\n```\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\n\r\nfeatures = Features({'text': Value('string'), 'ctext': Value('string')})\r\nfile_dict = {'train': PATH/'summary.csv'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n```",
"```python\r\nTraceback` (most recent call last):\r\n File \"main.py\", line 281, in <module>\r\n main()\r\n File \"main.py\", line 190, in main\r\n train_data, test_data = data_factory(\r\n File \"main.py\", line 129, in data_factory\r\n train_data = load_dataset('text', \r\n File \"/home/me/Downloads/datasets/src/datasets/load.py\", line 608, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 468, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 546, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 888, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/home/me/.local/lib/python3.8/site-packages/tqdm/std.py\", line 1130, in __iter__\r\n for obj in iterable:\r\n File \"/home/me/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014/text.py\", line 103, in _generate_tables\r\n pa_table = pac.read_csv(\r\n File \"pyarrow/_csv.pyx\", line 617, in pyarrow._csv.read_csv\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nUnfortunately i am still getting this issue on Linux. I installed datasets from source and specified script_version to master.\r\n\r\n",
"> \r\n> win10, py3.6\r\n> \r\n> ```\r\n> from datasets import Features, Value, ClassLabel, load_dataset\r\n> \r\n> \r\n> features = Features({'text': Value('string'), 'ctext': Value('string')})\r\n> file_dict = {'train': PATH/'summary.csv'}\r\n> \r\n> dataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n> ```\r\n\r\nSince #644 it should now work on windows @ScottishFold007 \r\n\r\n> Trying the following snippet, I get different problems on Linux and Windows.\r\n> \r\n> ```python\r\n> dataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n> # or \r\n> dataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n> ```\r\n>\r\n> Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n> \r\n> ```\r\n> Checking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\n> Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\n> Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\n> Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\n> Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\\dataset_infos.json\r\n> Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\n> Using custom data configuration default\r\n> ```\r\n\r\nSame for you @BramVanroy .\r\n\r\nNot sure about the one on linux though",
"> To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n> \r\n> ```python\r\n> dataset = load_dataset('text', script_version='master', data_files=XXX)\r\n> ```\r\n> \r\n> We do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).\r\n\r\nLinux here:\r\n\r\nI was using the 0.4.0 nlp library load_dataset to load a text dataset of 9-10Gb without collapsing the RAM memory. However, today I got the csv error message mentioned in this issue. After installing the new (datasets) library from source and specifying the script_verson = 'master' I'm still having this same error message. Furthermore, I cannot use the dictionary \"trick\" to load the dataset since the system kills the process due to a RAM out of memory problem. Is there any other solution to this error? Thank you in advance. ",
"Hi @raruidol \r\nTo fix the RAM issue you'll need to shard your text files into smaller files (see https://github.com/huggingface/datasets/issues/610#issuecomment-691672919 for example)\r\n\r\nI'm not sure why you're having the csv error on linux.\r\nDo you think you could to to reproduce it on google colab for example ?\r\nOr send me a dummy .txt file that reproduces the issue ?",
"@lhoestq \r\n\r\nThe crash message shows up when loading the dataset:\r\n```\r\nprint('Loading corpus...') \r\nfiles = glob.glob('corpora/shards/*') \r\n-> dataset = load_dataset('text', script_version='master', data_files=files) \r\nprint('Corpus loaded.')\r\n```\r\nAnd this is the exact message:\r\n```\r\nTraceback (most recent call last):\r\n File \"run_language_modeling.py\", line 27, in <module>\r\n dataset = load_dataset('text', script_version='master', data_files=files)\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/load.py\", line 611, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 471, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 548, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 892, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/tqdm/std.py\", line 1130, in __iter__\r\n for obj in iterable:\r\n File \"/home/jupyter-raruidol/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014/text.py\", line 107, in _generate_tables\r\n convert_options=self.config.convert_options,\r\n File \"pyarrow/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nAnd these are the pip packages I have atm and their versions:\r\n\r\n```\r\nPackage Version Location \r\n--------------- --------- -------------------------------------------------------------\r\ncertifi 2020.6.20 \r\nchardet 3.0.4 \r\nclick 7.1.2 \r\ndatasets 1.0.2 \r\ndill 0.3.2 \r\nfilelock 3.0.12 \r\nfuture 0.18.2 \r\nidna 2.10 \r\njoblib 0.16.0 \r\nnumpy 1.19.1 \r\npackaging 20.4 \r\npandas 1.1.1 \r\npip 19.0.3 \r\npyarrow 1.0.1 \r\npyparsing 2.4.7 \r\npython-dateutil 2.8.1 \r\npytz 2020.1 \r\nregex 2020.7.14 \r\nrequests 2.24.0 \r\nsacremoses 0.0.43 \r\nsentencepiece 0.1.91 \r\nsetuptools 40.8.0 \r\nsix 1.15.0 \r\ntokenizers 0.8.1rc2 \r\ntorch 1.6.0 \r\ntqdm 4.48.2 \r\ntransformers 3.0.2 /home/jupyter-raruidol/DebatAnalyser/env/src/transformers/src\r\n```\r\n\r\n\r\n",
"I tested on google colab which is also linux using this code:\r\n\r\n- first download an arbitrary text file\r\n```bash\r\nwget https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_train.txt\r\n```\r\n- then run\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"text\", data_files=\"all_train.txt\", script_version='master')\r\n```\r\nAnd I don't get this issue.\r\n\r\n\\> Could you test on your side if these lines work @raruidol ?\r\n\r\nalso cc @Skyy93 as it seems you have the same issue\r\n\r\nIf it works:\r\nIt could mean that the issue could come from unexpected patterns in the files you want to use.\r\nIn that case we should find a way to handle them.\r\n\r\nAnd if it doesn't work:\r\nIt could mean that it comes from the way pyarrow reads text files on linux.\r\nIn that case we should report it to pyarrow and find a workaround in the meantime\r\n\r\nEither way it should help to find where this bug comes from and fix it :)\r\n\r\nThank you in advance !",
"Update: also tested the above code in a docker container from [jupyter/minimal-notebook](https://hub.docker.com/r/jupyter/minimal-notebook/) (based on ubuntu) and still not able to reproduce",
"It looks like with your text input file works without any problem. I have been doing some experiments this morning with my input files and I'm almost certain that the crash is caused by some unexpected pattern in the files. However, I've not been able to spot the main cause of it. What I find strange is that this same corpus was being loaded by the nlp 0.4.0 library without any problem... Where can I find the code where you structure the input text data in order to use it with pyarrow?",
"Under the hood it does\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\n# Use csv reader from Pyarrow with one column for text files\r\n\r\n# To force the one-column setting, we set an arbitrary character\r\n# that is not in text files as delimiter, such as \\b or \\v.\r\n# The bell character, \\b, was used to make beeps back in the days\r\nparse_options = pa.csv.ParseOptions( \r\n delimiter=\"\\b\", \r\n quote_char=False, \r\n double_quote=False, \r\n escape_char=False, \r\n newlines_in_values=False, \r\n ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"all_train.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\nNote that we changed the parse options with datasets 1.0\r\nIn particular the delimiter used to be `\\r` but this delimiter doesn't work on windows.",
"Could you try with `\\a` instead of `\\b` ? It looks like the bell character is \\a in python and not \\b",
"I was just exploring if the crash was happening in every shard or not, and which shards were generating the error message. With \\b I got the following list of shards crashing:\r\n\r\n```\r\nErrors on files: ['corpora/shards/shard_0069', 'corpora/shards/shard_0043', 'corpora/shards/shard_0014', 'corpora/shards/shard_0032', 'corpora/shards/shard_0088', 'corpora/shards/shard_0018', 'corpora/shards/shard_0073', 'corpora/shards/shard_0079', 'corpora/shards/shard_0038', 'corpora/shards/shard_0041', 'corpora/shards/shard_0007', 'corpora/shards/shard_0004', 'corpora/shards/shard_0102', 'corpora/shards/shard_0096', 'corpora/shards/shard_0030', 'corpora/shards/shard_0076', 'corpora/shards/shard_0067', 'corpora/shards/shard_0052', 'corpora/shards/shard_0026', 'corpora/shards/shard_0024', 'corpora/shards/shard_0064', 'corpora/shards/shard_0044', 'corpora/shards/shard_0013', 'corpora/shards/shard_0062', 'corpora/shards/shard_0057', 'corpora/shards/shard_0097', 'corpora/shards/shard_0094', 'corpora/shards/shard_0078', 'corpora/shards/shard_0075', 'corpora/shards/shard_0039', 'corpora/shards/shard_0077', 'corpora/shards/shard_0021', 'corpora/shards/shard_0040', 'corpora/shards/shard_0009', 'corpora/shards/shard_0023', 'corpora/shards/shard_0095', 'corpora/shards/shard_0107', 'corpora/shards/shard_0063', 'corpora/shards/shard_0086', 'corpora/shards/shard_0047', 'corpora/shards/shard_0089', 'corpora/shards/shard_0037', 'corpora/shards/shard_0101', 'corpora/shards/shard_0093', 'corpora/shards/shard_0082', 'corpora/shards/shard_0091', 'corpora/shards/shard_0065', 'corpora/shards/shard_0020', 'corpora/shards/shard_0070', 'corpora/shards/shard_0008', 'corpora/shards/shard_0058', 'corpora/shards/shard_0060', 'corpora/shards/shard_0022', 'corpora/shards/shard_0059', 'corpora/shards/shard_0100', 'corpora/shards/shard_0027', 'corpora/shards/shard_0072', 'corpora/shards/shard_0098', 'corpora/shards/shard_0019', 'corpora/shards/shard_0066', 'corpora/shards/shard_0042', 'corpora/shards/shard_0053']\r\n```\r\n\r\nI also tried with \\a and the list decreased but there were still several crashes:\r\n\r\n```\r\nErrors on files: ['corpora/shards/shard_0069', 'corpora/shards/shard_0055', 'corpora/shards/shard_0043', 'corpora/shards/shard_0014', 'corpora/shards/shard_0073', 'corpora/shards/shard_0025', 'corpora/shards/shard_0068', 'corpora/shards/shard_0102', 'corpora/shards/shard_0096', 'corpora/shards/shard_0076', 'corpora/shards/shard_0067', 'corpora/shards/shard_0026', 'corpora/shards/shard_0024', 'corpora/shards/shard_0044', 'corpora/shards/shard_0087', 'corpora/shards/shard_0092', 'corpora/shards/shard_0074', 'corpora/shards/shard_0094', 'corpora/shards/shard_0078', 'corpora/shards/shard_0039', 'corpora/shards/shard_0077', 'corpora/shards/shard_0040', 'corpora/shards/shard_0009', 'corpora/shards/shard_0107', 'corpora/shards/shard_0063', 'corpora/shards/shard_0103', 'corpora/shards/shard_0047', 'corpora/shards/shard_0033', 'corpora/shards/shard_0089', 'corpora/shards/shard_0037', 'corpora/shards/shard_0082', 'corpora/shards/shard_0071', 'corpora/shards/shard_0091', 'corpora/shards/shard_0065', 'corpora/shards/shard_0070', 'corpora/shards/shard_0058', 'corpora/shards/shard_0081', 'corpora/shards/shard_0060', 'corpora/shards/shard_0002', 'corpora/shards/shard_0059', 'corpora/shards/shard_0027', 'corpora/shards/shard_0072', 'corpora/shards/shard_0098', 'corpora/shards/shard_0019', 'corpora/shards/shard_0045', 'corpora/shards/shard_0036', 'corpora/shards/shard_0066', 'corpora/shards/shard_0053']\r\n```\r\n\r\nWhich means that it is quite possible that the assumption of that some unexpected pattern in the files is causing the crashes is true. If I am able to reach any conclusion I will post It here asap.",
"Hmmm I was expecting it to work with \\a, not sure why they appear in your text files though",
"Hi @lhoestq, is there any input length restriction which was not before the update of the nlp library?",
"No we never set any input length restriction on our side (maybe arrow but I don't think so)",
"@lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong? \r\n\r\nIf I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?",
"Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones:\r\n\r\n\r\n_4. DE L’ORGANITZACIÓ ESTAMENTAL A L’ORGANITZACIÓ EN CLASSES A mesura que es desenvolupava un sistema econòmic capitalista i naixia una classe burgesa cada vegada més preparada per a substituir els dirigents de les velles monarquies absolutistes, es qüestionava l’abundància de béns amortitzats, que com s’ha dit estaven fora del mercat i no pagaven tributs, pels perjudicis que ocasionaven a les finances públiques i a l’economia en general. Aquest estat d’opinió revolucionari va desembocar en un conjunt de mesures pràctiques de caràcter liberal. D’una banda, les que intentaven desposseir les mans mortes del domini de béns acumulats, procés que acostumem a denominar desamortització, i que no és més que la nacionalització i venda d’aquests béns eclesiàstics o civils en subhasta pública al millor postor. D’altra banda, les que redimien o reduïen els censos i delmes o aixecaven les prohibicions de venda, és a dir, les vinculacions. La desamortització, que va afectar béns dels ordes religiosos, dels pobles i d’algunes corporacions civils, no va ser un camí fàcil, perquè costava i costa trobar algú que sigui indiferent a la pèrdua de béns, drets i privilegis. I té una gran transcendència, va privar els antics estaments de les Espanyes, clero i pobles —la noblesa en queda al marge—, de la força econòmica que els donaven bona part de les seves terres i, en última instància, va preparar el terreny per a la substitució de la vella societat estamental per la nova societat classista. En aquesta societat, en teoria, les agrupacions socials són obertes, no tenen cap estatut jurídic privilegiat i estan definides per la possessió o no d’uns béns econòmics que són lliurement alienables. A les Espanyes la transformació va afectar poc l’aristocràcia latifundista, allà on n’hi havia. Aquesta situació va afavorir, en part, la persistència de la vella cultura de la societat estamental en determinats ambients, i això ha influït decisivament en la manca de democràcia que caracteritza la majoria de règims polítics que s’han anat succeint. Una manera de pensar que sempre sura en un moment o altre, i que de fet no acaba de desaparèixer del tot. 5. INICI DE LA DESAMORTITZACIÓ A LES ESPANYES Durant el segle xviii, dins d’aquesta visió lliberal, va agafar força en alguns cercles de les Espanyes el corrent d’opinió contrari a les mans mortes. Durant el regnat de Carles III, s’arbitraren les primeres mesures desamortitzadores proposades per alguns ministres il·lustrats. Aquestes disposicions foren modestes i poc eficaces, no van aturar l’acumulació de terres per part dels estaments que constituïen les mans mortes i varen afectar principalment béns dels pobles. L’Església no va ser tocada, excepte en el cas de 110_\r\n\r\n_la revolució liberal, perquè, encara que havia perdut els seus drets jurisdiccionals, havia conservat la majoria de terres i fins i tot les havia incrementat amb d’altres que procedien de la desamortització. En la nova situació, les mans mortes del bosc públic eren l’Estat, que no cerca mai l’autofinançament de les despeses de gestió; els diners que manquin ja els posarà l’Estat. 9. DEFENSA I INTENTS DE RECUPERACIÓ DELS BÉNS COMUNALS DESAMORTITZATS El procés de centralització no era senzill, perquè, d’una banda, la nova organització apartava de la gestió moltes corporacions locals i molts veïns que l’havien portada des de l’edat mitjana, i, de l’altra, era difícil de coordinar la nova silvicultura amb moltes pràctiques forestals i drets tradicionals, com la pastura, fer llenya o tallar un arbre aquí i un altre allà quan tenia el gruix suficient, les pràctiques que s’havien fet sempre. Les primeres passes de la nova organització centralitzada varen tenir moltes dificultats en aquells indrets en què els terrenys municipals i comunals tenien un paper important en l’economia local. La desobediència a determinades normes imposades varen prendre formes diferents. Algunes institucions, com, per exemple, la Diputació de Lleida, varen retardar la tramitació d’alguns expedients i varen evitar la venda de béns municipals. Molts pobles permeteren deixar que els veïns continuessin amb les seves pràctiques tradicionals, d’altres varen boicotejar les subhastes d’aprofitaments. L’Estat va reaccionar encomanant a la Guàrdia Civil el compliment de les noves directrius. Imposar el nou règim va costar a l’Administració un grapat d’anys, però de mica en mica, amb molta, molta guarderia i gens de negociació, ho va aconseguir. La nova gestió estatal dels béns municipals va deixar, com hem comentat, molta gent sense uns recursos necessaris per a la supervivència, sobre tot en àrees on predominaven les grans propietats, i on els pagesos sense terra treballaven de jornalers temporers. Això va afavorir que, a bona part de les Espanyes, les primeres lluites camperoles de la segona meitat del segle xix defensessin la recuperació dels comunals desamortitzats; per a molts aquella expropiació i venda dirigida pels governs monàrquics era la causa de molta misèria. D’altres, més radicalitzats, varen entendre que l’eliminació de la propietat col·lectiva i la gestió estatal dels boscos no desamortitzats suposava una usurpació pura i dura. En les zones més afectades per la desamortització això va donar lloc a un imaginari centrat en la defensa del comunal. La Segona República va arribar en una conjuntura econòmica de crisi, generada pel crac del 1929. Al camp, aquesta situació va produir una forta caiguda dels preus dels productes agraris i un increment important de l’atur. QUADERNS AGRARIS 42 (juny 2017), p. 105-126_\r\n\r\nI think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.",
"So we're using the csv reader to read text files because arrow doesn't have a text reader.\r\nTo workaround the fact that text files are just csv with one column, we want to set a delimiter that doesn't appear in text files.\r\nUntil now I thought that it would do the job but unfortunately it looks like even characters like \\a appear in text files.\r\n\r\nSo we have to option:\r\n- find another delimiter that does the job (maybe `\\x1b` esc or `\\x18` cancel)\r\n- don't use the csv reader from arrow but the text reader from pandas instead (or any other reader). The only important thing is that it must be fast (arrow's reader has a nice and fast multithreaded for csv that we're using now but hopefully we can find an alternative)\r\n\r\n\r\n\r\n> @lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong?\r\n\r\nAs long as the text file follows some encoding it wouldn't make sense to have characters such as the bell character. However I agree it can happen.\r\n\r\n> If I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?\r\n\r\nExactly. Arrow doesn't allow the newline character unfortunately.",
"> Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones\r\n\r\nThanks for digging into it !\r\n\r\nCharacters like \\a or \\b are not shown when printing the text, so as it is I can't tell if it contains unexpected characters.\r\nMaybe could could open the file in python and check if `\"\\b\" in open(\"path/to/file\", \"r\").read()` ?\r\n\r\n> I think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.\r\n\r\nTo check that you could try to run \r\n\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\nopen(\"dummy.txt\", \"w\").write(((\"a\" * 10_000) + \"\\n\") * 4) # 4 lines of 10 000 'a'\r\n\r\nparse_options = pa.csv.ParseOptions( \r\n delimiter=\"\\b\", \r\n quote_char=False, \r\n double_quote=False, \r\n escape_char=False, \r\n newlines_in_values=False, \r\n ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"dummy.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\non my side it runs without error though",
"That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?",
"> That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?\r\n\r\nI don't think it would work since we only want one column, and \"\\b\" is set to be the delimiter between two columns, so it will raise the same issue again. Pyarrow would think that there is more than one column if the delimiter is found somewhere.\r\n\r\nAnyway, I I'll work on a new text reader if we don't find the right workaround about this delimiter issue."
] |
https://api.github.com/repos/huggingface/datasets/issues/4547 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4547/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4547/comments | https://api.github.com/repos/huggingface/datasets/issues/4547/events | https://github.com/huggingface/datasets/pull/4547 | 1,282,160,517 | PR_kwDODunzps46Ot5u | 4,547 | [CI] Fix some warnings | [] | closed | false | null | 4 | 2022-06-23T10:10:49Z | 2022-06-28T14:10:57Z | 2022-06-28T13:59:54Z | null | There are some warnings in the CI that are annoying, I tried to remove most of them | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"There is a CI failure only related to the missing content of the universal_dependencies dataset card, we can ignore this failure in this PR",
"good catch, I thought I resolved them all sorry",
"Alright it should be good now"
] |
https://api.github.com/repos/huggingface/datasets/issues/1282 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1282/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1282/comments | https://api.github.com/repos/huggingface/datasets/issues/1282/events | https://github.com/huggingface/datasets/pull/1282 | 759,208,335 | MDExOlB1bGxSZXF1ZXN0NTM0MjQ4NzI5 | 1,282 | add thaiqa_squad | [] | closed | false | null | 0 | 2020-12-08T08:14:38Z | 2020-12-08T18:36:18Z | 2020-12-08T18:36:18Z | null | Example format is a little different from SQuAD since `thaiqa` always have one answer per question so I added a check to convert answers to lists if they are not already one to future-proof additional questions that might have multiple answers.
`thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from Wikipedia articles and adapted to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format by [PyThaiNLP](https://github.com/PyThaiNLP/). | {
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https://api.github.com/repos/huggingface/datasets/issues/5365 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5365/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5365/comments | https://api.github.com/repos/huggingface/datasets/issues/5365/events | https://github.com/huggingface/datasets/pull/5365 | 1,498,422,466 | PR_kwDODunzps5Fi6ZD | 5,365 | fix: image array should support other formats than uint8 | [] | closed | false | null | 4 | 2022-12-15T13:17:50Z | 2023-01-26T18:46:45Z | 2023-01-26T18:39:36Z | null | Currently images that are provided as ndarrays, but not in `uint8` format are going to loose data. Namely, for example in a depth image where the data is in float32 format, the type-casting to uint8 will basically make the whole image blank.
`PIL.Image.fromarray` [does support mode `F`](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes).
although maybe some further metadata could be supplied via the [Image](https://huggingface.co./docs/datasets/v2.7.1/en/package_reference/main_classes#datasets.Image) object. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi, thanks for working on this! \r\n\r\nI agree that the current type-casting (always cast to `np.uint8` as Tensorflow Datasets does) is a bit too harsh. However, not all dtypes are supported in `Image.fromarray` (e.g. np.int64), so we need to treat these with special care (e.g. downcast to the closest supported dtype, maybe with warnings to let the user know what's happening).\r\n\r\nPS: To avoid the CI failures, we need to handle two more instances of the cast to `np.uint8` (both are in the `image.py` file).",
"I've made some changes to the PR.\r\n\r\nNow the encoding procedure behaves as follows:\r\n* for multi-channel arrays: if their dtype is `int`/`uint`, cast to np.uint8 (the only supported dtype for multi-channel arrays), throw an error otherwise\r\n* if the array dtype is of valid kind (\"u\", \"i\", \"f\", ...):\r\n * don't do anything if Pillow natively supports it\r\n * otherwise, downcast until it becomes compatible with Pillow\r\n* raise an error if nothing from above is true",
"<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) | 0.009537 / 0.011353 (-0.001816) | 0.004946 / 0.011008 (-0.006062) | 0.100552 / 0.038508 (0.062043) | 0.035119 / 0.023109 (0.012009) | 0.295989 / 0.275898 (0.020091) | 0.361326 / 0.323480 (0.037846) | 0.007608 / 0.007986 (-0.000378) | 0.004151 / 0.004328 (-0.000177) | 0.077301 / 0.004250 (0.073050) | 0.042921 / 0.037052 (0.005869) | 0.304804 / 0.258489 (0.046315) | 0.345934 / 0.293841 (0.052093) | 0.038987 / 0.128546 (-0.089559) | 0.012055 / 0.075646 (-0.063591) | 0.334035 / 0.419271 (-0.085236) | 0.052679 / 0.043533 (0.009146) | 0.291700 / 0.255139 (0.036561) | 0.335423 / 0.283200 (0.052223) | 0.107002 / 0.141683 (-0.034680) | 1.516780 / 1.452155 (0.064625) | 1.514137 / 1.492716 (0.021420) |\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) | 0.014719 / 0.018006 (-0.003287) | 0.545251 / 0.000490 (0.544761) | 0.004719 / 0.000200 (0.004519) | 0.000275 / 0.000054 (0.000220) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026633 / 0.037411 (-0.010779) | 0.106911 / 0.014526 (0.092385) | 0.120258 / 0.176557 (-0.056299) | 0.156196 / 0.737135 (-0.580940) | 0.123132 / 0.296338 (-0.173207) |\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) | 0.398018 / 0.215209 (0.182809) | 3.973992 / 2.077655 (1.896337) | 1.776436 / 1.504120 (0.272316) | 1.579036 / 1.541195 (0.037841) | 1.643345 / 1.468490 (0.174855) | 0.692408 / 4.584777 (-3.892369) | 3.757243 / 3.745712 (0.011531) | 3.226212 / 5.269862 (-2.043649) | 1.797845 / 4.565676 (-2.767831) | 0.085878 / 0.424275 (-0.338398) | 0.012451 / 0.007607 (0.004844) | 0.509755 / 0.226044 (0.283711) | 5.029035 / 2.268929 (2.760107) | 2.255507 / 55.444624 (-53.189117) | 1.892868 / 6.876477 (-4.983609) | 1.900017 / 2.142072 (-0.242055) | 0.853965 / 4.805227 (-3.951263) | 0.167268 / 6.500664 (-6.333396) | 0.062796 / 0.075469 (-0.012673) |\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) | 1.183361 / 1.841788 (-0.658427) | 15.103797 / 8.074308 (7.029489) | 14.112931 / 10.191392 (3.921539) | 0.167234 / 0.680424 (-0.513190) | 0.029487 / 0.534201 (-0.504713) | 0.444121 / 0.579283 (-0.135162) | 0.437821 / 0.434364 (0.003457) | 0.544900 / 0.540337 (0.004562) | 0.642142 / 1.386936 (-0.744794) |\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) | 0.007078 / 0.011353 (-0.004275) | 0.004983 / 0.011008 (-0.006026) | 0.097106 / 0.038508 (0.058598) | 0.033747 / 0.023109 (0.010637) | 0.382030 / 0.275898 (0.106132) | 0.410193 / 0.323480 (0.086713) | 0.006658 / 0.007986 (-0.001327) | 0.005358 / 0.004328 (0.001029) | 0.073878 / 0.004250 (0.069628) | 0.049292 / 0.037052 (0.012240) | 0.384053 / 0.258489 (0.125564) | 0.427826 / 0.293841 (0.133985) | 0.036780 / 0.128546 (-0.091766) | 0.012469 / 0.075646 (-0.063178) | 0.332989 / 0.419271 (-0.086283) | 0.059531 / 0.043533 (0.015998) | 0.378431 / 0.255139 (0.123292) | 0.402672 / 0.283200 (0.119473) | 0.110782 / 0.141683 (-0.030901) | 1.484570 / 1.452155 (0.032416) | 1.608081 / 1.492716 (0.115365) |\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) | 0.232356 / 0.018006 (0.214350) | 0.545648 / 0.000490 (0.545158) | 0.003113 / 0.000200 (0.002913) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028138 / 0.037411 (-0.009273) | 0.110786 / 0.014526 (0.096260) | 0.123615 / 0.176557 (-0.052941) | 0.165773 / 0.737135 (-0.571362) | 0.126401 / 0.296338 (-0.169937) |\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) | 0.440518 / 0.215209 (0.225309) | 4.393821 / 2.077655 (2.316166) | 2.295479 / 1.504120 (0.791359) | 2.116679 / 1.541195 (0.575485) | 2.215561 / 1.468490 (0.747071) | 0.722343 / 4.584777 (-3.862434) | 3.783360 / 3.745712 (0.037647) | 3.302242 / 5.269862 (-1.967620) | 1.681535 / 4.565676 (-2.884142) | 0.085738 / 0.424275 (-0.338537) | 0.012373 / 0.007607 (0.004766) | 0.540499 / 0.226044 (0.314455) | 5.384915 / 2.268929 (3.115986) | 2.766346 / 55.444624 (-52.678279) | 2.451994 / 6.876477 (-4.424483) | 2.505720 / 2.142072 (0.363647) | 0.833006 / 4.805227 (-3.972221) | 0.168206 / 6.500664 (-6.332458) | 0.064971 / 0.075469 (-0.010498) |\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) | 1.253499 / 1.841788 (-0.588289) | 15.381840 / 8.074308 (7.307532) | 13.519493 / 10.191392 (3.328101) | 0.165559 / 0.680424 (-0.514865) | 0.017682 / 0.534201 (-0.516519) | 0.422248 / 0.579283 (-0.157035) | 0.422750 / 0.434364 (-0.011614) | 0.524546 / 0.540337 (-0.015792) | 0.626956 / 1.386936 (-0.759980) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d9a8d8af0961c473103516dd018e2d34d23cea02 \"CML watermark\")\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/4185 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4185/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4185/comments | https://api.github.com/repos/huggingface/datasets/issues/4185/events | https://github.com/huggingface/datasets/issues/4185 | 1,209,429,743 | I_kwDODunzps5IFm7v | 4,185 | Librispeech documentation, clarification on format | [] | open | false | null | 8 | 2022-04-20T09:35:55Z | 2022-04-21T11:00:53Z | null | null | https://github.com/huggingface/datasets/blob/cd3ce34ab1604118351e1978d26402de57188901/datasets/librispeech_asr/librispeech_asr.py#L53
> Note that in order to limit the required storage for preparing this dataset, the audio
> is stored in the .flac format and is not converted to a float32 array. To convert, the audio
> file to a float32 array, please make use of the `.map()` function as follows:
>
> ```python
> import soundfile as sf
> def map_to_array(batch):
> speech_array, _ = sf.read(batch["file"])
> batch["speech"] = speech_array
> return batch
> dataset = dataset.map(map_to_array, remove_columns=["file"])
> ```
Is this still true?
In my case, `ds["train.100"]` returns:
```
Dataset({
features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],
num_rows: 28539
})
```
and taking the first instance yields:
```
{'file': '374-180298-0000.flac',
'audio': {'path': '374-180298-0000.flac',
'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ...,
-2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
'sampling_rate': 16000},
'text': 'CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED',
'speaker_id': 374,
'chapter_id': 180298,
'id': '374-180298-0000'}
```
The `audio` `array` seems to be already decoded. So such convert/decode code as mentioned in the doc is wrong?
But I wonder, is it actually stored as flac on disk, and the decoding is done on-the-fly? Or was it decoded already during the preparation and is stored as raw samples on disk?
Note that I also used `datasets.load_dataset("librispeech_asr", "clean").save_to_disk(...)` and then `datasets.load_from_disk(...)` in this example. Does this change anything on how it is stored on disk?
A small related question: Actually I would prefer to even store it as mp3 or ogg on disk. Is this easy to convert? | {
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"(@patrickvonplaten )",
"Also cc @lhoestq here",
"The documentation in the code is definitely outdated - thanks for letting me know, I'll remove it in https://github.com/huggingface/datasets/pull/4184 .\r\n\r\nYou're exactly right `audio` `array` already decodes the audio file to the correct waveform. This is done on the fly, which is also why one should **not** do `ds[\"audio\"][\"array\"][0]` as this will decode all dataset samples, but instead `ds[0][\"audio\"][\"array\"]` see: https://huggingface.co./docs/datasets/audio_process#audio-datasets\r\n\r\n",
"So, again to clarify: On disk, only the raw flac file content is stored? Is this also the case after `save_to_disk`?\r\n\r\nAnd is it simple to also store it re-encoded as ogg or mp3 instead?\r\n",
"Hey, \r\n\r\nSorry yeah I was just about to look into this! We actually had an outdated version of Librispeech ASR that didn't save any files, but instead converted the audio files to a byte string, then was then decoded on-the-fly. This however is not very user-friendly so we recently decided to instead show the full path of the audio files with the `path` parameter.\r\n\r\nI'm currently changing this for Librispeech here: https://github.com/huggingface/datasets/pull/4184 .\r\nYou should be able to see the audio file in the original `flac` format under `path` then. I don't think it's a good idea to convert to MP3 out-of-the-box, but we could maybe think about some kind of convert function for audio datasets cc @lhoestq ? ",
"> I don't think it's a good idea to convert to MP3 out-of-the-box, but we could maybe think about some kind of convert function for audio datasets cc @lhoestq ?\r\n\r\nSure, I would expect that `load_dataset(\"librispeech_asr\")` would give you the original (not re-encoded) data (flac or already decoded). So such re-encoding logic would be some separate generic function. So I could do sth like `dataset.reencode_as_ogg(**ogg_encode_opts).save_to_disk(...)` or so.\r\n",
"A follow-up question: I wonder whether a Parquet dataset is maybe more what we actually want to have? (Following also my comment here: https://github.com/huggingface/datasets/pull/4184#issuecomment-1105045491.) Because I think we actually would prefer to embed the data content in the dataset.\r\n\r\nSo, instead of `save_to_disk`/`load_from_disk`, we would use `to_parquet`,`from_parquet`? Is there any downside? Are arrow files more efficient?\r\n\r\nRelated is also the doc update in #4193.\r\n",
"`save_to_disk` saves the dataset as an Arrow file, which is the format we use to load a dataset using memory mapping. This way the dataset does not fill your RAM, but is read from your disk instead.\r\n\r\nTherefore you can directly reload a dataset saved with `save_to_disk` using `load_from_disk`.\r\n\r\nParquet files are used for cold storage: to use memory mapping on a Parquet dataset, you first have to convert it to Arrow. We use Parquet to reduce the I/O when pushing/downloading data from the Hugging face Hub. When you load a Parquet file from the Hub, it is converted to Arrow on the fly during the download."
] |
https://api.github.com/repos/huggingface/datasets/issues/3754 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3754/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3754/comments | https://api.github.com/repos/huggingface/datasets/issues/3754/events | https://github.com/huggingface/datasets/issues/3754 | 1,142,886,536 | I_kwDODunzps5EHxCI | 3,754 | Overflowing indices in `select` | [
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] | closed | false | null | 2 | 2022-02-18T11:30:52Z | 2022-02-18T11:38:23Z | 2022-02-18T11:38:23Z | null | ## Describe the bug
The `Dataset.select` function seems to accept indices that are larger than the dataset size and seems to effectively use `index %len(ds)`.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"test": [1,2,3]})
ds = ds.select(range(5))
print(ds)
print()
print(ds["test"])
```
Result:
```python
Dataset({
features: ['test'],
num_rows: 5
})
[1, 2, 3, 1, 2]
```
This behaviour is not documented and can lead to unexpected behaviour when for example taking a sample larger than the dataset and thus creating a lot of duplicates.
## Expected results
It think this should throw an error or at least a very big warning:
```python
IndexError: Invalid key: 5 is out of bounds for size 3
```
## Environment info
- `datasets` version: 1.18.3
- Platform: macOS-12.0.1-x86_64-i386-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0 | {
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"Fixed on master (see https://github.com/huggingface/datasets/pull/3719).",
"Awesome, I did not find that one! Thanks."
] |
https://api.github.com/repos/huggingface/datasets/issues/4045 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4045/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4045/comments | https://api.github.com/repos/huggingface/datasets/issues/4045/events | https://github.com/huggingface/datasets/pull/4045 | 1,183,661,091 | PR_kwDODunzps41KtfV | 4,045 | Fix CLI dummy data generation | [] | closed | false | null | 1 | 2022-03-28T16:09:15Z | 2022-03-31T15:04:12Z | 2022-03-31T14:59:06Z | null | PR:
- #3868
broke the CLI dummy data generation.
Fix #4044. | {
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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/5130 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5130/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5130/comments | https://api.github.com/repos/huggingface/datasets/issues/5130/events | https://github.com/huggingface/datasets/pull/5130 | 1,413,435,000 | PR_kwDODunzps5BBxXX | 5,130 | Avoid extra cast in `class_encode_column` | [] | closed | false | null | 1 | 2022-10-18T15:31:24Z | 2022-10-19T11:53:02Z | 2022-10-19T11:50:46Z | null | Pass the updated features to `map` to avoid the `cast` in `class_encode_column`. | {
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https://api.github.com/repos/huggingface/datasets/issues/2027 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2027/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2027/comments | https://api.github.com/repos/huggingface/datasets/issues/2027/events | https://github.com/huggingface/datasets/pull/2027 | 828,490,444 | MDExOlB1bGxSZXF1ZXN0NTkwMjkzNDA1 | 2,027 | Update format columns in Dataset.rename_columns | [] | closed | false | null | 0 | 2021-03-10T23:50:59Z | 2021-03-11T14:38:40Z | 2021-03-11T14:38:40Z | null | Fixes #2026 | {
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https://api.github.com/repos/huggingface/datasets/issues/3104 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3104/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3104/comments | https://api.github.com/repos/huggingface/datasets/issues/3104/events | https://github.com/huggingface/datasets/issues/3104 | 1,029,080,412 | I_kwDODunzps49VoVc | 3,104 | Missing Zenodo 1.13.3 release | [
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] | closed | false | null | 1 | 2021-10-18T12:57:18Z | 2021-10-22T13:22:25Z | 2021-10-22T13:22:24Z | null | After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305
TODO:
- [x] Contact Zenodo support
- [x] Check it is fixed | {
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"Zenodo has fixed on their side the 1.13.3 release: https://zenodo.org/record/5589150"
] |
https://api.github.com/repos/huggingface/datasets/issues/455 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/455/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/455/comments | https://api.github.com/repos/huggingface/datasets/issues/455/events | https://github.com/huggingface/datasets/pull/455 | 668,037,965 | MDExOlB1bGxSZXF1ZXN0NDU4NTk4NTUw | 455 | Add bleurt | [] | closed | false | null | 4 | 2020-07-29T18:08:32Z | 2020-07-31T13:56:14Z | 2020-07-31T13:56:14Z | null | This PR adds the BLEURT metric to the library.
The BLEURT `Metric` downloads a TF checkpoint corresponding to its `config_name` at creation (in the `_info` function). Default is set to `bleurt-base-128`.
Note that the default in the original package is `bleurt-tiny-128`, but they throw a warning and recommend using `bleurt-base-128` instead. I think it's safer to have our users have a functioning metric when they call the default behavior, we'll address discrepancies in the issues/discussions if it comes up.
In addition to the BLEURT file, `load.py` was changed so we can ask users to pip install the required packages from git when they have a `setup.py` but are not on PyPL
cc @ankparikh @tsellam | {
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"Sorry one nit: Could we use named arguments for the call to BLEURT?\r\n\r\ni.e. \r\n scores = self.scorer.score(references=references, candidates=predictions)\r\n\r\n(i.e. so it is less bug prone)\r\n",
"Following up on Ankur's comment---we are going to drop support for\npositional (not named) arguments in the future releases because it seems to\ncause bugs and confusion. I hope it doesn't create too much of a mess.\n\nLe jeu. 30 juil. 2020 à 10:44, ankparikh <[email protected]> a\nécrit :\n\n> Sorry one nit: Could we use named arguments for the call to BLEURT?\n>\n> i.e.\n> scores = self.scorer.score(references=references, candidates=predictions)\n>\n> (i.e. so it is less bug prone)\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/nlp/pull/455#issuecomment-666414514>, or\n> unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABTMRNGAN2PMECS5K4DIHJDR6GBMLANCNFSM4PL323FA>\n> .\n>\n",
"> Following up on Ankur's comment---we are going to drop support for positional (not named) arguments in the future releases because it seems to cause bugs and confusion. I hope it doesn't create too much of a mess. Le jeu. 30 juil. 2020 à 10:44, ankparikh <[email protected]> a écrit :\r\n> […](#)\r\n> Sorry one nit: Could we use named arguments for the call to BLEURT? i.e. scores = self.scorer.score(references=references, candidates=predictions) (i.e. so it is less bug prone) — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <[#455 (comment)](https://github.com/huggingface/nlp/pull/455#issuecomment-666414514)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABTMRNGAN2PMECS5K4DIHJDR6GBMLANCNFSM4PL323FA> .\r\n\r\nChanged @ankparikh @tsellam, thanks for taking a look!",
"We should avoid positional arguments in metrics on our side as well. It's a dangerous source of errors indeed."
] |
https://api.github.com/repos/huggingface/datasets/issues/4668 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4668/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4668/comments | https://api.github.com/repos/huggingface/datasets/issues/4668/events | https://github.com/huggingface/datasets/issues/4668 | 1,299,735,893 | I_kwDODunzps5NeGVV | 4,668 | Dataset Viewer issue for hungnm/multilingual-amazon-review-sentiment-processed | [
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] | closed | false | null | 1 | 2022-07-09T18:04:13Z | 2022-07-11T07:47:47Z | 2022-07-11T07:47:47Z | null | ### Link
https://huggingface.co./hungnm/multilingual-amazon-review-sentiment
### Description
_No response_
### Owner
Yes | {
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"It seems like a private dataset. The viewer is currently not supported on the private datasets."
] |
https://api.github.com/repos/huggingface/datasets/issues/3622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3622/comments | https://api.github.com/repos/huggingface/datasets/issues/3622/events | https://github.com/huggingface/datasets/issues/3622 | 1,112,831,661 | I_kwDODunzps5CVHat | 3,622 | Extend support for streaming datasets that use os.path.relpath | [
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This feature will also be useful to yield the relative path of audio or image files.
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End of preview. Expand
in Dataset Viewer.
Dataset Card for "github-issues_huggingface-datasets"
Dataset Name: GitHub Issues from Hugging Face Datasets
Description: The "github-issues_huggingface-datasets" dataset is a corpus of GitHub issues extracted from the Hugging Face Datasets repository. It includes valuable information and metadata related to the issues, such as titles, descriptions, labels, states, comments, and whether they are pull requests. The dataset was compiled using the GitHub REST API, which enabled the retrieval of issues and their corresponding comments. Additionally, a new column was added to indicate whether an issue is a pull request.
Dataset Contents: The dataset consists of two splits:
- Train Split: Contains 4,863 records, each with features like URL, repository URL, labels URL, comments URL, HTML URL, ID, node ID, issue number, Title, labels, state, locked status, milestone, comments, creation date, update date, closing date, reactions, timeline URL, and more.
- Test Split: Comprises 1,216 records with the same features as the train split.
Potential Uses: This dataset is valuable for various purposes, such as:
- Semantic search: Analyzing and retrieving issues based on semantic similarity.
- Multilabel classification: Classifying issues into multiple categories based on their labels.
- Exploratory analysis: Gaining insights into the trends and patterns within GitHub issues.
Limitations and Risks: Users of this dataset should be aware of potential limitations, such as data incompleteness, bias in issue labeling, or outdated information. Additionally, data privacy and ethical considerations should be taken into account when using GitHub issues data.
Access: The dataset is openly accessible to anyone interested in using it for research, analysis, or any other suitable applications. The dataset is publicly available for download and usage.
Note: Certain user-specific features, such as "user", "author_association", "assignee" and "assignees" have been excluded from the dataset to protect individual privacy and mitigate the risk of identifying users or contributors.
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