datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
open-llm-leaderboard/details_Henk717__airochronos-33B | ---
pretty_name: Evaluation run of Henk717/airochronos-33B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Henk717/airochronos-33B](https://huggingface.co/Henk717/airochronos-33B) 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 4 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_Henk717__airochronos-33B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T22:07:20.672645](https://huggingface.co/datasets/open-llm-leaderboard/details_Henk717__airochronos-33B/blob/main/results_2023-09-17T22-07-20.672645.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\": 0.003145973154362416,\n\
\ \"em_stderr\": 0.0005734993648436351,\n \"f1\": 0.06925440436241624,\n\
\ \"f1_stderr\": 0.0014771385536763682,\n \"acc\": 0.46521874156655235,\n\
\ \"acc_stderr\": 0.010430187536918111\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436351,\n\
\ \"f1\": 0.06925440436241624,\n \"f1_stderr\": 0.0014771385536763682\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1372251705837756,\n \
\ \"acc_stderr\": 0.009477808244600422\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7932123125493291,\n \"acc_stderr\": 0.011382566829235798\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Henk717/airochronos-33B
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_17T12_26_49.704789
path:
- '**/details_harness|arc:challenge|25_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_16T22_55_10.209177
path:
- '**/details_harness|drop|3_2023-09-16T22-55-10.209177.parquet'
- split: 2023_09_17T00_16_43.512970
path:
- '**/details_harness|drop|3_2023-09-17T00-16-43.512970.parquet'
- split: 2023_09_17T22_07_20.672645
path:
- '**/details_harness|drop|3_2023-09-17T22-07-20.672645.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T22-07-20.672645.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_16T22_55_10.209177
path:
- '**/details_harness|gsm8k|5_2023-09-16T22-55-10.209177.parquet'
- split: 2023_09_17T00_16_43.512970
path:
- '**/details_harness|gsm8k|5_2023-09-17T00-16-43.512970.parquet'
- split: 2023_09_17T22_07_20.672645
path:
- '**/details_harness|gsm8k|5_2023-09-17T22-07-20.672645.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T22-07-20.672645.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hellaswag|10_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T12:26:49.704789.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T12:26:49.704789.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T12:26:49.704789.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_16T22_55_10.209177
path:
- '**/details_harness|winogrande|5_2023-09-16T22-55-10.209177.parquet'
- split: 2023_09_17T00_16_43.512970
path:
- '**/details_harness|winogrande|5_2023-09-17T00-16-43.512970.parquet'
- split: 2023_09_17T22_07_20.672645
path:
- '**/details_harness|winogrande|5_2023-09-17T22-07-20.672645.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T22-07-20.672645.parquet'
- config_name: results
data_files:
- split: 2023_08_17T12_26_49.704789
path:
- results_2023-08-17T12:26:49.704789.parquet
- split: 2023_09_16T22_55_10.209177
path:
- results_2023-09-16T22-55-10.209177.parquet
- split: 2023_09_17T00_16_43.512970
path:
- results_2023-09-17T00-16-43.512970.parquet
- split: 2023_09_17T22_07_20.672645
path:
- results_2023-09-17T22-07-20.672645.parquet
- split: latest
path:
- results_2023-09-17T22-07-20.672645.parquet
---
# Dataset Card for Evaluation run of Henk717/airochronos-33B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Henk717/airochronos-33B
- **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 [Henk717/airochronos-33B](https://huggingface.co/Henk717/airochronos-33B) 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 4 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_Henk717__airochronos-33B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T22:07:20.672645](https://huggingface.co/datasets/open-llm-leaderboard/details_Henk717__airochronos-33B/blob/main/results_2023-09-17T22-07-20.672645.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": 0.003145973154362416,
"em_stderr": 0.0005734993648436351,
"f1": 0.06925440436241624,
"f1_stderr": 0.0014771385536763682,
"acc": 0.46521874156655235,
"acc_stderr": 0.010430187536918111
},
"harness|drop|3": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436351,
"f1": 0.06925440436241624,
"f1_stderr": 0.0014771385536763682
},
"harness|gsm8k|5": {
"acc": 0.1372251705837756,
"acc_stderr": 0.009477808244600422
},
"harness|winogrande|5": {
"acc": 0.7932123125493291,
"acc_stderr": 0.011382566829235798
}
}
```
### 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] |
ksaml/Stanford_dogs | ---
license: other
---
## Context
The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age. <b> I have used only images, so this does not contain any labels <b>.
## Content
Number of images: 20,580
## Acknowledgements
The original data source is found on http://vision.stanford.edu/aditya86/ImageNetDogs/ and contains additional information on the train/test splits and baseline results.
If you use this dataset in a publication, please cite the dataset on the following papers:
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex]
Secondary:
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex] |
samuelchan/art | ---
license: afl-3.0
---
|
heegyu/kowow | ---
license: cc-by-4.0
---
Original data: https://huggingface.co/datasets/KETI-AIR/kowow/blob/main/data/ko/data.json |
talkmap/banking-conversation-corpus | ---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- conversation
- banking
pretty_name: banking-300k
size_categories:
- 100K<n<1M
---
# Banking 300k Dataset Overview
This dataset consists of 300,000 synthetically generated conversations in a customer service setting for the telecom industry. There are two speakers: a customer, and an agent. |
kgr123/quality_counter_500_4_buckets | ---
dataset_info:
features:
- name: context
dtype: string
- name: word
dtype: string
- name: claim
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 2883824
num_examples: 1929
- name: train
num_bytes: 2868952
num_examples: 1935
- name: validation
num_bytes: 2941330
num_examples: 1941
download_size: 2089608
dataset_size: 8694106
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
CyberHarem/isuzu_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of isuzu/五十鈴 (Kantai Collection)
This is the dataset of isuzu/五十鈴 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `long_hair, twintails, breasts, ribbon, large_breasts, hair_ribbon, blue_hair, blue_eyes, 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 | 500 | 491.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isuzu_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 328.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isuzu_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1199 | 703.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isuzu_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 455.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isuzu_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1199 | 910.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isuzu_kantaicollection/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/isuzu_kantaicollection',
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 | 31 |  |  |  |  |  | 1girl, serafuku, solo, detached_sleeves, pleated_skirt, red_skirt, white_sailor_collar, looking_at_viewer, simple_background, white_thighhighs, brown_neckerchief, white_background, sleeveless, smile, cowboy_shot |
| 1 | 6 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, underwear_only, black_bra, black_panties, navel, smile, blush |
| 2 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, simple_background, solo, white_background, blue_bikini, floral_print, blush, cleavage, navel, collarbone, hair_between_eyes, sarong, smile, aqua_eyes, open_mouth |
| 3 | 6 |  |  |  |  |  | 1girl, blue_bikini, floral_print, navel, sarong, smile, solo, black_hair, looking_at_viewer, cowboy_shot |
| 4 | 14 |  |  |  |  |  | 1girl, day, solo, blue_bikini, looking_at_viewer, smile, ocean, beach, collarbone, outdoors, cloud, floral_print, sarong, cleavage, blue_sky, blush, navel, black_hair, green_hair, cowboy_shot, open_mouth, water |
| 5 | 6 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, covered_navel, cowboy_shot, school_swimsuit, simple_background, white_background, blue_one-piece_swimsuit, collarbone, dated, name_tag, twitter_username |
| 6 | 8 |  |  |  |  |  | 1girl, enmaided, looking_at_viewer, solo, white_apron, cleavage, frilled_apron, simple_background, waist_apron, white_background, black_dress, maid_headdress, blush, white_thighhighs, hair_between_eyes, open_mouth, short_sleeves, wrist_cuffs, bangs, dated, sweat |
| 7 | 5 |  |  |  |  |  | 1boy, 1girl, blush, cum_on_breasts, facial, hetero, nipples, open_mouth, penis, solo_focus, looking_at_viewer, mosaic_censoring, paizuri, black_hair, one_eye_closed, cum_on_tongue, detached_sleeves, huge_breasts, pov |
| 8 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, nude, open_mouth, solo_focus, sweat, girl_on_top, hairband, navel, penis, sex_from_behind, tears, vaginal, bar_censor, green_hair, reverse_cowgirl_position, bangs, collarbone, cum_in_pussy, motion_lines, trembling |
| 9 | 14 |  |  |  |  |  | 1girl, playboy_bunny, rabbit_ears, solo, detached_collar, fake_animal_ears, looking_at_viewer, cleavage, wrist_cuffs, simple_background, strapless_leotard, cowboy_shot, white_background, bowtie, pantyhose, alternate_costume, blush, rabbit_tail, highleg, blue_leotard, covered_navel, white_gloves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | serafuku | solo | detached_sleeves | pleated_skirt | red_skirt | white_sailor_collar | looking_at_viewer | simple_background | white_thighhighs | brown_neckerchief | white_background | sleeveless | smile | cowboy_shot | cleavage | underwear_only | black_bra | black_panties | navel | blush | blue_bikini | floral_print | collarbone | hair_between_eyes | sarong | aqua_eyes | open_mouth | black_hair | day | ocean | beach | outdoors | cloud | blue_sky | green_hair | water | covered_navel | school_swimsuit | blue_one-piece_swimsuit | dated | name_tag | twitter_username | enmaided | white_apron | frilled_apron | waist_apron | black_dress | maid_headdress | short_sleeves | wrist_cuffs | bangs | sweat | 1boy | cum_on_breasts | facial | hetero | nipples | penis | solo_focus | mosaic_censoring | paizuri | one_eye_closed | cum_on_tongue | huge_breasts | pov | nude | girl_on_top | hairband | sex_from_behind | tears | vaginal | bar_censor | reverse_cowgirl_position | cum_in_pussy | motion_lines | trembling | playboy_bunny | rabbit_ears | detached_collar | fake_animal_ears | strapless_leotard | bowtie | pantyhose | alternate_costume | rabbit_tail | highleg | blue_leotard | white_gloves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:-------|:-------------------|:----------------|:------------|:----------------------|:--------------------|:--------------------|:-------------------|:--------------------|:-------------------|:-------------|:--------|:--------------|:-----------|:-----------------|:------------|:----------------|:--------|:--------|:--------------|:---------------|:-------------|:--------------------|:---------|:------------|:-------------|:-------------|:------|:--------|:--------|:-----------|:--------|:-----------|:-------------|:--------|:----------------|:------------------|:--------------------------|:--------|:-----------|:-------------------|:-----------|:--------------|:----------------|:--------------|:--------------|:-----------------|:----------------|:--------------|:--------|:--------|:-------|:-----------------|:---------|:---------|:----------|:--------|:-------------|:-------------------|:----------|:-----------------|:----------------|:---------------|:------|:-------|:--------------|:-----------|:------------------|:--------|:----------|:-------------|:---------------------------|:---------------|:---------------|:------------|:----------------|:--------------|:------------------|:-------------------|:--------------------|:---------|:------------|:--------------------|:--------------|:----------|:---------------|:---------------|
| 0 | 31 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | | | | | X | | | | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | | X | | | | | X | X | | | X | | X | | X | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | | | | | X | | | | | | X | X | | | | | X | | X | X | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 14 |  |  |  |  |  | X | | X | | | | | X | | | | | | X | X | X | | | | X | X | X | X | X | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | X | | | | | X | X | | | X | | | X | | | | | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | | X | | | | | X | X | X | | X | | | | X | | | | | X | | | | X | | | X | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | | X | | | | X | | | | | | | | | | | | | X | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | X | X | | | X | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | | | X | X | X | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 9 | 14 |  |  |  |  |  | X | | X | | | | | X | X | | | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
magnosfalcao/Vinicius | ---
license: openrail
---
|
danjacobellis/audio_har_descript_44kHz_frames | ---
dataset_info:
features:
- name: codes
dtype:
array2_d:
shape:
- 9
- 180
dtype: float32
- name: label
dtype:
class_label:
names:
'0': No Activity
'1': Writing
'2': Drawing
'3': Cutting paper
'4': Typing on keyboard
'5': Typing on phone
'6': Browsing on phone
'7': Clapping
'8': Shuffling cards
'9': Scratching
'10': Wiping table
'11': Brushing hair
'12': Washing hands
'13': Drinking
'14': Eating snacks
'15': Brushing teeth
'16': Chopping
'17': Grating
'18': Frying
'19': Sweeping
'20': Vacuuming
'21': Washing dishes
'22': Filling water
'23': Using microwave
- name: label_str
dtype: string
- name: participant
dtype: int32
splits:
- name: train
num_bytes: 64432783
num_examples: 9841
download_size: 19881933
dataset_size: 64432783
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xianbao/my-dreambooth | ---
license: other
---
|
arefm/second_experiment_data | ---
license: apache-2.0
---
|
fromsite/online | ---
license: unlicense
---
|
htdung167/fleurs-vi-preprocessed | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: original_sentence
dtype: string
- name: preprocessed_sentence
dtype: string
splits:
- name: train
num_bytes: 2092459546.394
num_examples: 2994
- name: validation
num_bytes: 275319524.0
num_examples: 361
- name: test
num_bytes: 692444021.0
num_examples: 857
download_size: 3040363730
dataset_size: 3060223091.394
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
fadeke/clavis_studio_dataset_2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 31077171.0
num_examples: 72
download_size: 30449595
dataset_size: 31077171.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
eagle0504/larkin-web-scrape-dataset-qa-formatted | ---
dataset_info:
features:
- name: questions
dtype: string
- name: answers
dtype: string
splits:
- name: train
num_bytes: 115322
num_examples: 568
download_size: 62490
dataset_size: 115322
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
holistic-ai/LLM_Audit_Toxicity_Prompts | ---
license: mit
---
|
liuyanchen1015/MULTI_VALUE_wnli_plural_preposed | ---
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: 1019
num_examples: 5
- name: test
num_bytes: 8718
num_examples: 28
- name: train
num_bytes: 15381
num_examples: 77
download_size: 17960
dataset_size: 25118
---
# Dataset Card for "MULTI_VALUE_wnli_plural_preposed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1713096999 | ---
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: 9179
num_examples: 24
download_size: 12433
dataset_size: 9179
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713096999"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MartinKu/wikipedia_stage1_OC_20230331 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 10763367877
num_examples: 152512467
download_size: 6671458208
dataset_size: 10763367877
---
# Dataset Card for "wikipedia_stage1_OC_20230331"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Murali0604/Syringe-Dataset-Labelled-1 | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 137911723.0
num_examples: 12
download_size: 10051906
dataset_size: 137911723.0
---
# Dataset Card for "Syringe-Dataset-Labelled-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
marmofayezi/M3EditLandmark | ---
dataset_info:
features:
- name: id
dtype: string
- name: original_image
dtype: image
- name: prompt
dtype: string
- name: landmark
dtype: image
- name: edit_20_0.5
dtype: image
- name: edit_20_0.7
dtype: image
- name: edit_20_0.8
dtype: image
- name: edit_20_1.0
dtype: image
- name: edit_20_1.1
dtype: image
- name: edit_20_1.3
dtype: image
- name: edit_40_0.5
dtype: image
- name: edit_40_0.7
dtype: image
- name: edit_40_0.8
dtype: image
- name: edit_40_1.0
dtype: image
- name: edit_40_1.1
dtype: image
- name: edit_40_1.3
dtype: image
splits:
- name: train
num_bytes: 25887019.0
num_examples: 51
download_size: 24895686
dataset_size: 25887019.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_cola_absolute_reflex | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 6875
num_examples: 81
- name: test
num_bytes: 7485
num_examples: 92
- name: train
num_bytes: 70891
num_examples: 918
download_size: 41221
dataset_size: 85251
---
# Dataset Card for "MULTI_VALUE_cola_absolute_reflex"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/akagi_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of akagi/赤城/赤城 (Azur Lane)
This is the dataset of akagi/赤城/赤城 (Azur Lane), containing 500 images and their tags.
The core tags of this character are `animal_ears, fox_ears, long_hair, breasts, red_eyes, brown_hair, large_breasts, tail, fox_tail, multiple_tails, bangs, fox_girl, animal_ear_fluff`, 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 | 994.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 469.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1287 | 1.01 GiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 834.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1287 | 1.60 GiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_azurlane/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/akagi_azurlane',
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 | 5 |  |  |  |  |  | 2girls, black_gloves, cleavage, looking_at_viewer, smile, white_hair, wide_sleeves, kitsune, parted_lips, short_hair, simple_background, black_hair, black_kimono, blush, makeup, medium_breasts, red_skirt, solo_focus, white_background, white_kimono |
| 1 | 5 |  |  |  |  |  | 1girl, black_hair, looking_at_viewer, pleated_skirt, red_skirt, smile, solo, wide_sleeves, black_gloves, cleavage, hakama_short_skirt, kimono, parted_lips, airplane, blunt_bangs, cowboy_shot |
| 2 | 5 |  |  |  |  |  | 1girl, black_gloves, black_kimono, blunt_bangs, kyuubi, looking_at_viewer, partially_fingerless_gloves, pleated_skirt, red_skirt, smile, solo, wide_sleeves, cleavage, eyeshadow, long_sleeves, simple_background, airplane, cowboy_shot, eyeliner, hakama_short_skirt, holding, open_clothes, parted_lips, sidelocks, standing, zettai_ryouiki, black_thighhighs, collarbone, sash, shikigami, very_long_hair, white_background |
| 3 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, cleavage, black_gloves, black_hair, black_thighhighs, wide_sleeves, parted_lips, red_skirt, kimono, blush, simple_background |
| 4 | 12 |  |  |  |  |  | 1girl, cleavage, solo, wide_sleeves, black_gloves, looking_at_viewer, red_skirt, kyuubi, brown_tail, pleated_skirt, simple_background, white_background, black_kimono, makeup, sakuramon |
| 5 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, oil-paper_umbrella, wide_sleeves, cleavage, solo, black_kimono, kitsune, eyeshadow, fur-trimmed_kimono, holding_umbrella, blunt_bangs, eyeliner, x_hair_ornament, obi, smile, gloves, black_hair, official_alternate_costume |
| 6 | 26 |  |  |  |  |  | 1girl, cleavage, solo, smile, looking_at_viewer, red_bikini, navel, hair_flower, wrist_scrunchie, black_scrunchie, collarbone, bare_shoulders, kitsune, spider_lily, very_long_hair, blush, sarong, stomach, black_hair, official_alternate_costume, simple_background, thighs, white_background |
| 7 | 22 |  |  |  |  |  | official_alternate_costume, red_dress, cleavage, bare_shoulders, black_gloves, fingerless_gloves, looking_at_viewer, 1girl, solo, very_long_hair, smile, halter_dress, thighs, feather_boa, kitsune, o-ring, blush, champagne_flute, holding_cup, sitting, sleeveless_dress, black_choker, blunt_bangs, evening_gown, parted_lips, sidelocks |
| 8 | 6 |  |  |  |  |  | 1girl, erection, futanari, huge_breasts, huge_penis, looking_at_viewer, nipples, red_skirt, smile, solo, testicles, thick_thighs, uncensored, veiny_penis, artist_name, bare_shoulders, black_hair, blush, detached_sleeves, makeup, parted_lips, pleated_skirt, black_gloves, collarbone, japanese_clothes, tongue_out, wide_sleeves, breasts_out, miniskirt, no_panties |
| 9 | 8 |  |  |  |  |  | 1girl, black_gloves, looking_at_viewer, solo, red_necktie, sleeveless_shirt, white_shirt, bare_shoulders, black_skirt, collared_shirt, smile, black_pantyhose, cleavage, miniskirt, necktie_between_breasts, pleated_skirt, black_hair, guitar, holding_instrument, simple_background, standing, white_background, wide_sleeves, black_footwear, blush, closed_mouth, detached_sleeves, full_body, kitsune, open_shirt, shoes, very_long_hair |
| 10 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, navel, nipples, penis, sex, solo_focus, vaginal, completely_nude, heavy_breathing, open_mouth, collarbone, cowgirl_position, cum_in_pussy, heart, kitsune, looking_at_viewer, mosaic_censoring, pov, saliva, sweat, tongue_out, black_hair, cleavage, girl_on_top, huge_breasts, overflow, smile, spread_legs, steaming_body, stomach, symbol-shaped_pupils, thick_thighs, uncensored |
| 11 | 6 |  |  |  |  |  | 1girl, navel, solo, looking_at_viewer, brown_tail, cleavage, on_back, red_bra, red_panties |
| 12 | 5 |  |  |  |  |  | 1girl, solo, white_shirt, black_skirt, looking_at_viewer, simple_background, white_background, alternate_costume, brown_tail, school_uniform, cleavage, coat, collared_shirt, holding, kitsune, pantyhose, pleated_skirt, red_bowtie, short_sleeves, sitting, smile |
| 13 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, cleavage, detached_collar, simple_background, solo, wrist_cuffs, black_leotard, playboy_bunny, rabbit_ears, strapless_leotard, white_background, alternate_costume, bare_shoulders, black_pantyhose, blush, covered_navel, fake_animal_ears, red_bowtie, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 2girls | black_gloves | cleavage | looking_at_viewer | smile | white_hair | wide_sleeves | kitsune | parted_lips | short_hair | simple_background | black_hair | black_kimono | blush | makeup | medium_breasts | red_skirt | solo_focus | white_background | white_kimono | 1girl | pleated_skirt | solo | hakama_short_skirt | kimono | airplane | blunt_bangs | cowboy_shot | kyuubi | partially_fingerless_gloves | eyeshadow | long_sleeves | eyeliner | holding | open_clothes | sidelocks | standing | zettai_ryouiki | black_thighhighs | collarbone | sash | shikigami | very_long_hair | brown_tail | sakuramon | oil-paper_umbrella | fur-trimmed_kimono | holding_umbrella | x_hair_ornament | obi | gloves | official_alternate_costume | red_bikini | navel | hair_flower | wrist_scrunchie | black_scrunchie | bare_shoulders | spider_lily | sarong | stomach | thighs | red_dress | fingerless_gloves | halter_dress | feather_boa | o-ring | champagne_flute | holding_cup | sitting | sleeveless_dress | black_choker | evening_gown | erection | futanari | huge_breasts | huge_penis | nipples | testicles | thick_thighs | uncensored | veiny_penis | artist_name | detached_sleeves | japanese_clothes | tongue_out | breasts_out | miniskirt | no_panties | red_necktie | sleeveless_shirt | white_shirt | black_skirt | collared_shirt | black_pantyhose | necktie_between_breasts | guitar | holding_instrument | black_footwear | closed_mouth | full_body | open_shirt | shoes | 1boy | hetero | penis | sex | vaginal | completely_nude | heavy_breathing | open_mouth | cowgirl_position | cum_in_pussy | heart | mosaic_censoring | pov | saliva | sweat | girl_on_top | overflow | spread_legs | steaming_body | symbol-shaped_pupils | on_back | red_bra | red_panties | alternate_costume | school_uniform | coat | pantyhose | red_bowtie | short_sleeves | detached_collar | wrist_cuffs | black_leotard | playboy_bunny | rabbit_ears | strapless_leotard | covered_navel | fake_animal_ears |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------|:---------------|:-----------|:--------------------|:--------|:-------------|:---------------|:----------|:--------------|:-------------|:--------------------|:-------------|:---------------|:--------|:---------|:-----------------|:------------|:-------------|:-------------------|:---------------|:--------|:----------------|:-------|:---------------------|:---------|:-----------|:--------------|:--------------|:---------|:------------------------------|:------------|:---------------|:-----------|:----------|:---------------|:------------|:-----------|:-----------------|:-------------------|:-------------|:-------|:------------|:-----------------|:-------------|:------------|:---------------------|:---------------------|:-------------------|:------------------|:------|:---------|:-----------------------------|:-------------|:--------|:--------------|:------------------|:------------------|:-----------------|:--------------|:---------|:----------|:---------|:------------|:--------------------|:---------------|:--------------|:---------|:------------------|:--------------|:----------|:-------------------|:---------------|:---------------|:-----------|:-----------|:---------------|:-------------|:----------|:------------|:---------------|:-------------|:--------------|:--------------|:-------------------|:-------------------|:-------------|:--------------|:------------|:-------------|:--------------|:-------------------|:--------------|:--------------|:-----------------|:------------------|:--------------------------|:---------|:---------------------|:-----------------|:---------------|:------------|:-------------|:--------|:-------|:---------|:--------|:------|:----------|:------------------|:------------------|:-------------|:-------------------|:---------------|:--------|:-------------------|:------|:---------|:--------|:--------------|:-----------|:--------------|:----------------|:-----------------------|:----------|:----------|:--------------|:--------------------|:-----------------|:-------|:------------|:-------------|:----------------|:------------------|:--------------|:----------------|:----------------|:--------------|:--------------------|:----------------|:-------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | | X | X | X | X | | X | | X | | | X | | | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | | X | X | X | X | | X | | X | | X | X | | X | | | X | | | | X | | X | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | | X | X | X | | | X | | | | X | | X | | X | | X | | X | | X | X | X | | | | | | X | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 13 |  |  |  |  |  | | | X | X | X | | X | X | | | | X | X | | | | | | | | X | | X | | | | X | | | | X | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 26 |  |  |  |  |  | | | X | X | X | | | X | | | X | X | | X | | | | | X | | X | | X | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 22 |  |  |  |  |  | | 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 |  |  |  |  |  | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 8 |  |  |  |  |  | | 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 | 5 |  |  |  |  |  | | | 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 | | | | | | | | | | | | | | | | | |
| 11 | 6 |  |  |  |  |  | | | X | X | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | |
| 12 | 5 |  |  |  |  |  | | | X | X | X | | | X | | | X | | | | | | | | X | | X | X | X | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | |
| 13 | 6 |  |  |  |  |  | | | X | X | X | | | | | | X | | | X | | | | | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | X | X | X | X | X | X | X | X |
|
Jakelolipopp/truthful_qa-validation-german_q_n_a | ---
license: apache-2.0
language:
- de
--- |
AdapterOcean/Open_Platypus_standardized_cluster_11_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3418897
num_examples: 5304
download_size: 1538841
dataset_size: 3418897
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Open_Platypus_standardized_cluster_11_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-squad_v2-2eb94bfa-11695557 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/tinyroberta-6l-768d
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/tinyroberta-6l-768d
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ghpkishore](https://huggingface.co/ghpkishore) for evaluating this model. |
ipipan/maupqa | ---
task_categories:
- question-answering
- text-retrieval
task_ids:
- open-domain-qa
- document-retrieval
language:
- pl
pretty_name: MAUPQA
size_categories:
- 1M<n<10M
annotations_creators:
- found
- machine-generated
license: cc-by-sa-4.0
---
# Dataset Card for MAUPQA Dataset
## Dataset Description
- **Paper:** [MAUPQA: Massive Automatically-created Polish Question Answering Dataset](https://arxiv.org/abs/2305.05486), [SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering](https://arxiv.org/abs/2309.08469)
- **Point of Contact:** [Piotr Rybak](mailto:piotr.cezary.rybak@gmail.com)
### Dataset Summary
MAUPQA is a collection of 14 datasets for Polish document retrieval. Most of the datasets are either machine-generated or machine-translated from English. Across all datasets, it consists of over 1M questions, 1M positive, and 7M hard-negative question-passage pairs.
### Supported Tasks and Leaderboards
- `document-retrieval`: The dataset can be used to train a model for document retrieval. Success on this task is typically measured by [top-k retrieval accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html) or [NDCG](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html).
### Languages
The text is in Polish, as spoken by the [Internet users](https://github.com/facebookresearch/cc_net), [Polish Wikipedia](https://pl.wikipedia.org/) editors, or is an output of generative or translation models. The BCP-47 code for Polish is pl-PL.
## Dataset Structure
### Data Instances
The dataset consists of over 8 million question-passage pairs. For each instance, there is a `question`, a passage (`passage_title`, `passage_text`), and a boolean indicator if the passage is `relevant` for the given question (i.e. does it contain the answers).
For a small subset of `question` there is also a list of possible `answers` formulated in a natural language, in a way a Polish
speaker would answer the questions.
```
{
'question_id': 1,
'question': 'Na którym kontynencie leży państwo Gujana, panie Krzysztofie?',
'answers': "['W Ameryce Południowej']",
'passage_title': 'Gujana (ujednoznacznienie)',
'passage_text': 'Gujana (region) – region Ameryki Południowej Gujana – państwo w Ameryce Południowej Gujana Brytyjska – dawna kolonia brytyjska; obecnie państwo Gujana Gujana Francuska – departament zamorski Francji; dawniej kolonia francuska Gujana Holenderska – dawna kolonia holenderska; obecnie państwo Surinam',
'relevant': True,
'passage_source': 'crawling',
'subset': '1z10'
}
```
### Data Fields
Question-passage pairs:
- `question_id`: an integer id of the question
- `question`: a string containing the question
- `passage_title`: a string containing the title of the Wikipedia article
- `passage_text`: a string containing the passage text as extracted by the human annotator
- `relevant`: a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers)
- `annotated_by`: a string containing the name of the annotator who verified the relevance of the pair
- `answers`: a string containing a list of possible short answers to the question
- `passage_source`: a string containing the method of obtaining the passage. One of the following:
- `manual-annotation`: the question-passage pair was manually annotated
- `crawling`: the question-passage pairs were created by taking advantage of the specific structure of crawled website
- `dataset-translation`: the dataset was created by machine-translating the English dataset
- `generative-model`: the question was created by the generative model based on the given passage
- `bm25-negatives`: the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is not relevant
- `bm25-positives`: the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is relevant
- `subset`: a string containing the name of the dataset
### Data Splits
MAUPQA is a collection of 14 datasets and most of them are weakly labeled. Therefore, the intended use of MAUPQA is for training only. As such, all examples belong to a single `train` split. We recommend using the [PolQA](https://huggingface.co/datasets/ipipan/polqa) dataset for evaluation.
Basic statistics of all 14 datasets:
| dataset | # questions | # answers | # positive passages | # negative passages |
|-------------------|------------:|----------:|--------------------:|--------------------:|
| 1z10 | 22,835 | 21,415 | 22,014 | 139,471 |
| czy-wiesz-v2 | 29,078 | - | 29,078 | 143,306 |
| gpt3-cc | 10,146 | 10,146 | 10,177 | 89,203 |
| gpt3.5-cc | 29,591 | 29,583 | 29,720 | 251,959 |
| gpt3.5-wiki | 29,674 | 29,636 | 29,748 | 115,564 |
| mkqa | 4,036 | 4,036 | 3,968 | 19,814 |
| mqa | 172,768 | - | 178,131 | 1,249,659 |
| msmarco | 389,987 | - | 416,763 | 3,006,996 |
| multilingual-NLI | 100,752 | 64,900 | 68,096 | 743,857 |
| nq | 135,781 | - | 139,976 | 797,436 |
| poleval2021-pairs | 1,977 | - | 2,088 | 17,608 |
| poquad | 56,588 | 46,157 | 46,187 | 299,865 |
| templates | 15,993 | 14,504 | 15,993 | 45,228 |
| wiki-def | 18,093 | 18,092 | 18,093 | 84,956 |
| Total | 1,017,299 | 238,469 | 1,010,032 | 7,004,922 |
## Dataset Creation
### Curation Rationale
Open-domain question answering systems rely heavily on annotated datasets to train neural document retrievers. However, manually annotating such datasets is both difficult and time-consuming. To overcome these difficulties, we experimented with several methods for automatically collecting weakly labeled datasets. As a result, MAUPQA enables the development of robust document retrieval systems for Polish.
### Source Data
#### Initial Data Collection and Normalization
Below, we briefly describe each dataset. For a detailed description please refer to the [paper](https://arxiv.org/abs/2305.05486).
* `1z10`: We transcribe 333 recordings of the [Jeden z Dziesięciu](https://pl.wikipedia.org/wiki/Jeden_z_dziesi%C4%99ciu) TV show using the Whisper model and extract the question-answer pairs using GPT-3.5 model. We use the BM25 retriever and the GPT-3.5-based cross-encoder to match questions with Wikipedia passages.
* `czy-wiesz-v2`: We first crawl all questions from the [Did you know?](https://pl.wikipedia.org/wiki/Wikiprojekt:Czy_wiesz/archiwum) section on Polish Wikipedia together with a link to the relevant Wikipedia article. Then, we use the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage.
* `gpt3-cc`: We sample random passages from [CCNet](https://github.com/facebookresearch/cc_net) corpus and prompt GPT-3 to generate a relevant question.
* `gpt3.5-cc`: We sample random passages from [CCNet](https://github.com/facebookresearch/cc_net) corpus and prompt GPT-3.5 to generate a relevant question.
* `gpt3.5-wiki`: We sample random passages from Polish Wikipedia and prompt GPT-3.5 to generate a relevant question.
* `mkqa`: We clean the Polish subset of the [MKQA](https://huggingface.co/datasets/mkqa) dataset by removing questions without answers, requiring long answers (*Why?* and *How?* questions), and ambiguous ones ("Who is the *current* president?*). We use the BM25 retriever and the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage.
* `mqa`: We clean the Polish subset of the [MQA](https://huggingface.co/datasets/clips/mqa) dataset by removing artificially created questions like "What is the best hotel in *{city}*?" for hundreds of different *cities*. To clean the dataset, we cluster lexically similar questions/passages and remove clusters with over 5 questions.
* `msmarco`: We translate the [MS MARCO](https://huggingface.co/datasets/ms_marco) dataset into Polish using the machine translation model.
* `multilingual-NLI`: We extract question-answer pairs from the Polish subset of the [multilingual-NLI](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) dataset. We create questions using the following template: "Czy *{premise}*?" (Eng. "Does *{premise}*?") and use hypotheses as passages. We consider `entailment` and `contradiction` labels as relevant and `neutral` as negative.
* `nq`: We translate the [NQ](https://huggingface.co/datasets/natural_questions) dataset into Polish using the machine translation model.
* `poleval2021-pairs`: We take [allegro/polish-question-passage-pairs](https://huggingface.co/datasets/allegro/polish-question-passage-pairs) without any changes.
* `poquad`: We extract question-passages pairs from the training split of the [PoQuAD](https://huggingface.co/datasets/clarin-pl/poquad) dataset.
* `templates`: We take advantage of the Wikipedia structure to generate questions using predefined templates. For example, list pages group together similar entities (e.g. "Writers born in Poland") which allow generating questions like "Where was *{writer name}* born?". In total, we use 33 templates to generate questions. We use the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage from the linked article.
* `wiki-def`: We use [Wiktionary](https://www.wiktionary.org/) to generate questions based on word definitions. We use definitions that have links to Wikipedia articles to create the question-passage pairs. For example, the definition of "Monday" is "the first day of the week". Based on it, we generate the question "What is the name of *the first day of the week*?".
Additionally, we extend each dataset by sampling the hard negative passages using a BM25 retriever and score using a [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to ensure that passages are not relevant.
#### Who are the source language producers?
The text is in Polish, as spoken by the [Internet users](https://github.com/facebookresearch/cc_net), [Polish Wikipedia](https://pl.wikipedia.org/) editors, or is an output of generative or translation models.
### Annotations
#### Annotation process
The MAUPQA dataset doesn't provide additional annotation except for the annotation present in the source datasets.
#### Who are the annotators?
Please refer to the description of the source datasets.
### Personal and Sensitive Information
The dataset should not contain any personal or sensitive information. However, we use the [CCNet](https://github.com/facebookresearch/cc_net) dataset as a source of passages that we didn't manually inspect for personal and sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems.
### Discussion of Biases
The machine-translated datasets might not represent the natural language as used by native Polish speakers. Similarly, the questions generated by the generative models might not be representative or correct.
Most of the question-passage pairs are created automatically using the BM25 retriever and as such it is biased to lexically similar pairs.
### Other Known Limitations
The MAUPQA dataset is mostly automatically generated and can therefore contain a high proportion of noise and incorrectly labeled question-passage pairs.
## Additional Information
### Dataset Curators
The MAUPQA dataset was collected by Piotr Rybak and Maciej Ogrodniczuk from the [Institute of Computer Science, Polish Academy of Sciences](http://zil.ipipan.waw.pl/) but the source datasets were created by many more researchers. Please refer to the original dataset descriptions for the full authorship.
This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@inproceedings{rybak-2023-maupqa,
title = "{MAUPQA}: Massive Automatically-created {P}olish Question Answering Dataset",
author = "Rybak, Piotr",
booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bsnlp-1.2",
pages = "11--16",
abstract = "Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.",
}
```
```
@misc{rybak2023silver,
title={Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering},
author={Piotr Rybak and Maciej Ogrodniczuk},
year={2023},
eprint={2309.08469},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Multimodal-Fatima/cv-as-nlp-vision-example | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': abyssinian
'1': american bulldog
'2': american pit bull terrier
'3': basset hound
'4': beagle
'5': bengal
'6': birman
'7': bombay
'8': boxer
'9': british shorthair
'10': chihuahua
'11': egyptian mau
'12': english cocker spaniel
'13': english setter
'14': german shorthaired
'15': great pyrenees
'16': havanese
'17': japanese chin
'18': keeshond
'19': leonberger
'20': maine coon
'21': miniature pinscher
'22': newfoundland
'23': persian
'24': pomeranian
'25': pug
'26': ragdoll
'27': russian blue
'28': saint bernard
'29': samoyed
'30': scottish terrier
'31': shiba inu
'32': siamese
'33': sphynx
'34': staffordshire bull terrier
'35': wheaten terrier
'36': yorkshire terrier
- name: id
dtype: int64
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_simple_specific
sequence: string
splits:
- name: test
num_bytes: 413925401.0
num_examples: 3669
download_size: 412563763
dataset_size: 413925401.0
---
# Dataset Card for "cv-as-nlp-vision-example"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
severo/RedPajama-Tiny | ---
language:
- en
license: apache-2.0
size_categories:
- n<1K
task_categories:
- text-generation
pretty_name: RedPajama Tiny
dataset_info:
features:
- name: text
dtype: string
- name: meta
dtype: string
splits:
- name: train
num_bytes: 32428740
num_examples: 448
download_size: 18977228
dataset_size: 32428740
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Dataset Name
### Dataset Summary
This is a tiny version of the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).
It contains 64 samples from each of the 7 sources.
This dataset is intended for developing and testing data/training pipeline for loading the full RedPajama dataset or any general HuggingFace dataset.
It is very fast to download and easy to examine. You should not use it for training a full model, but you can use it for overfitting test or any other sanity checks.
## Dataset Structure
The dataset structure is as follows:
```
{
"text": ...,
"meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}
}
```
|
heliosprime/twitter_dataset_1713222662 | ---
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: 28449
num_examples: 78
download_size: 23140
dataset_size: 28449
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713222662"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lukintrees/guanaco-llama2-ru-1k-loli | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 163484.62969733903
num_examples: 788
- name: test
num_bytes: 10355.505791505791
num_examples: 46
download_size: 1083443
dataset_size: 173840.13548884483
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
yousaforever/likun_v1 | ---
license: agpl-3.0
---
|
mmmurf/gpt2-augmentation1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 260
num_examples: 4
- name: validation
num_bytes: 261
num_examples: 4
download_size: 2470
dataset_size: 521
---
# Dataset Card for "gpt2-augmentation1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
giuliadc/mlsum-fr-filtered | ---
task_categories:
- summarization
language:
- fr
---
French part of MLSUM dataset filtered by using the code by Aumiller et al. (1) available at https://github.com/dennlinger/summaries/tree/main
min_length_summary = 18; min_length_reference = 150; length_metric = "whitespace"
extractiveness = "fully"
min_compression_ratio = 2.5
Maximal article length = 512 tokens
(1): Aumiller, D., Fan, J., & Gertz, M. (2023). On the State of German (Abstractive) Text Summarization. arXiv preprint arXiv:2301.07095. |
yuvalkirstain/pokemon-split | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 107374569.23529412
num_examples: 749
- name: test
num_bytes: 12042007.764705881
num_examples: 84
download_size: 99425904
dataset_size: 119416577.0
---
# Dataset Card for "pokemon-split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
qgyd2021/few_shot_intent_sft | ---
license: apache-2.0
task_categories:
- text-classification
- text-generation
- text2text-generation
language:
- zh
- en
tags:
- few-shot
- intent
size_categories:
- 100M<n<1B
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: not_applicable
dtype: bool
- name: intent
dtype: string
- name: intent_version
dtype: string
- name: n_way
dtype: int32
- name: n_shot
dtype: int32
- name: description
dtype: string
splits:
- name: train
num_bytes: 22484898
num_examples: 22080
- name: test
num_bytes: 1853817
num_examples: 2477
download_size: 7816475
dataset_size: 24338715
---
## 小样本意图识别指令数据集
收集了意图识别的数据集, 将其制作成 prompt, 用于 few-shot 的意图识别 LLM 研究.
编写 prompt 模板需要想像力, 你可以在 community 中交流你的想法.
`{dataset_name}_prompt` 子集是从其对应的 `{dataset_name}` 数据集和 `{dataset_name}_template` 子集动态生成的, 因此每一次的结果都会不一样.
提示: 由于训练时 prompt 的长度可能超出最大限制而被 truncate, 因此尽量把 prompt 设计成即使被 truncate 也仍然可以用于 GPT 训练.
[提示工程指南](https://www.promptingguide.ai/zh/techniques/cot)
### 样本示例
<details>
<summary>train subset prompt 示例: (intent: Is it safe to go to the gym indoors if I'm vaccinated?)</summary>
<pre><code>intent recognition.<br>
Examples:
------------
text: will i be okay on the gym
intent: Is it safe to go to the gym indoors if I'm vaccinated?
------------
text: I want to go and exercise at the gym, indoors, but I don't know if it's safe?
intent: Is it safe to go to the gym indoors if I'm vaccinated?
------------
text: I worry I will catch Covid from the Gym even though I have been vaccinated?
intent: Is it safe to go to the gym indoors if I'm vaccinated?
------------
text: What does the fda think about the covid 19 vaccine?
intent: Is the vaccine FDA approved?
------------
text: it's never safe in a gym there are always bacteria everywhere
intent: Is it safe to go to the gym indoors if I'm vaccinated?
------------
text: who is the difference between FDA authorization and approval?
intent: Is the vaccine FDA approved?
------------
text: would the vaccine FDA be approved
intent: Is the vaccine FDA approved?
------------
text: If I had my vaccine, is it safe to go to the indoor gym?
intent:
</code></pre>
</details>
<details>
<summary>train subset prompt 示例: (intent: 考虑一下)</summary>
<pre><code>电销场景意图识别。如果不能确定，请输出 “未知意图”。<br>
Examples:
------------
text: 没关系啦 知道的
intent: 肯定答复
------------
text: 怎么能联系你
intent: 查联系方式
------------
text: 恩。让我想想吧。
intent: 考虑一下
------------
text: 说点有用的
intent: 请讲重点
------------
text: 唉唉
intent: 语气词
------------
text: 说快一点
intent: 请讲重点
------------
text: 再介绍一下
intent: 要求复述
------------
text: 从哪弄到我信息
intent: 质疑隐私安全
------------
text: 哎。。不是的
intent: 不是
------------
text: 给我电话号码
intent: 查联系方式
------------
text: 先看看吧
intent: 考虑一下
------------
text: 怎么知道道我的信息
intent: 质疑隐私安全
------------
text: 哎,再说吧,我再想想
intent: 考虑一下
------------
text: 不,我清醒。
intent: 不是
------------
text: 重说一次
intent: 要求复述
------------
text: 行了,晚安
intent: 肯定答复
------------
text: 额额额额
intent: 语气词
------------
text: 恩。哎再说吧我考虑一下hiahia
intent:
</code></pre>
</details>
<details>
<summary>train subset prompt 示例: (intent: 污言秽语)</summary>
<pre><code>电销场景意图识别。<br>
Examples:
text: 那留言
intent: 语音信箱<br>
text: 好啊,哈哈,没事,我再找其他的人
intent: 好的<br>
text: 在!
intent: 我在<br>
text: 要打副本,没时间
intent: 没时间<br>
text: 必须去学习!赶快去!
intent: 加快速度<br>
text: 好的。满汉全席送上
intent: 好的<br>
text: 你看到我给你的留言了么
intent: 语音信箱<br>
text: 我在呢。
intent: 我在<br>
text: 傻逼？
intent: 污言秽语<br>
text: 胸大无脑
intent: 污言秽语<br>
text: 不着急。
intent: 请等一等<br>
text: 恩 我是团子
intent: 做自我介绍<br>
text: 我是收电费的
intent: 做自我介绍<br>
text: 我现在没时间接电话呢,待会儿打给你。
intent: 没时间<br>
text: 好的。哈哈。初六见。我去睡觉啦
intent: 好的<br>
text: 在啊
intent: 我在<br>
text: 包皮猩
intent: 污言秽语<br>
text: 离开一下
intent: 请等一等<br>
text: 有病
intent: 污言秽语<br>
text: 给我留个言
intent: 语音信箱<br>
text: 你等一下
intent: 请等一等<br>
text: 立刻马上!!!快快快快
intent: 加快速度<br>
text: 我是郭钊源
intent: 做自我介绍<br>
text: 快点儿
intent: 加快速度<br>
text: 没时间睡觉怎么办吖
intent: 没时间<br>
text: 吃!你来
intent:
</code></pre>
</details>
<details>
<summary>test subset prompt 示例: (intent: 未能理解)</summary>
<pre><code>电销场景意图识别。如果不能确定，请输出 “未知意图”。<br>
Examples:
------------
text: 讲什么
intent: 未能理解
------------
text: 等着吧!
intent: 请等一等
------------
text: 搞不懂你
intent: 未能理解
------------
text: 我实在是不想弄了,我那时事多没时间啊!
intent: 没时间
------------
text: 这你自己不清楚自己啊,还不晓得
intent: 不清楚
------------
text: 没问题放心吧
intent: 肯定(没问题)
------------
text: 公司名字是什么
intent: 查公司介绍
------------
text: 不放弃
intent: 肯定(需要)
------------
text: 老师也不懂
intent:
</code></pre>
</details>
<details>
<summary>test subset prompt 示例: (intent: 肯定(嗯嗯))</summary>
<pre><code>电销场景意图识别。
不确定时请输出 “未知领域”。<br>
Examples:
------------
text: 截止期过了多少天
intent: 疑问(时长)
------------
text: 不了
intent: 不需要
------------
text: 不行,不够不够
intent: 否定(不可以)
------------
text: 4个1
intent: 答数值
------------
text: 辽宁
intent: 地址
------------
text: 不清楚
intent: 不清楚
------------
text: 店里
intent: 地址
------------
text: 嗯啊嗯嗯来吧
intent: 肯定(嗯嗯)
------------
text: 利息比别的贷款高
intent: 价格太高
------------
text: 算23点,[9,4,8,2
intent: 答数值
------------
text: 可以还得上
intent: 会按时处理
------------
text: 对啊 就是不行
intent: 否定(不可以)
------------
text: 真的不便宜
intent: 价格太高
------------
text: 嗯,thanks
intent: 肯定(嗯嗯)
------------
text: 这你自己不清楚自己啊,还不晓得
intent: 不清楚
------------
text: 我找找吧
intent: 会按时处理
------------
text: 这是拖欠几天了
intent: 疑问(时长)
------------
text: 不需要证据
intent: 不需要
------------
text: 噢,谢谢
intent: 肯定(嗯嗯)
------------
text: 恩恩,想我
intent:
</code></pre>
</details>
<details>
<summary>test subset prompt 示例: (intent: 不信任)</summary>
<pre><code>意图识别。<br>
Examples:
text: 你不要答非所问
intent: 答非所问<br>
text: 费用搞错了
intent: 否定(错误)<br>
text: 我给你留言了,你木有回
intent: 语音信箱<br>
text: 小骗子
intent: 不信任<br>
text: 昆明
intent: 实体(地址)<br>
text: 哦,行,好了你发信息给我
intent: 肯定(可以)<br>
text: 哦,这样啊,没时间就算了
intent: 没时间<br>
text: 我错了,别欺负我了
intent: 请求谅解<br>
text: 万一你们是骗子怎么办
intent: 不信任<br>
text: 我太乃刀了
intent: 无关领域<br>
text: 讲清楚重要的
intent: 请讲重点<br>
text: 骗子,好好说话
intent:
</code></pre>
</details>
### 数据来源
数据集从网上收集整理如下:
#### 意图识别
意图识别（英语）
| 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| ATIS | 英语 | [ATIS](https://paperswithcode.com/dataset/atis); [ATIS_dataset](https://github.com/howl-anderson/ATIS_dataset) | 4978(Training set)+893(Testing set) | 微软提供的公开数据集 (Airline Travel Information System)，实现意图识别任务。 | [atis_intents](https://huggingface.co/datasets/fathyshalab/atis_intents) |
| conv_intent | 英语 | [conv_intent](https://huggingface.co/datasets/generalization/conv_intent_Full-p_1) | 13.8K | | [intent-recogniton](https://www.kaggle.com/code/upsunny/intent-recogniton-based-on-bert) |
| banking77 | 英语 | [banking77](https://arxiv.org/abs/2003.04807); [task-specific-datasets](https://github.com/PolyAI-LDN/task-specific-datasets) | 13,083 | 在线银行查询数据集 | [banking77](https://huggingface.co/datasets/banking77) |
| mobile_assistant | 英语 | [Intent-Classification-large](https://huggingface.co/datasets/dipesh/Intent-Classification-large) | 17K (但是我去除了意图为 others 的样本.) | | |
| amazon_massive_intent_en_us | 英语 | [amazon_massive_intent_en_us](https://huggingface.co/datasets/SetFit/amazon_massive_intent_en-US) | 16.5K | Alexa virtual assistant | [nlu_evaluation_data](https://huggingface.co/datasets/nlu_evaluation_data) |
| snips_built_in_intents | 英语 | [nlu-benchmark](https://github.com/sonos/nlu-benchmark); [benchmarking](https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d) | 328 | | [snips_built_in_intents](https://huggingface.co/datasets/snips_built_in_intents) |
| vira_intents | 英语 | [vira-intent-classification](https://github.com/IBM/vira-intent-classification) | 10.9K | COVID-19 疫苗意图 | [vira_intents_live](https://huggingface.co/datasets/codesj/vira-intents-live); [vira_intents_live](https://huggingface.co/datasets/vira-chatbot/vira-intents-live) |
| intent_classification | 英语 | [intent_classification](https://huggingface.co/datasets/Bhuvaneshwari/intent_classification) | 13.8K | | |
| Out-of-Scope | 英语 | [范围外意图分类数据集](https://tianchi.aliyun.com/dataset/94112); [clinc150](https://archive.ics.uci.edu/dataset/570/clinc150); [clinc150](https://paperswithcode.com/dataset/clinc150) | | 该数据集提供了一种评估“Out-of-Scope”输入的意图分类模型的方法。 | [Out-of-Scope Intent Classification Dataset](https://www.kaggle.com/datasets/stefanlarson/outofscope-intent-classification-dataset); [clinc_oos](https://huggingface.co/datasets/clinc_oos); [xjlulu/ntu_adl_intent](https://huggingface.co/datasets/xjlulu/ntu_adl_intent); [cmaldona/Generalization-MultiClass-CLINC150-ROSTD](https://huggingface.co/datasets/cmaldona/Generalization-MultiClass-CLINC150-ROSTD) |
| finance21 | 英语 | [finance21](https://github.com/Dark-Sied/Intent_Classification/) | | | |
| book6 | 英语 | [book6](https://github.com/ajinkyaT/CNN_Intent_Classification) | 12000 | Six categories namely: AddToPlaylist, BookRestaurant, GetWeather , RateBook , SearchCreativeWork, SearchScreeningEvent each having nearly 2000 sentences. | [Intent Recognition Dataset](https://www.kaggle.com/datasets/himanshunayal/intent-recognition-dataset) |
| bi_text | 英语 | [bi_text](https://www.kaggle.com/datasets/bitext/training-dataset-for-chatbotsvirtual-assistants); [customer-support-intent-dataset](https://www.kaggle.com/datasets/scodepy/customer-support-intent-dataset) | 8175 | 该数据集涵盖“客户支持”领域，包括分为 11 个类别的 27 个意图。 这些意图是从 Bitext 的 20 个特定领域数据集（银行、零售、公用事业……）中选择的，保留了跨领域的通用意图。 | |
| small talk | 英语 | [Small Talk](https://www.kaggle.com/datasets/salmanfaroz/small-talk-intent-classification-data) | 3000 | 闲聊用于为用户提供与聊天机器人的随意对话流程 | |
| chatbots | 英语 | [Chatbots: Intent Recognition Dataset](https://www.kaggle.com/datasets/elvinagammed/chatbots-intent-recognition-dataset) | | 用于分类、识别和聊天机器人开发的数据 | |
| ide_intent | 英语 | [intent-classification-for-ide-functionalities](https://www.kaggle.com/datasets/abdullahusmani86/intent-classification-for-ide-functionalities) | 27019 | IDE 意图分类数据集。 | |
| star_wars | 英语 | [star-wars](https://www.kaggle.com/datasets/aslanahmedov/star-wars-chat-bot) | 100 | 包含有关星球大战宇宙的各种数据。 | |
| jarvis_intent | 英语 | [jarvisintent](https://www.kaggle.com/datasets/joelyu/jarvisintent) | 4556 | | |
| dnd_style_intents | 英语 | | train: 131K; eval: 16.3K; test: 16.3K; | 该数据集是为游戏开发者对话系统中的意图分类模块而设计的。 数据集中有超过 17 个意图的约 163K 个示例。 | [neurae/dnd_style_intents](https://huggingface.co/datasets/neurae/dnd_style_intents) |
意图识别（汉语）
| 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| amazon_massive_intent_zh_cn | 汉语 | [amazon_massive_intent_zh_cn](https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-CN) | 16.5K | Alexa virtual assistant | |
| THU Intent Corpus | 汉语 | | 共计约6,000个句子 | 清华大学发布的中文意图识别和词槽填充数据集，包含15个领域和27个意图类别 | |
| CrossWOZ | 汉语 | [CrossWOZ](https://github.com/thu-coai/CrossWOZ) | | CrossWOZ是第一个大规模中文跨域Wizard-of-Oz任务导向数据集。 它包含 5 个领域的 6K 对话会话和 102K 话语，包括酒店、餐厅、景点、地铁和出租车。 此外，该语料库还包含用户侧和系统侧丰富的对话状态和对话行为注释。 | |
| CMID | 汉语 | [CMID](https://github.com/ishine/CMID) | | 该数据集用于中文医学 QA 意图理解任务。 | |
| dmslots | 汉语 | [dmslots](https://raw.githubusercontent.com/kids/bert_nlu/main/data/dmslots.txt) | | 弱标注数据 | |
| SMP2017 | 汉语 | [SMP2017-ECDT](http://ir.hit.edu.cn/SMP2017-ECDT); [1709.10217](https://arxiv.org/abs/1709.10217); [SMP2017ECDT-DATA](https://github.com/HITlilingzhi/SMP2017ECDT-DATA) | | 第六届全国社会媒体处理大会之中文人机对话技术评测(SMP2017-ECDT) | [ChineseNLPCorpus](https://github.com/InsaneLife/ChineseNLPCorpus) |
| SMP2019 | 汉语 | [SMP2019](https://conference.cipsc.org.cn/smp2019/evaluation.html); [smp2019ecdt_task1](https://adamszq.github.io/smp2019ecdt_task1/) | | SMP2019 ECDT 中文人机对话技术测评 | [SMP2017-2019-ECDT-data](https://github.com/hml-ubt/SMP2017-2019-ECDT-data); [ChineseNLPCorpus](https://github.com/InsaneLife/ChineseNLPCorpus) |
| a_intent | 汉语 | [意图识别](https://blog.csdn.net/weixin_42551154/article/details/129480825); [意图识别](https://competition.coggle.club/); [a_intent](https://pan.baidu.com/s/19_oqY4bC_lJa_7Mc6lxU7w?pwd=v4bi) | 12000 | 该意图识别数据集是一个多分类任务，目标是根据用户的输入文本判断用户的意图 | |
| RiSAWOZ | 汉语 | [RiSAWOZ](https://gem-benchmark.com/data_cards/RiSAWOZ) | | RiSAWOZ 是一个中文对话数据集。 它可用于研究各种对话任务，例如对话状态跟踪、对话上下文到文本生成、共指消解以及统一生成省略号和共指消解。 | [GEM/RiSAWOZ](https://huggingface.co/datasets/GEM/RiSAWOZ) |
| IMCS-IR | 汉语 | [中文医疗信息处理评测基准CBLUE](https://tianchi.aliyun.com/dataset/95414); [CBLUE 智能对话诊疗意图识别 IMCS-IR](https://github.com/winninghealth/imcs-ir) | | 中文医疗信息处理挑战榜CBLUE | |
#### 文本分类
| 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| ag_news | 英语 | [AG_corpus_of_news_articles](http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html); [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626); [ag_news](https://huggingface.co/datasets/ag_news) | 120K | AG的新闻主题分类数据集 | |
| daily_dialog | 英语 | [DailyDialog](http://yanran.li/dailydialog) | 11.1K | 标签分类为：dummy (0), inform (1), question (2), directive (3), commissive (4). 情感分类为：no emotion (0), anger (1), disgust (2), fear (3), happiness (4), sadness (5), surprise (6). | [daily_dialog](https://huggingface.co/datasets/daily_dialog) |
| chinese_news_title | 汉语 | [中文新闻文本标题分类](https://aistudio.baidu.com/datasetdetail/103654) | | 中文新闻标题数据集包含可供训练的32类(即新闻主题)标题47,952个，可供测试的新闻标题15,986个。在删除这些包含不能处理的特殊字符的标题后，我们保留了47,850个训练标题和15,950个测试标题(即#DataSet1)。 | [百度网盘](https://pan.baidu.com/s/1mgBTFOO) |
#### 其它任务类型
| 数据 | 语言 | 任务类型 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :-----: | :---: | :---: | :---: | :---: |
| suicide_intent | 英语 | 情感分类 | [suicide-intent](https://www.kaggle.com/datasets/hetarthraval/suicide-intent-detection-dataset) | 3731 | 该数据集有四个类别：快乐、正常、悲伤和自杀意图。 | |
| CARER | 英语 | 情感分类 | [emotion](https://paperswithcode.com/dataset/emotion) | 20K | 情感是英语 Twitter 消息的数据集，包含六种基本情感：愤怒、恐惧、快乐、爱、悲伤和惊讶。 | [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) |
| COIG-CQIA | 汉语 | 指令微调 | [CValues](https://arxiv.org/abs/2307.09705); [paralym/COIG-CQIA](https://github.com/paralym/COIG-CQIA) | | 高质量指令微调数据集，旨在为中文NLP社区提供高质量且符合人类交互行为的指令微调数据。 | [m-a-p/COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA) |
| emo2019 | 英语 | 情感分类 | [SemEval-2019 Task 3](https://www.aclweb.org/anthology/S19-2005) | TRAIN: 30160, TEST: 5509 | 情绪检测。四个标签：others (0), happy (1), sad (2), angry (3). | [emo](https://huggingface.co/datasets/emo) |
### 数据加载
```python
#!/usr/bin/python3
# -*- coding: utf-8 -*-
from datasets import load_dataset, concatenate_datasets
name_list = [
"amazon_massive_intent_en_us_prompt",
"amazon_massive_intent_zh_cn_prompt",
"atis_intent_prompt",
"banking77_prompt",
"bi_text11_prompt",
"bi_text27_prompt",
"book6_prompt",
# "chinese_news_title_prompt",
"cmid_4class_prompt",
"cmid_36class_prompt",
"conv_intent_prompt",
"crosswoz_prompt",
"dmslots_prompt",
"finance21_prompt",
"intent_classification_prompt",
"mobile_assistant_prompt",
"mtop_intent_prompt",
"out_of_scope_prompt",
"small_talk_prompt",
"smp2017_task1_prompt",
"smp2019_task1_domain_prompt",
"smp2019_task1_intent_prompt",
"snips_built_in_intents_prompt",
"telemarketing_intent_en_prompt",
"telemarketing_intent_cn_prompt",
"vira_intents_prompt",
]
train_dataset = list()
for name in name_list:
dataset = load_dataset(
path="qgyd2021/few_shot_intent_sft",
name=name,
split="train",
)
train_dataset.append(dataset)
train_dataset = concatenate_datasets(train_dataset)
valid_dataset = list()
for name in name_list:
dataset = load_dataset(
path="qgyd2021/few_shot_intent_sft",
name=name,
split="test",
)
valid_dataset.append(dataset)
valid_dataset = concatenate_datasets(valid_dataset)
```
### 参考来源
<details>
<summary>参考的数据来源,展开查看</summary>
<pre><code>
https://huggingface.co/datasets/qanastek/MASSIVE
https://huggingface.co/datasets/fathyshalab/atis_intents
https://huggingface.co/datasets/generalization/conv_intent_Full-p_1
https://huggingface.co/datasets/banking77
https://huggingface.co/datasets/dipesh/Intent-Classification-large
https://huggingface.co/datasets/SetFit/amazon_massive_intent_en-US
https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-CN
https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-TW
https://huggingface.co/datasets/snips_built_in_intents
https://huggingface.co/datasets/zapsdcn/citation_intent
https://huggingface.co/datasets/ibm/vira-intents
https://huggingface.co/datasets/mteb/mtop_intent
https://huggingface.co/datasets/Bhuvaneshwari/intent_classification
https://huggingface.co/datasets/ibm/vira-intents-live
https://huggingface.co/datasets/ebrigham/nl_banking_intents
https://pan.baidu.com/s/19_oqY4bC_lJa_7Mc6lxU7w?pwd=v4bi
https://gitee.com/a2798063/SMP2019/tree/master
https://cold-eye.github.io/post/nlp-corpus/
https://www.cluebenchmarks.com/introduce.html
https://github.com/search?q=chinese%20intent&type=repositories
https://aistudio.baidu.com/projectdetail/3441337
JDDC Corpus (JingDong Dialogue Chanllenge)
https://arxiv.org/abs/1911.09969
https://github.com/SimonJYang/JDDC-Baseline-TFIDF
https://github.com/hrlinlp/jddc2.1
https://github.com/zhangbo2008/JDDC_for_train_gpt_data
https://github.com/anony-dev-res/JDDC
ECD Corpus (Ecommerce Dialogue Corpus) 多轮对话数据集，没有标注意图。
https://arxiv.org/abs/1806.09102
https://github.com/cooelf/DeepUtteranceAggregation
</code></pre>
</details>
|
zjsfxpm1/1231231 | ---
license: mit
---
|
distil-whisper/gigaspeech-l-timestamped | ---
license: other
task_categories:
- automatic-speech-recognition
language:
- en
extra_gated_prompt: |-
SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the Hub under certain conditions and terms.
Terms of Access:
The "Researcher" has requested permission to use the GigaSpeech database (the "Database") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
Please also fill out the Google Form https://forms.gle/UuGQAPyscGRrUMLq6 to request access to the GigaSpeech dataset.
extra_gated_fields:
Name: text
Email: text
Organization: text
Address: text
I hereby confirm that I have requested access via the Google Form provided above: checkbox
I accept the terms of access: checkbox
---
# Distil Whisper: GigaSpeech With Timestamps
This is a variant of the [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling and timestamp prediction. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/speechcolab/gigaspeech).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/gigaspeech-l", "l")
# take the first sample of the validation set
sample = dataset["validation"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/gigaspeech-l", "l", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under custom terms. To view the custom license for this dataset, refer to the original [dataset card](https://huggingface.co/datasets/speechcolab/gigaspeech).
|
Vinnyyw/Maitez | ---
license: openrail
---
|
Akg123/Akgandy1 | ---
pretty_name: Akg
size_categories:
- n<1K
--- |
kings-crown/summary_key | ---
license: mit
---
|
beelzeebuub/FJ-flagging | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## 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]
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## Uses
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### 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] |
Icannos/lichess_games | ---
license: cc0-1.0
task_categories:
- text-generation
language:
- en
pretty_name: Lichess Games
size_categories:
- 100B<n<1T
viewer: false
---
# Dataset Card for Lichess Games
## Dataset Description
- **Homepage:** https://database.lichess.org/
- **Point of Contact:** maxime.darrin@outlook.com
### Dataset Summary
This is an easy-to-use huggingface dataset to access the [lichess game database](https://database.lichess.org/). For now it supports only the standard games
but other variant will be added shortly.
Requirements:
```
chess
zstandard
```
### Supported Tasks and Leaderboards
It is intended for pretraining text generation models for chess games (in a PGN format).
## Dataset Structure
### Data Instances
Available configs consist on the year and month of the file as described here: https://database.lichess.org/.
For example to get a small sample one can try to download the dataset for june 2013 (~40mo).
```python
from datasets import load_dataset
dataset = load_dataset("Icannos/lichess_games", "2013-06", streaming=True)
```
Examples (3 rows from june 2013):
<details>
```
{'text': '[Event "Rated Bullet game"]\n'
'[Site "https://lichess.org/in28emmw"]\n'
'[Date "????.??.??"]\n'
'[Round "?"]\n'
'[White "Kazuma"]\n'
'[Black "kikeillana"]\n'
'[Result "1-0"]\n'
'[BlackElo "1684"]\n'
'[BlackRatingDiff "-9"]\n'
'[ECO "A07"]\n'
'[Opening "King\'s Indian Attack: Keres Variation #2"]\n'
'[Termination "Normal"]\n'
'[TimeControl "60+0"]\n'
'[UTCDate "2013.05.31"]\n'
'[UTCTime "22:00:22"]\n'
'[WhiteElo "1756"]\n'
'[WhiteRatingDiff "+11"]\n'
'\n'
'1. Nf3 d5 2. g3 Bg4 3. Bg2 Bxf3 4. Bxf3 e6 5. O-O Bb4 6. d4 Nd7 7. '
'c3 Ba5 8. Bf4 Bb6 9. b4 a6 10. a4 c6 11. Nd2 Ngf6 12. e4 dxe4 13. '
'Nxe4 Nxe4 14. Bxe4 f6 15. c4 h6 16. c5 Bc7 17. Qb3 Bxf4 18. Qxe6+ '
'Qe7 19. Bg6+ Kd8 20. Qxe7+ Kxe7 21. gxf4 Rhe8 22. Bxe8 Rxe8 23. '
'Rfe1+ Kf7 24. Rxe8 Kxe8 25. Re1+ Kf7 26. Re4 g6 27. Kg2 f5 28. Re3 '
'h5 29. Kf3 Kg7 30. Re7+ Kf6 31. Rxd7 g5 32. Rxb7 1-0'}
{'text': '[Event "Rated Bullet game"]\n'
'[Site "https://lichess.org/e174t8h7"]\n'
'[Date "????.??.??"]\n'
'[Round "?"]\n'
'[White "Aceves"]\n'
'[Black "calculus"]\n'
'[Result "0-1"]\n'
'[BlackElo "1568"]\n'
'[BlackRatingDiff "+9"]\n'
'[ECO "D00"]\n'
'[Opening "Queen\'s Pawn Game #3"]\n'
'[Termination "Time forfeit"]\n'
'[TimeControl "60+1"]\n'
'[UTCDate "2013.05.31"]\n'
'[UTCTime "22:02:13"]\n'
'[WhiteElo "1487"]\n'
'[WhiteRatingDiff "-9"]\n'
'\n'
'1. d4 d5 2. e3 Nf6 3. c3 Bg4 4. Qc2 e6 5. Bd3 Bd6 6. Nd2 c6 7. e4 '
'dxe4 8. Nxe4 Nxe4 9. Bxe4 Bc7 10. Bxh7 g6 11. h3 Bf5 12. Qe2 Rxh7 '
'13. Be3 Qd6 14. Nf3 Nd7 15. Ng5 Rh8 16. g3 f6 17. Bf4 e5 18. dxe5 '
'fxe5 19. Bxe5 Qxe5 20. Qe3 Qxe3+ 21. fxe3 Bxg3+ 22. Ke2 Bh4 23. Nf3 '
'Be4 24. Rad1 O-O-O 25. Rhf1 Rhf8 26. Nd4 Rxf1 27. Rxf1 Ne5 28. Ne6 '
'Re8 29. Ng7 Re7 30. Rf4 Bd3+ 31. Kd2 Rxg7 32. Rxh4 Nf3+ 33. Kd1 Nxh4 '
'34. Kd2 Bf5 0-1'}
{'text': '[Event "Rated Blitz game"]\n'
'[Site "https://lichess.org/d4ui60z6"]\n'
'[Date "????.??.??"]\n'
'[Round "?"]\n'
'[White "melro"]\n'
'[Black "patrimpas"]\n'
'[Result "0-1"]\n'
'[BlackElo "1912"]\n'
'[BlackRatingDiff "+0"]\n'
'[ECO "B20"]\n'
'[Opening "Sicilian Defense: Staunton-Cochrane Variation"]\n'
'[Termination "Normal"]\n'
'[TimeControl "240+0"]\n'
'[UTCDate "2013.05.31"]\n'
'[UTCTime "22:02:15"]\n'
'[WhiteElo "1144"]\n'
'[WhiteRatingDiff "-1"]\n'
'\n'
'1. e4 c5 2. c4 Nc6 3. d3 g6 4. Bd2 Bg7 5. Bc3 Nf6 6. Nd2 d6 7. Rb1 '
'O-O 8. Bxf6 Bxf6 9. b3 Qa5 10. a4 Bc3 11. f3 e6 12. Ne2 Bg7 13. g4 '
'd5 14. h3 Nd4 15. Nxd4 cxd4 16. Be2 dxe4 17. fxe4 Bh6 18. Rb2 e5 19. '
'O-O Be3+ 20. Kh1 Qd8 21. Nf3 Bf4 22. Rf2 h5 23. Rg2 hxg4 24. hxg4 '
'Kg7 25. Kg1 Rh8 26. Kf2 Qf6 27. Qc2 Rh3 28. Qd1 Be3+ 29. Ke1 Rh1+ '
'30. Rg1 0-1'}
```
</details>
### Data Fields
Only a single column "text". Each row contains a single game in PGN format.
### How to use with python-chess
```python
from datasets import load_dataset
import chess.pgn
import io
dataset = load_dataset("lichess_games", "2013-06", streaming=True)
for d in dataset['train']:
pgn = io.StringIO(d['text'])
game = chess.pgn.read_game(pgn)
print(game.headers['White'], game.headers['Black'])
print(game.headers['Result'])
print(game.mainline_moves())
break
```
### Data Splits
No splits only the file per dates.
### Source Data
The underlying data are provided and maintained by the Lichess team and provided under CC0 license (https://database.lichess.org/). I only provide the huggingface interface here.
The loading script download the zstd files and reads from them on the fly without decompressing the whole file, and parses the games using python-chess.
#### Initial Data Collection and Normalization
The data comes from all the standard rated games played on lichess.org. Every rated game played on lichess and its metadata are recorded and stored by lichess.
Lichess.org provides a forever free to use, libre and open-source plateform to play chess online.
### Annotations
Some of the games (~6% according to lichess: https://database.lichess.org/) comes annotated (directly in the PGN format) with computer analysis of the move:
```
About 6% of the games include Stockfish analysis evaluations: [%eval 2.35] (235 centipawn advantage), [%eval #-4] (getting mated in 4), always from White's point of view.
The WhiteElo and BlackElo tags contain Glicko2 ratings.
Games contain clock information as PGN %clk comments since April 2017.
Variant games have a Variant tag, e.g., [Variant "Antichess"].
```
### Personal and Sensitive Information
The metadata of the different PGN contains information of the players (their pseudo on lichess), the date and times when the game happened, the strength of the players
(in terms of ELO rating) and a link to the game on the platform.
An example of metadata from one the games.
```
[Event "Rated Bullet tournament https://lichess.org/tournament/yc1WW2Ox"]
[Site "https://lichess.org/PpwPOZMq"]
[Date "2017.04.01"]
[Round "-"]
[White "Abbot"]
[Black "Costello"]
[Result "0-1"]
[UTCDate "2017.04.01"]
[UTCTime "11:32:01"]
[WhiteElo "2100"]
[BlackElo "2000"]
[WhiteRatingDiff "-4"]
[BlackRatingDiff "+1"]
[WhiteTitle "FM"]
[ECO "B30"]
[Opening "Sicilian Defense: Old Sicilian"]
[TimeControl "300+0"]
[Termination "Time forfeit"]
```
## Additional Information
### Licensing Information
Lichess provides all the data under CC0.
### Citation Information
TO COME.
|
CyberHarem/suzuran_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of suzuran/スズラン/铃兰 (Arknights)
This is the dataset of suzuran/スズラン/铃兰 (Arknights), containing 500 images and their tags.
The core tags of this character are `animal_ears, fox_ears, blonde_hair, fox_girl, animal_ear_fluff, hairband, fox_tail, tail, green_eyes, multiple_tails, blue_hairband, multicolored_hair, white_hair, braid, hair_rings, braided_hair_rings, two-tone_hair, twin_braids`, 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 | 977.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuran_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 432.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuran_arknights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1327 | 1020.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuran_arknights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 797.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuran_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1327 | 1.58 GiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuran_arknights/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/suzuran_arknights',
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 | 14 |  |  |  |  |  | 1girl, bare_shoulders, black_footwear, dress, frills, infection_monitor_(arknights), kitsune, looking_at_viewer, oripathy_lesion_(arknights), solo, white_pantyhose, black_gloves, full_body, holding_staff, shoes, single_glove, closed_mouth, white_background, earpiece, purple_skirt, simple_background, smile, torn_pantyhose, standing, wrist_cuffs, blush, pouch, shirt |
| 1 | 5 |  |  |  |  |  | 1girl, bare_shoulders, black_gloves, closed_mouth, kitsune, looking_at_viewer, solo, white_shirt, dress, earpiece, frills, hair_between_eyes, holding_staff, infection_monitor_(arknights), oripathy_lesion_(arknights), purple_skirt, simple_background, single_glove, waist_apron, white_apron, white_background, wrist_cuffs, smile, blue_skirt |
| 2 | 14 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, simple_background, solo, upper_body, closed_mouth, infection_monitor_(arknights), white_background, white_shirt, blush, smile, oripathy_lesion_(arknights), earpiece, cropped_torso, hair_between_eyes, kitsune |
| 3 | 5 |  |  |  |  |  | 1girl, black_gloves, black_scarf, closed_mouth, fingerless_gloves, goggles_on_head, looking_at_viewer, official_alternate_costume, solo, long_hair, upper_body, holding_staff, kitsune, simple_background, white_background, hair_between_eyes, long_sleeves, outdoors, shirt, short_sleeves, smile |
| 4 | 9 |  |  |  |  |  | 1girl, goggles_on_head, official_alternate_costume, outdoors, solo, fingerless_gloves, long_hair, boots, brown_footwear, dirty_face, torn_pants, long_sleeves, looking_at_viewer, holding_staff, kyuubi, black_pants, black_scarf, full_body, parted_lips, sitting |
| 5 | 13 |  |  |  |  |  | 1girl, frilled_hairband, long_hair, neck_ribbon, official_alternate_costume, puffy_long_sleeves, red_ribbon, solo, white_shirt, looking_at_viewer, open_cardigan, blue_skirt, shoulder_bag, smile, high-waist_skirt, black_cat, brown_bag, jacket, on_head, blush, closed_mouth, cross-laced_clothes, outdoors, cross-laced_slit, holding_basket, crossover, kyuubi, open_mouth, white_background, yellow_cardigan |
| 6 | 6 |  |  |  |  |  | 1girl, black_hakama, frilled_apron, hair_flower, hakama_skirt, holding_umbrella, kyuubi, long_hair, long_sleeves, looking_at_viewer, obi, official_alternate_costume, oil-paper_umbrella, okobo, pink_kimono, pinwheel, red_hairband, red_umbrella, sandals, solo, tabi, waist_apron, white_apron, white_socks, wide_sleeves, yagasuri, coin_purse, full_body, parted_lips, bobby_socks, floral_print, pleated_skirt, tassel, blush, standing |
| 7 | 6 |  |  |  |  |  | 1girl, frilled_apron, hair_flower, holding_umbrella, kyuubi, long_hair, long_sleeves, obi, official_alternate_costume, oil-paper_umbrella, pink_kimono, pinwheel, red_hairband, red_umbrella, solo, waist_apron, white_apron, wide_sleeves, black_hakama, coin_purse, hakama_skirt, looking_at_viewer, yagasuri, parted_lips, blush, snow |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_footwear | dress | frills | infection_monitor_(arknights) | kitsune | looking_at_viewer | oripathy_lesion_(arknights) | solo | white_pantyhose | black_gloves | full_body | holding_staff | shoes | single_glove | closed_mouth | white_background | earpiece | purple_skirt | simple_background | smile | torn_pantyhose | standing | wrist_cuffs | blush | pouch | shirt | white_shirt | hair_between_eyes | waist_apron | white_apron | blue_skirt | upper_body | cropped_torso | black_scarf | fingerless_gloves | goggles_on_head | official_alternate_costume | long_hair | long_sleeves | outdoors | short_sleeves | boots | brown_footwear | dirty_face | torn_pants | kyuubi | black_pants | parted_lips | sitting | frilled_hairband | neck_ribbon | puffy_long_sleeves | red_ribbon | open_cardigan | shoulder_bag | high-waist_skirt | black_cat | brown_bag | jacket | on_head | cross-laced_clothes | cross-laced_slit | holding_basket | crossover | open_mouth | yellow_cardigan | black_hakama | frilled_apron | hair_flower | hakama_skirt | holding_umbrella | obi | oil-paper_umbrella | okobo | pink_kimono | pinwheel | red_hairband | red_umbrella | sandals | tabi | white_socks | wide_sleeves | yagasuri | coin_purse | bobby_socks | floral_print | pleated_skirt | tassel | snow |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------------|:--------|:---------|:--------------------------------|:----------|:--------------------|:------------------------------|:-------|:------------------|:---------------|:------------|:----------------|:--------|:---------------|:---------------|:-------------------|:-----------|:---------------|:--------------------|:--------|:-----------------|:-----------|:--------------|:--------|:--------|:--------|:--------------|:--------------------|:--------------|:--------------|:-------------|:-------------|:----------------|:--------------|:--------------------|:------------------|:-----------------------------|:------------|:---------------|:-----------|:----------------|:--------|:-----------------|:-------------|:-------------|:---------|:--------------|:--------------|:----------|:-------------------|:--------------|:---------------------|:-------------|:----------------|:---------------|:-------------------|:------------|:------------|:---------|:----------|:----------------------|:-------------------|:-----------------|:------------|:-------------|:------------------|:---------------|:----------------|:--------------|:---------------|:-------------------|:------|:---------------------|:--------|:--------------|:-----------|:---------------|:---------------|:----------|:-------|:--------------|:---------------|:-----------|:-------------|:--------------|:---------------|:----------------|:---------|:-------|
| 0 | 14 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | | X | | X | | X | X | X | X | X | X | X | | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | X | | | | X | X | X | X | X | | | | | | | X | X | X | | X | X | | | | X | | | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | | | | X | X | | X | | X | | X | | | X | X | | | X | X | | | | | | X | | X | | | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | | | | | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 13 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | 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 | |
| 7 | 6 |  |  |  |  |  | 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 |
|
hezarai/arman-ner | ---
task_categories:
- token-classification
language:
- fa
pretty_name: ARMAN-NER
--- |
usvsnsp/memories-semantic-memorization-filter-results | ---
dataset_info:
features:
- name: sequence_id
dtype: int64
- name: text
dtype: string
- name: sequence_duplicates
dtype: int64
- name: max_frequency
dtype: int64
- name: avg_frequency
dtype: float64
- name: min_frequency
dtype: int64
- name: median_frequency
dtype: float64
- name: p25_frequency
dtype: int64
- name: p75_frequency
dtype: int64
- name: frequencies
sequence: int64
- name: is_incrementing
dtype: bool
- name: tokens
sequence: int64
- name: repeating_offset
dtype: int32
- name: num_repeating
dtype: int32
- name: smallest_repeating_chunk
sequence: int64
- name: memorization_score
dtype: float64
- name: templating_frequency_0.9
dtype: int64
- name: templating_frequency_0.8
dtype: int64
- name: prompt_perplexity
dtype: float32
- name: generation_perplexity
dtype: float32
- name: sequence_perplexity
dtype: float32
splits:
- name: memories.duped.70m
num_bytes: 648141277
num_examples: 463953
- name: memories.duped.160m
num_bytes: 955903849
num_examples: 689673
- name: memories.duped.410m
num_bytes: 1337555782
num_examples: 970341
- name: memories.duped.1b
num_bytes: 1725540452
num_examples: 1256141
- name: memories.duped.1.4b
num_bytes: 1884519155
num_examples: 1373722
- name: memories.duped.2.8b
num_bytes: 2292743123
num_examples: 1675077
- name: memories.duped.6.9b
num_bytes: 2898035658
num_examples: 2120976
- name: memories.duped.12b
num_bytes: 3252649684
num_examples: 2382328
- name: memories.deduped.70m
num_bytes: 576211560
num_examples: 411448
- name: memories.deduped.160m
num_bytes: 809545073
num_examples: 581195
- name: memories.deduped.410m
num_bytes: 1126006111
num_examples: 811039
- name: memories.deduped.1b
num_bytes: 1430399436
num_examples: 1032865
- name: memories.deduped.1.4b
num_bytes: 1450336662
num_examples: 1048097
- name: memories.deduped.2.8b
num_bytes: 1871907415
num_examples: 1355211
- name: memories.deduped.6.9b
num_bytes: 2319039796
num_examples: 1680294
- name: memories.deduped.12b
num_bytes: 2581349436
num_examples: 1871216
download_size: 9223426756
dataset_size: 27159884469
---
# Dataset Card for "memories-semantic-memorization-filter-results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_cloudyu__mixtral_7bx4_moe | ---
pretty_name: Evaluation run of cloudyu/mixtral_7bx4_moe
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cloudyu/mixtral_7bx4_moe](https://huggingface.co/cloudyu/mixtral_7bx4_moe) 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_cloudyu__mixtral_7bx4_moe\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-23T17:37:28.145090](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__mixtral_7bx4_moe/blob/main/results_2023-12-23T17-37-28.145090.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\": 0.6311139010801706,\n\
\ \"acc_stderr\": 0.03229082356266579,\n \"acc_norm\": 0.632622270079106,\n\
\ \"acc_norm_stderr\": 0.0329353580988297,\n \"mc1\": 0.423500611995104,\n\
\ \"mc1_stderr\": 0.017297421448534727,\n \"mc2\": 0.5985125569293038,\n\
\ \"mc2_stderr\": 0.015744189058578734\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6126279863481229,\n \"acc_stderr\": 0.014235872487909865,\n\
\ \"acc_norm\": 0.6527303754266212,\n \"acc_norm_stderr\": 0.013913034529620451\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6685919139613623,\n\
\ \"acc_stderr\": 0.00469757396216943,\n \"acc_norm\": 0.8528181637124079,\n\
\ \"acc_norm_stderr\": 0.0035356302890914566\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720685,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720685\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n\
\ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438655,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438655\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\
\ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4312169312169312,\n \"acc_stderr\": 0.025506481698138215,\n \"\
acc_norm\": 0.4312169312169312,\n \"acc_norm_stderr\": 0.025506481698138215\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\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7645161290322581,\n\
\ \"acc_stderr\": 0.02413763242933771,\n \"acc_norm\": 0.7645161290322581,\n\
\ \"acc_norm_stderr\": 0.02413763242933771\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\
\ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932022,\n \"\
acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932022\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768776,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768776\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6435897435897436,\n \"acc_stderr\": 0.02428314052946731,\n \
\ \"acc_norm\": 0.6435897435897436,\n \"acc_norm_stderr\": 0.02428314052946731\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524575,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524575\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931666,\n\
\ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931666\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8256880733944955,\n \"acc_stderr\": 0.016265675632010323,\n \"\
acc_norm\": 0.8256880733944955,\n \"acc_norm_stderr\": 0.016265675632010323\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.04058042015646034,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.04058042015646034\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\
\ \"acc_stderr\": 0.019875655027867443,\n \"acc_norm\": 0.8974358974358975,\n\
\ \"acc_norm_stderr\": 0.019875655027867443\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.01362555690799345,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.01362555690799345\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577615,\n\
\ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577615\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3486033519553073,\n\
\ \"acc_stderr\": 0.015937484656687033,\n \"acc_norm\": 0.3486033519553073,\n\
\ \"acc_norm_stderr\": 0.015937484656687033\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.02617390850671858,\n\
\ \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.02617390850671858\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\
\ \"acc_stderr\": 0.026160584450140453,\n \"acc_norm\": 0.6945337620578779,\n\
\ \"acc_norm_stderr\": 0.026160584450140453\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495036,\n\
\ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495036\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\": 0.4556714471968709,\n\
\ \"acc_stderr\": 0.012719949543032205,\n \"acc_norm\": 0.4556714471968709,\n\
\ \"acc_norm_stderr\": 0.012719949543032205\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n\
\ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6470588235294118,\n \"acc_stderr\": 0.019333142020797164,\n \
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.019333142020797164\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399673,\n\
\ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399673\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.02553843336857833,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.02553843336857833\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263686,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263686\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.423500611995104,\n\
\ \"mc1_stderr\": 0.017297421448534727,\n \"mc2\": 0.5985125569293038,\n\
\ \"mc2_stderr\": 0.015744189058578734\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.77663772691397,\n \"acc_stderr\": 0.0117056975652052\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6209249431387415,\n \
\ \"acc_stderr\": 0.013363630295088361\n }\n}\n```"
repo_url: https://huggingface.co/cloudyu/mixtral_7bx4_moe
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_12_23T17_37_28.145090
path:
- '**/details_harness|arc:challenge|25_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|gsm8k|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hellaswag|10_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-23T17-37-28.145090.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-23T17-37-28.145090.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- '**/details_harness|winogrande|5_2023-12-23T17-37-28.145090.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-23T17-37-28.145090.parquet'
- config_name: results
data_files:
- split: 2023_12_23T17_37_28.145090
path:
- results_2023-12-23T17-37-28.145090.parquet
- split: latest
path:
- results_2023-12-23T17-37-28.145090.parquet
---
# Dataset Card for Evaluation run of cloudyu/mixtral_7bx4_moe
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cloudyu/mixtral_7bx4_moe](https://huggingface.co/cloudyu/mixtral_7bx4_moe) 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_cloudyu__mixtral_7bx4_moe",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-23T17:37:28.145090](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__mixtral_7bx4_moe/blob/main/results_2023-12-23T17-37-28.145090.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": 0.6311139010801706,
"acc_stderr": 0.03229082356266579,
"acc_norm": 0.632622270079106,
"acc_norm_stderr": 0.0329353580988297,
"mc1": 0.423500611995104,
"mc1_stderr": 0.017297421448534727,
"mc2": 0.5985125569293038,
"mc2_stderr": 0.015744189058578734
},
"harness|arc:challenge|25": {
"acc": 0.6126279863481229,
"acc_stderr": 0.014235872487909865,
"acc_norm": 0.6527303754266212,
"acc_norm_stderr": 0.013913034529620451
},
"harness|hellaswag|10": {
"acc": 0.6685919139613623,
"acc_stderr": 0.00469757396216943,
"acc_norm": 0.8528181637124079,
"acc_norm_stderr": 0.0035356302890914566
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720685,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720685
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6644736842105263,
"acc_stderr": 0.038424985593952694,
"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.038424985593952694
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.028254200344438655,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.028254200344438655
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6358381502890174,
"acc_stderr": 0.03669072477416907,
"acc_norm": 0.6358381502890174,
"acc_norm_stderr": 0.03669072477416907
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082636,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082636
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5914893617021276,
"acc_stderr": 0.032134180267015755,
"acc_norm": 0.5914893617021276,
"acc_norm_stderr": 0.032134180267015755
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4312169312169312,
"acc_stderr": 0.025506481698138215,
"acc_norm": 0.4312169312169312,
"acc_norm_stderr": 0.025506481698138215
},
"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": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7645161290322581,
"acc_stderr": 0.02413763242933771,
"acc_norm": 0.7645161290322581,
"acc_norm_stderr": 0.02413763242933771
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.49261083743842365,
"acc_stderr": 0.035176035403610084,
"acc_norm": 0.49261083743842365,
"acc_norm_stderr": 0.035176035403610084
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7575757575757576,
"acc_stderr": 0.030532892233932022,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.030532892233932022
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.022473253332768776,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768776
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6435897435897436,
"acc_stderr": 0.02428314052946731,
"acc_norm": 0.6435897435897436,
"acc_norm_stderr": 0.02428314052946731
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.028661201116524575,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.028661201116524575
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6596638655462185,
"acc_stderr": 0.030778057422931666,
"acc_norm": 0.6596638655462185,
"acc_norm_stderr": 0.030778057422931666
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.038020397601079024,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.038020397601079024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8256880733944955,
"acc_stderr": 0.016265675632010323,
"acc_norm": 0.8256880733944955,
"acc_norm_stderr": 0.016265675632010323
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7990196078431373,
"acc_stderr": 0.028125972265654373,
"acc_norm": 0.7990196078431373,
"acc_norm_stderr": 0.028125972265654373
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7805907172995781,
"acc_stderr": 0.026939106581553945,
"acc_norm": 0.7805907172995781,
"acc_norm_stderr": 0.026939106581553945
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6816143497757847,
"acc_stderr": 0.03126580522513713,
"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7633587786259542,
"acc_stderr": 0.03727673575596913,
"acc_norm": 0.7633587786259542,
"acc_norm_stderr": 0.03727673575596913
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990947,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990947
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252626,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252626
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7791411042944786,
"acc_stderr": 0.03259177392742179,
"acc_norm": 0.7791411042944786,
"acc_norm_stderr": 0.03259177392742179
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.04058042015646034,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.04058042015646034
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8974358974358975,
"acc_stderr": 0.019875655027867443,
"acc_norm": 0.8974358974358975,
"acc_norm_stderr": 0.019875655027867443
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.01362555690799345,
"acc_norm": 0.8237547892720306,
"acc_norm_stderr": 0.01362555690799345
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7196531791907514,
"acc_stderr": 0.024182427496577615,
"acc_norm": 0.7196531791907514,
"acc_norm_stderr": 0.024182427496577615
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3486033519553073,
"acc_stderr": 0.015937484656687033,
"acc_norm": 0.3486033519553073,
"acc_norm_stderr": 0.015937484656687033
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7026143790849673,
"acc_stderr": 0.02617390850671858,
"acc_norm": 0.7026143790849673,
"acc_norm_stderr": 0.02617390850671858
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6945337620578779,
"acc_stderr": 0.026160584450140453,
"acc_norm": 0.6945337620578779,
"acc_norm_stderr": 0.026160584450140453
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7098765432098766,
"acc_stderr": 0.025251173936495036,
"acc_norm": 0.7098765432098766,
"acc_norm_stderr": 0.025251173936495036
},
"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": 0.4556714471968709,
"acc_stderr": 0.012719949543032205,
"acc_norm": 0.4556714471968709,
"acc_norm_stderr": 0.012719949543032205
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6617647058823529,
"acc_stderr": 0.028739328513983572,
"acc_norm": 0.6617647058823529,
"acc_norm_stderr": 0.028739328513983572
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6470588235294118,
"acc_stderr": 0.019333142020797164,
"acc_norm": 0.6470588235294118,
"acc_norm_stderr": 0.019333142020797164
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302505,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302505
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.746938775510204,
"acc_stderr": 0.027833023871399673,
"acc_norm": 0.746938775510204,
"acc_norm_stderr": 0.027833023871399673
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.02553843336857833,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.02553843336857833
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.035887028128263686,
"acc_norm": 0.85,
"acc_norm_stderr": 0.035887028128263686
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.423500611995104,
"mc1_stderr": 0.017297421448534727,
"mc2": 0.5985125569293038,
"mc2_stderr": 0.015744189058578734
},
"harness|winogrande|5": {
"acc": 0.77663772691397,
"acc_stderr": 0.0117056975652052
},
"harness|gsm8k|5": {
"acc": 0.6209249431387415,
"acc_stderr": 0.013363630295088361
}
}
```
## 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] |
vaishali/spider-tableQA-pretraining | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: query
dtype: string
- name: table_names
sequence: string
- name: tables
sequence: string
- name: answer
dtype: string
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 1640743917
num_examples: 3816
- name: validation
num_bytes: 218540389
num_examples: 536
download_size: 390262655
dataset_size: 1859284306
---
# Dataset Card for "spider-tableQA-pretraining"
# Usage
```python
import pandas as pd
from datasets import load_dataset
spider_tableQA_pretraining = load_dataset("vaishali/spider-tableQA-pretraining")
for sample in spider_tableQA_pretraining['train']:
sql_query = sample['query']
input_table_names = sample["table_names"]
input_tables = [pd.read_json(table, orient='split') for table in sample['tables']]
answer = pd.read_json(sample['answer'], orient='split')
# flattened input/output
input_to_model = sample["source"]
target = sample["target"]
```
# BibTeX entry and citation info
```
@inproceedings{pal-etal-2023-multitabqa,
title = "{M}ulti{T}ab{QA}: Generating Tabular Answers for Multi-Table Question Answering",
author = "Pal, Vaishali and
Yates, Andrew and
Kanoulas, Evangelos and
de Rijke, Maarten",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.348",
doi = "10.18653/v1/2023.acl-long.348",
pages = "6322--6334",
abstract = "Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table. However, real-world queries are complex in nature, often over multiple tables in a relational database or web page. Single table questions do not involve common table operations such as set operations, Cartesian products (joins), or nested queries. Furthermore, multi-table operations often result in a tabular output, which necessitates table generation capabilities of tabular QA models. To fill this gap, we propose a new task of answering questions over multiple tables. Our model, MultiTabQA, not only answers questions over multiple tables, but also generalizes to generate tabular answers. To enable effective training, we build a pre-training dataset comprising of 132,645 SQL queries and tabular answers. Further, we evaluate the generated tables by introducing table-specific metrics of varying strictness assessing various levels of granularity of the table structure. MultiTabQA outperforms state-of-the-art single table QA models adapted to a multi-table QA setting by finetuning on three datasets: Spider, Atis and GeoQuery.",
}
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kristmh/flutter_testset_with_med_low_1 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: text_clean
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 2931904
num_examples: 2370
download_size: 1068446
dataset_size: 2931904
---
# Dataset Card for "flutter_testset_with_med_low_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nuprl/ts-eval-with-usages | ---
dataset_info:
features:
- name: hexsha
dtype: string
- name: size
dtype: int64
- name: ext
dtype: string
- name: lang
dtype: string
- name: max_stars_repo_path
dtype: string
- name: max_stars_repo_name
dtype: string
- name: max_stars_repo_head_hexsha
dtype: string
- name: max_stars_repo_licenses
sequence: string
- name: max_stars_count
dtype: float64
- name: max_stars_repo_stars_event_min_datetime
dtype: string
- name: max_stars_repo_stars_event_max_datetime
dtype: string
- name: max_issues_repo_path
dtype: string
- name: max_issues_repo_name
dtype: string
- name: max_issues_repo_head_hexsha
dtype: string
- name: max_issues_repo_licenses
sequence: string
- name: max_issues_count
dtype: float64
- name: max_issues_repo_issues_event_min_datetime
dtype: string
- name: max_issues_repo_issues_event_max_datetime
dtype: string
- name: max_forks_repo_path
dtype: string
- name: max_forks_repo_name
dtype: string
- name: max_forks_repo_head_hexsha
dtype: string
- name: max_forks_repo_licenses
sequence: string
- name: max_forks_count
dtype: float64
- name: max_forks_repo_forks_event_min_datetime
dtype: string
- name: max_forks_repo_forks_event_max_datetime
dtype: string
- name: content
dtype: string
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
- name: alphanum_fraction
dtype: float64
- name: loc
dtype: int64
- name: functions
dtype: int64
- name: function_signatures
dtype: int64
- name: function_parameters
dtype: int64
- name: variable_declarations
dtype: int64
- name: property_declarations
dtype: int64
- name: function_usages
dtype: int64
- name: trivial_types
dtype: int64
- name: predefined_types
dtype: int64
- name: type_definitions
dtype: int64
- name: dynamism_heuristic
dtype: int64
- name: loc_per_function
dtype: float64
- name: estimated_tokens
dtype: int64
- name: fun_ann_density
dtype: float64
- name: var_ann_density
dtype: float64
- name: prop_ann_density
dtype: float64
- name: typedef_density
dtype: float64
- name: dynamism_density
dtype: float64
- name: trivial_density
dtype: float64
- name: predefined_density
dtype: float64
- name: metric
dtype: float64
- name: content_without_annotations
dtype: string
splits:
- name: test
num_bytes: 6009683
num_examples: 744
download_size: 2426430
dataset_size: 6009683
---
# Dataset Card for "ts-eval-with-usages"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/random_letter_same_length_find_passage_train30_eval20_num | ---
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
splits:
- name: train
num_bytes: 25210
num_examples: 80
- name: validation
num_bytes: 7230
num_examples: 20
download_size: 23539
dataset_size: 32440
---
# Dataset Card for "random_letter_same_length_find_passage_train30_eval20_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alvations/c4p0-v1-en-es | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: target_backto_source
dtype: string
- name: raw_target
list:
- name: generated_text
dtype: string
- name: raw_target_backto_source
list:
- name: generated_text
dtype: string
- name: prompt
dtype: string
- name: reverse_prompt
dtype: string
- name: source_langid
dtype: string
- name: target_langid
dtype: string
- name: target_backto_source_langid
dtype: string
- name: doc_id
dtype: int64
- name: sent_id
dtype: int64
- name: timestamp
dtype: string
- name: url
dtype: string
- name: doc_hash
dtype: string
- name: dataset
dtype: string
- name: source_lang
dtype: string
- name: target_lang
dtype: string
splits:
- name: train
num_bytes: 23053968
num_examples: 18476
download_size: 10003956
dataset_size: 23053968
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
aminlouhichi/donut3 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 25755597.0
num_examples: 60
- name: validation
num_bytes: 25755597.0
num_examples: 60
- name: test
num_bytes: 25755597.0
num_examples: 60
download_size: 55055025
dataset_size: 77266791.0
---
# Dataset Card for "donut3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
qgallouedec/prj_gia_dataset_metaworld_sweep_v2_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the sweep-v2 environment, sample for the policy sweep-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_sweep_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_sweep_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
|
CyberHarem/kinu_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kinu/鬼怒/鬼怒 (Azur Lane)
This is the dataset of kinu/鬼怒/鬼怒 (Azur Lane), containing 86 images and their tags.
The core tags of this character are `yellow_eyes, hair_between_eyes, bangs, white_hair, horns, breasts, oni_horns, medium_hair, medium_breasts, short_hair`, 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 | 97.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 86 | 56.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 196 | 114.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 86 | 87.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 196 | 159.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_azurlane/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/kinu_azurlane',
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 | 33 |  |  |  |  |  | 1girl, solo, looking_at_viewer, black_cape, fingerless_gloves, military_uniform, long_sleeves, military_hat, black_jacket, black_headwear, closed_mouth, peaked_cap, holding_sword, red_gloves, black_shorts, multicolored_cape, simple_background, white_background, armor, low_ponytail, black_thighhighs, grey_hair, katana, retrofit_(azur_lane), earrings |
| 1 | 6 |  |  |  |  |  | 1girl, solo, bare_shoulders, blush, detached_sleeves, looking_at_viewer, see-through, red_horns, revealing_clothes, bikini, closed_mouth, grey_hair, holding_fan, open_mouth, paper_fan, pelvic_curtain, simple_background |
| 2 | 6 |  |  |  |  |  | 1girl, simple_background, solo, white_shirt, looking_at_viewer, white_background, earrings, hairclip, bare_shoulders, black_skirt, blush, bowtie, collared_shirt, sleeveless_shirt, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | black_cape | fingerless_gloves | military_uniform | long_sleeves | military_hat | black_jacket | black_headwear | closed_mouth | peaked_cap | holding_sword | red_gloves | black_shorts | multicolored_cape | simple_background | white_background | armor | low_ponytail | black_thighhighs | grey_hair | katana | retrofit_(azur_lane) | earrings | bare_shoulders | blush | detached_sleeves | see-through | red_horns | revealing_clothes | bikini | holding_fan | open_mouth | paper_fan | pelvic_curtain | white_shirt | hairclip | black_skirt | bowtie | collared_shirt | sleeveless_shirt | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------|:--------------------|:-------------------|:---------------|:---------------|:---------------|:-----------------|:---------------|:-------------|:----------------|:-------------|:---------------|:--------------------|:--------------------|:-------------------|:--------|:---------------|:-------------------|:------------|:---------|:-----------------------|:-----------|:-----------------|:--------|:-------------------|:--------------|:------------|:--------------------|:---------|:--------------|:-------------|:------------|:-----------------|:--------------|:-----------|:--------------|:---------|:-----------------|:-------------------|:--------|
| 0 | 33 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | | | | | | | X | | | | | | X | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | | | | | | | | | | | | | | X | X | | | | | | | X | X | X | | | | | | | | | | X | X | X | X | X | X | X |
|
eitanturok/ms-marco | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: train
path: data/train-*
- split: small
path: data/small-*
dataset_info:
features:
- name: passages
sequence: string
- name: query
dtype: string
- name: answers
sequence: string
- name: query_type
dtype: string
splits:
- name: validation
num_bytes: 181853458
num_examples: 55636
- name: train
num_bytes: 1789000138
num_examples: 503370
- name: small
num_bytes: 351268
num_examples: 100
download_size: 1049524677
dataset_size: 1971204864
---
# Dataset Card for "ms-marco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cicekliai/writingPromptsTR | ---
license: apache-2.0
---
|
stsudharsan/veshti-controlnet | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_img
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 14599706.0
num_examples: 143
download_size: 13484309
dataset_size: 14599706.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "veshti-controlnet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_monology__mixtral-4x7b_slerp | ---
pretty_name: Evaluation run of monology/mixtral-4x7b_slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [monology/mixtral-4x7b_slerp](https://huggingface.co/monology/mixtral-4x7b_slerp)\
\ 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_monology__mixtral-4x7b_slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-15T15:47:14.400970](https://huggingface.co/datasets/open-llm-leaderboard/details_monology__mixtral-4x7b_slerp/blob/main/results_2024-04-15T15-47-14.400970.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\": 0.2505114078493071,\n\
\ \"acc_stderr\": 0.03054397841862387,\n \"acc_norm\": 0.2513783678037018,\n\
\ \"acc_norm_stderr\": 0.03136051048389898,\n \"mc1\": 0.2631578947368421,\n\
\ \"mc1_stderr\": 0.015415241740237024,\n \"mc2\": 0.5013729099863928,\n\
\ \"mc2_stderr\": 0.01617486045768499\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.19539249146757678,\n \"acc_stderr\": 0.011586907189952911,\n\
\ \"acc_norm\": 0.2508532423208191,\n \"acc_norm_stderr\": 0.01266819862131543\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2650866361282613,\n\
\ \"acc_stderr\": 0.004404772735765965,\n \"acc_norm\": 0.2751443935471022,\n\
\ \"acc_norm_stderr\": 0.004456743108170734\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.24444444444444444,\n\
\ \"acc_stderr\": 0.03712537833614867,\n \"acc_norm\": 0.24444444444444444,\n\
\ \"acc_norm_stderr\": 0.03712537833614867\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\
\ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.18,\n\
\ \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.18,\n \
\ \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.02528839450289137,\n\
\ \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.24305555555555555,\n\
\ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.24305555555555555,\n\
\ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.28,\n\
\ \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\": 0.28,\n \
\ \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\
\ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\
\ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n\
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\
\ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n\
\ \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\
acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15873015873015872,\n\
\ \"acc_stderr\": 0.03268454013011743,\n \"acc_norm\": 0.15873015873015872,\n\
\ \"acc_norm_stderr\": 0.03268454013011743\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\"\
: 0.18064516129032257,\n \"acc_stderr\": 0.021886178567172548,\n \"\
acc_norm\": 0.18064516129032257,\n \"acc_norm_stderr\": 0.021886178567172548\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.17733990147783252,\n \"acc_stderr\": 0.02687433727680835,\n \"\
acc_norm\": 0.17733990147783252,\n \"acc_norm_stderr\": 0.02687433727680835\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\
acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.35233160621761656,\n \"acc_stderr\": 0.034474782864143586,\n\
\ \"acc_norm\": 0.35233160621761656,\n \"acc_norm_stderr\": 0.034474782864143586\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.29743589743589743,\n \"acc_stderr\": 0.023177408131465953,\n\
\ \"acc_norm\": 0.29743589743589743,\n \"acc_norm_stderr\": 0.023177408131465953\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712163,\n \
\ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712163\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.24369747899159663,\n \"acc_stderr\": 0.027886828078380558,\n\
\ \"acc_norm\": 0.24369747899159663,\n \"acc_norm_stderr\": 0.027886828078380558\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2185430463576159,\n \"acc_stderr\": 0.033742355504256936,\n \"\
acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.033742355504256936\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.22752293577981653,\n \"acc_stderr\": 0.017974463578776502,\n \"\
acc_norm\": 0.22752293577981653,\n \"acc_norm_stderr\": 0.017974463578776502\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.033723432716530624,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.033723432716530624\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25980392156862747,\n \"acc_stderr\": 0.030778554678693264,\n \"\
acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.030778554678693264\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n \
\ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3004484304932735,\n\
\ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.3004484304932735,\n\
\ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22699386503067484,\n \"acc_stderr\": 0.03291099578615771,\n\
\ \"acc_norm\": 0.22699386503067484,\n \"acc_norm_stderr\": 0.03291099578615771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\
\ \"acc_stderr\": 0.04464285714285714,\n \"acc_norm\": 0.33035714285714285,\n\
\ \"acc_norm_stderr\": 0.04464285714285714\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.18446601941747573,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.18446601941747573,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.23076923076923078,\n\
\ \"acc_stderr\": 0.027601921381417597,\n \"acc_norm\": 0.23076923076923078,\n\
\ \"acc_norm_stderr\": 0.027601921381417597\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\
\ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\
\ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574911,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574911\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.02355083135199509,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.02355083135199509\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2379421221864952,\n\
\ \"acc_stderr\": 0.02418515064781871,\n \"acc_norm\": 0.2379421221864952,\n\
\ \"acc_norm_stderr\": 0.02418515064781871\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\
\ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \
\ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\
\ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\
\ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\
\ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.23636363636363636,\n \"acc_stderr\": 0.04069306319721376,\n\
\ \"acc_norm\": 0.23636363636363636,\n \"acc_norm_stderr\": 0.04069306319721376\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.32653061224489793,\n\
\ \"acc_stderr\": 0.030021056238440307,\n \"acc_norm\": 0.32653061224489793,\n\
\ \"acc_norm_stderr\": 0.030021056238440307\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\
\ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-virology|5\"\
: {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\
\ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\
\ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.30994152046783624,\n\
\ \"acc_stderr\": 0.03546976959393163,\n \"acc_norm\": 0.30994152046783624,\n\
\ \"acc_norm_stderr\": 0.03546976959393163\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 0.2631578947368421,\n \"mc1_stderr\": 0.015415241740237024,\n\
\ \"mc2\": 0.5013729099863928,\n \"mc2_stderr\": 0.01617486045768499\n\
\ },\n \"harness|winogrande|5\": {\n \"acc\": 0.5153906866614049,\n\
\ \"acc_stderr\": 0.014045826789783666\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/monology/mixtral-4x7b_slerp
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_04_15T15_47_14.400970
path:
- '**/details_harness|arc:challenge|25_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|gsm8k|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hellaswag|10_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T15-47-14.400970.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T15-47-14.400970.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T15-47-14.400970.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T15-47-14.400970.parquet'
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- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T15-47-14.400970.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T15-47-14.400970.parquet'
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T15-47-14.400970.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T15-47-14.400970.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T15-47-14.400970.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- '**/details_harness|winogrande|5_2024-04-15T15-47-14.400970.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-15T15-47-14.400970.parquet'
- config_name: results
data_files:
- split: 2024_04_15T15_47_14.400970
path:
- results_2024-04-15T15-47-14.400970.parquet
- split: latest
path:
- results_2024-04-15T15-47-14.400970.parquet
---
# Dataset Card for Evaluation run of monology/mixtral-4x7b_slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [monology/mixtral-4x7b_slerp](https://huggingface.co/monology/mixtral-4x7b_slerp) 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_monology__mixtral-4x7b_slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-15T15:47:14.400970](https://huggingface.co/datasets/open-llm-leaderboard/details_monology__mixtral-4x7b_slerp/blob/main/results_2024-04-15T15-47-14.400970.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": 0.2505114078493071,
"acc_stderr": 0.03054397841862387,
"acc_norm": 0.2513783678037018,
"acc_norm_stderr": 0.03136051048389898,
"mc1": 0.2631578947368421,
"mc1_stderr": 0.015415241740237024,
"mc2": 0.5013729099863928,
"mc2_stderr": 0.01617486045768499
},
"harness|arc:challenge|25": {
"acc": 0.19539249146757678,
"acc_stderr": 0.011586907189952911,
"acc_norm": 0.2508532423208191,
"acc_norm_stderr": 0.01266819862131543
},
"harness|hellaswag|10": {
"acc": 0.2650866361282613,
"acc_stderr": 0.004404772735765965,
"acc_norm": 0.2751443935471022,
"acc_norm_stderr": 0.004456743108170734
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.24444444444444444,
"acc_stderr": 0.03712537833614867,
"acc_norm": 0.24444444444444444,
"acc_norm_stderr": 0.03712537833614867
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536955,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536955
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21509433962264152,
"acc_stderr": 0.02528839450289137,
"acc_norm": 0.21509433962264152,
"acc_norm_stderr": 0.02528839450289137
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.24305555555555555,
"acc_stderr": 0.0358687928008034,
"acc_norm": 0.24305555555555555,
"acc_norm_stderr": 0.0358687928008034
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
"acc_stderr": 0.030952890217749874,
"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749874
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.26382978723404255,
"acc_stderr": 0.028809989854102973,
"acc_norm": 0.26382978723404255,
"acc_norm_stderr": 0.028809989854102973
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.039994238792813365,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.039994238792813365
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2620689655172414,
"acc_stderr": 0.036646663372252565,
"acc_norm": 0.2620689655172414,
"acc_norm_stderr": 0.036646663372252565
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.20899470899470898,
"acc_stderr": 0.02094048156533486,
"acc_norm": 0.20899470899470898,
"acc_norm_stderr": 0.02094048156533486
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.15873015873015872,
"acc_stderr": 0.03268454013011743,
"acc_norm": 0.15873015873015872,
"acc_norm_stderr": 0.03268454013011743
},
"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": 0.18064516129032257,
"acc_stderr": 0.021886178567172548,
"acc_norm": 0.18064516129032257,
"acc_norm_stderr": 0.021886178567172548
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.17733990147783252,
"acc_stderr": 0.02687433727680835,
"acc_norm": 0.17733990147783252,
"acc_norm_stderr": 0.02687433727680835
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.17676767676767677,
"acc_stderr": 0.027178752639044915,
"acc_norm": 0.17676767676767677,
"acc_norm_stderr": 0.027178752639044915
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.35233160621761656,
"acc_stderr": 0.034474782864143586,
"acc_norm": 0.35233160621761656,
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"acc": 0.0,
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}
}
```
## 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]
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### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
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## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
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### Out-of-Scope Use
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## 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. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Who are the annotators?
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#### 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. -->
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## Bias, Risks, and Limitations
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### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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BangumiBase/versaillesnobara | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Versailles No Bara
This is the image base of bangumi Versailles No Bara, we detected 35 characters, 4981 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 468 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 126 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 154 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 659 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 105 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 47 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 44 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 51 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 173 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 48 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 25 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 168 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 150 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 290 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 30 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 1251 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 125 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 34 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 66 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 76 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 150 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 147 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 13 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 62 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 65 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 119 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 109 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 14 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 13 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 28 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 17 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 12 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 19 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 10 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 113 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
ctoraman/protest-event-prediction | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- tr
tags:
- protest event detection
- protest detection
- public reaction
---
# Public Protest Event Detection in Turkish
80 news events/articles occurred between 2015 and 017. Each event is listed with its origin date, place, news url, public-reaction category, and reaction tags.
Protest labels are determined in terms of dimensions and directions.
Dimensions are in terms of national, local, and social media. National categories represent public reactions occurred in at least two different cities. Local categories have events occurred at only a specific place. Social categories
represent reactions that people share opinions only in social media, such as microblogs.
Directions are either negative or positive.
Overall, there are 7 classes:
- national positive
- national negative
- local positive
- local negative
- social positive
- social negative
- no reaction
GitHub Repo: https://github.com/BilkentInformationRetrievalGroup/BilPredict2017
# If you would like to use any material in this repository, please cite the following paper:
- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017. |
ghbacct/gold-headlines-grammatical-tense-classification | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 573485.5078864354
num_examples: 9129
- name: test
num_bytes: 143418.49211356466
num_examples: 2283
download_size: 382001
dataset_size: 716904.0
---
# Dataset Card for "gold-headlines-grammatical-tense-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jtz18/sutd_qa_dataset | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 95929
num_examples: 200
download_size: 45101
dataset_size: 95929
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Zayt/oasst1-vi | ---
license: apache-2.0
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_count
dtype: int32
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: int32
- name: synthetic
dtype: bool
- name: model_name
dtype: string
- name: detoxify
struct:
- name: toxicity
dtype: float64
- name: severe_toxicity
dtype: float64
- name: obscene
dtype: float64
- name: identity_attack
dtype: float64
- name: insult
dtype: float64
- name: threat
dtype: float64
- name: sexual_explicit
dtype: float64
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
sequence:
- name: name
dtype: string
- name: count
dtype: int32
- name: labels
sequence:
- name: name
dtype: string
- name: value
dtype: float64
- name: count
dtype: int32
- name: text_chunks
sequence: string
- name: text_translation
dtype: string
splits:
- name: train
num_bytes: 59922108.85834358
num_examples: 38537
download_size: 39428167
dataset_size: 59922108.85834358
task_categories:
- conversational
language:
- vi
size_categories:
- 10K<n<100K
---
This dataset contains vi subsets (first 191 examples) and auto-translation from en to vi subsets (the rest, 38346 examples) from [OASST1](https://huggingface.co/datasets/OpenAssistant/oasst1). All auto-translation examples are generated using [VietAI envit5-translation](https://huggingface.co/VietAI/envit5-translation).
The vi subsets have the same features as the original dataset. Meanwhile, the auto-translation subsets introduce two new features:
- `"text_chunks"` is a list that contains chunked text split from `"text"`, each chunk has no more than 300 tokens. The sent_tokenizer and word_tokenzier used are from spacy en_core_web_sm model.
- `"text_translation"` contains merged of all translated chunks. Due to the auto-translation model, all new-line symbols (`\n`) are removed.
The translation script can be found at `translate_en_to_vi.py` |
NamCyan/tesoro | ---
dataset_info:
features:
- name: id
dtype: int64
- name: comment_id
dtype: int64
- name: comment
dtype: string
- name: code
dtype: string
- name: classification
dtype: string
- name: isFinished
dtype: bool
- name: code_context_2
dtype: string
- name: code_context_10
dtype: string
- name: code_context_20
dtype: string
splits:
- name: train
num_bytes: 88392573
num_examples: 4981
download_size: 3689503
dataset_size: 88392573
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kwaikeg/Kuaipedia | ---
license: cc-by-nc-sa-4.0
language:
- zh
---
[**Kuaipedia**](https://github.com/KwaiKEG/Kuaipedia) is developed by [KwaiKEG](https://github.com/KwaiKEG), collaborating with HIT and HKUST. It is the world's first large-scale multi-modal short-video encyclopedia where the primitive units are items, aspects, and short videos.
* **Items** is a set of entities and concepts, such as [Shiba Inu](https://en.wikipedia.org/wiki/Shiba_Inu), [Moon](https://en.wikipedia.org/wiki/Moon) and [Galileo Galilei](https://en.wikipedia.org/wiki/Galileo_Galilei), which can be edited at one Wikipedia page. An item may have a title, a subtitle, a summary, attributes, and other detailed information of the item.
* **Aspects** is a set of keywords or keyphrases attached to items. Those keywords are used to describe specific aspects of the item. For example, "selection", "food-protecting", "color" of item [Shiba Inu](https://en.wikipedia.org/wiki/Shiba_Inu), or "formation", "surface conditions", "how-to-draw" of item [Moon](https://en.wikipedia.org/wiki/Moon).
* **Videos** is a set of short-videos whose duration may not exceed 5 minutes. In this paper, we only focus on knowledge videos we detected, Where we follow OECD to define knowledge as:
* *Know-what* refers to knowledge about facts. E.g. How many people live in New York?
* *Know-why* refers to scientific knowledge of the principles and laws of nature. E.g. Why does the earth revolve around the sun?
* *Know-how* refers to skills or the capability to do something. E.g. How to cook bacon in the oven.
Please refer to the paper for more details.
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia [[Manuscript]](https://arxiv.org/abs/2211.00732)
## Data
**Statistics**
| | Full Dump | Subset Dump |
|------------|-----------------|-------------|
| #Items | > 26 million | 51,702 |
| #Aspects | > 2.5 million | 1,074,539 |
| #Videos | > 200 million | 769,096 |
The comparative results with the baseline models are as follows:
| Model | Item P | Item R | Item-Aspect P | Item-Aspect R |
| ---- | ---- | ---- | ---- | ---- |
| Random | 87.7 | 49.8 | 36.4 | 49.6 |
| LR | 90.4 | 68.3 | 55.1 | 2.7 |
| T5-small | 93.7 | 76.1 | 79.3 | 58.5 |
| BERT-base | 94.3 | 77.8 | 81.5 | 62.7 |
| GPT-3.5 | 90.5 | 86.4 | 41.8 | 95.7 |
| Ours | 94.7 | 79.7 | 83.0 | 65.7 |
Feel free to explore and utilize this valuable dataset for your research and projects.
## Reference
```
@article{Kuaipedia22,
author = {Haojie Pan and
Zepeng Zhai and
Yuzhou Zhang and
Ruiji Fu and
Ming Liu and
Yangqiu Song and
Zhongyuan Wang and
Bing Qin
},
title = {{Kuaipedia:} a Large-scale Multi-modal Short-video Encyclopedia},
journal = {CoRR},
volume = {abs/2211.00732},
year = {2022}
}
``` |
Vipitis/Shadertoys-fine | ---
annotations_creators:
- no-annotation
language:
- en
- code
language_creators:
- machine-generated
license:
- cc-by-nc-sa-3.0
multilinguality: []
pretty_name: Shadertoys-fine
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- code
task_categories:
- text-generation
task_ids: []
dataset_info:
- config_name: default
features:
- name: name
dtype: string
- name: code
dtype: string
- name: source
dtype: string
- name: author
dtype: string
splits:
- name: train
- name: test
download_size: 154529204
dataset_size: 0
- config_name: fine
features:
- name: name
dtype: string
- name: code
dtype: string
- name: source
dtype: string
- name: author
dtype: string
splits:
- name: train
num_bytes: 119963236
num_examples: 226910
- name: test
num_bytes: 20003783
num_examples: 38356
download_size: 154529204
dataset_size: 139967019
- config_name: return_completion
features:
- name: body
dtype: string
- name: return_statement
dtype: string
splits:
- name: train
num_bytes: 37597125
num_examples: 84843
- name: test
num_bytes: 6360131
num_examples: 14248
download_size: 154529204
dataset_size: 43957256
---
# Dataset Card for Shadertoys-fine
## 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)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Repository:** https://github.com/Vipitis/project (private placeholder)
### Dataset Summary
fine variant of the Shadertoys dataset (still WIP), where individual functions are avaialable as Datapoints.
### Supported Tasks and Leaderboards
`language-modeling`: The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages.
### Languages
- English (names, comments)
- Shadercode **programming** language
## Dataset Structure
### Data Instances
A data point consists of the function string, it's name as well as a bit of metadata like the author and source URL. (in the future there might be a function string without comments)
```
{
'name': '<type> <name>',
'code': '<type> <name>(<inputs>) { <body> return <outputs>; }\n',
'source': 'https://shadertoy.com/view/<shaderID>',
'author': '<username>'
}
```
A data point in the `return_completion` subset for the return-completion task in [ShaderEval](https://huggingface.co/spaces/Vipitis/ShaderEval) includes just two features:
```
{
'body': '<type> <name> <type> <name>(<inputs>) { <body> return',
'return_statment': ' <outputs>: }\n',
}
```
### Data Fields
- 'name' funciton identifier composed of the type and the name of the function
- 'code' the raw code (including comments) of function.
- 'source' URL to the shader. It might be on a different renderpass
- 'author' username of the shader author
- 'body' the body of the function without the return statement (no comments)
- 'return_statment' the return statement of the function. everything infront of the semicolon is kept and white sapces are stripped in the custome Evaluator.
### Data Splits
Currently available (shuffled):
- train (85.0%)
- test (15.0%)
These splits should be indexed the same across both subsets. So if you are fine-tuning on the `fine` subset you won't get exposed to the `return_completion` test split. However there are many duplicates among both subsets and splits.
## Dataset Creation
Data retrieved starting 2022-07-20
### Source Data
#### Initial Data Collection and Normalization
All data was collected via the [Shadertoy.com API](https://www.shadertoy.com/howto#q2) and then by looking for keywords and counting curly brackets to figure out what is part of a function and what isn't.
#### Who are the source language producers?
Shadertoy.com contributers which publish shaders as 'public+API'
## Licensing Information
The Default [licnese for each Shader](https://www.shadertoy.com/terms) is CC BY-NC-SA 3.0. However, some Shaders might have a different license attached. The Dataset is currently not filtering for any licensis. |
tqhuyen/MC_OCR2021 | ---
license: unknown
---
|
NetherlandsForensicInstitute/sentence-compression-translated-nl | ---
task_categories:
- sentence-similarity
language:
- nl
size_categories:
- 100K<n<1M
---
This is a Dutch version of the [Sentence Compression dataset](https://github.com/google-research-datasets/sentence-compression). Which we have auto-translated from English into Dutch using Meta's [No Language Left Behind](https://ai.facebook.com/research/no-language-left-behind/) model, specifically the [huggingface implementation](https://huggingface.co/facebook/nllb-200-distilled-600M). |
ealbilali/KaifLematha | ---
license: cc-by-4.0
---
|
BangumiBase/kanojookarishimasu | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kanojo, Okarishimasu
This is the image base of bangumi Kanojo, Okarishimasu, we detected 44 characters, 6680 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1417 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 82 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 105 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 58 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 31 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 35 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 32 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 45 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 32 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 15 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 31 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 33 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 36 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 20 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 15 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 18 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 13 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 555 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 71 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 2254 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 20 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 33 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 148 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 31 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 121 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 92 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 88 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 74 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 34 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 14 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 72 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 318 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 16 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 20 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 8 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 264 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 8 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 21 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 11 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 7 | [Download](40/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 41 | 219 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 8 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 146 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
GAIR/ReAlign-Alpaca | ---
task_categories:
- question-answering
- conversational
language:
- en
size_categories:
- 10K<n<100K
---
Please refer to our [GitHub repo](https://github.com/GAIR-NLP/ReAlign) for more details. |
liuyanchen1015/MULTI_VALUE_qqp_definite_abstract | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 840463
num_examples: 4737
- name: test
num_bytes: 7921652
num_examples: 44767
- name: train
num_bytes: 7560356
num_examples: 42436
download_size: 9919049
dataset_size: 16322471
---
# Dataset Card for "MULTI_VALUE_qqp_definite_abstract"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Larvik/gelb | ---
license: unknown
---
|
allmalab/azwiki | ---
language:
- az
license: cc-by-sa-3.0
size_categories:
- 100K<n<1M
task_categories:
- text-generation
pretty_name: Azerbaijani Wikipedia
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 360206818
num_examples: 129433
download_size: 204669649
dataset_size: 360206818
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
**You can find the scripts [here](https://github.com/ceferisbarov/azwiki)**
AzWiki is a snapshot of Azerbaijani Wikipedia processed specifically for the text generation task. More details regarding the cleaning and processing steps will be released.
Please go to the GitHub repository for all discussions and PRs. |
Francesco/cells-uyemf | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': cells
'1': celula
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: cells-uyemf
tags:
- rf100
---
# Dataset Card for cells-uyemf
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cells-uyemf
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
cells-uyemf
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cells-uyemf
### Citation Information
```
@misc{ cells-uyemf,
title = { cells uyemf Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cells-uyemf } },
url = { https://universe.roboflow.com/object-detection/cells-uyemf },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
CyberHarem/salome_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of salome/サロメ/莎乐美 (Fate/Grand Order)
This is the dataset of salome/サロメ/莎乐美 (Fate/Grand Order), containing 54 images and their tags.
The core tags of this character are `green_hair, purple_eyes, braid, breasts, twin_braids, long_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 | 54 | 82.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/salome_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 54 | 72.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/salome_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 117 | 130.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/salome_fgo/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/salome_fgo',
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 | 16 |  |  |  |  |  | 1girl, solo, looking_at_viewer, smile, detached_sleeves, navel, veil, revealing_clothes, skull, bare_shoulders, jewelry, nail_polish, thighhighs, parted_lips |
| 1 | 7 |  |  |  |  |  | 1girl, solo, bare_shoulders, looking_at_viewer, smile, upper_body, veil, jewelry, black_background, open_mouth, simple_background, long_sleeves, parted_bangs, puffy_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | smile | detached_sleeves | navel | veil | revealing_clothes | skull | bare_shoulders | jewelry | nail_polish | thighhighs | parted_lips | upper_body | black_background | open_mouth | simple_background | long_sleeves | parted_bangs | puffy_sleeves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:-------------------|:--------|:-------|:--------------------|:--------|:-----------------|:----------|:--------------|:-------------|:--------------|:-------------|:-------------------|:-------------|:--------------------|:---------------|:---------------|:----------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | | | X | | | X | X | | | | X | X | X | X | X | X | X |
|
CyberHarem/mimi_pearlbaton_rezero | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of mimi_pearlbaton (Re:Zero Kara Hajimeru Isekai Seikatsu)
This is the dataset of mimi_pearlbaton (Re:Zero Kara Hajimeru Isekai Seikatsu), containing 20 images and their tags.
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)).
|
open-llm-leaderboard/details_Gille__StrangeMerges_53-7B-model_stock | ---
pretty_name: Evaluation run of Gille/StrangeMerges_53-7B-model_stock
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Gille/StrangeMerges_53-7B-model_stock](https://huggingface.co/Gille/StrangeMerges_53-7B-model_stock)\
\ 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_Gille__StrangeMerges_53-7B-model_stock\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-02T22:26:33.194666](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_53-7B-model_stock/blob/main/results_2024-04-02T22-26-33.194666.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\": 0.6557829300668468,\n\
\ \"acc_stderr\": 0.03189334507100312,\n \"acc_norm\": 0.6550160624480947,\n\
\ \"acc_norm_stderr\": 0.032560414157835546,\n \"mc1\": 0.587515299877601,\n\
\ \"mc1_stderr\": 0.017233299399571213,\n \"mc2\": 0.7386394132081768,\n\
\ \"mc2_stderr\": 0.014357973440118963\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.697098976109215,\n \"acc_stderr\": 0.013428241573185349,\n\
\ \"acc_norm\": 0.7278156996587031,\n \"acc_norm_stderr\": 0.013006600406423704\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.708424616610237,\n\
\ \"acc_stderr\": 0.004535589759202659,\n \"acc_norm\": 0.8845847440748855,\n\
\ \"acc_norm_stderr\": 0.003188694028453636\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\
\ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\
\ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"\
acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \
\ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\
\ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\
acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n\
\ \"acc_stderr\": 0.023287665127268545,\n \"acc_norm\": 0.7870967741935484,\n\
\ \"acc_norm_stderr\": 0.023287665127268545\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218967,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218967\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\
\ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\
\ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815632,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815632\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8627450980392157,\n \"acc_stderr\": 0.02415222596280158,\n \"\
acc_norm\": 0.8627450980392157,\n \"acc_norm_stderr\": 0.02415222596280158\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\
\ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\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\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\
\ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\
\ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545546,\n\
\ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545546\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4446927374301676,\n\
\ \"acc_stderr\": 0.01661988198817702,\n \"acc_norm\": 0.4446927374301676,\n\
\ \"acc_norm_stderr\": 0.01661988198817702\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729484,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729484\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\
: {\n \"acc\": 0.4758800521512386,\n \"acc_stderr\": 0.012755368722863937,\n\
\ \"acc_norm\": 0.4758800521512386,\n \"acc_norm_stderr\": 0.012755368722863937\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031208,\n \"\
acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031208\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\
\ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\
\ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.587515299877601,\n\
\ \"mc1_stderr\": 0.017233299399571213,\n \"mc2\": 0.7386394132081768,\n\
\ \"mc2_stderr\": 0.014357973440118963\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8366219415943172,\n \"acc_stderr\": 0.010390695970273766\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7270659590598939,\n \
\ \"acc_stderr\": 0.012270381151108754\n }\n}\n```"
repo_url: https://huggingface.co/Gille/StrangeMerges_53-7B-model_stock
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_04_02T22_26_33.194666
path:
- '**/details_harness|arc:challenge|25_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|gsm8k|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hellaswag|10_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T22-26-33.194666.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-02T22-26-33.194666.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- '**/details_harness|winogrande|5_2024-04-02T22-26-33.194666.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-02T22-26-33.194666.parquet'
- config_name: results
data_files:
- split: 2024_04_02T22_26_33.194666
path:
- results_2024-04-02T22-26-33.194666.parquet
- split: latest
path:
- results_2024-04-02T22-26-33.194666.parquet
---
# Dataset Card for Evaluation run of Gille/StrangeMerges_53-7B-model_stock
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Gille/StrangeMerges_53-7B-model_stock](https://huggingface.co/Gille/StrangeMerges_53-7B-model_stock) 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_Gille__StrangeMerges_53-7B-model_stock",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-02T22:26:33.194666](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_53-7B-model_stock/blob/main/results_2024-04-02T22-26-33.194666.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": 0.6557829300668468,
"acc_stderr": 0.03189334507100312,
"acc_norm": 0.6550160624480947,
"acc_norm_stderr": 0.032560414157835546,
"mc1": 0.587515299877601,
"mc1_stderr": 0.017233299399571213,
"mc2": 0.7386394132081768,
"mc2_stderr": 0.014357973440118963
},
"harness|arc:challenge|25": {
"acc": 0.697098976109215,
"acc_stderr": 0.013428241573185349,
"acc_norm": 0.7278156996587031,
"acc_norm_stderr": 0.013006600406423704
},
"harness|hellaswag|10": {
"acc": 0.708424616610237,
"acc_stderr": 0.004535589759202659,
"acc_norm": 0.8845847440748855,
"acc_norm_stderr": 0.003188694028453636
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.04094376269996792,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.04094376269996792
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.02804918631569525,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.02804918631569525
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7847222222222222,
"acc_stderr": 0.03437079344106135,
"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.03437079344106135
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5379310344827586,
"acc_stderr": 0.04154659671707548,
"acc_norm": 0.5379310344827586,
"acc_norm_stderr": 0.04154659671707548
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.025279850397404904,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.025279850397404904
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4603174603174603,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.023287665127268545,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.023287665127268545
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.032568666616811015,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.032568666616811015
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8131313131313131,
"acc_stderr": 0.027772533334218967,
"acc_norm": 0.8131313131313131,
"acc_norm_stderr": 0.027772533334218967
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.917098445595855,
"acc_stderr": 0.01989934131572178,
"acc_norm": 0.917098445595855,
"acc_norm_stderr": 0.01989934131572178
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6717948717948717,
"acc_stderr": 0.023807633198657266,
"acc_norm": 0.6717948717948717,
"acc_norm_stderr": 0.023807633198657266
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3074074074074074,
"acc_stderr": 0.028133252578815632,
"acc_norm": 0.3074074074074074,
"acc_norm_stderr": 0.028133252578815632
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.030283995525884396,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.030283995525884396
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8440366972477065,
"acc_stderr": 0.01555580271359017,
"acc_norm": 0.8440366972477065,
"acc_norm_stderr": 0.01555580271359017
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5092592592592593,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8627450980392157,
"acc_stderr": 0.02415222596280158,
"acc_norm": 0.8627450980392157,
"acc_norm_stderr": 0.02415222596280158
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944856,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944856
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6816143497757847,
"acc_stderr": 0.03126580522513713,
"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8091603053435115,
"acc_stderr": 0.03446513350752598,
"acc_norm": 0.8091603053435115,
"acc_norm_stderr": 0.03446513350752598
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.0401910747255735,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.0401910747255735
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"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": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406957,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406957
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8275862068965517,
"acc_stderr": 0.013507943909371803,
"acc_norm": 0.8275862068965517,
"acc_norm_stderr": 0.013507943909371803
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7456647398843931,
"acc_stderr": 0.023445826276545546,
"acc_norm": 0.7456647398843931,
"acc_norm_stderr": 0.023445826276545546
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4446927374301676,
"acc_stderr": 0.01661988198817702,
"acc_norm": 0.4446927374301676,
"acc_norm_stderr": 0.01661988198817702
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7352941176470589,
"acc_stderr": 0.025261691219729484,
"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.025261691219729484
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694912,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694912
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7469135802469136,
"acc_stderr": 0.024191808600712995,
"acc_norm": 0.7469135802469136,
"acc_norm_stderr": 0.024191808600712995
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5,
"acc_stderr": 0.029827499313594685,
"acc_norm": 0.5,
"acc_norm_stderr": 0.029827499313594685
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4758800521512386,
"acc_stderr": 0.012755368722863937,
"acc_norm": 0.4758800521512386,
"acc_norm_stderr": 0.012755368722863937
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6801470588235294,
"acc_stderr": 0.028332959514031208,
"acc_norm": 0.6801470588235294,
"acc_norm_stderr": 0.028332959514031208
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6830065359477124,
"acc_stderr": 0.018824219512706207,
"acc_norm": 0.6830065359477124,
"acc_norm_stderr": 0.018824219512706207
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302506,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302506
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.028263889943784596,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784596
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8258706467661692,
"acc_stderr": 0.026814951200421603,
"acc_norm": 0.8258706467661692,
"acc_norm_stderr": 0.026814951200421603
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.027539122889061456,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061456
},
"harness|truthfulqa:mc|0": {
"mc1": 0.587515299877601,
"mc1_stderr": 0.017233299399571213,
"mc2": 0.7386394132081768,
"mc2_stderr": 0.014357973440118963
},
"harness|winogrande|5": {
"acc": 0.8366219415943172,
"acc_stderr": 0.010390695970273766
},
"harness|gsm8k|5": {
"acc": 0.7270659590598939,
"acc_stderr": 0.012270381151108754
}
}
```
## 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] |
MrDre/autotrain-data-feets | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: feets
## Dataset Description
This dataset has been automatically processed by AutoTrain for project feets.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<206x320 RGB PIL image>",
"target": 0
},
{
"image": "<173x320 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['gettyimagefeet'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 122 |
| valid | 122 |
|
Shurius/Public_TRAIN | ---
license: afl-3.0
---
|
yzhuang/metatree_cpu_small | ---
dataset_info:
features:
- name: id
dtype: int64
- name: X
sequence: float64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 655400
num_examples: 5650
- name: validation
num_bytes: 294872
num_examples: 2542
download_size: 703010
dataset_size: 950272
---
# Dataset Card for "metatree_cpu_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jlbaker361/flickr_humans_5k_vangogh | ---
dataset_info:
features:
- name: image
dtype: image
- name: split
dtype: string
- name: style
dtype: string
splits:
- name: train
num_bytes: 2760853501.0
num_examples: 5000
download_size: 0
dataset_size: 2760853501.0
---
# Dataset Card for "flickr_humans_5k_vangogh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/clueweb09_es | ---
pretty_name: '`clueweb09/es`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `clueweb09/es`
The `clueweb09/es` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb09#clueweb09/es).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=79,333,950
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/clueweb09_es', 'docs')
for record in docs:
record # {'doc_id': ..., 'url': ..., 'date': ..., 'http_headers': ..., 'body': ..., 'body_content_type': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
|
sahityas/goodreads-llama-7b | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 27512
num_examples: 254
download_size: 15892
dataset_size: 27512
---
# Dataset Card for "goodreads-llama-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ishannbx/arXiv-one-shot-classification-l27b-E02-large-b05 | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 3103900
num_examples: 467
- name: test
num_bytes: 780031
num_examples: 117
download_size: 654972
dataset_size: 3883931
---
|
ibivibiv/alpaca_lamini20 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 56007787
num_examples: 129280
download_size: 36175612
dataset_size: 56007787
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_ChuckMcSneed__ArcaneEntanglement-model64-70b | ---
pretty_name: Evaluation run of ChuckMcSneed/ArcaneEntanglement-model64-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ChuckMcSneed/ArcaneEntanglement-model64-70b](https://huggingface.co/ChuckMcSneed/ArcaneEntanglement-model64-70b)\
\ 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_ChuckMcSneed__ArcaneEntanglement-model64-70b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-03T04:00:35.269835](https://huggingface.co/datasets/open-llm-leaderboard/details_ChuckMcSneed__ArcaneEntanglement-model64-70b/blob/main/results_2024-04-03T04-00-35.269835.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\": 0.7081319875868494,\n\
\ \"acc_stderr\": 0.03007989681657682,\n \"acc_norm\": 0.7112822557850792,\n\
\ \"acc_norm_stderr\": 0.030663388669225966,\n \"mc1\": 0.4430844553243574,\n\
\ \"mc1_stderr\": 0.017389730346877106,\n \"mc2\": 0.6052983910894114,\n\
\ \"mc2_stderr\": 0.01490057109922886\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6706484641638225,\n \"acc_stderr\": 0.013734057652635474,\n\
\ \"acc_norm\": 0.7141638225255973,\n \"acc_norm_stderr\": 0.01320319608853737\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6931886078470424,\n\
\ \"acc_stderr\": 0.004602279238122068,\n \"acc_norm\": 0.8796056562437762,\n\
\ \"acc_norm_stderr\": 0.003247570330456916\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.03110318238312338,\n\
\ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.03110318238312338\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\
\ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7283018867924528,\n \"acc_stderr\": 0.027377706624670713,\n\
\ \"acc_norm\": 0.7283018867924528,\n \"acc_norm_stderr\": 0.027377706624670713\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.031164899666948617,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.031164899666948617\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n\
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\
\ \"acc_stderr\": 0.035149425512674394,\n \"acc_norm\": 0.6936416184971098,\n\
\ \"acc_norm_stderr\": 0.035149425512674394\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6936170212765957,\n \"acc_stderr\": 0.030135906478517563,\n\
\ \"acc_norm\": 0.6936170212765957,\n \"acc_norm_stderr\": 0.030135906478517563\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.040434618619167466,\n\
\ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.040434618619167466\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.47354497354497355,\n \"acc_stderr\": 0.02571523981134676,\n \"\
acc_norm\": 0.47354497354497355,\n \"acc_norm_stderr\": 0.02571523981134676\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.49206349206349204,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8225806451612904,\n\
\ \"acc_stderr\": 0.021732540689329286,\n \"acc_norm\": 0.8225806451612904,\n\
\ \"acc_norm_stderr\": 0.021732540689329286\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.03481904844438804,\n\
\ \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.03481904844438804\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\"\
: 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8424242424242424,\n \"acc_stderr\": 0.02845038880528437,\n\
\ \"acc_norm\": 0.8424242424242424,\n \"acc_norm_stderr\": 0.02845038880528437\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8838383838383839,\n \"acc_stderr\": 0.02282888177524938,\n \"\
acc_norm\": 0.8838383838383839,\n \"acc_norm_stderr\": 0.02282888177524938\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678178,\n\
\ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7153846153846154,\n \"acc_stderr\": 0.0228783227997063,\n \
\ \"acc_norm\": 0.7153846153846154,\n \"acc_norm_stderr\": 0.0228783227997063\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.31851851851851853,\n \"acc_stderr\": 0.028406533090608463,\n \
\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.028406533090608463\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295893,\n \
\ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295893\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.46357615894039733,\n \"acc_stderr\": 0.04071636065944215,\n \"\
acc_norm\": 0.46357615894039733,\n \"acc_norm_stderr\": 0.04071636065944215\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9064220183486239,\n \"acc_stderr\": 0.012486841824601963,\n \"\
acc_norm\": 0.9064220183486239,\n \"acc_norm_stderr\": 0.012486841824601963\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5879629629629629,\n \"acc_stderr\": 0.03356787758160831,\n \"\
acc_norm\": 0.5879629629629629,\n \"acc_norm_stderr\": 0.03356787758160831\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813902,\n \"\
acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813902\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065498,\n \
\ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065498\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8251121076233184,\n\
\ \"acc_stderr\": 0.02549528462644497,\n \"acc_norm\": 0.8251121076233184,\n\
\ \"acc_norm_stderr\": 0.02549528462644497\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\
\ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8677685950413223,\n \"acc_stderr\": 0.030922788320445795,\n \"\
acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.030922788320445795\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8220858895705522,\n \"acc_stderr\": 0.03004735765580663,\n\
\ \"acc_norm\": 0.8220858895705522,\n \"acc_norm_stderr\": 0.03004735765580663\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.04742762361243011,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.04742762361243011\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9145299145299145,\n\
\ \"acc_stderr\": 0.018315891685625835,\n \"acc_norm\": 0.9145299145299145,\n\
\ \"acc_norm_stderr\": 0.018315891685625835\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8710089399744572,\n\
\ \"acc_stderr\": 0.011986371548086858,\n \"acc_norm\": 0.8710089399744572,\n\
\ \"acc_norm_stderr\": 0.011986371548086858\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8121387283236994,\n \"acc_stderr\": 0.021029269752423214,\n\
\ \"acc_norm\": 0.8121387283236994,\n \"acc_norm_stderr\": 0.021029269752423214\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.016384638410380816,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.016384638410380816\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.024404394928087873,\n\
\ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.024404394928087873\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7781350482315113,\n\
\ \"acc_stderr\": 0.02359885829286305,\n \"acc_norm\": 0.7781350482315113,\n\
\ \"acc_norm_stderr\": 0.02359885829286305\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.02058146613825711,\n\
\ \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.02058146613825711\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5815602836879432,\n \"acc_stderr\": 0.02942799403942,\n \"\
acc_norm\": 0.5815602836879432,\n \"acc_norm_stderr\": 0.02942799403942\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5554106910039114,\n\
\ \"acc_stderr\": 0.012691575792657112,\n \"acc_norm\": 0.5554106910039114,\n\
\ \"acc_norm_stderr\": 0.012691575792657112\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7463235294117647,\n \"acc_stderr\": 0.026431329870789524,\n\
\ \"acc_norm\": 0.7463235294117647,\n \"acc_norm_stderr\": 0.026431329870789524\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7696078431372549,\n \"acc_stderr\": 0.017035229258034034,\n \
\ \"acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.017035229258034034\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7545454545454545,\n\
\ \"acc_stderr\": 0.041220665028782855,\n \"acc_norm\": 0.7545454545454545,\n\
\ \"acc_norm_stderr\": 0.041220665028782855\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8163265306122449,\n \"acc_stderr\": 0.024789071332007636,\n\
\ \"acc_norm\": 0.8163265306122449,\n \"acc_norm_stderr\": 0.024789071332007636\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\
\ \"acc_stderr\": 0.021628920516700637,\n \"acc_norm\": 0.8955223880597015,\n\
\ \"acc_norm_stderr\": 0.021628920516700637\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072864,\n\
\ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072864\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4430844553243574,\n\
\ \"mc1_stderr\": 0.017389730346877106,\n \"mc2\": 0.6052983910894114,\n\
\ \"mc2_stderr\": 0.01490057109922886\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8303078137332282,\n \"acc_stderr\": 0.010549542647363682\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6300227445034117,\n \
\ \"acc_stderr\": 0.013298661207727124\n }\n}\n```"
repo_url: https://huggingface.co/ChuckMcSneed/ArcaneEntanglement-model64-70b
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_04_03T04_00_35.269835
path:
- '**/details_harness|arc:challenge|25_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|gsm8k|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hellaswag|10_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-03T04-00-35.269835.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-03T04-00-35.269835.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- '**/details_harness|winogrande|5_2024-04-03T04-00-35.269835.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-03T04-00-35.269835.parquet'
- config_name: results
data_files:
- split: 2024_04_03T04_00_35.269835
path:
- results_2024-04-03T04-00-35.269835.parquet
- split: latest
path:
- results_2024-04-03T04-00-35.269835.parquet
---
# Dataset Card for Evaluation run of ChuckMcSneed/ArcaneEntanglement-model64-70b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [ChuckMcSneed/ArcaneEntanglement-model64-70b](https://huggingface.co/ChuckMcSneed/ArcaneEntanglement-model64-70b) 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_ChuckMcSneed__ArcaneEntanglement-model64-70b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-03T04:00:35.269835](https://huggingface.co/datasets/open-llm-leaderboard/details_ChuckMcSneed__ArcaneEntanglement-model64-70b/blob/main/results_2024-04-03T04-00-35.269835.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
{
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"mc1": 0.4430844553243574,
"mc1_stderr": 0.017389730346877106,
"mc2": 0.6052983910894114,
"mc2_stderr": 0.01490057109922886
},
"harness|arc:challenge|25": {
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"acc_norm": 0.7141638225255973,
"acc_norm_stderr": 0.01320319608853737
},
"harness|hellaswag|10": {
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},
"harness|hendrycksTest-abstract_algebra|5": {
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},
"harness|hendrycksTest-anatomy|5": {
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},
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},
"harness|hendrycksTest-clinical_knowledge|5": {
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},
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},
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},
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},
"harness|hendrycksTest-conceptual_physics|5": {
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},
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},
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},
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},
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"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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},
"harness|hendrycksTest-high_school_statistics|5": {
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},
"harness|hendrycksTest-high_school_us_history|5": {
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},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8987341772151899,
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},
"harness|hendrycksTest-human_aging|5": {
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},
"harness|hendrycksTest-human_sexuality|5": {
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},
"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-logical_fallacies|5": {
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},
"harness|hendrycksTest-machine_learning|5": {
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},
"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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},
"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
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},
"harness|hendrycksTest-nutrition|5": {
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},
"harness|hendrycksTest-philosophy|5": {
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},
"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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},
"harness|truthfulqa:mc|0": {
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},
"harness|winogrande|5": {
"acc": 0.8303078137332282,
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},
"harness|gsm8k|5": {
"acc": 0.6300227445034117,
"acc_stderr": 0.013298661207727124
}
}
```
## 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]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
DataStudio/OCR_document_bluir_part_08 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1017152623.875
num_examples: 137585
download_size: 1018902673
dataset_size: 1017152623.875
---
# Dataset Card for "OCR_document_bluir_part_08"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xedwin23x/VegFru | ---
license: unknown
---
|
pythonist/staf_alpa_kkm | ---
license: mit
---
|
hongrui/xray_v_1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: condition
dtype: string
splits:
- name: train
num_bytes: 136857730.392
num_examples: 5216
download_size: 121759783
dataset_size: 136857730.392
---
# Dataset Card for "xray_v_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ybelkada/food101-tiny | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': apple_pie
'1': baby_back_ribs
'2': baklava
'3': beef_carpaccio
'4': beef_tartare
'5': beet_salad
'6': beignets
'7': bibimbap
'8': bread_pudding
'9': breakfast_burrito
'10': bruschetta
'11': caesar_salad
'12': cannoli
'13': caprese_salad
'14': carrot_cake
'15': ceviche
'16': cheesecake
'17': cheese_plate
'18': chicken_curry
'19': chicken_quesadilla
'20': chicken_wings
'21': chocolate_cake
'22': chocolate_mousse
'23': churros
'24': clam_chowder
'25': club_sandwich
'26': crab_cakes
'27': creme_brulee
'28': croque_madame
'29': cup_cakes
'30': deviled_eggs
'31': donuts
'32': dumplings
'33': edamame
'34': eggs_benedict
'35': escargots
'36': falafel
'37': filet_mignon
'38': fish_and_chips
'39': foie_gras
'40': french_fries
'41': french_onion_soup
'42': french_toast
'43': fried_calamari
'44': fried_rice
'45': frozen_yogurt
'46': garlic_bread
'47': gnocchi
'48': greek_salad
'49': grilled_cheese_sandwich
'50': grilled_salmon
'51': guacamole
'52': gyoza
'53': hamburger
'54': hot_and_sour_soup
'55': hot_dog
'56': huevos_rancheros
'57': hummus
'58': ice_cream
'59': lasagna
'60': lobster_bisque
'61': lobster_roll_sandwich
'62': macaroni_and_cheese
'63': macarons
'64': miso_soup
'65': mussels
'66': nachos
'67': omelette
'68': onion_rings
'69': oysters
'70': pad_thai
'71': paella
'72': pancakes
'73': panna_cotta
'74': peking_duck
'75': pho
'76': pizza
'77': pork_chop
'78': poutine
'79': prime_rib
'80': pulled_pork_sandwich
'81': ramen
'82': ravioli
'83': red_velvet_cake
'84': risotto
'85': samosa
'86': sashimi
'87': scallops
'88': seaweed_salad
'89': shrimp_and_grits
'90': spaghetti_bolognese
'91': spaghetti_carbonara
'92': spring_rolls
'93': steak
'94': strawberry_shortcake
'95': sushi
'96': tacos
'97': takoyaki
'98': tiramisu
'99': tuna_tartare
'100': waffles
splits:
- name: train
num_bytes: 5343359.0
num_examples: 100
download_size: 5256650
dataset_size: 5343359.0
---
# Dataset Card for "food101-tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sayan1101/instr_finetune_modelv1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 27407564
num_examples: 52000
download_size: 0
dataset_size: 27407564
---
# Dataset Card for "instr_finetune_modelv1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
satwikapaul/braille_dataset_4 | ---
license: openrail
---
|
senti_lex | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
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- 'no'
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
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pretty_name: SentiWS
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- name: train
num_bytes: 1944
num_examples: 111
download_size: 0
dataset_size: 1944
- config_name: vi
features:
- name: word
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 18100
num_examples: 1016
download_size: 0
dataset_size: 18100
- config_name: vo
features:
- name: word
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 775
num_examples: 43
download_size: 0
dataset_size: 775
- config_name: wa
features:
- name: word
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 3450
num_examples: 193
download_size: 0
dataset_size: 3450
- config_name: yi
features:
- name: word
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 9001
num_examples: 395
download_size: 0
dataset_size: 9001
- config_name: zh
features:
- name: word
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 33025
num_examples: 1879
download_size: 0
dataset_size: 33025
- config_name: zhw
features:
- name: word
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 67675
num_examples: 3828
download_size: 0
dataset_size: 67675
config_names:
- 'no'
- af
- an
- ar
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- eo
- es
- et
- eu
- fa
- fi
- fo
- fr
- fy
- ga
- gd
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- ia
- id
- io
- is
- it
- ja
- ka
- km
- kn
- ko
- ku
- ky
- la
- lb
- lt
- lv
- mk
- mr
- ms
- mt
- nl
- nn
- pl
- pt
- rm
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- sw
- ta
- te
- th
- tk
- tl
- tr
- uk
- ur
- uz
- vi
- vo
- wa
- yi
- zh
- zhw
---
# Dataset Card for SentiWS
## 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)
- [Annotations](#annotations)
- [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:** https://sites.google.com/site/datascienceslab/projects/multilingualsentiment
- **Repository:** https://www.kaggle.com/rtatman/sentiment-lexicons-for-81-languages
- **Paper:** https://aclanthology.org/P14-2063/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset add sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them
### Supported Tasks and Leaderboards
Sentiment-Classification
### Languages
Afrikaans
Aragonese
Arabic
Azerbaijani
Belarusian
Bulgarian
Bengali
Breton
Bosnian
Catalan; Valencian
Czech
Welsh
Danish
German
Greek, Modern
Esperanto
Spanish; Castilian
Estonian
Basque
Persian
Finnish
Faroese
French
Western Frisian
Irish
Scottish Gaelic; Gaelic
Galician
Gujarati
Hebrew (modern)
Hindi
Croatian
Haitian; Haitian Creole
Hungarian
Armenian
Interlingua
Indonesian
Ido
Icelandic
Italian
Japanese
Georgian
Khmer
Kannada
Korean
Kurdish
Kirghiz, Kyrgyz
Latin
Luxembourgish, Letzeburgesch
Lithuanian
Latvian
Macedonian
Marathi (Marāṭhī)
Malay
Maltese
Dutch
Norwegian Nynorsk
Norwegian
Polish
Portuguese
Romansh
Romanian, Moldavian, Moldovan
Russian
Slovak
Slovene
Albanian
Serbian
Swedish
Swahili
Tamil
Telugu
Thai
Turkmen
Tagalog
Turkish
Ukrainian
Urdu
Uzbek
Vietnamese
Volapük
Walloon
Yiddish
Chinese
Zhoa
## Dataset Structure
### Data Instances
```
{
"word":"die",
"sentiment": 0, #"negative"
}
```
### Data Fields
- word: one word as a string,
- sentiment-score: the sentiment classification of the word as a string either negative (0) or positive (1)
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
GNU General Public License v3.
It is distributed here under the [GNU General Public License](http://www.gnu.org/licenses/gpl-3.0.html).
Note that this is the full GPL, which allows many free uses, but does not allow its incorporation into any type of distributed proprietary software, even in part or in translation.
For commercial applications please contact the dataset creators (see "Citation Information").
### Citation Information
This dataset was collected by Yanqing Chen and Steven Skiena. If you use it in your work, please cite the following paper:
```bibtex
@inproceedings{chen-skiena-2014-building,
title = "Building Sentiment Lexicons for All Major Languages",
author = "Chen, Yanqing and
Skiena, Steven",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-2063",
doi = "10.3115/v1/P14-2063",
pages = "383--389",
}
```
### Contributions
Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset. |
facat/Socratic | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: num_of_rounds
dtype: int64
splits:
- name: train
num_bytes: 457252754
num_examples: 50728
download_size: 200925490
dataset_size: 457252754
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
monsoonery/voxpopuli_nl_TRAIN_pseudo_labelled | ---
dataset_info:
config_name: nl
features:
- name: audio_id
dtype: string
- name: language
dtype:
class_label:
names:
'0': en
'1': de
'2': fr
'3': es
'4': pl
'5': it
'6': ro
'7': hu
'8': cs
'9': nl
'10': fi
'11': hr
'12': sk
'13': sl
'14': et
'15': lt
'16': en_accented
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: raw_text
dtype: string
- name: normalized_text
dtype: string
- name: gender
dtype: string
- name: speaker_id
dtype: string
- name: is_gold_transcript
dtype: bool
- name: accent
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 10456321909.608
num_examples: 20968
download_size: 8900264489
dataset_size: 10456321909.608
configs:
- config_name: nl
data_files:
- split: train
path: nl/train-*
---
|
CyberHarem/eve_santaclaus_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of eve_santaclaus/イヴ・サンタクロース (THE iDOLM@STER: Cinderella Girls)
This is the dataset of eve_santaclaus/イヴ・サンタクロース (THE iDOLM@STER: Cinderella Girls), containing 128 images and their tags.
The core tags of this character are `long_hair, yellow_eyes, white_hair, breasts, bangs, hat, medium_breasts, santa_hat`, 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 | 128 | 141.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eve_santaclaus_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 128 | 93.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eve_santaclaus_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 294 | 191.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eve_santaclaus_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 128 | 129.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eve_santaclaus_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 294 | 250.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eve_santaclaus_idolmastercinderellagirls/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/eve_santaclaus_idolmastercinderellagirls',
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 | 5 |  |  |  |  |  | :d, open_mouth, 1girl, blush, looking_at_viewer, solo, bikini, hair_flower, navel, armpits, cleavage, convenient_censoring, dark_skin, frills, nude, water |
| 1 | 6 |  |  |  |  |  | 1girl, open_mouth, solo, blush, christmas, smile, looking_at_viewer, reindeer, cardboard_box, nude |
| 2 | 13 |  |  |  |  |  | 1girl, christmas, santa_costume, solo, midriff, open_mouth, reindeer, looking_at_viewer, navel, thighhighs, bell, belt, blush, skirt, star_(symbol), :d, cleavage |
| 3 | 8 |  |  |  |  |  | 1girl, christmas, looking_at_viewer, midriff, red_gloves, red_headwear, red_skirt, santa_costume, solo, blush, crop_top, fur-trimmed_gloves, fur-trimmed_skirt, green_bow, miniskirt, navel, puffy_short_sleeves, red_shirt, bell, belt, fur-trimmed_headwear, sack, smile, bowtie, cropped_jacket, white_thighhighs, closed_mouth, fur-trimmed_jacket, red_footwear, santa_gloves, sitting, striped_bow, zettai_ryouiki, bag, box, cleavage, cowboy_shot, fur-trimmed_boots, gift, print_skirt, red_bow, red_jacket, simple_background, standing, stomach, very_long_hair, white_background |
| 4 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, collarbone, smile, bare_shoulders, blush, closed_mouth, large_breasts, simple_background, upper_body, white_background, cleavage |
| 5 | 11 |  |  |  |  |  | 1girl, blush, solo, bare_shoulders, looking_at_viewer, white_gloves, jewelry, open_mouth, tiara, cleavage, pendant_watch, :d, brown_eyes, collarbone, heart, strapless_dress |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | :d | open_mouth | 1girl | blush | looking_at_viewer | solo | bikini | hair_flower | navel | armpits | cleavage | convenient_censoring | dark_skin | frills | nude | water | christmas | smile | reindeer | cardboard_box | santa_costume | midriff | thighhighs | bell | belt | skirt | star_(symbol) | red_gloves | red_headwear | red_skirt | crop_top | fur-trimmed_gloves | fur-trimmed_skirt | green_bow | miniskirt | puffy_short_sleeves | red_shirt | fur-trimmed_headwear | sack | bowtie | cropped_jacket | white_thighhighs | closed_mouth | fur-trimmed_jacket | red_footwear | santa_gloves | sitting | striped_bow | zettai_ryouiki | bag | box | cowboy_shot | fur-trimmed_boots | gift | print_skirt | red_bow | red_jacket | simple_background | standing | stomach | very_long_hair | white_background | collarbone | bare_shoulders | large_breasts | upper_body | white_gloves | jewelry | tiara | pendant_watch | brown_eyes | heart | strapless_dress |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----|:-------------|:--------|:--------|:--------------------|:-------|:---------|:--------------|:--------|:----------|:-----------|:-----------------------|:------------|:---------|:-------|:--------|:------------|:--------|:-----------|:----------------|:----------------|:----------|:-------------|:-------|:-------|:--------|:----------------|:-------------|:---------------|:------------|:-----------|:---------------------|:--------------------|:------------|:------------|:----------------------|:------------|:-----------------------|:-------|:---------|:-----------------|:-------------------|:---------------|:---------------------|:---------------|:---------------|:----------|:--------------|:-----------------|:------|:------|:--------------|:--------------------|:-------|:--------------|:----------|:-------------|:--------------------|:-----------|:----------|:-----------------|:-------------------|:-------------|:-----------------|:----------------|:-------------|:---------------|:----------|:--------|:----------------|:-------------|:--------|:------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | | X | X | X | X | X | | | | | | | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 13 |  |  |  |  |  | X | X | X | X | X | X | | | X | | X | | | | | | X | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 8 |  |  |  |  |  | | | 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 | X | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | | | X | X | X | X | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | X | X | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | X | X | X | X | X | X | X |
|
MobeenHameed/khan_final | ---
license: mit
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 1205698214.0
num_examples: 985
download_size: 1154686371
dataset_size: 1205698214.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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