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davanstrien
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summary-test

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alvarobartt/mmlu-okapi-eval-es
--- language: - es license: cc-by-nc-4.0 size_categories: - 10K<n<100K task_categories: - multiple-choice - question-answering task_ids: - multiple-choice-qa - open-domain-qa tags: - chatgpt-translated dataset_info: - config_name: abstract_algebra features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 1760 num_examples: 5 - name: validation num_bytes: 4246 num_examples: 11 - name: test num_bytes: 40783 num_examples: 100 download_size: 31838 dataset_size: 46789 - config_name: anatomy features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2024 num_examples: 5 - name: validation num_bytes: 6533 num_examples: 14 - name: test num_bytes: 68781 num_examples: 134 download_size: 55543 dataset_size: 77338 - config_name: astronomy features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 4476 num_examples: 5 - name: validation num_bytes: 10713 num_examples: 16 - name: test num_bytes: 100179 num_examples: 152 download_size: 78498 dataset_size: 115368 - config_name: business_ethics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 4608 num_examples: 5 - name: validation num_bytes: 6415 num_examples: 11 - name: test num_bytes: 69628 num_examples: 98 download_size: 62408 dataset_size: 80651 - config_name: clinical_knowledge features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2594 num_examples: 5 - name: validation num_bytes: 14438 num_examples: 29 - name: test num_bytes: 135086 num_examples: 263 download_size: 103601 dataset_size: 152118 - config_name: college_biology features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3177 num_examples: 5 - name: validation num_bytes: 10330 num_examples: 16 - name: test num_bytes: 103413 num_examples: 143 download_size: 87080 dataset_size: 116920 - config_name: college_chemistry features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2779 num_examples: 5 - name: validation num_bytes: 4874 num_examples: 8 - name: test num_bytes: 52123 num_examples: 100 download_size: 51328 dataset_size: 59776 - config_name: college_computer_science features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 5805 num_examples: 5 - name: validation num_bytes: 9920 num_examples: 11 - name: test num_bytes: 89185 num_examples: 98 download_size: 82341 dataset_size: 104910 - config_name: college_mathematics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3089 num_examples: 5 - name: validation num_bytes: 5484 num_examples: 11 - name: test num_bytes: 50044 num_examples: 97 download_size: 51658 dataset_size: 58617 - config_name: college_medicine features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3649 num_examples: 5 - name: validation num_bytes: 16728 num_examples: 22 - name: test num_bytes: 171553 num_examples: 171 download_size: 115167 dataset_size: 191930 - config_name: college_physics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2898 num_examples: 5 - name: validation num_bytes: 7335 num_examples: 11 - name: test num_bytes: 62094 num_examples: 100 download_size: 57235 dataset_size: 72327 - config_name: computer_security features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 1733 num_examples: 4 - name: validation num_bytes: 9678 num_examples: 11 - name: test num_bytes: 58507 num_examples: 100 download_size: 57512 dataset_size: 69918 - config_name: conceptual_physics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 1965 num_examples: 5 - name: validation num_bytes: 9503 num_examples: 26 - name: test num_bytes: 86744 num_examples: 235 download_size: 69227 dataset_size: 98212 - config_name: econometrics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3459 num_examples: 5 - name: validation num_bytes: 10475 num_examples: 12 - name: test num_bytes: 94779 num_examples: 110 download_size: 71152 dataset_size: 108713 - config_name: electrical_engineering features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2046 num_examples: 5 - name: validation num_bytes: 6173 num_examples: 16 - name: test num_bytes: 54302 num_examples: 145 download_size: 51393 dataset_size: 62521 - config_name: elementary_mathematics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2726 num_examples: 4 - name: validation num_bytes: 18444 num_examples: 40 - name: test num_bytes: 144531 num_examples: 369 download_size: 109491 dataset_size: 165701 - config_name: formal_logic features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3619 num_examples: 5 - name: validation num_bytes: 12939 num_examples: 14 - name: test num_bytes: 96569 num_examples: 118 download_size: 63090 dataset_size: 113127 - config_name: global_facts features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2553 num_examples: 5 - name: validation num_bytes: 3941 num_examples: 10 - name: test num_bytes: 39400 num_examples: 100 download_size: 37322 dataset_size: 45894 - config_name: high_school_biology features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3476 num_examples: 5 - name: validation num_bytes: 23395 num_examples: 32 - name: test num_bytes: 232885 num_examples: 309 download_size: 158998 dataset_size: 259756 - config_name: high_school_chemistry features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2507 num_examples: 5 - name: validation num_bytes: 14839 num_examples: 22 - name: test num_bytes: 120079 num_examples: 200 download_size: 90031 dataset_size: 137425 - config_name: high_school_computer_science features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 6121 num_examples: 5 - name: validation num_bytes: 6988 num_examples: 8 - name: test num_bytes: 93799 num_examples: 97 download_size: 75958 dataset_size: 106908 - config_name: high_school_european_history features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 14265 num_examples: 3 - name: validation num_bytes: 57480 num_examples: 17 - name: test num_bytes: 479646 num_examples: 148 download_size: 359755 dataset_size: 551391 - config_name: high_school_geography features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3101 num_examples: 5 - name: validation num_bytes: 9160 num_examples: 22 - name: test num_bytes: 87342 num_examples: 192 download_size: 75260 dataset_size: 99603 - config_name: high_school_government_and_politics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3772 num_examples: 5 - name: validation num_bytes: 14984 num_examples: 21 - name: test num_bytes: 141849 num_examples: 193 download_size: 106607 dataset_size: 160605 - config_name: high_school_macroeconomics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2933 num_examples: 5 - name: validation num_bytes: 27945 num_examples: 43 - name: test num_bytes: 249710 num_examples: 387 download_size: 141531 dataset_size: 280588 - config_name: high_school_mathematics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2617 num_examples: 5 - name: validation num_bytes: 11798 num_examples: 28 - name: test num_bytes: 112551 num_examples: 266 download_size: 89117 dataset_size: 126966 - config_name: high_school_microeconomics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2710 num_examples: 5 - name: validation num_bytes: 16309 num_examples: 26 - name: test num_bytes: 160145 num_examples: 234 download_size: 98676 dataset_size: 179164 - config_name: high_school_physics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3173 num_examples: 5 - name: validation num_bytes: 14127 num_examples: 17 - name: test num_bytes: 123938 num_examples: 149 download_size: 90127 dataset_size: 141238 - config_name: high_school_psychology features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3883 num_examples: 5 - name: validation num_bytes: 36566 num_examples: 60 - name: test num_bytes: 318886 num_examples: 513 download_size: 221819 dataset_size: 359335 - config_name: high_school_statistics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 5264 num_examples: 5 - name: validation num_bytes: 21199 num_examples: 23 - name: test num_bytes: 234094 num_examples: 215 download_size: 150556 dataset_size: 260557 - config_name: high_school_us_history features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 10940 num_examples: 3 - name: validation num_bytes: 57373 num_examples: 19 - name: test num_bytes: 415443 num_examples: 149 download_size: 309982 dataset_size: 483756 - config_name: high_school_world_history features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 10120 num_examples: 5 - name: validation num_bytes: 70014 num_examples: 21 - name: test num_bytes: 629850 num_examples: 201 download_size: 441428 dataset_size: 709984 - config_name: human_aging features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2066 num_examples: 5 - name: validation num_bytes: 10131 num_examples: 23 - name: test num_bytes: 96475 num_examples: 219 download_size: 81152 dataset_size: 108672 - config_name: human_sexuality features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 1251 num_examples: 3 - name: validation num_bytes: 5129 num_examples: 12 - name: test num_bytes: 53324 num_examples: 110 download_size: 53146 dataset_size: 59704 - config_name: international_law features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 5118 num_examples: 5 - name: validation num_bytes: 13609 num_examples: 13 - name: test num_bytes: 114851 num_examples: 121 download_size: 83492 dataset_size: 133578 - config_name: jurisprudence features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2642 num_examples: 5 - name: validation num_bytes: 7940 num_examples: 11 - name: test num_bytes: 71653 num_examples: 108 download_size: 66964 dataset_size: 82235 - config_name: logical_fallacies features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3160 num_examples: 5 - name: validation num_bytes: 10588 num_examples: 18 - name: test num_bytes: 103636 num_examples: 161 download_size: 66840 dataset_size: 117384 - config_name: machine_learning features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 5036 num_examples: 5 - name: validation num_bytes: 6891 num_examples: 11 - name: test num_bytes: 73135 num_examples: 112 download_size: 60833 dataset_size: 85062 - config_name: management features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 1872 num_examples: 5 - name: validation num_bytes: 3799 num_examples: 11 - name: test num_bytes: 42556 num_examples: 103 download_size: 43017 dataset_size: 48227 - config_name: marketing features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3239 num_examples: 5 - name: validation num_bytes: 15704 num_examples: 25 - name: test num_bytes: 132425 num_examples: 231 download_size: 98948 dataset_size: 151368 - config_name: medical_genetics features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2284 num_examples: 5 - name: validation num_bytes: 6400 num_examples: 11 - name: test num_bytes: 44372 num_examples: 100 download_size: 48735 dataset_size: 53056 - config_name: miscellaneous features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 1437 num_examples: 5 - name: validation num_bytes: 30333 num_examples: 86 - name: test num_bytes: 304980 num_examples: 760 download_size: 231606 dataset_size: 336750 - config_name: moral_disputes features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3671 num_examples: 5 - name: validation num_bytes: 25869 num_examples: 38 - name: test num_bytes: 214143 num_examples: 327 download_size: 147774 dataset_size: 243683 - config_name: moral_scenarios features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2657 num_examples: 3 - name: validation num_bytes: 71335 num_examples: 78 - name: test num_bytes: 683382 num_examples: 752 download_size: 213484 dataset_size: 757374 - config_name: nutrition features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 4523 num_examples: 5 - name: validation num_bytes: 17721 num_examples: 32 - name: test num_bytes: 199634 num_examples: 305 download_size: 138805 dataset_size: 221878 - config_name: philosophy features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 2058 num_examples: 5 - name: validation num_bytes: 19167 num_examples: 34 - name: test num_bytes: 161737 num_examples: 302 download_size: 121059 dataset_size: 182962 - config_name: prehistory features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3992 num_examples: 5 - name: validation num_bytes: 21214 num_examples: 34 - name: test num_bytes: 181683 num_examples: 314 download_size: 136059 dataset_size: 206889 - config_name: professional_accounting features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 4568 num_examples: 5 - name: validation num_bytes: 29165 num_examples: 30 - name: test num_bytes: 266225 num_examples: 282 download_size: 181436 dataset_size: 299958 - config_name: professional_law features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 9431 num_examples: 4 - name: validation num_bytes: 363635 num_examples: 145 - name: test num_bytes: 3285957 num_examples: 1292 download_size: 1993775 dataset_size: 3659023 - config_name: professional_medicine features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 7060 num_examples: 4 - name: validation num_bytes: 47479 num_examples: 30 - name: test num_bytes: 446995 num_examples: 265 download_size: 311538 dataset_size: 501534 - config_name: professional_psychology features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 4597 num_examples: 5 - name: validation num_bytes: 60499 num_examples: 68 - name: test num_bytes: 471519 num_examples: 604 download_size: 325283 dataset_size: 536615 - config_name: public_relations features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3165 num_examples: 5 - name: validation num_bytes: 9669 num_examples: 12 - name: test num_bytes: 60281 num_examples: 109 download_size: 61213 dataset_size: 73115 - config_name: security_studies features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 11199 num_examples: 5 - name: validation num_bytes: 47992 num_examples: 27 - name: test num_bytes: 427743 num_examples: 240 download_size: 282999 dataset_size: 486934 - config_name: sociology features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3456 num_examples: 5 - name: validation num_bytes: 14660 num_examples: 21 - name: test num_bytes: 138231 num_examples: 196 download_size: 111807 dataset_size: 156347 - config_name: us_foreign_policy features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 3440 num_examples: 5 - name: validation num_bytes: 6883 num_examples: 11 - name: test num_bytes: 60635 num_examples: 99 download_size: 56816 dataset_size: 70958 - config_name: virology features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 1836 num_examples: 4 - name: validation num_bytes: 10140 num_examples: 17 - name: test num_bytes: 82841 num_examples: 165 download_size: 73952 dataset_size: 94817 - config_name: world_religions features: - name: en_question dtype: string - name: es_question dtype: string - name: en_choices sequence: string - name: es_choices sequence: string - name: en_answer dtype: int64 - name: es_answer dtype: int64 splits: - name: dev num_bytes: 1357 num_examples: 5 - name: validation num_bytes: 5669 num_examples: 19 - name: test num_bytes: 51954 num_examples: 171 download_size: 51989 dataset_size: 58980 configs: - config_name: abstract_algebra data_files: - split: dev path: abstract_algebra/dev-* - split: validation path: abstract_algebra/validation-* - split: test path: abstract_algebra/test-* - config_name: anatomy data_files: - split: dev path: anatomy/dev-* - split: validation path: anatomy/validation-* - split: test path: anatomy/test-* - config_name: astronomy data_files: - split: dev path: astronomy/dev-* - split: validation path: astronomy/validation-* - split: test path: astronomy/test-* - config_name: business_ethics data_files: - split: dev path: business_ethics/dev-* - split: validation path: business_ethics/validation-* - split: test path: business_ethics/test-* - config_name: clinical_knowledge data_files: - split: dev path: clinical_knowledge/dev-* - split: validation path: clinical_knowledge/validation-* - split: test path: clinical_knowledge/test-* - config_name: college_biology data_files: - split: dev path: college_biology/dev-* - split: validation path: college_biology/validation-* - split: test path: college_biology/test-* - config_name: college_chemistry data_files: - split: dev path: college_chemistry/dev-* - split: validation path: college_chemistry/validation-* - split: test path: college_chemistry/test-* - config_name: college_computer_science data_files: - split: dev path: college_computer_science/dev-* - split: validation path: college_computer_science/validation-* - split: test path: college_computer_science/test-* - config_name: college_mathematics data_files: - split: dev path: college_mathematics/dev-* - split: validation path: college_mathematics/validation-* - split: test path: college_mathematics/test-* - config_name: college_medicine data_files: - split: dev path: college_medicine/dev-* - split: validation path: college_medicine/validation-* - split: test path: college_medicine/test-* - config_name: college_physics data_files: - split: dev path: college_physics/dev-* - split: validation path: college_physics/validation-* - split: test path: college_physics/test-* - config_name: computer_security data_files: - split: dev path: computer_security/dev-* - split: validation path: computer_security/validation-* - split: test path: computer_security/test-* - config_name: conceptual_physics data_files: - split: dev path: conceptual_physics/dev-* - split: validation path: conceptual_physics/validation-* - split: test path: conceptual_physics/test-* - config_name: econometrics data_files: - split: dev path: econometrics/dev-* - split: validation path: econometrics/validation-* - split: test path: econometrics/test-* - config_name: electrical_engineering data_files: - split: dev path: electrical_engineering/dev-* - split: validation path: electrical_engineering/validation-* - split: test path: electrical_engineering/test-* - config_name: elementary_mathematics data_files: - split: dev path: elementary_mathematics/dev-* - split: validation path: elementary_mathematics/validation-* - split: test path: elementary_mathematics/test-* - config_name: formal_logic data_files: - split: dev path: formal_logic/dev-* - split: validation path: formal_logic/validation-* - split: test path: formal_logic/test-* - config_name: global_facts data_files: - split: dev path: global_facts/dev-* - split: validation path: global_facts/validation-* - split: test path: global_facts/test-* - config_name: high_school_biology data_files: - split: dev path: high_school_biology/dev-* - split: validation path: high_school_biology/validation-* - split: test path: high_school_biology/test-* - config_name: high_school_chemistry data_files: - split: dev path: high_school_chemistry/dev-* - split: validation path: high_school_chemistry/validation-* - split: test path: high_school_chemistry/test-* - config_name: high_school_computer_science data_files: - split: dev path: high_school_computer_science/dev-* - split: validation path: high_school_computer_science/validation-* - split: test path: high_school_computer_science/test-* - config_name: high_school_european_history data_files: - split: dev path: high_school_european_history/dev-* - split: validation path: high_school_european_history/validation-* - split: test path: high_school_european_history/test-* - config_name: high_school_geography data_files: - split: dev path: high_school_geography/dev-* - split: validation path: high_school_geography/validation-* - split: test path: high_school_geography/test-* - config_name: high_school_government_and_politics data_files: - split: dev path: high_school_government_and_politics/dev-* - split: validation path: high_school_government_and_politics/validation-* - split: test path: high_school_government_and_politics/test-* - config_name: high_school_macroeconomics data_files: - split: dev path: high_school_macroeconomics/dev-* - split: validation path: high_school_macroeconomics/validation-* - split: test path: high_school_macroeconomics/test-* - config_name: high_school_mathematics data_files: - split: dev path: high_school_mathematics/dev-* - split: validation path: high_school_mathematics/validation-* - split: test path: high_school_mathematics/test-* - config_name: high_school_microeconomics data_files: - split: dev path: high_school_microeconomics/dev-* - split: validation path: high_school_microeconomics/validation-* - split: test path: high_school_microeconomics/test-* - config_name: high_school_physics data_files: - split: dev path: high_school_physics/dev-* - split: validation path: high_school_physics/validation-* - split: test path: high_school_physics/test-* - config_name: high_school_psychology data_files: - split: dev path: high_school_psychology/dev-* - split: validation path: high_school_psychology/validation-* - split: test path: high_school_psychology/test-* - config_name: high_school_statistics data_files: - split: dev path: high_school_statistics/dev-* - split: validation path: high_school_statistics/validation-* - split: test path: high_school_statistics/test-* - config_name: high_school_us_history data_files: - split: dev path: high_school_us_history/dev-* - split: validation path: high_school_us_history/validation-* - split: test path: high_school_us_history/test-* - config_name: high_school_world_history data_files: - split: dev path: high_school_world_history/dev-* - split: validation path: high_school_world_history/validation-* - split: test path: high_school_world_history/test-* - config_name: human_aging data_files: - split: dev path: human_aging/dev-* - split: validation path: human_aging/validation-* - split: test path: human_aging/test-* - config_name: human_sexuality data_files: - split: dev path: human_sexuality/dev-* - split: validation path: human_sexuality/validation-* - split: test path: human_sexuality/test-* - config_name: international_law data_files: - split: dev path: international_law/dev-* - split: validation path: international_law/validation-* - split: test path: international_law/test-* - config_name: jurisprudence data_files: - split: dev path: jurisprudence/dev-* - split: validation path: jurisprudence/validation-* - split: test path: jurisprudence/test-* - config_name: logical_fallacies data_files: - split: dev path: logical_fallacies/dev-* - split: validation path: logical_fallacies/validation-* - split: test path: logical_fallacies/test-* - config_name: machine_learning data_files: - split: dev path: machine_learning/dev-* - split: validation path: machine_learning/validation-* - split: test path: machine_learning/test-* - config_name: management data_files: - split: dev path: management/dev-* - split: validation path: management/validation-* - split: test path: management/test-* - config_name: marketing data_files: - split: dev path: marketing/dev-* - split: validation path: marketing/validation-* - split: test path: marketing/test-* - config_name: medical_genetics data_files: - split: dev path: medical_genetics/dev-* - split: validation path: medical_genetics/validation-* - split: test path: medical_genetics/test-* - config_name: miscellaneous data_files: - split: dev path: miscellaneous/dev-* - split: validation path: miscellaneous/validation-* - split: test path: miscellaneous/test-* - config_name: moral_disputes data_files: - split: dev path: moral_disputes/dev-* - split: validation path: moral_disputes/validation-* - split: test path: moral_disputes/test-* - config_name: moral_scenarios data_files: - split: dev path: moral_scenarios/dev-* - split: validation path: moral_scenarios/validation-* - split: test path: moral_scenarios/test-* - config_name: nutrition data_files: - split: dev path: nutrition/dev-* - split: validation path: nutrition/validation-* - split: test path: nutrition/test-* - config_name: philosophy data_files: - split: dev path: philosophy/dev-* - split: validation path: philosophy/validation-* - split: test path: philosophy/test-* - config_name: prehistory data_files: - split: dev path: prehistory/dev-* - split: validation path: prehistory/validation-* - split: test path: prehistory/test-* - config_name: professional_accounting data_files: - split: dev path: professional_accounting/dev-* - split: validation path: professional_accounting/validation-* - split: test path: professional_accounting/test-* - config_name: professional_law data_files: - split: dev path: professional_law/dev-* - split: validation path: professional_law/validation-* - split: test path: professional_law/test-* - config_name: professional_medicine data_files: - split: dev path: professional_medicine/dev-* - split: validation path: professional_medicine/validation-* - split: test path: professional_medicine/test-* - config_name: professional_psychology data_files: - split: dev path: professional_psychology/dev-* - split: validation path: professional_psychology/validation-* - split: test path: professional_psychology/test-* - config_name: public_relations data_files: - split: dev path: public_relations/dev-* - split: validation path: public_relations/validation-* - split: test path: public_relations/test-* - config_name: security_studies data_files: - split: dev path: security_studies/dev-* - split: validation path: security_studies/validation-* - split: test path: security_studies/test-* - config_name: sociology data_files: - split: dev path: sociology/dev-* - split: validation path: sociology/validation-* - split: test path: sociology/test-* - config_name: us_foreign_policy data_files: - split: dev path: us_foreign_policy/dev-* - split: validation path: us_foreign_policy/validation-* - split: test path: us_foreign_policy/test-* - config_name: virology data_files: - split: dev path: virology/dev-* - split: validation path: virology/validation-* - split: test path: virology/test-* - config_name: world_religions data_files: - split: dev path: world_religions/dev-* - split: validation path: world_religions/validation-* - split: test path: world_religions/test-* --- # MMLU translated to Spanish This dataset was generated by the Natural Language Processing Group of the University of Oregon, where they used the original MMLU dataset in English and translated it into different languages using ChatGPT. This dataset only contains the Spanish translation, but the following languages are also covered within the original subsets posted by the University of Oregon at http://nlp.uoregon.edu/download/okapi-eval/datasets/. ## Disclaimer All the credits for this dataset go to the original authors of MMLU (licensed as MIT), and to the authors of this translation via ChatGPT (licensed as CC BY NC 4.0, allowing only non-commercial use). ## References * [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) * [Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2307.16039)
liuyanchen1015/MULTI_VALUE_wnli_it_is_non_referential
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 976 num_examples: 3 - name: train num_bytes: 643 num_examples: 4 download_size: 7033 dataset_size: 1619 --- # Dataset Card for "MULTI_VALUE_wnli_it_is_non_referential" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
salokr/MailEx
--- license: cc-by-4.0 ---
schrilax/marketing_campaign_data
--- license: openrail ---
open-llm-leaderboard/details_IkariDev__Athnete-13B
--- pretty_name: Evaluation run of IkariDev/Athnete-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [IkariDev/Athnete-13B](https://huggingface.co/IkariDev/Athnete-13B) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_IkariDev__Athnete-13B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-22T19:52:46.910811](https://huggingface.co/datasets/open-llm-leaderboard/details_IkariDev__Athnete-13B/blob/main/results_2024-03-22T19-52-46.910811.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5709192487354107,\n\ \ \"acc_stderr\": 0.03334034722216404,\n \"acc_norm\": 0.5810771448544826,\n\ \ \"acc_norm_stderr\": 0.03424492177617554,\n \"mc1\": 0.3598531211750306,\n\ \ \"mc1_stderr\": 0.016801860466677157,\n \"mc2\": 0.5105497381067257,\n\ \ \"mc2_stderr\": 0.015603754710210896\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5947098976109215,\n \"acc_stderr\": 0.014346869060229321,\n\ \ \"acc_norm\": 0.621160409556314,\n \"acc_norm_stderr\": 0.014175915490000322\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6532563234415455,\n\ \ \"acc_stderr\": 0.004749606196363344,\n \"acc_norm\": 0.8435570603465445,\n\ \ \"acc_norm_stderr\": 0.003625323221166242\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4888888888888889,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5592105263157895,\n \"acc_stderr\": 0.040403110624904356,\n\ \ \"acc_norm\": 0.5592105263157895,\n \"acc_norm_stderr\": 0.040403110624904356\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.03000048544867599,\n\ \ \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.03000048544867599\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6388888888888888,\n\ \ \"acc_stderr\": 0.04016660030451233,\n \"acc_norm\": 0.6388888888888888,\n\ \ \"acc_norm_stderr\": 0.04016660030451233\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5491329479768786,\n\ \ \"acc_stderr\": 0.0379401267469703,\n \"acc_norm\": 0.5491329479768786,\n\ \ \"acc_norm_stderr\": 0.0379401267469703\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.04440521906179328,\n\ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.04440521906179328\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4765957446808511,\n \"acc_stderr\": 0.032650194750335815,\n\ \ \"acc_norm\": 0.4765957446808511,\n \"acc_norm_stderr\": 0.032650194750335815\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3157894736842105,\n\ \ \"acc_stderr\": 0.04372748290278007,\n \"acc_norm\": 0.3157894736842105,\n\ \ \"acc_norm_stderr\": 0.04372748290278007\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.041665675771015785,\n\ \ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.041665675771015785\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3386243386243386,\n \"acc_stderr\": 0.024373197867983067,\n \"\ acc_norm\": 0.3386243386243386,\n \"acc_norm_stderr\": 0.024373197867983067\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n\ \ \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n\ \ \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.6645161290322581,\n \"acc_stderr\": 0.026860206444724335,\n \"\ acc_norm\": 0.6645161290322581,\n \"acc_norm_stderr\": 0.026860206444724335\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.45320197044334976,\n \"acc_stderr\": 0.035025446508458714,\n \"\ acc_norm\": 0.45320197044334976,\n \"acc_norm_stderr\": 0.035025446508458714\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6848484848484848,\n \"acc_stderr\": 0.0362773057502241,\n\ \ \"acc_norm\": 0.6848484848484848,\n \"acc_norm_stderr\": 0.0362773057502241\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7121212121212122,\n \"acc_stderr\": 0.03225883512300992,\n \"\ acc_norm\": 0.7121212121212122,\n \"acc_norm_stderr\": 0.03225883512300992\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8290155440414507,\n \"acc_stderr\": 0.02717121368316455,\n\ \ \"acc_norm\": 0.8290155440414507,\n \"acc_norm_stderr\": 0.02717121368316455\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5538461538461539,\n \"acc_stderr\": 0.02520357177302833,\n \ \ \"acc_norm\": 0.5538461538461539,\n \"acc_norm_stderr\": 0.02520357177302833\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253252,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253252\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6092436974789915,\n \"acc_stderr\": 0.03169380235712996,\n \ \ \"acc_norm\": 0.6092436974789915,\n \"acc_norm_stderr\": 0.03169380235712996\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7577981651376147,\n \"acc_stderr\": 0.01836817630659862,\n \"\ acc_norm\": 0.7577981651376147,\n \"acc_norm_stderr\": 0.01836817630659862\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4212962962962963,\n \"acc_stderr\": 0.03367462138896078,\n \"\ acc_norm\": 0.4212962962962963,\n \"acc_norm_stderr\": 0.03367462138896078\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7745098039215687,\n \"acc_stderr\": 0.02933116229425174,\n \"\ acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.02933116229425174\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7721518987341772,\n \"acc_stderr\": 0.02730348459906943,\n \ \ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.02730348459906943\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.03114679648297246,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.03114679648297246\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n\ \ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.743801652892562,\n \"acc_stderr\": 0.039849796533028725,\n \"\ acc_norm\": 0.743801652892562,\n \"acc_norm_stderr\": 0.039849796533028725\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6932515337423313,\n \"acc_stderr\": 0.03623089915724145,\n\ \ \"acc_norm\": 0.6932515337423313,\n \"acc_norm_stderr\": 0.03623089915724145\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\ \ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\ \ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6893203883495146,\n \"acc_stderr\": 0.0458212416016155,\n\ \ \"acc_norm\": 0.6893203883495146,\n \"acc_norm_stderr\": 0.0458212416016155\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8162393162393162,\n\ \ \"acc_stderr\": 0.025372139671722933,\n \"acc_norm\": 0.8162393162393162,\n\ \ \"acc_norm_stderr\": 0.025372139671722933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7739463601532567,\n\ \ \"acc_stderr\": 0.014957458504335844,\n \"acc_norm\": 0.7739463601532567,\n\ \ \"acc_norm_stderr\": 0.014957458504335844\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6445086705202312,\n \"acc_stderr\": 0.025770292082977254,\n\ \ \"acc_norm\": 0.6445086705202312,\n \"acc_norm_stderr\": 0.025770292082977254\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5005586592178771,\n\ \ \"acc_stderr\": 0.016722491114073354,\n \"acc_norm\": 0.5005586592178771,\n\ \ \"acc_norm_stderr\": 0.016722491114073354\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6405228758169934,\n \"acc_stderr\": 0.027475969910660952,\n\ \ \"acc_norm\": 0.6405228758169934,\n \"acc_norm_stderr\": 0.027475969910660952\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.662379421221865,\n\ \ \"acc_stderr\": 0.026858825879488544,\n \"acc_norm\": 0.662379421221865,\n\ \ \"acc_norm_stderr\": 0.026858825879488544\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6481481481481481,\n \"acc_stderr\": 0.026571483480719967,\n\ \ \"acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.026571483480719967\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4397163120567376,\n \"acc_stderr\": 0.029609912075594106,\n \ \ \"acc_norm\": 0.4397163120567376,\n \"acc_norm_stderr\": 0.029609912075594106\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4406779661016949,\n\ \ \"acc_stderr\": 0.012680037994097079,\n \"acc_norm\": 0.4406779661016949,\n\ \ \"acc_norm_stderr\": 0.012680037994097079\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5514705882352942,\n \"acc_stderr\": 0.030211479609121596,\n\ \ \"acc_norm\": 0.5514705882352942,\n \"acc_norm_stderr\": 0.030211479609121596\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5915032679738562,\n \"acc_stderr\": 0.01988622103750187,\n \ \ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.01988622103750187\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6530612244897959,\n \"acc_stderr\": 0.030472526026726492,\n\ \ \"acc_norm\": 0.6530612244897959,\n \"acc_norm_stderr\": 0.030472526026726492\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7512437810945274,\n\ \ \"acc_stderr\": 0.030567675938916714,\n \"acc_norm\": 0.7512437810945274,\n\ \ \"acc_norm_stderr\": 0.030567675938916714\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.46987951807228917,\n\ \ \"acc_stderr\": 0.03885425420866766,\n \"acc_norm\": 0.46987951807228917,\n\ \ \"acc_norm_stderr\": 0.03885425420866766\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.031581495393387324,\n\ \ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.031581495393387324\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3598531211750306,\n\ \ \"mc1_stderr\": 0.016801860466677157,\n \"mc2\": 0.5105497381067257,\n\ \ \"mc2_stderr\": 0.015603754710210896\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224178\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/IkariDev/Athnete-13B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|arc:challenge|25_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-22T19-52-46.910811.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|gsm8k|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hellaswag|10_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T19-52-46.910811.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T19-52-46.910811.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T19-52-46.910811.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_22T19_52_46.910811 path: - '**/details_harness|winogrande|5_2024-03-22T19-52-46.910811.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-22T19-52-46.910811.parquet' - config_name: results data_files: - split: 2024_03_22T19_52_46.910811 path: - results_2024-03-22T19-52-46.910811.parquet - split: latest path: - results_2024-03-22T19-52-46.910811.parquet --- # Dataset Card for Evaluation run of IkariDev/Athnete-13B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [IkariDev/Athnete-13B](https://huggingface.co/IkariDev/Athnete-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_IkariDev__Athnete-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-22T19:52:46.910811](https://huggingface.co/datasets/open-llm-leaderboard/details_IkariDev__Athnete-13B/blob/main/results_2024-03-22T19-52-46.910811.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5709192487354107, "acc_stderr": 0.03334034722216404, "acc_norm": 0.5810771448544826, "acc_norm_stderr": 0.03424492177617554, "mc1": 0.3598531211750306, "mc1_stderr": 0.016801860466677157, "mc2": 0.5105497381067257, "mc2_stderr": 0.015603754710210896 }, "harness|arc:challenge|25": { "acc": 0.5947098976109215, "acc_stderr": 0.014346869060229321, "acc_norm": 0.621160409556314, "acc_norm_stderr": 0.014175915490000322 }, "harness|hellaswag|10": { "acc": 0.6532563234415455, "acc_stderr": 0.004749606196363344, "acc_norm": 0.8435570603465445, "acc_norm_stderr": 0.003625323221166242 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4888888888888889, "acc_stderr": 0.04318275491977976, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.04318275491977976 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5592105263157895, "acc_stderr": 0.040403110624904356, "acc_norm": 0.5592105263157895, "acc_norm_stderr": 0.040403110624904356 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6113207547169811, "acc_stderr": 0.03000048544867599, "acc_norm": 0.6113207547169811, "acc_norm_stderr": 0.03000048544867599 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6388888888888888, "acc_stderr": 0.04016660030451233, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.04016660030451233 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5491329479768786, "acc_stderr": 0.0379401267469703, "acc_norm": 0.5491329479768786, "acc_norm_stderr": 0.0379401267469703 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.27450980392156865, "acc_stderr": 0.04440521906179328, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.04440521906179328 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4765957446808511, "acc_stderr": 0.032650194750335815, "acc_norm": 0.4765957446808511, "acc_norm_stderr": 0.032650194750335815 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3157894736842105, "acc_stderr": 0.04372748290278007, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.04372748290278007 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.503448275862069, "acc_stderr": 0.041665675771015785, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.041665675771015785 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3386243386243386, "acc_stderr": 0.024373197867983067, "acc_norm": 0.3386243386243386, "acc_norm_stderr": 0.024373197867983067 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.36507936507936506, "acc_stderr": 0.04306241259127153, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.04306241259127153 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6645161290322581, "acc_stderr": 0.026860206444724335, "acc_norm": 0.6645161290322581, "acc_norm_stderr": 0.026860206444724335 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.45320197044334976, "acc_stderr": 0.035025446508458714, "acc_norm": 0.45320197044334976, "acc_norm_stderr": 0.035025446508458714 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6848484848484848, "acc_stderr": 0.0362773057502241, "acc_norm": 0.6848484848484848, "acc_norm_stderr": 0.0362773057502241 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7121212121212122, "acc_stderr": 0.03225883512300992, "acc_norm": 0.7121212121212122, "acc_norm_stderr": 0.03225883512300992 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8290155440414507, "acc_stderr": 0.02717121368316455, "acc_norm": 0.8290155440414507, "acc_norm_stderr": 0.02717121368316455 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5538461538461539, "acc_stderr": 0.02520357177302833, "acc_norm": 0.5538461538461539, "acc_norm_stderr": 0.02520357177302833 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253252, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253252 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6092436974789915, "acc_stderr": 0.03169380235712996, "acc_norm": 0.6092436974789915, "acc_norm_stderr": 0.03169380235712996 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7577981651376147, "acc_stderr": 0.01836817630659862, "acc_norm": 0.7577981651376147, "acc_norm_stderr": 0.01836817630659862 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4212962962962963, "acc_stderr": 0.03367462138896078, "acc_norm": 0.4212962962962963, "acc_norm_stderr": 0.03367462138896078 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7745098039215687, "acc_stderr": 0.02933116229425174, "acc_norm": 0.7745098039215687, "acc_norm_stderr": 0.02933116229425174 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7721518987341772, "acc_stderr": 0.02730348459906943, "acc_norm": 0.7721518987341772, "acc_norm_stderr": 0.02730348459906943 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.03114679648297246, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.03114679648297246 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.648854961832061, "acc_stderr": 0.04186445163013751, "acc_norm": 0.648854961832061, "acc_norm_stderr": 0.04186445163013751 }, "harness|hendrycksTest-international_law|5": { "acc": 0.743801652892562, "acc_stderr": 0.039849796533028725, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.039849796533028725 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6932515337423313, "acc_stderr": 0.03623089915724145, "acc_norm": 0.6932515337423313, "acc_norm_stderr": 0.03623089915724145 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness|hendrycksTest-management|5": { "acc": 0.6893203883495146, "acc_stderr": 0.0458212416016155, "acc_norm": 0.6893203883495146, "acc_norm_stderr": 0.0458212416016155 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8162393162393162, "acc_stderr": 0.025372139671722933, "acc_norm": 0.8162393162393162, "acc_norm_stderr": 0.025372139671722933 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7739463601532567, "acc_stderr": 0.014957458504335844, "acc_norm": 0.7739463601532567, "acc_norm_stderr": 0.014957458504335844 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6445086705202312, "acc_stderr": 0.025770292082977254, "acc_norm": 0.6445086705202312, "acc_norm_stderr": 0.025770292082977254 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5005586592178771, "acc_stderr": 0.016722491114073354, "acc_norm": 0.5005586592178771, "acc_norm_stderr": 0.016722491114073354 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6405228758169934, "acc_stderr": 0.027475969910660952, "acc_norm": 0.6405228758169934, "acc_norm_stderr": 0.027475969910660952 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.662379421221865, "acc_stderr": 0.026858825879488544, "acc_norm": 0.662379421221865, "acc_norm_stderr": 0.026858825879488544 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6481481481481481, "acc_stderr": 0.026571483480719967, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.026571483480719967 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4397163120567376, "acc_stderr": 0.029609912075594106, "acc_norm": 0.4397163120567376, "acc_norm_stderr": 0.029609912075594106 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4406779661016949, "acc_stderr": 0.012680037994097079, "acc_norm": 0.4406779661016949, "acc_norm_stderr": 0.012680037994097079 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5514705882352942, "acc_stderr": 0.030211479609121596, "acc_norm": 0.5514705882352942, "acc_norm_stderr": 0.030211479609121596 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5915032679738562, "acc_stderr": 0.01988622103750187, "acc_norm": 0.5915032679738562, "acc_norm_stderr": 0.01988622103750187 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425465, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425465 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6530612244897959, "acc_stderr": 0.030472526026726492, "acc_norm": 0.6530612244897959, "acc_norm_stderr": 0.030472526026726492 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7512437810945274, "acc_stderr": 0.030567675938916714, "acc_norm": 0.7512437810945274, "acc_norm_stderr": 0.030567675938916714 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness|hendrycksTest-virology|5": { "acc": 0.46987951807228917, "acc_stderr": 0.03885425420866766, "acc_norm": 0.46987951807228917, "acc_norm_stderr": 0.03885425420866766 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.783625730994152, "acc_stderr": 0.031581495393387324, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.031581495393387324 }, "harness|truthfulqa:mc|0": { "mc1": 0.3598531211750306, "mc1_stderr": 0.016801860466677157, "mc2": 0.5105497381067257, "mc2_stderr": 0.015603754710210896 }, "harness|winogrande|5": { "acc": 0.7592738752959748, "acc_stderr": 0.012015559212224178 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
hkss/tempsets
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 8823804 num_examples: 20324 - name: test num_bytes: 21679 num_examples: 50 download_size: 4608398 dataset_size: 8845483 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
naomi-laker/chess-games-base
--- license: apache-2.0 ---
johko/fashion-products-small-clip-embeddings
--- dataset_info: features: - name: filename dtype: string - name: link dtype: string - name: id dtype: string - name: masterCategory dtype: string - name: gender dtype: string - name: subCategory dtype: string - name: image dtype: image - name: clip_embeddings sequence: sequence: float32 splits: - name: train num_bytes: 795996150.5 num_examples: 42700 download_size: 799221195 dataset_size: 795996150.5 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/yokoyama_nao_theidolmstermillionlive
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yokoyama_nao/横山奈緒 (THE iDOLM@STER: Million Live!) This is the dataset of yokoyama_nao/横山奈緒 (THE iDOLM@STER: Million Live!), containing 500 images and their tags. The core tags of this character are `brown_hair, ahoge, purple_eyes, side_ponytail, bangs, drill_hair, side_drill, sidelocks, hair_ornament, medium_hair, breasts, scrunchie, hair_scrunchie`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 409.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yokoyama_nao_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 303.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yokoyama_nao_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1169 | 614.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yokoyama_nao_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 387.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yokoyama_nao_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1169 | 748.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yokoyama_nao_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/yokoyama_nao_theidolmstermillionlive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 7 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, maid_headdress, solo, puffy_short_sleeves, wrist_cuffs, blush, white_background, enmaided, medium_breasts, pink_bowtie, smile, waist_apron, white_shirt, collared_shirt, frilled_apron, frilled_cuffs, heart_hands, long_hair, pink_dress, skirt, upper_body, white_apron | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, solo, blush, tongue_out, long_hair, smile, food, white_background | | 2 | 50 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, black_shirt, solo, blue_scrunchie, short_sleeves, star_print, blush, looking_at_viewer, t-shirt, smile, print_shirt, open_mouth, wrist_scrunchie, star_necklace, simple_background, upper_body | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, looking_at_viewer, solo, long_hair, medium_breasts, nipples, open_mouth, :d, completely_nude, barefoot, collarbone, navel, white_background | | 4 | 16 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, solo, looking_at_viewer, bare_shoulders, blush, earrings, necklace, smile, flower, upper_body, strapless_dress, cleavage, collarbone, medium_breasts, pink_dress, bracelet, open_mouth | | 5 | 14 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, solo, looking_at_viewer, blush, medium_breasts, open_mouth, cleavage, collarbone, navel, smile, side-tie_bikini_bottom, cowboy_shot | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1boy, 1girl, blush, hetero, penis, sex, solo_focus, sweat, vaginal, female_pubic_hair, open_mouth, completely_nude, mosaic_censoring, nipples, spread_legs, on_back, pov, bar_censor, cum_in_pussy, medium_breasts, missionary, navel | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, kneehighs, looking_at_viewer, plaid_skirt, school_uniform, solo, wing_collar, holding, long_sleeves, miniskirt, pleated_skirt, red_skirt, white_shirt, black_socks, blue_scrunchie, blush, brown_footwear, dress_shirt, full_body, loafers, open_mouth, red_necktie, simple_background, standing, bag, blazer, grey_jacket, grey_sweater, grin, open_jacket, sitting, striped, v-neck, white_background, white_jacket, white_socks, wrist_scrunchie | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, looking_at_viewer, school_uniform, short_sleeves, white_shirt, plaid_skirt, solo, wing_collar, blue_necktie, blush, collared_shirt, dress_shirt, hair_bow, smile, blue_skirt, blurry, closed_mouth, hair_ribbon, miniskirt, open_mouth | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, black_choker, blue_shorts, blush, denim_shorts, heart-shaped_eyewear, long_sleeves, looking_at_viewer, midriff, navel, short_shorts, solo, standing, sunglasses, bracelet, crop_top, cutoffs, eyewear_on_head, necklace, simple_background, suspender_shorts, white_background, off-shoulder_shirt, single_thighhigh, star_(symbol), thigh_strap, white_thighhighs, wristband, yellow_jacket, black_footwear, blue_belt, boots, closed_mouth, cowboy_shot, cross-laced_footwear, full_body, garter_straps, grin, hair_bobbles, orange_shirt, purple_scrunchie, red-framed_eyewear, shoes, wrist_ribbon, wrist_scrunchie, yellow_shirt | | 10 | 6 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, looking_at_viewer, red_bow, smile, solo, white_gloves, white_shirt, miniskirt, sleeveless_shirt, blue_skirt, open_mouth, pleated_skirt, red_neckerchief, standing, armpits, back_bow, blush, cowboy_shot, hair_bow, holding, idol, medium_breasts, white_sailor_collar, white_shorts | | 11 | 6 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, blush, china_dress, looking_at_viewer, print_dress, solo, floral_print, holding, medium_breasts, black_dress, black_ribbon, hair_ribbon, open_mouth, sleeveless_dress, standing, :d, bamboo_steamer, baozi, bracelet, double_bun, side_slit, simple_background, white_background | | 12 | 7 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | 1girl, blush, looking_at_viewer, one_eye_closed, smile, solo, wrist_cuffs, ;d, necktie, open_mouth, short_sleeves, character_name, choker, cowboy_shot, hair_bow, holding_microphone, midriff, navel, pink_shorts, simple_background, white_background | | 13 | 10 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | 1girl, detached_collar, looking_at_viewer, playboy_bunny, strapless_leotard, cleavage, fake_animal_ears, rabbit_ears, solo, bare_shoulders, black_bowtie, black_leotard, white_background, wrist_cuffs, medium_breasts, open_mouth, pantyhose, simple_background, smile, blush, white_collar, collarbone, covered_navel, one_eye_closed | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | maid_headdress | solo | puffy_short_sleeves | wrist_cuffs | blush | white_background | enmaided | medium_breasts | pink_bowtie | smile | waist_apron | white_shirt | collared_shirt | frilled_apron | frilled_cuffs | heart_hands | long_hair | pink_dress | skirt | upper_body | white_apron | tongue_out | food | black_shirt | blue_scrunchie | short_sleeves | star_print | t-shirt | print_shirt | open_mouth | wrist_scrunchie | star_necklace | simple_background | nipples | :d | completely_nude | barefoot | collarbone | navel | bare_shoulders | earrings | necklace | flower | strapless_dress | cleavage | bracelet | side-tie_bikini_bottom | cowboy_shot | 1boy | hetero | penis | sex | solo_focus | sweat | vaginal | female_pubic_hair | mosaic_censoring | spread_legs | on_back | pov | bar_censor | cum_in_pussy | missionary | kneehighs | plaid_skirt | school_uniform | wing_collar | holding | long_sleeves | miniskirt | pleated_skirt | red_skirt | black_socks | brown_footwear | dress_shirt | full_body | loafers | red_necktie | standing | bag | blazer | grey_jacket | grey_sweater | grin | open_jacket | sitting | striped | v-neck | white_jacket | white_socks | blue_necktie | hair_bow | blue_skirt | blurry | closed_mouth | hair_ribbon | black_choker | blue_shorts | denim_shorts | heart-shaped_eyewear | midriff | short_shorts | sunglasses | crop_top | cutoffs | eyewear_on_head | suspender_shorts | off-shoulder_shirt | single_thighhigh | star_(symbol) | thigh_strap | white_thighhighs | wristband | yellow_jacket | black_footwear | blue_belt | boots | cross-laced_footwear | garter_straps | hair_bobbles | orange_shirt | purple_scrunchie | red-framed_eyewear | shoes | wrist_ribbon | yellow_shirt | red_bow | white_gloves | sleeveless_shirt | red_neckerchief | armpits | back_bow | idol | white_sailor_collar | white_shorts | china_dress | print_dress | floral_print | black_dress | black_ribbon | sleeveless_dress | bamboo_steamer | baozi | double_bun | side_slit | one_eye_closed | ;d | necktie | character_name | choker | holding_microphone | pink_shorts | detached_collar | playboy_bunny | strapless_leotard | fake_animal_ears | rabbit_ears | black_bowtie | black_leotard | pantyhose | white_collar | covered_navel | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:-----------------|:-------|:----------------------|:--------------|:--------|:-------------------|:-----------|:-----------------|:--------------|:--------|:--------------|:--------------|:-----------------|:----------------|:----------------|:--------------|:------------|:-------------|:--------|:-------------|:--------------|:-------------|:-------|:--------------|:-----------------|:----------------|:-------------|:----------|:--------------|:-------------|:------------------|:----------------|:--------------------|:----------|:-----|:------------------|:-----------|:-------------|:--------|:-----------------|:-----------|:-----------|:---------|:------------------|:-----------|:-----------|:-------------------------|:--------------|:-------|:---------|:--------|:------|:-------------|:--------|:----------|:--------------------|:-------------------|:--------------|:----------|:------|:-------------|:---------------|:-------------|:------------|:--------------|:-----------------|:--------------|:----------|:---------------|:------------|:----------------|:------------|:--------------|:-----------------|:--------------|:------------|:----------|:--------------|:-----------|:------|:---------|:--------------|:---------------|:-------|:--------------|:----------|:----------|:---------|:---------------|:--------------|:---------------|:-----------|:-------------|:---------|:---------------|:--------------|:---------------|:--------------|:---------------|:-----------------------|:----------|:---------------|:-------------|:-----------|:----------|:------------------|:-------------------|:---------------------|:-------------------|:----------------|:--------------|:-------------------|:------------|:----------------|:-----------------|:------------|:--------|:-----------------------|:----------------|:---------------|:---------------|:-------------------|:---------------------|:--------|:---------------|:---------------|:----------|:---------------|:-------------------|:------------------|:----------|:-----------|:-------|:----------------------|:---------------|:--------------|:--------------|:---------------|:--------------|:---------------|:-------------------|:-----------------|:--------|:-------------|:------------|:-----------------|:-----|:----------|:-----------------|:---------|:---------------------|:--------------|:------------------|:----------------|:--------------------|:-------------------|:--------------|:---------------|:----------------|:------------|:---------------|:----------------| | 0 | 7 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | | | X | X | | | | X | | | | | | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 50 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | | | X | | | | | X | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | | | X | X | | X | | | | | | | | | X | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 16 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | X | | | X | | | X | | X | | | | | | | | X | | X | | | | | | | | | | X | | | | | | | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 14 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | X | | | X | | | X | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | X | | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | | X | | | X | X | | | | | | X | | | | | | | | | | | | | X | | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | X | | X | | | X | | | | | X | | X | X | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | X | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | X | | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | X | | | | | X | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 6 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | X | | X | | | X | | | X | | X | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | | X | X | | | | | | | | X | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 11 | 6 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | X | X | | X | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | X | | | X | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 12 | 7 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | X | X | | X | | X | X | X | | | | X | | | | | | | | | | | | | | | | X | | | | X | | | X | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | 13 | 10 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | X | X | | X | | X | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X |
Nexdata/Thai_Children_Spontaneous_Speech_Data
--- task_categories: - automatic-speech-recognition language: - th --- # Dataset Card for Nexdata/Thai_Children_Spontaneous_Speech_Data ## Description The 100 Hours - Thai Child's Spontaneous Speech Data, manually screened and processed. Annotation contains transcription text, speaker identification, gender and other informantion. This dataset can be applied in speech recognition (acoustic model or language model training), caption generation, voice content moderation and other AI algorithm research. For more details, please refer to the link: https://www.nexdata.ai/datasets/1330?source=Huggingface # Specifications ## Format 16k Hz, 16 bit, wav, mono channel; ## Age 12 years old and younger children; ## Content category including self-media, conversation, live, lecture, variety show; ## Language Thai; ## Annotation annotation for the transcription text, speaker identification, gender; ## Accuracy Word Accuracy Rate (WAR) at least 98%. # Licensing Information Commercial License
Tippawan/test2-data-semi-p3-WLV
--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: int64 - name: prob sequence: float64 - name: ifpass sequence: int64 - name: pred dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 2511262 num_examples: 1762 download_size: 379404 dataset_size: 2511262 configs: - config_name: default data_files: - split: train path: data/train-* ---
ccj692709344/data_demo
--- size_categories: - n<1K ---
liyongsea/un_linebreak-1000
--- dataset_info: features: - name: text dtype: string - name: label dtype: bool splits: - name: train num_bytes: 81448559 num_examples: 668128 - name: test num_bytes: 10851041 num_examples: 84617 download_size: 32630643 dataset_size: 92299600 --- # Dataset Card for "un_linebreak-1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deivsu/lena
--- license: openrail ---
valashir/SMM2-levels-all-v2
--- dataset_info: features: - name: id dtype: int64 - name: level sequence: sequence: sequence: uint8 - name: text dtype: string - name: text-baseline dtype: string splits: - name: train num_bytes: 30754194471 num_examples: 202096 - name: val num_bytes: 308873455 num_examples: 2048 download_size: 271999803 dataset_size: 31063067926 --- # Dataset Card for "SMM2-levels-all-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/sentiment_analysis_affix
--- dataset_info: features: - name: label dtype: class_label: names: '0': neg '1': pos - name: text dtype: string splits: - name: train num_bytes: 390794.9858712716 num_examples: 7318 download_size: 194325 dataset_size: 390794.9858712716 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sentiment_analysis_affix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aldenn13l/182-fine-tune
--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: new_image dtype: image splits: - name: train num_bytes: 1432179908.95 num_examples: 1291 download_size: 1428584756 dataset_size: 1432179908.95 configs: - config_name: default data_files: - split: train path: data/train-* --- data for 182
KoladeOdunope/rlhf_report_dataset
--- dataset_info: features: - name: query dtype: string - name: response1 dtype: string - name: response2 dtype: string splits: - name: train num_bytes: 6399268 num_examples: 1029 download_size: 2606759 dataset_size: 6399268 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChangeMavens/OrgChange
--- license: afl-3.0 ---
iblai/fordham-university
--- license: apache-2.0 task_categories: - question-answering language: - en size_categories: - 1K<n<10K --- # ibleducation/fordham-university This dataset contains a set of query and response pairs about Fordham university Data for the dataset was scrapped from [fordham.edu](https://fordham.edu) using [GptCrawler](https://github.com/BuilderIO/gpt-crawler). The resulting pages were then converted to query response pairs using GPT-3.5 A total of **2707** data points exist in this dataset.
Xinyue123/LIMA_instructions_generate
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 107329.5 num_examples: 51 download_size: 80121 dataset_size: 107329.5 configs: - config_name: default data_files: - split: train path: data/train-* ---
datadreamer-dev/cnn_dailymail_sports
--- size_categories: - n<1K source_datasets: - cnn_dailymail dataset_info: features: - name: article dtype: string - name: highlights dtype: string - name: id dtype: string splits: - name: train num_bytes: 163568 num_examples: 47 download_size: 115819 dataset_size: 163568 configs: - config_name: default data_files: - split: train path: data/train-* library_name: datadreamer tags: - datadreamer - datadreamer-0.1.0 - synthetic - gpt-4 --- # Dataset Card See: https://datadreamer.dev/docs/latest/pages/get_started/quick_tour/dataset_cleaning.html --- This dataset was produced with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card can be found [here](datadreamer.json).
potatoSeop/chimsuja_dataset
--- dataset_info: features: - name: audio dtype: audio - name: script dtype: string splits: - name: train num_bytes: 2614465503.562 num_examples: 2521 download_size: 3076362475 dataset_size: 2614465503.562 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "chimsuja_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Sowmya15/gibberish_april12
--- license: apache-2.0 ---
arieg/bw_spec_cls_4_16_s_200
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1482' '1': '1510' '2': '1544' '3': '1642' splits: - name: train num_bytes: 43983230.0 num_examples: 800 - name: test num_bytes: 1108325.0 num_examples: 20 download_size: 38471730 dataset_size: 45091555.0 --- # Dataset Card for "bw_spec_cls_4_16_s_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tasksource/QA-Feedback
--- license: cc ---
CyberHarem/nopht_sukasuka
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Nopht Keh Desperatio/ノフト・ケー・デスペラティオ (Shuumatsu Nani Shitemasu Ka? Isogashii Desu Ka?) This is the dataset of Nopht Keh Desperatio/ノフト・ケー・デスペラティオ (Shuumatsu Nani Shitemasu Ka? Isogashii Desu Ka?), containing 158 images and their tags. The core tags of this character are `short_hair, pink_hair, red_hair, red_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 158 | 94.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nopht_sukasuka/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 158 | 94.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nopht_sukasuka/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 278 | 149.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nopht_sukasuka/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/nopht_sukasuka', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 2girls, hoodie, blue_hair, solo_focus, long_hair | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, hoodie, open_mouth, solo, upper_body, v-shaped_eyebrows | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, closed_mouth, portrait, solo | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blood_on_face, holding_weapon, solo, hoodie, open_mouth, suspenders, blood_on_clothes | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, hoodie, open_mouth, solo, :d, closed_eyes, shorts | | 5 | 9 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | sky, solo, cloud, outdoors, profile, 1boy, closed_eyes, from_side, holding_weapon, male_focus, open_mouth, sword, 1girl, hood, shorts, standing, day, wings | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 2girls | hoodie | blue_hair | solo_focus | long_hair | 1girl | open_mouth | solo | upper_body | v-shaped_eyebrows | closed_mouth | portrait | blood_on_face | holding_weapon | suspenders | blood_on_clothes | :d | closed_eyes | shorts | sky | cloud | outdoors | profile | 1boy | from_side | male_focus | sword | hood | standing | day | wings | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------|:---------|:------------|:-------------|:------------|:--------|:-------------|:-------|:-------------|:--------------------|:---------------|:-----------|:----------------|:-----------------|:-------------|:-------------------|:-----|:--------------|:---------|:------|:--------|:-----------|:----------|:-------|:------------|:-------------|:--------|:-------|:-----------|:------|:--------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | | | | | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | | X | | | | X | X | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | | X | | | | X | X | X | | | | | | | | | X | X | X | | | | | | | | | | | | | | 5 | 9 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | | | | | | X | X | X | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
larrylawl/multilexnorm
--- license: cc-by-4.0 task_categories: - text-generation language: - en - da - de - es - hr - it - nl - sl - sr - tr - id size_categories: - 100K<n<1M --- # Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://noisy-text.github.io/2021/multi-lexnorm.html]() - **Paper:** [https://aclanthology.org/2021.wnut-1.55/]() ### Dataset Summary This is the huggingface version of the MultiLexnorm dataset. I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing. ### Citation Information ``` @inproceedings{van-der-goot-etal-2021-multilexnorm, title = "{M}ulti{L}ex{N}orm: A Shared Task on Multilingual Lexical Normalization", author = {van der Goot, Rob and Ramponi, Alan and Zubiaga, Arkaitz and Plank, Barbara and Muller, Benjamin and San Vicente Roncal, I{\~n}aki and Ljube{\v{s}}i{\'c}, Nikola and {\c{C}}etino{\u{g}}lu, {\"O}zlem and Mahendra, Rahmad and {\c{C}}olako{\u{g}}lu, Talha and Baldwin, Timothy and Caselli, Tommaso and Sidorenko, Wladimir}, booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.wnut-1.55", doi = "10.18653/v1/2021.wnut-1.55", pages = "493--509", abstract = "Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.", } ``` ### Contributions Thanks to [@larrylawl](https://github.com/larrylawl) for adding this dataset.
open-llm-leaderboard/details_Josephgflowers__TinyLlama-748M-Reason-With-Cinder-Test-2
--- pretty_name: Evaluation run of Josephgflowers/TinyLlama-748M-Reason-With-Cinder-Test-2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Josephgflowers/TinyLlama-748M-Reason-With-Cinder-Test-2](https://huggingface.co/Josephgflowers/TinyLlama-748M-Reason-With-Cinder-Test-2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Josephgflowers__TinyLlama-748M-Reason-With-Cinder-Test-2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-16T18:21:24.569209](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__TinyLlama-748M-Reason-With-Cinder-Test-2/blob/main/results_2024-02-16T18-21-24.569209.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2521207309170715,\n\ \ \"acc_stderr\": 0.030556259826906736,\n \"acc_norm\": 0.2529609814071766,\n\ \ \"acc_norm_stderr\": 0.03131972311648323,\n \"mc1\": 0.2558139534883721,\n\ \ \"mc1_stderr\": 0.015274176219283352,\n \"mc2\": 0.42762316543412854,\n\ \ \"mc2_stderr\": 0.015330016474026912\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.22781569965870307,\n \"acc_stderr\": 0.012256708602326912,\n\ \ \"acc_norm\": 0.24658703071672355,\n \"acc_norm_stderr\": 0.012595726268790134\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.304919338777136,\n\ \ \"acc_stderr\": 0.004594323838650341,\n \"acc_norm\": 0.34495120493925513,\n\ \ \"acc_norm_stderr\": 0.004743808792037872\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \ \ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3037037037037037,\n\ \ \"acc_stderr\": 0.039725528847851375,\n \"acc_norm\": 0.3037037037037037,\n\ \ \"acc_norm_stderr\": 0.039725528847851375\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.03110318238312337,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.03110318238312337\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.21,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.21132075471698114,\n \"acc_stderr\": 0.025125766484827842,\n\ \ \"acc_norm\": 0.21132075471698114,\n \"acc_norm_stderr\": 0.025125766484827842\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.24305555555555555,\n\ \ \"acc_stderr\": 0.03586879280080342,\n \"acc_norm\": 0.24305555555555555,\n\ \ \"acc_norm_stderr\": 0.03586879280080342\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\ : 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2543352601156069,\n\ \ \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.2543352601156069,\n\ \ \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\ \ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.17,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.17,\n\ \ \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.23829787234042554,\n \"acc_stderr\": 0.027851252973889778,\n\ \ \"acc_norm\": 0.23829787234042554,\n \"acc_norm_stderr\": 0.027851252973889778\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\ \ \"acc_stderr\": 0.040969851398436695,\n \"acc_norm\": 0.2543859649122807,\n\ \ \"acc_norm_stderr\": 0.040969851398436695\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.22758620689655173,\n \"acc_stderr\": 0.03493950380131184,\n\ \ \"acc_norm\": 0.22758620689655173,\n \"acc_norm_stderr\": 0.03493950380131184\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.23015873015873015,\n \"acc_stderr\": 0.02167921966369314,\n \"\ acc_norm\": 0.23015873015873015,\n \"acc_norm_stderr\": 0.02167921966369314\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.16666666666666666,\n\ \ \"acc_stderr\": 0.03333333333333337,\n \"acc_norm\": 0.16666666666666666,\n\ \ \"acc_norm_stderr\": 0.03333333333333337\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.3161290322580645,\n \"acc_stderr\": 0.02645087448904277,\n \"\ acc_norm\": 0.3161290322580645,\n \"acc_norm_stderr\": 0.02645087448904277\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.29064039408866993,\n \"acc_stderr\": 0.0319474007226554,\n \"\ acc_norm\": 0.29064039408866993,\n \"acc_norm_stderr\": 0.0319474007226554\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\"\ : 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.2606060606060606,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.2606060606060606,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2676767676767677,\n \"acc_stderr\": 0.03154449888270285,\n \"\ acc_norm\": 0.2676767676767677,\n \"acc_norm_stderr\": 0.03154449888270285\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.27979274611398963,\n \"acc_stderr\": 0.03239637046735703,\n\ \ \"acc_norm\": 0.27979274611398963,\n \"acc_norm_stderr\": 0.03239637046735703\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2692307692307692,\n \"acc_stderr\": 0.022489389793654845,\n\ \ \"acc_norm\": 0.2692307692307692,\n \"acc_norm_stderr\": 0.022489389793654845\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.02606715922227579,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.02606715922227579\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.33613445378151263,\n \"acc_stderr\": 0.03068473711513537,\n\ \ \"acc_norm\": 0.33613445378151263,\n \"acc_norm_stderr\": 0.03068473711513537\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.23178807947019867,\n \"acc_stderr\": 0.03445406271987054,\n \"\ acc_norm\": 0.23178807947019867,\n \"acc_norm_stderr\": 0.03445406271987054\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.24770642201834864,\n \"acc_stderr\": 0.01850814360254782,\n \"\ acc_norm\": 0.24770642201834864,\n \"acc_norm_stderr\": 0.01850814360254782\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4675925925925926,\n \"acc_stderr\": 0.03402801581358966,\n \"\ acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.03402801581358966\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.27941176470588236,\n \"acc_stderr\": 0.031493281045079556,\n \"\ acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.031493281045079556\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.23628691983122363,\n \"acc_stderr\": 0.027652153144159263,\n \ \ \"acc_norm\": 0.23628691983122363,\n \"acc_norm_stderr\": 0.027652153144159263\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.21524663677130046,\n\ \ \"acc_stderr\": 0.027584066602208263,\n \"acc_norm\": 0.21524663677130046,\n\ \ \"acc_norm_stderr\": 0.027584066602208263\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2892561983471074,\n \"acc_stderr\": 0.041391127276354626,\n \"\ acc_norm\": 0.2892561983471074,\n \"acc_norm_stderr\": 0.041391127276354626\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.2392638036809816,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.2392638036809816,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.23214285714285715,\n\ \ \"acc_stderr\": 0.04007341809755807,\n \"acc_norm\": 0.23214285714285715,\n\ \ \"acc_norm_stderr\": 0.04007341809755807\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.039166677628225836,\n\ \ \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.039166677628225836\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.20085470085470086,\n\ \ \"acc_stderr\": 0.02624677294689048,\n \"acc_norm\": 0.20085470085470086,\n\ \ \"acc_norm_stderr\": 0.02624677294689048\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26309067688378035,\n\ \ \"acc_stderr\": 0.01574549716904906,\n \"acc_norm\": 0.26309067688378035,\n\ \ \"acc_norm_stderr\": 0.01574549716904906\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.2254335260115607,\n \"acc_stderr\": 0.022497230190967547,\n\ \ \"acc_norm\": 0.2254335260115607,\n \"acc_norm_stderr\": 0.022497230190967547\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\ \ \"acc_stderr\": 0.014422292204808871,\n \"acc_norm\": 0.24692737430167597,\n\ \ \"acc_norm_stderr\": 0.014422292204808871\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.024954184324879912,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.024954184324879912\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.29260450160771706,\n\ \ \"acc_stderr\": 0.025839898334877983,\n \"acc_norm\": 0.29260450160771706,\n\ \ \"acc_norm_stderr\": 0.025839898334877983\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.02492200116888633,\n\ \ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.02492200116888633\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2198581560283688,\n \"acc_stderr\": 0.024706141070705474,\n \ \ \"acc_norm\": 0.2198581560283688,\n \"acc_norm_stderr\": 0.024706141070705474\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2196870925684485,\n\ \ \"acc_stderr\": 0.010574639934167518,\n \"acc_norm\": 0.2196870925684485,\n\ \ \"acc_norm_stderr\": 0.010574639934167518\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.39338235294117646,\n \"acc_stderr\": 0.02967428828131118,\n\ \ \"acc_norm\": 0.39338235294117646,\n \"acc_norm_stderr\": 0.02967428828131118\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.22549019607843138,\n \"acc_stderr\": 0.016906615927288145,\n \ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.016906615927288145\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2727272727272727,\n\ \ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.2727272727272727,\n\ \ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.3551020408163265,\n \"acc_stderr\": 0.030635655150387638,\n\ \ \"acc_norm\": 0.3551020408163265,\n \"acc_norm_stderr\": 0.030635655150387638\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n\ \ \"acc_stderr\": 0.029705284056772436,\n \"acc_norm\": 0.22885572139303484,\n\ \ \"acc_norm_stderr\": 0.029705284056772436\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.21686746987951808,\n\ \ \"acc_stderr\": 0.03208284450356365,\n \"acc_norm\": 0.21686746987951808,\n\ \ \"acc_norm_stderr\": 0.03208284450356365\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.28654970760233917,\n \"acc_stderr\": 0.03467826685703826,\n\ \ \"acc_norm\": 0.28654970760233917,\n \"acc_norm_stderr\": 0.03467826685703826\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2558139534883721,\n\ \ \"mc1_stderr\": 0.015274176219283352,\n \"mc2\": 0.42762316543412854,\n\ \ \"mc2_stderr\": 0.015330016474026912\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.014051745961790516\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.008339651250947688,\n \ \ \"acc_stderr\": 0.002504942226860505\n }\n}\n```" repo_url: https://huggingface.co/Josephgflowers/TinyLlama-748M-Reason-With-Cinder-Test-2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|arc:challenge|25_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-16T18-21-24.569209.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|gsm8k|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hellaswag|10_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-16T18-21-24.569209.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-management|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T18-21-24.569209.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|truthfulqa:mc|0_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-16T18-21-24.569209.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_16T18_21_24.569209 path: - '**/details_harness|winogrande|5_2024-02-16T18-21-24.569209.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-16T18-21-24.569209.parquet' - config_name: results data_files: - split: 2024_02_16T18_21_24.569209 path: - results_2024-02-16T18-21-24.569209.parquet - split: latest path: - results_2024-02-16T18-21-24.569209.parquet --- # Dataset Card for Evaluation run of Josephgflowers/TinyLlama-748M-Reason-With-Cinder-Test-2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Josephgflowers/TinyLlama-748M-Reason-With-Cinder-Test-2](https://huggingface.co/Josephgflowers/TinyLlama-748M-Reason-With-Cinder-Test-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Josephgflowers__TinyLlama-748M-Reason-With-Cinder-Test-2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T18:21:24.569209](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__TinyLlama-748M-Reason-With-Cinder-Test-2/blob/main/results_2024-02-16T18-21-24.569209.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.2521207309170715, "acc_stderr": 0.030556259826906736, "acc_norm": 0.2529609814071766, "acc_norm_stderr": 0.03131972311648323, "mc1": 0.2558139534883721, "mc1_stderr": 0.015274176219283352, "mc2": 0.42762316543412854, "mc2_stderr": 0.015330016474026912 }, "harness|arc:challenge|25": { "acc": 0.22781569965870307, "acc_stderr": 0.012256708602326912, "acc_norm": 0.24658703071672355, "acc_norm_stderr": 0.012595726268790134 }, "harness|hellaswag|10": { "acc": 0.304919338777136, "acc_stderr": 0.004594323838650341, "acc_norm": 0.34495120493925513, "acc_norm_stderr": 0.004743808792037872 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.19, "acc_stderr": 0.039427724440366234, "acc_norm": 0.19, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3037037037037037, "acc_stderr": 0.039725528847851375, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.039725528847851375 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.03110318238312337, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.03110318238312337 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.21132075471698114, "acc_stderr": 0.025125766484827842, "acc_norm": 0.21132075471698114, "acc_norm_stderr": 0.025125766484827842 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.24305555555555555, "acc_stderr": 0.03586879280080342, "acc_norm": 0.24305555555555555, "acc_norm_stderr": 0.03586879280080342 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2543352601156069, "acc_stderr": 0.0332055644308557, "acc_norm": 0.2543352601156069, "acc_norm_stderr": 0.0332055644308557 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.28431372549019607, "acc_stderr": 0.04488482852329017, "acc_norm": 0.28431372549019607, "acc_norm_stderr": 0.04488482852329017 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.17, "acc_stderr": 0.0377525168068637, "acc_norm": 0.17, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.23829787234042554, "acc_stderr": 0.027851252973889778, "acc_norm": 0.23829787234042554, "acc_norm_stderr": 0.027851252973889778 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2543859649122807, "acc_stderr": 0.040969851398436695, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.040969851398436695 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.22758620689655173, "acc_stderr": 0.03493950380131184, "acc_norm": 0.22758620689655173, "acc_norm_stderr": 0.03493950380131184 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.23015873015873015, "acc_stderr": 0.02167921966369314, "acc_norm": 0.23015873015873015, "acc_norm_stderr": 0.02167921966369314 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.16666666666666666, "acc_stderr": 0.03333333333333337, "acc_norm": 0.16666666666666666, "acc_norm_stderr": 0.03333333333333337 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3161290322580645, "acc_stderr": 0.02645087448904277, "acc_norm": 0.3161290322580645, "acc_norm_stderr": 0.02645087448904277 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.29064039408866993, "acc_stderr": 0.0319474007226554, "acc_norm": 0.29064039408866993, "acc_norm_stderr": 0.0319474007226554 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.23, "acc_stderr": 0.042295258468165065, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.2606060606060606, "acc_stderr": 0.034277431758165236, "acc_norm": 0.2606060606060606, "acc_norm_stderr": 0.034277431758165236 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2676767676767677, "acc_stderr": 0.03154449888270285, "acc_norm": 0.2676767676767677, "acc_norm_stderr": 0.03154449888270285 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.27979274611398963, "acc_stderr": 0.03239637046735703, "acc_norm": 0.27979274611398963, "acc_norm_stderr": 0.03239637046735703 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2692307692307692, "acc_stderr": 0.022489389793654845, "acc_norm": 0.2692307692307692, "acc_norm_stderr": 0.022489389793654845 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24074074074074073, "acc_stderr": 0.02606715922227579, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.02606715922227579 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.33613445378151263, "acc_stderr": 0.03068473711513537, "acc_norm": 0.33613445378151263, "acc_norm_stderr": 0.03068473711513537 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.23178807947019867, "acc_stderr": 0.03445406271987054, "acc_norm": 0.23178807947019867, "acc_norm_stderr": 0.03445406271987054 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.24770642201834864, "acc_stderr": 0.01850814360254782, "acc_norm": 0.24770642201834864, "acc_norm_stderr": 0.01850814360254782 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4675925925925926, "acc_stderr": 0.03402801581358966, "acc_norm": 0.4675925925925926, "acc_norm_stderr": 0.03402801581358966 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.27941176470588236, "acc_stderr": 0.031493281045079556, "acc_norm": 0.27941176470588236, "acc_norm_stderr": 0.031493281045079556 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.23628691983122363, "acc_stderr": 0.027652153144159263, "acc_norm": 0.23628691983122363, "acc_norm_stderr": 0.027652153144159263 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.21524663677130046, "acc_stderr": 0.027584066602208263, "acc_norm": 0.21524663677130046, "acc_norm_stderr": 0.027584066602208263 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2748091603053435, "acc_stderr": 0.03915345408847836, "acc_norm": 0.2748091603053435, "acc_norm_stderr": 0.03915345408847836 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2892561983471074, "acc_stderr": 0.041391127276354626, "acc_norm": 0.2892561983471074, "acc_norm_stderr": 0.041391127276354626 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.21296296296296297, "acc_stderr": 0.0395783547198098, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.2392638036809816, "acc_stderr": 0.033519538795212696, "acc_norm": 0.2392638036809816, "acc_norm_stderr": 0.033519538795212696 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.23214285714285715, "acc_stderr": 0.04007341809755807, "acc_norm": 0.23214285714285715, "acc_norm_stderr": 0.04007341809755807 }, "harness|hendrycksTest-management|5": { "acc": 0.1941747572815534, "acc_stderr": 0.039166677628225836, "acc_norm": 0.1941747572815534, "acc_norm_stderr": 0.039166677628225836 }, "harness|hendrycksTest-marketing|5": { "acc": 0.20085470085470086, "acc_stderr": 0.02624677294689048, "acc_norm": 0.20085470085470086, "acc_norm_stderr": 0.02624677294689048 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.26309067688378035, "acc_stderr": 0.01574549716904906, "acc_norm": 0.26309067688378035, "acc_norm_stderr": 0.01574549716904906 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2254335260115607, "acc_stderr": 0.022497230190967547, "acc_norm": 0.2254335260115607, "acc_norm_stderr": 0.022497230190967547 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24692737430167597, "acc_stderr": 0.014422292204808871, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.014422292204808871 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2549019607843137, "acc_stderr": 0.024954184324879912, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.024954184324879912 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.29260450160771706, "acc_stderr": 0.025839898334877983, "acc_norm": 0.29260450160771706, "acc_norm_stderr": 0.025839898334877983 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2777777777777778, "acc_stderr": 0.02492200116888633, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.02492200116888633 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2198581560283688, "acc_stderr": 0.024706141070705474, "acc_norm": 0.2198581560283688, "acc_norm_stderr": 0.024706141070705474 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2196870925684485, "acc_stderr": 0.010574639934167518, "acc_norm": 0.2196870925684485, "acc_norm_stderr": 0.010574639934167518 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.39338235294117646, "acc_stderr": 0.02967428828131118, "acc_norm": 0.39338235294117646, "acc_norm_stderr": 0.02967428828131118 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.22549019607843138, "acc_stderr": 0.016906615927288145, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.016906615927288145 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2727272727272727, "acc_stderr": 0.04265792110940589, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.04265792110940589 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.3551020408163265, "acc_stderr": 0.030635655150387638, "acc_norm": 0.3551020408163265, "acc_norm_stderr": 0.030635655150387638 }, "harness|hendrycksTest-sociology|5": { "acc": 0.22885572139303484, "acc_stderr": 0.029705284056772436, "acc_norm": 0.22885572139303484, "acc_norm_stderr": 0.029705284056772436 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness|hendrycksTest-virology|5": { "acc": 0.21686746987951808, "acc_stderr": 0.03208284450356365, "acc_norm": 0.21686746987951808, "acc_norm_stderr": 0.03208284450356365 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.28654970760233917, "acc_stderr": 0.03467826685703826, "acc_norm": 0.28654970760233917, "acc_norm_stderr": 0.03467826685703826 }, "harness|truthfulqa:mc|0": { "mc1": 0.2558139534883721, "mc1_stderr": 0.015274176219283352, "mc2": 0.42762316543412854, "mc2_stderr": 0.015330016474026912 }, "harness|winogrande|5": { "acc": 0.505130228887135, "acc_stderr": 0.014051745961790516 }, "harness|gsm8k|5": { "acc": 0.008339651250947688, "acc_stderr": 0.002504942226860505 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended 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etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. 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More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-guanaco
--- pretty_name: Evaluation run of TheTravellingEngineer/llama2-7b-chat-hf-guanaco dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheTravellingEngineer/llama2-7b-chat-hf-guanaco](https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-guanaco)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-guanaco\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-16T15:24:09.297572](https://huggingface.co/datasets/open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-guanaco/blob/main/results_2023-09-16T15-24-09.297572.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0028313758389261743,\n\ \ \"em_stderr\": 0.0005441551135494218,\n \"f1\": 0.05759857382550368,\n\ \ \"f1_stderr\": 0.0013970900427636582,\n \"acc\": 0.4074763654032228,\n\ \ \"acc_stderr\": 0.01009856180825454\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0028313758389261743,\n \"em_stderr\": 0.0005441551135494218,\n\ \ \"f1\": 0.05759857382550368,\n \"f1_stderr\": 0.0013970900427636582\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08567096285064443,\n \ \ \"acc_stderr\": 0.007709218855882777\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7292817679558011,\n \"acc_stderr\": 0.012487904760626304\n\ \ }\n}\n```" repo_url: https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-guanaco leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|arc:challenge|25_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-02T15:25:50.809561.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_16T15_24_09.297572 path: - '**/details_harness|drop|3_2023-09-16T15-24-09.297572.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-16T15-24-09.297572.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_16T15_24_09.297572 path: - '**/details_harness|gsm8k|5_2023-09-16T15-24-09.297572.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-16T15-24-09.297572.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hellaswag|10_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-02T15:25:50.809561.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-management|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T15:25:50.809561.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_02T15_25_50.809561 path: - '**/details_harness|truthfulqa:mc|0_2023-08-02T15:25:50.809561.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-02T15:25:50.809561.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_16T15_24_09.297572 path: - '**/details_harness|winogrande|5_2023-09-16T15-24-09.297572.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-16T15-24-09.297572.parquet' - config_name: results data_files: - split: 2023_08_02T15_25_50.809561 path: - results_2023-08-02T15:25:50.809561.parquet - split: 2023_09_16T15_24_09.297572 path: - results_2023-09-16T15-24-09.297572.parquet - split: latest path: - results_2023-09-16T15-24-09.297572.parquet --- # Dataset Card for Evaluation run of TheTravellingEngineer/llama2-7b-chat-hf-guanaco ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-guanaco - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TheTravellingEngineer/llama2-7b-chat-hf-guanaco](https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-guanaco) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-guanaco", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-16T15:24:09.297572](https://huggingface.co/datasets/open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-guanaco/blob/main/results_2023-09-16T15-24-09.297572.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0028313758389261743, "em_stderr": 0.0005441551135494218, "f1": 0.05759857382550368, "f1_stderr": 0.0013970900427636582, "acc": 0.4074763654032228, "acc_stderr": 0.01009856180825454 }, "harness|drop|3": { "em": 0.0028313758389261743, "em_stderr": 0.0005441551135494218, "f1": 0.05759857382550368, "f1_stderr": 0.0013970900427636582 }, "harness|gsm8k|5": { "acc": 0.08567096285064443, "acc_stderr": 0.007709218855882777 }, "harness|winogrande|5": { "acc": 0.7292817679558011, "acc_stderr": 0.012487904760626304 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
thudoann/finetuningllm2
--- task_categories: - table-question-answering language: - en size_categories: - 10K<n<100K ---
medmac01/dreambooth-moroccan-design
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 2734939.0 num_examples: 47 download_size: 0 dataset_size: 2734939.0 --- # Dataset Card for "dreambooth-moroccan-design" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
maghwa/OpenHermes-2-AR-10K-26-680k-690k
--- dataset_info: features: - name: language dtype: 'null' - name: system_prompt dtype: 'null' - name: conversations dtype: string - name: category dtype: 'null' - name: id dtype: 'null' - name: topic dtype: 'null' - name: hash dtype: 'null' - name: model_name dtype: 'null' - name: idx dtype: 'null' - name: skip_prompt_formatting dtype: 'null' - name: model dtype: 'null' - name: avatarUrl dtype: 'null' - name: title dtype: 'null' - name: views dtype: float64 - name: source dtype: string - name: custom_instruction dtype: 'null' splits: - name: train num_bytes: 25324996 num_examples: 10001 download_size: 11501943 dataset_size: 25324996 configs: - config_name: default data_files: - split: train path: data/train-* ---
evoosa/gemstones
--- license: apache-2.0 ---
autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-e19ec2-2251271758
--- type: predictions tags: - autotrain - evaluation datasets: - kmfoda/booksum eval_info: task: summarization model: pszemraj/long-t5-tglobal-large-booksum-WIP metrics: ['bertscore'] dataset_name: kmfoda/booksum dataset_config: kmfoda--booksum dataset_split: test col_mapping: text: chapter target: summary_text --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/long-t5-tglobal-large-booksum-WIP * Dataset: kmfoda/booksum * Config: kmfoda--booksum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
anan-2024/twitter_dataset_1712994596
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 376701 num_examples: 1018 download_size: 203944 dataset_size: 376701 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/charlotta_granbluefantasy
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of charlotta (Granblue Fantasy) This is the dataset of charlotta (Granblue Fantasy), containing 302 images and their tags. The core tags of this character are `blonde_hair, long_hair, pointy_ears, blue_eyes, crown, very_long_hair, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 302 | 251.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotta_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 302 | 179.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotta_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 594 | 335.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotta_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 302 | 233.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotta_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 594 | 408.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotta_granbluefantasy/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/charlotta_granbluefantasy', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blue_dress, breastplate, gauntlets, harvin, holding_sword, solo, simple_background, armored_boots, frilled_dress, v-shaped_eyebrows, white_background, puffy_short_sleeves, open_mouth, armored_dress, full_body | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blue_dress, closed_mouth, gauntlets, harvin, holding_sword, looking_at_viewer, solo, simple_background, smile, white_background, puffy_sleeves, blush, breastplate, hair_between_eyes, v-shaped_eyebrows | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blue_dress, blush, breastplate, harvin, looking_at_viewer, puffy_short_sleeves, solo, simple_background, white_background, gauntlets, v-shaped_eyebrows, open_mouth, smile, upper_body | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, armor, dress, harvin, solo, sword, gauntlets, looking_at_viewer, smile, open_mouth | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, bat_wings, blush, hair_bow, halloween, harvin, jack-o'-lantern, pumpkin, solo, black_bow, puffy_short_sleeves, armored_boots, breastplate, gauntlets, looking_at_viewer, orange_dress, sword, closed_mouth, frilled_dress, full_body, holding, smile, v-shaped_eyebrows | | 5 | 12 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, harvin, solo, bare_shoulders, blush, collarbone, looking_at_viewer, navel, smile, white_background, hair_between_eyes, closed_mouth, official_alternate_costume, simple_background, full_body, >:), bikini_skirt, standing, star_(symbol), white_bikini | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, fake_animal_ears, harvin, leotard, playboy_bunny, rabbit_ears, solo, bare_shoulders, detached_collar, looking_at_viewer, wrist_cuffs, black_pantyhose, blush, bowtie, full_body, rabbit_tail, simple_background, small_breasts, cowboy_shot, grey_background, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_dress | breastplate | gauntlets | harvin | holding_sword | solo | simple_background | armored_boots | frilled_dress | v-shaped_eyebrows | white_background | puffy_short_sleeves | open_mouth | armored_dress | full_body | closed_mouth | looking_at_viewer | smile | puffy_sleeves | blush | hair_between_eyes | upper_body | armor | dress | sword | bat_wings | hair_bow | halloween | jack-o'-lantern | pumpkin | black_bow | orange_dress | holding | bare_shoulders | collarbone | navel | official_alternate_costume | >:) | bikini_skirt | standing | star_(symbol) | white_bikini | fake_animal_ears | leotard | playboy_bunny | rabbit_ears | detached_collar | wrist_cuffs | black_pantyhose | bowtie | rabbit_tail | small_breasts | cowboy_shot | grey_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------------|:------------|:---------|:----------------|:-------|:--------------------|:----------------|:----------------|:--------------------|:-------------------|:----------------------|:-------------|:----------------|:------------|:---------------|:--------------------|:--------|:----------------|:--------|:--------------------|:-------------|:--------|:--------|:--------|:------------|:-----------|:------------|:------------------|:----------|:------------|:---------------|:----------|:-----------------|:-------------|:--------|:-----------------------------|:------|:---------------|:-----------|:----------------|:---------------|:-------------------|:----------|:----------------|:--------------|:------------------|:--------------|:------------------|:---------|:--------------|:----------------|:--------------|:------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | | | X | X | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | | X | X | | | X | X | X | X | | | | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | X | X | | X | | | | | | | X | | | | X | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | X | X | | X | | X | X | X | | X | | | X | X | X | X | | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 5 | 12 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | | X | | X | X | | | | X | | | | X | X | X | X | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | | X | | X | X | | | | | | X | | X | | X | | | X | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
jilp00/YouToks-Instruct-Thermodynamics-of-Materials
--- dataset_info: features: - name: text dtype: string - name: token_count dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 1805620 num_examples: 979 download_size: 842980 dataset_size: 1805620 configs: - config_name: default data_files: - split: train path: data/train-* ---
pradeep239/Philip_Plain_and_Image_Together
--- license: mit dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 2257962683.398 num_examples: 4793 - name: validation num_bytes: 269291994.0 num_examples: 564 - name: test num_bytes: 133776859.0 num_examples: 282 download_size: 1981128197 dataset_size: 2661031536.398 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
Sleoruiz/speeches-separated-by-idx
--- dataset_info: features: - name: text dtype: string - name: gaceta_numero dtype: string - name: fecha_gaceta dtype: string - name: comision dtype: string - name: name dtype: string - name: idx dtype: int64 splits: - name: train num_bytes: 185409277 num_examples: 149249 download_size: 93663216 dataset_size: 185409277 --- # Dataset Card for "speeches-separated-by-idx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
K00B404/simpsonspix2pixdataset
--- license: apache-2.0 task_categories: - feature-extraction tags: - imagedataset - SideBySide - Pix2Pix - colorization - img2img pretty_name: GarbagePailKids cards in a sidebyside org/grayscaler image for pix2pix size_categories: - 1K<n<10K ---
tasksource/naturallogic
--- language: - en license: apache-2.0 task_categories: - text-classification configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: 'original_id ' dtype: int64 - name: ' sent1 ' dtype: string - name: ' sent2 ' dtype: string - name: ' keyword_before ' dtype: string - name: ' relation 1to2 ' dtype: string - name: ' pattern ' dtype: string - name: ' original_label ' dtype: string - name: ' original_genre ' dtype: string - name: ' consistent ' dtype: bool - name: ' formula ' dtype: string - name: ' start_ends ' dtype: string - name: ' new_label ' dtype: string splits: - name: train num_bytes: 2011728.0534709194 num_examples: 6390 download_size: 227618 dataset_size: 2011728.0534709194 --- https://github.com/feng-yufei/Neural-Natural-Logic ```bib @inproceedings{feng2020exploring, title={Exploring End-to-End Differentiable Natural Logic Modeling}, author={Feng, Yufei, Ziou Zheng, and Liu, Quan and Greenspan, Michael and Zhu, Xiaodan}, booktitle={Proceedings of the 28th International Conference on Computational Linguistics}, pages={1172--1185}, year={2020} } ```
ConvLab/sgd1
--- language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: SGD-X v1 size_categories: - 10K<n<100K task_categories: - conversational --- # Dataset Card for SGD-X v1 - **Repository:** https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/tree/master/sgd_x - **Paper:** https://arxiv.org/pdf/2110.06800.pdf - **Leaderboard:** None - **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com) To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via: ``` from convlab.util import load_dataset, load_ontology, load_database dataset = load_dataset('sgd1') ontology = load_ontology('sgd1') database = load_database('sgd1') ``` For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). ### Dataset Summary The **Schema-Guided Dialogue (SGD)** dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, such as banks, events, media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, and user simulation learning, among other tasks for developing large-scale virtual assistants. Additionally, the dataset contains unseen domains and services in the evaluation set to quantify the performance in zero-shot or few-shot settings. The **SGD-X** dataset consists of 5 linguistic variants of every schema in the original SGD dataset. Linguistic variants were written by hundreds of paid crowd-workers. In the SGD-X directory, v1 represents the variant closest to the original schemas and v5 the farthest in terms of linguistic distance. To evaluate model performance on SGD-X schemas, dialogues must be converted using the script generate_sgdx_dialogues.py. - **How to get the transformed data from original data:** - Download [dstc8-schema-guided-dialogue-master.zip](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/archive/refs/heads/master.zip). - Modified `sgd_x/generate_sgdx_dialogues.py` as https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/issues/57 - Run `python -m sgd_x.generate_sgdx_dialogues` under `dstc8-schema-guided-dialogue-master` dir which need tensorflow installed. - Run `python preprocess.py` in the current directory. - **Main changes of the transformation:** - Lower case original `act` as `intent`. - Add `count` slot for each domain, non-categorical, find span by text matching. - Categorize `dialogue acts` according to the `intent`. - Concatenate multiple values using `|`. - Retain `active_intent`, `requested_slots`, `service_call`. - **Annotations:** - dialogue acts, state, db_results, service_call, active_intent, requested_slots. ### Supported Tasks and Leaderboards NLU, DST, Policy, NLG, E2E ### Languages English ### Data Splits | split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) | |------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------| | train | 16142 | 329964 | 20.44 | 9.75 | 1.84 | 100 | - | 100 | 100 | | validation | 2482 | 48726 | 19.63 | 9.66 | 1.84 | 100 | - | 100 | 100 | | test | 4201 | 84594 | 20.14 | 10.4 | 2.02 | 100 | - | 100 | 100 | | all | 22825 | 463284 | 20.3 | 9.86 | 1.87 | 100 | - | 100 | 100 | 45 domains: ['Banks_11', 'Buses_11', 'Buses_21', 'Calendar_11', 'Events_11', 'Events_21', 'Flights_11', 'Flights_21', 'Homes_11', 'Hotels_11', 'Hotels_21', 'Hotels_31', 'Media_11', 'Movies_11', 'Music_11', 'Music_21', 'RentalCars_11', 'RentalCars_21', 'Restaurants_11', 'RideSharing_11', 'RideSharing_21', 'Services_11', 'Services_21', 'Services_31', 'Travel_11', 'Weather_11', 'Alarm_11', 'Banks_21', 'Flights_31', 'Hotels_41', 'Media_21', 'Movies_21', 'Restaurants_21', 'Services_41', 'Buses_31', 'Events_31', 'Flights_41', 'Homes_21', 'Media_31', 'Messaging_11', 'Movies_31', 'Music_31', 'Payment_11', 'RentalCars_31', 'Trains_11'] - **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage. - **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage. ### Citation ``` @inproceedings{lee2022sgd, title={SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems}, author={Lee, Harrison and Gupta, Raghav and Rastogi, Abhinav and Cao, Yuan and Zhang, Bin and Wu, Yonghui}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={36}, number={10}, pages={10938--10946}, year={2022} } ``` ### Licensing Information [**CC BY-SA 4.0**](https://creativecommons.org/licenses/by-sa/4.0/)
sled-umich/Action-Effect
--- annotations_creators: - crowdsourced language: - eng language_creators: - crowdsourced license: [] multilinguality: - monolingual pretty_name: Action-Effect-Prediction size_categories: - 1K<n<10K source_datasets: - original tags: [] task_categories: - image-classification - image-to-text task_ids: [] --- # Physical-Action-Effect-Prediction Official dataset for ["What Action Causes This? Towards Naive Physical Action-Effect Prediction"](https://aclanthology.org/P18-1086/), ACL 2018. ![What Action Causes This? Towards Naive Physical Action-Effect Prediction](https://sled.eecs.umich.edu/media/datasets/action-effect-pred.png) ## Overview Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples. ### Datasets - This dataset contains action-effect information for 140 verb-noun pairs. It has two parts: effects described by natural language, and effects depicted in images. - The language data contains verb-noun pairs and their effects described in natural language. For each verb-noun pair, its possible effects are described by 10 different annotators. The format for each line is `verb noun, effect_sentence, [effect_phrase_1, effect_phrase_2, effect_phrase_3, ...]`. Effect_phrases were automatically extracted from their corresponding effect_sentences. - The image data contains images depicting action effects. For each verb-noun pair, an average of 15 positive images and 15 negative images were collected. Positive images are those deemed to capture the resulting world state of the action. And negative images are those deemed to capture some state of the related object (*i.e.*, the nouns in the verb-noun pairs), but are not the resulting state of the corresponding action. ### Download ```python from datasets import load_dataset dataset = load_dataset("sled-umich/Action-Effect") ``` * [HuggingFace](https://huggingface.co/datasets/sled-umich/Action-Effect) * [Google Drive](https://drive.google.com/drive/folders/1P1_xWdCUoA9bHGlyfiimYAWy605tdXlN?usp=sharing) * Dropbox: * [Language Data](https://www.dropbox.com/s/pi1ckzjipbqxyrw/action_effect_sentence_phrase.txt?dl=0) * [Image Data](https://www.dropbox.com/s/ilmfrqzqcbdf22k/action_effect_image_rs.tar.gz?dl=0) ### Cite [What Action Causes This? Towards Naïve Physical Action-Effect Prediction](https://sled.eecs.umich.edu/publication/dblp-confacl-vanderwende-cyg-18/). *Qiaozi Gao, Shaohua Yang, Joyce Chai, Lucy Vanderwende*. ACL, 2018. [[Paper]](https://aclanthology.org/P18-1086/) [[Slides]](https://aclanthology.org/attachments/P18-1086.Presentation.pdf) ```tex @inproceedings{gao-etal-2018-action, title = "What Action Causes This? Towards Naive Physical Action-Effect Prediction", author = "Gao, Qiaozi and Yang, Shaohua and Chai, Joyce and Vanderwende, Lucy", booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2018", address = "Melbourne, Australia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P18-1086", doi = "10.18653/v1/P18-1086", pages = "934--945", abstract = "Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.", } ```
makedelta/analyst_report_raw
--- dataset_info: features: - name: content dtype: string - name: created_at dtype: string - name: pdf_content dtype: string - name: sentiment dtype: float64 - name: summary1 dtype: string - name: summary2 dtype: string - name: title dtype: string - name: updated_at dtype: string splits: - name: train num_bytes: 151542728 num_examples: 5847 download_size: 77730208 dataset_size: 151542728 --- # Dataset Card for "analyst_report_raw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/find_marker_both_sent_train_400_eval_40_random_permute_2
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 3710817.0470730234 num_examples: 2874 - name: validation num_bytes: 220570 num_examples: 200 download_size: 981619 dataset_size: 3931387.0470730234 --- # Dataset Card for "find_marker_both_sent_train_400_eval_40_random_permute_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
adarshxs/gita
--- license: apache-2.0 ---
gorovuha/ru-image-captions-test100
--- dataset_info: features: - name: image dtype: image - name: 'Unnamed: 0' dtype: int64 - name: capt1 dtype: string - name: capt2 dtype: string - name: url dtype: string splits: - name: train num_bytes: 5054684.0 num_examples: 2 - name: validation num_bytes: 68687378.0 num_examples: 26 - name: test num_bytes: 103920477.0 num_examples: 55 download_size: 177270715 dataset_size: 177662539.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
tilyupo/trivia_cqa
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: context dtype: string - name: context_score dtype: float64 - name: context_source dtype: string splits: - name: train num_bytes: 44625505.0 num_examples: 79682 - name: validation num_bytes: 5750820.0 num_examples: 10291 download_size: 33689157 dataset_size: 50376325.0 --- # Dataset Card for "trivia_cqa_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AdapterOcean/python-code-instructions-18k-alpaca-standardized_cluster_5
--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 10880578 num_examples: 1200 download_size: 2948739 dataset_size: 10880578 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "python-code-instructions-18k-alpaca-standardized_cluster_5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
siddharthbulia/therapy-data-set-llama
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 85705 num_examples: 661 download_size: 26557 dataset_size: 85705 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "therapy-data-set-llama" Created Dataset particularly focussed on conversations between a therapist and a patient which can be directly used for training of llama models. Raw Dataset is picked from [Pandora](https://github.com/avocadopelvis/pandora) Eg. Patient: Hi Therapist: Hello there. Tell me how are you feeling today? Patient: Is anyone there? Therapist: Hello there. Glad to see you're back. What's going on in your world right now? Patient: Good morning Therapist: Good morning. I hope you had a good night's sleep. How are you feeling today? [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vegeta/testargilla2
--- dataset_info: features: - name: metadata dtype: string - name: text dtype: string id: field - name: label dtype: string id: field - name: question-1 sequence: - name: user_id dtype: string - name: value dtype: string - name: status dtype: string id: question - name: question-2 sequence: - name: user_id dtype: string - name: value dtype: int32 - name: status dtype: string id: question - name: external_id dtype: string id: external_id splits: - name: train num_bytes: 148 num_examples: 1 download_size: 6115 dataset_size: 148 --- # Dataset Card for "testargilla2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
metaeval/nli4wills
--- license: apache-2.0 ---
MRezaPournader/CommonVoice11FarsiRomanized
--- license: unknown dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 66411421.5 num_examples: 3125 download_size: 61424238 dataset_size: 66411421.5 configs: - config_name: default data_files: - split: train path: data/train-* ---
centroIA/zephyrJavaCucumber
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input dtype: string - name: output dtype: string - name: text dtype: string - name: __index_level_0__ dtype: string - name: __index_level_1__ dtype: string - name: __index_level_2__ dtype: string - name: __index_level_3__ dtype: string - name: __index_level_4__ dtype: string - name: __index_level_5__ dtype: string - name: __index_level_6__ dtype: string - name: __index_level_7__ dtype: string - name: __index_level_8__ dtype: string - name: __index_level_9__ dtype: string - name: __index_level_10__ dtype: string - name: __index_level_11__ dtype: string - name: __index_level_12__ dtype: string - name: __index_level_13__ dtype: string - name: __index_level_14__ dtype: string - name: __index_level_15__ dtype: string splits: - name: train num_bytes: 1137504 num_examples: 165 download_size: 318943 dataset_size: 1137504 --- # Dataset Card for "zephyrJavaCucumber" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_The-Face-Of-Goonery__Chronos-Beluga-v2-13bfp16
--- pretty_name: Evaluation run of The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16](https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_The-Face-Of-Goonery__Chronos-Beluga-v2-13bfp16\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-18T19:11:05.927806](https://huggingface.co/datasets/open-llm-leaderboard/details_The-Face-Of-Goonery__Chronos-Beluga-v2-13bfp16/blob/main/results_2023-09-18T19-11-05.927806.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.1196518456375839,\n\ \ \"em_stderr\": 0.0033237364616341856,\n \"f1\": 0.18612311241610655,\n\ \ \"f1_stderr\": 0.003456805841321019,\n \"acc\": 0.3921054253509362,\n\ \ \"acc_stderr\": 0.009071968047164727\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.1196518456375839,\n \"em_stderr\": 0.0033237364616341856,\n\ \ \"f1\": 0.18612311241610655,\n \"f1_stderr\": 0.003456805841321019\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04624715693707354,\n \ \ \"acc_stderr\": 0.005784991662691891\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.012358944431637561\n\ \ }\n}\n```" repo_url: https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|arc:challenge|25_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-09T10:53:07.090454.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_18T19_11_05.927806 path: - '**/details_harness|drop|3_2023-09-18T19-11-05.927806.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-18T19-11-05.927806.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_18T19_11_05.927806 path: - '**/details_harness|gsm8k|5_2023-09-18T19-11-05.927806.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-18T19-11-05.927806.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hellaswag|10_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:53:07.090454.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:53:07.090454.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T10_53_07.090454 path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T10:53:07.090454.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T10:53:07.090454.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_18T19_11_05.927806 path: - '**/details_harness|winogrande|5_2023-09-18T19-11-05.927806.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-18T19-11-05.927806.parquet' - config_name: results data_files: - split: 2023_08_09T10_53_07.090454 path: - results_2023-08-09T10:53:07.090454.parquet - split: 2023_09_18T19_11_05.927806 path: - results_2023-09-18T19-11-05.927806.parquet - split: latest path: - results_2023-09-18T19-11-05.927806.parquet --- # Dataset Card for Evaluation run of The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16](https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_The-Face-Of-Goonery__Chronos-Beluga-v2-13bfp16", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-18T19:11:05.927806](https://huggingface.co/datasets/open-llm-leaderboard/details_The-Face-Of-Goonery__Chronos-Beluga-v2-13bfp16/blob/main/results_2023-09-18T19-11-05.927806.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.1196518456375839, "em_stderr": 0.0033237364616341856, "f1": 0.18612311241610655, "f1_stderr": 0.003456805841321019, "acc": 0.3921054253509362, "acc_stderr": 0.009071968047164727 }, "harness|drop|3": { "em": 0.1196518456375839, "em_stderr": 0.0033237364616341856, "f1": 0.18612311241610655, "f1_stderr": 0.003456805841321019 }, "harness|gsm8k|5": { "acc": 0.04624715693707354, "acc_stderr": 0.005784991662691891 }, "harness|winogrande|5": { "acc": 0.7379636937647988, "acc_stderr": 0.012358944431637561 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
ziejhean/medmcqa-llama2-1k-v2
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 253922 num_examples: 1000 download_size: 127563 dataset_size: 253922 configs: - config_name: default data_files: - split: train path: data/train-* ---
Lycoris53/Japanese-Amitaro-VITS-TTS-Voice-data
--- license: apache-2.0 --- # Japanese-Amitaro-VITS-TTS-Voice-data Annotated Japanese voice data for VITS TTS training All credits goes to Amitaro　: [あみたろの声素材工房](https://amitaro.net) - Annotated Json data : amitaro_with_kana.json - Annotated txt data : amitaro_train.txt - Python file : amitaro_html_parse.py Due to direct link restriction from creators, wav files can be found at [Amitaro Voice Lab.](https://amitaro.net/voice/voice_dl/) (press the link on あみたろの声素材工房・圧縮ファイル置き場 section to download)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/411beded
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1338 dataset_size: 184 --- # Dataset Card for "411beded" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ProgramComputer/VGGFace2
--- license: cc-by-nc-4.0 paperswithcode_id: vggface2 pretty_name: vggface2 --- ``` @article{DBLP:journals/corr/abs-1710-08092, author = {Qiong Cao and Li Shen and Weidi Xie and Omkar M. Parkhi and Andrew Zisserman}, title = {VGGFace2: {A} dataset for recognising faces across pose and age}, journal = {CoRR}, volume = {abs/1710.08092}, year = {2017}, url = {http://arxiv.org/abs/1710.08092}, eprinttype = {arXiv}, eprint = {1710.08092}, timestamp = {Wed, 04 Aug 2021 07:50:14 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1710-08092.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` # README ## 关于超神经 Hyper.AI 超神经 Hyper.AI（https://hyper.ai）是科技实验媒体，专注报道人工智能与其适用场景。致力于推动中文领域对机器智能的认知与普及，探讨机器智能的对社会的影响。超神经为提高科研效率，提供大陆范围内最快最全的公开数据集下载节点、人工智能百科词条等多个产品，服务产业相关从业者和科研院所的师生。 ## 关于数据集 - 数据集名称：VGG-Face2 - 发布机构：牛津大学工程科学系视觉几何组 Visual Geometry Group, Department of Engineering Science, University of Oxford - 网址：http://www.robots.ox.ac.uk/~vgg/data/vgg_face/ - 大小：nan GB - 简介：VGGFace2是一个大规模的人脸识别数据集，包含9131个人的面部。 图像从Google图片搜索下载，在姿势，年龄，照明，种族和职业方面有很大差异。该数据集于2015年由牛津大学工程科学系视觉几何组发布，相关论文为Deep Face Recognition。
open-llm-leaderboard/details_kalisai__Nusantara-7b-Indo-Chat
--- pretty_name: Evaluation run of kalisai/Nusantara-7b-Indo-Chat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kalisai/Nusantara-7b-Indo-Chat](https://huggingface.co/kalisai/Nusantara-7b-Indo-Chat)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kalisai__Nusantara-7b-Indo-Chat\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-11T04:41:04.791049](https://huggingface.co/datasets/open-llm-leaderboard/details_kalisai__Nusantara-7b-Indo-Chat/blob/main/results_2024-03-11T04-41-04.791049.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.518065189216087,\n\ \ \"acc_stderr\": 0.03452032575821158,\n \"acc_norm\": 0.5232323706514371,\n\ \ \"acc_norm_stderr\": 0.03526088253510083,\n \"mc1\": 0.3108935128518972,\n\ \ \"mc1_stderr\": 0.016203316673559696,\n \"mc2\": 0.4562685899562633,\n\ \ \"mc2_stderr\": 0.015084081745078533\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4598976109215017,\n \"acc_stderr\": 0.014564318856924848,\n\ \ \"acc_norm\": 0.4854948805460751,\n \"acc_norm_stderr\": 0.014605241081370056\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.54052977494523,\n \ \ \"acc_stderr\": 0.00497336133916964,\n \"acc_norm\": 0.7284405496912966,\n\ \ \"acc_norm_stderr\": 0.004438549152538034\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\ \ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.45925925925925926,\n\ \ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309173,\n\ \ \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309173\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\ \ \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5773584905660377,\n \"acc_stderr\": 0.030402331445769544,\n\ \ \"acc_norm\": 0.5773584905660377,\n \"acc_norm_stderr\": 0.030402331445769544\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5347222222222222,\n\ \ \"acc_stderr\": 0.04171115858181618,\n \"acc_norm\": 0.5347222222222222,\n\ \ \"acc_norm_stderr\": 0.04171115858181618\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4508670520231214,\n\ \ \"acc_stderr\": 0.037940126746970296,\n \"acc_norm\": 0.4508670520231214,\n\ \ \"acc_norm_stderr\": 0.037940126746970296\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4978723404255319,\n \"acc_stderr\": 0.032685726586674915,\n\ \ \"acc_norm\": 0.4978723404255319,\n \"acc_norm_stderr\": 0.032685726586674915\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.34656084656084657,\n \"acc_stderr\": 0.024508777521028438,\n \"\ acc_norm\": 0.34656084656084657,\n \"acc_norm_stderr\": 0.024508777521028438\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\ \ \"acc_stderr\": 0.04285714285714281,\n \"acc_norm\": 0.35714285714285715,\n\ \ \"acc_norm_stderr\": 0.04285714285714281\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.635483870967742,\n \"acc_stderr\": 0.027379871229943245,\n \"\ acc_norm\": 0.635483870967742,\n \"acc_norm_stderr\": 0.027379871229943245\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.43842364532019706,\n \"acc_stderr\": 0.03491207857486518,\n \"\ acc_norm\": 0.43842364532019706,\n \"acc_norm_stderr\": 0.03491207857486518\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6242424242424243,\n \"acc_stderr\": 0.03781887353205982,\n\ \ \"acc_norm\": 0.6242424242424243,\n \"acc_norm_stderr\": 0.03781887353205982\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6818181818181818,\n \"acc_stderr\": 0.03318477333845331,\n \"\ acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.03318477333845331\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7098445595854922,\n \"acc_stderr\": 0.03275264467791516,\n\ \ \"acc_norm\": 0.7098445595854922,\n \"acc_norm_stderr\": 0.03275264467791516\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5358974358974359,\n \"acc_stderr\": 0.02528558599001785,\n \ \ \"acc_norm\": 0.5358974358974359,\n \"acc_norm_stderr\": 0.02528558599001785\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.25555555555555554,\n \"acc_stderr\": 0.026593939101844072,\n \ \ \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.026593939101844072\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5462184873949579,\n \"acc_stderr\": 0.03233943468182088,\n \ \ \"acc_norm\": 0.5462184873949579,\n \"acc_norm_stderr\": 0.03233943468182088\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7247706422018348,\n \"acc_stderr\": 0.019149093743155196,\n \"\ acc_norm\": 0.7247706422018348,\n \"acc_norm_stderr\": 0.019149093743155196\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538271,\n \"\ acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538271\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6862745098039216,\n \"acc_stderr\": 0.03256685484460389,\n \"\ acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.03256685484460389\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6624472573839663,\n \"acc_stderr\": 0.030781549102026216,\n \ \ \"acc_norm\": 0.6624472573839663,\n \"acc_norm_stderr\": 0.030781549102026216\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5874439461883408,\n\ \ \"acc_stderr\": 0.03304062175449297,\n \"acc_norm\": 0.5874439461883408,\n\ \ \"acc_norm_stderr\": 0.03304062175449297\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5267175572519084,\n \"acc_stderr\": 0.04379024936553894,\n\ \ \"acc_norm\": 0.5267175572519084,\n \"acc_norm_stderr\": 0.04379024936553894\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6859504132231405,\n \"acc_stderr\": 0.04236964753041018,\n \"\ acc_norm\": 0.6859504132231405,\n \"acc_norm_stderr\": 0.04236964753041018\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.04668408033024931,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.04668408033024931\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5766871165644172,\n \"acc_stderr\": 0.03881891213334384,\n\ \ \"acc_norm\": 0.5766871165644172,\n \"acc_norm_stderr\": 0.03881891213334384\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6893203883495146,\n \"acc_stderr\": 0.04582124160161552,\n\ \ \"acc_norm\": 0.6893203883495146,\n \"acc_norm_stderr\": 0.04582124160161552\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7564102564102564,\n\ \ \"acc_stderr\": 0.028120966503914397,\n \"acc_norm\": 0.7564102564102564,\n\ \ \"acc_norm_stderr\": 0.028120966503914397\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7177522349936143,\n\ \ \"acc_stderr\": 0.01609530296987855,\n \"acc_norm\": 0.7177522349936143,\n\ \ \"acc_norm_stderr\": 0.01609530296987855\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5404624277456648,\n \"acc_stderr\": 0.026830805998952243,\n\ \ \"acc_norm\": 0.5404624277456648,\n \"acc_norm_stderr\": 0.026830805998952243\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.22793296089385476,\n\ \ \"acc_stderr\": 0.014030149950805097,\n \"acc_norm\": 0.22793296089385476,\n\ \ \"acc_norm_stderr\": 0.014030149950805097\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5947712418300654,\n \"acc_stderr\": 0.02811092849280907,\n\ \ \"acc_norm\": 0.5947712418300654,\n \"acc_norm_stderr\": 0.02811092849280907\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5819935691318328,\n\ \ \"acc_stderr\": 0.028013651891995072,\n \"acc_norm\": 0.5819935691318328,\n\ \ \"acc_norm_stderr\": 0.028013651891995072\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5679012345679012,\n \"acc_stderr\": 0.027563010971606672,\n\ \ \"acc_norm\": 0.5679012345679012,\n \"acc_norm_stderr\": 0.027563010971606672\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3971631205673759,\n \"acc_stderr\": 0.029189805673587102,\n \ \ \"acc_norm\": 0.3971631205673759,\n \"acc_norm_stderr\": 0.029189805673587102\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3956975228161669,\n\ \ \"acc_stderr\": 0.012489290735449007,\n \"acc_norm\": 0.3956975228161669,\n\ \ \"acc_norm_stderr\": 0.012489290735449007\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.030372836961539352,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.030372836961539352\n \ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\"\ : 0.4820261437908497,\n \"acc_stderr\": 0.020214761037872408,\n \"\ acc_norm\": 0.4820261437908497,\n \"acc_norm_stderr\": 0.020214761037872408\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5727272727272728,\n\ \ \"acc_stderr\": 0.047381987035454834,\n \"acc_norm\": 0.5727272727272728,\n\ \ \"acc_norm_stderr\": 0.047381987035454834\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.4857142857142857,\n \"acc_stderr\": 0.03199615232806287,\n\ \ \"acc_norm\": 0.4857142857142857,\n \"acc_norm_stderr\": 0.03199615232806287\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6417910447761194,\n\ \ \"acc_stderr\": 0.03390393042268814,\n \"acc_norm\": 0.6417910447761194,\n\ \ \"acc_norm_stderr\": 0.03390393042268814\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.42168674698795183,\n\ \ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n\ \ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.6608187134502924,\n \"acc_stderr\": 0.03631053496488904,\n\ \ \"acc_norm\": 0.6608187134502924,\n \"acc_norm_stderr\": 0.03631053496488904\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3108935128518972,\n\ \ \"mc1_stderr\": 0.016203316673559696,\n \"mc2\": 0.4562685899562633,\n\ \ \"mc2_stderr\": 0.015084081745078533\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6953433307024467,\n \"acc_stderr\": 0.012935646499325297\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2494313874147081,\n \ \ \"acc_stderr\": 0.011918265218445521\n }\n}\n```" repo_url: https://huggingface.co/kalisai/Nusantara-7b-Indo-Chat leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|arc:challenge|25_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-11T04-41-04.791049.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|gsm8k|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hellaswag|10_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-11T04-41-04.791049.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-management|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T04-41-04.791049.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|truthfulqa:mc|0_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-11T04-41-04.791049.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_11T04_41_04.791049 path: - '**/details_harness|winogrande|5_2024-03-11T04-41-04.791049.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-11T04-41-04.791049.parquet' - config_name: results data_files: - split: 2024_03_11T04_41_04.791049 path: - results_2024-03-11T04-41-04.791049.parquet - split: latest path: - results_2024-03-11T04-41-04.791049.parquet --- # Dataset Card for Evaluation run of kalisai/Nusantara-7b-Indo-Chat <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [kalisai/Nusantara-7b-Indo-Chat](https://huggingface.co/kalisai/Nusantara-7b-Indo-Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_kalisai__Nusantara-7b-Indo-Chat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-11T04:41:04.791049](https://huggingface.co/datasets/open-llm-leaderboard/details_kalisai__Nusantara-7b-Indo-Chat/blob/main/results_2024-03-11T04-41-04.791049.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.518065189216087, "acc_stderr": 0.03452032575821158, "acc_norm": 0.5232323706514371, "acc_norm_stderr": 0.03526088253510083, "mc1": 0.3108935128518972, "mc1_stderr": 0.016203316673559696, "mc2": 0.4562685899562633, "mc2_stderr": 0.015084081745078533 }, "harness|arc:challenge|25": { "acc": 0.4598976109215017, "acc_stderr": 0.014564318856924848, "acc_norm": 0.4854948805460751, "acc_norm_stderr": 0.014605241081370056 }, "harness|hellaswag|10": { "acc": 0.54052977494523, "acc_stderr": 0.00497336133916964, "acc_norm": 0.7284405496912966, "acc_norm_stderr": 0.004438549152538034 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464242, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464242 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5131578947368421, "acc_stderr": 0.04067533136309173, "acc_norm": 0.5131578947368421, "acc_norm_stderr": 0.04067533136309173 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.55, "acc_stderr": 0.04999999999999999, "acc_norm": 0.55, "acc_norm_stderr": 0.04999999999999999 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5773584905660377, "acc_stderr": 0.030402331445769544, "acc_norm": 0.5773584905660377, "acc_norm_stderr": 0.030402331445769544 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5347222222222222, "acc_stderr": 0.04171115858181618, "acc_norm": 0.5347222222222222, "acc_norm_stderr": 0.04171115858181618 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4508670520231214, "acc_stderr": 0.037940126746970296, "acc_norm": 0.4508670520231214, "acc_norm_stderr": 0.037940126746970296 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4978723404255319, "acc_stderr": 0.032685726586674915, "acc_norm": 0.4978723404255319, "acc_norm_stderr": 0.032685726586674915 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.34656084656084657, "acc_stderr": 0.024508777521028438, "acc_norm": 0.34656084656084657, "acc_norm_stderr": 0.024508777521028438 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04285714285714281, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04285714285714281 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.635483870967742, "acc_stderr": 0.027379871229943245, "acc_norm": 0.635483870967742, "acc_norm_stderr": 0.027379871229943245 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.43842364532019706, "acc_stderr": 0.03491207857486518, "acc_norm": 0.43842364532019706, "acc_norm_stderr": 0.03491207857486518 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6242424242424243, "acc_stderr": 0.03781887353205982, "acc_norm": 0.6242424242424243, "acc_norm_stderr": 0.03781887353205982 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6818181818181818, "acc_stderr": 0.03318477333845331, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.03318477333845331 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7098445595854922, "acc_stderr": 0.03275264467791516, "acc_norm": 0.7098445595854922, "acc_norm_stderr": 0.03275264467791516 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5358974358974359, "acc_stderr": 0.02528558599001785, "acc_norm": 0.5358974358974359, "acc_norm_stderr": 0.02528558599001785 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25555555555555554, "acc_stderr": 0.026593939101844072, "acc_norm": 0.25555555555555554, "acc_norm_stderr": 0.026593939101844072 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5462184873949579, "acc_stderr": 0.03233943468182088, "acc_norm": 0.5462184873949579, "acc_norm_stderr": 0.03233943468182088 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763743, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763743 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7247706422018348, "acc_stderr": 0.019149093743155196, "acc_norm": 0.7247706422018348, "acc_norm_stderr": 0.019149093743155196 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538271, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538271 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6862745098039216, "acc_stderr": 0.03256685484460389, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.03256685484460389 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6624472573839663, "acc_stderr": 0.030781549102026216, "acc_norm": 0.6624472573839663, "acc_norm_stderr": 0.030781549102026216 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5874439461883408, "acc_stderr": 0.03304062175449297, "acc_norm": 0.5874439461883408, "acc_norm_stderr": 0.03304062175449297 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5267175572519084, "acc_stderr": 0.04379024936553894, "acc_norm": 0.5267175572519084, "acc_norm_stderr": 0.04379024936553894 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6859504132231405, "acc_stderr": 0.04236964753041018, "acc_norm": 0.6859504132231405, "acc_norm_stderr": 0.04236964753041018 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6296296296296297, "acc_stderr": 0.04668408033024931, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.04668408033024931 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5766871165644172, "acc_stderr": 0.03881891213334384, "acc_norm": 0.5766871165644172, "acc_norm_stderr": 0.03881891213334384 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.41964285714285715, "acc_stderr": 0.04684099321077106, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.04684099321077106 }, "harness|hendrycksTest-management|5": { "acc": 0.6893203883495146, "acc_stderr": 0.04582124160161552, "acc_norm": 0.6893203883495146, "acc_norm_stderr": 0.04582124160161552 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7564102564102564, "acc_stderr": 0.028120966503914397, "acc_norm": 0.7564102564102564, "acc_norm_stderr": 0.028120966503914397 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7177522349936143, "acc_stderr": 0.01609530296987855, "acc_norm": 0.7177522349936143, "acc_norm_stderr": 0.01609530296987855 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5404624277456648, "acc_stderr": 0.026830805998952243, "acc_norm": 0.5404624277456648, "acc_norm_stderr": 0.026830805998952243 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.22793296089385476, "acc_stderr": 0.014030149950805097, "acc_norm": 0.22793296089385476, "acc_norm_stderr": 0.014030149950805097 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5947712418300654, "acc_stderr": 0.02811092849280907, "acc_norm": 0.5947712418300654, "acc_norm_stderr": 0.02811092849280907 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5819935691318328, "acc_stderr": 0.028013651891995072, "acc_norm": 0.5819935691318328, "acc_norm_stderr": 0.028013651891995072 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5679012345679012, "acc_stderr": 0.027563010971606672, "acc_norm": 0.5679012345679012, "acc_norm_stderr": 0.027563010971606672 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3971631205673759, "acc_stderr": 0.029189805673587102, "acc_norm": 0.3971631205673759, "acc_norm_stderr": 0.029189805673587102 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3956975228161669, "acc_stderr": 0.012489290735449007, "acc_norm": 0.3956975228161669, "acc_norm_stderr": 0.012489290735449007 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5, "acc_stderr": 0.030372836961539352, "acc_norm": 0.5, "acc_norm_stderr": 0.030372836961539352 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4820261437908497, "acc_stderr": 0.020214761037872408, "acc_norm": 0.4820261437908497, "acc_norm_stderr": 0.020214761037872408 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5727272727272728, "acc_stderr": 0.047381987035454834, "acc_norm": 0.5727272727272728, "acc_norm_stderr": 0.047381987035454834 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4857142857142857, "acc_stderr": 0.03199615232806287, "acc_norm": 0.4857142857142857, "acc_norm_stderr": 0.03199615232806287 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6417910447761194, "acc_stderr": 0.03390393042268814, "acc_norm": 0.6417910447761194, "acc_norm_stderr": 0.03390393042268814 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-virology|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6608187134502924, "acc_stderr": 0.03631053496488904, "acc_norm": 0.6608187134502924, "acc_norm_stderr": 0.03631053496488904 }, "harness|truthfulqa:mc|0": { "mc1": 0.3108935128518972, "mc1_stderr": 0.016203316673559696, "mc2": 0.4562685899562633, "mc2_stderr": 0.015084081745078533 }, "harness|winogrande|5": { "acc": 0.6953433307024467, "acc_stderr": 0.012935646499325297 }, "harness|gsm8k|5": { "acc": 0.2494313874147081, "acc_stderr": 0.011918265218445521 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
MasahiroKaneko/eagle
--- configs: - config_name: default data_files: - split: subset path: subset.csv - split: fullset1 path: fullset1.csv - split: fullset2 path: fullset2.csv license: - mit task_categories: - text-generation size_categories: - 1M<n<10M language: - en - zh - fr - ko - de - es - ja --- # Eagle 🦅: Ethical Dataset Given from Real Interactions ![](eagle.png) ## Introduction This repository contains the Eagle dataset, which is an ethical dataset of real interactions between humans and ChatGPT. This dataset is created to evaluate social bias, opinion bias, toxic language, and morality in Large Language Models (LLMs). If you use the Eagle dataset in your research, please cite the following: ```sql @inproceedings{Eagle:arxiv:2024, title={Eagle: Ethical Dataset Given from Real Interactions}, author={Kaneko, Masahiro and Bollegala, Danushka and Baldwin, Timothy}, booktitle={arXiv}, year={2024} } ``` The Eagle dataset has `fullset1.csv`, `fullset2.csv`, and `subset.csv` files. Due to data size limitations on uploads, we have split one dataset into two files, named `fullset1.csv` and `fullset2.csv`. They contain multilingual neutral, social bias, opinion bias, toxic language, and molarity instances. `subset.csv` contains English social bias, opinion bias, toxic language, and molarity instances. The subset dataset has 2.3K instances, and the fullset dataset has 1.4M instances. These CSV files have the following fields: - `original_id`: Original dataset ID - `conversation_num`: Number within the same conversation - `utterance_num`: Order of ChatGPT's response within the conversation - `language`: Identified language of utterance - `ethical_labels`: Classified ethical labels (social bias, opinion bias, toxic language, and molarity) - `context`: {"role": "gpt or human", "content": "context utterances"} - `output`: {"role": "gpt": "content": "chatgpt output"} ## How to Evaluate LLMs using the Eagle Dataset We use a likelihood-based evaluation based on this [code](https://github.com/kanekomasahiro/transformers_llm). ## License You can find the full text of the license in the LICENSE file.
boapps/kmdb_institution_classification
--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: text dtype: string - name: positive_institutions sequence: string - name: negative_institutions sequence: string splits: - name: test num_bytes: 2249637 num_examples: 494 - name: train num_bytes: 34353884 num_examples: 7191 - name: validation num_bytes: 4170449 num_examples: 919 download_size: 24136916 dataset_size: 40773970 --- # Dataset Card for "kmdb_institution_classification" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/chloe_lapisrelights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Chloe (Lapis Re:LiGHTs) This is the dataset of Chloe (Lapis Re:LiGHTs), containing 45 images and their tags. The core tags of this character are `blue_hair, short_hair, glasses, hair_over_one_eye, red-framed_eyewear, green_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 45 | 27.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_lapisrelights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 45 | 21.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_lapisrelights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 84 | 40.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_lapisrelights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 45 | 27.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_lapisrelights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 84 | 50.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_lapisrelights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/chloe_lapisrelights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------| | 0 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, closed_mouth, upper_body, indoors, red_ascot, anime_coloring, jacket, school_uniform, window | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | closed_mouth | upper_body | indoors | red_ascot | anime_coloring | jacket | school_uniform | window | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:-------------|:----------|:------------|:-----------------|:---------|:-----------------|:---------| | 0 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X |
AdrianGonzalezSanchez/AISBOM
--- license: mit language: - en tags: - AI Act - AI - Regulation - EU - GDPR - RAI - Ethics --- # AISBOM - AI Software Bill of Materials [JSON Spec for Transparency Obligations of the EU AI Act](https://huggingface.co/datasets/AdrianGonzalezSanchez/AISBOM/blob/main/AISBOM_spec.json), including LLM / foundation models Version 0.1 (December 11, 2023) > [!NOTE] > - This JSON file is intended as a means to address the transparency requirements in the upcoming EU AI Act (focus on Article 13 & 52). > - The file is an illustrative example as the basis for discussion and feedback. > - To use the file, copy the template and insert the values of the AI System at hand, using the descriptions given in the template as a guidance). > - The file is not a formal JSON Schema, but we may adopt the schema in the future for improved automated processing. ## Call to action - Please share your feedback in [Hugging Face Discussions](https://huggingface.co/datasets/AdrianGonzalezSanchez/AISBOM/discussions). - See the call for contributions at the end of this document. ## How to cite this work [@AdrianGonzalezSanchez](https://huggingface.co/AdrianGonzalezSanchez) (OdiseIA, HEC Montréal, IE University, Microsoft) & appliedAI Institute for Europe gGmbH (2024). AI Software Bill of Material - Transparency (AI-SBOM). [Hugging Face](https://huggingface.co/datasets/AdrianGonzalezSanchez/AISBOM) ## Overview EU AI Act. It addresses mainly the transparency obligations outlined in Articles 13 and 52 of the AI Act to share and emphasize relevant information with various stakeholders and interested parties BOM = Bill of Material; The set of elements, an inventory, that are needed to compile or produce a product; Adopted to an AI System and inspired from areas like manufacturing and cybersecurity. ## Purpose of the AI-SBOM Transparency Collecting and providing the information required by Articles 13 and 52 can be challenging in complex AI value chains involving multiple entities who control or need certain information. The AI-SBOM Transparency is intended as the single point of truth for collecting and sharing the necessary information, keeping the following benefits in mind: - Overview of transparency obligations: Reducing the need for an in-depth understanding of the AI Act (saves time and effort to read 160+ pages). - Improves risk management in transparency: Completing the AI-SBOM helps in identifying and addressing potential vulnerabilities and dependencies related to transparency throughout the development cycle of high-risk AI systems. - Approach to simplify compliance with transparency requirements: Helps to ensure adherence to the AI Act's transparency requirements by collecting the relevant information, which, in turn, reduces deployment and liability risks. - AI-SBOM Transparency may complement and/or refer to the instructions for use (“User Manual”). It could be a first “draft” of a “User Manual” which has to be provided to the Deployer. ## Target group of the AI-BOM AI-SBOM Transparency targets technical professionals engaged in compliance matters as well as compliance experts delving into technical aspects. Our goal is to support providers and deployers in managing, maintaining, and making knowledgeable choices about AI systems within the AI Act's regulations (Articles 13 and 52). Achieving this is more feasible through a collaborative approach. ## What is the scope of Article 13 AI Act? [EU Parlaments Proposal] Article 13 AI Act applies to high-risk AI Systems (details in Article 6) and outlines requirements and considerations related to transparency and accountability in the deployment of an AI System. In a nutshell: **Article 13 (1)**: The transparency obligations are set to enable the understanding of the outcomes and functioning of the respective AI System. Specifically, it entails the obligation to ensure that: (i) the AI System will be used properly, i.e., according to its intended purpose by stating how the AI System actually works, (ii) details about the processed data are known and (iii) the AI Systems output is interpretable and can be explained to affected persons. **Article 13 (2)** Requires that the high-risk AI System shall be accompanied by **instructions for** use** [Like a “**(Digital) User Manual**”] that helps the deployer (the entity who is putting the AI System into use) operate and maintain the AI System as intended, as well as supporting an informed decision making by the deployer. Such a User Manual has to incorporate information referred to in Article 13 (3) and be available prior to putting the AI System into service or placing the AI System on the market. **Article 13 (3)** Specifies concrete information that shall be communicated for reaching sufficient transparency to satisfy Article 13 (1). This is the focus of the AI-SBOM and includes information such as the intended purpose of the AI System, known/foreseeable risks/misuses, desired input data, affected persons etc. The AI-SBOM is not meant to replace or implement the instructions for use. The AI-SBOM aims to support in collecting such relevant information for the instructions of use during the development process of an AI System. Thus, high-risk AI Systems shall be designed and developed in such a way that their operation is sufficiently transparent to assure the respective deployer (and provider themselves if they deploy their own AI System internally) appropriately interpret and use the results of the AI System [“Procedural Transparency”]. Such Procedural Transparency, as outlined in Article 13, is particularly crucial in the AI value chain perspective from the provider to the actual deployer of the AI System. ## What is the scope of Article 52 AI Act? [EU Parlaments Proposal] Article 52 AI Act aims to ensure the transparency of AI Systems in case natural persons and/or the general public are exposed to an AI System. This is ensured in three ways: (i) **Article 52 (1)**: If there is an interaction of the AI System with a natural person - like a Chatbot, Healthcare Diagnosis Tools used by doctors, or AI-driven robot financial advisors - such interactions have to be made transparent through a notification to the affected natural persons [“**Interaction Transparency**”]. (ii) **Article 52 (2)**: If the AI System is an emotion recognition or biometric categorization system, prior to the processing of such data, the affected person has to give their consent for such (connection to the GDPR) [“**Consent Transparency**”]. (iii) **Article 52 (3)**: If the AI System is generating so-called “deep fakes”, such artificially generated content shall be disclosed in a visible manner like “watermarks” [“**Content Transparency**”]. Notably, an AI System that is not classified as high-risk and therefore exempt from compliance with Article 13 may still be subject to the provisions of Article 52 if one of the three paragraphs applies. Conversely, if an AI System is classified as high risk, Article 52 might apply in addition. ## Contributing This draft is understood as a “living paper” mapping the state of an ongoing discussion and open for feedback. We invite all stakeholders to share their insights and suggestions to enhance the tool's effectiveness and compliance capabilities. Please consider our notes for feedback and discussion. **Note #1**: This AI-SBOM Transparency is for discussion purposes and does not constitute legal advice. It is essential to consult with legal experts to ensure full compliance with the AI Act. **Note #2**: We mainly worked with the proposal of the EU Parlament. The final text of the AI Act is still unknown. Also, any standards for Article 13 and Article 52 are under development and not published at the moment. The AI-SBOM is current as of the date of its publication and does not necessarily reflect the present state of the law or relevant regulation. **Note #3**: Recognizing the variety of stakeholders involved in the AI lifecycle, each possessing varying degrees of technical know-how, we understand that transparency is not a one-size-fits-all attribute. AI systems should offer tailored transparency across the AI value chain, catering to the unique needs and perspectives of each stakeholder. This calls for a collaborative effort among all parties involved to ensure effective transparency." **Note #4**: Please be aware that transparency has an intense tension (especially proprietary AI Systems) with **Data Privacy** (access/description to training data) and **IP/trade secrets** (access/description to the model) and **Cyber Security** (access/description to training data + the model vulnerabilities) - [altogether “Sensitive Information”]
onyou611/ko-alpaca-nms
--- license: apache-2.0 ---
HuggingFaceM4/VQAv2_modif_support_query_sets_part_0
Invalid username or password.
Shiveswarran/llm_instruction_code_v6
--- license: mit ---
IDQO/test_jules_cat_2023-06-12-10-39-03
--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: label dtype: class_label: names: '0': 'Automation & Process Control ' '1': 'Batteries & Chargers ' '2': 'Cable, Wire & Cable Assemblies ' '3': 'Chemicals & Adhesives ' '4': Company Fashion '5': 'Connectors ' '6': 'Electrical ' '7': Eye and face protection '8': Fall protection '9': First aid and fire protection '10': Foot protection '11': Hand protection '12': Head protection '13': Hearing protection '14': Hydraulics '15': Hygiene & maintenance '16': 'LED Lighting Components ' '17': 'Lighting Products ' '18': 'Passive Components ' '19': 'Power & Line Protection ' '20': Power Tools '21': Power Transmission '22': Protective Wear '23': 'Semiconductors - Discretes ' '24': 'Semiconductors - ICs ' '25': 'Sensors & Transducers ' '26': Signaling '27': Storage and Tools '28': 'Switches & Relays ' '29': 'Wireless Modules & Adaptors ' '30': Workwear splits: - name: train num_bytes: 260560.0 num_examples: 2400 - name: test num_bytes: 65140.0 num_examples: 600 download_size: 241386 dataset_size: 325700.0 --- # Dataset Card for "test_jules_cat_2023-06-12-10-39-03" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
llm-aes/meva_full_analyze_rate
--- dataset_info: features: - name: task_id dtype: string - name: worker_id dtype: string - name: human_label dtype: int64 - name: llm_label dtype: int64 - name: generator_1 dtype: string - name: generator_2 dtype: string - name: premise dtype: string splits: - name: train num_bytes: 390143 num_examples: 2000 download_size: 49344 dataset_size: 390143 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-eval-samsum-samsum-2d4eb1-47303145208
--- type: predictions tags: - autotrain - evaluation datasets: - samsum eval_info: task: summarization model: google/pegasus-large metrics: [] dataset_name: samsum dataset_config: samsum dataset_split: test col_mapping: text: dialogue target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: google/pegasus-large * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@sohamchougule](https://huggingface.co/sohamchougule) for evaluating this model.
zycjlsj123/ragsummdata
--- dataset_info: features: - name: text dtype: string - name: scenario dtype: int64 splits: - name: train num_bytes: 13076000 num_examples: 2690 - name: test num_bytes: 2141304 num_examples: 378 download_size: 5633239 dataset_size: 15217304 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "ragsumm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tsuyuan/gptspeech_amazon_google_tencent
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: decoder_input_ids sequence: sequence: int64 - name: decoder_attention_mask sequence: int64 - name: labels sequence: int64 splits: - name: train num_bytes: 526153002921 num_examples: 6675459 - name: eval num_bytes: 13396628973 num_examples: 169967 download_size: 16698860181 dataset_size: 539549631894 --- # Dataset Card for "gptspeech_amazon_google_tencent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vivekraina/stanford_dataset_qa_final
--- dataset_info: features: - name: paragraphs list: - name: context dtype: string - name: qas list: - name: answers list: - name: answer_start dtype: int64 - name: text dtype: string - name: id dtype: string - name: question dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3745671 num_examples: 48 download_size: 1775277 dataset_size: 3745671 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "stanford_dataset_qa_final" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pharaouk/cortex_quatro
--- dataset_info: features: - name: prompts dtype: string - name: responses dtype: string splits: - name: train num_bytes: 86707763 num_examples: 25332 download_size: 45279280 dataset_size: 86707763 configs: - config_name: default data_files: - split: train path: data/train-* ---
pierreguillou/DocLayNet-small
--- language: - en - de - fr - ja annotations_creators: - crowdsourced license: other pretty_name: DocLayNet small size_categories: - 1K<n<10K tags: - DocLayNet - COCO - PDF - IBM - Financial-Reports - Finance - Manuals - Scientific-Articles - Science - Laws - Law - Regulations - Patents - Government-Tenders - object-detection - image-segmentation - token-classification task_categories: - object-detection - image-segmentation - token-classification task_ids: - instance-segmentation --- # Dataset Card for DocLayNet small ## About this card (01/27/2023) ### Property and license All information from this page but the content of this paragraph "About this card (01/27/2023)" has been copied/pasted from [Dataset Card for DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet). DocLayNet is a dataset created by Deep Search (IBM Research) published under [license CDLA-Permissive-1.0](https://huggingface.co/datasets/ds4sd/DocLayNet#licensing-information). I do not claim any rights to the data taken from this dataset and published on this page. ### DocLayNet dataset [DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets: - direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB) - Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet) Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022) ### Processing into a format facilitating its use by HF notebooks These 2 options require the downloading of all the data (approximately 30GBi), which requires downloading time (about 45 mn in Google Colab) and a large space on the hard disk. These could limit experimentation for people with low resources. Moreover, even when using the download via HF datasets library, it is necessary to download the EXTRA zip separately ([doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip), 7.5 GiB) to associate the annotated bounding boxes with the text extracted by OCR from the PDFs. This operation also requires additional code because the boundings boxes of the texts do not necessarily correspond to those annotated (a calculation of the percentage of area in common between the boundings boxes annotated and those of the texts makes it possible to make a comparison between them). At last, in order to use Hugging Face notebooks on fine-tuning layout models like LayoutLMv3 or LiLT, DocLayNet data must be processed in a proper format. For all these reasons, I decided to process the DocLayNet dataset: - into 3 datasets of different sizes: - [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test) - [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test) - [DocLayNet large](https://huggingface.co/datasets/pierreguillou/DocLayNet-large) (about 100% of DocLayNet) < 100.000k document images (69.103 train, 6.480 val, 4.994 test) - with associated texts and PDFs (base64 format), - and in a format facilitating their use by HF notebooks. *Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!* ### About PDFs languages Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062): "We did not control the document selection with regard to language. **The vast majority of documents contained in DocLayNet (close to 95%) are published in English language.** However, **DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%)**. While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features." ### About PDFs categories distribution Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062): "The pages in DocLayNet can be grouped into **six distinct categories**, namely **Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders**. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes." ![DocLayNet PDFs categories distribution (source: DocLayNet paper)](https://huggingface.co/datasets/pierreguillou/DocLayNet-small/resolve/main/DocLayNet_PDFs_categories_distribution.png) ### Download & overview The size of the DocLayNet small is about 1% of the DocLayNet dataset (random selection respectively in the train, val and test files). ``` # !pip install -q datasets from datasets import load_dataset dataset_small = load_dataset("pierreguillou/DocLayNet-small") # overview of dataset_small DatasetDict({ train: Dataset({ features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'], num_rows: 691 }) validation: Dataset({ features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'], num_rows: 64 }) test: Dataset({ features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'], num_rows: 49 }) }) ``` ### Annotated bounding boxes The DocLayNet base makes easy to display document image with the annotaed bounding boxes of paragraphes or lines. Check the notebook [processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb](https://github.com/piegu/language-models/blob/master/processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb) in order to get the code. #### Paragraphes ![Annotated DocLayNet document image with bounding boxes and categories of paragraphes](https://huggingface.co/datasets/pierreguillou/DocLayNet-small/resolve/main/DocLayNet_image_annotated_bounding_boxes_paragraph.png) #### Lines ![Annotated DocLayNet document image with bounding boxes and categories of lines](https://huggingface.co/datasets/pierreguillou/DocLayNet-small/resolve/main/DocLayNet_image_annotated_bounding_boxes_line.png) ### HF notebooks - [notebooks LayoutLM](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLM) (Niels Rogge) - [notebooks LayoutLMv2](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2) (Niels Rogge) - [notebooks LayoutLMv3](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3) (Niels Rogge) - [notebooks LiLT](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT) (Niels Rogge) - [Document AI: Fine-tuning LiLT for document-understanding using Hugging Face Transformers](https://github.com/philschmid/document-ai-transformers/blob/main/training/lilt_funsd.ipynb) ([post](https://www.philschmid.de/fine-tuning-lilt#3-fine-tune-and-evaluate-lilt) of Phil Schmid) ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/ - **Repository:** https://github.com/DS4SD/DocLayNet - **Paper:** https://doi.org/10.1145/3534678.3539043 - **Leaderboard:** - **Point of Contact:** ### Dataset Summary DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank: 1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout 2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals 3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail. 4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models 5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets. ### Supported Tasks and Leaderboards We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/. ## Dataset Structure ### Data Fields DocLayNet provides four types of data assets: 1. PNG images of all pages, resized to square `1025 x 1025px` 2. Bounding-box annotations in COCO format for each PNG image 3. Extra: Single-page PDF files matching each PNG image 4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content The COCO image record are defined like this example ```js ... { "id": 1, "width": 1025, "height": 1025, "file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png", // Custom fields: "doc_category": "financial_reports" // high-level document category "collection": "ann_reports_00_04_fancy", // sub-collection name "doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename "page_no": 9, // page number in original document "precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation }, ... ``` The `doc_category` field uses one of the following constants: ``` financial_reports, scientific_articles, laws_and_regulations, government_tenders, manuals, patents ``` ### Data Splits The dataset provides three splits - `train` - `val` - `test` ## Dataset Creation ### Annotations #### Annotation process The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf). #### Who are the annotators? Annotations are crowdsourced. ## Additional Information ### Dataset Curators The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research. You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com). Curators: - Christoph Auer, [@cau-git](https://github.com/cau-git) - Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm) - Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial) - Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM) ### Licensing Information License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/) ### Citation Information ```bib @article{doclaynet2022, title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation}, doi = {10.1145/3534678.353904}, url = {https://doi.org/10.1145/3534678.3539043}, author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J}, year = {2022}, isbn = {9781450393850}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, pages = {3743–3751}, numpages = {9}, location = {Washington DC, USA}, series = {KDD '22} } ``` ### Contributions Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset.
hoy980514/whisper_finetuning
--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 22355037360 num_examples: 23274 - name: test num_bytes: 2795100320 num_examples: 2910 - name: valid num_bytes: 2794136992 num_examples: 2909 download_size: 3535186561 dataset_size: 27944274672 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
result-kand2-sdxl-wuerst-karlo/166c9db0
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 185 num_examples: 10 download_size: 1392 dataset_size: 185 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "166c9db0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-70000
--- dataset_info: features: - name: tables sequence: string - name: table_names sequence: string - name: query dtype: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 13893171169 num_examples: 2500 download_size: 2732018562 dataset_size: 13893171169 configs: - config_name: default data_files: - split: train path: data/train-* ---
dlproject/msp_val_hubert_large
--- dataset_info: features: - name: input_values sequence: sequence: sequence: float32 - name: attention_mask sequence: sequence: int32 - name: labels dtype: int64 splits: - name: train num_bytes: 1895911184 num_examples: 5213 download_size: 1773617134 dataset_size: 1895911184 --- # Dataset Card for "msp_val_hubert_large" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
moooji/controlnet_test3
--- dataset_info: features: - name: source dtype: image - name: target dtype: image - name: prompt dtype: string splits: - name: train num_bytes: 32228.0 num_examples: 1 download_size: 33477 dataset_size: 32228.0 --- # Dataset Card for "controlnet_test3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
atharva7ak/mini-platypus
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245921 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---
Finnish-NLP/ultrachat_dpo_sft_deepl_kaannetty
--- dataset_info: features: - name: instruction dtype: string - name: response_accepted dtype: string - name: response_rejected dtype: string - name: instruction_orig dtype: string - name: response_accepted_orig dtype: string - name: response_rejected_orig dtype: string - name: response_orig_grade dtype: string - name: response_judgelm dtype: string splits: - name: train num_bytes: 101503630 num_examples: 16581 download_size: 59174494 dataset_size: 101503630 configs: - config_name: default data_files: - split: train path: data/train-* --- README TO DO BUT RELEASED NEVERTHELESS
Michaelkassouf/Ferrari_AI4A
--- dataset_info: features: - name: image dtype: string - name: caption dtype: string splits: - name: train num_bytes: 3495120 num_examples: 35553 download_size: 1051219 dataset_size: 3495120 configs: - config_name: default data_files: - split: train path: data/train-* ---
Nexdata/Chinese_Mandarin_Entertainment_anchor_Style_Multi-emotional_Synthesis_Corpus
--- task_categories: - text-to-speech language: - zh --- # Dataset Card for Nexdata/Chinese_Mandarin_Entertainment_anchor_Style_Multi-emotional_Synthesis_Corpus ## Description 12 Hours - Chinese Mandarin Entertainment anchor Style Multi-emotional Synthesis Corpus. It is recorded by Chinese native speaker. six emotional text+modal particles, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis. For more details, please refer to the link: https://www.nexdata.ai/datasets/1304?source=Huggingface # Specifications ## Format 48,000Hz, 24bit, uncompressed wav, mono channel ## Recording environment professional recording studio ## Recording content seven emotions (happiness, anger, sadness, surprise, fear, disgust)+sentences with filler word ## Speaker professional CharacterVoice; Role: An 18-year-old girl who works as an entertainment anchor and enjoys singing and dancing ## Device microphone ## Language Mandarin ## Annotation word and pinyin transcription, prosodic boundary annotation, phoneme boundary annotation ## The amount of data The amount of neutral data is not less than 1.6 hours; the amount of data with filler word is not less than 0.4 hours; and the remaining six types of emotional data is not less than 1.67 hours each # Licensing Information Commercial License
FinGPT/fingpt-fineval
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 441991 num_examples: 1056 - name: test num_bytes: 117516 num_examples: 265 download_size: 269193 dataset_size: 559507 --- # Dataset Card for "fingpt-fineval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Shahnawaj/13MedicareFAQ
--- license: mit ---
CyberHarem/diesel_nikke
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of diesel/ディーゼル/迪塞尔/디젤 (Nikke: Goddess of Victory) This is the dataset of diesel/ディーゼル/迪塞尔/디젤 (Nikke: Goddess of Victory), containing 86 images and their tags. The core tags of this character are `long_hair, bangs, hat, black_hair, breasts, earrings, brown_eyes, blue_headwear, brown_hair, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 86 | 169.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diesel_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 86 | 80.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diesel_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 216 | 174.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diesel_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 86 | 140.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diesel_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 216 | 271.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diesel_nikke/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/diesel_nikke', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blue_jacket, blue_necktie, solo, uniform, white_background, white_shirt, long_sleeves, simple_background, smile, white_gloves, white_skirt, collared_shirt, jewelry, looking_at_viewer, pleated_skirt, belt, open_mouth, blush, thigh_strap | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, holding_gun, military_hat, solo, white_gloves, jewelry, looking_at_viewer, military_uniform, assault_rifle, blue_jacket, white_skirt, coat_on_shoulders, feet_out_of_frame, long_sleeves, standing, white_shirt, blue_necktie, closed_mouth, peaked_cap, pleated_skirt, pouch, thigh_strap | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_jacket | blue_necktie | solo | uniform | white_background | white_shirt | long_sleeves | simple_background | smile | white_gloves | white_skirt | collared_shirt | jewelry | looking_at_viewer | pleated_skirt | belt | open_mouth | blush | thigh_strap | holding_gun | military_hat | military_uniform | assault_rifle | coat_on_shoulders | feet_out_of_frame | standing | closed_mouth | peaked_cap | pouch | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:---------------|:-------|:----------|:-------------------|:--------------|:---------------|:--------------------|:--------|:---------------|:--------------|:-----------------|:----------|:--------------------|:----------------|:-------|:-------------|:--------|:--------------|:--------------|:---------------|:-------------------|:----------------|:--------------------|:--------------------|:-----------|:---------------|:-------------|:--------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | | X | X | | | X | X | | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X |
liuyanchen1015/MULTI_VALUE_wnli_completive_finish
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 648 num_examples: 3 - name: train num_bytes: 6299 num_examples: 28 download_size: 8402 dataset_size: 6947 --- # Dataset Card for "MULTI_VALUE_wnli_completive_finish" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
karimasbar/data_chat
--- license: mit ---
pembelajarff/3500_more_movie_reviews
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: review dtype: string - name: review_length dtype: int64 splits: - name: train num_bytes: 4631655.01 num_examples: 3592 - name: validation num_bytes: 515774.5 num_examples: 400 download_size: 3424005 dataset_size: 5147429.51 --- # Dataset Card for "3500_more_movie_reviews" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kunishou/cnn-dailymail-27k-ja
--- license: mit --- This dataset was created by automatically translating part of "cnn_dailymail" into Japanese. cnn_dailymail repository https://github.com/abisee/cnn-dailymail cnn_dailymail https://huggingface.co/datasets/cnn_dailymail
akash-soni/resume-dataset
--- dataset_info: features: - name: ID dtype: int64 - name: Resume_str dtype: string - name: Resume_html dtype: string - name: Category dtype: string splits: - name: train num_bytes: 43835582.16223832 num_examples: 1987 - name: valid num_bytes: 5471174.824476651 num_examples: 248 - name: test num_bytes: 5493236.013285024 num_examples: 249 download_size: 20342316 dataset_size: 54799993.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
justram/AToMiC-Texts-Dedup
--- dataset_info: features: - name: language dtype: string - name: text_id dtype: string - name: page_url dtype: string - name: page_title dtype: string - name: section_title dtype: string - name: hierarchical_section_title dtype: string - name: context_page_description dtype: string - name: context_section_description dtype: string splits: - name: train num_bytes: 4768023667.871489 num_examples: 3220639 - name: validation num_bytes: 35066965.650891684 num_examples: 21466 - name: test num_bytes: 26076287.261490725 num_examples: 16362 download_size: 2976408849 dataset_size: 4829166920.783871 --- # Dataset Card for "AToMiC-Texts-Dedup" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vwxyzjn/ultrafeedback_binarized_1707921333
--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: query list: - name: content dtype: string - name: role dtype: string - name: query_token sequence: int64 - name: query_token_len dtype: int64 - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 splits: - name: test_prefs num_bytes: 16973857 num_examples: 1000 - name: train_prefs num_bytes: 16589732 num_examples: 1000 download_size: 12976788 dataset_size: 33563589 --- # Dataset Card for "ultrafeedback_binarized_1707921333" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sethapun/arithmetic_2as_1to250
--- dataset_info: features: - name: expression dtype: string - name: answer dtype: int64 - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 60140 num_examples: 2000 - name: validation num_bytes: 12060 num_examples: 400 download_size: 23655 dataset_size: 72200 --- # Dataset Card for "arithmetic_2as_1to250" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FrederikMH/farright-test
--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for farright-test This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("FrederikMH/farright-test") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("FrederikMH/farright-test") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | text | Text | text | True | False | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | sentiment | Sentiment | label_selection | True | N/A | ['positive', 'neutral', 'negative'] | | mixed-emotion | Mixed-emotion | multi_label_selection | True | N/A | ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'] | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "text": "i didnt feel humiliated" }, "metadata": {}, "responses": [ { "status": "submitted", "user_id": "3314b00d-2477-4606-b8f7-5cc2c52b2e28", "values": { "mixed-emotion": { "value": [ "fear" ] }, "sentiment": { "value": "neutral" } } } ], "suggestions": [], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": null, "metadata": "{}", "mixed-emotion": [ { "status": "submitted", "user_id": "3314b00d-2477-4606-b8f7-5cc2c52b2e28", "value": [ "fear" ] } ], "mixed-emotion-suggestion": null, "mixed-emotion-suggestion-metadata": { "agent": null, "score": null, "type": null }, "sentiment": [ { "status": "submitted", "user_id": "3314b00d-2477-4606-b8f7-5cc2c52b2e28", "value": "neutral" } ], "sentiment-suggestion": null, "sentiment-suggestion-metadata": { "agent": null, "score": null, "type": null }, "text": "i didnt feel humiliated" } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **text** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **sentiment** is of type `label_selection` with the following allowed values ['positive', 'neutral', 'negative']. * **mixed-emotion** is of type `multi_label_selection` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love']. * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **sentiment-suggestion** is of type `label_selection` with the following allowed values ['positive', 'neutral', 'negative']. * (optional) **mixed-emotion-suggestion** is of type `multi_label_selection` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love']. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
JennnDexter/ddpm-butterflies-128
--- license: unknown ---
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-59000
--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 658320 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
DnerE/ImpartialDataset
--- license: mit ---