datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
TornikeO/imigue | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': arms_akimbo
'1': biting_nails
'2': buckle_button,_pulling_shirt_collar,_adjusting_tie
'3': bulging_face,_deep_breath
'4': covering_face
'5': crossing_fingers
'6': dustoffing_clothes
'7': folding_arms
'8': head_up
'9': hold_back_arms
'10': illustrative_body_language
'11': minaret_gesture
'12': moving_torso
'13': playing_with_or_adjusting_hair
'14': playing_with_or_manipulating_objects
'15': pressing_lips
'16': putting_arms_behind_body
'17': rubbing_eyes
'18': rubbing_or_holding_hands
'19': scratching_back
'20': scratching_or_touching_arms
'21': shaking_shoulders
'22': sitting_upright
'23': touching_ears
'24': touching_hat
'25': touching_jaw
'26': touching_or_covering_suprasternal_notch
'27': touching_or_scratching_facial_parts
'28': touching_or_scratching_forehead
'29': touching_or_scratching_head
'30': touching_or_scratching_neck
'31': turtle_neck
splits:
- name: train
num_bytes: 1244324327.526
num_examples: 60514
download_size: 1235543437
dataset_size: 1244324327.526
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Seanxh/twitter_dataset_1713150438 | ---
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: 23529
num_examples: 57
download_size: 13536
dataset_size: 23529
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jamestalentium/dialogsum_1000_rm | ---
dataset_info:
features:
- name: id
dtype: string
- name: input_text
dtype: string
- name: output_text
dtype: string
- name: topic
dtype: string
splits:
- name: train
num_bytes: 918108.1861958266
num_examples: 1000
download_size: 509997
dataset_size: 918108.1861958266
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dialogsum_1000_rm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
growth-cadet/jobpost_signals-to-json_test_mistral01gen | ---
dataset_info:
features:
- name: id
dtype: string
- name: ats
dtype: string
- name: context
dtype: string
- name: context_token_count
dtype: int64
- name: gpt-4_response
dtype: string
- name: gpt-4_cost
dtype: float64
- name: gpt-4_sys5_response
dtype: string
- name: gpt-4_sys5_cost
dtype: float64
- name: sys5_obj
struct:
- name: focus_areas
list:
- name: description
dtype: string
- name: subject
dtype: string
- name: industries
list:
- name: description
dtype: string
- name: subject
dtype: string
- name: products_and_technologies
list:
- name: description
dtype: string
- name: subject
dtype: string
- name: mistral01_gen
dtype: string
splits:
- name: train
num_bytes: 18559925
num_examples: 1806
download_size: 8484289
dataset_size: 18559925
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-project-squad_v2-1e2c143e-1305549899 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: nbroad/rob-base-superqa1
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: nbroad/rob-base-superqa1
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
felipesampaio2010/curtishi5 | ---
license: openrail
---
|
MASTERREDE/vozjose500 | ---
license: openrail
---
|
open-llm-leaderboard/details_Weyaxi__Qwen-72B-Llama | ---
pretty_name: Evaluation run of Weyaxi/Qwen-72B-Llama
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/Qwen-72B-Llama](https://huggingface.co/Weyaxi/Qwen-72B-Llama) 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_Weyaxi__Qwen-72B-Llama\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-02T06:36:25.719099](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Qwen-72B-Llama/blob/main/results_2024-02-02T06-36-25.719099.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\": 0.7316507461013225,\n\
\ \"acc_stderr\": 0.02876333054548078,\n \"acc_norm\": 0.7367632357692079,\n\
\ \"acc_norm_stderr\": 0.029299960559549205,\n \"mc1\": 0.40514075887392903,\n\
\ \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5759942995358094,\n\
\ \"mc2_stderr\": 0.015126059763108792\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6168941979522184,\n \"acc_stderr\": 0.014206472661672877,\n\
\ \"acc_norm\": 0.6484641638225256,\n \"acc_norm_stderr\": 0.013952413699600931\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6480780720971918,\n\
\ \"acc_stderr\": 0.004765937515197188,\n \"acc_norm\": 0.8327026488747261,\n\
\ \"acc_norm_stderr\": 0.00372478338925333\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\": 0.6962962962962963,\n\
\ \"acc_stderr\": 0.039725528847851355,\n \"acc_norm\": 0.6962962962962963,\n\
\ \"acc_norm_stderr\": 0.039725528847851355\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \
\ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.8113207547169812,\n \"acc_stderr\": 0.02407999513006224,\n\
\ \"acc_norm\": 0.8113207547169812,\n \"acc_norm_stderr\": 0.02407999513006224\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9027777777777778,\n\
\ \"acc_stderr\": 0.02477451625044018,\n \"acc_norm\": 0.9027777777777778,\n\
\ \"acc_norm_stderr\": 0.02477451625044018\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\"\
: 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n\
\ \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7745664739884393,\n\
\ \"acc_stderr\": 0.03186209851641143,\n \"acc_norm\": 0.7745664739884393,\n\
\ \"acc_norm_stderr\": 0.03186209851641143\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.84,\n \"acc_stderr\": 0.036845294917747094,\n \"acc_norm\": 0.84,\n\
\ \"acc_norm_stderr\": 0.036845294917747094\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.8170212765957446,\n \"acc_stderr\": 0.025276041000449966,\n\
\ \"acc_norm\": 0.8170212765957446,\n \"acc_norm_stderr\": 0.025276041000449966\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5614035087719298,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\
\ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.6851851851851852,\n\
\ \"acc_stderr\": 0.023919984164047732,\n \"acc_norm\": 0.6851851851851852,\n\
\ \"acc_norm_stderr\": 0.023919984164047732\n },\n \"harness|hendrycksTest-formal_logic|5\"\
: {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n\
\ \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n\
\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.54,\n\
\ \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.864516129032258,\n \"acc_stderr\": 0.019469334586486933,\n\
\ \"acc_norm\": 0.864516129032258,\n \"acc_norm_stderr\": 0.019469334586486933\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.6945812807881774,\n \"acc_stderr\": 0.03240661565868408,\n \"\
acc_norm\": 0.6945812807881774,\n \"acc_norm_stderr\": 0.03240661565868408\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\
: 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.22424242424242424,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.22424242424242424,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9141414141414141,\n \"acc_stderr\": 0.01996022556317289,\n \"\
acc_norm\": 0.9141414141414141,\n \"acc_norm_stderr\": 0.01996022556317289\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9844559585492227,\n \"acc_stderr\": 0.008927492715084317,\n\
\ \"acc_norm\": 0.9844559585492227,\n \"acc_norm_stderr\": 0.008927492715084317\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7948717948717948,\n \"acc_stderr\": 0.02047323317355199,\n \
\ \"acc_norm\": 0.7948717948717948,\n \"acc_norm_stderr\": 0.02047323317355199\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.4444444444444444,\n \"acc_stderr\": 0.030296771286067323,\n \
\ \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.030296771286067323\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8025210084033614,\n \"acc_stderr\": 0.025859164122051453,\n\
\ \"acc_norm\": 0.8025210084033614,\n \"acc_norm_stderr\": 0.025859164122051453\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4966887417218543,\n \"acc_stderr\": 0.04082393379449654,\n \"\
acc_norm\": 0.4966887417218543,\n \"acc_norm_stderr\": 0.04082393379449654\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769553,\n \"\
acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769553\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6620370370370371,\n \"acc_stderr\": 0.03225941352631295,\n \"\
acc_norm\": 0.6620370370370371,\n \"acc_norm_stderr\": 0.03225941352631295\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.43137254901960786,\n \"acc_stderr\": 0.03476099060501636,\n \"\
acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.03476099060501636\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8396624472573839,\n \"acc_stderr\": 0.02388438092596567,\n \
\ \"acc_norm\": 0.8396624472573839,\n \"acc_norm_stderr\": 0.02388438092596567\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n\
\ \"acc_stderr\": 0.02693611191280227,\n \"acc_norm\": 0.7982062780269058,\n\
\ \"acc_norm_stderr\": 0.02693611191280227\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8854961832061069,\n \"acc_stderr\": 0.027927473753597453,\n\
\ \"acc_norm\": 0.8854961832061069,\n \"acc_norm_stderr\": 0.027927473753597453\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.859504132231405,\n \"acc_stderr\": 0.03172233426002158,\n \"acc_norm\"\
: 0.859504132231405,\n \"acc_norm_stderr\": 0.03172233426002158\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.03826076324884866,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.03826076324884866\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8404907975460123,\n \"acc_stderr\": 0.02876748172598386,\n\
\ \"acc_norm\": 0.8404907975460123,\n \"acc_norm_stderr\": 0.02876748172598386\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5892857142857143,\n\
\ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.5892857142857143,\n\
\ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8932038834951457,\n \"acc_stderr\": 0.030581088928331352,\n\
\ \"acc_norm\": 0.8932038834951457,\n \"acc_norm_stderr\": 0.030581088928331352\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\
\ \"acc_stderr\": 0.01700436856813234,\n \"acc_norm\": 0.9273504273504274,\n\
\ \"acc_norm_stderr\": 0.01700436856813234\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.0368452949177471,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.0368452949177471\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9169859514687101,\n\
\ \"acc_stderr\": 0.009866287394639552,\n \"acc_norm\": 0.9169859514687101,\n\
\ \"acc_norm_stderr\": 0.009866287394639552\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8208092485549133,\n \"acc_stderr\": 0.02064759002967933,\n\
\ \"acc_norm\": 0.8208092485549133,\n \"acc_norm_stderr\": 0.02064759002967933\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7832402234636872,\n\
\ \"acc_stderr\": 0.013780598486443356,\n \"acc_norm\": 0.7832402234636872,\n\
\ \"acc_norm_stderr\": 0.013780598486443356\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8464052287581699,\n \"acc_stderr\": 0.020645597910418763,\n\
\ \"acc_norm\": 0.8464052287581699,\n \"acc_norm_stderr\": 0.020645597910418763\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8360128617363344,\n\
\ \"acc_stderr\": 0.021029576464662695,\n \"acc_norm\": 0.8360128617363344,\n\
\ \"acc_norm_stderr\": 0.021029576464662695\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8641975308641975,\n \"acc_stderr\": 0.01906158818150541,\n\
\ \"acc_norm\": 0.8641975308641975,\n \"acc_norm_stderr\": 0.01906158818150541\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6028368794326241,\n \"acc_stderr\": 0.02918980567358709,\n \
\ \"acc_norm\": 0.6028368794326241,\n \"acc_norm_stderr\": 0.02918980567358709\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5827900912646675,\n\
\ \"acc_stderr\": 0.012593959992906426,\n \"acc_norm\": 0.5827900912646675,\n\
\ \"acc_norm_stderr\": 0.012593959992906426\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8161764705882353,\n \"acc_stderr\": 0.023529242185193106,\n\
\ \"acc_norm\": 0.8161764705882353,\n \"acc_norm_stderr\": 0.023529242185193106\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7990196078431373,\n \"acc_stderr\": 0.01621193888965559,\n \
\ \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.01621193888965559\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7818181818181819,\n\
\ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.7818181818181819,\n\
\ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7836734693877551,\n \"acc_stderr\": 0.026358916334904017,\n\
\ \"acc_norm\": 0.7836734693877551,\n \"acc_norm_stderr\": 0.026358916334904017\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8756218905472637,\n\
\ \"acc_stderr\": 0.023335401790166323,\n \"acc_norm\": 0.8756218905472637,\n\
\ \"acc_norm_stderr\": 0.023335401790166323\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.97,\n \"acc_stderr\": 0.01714466079977655,\n \
\ \"acc_norm\": 0.97,\n \"acc_norm_stderr\": 0.01714466079977655\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n\
\ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n\
\ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015577,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015577\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40514075887392903,\n\
\ \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5759942995358094,\n\
\ \"mc2_stderr\": 0.015126059763108792\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8153117600631413,\n \"acc_stderr\": 0.010905978112156885\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5625473843821076,\n \
\ \"acc_stderr\": 0.013664299060751915\n }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/Qwen-72B-Llama
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_02T06_36_25.719099
path:
- '**/details_harness|arc:challenge|25_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|gsm8k|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hellaswag|10_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-36-25.719099.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T06-36-25.719099.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- '**/details_harness|winogrande|5_2024-02-02T06-36-25.719099.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-02T06-36-25.719099.parquet'
- config_name: results
data_files:
- split: 2024_02_02T06_36_25.719099
path:
- results_2024-02-02T06-36-25.719099.parquet
- split: latest
path:
- results_2024-02-02T06-36-25.719099.parquet
---
# Dataset Card for Evaluation run of Weyaxi/Qwen-72B-Llama
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Weyaxi/Qwen-72B-Llama](https://huggingface.co/Weyaxi/Qwen-72B-Llama) 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_Weyaxi__Qwen-72B-Llama",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-02T06:36:25.719099](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Qwen-72B-Llama/blob/main/results_2024-02-02T06-36-25.719099.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": 0.7316507461013225,
"acc_stderr": 0.02876333054548078,
"acc_norm": 0.7367632357692079,
"acc_norm_stderr": 0.029299960559549205,
"mc1": 0.40514075887392903,
"mc1_stderr": 0.01718561172775337,
"mc2": 0.5759942995358094,
"mc2_stderr": 0.015126059763108792
},
"harness|arc:challenge|25": {
"acc": 0.6168941979522184,
"acc_stderr": 0.014206472661672877,
"acc_norm": 0.6484641638225256,
"acc_norm_stderr": 0.013952413699600931
},
"harness|hellaswag|10": {
"acc": 0.6480780720971918,
"acc_stderr": 0.004765937515197188,
"acc_norm": 0.8327026488747261,
"acc_norm_stderr": 0.00372478338925333
},
"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": 0.6962962962962963,
"acc_stderr": 0.039725528847851355,
"acc_norm": 0.6962962962962963,
"acc_norm_stderr": 0.039725528847851355
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.875,
"acc_stderr": 0.026913523521537846,
"acc_norm": 0.875,
"acc_norm_stderr": 0.026913523521537846
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932262,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932262
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8113207547169812,
"acc_stderr": 0.02407999513006224,
"acc_norm": 0.8113207547169812,
"acc_norm_stderr": 0.02407999513006224
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.9027777777777778,
"acc_stderr": 0.02477451625044018,
"acc_norm": 0.9027777777777778,
"acc_norm_stderr": 0.02477451625044018
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7745664739884393,
"acc_stderr": 0.03186209851641143,
"acc_norm": 0.7745664739884393,
"acc_norm_stderr": 0.03186209851641143
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.84,
"acc_stderr": 0.036845294917747094,
"acc_norm": 0.84,
"acc_norm_stderr": 0.036845294917747094
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.8170212765957446,
"acc_stderr": 0.025276041000449966,
"acc_norm": 0.8170212765957446,
"acc_norm_stderr": 0.025276041000449966
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5614035087719298,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.5614035087719298,
"acc_norm_stderr": 0.04668000738510455
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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. -->
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## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
huggingartists/snoop-dogg | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/snoop-dogg"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 4.603835 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/91bd22f5e53a3ea3cb1436de8f4a3722.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/snoop-dogg">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Snoop Dogg</div>
<a href="https://genius.com/artists/snoop-dogg">
<div style="text-align: center; font-size: 14px;">@snoop-dogg</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/snoop-dogg).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/snoop-dogg")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|1773| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/snoop-dogg")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
abhayesian/augmented_advbench_v2 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion_1
dtype: string
- name: completion_2
dtype: string
- name: completion_3
dtype: string
- name: completion_4
dtype: string
- name: completion_5
dtype: string
- name: refusal
dtype: string
splits:
- name: train
num_bytes: 12104803
num_examples: 5230
download_size: 6465995
dataset_size: 12104803
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1 | ---
pretty_name: Evaluation run of AIDC-ai-business/Marcoroni-70B-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_AIDC-ai-business__Marcoroni-70B-v1_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-09T10:10:41.822023](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1_public/blob/main/results_2023-11-09T10-10-41.822023.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\": 0.3132340604026846,\n\
\ \"em_stderr\": 0.004749834913438157,\n \"f1\": 0.456531040268459,\n\
\ \"f1_stderr\": 0.004364621394991152,\n \"acc\": 0.5835410217852969,\n\
\ \"acc_stderr\": 0.01171539602098445\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3132340604026846,\n \"em_stderr\": 0.004749834913438157,\n\
\ \"f1\": 0.456531040268459,\n \"f1_stderr\": 0.004364621394991152\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.332827899924185,\n \
\ \"acc_stderr\": 0.012979892496598271\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.010450899545370628\n\
\ }\n}\n```"
repo_url: https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_09T10_10_41.822023
path:
- '**/details_harness|drop|3_2023-11-09T10-10-41.822023.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-09T10-10-41.822023.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_09T10_10_41.822023
path:
- '**/details_harness|gsm8k|5_2023-11-09T10-10-41.822023.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-09T10-10-41.822023.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_09T10_10_41.822023
path:
- '**/details_harness|winogrande|5_2023-11-09T10-10-41.822023.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-09T10-10-41.822023.parquet'
- config_name: results
data_files:
- split: 2023_11_09T10_10_41.822023
path:
- results_2023-11-09T10-10-41.822023.parquet
- split: latest
path:
- results_2023-11-09T10-10-41.822023.parquet
---
# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1
- **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 [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_AIDC-ai-business__Marcoroni-70B-v1_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T10:10:41.822023](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1_public/blob/main/results_2023-11-09T10-10-41.822023.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": 0.3132340604026846,
"em_stderr": 0.004749834913438157,
"f1": 0.456531040268459,
"f1_stderr": 0.004364621394991152,
"acc": 0.5835410217852969,
"acc_stderr": 0.01171539602098445
},
"harness|drop|3": {
"em": 0.3132340604026846,
"em_stderr": 0.004749834913438157,
"f1": 0.456531040268459,
"f1_stderr": 0.004364621394991152
},
"harness|gsm8k|5": {
"acc": 0.332827899924185,
"acc_stderr": 0.012979892496598271
},
"harness|winogrande|5": {
"acc": 0.8342541436464088,
"acc_stderr": 0.010450899545370628
}
}
```
### 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] |
malaysia-ai/mosaic-instructions | ---
language:
- ms
---
# Mosaic format for instructions dataset to train Malaysian LLM
This repository is to store dataset shards using mosaic format.
1. prepared at https://github.com/malaysia-ai/dedup-text-dataset/blob/main/pretrain-llm/combine-instructions.ipynb
2. using tokenizer https://huggingface.co/malaysia-ai/bpe-tokenizer
3. 4096 context length.
## how-to
1. git clone,
```bash
git lfs clone https://huggingface.co/datasets/malaysia-ai/mosaic-instructions
```
2. load it,
```python
from streaming import LocalDataset
import numpy as np
from streaming.base.format.mds.encodings import Encoding, _encodings
class UInt16(Encoding):
def encode(self, obj) -> bytes:
return obj.tobytes()
def decode(self, data: bytes):
return np.frombuffer(data, np.uint16)
_encodings['uint16'] = UInt16
dataset = LocalDataset('mosaic-instructions')
len(dataset)
``` |
Falah/landscape_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 38293009
num_examples: 100000
download_size: 4530457
dataset_size: 38293009
---
# Dataset Card for "landscape_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ThinZinc/fuego-20230214-101615-0a9f24 | ---
tags:
- fuego
fuego:
id: 20230214-101615-0a9f24
status: done
script: main.py
requirements_file: requirements.txt
space_id: ThinZinc/fuego-20230214-101615-0a9f24
space_hardware: cpu-basic
github_repo_id: pytorch/examples
github_repo_branch: main
github_repo_sha: e4e8da8467d55d28920dbd137261d82255f68c71
---
|
Prometutas/elidata | ---
license: openrail
---
|
Nexdata/399_Asians_35112_Images_Multi_pose_Face_Data_with_21_Facial_Landmarks_Annotation | ---
license: cc-by-nc-nd-4.0
---
## Description
399 Asian People 35,112 Images Multi-pose Face Data with 21 Facial Landmarks Annotation, this data collected 399 people(88 images per person). The data diversity includes multiple poses, different ages, different light conditions and multiple scenes. This data can be used for tasks such as face detection and face recognition.
For more details, please refer to the link: https://www.nexdata.ai/dataset/173?source=Huggingface
## Data size
399 people, 88 images per person
## Race distribution
Asian
## Gender distribution
199 males, 200 females
## Age distribution:
the middle-aged and young people are the majorities
## Collecting environment
including indoor and outdoor scenes
## Data diversity
different face poses, ages, light conditions and scenes
## Device
cellphone
## Data format
.jpg, .metadata, .txt
## Annotation content
21 facial landmarks
## Accuracy
the accuracy of labels of gender, face pose, year of birth, light condition, scene and wearing glasses or not is more than 97%；annotation accuracy of facial landmarks is more than 97%
# Licensing Information
Commercial License
|
imone/FLAN_NIv2_NoExplanation | ---
license: mit
---
# FLAN NIv2 without explanation
This is a subset of [FLAN NIv2](https://huggingface.co/datasets/Open-Orca/FLAN). We removed all examples with explanations in the few-shot template, as the final answers also don't have explanations. |
Dulsara/glaive-function-calling-v2 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
Modified version of the [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) dataset
All samples in the glaive dataset is converted into the following format for better interoperability
```json
[
{
"role":"system",
"content":"You are a helpful assistant with access to the functions.",
"functions":[
{
"name":"generate_password",
"description":"Generate a random password with specified criteria",
"parameters":{
"type":"object",
"properties":{
"length":{
"type":"integer",
"description":"The length of the password"
},
"include_numbers":{
"type":"boolean",
"description":"Include numbers in the password"
},
"include_special_characters":{
"type":"boolean",
"description":"Include special characters in the password"
}
},
"required":[
"length"
]
}
}
]
},
{
"role":"user",
"content":"I need a new password. Can you generate one for me?"
},
{
"role":"assistant",
"content":"Of course! How long would you like your password to be? And do you want it to include numbers and special characters?"
},
{
"role":"user",
"content":"I want it to be 12 characters long and yes, it should include both numbers and special characters."
},
{
"role":"function call",
"content":{
"name":"generate_password",
"arguments":{
"length": 12,
"include_numbers": true,
"include_special_characters": true
}
}
},
{
"role":"function response",
"content":{
"password":"4#7gB6&9L1!0"
}
},
{
"role":"assistant",
"content":"Here is your new password: 4#7gB6&9L1!0. Please make sure to save it in a secure place."
}
]
``` |
bigscience-data/roots_vi_data_on_covid_19_news_coverage_in_vietnam | ---
language: vi
license: cc-by-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_vi_data_on_covid_19_news_coverage_in_vietnam
# Data on COVID-19 News Coverage in Vietnam
- Dataset uid: `data_on_covid_19_news_coverage_in_vietnam`
### Description
The dataset extracted from the AI-enabled news crawler contains the following information:
Date: The date of publication of the crawled news articles.
Title: The title of the crawled news articles.
Url: The Uniform Resource Locators (URLs), or the web addresses, of the crawled news articles.
Detail: The content of the crawled news articles
### Homepage
https://www.mdpi.com/2306-5729/6/7/70/htm
### Licensing
Unclear
### Speaker Locations
- South-eastern Asia
### Sizes
- 0.0074 % of total
- 0.5425 % of vi
### BigScience processing steps
#### Filters applied to: vi
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
|
jarod0411/zinc10M_linker | ---
dataset_info:
features:
- name: smiles
dtype: string
- name: p1_smiles
dtype: string
- name: p2_smiles
dtype: string
- name: p1_atom
dtype: string
- name: p2_atom
dtype: string
splits:
- name: train
num_bytes: 1460441372.8248076
num_examples: 9625323
- name: validation
num_bytes: 162277642.92025834
num_examples: 1069408
download_size: 873925320
dataset_size: 1622719015.745066
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
elory123/WizMap | ---
license: apache-2.0
---
|
Harshithacj123/CCU_Llama_QandA | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 25578
num_examples: 50
download_size: 15050
dataset_size: 25578
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tyzhu/find_last_sent_train_10_eval_10_hint5 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 39698
num_examples: 30
- name: validation
num_bytes: 9384
num_examples: 10
download_size: 45959
dataset_size: 49082
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "find_last_sent_train_10_eval_10_hint5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/VALUE_wikitext103_uninflect | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 849818
num_examples: 1118
- name: train
num_bytes: 368188365
num_examples: 469979
- name: validation
num_bytes: 796548
num_examples: 1053
download_size: 215340313
dataset_size: 369834731
---
# Dataset Card for "VALUE_wikitext103_uninflect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TheHolyPacman/test_dataset_2_mod | ---
dataset_info:
features:
- name: file_name
dtype: string
- name: accent
dtype: string
- name: sound_array
struct:
- name: array
sequence: float64
- name: input_values
sequence: float32
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 447549854
num_examples: 419
download_size: 375726911
dataset_size: 447549854
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
my-north-ai/fixed-scriber-data | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: description
dtype: string
- name: n_speakers
dtype: int32
- name: transcription
dtype: string
- name: gender
dtype:
class_label:
names:
'0': M
'1': F
'2': M-F
'3': M-M
'4': F-M
- name: language
dtype:
class_label:
names:
'0': EN
'1': PT
'2': FR
- name: music
dtype:
class_label:
names:
'0': 'YES'
'1': 'NO'
- name: lyrics
dtype:
class_label:
names:
'0': 'YES'
'1': 'NO'
- name: volume
dtype:
class_label:
names:
'0': 'NO'
'1': LOW
'2': MID
'3': HIGH
- name: type_interaction
dtype:
class_label:
names:
'0': TEST
'1': ASSESSMENT
'2': SOAP
'3': GYM
'4': MARQUISE
- name: status
dtype:
class_label:
names:
'0': RAW
'1': NOT-TRANSCRIBED
'2': TRANSCRIBED
'3': VERIFIED
splits:
- name: train
num_bytes: 5939860.0
num_examples: 8
download_size: 5944046
dataset_size: 5939860.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nirantk/esci_ranking_lite | ---
dataset_info:
features:
- name: example_id
dtype: int64
- name: query
dtype: string
- name: query_id
dtype: int64
- name: product_id
dtype: string
- name: product_locale
dtype: string
- name: esci_label
dtype: string
- name: small_version
dtype: int64
- name: large_version
dtype: int64
- name: split
dtype: string
- name: product_title
dtype: string
- name: product_description
dtype: string
- name: product_bullet_point
dtype: string
- name: product_brand
dtype: string
- name: product_color
dtype: string
- name: relevance_pos
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1481733293
num_examples: 1118011
download_size: 714939828
dataset_size: 1481733293
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_117 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1503153308.0
num_examples: 295199
download_size: 1533916152
dataset_size: 1503153308.0
---
# Dataset Card for "chunk_117"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
approach0/annotated-topics-perfect | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: problem
dtype: string
- name: query
dtype: string
- name: prompt
dtype: string
- name: solution
dtype: string
- name: ground_truth
dtype: 'null'
- name: judge_buffer
dtype: 'null'
- name: manual_query
dtype: 'null'
- name: manual_rating
dtype: int64
- name: args
dtype: string
- name: out_str
dtype: string
- name: tool_res
sequence: string
splits:
- name: test
num_bytes: 73300
num_examples: 9
download_size: 56937
dataset_size: 73300
---
# Dataset Card for "annotated-topic-perfect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kennytat/test_dataset | ---
dataset_info:
features:
- name: filename
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: transcript
dtype: string
- name: sample_rate
dtype: int32
- name: duration
dtype: float32
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 10575340.0
num_examples: 20
download_size: 8562282
dataset_size: 10575340.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Kamyar-zeinalipour/tr_quiz_multiple_choice | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6108405
num_examples: 3723
- name: test
num_bytes: 505308
num_examples: 300
download_size: 2944903
dataset_size: 6613713
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
justram/AToMiC-Qrels-Dedupe | ---
dataset_info:
features:
- name: text_id
dtype: string
- name: Q0
dtype: string
- name: image_id
dtype: string
- name: rel
dtype: int64
splits:
- name: train
num_bytes: 417552084
num_examples: 5030748
- name: validation
num_bytes: 3336587
num_examples: 38859
- name: test
num_bytes: 2551669
num_examples: 30938
download_size: 226715065
dataset_size: 423440340
---
# Dataset Card for "AToMiC-Qrels-Dedupe"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aloobun/ldjnr-combined | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
- conversational
language:
- en
---
LDJnr/Capybara + Pure-Dove + Verified-Camel
Fork of [M4-ai/LDJnr_combined_inout_format](https://huggingface.co/datasets/M4-ai/LDJnr_combined_inout_format) |
SEACrowd/id_stance | ---
tags:
- textual-entailment
language:
- ind
---
# id_stance
Stance Classification Towards Political Figures on Blog Writing.
This dataset contains dataset from the second research, which is combined from the first research and new dataset.
The dataset consist of 337 data, about five target and every target have 1 different event.
Two label are used: 'For' and 'Againts'.
1. For - the text that is created by author is support the target in an event
2. Against - the text that is created by author is oppose the target in an event
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@INPROCEEDINGS{8629144,
author={R. {Jannati} and R. {Mahendra} and C. W. {Wardhana} and M. {Adriani}},
booktitle={2018 International Conference on Asian Language Processing (IALP)},
title={Stance Classification Towards Political Figures on Blog Writing},
year={2018},
volume={},
number={},
pages={96-101},
}
```
## License
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
## Homepage
[https://github.com/reneje/id_stance_dataset_article-Stance-Classification-Towards-Political-Figures-on-Blog-Writing](https://github.com/reneje/id_stance_dataset_article-Stance-Classification-Towards-Political-Figures-on-Blog-Writing)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
Yihyun/custom1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6349443
num_examples: 13638
download_size: 1391306
dataset_size: 6349443
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gvlk/dialogsummaryv1 | ---
dataset_info:
features:
- name: id
dtype: string
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
splits:
- name: train
num_bytes: 11439628
num_examples: 12460
- name: test
num_bytes: 1367451
num_examples: 1500
- name: validation
num_bytes: 446639
num_examples: 500
download_size: 7116819
dataset_size: 13253718
---
# Dataset Card for "dialogsummaryv1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/m14_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of m14/M14/M14 (Girls' Frontline)
This is the dataset of m14/M14/M14 (Girls' Frontline), containing 198 images and their tags.
The core tags of this character are `brown_hair, long_hair, twintails, yellow_eyes, breasts, antenna_hair, bangs, ribbon, hair_ribbon`, 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 | 198 | 229.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m14_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 198 | 131.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m14_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 507 | 292.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m14_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 198 | 204.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m14_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 507 | 402.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m14_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/m14_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 | 22 |  |  |  |  |  | 1girl, skirt, solo, battle_rifle, white_thighhighs, looking_at_viewer, smile, holding_weapon, multicolored_hair, white_background, blush, shoes, sitting |
| 1 | 17 |  |  |  |  |  | 1girl, solo, looking_at_viewer, pleated_skirt, simple_background, blush, long_sleeves, multicolored_hair, smile, blue_skirt, white_background, white_thighhighs, jacket, medium_breasts, very_long_hair, collarbone, black_skirt, ahoge, full_body, sailor_collar, white_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | skirt | solo | battle_rifle | white_thighhighs | looking_at_viewer | smile | holding_weapon | multicolored_hair | white_background | blush | shoes | sitting | pleated_skirt | simple_background | long_sleeves | blue_skirt | jacket | medium_breasts | very_long_hair | collarbone | black_skirt | ahoge | full_body | sailor_collar | white_shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:---------------|:-------------------|:--------------------|:--------|:-----------------|:--------------------|:-------------------|:--------|:--------|:----------|:----------------|:--------------------|:---------------|:-------------|:---------|:-----------------|:-----------------|:-------------|:--------------|:--------|:------------|:----------------|:--------------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 17 |  |  |  |  |  | X | | X | | X | X | X | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
NarouMas/VarientRewardCommand | ---
license: mit
---
|
lgris/common_voice_13_0_pt_pseudo_labelled | ---
dataset_info:
config_name: pt
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 575552839.688
num_examples: 19948
- name: validation
num_bytes: 260552460.856
num_examples: 9072
- name: test
num_bytes: 283043426.928
num_examples: 9072
download_size: 1108032724
dataset_size: 1119148727.472
configs:
- config_name: pt
data_files:
- split: train
path: pt/train-*
- split: validation
path: pt/validation-*
- split: test
path: pt/test-*
---
|
thercyl/JNJ | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: float64
- name: Ticker
dtype: string
- name: Year
dtype: string
- name: Text
dtype: string
- name: Embedding
dtype: string
splits:
- name: train
num_bytes: 41199241
num_examples: 1185
download_size: 22949643
dataset_size: 41199241
---
# Dataset Card for "JNJ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Goorm-AI-04/Drone_RCS_Measurement | ---
configs:
- config_name: default
data_files:
- split: Heli_HH
path: data/Heli_HH-*
- split: Y600_HH
path: data/Y600_HH-*
- split: Hexa_VV
path: data/Hexa_VV-*
- split: M100_HV
path: data/M100_HV-*
- split: M100_VH
path: data/M100_VH-*
- split: P4P_HH
path: data/P4P_HH-*
- split: battery_HH
path: data/battery_HH-*
- split: Hexa_HH
path: data/Hexa_HH-*
- split: Walkera_VV
path: data/Walkera_VV-*
- split: Walkera_HH
path: data/Walkera_HH-*
- split: M100_VV
path: data/M100_VV-*
- split: Y600_VV
path: data/Y600_VV-*
- split: Mavic_HH
path: data/Mavic_HH-*
- split: P4P_VV
path: data/P4P_VV-*
- split: Parrot_HH
path: data/Parrot_HH-*
- split: F450_HH
path: data/F450_HH-*
- split: M100_HH
path: data/M100_HH-*
dataset_info:
features:
- name: f
dtype: int64
- name: theta
dtype: int64
- name: phi
dtype: int64
- name: RCS
dtype: float64
splits:
- name: Heli_HH
num_bytes: 15725280
num_examples: 491415
- name: Y600_HH
num_bytes: 16594080
num_examples: 518565
- name: Hexa_VV
num_bytes: 16594080
num_examples: 518565
- name: M100_HV
num_bytes: 16594080
num_examples: 518565
- name: M100_VH
num_bytes: 16594080
num_examples: 518565
- name: P4P_HH
num_bytes: 16594080
num_examples: 518565
- name: battery_HH
num_bytes: 3974880
num_examples: 124215
- name: Hexa_HH
num_bytes: 15725280
num_examples: 491415
- name: Walkera_VV
num_bytes: 16594080
num_examples: 518565
- name: Walkera_HH
num_bytes: 16594080
num_examples: 518565
- name: M100_VV
num_bytes: 16594080
num_examples: 518565
- name: Y600_VV
num_bytes: 16594080
num_examples: 518565
- name: Mavic_HH
num_bytes: 15725280
num_examples: 491415
- name: P4P_VV
num_bytes: 16594080
num_examples: 518565
- name: Parrot_HH
num_bytes: 15725280
num_examples: 491415
- name: F450_HH
num_bytes: 15725280
num_examples: 491415
- name: M100_HH
num_bytes: 16594080
num_examples: 518565
download_size: 4506112
dataset_size: 265136160
---
# Dataset Card for "Drone_RCS_Measurement"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
johannes-garstenauer/embeddings_from_distilbert_class_heaps | ---
dataset_info:
features:
- name: struct
dtype: string
- name: label
dtype: int64
- name: pred
dtype: int64
- name: cls_layer_6
sequence: float32
- name: cls_layer_5
sequence: float32
- name: cls_layer_4
sequence: float32
splits:
- name: train
num_bytes: 2564388529
num_examples: 269087
download_size: 2985131227
dataset_size: 2564388529
---
# Dataset Card for "embeddings_from_distilbert_class_heaps"
Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer.
This dataset contains representations of heap data structures along with their labels and the predicted label.
The representations are the [CLS] token embeddings of the last 3 layers of the DistilBERT model.
The representation-generating model is: https://huggingface.co/johannes-garstenauer/distilbert_class_heaps
The dataset from which representations were generated is: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_eval
The twin dataset (model with different training used to generate embeddings) is: https://huggingface.co/datasets/johannes-garstenauer/embeddings_from_distilbert_masking_heaps/
Thesis and associated scripts: https://zenodo.org/records/10053730 |
atgarcia/EMGSoundTrain4 | ---
dataset_info:
features:
- name: text
dtype: string
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: emg
sequence:
sequence: float64
- name: emg_sound
sequence: float64
splits:
- name: train
num_bytes: 2899691741
num_examples: 1296
download_size: 974979969
dataset_size: 2899691741
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
benayas/banking_augmented_5pct_v1 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1025004
num_examples: 10003
download_size: 399224
dataset_size: 1025004
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
datahrvoje/twitter_dataset_1713030768 | ---
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: 31664
num_examples: 74
download_size: 16373
dataset_size: 31664
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Sangle/test-book | ---
license: apache-2.0
language:
- en
pretty_name: book-card
--- |
elsaEU/ELSA_D3 | ---
configs:
- config_name: default
data_files:
- split: train
path:
- data/train-*
- data/val-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: original_prompt
dtype: string
- name: positive_prompt
dtype: string
- name: negative_prompt
dtype: string
- name: url
dtype: string
- name: model_gen0
dtype: string
- name: model_gen1
dtype: string
- name: model_gen2
dtype: string
- name: model_gen3
dtype: string
- name: width_gen0
dtype: int64
- name: width_gen1
dtype: int64
- name: width_gen2
dtype: int64
- name: width_gen3
dtype: int64
- name: height_gen0
dtype: int64
- name: height_gen1
dtype: int64
- name: height_gen2
dtype: int64
- name: height_gen3
dtype: int64
- name: num_inference_steps_gen0
dtype: int64
- name: num_inference_steps_gen1
dtype: int64
- name: num_inference_steps_gen2
dtype: int64
- name: num_inference_steps_gen3
dtype: int64
- name: filepath_gen0
dtype: string
- name: filepath_gen1
dtype: string
- name: filepath_gen2
dtype: string
- name: filepath_gen3
dtype: string
- name: image_gen0
dtype: image
- name: image_gen1
dtype: image
- name: image_gen2
dtype: image
- name: image_gen3
dtype: image
splits:
- name: train
num_bytes: 2626848010531.5
num_examples: 2306629
- name: validation
num_bytes: 5318900038.0
num_examples: 4800
download_size: 2568003790242
dataset_size: 2632166910569.5
---
# ELSA - Multimedia use case
**ELSA Multimedia is a large collection of Deep Fake images, generated using diffusion models**
### Dataset Summary
This dataset was developed as part of the EU project ELSA. Specifically for the Multimedia use-case.
Official webpage: https://benchmarks.elsa-ai.eu/
This dataset aims to develop effective solutions for detecting and mitigating the spread of deep fake images in multimedia content. Deep fake images, which are highly realistic and deceptive manipulations, pose significant risks to privacy, security, and trust in digital media. This dataset can be used to train robust and accurate models that can identify and flag instances of deep fake images.
### ELSA versions
| Name | Description | Link |
| ------------- | ------------- | ---------------------|
| ELSA1M_track1 | Dataset of 1M images generated using diffusion model | https://huggingface.co/datasets/elsaEU/ELSA1M_track1 |
| ELSA10M_track1 | Dataset of 10M images generated using four different diffusion models for each caption, multiple image compression formats, multiple aspect ration | https://huggingface.co/datasets/elsaEU/ELSA_D3 |
| ELSA500k_track2 | Dataset of 500k images generated using diffusion model with diffusion attentive attribution maps [1] | https://huggingface.co/datasets/elsaEU/ELSA500k_track2 |
```python
from datasets import load_dataset
elsa_data = load_dataset("elsaEU/ELSA_D3", split="train", streaming=True)
```
Using <a href="https://huggingface.co/docs/datasets/stream">streaming=True</a> lets you work with the dataset without downloading it.
## Dataset Structure
Each parquet file contains nearly 1k images and a JSON file with metadata.
The Metadata for generated images are:
- ID: Laion image ID
- original_prompt: Laion Prompt
- positive_prompt: positive prompt used for image generation
- negative_prompt: negative prompt used for image generation
- url: Url of the real image associated with the same prompt
- width: width generated image
- height: height generated image
- num_inference_steps: diffusion steps of the generator
- filepath: path of the generated image
- model_gen0: Generator 0 name
- model_gen1: Generator 1 name
- model_gen2: Generator 2 name
- model_gen3: Generator 3 name
- image_gen0: image generated with generator 0
- image_gen1: image generated with generator 1
- image_gen2: image generated with generator 2
- image_gen3: image generated with generator 3
- aspect_ratio: aspect ratio of the generated image
### Dataset Curators
- Leonardo Labs (rosario.dicarlo.ext@leonardo.com)
- UNIMORE (https://aimagelab.ing.unimore.it/imagelab/) |
Akhil2507/oneAPI_Dataset | ---
task_categories:
- question-answering
- text2text-generation
language:
- en
tags:
- text
- QA
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is given by intel oneAPI for oneAPI_Hackathon
### Supported Tasks and Leaderboards
Question_Answering
### Languages
English
## 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] |
furry-br/mammon | ---
license: openrail
---
|
result-kand2-sdxl-wuerst-karlo/897fa162 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 226
num_examples: 10
download_size: 1465
dataset_size: 226
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "897fa162"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KimDongH/spam_dataset_2 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: subject
dtype: string
- name: message
dtype: string
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 75055573
num_examples: 25372
- name: validation
num_bytes: 18107843
num_examples: 6344
- name: test
num_bytes: 5822612
num_examples: 2000
download_size: 46324581
dataset_size: 98986028
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
DanteMerlino/ENT-papers | ---
license: afl-3.0
---
|
SneakyInsect/maestro-rollingsplit | ---
dataset_info:
features:
- name: name
dtype: string
- name: start
sequence: float64
- name: duration
sequence: float64
- name: pitch
sequence: float64
- name: velocity
sequence: float64
splits:
- name: train
num_bytes: 745208510
num_examples: 373963
- name: validation
num_bytes: 84002977
num_examples: 42153
- name: test
num_bytes: 97390221
num_examples: 48820
download_size: 144295382
dataset_size: 926601708
---
# Dataset Card for "maestro-rollingsplit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
eagle0504/warren-buffett-annual-letters-1977-2022 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9131475
num_examples: 17000
- name: test
num_bytes: 564952
num_examples: 1115
download_size: 3718546
dataset_size: 9696427
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
rikdas/fabric_dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 41259319.0
num_examples: 20
download_size: 41261924
dataset_size: 41259319.0
---
# Dataset Card for "fabric_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
1rsh/speech-qa-bhojpuri-hi-karya | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 104532045.95384616
num_examples: 358
- name: test
num_bytes: 6472445.046153846
num_examples: 32
download_size: 108250762
dataset_size: 111004491.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
chengli-thu/Sherlock-Holmes-and-Thor | ---
license: cc-by-4.0
---
|
nguyenminh871/multi_class_solidity_function_vulnerabilty | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: func
dtype: string
- name: target
dtype: int64
- name: project
dtype: string
splits:
- name: train
num_bytes: 5745139.2
num_examples: 14889
- name: test
num_bytes: 1915046.4
num_examples: 4963
- name: validation
num_bytes: 1915046.4
num_examples: 4963
download_size: 2325677
dataset_size: 9575232.0
---
# Dataset Card for "multi_class_solidity_function_vulnerabilty"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Aditya757864/DATA | ---
license: mit
task_categories:
- translation
language:
- en
tags:
- code
configs:
- config_name: default
data_files:
- split: train
path: "data/*.csv"
- split: test
path: "holdout/*.csv"
---
|
mlabonne/bactrian-fr | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: id
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 41488334
num_examples: 50000
download_size: 24344870
dataset_size: 41488334
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-conll2003-conll2003-c67e3d-2126868714 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: 51la5/distilbert-base-NER
metrics: []
dataset_name: conll2003
dataset_config: conll2003
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# 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: 51la5/distilbert-base-NER
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@aniketrawat97](https://huggingface.co/aniketrawat97) for evaluating this model. |
Wrathless/Assist | ---
license: apache-2.0
---
|
promptora11/finetune | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 74044
num_examples: 100
download_size: 7883
dataset_size: 74044
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Alljoined/05_125 | ---
dataset_info:
features:
- name: EEG
sequence:
sequence: float64
- name: subject_id
dtype: int32
- name: session
dtype: int32
- name: block
dtype: int32
- name: trial
dtype: int32
- name: 73k_id
dtype: int32
- name: coco_id
dtype: int32
- name: curr_time
dtype: float32
splits:
- name: train
num_bytes: 5884702784
num_examples: 34354
- name: test
num_bytes: 1493015936
num_examples: 8716
download_size: 5656042891
dataset_size: 7377718720
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
tyzhu/lmind_hotpot_train300_eval100_v1_qa | ---
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: all_docs
path: data/all_docs-*
- split: all_docs_eval
path: data/all_docs_eval-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
splits:
- name: train_qa
num_bytes: 51441
num_examples: 300
- name: train_recite_qa
num_bytes: 312070
num_examples: 300
- name: eval_qa
num_bytes: 16148
num_examples: 100
- name: eval_recite_qa
num_bytes: 104950
num_examples: 100
- name: all_docs
num_bytes: 361191
num_examples: 797
- name: all_docs_eval
num_bytes: 361140
num_examples: 797
- name: train
num_bytes: 51441
num_examples: 300
- name: validation
num_bytes: 16148
num_examples: 100
download_size: 596980
dataset_size: 1274529
---
# Dataset Card for "lmind_hotpot_train300_eval100_v1_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kz919/open-orca-flan-50k-synthetic-reward-e5-mistral-7b-instruct-v6 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: task
dtype: string
- name: ignos-Mistral-T5-7B-v1
dtype: string
- name: cognAI-lil-c3po
dtype: string
- name: viethq188-Rabbit-7B-DPO-Chat
dtype: string
- name: cookinai-DonutLM-v1
dtype: string
- name: v1olet-v1olet-merged-dpo-7B
dtype: string
- name: normalized_rewards
sequence: float64
- name: router_label
dtype: int64
splits:
- name: train
num_bytes: 69684479
num_examples: 32543
download_size: 30952266
dataset_size: 69684479
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ApoAlquaary/Game-of-Thrones-IMDB | ---
task_categories:
- text-classification
- zero-shot-classification
language:
- en
size_categories:
- 10K<n<100K
---
Table of Contents
Dataset Description
Dataset Summary
Supported Tasks
Languages
Dataset Structure
Data Instances
Data Fields
Data Splits
Dataset Creation
Curation Rationale
Source Data
Annotations
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
**Dataset Description
Dataset Summary
This dataset comprises comments from IMDb on "Game of Thrones" episodes, including ratings (POINT), dates of the comments (DATE), titles of the episodes (TITLE), usernames (USER_NAME), and the content of the comments (CONTENT).
Supported Tasks
Sentiment Analysis: Determine the sentiment of comments.
Text Classification: Classify comments by sentiment or episode.
Language Modeling: Train models on entertainment-specific text.
Languages
The dataset is primarily in English.
Dataset Structure
Data Instances
A data instance might look like this:
json
Copy code
{
"POINT": 8,
"DATE": "2019-04-14",
"TITLE": "Winterfell",
"USER_NAME": "john_doe",
"CONTENT": "Great episode but expected more from the storyline."
}
Data Fields
POINT: Rating given by the user.
DATE: Date when the comment was posted.
TITLE: Title of the episode being commented on.
USER_NAME: Username of the commenter.
CONTENT: Text of the comment.
Data Splits
The dataset documentation should detail the division into training, validation, and test sets, if applicable.
Dataset Creation
Curation Rationale
Curated to analyze viewer reactions towards "Game of Thrones" episodes, aiming to provide insights into the series' reception and engagement levels.
Source Data
Comments were collected from IMDb's episode pages for "Game of Thrones".
Annotations
The dataset does not include additional annotations beyond the user-provided ratings and comments.
Personal and Sensitive Information
Includes usernames that could be considered personal information. Users should handle this data responsibly.
Considerations for Using the Data
Social Impact of Dataset
Facilitates understanding of viewer sentiments and can contribute to cultural impact studies on popular television series.
Discussion of Biases
May contain biases towards English-speaking and online-commenting populations.
Other Known Limitations
Sentiments expressed may not represent the broader audience's views accurately.
Additional Information
Dataset Curators
Curated by Abdalrhman Alquaary in 2023.
Licensing Information
Specify the dataset's licensing here.
Citation Information
bibtex
Copy code
@misc{game_of_thrones_imdb_comments_2023,
title={Game of Thrones Comments on IMDb},
author={Alquaary, Abdalrhman},
year={2023}
} |
giux78/ultrafeedback-binarized-preferences-cleaned-ita-ready | ---
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen-rating
dtype: float64
- name: chosen-model
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected-rating
dtype: float64
- name: rejected-model
dtype: string
splits:
- name: train
num_bytes: 377529380.1
num_examples: 54810
- name: test
num_bytes: 41947708.9
num_examples: 6090
download_size: 203255015
dataset_size: 419477089.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
atmallen/popqa_90 | ---
dataset_info:
features:
- name: question
dtype: string
- name: statement
dtype: string
- name: object
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: train
num_bytes: 191024
num_examples: 1996
- name: validation
num_bytes: 41383
num_examples: 428
- name: test
num_bytes: 41727
num_examples: 430
download_size: 87135
dataset_size: 274134
---
# Dataset Card for "popqa_90"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BangumiBase/fatezero | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Fate/zero
This is the image base of bangumi Fate/Zero, we detected 26 characters, 2067 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 145 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 14 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 244 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 109 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 285 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 151 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 71 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 40 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 36 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 70 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 27 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 16 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 14 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 23 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 16 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 167 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 72 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 59 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 34 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 9 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 286 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 17 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 25 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 20 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 6 | [Download](24/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 111 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Farisya/ft-usermanual | ---
dataset_info:
features:
- name: example
dtype: string
splits:
- name: train
num_bytes: 19361
num_examples: 22
- name: test
num_bytes: 8845
num_examples: 9
download_size: 20017
dataset_size: 28206
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
petricevich/macocu-hr-v2.0 | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 15770314647
num_examples: 7850602
download_size: 10313671198
dataset_size: 15770314647
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
See source for details on dataset: https://www.clarin.si/repository/xmlui/handle/11356/1806
This upload has some filtering. |
BangumiBase/tokyoghoul | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Tokyo Ghoul
This is the image base of bangumi Tokyo Ghoul, we detected 74 characters, 3651 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 551 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 173 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 25 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 37 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 132 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 211 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 51 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 48 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 121 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 43 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 28 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 31 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 22 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 29 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 50 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 55 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 208 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 23 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 57 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 28 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 19 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 40 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 31 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 26 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 49 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 45 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 56 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 66 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 30 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 75 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 46 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 50 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 12 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 35 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 37 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 22 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 33 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 30 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 10 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 7 | [Download](40/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 41 | 10 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 115 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 26 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 30 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 32 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 38 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 20 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 24 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 58 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 36 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 16 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 13 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 20 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 12 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 20 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 58 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 17 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 13 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 33 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 19 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 10 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 41 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 11 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 24 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 7 | [Download](65/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 66 | 9 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 20 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 8 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 19 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 11 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 9 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 13 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 198 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
kewu93/three_styles_prompted_all_512x512 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: style_class
dtype: string
splits:
- name: train
num_bytes: 278199128.832
num_examples: 11312
- name: val
num_bytes: 68747668.084
num_examples: 2828
download_size: 341189285
dataset_size: 346946796.916
---
# Dataset Card for "three_styles_prompted_all_512x512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/arare_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of arare/霰/霰 (Kantai Collection)
This is the dataset of arare/霰/霰 (Kantai Collection), containing 275 images and their tags.
The core tags of this character are `short_hair, black_hair, brown_eyes, hat`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 275 | 156.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/arare_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 275 | 121.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/arare_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 551 | 236.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/arare_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 275 | 149.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/arare_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 551 | 283.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/arare_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/arare_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, black_dress, full_body, long_sleeves, pinafore_dress, simple_background, solo, white_background, white_shirt, white_socks, looking_at_viewer, machinery, rigging, torpedo_tubes, twitter_username, adapted_turret, cannon, one-hour_drawing_challenge, shoes, standing, torpedo_launcher |
| 1 | 5 |  |  |  |  |  | 2girls, long_sleeves, pinafore_dress, school_uniform, solo_focus, white_shirt, belt, black_dress, looking_at_viewer, white_background, bangs, blush, clenched_hand, grey_hair, long_hair, open_mouth, simple_background |
| 2 | 34 |  |  |  |  |  | 1girl, suspenders, solo, school_uniform, looking_at_viewer, arm_warmers, short_sleeves, pleated_skirt, white_background, white_shirt |
| 3 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blue_one-piece_swimsuit, simple_background, collarbone, cowboy_shot, bangs, white_background, competition_school_swimsuit, small_breasts, black_one-piece_swimsuit, covered_navel, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_dress | full_body | long_sleeves | pinafore_dress | simple_background | solo | white_background | white_shirt | white_socks | looking_at_viewer | machinery | rigging | torpedo_tubes | twitter_username | adapted_turret | cannon | one-hour_drawing_challenge | shoes | standing | torpedo_launcher | 2girls | school_uniform | solo_focus | belt | bangs | blush | clenched_hand | grey_hair | long_hair | open_mouth | suspenders | arm_warmers | short_sleeves | pleated_skirt | blue_one-piece_swimsuit | collarbone | cowboy_shot | competition_school_swimsuit | small_breasts | black_one-piece_swimsuit | covered_navel |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:------------|:---------------|:-----------------|:--------------------|:-------|:-------------------|:--------------|:--------------|:--------------------|:------------|:----------|:----------------|:-------------------|:-----------------|:---------|:-----------------------------|:--------|:-----------|:-------------------|:---------|:-----------------|:-------------|:-------|:--------|:--------|:----------------|:------------|:------------|:-------------|:-------------|:--------------|:----------------|:----------------|:--------------------------|:-------------|:--------------|:------------------------------|:----------------|:---------------------------|:----------------|
| 0 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | |
| 2 | 34 |  |  |  |  |  | X | | | | | | X | X | X | | X | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | | | | | X | X | X | | | X | | | | | | | | | X | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X |
|
rusheeliyer/uk-abs | ---
dataset_info:
features:
- name: judgement
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 52800141
num_examples: 589
- name: test
num_bytes: 8174530
num_examples: 100
- name: validation
num_bytes: 10432092
num_examples: 104
download_size: 32973908
dataset_size: 71406763
---
# Dataset Card for "uk-abs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
keyon008/asiga-train | ---
license: openrail
---
|
medalpaca/medical_meadow_medqa | ---
task_categories:
- question-answering
language:
- en
- zh
tags:
- medical
---
# Dataset Card for MedQA
## Dataset Description
- **Paper:**
### Dataset Summary
This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams."
From https://github.com/jind11/MedQA:
>The data that contains both the QAs and textbooks can be downloaded from [this google drive folder](https://drive.google.com/file/d/1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw/view?usp=sharing). A bit of details of data are explained as below:
>
> For QAs, we have three sources: US, Mainland of China, and Taiwan District, which are put in folders, respectively. All files for QAs are in jsonl file format, where each line is a data sample as a dict. The "XX_qbank.jsonl" files contain all data samples while we also provide an official random split into train, dev, and test sets. Those files in the "metamap" folders are extracted medical related phrases using the Metamap tool.
>
> For QAs, we also include the "4_options" version in for US and Mainland of China since we reported results for 4 options in the paper.
>
> For textbooks, we have two languages: English and simplified Chinese. For simplified Chinese, we provide two kinds of sentence spliting: one is split by sentences, and the other is split by paragraphs.
### Citation Information
```
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
``` |
joey234/mmlu-computer_security-neg-prepend-verbal | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
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: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: ori_prompt
dtype: string
- name: fewshot_context_neg
dtype: string
- name: fewshot_context_ori
dtype: string
- name: neg_prompt
dtype: string
splits:
- name: dev
num_bytes: 6196
num_examples: 5
- name: test
num_bytes: 689900
num_examples: 100
download_size: 128678
dataset_size: 696096
---
# Dataset Card for "mmlu-computer_security-neg-prepend-verbal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
llm-aes/gemini_meva_full_score_only | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: worker_id
dtype: string
- name: human_label
dtype: int64
- name: llm_label
dtype: int64
- name: generator_1
dtype: string
- name: generator_2
dtype: string
- name: premise
dtype: string
splits:
- name: train
num_bytes: 371250
num_examples: 2000
download_size: 49199
dataset_size: 371250
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
eloducrest/cdr | ---
license: apache-2.0
---
|
irds/lotte_science_test_search | ---
pretty_name: '`lotte/science/test/search`'
viewer: false
source_datasets: ['irds/lotte_science_test']
task_categories:
- text-retrieval
---
# Dataset Card for `lotte/science/test/search`
The `lotte/science/test/search` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/science/test/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=617
- `qrels`: (relevance assessments); count=1,738
- For `docs`, use [`irds/lotte_science_test`](https://huggingface.co/datasets/irds/lotte_science_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_science_test_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_science_test_search', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
WanYuanYi/WanYuanYi | ---
license: mit
---
|
CyberHarem/sena_shiori_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of sena_shiori/瀬名詩織 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of sena_shiori/瀬名詩織 (THE iDOLM@STER: Cinderella Girls), containing 23 images and their tags.
The core tags of this character are `long_hair, brown_eyes, black_hair, hat`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 23 | 17.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sena_shiori_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 23 | 16.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sena_shiori_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 43 | 27.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sena_shiori_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 23 | 17.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sena_shiori_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 43 | 28.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sena_shiori_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sena_shiori_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------|
| 0 | 23 |  |  |  |  |  | 1girl, solo, dress, smile, card_(medium), character_name, gem_(symbol), looking_at_viewer, blue_background, necklace |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | dress | smile | card_(medium) | character_name | gem_(symbol) | looking_at_viewer | blue_background | necklace |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------|:----------------|:-----------------|:---------------|:--------------------|:------------------|:-----------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X |
|
fewshot-goes-multilingual/cs_csfd-movie-reviews | ---
annotations_creators:
- crowdsourced
language:
- cs
language_creators:
- crowdsourced
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: CSFD movie reviews (Czech)
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- movie reviews
- rating prediction
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for CSFD movie reviews (Czech)
## Dataset Description
The dataset contains user reviews from Czech/Slovak movie databse website <https://csfd.cz>.
Each review contains text, rating, date, and basic information about the movie (or TV series).
The dataset has in total (train+validation+test) 30,000 reviews. The data is balanced - each rating has approximately the same frequency.
## Dataset Features
Each sample contains:
- `review_id`: unique string identifier of the review.
- `rating_str`: string representation of the rating (from "0/5" to "5/5")
- `rating_int`: integer representation of the rating (from 0 to 5)
- `date`: date of publishing the review (just date, no time nor timezone)
- `comment_language`: language of the review (always "cs")
- `comment`: the string of the review
- `item_title`: title of the reviewed item
- `item_year`: publishing year of the item (string, can also be a range)
- `item_kind`: kind of the item - either "film" or "seriál"
- `item_genres`: list of genres of the item
- `item_directors`: list of director names of the item
- `item_screenwriters`: list of screenwriter names of the item
- `item_cast`: list of actors and actress in the item
## Dataset Source
The data was mined and sampled from the <https://csfd.cz> website.
Make sure to comply with the terms of conditions of the website operator when using the data.
|
MicPie/unpredictable_phonearena-com | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-phonearena-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-phonearena-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
open-llm-leaderboard/details_jsfs11__WONMSeverusDevil-TIES-7B | ---
pretty_name: Evaluation run of jsfs11/WONMSeverusDevil-TIES-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jsfs11/WONMSeverusDevil-TIES-7B](https://huggingface.co/jsfs11/WONMSeverusDevil-TIES-7B)\
\ 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_jsfs11__WONMSeverusDevil-TIES-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-31T04:23:08.012939](https://huggingface.co/datasets/open-llm-leaderboard/details_jsfs11__WONMSeverusDevil-TIES-7B/blob/main/results_2024-03-31T04-23-08.012939.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\": 0.6533326545449751,\n\
\ \"acc_stderr\": 0.03207044293149858,\n \"acc_norm\": 0.6531305994965103,\n\
\ \"acc_norm_stderr\": 0.032734831964402945,\n \"mc1\": 0.565483476132191,\n\
\ \"mc1_stderr\": 0.01735273874925956,\n \"mc2\": 0.7199805875561548,\n\
\ \"mc2_stderr\": 0.014740226461810198\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7064846416382252,\n \"acc_stderr\": 0.013307250444941118,\n\
\ \"acc_norm\": 0.7295221843003413,\n \"acc_norm_stderr\": 0.012980954547659554\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.709520015933081,\n\
\ \"acc_stderr\": 0.004530560646902538,\n \"acc_norm\": 0.8844851623182632,\n\
\ \"acc_norm_stderr\": 0.0031898897894046692\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n\
\ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \
\ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\"\
: 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n\
\ \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.035149425512674394,\n\
\ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.035149425512674394\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n\
\ \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n\
\ \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768077,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768077\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.574468085106383,\n\
\ \"acc_stderr\": 0.03232146916224468,\n \"acc_norm\": 0.574468085106383,\n\
\ \"acc_norm_stderr\": 0.03232146916224468\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n\
\ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n \"\
acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.0235407993587233,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.0235407993587233\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\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\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\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\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971114,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971114\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\
acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\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\"\
: 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"\
acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\
\ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993464,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993464\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n\
\ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4145251396648045,\n\
\ \"acc_stderr\": 0.016476342210254,\n \"acc_norm\": 0.4145251396648045,\n\
\ \"acc_norm_stderr\": 0.016476342210254\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\
\ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n\
\ \"acc_stderr\": 0.025403832978179615,\n \"acc_norm\": 0.7234726688102894,\n\
\ \"acc_norm_stderr\": 0.025403832978179615\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4706649282920469,\n\
\ \"acc_stderr\": 0.012748238397365549,\n \"acc_norm\": 0.4706649282920469,\n\
\ \"acc_norm_stderr\": 0.012748238397365549\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\
\ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \
\ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233278,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233278\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.565483476132191,\n\
\ \"mc1_stderr\": 0.01735273874925956,\n \"mc2\": 0.7199805875561548,\n\
\ \"mc2_stderr\": 0.014740226461810198\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.010450899545370625\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6823351023502654,\n \
\ \"acc_stderr\": 0.012824066621488842\n }\n}\n```"
repo_url: https://huggingface.co/jsfs11/WONMSeverusDevil-TIES-7B
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_31T04_23_08.012939
path:
- '**/details_harness|arc:challenge|25_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|gsm8k|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hellaswag|10_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-31T04-23-08.012939.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-31T04-23-08.012939.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- '**/details_harness|winogrande|5_2024-03-31T04-23-08.012939.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-31T04-23-08.012939.parquet'
- config_name: results
data_files:
- split: 2024_03_31T04_23_08.012939
path:
- results_2024-03-31T04-23-08.012939.parquet
- split: latest
path:
- results_2024-03-31T04-23-08.012939.parquet
---
# Dataset Card for Evaluation run of jsfs11/WONMSeverusDevil-TIES-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jsfs11/WONMSeverusDevil-TIES-7B](https://huggingface.co/jsfs11/WONMSeverusDevil-TIES-7B) 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_jsfs11__WONMSeverusDevil-TIES-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-31T04:23:08.012939](https://huggingface.co/datasets/open-llm-leaderboard/details_jsfs11__WONMSeverusDevil-TIES-7B/blob/main/results_2024-03-31T04-23-08.012939.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": 0.6533326545449751,
"acc_stderr": 0.03207044293149858,
"acc_norm": 0.6531305994965103,
"acc_norm_stderr": 0.032734831964402945,
"mc1": 0.565483476132191,
"mc1_stderr": 0.01735273874925956,
"mc2": 0.7199805875561548,
"mc2_stderr": 0.014740226461810198
},
"harness|arc:challenge|25": {
"acc": 0.7064846416382252,
"acc_stderr": 0.013307250444941118,
"acc_norm": 0.7295221843003413,
"acc_norm_stderr": 0.012980954547659554
},
"harness|hellaswag|10": {
"acc": 0.709520015933081,
"acc_stderr": 0.004530560646902538,
"acc_norm": 0.8844851623182632,
"acc_norm_stderr": 0.0031898897894046692
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.04094376269996792,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.04094376269996792
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.65,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.02825420034443866,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.02825420034443866
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6936416184971098,
"acc_stderr": 0.035149425512674394,
"acc_norm": 0.6936416184971098,
"acc_norm_stderr": 0.035149425512674394
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768077,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768077
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
"acc_stderr": 0.03232146916224468,
"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224468
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.02546714904546955,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.02546714904546955
},
"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-machine_learning|5": {
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"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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"acc_norm_stderr": 0.02093019318517933
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"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
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"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
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"harness|truthfulqa:mc|0": {
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"mc1_stderr": 0.01735273874925956,
"mc2": 0.7199805875561548,
"mc2_stderr": 0.014740226461810198
},
"harness|winogrande|5": {
"acc": 0.8342541436464088,
"acc_stderr": 0.010450899545370625
},
"harness|gsm8k|5": {
"acc": 0.6823351023502654,
"acc_stderr": 0.012824066621488842
}
}
```
## 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:**
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[More Information Needed]
## 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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## Dataset Card Contact
[More Information Needed] |
ExampleCode/github-issues-dataset | ---
license: mit
---
|
pythainlp/thai_wikipedia_clean_20230101 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 686139541
num_examples: 1436054
download_size: 260540997
dataset_size: 686139541
license: cc-by-sa-3.0
task_categories:
- text-generation
language:
- th
---
# Dataset Card for "thai_wikipedia_clean_20230101"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Thai Wikipedia Database dumps to plain text for NLP work.
This dataset was dump on 1 January 2023 from [Thai wikipedia](https://th.wikipedia.org).
- GitHub: [PyThaiNLP / ThaiWiki-clean](https://github.com/PyThaiNLP/ThaiWiki-clean)
- Notebook for upload to HF: [https://github.com/PyThaiNLP/ThaiWiki-clean/blob/main/thai_wikipedia_clean_20230101_hf.ipynb](https://github.com/PyThaiNLP/ThaiWiki-clean/blob/main/thai_wikipedia_clean_20230101_hf.ipynb) |
CyberHarem/agano_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of agano/阿賀野/阿贺野 (Azur Lane)
This is the dataset of agano/阿賀野/阿贺野 (Azur Lane), containing 32 images and their tags.
The core tags of this character are `breasts, long_hair, red_eyes, black_hair, bangs, large_breasts, very_long_hair, ponytail, hair_ornament, ahoge, bow, hair_bow, red_bow`, 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 | 32 | 44.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 32 | 26.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 78 | 52.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 32 | 39.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 78 | 72.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/agano_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, collarbone, solo, looking_at_viewer, smile, blush, detached_sleeves, wide_sleeves, simple_background, black_pantyhose, black_skirt, kimono, obi, pleated_skirt, ribbon_trim, white_background, closed_mouth, open_mouth |
| 1 | 10 |  |  |  |  |  | blush, looking_at_viewer, 1girl, smile, solo, brown_coat, closed_mouth, black_pantyhose, hair_ribbon, turtleneck_sweater, aran_sweater, open_coat, sweater_dress, bag, holding, long_sleeves, red_ribbon, sleeveless_turtleneck, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | cleavage | collarbone | solo | looking_at_viewer | smile | blush | detached_sleeves | wide_sleeves | simple_background | black_pantyhose | black_skirt | kimono | obi | pleated_skirt | ribbon_trim | white_background | closed_mouth | open_mouth | brown_coat | hair_ribbon | turtleneck_sweater | aran_sweater | open_coat | sweater_dress | bag | holding | long_sleeves | red_ribbon | sleeveless_turtleneck |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------|:-------------|:-------|:--------------------|:--------|:--------|:-------------------|:---------------|:--------------------|:------------------|:--------------|:---------|:------|:----------------|:--------------|:-------------------|:---------------|:-------------|:-------------|:--------------|:---------------------|:---------------|:------------|:----------------|:------|:----------|:---------------|:-------------|:------------------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | | | | X | X | X | X | | | | X | | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_MaziyarPanahi__Qwen1.5-8x7b-v0.1 | ---
pretty_name: Evaluation run of MaziyarPanahi/Qwen1.5-8x7b-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [MaziyarPanahi/Qwen1.5-8x7b-v0.1](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1)\
\ 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_MaziyarPanahi__Qwen1.5-8x7b-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-13T08:09:38.423036](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Qwen1.5-8x7b-v0.1/blob/main/results_2024-03-13T08-09-38.423036.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\": 0.5944164816473096,\n\
\ \"acc_stderr\": 0.03362011086351357,\n \"acc_norm\": 0.5974331678004989,\n\
\ \"acc_norm_stderr\": 0.03429741428253657,\n \"mc1\": 0.379436964504284,\n\
\ \"mc1_stderr\": 0.016987039266142995,\n \"mc2\": 0.5577993030725962,\n\
\ \"mc2_stderr\": 0.015324882744176647\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4803754266211604,\n \"acc_stderr\": 0.014600132075947094,\n\
\ \"acc_norm\": 0.5162116040955631,\n \"acc_norm_stderr\": 0.014603708567414945\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5624377614021111,\n\
\ \"acc_stderr\": 0.004950723480149754,\n \"acc_norm\": 0.7571200955984864,\n\
\ \"acc_norm_stderr\": 0.004279467128560753\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\": 0.48148148148148145,\n\
\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\
\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.038947344870133176,\n\
\ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.038947344870133176\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6188679245283019,\n \"acc_stderr\": 0.02989060968628664,\n\
\ \"acc_norm\": 0.6188679245283019,\n \"acc_norm_stderr\": 0.02989060968628664\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6875,\n\
\ \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n\
\ \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5838150289017341,\n\
\ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\
\ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.03252909619613197,\n\
\ \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.03252909619613197\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3508771929824561,\n\
\ \"acc_stderr\": 0.044895393502707,\n \"acc_norm\": 0.3508771929824561,\n\
\ \"acc_norm_stderr\": 0.044895393502707\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\"\
: 0.5,\n \"acc_stderr\": 0.025751310131230234,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.025751310131230234\n },\n \"harness|hendrycksTest-formal_logic|5\"\
: {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.04426266681379909,\n\
\ \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.04426266681379909\n\
\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.39,\n\
\ \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \
\ \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.026069362295335144,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.026069362295335144\n \
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5566502463054187,\n \"acc_stderr\": 0.034953345821629324,\n \"\
acc_norm\": 0.5566502463054187,\n \"acc_norm_stderr\": 0.034953345821629324\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.0347769116216366,\n\
\ \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.0347769116216366\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7373737373737373,\n \"acc_stderr\": 0.03135305009533084,\n \"\
acc_norm\": 0.7373737373737373,\n \"acc_norm_stderr\": 0.03135305009533084\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\
\ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5846153846153846,\n \"acc_stderr\": 0.024985354923102342,\n\
\ \"acc_norm\": 0.5846153846153846,\n \"acc_norm_stderr\": 0.024985354923102342\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35555555555555557,\n \"acc_stderr\": 0.02918571494985741,\n \
\ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.02918571494985741\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5840336134453782,\n \"acc_stderr\": 0.032016501007396114,\n\
\ \"acc_norm\": 0.5840336134453782,\n \"acc_norm_stderr\": 0.032016501007396114\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3973509933774834,\n \"acc_stderr\": 0.03995524007681681,\n \"\
acc_norm\": 0.3973509933774834,\n \"acc_norm_stderr\": 0.03995524007681681\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7908256880733945,\n \"acc_stderr\": 0.01743793717334323,\n \"\
acc_norm\": 0.7908256880733945,\n \"acc_norm_stderr\": 0.01743793717334323\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.44907407407407407,\n \"acc_stderr\": 0.03392238405321616,\n \"\
acc_norm\": 0.44907407407407407,\n \"acc_norm_stderr\": 0.03392238405321616\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7107843137254902,\n \"acc_stderr\": 0.031822318676475524,\n \"\
acc_norm\": 0.7107843137254902,\n \"acc_norm_stderr\": 0.031822318676475524\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7426160337552743,\n \"acc_stderr\": 0.028458820991460302,\n \
\ \"acc_norm\": 0.7426160337552743,\n \"acc_norm_stderr\": 0.028458820991460302\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6188340807174888,\n\
\ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n\
\ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\
\ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\
: 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.04236511258094632,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.04236511258094632\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n\
\ \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7087378640776699,\n \"acc_stderr\": 0.044986763205729224,\n\
\ \"acc_norm\": 0.7087378640776699,\n \"acc_norm_stderr\": 0.044986763205729224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406978,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406978\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7547892720306514,\n\
\ \"acc_stderr\": 0.015384352284543937,\n \"acc_norm\": 0.7547892720306514,\n\
\ \"acc_norm_stderr\": 0.015384352284543937\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.661849710982659,\n \"acc_stderr\": 0.02546977014940018,\n\
\ \"acc_norm\": 0.661849710982659,\n \"acc_norm_stderr\": 0.02546977014940018\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2905027932960894,\n\
\ \"acc_stderr\": 0.015183844307206157,\n \"acc_norm\": 0.2905027932960894,\n\
\ \"acc_norm_stderr\": 0.015183844307206157\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6535947712418301,\n \"acc_stderr\": 0.027245613047215362,\n\
\ \"acc_norm\": 0.6535947712418301,\n \"acc_norm_stderr\": 0.027245613047215362\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6752411575562701,\n\
\ \"acc_stderr\": 0.026596782287697046,\n \"acc_norm\": 0.6752411575562701,\n\
\ \"acc_norm_stderr\": 0.026596782287697046\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6327160493827161,\n \"acc_stderr\": 0.0268228017595079,\n\
\ \"acc_norm\": 0.6327160493827161,\n \"acc_norm_stderr\": 0.0268228017595079\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.42907801418439717,\n \"acc_stderr\": 0.02952591430255856,\n \
\ \"acc_norm\": 0.42907801418439717,\n \"acc_norm_stderr\": 0.02952591430255856\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4178617992177314,\n\
\ \"acc_stderr\": 0.012596744108998562,\n \"acc_norm\": 0.4178617992177314,\n\
\ \"acc_norm_stderr\": 0.012596744108998562\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5367647058823529,\n \"acc_stderr\": 0.030290619180485687,\n\
\ \"acc_norm\": 0.5367647058823529,\n \"acc_norm_stderr\": 0.030290619180485687\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5833333333333334,\n \"acc_stderr\": 0.01994491413687358,\n \
\ \"acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.01994491413687358\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n\
\ \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n\
\ \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7711442786069652,\n\
\ \"acc_stderr\": 0.029705284056772436,\n \"acc_norm\": 0.7711442786069652,\n\
\ \"acc_norm_stderr\": 0.029705284056772436\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\
\ \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n\
\ \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7192982456140351,\n \"acc_stderr\": 0.03446296217088427,\n\
\ \"acc_norm\": 0.7192982456140351,\n \"acc_norm_stderr\": 0.03446296217088427\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.379436964504284,\n\
\ \"mc1_stderr\": 0.016987039266142995,\n \"mc2\": 0.5577993030725962,\n\
\ \"mc2_stderr\": 0.015324882744176647\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6992896606156275,\n \"acc_stderr\": 0.012888010494704734\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5420773313115997,\n \
\ \"acc_stderr\": 0.013723629649844082\n }\n}\n```"
repo_url: https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1
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_13T08_09_38.423036
path:
- '**/details_harness|arc:challenge|25_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|gsm8k|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hellaswag|10_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-13T08-09-38.423036.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2024-03-13T08-09-38.423036.parquet'
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-13T08-09-38.423036.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-13T08-09-38.423036.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-13T08-09-38.423036.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-13T08-09-38.423036.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- '**/details_harness|winogrande|5_2024-03-13T08-09-38.423036.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-13T08-09-38.423036.parquet'
- config_name: results
data_files:
- split: 2024_03_13T08_09_38.423036
path:
- results_2024-03-13T08-09-38.423036.parquet
- split: latest
path:
- results_2024-03-13T08-09-38.423036.parquet
---
# Dataset Card for Evaluation run of MaziyarPanahi/Qwen1.5-8x7b-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [MaziyarPanahi/Qwen1.5-8x7b-v0.1](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b-v0.1) 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_MaziyarPanahi__Qwen1.5-8x7b-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-13T08:09:38.423036](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Qwen1.5-8x7b-v0.1/blob/main/results_2024-03-13T08-09-38.423036.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": 0.5944164816473096,
"acc_stderr": 0.03362011086351357,
"acc_norm": 0.5974331678004989,
"acc_norm_stderr": 0.03429741428253657,
"mc1": 0.379436964504284,
"mc1_stderr": 0.016987039266142995,
"mc2": 0.5577993030725962,
"mc2_stderr": 0.015324882744176647
},
"harness|arc:challenge|25": {
"acc": 0.4803754266211604,
"acc_stderr": 0.014600132075947094,
"acc_norm": 0.5162116040955631,
"acc_norm_stderr": 0.014603708567414945
},
"harness|hellaswag|10": {
"acc": 0.5624377614021111,
"acc_stderr": 0.004950723480149754,
"acc_norm": 0.7571200955984864,
"acc_norm_stderr": 0.004279467128560753
},
"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": 0.48148148148148145,
"acc_stderr": 0.043163785995113245,
"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6447368421052632,
"acc_stderr": 0.038947344870133176,
"acc_norm": 0.6447368421052632,
"acc_norm_stderr": 0.038947344870133176
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6188679245283019,
"acc_stderr": 0.02989060968628664,
"acc_norm": 0.6188679245283019,
"acc_norm_stderr": 0.02989060968628664
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6875,
"acc_stderr": 0.038760854559127644,
"acc_norm": 0.6875,
"acc_norm_stderr": 0.038760854559127644
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5838150289017341,
"acc_stderr": 0.03758517775404947,
"acc_norm": 0.5838150289017341,
"acc_norm_stderr": 0.03758517775404947
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.047840607041056527,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.047840607041056527
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.548936170212766,
"acc_stderr": 0.03252909619613197,
"acc_norm": 0.548936170212766,
"acc_norm_stderr": 0.03252909619613197
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3508771929824561,
"acc_stderr": 0.044895393502707,
"acc_norm": 0.3508771929824561,
"acc_norm_stderr": 0.044895393502707
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555497,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.5,
"acc_stderr": 0.025751310131230234,
"acc_norm": 0.5,
"acc_norm_stderr": 0.025751310131230234
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04426266681379909,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04426266681379909
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7,
"acc_stderr": 0.026069362295335144,
"acc_norm": 0.7,
"acc_norm_stderr": 0.026069362295335144
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5566502463054187,
"acc_stderr": 0.034953345821629324,
"acc_norm": 0.5566502463054187,
"acc_norm_stderr": 0.034953345821629324
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.0347769116216366,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.0347769116216366
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7373737373737373,
"acc_stderr": 0.03135305009533084,
"acc_norm": 0.7373737373737373,
"acc_norm_stderr": 0.03135305009533084
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7979274611398963,
"acc_stderr": 0.02897908979429673,
"acc_norm": 0.7979274611398963,
"acc_norm_stderr": 0.02897908979429673
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5846153846153846,
"acc_stderr": 0.024985354923102342,
"acc_norm": 0.5846153846153846,
"acc_norm_stderr": 0.024985354923102342
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35555555555555557,
"acc_stderr": 0.02918571494985741,
"acc_norm": 0.35555555555555557,
"acc_norm_stderr": 0.02918571494985741
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5840336134453782,
"acc_stderr": 0.032016501007396114,
"acc_norm": 0.5840336134453782,
"acc_norm_stderr": 0.032016501007396114
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3973509933774834,
"acc_stderr": 0.03995524007681681,
"acc_norm": 0.3973509933774834,
"acc_norm_stderr": 0.03995524007681681
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7908256880733945,
"acc_stderr": 0.01743793717334323,
"acc_norm": 0.7908256880733945,
"acc_norm_stderr": 0.01743793717334323
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.44907407407407407,
"acc_stderr": 0.03392238405321616,
"acc_norm": 0.44907407407407407,
"acc_norm_stderr": 0.03392238405321616
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7107843137254902,
"acc_stderr": 0.031822318676475524,
"acc_norm": 0.7107843137254902,
"acc_norm_stderr": 0.031822318676475524
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7426160337552743,
"acc_stderr": 0.028458820991460302,
"acc_norm": 0.7426160337552743,
"acc_norm_stderr": 0.028458820991460302
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6188340807174888,
"acc_stderr": 0.03259625118416827,
"acc_norm": 0.6188340807174888,
"acc_norm_stderr": 0.03259625118416827
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7175572519083969,
"acc_stderr": 0.03948406125768361,
"acc_norm": 0.7175572519083969,
"acc_norm_stderr": 0.03948406125768361
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.743801652892562,
"acc_stderr": 0.03984979653302872,
"acc_norm": 0.743801652892562,
"acc_norm_stderr": 0.03984979653302872
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.04236511258094632,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.04236511258094632
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7055214723926381,
"acc_stderr": 0.03581165790474082,
"acc_norm": 0.7055214723926381,
"acc_norm_stderr": 0.03581165790474082
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.047184714852195886,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.047184714852195886
},
"harness|hendrycksTest-management|5": {
"acc": 0.7087378640776699,
"acc_stderr": 0.044986763205729224,
"acc_norm": 0.7087378640776699,
"acc_norm_stderr": 0.044986763205729224
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406978,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406978
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7547892720306514,
"acc_stderr": 0.015384352284543937,
"acc_norm": 0.7547892720306514,
"acc_norm_stderr": 0.015384352284543937
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.661849710982659,
"acc_stderr": 0.02546977014940018,
"acc_norm": 0.661849710982659,
"acc_norm_stderr": 0.02546977014940018
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2905027932960894,
"acc_stderr": 0.015183844307206157,
"acc_norm": 0.2905027932960894,
"acc_norm_stderr": 0.015183844307206157
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6535947712418301,
"acc_stderr": 0.027245613047215362,
"acc_norm": 0.6535947712418301,
"acc_norm_stderr": 0.027245613047215362
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6752411575562701,
"acc_stderr": 0.026596782287697046,
"acc_norm": 0.6752411575562701,
"acc_norm_stderr": 0.026596782287697046
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6327160493827161,
"acc_stderr": 0.0268228017595079,
"acc_norm": 0.6327160493827161,
"acc_norm_stderr": 0.0268228017595079
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.42907801418439717,
"acc_stderr": 0.02952591430255856,
"acc_norm": 0.42907801418439717,
"acc_norm_stderr": 0.02952591430255856
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4178617992177314,
"acc_stderr": 0.012596744108998562,
"acc_norm": 0.4178617992177314,
"acc_norm_stderr": 0.012596744108998562
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5367647058823529,
"acc_stderr": 0.030290619180485687,
"acc_norm": 0.5367647058823529,
"acc_norm_stderr": 0.030290619180485687
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.01994491413687358,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.01994491413687358
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5818181818181818,
"acc_stderr": 0.04724577405731572,
"acc_norm": 0.5818181818181818,
"acc_norm_stderr": 0.04724577405731572
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.0289205832206756,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.0289205832206756
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7711442786069652,
"acc_stderr": 0.029705284056772436,
"acc_norm": 0.7711442786069652,
"acc_norm_stderr": 0.029705284056772436
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-virology|5": {
"acc": 0.463855421686747,
"acc_stderr": 0.03882310850890593,
"acc_norm": 0.463855421686747,
"acc_norm_stderr": 0.03882310850890593
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7192982456140351,
"acc_stderr": 0.03446296217088427,
"acc_norm": 0.7192982456140351,
"acc_norm_stderr": 0.03446296217088427
},
"harness|truthfulqa:mc|0": {
"mc1": 0.379436964504284,
"mc1_stderr": 0.016987039266142995,
"mc2": 0.5577993030725962,
"mc2_stderr": 0.015324882744176647
},
"harness|winogrande|5": {
"acc": 0.6992896606156275,
"acc_stderr": 0.012888010494704734
},
"harness|gsm8k|5": {
"acc": 0.5420773313115997,
"acc_stderr": 0.013723629649844082
}
}
```
## 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. -->
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## Dataset Card Contact
[More Information Needed] |
autoevaluate/autoeval-staging-eval-project-31466167-6d47-4d63-9ebd-59fe66b62d96-5553 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: autoevaluate/natural-language-inference
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: validation
col_mapping:
text1: sentence1
text2: sentence2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: autoevaluate/natural-language-inference
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
Lollitor/MyPubChem100 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 13325929.2
num_examples: 81000
- name: validation
num_bytes: 1480658.8
num_examples: 9000
download_size: 4647998
dataset_size: 14806588.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "MyPubChem100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nithin1995/dfc_sroie_caption3 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 560622934.0
num_examples: 973
download_size: 499273882
dataset_size: 560622934.0
---
# Dataset Card for "dfc_sroie_caption3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
james-burton/imdb_genre_prediction2 | ---
dataset_info:
features:
- name: Rank
dtype: int64
- name: Title
dtype: string
- name: Description
dtype: string
- name: Director
dtype: string
- name: Actors
dtype: string
- name: Year
dtype: int64
- name: Runtime (Minutes)
dtype: int64
- name: Rating
dtype: float64
- name: Votes
dtype: int64
- name: Revenue (Millions)
dtype: float64
- name: Metascore
dtype: float64
- name: Genre_is_Drama
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 224399.15
num_examples: 680
- name: validation
num_bytes: 39599.85
num_examples: 120
- name: test
num_bytes: 65392
num_examples: 200
download_size: 0
dataset_size: 329391.0
---
# Dataset Card for "imdb_genre_prediction2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jonathan-roberts1/NWPU-RESISC45 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': airport
'2': baseball diamond
'3': basketball court
'4': beach
'5': bridge
'6': chaparral
'7': church
'8': circular farmland
'9': cloud
'10': commercial area
'11': dense residential
'12': desert
'13': forest
'14': freeway
'15': golf course
'16': ground track field
'17': harbor
'18': industrial area
'19': intersection
'20': island
'21': lake
'22': meadow
'23': medium residential
'24': mobile home park
'25': mountain
'26': overpass
'27': palace
'28': parking lot
'29': railway
'30': railway station
'31': rectangular farmland
'32': river
'33': roundabout
'34': runway
'35': sea ice
'36': ship
'37': snowberg
'38': sparse residential
'39': stadium
'40': storage tank
'41': tennis court
'42': terrace
'43': thermal power station
'44': wetland
splits:
- name: train
num_bytes: 381151705
num_examples: 31500
download_size: 424827902
dataset_size: 381151705
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "NWPU-RESISC45"
## Dataset Description
- **Paper** [Remote sensing image scene classification: Benchmark and state of the art](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
### Licensing Information
[CC-BY-SA]
## Citation Information
[Remote sensing image scene classification: Benchmark and state of the art](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
```
@article{cheng2017remote,
title = {Remote sensing image scene classification: Benchmark and state of the art},
author = {Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
year = 2017,
journal = {Proceedings of the IEEE},
publisher = {IEEE},
volume = 105,
number = 10,
pages = {1865--1883}
}
``` |
atmallen/sharegpt-binary | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: statement
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: model
dtype: string
splits:
- name: test
num_bytes: 1090167
num_examples: 243
download_size: 188810
dataset_size: 1090167
---
# Dataset Card for "sharegpt-binary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
longevity-genie/moskalev_papers | ---
license: openrail
---
All Alexey Moskalev paper found inside semantic-scholar that have pubmed ids.
The parquet file schema is:
```
root
|-- corpusid: long (nullable = true)
|-- updated: string (nullable = true)
|-- content_source_oainfo_license: string (nullable = true)
|-- content_source_oainfo_openaccessurl: string (nullable = true)
|-- content_source_oainfo_status: string (nullable = true)
|-- content_source_pdfsha: string (nullable = true)
|-- content_source_pdfurls: array (nullable = true)
| |-- element: string (containsNull = true)
|-- externalids_acl: string (nullable = true)
|-- externalids_arxiv: string (nullable = true)
|-- externalids_dblp: string (nullable = true)
|-- externalids_doi: string (nullable = true)
|-- externalids_mag: string (nullable = true)
|-- externalids_pubmed: string (nullable = true)
|-- externalids_pubmedcentral: string (nullable = true)
|-- content_text: string (nullable = true)
|-- annotations_abstract: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_author: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_authoraffiliation: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_authorfirstname: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_authorlastname: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibauthor: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibauthorfirstname: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibauthorlastname: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibentry: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibref: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibtitle: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibvenue: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_figure: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_figurecaption: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_figureref: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_formula: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_paragraph: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_publisher: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_sectionheader: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_table: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_tableref: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_title: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_venue: array (nullable = true)
| |-- element: string (containsNull = true)
``` |
Mcholo/VFRDataset | ---
license: cc-by-nc-nd-4.0
size_categories:
- 10K<n<100K
task_categories:
- image-segmentation
pretty_name: Visaul Font Recognition Dataset
dataset_info:
features:
- name: image
dtype: image
- name: annotation
dtype: image
splits:
- name: train
num_bytes: 1011715449.0
num_examples: 10000
download_size: 985371934
dataset_size: 1011715449.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- art
- design
- font
- fonts
---
# Dataset Card
<!-- Provide a quick summary of the dataset. -->
this dataset is designed for training a model to recognize fonts in images. It comprises images generated from various fonts and placed on canvases with varying RGB color combinations, ensuring the model learns to identify fonts under diverse conditions.
### Dataset Description
this dataset is designed for training a model to recognize fonts in images. It comprises images generated from various fonts and placed on canvases with varying RGB color combinations, ensuring the model learns to identify fonts under diverse conditions.
- **Curated by:** Mate Tcholokava
- **License:** cc-by-nc-nd-4.0 |
Christian4145/BigRandom | ---
license: apache-2.0
---
|
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