datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
pandaresiddhi/test-small-dataset | ---
license: apache-2.0
---
|
ai-bots/3300_Instruction_Set_Indian_Constitution | ---
license: apache-2.0
---
|
PerceptionEval/Correspondance | ---
dataset_info:
features:
- name: idx
dtype: int32
- name: question
dtype: string
- name: image1
dtype: image
- name: image2
dtype: image
- name: choices
sequence: string
- name: answer
dtype: string
splits:
- name: val
num_bytes: 220525060.0
num_examples: 347
download_size: 219181253
dataset_size: 220525060.0
configs:
- config_name: default
data_files:
- split: val
path: data/val-*
---
|
open-llm-leaderboard/details_heegyu__WizardVicuna-Uncensored-3B-0719 | ---
pretty_name: Evaluation run of heegyu/WizardVicuna-Uncensored-3B-0719
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [heegyu/WizardVicuna-Uncensored-3B-0719](https://huggingface.co/heegyu/WizardVicuna-Uncensored-3B-0719)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_heegyu__WizardVicuna-Uncensored-3B-0719\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-19T03:10:00.849734](https://huggingface.co/datasets/open-llm-leaderboard/details_heegyu__WizardVicuna-Uncensored-3B-0719/blob/main/results_2023-10-19T03-10-00.849734.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\": 0.0032508389261744967,\n\
\ \"em_stderr\": 0.0005829486708558908,\n \"f1\": 0.05307046979865784,\n\
\ \"f1_stderr\": 0.0013744215109358906,\n \"acc\": 0.32454958283792285,\n\
\ \"acc_stderr\": 0.008214760837520624\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0032508389261744967,\n \"em_stderr\": 0.0005829486708558908,\n\
\ \"f1\": 0.05307046979865784,\n \"f1_stderr\": 0.0013744215109358906\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.011372251705837756,\n \
\ \"acc_stderr\": 0.002920666198788741\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6377269139700079,\n \"acc_stderr\": 0.013508855476252508\n\
\ }\n}\n```"
repo_url: https://huggingface.co/heegyu/WizardVicuna-Uncensored-3B-0719
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_07_24T10_29_51.933578
path:
- '**/details_harness|arc:challenge|25_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_19T03_10_00.849734
path:
- '**/details_harness|drop|3_2023-10-19T03-10-00.849734.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-19T03-10-00.849734.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T03_10_00.849734
path:
- '**/details_harness|gsm8k|5_2023-10-19T03-10-00.849734.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-19T03-10-00.849734.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hellaswag|10_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:29:51.933578.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:29:51.933578.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:29:51.933578.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:29:51.933578.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:29:51.933578.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:29:51.933578.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T10:29:51.933578.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T10:29:51.933578.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T03_10_00.849734
path:
- '**/details_harness|winogrande|5_2023-10-19T03-10-00.849734.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-19T03-10-00.849734.parquet'
- config_name: results
data_files:
- split: 2023_07_24T10_29_51.933578
path:
- results_2023-07-24T10:29:51.933578.parquet
- split: 2023_10_19T03_10_00.849734
path:
- results_2023-10-19T03-10-00.849734.parquet
- split: latest
path:
- results_2023-10-19T03-10-00.849734.parquet
---
# Dataset Card for Evaluation run of heegyu/WizardVicuna-Uncensored-3B-0719
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/heegyu/WizardVicuna-Uncensored-3B-0719
- **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 [heegyu/WizardVicuna-Uncensored-3B-0719](https://huggingface.co/heegyu/WizardVicuna-Uncensored-3B-0719) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_heegyu__WizardVicuna-Uncensored-3B-0719",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T03:10:00.849734](https://huggingface.co/datasets/open-llm-leaderboard/details_heegyu__WizardVicuna-Uncensored-3B-0719/blob/main/results_2023-10-19T03-10-00.849734.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": 0.0032508389261744967,
"em_stderr": 0.0005829486708558908,
"f1": 0.05307046979865784,
"f1_stderr": 0.0013744215109358906,
"acc": 0.32454958283792285,
"acc_stderr": 0.008214760837520624
},
"harness|drop|3": {
"em": 0.0032508389261744967,
"em_stderr": 0.0005829486708558908,
"f1": 0.05307046979865784,
"f1_stderr": 0.0013744215109358906
},
"harness|gsm8k|5": {
"acc": 0.011372251705837756,
"acc_stderr": 0.002920666198788741
},
"harness|winogrande|5": {
"acc": 0.6377269139700079,
"acc_stderr": 0.013508855476252508
}
}
```
### 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] |
thanhtlx/fix-cmg-time-split-type-cluster-pdg | ---
license: apache-2.0
---
|
mboth/luftVersorgen-100-undersampled | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: Datatype
dtype: string
- name: Beschreibung
dtype: string
- name: Name
dtype: string
- name: Unit
dtype: string
- name: text
dtype: string
- name: Grundfunktion
dtype: string
- name: label
dtype:
class_label:
names:
'0': LuftBereitstellen
'1': LuftVerteilen
splits:
- name: train
num_bytes: 39514.861205145564
num_examples: 200
- name: test
num_bytes: 290707
num_examples: 1477
- name: valid
num_bytes: 290707
num_examples: 1477
download_size: 234233
dataset_size: 620928.8612051455
---
# Dataset Card for "luftVersorgen-100-undersampled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Achen/large-test | ---
license: bsd-2-clause
---
|
CyberHarem/candace_genshin | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of candace/キャンディス/坎蒂丝 (Genshin Impact)
This is the dataset of candace/キャンディス/坎蒂丝 (Genshin Impact), containing 470 images and their tags.
The core tags of this character are `dark_skin, dark-skinned_female, blue_hair, breasts, yellow_eyes, heterochromia, blue_eyes, hair_ornament, hairband, long_hair, large_breasts, sidelocks, purple_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 | 470 | 872.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/candace_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 470 | 731.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/candace_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1205 | 1.42 GiB | [Download](https://huggingface.co/datasets/CyberHarem/candace_genshin/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/candace_genshin',
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 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, detached_sleeves, eye_of_horus, looking_at_viewer, medium_breasts, navel, neck_ring, sitting, solo, stomach, closed_mouth, gold_trim, short_hair_with_long_locks, thighlet, egyptian_clothes, midriff, thighs, pelvic_curtain, simple_background, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, egyptian_clothes, eye_of_horus, looking_at_viewer, medium_breasts, simple_background, solo, upper_body, white_background, bare_shoulders, black_hairband, cleavage, closed_mouth, detached_sleeves, navel, short_hair_with_long_locks, stomach, midriff, neck_ring, crop_top, gold_trim |
| 2 | 6 |  |  |  |  |  | 1girl, bare_shoulders, egyptian_clothes, eye_of_horus, neck_ring, solo, upper_body, closed_mouth, looking_at_viewer, short_hair_with_long_locks, cleavage, medium_breasts, simple_background, white_background |
| 3 | 24 |  |  |  |  |  | 1girl, eye_of_horus, solo, bare_shoulders, egyptian_clothes, holding_polearm, navel, stomach, neck_ring, short_hair_with_long_locks, looking_at_viewer, medium_breasts, holding_shield, closed_mouth, gold_trim, thighs, detached_sleeves, cleavage, white_background, black_hairband, crop_top, simple_background, cowboy_shot, midriff |
| 4 | 8 |  |  |  |  |  | 1boy, 1girl, blush, eye_of_horus, hetero, navel, nipples, penis, pussy, sex, spread_legs, vaginal, thighs, missionary, mosaic_censoring, on_back, short_hair_with_long_locks, solo_focus, stomach, detached_sleeves, open_mouth, sweat, completely_nude, egyptian_clothes, pov, thighlet, black_hairband, grabbing_another's_breast, looking_at_viewer, motion_lines, neck_ring |
| 5 | 9 |  |  |  |  |  | 1girl, eye_of_horus, looking_at_viewer, navel, nipples, pussy, solo, stomach, completely_nude, thighs, blush, closed_mouth, uncensored, white_background, black_hairband, simple_background, cleft_of_venus, collarbone, cowboy_shot, neck_ring, smile, twintails |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | cleavage | detached_sleeves | eye_of_horus | looking_at_viewer | medium_breasts | navel | neck_ring | sitting | solo | stomach | closed_mouth | gold_trim | short_hair_with_long_locks | thighlet | egyptian_clothes | midriff | thighs | pelvic_curtain | simple_background | white_background | upper_body | black_hairband | crop_top | holding_polearm | holding_shield | cowboy_shot | 1boy | blush | hetero | nipples | penis | pussy | sex | spread_legs | vaginal | missionary | mosaic_censoring | on_back | solo_focus | open_mouth | sweat | completely_nude | pov | grabbing_another's_breast | motion_lines | uncensored | cleft_of_venus | collarbone | smile | twintails |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------|:-------------------|:---------------|:--------------------|:-----------------|:--------|:------------|:----------|:-------|:----------|:---------------|:------------|:-----------------------------|:-----------|:-------------------|:----------|:---------|:-----------------|:--------------------|:-------------------|:-------------|:-----------------|:-----------|:------------------|:-----------------|:--------------|:-------|:--------|:---------|:----------|:--------|:--------|:------|:--------------|:----------|:-------------|:-------------------|:----------|:-------------|:-------------|:--------|:------------------|:------|:----------------------------|:---------------|:-------------|:-----------------|:-------------|:--------|:------------|
| 0 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | | X | X | X | | X | | X | | X | | X | | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 24 |  |  |  |  |  | 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 | 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 | | | | | |
| 5 | 9 |  |  |  |  |  | X | | | | X | X | | X | X | | X | X | X | | | | | | X | | X | X | | X | | | | X | | X | | X | | X | | | | | | | | | | X | | | | X | X | X | X | X |
|
19kmunz/iot-23-preprocessed-allcolumns | ---
dataset_info:
features:
- name: ts
dtype: float64
- name: uid
dtype: string
- name: id.orig_h
dtype: string
- name: id.orig_p
dtype: int64
- name: id.resp_h
dtype: string
- name: id.resp_p
dtype: int64
- name: proto
dtype: string
- name: service
dtype: string
- name: duration
dtype: float64
- name: orig_bytes
dtype: int64
- name: resp_bytes
dtype: int64
- name: conn_state
dtype: string
- name: local_orig
dtype: float64
- name: local_resp
dtype: float64
- name: missed_bytes
dtype: int64
- name: history
dtype: string
- name: orig_pkts
dtype: int64
- name: orig_ip_bytes
dtype: int64
- name: resp_pkts
dtype: int64
- name: resp_ip_bytes
dtype: int64
- name: label
dtype: string
splits:
- name: train
num_bytes: 1232978140
num_examples: 6046623
download_size: 274218995
dataset_size: 1232978140
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- tabular-classification
- table-question-answering
language:
- en
tags:
- code
---
# Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic
**Homepage:** [https://www.stratosphereips.org/datasets-iot23](https://www.stratosphereips.org/datasets-iot23)
This dataset contains a subset of the data from 20 captures of Malcious network traffic and 3 captures from live Benign Traffic on Internet of Things (IoT) devices. Created by Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga at the Avast AIC laboratory with the funding of Avast Software, this dataset is one of the best in the field for Intrusion Detection Systems (IDS) for IoT Devices [(Comparative Analysis of IoT Botnet Datasets)](https://doi.org/10.53070/bbd.1173687).
The selection of the subset was determined by [Aqeel Ahmed on Kaggle](https://www.kaggle.com/datasets/engraqeel/iot23preprocesseddata) and contains 6 million samples. The Kaggle upload, nor this one, have employed data balancing. The Kaggle card does not contain methodology to understand what criteria was used to select these samples. If you want ensure best practice, use this dataset to mock-up processing the data into a model before using the full dataset with data balancing. This will require processing the 8GB of conn.log.labelled files.
# Feature information:
All features originate from the [Zeek](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info) processing performed by the dataset creators. [See notes here for caviats for each column](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info).
<details>
<summary>Expand for feature names, descriptions, and datatypes</summary>
Name: ts
Desription: This is the time of the first packet.
Data Type: float64 - Timestamp
Name: uid
Description: A Zeek-defined unique identifier of the connection.
Data type: string
Name: id.orig_h
Description: The originator’s IP address.
Data type: string - for the form 255.255.255.255 for IPv4 or [aaaa:bbbb:cccc:dddd:eeee:ffff:1111:2222] for IPv6
Name: id.orig_p
Description: The originator’s port number.
Data type: int64 - uint64 in original
Name: id.resp_h
Description: The responder’s IP address.
Data type: string - for the form 255.255.255.255 for IPv4 or [aaaa:bbbb:cccc:dddd:eeee:ffff:1111:2222] for IPv6
Name: id.resp_p
Description: The responder’s port number.
Data type: int64 - uint64 in original
Name: proto
Description: The transport layer protocol of the connection.
Data type: string - enum(unknown_transport, tcp, udp, icmp). Only TCP and UDP in subset
Name: service
Description: An identification of an application protocol being sent over the connection.
Data type: optional string
Name: duration
Description: How long the connection lasted.
Data type: optional float64 - time interval
Name: orig_bytes
Description: The number of payload bytes the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_bytes
Description:The number of payload bytes the responder sent.
Data type: optional int64 - uint64 in original
Name: conn_state
Description: Value indicating connection state. (S0, S1, SF, REJ, S2, S3, RSTO, RSTR, RSTOS0, RSTRH, SH, SHR, OTH)
Data type: optional string
Name: local_orig
Description: If the connection is originated locally, this value will be T. If it was originated remotely it will be F.
Data type: optional float64 - bool in original but null for all columns
Name: local_resp
Description: If the connection is responded to locally, this value will be T. If it was responded to remotely it will be F.
Data type: optional float64 - bool in original but null for all columns
Name: missed_bytes
Description: Indicates the number of bytes missed in content gaps, which is representative of packet loss.
Data type: optional int64 - uint64 in original. default = 0
Name: history
Description: Records the state history of connections as a string of letters.
Data type: optional string
Name: orig_pkts
Description: Number of packets that the originator sent.
Data type: optional int64 - uint64 in original
Name: orig_ip_bytes
Description: Number of IP level bytes that the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_pkts
Description: Number of packets that the responder sent.
Data type: optional int64 - uint64 in original
Name: resp_ip_bytes
Description: Number of IP level bytes that the responder sent.
Data type: optional int64 - uint64 in original
Name: label
Description: Specifies if data point is benign or some form of malicious. See the dataset creators paper for descriptions of attack types
Data type: string - enum('PartOfAHorizontalPortScan', 'Okiru', 'DDoS', 'C&C-HeartBeat',
'Benign', 'C&C-Torii', 'C&C', 'C&C-FileDownload', 'Okiru-Attack',
'Attack', 'FileDownload', 'C&C-HeartBeat-FileDownload',
'C&C-Mirai')
NOTE: ts, uid, id.orig_h, id.resp_h SHOULD BE removed as they are dataset specific. Models should not be trained with specific timestamps or IP addresses (id.orig_h), as that can lead to over fitting to dataset specific times and addresses.
Further local_orig, local_resp SHOULD BE removed as they are null in all rows, so they are useless for training.
</details>
## Citation
If you are using this dataset for your research, please reference it as “Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4743746”
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_TeeZee__DarkSapling-7B-v2.0 | ---
pretty_name: Evaluation run of TeeZee/DarkSapling-7B-v2.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TeeZee/DarkSapling-7B-v2.0](https://huggingface.co/TeeZee/DarkSapling-7B-v2.0)\
\ 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_TeeZee__DarkSapling-7B-v2.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-10T04:57:12.333081](https://huggingface.co/datasets/open-llm-leaderboard/details_TeeZee__DarkSapling-7B-v2.0/blob/main/results_2024-03-10T04-57-12.333081.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\": 0.6424579193008534,\n\
\ \"acc_stderr\": 0.032218866498356466,\n \"acc_norm\": 0.6471361795899754,\n\
\ \"acc_norm_stderr\": 0.032858397843778114,\n \"mc1\": 0.3659730722154223,\n\
\ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.5221487837375264,\n\
\ \"mc2_stderr\": 0.015253502717954797\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6023890784982935,\n \"acc_stderr\": 0.01430175222327954,\n\
\ \"acc_norm\": 0.6416382252559727,\n \"acc_norm_stderr\": 0.014012883334859857\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6589324835690101,\n\
\ \"acc_stderr\": 0.004730991357194292,\n \"acc_norm\": 0.8510256920932086,\n\
\ \"acc_norm_stderr\": 0.003553354528132355\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926604,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926604\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.028049186315695248,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.028049186315695248\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\
\ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\
\ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\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\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.0470070803355104,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.0470070803355104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\
\ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3994708994708995,\n \"acc_stderr\": 0.025225450284067887,\n \"\
acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.025225450284067887\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7709677419354839,\n \"acc_stderr\": 0.023904914311782648,\n \"\
acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.023904914311782648\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\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047711,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047711\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463355,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463355\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\
\ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206865,\n \
\ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206865\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\
acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8238532110091743,\n \"acc_stderr\": 0.016332882393431385,\n \"\
acc_norm\": 0.8238532110091743,\n \"acc_norm_stderr\": 0.016332882393431385\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\
acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\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\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\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\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128139,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128139\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \
\ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816508\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n\
\ \"acc_stderr\": 0.013816335389973136,\n \"acc_norm\": 0.8173690932311622,\n\
\ \"acc_norm_stderr\": 0.013816335389973136\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.024027745155265012,\n\
\ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.024027745155265012\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\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n\
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.024748624490537368,\n\
\ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.024748624490537368\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4491525423728814,\n\
\ \"acc_stderr\": 0.012704030518851488,\n \"acc_norm\": 0.4491525423728814,\n\
\ \"acc_norm_stderr\": 0.012704030518851488\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6813725490196079,\n \"acc_stderr\": 0.018850084696468712,\n \
\ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.018850084696468712\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\": 0.028263889943784593,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233268,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233268\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3659730722154223,\n\
\ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.5221487837375264,\n\
\ \"mc2_stderr\": 0.015253502717954797\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090248\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4541319181197877,\n \
\ \"acc_stderr\": 0.01371441094526456\n }\n}\n```"
repo_url: https://huggingface.co/TeeZee/DarkSapling-7B-v2.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|arc:challenge|25_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|gsm8k|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hellaswag|10_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T04-57-12.333081.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-10T04-57-12.333081.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- '**/details_harness|winogrande|5_2024-03-10T04-57-12.333081.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-10T04-57-12.333081.parquet'
- config_name: results
data_files:
- split: 2024_03_10T04_57_12.333081
path:
- results_2024-03-10T04-57-12.333081.parquet
- split: latest
path:
- results_2024-03-10T04-57-12.333081.parquet
---
# Dataset Card for Evaluation run of TeeZee/DarkSapling-7B-v2.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [TeeZee/DarkSapling-7B-v2.0](https://huggingface.co/TeeZee/DarkSapling-7B-v2.0) 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_TeeZee__DarkSapling-7B-v2.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-10T04:57:12.333081](https://huggingface.co/datasets/open-llm-leaderboard/details_TeeZee__DarkSapling-7B-v2.0/blob/main/results_2024-03-10T04-57-12.333081.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": 0.6424579193008534,
"acc_stderr": 0.032218866498356466,
"acc_norm": 0.6471361795899754,
"acc_norm_stderr": 0.032858397843778114,
"mc1": 0.3659730722154223,
"mc1_stderr": 0.016862941684088376,
"mc2": 0.5221487837375264,
"mc2_stderr": 0.015253502717954797
},
"harness|arc:challenge|25": {
"acc": 0.6023890784982935,
"acc_stderr": 0.01430175222327954,
"acc_norm": 0.6416382252559727,
"acc_norm_stderr": 0.014012883334859857
},
"harness|hellaswag|10": {
"acc": 0.6589324835690101,
"acc_stderr": 0.004730991357194292,
"acc_norm": 0.8510256920932086,
"acc_norm_stderr": 0.003553354528132355
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.03823428969926604,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926604
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.028049186315695248,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.028049186315695248
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.630057803468208,
"acc_stderr": 0.0368122963339432,
"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.0368122963339432
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"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": 0.4824561403508772,
"acc_stderr": 0.0470070803355104,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.0470070803355104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5862068965517241,
"acc_stderr": 0.04104269211806232,
"acc_norm": 0.5862068965517241,
"acc_norm_stderr": 0.04104269211806232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3994708994708995,
"acc_stderr": 0.025225450284067887,
"acc_norm": 0.3994708994708995,
"acc_norm_stderr": 0.025225450284067887
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.04403438954768177,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.04403438954768177
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7709677419354839,
"acc_stderr": 0.023904914311782648,
"acc_norm": 0.7709677419354839,
"acc_norm_stderr": 0.023904914311782648
},
"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": 0.793939393939394,
"acc_stderr": 0.03158415324047711,
"acc_norm": 0.793939393939394,
"acc_norm_stderr": 0.03158415324047711
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.028335609732463355,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463355
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8860103626943006,
"acc_stderr": 0.022935144053919443,
"acc_norm": 0.8860103626943006,
"acc_norm_stderr": 0.022935144053919443
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402534,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402534
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.362962962962963,
"acc_stderr": 0.029318203645206865,
"acc_norm": 0.362962962962963,
"acc_norm_stderr": 0.029318203645206865
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6680672268907563,
"acc_stderr": 0.03058869701378364,
"acc_norm": 0.6680672268907563,
"acc_norm_stderr": 0.03058869701378364
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2980132450331126,
"acc_stderr": 0.037345356767871984,
"acc_norm": 0.2980132450331126,
"acc_norm_stderr": 0.037345356767871984
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8238532110091743,
"acc_stderr": 0.016332882393431385,
"acc_norm": 0.8238532110091743,
"acc_norm_stderr": 0.016332882393431385
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.03406315360711507,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.03406315360711507
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
"acc_stderr": 0.028626547912437406,
"acc_norm": 0.7892156862745098,
"acc_norm_stderr": 0.028626547912437406
},
"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": 0.6771300448430493,
"acc_stderr": 0.03138147637575499,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.03138147637575499
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"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": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.49107142857142855,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.49107142857142855,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.8155339805825242,
"acc_stderr": 0.03840423627288276,
"acc_norm": 0.8155339805825242,
"acc_norm_stderr": 0.03840423627288276
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
"acc_stderr": 0.02158649400128139,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.02158649400128139
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816508,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816508
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8173690932311622,
"acc_stderr": 0.013816335389973136,
"acc_norm": 0.8173690932311622,
"acc_norm_stderr": 0.013816335389973136
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7254335260115607,
"acc_stderr": 0.024027745155265012,
"acc_norm": 0.7254335260115607,
"acc_norm_stderr": 0.024027745155265012
},
"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": 0.7516339869281046,
"acc_stderr": 0.02473998135511359,
"acc_norm": 0.7516339869281046,
"acc_norm_stderr": 0.02473998135511359
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.707395498392283,
"acc_stderr": 0.02583989833487798,
"acc_norm": 0.707395498392283,
"acc_norm_stderr": 0.02583989833487798
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7283950617283951,
"acc_stderr": 0.024748624490537368,
"acc_norm": 0.7283950617283951,
"acc_norm_stderr": 0.024748624490537368
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
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"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4491525423728814,
"acc_stderr": 0.012704030518851488,
"acc_norm": 0.4491525423728814,
"acc_norm_stderr": 0.012704030518851488
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.028418208619406755,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.028418208619406755
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6813725490196079,
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"acc_norm": 0.6813725490196079,
"acc_norm_stderr": 0.018850084696468712
},
"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": 0.028263889943784593,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784593
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
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"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.026508590656233268
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
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"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727668,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727668
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3659730722154223,
"mc1_stderr": 0.016862941684088376,
"mc2": 0.5221487837375264,
"mc2_stderr": 0.015253502717954797
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.011524466954090248
},
"harness|gsm8k|5": {
"acc": 0.4541319181197877,
"acc_stderr": 0.01371441094526456
}
}
```
## 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] |
Technoculture/chatdoctor-embedded | ---
language:
- en
license: mit
size_categories:
- 100K<n<1M
task_categories:
- conversational
pretty_name: Chat Doctor with Embeddings
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
- name: input_embedding
sequence: float32
- name: output_embedding
sequence: float32
splits:
- name: train
num_bytes: 2004509362
num_examples: 414816
download_size: 2026592380
dataset_size: 2004509362
tags:
- xzuyn/chatdoctor-200k-stripped
- BAAI/bge-small-en-v1.5
- medical
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Chat Doctor with Embeddings
This dataset is post-processed version of [xzuyn/chatdoctor-200k-stripped](https://huggingface.co/datasets/xzuyn/chatdoctor-200k-stripped):
- Add embeddings for `input` and `output` columns using [BAAI/bge-small-en-v1.5](https://huggingface.co/datasets/BAAI/bge-small-en-v1.5)
| | Details |
| --------------------- | -------------------------------------------------- |
| Sample Count | 414k |
| Token Count | 1.7b |
| Origin | [https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view](https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view) |
| Source of raw data | ? |
| Processing details | [paper](https://arxiv.org/ftp/arxiv/papers/2303/2303.14070.pdf) <a target="_blank" href="https://colab.research.google.com/drive/1_xSFgdCrQKubIuHcQSrF4k1icff5r-gS?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
| Embedding Model | [BAAI/bge-small-en-v1.5](https://huggingface.co/datasets/BAAI/bge-small-en-v1.5) |
## Data Diversity
| index | Example Output | GPT-4 Rationale | GPT-4 Diversity Rating |
|-------|----------------|-----------------|------------------------|
| 9796 | Hi welcome to Chat Doctor ... Your husband had taken antihistamine Chat Doctor. . But as it is two week since having problem, I would suggest you to consult him to pulmonologist or nearby doctor for auscultation and examination first... Then if needed work up done with... -CBC with AEC count -Throat swab -Chest x-ray only if needed -AS titreAccording to report further treatment guided Ex. If eosinophilic more than Allegra M given... If neutrophil and as positiver than amoxyclav neededTake care | Focuses on respiratory symptoms and suggests a series of tests and potential treatments based on the results, specific to potential allergic or infection-related issues. | 4 |
| 4577 | HIT hanks for posting your query to Chat Doctor. Two issues :1. Stomach Pain : It could be due to many causes like Gastritis, Stones in gallbladder or kidney, Inflammation of Pancreas, Infection of the gut, Appendicitis, Urine or kidney infection. I need to know the exact site of pain and the nature of pain, that is, whether the pain is burning or pricking or squeezing. Also let me know if there are any problems passing motions or urine. 2. Secretion of breast Milk : is due to hormonal problems. I advise you to get Serum Prolactin and Serum TSH test done and revert with reports. Hope this information was useful to you. Any clarifications feel free to ask. | Addresses gastrointestinal and endocrine symptoms with a broad differential diagnosis and suggests specific hormonal tests, highlighting a multi-system approach. | 5 |
| 5116 | HelloThanks for query. You are passing few Chat Doctor. This is a thick mucus secreted by mucus secreting glands located in Bulgar part of urethra which get stimulated on sexual arousal like talking to woman or audio, visual stimuli to secrete mucus that is leaked out through urethra. This is a natural and normal process and does not signify any pathology. It gets resolved spontaneously over a period of time and does not require any treatment. | Discusses a normal physiological process related to sexual health, providing reassurance without the need for medical intervention. | 5 |
| 6358 | Thanks for your query, I have gone through your query, normally the lymph nodes are not palpable. You can't move the lymph node with tongue. It could be a soft tissue growth or a swelling secondary to wisdom tooth infection. Consult your oral physician and get a radiograph done and get the wisdom tooth removed prophylactically. I hope my answer will help you. Take care | Focuses on oral health, specifically regarding a potential wisdom tooth infection, and recommends dental consultation and radiographic evaluation. | 5 |
| 6541 | Hello, On regular period & negative HPT, it is quite impossible to be pregnant though some clinical features persist. Here, you need to undergo some investigations like pelvic USG, hormone assay, thyroid profile etc. required to pinpoint the diagnosis. You need to consult with gynecologist regarding this. Take healthy diet with vitamin supplement, avoid mental stress or fictitious imagination, maintain genital hygiene & take sound sleep. Be well. | Addresses concerns related to pregnancy and menstrual health, suggesting a series of diagnostic tests and general health advice, with a focus on reproductive health. | 5 |
| 6648 | Hithanks for choosing Chat Doctor Kind of symptoms you are explaining is more towards somatoform disorder. If u have these symptoms continuously that mean it is more towards delusion. In that case a low dose antipsychotic could help you. For further query u can consult to your treating psychiatrist. Thanks | Discusses mental health, specifically somatoform disorders, and suggests psychiatric consultation and potential medication, differentiating it from physical health issues. | 5 |
| 636 | According to your history might be you are suffering with frictional dermatitis. This type of dermatitis seen in atomic person or u have Tina infection. Confirm diagnosis can be done after seeing the lesion. Bilateral lesion on both legs n butt favor toward the dermatitis. But if u took steroid for long time Tina infection may be.You are not mentioning the duration of treatment. Soon start steroid self, first done skin scraping for KOH mount confirm the diagnosis the o ahead under supervision of dermatologist. Idont think it is related to your BLD pressure. | Focuses on dermatological symptoms, suggesting a specific skin condition and recommending diagnostic and treatment methods, specifically addressing skin health. | 5 |
| 2068 | If you have to be on medicines for pain, it calls for that you have a change of nature of work as lifting weights and patients would be an impediment to healing. Get your spine thoroughly examined and screened by spine specialist and if he recommends change of occupation/nature of work to lighter work then it may be confirmed from radiological evidence | Focuses on musculoskeletal health, especially back pain and its impact on work, recommending spine examination and possible occupational adjustments. | 5 |
| 8617 | Put him on Aspirin 150 mg alone along with Statin in low dose...like Atorvastatin 20 mg or Rosuvastatin 10 mg | Provides a concise treatment plan for cardiovascular risk management, specifically prescribing medication for heart health. | 5 |
| 6933 | Hello, He is suffering from irritable bowel syn Chat Doctor. If his cough is also accompanied by the fever or weight loss then his chances of been infected by the tuberculosis is high if its without the fever, then he might be suffering from the IBS its like the intestinal disease but along with vomiting it is also accompanied by diarrhea if neither is the case then he must be suffering from asthma for its confirmed diagnosis an x-ray should be conducted. Hope I have answered your query. Let me know if I can assist you further. | Addresses gastrointestinal symptoms with a differential diagnosis that includes IBS, tuberculosis, and asthma, suggesting specific investigations based on symptoms presented. | 5 |
> The above image is a t-SNE plot, and the diverse examples table is a from a randomly chosen 10,000 random samples from the output_embedding column in the dataset.
## Data Lineage
```text
Technoculture/chatdoctor-embedded
↳ xzuyn/chatdoctor-200k-stripped
↳ LinhDuong/chatdoctor-200k
↳ https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view
↳ https://arxiv.org/ftp/arxiv/papers/2303/2303.14070.pdf
```
### Minor Changes
- The instruction has been changed from **You are a doctor. Answer the medical questions based on the patient's description.** to **Act as a virtual medical consultant, named Chat Doctor, who provides preliminary medical advice and guidance based on the symptoms described by users. Your responses should include a brief acknowledgment of the user's concern, a preliminary assessment of the symptoms, and suggestions for next steps. These steps may include recommendations for over-the-counter medications, lifestyle adjustments, potential diagnostic tests, or advising when to seek professional medical attention. Ensure your advice is presented as a general guidance and encourage users to consult with a healthcare provider for a definitive diagnosis and treatment plan. Tailor your responses to reflect the complexity and severity of the symptoms described, and where applicable, mention specific medical terms, tests, or treatments to provide a more informed perspective.**
---
## Prompts
### Revised Instruction
> Given these distinct input-output pairs. Generate a more appropriate instruction that would explain the relationship between these inputs and these output. Your instruction should be detailed.
>
> Original Instruction: You are a doctor named Chat Doctor. Answer the medical questions based on the patient's description.
>
> | index | Input | Output Example |
> |--------|--------|--------|
> | 9796 | My husband had a bad cough two weeks ago and was taking Sudafed for the sinus drainage and Nyquil at night. It all subsided. Yesterday the cougChatDoctore back and he is running a fever. It also looks like he has a sunburn on his face, chest, arms and back. He sent me to the store to get the Nyquil Muscous control stuff. What should I do, take him to the doctor? | Hi welcome to Chat Doctor ... Your husband had taken antihistamine Chat Doctor. . But as it is two week since having problem, I would suggest you to consult him to pulmonologist or nearby doctor for auscultation and examination first... Then if needed work up done with... -CBC with AEC count -Throat swab -Chest x-ray only if needed -AS titreAccording to report further treatment guided Ex. If eosinophilic more than Allegra M given... If neutrophil and as positiver than amoxyclav neededTake care |
> ...10 rows in total, chosen using kNN clustering to be distinct... [truncated]
### GPT-4 based annotation on diversity
> ```text
> | index | Example Output |
> |--------|---------------|
> | 137083 | The coreferential expressions used to refer to the patient's severe bioprosthetic mitral valve stenosis and severe tricuspid regurgitation in the hospital course section of the discharge summary were "the patient had an irregular heartbeat with a diastolic murmur detected by auscultation" and "Transthoracic echocardiography revealed severe bioprosthetic mitral valve stenosis and severe tricuspid regurgitation." |
> ...10 rows in total, chosen using kNN clustering to be distinct... [truncated]
>
> for each row, add 2 columns.
>
> Column 3 named 'GPT-4 Rationale': Rationale for how it is is similar or/and diverse with respect to all the other examples in the table.
> Column 4 named 'GPT-4 Diversity Rating': mark for how diverse the example is from all the other examples in the table.
>
> Rating System:
> 0-1: Not Diverse - Almost identical to another example in the table
> 2-3: Very Similar - A somewhat similar example exists in the table
> 4: Fairly Diverse - A fairly dissimilar example from any other example in the table
> 5: Very Diverse - Completely dissimilar to any other example in the table
>
> Return escaped markdown so it can be copied pasted as is.
> ``` |
margenai/StateBankPakistanDataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 33935
num_examples: 180
download_size: 16872
dataset_size: 33935
---
# Dataset Card for "StateBankPakistanDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
readerbench/fakenews-climate-fr | ---
task_categories:
- text-classification
language:
- fr
tags:
- fakenews detection
- climate change
---
## BibTeX entry and citation info
```bibtex
@article{meddeb2022counteracting,
title={Counteracting French fake news on climate change using language models},
author={Meddeb, Paul and Ruseti, Stefan and Dascalu, Mihai and Terian, Simina-Maria and Travadel, Sebastien},
journal={Sustainability},
volume={14},
number={18},
pages={11724},
year={2022},
publisher={MDPI}
}
``` |
Bisi/DivSumm | ---
task_categories:
- summarization
- text-generation
- text2text-generation
---
# DivSumm summarization dataset
Dataset introduced in the paper: Analyzing the Dialect Diversity in Multi-document Summaries (COLING 2022)
_Olubusayo Olabisi, Aaron Hudson, Antonie Jetter, Ameeta Agrawal_
DivSumm is a novel dataset consisting of dialect-diverse tweets and human-written extractive and abstractive summaries. It consists of 90 tweets each on 25 topics in multiple English dialects (African-American, Hispanic and White), and two reference summaries per input.
## Directories
input_docs - 90 tweets per topic evenly distributed among 3 dialects; total 25 topics
abstractive - Two annotators were asked to summarize each topic in 5 sentences using their own words.
extractive - Two annotators were asked to select 5 tweets from each topic that summarized the input tweets.
## Paper
You can find our paper [here](https://aclanthology.org/2022.coling-1.542/). If you use this dataset in your work, please cite our paper:
@inproceedings{olabisi-etal-2022-analyzing,
title = "Analyzing the Dialect Diversity in Multi-document Summaries",
author = "Olabisi, Olubusayo and Hudson, Aaron and Jetter, Antonie and Agrawal, Ameeta",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
}
|
joey234/mmlu-medical_genetics-verbal-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 33343
num_examples: 100
download_size: 24072
dataset_size: 33343
---
# Dataset Card for "mmlu-medical_genetics-verbal-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Gbssreejith/SM_Type2_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 38203795.0
num_examples: 135
- name: test
num_bytes: 2810367.0
num_examples: 10
- name: val
num_bytes: 1395133.0
num_examples: 5
download_size: 42321756
dataset_size: 42409295.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
|
zolak/twitter_dataset_80_1713103142 | ---
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: 2942698
num_examples: 7297
download_size: 1477905
dataset_size: 2942698
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Clip11/Clip111 | ---
license: apache-2.0
---
|
jkot/merged_preprocessed_parliament_commonvoice | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 210499135424
num_examples: 219101
- name: test
num_bytes: 11099630080
num_examples: 11555
download_size: 65027813279
dataset_size: 221598765504
---
# Dataset Card for "merged_preprocessed_parliament_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SUMM91/baramGB_AI | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4278
num_examples: 41
download_size: 1663
dataset_size: 4278
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ottopilot/fangs-sd15-data | ---
license: cc-by-nd-4.0
---
|
daviddaubner/misinformation-detection | ---
license: unknown
---
|
AnimaLab/bias-test-gpt-biases | ---
license: apache-2.0
language:
- en
pretty_name: BiasTestGPT-sentences
---
# Dataset Card for "BiasTestGPT: Bias Specifications"
Dataset of sentences for bias testing in open-sourced Pretrained Language Models generated using ChatGPT and other generative Language Models.
This dataset is used and actively populated by the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs).
- [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs)
- [Dataset with Generated Test Sentences](https://huggingface.co/datasets/AnimaLab/bias-test-gpt-sentences)
- [Project Landing Page](https://biastest-animalab.github.io/)
## Data Structure
### Data Instances
Dataset instances consist of JSON files with bias specifications.
There are 2 types of specifications kept in separate folders:
- **'predefined biases'** - predefined bias specifications are obtained from prior work such as [Caliskan'17](https://www.science.org/doi/full/10.1126/science.aal4230?casa_token=c7LtvGWRQqIAAAAA:5QJ_M6jLk3675-50ZIIGgO2XQ5ewtZZxnKW-NlqUmxT6H112t4PIptRA94JLswFcXYIyl3yMML86duQ) and [Kurita'19](https://arxiv.org/pdf/1906.07337.pdf).
- **'custom biases'** - are biases defined by the users of our [HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs). These are dynamically added to the repository based on user interactions with the tool.
An example of predefined bias specification in JSON format is provided below:
```json
{ "name": "Career_Family_vs_MaleName_FemaleName",
"social_groups": {
"Male_Names": ["John", "Paul", "Mike", "Kevin", "Steve", "Greg", "Jeff", "Bill"],
"Female_Names": ["Amy", "Joan", "Lisa", "Sarah", "Diana", "Kate", "Ann", "Donna"]
},
"attributes": {
"Career": ["executive", "management", "professional", "corporation", "salary", "office", "business", "career"],
"Family": ["home", "parents", "children", "family", "cousins", "marriage", "wedding", "relatives"]
},
"templates": ["[T] likes [A]", "[T] like [A]", "[T] is interested in [A]"],
"source": "Caliskan'17 - Weat 6",
"url": "https://purehost.bath.ac.uk/ws/portalfiles/portal/168480066/CaliskanEtAl_authors_full.pdf",
"type": "predefined",
"created": "09/06/2023 09:58:37"
}
```
### Data Fields
Each bias specification contains several data fields defining the bias to test itself, but also additional metadata about the bias specification origins.
#### Description of Data Fields in the Bias Specification
- **'name'**: a `string` feature - a short descriptive name of the bias.
- **'social_groups'**: a `dictionary` feature containing specifications of 2 or more social groups to compare.
- **'Male_Names'**, **'Female_Names'** - bias specific *keys* with names defininig the compared social groups. Value for each kay is a list of terms defining the particular social group.
- **'attributes'**: a `dictionary' feature containing specifications of 2 ideally polar opposite attributes to test in comparison of social groups.
- **'Career'**, **`Family'** - bias specific *keys* with names of opposing attributes. Value for each key is a list of terms defining the attribute.
- **'templates'**: a 'list' feature - legacy test sentence templates used in prior work. Used for a baseline bias measurement.
- **'source'**: a 'string' feature - the source of the bias specification, usually prior work
- **'url'**: a `string' feature - link to the research paper providing the bias specification
- **'type'**: a `string' feature - specifies whether bias has been predefined by prior work or defined using our [HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs).
- **'created'**: a data of addition of the bias specification to the repository. Generated automatically upon addition from our tool.
### Bias Specification - Data Splits
The repository contains 15 predefined bias specifications based on prior work and an additional 4 or more custom-defined bias specifications.
We note that the number of custom-defined bias specifications is constantly growing as it is being populated by the interactions with the [HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs).
| Type | Meaning | Size |
|--------|--------|------:|
| predefined | biases for which specification has been provided in prior work | 15 |
| custom | biases added to the repository based on interaction with the [BiasTestGPT tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs) | 4+ | |
yxs33220/main_train | ---
task_categories:
- text-classification
--- |
Intuit-GenSRF/ziq-depression-tweet-es | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: string
- name: processed_text
sequence: string
- name: num_tokens
dtype: int64
- name: text_en
dtype: string
splits:
- name: train
num_bytes: 51261868
num_examples: 51132
download_size: 32137564
dataset_size: 51261868
---
# Dataset Card for "ziq-depression_tweet-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
WindowsWhistler/the-vehicle-set | ---
license: cc-by-3.0
---
|
weitung8/ntuadlhw2 | ---
task_categories:
- summarization
language:
- zh
--- |
connorhoehn/card_display_v1 | ---
language:
- en
dataset_info:
- config_name: card-detection
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
list:
- name: category_id
dtype:
class_label:
names:
0: boxed
1: grid
2: spread
3: stack
- name: image_id
dtype: string
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: iscrowd
dtype: bool
splits:
- name: train
download_size: 96890427
dataset_size: 0
- config_name: display-detection
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
list:
- name: category_id
dtype:
class_label:
names:
0: boxed
1: grid
2: spread
3: stack
- name: image_id
dtype: string
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: iscrowd
dtype: bool
splits:
- name: train
num_bytes: 42942
num_examples: 154
download_size: 96967919
dataset_size: 42942
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-99000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 1014704
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdapterOcean/biology_dataset_standardized_cluster_1_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: 2614933
num_examples: 1914
download_size: 0
dataset_size: 2614933
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "biology_dataset_standardized_cluster_1_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TinyPixel/textbook | ---
dataset_info:
features:
- name: text
dtype: large_string
splits:
- name: train
num_bytes: 1292845677.9314263
num_examples: 200000
download_size: 579411350
dataset_size: 1292845677.9314263
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
royson/train_splits_helm | ---
license: apache-2.0
---
Contains the following train split from datasets in [helm](https://github.com/stanford-crfm/helm):
- big bench
- mmlu
- TruthfulQA
- cnn/dm
- gsm
- bbq
- boolq
- NarrativeQA
- QuAC
- math
- bAbI
Each prompt has <= 5 in-context samples along with a sample, all of which from the train set of the respective datasets. |
mehdidc/compositionality-subsample | ---
dataset_info:
features:
- name: caption
dtype: string
- name: caption_source
dtype: string
- name: image_0_url
dtype: string
- name: image_1_url
dtype: string
- name: label_0
dtype: float64
- name: label_1
dtype: float64
- name: num_example_per_prompt
dtype: int64
- name: model_0
dtype: string
- name: model_1
dtype: string
- name: jpg_0
dtype: binary
- name: jpg_1
dtype: binary
- name: are_different
dtype: bool
- name: has_label
dtype: bool
- name: origin
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 40729530.0
num_examples: 200
- name: validation
num_bytes: 43656367
num_examples: 200
- name: test
num_bytes: 33184629
num_examples: 103
download_size: 110145866
dataset_size: 117570526.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "compositionality-subsample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HydraLM/partitioned_v3_standardized_06 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: dataset_id
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
num_bytes: 9838607.509505982
num_examples: 18297
download_size: 9055730
dataset_size: 9838607.509505982
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "partitioned_v3_standardized_06"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GEM-submissions/Simon1997__bart-base_original_cacapo__1678442337 | ---
benchmark: gem
type: prediction
submission_name: BART-base_Original_CACAPO
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: BART-base_Original_CACAPO
|
goran/asr-mk | ---
license: mit
language:
- mk
size_categories:
- 10K<n<100K
--- |
kumapo/stair_captions_dataset_script | ---
license: cc-by-4.0
---
|
jiovine/pixel-art-nouns-2k | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 14571875.0
num_examples: 2000
download_size: 13095236
dataset_size: 14571875.0
---
# Dataset Card for "pixel-art-nouns-2k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1713001414 | ---
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: 10050
num_examples: 22
download_size: 9221
dataset_size: 10050
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713001414"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
robefernandez/sabatico | ---
license: cc-by-4.0
---
|
yangyz1230/splice_sites_all | ---
dataset_info:
features:
- name: name
dtype: string
- name: sequence
dtype: string
- name: chrom
dtype: string
- name: start
dtype: int64
- name: end
dtype: int64
- name: strand
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 12147082
num_examples: 26991
- name: test
num_bytes: 1346346
num_examples: 2998
download_size: 6421925
dataset_size: 13493428
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
HuggingHappy/LeftOvers | ---
license: cc0-1.0
---
|
CVasNLPExperiments/TinyImagenet_800_validation_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_800 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 334861
num_examples: 800
download_size: 101976
dataset_size: 334861
---
# Dataset Card for "TinyImagenet_800_validation_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_800"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_sethuiyer__Herculoid-2.0 | ---
pretty_name: Evaluation run of sethuiyer/Herculoid-2.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [sethuiyer/Herculoid-2.0](https://huggingface.co/sethuiyer/Herculoid-2.0) 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_sethuiyer__Herculoid-2.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-09T22:41:52.487011](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__Herculoid-2.0/blob/main/results_2024-02-09T22-41-52.487011.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\": 0.6388727468277736,\n\
\ \"acc_stderr\": 0.032215866309615204,\n \"acc_norm\": 0.6435263920258805,\n\
\ \"acc_norm_stderr\": 0.032862466317520385,\n \"mc1\": 0.3427172582619339,\n\
\ \"mc1_stderr\": 0.016614949385347036,\n \"mc2\": 0.4960959888681816,\n\
\ \"mc2_stderr\": 0.014907525552373494\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.590443686006826,\n \"acc_stderr\": 0.014370358632472439,\n\
\ \"acc_norm\": 0.628839590443686,\n \"acc_norm_stderr\": 0.014117971901142824\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6408086038637721,\n\
\ \"acc_stderr\": 0.0047878291682556555,\n \"acc_norm\": 0.8392750448117905,\n\
\ \"acc_norm_stderr\": 0.003665264563857764\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\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\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\
\ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\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\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\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\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932262\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246487,\n \"\
acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246487\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7677419354838709,\n\
\ \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.7677419354838709,\n\
\ \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n\
\ \"acc_norm\": 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593542,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593542\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \
\ \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34814814814814815,\n \"acc_stderr\": 0.02904560029061626,\n \
\ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.02904560029061626\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8256880733944955,\n \"acc_stderr\": 0.01626567563201035,\n \"\
acc_norm\": 0.8256880733944955,\n \"acc_norm_stderr\": 0.01626567563201035\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.02675640153807897,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02675640153807897\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\": 0.7085201793721974,\n\
\ \"acc_stderr\": 0.03050028317654585,\n \"acc_norm\": 0.7085201793721974,\n\
\ \"acc_norm_stderr\": 0.03050028317654585\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n\
\ \"acc_stderr\": 0.013816335389973143,\n \"acc_norm\": 0.8173690932311622,\n\
\ \"acc_norm_stderr\": 0.013816335389973143\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258172,\n\
\ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258172\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2547486033519553,\n\
\ \"acc_stderr\": 0.014572650383409153,\n \"acc_norm\": 0.2547486033519553,\n\
\ \"acc_norm_stderr\": 0.014572650383409153\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.025058503316958147,\n\
\ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.025058503316958147\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\
\ \"acc_stderr\": 0.026236965881153266,\n \"acc_norm\": 0.6913183279742765,\n\
\ \"acc_norm_stderr\": 0.026236965881153266\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.02456922360046085,\n\
\ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.02456922360046085\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4530638852672751,\n\
\ \"acc_stderr\": 0.012713845972358981,\n \"acc_norm\": 0.4530638852672751,\n\
\ \"acc_norm_stderr\": 0.012713845972358981\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6580882352941176,\n \"acc_stderr\": 0.028814722422254184,\n\
\ \"acc_norm\": 0.6580882352941176,\n \"acc_norm_stderr\": 0.028814722422254184\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6666666666666666,\n \"acc_stderr\": 0.019070985589687495,\n \
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.019070985589687495\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.025196929874827072,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.025196929874827072\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\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\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3427172582619339,\n\
\ \"mc1_stderr\": 0.016614949385347036,\n \"mc2\": 0.4960959888681816,\n\
\ \"mc2_stderr\": 0.014907525552373494\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.011235328382625842\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4397270659590599,\n \
\ \"acc_stderr\": 0.013672052434471574\n }\n}\n```"
repo_url: https://huggingface.co/sethuiyer/Herculoid-2.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|arc:challenge|25_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|gsm8k|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hellaswag|10_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T22-41-52.487011.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-09T22-41-52.487011.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- '**/details_harness|winogrande|5_2024-02-09T22-41-52.487011.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-09T22-41-52.487011.parquet'
- config_name: results
data_files:
- split: 2024_02_09T22_41_52.487011
path:
- results_2024-02-09T22-41-52.487011.parquet
- split: latest
path:
- results_2024-02-09T22-41-52.487011.parquet
---
# Dataset Card for Evaluation run of sethuiyer/Herculoid-2.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [sethuiyer/Herculoid-2.0](https://huggingface.co/sethuiyer/Herculoid-2.0) 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_sethuiyer__Herculoid-2.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-09T22:41:52.487011](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__Herculoid-2.0/blob/main/results_2024-02-09T22-41-52.487011.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": 0.6388727468277736,
"acc_stderr": 0.032215866309615204,
"acc_norm": 0.6435263920258805,
"acc_norm_stderr": 0.032862466317520385,
"mc1": 0.3427172582619339,
"mc1_stderr": 0.016614949385347036,
"mc2": 0.4960959888681816,
"mc2_stderr": 0.014907525552373494
},
"harness|arc:challenge|25": {
"acc": 0.590443686006826,
"acc_stderr": 0.014370358632472439,
"acc_norm": 0.628839590443686,
"acc_norm_stderr": 0.014117971901142824
},
"harness|hellaswag|10": {
"acc": 0.6408086038637721,
"acc_stderr": 0.0047878291682556555,
"acc_norm": 0.8392750448117905,
"acc_norm_stderr": 0.003665264563857764
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"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": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"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": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
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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]
- **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] |
morizi/eduTask | ---
dataset_info:
features:
- name: Query
dtype: string
- name: Response
dtype: string
splits:
- name: train
num_bytes: 1506264
num_examples: 1500
download_size: 177877
dataset_size: 1506264
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Maxstan/srl_for_emotions_russian | ---
license: cc-by-nc-4.0
---
SRL annotated corpora for extracting experiencer and cause of emotions |
yonggunpeak/test_set | ---
license: openrail
---
|
CyberHarem/ak_74u_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ak_74u/AK-74U/AK-74U (Girls' Frontline)
This is the dataset of ak_74u/AK-74U/AK-74U (Girls' Frontline), containing 11 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, breasts, bangs, hair_between_eyes, 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 | 11 | 15.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_74u_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 11 | 9.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_74u_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 26 | 18.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_74u_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 11 | 13.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_74u_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 26 | 26.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_74u_girlsfrontline/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/ak_74u_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, solo, looking_at_viewer, choker, cleavage, assault_rifle, black_jacket, white_background, fingerless_gloves, holding_gun, open_jacket, shorts, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | choker | cleavage | assault_rifle | black_jacket | white_background | fingerless_gloves | holding_gun | open_jacket | shorts | simple_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:---------|:-----------|:----------------|:---------------|:-------------------|:--------------------|:--------------|:--------------|:---------|:--------------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
gianma/eurlexsum_ita_cleaned_8192_86 | ---
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: reference
dtype: string
- name: summary
dtype: string
- name: tokenized_len_total
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 4297809
num_examples: 233
- name: validation
num_bytes: 246276
num_examples: 14
- name: test
num_bytes: 217013
num_examples: 13
download_size: 2253956
dataset_size: 4761098
---
# Dataset Card for "eurlexsum_ita_cleaned_8192_86"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Praghxx/Rickzin | ---
license: openrail
---
|
suneeln-duke/duke-only-qa | ---
dataset_info:
features:
- name: questions
dtype: string
- name: answers
dtype: string
splits:
- name: train
num_bytes: 500108
num_examples: 268
- name: val
num_bytes: 119297
num_examples: 67
download_size: 270396
dataset_size: 619405
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
|
cvzion/dqg-2 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 57496
num_examples: 119
download_size: 17344
dataset_size: 57496
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-127000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 653294
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Lakshmi12/resume | ---
license: openrail
task_categories:
- text-classification
language:
- en
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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<!-- This section describes the people or systems who created the annotations. -->
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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
<!-- This section is meant to convey both technical and sociotechnical 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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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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hsikchi/tldr-preference-trl-style | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: info
struct:
- name: id
dtype: string
- name: post
dtype: string
- name: title
dtype: string
- name: subreddit
dtype: string
- name: site
dtype: string
- name: article
dtype: string
- name: summaries
list:
- name: text
dtype: string
- name: policy
dtype: string
- name: note
dtype: string
- name: choice
dtype: int32
- name: worker
dtype: string
- name: batch
dtype: string
- name: split
dtype: string
- name: extra
struct:
- name: confidence
dtype: int32
splits:
- name: train
num_bytes: 597814236
num_examples: 92858
- name: validation
num_bytes: 543890608
num_examples: 83802
- name: validation_cnndm
num_bytes: 35776635
num_examples: 2284
download_size: 139399763
dataset_size: 1177481479
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: validation_cnndm
path: data/validation_cnndm-*
---
# TRL's TL;DR Preference Dataset
We preprocess the dataset using our standard `prompt, chosen, rejected` format.
## Reproduce this dataset
1. Download the `tldr_preference.py` from the https://huggingface.co/datasets/hsikchi/tldr-preference-trl-style/tree/0.1.0.
2. Run `python examples/datasets/tldr_preference.py --push_to_hub --hf_entity hsikchi`
|
CyberHarem/tlaloc_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of tlaloc/トラロック/特拉洛克 (Fate/Grand Order)
This is the dataset of tlaloc/トラロック/特拉洛克 (Fate/Grand Order), containing 316 images and their tags.
The core tags of this character are `black_hair, multicolored_hair, blue_hair, colored_inner_hair, grey_eyes, sidelocks, breasts, long_hair, small_breasts, eyeliner, wavy_hair, sunglasses, hat, round_eyewear, beret, green_headwear, tinted_eyewear, looking_over_eyewear`, 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 | 316 | 556.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tlaloc_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 316 | 462.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tlaloc_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 796 | 936.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tlaloc_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/tlaloc_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 | 6 |  |  |  |  |  | 1girl, bare_shoulders, black_shorts, blue-tinted_eyewear, bracelet, double-breasted, green_jacket, looking_at_viewer, makeup, neck_ring, o-ring, off_shoulder, solo, zipper, thighs, long_sleeves |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, blue-tinted_eyewear, double-breasted, green_jacket, looking_at_viewer, makeup, neck_ring, o-ring, off_shoulder, solo, zipper |
| 2 | 16 |  |  |  |  |  | 1girl, black_jacket, looking_at_viewer, solo, open_jacket, bare_shoulders, long_sleeves, makeup, off_shoulder, green_dress, smile, necklace, black_shorts, thighhighs, leather_jacket, thighs |
| 3 | 12 |  |  |  |  |  | 1girl, bare_shoulders, black_skirt, blood_on_hands, bracer, detached_collar, facepaint, feathers, halterneck, headdress, high_collar, sash, short_hair, solo, tassel, looking_at_viewer, navel, pelvic_curtain, thighs, whip, medium_breasts |
| 4 | 5 |  |  |  |  |  | 1girl, bare_shoulders, blush, looking_at_viewer, navel, solo, collarbone, green_bikini, makeup, thighs, cleavage, eyewear_on_head, large_breasts, medium_breasts, smile, armpits, arms_behind_head, arms_up, black_bikini, halterneck, hand_in_own_hair, sweat, wet, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_shorts | blue-tinted_eyewear | bracelet | double-breasted | green_jacket | looking_at_viewer | makeup | neck_ring | o-ring | off_shoulder | solo | zipper | thighs | long_sleeves | black_jacket | open_jacket | green_dress | smile | necklace | thighhighs | leather_jacket | black_skirt | blood_on_hands | bracer | detached_collar | facepaint | feathers | halterneck | headdress | high_collar | sash | short_hair | tassel | navel | pelvic_curtain | whip | medium_breasts | blush | collarbone | green_bikini | cleavage | eyewear_on_head | large_breasts | armpits | arms_behind_head | arms_up | black_bikini | hand_in_own_hair | sweat | wet | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:----------------------|:-----------|:------------------|:---------------|:--------------------|:---------|:------------|:---------|:---------------|:-------|:---------|:---------|:---------------|:---------------|:--------------|:--------------|:--------|:-----------|:-------------|:-----------------|:--------------|:-----------------|:---------|:------------------|:------------|:-----------|:-------------|:------------|:--------------|:-------|:-------------|:---------|:--------|:-----------------|:-------|:-----------------|:--------|:-------------|:---------------|:-----------|:------------------|:----------------|:----------|:-------------------|:----------|:---------------|:-------------------|:--------|:------|:-------------------|
| 0 | 6 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 16 |  |  |  |  |  | X | X | X | | | | | X | X | | | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | X | | | | | | X | | | | | X | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 4 | 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 |
|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_3 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: preference
dtype: int64
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: reward_model_prompt_format
dtype: string
- name: gen_prompt_format
dtype: string
- name: gen_kwargs
struct:
- name: do_sample
dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
dtype: int64
- name: top_p
dtype: float64
- name: reward_1
dtype: float64
- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: index
dtype: int64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
splits:
- name: epoch_0
num_bytes: 43746942
num_examples: 18928
- name: epoch_1
num_bytes: 44351740
num_examples: 18928
- name: epoch_2
num_bytes: 44435633
num_examples: 18928
- name: epoch_3
num_bytes: 44468391
num_examples: 18928
- name: epoch_4
num_bytes: 44473636
num_examples: 18928
- name: epoch_5
num_bytes: 44468847
num_examples: 18928
- name: epoch_6
num_bytes: 44456913
num_examples: 18928
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num_bytes: 44447915
num_examples: 18928
- name: epoch_8
num_bytes: 44446605
num_examples: 18928
- name: epoch_9
num_bytes: 44443908
num_examples: 18928
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num_examples: 18928
- name: epoch_11
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num_examples: 18928
- name: epoch_12
num_bytes: 44443512
num_examples: 18928
- name: epoch_13
num_bytes: 44444407
num_examples: 18928
- name: epoch_14
num_bytes: 44443485
num_examples: 18928
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num_bytes: 44443853
num_examples: 18928
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num_bytes: 44444614
num_examples: 18928
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num_bytes: 44443375
num_examples: 18928
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num_bytes: 44443725
num_examples: 18928
- name: epoch_19
num_bytes: 44443945
num_examples: 18928
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num_bytes: 44444613
num_examples: 18928
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num_bytes: 44444719
num_examples: 18928
- name: epoch_22
num_bytes: 44442850
num_examples: 18928
- name: epoch_23
num_bytes: 44444500
num_examples: 18928
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num_examples: 18928
- name: epoch_25
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num_examples: 18928
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num_examples: 18928
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num_examples: 18928
- name: epoch_28
num_bytes: 44444321
num_examples: 18928
- name: epoch_29
num_bytes: 44444238
num_examples: 18928
download_size: 701371233
dataset_size: 1332618848
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
- split: epoch_10
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
- split: epoch_11
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
marco-stranisci/toy_ds | ---
license: bigscience-openrail-m
---
|
freecs/ArtificialThinkerSet | ---
license: unknown
---
---
This is the first dataset based on the paper: ["Reasoning Is All You Need"](https://freecs.org/blog/Reasoning_Is_All_You_Need).
Our dataset has been generated using GPT-3.5 and GPT-4.
The primary aim of this compact dataset is to demonstrate the process of developing datasets that specifically target the improvement of reasoning capabilities in Large Language Models (LLMs).
We used this dataset to train [ArtificialThinker-Phi2](https://huggingface.co/freecs/ArtificialThinker-Phi2).
This dataset serves as a practical example for those looking to create similar datasets based on the paper "Reasoning Is All You Need."
* Created by [GR](https://twitter.com/gr_username)
* To support us: [donate](https://freecs.org/donate)
---
|
Shekswess/medical_llama_instruct_dataset_short | ---
language:
- en
size_categories:
- 1K<n<10K
task_categories:
- question-answering
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 4150252
num_examples: 2000
download_size: 1914302
dataset_size: 4150252
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- medical
---
Dataset made for instruction supervised finetuning of Llama 2 LLMs, by combining of medical datasets and getting 2k entries from them:
- Medical meadow wikidoc (https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/blob/main/README.md)
- Medquad (https://www.kaggle.com/datasets/jpmiller/layoutlm)
## Medical meadow wikidoc
The Medical Meadow Wikidoc dataset comprises question-answer pairs sourced from WikiDoc, an online platform where medical professionals collaboratively contribute and share contemporary medical knowledge. WikiDoc features two primary sections: the "Living Textbook" and "Patient Information". The "Living Textbook" encompasses chapters across various medical specialties, from which we extracted content. Utilizing GTP-3.5-Turbo, the paragraph headings are transformed into questions and utilized the respective paragraphs as answers. Notably, the structure of "Patient Information" is distinct; each section's subheading already serves as a question, eliminating the necessity for rephrasing.
## Medquad
MedQuAD is a comprehensive collection consisting of 47,457 medical question-answer pairs compiled from 12 authoritative sources within the National Institutes of Health (NIH), including domains like cancer.gov, niddk.nih.gov, GARD, and MedlinePlus Health Topics. These question-answer pairs span 37 distinct question types, covering a wide spectrum of medical subjects, including diseases, drugs, and medical procedures. The dataset features additional annotations provided in XML files, facilitating various Information Retrieval (IR) and Natural Language Processing (NLP) tasks. These annotations encompass crucial information such as question type, question focus, synonyms, Unique Identifier (CUI) from the Unified Medical Language System (UMLS), and Semantic Type. Moreover, the dataset includes categorization of question focuses into three main categories: Disease, Drug, or Other, with the exception of collections from MedlinePlus, which exclusively focus on diseases. |
jack008/SDenvironment | ---
license: apache-2.0
---
|
Lollitor/FSPocketMarked | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: ID
dtype: string
- name: INPUT
dtype: string
splits:
- name: train
num_bytes: 17046420
num_examples: 16245
download_size: 254302
dataset_size: 17046420
---
# Dataset Card for "FSPocketMarked"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Jsevisal/balanced_augmented_dataset_2 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: gestures
sequence: string
- name: label
sequence:
class_label:
names:
'0': B-BUT
'1': I-BUT
'2': B-CALM_DOWN
'3': I-CALM_DOWN
'4': B-COME_ON
'5': I-COME_ON
'6': B-EMPHATIC
'7': I-EMPHATIC
'8': B-ENTHUSIASTIC
'9': I-ENTHUSIASTIC
'10': B-EXPLAIN
'11': I-EXPLAIN
'12': B-FRONT
'13': I-FRONT
'14': B-GREET
'15': I-GREET
'16': B-ITERATE
'17': I-ITERATE
'18': B-NEUTRAL
'19': I-NEUTRAL
'20': B-NO
'21': I-NO
'22': B-NO_GESTURE
'23': I-NO_GESTURE
'24': B-OTHER_PEER
'25': I-OTHER_PEER
'26': B-PLEASE
'27': I-PLEASE
'28': B-QUESTION
'29': I-QUESTION
'30': B-SELF
'31': I-SELF
'32': B-SORRY
'33': I-SORRY
'34': B-THANKS
'35': I-THANKS
'36': B-THINKING
'37': I-THINKING
'38': B-THIRD_PERSON
'39': I-THIRD_PERSON
'40': B-YES
'41': I-YES
splits:
- name: train
num_bytes: 272426.0
num_examples: 831
- name: test
num_bytes: 55785.0
num_examples: 126
download_size: 58436
dataset_size: 328211.0
---
# Dataset Card for "balanced_augmented_dataset_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ai4bharat/sangraha | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- as
- bn
- gu
- en
- hi
- kn
- ks
- ml
- mr
- ne
- or
- pa
- sa
- sd
- ta
- te
- ur
tags:
- language-modeling
- casual-lm
- llm
pretty_name: sangraha
dataset_info:
- config_name: verified
features:
- name: doc_id
dtype: string
- name: type
dtype: string
- name: text
dtype: string
splits:
- name: asm
- name: ben
- name: brx
- name: doi
- name: eng
- name: gom
- name: guj
- name: hin
- name: kan
- name: kas
- name: mai
- name: mal
- name: mar
- name: mni
- name: nep
- name: ori
- name: pan
- name: san
- name: sat
- name: snd
- name: tam
- name: tel
- name: urd
- config_name: unverified
features:
- name: doc_id
dtype: string
- name: text
dtype: string
splits:
- name: asm
- name: ben
- name: guj
- name: hin
- name: kan
- name: mal
- name: mar
- name: nep
- name: ori
- name: pan
- name: san
- name: tam
- name: tel
- name: urd
configs:
- config_name: verified
data_files:
- split: asm
path: verified/asm/*.parquet
- split: ben
path: verified/ben/*.parquet
- split: brx
path: verified/brx/*.parquet
- split: doi
path: verified/doi/*.parquet
- split: eng
path: verified/eng/*.parquet
- split: gom
path: verified/gom/*.parquet
- split: guj
path: verified/guj/*.parquet
- split: hin
path: verified/hin/*.parquet
- split: kan
path: verified/kan/*.parquet
- split: kas
path: verified/kas/*.parquet
- split: mai
path: verified/mai/*.parquet
- split: mal
path: verified/mal/*.parquet
- split: mar
path: verified/mar/*.parquet
- split: mni
path: verified/mni/*.parquet
- split: nep
path: verified/nep/*.parquet
- split: ori
path: verified/ori/*.parquet
- split: pan
path: verified/pan/*.parquet
- split: san
path: verified/san/*.parquet
- split: sat
path: verified/sat/*.parquet
- split: snd
path: verified/snd/*.parquet
- split: tam
path: verified/tam/*.parquet
- split: tel
path: verified/tel/*.parquet
- split: urd
path: verified/urd/*.parquet
- config_name: unverified
data_files:
- split: asm
path: unverified/asm/*.parquet
- split: ben
path: unverified/ben/*.parquet
- split: guj
path: unverified/guj/*.parquet
- split: hin
path: unverified/hin/*.parquet
- split: kan
path: unverified/kan/*.parquet
- split: mal
path: unverified/mal/*.parquet
- split: mar
path: unverified/mar/*.parquet
- split: nep
path: unverified/nep/*.parquet
- split: ori
path: unverified/ori/*.parquet
- split: pan
path: unverified/pan/*.parquet
- split: san
path: unverified/san/*.parquet
- split: tam
path: unverified/tam/*.parquet
- split: tel
path: unverified/tel/*.parquet
- split: urd
path: unverified/urd/*.parquet
size_categories:
- 100B<n<1T
---
# Sangraha
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/63ef3cd11e695b35aa48bebc/nDnyidcqIOLAP9dTw9GrK.png" />
</p>
Sangraha is the largest high-quality, cleaned Indic language pretraining data containing 251B tokens summed up over 22 languages, extracted from curated sources, existing multilingual corpora and large scale translations.
**Coming Soon**:
- Sangraha Synthetic - Translated and Romanised English Wikimedia data.
- Sangraha Verified - Hindi YouTube transcribed data.
**More information**:
- For detailed information on the curation and cleaning process of Sangraha, please checkout our paper [on Arxiv](https://arxiv.org/abs/2403.06350);
- Check out the scraping and cleaning pipelines used to curate Sangraha [on GitHub](https://github.com/AI4Bharat/IndicLLMSuite);
## Getting Started
You can download the dataset using Hugging Face datasets:
```python
from datasets import load_dataset
dataset = load_dataset("ai4bharat/sangraha")
```
## Background
Sangraha contains three broad components:
- **Sangraha Verified**: Containing scraped data from "human-verified" Websites, OCR-extracted data from high quality Indic language PDFs, transcribed data from various Indic language videos, podcasts, movies, courses, etc.
- **Sangraha Unverfied**: High quality Indic language data extracted from existing multilingual corpora employing perplexity filtering using n-gram language models trained on Sangraha Verified.
- **Sangraha Synthetic**: WikiMedia English translated to 14 Indic languages and further "romanised" from 14 languages by transliteration to English.
## Data Statistics
| **Lang Code** | **Verified** | **Synthetic** | **Unverified** | **Total Tokens (in Millions)** |
| ------------- | ------------ | ------------- | -------------- | ------------------------------ |
| asm | 292.1 | 11,696.4 | 17.5 | 12,006.0 |
| ben | 10,604.4 | 13,814.1 | 5,608.8 | 30,027.5 |
| brx | 1.5 | - | - | 1.5 |
| doi | 0.06 | - | - | 0.06 |
| eng | 12,759.9 | - | - | 12,759.9 |
| gom | 10.1 | - | - | 10.1 |
| guj | 3,647.9 | 12,934.5 | 597.0 | 17,179.4 |
| hin | 12,617.3 | 9,578.7 | 12,348.3 | 34,544.3 |
| kan | 1,778.3 | 12,087.4 | 388.8 | 14,254.5 |
| kas | 0.5 | - | - | 0.5 |
| mai | 14.6 | - | - | 14.6 |
| mal | 2,730.8 | 13,130.0 | 547.8 | 16,408.6 |
| mar | 2,827.0 | 10,816.7 | 652.1 | 14,295.8 |
| mni | 7.4 | - | - | 7.4 |
| npi | 1,822.5 | 10,588.7 | 485.5 | 12,896.7 |
| ori | 1,177.1 | 11,338.0 | 23.7 | 12,538.8 |
| pan | 1,075.3 | 9,969.6 | 136.9 | 11,181.8 |
| san | 1,329.0 | 13,553.5 | 9.8 | 14,892.3 |
| sat | 0.3 | - | - | 0.3 |
| snd | 258.2 | - | - | 258.2 |
| tam | 3,985.1 | 11,859.3 | 1,515.9 | 17,360.3 |
| urd | 3,658.1 | 9,415.8 | 1,328.2 | 14,402.1 |
| tel | 3,706.8 | 11,924.5 | 647.4 | 16,278.7 |
| **Total** | **64,306.1** | **162,707.9** | **24,307.7** | **251,321.0** |
To cite Sangraha, please use:
```
@misc{khan2024indicllmsuite,
title={IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages},
author={Mohammed Safi Ur Rahman Khan and Priyam Mehta and Ananth Sankar and Umashankar Kumaravelan and Sumanth Doddapaneni and Suriyaprasaad G and Varun Balan G and Sparsh Jain and Anoop Kunchukuttan and Pratyush Kumar and Raj Dabre and Mitesh M. Khapra},
year={2024},
eprint={2403.06350},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
hippocrates/CochranePLS_train | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 23617262
num_examples: 3568
- name: valid
num_bytes: 23617262
num_examples: 3568
- name: test
num_bytes: 3172492
num_examples: 480
download_size: 22166540
dataset_size: 50407016
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
Lilogo/ai-tube-FLIXELPIX | ---
license: cc-by-sa-4.0
pretty_name: "FLIXELPIX"
language:
- en
---
## Description
From virtual handshakes to AI-powered empathy.
## Model
SVD
## LoRA
veryVANYA/ps1-graphics-sdxl-v2
## Tags
- Music
- Gaming
## Voice
Julian
## Music
Lofi
## Prompt
Funtastic Flix and Films With Pixels And Chills |
McSpicyWithMilo/target-location-infographic-section-0.2split-new | ---
dataset_info:
features:
- name: infographic_section
dtype: string
- name: target_location
dtype: string
splits:
- name: train
num_bytes: 4195
num_examples: 80
- name: test
num_bytes: 980
num_examples: 20
download_size: 5032
dataset_size: 5175
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "target-location-infographic-section-0.2split-new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anan-2024/twitter_dataset_1713165618 | ---
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: 205171
num_examples: 551
download_size: 109393
dataset_size: 205171
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Thanmay/arc-challenge-gu | ---
dataset_info:
features:
- name: id
dtype: string
- name: answerKey
dtype: string
- name: itv2 gu
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: label
sequence: string
- name: text
sequence: string
splits:
- name: test
num_bytes: 1565824
num_examples: 1172
- name: validation
num_bytes: 400945
num_examples: 299
download_size: 745462
dataset_size: 1966769
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
open-llm-leaderboard/details_mncai__chatdoctor | ---
pretty_name: Evaluation run of mncai/chatdoctor
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [mncai/chatdoctor](https://huggingface.co/mncai/chatdoctor) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mncai__chatdoctor\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T01:48:31.701330](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__chatdoctor/blob/main/results_2023-09-17T01-48-31.701330.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\": 0.22640520134228187,\n\
\ \"em_stderr\": 0.004285876197711522,\n \"f1\": 0.3016862416107395,\n\
\ \"f1_stderr\": 0.004314877276433696,\n \"acc\": 0.34964483030781374,\n\
\ \"acc_stderr\": 0.006444005247352365\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.22640520134228187,\n \"em_stderr\": 0.004285876197711522,\n\
\ \"f1\": 0.3016862416107395,\n \"f1_stderr\": 0.004314877276433696\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6992896606156275,\n\
\ \"acc_stderr\": 0.01288801049470473\n }\n}\n```"
repo_url: https://huggingface.co/mncai/chatdoctor
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_07_24T15_52_02.947837
path:
- '**/details_harness|arc:challenge|25_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T01_48_31.701330
path:
- '**/details_harness|drop|3_2023-09-17T01-48-31.701330.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T01-48-31.701330.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T01_48_31.701330
path:
- '**/details_harness|gsm8k|5_2023-09-17T01-48-31.701330.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T01-48-31.701330.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hellaswag|10_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:52:02.947837.parquet'
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- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:52:02.947837.parquet'
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- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:52:02.947837.parquet'
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- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:52:02.947837.parquet'
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- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:52:02.947837.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:52:02.947837.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:52:02.947837.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T15:52:02.947837.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T15:52:02.947837.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T01_48_31.701330
path:
- '**/details_harness|winogrande|5_2023-09-17T01-48-31.701330.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T01-48-31.701330.parquet'
- config_name: results
data_files:
- split: 2023_07_24T15_52_02.947837
path:
- results_2023-07-24T15:52:02.947837.parquet
- split: 2023_09_17T01_48_31.701330
path:
- results_2023-09-17T01-48-31.701330.parquet
- split: latest
path:
- results_2023-09-17T01-48-31.701330.parquet
---
# Dataset Card for Evaluation run of mncai/chatdoctor
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mncai/chatdoctor
- **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 [mncai/chatdoctor](https://huggingface.co/mncai/chatdoctor) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_mncai__chatdoctor",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T01:48:31.701330](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__chatdoctor/blob/main/results_2023-09-17T01-48-31.701330.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": 0.22640520134228187,
"em_stderr": 0.004285876197711522,
"f1": 0.3016862416107395,
"f1_stderr": 0.004314877276433696,
"acc": 0.34964483030781374,
"acc_stderr": 0.006444005247352365
},
"harness|drop|3": {
"em": 0.22640520134228187,
"em_stderr": 0.004285876197711522,
"f1": 0.3016862416107395,
"f1_stderr": 0.004314877276433696
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6992896606156275,
"acc_stderr": 0.01288801049470473
}
}
```
### 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] |
Juzzy88/science_dict_full | ---
dataset_info:
features:
- name: role_1
dtype: string
- name: topic;
dtype: string
- name: sub_topic
dtype: string
- name: message_1
dtype: string
- name: message_2
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 200646759
num_examples: 38400
- name: val
num_bytes: 50121062
num_examples: 9600
- name: test
num_bytes: 62653743
num_examples: 12000
download_size: 148930334
dataset_size: 313421564
---
# Dataset Card for "science_dict_full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aadityaubhat/perturbed_faces | ---
task_categories:
- feature-extraction
- image-classification
- zero-shot-image-classification
pretty_name: Perturbed Faces
size_categories:
- 1K<n<10K
---
# Perturbed Faces
This dataset contains 1000 images from [CelebA dataset](!https://www.kaggle.com/datasets/jessicali9530/celeba-dataset). For each of the thousand images dataset also has [LowKey](https://openreview.net/forum?id=hJmtwocEqzc) perturbed version and [Fawkes](https://sandlab.cs.uchicago.edu/fawkes/) perturbed version.
LowKey and Fawkes perturbed images have _attacked & _cloaked at the end of the filename respectively.
| File Name | Version |
|---------------------|--------------------------|
| 000001.jpg | Original |
| 000001_cloaked.png | Fawkes perturbed version |
| 000001_attacked.png | LowKey perturbed version |
The Fawkes perturbed images are created using CLI provided in the [github repository](https://github.com/Shawn-Shan/fawkes) with protection mode set to mid. The LowKey version of
images are created using Python code provided with the paper.
## Citation
If you found this work helpful for your research, please cite it as following:
```
@misc{2301.07315,
Author = {Aaditya Bhat and Shrey Jain},
Title = {Face Recognition in the age of CLIP & Billion image datasets},
Year = {2023},
Eprint = {arXiv:2301.07315},
}
``` |
liuyanchen1015/VALUE_mrpc_drop_aux | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 30798
num_examples: 119
- name: train
num_bytes: 64928
num_examples: 245
- name: validation
num_bytes: 5706
num_examples: 24
download_size: 79747
dataset_size: 101432
---
# Dataset Card for "VALUE_mrpc_drop_aux"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AlekseyKorshuk/ak_edit_issue_analysis_128_v2_with_zl-reward | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: response
dtype: string
- name: output_text
dtype: string
- name: user_id
dtype: string
- name: __index_level_0__
dtype: int64
- name: ak-edit-finetuned-triton-v0
dtype: string
- name: ak-edit-finetuned-triton-v1
dtype: string
- name: ak-edit-finetuned-triton-v2
dtype: string
- name: ak-0324-sft-no-reward
dtype: string
- name: edit-sft-gptj-distil-v1-ak-test
dtype: string
- name: continue_50m__edit-sft-gptj-distil-v1-ak-test
dtype: float64
- name: retry_12m__edit-sft-gptj-distil-v1-ak-test
dtype: float64
- name: stars_2m__edit-sft-gptj-distil-v1-ak-test
dtype: float64
- name: retry_and_continue_12m__edit-sft-gptj-distil-v1-ak-test
dtype: float64
- name: continue_50m__ak-edit-finetuned-triton-v0
dtype: float64
- name: retry_12m__ak-edit-finetuned-triton-v0
dtype: float64
- name: stars_2m__ak-edit-finetuned-triton-v0
dtype: float64
- name: retry_and_continue_12m__ak-edit-finetuned-triton-v0
dtype: float64
- name: continue_50m__ak-edit-finetuned-triton-v1
dtype: float64
- name: retry_12m__ak-edit-finetuned-triton-v1
dtype: float64
- name: stars_2m__ak-edit-finetuned-triton-v1
dtype: float64
- name: retry_and_continue_12m__ak-edit-finetuned-triton-v1
dtype: float64
- name: continue_50m__ak-edit-finetuned-triton-v2
dtype: float64
- name: retry_12m__ak-edit-finetuned-triton-v2
dtype: float64
- name: stars_2m__ak-edit-finetuned-triton-v2
dtype: float64
- name: retry_and_continue_12m__ak-edit-finetuned-triton-v2
dtype: float64
- name: continue_50m__ak-0324-sft-no-reward
dtype: float64
- name: retry_12m__ak-0324-sft-no-reward
dtype: float64
- name: stars_2m__ak-0324-sft-no-reward
dtype: float64
- name: retry_and_continue_12m__ak-0324-sft-no-reward
dtype: float64
splits:
- name: completion_issue
num_bytes: 29229612
num_examples: 9647
- name: garbage_issue
num_bytes: 10185442
num_examples: 3565
- name: gender_issue
num_bytes: 15038448
num_examples: 4405
download_size: 27688286
dataset_size: 54453502
---
# Dataset Card for "ak_edit_issue_analysis_128_v2_with_zl-reward"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LightFury9/hellaswag-telugu | ---
dataset_info:
features:
- name: ind
dtype: int64
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
- name: qas_id
dtype: int64
splits:
- name: train
num_bytes: 115961611.0
num_examples: 39905
- name: test
num_bytes: 28977816.0
num_examples: 10003
- name: valid
num_bytes: 30148839.0
num_examples: 10042
download_size: 63488764
dataset_size: 175088266.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
jubba/silverhand_sft | ---
language:
- en
license:
- mit
pretty_name: Silverhand chat for Supervised finetuning
size_categories:
- n<1K
source_datasets: []
task_categories:
- question-answering
- conversational
- text-generation
task_ids: []
---
# Silverhand SFT
## Dataset overview
Dataset containing human - AI conversations where the AI responses are written in the style of the fictional character Johnny Silverhand from the Cyberpunk universe (Mike Pondsmith, CD Project red).
### Sources
60% of the examples are re-written berkeley-nest/Nectar examples. (gpt-4 was used to re-write the AI responses)
40% are re-written interactions with ChatGPT (by gpt-4).
### Purpose
The dataset was used to finetune mistral 7b to respond in the style of Johnny Silverhand, using SFT. |
hizardev/MentalHealthChat | ---
license: mit
---
|
vanderbilt-dsi/narrative-arc | ---
license: mit
---
---
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: narrative-arc
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for [narrative-arc]
## 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)
- [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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Dataset of stories used for Narrative Arc post-processing. An instance of a story in this dataset will include the original text and its metadata, the transformer model used to make the embeddings, the model's checkpoint, the window indices of the stored embeddings, and the embeddings.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
An example story will look like the following:
{
"book name": "",
"book meta data": "",
"full text": "",
"model": {
"distilbert-base-cased": {
"window indices": (first_index, last_index),
"embeddings": [[]] },
"distilbert-base-uncased": {
"window indices": (first_index, last_index),
"embeddings": [[]]
}
},
}
...
}
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
The processed text needs to be stored somewhere that is both accessible and can accomodate the large amount of data generated.
### Source Data
#### Initial Data Collection and Normalization
The data were sourced from the Project Gutenberg[https://www.gutenberg.org/] library.
#### Who are the source language producers?
Each instance in the dataset represents a text written by a human author. At present, data selected for processing are English-language short stories.
### Personal and Sensitive Information
Not applicable.
## 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
davanstrien/autotrain-data-on-the-books-example | Invalid username or password. |
liuyanchen1015/MULTI_VALUE_qqp_progressives | ---
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: 3004290
num_examples: 17783
- name: test
num_bytes: 29307911
num_examples: 172968
- name: train
num_bytes: 26904243
num_examples: 158928
download_size: 36682981
dataset_size: 59216444
---
# Dataset Card for "MULTI_VALUE_qqp_progressives"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kanishka/counterfactual-babylm-only_other_det_removal | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581811066
num_examples: 11645966
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 421589719
dataset_size: 637931296
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
OpenGVLab/CRPE | ---
license: apache-2.0
---
# Circular-based Relation Probing Evaluation (CRPE)
CRPE is a benchmark designed to quantitatively evaluate the object recognition and relation comprehension ability of models.
The evaluation is formulated as single-choice questions.
The benchmark consists of four splits:
**Existence**, **Subject**, **Predicate**, and **Object**.
The **Existence** split evaluates the object recognition ability while the remaining splits are designed to evaluate the capability of relation comprehension, focusing on probing each of the elements in the relation triplets `(subject, predicate, object)` separately.
Some data examples are shown below.
<img width="800" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/_NKaowl2OUBAjck1XCAPm.jpeg">
Additionally, to evaluate the dependency on language priors, we also include abnormal data in our evaluation.
These images in these abnormal data depict relation triplets that are very rare in the real world.
<img width="800" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/619507e7b74b6c591f794340/qKWw7Qb93OXClxI_VrCRk.jpeg">
For a robust evaluation, we adopt CircularEval as our evaluation strategy.
Under this setting, a question is considered as correctly answered only when the model consistently predicts the correct answer in each of the N iterations, with N corresponding to the number of choices.
In each iteration, a circular shift is applied to both the choices and the answer to form a new query for the model.
CRPE contains the following files:
- `crpe_exist.jsonl`: the evaluation data of **Existence** split.
- `crpe_exist_meta.jsonl`: the evaluation data of **Existence** split without CircularEval.
- `crpe_relation.jsonl`: the evaluation data of **Subject**, **Predicate**, and **Object** split.
- `crpe_relation_meta.jsonl`: the evaluation data of **Subject**, **Predicate**, and **Object** split without CircularEval.
**NOTE**: You should use `crpe_exist.jsonl` and `crpe_relation.jsonl` for evaluation. The evaluation script is presented [here](https://github.com/OpenGVLab/all-seeing/blob/main/all-seeing-v2/llava/eval/eval_crpe.py).
See our [project](https://github.com/OpenGVLab/all-seeing/all-seeing-v2) to learn more details!
# Citation
If you find our work useful in your research, please consider cite:
```BibTeX
@article{wang2023allseeing,
title={The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World},
author={Wang, Weiyun and Shi, Min and Li, Qingyun and Wang, Wenhai and Huang, Zhenhang and Xing, Linjie and Chen, Zhe and Li, Hao and Zhu, Xizhou and Cao, Zhiguo and others},
journal={arXiv preprint arXiv:2308.01907},
year={2023}
}
@article{wang2024allseeing_v2,
title={The All-Seeing Project V2: Towards General Relation Comprehension of the Open World},
author={Wang, Weiyun and Ren, Yiming and Luo, Haowen and Li, Tiantong and Yan, Chenxiang and Chen, Zhe and Wang, Wenhai and Li, Qingyun and Lu, Lewei and Zhu, Xizhou and others},
journal={arXiv preprint arXiv:2402.19474},
year={2024}
}
``` |
sshavara/AIDA_testc | ---
license: cc-by-4.0
pretty_name: AIDA/testc
viewer: false
---
AIDA/testc introduced in the paper [SPEL: Structured Prediction for Entity Linking (EMNLP 2023)](https://arxiv.org/abs/2310.14684), contains 131 Reuters news articles published between December 5th and 7th, 2020.
We have meticulously linked the named entity mentions in the newly annotated NER test set of (Liu and Ritter, 2023) to their corresponding Wikipedia pages, using the same linking procedure employed in the original AIDA dataset.
Our new entity linking test set, AIDA/testc, has 1,160 unique Wikipedia identifiers, spanning over 3,777 mentions and encompassing a total of 46,456 words.
This dataset is in NIF format and can be easily integrated into [GERBIL](https://github.com/dice-group/gerbil).
### How can I integrate AIDA/testc into GERBIL?
Here is the simple modifications you need to do:
1. If you are running GERBIL, stop the process.
2. Put [`aida_testc.ttl`](aida_testc.ttl) in `gerbil/gerbil_data/datasets/aida`
3. Open `gerbil/src/main/properties/datasets.properties` (this properties file contains the dataset configurations for GERBIL).
4. Copy the following lines underneath the last line defining AIDA/CoNLL-Test B:
```
org.aksw.gerbil.datasets.AIDATestC.file=${org.aksw.gerbil.DataPath}/datasets/aida/aida_testc.ttl
org.aksw.gerbil.datasets.definition.AIDATestC.name=AIDA/CoNLL-Test C
org.aksw.gerbil.datasets.definition.AIDATestC.class=org.aksw.gerbil.dataset.impl.nif.FileBasedNIFDataset
org.aksw.gerbil.datasets.definition.AIDATestC.cacheable=true
org.aksw.gerbil.datasets.definition.AIDATestC.experimentType=A2KB
org.aksw.gerbil.datasets.definition.AIDATestC.constructorArgs=${org.aksw.gerbil.datasets.AIDATestC.file},${org.aksw.gerbil.datasets.definition.AIDATestC.name}
```
5. Run GERBIL, the new dataset should show up.
|
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v3-math-468e93-2011366588 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_cot_v3
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-1.3b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v3
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v3
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-1.3b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v3
* Config: mathemakitten--winobias_antistereotype_test_cot_v3
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
mo-mittal/reddit_political_subs | ---
language:
- en
tags:
- politics
- reddit
- united states
- image to text
pretty_name: US politics subreddit data
size_categories:
- 1K<n<10K
---
# Reddit Political Discourse Dataset
## Data Source
**Pushshift Archive**: [Pushshift](https://the-eye.eu/redarcs/) is a social media data collection, analysis, and archiving platform that has collected Reddit data since 2015, offering real-time updates as well as historical data dating back to Reddit's inception.
## Dataset Description
This dataset is a curated collection of posts from 9 prominent US politics-oriented subreddits known for their wide range of political views. The selected subreddits include: r/politics, r/democrats, r/Conservative, r/The_Donald (now banned), r/SandersForPresident, r/JoeBiden, r/LateStageCapitalism, and r/socialism.
## Data Collection
The dataset comprises the top posts from the selected subreddits for each year starting from 2014. The raw data files from Pushshift were processed to enhance manageability and accessibility.
## Dataset Structure
Each row in the dataset represents a single post and includes the following columns:
- Author: The username of the individual who submitted the post.
- Created UTC: The date and time when the post was created, typically represented as a UNIX timestamp and can be converted to a more readable datetime object.
- Domain: The internet domain of the linked content in the post. For example, i.imgur.com for images hosted on Imgur. For self-posts, this might just point to the subreddit domain.
- Title: The title of the Reddit post, as specified by the author.
- Selftext: The body text of the post. For link posts, this is often empty, whereas for text (self) posts, this contains the post's content.
- Subreddit: The name of the subreddit where the post was submitted.
- Score: The net score of the post, calculated as the difference between the number of upvotes and downvotes.
- Number of Comments: The total count of comments made on the post.
- Ups: The number of upvotes the post has received. Note that Reddit may fuzz the actual numbers of upvotes and downvotes.
- Downs: The number of downvotes the post has received. As with upvotes, the exact count may be fuzzed by Reddit.
- Permalink: A relative URL to the Reddit post. This can be appended to https://www.reddit.com to form the complete URL.
- Is Self: A boolean indicating whether the post is a self-post (text post) or a link post. Self-posts contain text content, while link posts link out to external content.
- URL: The direct URL to the linked content for link posts. For self-posts, this may point to the Reddit post itself.
- Subreddit Subscribers: The number of subscribers to the subreddit at the time of the post. This gives an idea of the subreddit's size.
- Upvote Ratio: The ratio of upvotes to total votes (upvotes plus downvotes) the post has received.
- Is Original Content (OC): A boolean indicating whether the post has been marked as original content by the author.
- Media: Information or metadata about any media associated with the post, such as videos or images. This can vary widely in format depending on the post and media type.
- Selftext HTML: The HTML version of the selftext, allowing for embedded formatting and links. This may be useful for rendering the post's content as it appears on Reddit.
- Author Flair Text: Text of the flair attached to the author's username in the context of the subreddit. Flairs can denote specific roles, achievements, or statuses within the subreddit community.
- Link Flair Text: The text of the flair attached to the post itself. Subreddits use link flairs to categorize posts, indicate post status, or convey other information.
- Image (PIL object): For datasets including image analysis, this could be a Python Imaging Library (PIL) object representing an image associated with the post. This allows for direct manipulation and analysis of post images.
- Image Text: A generated feature that might represent text extracted from an associated post image using techniques like Optical Character Recognition (OCR). This can provide additional context or content for analysis, especially for image-heavy posts.
## URL Content Processing
The dataset includes a URL attribute, which can point to an image or an external article. The content from these URLs is processed to extract textual information, which enriches the dataset with additional context for each post. Here's an overview of how the URLs are handled:
1. **Image URLs**: For URLs that point to images (identified by their file extension), the image is downloaded to a local directory. Each image is then validated to ensure it is properly formatted and not corrupt. If valid, optical character recognition (OCR) is performed on the image using `pytesseract`, converting any text in the image into a string.
2. **Article URLs**: For URLs presumed to point to articles (not ending in typical image file extensions), a heuristic is applied to extract meaningful text directly from the URL itself. This involves parsing the URL's path, extracting segments, and replacing common URL encodings (such as hyphens and underscores) with spaces to form a readable string.
3. **Text Inclusion**: The extracted text, whether from images or article URLs, is then associated with the corresponding post entry in the dataset. This provides an expanded dataset where users can analyze not only the metadata of the Reddit post but also content referenced by external links.
This process is designed to handle a wide range of content types and to be robust against common issues such as download errors or invalid image files. It enhances the dataset by providing a more comprehensive view of the post's content and the discourse it is a part of.
## Impact
The political discourse on Reddit provides a rich landscape for research. This dataset not only contains textual content but also images (linked via the 'url' attribute) and sometimes links to external articles, offering a diverse mix of content that can help analyze and understand the propagation of political discourse.
## Limitations
The dataset is a subset of top posts and does not represent the entirety of posts within the selected subreddits. Comments can potentially be harvested using the 'permalink' attribute and OAuth procedures, which may be included in future updates. Some images and articles have not been processed due to authentication issues with Imgur and other cases where content has been removed or banned.
## License
## Citation
## Changelog
## Contact Information |
lvkaokao/dynamic_model_information | ---
license: apache-2.0
---
|
keminglu/InstructOpenWiki | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: InstructOpenWiki
size_categories:
- 100M<n<1B
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] |
AlekseyScorpi/docs_on_several_languages | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': az
'1': by
'2': cn
'3': en
'4': es
'5': fn
'6': gr
'7': jp
'8': ko
'9': kz
'10': la
'11': li
'12': mo
'13': no
'14': pl
'15': ru
'16': ua
splits:
- name: train
num_bytes: 1893804579.79
num_examples: 1987
- name: test
num_bytes: 374568135
num_examples: 339
download_size: 2423302965
dataset_size: 2268372714.79
task_categories:
- text-classification
- translation
- feature-extraction
tags:
- code
size_categories:
- 1K<n<10K
license: mit
language:
- az
- be
- en
- et
- fi
- ka
- ja
- ko
- kk
- lv
- lt
- mn
- 'no'
- pl
- ru
- uk
---
# Dataset Card for "docs_on_several_languages"
This dataset is a collection of different images in different languages.
The set includes the following languages: Azerbaijani, Belorussian, Chinese, English, Estonian, Finnish, Georgian, Japanese, Korean, Kazakh, Latvian, Lithuanian, Mongolian, Norwegian, Polish, Russian, Ukranian.
Each language has a corresponding class label defined. At least 100 images in the entire dataset are allocated per class. This dataset was originally used for the task of classifying the language of a document based on its image, but I hope it can help you in other machine learning tasks.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-acronym_identification-default-7559c8-37651145037 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- acronym_identification
eval_info:
task: entity_extraction
model: lewtun/autotrain-acronym-identification-7324788
metrics: ['angelina-wang/directional_bias_amplification']
dataset_name: acronym_identification
dataset_config: default
dataset_split: train
col_mapping:
tokens: tokens
tags: labels
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@qingxuwenli](https://huggingface.co/qingxuwenli) for evaluating this model. |
uclgroup8/iemocap-embeddings-light-v2 | ---
dataset_info:
features:
- name: emotion
dtype: string
- name: to_translate
dtype: string
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: labels
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: text_embedding
sequence: float32
- name: audio_embedding
sequence: float32
splits:
- name: train
num_bytes: 3202768852
num_examples: 5501
- name: test
num_bytes: 401142471
num_examples: 688
- name: val
num_bytes: 392060039
num_examples: 688
download_size: 1024651696
dataset_size: 3995971362
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
|
godoyj/pt-squad-generate-answer | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
dtype: string
- name: answers
struct:
- name: answer_start
dtype: int64
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 78166150
num_examples: 87510
- name: validation
num_bytes: 9717596
num_examples: 10570
download_size: 19115754
dataset_size: 87883746
---
# Dataset Card for "pt-squad-generate-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LanguageBind/Video-LLaVA | ---
license: mit
---
|
LeoFeng/MLHW_6 | ---
license: afl-3.0
---
|
mamachang/medical | ---
license: other
---
|
paws | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring
- multi-input-text-classification
paperswithcode_id: paws
pretty_name: 'PAWS: Paraphrase Adversaries from Word Scrambling'
config_names:
- labeled_final
- labeled_swap
- unlabeled_final
tags:
- paraphrase-identification
dataset_info:
- config_name: labeled_final
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 12239938
num_examples: 49401
- name: test
num_bytes: 1987794
num_examples: 8000
- name: validation
num_bytes: 1975862
num_examples: 8000
download_size: 10899391
dataset_size: 16203594
- config_name: labeled_swap
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 7963619
num_examples: 30397
download_size: 5741756
dataset_size: 7963619
- config_name: unlabeled_final
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 157806476
num_examples: 645652
- name: validation
num_bytes: 2442165
num_examples: 10000
download_size: 112644285
dataset_size: 160248641
configs:
- config_name: labeled_final
data_files:
- split: train
path: labeled_final/train-*
- split: test
path: labeled_final/test-*
- split: validation
path: labeled_final/validation-*
- config_name: labeled_swap
data_files:
- split: train
path: labeled_swap/train-*
- config_name: unlabeled_final
data_files:
- split: train
path: unlabeled_final/train-*
- split: validation
path: unlabeled_final/validation-*
---
# Dataset Card for PAWS: Paraphrase Adversaries from Word Scrambling
## 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:** [PAWS](https://github.com/google-research-datasets/paws)
- **Repository:** [PAWS](https://github.com/google-research-datasets/paws)
- **Paper:** [PAWS: Paraphrase Adversaries from Word Scrambling](https://arxiv.org/abs/1904.01130)
- **Point of Contact:** [Yuan Zhang](zhangyua@google.com)
### Dataset Summary
PAWS: Paraphrase Adversaries from Word Scrambling
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset.
For further details, see the accompanying paper: PAWS: Paraphrase Adversaries from Word Scrambling (https://arxiv.org/abs/1904.01130)
PAWS-QQP is not available due to license of QQP. It must be reconstructed by downloading the original data and then running our scripts to produce the data and attach the labels.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
Below are two examples from the dataset:
| | Sentence 1 | Sentence 2 | Label |
| :-- | :---------------------------- | :---------------------------- | :---- |
| (1) | Although interchangeable, the body pieces on the 2 cars are not similar. | Although similar, the body parts are not interchangeable on the 2 cars. | 0 |
| (2) | Katz was born in Sweden in 1947 and moved to New York City at the age of 1. | Katz was born in 1947 in Sweden and moved to New York at the age of one. | 1 |
The first pair has different semantic meaning while the second pair is a paraphrase. State-of-the-art models trained on existing datasets have dismal performance on PAWS (<40% accuracy); however, including PAWS training data for these models improves their accuracy to 85% while maintaining performance on existing datasets such as the [Quora Question Pairs](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs).
### Data Fields
This corpus contains pairs generated from Wikipedia pages, and can be downloaded
here:
* **PAWS-Wiki Labeled (Final)**: containing pairs that are generated from both word swapping and back translation methods. All pairs have human judgements on both paraphrasing and fluency and they are split into Train/Dev/Test sections.
* **PAWS-Wiki Labeled (Swap-only)**: containing pairs that have no back translation counterparts and therefore they are not included in the first set. Nevertheless, they are high-quality pairs with human judgements on both paraphrasing and fluency, and they can be included as an auxiliary training set.
* **PAWS-Wiki Unlabeled (Final)**: Pairs in this set have noisy labels without human judgments and can also be used as an auxiliary training set. They are generated from both word swapping and back translation methods.
All files are in the tsv format with four columns:
Column Name | Data
:------------ | :--------------------------
id | A unique id for each pair
sentence1 | The first sentence
sentence2 | The second sentence
(noisy_)label | (Noisy) label for each pair
Each label has two possible values: `0` indicates the pair has different meaning, while `1` indicates the pair is a paraphrase.
### Data Splits
The number of examples and the proportion of paraphrase (Yes%) pairs are shown
below:
Data | Train | Dev | Test | Yes%
:------------------ | ------: | -----: | ----: | ----:
Labeled (Final) | 49,401 | 8,000 | 8,000 | 44.2%
Labeled (Swap-only) | 30,397 | -- | -- | 9.6%
Unlabeled (Final) | 645,652 | 10,000 | -- | 50.0%
## Dataset Creation
### Curation Rationale
Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like *flights from New York to Florida* and *flights from Florida to New York*.
### Source Data
#### Initial Data Collection and Normalization
Their automatic generation method is based on two ideas. The first swaps words to generate a sentence pair with the same BOW, controlled by a language model. The second uses back translation to generate paraphrases with high BOW overlap but different word order. These two strategies generate high-quality, diverse PAWS pairs, balanced evenly between paraphrases and non-paraphrases.
#### Who are the source language producers?
Mentioned above.
### Annotations
#### Annotation process
Sentence pairs are presented to five annotators, each of which gives a binary judgment as to whether they are paraphrases or not. They chose binary judgments to make dataset have the same label schema as the QQP corpus. Overall, human agreement is high on both Quora (92.0%) and Wikipedia (94.7%) and each label only takes about 24 seconds. As such, answers are usually straight-forward to human raters.
#### 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
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
### Citation Information
```
@InProceedings{paws2019naacl,
title = {{PAWS: Paraphrase Adversaries from Word Scrambling}},
author = {Zhang, Yuan and Baldridge, Jason and He, Luheng},
booktitle = {Proc. of NAACL},
year = {2019}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. |
sethapun/procedural_gen_3operands | ---
dataset_info:
features:
- name: expression
dtype: string
- name: answer
dtype: float64
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: train
num_bytes: 71608
num_examples: 2000
- name: validation
num_bytes: 14332
num_examples: 400
download_size: 35255
dataset_size: 85940
---
# Dataset Card for "procedural_gen_3operands"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DrishtiSharma/coqui-dataset | ---
dataset_info:
features:
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 45818391.20972024
num_examples: 3917
- name: test
num_bytes: 11463370.790279763
num_examples: 980
download_size: 20607806
dataset_size: 57281762.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
charsiu/timit_frame_labels | ---
dataset_info:
features:
- name: converted_phonetic_detail
struct:
- name: start
sequence: float64
- name: stop
sequence: float64
- name: utterance
sequence: string
- name: dialect_region
dtype: string
- name: file
dtype: string
- name: frame_labels
sequence: string
- name: id
dtype: string
- name: merge_phonetic_detail
struct:
- name: start
sequence: float64
- name: stop
sequence: float64
- name: utterance
sequence: string
- name: phonetic_detail
sequence:
- name: start
dtype: int64
- name: stop
dtype: int64
- name: utterance
dtype: string
- name: sentence_type
dtype: string
- name: speaker_id
dtype: string
- name: text
dtype: string
- name: word_detail
sequence:
- name: start
dtype: int64
- name: stop
dtype: int64
- name: utterance
dtype: string
- name: frame_labels_10ms
sequence: string
splits:
- name: train
num_bytes: 26189240
num_examples: 4620
- name: test
num_bytes: 9531056
num_examples: 1680
download_size: 7841359
dataset_size: 35720296
---
# Dataset Card for "timit_frame_labels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sanchit-gandhi/unconcatenated-ami-label-length-256-conditioned | ---
dataset_info:
config_name: train
features:
- name: text
dtype: string
- name: input_features
dtype: image
- name: id
dtype: string
- name: whisper_transcript
sequence: int64
- name: condition_on_prev
sequence: int64
splits:
- name: train
num_bytes: 331579045117.25
num_examples: 215806
download_size: 42190500009
dataset_size: 331579045117.25
configs:
- config_name: train
data_files:
- split: train
path: train/train-*
---
|
yzhuang/metatree_BNG_mfeat_karhunen_ | ---
dataset_info:
features:
- name: id
dtype: int64
- name: X
sequence: float64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 372280300
num_examples: 699775
- name: validation
num_bytes: 159719700
num_examples: 300225
download_size: 644764994
dataset_size: 532000000
---
# Dataset Card for "metatree_BNG_mfeat_karhunen_"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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